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És molt important que com ha individus fem petites accions per tal de millorar el planeta, o no destruïr-lo tant rapidament. Però també veig que és molt necessaria la implàntació de noves polítiques i plans de millora per a la cuida del nostre planeta. No és just que entre la població ens intentem coscienciar al màxim i següidament, les persones amb poder que les petites accions que hem fet nosaltres quedin amb res.

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L'humà és l'element detonant i causant de la situació actual del planeta. No obstant això, som els únics que podem mirar de millorar-la. "És feina dels de dalt", pensen alguns/es i és cert, perquè calen, amb urgència, polítiques i plans de millora per reformular les accions i les decisions que s'han pres.
Cal actuar, encara que sigui a petita escala i nosaltres, les futures docents, tindrem la gran tasca de guiar la societat del futur.

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Crec que la gent ho veu com una cosa llunyana i no li dona la importància que té realment. Hauríem d'actuar tot des d'aquest mateix moment perquè les conseqüències seran molt greus.

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Crec que més enllà de la possibilitat que tot això passi o no passi, sí que caldria educar a les persones en tot això que hem parlat anteriorment: consum responsable, reciclatge, etc. Això sí, és molt difícil perquè les societats no funcionen d'aquesta manera, caldrien persones molt conscients per poder frenar aquestes conseqüències i lluitar contra com funcionem.

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és una qüestió de la qual no hi ha molta informació (crec jo) i que molta part de la població no se sent identificada o que la problemàtica no va amb ells, ja que a les nostres vides, per sort, no hi ha hagut canvis. S'ha d'evitar arribar al moment on sí que n'hi hagi, perquè no cal arribar a aquest punt per adonar-se de la importància que té.

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Considero que som persones molt consumistes i no valorem el que tenim, és a dir, sempre volem més del que tenim i no pensem en quina repercussió pot tenir això. A més, crec que no som conscients de com pot arribar a afectar-nos el canvi climàtic i de la urgència que hi ha en aturar-lo. Hem de començar a cuidar el nostre planeta per poder conservar-lo en bon estat per les generacions futures.

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Penso que es tracta d'una problemàtica de la qual ens fem responsables com a persones (individus) i no com a col·lectiu, nació, govern, etc. En altres paraules, crec que els governs ens responsabilitzen únicament a les persones de la problemàtica i dels canvis que hem de dur a terme, però que ells com a dirigents dels països es neguen a dur a terme determinades actuacions "pel seu propi bé" i aquest fet obstaculitza les de les persones.

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Jo crec que les petites accions que podem fer nosaltres són importants i que cal educar els infants perquè les integrin però el que realment marca la diferència són les accions que poden dur a terme les grans empreses. I que calen polítiques consistents per tal de fer front a aquest problema, però evidentment és molt difícil en la societat capitalista en la que vivim

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Penso que s'ha d'actuar col·lectivament, i encara hi ha molta gent que no és conscient de les conseqüències del canvi climàtic. Per aquesta raó, crec que hi haura d'haver mesures a nivell estatal, les quals permetessin una acció gobal.

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Penso que s'ha d'actuar col·lectivament, i encara hi ha molta gent que no és conscient de les conseqüències del canvi climàtic. Per aquesta raó, crec que hi haura d'haver mesures a nivell estatal, les quals permetessin una acció gobal.

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Actualment, ens trobem submergits en una situació d'emergència planetària sense precedents. Probablement, ens queden uns 60 o 70 anys abans del final de la Terra i estem tardant a actuar. Tota la societat hauria d'aplicar diàriament totes les solucions plantejades al llarg del documental a escala global per poder garantir un futur viable per a la humanitat. S'ha de tenir molta consciència sobre aquesta gran problemàtica i fer un canvi de mentalitat per poder fer-hi front.

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Jo penso que el que es diu al documental és cert, ens trobem en una situació perillosa no si volem canviar-ho penso que s'hauria de prendre alguna mesura a nivell més global (per exemple des del govern), perquè de manera individual no sembla que estigui funcionant.

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Jo penso que, tal com indica el documental, anem cap a una situació catastròfica. Les grans companyies i els polítics no fan res per aturar-ho i nosaltres, individualment, podem contribuir per reduir la petjada, però no es podrà millorar la situació fins que els governs actuïn.

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Penso que no som gens conscients del que passarà. Es parla sempre del canvi climàtic, de l'extinció de les espècies, però les evidències que veiem en el nostre dia a dia són poques, per això no fem canvis més eficients.

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Nosaltres només podem actuar com a persones individuals fent petites accions per reduir el nostre impacte però em sembla contardictori i injust que persones en posició de poder es "carreguin" tot el que la població tracta de construir (per exemple, fent ús d'avions privats per trajectes molt curts).

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Penso que ens adonarem de la situació quan realment estiguem en un moment de no poder tornar enrere. La societat no crec que faci els canvis que realment són necessaris, ja que molta gent no vol renunciar a deixar de consumir perquè al final és la mentalitat amb la que hem crescut i estem creixent. A més, molta gent és conscient i actua de manera individual però si la majoria no ho fan poc podem aconseguir...

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-Onades de calor
- Sequeres
-Desertificació

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Reutilitzar, reciclar les deixalles i reduir el consum

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Conscienciar en petites accions com la compra a granel, l'ús de tuppers de vidre, no embolicar els entrepans amb plàstic, etc

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Hem de conscienciar-los sobre l'estalvi de l'aigua, no al consumisme, respecte al medi ambient

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Aigua com a font de vida, cuidar-la i fer-ne un ús responsable

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Fomentar el consum de proximitat i el tipus d'emagatzematge que utilitzen. I també un consum i compra responsable, a més a més d'una cultura del reaprofitament i donar una segona vida a les coses que tenim (dispositius, roba...)

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Fomentar el consum de proximitat i el tipus d'emagatzematge que utilitzen. I també un consum i compra responsable, a més a més d'una cultura del reaprofitament i donar una segona vida a les coses que tenim (dispositius, roba...)

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-  Reflexionar sobre el transport i les emissions que genera

-  Conèixer la importància de les energies renovables

-  Ser conscients de la situació de sequera i evitar el malbaratament d’aigua
- Reutilitzar recursos per minimitzar el consum precipitat.

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consum responsable, reciclatge, cura de l'entorn, estalvi i eficiència d'aigua

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Recalcar la importància de fer un consum responsable, sobretot pel que fa a l'aigua. També, animar a reciclar. 

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Recalcar la importància de fer un consum responsable, sobretot pel que fa a l'aigua. També, animar a reciclar. 

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Des de l'escola hauriem de conscienciar sobre el consum responsable i l'impacte de les nostres accions al medi.
Han de reciclar, fer ús de transports públics o vehicles compartits, no malbaratar aigua, reduir el consum de productes innecessaris,...

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Com a docent voldria conscienciar a l'alumnat de la situació actual i
com canviant el seu comportament pot ajudar a la solució: fer ús del transport públic, reciclar, reduir el consum de carn, no consumir excessivament...

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Jo com a docent, m'agradaria fer incis a:

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Jo com a docent, m'agradaria fer incis a:

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- Han de saber com no malbaratar l'aigua i com podem contribuir-hi.
- Han de ser conscients sobre la importància dels aliments per no tirar menjar.
- Consum responsable d'electricitat, llum, gas...

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He de fer incís en què és el consum excessiu de qualsevol producte. És a dir, voldria que entenguessin que d'entre tots els productes que comprem no tots són essencials i de vegades ens deixem anar comprant capricis que no sempre necessitem.

També els faria entendre que és molt important saber d'on venen aquests productes i quin és el seu origen per poder prevenir la contaminació deguda al transport d'aquests productes o al material del qual estan fets.

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He de fer èmfasi en la importància del consum responsable, comprar allò que realment necessitem. Per tant, conscienciar als alumnes en les conseqüències de l'augment del consum.

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Jo com a docent penso que he de fer èmfasis en inculcar als alumnes la reducció de consum, és a dir, consumir el que realment necessitem i sobretot tenir cura del consum d'aigua per tal de reduir-lo.

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guerres

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fam
pèrdua d'ecosistemes

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Augment de la fam

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Degradació dels sistemes ecosistèmics vitals.
Augment d'epidèmies i pandèmies

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- Canvis metereològics greus (inundacions, augment de la calor)
- Desglaç i augment del gas metà
- Mort i desaparició de poblacions causat per fam i falta de recursos

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disminució de la producció agrícola

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Desertificació de la terra que impossibilita el conreu d'aquestes

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Desertificació de la terra que impossibilita el conreu d'aquestes

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aproximadament el 31% d'amfibis, el 21% dels mamífers i el 13% d'aus estan en perill d'extinció.

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Si la temperatura global continua augmentant, la producció de menjar disminuirà

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Augment del gas metà a l'atmosfera pel desgel dels glacials.

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Inundacions
Ones de calor extremes
Augment del metà
Extinció d'espècies
Trencament del cicle global del carboni
Sequeres
Canvi de vida en el nostre planeta

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- Canvi climàtic
- Elevades emissions de CO2
- Desglaç del gel

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- Necessitat de més àrees de conreu per abastir a la població --> deforestació
- Migrants climàtics
- Augment del nivell de l'aigua --> enfonsament de ciutats

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Augment del nivell d'aigua i com a conseqüència inundació de poblacions

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Deforestació --> sense zones pel consum i agricultura
Escassetat aigua --> afectarà agricultura, ecosistemes i vida humana
Canvi climàtic --> ecosistemes i clima global
Augment temperatures --> augment nivell aigua, incendis, zones no habitables

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- Canvi climàtic
- Augment de la temperatura
- Escassetat d'aigua i aliments
- Augment de la població
- Augmet Co2

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Stephen Emmott's population book is unscientific and misanthropic

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Stephen Emmott's population book is unscientific and misanthropic

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https://www.theguardian.com/environment/2013/jul/09/stephen-emmott-population-book-misanthropic

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tephen Emmott's book on global ecological challenges is attracting much attention. The work is extremely short - perhaps about 15,000 words - and is in the form of notes that provide terse commentary on a series of graphs. It is little more than a Powerpoint presentation turned into a slim paperback. Although any attempt to increase mankind's alarm at the threat from climate change is welcome, Emmott's book is error-strewn, full of careless exaggeration and weak on basic science. Its reliance on random facts pulled from the internet is truly shocking and it will harm the cause of environmental protection. As might be expected, the best sceptic bloggers are already deconstructing its excesses line-by-line.

Things are indeed pretty bad. The steps to address climate change are lamentably slow and ineffectual. Biodiversity is in sharp decline in some parts of the world. Water supplies are becoming tighter in many countries. The pressures on global forests are declining but still acute in some places. Air quality is appalling in big cities in Asia and quite bad in major Western capitals. But we don't help solve these problems by exaggerating their seriousness and picking up gobbets of data from dodgy sources we found on the web

All of us see these difficult problems but most see them as soluble. Not so Stephen Emmott. In his eyes the world is hurtling towards disaster at ever-increasing speed. To him, every global issue is of 'accelerating' seriousness. At one point the word 'accelerates' is used 8 times in just over 100 words to describe a tip into some future hell. (It's also striking that a senior scientist uses this word inexactly. He generally doesn't mean that the rate of change is increasing but merely that the particular phenomenon he is worried about is continuing to grow).

Emmott starts by looking at population from the year 10,000 BC. He says he uses data from the UN but I cannot find anything produced by this organisation that records estimates prior to 1950. He seems instead to have employed a file found on the internet at http://commons.wikimedia.org/wiki/File:Population_curve.svg. This file has the unusual features of Emmott's first chart, such as the use of a year called 'AD1'. He uses the curve of population growth to tell us we might see a fourfold increase in population by 2100.

Despite Emmott's assertions to the contrary, population growth has been slowing steadily since the 1960's. The number of people in the world is increasing by about 1% a year and the slowdown will almost certainly continue. This is never mentioned, let alone discussed by Emmott. Reasonable 2050 predictions are almost all in the 9 to 10 billion range, with most people seeing declines after that date but he tells us that we might actually see 28 billion by 2100. Does he have an argument why his number could be right? No, he just asserts it.

Similarly, he addresses food supply with passionate language and few facts. He ignores the relatively stable and gradual increases in food availability per person over the last half-century and predicts coming apocalypse. A huge increase in land needed for food production is forecast, something completely unpredicted by any experts in the field. He reserves particular scorn for the impact of improved agricultural technologies such as pesticides and fertilisers. 'The Green Revolution is a myth' he writes, ignoring the extraordinary and reliable increase in food production launched by the plant breeder Norman Borlaug in India that made famine thankfully rare. His assessments of the need for more land on which to grow crops seem to crudely assume no increase whatsoever in yields per hectare, ignoring reliable evidence since the 1960s.

Very strangely, I don't think any of of the thirteen apocalyptic charts in his book are taken from primary sources. The data he uses is almost always 'adapted' from other work, something which doesn't appear to embarrass him. The figures employed aren't traceable and checking is difficult. But those charts that I was able to source are generally mis-drawn or downright misleading. For example, the worrying chart on species extinction on page 54 of the Kindle edition seems to present a sharp, abrupt and catastrophic rise from the year 2045 without any basis in fact or scientific research. Others are similarly invented.

When it comes to discussion of fish production, Emmott shows an equally disturbing lack of knowledge. He writes that a 'fully exploited' fishing ground has 'no fish left'. Actually, the words mean that the rate of fish extraction cannot be increased without loss of long term fish extraction potential.

He's also worried about water availability. And this is indeed going to be one of the world's most pressing problems by 2050. But he exaggerates, as in so many other instances, the current seriousness of the issue. Several of his very sparsely filled pages are given over to discussing how much water is needed to grow food but nowhere does he discuss the global availability. Yes, the world does use 6,000 cubic kilometres of fresh water a year but we probably have about twenty times this much available in one form or another. Small improvements in irrigation practices will almost certainly help us decrease water stress in the most threatened global food production areas. I'm not trying to diminish the severity of the challenge but to ask Emmott to give us careful argument, not overplayed assertion.

Although I think that most of what he says on other subjects is ill-researched, most of the readers of this blog will agree strongly with his views on climate change. He exaggerates the likely increase in global energy need - a few well researched charts from the recent BP statistical review would have helped him - but we do face a real likelihood of a 4 degree temperature rise on current trends. Emmott is right to emphasise the weak global response to this threat.

He's not a fan of renewable energy sources but his opinions are surprisingly casual. PV, for example, is flawed because ' the production of the new generation of solar panels involves nitrogen triflouride (sic) - one of the most potent greenhouse gases on earth'. Nowhere does he tell us that this molecule has been used for years and that when calculations have been done, the carbon benefit of the renewable electricity generated by the panel dwarfs the global warming impact of the nitrogen trifluoride used. This is typical of the book: lots of strong assertions, no analysis and lots of factual mistakes.

In the end, his ambivalent feelings towards humanity come out all too clearly. Every which way you look at it, a planet of ten billion looks like a nightmare, he writes. I wanted him to come up with solutions to humanity's problems, not to exhibit a troubling misanthropism and astonishingly careless use of data and basic science.
Appendix
There are scores and scores of errors and exaggerations in this short book. I've mentioned a few of them below to demonstrate the range of surprising misstatements.

(Page numbers refer to the Kindle edition)
Land grabs
Emmott writes (page 50)
During just the past twelve years, almost 50 million hectares of land have been traded. That is an area of land the size of half of western Europe being bought and sold
Depending on which countries you include, 50 million hectares of land is approximately equal to 15% of the land area of western Europe, not 50%. 50million hectares is less than size of France.
Keystone XL pipeline
Emmott says (page 76)
And Barack Obama has committed to extending the importation of tar sands oil from Alberta in Canada to the US through the development of the 'Keystone XL' project - providing US consumers with close to one million barrels of oil per day from Canadian tar sands.
President Obama has not committed to Keystone XL. This is how the LA Times reported his comments Tuesday June 25 th 2013:
President Obama set a high bar for approval of the controversial Keystone XL pipeline, declaring for the first time that he would let the project go forward only if it does not "significantly increase" emissions of greenhouse gases. UK oil and gas exploration
Emmott says (page 77)
In the UK, despite its stated commitment to tackling climate change, the British government issued 197 new licences to drill for oil and gas in the North Sea - the largest number since North Sea oil drilling began in 1964.
Emmott appears to be referring to the 2012 North Sea licensing round. His comments seem to have been taken from the Financial Times of October 25 th 2012. The paper said
The government has issued 167 new oil and gas licences to companies seeking to drill in the North Sea, in what John Hayes, the energy minister, described as a "bonanza" for the oil industry. The UK's 27 th licensing round for the North Sea attracted its greatest ever level of interest, with 224 applications - the largest number since offshore licensing began in 1964. Melting of sea ice
Emmott has made small but sloppy mistakes. The number of licences wasn't 197, it was 167. The record was not of granted licences but the number of licence applications.
Arctic coastlines are retreating by 14 metres per year.
a) Emmott writes (page 108)
As their ancient ice buffers vanish, Arctic coastlines are eroding; parts of Alaska are receding at 14 metres (45 feet) a year.
This statement seems to have originated in an Economist article of June 16 th 2012 ('The Melting North). The paper wrote
The coastline in Arctic regions reacts to climate change with increased erosion and retreats by half a metre per year on average.
Note that what the Economist said was of , not all Arctic coastlines. The average rate of recession is much, much slower. A 2011 article in Science Daily summarised the conventional view when it wrote ( http://www.sciencedaily.com/releases/2011/04/110417185342.htm)
b) Loss of Arctic and Greenland ice
Emmott exaggerates about thirty fold.

Emmott says (page 108)
Methane hydrates
Greenland and Arctic ice sheets are now losing some 475 billion tonnes of mass per year into the sea. This is going to contribute to rising sea levels.
For the first time, hundreds of plumes of methane - many of them kilometres across - have been observed rising from previously frozen methane stores in the Laptev Sea, off the East Siberian Arctic shelf.
Prof Emmott appears to be confusing the Antarctic and the Arctic. The study he seems to be referring to was carried out by Eric Rignot and others ( http://onlinelibrary.wiley.com/doi/10.1029/2011GL046583/full) and refers to the loss of ice from the Antarctic and Greenland ice sheets. Arctic ice floats on water and when it melts it does not raise sea levels by a measurable amount.
Russian scientists have discovered more than 200 sources of methane emissions in the Arctic, particularly in the north of the Laptev Sea. Two of the methane fields exceed 1 kilometer in diameter, said Igor Semiletov, expedition head aboard the Viktor Buinitsky research vessel. Methane emissions in the Arctic have been observed before and are explained by bacterial activity.
Emmott writes
Demand for land for food
Once again, he doesn't provide a source for this comment. However he seems to have used research work by Russian scientists from 2012. The Voice of Russia summarised their results as follows.( http://english.ruvr.ru/2012_09_18/Methane-emission-in-the-Arctic-a-possible-key-to-the-global-warming/)
Yet demand for land for food is going to double - at least - by 2050, and triple - at least - by the end of this century.
The release of trapped methane from the world's cold oceans is an extremely serious threat. But Emmott exaggerated the findings of the research, stating, for example, that ' many' of the plumes were kilometres across rather than the two mentioned by the researchers. He also incorrectly says that this is the first time the methane has been observed.

Emmott writes (page 118)
Food production
Emmott elsewhere tells us that 40% of the world's land area is used for agriculture (page 155), so it is difficult to see how the numbers above are remotely possible.
We currently have no known means of being able to feed ten billion of us at our current rate of consumption and with our current agricultural system.
The Food and Agriculture Organisation (FAO) makes regular assessments of the need for arable land to grow food. Land needs are reduced by improving agricultural yields and increased by rising population and increasing space allocation to low productivity uses such as meat production and fruits. The latest assessment for 2030 and 2050 ( http://www.fao.org/docrep/016/ap106e/ap106e.pdf) suggests that land use for crop production will rise by 70 million hectares to 2050 from a figure of about 1.6 million hectares today. This means the area under cultivation will increase by slightly less than 5%, not the 100%+ suggested by Emmott.
b) Shortage of phosphates
Emmott writes (page 122)

On the contrary, we understand well how to feed people. The current agricultural system has increased productivity for the last fifty years and that increase continues, albeit at a slowing rate. Climate change will probably cause increasing variability of yield but Emmott's assertion has no substance to back it up.

Emmott asserts (page 129)

The amount of food we produce is almost entirely dependent upon phosphate-based fertilizers. But phosphate reserves are finite, and it is becoming apparent that we are going to run out of it, almost certainly some time this century.

The US Geological service estimates ( http://minerals.usgs.gov/minerals/pubs/commodity/phosphate_rock/mcs-2013-phosp.pdf) the usage rates of the key minerals and the amount of reserves still in the ground. In the case of phosphate rock it states that annual usage is about 210 million tonnes. World reserves are put at 67 billion tonnes, or enough for 300+ years at current rates. (In mature economies such as the UK demand for phosphates for farming is tending to fall).

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Stephen Emmott's 10 billion book is unscientific and misanthropic

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tephen Emmott's book on global ecological challenges is attracting much attention. The work is extremely short - perhaps about 15,000 words - and is in the form of notes that provide terse commentary on a series of graphs. It is little more than a Powerpoint presentation turned into a slim paperback.

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-sobrepoblació
- aigua
-pocs espais naturals

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-Canvi climàtic
-Ús excessiu de l'aigua
-Augment del consum
-Augment de la població
-Extinció d'animals

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- Falta d’aigua
- Menys espais naturals
- Sobrepoblació
- Més productes contaminants
- CO2

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- Pèrdua d'hàbitat
- Increment del consumisme
- Pèrdua d'espècies animals
- Poc ús d'energies renovables

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- Canvi climàtic
- Contaminació
- Producció massiva
- Gran augment de consum
- Escassetat d'aigua i recursos
- Augment de la població

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- Species loss
- Water scarce
- Game-changing revolution
- Green revolution
- Global carbon cycle
- Climate migrants

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- Canvi climàtic
- Biodiversitat
- Extincions massives (degradació dels ecosistemes)
- Increment de la demanda alimentària
- Augment del consum
- Increment de la població
- Escassetat daigua i recursos
- Crisi energètica

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Canvi climàtic
Deforestació
Animals en perill d'extinció
Escassetat d'aigua
Biosfera afectada

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- Sequera
- Degradació del sòl terrestre
- Desaparició d’espècies
- Poc ús d'energies renovables
- Canvi climàtic

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- Canvi climàtic
- Extincions massives
- Increment de la demanda alimentària
- Degradació dels ecosistemes
- Intervenció en el circuit de CO2

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- l'augment de la densitat poblacional a les ciutats.
- canvi climàtic i els seus efectes.
- pèrdua de la biodiversitat.
- escassetat d'aigua.
-increment de la demanda i el consum d'energia.
- contaminació.
- mals usos, contaminacions i decisions per l'home.
- manca de pluja. (desertificació)
- animals en perill d'extinció.

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- canvi climàtic
- pèrdua de biodiversitat
- escassetat d'aigua
- contaminació
- pèrdua de recursos naturals
- crisi energètica

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1. Augment de població
2. Augment de demanda de l'agricultura
3. Major consum d'aigua, carn, entre d'altres
4. Desforestació
5. Pèrdua de gel dels pols
6. Alteració del temps

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- Pèrdua de la Biodiversitat
- Canvi Climàtic
- Escassetat d'aigua
- Contaminació
- Transport
- Degradació de l'ecosistema
- Desertificació
- Producció massiva

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Affiliations

1. School of Natural Resources and Environment, University of Michigan, Ann Arbor, 48109, Michigan, USA

Bradley J. Cardinale, Patrick Venail & Anita Narwani

2. Virginia Institute of Marine Science, The College of William and Mary, Gloucester Point, 23062, Virginia, USA

J. Emmett Duffy

3. Department of Biology, McGill University, Montreal, Quebec H3A 1B1, Canada

Andrew Gonzalez

4. Department of Biology, Western Washington University, Bellingham, 98225, Washington, USA

David U. Hooper

5. School of Life Sciences, Arizona State University, Tempe, 85287, Arizona, USA

Charles Perrings & Ann P. Kinzig

6. Centre for Population Biology, Imperial College London, Silwood Park SL5 7PY, UK

Georgina M. Mace

7. Department of Ecology, Evolution & Behavior, University of Minnesota, Saint Paul, 55108, Minnesota, USA

David Tilman

8. Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, S- 901 83 Umeå, Sweden

David A. Wardle

9. Department of Biology and Woods Institute, Stanford University, Stanford, 94305, California, USA

Gretchen C. Daily

10. Station d'Ecologie Expérimentale, Centre National de la Recherche Scientifique, 09200 Moulis, France

Michel Loreau

11. US Geological Survey, National Wetlands Research Center, Lafayette, 70506, Louisiana, USA

James B. Grace

12. Museum National d'Histoire Naturelle, 57, Rue Cuvier, CP 41 75231, Paris Cedex 05, France

Anne Larigauderie

13. Department of Zoology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada

Diane S. Srivastava

14. Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, 10027, New York, USA

Shahid Naeem

Corresponding author

Correspondence to Bradley J. Cardinale.

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Biodiversity loss and its impact on humanity - Nature

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Two decades ago the first Earth Summit raised the question of how biological diversity loss alters ecosystem functioning and affects humanity; this Review looks at the progress made towards answering this question. The 1992 Earth Summit in Rio de Janeiro, Brazil, stimulated an explosion of research into how biodiversity loss influences the processes that underpin the goods and services that ecosystems provide to humanity.

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https://www.wildculture.com/article/review-stephen-emmott-10-billion/1240

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Review: Stephen Emmott - 10 Billion

BOOKS: London-based architecture and urbanism writer Herbert Wright reviews 10 Billion, a pessimistic view of the future of humanity by Microsoft's Head of Computational Science, Stephen Emmott.

"I think we're fucked," says Stephen Emmott, head of Microsoft's Computational Research Centre. That's his view on the future for humanity, as the population climbs from seven to ten billion this century. He spells out his reasoning in a flourescent orange-covered paperback, 10 Billion. It's a straight-talking overview of how we're trashing the only habitat we have, planet Earth, and why it's too late to make it right.

Emmott is a credible voice. He's a professor at Oxford and University College London. He's worked in great corporations and advised governments. With an education in biology and computational neuroscience, his trans-disciplinary work now encompasses fields from marine ecology to artificial photosynthesis. The latter, like nuclear fusion, offers the hope of a magic bullet to the world's insatiable energy demands, but sadly they're both long shots in the short term.

He uses plain English, sometimes so sparsely it's stuccatto. His stripped-back language is personal and direct. Brevity emphasises points. Effectively. And often, a point is underlined simply by concluding it as a new paragraph. And paragraphs are widely spaced. So much so that a page may contain just a paragraph. Or even a single sentence. But I shan't do that here.

That would look silly.

And Emmott's thesis is far from silly. Like a man in a hurry, Emmott lays out in broad brush strokes the familiar matters of anthropogenic climate change and resource stress. Photos and graphs punctuate the text, the former without explanation. For example, what is that gargantuan hole almost swallowing a town, in the shot labelled merely "Mirny, Russia"? It looks like the entrance to Hell - in fact, it's an abandoned open diamond mine 525m deep. By contrast, graphs come with sources and explanations. One illustrates the 7.5-fold rise in total annual water consumption from 600 km3 in 1900 to today, and projected to be ten-fold by 2025. The graph's line looks almost harmless, but consider this. We consume four litres of water to make a litre bottle of it, and 72,000 to make a semiconductor chip. In facts like those lies the terrible power of the word 'unsustainable'.

Habitat destruction often heralds the arrival of men with guns.

Previously, population pressure and resource stress have not been without solutions. Here's an interesting science-led miracle solution that Emmott swiftly dissects. The post-war 'Green Revolution' boosted crop yields enormously, feeding hundreds of millions that would otherwise have starved. But Emmott points out that engineered crops expended finite biological resources on growing quicker rather than on their natural defences, so we compensated with toxic insecticides; they were shorter so we deployed herbicides to stop weeds taking their light; and they needed vastly more water. We got away with that because the limits to the Earth's capacity for absorbing toxins and providing water weren't reached. Consider, though, the current miracle. The biggest demographic trend recognised today is that education and urbanisation mean dropping birth levels. But they also produce consumer societies, which means resource demand and carbon footprint skyrockets. And if ten billion want to live like we do now in the West, we breach the limits in resources, and carbon-induced global warming could flip to runaway.

Scare tactics? Perhaps, but he breezes through the apparently-viable alternatives, including not just renewables and geo-engineering but the taboo of nuclear (fission) power. Even if they go full steam ahead, they're not enough. There are slivers of hope he doesn't address at all, such as energy-intensification trends (getting more buck for your kilowatt produced, something China sets targets on), or near-feasible projects like asteroid mining. And he doesn't spell out that actually, demographic trends suggest ten billion is around the peak projected population, and then it will fall. But what kind of a world will it be that it falls in?

Emmott even offers a dreadful thought on that score as well. Oddly, it aligns him with some climate-change skeptics, those idiots arrogant enough to think they know better than the global scientific community. The most vocal of those are often American gun lobbyists. Emmott's last line quotes a scientist whose personal reaction to the situation was: "Teach my son to use a gun." I wonder if Emmott saw the irony of a climate scientist talking like a Tea Party-type.

If the line seems melodramataic, tell that to the tribes of desertificating Dafur or across the depleting Amazon. Their communities have already been trashed by humanity's rape of Earth, and they've already experienced the ominous coincidence that habitat destruction often heralds the arrival of men with guns.

Herbert Wright is a London-based writer specialising in urbanism, architecture and art. He is Contributing Editor to UK architecture/design magazine Blueprint and contributor to others. Previously, he has written for magazines covering contemporary and twentieth century art, real estate, technology, and music. Recent work includes contributing to architectural encyclopedias for Phaidon, editing the London ICA website, and articles in journals in France, Mexico and Croatia. Herbert is the author of three non-fiction books about skyscrapers and urbanism.
www.herbertwright.co.uk

10 Billion is published by Penguin Books UK.

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Review: Stephen Emmott - 10 Billion BOOKS: London-based architecture and urbanism writer Herbert Wright reviews 10 Billion, a pessimistic view of the future of humanity by Microsoft's Head of Computational Science, Stephen Emmott. "I think we're fucked," says Stephen Emmott, head of Microsoft's Computational Research Centre.

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Visual DSD: a design and analysis tool for DNA strand displacement systems

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Abstract. Summary: The Visual DSD (DNA Strand Displacement) tool allows rapid prototyping and analysis of computational devices implemented using DNA strand dis

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Climate Change and the Inescapable PresentAuthors

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Climate Change and the Inescapable PresentAuthors

# data[].attributes.attachment_link.readable_text
Adam, Barbara. Time. 2004. Cambridge, UK: Polity.

Arons, Wendy and Theresa J. May. 2012. "Introduction." In Readings in Performance and Ecology. Edited by Wendy Arons and Theresa J. May, 1-10. New York: Palgrave Macmillan. https://doi.org/10.1057/9781137011695_1

Billington, Michael. 2012. "Ten Billion - review." Guardian, July 19. Accessed July 15, 2017. https://www.theguardian.com/stage/2012/jul/19/ten-billion-review-royal-court.

Billington, Michael. 2011. "Greenland - review". Guardian, February 1. Accessed September 15, 2016. https://www.theguardian.com/stage/2011/feb/02/greenland-review

Bourne, Craig. 2006. "A Theory of Presentism." Canadian Journal of Philosophy 36 (1): 1-24. https://doi.org/10.1093/acprof:oso/9780199212804.003.

Buffini, Moira, Matt Charman, Penelope Skinner, and Jack Thorne. 2011. Greenland. London: Faber and Faber.

Chaudhuri, Una. 2014. "Introduction: Animal Acts for Changing Times, 2.0: A Field Guide to Interspecies Performance." In Animal Acts: Performing Species Today. Edited by Una Chaudhuri and Holly Hughes, 1-12. Ann Arbor: University of Michigan Press. https://doi.org/10.3998/mpub.5633302

Conway, Erik M. 2008. "What's in a Name? Global Warming vs. Climate Change." National Aeronautics and Space Administration, Dec. 12. Accessed May 15, 2018. https://www.nasa.gov/topics/earth/features/climate_by_any_other_name.html

Emmott, Stephen. 2013. Ten Billion. New York: Random House.

Fortier, Mark. 2002. Theory/Theatre: An Introduction. New York: Routledge.

Friedman, Lisa. 2017. "E.P.A. Scrubs a Climate Website of 'Climate Change.'" New York Times, October 20. Accessed February 13, 2018. https://www.nytimes.com/2017/10/20/climate/epa-climate-change.html

Gabbatiss, Josh. 2018. "Sea levels could be rising faster than predicted due to new source of Antarctic ice melting." Independent, April 18. Accessed June 9, 2018. https://www.independent.co.uk/environment/antarctica-sea-level-rise-ice-melting-climate-change-global-warming-positive-feedback-a8310681.html

Garner Jr., Stanton B. 1994. Bodied Spaces: Phenomenology and Performances in Contemporary Drama. Ithaca: Cornell University Press.

Ghosh, Amitav. 2016. The Great Derangement: Climate Change and the Unthinkable. Chicago: University of Chicago Press.

Heidegger, Martin. Being and Time. 1996. Translated by Joan Stambaugh. Albany: State University of New York Press.

Heise, Ursula. 2016. Imagining Extinction: The Cultural Meanings of Endangered Species. Chicago: University of Chicago Press. https://doi.org/10.7208/chicago/9780226358338.001.0001

Hoffman, Andrew. 2015. How Culture Shapes the Climate Change Debate. Stanford: Stanford University Press.

Hoffman, Piotr. 2006. "Death, time, history: Division II of Being and Time." In The Cambridge Companion to Heidegger, second edition. Edited by Charles B. Guignon, 222-240. Cambridge: Cambridge University Press. https://doi.org/10.1017/CCOL0521821363.009

Hudson, Julie. 2012. "'If You Want to Be Green Hold Your Breath': Climate Change in British Theatre." New Theatre Quarterly, 28 (3): 260-271. https://doi.org/10.1017/S0266464X12000449

Husserl, Edmund. 1991. On the Phenomenology of the Consciousness of Internal Time (1893-1917). Translated by John Barnett Brough. Norwell, MA: Kluwer Academic Publishers.

Johnston, Daniel. 2017. Theatre and Phenomenology: Manual Philosophy. London: Palgrave. https://doi.org/10.1057/978-1-137-53052-3

Klein, Naomi. 2014. This Changes Everything: Capitalism vs. The Climate. New York: Simon & Schuster.

Kolbert, Elizabeth. 2014. The Sixth Extinction: An Unnatural History. New York: Henry Hold and Company.

McKibben, Bill. 2009. "Climate Change." Foreign Policy 170: 32-38.

McKie Robin. 2012. "Stephen Emmott, co-creator of Ten Billion: Interview." Population Matters, July 22. Accessed July 23, 2016. http://www.populationmatters.org/stephen-emmott-cocreator-ten-billion-interview

Mellor, D.H. 1981. Real Time. Cambridge: Cambridge University Press.

Mellor, D. H. 1998. Real Time II. New York: Routledge. https://doi.org/10.4324/9780203302675

Rayner, Alice. 2006. Ghosts: Death's Double and Phenomena of Theatre. Minneapolis: University of Minnesota Press.

Rushkoff, Douglas. 2013. Present Shock: When Everything Happens Now. New York: Penguin.

States, Bert O. 1985. Great Reckonings in Little Rooms: On the Phenomenology of Theater. Los Angeles: University of California Press.

Taylor, Paul. 2011. "Greenland, National Theatre: Lyttelton, London." Independent, February 2. Accessed July 12, 2016. http://www.independent.co.uk/arts-entertainment/theatre-dance/reviews/greenland-national-theatre-lyttelton-london-2202512.html

Trueman, Matt. 2014. "Katie Mitchell and Duncan Macmillan on 2071." Matt Trueman: Theatre Critic and Journalist. November 23. Accessed August 15, 2016. http://matttrueman.co.uk/2014/11/katie-mitchell-and-duncan-macmillan-on-2071.html

Wacjman, Judy. 2008. "Life in the fast lane? Towards a sociology of technology and time." The British Journal of Sociology 59 (1): 59-77. https://doi.org/10.1111/j.1468-4446.2007.00182.x

Wiles, David. 2014. Theatre & Time. New York: Palgrave Macmillan. https://doi.org/10.1007/978-1-137-34387-1

Wolf, Matt. 2011. "In London, Climate Change Comes to the Stage." New York Times, February 8. Accessed July 9, 2016. http://www.nytimes.com/2011/02/09/arts/09iht-lon09.html

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Adam, Barbara. Time. 2004. Cambridge, UK: Polity. Arons, Wendy and Theresa J. May. 2012. "Introduction." In Readings in Performance and Ecology. Edited by Wendy Arons and Theresa J. May, 1-10. New York: Palgrave Macmillan. https://doi.org/10.1057/9781137011695_1 Billington, Michael. 2012. "Ten Billion - review." Guardian, July 19. Accessed July 15, 2017. https://www.theguardian.com/stage/2012/jul/19/ten-billion-review-royal-court.

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Hania Zlotnik assesses two polarized takes on population growth and planetary capacity.

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Defining an essential transcription factor program for naïve pluripotency

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https://royalsocietypublishing.org/doi/full/10.1098/rspb.2013.1452

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Scientists and software – surveying the species distribution modelling community

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Scientists and software – surveying the species distribution modelling community

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https://onlinelibrary.wiley.com/doi/full/10.1111/ddi.12305

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https://www.frontiersin.org/articles/10.3389/fmats.2015.00006/full

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Cavendish Laboratory, University of Cambridge, Cambridge, UK

Computational Science Laboratory, Microsoft Research, Cambridge, UK

Introduction and Background

Photovoltaic (PV) solar energy conversion is one of the most promising emerging renewable energy technologies and is likely to play an ever-increasing role in the future of electricity generation. The potential of photovoltaics is considered to be much larger than the current energy demand, which is equivalent to ≈13,000 millions tons of oil ( BP, 2014). Together with other renewable energy sources, photovoltaics could thus considerably aid the decarbonization of the energy sector in the near future, an objective, which is (should be) seriously sought after to meet the IPCC 2°C target. Indeed, PV energy conversion is an ever-increasing research field having an impact on other sectors: technological (e.g., dealing with energy storage and distribution issues) and economical (e.g., evaluating the costs of PV devices compared to conventional energy technologies).

The typical working principle of a photovoltaic cell is quite simple, and well understood in terms of doped inorganic semiconductors ( Wurfel, 2009). Photovoltaic technology has significantly moved forward since the first silicon (Si) solar cell having an efficiency of ≈4% was invented in 1950s in Bell Laboratories, following the breakthrough of the p-n junction developed by Shockley, Brattain, and Bardeen ( Chapin et al., 1954). Since then, research on PV has led to single-junction and multi-junction solar cells with record efficiencies in controlled (laboratory) environments of ≈29% ( Green et al., 2014) and 46% ( Fraunhofer-ISE, 2014), respectively. However, the efficiency of the most popular technologies in the commercial market - which belong to the so called first generation of solar cells, mainly based on crystalline Si - is in the 10-18% range and around 25% in laboratory. A second generation of cells, mainly aimed at reducing the fabrication costs, is based on thin film architectures, where light is absorbed and charge generated in a solid thin layer of semiconductor (e.g., CdTe, CIGS), with efficiencies restrained to around 22%. Beyond these technologies, a variety of other PV designs has emerged, mainly driven by the progress on materials research and the need of low-cost manufacture devices, organic photovoltaic (OPV) being one of the most interesting solutions. The latest scientific developments in this field have taken various directions (using different concepts, materials, and geometries), all having the potential to result in high-efficiency PV devices. Most current approaches, which have been termed third generation PV [see e.g., Green (2003)], explore schemes beyond the guidelines given by Shockley and Queisser (1961), which are based on the assumption that each photon absorbed above the band gap of the semiconductor photovoltaic material generates a single electron-hole pair. These methods include solar cells exploiting multiple-exciton generation in both inorganic and organic materials, hot carriers collection, up- and down-conversions.

Reviewing the extensive literature about the past and present PV schemes is beyond the scope of this Opinion, many excellent reviews having already succeeded in this (challenging) task. Here, we will illustrate and highlight our point of view on an emergent strategy for PV and artificial light-harvesting in general. This strategy holds onto an interdisciplinary framework encompassing biology and quantum mechanics. Ideas, which have been developed within this nascent trend, are inspired by the recent discovery of quantum effects in biological systems ( Mohseni et al., 2014), and aim to model and reproduce the highly efficient reactions often exhibited by these systems using the tools of quantum mechanics and nanotechnology platforms. Novel theoretical proposals, for instance ( Scully, 2010; Blankenship et al., 2011; Scully et al., 2011; Creatore et al., 2013; Zhang et al., 2015), have underlined the relevance of effects of quantum coherence to enhance the performance of photocells whose working principle is inspired by the architecture of biological light-harvesting complexes (LHCs).

Emergent Schemes for Artificial Light-Harvesting

Mimicking photosynthesis is perhaps the most intuitive scheme for building photovoltaic devices: as photovoltaic solar cells convert sunlight into electricity, biological LHCs use photosynthesis to convert sunlight into chemical energy necessary for sustaining their life. Biological photosynthesis begins with an ultrafast sequence of photophysical events, known as light reactions, which occur via a diverse range of nanoscaled pigment-protein complexes (PPCs) found in photosynthetic plants, algae, and bacteria ( Blankenship, 2008). There reactions proceed through the creation of molecular excited states (excitons) by the absorption of solar photons, then followed by intra- and/or inter-PPC-exciton energy transfer (EET); finally, the excited state energy reaches a reaction center (RC) where it is used to initiate charge separation, by releasing an energetic electron for the later stages of the photosynthetic chemistry, i.e., to start chemical reactions to obtain carbohydrates. Intriguingly, the efficiency of these light reactions can often be close to 100%, suggesting a carefully optimization of the deleterious processes (decay and charge recombination, excited state trapping), which plague the attempts to carry out similar processes in artificial solar cells.

Recent experimental and theoretical studies demonstrate that quantum-mechanical effects (e.g., coherent quantum dynamics) play a previously unexpected and relevant role in biological EET ( Mohseni et al., 2014). Quantum effects have been shown to survive even at ambient temperatures and are possibly beneficial to the observed very high efficiencies exhibited by these biological structures. Consequently, there has been an ever-increasing interest in understanding and modeling the nanoscaled structures and the related pigment-pigment interactions, which drive the natural light reactions so efficiently. Mimicking the physics of the photosynthetic PPCs could be extremely valuable for the future design of more efficient artificial light-harvesting structures based on the exploitation of quantum effects.

A powerful idea for recognizing the role played by quantum effects in the light reactions, one based on the fundamental principles of quantum mechanics, has recently been proposed by Scully (2010), a pioneer of theoretical quantum optics. By modeling the light reactions (photon absorption/emission, excitonic energy transfer) as a cyclic working process, analogous to a thermodynamic Carnot cycle, Scully has presented a formalism, which allows the performance of the light reactions to be analyzed in a way similar to standard thermodynamics heat engines. Within this framework, the solar photonic energy is described in terms of a "hot" thermal bath that is then converted through photon absorption to low-entropy useful work, i.e., the production of high energy electrons later used for the subsequent photosynthetic chemistry. The relevance of the quantum heat engine (QHE) approach is exemplified by the famous calculation of Shockley and Queisser (1961) who used similar thermodynamical considerations to determine the fundamental maximum efficiency of the single band-gap (junction) semiconductor solar cells ( Wurfel, 2009). More specifically, Scully has shown that in photocells consisting of elements that support quantum coherence, it is possible to suppress the emission process. Spontaneous emission of photons before the absorbed energy can be used to perform any work is indeed the most important as well as unavoidable loss mechanisms in optical energy extraction. Under open-circuit conditions, i.e., when no work current is extracted, the maximum voltage that can be obtained is determined by the detailed balance of absorption and spontaneous emission. Hence, breaking the detailed balance by quenching the spontaneous emission may lead to enhanced power extraction and current generation in photocells ( Scully, 2010).

An appealing direction to explore will thus combine the utility and elegance of the quantum heat engine framework and the nanoscale architecture of the highly efficient photosynthetic complexes. As an example developed along these lines, we have recently presented a scheme of a photocell inspired by the molecular elements of biological RCs ( Creatore et al., 2013). In photosynthesis, the RC consists of a "special pair" of photon-absorbing pigments (donors), which are coupled through an electron-accepting molecule to a work extraction stage.

The scheme we propose harnesses well understood and experimentally observed quantum effects, such as the formation of delocalized excited (exciton) states resulting from the long-range dipole-dipole interaction (excitonic coupling) between closely spaced pigments. In our RC-inspired photocell, such dipolar interaction between the pair or light-absorbing donor molecules leads to the formation of new optically excitable states and quantum interference effects, which can enhance the photocell performance relative to a "classical" scheme in which these effects are not present (Figure 1, left panel). In particular, we have shown how dipolar couplings and related quantum interference effects can be tailored to increase the efficiency of charge transfer at the acceptor molecule, thus leading to enhanced light-to-current generation and power extraction at the reaction center. Although not a model of an actual RC, this proposal shows what could be achieved with the same resources, i.e., a pair of absorbers, an electron acceptor, and the ability to "build" the electronic structure, the quantum states, and their interactions. A recent follow-up ( Zhang et al., 2015) (proposed by the Group of S. Kais and one of the groundbreakers of quantum biology, G. S. Engel) of our model has highlighted the originality of the QHE strategy for realizing PV devices based on photosynthesis, finding even greater efficiency gains in coherent trimer structures. The relevance of a new class of bio-inspired quantum nanotechnology, however, is not just restrained to reproduce/enhance efficient operations observed in nature. Higgins et al. (2014), for instance, have shown theoretically that well-established quantum control techniques can be used to achieve an enhanced photon absorption (a phenomenon called "superabsorption") in simple ring nanostructures reminiscent of photosynthetic LHCs (Figure 1, right panel), whereas these natural systems themselves are not designed to exploit this effect. Superabsorption, which is the absorptive analog of quantum superradiance, has a range of applications including photon detection in the context of optical sensors and light-based power transmissions ( Higgins et al., 2014).

Figure 1. Left panel: the photosynthetic reaction center modeled in Creatore et al. (2013), consisting of two donor molecules (red spheres) and one acceptor molecule (green sphere). In (A), the donor and are identical and uncoupled and both able to absorb and re-emit light (see the dipole moments and ). In (B), the dipolar excitonic coupling between and leads to new excitable donor states ( and ) resulting from the quantum superposition of the original donor states: is a bright state with optical dipole moment , while is a dark state, which cannot absorb or emit photons. Under a variety of realistic and experimentally realizable parameters, the presence of a dark state lowers the role of deleterious radiative recombination processes, while increases the efficiency of charge transfer at the acceptor A. This leads to an enhanced current and power generation at the RC, compared to the uncoupled/ classical scheme of (A); see Creatore et al. (2013) for details. Right panel: a collection of N dipoles arranged in a ring structure reminiscent of the photosynthetic light-harvesting complex LH1. By using environmental quantum control techniques, Higgins et al. (2014) have shown that it is possible to achieve a regime ( superabsorption), in which the N dipoles absorb incident photons with a rate proportional to , this effect being the absorptive analog of quantum superradiance. This model could be relevant to applications to solar energy harvesting, where the excitation induced by the absorption of solar photons are efficiently transferred from the ring structure to a central core absorber and then to a center converting such excitations into stored energy; see Higgins et al. (2014) for details. [Copied with permission from the original paper: Nat. Commun. 5, Article number: 4705 (2014).]

These bio-inspired schemes are potentially realizable in artificial molecular light-harvesting systems through existing organic synthesis routes. A fundamental breakthrough in this direction has been recently achieved by Hayes et al. (2013), who have engineered an artificial synthetic system (consisting of a series of rigid synthetic heterodimers) supporting the persistent electronic coherences observed in biological systems and believed to be beneficial for the process of energy transfer. Another direction for the realization of these models, one based on solid-state nanotechnology architectures, is now viable due to the increasing level of experimental control over nano-optical systems gained in recent years, and involves the use of solid-state chromophores such as quantum dots, superconducting qubits, plasmonic nanostructures, and possibly hybrid combinations of the above. From the theoretical/computational point of view, advanced simulation techniques to complement the Hamiltonian/microscopic approaches (based on setting up and solving quantum optical master equations) used in the aforementioned proposals, are required. These techniques are useful to precisely assess the optical geometry and the stability of the engineered quantum states, including the role of environmental degrees of freedom (using, e.g., time-dependent density matrix renormalization group methods) and details of additional states, e.g., charge-transfer states (using, e.g., tight binding models).

Conclusion

Innovative ideas are needed to satisfy the ever-growing interest and expectations on solar energy conversion. In this Opinion, we have presented the prospects of an emergent novel strategy to harvest solar energy based on replicating mechanisms developed in nature, and realizable using the theoretical and experimental tools of quantum mechanics. Although at a nascent stage, this framework is attracting an increasing attention and we think that the proposals here discussed suggest an encouraging trend for biologically inspired PV. Further, the technological capabilities are now fully developed for translating the theoretical schemes in experimental proofs of concept/prototypes. Besides, we believe that results and solutions established along these new lines will be beneficial to the evolving and interconnected fields of non-equilibrium ( Bustamante et al., 2005) and quantum thermodynamics ( Gemmer et al., 2004; Kosloff, 2013) as well. Biological processes usually take place at the micro- and nano-scale and evolve in out-of-equilibrium conditions - energy transfer and the motion of molecular motors being paradigmatic examples. The same scenario applies to nanoscale devices, which, through precise quantum-mechanical control, could be employed to unravel the thermodynamic relations between the non-equilibrium evolution of molecular quantum states and the enhanced (or reduced) working efficiency of biological or biologically inspired processes harnessing these states.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnote

References

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Emergent Models for Artificial Light-Harvesting

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Introduction and backgroundPhotovoltaic (PV) solar energy conversion is one of the most promising emerging renewable energy technologies and is likely to play an ever increasing role in the future of electricity generation. The potential of photovoltaics is considered to be much larger than the current energy demand, which is equivalent to ~ 13,000 millions tons of oil [3].

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Humans: the real threat to life on Earth

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Humans: the real threat to life on Earth

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arth is home to millions of species. Just one dominates it. Us. Our cleverness, our inventiveness and our activities have modified almost every part of our planet. In fact, we are having a profound impact on it. Indeed, our cleverness, our inventiveness and our activities are now the drivers of every global problem we face. And every one of these problems is accelerating as we continue to grow towards a global population of 10 billion. In fact, I believe we can rightly call the situation we're in right now an emergency - an unprecedented planetary emergency.

We humans emerged as a species about 200,000 years ago. In geological time, that is really incredibly recent. Just 10,000 years ago, there were one million of us. By 1800, just over 200 years ago, there were 1 billion of us. By 1960, 50 years ago, there were 3 billion of us. There are now over 7 billion of us. By 2050, your children, or your children's children, will be living on a planet with at least 9 billion other people. Some time towards the end of this century, there will be at least 10 billion of us. Possibly more.

We got to where we are now through a number of civilisation- and society-shaping "events", most notably the agricultural revolution, the scientific revolution, the industrial revolution and - in the West - the public-health revolution. By 1980, there were 4 billion of us on the planet. Just 10 years later, in 1990, there were 5 billion of us. By this point initial signs of the consequences of our growth were starting to show. Not the least of these was on water. Our demand for water - not just the water we drank but the water we needed for food production and to make all the stuff we were consuming - was going through the roof. But something was starting to happen to water.

Back in 1984, journalists reported from Ethiopia about a famine of biblical proportions caused by widespread drought. Unusual drought, and unusual flooding, was increasing everywhere: Australia, Asia, the US, Europe. Water, a vital resource we had thought of as abundant, was now suddenly something that had the potential to be scarce.

By 2000 there were 6 billion of us. It was becoming clear to the world's scientific community that the accumulation of CO ₂, methane and other greenhouse gases in the atmosphere - as a result of increasing agriculture, land use and the production, processing and transportation of everything we were consuming - was changing the climate. And that, as a result, we had a serious problem on our hands; 1998 had been the warmest year on record. The 10 warmest years on record have occurred since 1998.

We hear the term "climate" every day, so it is worth thinking about what we actually mean by it. Obviously, "climate" is not the same as weather. The climate is one of the Earth's fundamental life support systems, one that determines whether or not we humans are able to live on this planet. It is generated by four components: the atmosphere (the air we breathe); the hydrosphere (the planet's water); the cryosphere (the ice sheets and glaciers); the biosphere (the planet's plants and animals). By now, our activities had started to modify every one of these components.

Our emissions of CO ₂ modify our atmosphere. Our increasing water use had started to modify our hydrosphere. Rising atmospheric and sea-surface temperature had started to modify the cryosphere, most notably in the unexpected shrinking of the Arctic and Greenland ice sheets. Our increasing use of land, for agriculture, cities, roads, mining - as well as all the pollution we were creating - had started to modify our biosphere. Or, to put it another way: we had started to change our climate.

There are now more than 7 billion of us on Earth. As our numbers continue to grow, we continue to increase our need for far more water, far more food, far more land, far more transport and far more energy. As a result, we are accelerating the rate at which we're changing our climate. In fact, our activities are not only completely interconnected with but now also interact with, the complex system we live on: Earth. It is important to understand how all this is connected.

Let's take one important, yet little known, aspect of increasing water use: "hidden water". Hidden water is water used to produce things we consume but typically do not think of as containing water. Such things include chicken, beef, cotton, cars, chocolate and mobile phones. For example: it takes around 3,000 litres of water to produce a burger. In 2012 around five billion burgers were consumed in the UK alone. That's 15 trillion litres of water - on burgers. Just in the UK. Something like 14 billion burgers were consumed in the United States in 2012. That's around 42 trillion litres of water. To produce burgers in the US. In one year. It takes around 9,000 litres of water to produce a chicken. In the UK alone we consumed around one billion chickens in 2012. It takes around 27,000 litres of water to produce one kilogram of chocolate. That's roughly 2,700 litres of water per bar of chocolate. This should surely be something to think about while you're curled up on the sofa eating it in your pyjamas.

But I have bad news about pyjamas. Because I'm afraid your cotton pyjamas take 9,000 litres of water to produce. And it takes 100 litres of water to produce a cup of coffee. And that's before any water has actually been added to your coffee. We probably drank about 20 billion cups of coffee last year in the UK. And - irony of ironies - it takes something like four litres of water to produce a one-litre plastic bottle of water. Last year, in the UK alone, we bought, drank and threw away nine billion plastic water bottles. That is 36 billion litres of water, used completely unnecessarily. Water wasted to produce bottles - for water. And it takes around 72,000 litres of water to produce one of the 'chips' that typically powers your laptop, Sat Nav, phone, iPad and your car. There were over two billion such chips produced in 2012. That is at least 145 trillion litres of water. On semiconductor chips. In short, we're consuming water, like food, at a rate that is completely unsustainable.

Demand for land for food is going to double - at least - by 2050, and triple - at least - by the end of this century. This means that pressure to clear many of the world's remaining tropical rainforests for human use is going to intensify every decade, because this is predominantly the only available land that is left for expanding agriculture at scale. Unless Siberia thaws out before we finish deforestation. By 2050, 1bn hectares of land is likely to be cleared to meet rising food demands from a growing population. This is an area greater than the US. And accompanying this will be three gigatons per year extra CO ₂ emissions.If Siberia does thaw out before we finish our deforestation, it would result in a vast amount of new land being available for agriculture, as well as opening up a very rich source of minerals, metals, oil and gas. In the process this would almost certainly completely change global geopolitics. Siberia thawing would turn Russia into a remarkable economic and political force this century because of its newly uncovered mineral, agricultural and energy resources. It would also inevitably be accompanied by vast stores of methane - currently sealed under the Siberian permafrost tundra - being released, greatly accelerating our climate problem even further.

Amazon rainforest smoulders after being cleared for cattle pasture in Brazil. Photograph: Michael Nichols/Getty Images

Meanwhile, another 3 billion people are going to need somewhere to live. By 2050, 70% of us are going to be living in cities. This century will see the rapid expansion of cities, as well as the emergence of entirely new cities that do not yet exist. It's worth mentioning that of the 19 Brazilian cities that have doubled in population in the past decade, 10 are in the Amazon. All this is going to use yet more land.

We currently have no known means of being able to feed 10 billion of us at our current rate of consumption and with our current agricultural system. Indeed, simply to feed ourselves in the next 40 years, we will need to produce more food than the entire agricultural output of the past 10,000 years combined. Yet food productivity is set to decline, possibly very sharply, over the coming decades due to: climate change; soil degradation and desertification - both of which are increasing rapidly in many parts of the world; and water stress. By the end of this century, large parts of the planet will not have any usable water.

At the same time, the global shipping and airline sectors are projected to continue to expand rapidly every year, transporting more of us, and more of the stuff we want to consume, around the planet year on year. That is going to cause enormous problems for us in terms of more CO ₂ emissions, more black carbon, and more pollution from mining and processing to make all this stuff.

But think about this. In transporting us and our stuff all over the planet, we are also creating a highly efficient network for the global spread of potentially catastrophic diseases. There was a global pandemic just 95 years ago - the Spanish flu pandemic, which is now estimated to have killed up to 100 million people. And that's before one of our more questionable innovations - the budget airline - was invented. The combination of millions of people travelling around the world every day, plus millions more people living in extremely close proximity to pigs and poultry - often in the same room, making a new virus jumping the species barrier more likely - means we are increasing, significantly, the probability of a new global pandemic. So no wonder then that epidemiologists increasingly agree that a new global pandemic is now a matter of "when" not "if".

We are going to have to triple - at least - energy production by the end of this century to meet expected demand. To meet that demand, we will need to build, roughly speaking, something like: 1,800 of the world's largest dams, or 23,000 nuclear power stations, 14m wind turbines, 36bn solar panels, or just keep going with predominantly oil, coal and gas - and build the 36,000 new power stations that means we will need.Our existing oil, coal and gas reserves alone are worth trillions of dollars. Are governments and the world's major oil, coal and gas companies - some of the most influential corporations on Earth - really going to decide to leave the money in the ground, as demand for energy increases relentlessly? I doubt it.

Meanwhile the emerging climate problem is on an entirely different scale. The problem is that we may well be heading towards a number of critical "tipping points" in the global climate system. There is a politically agreed global target - driven by the Intergovernmental Panel on Climate Change (IPCC) - to limit the global average temperature rise to 2C. The rationale for this target is that a rise above 2C carries a significant risk of catastrophic climate change that would almost certainly lead to irreversible planetary "tipping points", caused by events such as the melting of the Greenland ice shelf, the release of frozen methane deposits from Arctic tundra, or dieback of the Amazon. In fact, the first two are happening now - at below the 2C threshold.

As for the third, we're not waiting for climate change to do this: we're doing it right now through deforestation. And recent research shows that we look certain to be heading for a larger rise in global average temperatures than 2C - a far larger rise. It is now very likely that we are looking at a future global average rise of 4C - and we can't rule out a rise of 6C. This will be absolutely catastrophic. It will lead to runaway climate change, capable of tipping the planet into an entirely different state, rapidly. Earth will become a hellhole. In the decades along the way, we will witness unprecedented extremes in weather, fires, floods, heatwaves, loss of crops and forests, water stress and catastrophic sea-level rises. Large parts of Africa will become permanent disaster areas. The Amazon could be turned into savannah or even desert. And the entire agricultural system will be faced with an unprecedented threat.

More "fortunate" countries, such as the UK, the US and most of Europe, may well look like something approaching militarised countries, with heavily defended border controls designed to prevent millions of people from entering, people who are on the move because their own country is no longer habitable, or has insufficient water or food, or is experiencing conflict over increasingly scarce resources. These people will be "climate migrants". The term "climate migrants" is one we will increasingly have to get used to. Indeed, anyone who thinks that the emerging global state of affairs does not have great potential for civil and international conflict is deluding themselves. It is no coincidence that almost every scientific conference that I go to about climate change now has a new type of attendee: the military.

Every which way you look at it, a planet of 10 billion looks like a nightmare. What, then, are our options?

The only solution left to us is to change our behaviour, radically and globally, on every level. In short, we urgently need to consume less. A lot less. Radically less. And we need to conserve more. A lot more. To accomplish such a radical change in behaviour would also need radical government action. But as far as this kind of change is concerned, politicians are currently part of the problem, not part of the solution, because the decisions that need to be taken to implement significant behaviour change inevitably make politicians very unpopular - as they are all too aware.

So what politicians have opted for instead is failed diplomacy. For example: The UN Framework Convention on Climate Change, whose job it has been for 20 years to ensure the stabilisation of greenhouse gases in the Earth's atmosphere: Failed. The UN Convention to Combat Desertification, whose job it's been for 20 years to stop land degrading and becoming desert: Failed. The Convention on Biological Diversity, whose job it's been for 20 years to reduce the rate of biodiversity loss: Failed. Those are only three examples of failed global initiatives. The list is a depressingly long one. And the way governments justify this level of inaction is by exploiting public opinion and scientific uncertainty. It used to be a case of, "We need to wait for science to prove climate change is happening". This is now beyond doubt. So now it's, "We need to wait for scientists to be able to tell us what the impact will be and the costs". And, "We need to wait for public opinion to get behind action". But climate models will never be free from uncertainties. And as for public opinion, politicians feel remarkably free to ignore it when it suits them - wars, bankers' bonuses and healthcare reforms, to give just three examples.

What politicians and governments say about their commitment to tackling climate change is completely different from what they are doing about it.

What about business? In 2008 a group of highly respected economists and scientists led by Pavan Sukhdev, then a senior Deutsche Bank economist, conducted an authoritative economic analysis of the value of biodiversity. Their conclusion? The cost of the business activities of the world's 3,000 largest corporations in loss or damage to nature and the environment now stands at $2.2tn per year. And rising. These costs will have to be paid for in the future. By your children and your grandchildren. To quote Sukhdev: "The rules of business urgently need to be changed, so corporations compete on the basis of innovation, resource conservation and satisfaction of multiple stakeholder demands, rather than on the basis of who is most effective in influencing government regulation, avoiding taxes and obtaining subsidies for harmful activities to maximise the return for shareholders." Do I think that will happen? No. What about us?

I confess I used to find it amusing, but I am now sick of reading in the weekend papers about some celebrity saying, "I gave up my 4×4 and now I've bought a Prius. Aren't I doing my bit for the environment?" They are not doing their bit for the environment. But it's not their fault. The fact is that they - we - are not being well informed. And that's part of the problem. We're not getting the information we need. The scale and the nature of the problem is simply not being communicated to us. And when we are advised to do something, it barely makes a dent in the problem. Here are some of the changes we've been asked to make recently, by celebrities who like to pronounce on this sort of thing, and by governments, who should know better than to give out this kind of nonsense as 'solutions': Switch off your mobile phone charger; wee in the shower (my favourite); buy an electric car (no, don't); use two sheets of loo roll rather than three. All of these are token gestures that miss the fundamental fact that the scale and nature of the problems we face are immense, unprecedented and possibly unsolvable.

The behavioural changes that are required of us are so fundamental that no one wants to make them. What are they? We need to consume less. A lot less. Less food, less energy, less stuff. Fewer cars, electric cars, cotton T-shirts, laptops, mobile phone upgrades. Far fewer.And here it is worth pointing out that "we" refers to the people who live in the west and the north of the globe. There are currently almost 3 billion people in the world who urgently need to consume more: more water, more food, more energy. Saying "Don't have children" is utterly ridiculous. It contradicts every genetically coded piece of information we contain, and one of the most important (and fun) impulses we have. That said, the worst thing we can continue to do - globally - is have children at the current rate. If the current global rate of reproduction continues, by the end of this century there will not be 10 billion of us. According to the United Nations, Zambia's population is projected to increase by 941% by the end of this century. The population of Nigeria is projected to grow by 349% - to 730 million people.

Afghanistan by 242%.

Democratic Republic of Congo 213%.

Gambia by 242%.

Guatemala by 369%.

Iraq by 344%.

Kenya by 284%.

Liberia by 300%.

Malawi by 741%.

Mali by 408%.

Niger by 766%.

Somalia by 663%.

Uganda by 396%.

Yemen by 299%.

Even the United States' population is projected to grow by 54% by 2100, from 315 million in 2012 to 478 million. I do just want to point out that if the current global rate of reproduction continues, by the end of this century there will not be 10 billion of us - there will be 28 billion of us.

Where does this leave us? Let's look at it like this. If we discovered tomorrow that there was an asteroid on a collision course with Earth and - because physics is a fairly simple science - we were able to calculate that it was going to hit Earth on 3 June 2072, and we knew that its impact was going to wipe out 70% of all life on Earth, governments worldwide would marshal the entire planet into unprecedented action. Every scientist, engineer, university and business would be enlisted: half to find a way of stopping it, the other half to find a way for our species to survive and rebuild if the first option proved unsuccessful. We are in almost precisely that situation now, except that there isn't a specific date and there isn't an asteroid. The problem is us. Why are we not doing more about the situation we're in - given the scale of the problem and the urgency needed - I simply cannot understand. We're spending €8bn at Cern to discover evidence of a particle called the Higgs boson, which may or may not eventually explain mass and provide a partial thumbs-up for the standard model of particle physics. And Cern's physicists are keen to tell us it is the biggest, most important experiment on Earth. It isn't. The biggest and most important experiment on Earth is the one we're all conducting, right now, on Earth itself. Only an idiot would deny that there is a limit to how many people our Earth can support. The question is, is it seven billion (our current population), 10 billion or 28 billion? I think we've already gone past it. Well past it.

Science is essentially organised scepticism. I spend my life trying to prove my work wrong or look for alternative explanations for my results. It's called the Popperian condition of falsifiability. I hope I'm wrong. But the science points to my not being wrong. We can rightly call the situation we're in an unprecedented emergency. We urgently need to do - and I mean actually do - something radical to avert a global catastrophe. But I don't think we will. I think we're fucked. I asked one of the most rational, brightest scientists I know - a scientist working in this area, a young scientist, a scientist in my lab - if there was just one thing he had to do about the situation we face, what would it be? His reply? "Teach my son how to use a gun."
This is an edited extract from Ten Billion, by Stephen Emmott (Penguin, £6.99)

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Humans - the real threat to life on Earth

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arth is home to millions of species. Just one dominates it. Us. Our cleverness, our inventiveness and our activities have modified almost every part of our planet. In fact, we are having a profound impact on it. Indeed, our cleverness, our inventiveness and our activities are now the drivers of every global problem we face.

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Plagues have inflicted misery and suffering throughout history. They can be traced through generations in our genes, with echoes in religion and literature. Featuring essays arising from the 2014 Darwin College Lectures, this book examines the spectrum of tragic consequences of different types of plagues, from infectious diseases to over-population and computer viruses.

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New details about natural and anthropogenic processes are continually added to models of the Earth system, anticipating that the increased realism will increase the accuracy of their predictions. However, perspectives differ about whether this approach will improve the value of the information the models provide to decision makers, scientists, and societies.

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https://core.ac.uk/download/pdf/80367885.pdf

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Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model

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Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model

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https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001841

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Michael B. J. Harfoot,

Tim Newbold,

Derek P. Tittensor,

Stephen Emmott,

Jon Hutton,

Vassily Lyutsarev,

Matthew J. Smith,

Jörn P. W. Scharlemann,

Drew W. Purves

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Abstract

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures.

Author Summary

Ecosystems across the world are being rapidly degraded. This threatens their provision of natural goods and services, upon which all life depends. To be able to reduce-and one day reverse-this damage, we need to be able to predict the effects of human actions on ecosystems. Here, we present the first example of a General Ecosystem Model (GEM)-called the Madingley Model-a novel class of computational model that can be applied to any ecosystem, marine or terrestrial, and can be simulated at any spatial scale from local up to global. It covers almost all organisms in ecosystems, from the smallest to the largest, encoding the underlying biology and behaviour of individual organisms to capture the interactions between them and with the environment, to model the fate of each individual organism, and to make predictions about ecosystem structure and function. Predictions made by the Madingley Model broadly resemble what we observe in real-world ecosystems across scales from individuals through to communities, ecosystems, and the world as a whole. Our results show that ecologists can now begin modelling all nonhuman life on earth, and we suggest that this type of approach may hold promise for predicting the ecological implications of different future trajectories of human activity on our shared planet.

Citation: Harfoot MBJ, Newbold T, Tittensor DP, Emmott S, Hutton J, Lyutsarev V, et al. (2014) Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model. PLoS Biol 12(4): e1001841. https://doi.org/10.1371/journal.pbio.1001841

Academic Editor: Michel Loreau, Centre National de la Recherche Scientifique, France

Received: December 19, 2013; Accepted: March 10, 2014; Published: April 22, 2014

Copyright: © 2014 Harfoot et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This research was funded by United Nations Environment Programme World Conservation Monitoring Centre and the Computational Science Laboratory, Microsoft Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: DGVMs, dynamic global vegetation models; DOC, dissolved organic carbon; ESMs, Earth System Models; EwE, Ecopath With Ecosim; GEM, General Ecosystem Model; NBS, normalised body mass spectra; NPP, net primary productivity; POC, particulate organic carbon

Introduction

The pace and scale of anthropogenic environmental change has caused the widespread degradation of ecosystems and the services they provide that ultimately support human life on Earth [1]. Understanding and mitigating these impacts necessitates the development of a suite of tools, including policy instruments, practical conservation measures, and empirical research. At present, a variety of models are used to assist decision-making in relation to biodiversity and ecosystem services. Most are correlative, relying on statistical relationships derived from limited observational data without explicit reference to the underlying mechanisms; examples include the GLOBIO model, species distribution models, and models of local extinction based on species-area relationships [2]-[4]. All of these models are useful, for different purposes. However, what is urgently needed is mechanistic models, which explicitly represent the biological, physiological, and ecological mechanisms underlying the systems in question [5]. One of the key benefits of mechanistic models is that they are likely to make more accurate predictions under novel conditions [6]. For example, Earth System Models (ESMs), containing mechanistic descriptions of multiple interacting components of the climate, atmosphere, and ocean, are used to project properties and dynamics under future climate conditions that have not been observed previously (at least in relation to historical data) [7]. Similarly, mechanistic models of ecosystems would allow us to predict a given combination of human pressures on a given ecosystem, even when there is no or little historical data on which to rely. Mechanistic models can also improve our understanding of the systems being modelled, allowing predictions to be understood in relation to the underlying mechanisms that generate them [8]. This in turn might lead to novel ways to mitigate or even reverse the degradation of ecosystems.

We present the first process-based, mechanistic General Ecosystem Model (GEM) (called the Madingley Model). It is general in the sense that it strives to use a unified set of fundamental ecological concepts and processes for any ecosystem to which it is applied, either terrestrial or marine, and it can be simulated at any spatial resolution from local up to global scales. Applying a general modelling approach globally has three main advantages: (1) it allows testing of whether the same set of ecological mechanisms and concepts can adequately capture broad-scale ecosystem behaviour in both the marine and terrestrial realms; (2) it enables the development of a suite of predictive outputs common to both realms, from which standardised metrics of ecosystem health can be calculated; and (3) it enables links between marine and terrestrial ecosystems, both natural and human-driven, to be modelled. The model is also spatially explicit, with dynamics in a given location driven by the climate and other local factors, as well as by connections with other ecosystems through dispersal, and is mechanistic, with dynamics being driven by ecological processes defined at the level of individual organisms. Specifically, we model autotroph (plant) stocks, and individual herbivorous, omnivorous, and carnivorous animals of all body sizes, and their interactions. From these interactions, patterns emerge at larger spatial and temporal scales, including communities, ecosystems, and global macroecological gradients, without any direct model constraints imposed on those properties.

Such a GEM has great potential if it can, at a minimum, reproduce the observed properties of ecosystem structure and function, and enable the formation of valuable, novel hypotheses, and precise, testable predictions. Here, we test the model's ability to simulate ecosystems that persist over long time scales (1,000 y) by comparing model predictions with empirical data and test two theoretical predictions that to date have not been assessed empirically and have only been studied with simple trophic models: that the net primary productivity (NPP) of ecosystems determines the length of trophic chains [9],[10] and that herbivore pressure on autotrophs will reduce once a critical level of carnivore biomass is supported ("trophic release hypothesis" [11]). Finally, we provide a suite of other novel predictions that demonstrate the potential utility of the model as an operational tool with which the effects of human impacts on ecosystems can be explored.

Mechanistic models of specific ecosystems have been developed previously, and to date these have been constrained to particular spatial locations or to particular sets of organisms within ecosystems. For example, dynamic global vegetation models (DGVMs) are used to represent the physiological and ecological processes driving plant community dynamics on the global land surface, enabling investigations into how terrestrial vegetation interacts with climate [12]. However, these do not include animals or other heterotrophs, and so are limited in the extent to which they can be used to understand the roles of heterotrophs in ecosystems, or to address questions about the conservation of organisms other than plants. For the marine realm, "end-to-end" ecosystem models have been developed, which include most trophic levels for particular regions [13]. Examples are the Ecopath With Ecosim (EwE) [14] and Atlantis [15] models. But marine models tend to focus either on biogeochemical cycles or on organisms of economic importance, such as fish, rather than on the properties of the ecosystem as a whole. They also generally either use a stock-and-flow formulation [14],[15], making them unable to follow trajectories of individual organisms, or are restricted to simulating particular guilds of organisms [16].

There have also been previous theoretical examinations of the potential effect of select processes on select ecosystem metrics, such as the role of bioenergetics in determining size distributions [17]. Such theoretical studies have been very useful in providing insight into the potential mechanisms underlying ecosystem structure; but they have tended to be carried out for single, abstract locations that are not tied to any real geographical location and to omit most of the key processes affecting ecosystem structure and function in reality. We are not aware of any previous attempt to model emergent ecosystem structure and function by identifying a minimal, but putatively complete, set of key processes and then simulating these processes for all organisms globally, over the actual climate and geography of all marine and terrestrial environments. It is for these reasons that we refer to the model presented here as a GEM.

Methods

Model Scope

We identified a set of core biological and ecological processes necessary to predict ecosystem-level properties: primary production for autotrophs, and eating, metabolism, growth, reproduction, dispersal, and mortality for heterotrophs ( Figure 1 and Box 1). We modelled both marine and terrestrial environments but excluded freshwater ecosystems. We included all photoautotrophs, and all heterotrophs that feed on living organisms (i.e., we did not include chemoautotrophs and detritivores). We generally represented only macroscopic organisms (>1×10 −3 g), except that we included plankton in the marine realm because of their known importance to the marine food web [18]. All plant biomass in the terrestrial realm was modelled, but only leaves, flowers, fruits, and seeds were available as a food source for herbivores and omnivores. The marine component included phytoplanktonic autotrophs, which provide more than 90% of primary productivity in the oceans [18]. Seagrasses, mangroves, macroalgae, and corals, which are important autotrophs in coastal systems, are not yet included. In this proof-of-concept model, we consider a world without any human impacts, except that we used modern-day climatic conditions. The model and user guide can be downloaded from www.madingleymodel.org, and simulation outputs for main manuscript figures can be downloaded from the Dryad Digital Repository: doi:10.5061/dryad.5m96v [19].

Box 1. Running simulations within our GEM

Each simulation is initialised with environmental information from multiple datasets (as described in the Methods section and in Table S1) and spatially distributed stocks and cohorts (see Methods) according to user specifications (functional groups and grid locations). During a simulation, the stocks and cohorts then interact through time and space. For each time step and each grid cell, the first computation is to increment the biomass of autotrophs according to the relevant growth function ( Table 5). Next, the ecology of the heterotroph cohorts is modelled (see functions in Table 6). The order in which heterotroph cohorts act is randomised at each time step. Each heterotroph cohort performs multiple ecological processes per time step ( Figure 1A): Individuals in the cohort (i) metabolise biomass to sustain their activities; (ii) eat biomass from either autotrophs if herbivorous or other heterotrophs if carnivorous, or from both if omnivorous; (iii) use net biomass gain to grow, if juvenile, or store biomass for future reproduction if they have reached sufficient body mass to be reproductively mature; (iv) give birth to a separate, offspring cohort if sufficient reproductive biomass has been accumulated; (v) suffer mortality as a result of being eaten by other cohorts feeding on them, or as a result of background mortality processes, starvation, or senescence; and (vi) disperse from their current grid cell to another grid cell. As a result of these interactions across space and time, communities of cohorts possessing different functional traits, individual biomasses, and abundances self-assemble ( Figure 1B and 1C).

Traits, Cohorts, and Stocks

Traditional approaches to mechanistic modelling in community ecology focus on densities or abundances of individuals belonging to different species [16],[20]. These are well suited to modelling a small set of focal species, but are unfeasible for modelling whole ecosystems at a global scale, because the vast majority of the world's species remain to be described, or are at best represented by few data describing their distribution and ecology [21]-[23].

A separate issue is whether to define the model in terms of population densities and biomasses within functional groups (i.e., "stocks" or "pools"), or as collections of interacting individuals each characterized by their combination of functional traits (individual-based). For all organisms except autotrophs, we used an individual-based approach, because this allowed the model to be more finely resolved, and because it enabled us to capture variation in body mass-one of the most important traits for determining the rates of ecological processes [27]-[29] over the lifetime of an individual. It also enabled us to follow the fates of individual organisms. Higher level ecosystem properties emerge from these individual-based rules. Capturing this emergence was a central aim of this initial work.

Autotrophs were represented as stocks -that is, the total biomasses of such organisms-because either the definition of an individual was problematic (terrestrial plants) or rates of turnover were faster than the modelled time step (marine phytoplankton).

For heterotrophs, simulating every individual separately would have been computationally intractable given the vast number of individuals in global ecosystems. Therefore, we adopted a computational approach based on cohorts. A cohort consisted of a group of organisms occurring within the same grid cell, with identical traits-that is, in the same functional group and with identical continuous traits-but not necessarily belonging to the same taxon. This cohort-based approach allowed us to define the model in exactly the way that one would do in a fully individual-based model (i.e., processes defined at an individual level), but also allowed us to keep the number of computations low enough to be feasible.

Functional Groups

All stocks and cohorts belong to a functional group, defined according to the categorical traits of the individuals in that stock or cohort ( Tables 1 and 2).

Individuals in the same functional group interact with one another and with their environment in a qualitatively similar manner. Therefore, cohorts within the same functional group are modelled using the same mathematical representations of the ecological processes, though the rates predicted by those functions differ according to continuous traits that differ between cohorts, such as body mass. Individuals belonging to different functional groups have at least one qualitatively different interaction with other individuals or with their environment. We use the same functional forms for analogous functional groups in the ocean and on land, but with different parameter values, where justified by previous research.

The Environment

The environment is defined as a two-dimensional layer representing the land surface and the upper mixed layer (top 100 m) of the oceans. This layer is divided into grid cells within which individuals and stocks are assumed to be well mixed. The ecological processes can be affected by the size of the grid cell, the physical environment at that cell, and dispersal of organisms among adjacent grid cells. The model can be employed for any number of grid cells, at any resolution, locally or globally, subject to computational limitations. For the results presented here, we used either simulations for individual 1°×1° grid cells, or a 2°×2° grid covering the whole globe (see below, and Tables 3 and 4) except for high latitudes (>65°) because remotely sensed, exogenous environmental data currently used are not available for the polar regions. Each grid cell in the model is assigned to either the terrestrial or marine realm based on a land/ocean mask [30]. Environmental conditions for each grid cell are read as model inputs from publicly available datasets: for the terrestrial realm air temperature [31], precipitation [31], soil water availability [32], number of frost days [31], and seasonality of primary productivity [33]; and for the marine realm sea-surface temperature [34], NPP [35], and ocean current velocity ( Table S1) [34]. The model is flexible with respect to the specific environmental data used, and future simulated environmental conditions can be used.

The Ecology

Autotroph Ecology.

In the marine realm, one stock of phytoplankton per grid cell is modelled, characterised by a total wet matter biomass at time t, . For terrestrial autotrophs, we track two stocks of leaves, l, one deciduous and one evergreen, each characterized by a total biomass at time t, . The biomass of an autotroph stock s, which could be either phytoplankton ( p) or leaves ( l), is incremented in each time step (of length Δ t) as follows: (1) where and are the gain and loss of biomass from stock s over the time interval Δ t, respectively. includes losses due to herbivory. We use different approaches to model the gain and loss of biomass from marine and terrestrial autotroph stocks ( Table 5).

In the marine realm, we model growth of the phytoplankton stock by incrementing the total biomass of phytoplankton in each cell using satellite-derived estimates of NPP ( Table 5). This avoids us having to adopt computationally impractical time-steps (days) in order to accurately simulate the dynamics of phytoplankton, which have rapid turnover rates (in our opinion, an explicit nutrient-phytoplankton-zooplankton model would be a major improvement to the marine part of the model; see Table 7). Loss of phytoplankton arises directly from modelling grazing by marine organisms ( Table 5; Equations 3 and 4 in Text S1).

Terrestrial autotrophs are modelled using the climate-driven terrestrial carbon model of Smith et al. [36]. We selected this model because it has been parameterized and tested against empirical data on carbon stocks and flows more rigorously than similar models of which we are aware (Equations 5-19 in Text S1) [36]. Moreover, it has a similar level of complexity to that used to represent heterotrophic organisms. However, like the other model components we adopted, the vegetation model could be replaced by alternatives in future studies, including more complex models able to address particular issues like CO ₂ fertilization (e.g., [12],[37]). Terrestrial plant growth is modelled as a function of NPP, the proportion of NPP that is produced by evergreen or deciduous leaves, and the fraction of NPP allocated to structural tissues ( Table 5), all of which depend on the local climate. The loss of plant biomass is determined by leaf mortality rate, which is also function of the climate, as well as the consumption of biomass by herbivorous terrestrial organisms ( Table 5).

Heterotroph Ecology.

Heterotrophs are modelled as cohorts. Each cohort i is characterized by a functional group ( Table 2), by two traits that do not change through time-body mass at birth, , and body mass at reproductive maturity (for cohort, i)-and by three state variables whose values do change through time-the abundance of individuals Ni,(t), the wet matter body mass of each individual within the cohort Mi,(t), and a stored reproductive mass of each individual, Ri,(t) ( Figure 1). The values of these state variables are updated each time step according to the effects of ecological processes ( Table 6). Individuals in each cohort are assumed to interact only with stocks and cohorts in the same grid cell.

The growth of individual body mass is modelled as follows: (2) where is the total biomass assimilated as food, which is the sum of the biomasses assimilated through herbivory and predation, and , respectively; is mass lost through metabolism; and is mass lost by allocation to reproduction ( Table 6, see also Figure 1).

Predation and herbivory are modelled using a Holling's Type III functional response, which assumes that the number (or biomass) of prey (or plant material) eaten by an individual predator (or herbivore) is a sigmoidal function of prey density (or biomass density) ( Table 6) [38]. The Holling's functions require definition of the attack rate and handling time for each predator (or herbivore) cohort on each prey cohort (or plant stock). Attack rates of herbivores on plants scaled according to the body mass of herbivore. Attack rates of predators on animals were derived from the size-structured model of Williams et al. [39], where the probability of predation is a Gaussian function around an optimal prey body size (as a proportion of predator size) (see Equations 35 and 36 in Text S1) estimated from large empirical datasets on feeding relationships [40]. This size-structured model is an extension of the long-standing niche model [41] but could be replaced with other predator-prey interaction models in future studies if desired. For carnivores and omnivores, the handling time of each predator on each prey increases linearly with prey body mass (larger prey take longer to eat) but decreases as defined by a power-law relationship with predator body mass (larger predators handle prey more quickly) (Equation 40 in Text S1). For herbivores, handling time depends on herbivore body mass only (a decreasing power-law relationship) (Equation 32 in Text S1).

Metabolic costs are modelled as a power-law relationship with body mass, following Brown [29], using parameter values derived from field metabolic rates ( Table 6) [42]. We assume that each cohort is active for some proportion of each time step according to ambient temperature (Equations 41-47 in Text S1). Endotherms are assumed to thermoregulate, and thus are active for 100% of each time step. Marine ectotherms are active for 100% of each time step. Terrestrial ectotherms do not thermoregulate, and thus are only active for the proportion of each time step during which ambient temperature was within their upper and lower activity temperature limits, estimated following Deutsch et al. [43].

Once an individual reaches its adult mass, we assume that all further mass gained is stored as reproductive potential. An individual's reproductive potential mass is incremented as follows: (3) where is the potential reproductive biomass lost by each individual of cohort i through reproductive events ( Figure 1). Reproductive events occur when an individuals' stored reproductive potential reaches a threshold proportion of adult mass ( Table 6). During reproductive events, iteroparous organisms devote all of their stored reproductive potential mass to producing offspring; semelparous organisms devote all of their stored reproductive potential mass, and also a proportion (Equations 50-52 in Text S1, Table S2) of their adult mass.

The number of individuals in each cohort is incremented as follows: (4) where is the number of individuals of cohort i lost to nonpredation mortality, and is the total number of individuals of cohort i lost through predation, summed over all predator cohorts k as outlined above ( Figure 1). We model three sources of nonpredation mortality: a constant proportional rate of background mortality, which applies to all individuals; starvation mortality, which is applied according to how much body mass has been lost compared to the maximum body mass ever obtained by an individual; and senescence mortality, which increases exponentially after maturity with a functional form similar to the Gompertz model (e.g., [44],[45]) ( Table 6). Note that abundance only ever decreases within a cohort. New individuals generated through reproduction produced new offspring cohorts (see below) (Equations 52-54 in Text S1). For computational efficiency, once the number of cohorts exceeds a user-specified, computationally tractable threshold, a number of pairs of cohorts equal to the excess are merged together. On merging, the biomass of one of the cohort pair is converted into an equivalent number of individuals of the other cohort in the pair (Equations 68-69 in Text S1). The cohort pairs identified for merging are those lying closest together in continuous trait space, and belonging to the same functional group (Equation 67 in Text S1).

Individuals were exchanged among the grid cell via three types of dispersal: (1) random diffusive dispersal of newly produced (juvenile) cohorts, (2) active dispersal of individuals determined by the degree of starvation experienced, and (3) advective-diffusive dispersal driven by ocean currents (in the marine realm only) ( Table 6). Dispersal occurred via the movement of whole cohorts, such that cohorts remained intact. This was necessary numerically to keep the number of cohorts manageable. We carried out some targeted simulations to explore the effects of allowing cohorts to split on dispersal, and found that could have quantitative effects, but does not fundamentally alter dynamics ( Figure S1). Assumptions and functional forms about dispersal, and numerical schemes to implement them, are another potentially important area for future research.

When the model was applied to a specific grid cell in isolation, dispersal into or out of the grid cell was not modelled.

Emergent Properties

The properties of individuals and communities that we present below are "emergent"; that is, they are not prescribed, but instead emerge through time as a result of the large number of interactions that take place between individual organisms (approximated as cohorts). As a result of these interactions, life histories of individuals are formed over time and can be tracked, and communities and ecosystems of individuals self-assemble. Moreover, the dynamics of any one grid cell are affected by the exchange of individuals with other grid cells, which occurs due to dispersal. Thus macroscale predictions (e.g., over the generation length of an individual cohort, across functional groups, or across entire ecosystems) emerge from microscale biological mechanisms. The macroscale predictions differ for ecosystems in different climates, but only because the microscale biology is sensitive to the climate. Similarly, the macroscale predictions differ between the land and sea, but only because microscale biology differs between land and sea. We compared these emergent properties to empirical data.

Model Simulations and Comparison to Data

We carried out four distinct types of simulations for different assessments of model capabilities ( Table 3).

Terrestrial grid cells were seeded with two autotroph stocks, deciduous and evergreen, as detailed above, and marine grid cells were seeded with a single phytoplankton stock. Grid cells were seeded with around 1,000 cohorts each, with 112 cohorts in each of nine functional groups in the terrestrial realm and 100 cohorts in each of 10 functional groups in the marine realm. Juvenile and adult body masses of cohorts were drawn at random from a prespecified range ( Table 2), and initial abundance was scaled negatively with initial body mass to provide reasonable initial densities (see Text S1 for full details).

Detailed numerical analyses were conducted on four focal grid cells (Study 1, Table 3) to investigate ecosystem dynamics over longer time scales. These simulations were used to check for the persistence of key community components (autotrophs, herbivores, carnivores, and omnivores), and to determine the typical time scales for the dynamics to reach some form of equilibrium. These analyses used the same climatological time series per year to remove the effects of interannual environmental variation. For each focal grid cell, we ran 10 model simulations for 1,000 y at a monthly time step. To test the effect of the cohort-merging regime on modelled dynamics, we repeated the simulation ensembles for each focal grid cell with the threshold number of cohorts at which merging is activated set at 500, 1,000, 5,000, and 10,000 cohorts and for a shorter period of 100 y.

Additional detailed simulations were carried out for the focal grid cells over a 100-y period to generate highly resolved predictions of emergent ecosystem properties at two levels of biological organisation: individual and ecosystem level (Study 2, Table 3).

We compared the properties of individual organisms with empirical data. Importantly, none of these properties were defined in the model as parameters, but rather they emerged as a result of the ecological interactions among individuals. The predicted relationship between body mass and growth rates was compared to estimates for reptiles, mammals, birds, and fish [46]-[48], and the relationship between body mass and time to reach maturity to estimates for invertebrates, reptiles, mammals, birds, and fish [49]-[57] (where necessary body masses were estimated from body lengths using relationships in [58]-[61]). The predicted relationship between body mass and mortality rates was compared with data for invertebrates, mammals, birds, and fish, taken from a single study [62]. The predicted relationship between body mass and lifetime reproductive success was compared with data for mammals, birds, and a few insect species [63]-[71]. Of these emergent properties, the growth rate of organisms derives most closely from the functional forms and input parameters. Specifically, growth rate could theoretically be a simple sum of food assimilation rates under conditions of saturating prey density minus metabolic costs. To test whether this was the case, we calculated the theoretical growth rate for organisms of a range of body masses under these conditions.

We also generated model outputs at a global scale (Study 4, Table 3). These were used in two ways: firstly, to investigate the mechanisms giving rise to variation in ecosystem structure by assessing the relationship between trophic structure and productivity in the model along large gradients of autotroph productivity in both terrestrial (along a meridional transect from the low productivity Saharan desert to high productivity Congo Basin tropical forest region) and marine (along a meridional transect from low productivity Antarctic waters to high productivity East Atlantic upwelling zones) realms; and secondly, to make novel predictions of as-yet unmeasured global properties (e.g., latitudinal gradients in total biomass) and to compare to other modelled estimates of total biomass and density at a global scale [76]. We also compared modelled global average ratios of herbivore to autotroph biomass with the average ratios observed in the same dataset that we used to test the predictions made by individual cells [73]. Finally, we investigated how modelled marine ecosystem structure responded to mechanisms that have been proposed to cause inverted trophic biomass pyramids, such as dispersal [77], turnover rate of autotrophs, and turnover rate of consumers [77]-[79]. To do this, we simulated the global marine realm with no dispersal permitted between grid cells (Study 5, Table 3) and the effects of reducing the turnover rate of biomass through the ecosystem (Study 6, Table 3).

Results

Grid Cell: Individuals

The power-law relationships between body mass and the properties of individual organisms that emerged from the model were generally consistent with empirical data ( Figure 3). For growth rates and times taken to reach reproductive maturity, the modelled and empirical values were very similar, although the slope of the relationship between predicted growth rates and body mass was steeper. In absolute terms the predicted growth rates tended to be higher and the predicted times to maturity tended to be lower than those observed in the empirical data ( Table S4).

Our assumptions about underlying ecological processes, such as handling times and metabolic rates, place a fundamental limit on the growth rate of organisms of a given body mass (i.e., the net growth rate of individuals that are feeding at the maximum rate). If most individuals attained this maximum, then the growth rates would not be emergent, so much as defined by the model assumptions. But this was not the case. The emergent relationship between body mass and growth rate was not a simple function of maximum possible food assimilation and metabolic costs: modelled growth rates were typically one-tenth of theoretical maximum model growth rates and showed large variation for any given body mass ( Figure S3). This variation resulted from the many other factors that affected growth rate, most notably the abundance and body masses of potential prey and predators competing for the same prey.

Grid Cell: Community

For the four focal grid cells, terrestrial ecosystems exhibited a pyramid of biomass across the different trophic levels ( Figure 4A), which is widely accepted to be present in terrestrial systems [80]. The predicted herbivore biomass as a proportion of producer biomass (0.98%) was consistent with empirical terrestrial estimates (median value = 0.93%) ( Table S5) [73]. However, predicted biomass of large-bodied herbivores was four to six times higher than estimated from a field study [72]. Consistent with current opinion and observations [78],[81]-[84], marine ecosystems exhibited an inverted pyramid of biomass structure [73],[82], with the highest biomasses in the highest trophic levels ( Figure 4C). Marine systems exhibited relatively faster flow rates of productivity from autotrophs to higher trophic levels compared with terrestrial ecosystems-and at rates much higher than those estimated to date ( Figure 4A and 4C) [85]. Predicted herbivore biomass as a proportion of producer biomass for the marine grid cells was much higher than in terrestrial ecosystems (63%), and of a similar magnitude to empirical estimates (median value = 52%) ( Table S5) [73].

Geographical Patterns in Ecosystem Structure Across 14 Sites

Modelled ecosystems in the marine realm generally showed closer agreement with empirical estimates than in the terrestrial realm. However, in both Santa Monica Bay, San Francisco ( Figure 5A), and the Swartkops estuary in South Africa ( Figure 5F), median modelled herbivore to autotroph biomass ratios were three orders of magnitude larger than empirical estimates.

Trophic Structure Along Productivity Gradients

Global Ecosystems

Global patterns of total heterotroph biomass, averaged across the final year of the simulation ( Figure 7A), were similar to patterns of primary productivity ( Figure S6). In the marine realm, our modelled estimate of median heterotroph biomass density was 167,147 kg km −2, approximately 7-30 times greater than previous modelled estimates, which range from 5,500-25,000 kg km −2 [76],[88]-[90]. However, a recent empirical study into mesopelagic fish biomass suggests that some fish biomass densities are likely to be an order of magnitude higher than these previous estimates [90] and so our prediction is plausible. Global median ratios of herbivore to primary producer biomass estimated by the model were 0.8% for terrestrial and 189% for marine ecosystems, compared to 0.93% and 52% for empirical estimates ( Table S5) [73]. Our modelled estimate of median total terrestrial heterotroph biomass density was 151,089 kg km −2, a prediction which, as far as we are aware, has never been made previously.

In the marine realm, high heterotroph biomass is predicted in upwelling systems and areas of high annual productivity (e.g., the North Atlantic). In the terrestrial realm, predicted heterotroph biomass was highest in naturally forested areas and lowest in deserts. There was no clear latitudinal gradient of biomass density in either system, but latitudinal variability was substantially greater in the terrestrial realm. At subtropical latitudes in the northern hemisphere in the terrestrial realm, there was a band in which carnivores had higher biomass density than omnivores, whereas elsewhere omnivores had greater biomass density. This switch in the relative dominance of omnivores in the northern hemisphere coincided with a decline in mean herbivore biomass density. No discernible decline in mean herbivore biomass densities was observed at subtropical latitudes in the terrestrial southern hemisphere.

Discussion

We have shown that it is possible to derive global predictions about the emergent properties of ecosystem structure and function from a GEM based on processes of, and interactions among, individual organisms, without any model-imposed constraints on those properties.

Stability of Emergent Dynamics

The model reached a dynamic steady state in all grid cells, with the persistence of all trophic levels, which is expected in the absence of perturbation [91]. Real ecosystems are not expected to exhibit such stable dynamics because they are subject to numerous interannual environmental fluctuations and perturbations. These were not incorporated for this study, but could be in the future. The global simulations (which included dispersal) converged to equilibria with similar characteristics to the focal grid cells, although with higher biomass densities. The higher biomass must have been a result of dispersal, as this is the only difference between the focal-cell simulations and may be owing to a rescue effect from neighbouring grid cells. Nonetheless, the similarity of the simulations with and without dispersal provides support for the use of isolated focal grid cells in more detailed studies.

One form of instability in the dynamics was the large temporal variation in biomasses in the high-productivity, seasonal marine grid cell. Plankton and zooplankton are known to exhibit marked biomass fluctuations, but the modelled variation was much greater, and has a different temporal signature (chaotic variation within the productive season, whereas real cycles are repeatable year to year). This appears to be an artefact of the long time step used, allowing massive unconstrained changes in biomass over a single time step. Model simulations carried out using a daily time step (only feasible over shorter time periods and for single grid cells due to the computational demand) produced substantially more stable dynamics. However, the mean predicted biomass was similar between simulations using different time steps, suggesting that most of the properties of our results are robust. Nevertheless, future research is needed into numerical and computational methods for more appropriately simulating small organisms in GEMs (see Table 7 and Text S2).

Emergent Life Histories and Trophic Structure

Growth rates were slightly faster and times to maturity slightly shorter than those from observation. Modelled rates of biomass transfer calculated for the two focal grid cells were higher than empirically observed rates, particularly in the marine realm ( Figure 4) [85]. At least in part, this discrepancy may result from a bias in the data toward larger organisms, leading to empirical estimates underestimating real biomass flow rates.

Another difference between the model and data was that the modelled biomass of large-bodied herbivores was several times higher than observations [72]. Modelled ratios of herbivore to autotroph biomass were also higher than empirical estimates in most cases, especially for terrestrial ecosystems. There are three potential explanations for this. The first is that herbivores, like predators, are eating too efficiently in the model, which might result from incorrect functional forms or missing processes. The second explanation is that because the available data are extremely sparse, the data are thus potentially not representative of the system. Thirdly, human impacts might have reduced large herbivore biomass in the empirical data compared to the undisturbed ecosystem assumed in these simulations of the model.

The closer match between modelled and empirical trophic structure for marine and savannah ecosystems, compared to forests, might be because these ecosystems conform much better to the assumption that individuals are well mixed. This assumption implies that herbivores can find all plant material and predators can find all prey. In all ecosystems, but most obviously in forests, many leaves are physically out of reach of many herbivores, and many prey are able to find refuge from predation. This may act to slow down biomass turnover in forests in comparison to the ocean and savannas, in a way that cannot be captured by a model that assumes complete mixing within a grid cell. On the other hand, the discrepancy could also be caused by incomplete empirical data. It is easier to estimate whole-ecosystem properties in savannas (where animals tend to be highly visible) and in the ocean (which can be trawled with nets) compared with forests. Alternatively, the terrestrial plant model might be inaccurately capturing the allocation of productivity to structural or nonstructural matter at these specific forest sites. However, this appears less likely, as the allocation function of this model has been rigorously constrained using a global dataset, and evaluation shows the model predicts well for this property [36]. We note, however, that at finer, site-level scales, plant allocation strategies will certainly be more heterogeneous and will certainly be different from the larger scale average predicted by the global model.

It is important to note that differences between the marine and terrestrial realms were not imposed on the model as top-down limits on the structure of ecosystems but rather emerged from individual-scale ecology. A necessarily different set of representative functional groups was defined for the marine environment compared to the terrestrial environment, but all functional groups in the model followed the same fundamental ecological functions. The only other encoded differences between marine and terrestrial cohorts were the proportion of total autotroph biomass available for consumption by a given herbivore cohort, the different optimal prey body sizes for predators, and the assumption that marine ectotherms are not thermally restricted in their capacity to function. The primary cause of the much lower ratio of herbivore biomass to autotroph biomass in the focal terrestrial cells versus in the oceans was the allocation by terrestrial plants of primary productivity to structural tissues, which are inedible to all herbivores in the model ( Figure S11). This assumption probably made it more likely that terrestrial cells would exhibit a conventional pyramid of biomass structure. However, the model predicted considerable variation in the shape of the trophic biomass pyramid across terrestrial and marine cells, including an inverted structure in some terrestrial cells and a noninverted structure in some marine cells ( Figure S7).

Emergent Global Patterns: Model Results and Predictions

The modelled global estimates of biomass density represent the first attempt to assess the scaling of ecosystem properties from individuals to communities at a global level, and to apply a general ecological modelling methodology consistently across both the marine and terrestrial realms.

Some of these properties have been estimated previously in specific locations, but none have been estimated globally using mechanistic models (although marine animal biomass and biomass densities have been predicted mechanistically [76],[88],[89]). In addition, we have calculated emergent properties that are, as far as we are aware, unprecedented ( Table 8), and we discuss these below.

Heterotroph Biomass and NPP.

At a global scale, the spatial pattern of heterotroph biomass was broadly consistent with observed NPP [97], which is unsurprising, as NPP is the basal resource in all of the modelled ecosystems. However, the relationship between primary productivity and heterotroph biomass was not simple: a given level of primary productivity could result in a wide range of heterotroph biomass ( Figure S6A-D). This variation arises from differences in climate, which lead to differences in biomass allocation to different plant tissues and to variation in animal metabolic rates and levels of activity. The predicted peak in terrestrial heterotroph biomass densities observed in locations with intermediate levels of NPP is also likely to result from these factors; for example, plants in the most productive areas allocate a greater proportion of biomass to structural tissues, whereas plants in intermediate productivity ecosystems such as grasslands have a greater relative allocation to nonstructural tissues. Autotroph biomass was also influenced by herbivory, as demonstrated by removing all heterotrophs from the model and representing mortality of plants through herbivory using a constant loss term ( Figure S6E and F). This experiment suggests the impact of herbivory on plants varies significantly across the world, indicating the potential importance of explicitly considering herbivores in carbon cycle models, such as DGVMs, or models of ocean biogeochemistry, which is not done at present. Exploring the impacts of heterotrophs on carbon cycling and predicted carbon dynamics is an important avenue of research for future development of GEMs.

Emergent Structure and Productivity.

Modelled variation in trophic structure along gradients of productivity supported theoretical expectations that the NPP of systems will determine the length of trophic chains [9],[10],[98]. In low-productivity terrestrial systems, there is insufficient autotrophic biomass propagating to higher trophic levels to support carnivores, whereas in high-productivity ecosystems, carnivore biomass is greater than omnivore biomass. In the marine realm, autotroph biomass decreased with increasing primary productivity beyond NPP of 40,000 kg km −2 y −1, inconsistent with expectations from the trophic release hypothesis ( Figure 6B) [11]. The model predicts for marine ecosystems that with increasing primary productivity herbivore biomass increases and carnivore biomass, as a proportion of total biomass, decreases. For terrestrial ecosystems, herbivore biomass increased to around 40,000 kg km −2 and then decreased, whereas the carnivore biomass proportion decreased only after around 550,000 kg km −2. The relationship predicted for marine ecosystems is in agreement with the general findings from available empirical data on changes in trophic structure associated with changes in productivity, derived from freshwater, intertidal, and reef fish communities [99]-[103]. However, our results indicate that a qualitatively different relationship between trophic structure and productivity appears to exist in the terrestrial realm. There are additional factors that influence modelled community structure along productivity gradients that will require detailed investigation in the future, including (i) seasonality, which will vary among different modelled locations with similar productivity; (ii) climatic differences, which might affect the composition of autotroph biomass in terrestrial ecosystems; and (iii) dispersal regimes.

Emergent Latitudinal Patterns of Community Structure.

Predictions of latitudinal variation in community structure (as shown in Figure 7B and 7C) give an insight into the mechanistic basis of these patterns at a global scale. On land, there was greater variation in the biomass density of all trophic groups, both latitudinally and longitudinally, compared to in the oceans ( Table 8). The greater variation in terrestrial biomass densities was likely driven by greater environmental heterogeneity in the terrestrial realm and the greater range and speed of dispersal processes (e.g., advective dispersal) in the oceans. For example, both temporal [104],[105] and spatial variability [106] in environmental temperature is higher on land versus the ocean.

In the mid to high latitudes of the terrestrial northern hemisphere, omnivorous organisms account for greater biomass than carnivores. However, in equatorial latitudes and extending to 30°S, carnivorous organisms constituted a greater fraction of the total ecosystem biomass than omnivores ( Figure 7C and Table 8). The equatorial and low southern hemisphere latitudes correspond to more stable environmental conditions [106] compared with mid to high northern latitudes. Therefore, the model appears, in the terrestrial realm, to be selecting for generalist feeding strategies in more variable environments, while favouring specialism where the environment is more stable.

Mechanisms Giving Rise to Inverted Marine Trophic Structure.

The model showed that dispersal of organisms plays an important role in the spatial extent and frequency of inversion of marine trophic structure ( Figures S8 and S9), which is consistent with the hypothesis that the import of allocthonous organic matter supports relatively higher heterotrophic biomass in oligotrophic freshwater systems, which has been postulated previously but not investigated in detail [77]. Furthermore, we show that noninverted trophic biomass structure is more frequent when the turnover rate of phytoplankton is reduced and when the rate and efficiency of biomass transfer through the ecosystem is lower ( Figure S10), in agreement with current theory [77]-[79]. The generality of inverted trophic pyramids in the marine realm remains unknown, but there are numerous examples of herbivore biomass being larger than producer biomass in ocean environments [73]. Inversion at the top of the trophic pyramid has apparently been observed in some near-pristine reef habitats [84], though this may simply be an overestimation artefact of the visual census approach used [107].

Future Model Development

Future Model Development: Ecology.

The physical environment is currently represented very simply in the model. For example, in reality, variation in habitat structure within grid cells will alter interactions between organisms. And a fully integrated and explicitly resolved mechanistic model of phytoplankton is required in future iterations of the model, as is representation of nonphytoplanktonic coastal autotrophs (e.g., seagrass beds or coral reefs) and detritivores. The detrital loop in particular is important in "closing the system." Other ecological processes that should be incorporated in future iterations of the model include ecological stoichiometry; intelligent behaviour, such as directed dispersal, hibernation, and stasis; and complex predator-prey and herbivore-plant interactions ( Table 7).

Future Model Development: Data.

Robust data-constrained parameterisation of the model and rigorous evaluation of our novel but testable outputs was not possible because the necessary data were not generally available. Therefore, another set of developments are needed around the acquisition and collation of data for use in GEM modelling ( Table 8 and Table 7), which are reflected by the biodiversity informatics community [108].

Future Model Development: Numerical and Mathematical Methods.

The development of the Madingley Model was enabled by combination of numerical methods that is, far as we are aware, novel (e.g., the cohort approach to trait-driven interactions, combined with cohort merging, randomly ordered updating, the treatment of autotrophs as continuous state variables, and exponential differencing of loss rates within time steps; see Text S2 for further examples). Given the novelty of this modelling paradigm in ecology, at least five areas of development will be required: detailed analysis and understanding of the mathematical representations of ecology, analytical tractability of predicted ecosystem properties, the implications of alternative abstractions and numerical methods used to implement them, the infrastructure to allow flexibility in scale, and the infrastructure to constrain and evaluate the model so that the benefits of additional components or realism can be assessed objectively ( Table 7). In addition, it would be interesting to explore the approach referred to as "parameterization" in the biogeosciences modelling community-that is, to fit phenomenological relationships to the output of GEMs, thus allowing approximate versions of them to be run much more quickly, or within the context of more general ESMs.

Future Model Development: Community.

The further development of GEMs discussed above will be expedited by a community of researchers improving upon the model presented here or developing alternative GEMs. We call for such a community of ecologists, biologists, mathematicians, and computer scientists to form around the GEM concept so that this class of model can be rapidly advanced to better meet the pressing needs of conservationists and policymakers. In this spirit, we have developed our model architecture in such a way that it is relatively easy to alter the representation of ecological processes and have made our model code freely available to the community ( www.madingleymodel.org).

Use of GEMs

Our model is the first step towards the development of a more ecologically refined GEM (and GEMs) that occupies a very different niche to the more specialised modelling approaches that have been used to inform biodiversity policy to date, and is more akin to the global climate models that inform global climate science and policy [8]. Our GEM necessarily made numerous simplifying assumptions in order to capture a broad range of ecological processes and organisms. This nevertheless provides a new approach to answering important outstanding questions in ecology and with which to begin to ask entirely new questions-questions that require an integrated, mechanistic understanding of whole ecosystems; the connections among them; and their response to environmental variation and natural or human perturbations. In the following sections, we briefly discuss how GEMs could be used to inform ecological science and conservation policy.

Use of GEMs: Ecology.

GEMs will help in the development of ecological theory. The study of ecology is so broad that most pieces of ecological research necessarily focus on a small subset, whether in terms of scale, taxa, or the processes concerned. The implications of the findings in these disparate research areas for the longer term and larger scale dynamics of whole communities and ecosystems are hard to assess. This has led some ecologists to call for a renewed interest in "systems ecology," which aims to study important ecological processes within the context of other important ecosystem and earth-system processes to enable a better understanding of the natural environment [109]. Models like ours provide one way to do this: new ecological findings can be used to modify the model, or different competing formulations of ecological theories (e.g., prey density or ratio-dependent predation functions [110]) can be represented in the model and the results assessed against known ecosystem structure. By doing so, the processes of critical importance for ecosystem structure and functioning could be identified and priorities for development of new ecological hypotheses or data-gathering determined. Our preliminary investigations of community trophic structure along productivity gradients and of the mechanisms giving rise to the inversion of marine cells illustrate the potential of GEMs in this regard.

Use of GEMs: Conservation.

The simulations presented here are not directly relevant to conservation decision-making, as the model currently does not directly include anthropogenic influences. In principle, a model of this kind could be used to better inform the management of the world's ecosystems, in much the same way that mechanistic ESMs are used to predict and explore scenarios of anthropogenic climate change [7]. Forcing the model with time-varying historic or future projections of environmental variables would provide the kinds of novel and highly relevant outputs that could enable a mechanistic approach to conservation decision-making.

By taking a system-level approach, GEMs are uniquely able to simulate the interacting relationships between various simultaneous human pressures (such as climate change, land use change, and harvesting of wild animals) and various metrics of ecosystem structure and function, exploring the trajectories derived from external scenarios of human pressure. For example, they could predict (1) static measures of ecosystem properties, such as the variation in the functional traits represented in ecosystems, an important measure of biodiversity thought to be related to ecosystem functioning [26]; (2) metrics that are currently used to monitor the state of ecological systems, such as the total abundance of large endotherms as an analogue of the Living Planet Index [111]; (3) metrics relating to focal species, by examining the fate of organisms with similar traits to those species, although important species-specific factors may not be captured that way; and (4) dynamic ecosystem measures, such as stability (the magnitude of temporal variation in ecosystem properties) or resilience (predicted time to recover from a perturbation). A unique advantage of GEM-like models relative to statistical modelling approaches is their capacity to model completely novel perturbation scenarios-that is, perturbations for which there are no data, experiments, or observations.

The structure of this type of model also allows for a wide set of anthropogenic perturbations to be considered simultaneously. For example, such models will be able to simulate how whole ecosystems respond to, and feed back upon, the effects of changes in atmospheric carbon dioxide concentrations on plants; altered climate on all organisms and ecological processes; harvesting of animals, land-use change, and harvesting of vegetation; invasive species; and toxic pollutants. The effects of these disparate perturbations could be measured using a common set of metrics. Considering these impacts together in a GEM should allow for much more holistic understanding, and management, of the world's ecosystems, which will make GEMs a powerful tool for bodies such as the Intergovernmental Platform on Biodiversity and Ecosystem Services.

Conclusions

We have developed the first General Ecosystem Model (GEM) that synthesizes fundamental aspects of ecological theory, to model, simulate, and predict how the structure and function of ecosystems at multiple scales emerges from the biology and interactions of individual organisms. Our model matches a broad range of empirical data well, to a first approximation. Where it departs from either observations or expectations, it does so in ways that afford interesting avenues for future ecological research, future model development, and empirical data collection. It also provides a set of novel predictions that can be independently assessed. We anticipate that this will be the beginning of a long-term exploration of GEMs, and we call upon ecologists, biologists, mathematicians, and computer scientists to join in creating a modelling community surrounding GEMs that can catalyse the development of more realistic, sophisticated, yet better understood models of ecosystems worldwide. Our hope is that GEMs will form the basis of new science in ecology and, in particular, science that proves actionable to those charged with conserving the biosphere on which we all depend.

Supporting Information

Trophic abundance pyramids. Community-level abundance pyramids across all cohorts belonging to each trophic level emergent from the model for an example of terrestrial and marine grid cell (grid cells T1 and M1 from Table 4). Results are from the final year of a 100-y model run. Light green represents herbivores, blue represents omnivores, and red represents carnivores. Total abundance densities (1,000 s individuals/km ²) are indicated by the widths (after log-transformation) and numbers within the boxes.
https://doi.org/10.1371/journal.pbio.1001841.s004
(TIF)

Comparison of model predicted with empirical normalised body mass spectra (NBS). Frequency distribution of the slope of NBS from [75] with model-derived NBS slope values, calculated following Sprules and Munawar [114], for carnivores (red), omnivores (blue), and herbivores (green). Triangles correspond to slopes for the low productivity, aseasonal marine cell (M1, Table 4) and circles to the high productivity, aseasonal terrestrial cell (T1, Table 4).
https://doi.org/10.1371/journal.pbio.1001841.s005
(TIF)

Community-level properties for cell T1 with all edible plant matter available for herbivory. Trophic pyramid and size distribution spectra for focal cell T1 with the value of parameter equal to 1 for terrestrial herbivores, which means that each terrestrial herbivore cohort experiences 100% of the edible plant matter in the grid cell when it is eating. The release of this parameter does not affect the low herbivore to primary producer ratio for terrestrial communities. Here, the ratio is 1.0%, marginally higher than that calculated for the same location using a value for of 10% ( Figure 4).
https://doi.org/10.1371/journal.pbio.1001841.s011
(TIFF)

Environmental data sources. External global environmental data sources used within the model. Units represent those used within the Madingley model, not those of the original source data. a Environmental variables were long-term/multidecadal average values.
https://doi.org/10.1371/journal.pbio.1001841.s012
(DOCX)

Empirical estimates of trophic-level biomasses in globally widespread ecosystems in both marine and terrestrial environments. Notes: 1. Consider only the pelagic producers. Assume that dissolved organic carbon (DOC) and suspended particulate organic carbon (POC) are available to the pelagic community. Do not include benthic suspension feeders. Assume micro-zooplankton are herbivores (as defined in our model). For omnivores and carnivores, assume that the proportion of biomass that is supported originally by pelagic primary production is equal to the proportional rate of consumption of pelagically derived foods relative to consumption of foods from all sources. 2. From Table 12.2 (plants taken as above ground vascular + Moss + Algae + Lichens) of Chapin et al [115]. 3. Herbivore biomass taken from [73]. 4. Producer and herbivore biomasses from [73]. 5. Producer biomass of 9,999 g C m-2 comes from Figure 8 of Frangi and Lugo [116]. Montane palm floodplain forest; herbivore biomass taken from [73].
https://doi.org/10.1371/journal.pbio.1001841.s014
(DOCX)

Comparison of emergent individual-level properties from the model with observations. The slopes and intercepts of the relationships between predicted properties and body mass compared to empirical data. The probability that the slope and intercept of the predicted relationship were different from those for the empirical data was calculated as the t statistic of linear models fitted to combined model and empirical data for each emergent property using a categorical factor to indicate a model or empirical datum. The probability that the t statistic for the model including the categorical factor indicates the significance of the difference.
https://doi.org/10.1371/journal.pbio.1001841.s015
(DOCX)

Summary statistics of empirical community herbivore and primary producer biomasses. Summary statistics, derived from [73], for those ecosystem types most closely representing the ecosystems within the two grid cells shown in Figure 3. PB, primary producer biomass; HB, herbivore biomass. Median herbivore biomass in temperate and tropical grassland ecosystems is 52.3% of that in communities of marine phytoplankton, whereas median primary producer biomass is 57 times larger in the terrestrial compared to the marine ecosystem.
https://doi.org/10.1371/journal.pbio.1001841.s016
(DOCX)

Comparison of abundance-density slopes predicted by the model with observed slopes. Abundance-density relationships predicted by the model (for cells T1 and M1, Table 4) compared with observations. Empirical abundance-density relationships were derived from Jennings et al. [74]. The reported total biomass versus body mass relationships were converted to abundance versus body mass relationships, by dividing the total biomass in each mass bin by the central mass of that mass bin, which can be approximated as subtracting 1 from the slope of total biomass against body mass.
https://doi.org/10.1371/journal.pbio.1001841.s017
(DOCX)

Acknowledgments

We thank L. Joppa for engaging in discussion and ideas that helped us to conceptualize the model and A. Blandon, K. Boughey, and E. Pynegar for compiling empirical data with which to compare the model.

Author Contributions

The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: MH TN DT SE JH VL MJS JPWS DWP. Performed the experiments: MH TN DT. Analyzed the data: MH TN DT MS DP. Contributed reagents/materials/analysis tools: MH TN DT MS VL DP. Wrote the paper: MH TN DT SE JH MJS JPWS DWP. Contributed equally to this manuscript: MH TN DT.

# data[].attributes.attachment_link.title
Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model

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Michael B. J. Harfoot, Tim Newbold, Derek P. Tittensor, Stephen Emmott, Jon Hutton, Vassily Lyutsarev, Matthew J. Smith, Jörn P. W. Scharlemann, Drew W. Purves Subject Areas For more information about PLOS Subject Areas, click here. We want your feedback. Do these Subject Areas make sense for this article?

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Michael B. J. Harfoot,

Tim Newbold,

Derek P. Tittensor,

Stephen Emmott,

Jon Hutton,

Vassily Lyutsarev,

Matthew J. Smith,

Jörn P. W. Scharlemann,

Drew W. Purves

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Abstract

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures.

Author Summary

Ecosystems across the world are being rapidly degraded. This threatens their provision of natural goods and services, upon which all life depends. To be able to reduce-and one day reverse-this damage, we need to be able to predict the effects of human actions on ecosystems. Here, we present the first example of a General Ecosystem Model (GEM)-called the Madingley Model-a novel class of computational model that can be applied to any ecosystem, marine or terrestrial, and can be simulated at any spatial scale from local up to global. It covers almost all organisms in ecosystems, from the smallest to the largest, encoding the underlying biology and behaviour of individual organisms to capture the interactions between them and with the environment, to model the fate of each individual organism, and to make predictions about ecosystem structure and function. Predictions made by the Madingley Model broadly resemble what we observe in real-world ecosystems across scales from individuals through to communities, ecosystems, and the world as a whole. Our results show that ecologists can now begin modelling all nonhuman life on earth, and we suggest that this type of approach may hold promise for predicting the ecological implications of different future trajectories of human activity on our shared planet.

Citation: Harfoot MBJ, Newbold T, Tittensor DP, Emmott S, Hutton J, Lyutsarev V, et al. (2014) Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model. PLoS Biol 12(4): e1001841. https://doi.org/10.1371/journal.pbio.1001841

Academic Editor: Michel Loreau, Centre National de la Recherche Scientifique, France

Received: December 19, 2013; Accepted: March 10, 2014; Published: April 22, 2014

Copyright: © 2014 Harfoot et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This research was funded by United Nations Environment Programme World Conservation Monitoring Centre and the Computational Science Laboratory, Microsoft Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: DGVMs, dynamic global vegetation models; DOC, dissolved organic carbon; ESMs, Earth System Models; EwE, Ecopath With Ecosim; GEM, General Ecosystem Model; NBS, normalised body mass spectra; NPP, net primary productivity; POC, particulate organic carbon

Introduction

The pace and scale of anthropogenic environmental change has caused the widespread degradation of ecosystems and the services they provide that ultimately support human life on Earth [1]. Understanding and mitigating these impacts necessitates the development of a suite of tools, including policy instruments, practical conservation measures, and empirical research. At present, a variety of models are used to assist decision-making in relation to biodiversity and ecosystem services. Most are correlative, relying on statistical relationships derived from limited observational data without explicit reference to the underlying mechanisms; examples include the GLOBIO model, species distribution models, and models of local extinction based on species-area relationships [2]-[4]. All of these models are useful, for different purposes. However, what is urgently needed is mechanistic models, which explicitly represent the biological, physiological, and ecological mechanisms underlying the systems in question [5]. One of the key benefits of mechanistic models is that they are likely to make more accurate predictions under novel conditions [6]. For example, Earth System Models (ESMs), containing mechanistic descriptions of multiple interacting components of the climate, atmosphere, and ocean, are used to project properties and dynamics under future climate conditions that have not been observed previously (at least in relation to historical data) [7]. Similarly, mechanistic models of ecosystems would allow us to predict a given combination of human pressures on a given ecosystem, even when there is no or little historical data on which to rely. Mechanistic models can also improve our understanding of the systems being modelled, allowing predictions to be understood in relation to the underlying mechanisms that generate them [8]. This in turn might lead to novel ways to mitigate or even reverse the degradation of ecosystems.

We present the first process-based, mechanistic General Ecosystem Model (GEM) (called the Madingley Model). It is general in the sense that it strives to use a unified set of fundamental ecological concepts and processes for any ecosystem to which it is applied, either terrestrial or marine, and it can be simulated at any spatial resolution from local up to global scales. Applying a general modelling approach globally has three main advantages: (1) it allows testing of whether the same set of ecological mechanisms and concepts can adequately capture broad-scale ecosystem behaviour in both the marine and terrestrial realms; (2) it enables the development of a suite of predictive outputs common to both realms, from which standardised metrics of ecosystem health can be calculated; and (3) it enables links between marine and terrestrial ecosystems, both natural and human-driven, to be modelled. The model is also spatially explicit, with dynamics in a given location driven by the climate and other local factors, as well as by connections with other ecosystems through dispersal, and is mechanistic, with dynamics being driven by ecological processes defined at the level of individual organisms. Specifically, we model autotroph (plant) stocks, and individual herbivorous, omnivorous, and carnivorous animals of all body sizes, and their interactions. From these interactions, patterns emerge at larger spatial and temporal scales, including communities, ecosystems, and global macroecological gradients, without any direct model constraints imposed on those properties.

Such a GEM has great potential if it can, at a minimum, reproduce the observed properties of ecosystem structure and function, and enable the formation of valuable, novel hypotheses, and precise, testable predictions. Here, we test the model's ability to simulate ecosystems that persist over long time scales (1,000 y) by comparing model predictions with empirical data and test two theoretical predictions that to date have not been assessed empirically and have only been studied with simple trophic models: that the net primary productivity (NPP) of ecosystems determines the length of trophic chains [9],[10] and that herbivore pressure on autotrophs will reduce once a critical level of carnivore biomass is supported ("trophic release hypothesis" [11]). Finally, we provide a suite of other novel predictions that demonstrate the potential utility of the model as an operational tool with which the effects of human impacts on ecosystems can be explored.

Mechanistic models of specific ecosystems have been developed previously, and to date these have been constrained to particular spatial locations or to particular sets of organisms within ecosystems. For example, dynamic global vegetation models (DGVMs) are used to represent the physiological and ecological processes driving plant community dynamics on the global land surface, enabling investigations into how terrestrial vegetation interacts with climate [12]. However, these do not include animals or other heterotrophs, and so are limited in the extent to which they can be used to understand the roles of heterotrophs in ecosystems, or to address questions about the conservation of organisms other than plants. For the marine realm, "end-to-end" ecosystem models have been developed, which include most trophic levels for particular regions [13]. Examples are the Ecopath With Ecosim (EwE) [14] and Atlantis [15] models. But marine models tend to focus either on biogeochemical cycles or on organisms of economic importance, such as fish, rather than on the properties of the ecosystem as a whole. They also generally either use a stock-and-flow formulation [14],[15], making them unable to follow trajectories of individual organisms, or are restricted to simulating particular guilds of organisms [16].

There have also been previous theoretical examinations of the potential effect of select processes on select ecosystem metrics, such as the role of bioenergetics in determining size distributions [17]. Such theoretical studies have been very useful in providing insight into the potential mechanisms underlying ecosystem structure; but they have tended to be carried out for single, abstract locations that are not tied to any real geographical location and to omit most of the key processes affecting ecosystem structure and function in reality. We are not aware of any previous attempt to model emergent ecosystem structure and function by identifying a minimal, but putatively complete, set of key processes and then simulating these processes for all organisms globally, over the actual climate and geography of all marine and terrestrial environments. It is for these reasons that we refer to the model presented here as a GEM.

Methods

Model Scope

We identified a set of core biological and ecological processes necessary to predict ecosystem-level properties: primary production for autotrophs, and eating, metabolism, growth, reproduction, dispersal, and mortality for heterotrophs ( Figure 1 and Box 1). We modelled both marine and terrestrial environments but excluded freshwater ecosystems. We included all photoautotrophs, and all heterotrophs that feed on living organisms (i.e., we did not include chemoautotrophs and detritivores). We generally represented only macroscopic organisms (>1×10 −3 g), except that we included plankton in the marine realm because of their known importance to the marine food web [18]. All plant biomass in the terrestrial realm was modelled, but only leaves, flowers, fruits, and seeds were available as a food source for herbivores and omnivores. The marine component included phytoplanktonic autotrophs, which provide more than 90% of primary productivity in the oceans [18]. Seagrasses, mangroves, macroalgae, and corals, which are important autotrophs in coastal systems, are not yet included. In this proof-of-concept model, we consider a world without any human impacts, except that we used modern-day climatic conditions. The model and user guide can be downloaded from www.madingleymodel.org, and simulation outputs for main manuscript figures can be downloaded from the Dryad Digital Repository: doi:10.5061/dryad.5m96v [19].

Box 1. Running simulations within our GEM

Each simulation is initialised with environmental information from multiple datasets (as described in the Methods section and in Table S1) and spatially distributed stocks and cohorts (see Methods) according to user specifications (functional groups and grid locations). During a simulation, the stocks and cohorts then interact through time and space. For each time step and each grid cell, the first computation is to increment the biomass of autotrophs according to the relevant growth function ( Table 5). Next, the ecology of the heterotroph cohorts is modelled (see functions in Table 6). The order in which heterotroph cohorts act is randomised at each time step. Each heterotroph cohort performs multiple ecological processes per time step ( Figure 1A): Individuals in the cohort (i) metabolise biomass to sustain their activities; (ii) eat biomass from either autotrophs if herbivorous or other heterotrophs if carnivorous, or from both if omnivorous; (iii) use net biomass gain to grow, if juvenile, or store biomass for future reproduction if they have reached sufficient body mass to be reproductively mature; (iv) give birth to a separate, offspring cohort if sufficient reproductive biomass has been accumulated; (v) suffer mortality as a result of being eaten by other cohorts feeding on them, or as a result of background mortality processes, starvation, or senescence; and (vi) disperse from their current grid cell to another grid cell. As a result of these interactions across space and time, communities of cohorts possessing different functional traits, individual biomasses, and abundances self-assemble ( Figure 1B and 1C).

Traits, Cohorts, and Stocks

Traditional approaches to mechanistic modelling in community ecology focus on densities or abundances of individuals belonging to different species [16],[20]. These are well suited to modelling a small set of focal species, but are unfeasible for modelling whole ecosystems at a global scale, because the vast majority of the world's species remain to be described, or are at best represented by few data describing their distribution and ecology [21]-[23].

A separate issue is whether to define the model in terms of population densities and biomasses within functional groups (i.e., "stocks" or "pools"), or as collections of interacting individuals each characterized by their combination of functional traits (individual-based). For all organisms except autotrophs, we used an individual-based approach, because this allowed the model to be more finely resolved, and because it enabled us to capture variation in body mass-one of the most important traits for determining the rates of ecological processes [27]-[29] over the lifetime of an individual. It also enabled us to follow the fates of individual organisms. Higher level ecosystem properties emerge from these individual-based rules. Capturing this emergence was a central aim of this initial work.

Autotrophs were represented as stocks -that is, the total biomasses of such organisms-because either the definition of an individual was problematic (terrestrial plants) or rates of turnover were faster than the modelled time step (marine phytoplankton).

For heterotrophs, simulating every individual separately would have been computationally intractable given the vast number of individuals in global ecosystems. Therefore, we adopted a computational approach based on cohorts. A cohort consisted of a group of organisms occurring within the same grid cell, with identical traits-that is, in the same functional group and with identical continuous traits-but not necessarily belonging to the same taxon. This cohort-based approach allowed us to define the model in exactly the way that one would do in a fully individual-based model (i.e., processes defined at an individual level), but also allowed us to keep the number of computations low enough to be feasible.

Functional Groups

All stocks and cohorts belong to a functional group, defined according to the categorical traits of the individuals in that stock or cohort ( Tables 1 and 2).

Individuals in the same functional group interact with one another and with their environment in a qualitatively similar manner. Therefore, cohorts within the same functional group are modelled using the same mathematical representations of the ecological processes, though the rates predicted by those functions differ according to continuous traits that differ between cohorts, such as body mass. Individuals belonging to different functional groups have at least one qualitatively different interaction with other individuals or with their environment. We use the same functional forms for analogous functional groups in the ocean and on land, but with different parameter values, where justified by previous research.

The Environment

The environment is defined as a two-dimensional layer representing the land surface and the upper mixed layer (top 100 m) of the oceans. This layer is divided into grid cells within which individuals and stocks are assumed to be well mixed. The ecological processes can be affected by the size of the grid cell, the physical environment at that cell, and dispersal of organisms among adjacent grid cells. The model can be employed for any number of grid cells, at any resolution, locally or globally, subject to computational limitations. For the results presented here, we used either simulations for individual 1°×1° grid cells, or a 2°×2° grid covering the whole globe (see below, and Tables 3 and 4) except for high latitudes (>65°) because remotely sensed, exogenous environmental data currently used are not available for the polar regions. Each grid cell in the model is assigned to either the terrestrial or marine realm based on a land/ocean mask [30]. Environmental conditions for each grid cell are read as model inputs from publicly available datasets: for the terrestrial realm air temperature [31], precipitation [31], soil water availability [32], number of frost days [31], and seasonality of primary productivity [33]; and for the marine realm sea-surface temperature [34], NPP [35], and ocean current velocity ( Table S1) [34]. The model is flexible with respect to the specific environmental data used, and future simulated environmental conditions can be used.

The Ecology

Autotroph Ecology.

In the marine realm, one stock of phytoplankton per grid cell is modelled, characterised by a total wet matter biomass at time t, . For terrestrial autotrophs, we track two stocks of leaves, l, one deciduous and one evergreen, each characterized by a total biomass at time t, . The biomass of an autotroph stock s, which could be either phytoplankton ( p) or leaves ( l), is incremented in each time step (of length Δ t) as follows: (1) where and are the gain and loss of biomass from stock s over the time interval Δ t, respectively. includes losses due to herbivory. We use different approaches to model the gain and loss of biomass from marine and terrestrial autotroph stocks ( Table 5).

In the marine realm, we model growth of the phytoplankton stock by incrementing the total biomass of phytoplankton in each cell using satellite-derived estimates of NPP ( Table 5). This avoids us having to adopt computationally impractical time-steps (days) in order to accurately simulate the dynamics of phytoplankton, which have rapid turnover rates (in our opinion, an explicit nutrient-phytoplankton-zooplankton model would be a major improvement to the marine part of the model; see Table 7). Loss of phytoplankton arises directly from modelling grazing by marine organisms ( Table 5; Equations 3 and 4 in Text S1).

Terrestrial autotrophs are modelled using the climate-driven terrestrial carbon model of Smith et al. [36]. We selected this model because it has been parameterized and tested against empirical data on carbon stocks and flows more rigorously than similar models of which we are aware (Equations 5-19 in Text S1) [36]. Moreover, it has a similar level of complexity to that used to represent heterotrophic organisms. However, like the other model components we adopted, the vegetation model could be replaced by alternatives in future studies, including more complex models able to address particular issues like CO ₂ fertilization (e.g., [12],[37]). Terrestrial plant growth is modelled as a function of NPP, the proportion of NPP that is produced by evergreen or deciduous leaves, and the fraction of NPP allocated to structural tissues ( Table 5), all of which depend on the local climate. The loss of plant biomass is determined by leaf mortality rate, which is also function of the climate, as well as the consumption of biomass by herbivorous terrestrial organisms ( Table 5).

Heterotroph Ecology.

Heterotrophs are modelled as cohorts. Each cohort i is characterized by a functional group ( Table 2), by two traits that do not change through time-body mass at birth, , and body mass at reproductive maturity (for cohort, i)-and by three state variables whose values do change through time-the abundance of individuals Ni,(t), the wet matter body mass of each individual within the cohort Mi,(t), and a stored reproductive mass of each individual, Ri,(t) ( Figure 1). The values of these state variables are updated each time step according to the effects of ecological processes ( Table 6). Individuals in each cohort are assumed to interact only with stocks and cohorts in the same grid cell.

The growth of individual body mass is modelled as follows: (2) where is the total biomass assimilated as food, which is the sum of the biomasses assimilated through herbivory and predation, and , respectively; is mass lost through metabolism; and is mass lost by allocation to reproduction ( Table 6, see also Figure 1).

Predation and herbivory are modelled using a Holling's Type III functional response, which assumes that the number (or biomass) of prey (or plant material) eaten by an individual predator (or herbivore) is a sigmoidal function of prey density (or biomass density) ( Table 6) [38]. The Holling's functions require definition of the attack rate and handling time for each predator (or herbivore) cohort on each prey cohort (or plant stock). Attack rates of herbivores on plants scaled according to the body mass of herbivore. Attack rates of predators on animals were derived from the size-structured model of Williams et al. [39], where the probability of predation is a Gaussian function around an optimal prey body size (as a proportion of predator size) (see Equations 35 and 36 in Text S1) estimated from large empirical datasets on feeding relationships [40]. This size-structured model is an extension of the long-standing niche model [41] but could be replaced with other predator-prey interaction models in future studies if desired. For carnivores and omnivores, the handling time of each predator on each prey increases linearly with prey body mass (larger prey take longer to eat) but decreases as defined by a power-law relationship with predator body mass (larger predators handle prey more quickly) (Equation 40 in Text S1). For herbivores, handling time depends on herbivore body mass only (a decreasing power-law relationship) (Equation 32 in Text S1).

Metabolic costs are modelled as a power-law relationship with body mass, following Brown [29], using parameter values derived from field metabolic rates ( Table 6) [42]. We assume that each cohort is active for some proportion of each time step according to ambient temperature (Equations 41-47 in Text S1). Endotherms are assumed to thermoregulate, and thus are active for 100% of each time step. Marine ectotherms are active for 100% of each time step. Terrestrial ectotherms do not thermoregulate, and thus are only active for the proportion of each time step during which ambient temperature was within their upper and lower activity temperature limits, estimated following Deutsch et al. [43].

Once an individual reaches its adult mass, we assume that all further mass gained is stored as reproductive potential. An individual's reproductive potential mass is incremented as follows: (3) where is the potential reproductive biomass lost by each individual of cohort i through reproductive events ( Figure 1). Reproductive events occur when an individuals' stored reproductive potential reaches a threshold proportion of adult mass ( Table 6). During reproductive events, iteroparous organisms devote all of their stored reproductive potential mass to producing offspring; semelparous organisms devote all of their stored reproductive potential mass, and also a proportion (Equations 50-52 in Text S1, Table S2) of their adult mass.

The number of individuals in each cohort is incremented as follows: (4) where is the number of individuals of cohort i lost to nonpredation mortality, and is the total number of individuals of cohort i lost through predation, summed over all predator cohorts k as outlined above ( Figure 1). We model three sources of nonpredation mortality: a constant proportional rate of background mortality, which applies to all individuals; starvation mortality, which is applied according to how much body mass has been lost compared to the maximum body mass ever obtained by an individual; and senescence mortality, which increases exponentially after maturity with a functional form similar to the Gompertz model (e.g., [44],[45]) ( Table 6). Note that abundance only ever decreases within a cohort. New individuals generated through reproduction produced new offspring cohorts (see below) (Equations 52-54 in Text S1). For computational efficiency, once the number of cohorts exceeds a user-specified, computationally tractable threshold, a number of pairs of cohorts equal to the excess are merged together. On merging, the biomass of one of the cohort pair is converted into an equivalent number of individuals of the other cohort in the pair (Equations 68-69 in Text S1). The cohort pairs identified for merging are those lying closest together in continuous trait space, and belonging to the same functional group (Equation 67 in Text S1).

Individuals were exchanged among the grid cell via three types of dispersal: (1) random diffusive dispersal of newly produced (juvenile) cohorts, (2) active dispersal of individuals determined by the degree of starvation experienced, and (3) advective-diffusive dispersal driven by ocean currents (in the marine realm only) ( Table 6). Dispersal occurred via the movement of whole cohorts, such that cohorts remained intact. This was necessary numerically to keep the number of cohorts manageable. We carried out some targeted simulations to explore the effects of allowing cohorts to split on dispersal, and found that could have quantitative effects, but does not fundamentally alter dynamics ( Figure S1). Assumptions and functional forms about dispersal, and numerical schemes to implement them, are another potentially important area for future research.

When the model was applied to a specific grid cell in isolation, dispersal into or out of the grid cell was not modelled.

Emergent Properties

The properties of individuals and communities that we present below are "emergent"; that is, they are not prescribed, but instead emerge through time as a result of the large number of interactions that take place between individual organisms (approximated as cohorts). As a result of these interactions, life histories of individuals are formed over time and can be tracked, and communities and ecosystems of individuals self-assemble. Moreover, the dynamics of any one grid cell are affected by the exchange of individuals with other grid cells, which occurs due to dispersal. Thus macroscale predictions (e.g., over the generation length of an individual cohort, across functional groups, or across entire ecosystems) emerge from microscale biological mechanisms. The macroscale predictions differ for ecosystems in different climates, but only because the microscale biology is sensitive to the climate. Similarly, the macroscale predictions differ between the land and sea, but only because microscale biology differs between land and sea. We compared these emergent properties to empirical data.

Model Simulations and Comparison to Data

We carried out four distinct types of simulations for different assessments of model capabilities ( Table 3).

Terrestrial grid cells were seeded with two autotroph stocks, deciduous and evergreen, as detailed above, and marine grid cells were seeded with a single phytoplankton stock. Grid cells were seeded with around 1,000 cohorts each, with 112 cohorts in each of nine functional groups in the terrestrial realm and 100 cohorts in each of 10 functional groups in the marine realm. Juvenile and adult body masses of cohorts were drawn at random from a prespecified range ( Table 2), and initial abundance was scaled negatively with initial body mass to provide reasonable initial densities (see Text S1 for full details).

Detailed numerical analyses were conducted on four focal grid cells (Study 1, Table 3) to investigate ecosystem dynamics over longer time scales. These simulations were used to check for the persistence of key community components (autotrophs, herbivores, carnivores, and omnivores), and to determine the typical time scales for the dynamics to reach some form of equilibrium. These analyses used the same climatological time series per year to remove the effects of interannual environmental variation. For each focal grid cell, we ran 10 model simulations for 1,000 y at a monthly time step. To test the effect of the cohort-merging regime on modelled dynamics, we repeated the simulation ensembles for each focal grid cell with the threshold number of cohorts at which merging is activated set at 500, 1,000, 5,000, and 10,000 cohorts and for a shorter period of 100 y.

Additional detailed simulations were carried out for the focal grid cells over a 100-y period to generate highly resolved predictions of emergent ecosystem properties at two levels of biological organisation: individual and ecosystem level (Study 2, Table 3).

We compared the properties of individual organisms with empirical data. Importantly, none of these properties were defined in the model as parameters, but rather they emerged as a result of the ecological interactions among individuals. The predicted relationship between body mass and growth rates was compared to estimates for reptiles, mammals, birds, and fish [46]-[48], and the relationship between body mass and time to reach maturity to estimates for invertebrates, reptiles, mammals, birds, and fish [49]-[57] (where necessary body masses were estimated from body lengths using relationships in [58]-[61]). The predicted relationship between body mass and mortality rates was compared with data for invertebrates, mammals, birds, and fish, taken from a single study [62]. The predicted relationship between body mass and lifetime reproductive success was compared with data for mammals, birds, and a few insect species [63]-[71]. Of these emergent properties, the growth rate of organisms derives most closely from the functional forms and input parameters. Specifically, growth rate could theoretically be a simple sum of food assimilation rates under conditions of saturating prey density minus metabolic costs. To test whether this was the case, we calculated the theoretical growth rate for organisms of a range of body masses under these conditions.

We also generated model outputs at a global scale (Study 4, Table 3). These were used in two ways: firstly, to investigate the mechanisms giving rise to variation in ecosystem structure by assessing the relationship between trophic structure and productivity in the model along large gradients of autotroph productivity in both terrestrial (along a meridional transect from the low productivity Saharan desert to high productivity Congo Basin tropical forest region) and marine (along a meridional transect from low productivity Antarctic waters to high productivity East Atlantic upwelling zones) realms; and secondly, to make novel predictions of as-yet unmeasured global properties (e.g., latitudinal gradients in total biomass) and to compare to other modelled estimates of total biomass and density at a global scale [76]. We also compared modelled global average ratios of herbivore to autotroph biomass with the average ratios observed in the same dataset that we used to test the predictions made by individual cells [73]. Finally, we investigated how modelled marine ecosystem structure responded to mechanisms that have been proposed to cause inverted trophic biomass pyramids, such as dispersal [77], turnover rate of autotrophs, and turnover rate of consumers [77]-[79]. To do this, we simulated the global marine realm with no dispersal permitted between grid cells (Study 5, Table 3) and the effects of reducing the turnover rate of biomass through the ecosystem (Study 6, Table 3).

Results

Grid Cell: Individuals

The power-law relationships between body mass and the properties of individual organisms that emerged from the model were generally consistent with empirical data ( Figure 3). For growth rates and times taken to reach reproductive maturity, the modelled and empirical values were very similar, although the slope of the relationship between predicted growth rates and body mass was steeper. In absolute terms the predicted growth rates tended to be higher and the predicted times to maturity tended to be lower than those observed in the empirical data ( Table S4).

Our assumptions about underlying ecological processes, such as handling times and metabolic rates, place a fundamental limit on the growth rate of organisms of a given body mass (i.e., the net growth rate of individuals that are feeding at the maximum rate). If most individuals attained this maximum, then the growth rates would not be emergent, so much as defined by the model assumptions. But this was not the case. The emergent relationship between body mass and growth rate was not a simple function of maximum possible food assimilation and metabolic costs: modelled growth rates were typically one-tenth of theoretical maximum model growth rates and showed large variation for any given body mass ( Figure S3). This variation resulted from the many other factors that affected growth rate, most notably the abundance and body masses of potential prey and predators competing for the same prey.

Grid Cell: Community

For the four focal grid cells, terrestrial ecosystems exhibited a pyramid of biomass across the different trophic levels ( Figure 4A), which is widely accepted to be present in terrestrial systems [80]. The predicted herbivore biomass as a proportion of producer biomass (0.98%) was consistent with empirical terrestrial estimates (median value = 0.93%) ( Table S5) [73]. However, predicted biomass of large-bodied herbivores was four to six times higher than estimated from a field study [72]. Consistent with current opinion and observations [78],[81]-[84], marine ecosystems exhibited an inverted pyramid of biomass structure [73],[82], with the highest biomasses in the highest trophic levels ( Figure 4C). Marine systems exhibited relatively faster flow rates of productivity from autotrophs to higher trophic levels compared with terrestrial ecosystems-and at rates much higher than those estimated to date ( Figure 4A and 4C) [85]. Predicted herbivore biomass as a proportion of producer biomass for the marine grid cells was much higher than in terrestrial ecosystems (63%), and of a similar magnitude to empirical estimates (median value = 52%) ( Table S5) [73].

Geographical Patterns in Ecosystem Structure Across 14 Sites

Modelled ecosystems in the marine realm generally showed closer agreement with empirical estimates than in the terrestrial realm. However, in both Santa Monica Bay, San Francisco ( Figure 5A), and the Swartkops estuary in South Africa ( Figure 5F), median modelled herbivore to autotroph biomass ratios were three orders of magnitude larger than empirical estimates.

Trophic Structure Along Productivity Gradients

Global Ecosystems

Global patterns of total heterotroph biomass, averaged across the final year of the simulation ( Figure 7A), were similar to patterns of primary productivity ( Figure S6). In the marine realm, our modelled estimate of median heterotroph biomass density was 167,147 kg km −2, approximately 7-30 times greater than previous modelled estimates, which range from 5,500-25,000 kg km −2 [76],[88]-[90]. However, a recent empirical study into mesopelagic fish biomass suggests that some fish biomass densities are likely to be an order of magnitude higher than these previous estimates [90] and so our prediction is plausible. Global median ratios of herbivore to primary producer biomass estimated by the model were 0.8% for terrestrial and 189% for marine ecosystems, compared to 0.93% and 52% for empirical estimates ( Table S5) [73]. Our modelled estimate of median total terrestrial heterotroph biomass density was 151,089 kg km −2, a prediction which, as far as we are aware, has never been made previously.

In the marine realm, high heterotroph biomass is predicted in upwelling systems and areas of high annual productivity (e.g., the North Atlantic). In the terrestrial realm, predicted heterotroph biomass was highest in naturally forested areas and lowest in deserts. There was no clear latitudinal gradient of biomass density in either system, but latitudinal variability was substantially greater in the terrestrial realm. At subtropical latitudes in the northern hemisphere in the terrestrial realm, there was a band in which carnivores had higher biomass density than omnivores, whereas elsewhere omnivores had greater biomass density. This switch in the relative dominance of omnivores in the northern hemisphere coincided with a decline in mean herbivore biomass density. No discernible decline in mean herbivore biomass densities was observed at subtropical latitudes in the terrestrial southern hemisphere.

Discussion

We have shown that it is possible to derive global predictions about the emergent properties of ecosystem structure and function from a GEM based on processes of, and interactions among, individual organisms, without any model-imposed constraints on those properties.

Stability of Emergent Dynamics

The model reached a dynamic steady state in all grid cells, with the persistence of all trophic levels, which is expected in the absence of perturbation [91]. Real ecosystems are not expected to exhibit such stable dynamics because they are subject to numerous interannual environmental fluctuations and perturbations. These were not incorporated for this study, but could be in the future. The global simulations (which included dispersal) converged to equilibria with similar characteristics to the focal grid cells, although with higher biomass densities. The higher biomass must have been a result of dispersal, as this is the only difference between the focal-cell simulations and may be owing to a rescue effect from neighbouring grid cells. Nonetheless, the similarity of the simulations with and without dispersal provides support for the use of isolated focal grid cells in more detailed studies.

One form of instability in the dynamics was the large temporal variation in biomasses in the high-productivity, seasonal marine grid cell. Plankton and zooplankton are known to exhibit marked biomass fluctuations, but the modelled variation was much greater, and has a different temporal signature (chaotic variation within the productive season, whereas real cycles are repeatable year to year). This appears to be an artefact of the long time step used, allowing massive unconstrained changes in biomass over a single time step. Model simulations carried out using a daily time step (only feasible over shorter time periods and for single grid cells due to the computational demand) produced substantially more stable dynamics. However, the mean predicted biomass was similar between simulations using different time steps, suggesting that most of the properties of our results are robust. Nevertheless, future research is needed into numerical and computational methods for more appropriately simulating small organisms in GEMs (see Table 7 and Text S2).

Emergent Life Histories and Trophic Structure

Growth rates were slightly faster and times to maturity slightly shorter than those from observation. Modelled rates of biomass transfer calculated for the two focal grid cells were higher than empirically observed rates, particularly in the marine realm ( Figure 4) [85]. At least in part, this discrepancy may result from a bias in the data toward larger organisms, leading to empirical estimates underestimating real biomass flow rates.

Another difference between the model and data was that the modelled biomass of large-bodied herbivores was several times higher than observations [72]. Modelled ratios of herbivore to autotroph biomass were also higher than empirical estimates in most cases, especially for terrestrial ecosystems. There are three potential explanations for this. The first is that herbivores, like predators, are eating too efficiently in the model, which might result from incorrect functional forms or missing processes. The second explanation is that because the available data are extremely sparse, the data are thus potentially not representative of the system. Thirdly, human impacts might have reduced large herbivore biomass in the empirical data compared to the undisturbed ecosystem assumed in these simulations of the model.

The closer match between modelled and empirical trophic structure for marine and savannah ecosystems, compared to forests, might be because these ecosystems conform much better to the assumption that individuals are well mixed. This assumption implies that herbivores can find all plant material and predators can find all prey. In all ecosystems, but most obviously in forests, many leaves are physically out of reach of many herbivores, and many prey are able to find refuge from predation. This may act to slow down biomass turnover in forests in comparison to the ocean and savannas, in a way that cannot be captured by a model that assumes complete mixing within a grid cell. On the other hand, the discrepancy could also be caused by incomplete empirical data. It is easier to estimate whole-ecosystem properties in savannas (where animals tend to be highly visible) and in the ocean (which can be trawled with nets) compared with forests. Alternatively, the terrestrial plant model might be inaccurately capturing the allocation of productivity to structural or nonstructural matter at these specific forest sites. However, this appears less likely, as the allocation function of this model has been rigorously constrained using a global dataset, and evaluation shows the model predicts well for this property [36]. We note, however, that at finer, site-level scales, plant allocation strategies will certainly be more heterogeneous and will certainly be different from the larger scale average predicted by the global model.

It is important to note that differences between the marine and terrestrial realms were not imposed on the model as top-down limits on the structure of ecosystems but rather emerged from individual-scale ecology. A necessarily different set of representative functional groups was defined for the marine environment compared to the terrestrial environment, but all functional groups in the model followed the same fundamental ecological functions. The only other encoded differences between marine and terrestrial cohorts were the proportion of total autotroph biomass available for consumption by a given herbivore cohort, the different optimal prey body sizes for predators, and the assumption that marine ectotherms are not thermally restricted in their capacity to function. The primary cause of the much lower ratio of herbivore biomass to autotroph biomass in the focal terrestrial cells versus in the oceans was the allocation by terrestrial plants of primary productivity to structural tissues, which are inedible to all herbivores in the model ( Figure S11). This assumption probably made it more likely that terrestrial cells would exhibit a conventional pyramid of biomass structure. However, the model predicted considerable variation in the shape of the trophic biomass pyramid across terrestrial and marine cells, including an inverted structure in some terrestrial cells and a noninverted structure in some marine cells ( Figure S7).

Emergent Global Patterns: Model Results and Predictions

The modelled global estimates of biomass density represent the first attempt to assess the scaling of ecosystem properties from individuals to communities at a global level, and to apply a general ecological modelling methodology consistently across both the marine and terrestrial realms.

Some of these properties have been estimated previously in specific locations, but none have been estimated globally using mechanistic models (although marine animal biomass and biomass densities have been predicted mechanistically [76],[88],[89]). In addition, we have calculated emergent properties that are, as far as we are aware, unprecedented ( Table 8), and we discuss these below.

Heterotroph Biomass and NPP.

At a global scale, the spatial pattern of heterotroph biomass was broadly consistent with observed NPP [97], which is unsurprising, as NPP is the basal resource in all of the modelled ecosystems. However, the relationship between primary productivity and heterotroph biomass was not simple: a given level of primary productivity could result in a wide range of heterotroph biomass ( Figure S6A-D). This variation arises from differences in climate, which lead to differences in biomass allocation to different plant tissues and to variation in animal metabolic rates and levels of activity. The predicted peak in terrestrial heterotroph biomass densities observed in locations with intermediate levels of NPP is also likely to result from these factors; for example, plants in the most productive areas allocate a greater proportion of biomass to structural tissues, whereas plants in intermediate productivity ecosystems such as grasslands have a greater relative allocation to nonstructural tissues. Autotroph biomass was also influenced by herbivory, as demonstrated by removing all heterotrophs from the model and representing mortality of plants through herbivory using a constant loss term ( Figure S6E and F). This experiment suggests the impact of herbivory on plants varies significantly across the world, indicating the potential importance of explicitly considering herbivores in carbon cycle models, such as DGVMs, or models of ocean biogeochemistry, which is not done at present. Exploring the impacts of heterotrophs on carbon cycling and predicted carbon dynamics is an important avenue of research for future development of GEMs.

Emergent Structure and Productivity.

Modelled variation in trophic structure along gradients of productivity supported theoretical expectations that the NPP of systems will determine the length of trophic chains [9],[10],[98]. In low-productivity terrestrial systems, there is insufficient autotrophic biomass propagating to higher trophic levels to support carnivores, whereas in high-productivity ecosystems, carnivore biomass is greater than omnivore biomass. In the marine realm, autotroph biomass decreased with increasing primary productivity beyond NPP of 40,000 kg km −2 y −1, inconsistent with expectations from the trophic release hypothesis ( Figure 6B) [11]. The model predicts for marine ecosystems that with increasing primary productivity herbivore biomass increases and carnivore biomass, as a proportion of total biomass, decreases. For terrestrial ecosystems, herbivore biomass increased to around 40,000 kg km −2 and then decreased, whereas the carnivore biomass proportion decreased only after around 550,000 kg km −2. The relationship predicted for marine ecosystems is in agreement with the general findings from available empirical data on changes in trophic structure associated with changes in productivity, derived from freshwater, intertidal, and reef fish communities [99]-[103]. However, our results indicate that a qualitatively different relationship between trophic structure and productivity appears to exist in the terrestrial realm. There are additional factors that influence modelled community structure along productivity gradients that will require detailed investigation in the future, including (i) seasonality, which will vary among different modelled locations with similar productivity; (ii) climatic differences, which might affect the composition of autotroph biomass in terrestrial ecosystems; and (iii) dispersal regimes.

Emergent Latitudinal Patterns of Community Structure.

Predictions of latitudinal variation in community structure (as shown in Figure 7B and 7C) give an insight into the mechanistic basis of these patterns at a global scale. On land, there was greater variation in the biomass density of all trophic groups, both latitudinally and longitudinally, compared to in the oceans ( Table 8). The greater variation in terrestrial biomass densities was likely driven by greater environmental heterogeneity in the terrestrial realm and the greater range and speed of dispersal processes (e.g., advective dispersal) in the oceans. For example, both temporal [104],[105] and spatial variability [106] in environmental temperature is higher on land versus the ocean.

In the mid to high latitudes of the terrestrial northern hemisphere, omnivorous organisms account for greater biomass than carnivores. However, in equatorial latitudes and extending to 30°S, carnivorous organisms constituted a greater fraction of the total ecosystem biomass than omnivores ( Figure 7C and Table 8). The equatorial and low southern hemisphere latitudes correspond to more stable environmental conditions [106] compared with mid to high northern latitudes. Therefore, the model appears, in the terrestrial realm, to be selecting for generalist feeding strategies in more variable environments, while favouring specialism where the environment is more stable.

Mechanisms Giving Rise to Inverted Marine Trophic Structure.

The model showed that dispersal of organisms plays an important role in the spatial extent and frequency of inversion of marine trophic structure ( Figures S8 and S9), which is consistent with the hypothesis that the import of allocthonous organic matter supports relatively higher heterotrophic biomass in oligotrophic freshwater systems, which has been postulated previously but not investigated in detail [77]. Furthermore, we show that noninverted trophic biomass structure is more frequent when the turnover rate of phytoplankton is reduced and when the rate and efficiency of biomass transfer through the ecosystem is lower ( Figure S10), in agreement with current theory [77]-[79]. The generality of inverted trophic pyramids in the marine realm remains unknown, but there are numerous examples of herbivore biomass being larger than producer biomass in ocean environments [73]. Inversion at the top of the trophic pyramid has apparently been observed in some near-pristine reef habitats [84], though this may simply be an overestimation artefact of the visual census approach used [107].

Future Model Development

Future Model Development: Ecology.

The physical environment is currently represented very simply in the model. For example, in reality, variation in habitat structure within grid cells will alter interactions between organisms. And a fully integrated and explicitly resolved mechanistic model of phytoplankton is required in future iterations of the model, as is representation of nonphytoplanktonic coastal autotrophs (e.g., seagrass beds or coral reefs) and detritivores. The detrital loop in particular is important in "closing the system." Other ecological processes that should be incorporated in future iterations of the model include ecological stoichiometry; intelligent behaviour, such as directed dispersal, hibernation, and stasis; and complex predator-prey and herbivore-plant interactions ( Table 7).

Future Model Development: Data.

Robust data-constrained parameterisation of the model and rigorous evaluation of our novel but testable outputs was not possible because the necessary data were not generally available. Therefore, another set of developments are needed around the acquisition and collation of data for use in GEM modelling ( Table 8 and Table 7), which are reflected by the biodiversity informatics community [108].

Future Model Development: Numerical and Mathematical Methods.

The development of the Madingley Model was enabled by combination of numerical methods that is, far as we are aware, novel (e.g., the cohort approach to trait-driven interactions, combined with cohort merging, randomly ordered updating, the treatment of autotrophs as continuous state variables, and exponential differencing of loss rates within time steps; see Text S2 for further examples). Given the novelty of this modelling paradigm in ecology, at least five areas of development will be required: detailed analysis and understanding of the mathematical representations of ecology, analytical tractability of predicted ecosystem properties, the implications of alternative abstractions and numerical methods used to implement them, the infrastructure to allow flexibility in scale, and the infrastructure to constrain and evaluate the model so that the benefits of additional components or realism can be assessed objectively ( Table 7). In addition, it would be interesting to explore the approach referred to as "parameterization" in the biogeosciences modelling community-that is, to fit phenomenological relationships to the output of GEMs, thus allowing approximate versions of them to be run much more quickly, or within the context of more general ESMs.

Future Model Development: Community.

The further development of GEMs discussed above will be expedited by a community of researchers improving upon the model presented here or developing alternative GEMs. We call for such a community of ecologists, biologists, mathematicians, and computer scientists to form around the GEM concept so that this class of model can be rapidly advanced to better meet the pressing needs of conservationists and policymakers. In this spirit, we have developed our model architecture in such a way that it is relatively easy to alter the representation of ecological processes and have made our model code freely available to the community ( www.madingleymodel.org).

Use of GEMs

Our model is the first step towards the development of a more ecologically refined GEM (and GEMs) that occupies a very different niche to the more specialised modelling approaches that have been used to inform biodiversity policy to date, and is more akin to the global climate models that inform global climate science and policy [8]. Our GEM necessarily made numerous simplifying assumptions in order to capture a broad range of ecological processes and organisms. This nevertheless provides a new approach to answering important outstanding questions in ecology and with which to begin to ask entirely new questions-questions that require an integrated, mechanistic understanding of whole ecosystems; the connections among them; and their response to environmental variation and natural or human perturbations. In the following sections, we briefly discuss how GEMs could be used to inform ecological science and conservation policy.

Use of GEMs: Ecology.

GEMs will help in the development of ecological theory. The study of ecology is so broad that most pieces of ecological research necessarily focus on a small subset, whether in terms of scale, taxa, or the processes concerned. The implications of the findings in these disparate research areas for the longer term and larger scale dynamics of whole communities and ecosystems are hard to assess. This has led some ecologists to call for a renewed interest in "systems ecology," which aims to study important ecological processes within the context of other important ecosystem and earth-system processes to enable a better understanding of the natural environment [109]. Models like ours provide one way to do this: new ecological findings can be used to modify the model, or different competing formulations of ecological theories (e.g., prey density or ratio-dependent predation functions [110]) can be represented in the model and the results assessed against known ecosystem structure. By doing so, the processes of critical importance for ecosystem structure and functioning could be identified and priorities for development of new ecological hypotheses or data-gathering determined. Our preliminary investigations of community trophic structure along productivity gradients and of the mechanisms giving rise to the inversion of marine cells illustrate the potential of GEMs in this regard.

Use of GEMs: Conservation.

The simulations presented here are not directly relevant to conservation decision-making, as the model currently does not directly include anthropogenic influences. In principle, a model of this kind could be used to better inform the management of the world's ecosystems, in much the same way that mechanistic ESMs are used to predict and explore scenarios of anthropogenic climate change [7]. Forcing the model with time-varying historic or future projections of environmental variables would provide the kinds of novel and highly relevant outputs that could enable a mechanistic approach to conservation decision-making.

By taking a system-level approach, GEMs are uniquely able to simulate the interacting relationships between various simultaneous human pressures (such as climate change, land use change, and harvesting of wild animals) and various metrics of ecosystem structure and function, exploring the trajectories derived from external scenarios of human pressure. For example, they could predict (1) static measures of ecosystem properties, such as the variation in the functional traits represented in ecosystems, an important measure of biodiversity thought to be related to ecosystem functioning [26]; (2) metrics that are currently used to monitor the state of ecological systems, such as the total abundance of large endotherms as an analogue of the Living Planet Index [111]; (3) metrics relating to focal species, by examining the fate of organisms with similar traits to those species, although important species-specific factors may not be captured that way; and (4) dynamic ecosystem measures, such as stability (the magnitude of temporal variation in ecosystem properties) or resilience (predicted time to recover from a perturbation). A unique advantage of GEM-like models relative to statistical modelling approaches is their capacity to model completely novel perturbation scenarios-that is, perturbations for which there are no data, experiments, or observations.

The structure of this type of model also allows for a wide set of anthropogenic perturbations to be considered simultaneously. For example, such models will be able to simulate how whole ecosystems respond to, and feed back upon, the effects of changes in atmospheric carbon dioxide concentrations on plants; altered climate on all organisms and ecological processes; harvesting of animals, land-use change, and harvesting of vegetation; invasive species; and toxic pollutants. The effects of these disparate perturbations could be measured using a common set of metrics. Considering these impacts together in a GEM should allow for much more holistic understanding, and management, of the world's ecosystems, which will make GEMs a powerful tool for bodies such as the Intergovernmental Platform on Biodiversity and Ecosystem Services.

Conclusions

We have developed the first General Ecosystem Model (GEM) that synthesizes fundamental aspects of ecological theory, to model, simulate, and predict how the structure and function of ecosystems at multiple scales emerges from the biology and interactions of individual organisms. Our model matches a broad range of empirical data well, to a first approximation. Where it departs from either observations or expectations, it does so in ways that afford interesting avenues for future ecological research, future model development, and empirical data collection. It also provides a set of novel predictions that can be independently assessed. We anticipate that this will be the beginning of a long-term exploration of GEMs, and we call upon ecologists, biologists, mathematicians, and computer scientists to join in creating a modelling community surrounding GEMs that can catalyse the development of more realistic, sophisticated, yet better understood models of ecosystems worldwide. Our hope is that GEMs will form the basis of new science in ecology and, in particular, science that proves actionable to those charged with conserving the biosphere on which we all depend.

Supporting Information

Trophic abundance pyramids. Community-level abundance pyramids across all cohorts belonging to each trophic level emergent from the model for an example of terrestrial and marine grid cell (grid cells T1 and M1 from Table 4). Results are from the final year of a 100-y model run. Light green represents herbivores, blue represents omnivores, and red represents carnivores. Total abundance densities (1,000 s individuals/km ²) are indicated by the widths (after log-transformation) and numbers within the boxes.
https://doi.org/10.1371/journal.pbio.1001841.s004
(TIF)

Comparison of model predicted with empirical normalised body mass spectra (NBS). Frequency distribution of the slope of NBS from [75] with model-derived NBS slope values, calculated following Sprules and Munawar [114], for carnivores (red), omnivores (blue), and herbivores (green). Triangles correspond to slopes for the low productivity, aseasonal marine cell (M1, Table 4) and circles to the high productivity, aseasonal terrestrial cell (T1, Table 4).
https://doi.org/10.1371/journal.pbio.1001841.s005
(TIF)

Community-level properties for cell T1 with all edible plant matter available for herbivory. Trophic pyramid and size distribution spectra for focal cell T1 with the value of parameter equal to 1 for terrestrial herbivores, which means that each terrestrial herbivore cohort experiences 100% of the edible plant matter in the grid cell when it is eating. The release of this parameter does not affect the low herbivore to primary producer ratio for terrestrial communities. Here, the ratio is 1.0%, marginally higher than that calculated for the same location using a value for of 10% ( Figure 4).
https://doi.org/10.1371/journal.pbio.1001841.s011
(TIFF)

Environmental data sources. External global environmental data sources used within the model. Units represent those used within the Madingley model, not those of the original source data. a Environmental variables were long-term/multidecadal average values.
https://doi.org/10.1371/journal.pbio.1001841.s012
(DOCX)

Empirical estimates of trophic-level biomasses in globally widespread ecosystems in both marine and terrestrial environments. Notes: 1. Consider only the pelagic producers. Assume that dissolved organic carbon (DOC) and suspended particulate organic carbon (POC) are available to the pelagic community. Do not include benthic suspension feeders. Assume micro-zooplankton are herbivores (as defined in our model). For omnivores and carnivores, assume that the proportion of biomass that is supported originally by pelagic primary production is equal to the proportional rate of consumption of pelagically derived foods relative to consumption of foods from all sources. 2. From Table 12.2 (plants taken as above ground vascular + Moss + Algae + Lichens) of Chapin et al [115]. 3. Herbivore biomass taken from [73]. 4. Producer and herbivore biomasses from [73]. 5. Producer biomass of 9,999 g C m-2 comes from Figure 8 of Frangi and Lugo [116]. Montane palm floodplain forest; herbivore biomass taken from [73].
https://doi.org/10.1371/journal.pbio.1001841.s014
(DOCX)

Comparison of emergent individual-level properties from the model with observations. The slopes and intercepts of the relationships between predicted properties and body mass compared to empirical data. The probability that the slope and intercept of the predicted relationship were different from those for the empirical data was calculated as the t statistic of linear models fitted to combined model and empirical data for each emergent property using a categorical factor to indicate a model or empirical datum. The probability that the t statistic for the model including the categorical factor indicates the significance of the difference.
https://doi.org/10.1371/journal.pbio.1001841.s015
(DOCX)

Summary statistics of empirical community herbivore and primary producer biomasses. Summary statistics, derived from [73], for those ecosystem types most closely representing the ecosystems within the two grid cells shown in Figure 3. PB, primary producer biomass; HB, herbivore biomass. Median herbivore biomass in temperate and tropical grassland ecosystems is 52.3% of that in communities of marine phytoplankton, whereas median primary producer biomass is 57 times larger in the terrestrial compared to the marine ecosystem.
https://doi.org/10.1371/journal.pbio.1001841.s016
(DOCX)

Comparison of abundance-density slopes predicted by the model with observed slopes. Abundance-density relationships predicted by the model (for cells T1 and M1, Table 4) compared with observations. Empirical abundance-density relationships were derived from Jennings et al. [74]. The reported total biomass versus body mass relationships were converted to abundance versus body mass relationships, by dividing the total biomass in each mass bin by the central mass of that mass bin, which can be approximated as subtracting 1 from the slope of total biomass against body mass.
https://doi.org/10.1371/journal.pbio.1001841.s017
(DOCX)

Acknowledgments

We thank L. Joppa for engaging in discussion and ideas that helped us to conceptualize the model and A. Blandon, K. Boughey, and E. Pynegar for compiling empirical data with which to compare the model.

Author Contributions

The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: MH TN DT SE JH VL MJS JPWS DWP. Performed the experiments: MH TN DT. Analyzed the data: MH TN DT MS DP. Contributed reagents/materials/analysis tools: MH TN DT MS VL DP. Wrote the paper: MH TN DT SE JH MJS JPWS DWP. Contributed equally to this manuscript: MH TN DT.

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Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model

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Michael B. J. Harfoot, Tim Newbold, Derek P. Tittensor, Stephen Emmott, Jon Hutton, Vassily Lyutsarev, Matthew J. Smith, Jörn P. W. Scharlemann, Drew W. Purves Subject Areas For more information about PLOS Subject Areas, click here. We want your feedback. Do these Subject Areas make sense for this article?

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No report from the Intergovernmental Panel on Climate Change would fail to mention global climate models. Yet the international bodies that are charged with addressing global challenges in conservation - including the Intergovernmental Platform on Biodiversity and Ecosystem Services, which holds its first plenary meeting next week in Bonn, Germany - cannot refer to analogous models of the world's ecosystems. Why? Because ecologists have not yet built them.

General circulation models, which simulate the physics and chemistry of Earth's land, ocean and atmosphere, embody scientists' best understanding of how the climate system works and are crucial to making predictions and shaping policies. We think that analogous general ecosystem models (GEMs) could radically improve understanding of the biosphere and inform policy decisions about biodiversity and conservation. Currently, decisions in conservation are based on disparate correlational studies, such as those showing that the diversity of bird species tends to decline in deforested landscapes. GEMs could provide a way to base conservation policy on an understanding of how ecosystems actually work.

Such models could capture the broad-scale structure and function of any ecosystem in the world by simulating processes - including feeding, reproduction and death - that drive the distribution and abundance of organisms within that ecosystem. Ecologists could apply a GEM to African savannas, for instance, to model the total biomass of all the plants, the grazers that feed on the plants, the carnivores that feed on the grazers and so on. Over time, the flows of energy and nutrients could be mapped between them. All of the organisms would be grouped not by species, but according to a few key traits such as whether they are plants, birds or mammals, cold blooded or warm blooded, diurnal or nocturnal. By encoding processes such as migration and predation into simple mathematical and computational forms, ecologists could model what happens to the various groups over time.

Metrics such as the diversity of animal types inhabiting the grasslands could be used to assess the savannas' health, stability and resilience, and to analyse the fate of particular groups of organisms such as top predators. Ecologists could explore how these attributes might change in response to, say, climate change, the introduction of invasive species or poaching. And, because the rules of play are likely to be broadly similar no matter what the ecosystem, the GEM could equally be applied to forests, lakes or the remotest parts of the ocean, providing a common framework for understanding and managing disparate ecosystems on local and global scales.

There are huge challenges to building GEMs - not least, obtaining the appropriate types of data to validate the models' predictions. But the difficulties are not insurmountable. Theories abound for describing the processes that drive ecosystems, many of which are backed up by data.

Over the past two years, we at Microsoft Research and at the United Nations Environment Programme World Conservation Monitoring Centre, both in Cambridge, UK, have built a prototype GEM for terrestrial and marine ecosystems. Called the Madingley model, it uses real data on carbon flows as a starting point. We have hit all sorts of computational and technical hurdles, and are expecting more as we develop the model. Yet the project demonstrates that building GEMs is possible. From the relationship between the mass of individual organisms and how long they live, or the effects of human perturbations such as hunting, to the distribution of biomass across Earth (see 'Model life'), the model's outputs are broadly consistent with current understanding of ecosystems.

Modelling ecological phenomena at various scales is not new. Conservationists frequently use models to predict how much habitat fragmentation an endangered species can tolerate. But few ecologists have tried to build models of how general ecosystem properties emerge from the interactions of individuals. One attempt - known as Ecopath with Ecosim1 - is being developed at the University of British Columbia's Fisheries Centre to address management and ecological questions. This combines modelling at the phytoplankton scale with that at the level of fisheries and marine mammals. Another, called Atlantis2, developed by Australia's Commonwealth Scientific and Industrial Research Organisation, incorporates biophysical, economic and social factors to provide an integrated tool for modelling marine ecosystems. But these consider fewer processes and organisms than would a GEM.

Throughout the history of ecology, most researchers have resisted abstraction because ecological complexity is so obvious in nature. Ignoring the myriad shapes and colours of different bird species, for example, seems instinctively wrong. Instead, ecologists have tended to stress the importance of species identification as well as a vast number of ecological processes, from individual adaptation to the social dynamics of groups. Many in the field also emphasize that findings in one ecosystem do not generalize to others, and that randomness and history could be as important in affecting some particular measurement as any deterministic rules3. But comprehensive species-specific data will always be in short supply (at least 80% of the millions of species on Earth are undescribed4), so a better understanding of ecosystems demands a broad-brush approach.

Building a GEM will require different types of data - to help define the ecological rules at play, to provide reasonable starting conditions for the simulation (such as a realistic ratio of herbivores to top predators) and to evaluate the model's predictions. For these three goals, enough data are available to get started, although information on ecological processes far outweighs the rest.

Metabolic rates, for instance, have been measured in hundreds of animals in the lab, and researchers in the field have documented lifespans, growth rates and reproductive success for thousands (in some cases, millions) of birds, mammals, plants and bacteria. Ecologists have also mathematically determined numerous 'rules of existence' for some organisms, such as that an animal's metabolic rate is proportional to its mass raised to a power of around 0.70 (ref. 5).

Obviously, modelling every organism within an ecosystem is impossible. (We estimate that it would take a standard laptop computer around 47 billion years to model for 100 years every multicellular animal within just one of the 1-degree grid cells covering Earth.) Yet certain computational techniques have been developed, mainly in marine ecology, that could allow researchers to model entire ecosystems using rules about the behaviour of individuals.

One approach is to model collections of organisms or 'cohorts'. The idea is that within a cohort, individuals are similar enough to be considered identical. For a shoal of small herbivorous fish meeting a cloud of plankton, say, ecologists could calculate the feeding rate for an exemplar fish and then apply that rate to the whole shoal. (In the simulation just described, we found that grouping organisms into cohorts on the basis of body size, functional group such as omnivore or carnivore and a few other traits reduced the computation time to 10 hours).

The biggest stumbling block to constructing GEMs (after convincing ecologists that they can and should be built!) is obtaining the data to parameterize and validate them.

Records of what species of plants and animals live in the world's forests, grasslands and oceans are often available to some extent, but far fewer data exist on the abundance of those species. And almost no data have been collected on the properties of whole ecosystems, such as on the distribution of body sizes from plankton to whales. Marine trawl surveys carried out for research or to assess fish stocks probably come closest to providing this type of information, although even these are restricted as to what size range of organisms they survey.

A new programme of data gathering is easy to envisage. Using automated cameras and image recognition, it should be possible to sample thousands of animals and determine their approximate size and what broad group they belong to: reptile or mammal, flying or non-flying. Motion-activated cameras used by conservationists and wildlife enthusiasts already produce tens of thousands of images of fish, birds and mammals every day. And stored away in numerous research institutions are vast samples of insects collected in traps that suck them out of the air, and data from continuous plankton recorders towed beneath ships for millions of kilometres.

Naturally, a major new data-gathering programme would be costly. But globally, governments already spend billions of dollars on satellite observations of vegetation and habitat distribution, fisheries surveys, forest inventories and species surveys. Diverting a small fraction of these funds to gathering the data needed to develop and evaluate GEMs could pay dividends. A first step would be for governments around the world to support programmes similar to the National Ecological Observatory Network - an international cooperation funded by the US National Science Foundation to manage large-scale collection of ecological and climate data.

To reduce costs and to harness the power of citizen science, data collection could even be crowd-sourced. Rapidly growing websites such as iNaturalist and eBird (on which users can share their observations of wildlife) currently focus on traditional species identification. Such sites could potentially collate an extraordinary amount of information on functional groups of organisms and traits such as body size.

Constructing realistic GEMs is one thing. The real challenge is to produce models from which the predictions are trustworthy enough to guide the decisions of conservationists and policy-makers. Recent progress in computational statistical methods offers a way for ecologists to formally build trustworthiness into models. For instance, tools are available to quantify the uncertainty associated with models' predictions. A healthy crop of alternative, competing GEMs will be crucial, together with mechanisms that enable their fair assessment. In blind-testing, for example, different models could be used to predict an ecosystem property that has been measured but not reported, allowing the models to be ranked in terms of how well they do.

We are not proposing that GEM predictions (which will always be simplistic) provide the only guide to conservation policy and the management of ecosystems. But coupled with models from other fields, such as economics and epidemiology, they could offer a means of managing human actions and the biosphere in an integrated, consistent and evidence-based way. Far from eclipsing traditional ecological research, GEMs would draw on it and give such work more focus. Using GEMs, ecologists could identify processes that are poorly understood yet crucial to ecosystem structure and function, rather than delve deeper into well-studied areas.

Ecological systems do not have the equivalent of the precise laws used by climate scientists.

Ecological systems do not have the equivalent of the precise laws used by climate scientists. This is a significant challenge to building GEMs, along with the complexity of nature, the small number of GEM-like models under development and the paucity of data with which to constrain them. But just by attempting to build general models, ecologists will find out what they need to know to truly understand ecosystems.

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Time to model all life on Earth - Nature

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No report from the Intergovernmental Panel on Climate Change would fail to mention global climate models. Yet the international bodies that are charged with addressing global challenges in conservation - including the Intergovernmental Platform on Biodiversity and Ecosystem Services, which holds its first plenary meeting next week in Bonn, Germany - cannot refer to analogous models of the world's ecosystems. Why?

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TOWARDS 2020 SCIENCE AND THE EUROPEAN SCIENCE INITIATIVE

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TOWARDS 2020 SCIENCE AND THE EUROPEAN SCIENCE INITIATIVE

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https://en.wikipedia.org/wiki/Stephen_Emmott

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From Wikipedia, the free encyclopedia

Stephen Emmott (born 3 June 1960) is a British scientist, entrepreneur and chief scientist of Scientific.[4][5] Emmott was named one of London's most influential scientists, and one of the most influential people in London by the Evening Standard in 2012.[6]

Emmott studied at the University of York, where he completed a B.Sc. in Biological Sciences (experimental psychology), graduating in 1987 with First Class honours.

He obtained a PhD in computational neuroscience from the Centre for Cognitive and Computational Neuroscience, University of Stirling in 1993, supervised by Professor Roger Watt. Having been deeply influenced and inspired by the work of David Marr and David Rumelhart, Emmott's doctoral research focused on modelling and understanding the computations the brain performs to produce vision. He chose the visual processing of text because it is the canonical example of a physical structure designed around how the brain works, rather than the other way round.[7]

After obtaining his PhD., Emmott took up a post-doctoral scientist position at AT&T Bell Labs in Holmdel, N.J, US, between 1993 and 1996. He worked in Nobel Laureate Arno Penzias's division, undertaking research into biological-inspired computing, applied to some of the earliest medical, financial and e-commerce applications of the Internet.

Emmott returned to London in January 1997 to lead the advanced research laboratory of NCR Corp (which then owned teradata). His work and the work of his Laboratory became recognised for numerous innovations at the intersection of science, technology and finance. This included the invention of Agent-based Internet auctions,[8] the digital wallet[9] and a biologically-inspired, probabilistic method to predict the behaviour of financial markets.[10]

Microsoft

In 2003, Emmott joined Microsoft as chief scientist, and head of computational science.[11] Emmott's vision was 'to pioneer new computational methods, tools and technologies to enable new kinds of science and accelerate radical solutions to the unprecedented challenges and opportunities of our time.'

Emmott's scientific leadership created numerous scientific and technological advances. These include the new science of biological computation and 'Living Software';[12][11][13][14] programmable Artificial Photosynthesis as a potentially breakthrough cheap, global energy technology;[15][16] the world's first predictive mechanistic model of all life on Earth (General Ecosystem Model);[17][18][19] the first mechanistic model of the Global Carbon Cycle;[20] and the development of a computational platform enabling hyper-parallelisation of the Scientific Method (e.g.,[21]).

Scientific

In 2019, Emmott created Scientific,[5] a new science and technology company dedicated to creating transformational science-based innovation that generates a step-change in returns for investors, and genuinely radical global impact outcomes.

From 2005 to 2010, Emmott was scientific advisor to the Chancellor of The Exchequer. He was a member of the UK Government's 10 Year Science & Innovation Framework Committee, 2004. He was appointed by the UK government Minister for Science as a trustee of the UK's National Endowment for Science, Technology & the Arts, 2008-2012. He was an adviser to the Finnish Prime Minister's Science & Innovation Strategy, 2008. Emmott is Professor of Biological Computation, University College London, and was Professor of Computational Science (visiting), University of Oxford, 2007-2014.

[ Ten Billion edit]

Emmott is author of 10 Billion.[22][23][24][25] It is about the climate, ecological, agricultural, resource, pollution, energy, migration and geo-political impact of a human population of 10 Billion.

Royal Court

10 Billion began as 'a new kind of scientific lecture', delivered by Emmott, over twenty nights at London's Royal Court Theatre, directed by Katie Mitchell.[22] It won widespread critical acclaim. Sarah Hemming of the Financial Times described it as 'immensely, distressingly powerful' and 'one of the most disturbing evenings I've ever spent in a Theatre'.[26] The Guardian's Michael Billington stated that "what is impressive is that Professor Emmott argues his case with an implacable logic. He is quiet, humane and deeply concerned and when he says, at the end, "I think we're fucked" you have to believe him."[27] Ten Billion was named as 'Theatrical Performance of the Year' in 2012 by The Guardian.

Book

10 Billion was published as a book in 2013.[23]

The Guardian's John Gray concluded that, "The shift in thinking that will be needed if we are to prepare ourselves for living in a different world begins with reading Emmott's indispensable book."[28] Clive Cookson of the Financial Times said the book was "a stark, simple and short warning about the coming catastrophe, which [Emmott] feels is inevitable, resulting from human population [growing from 7bn to 10 Billion] and over-exploitation of the world's resources."[29] In a review of 10 Billion in Nature, Hania Zlotnik noted: "His slim, even terse book [presenting] his view on the "unprecedented planetary emergency we've created" - primarily examines the transformation of the global environment by human activity, a transformation that includes climate change, increasing water shortages and growing urbanization. Emmott's assessment of the capacity of people and technology to prevent the global crises that confront us is grim."[30]

On the Guardian's environment network, Chris Goodall called it "error-strewn, full of careless exaggeration and weak on basic science."[31]

Film

10 Billion was made into a feature-length documentary film, produced by Oxford Film & Television[25] directed by Peter Webber. It was supported financially by Ingenious Media, Prince Albert II of Monaco Foundation, Sky Atlantic, HanWay Films and the Roland Mouret Foundation. It was premiered at London's Curzon cinema in December 2015, and screened at cinemas worldwide. Subsequently, it premiered on Sky Atlantic in 2016.

1. ^ "EMMOTT, Stephen". Who's Who 2013, A & C Black, an imprint of Bloomsbury Publishing plc, 2013; online edn, Oxford University Press.(subscription required)

2. ^ "Ten Billion".

3. ^ "Scientific".

4. ^ Emmott, Stephen J. (1993). The visual processing of text (Ph.D. thesis). University of Stirling. hdl:1893/1837.

5. ^ US 6871190, Seymour, Mark & Emmott, Stephen J., "System and method for conducting an electronic auction over an open communications network", published 2005-03-22, assigned to NCR Corp.

6. ^ US 6424845, Emmott, Stephen J.; Woods, Sarah & Johnson, Graham I., "Portable communication device", published 2002-07-23, assigned to NCR Corp.

7. ^ US 6480832, Nakisa, Ramin C., "Method and apparatus to model the variables of a data set", published 2002-11-12, assigned to NCR Corp.

8. Heger, M. (2010). "Tech meets bio: software and technology companies have increasingly been taking a more active role in biological research: Roni Zeiger, Michael Montalto, Ajay Royyuru and Stephen Emmott" (PDF). Nature Medicine. 16 (8): 844-847. doi:10.1038/nm0810-844. PMID 20689541. S2CID 205381392.

9. ^ Lakin, M. R.; Youssef, S.; Polo, F.; Emmott, S.; Phillips, A. (2011). "Visual DSD: A design and analysis tool for DNA strand displacement systems". Bioinformatics. 27 (22): 3211-3213. doi:10.1093/bioinformatics/btr543. PMC 3208393. PMID 21984756.

10. ^ Dalchau, N.; Smith, M. J.; Martin, S.; Brown, J. R.; Emmott, S.; Phillips, A. (2012). "Towards the rational design of synthetic cells with prescribed population dynamics". Journal of the Royal Society Interface. 9 (76): 2883-2898. doi:10.1098/rsif.2012.0280. PMC 3479904. PMID 22683525.

11. ^ Creatore, C.; Parker, M. A.; Emmott, S.; Chin, A. W. (18 December 2013). "Efficient Biologically Inspired Photocell Enhanced by Delocalized Quantum States". Physical Review Letters. 111 (25): 253601. arXiv:1307.5093. Bibcode:2013PhRvL.111y3601C. doi:10.1103/PhysRevLett.111.253601. PMID 24483744. S2CID 35661971.

12. ^ Hemmig, Elisa A.; Creatore, Celestino; Wünsch, Bettina; Hecker, Lisa; Mair, Philip; Parker, M. Andy; Emmott, Stephen; Tinnefeld, Philip; Keyser, Ulrich F. (13 April 2016). "Programming Light-Harvesting Efficiency Using DNA Origami". Nano Letters. 16 (4): 2369-2374. Bibcode:2016NanoL..16.2369H. doi:10.1021/acs.nanolett.5b05139. ISSN 1530-6984. PMC 5003508. PMID 26906456.

13. ^

14. ^ Purves, Drew W.; Scharlemann, Jörn P. W.; Smith, Matthew J.; Lyutsarev, Vassily; Hutton, Jon; Emmott, Stephen; Tittensor, Derek P.; Newbold, Tim; Harfoot, Michael B. J. (22 April 2014). "Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model". PLOS Biology. 12 (4): e1001841. doi:10.1371/journal.pbio.1001841. ISSN 1545-7885. PMC 3995663. PMID 24756001.

15. ^ Smith, M. J.; Purves, D. W.; Vanderwel, M. C.; Lyutsarev, V.; Emmott, S. (29 January 2013). "The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties". Biogeosciences. 10 (1): 583-606. Bibcode:2013BGeo...10..583S. doi:10.5194/bg-10-583-2013. ISSN 1726-4189.

16. ^ Yordanov, Boyan; Dunn, Sara-Jane; Kugler, Hillel; Smith, Austin; Martello, Graziano; Emmott, Stephen (7 July 2016). "A method to identify and analyze biological programs through automated reasoning". NPJ Systems Biology and Applications. 2: 16010-. doi:10.1038/npjsba.2016.10. ISSN 2056-7189. PMC 5034891. PMID 27668090.

17. Emmott, Stephen (2013). 10 billion. London: Penguin. ISBN 9780141976327. OCLC 829960881.

18. ^ Billington, Michael (19 July 2012). "Ten Billion - review". The Guardian. ISSN 0261-3077.

19. ^ Gray, John (5 July 2013). "Population 10 Billion by Danny Dorling and Ten Billion by Stephen Emmott - review". The Guardian. ISSN 0261-3077.

20. ^ Zlotnik, Hania (4 September 2013). "Population: Crowd control". Nature. 501 (7465): 30-31. Bibcode:2013Natur.501...30Z. doi:10.1038/501030a. ISSN 1476-4687.

21. ^ Goodall, Chris (9 July 2013). "Stephen Emmott's 10 billion book is unscientific and misanthropic". The Guardian. ISSN 0261-3077.

# data[].attributes.attachment_link.description
Toggle the table of contents From Wikipedia, the free encyclopedia Stephen Emmott (born 3 June 1960) is a British scientist, entrepreneur and chief scientist of Scientific.[4] [5] Emmott was named one of London's most influential scientists, and one of the most influential people in London by the Evening Standard in 2012.[6] Emmott studied at the University of York, where he completed a B.Sc.