# data[].attributes.headline
Beyond Traditional Hardiness Zones: Using Climate Envelopes to Map Plant Range Limits

# data[].attributes.subject
Beyond Traditional Hardiness Zones: Using Climate Envelopes to Map Plant Range Limits

# data[].attributes.body
https://academic.oup.com/bioscience/article/57/11/929/234249

~Shania

# data[].attributes.attachment_link.readable_text
Abstract

Plant hardiness zones identify the location of environmental conditions under which a species or variety of plant can successfully survive and grow. Knowledge of the hardiness characteristics of plants is useful for disciplines such as horticulture, agriculture, and silviculture, in which high levels of plant survival and growth are desirable. The seemingly simple question of where a plant can grow can have important implications, given the magnitude of the horticultural sector, the introduction of new species, the viability of native species outside their natural ranges, and the prospect of rapid climate change over the course of this century.

Several systems have been developed for mapping plant hardiness in North America. In the United States, plant hardiness zones have traditionally been defined using extreme minimum temperature ( USDA 1965), that is, the average coldest winter day for any given location. These zones were updated in 1990 using averages of the lowest temperatures recorded for each of the years 1974-1986 in the United States and Canada and 1971-1984 in Mexico ( Cathey 1990), and again in 2006 for the United States ( National Arbor Day Foundation 2006; www.freetrees.com/media/zones.cfm). This system identifies 11 different zones, each of which represents a 10-degree-Fahrenheit (5.6-degree-Celsius [°C]) range in average annual minimum temperature. In Canada, Ouellet and Sherk (1967a) developed a map based on an index of winter hardiness using plant survival data on 174 woody plant species and cultivars at 108 stations across the country. Their model was generated using regression analysis that included seven climate variables: mean minimum temperature of the coldest month, frost-free period in days, rainfall from June through November, mean maximum temperature of the warmest month, rainfall in January, mean maximum snow depth, and maximum wind gust in 30 years ( Ouellet and Sherk 1967b). The result was a map that defined 11 hardiness zones (0-10) across southern Canada ( Ouellet and Sherk 1967c). Both of these approaches to mapping plant hardiness zones have also been updated using thin-plate smoothing spline interpolation techniques ( McKenney et al. 2001, 2006). Finally, the Sunset climate zone system ( Brenzel 2001) incorporates information on length of growing season, timing and amount of rainfall, extreme low and high temperatures, and humidity. Though this system provides a high degree of detail for some regions, here we focus on comparisons with the US Department of Agriculture (USDA) system because it is widely used and accepted throughout North America.

Although plant hardiness systems are part of the lore of gardening in North America, there are a number of limitations to the systems employed in Canada and the United States. The USDA zones rely entirely on extreme minimum temperature, which, though important for plant survival, is not the only climatic factor that determines the suitability of a location. For example, snow cover can provide critical winter insulation in many regions. The Canadian system was in fact developed for trees and shrubs, but is used extensively for herbaceous perennials, which may not respond to the same climatic controls as woody plants. For instance, maximum wind gust is likely to be more important to a tree than to a low-growing, herbaceous plant. The categorical nature of both maps also introduces a degree of coarseness to the zone designations, particularly if the boundary of a plant's growing range falls in the middle of a hardiness zone. Finally, although both systems have been mapped to a high degree of detail and accuracy, the method used to designate plants into hardiness zones is not well defined. In some cases, hardiness ratings may be derived from trials at a few sites around the continent; in other cases, ratings may simply reflect the opinion of a select few horticulturalists. Often, in the rush to get new products to market, the gardens of the general public act as test plots for plants with poorly understood hardiness characteristics (John Valleau, Valleybrook Gardens/Heritage Perennials, Niagara-on-the-Lake, Ontario, Canada, personal communication, 14 November 2006).

In recognition of these limitations, it is our aim to go beyond the traditional plant hardiness zone approach and outline a generic, transparent, and repeatable process for delineating the potential distribution of large numbers of perennial species. Our approach involves developing climate envelopes ( Nix 1986) for individual plant species using accurately georeferenced occurrence data and spatially continuous, continent-wide climate models. This method works by summarizing the climatic extents (defined as the ranges between the minimum and maximum values for different climatic variables) at locations where a species is known to survive and then mapping the potential range of the species by identifying all locations (i.e., grid cells on a map) with conditions that fall within those extents. Furthermore, the potential effects of climate change on a species' distribution can be explored by identifying where these climate conditions are located on maps of future climate as estimated by general circulation models (GCMs).

To place this work in an ecological context, we draw on the niche concept proposed by Hutchinson (1957). He defined the fundamental niche of a species as the potential space that it could occupy, based on the full set of environmental conditions (e.g., climate, soil) that it can tolerate. Conversely, the realized niche, which is a subset of the fundamental niche, is the actual space occupied by a species, given the further constraints of biotic factors such as competition and predation. The realized niche is typically represented in species' range maps. The approach used here defines the climatic niche ( Pearson and Dawson 2003) of the species under study, which, in theory, should actually be larger than the fundamental niche, because it considers only climatic limitations to plant growth. In the horticultural context, the climatic niche should be a reasonable estimate of the full potential growing area of a plant, given that gardening activities often minimize both abiotic (e.g., fertilization to boost soil nutrients) and biotic (e.g., weeding to reduce competition) constraints on plant growth. Success in defining the potential growing area for any given species will depend on the extent to which the observed data encompass a plant's climatic tolerances and the chosen climate variables represent primary climatic controls on survival and growth.

An alternative approach to identifying a plant's fundamental niche is to employ physiologically based process models ( Sykes et al. 1996, Chuine and Beaubien 2001, Porter et al. 2002, Walther et al. 2005). However, the physiological details required for such an approach are time-consuming and expensive to gather, and thus are not likely to be determined for very many species. The approach used here makes use of georeferenced distribution data, which are more readily accessible. We have therefore undertaken a major data-gathering effort that involves obtaining geographically referenced plant occurrence information from agencies, from botanically reputable experts, and from the enthusiastic gardening public across both Canada and the United States ( McKenney 2006).

Here we advance the concept of using bioclimatic envelopes to define species-specific potential distributions as an alternative approach to defining hardiness zones for individual species across North America. Specifically, we have three objectives: (1) to summarize the current status of our data-gathering efforts, (2) to present results for a representative species and compare them to those using the standard hardiness zones, and (3) to demonstrate how predicted climate-change impacts can be incorporated into the climate-envelope approach. We demonstrate our approach using sugar maple ( Acer saccharum)-a popular and economically important species, with well-established hardiness ratings in both Canada and the United States, and for which we have obtained extensive distribution data.

Climate envelopes: Concepts and considerations

Climate envelopes were generated using ANUCLIM computer software ( Nix 1986, Houlder et al. 2000). This system works by first generating an estimate for all climate variables of interest at each location where the species was observed. The climatic extents of the species' range are then defined by obtaining the minimum and maximum value for each of the climate variables. ANUCLIM is an early-generation climate-envelope program that fits a simple rectilinear model-that is, it essentially fits a box around extreme values in multivariate ecological space. In recent years, ecologists have developed a variety of niche-modeling techniques ( Segurado and Araújo 2004, Guisan and Thuiller 2005, Elith et al. 2006, Heikkinen et al. 2006, Pearson et al. 2006), many of which attempt to fit a more refined (i.e., nonrectilinear) envelope by incorporating patterns of covariance among the environmental variables. We note, however, that in a horticultural setting, typical climatic covariation (e.g., hot and dry conditions) may no longer hold because of human intervention (e.g., irrigation), and thus the simple, direct approach of ANUCLIM may be very suitable in this context. The transparency of the method also aids in generating interest and involvement from the general public.

It is common practice to delineate a "core range"-a subset of the full climatic range within which an organism is thought to maintain high rates of survival, growth, and reproductive success. Since climate values for a given species are summarized in the form of a cumulative frequency distribution, users can define upper and lower percentiles as limits for a reduced or core range. In this study, we defined the core range as the climatic space bounded by the 5th and 95th percentiles, thus encompassing 90% of the climate values for each species. Because the core range is quantified on the basis of the density of species observations in climate space, its shape and location can be sensitive to variations in observed spatial data density that may not reflect its possible distribution in climate space. For example, occurrence records for a tree species with a climatic niche centered in northern Canada may be biased toward the southern portion of the species' range because of a greater density of data collections in southern areas. To help reduce this geographic bias, we pre-filtered the species occurrence data by overlaying them with a 300-arc-second (approximately 10-kilometer [km]) grid and randomly selecting a single observation from any grid cell that contained multiple data points. We explored several grid sizes for this operation and found that the 10-km grid represented a reasonable trade-off between the degree of coarseness required to filter out spurious data density effects and the degree of fineness needed to capture the range of climate variability contained in the species occurrence data.

Another important consideration for this approach is the set of climate variables used to define the envelope. ANUCLIM generates 19 climate variables by default when supplied with spatially continuous models of monthly mean daily maximum and minimum temperature and monthly mean precipitation. Differences in the size and shape of the predicted climatic niche can arise depending on the variables involved ( Beaumont et al. 2005). Thus, the best choice of climate variables is a parsimonious set that defines important climatic constraints on plant survival and growth; larger sets can unnecessarily constrain potential plant ranges with climatic requirements that are superfluous to plant survival ( Box 1981, Beaumont et al. 2005). On the basis of extensive testing, and of a literature survey on climate controls on plants (e.g., Woodward 1987, Shao and Halpin 1995, Stephenson 1998), we use six climate variables in the models presented here: annual mean temperature, minimum temperature of the coldest month, maximum temperature of the warmest month, annual precipitation, precipitation in the warmest quarter, and precipitation in the coldest quarter. Irrigation is often provided in the horticultural setting, which removes water supply (i.e., precipitation) as a constraint. Therefore, we also generate potential distribution results using only the three temperature-related variables. The resultant pair of maps for each species can be interpreted as indicating potential distribution under irrigated (i.e., cultivated) and nonirrigated (i.e., wild) conditions.

The temperature variables listed above are highly correlated ( r > 0.90) with other familiar climatic controls on plants, such as extreme minimum temperature, growing season length, and degree-days. Ideally, moisture constraints would be quantified with a water budget model rather than with precipitation variables. However, this would require high-resolution maps of soil water capacity, which are not available for much of Canada. Nevertheless, we have found high levels of correlation (i.e., r values of 0.7 to 0.8) between the precipitation variables outlined above and coarse-scale, global water budget variables ( Willmott and Matsuura 2007).

Present and future climate

Climate data are provided in the form of spatially continuous models generated from 1971 to 2000 US and Canada-wide climate station data using the ANUSPLIN suite of programs ( Hutchinson 2004). This approach employs thin-plate smoothing splines to model each climate variable as a function of latitude, longitude, and elevation; errors from withheld data tests for any given location are 10% to 20% for precipitation and less than 0.5°C for temperature ( McKenney et al. 2006). Because the models are spatially continuous, climate estimates can easily be generated for any georeferenced entry in the plant database.

To map the climatic niche, we created regular grid estimates of the selected climate variables at a 300-arc-second (approximately 10-km) resolution. Although finer resolutions are possible-and arguably appropriate in regions with significant elevation gradients-we found this resolution to be a reasonable compromise between computational intensity, spatial relevance, and convenience for users. We note, however, that this resolution may not completely capture the abrupt climatic gradients that exist in the mountainous regions of the western United States and Canada. As our computing power increases, our map resolution will increase.

One advantage of the climate-envelope approach is that the effect of predicted changes in future climate can be easily explored. Here we compare the climate habitat for sugar maple in current climate conditions with the climate habitat predicted for the end of the century in the conditions projected by the Canadian Centre for Climate Modelling and Analysis's second-generation coupled global climate model (CGCM2; Boer et al. 2000) under the A2 emissions scenario outlined by the Intergovernmental Panel on Climate Change (IPCC; Nakicenovic and Swart 2000). This is considered by some to be a relatively extreme scenario that assumes, among other things, high human population, rapid rates of deforestation, and high levels of pollution and carbon dioxide emissions; however, recent evidence suggests that carbon dioxide levels have been increasing at rates higher than the most pessimistic IPCC scenarios ( Canadell et al. 2007). We have developed grids of future climate for several other GCMs, emissions scenarios, and time periods (see McKenney et al. 2006), but here we present only the findings for the CGCM2/A2 emissions scenario to demonstrate the approach.

The climate-envelope approach has been criticized as a method for studying the effect of climate change on species' distributions. The approach does not take into account non-climatic factors that play an important role in determining species distributions, such as biotic interactions ( Davis et al. 1998, Hampe 2004), dispersal limitations ( Lawton 2000, Hampe 2004), and genetic adaptation in response to environmental change ( Etterson and Shaw 2001, Hampe 2004). In response to these criticisms, Pearson and Dawson (2003, 2004) point out that most of these concerns are minimized when bioclimatic envelopes are employed at broad spatial scales, at which climate factors tend to be the primary controls on species distributions. Furthermore, as noted previously, issues such as dispersal and competition are often minimized in the horticultural setting.

Plant observations

We have adopted a two-part strategy to acquiring plant data for the project. First, we have approached various agencies that collect or store georeferenced plant observations. In Canada, these agencies are the Ministry of Natural Resources within each province; Conservation Data Centres; ad hoc vegetation surveys by experts; botanical gardens; herbaria; master gardeners; and other botanically reputable groups, such as community-based horticultural societies. In the United States, we have obtained information from similar sources and from the US Forest Service's Forest Inventory and Analysis program ( Alerich et al. 2005). This data collection is ongoing.

Another source of plant occurrence data is submission from the general public, a form of citizen science. This approach has been facilitated by a Web application developed to accept the required information on plant distributions. Before submitting data, participants are asked to register and identify themselves as professional botanists/horticulturalists, master gardeners, or members of the general public. We recognize that there may be some concerns over the taxonomic skills of contributors; thus, this categorization will allow models to be generated using data with varying degrees of botanical certainty. The user interface then accepts information concerning the plant name and location of the sighting. Plants recorded in the database must have survived at a particular location for at least three years. All data submitted are confidential, in that the location coordinates are coarsened before being mapped for public viewing. This not only protects the location of sensitive plant species but ensures privacy for contributors.

To date, more than 1.8 million records for more than 4100 plant species have been gathered. A large majority of these records have come from data agreements with government agencies. Approximately 5000 records have been submitted online, and this number continues to grow as the project generates interest. At this point, 1499 species have more than 50 observations each ( figure 1); previous work indicates that approximately that number of observations is required to produce reliable climate-envelope models, though this number varies depending on the dispersion of the data and the distributional qualities of the species under study ( Kadmon et al. 2003). At http://planthardiness.gc.ca, users can view the occurrence data, potential range maps for both the "irrigated" and "nonirrigated" scenarios, and the projected future climate habitat for all species with at least 30 observations. We have chosen this number of observations (as opposed to 50) as a cutoff to provide preliminary models for a larger number of species and to encourage the submission of observations from data-sparse areas. Users are reminded that the models are preliminary and a work in progress.

An alternative approach to collecting occurrence records is to make use of published range maps (see Shafer et al. 2001). However, range maps do not exist for many plant groups and are usually relatively coarse representations of species' natural, not potential, range. As an example, figure 2 shows the natural range of sugar maple (digitized from Little 1971) overlaid with the data collected for this species. The data represent the natural range well, but they also include verified occurrences as far south as Texas (more than 1000 km south of its natural range; figure 2) and as far west as British Columbia (about 3000 km west of the published range limits; figure 2). Clearly, sugar maple, like most species, is capable of survival and growth far outside its accepted range limits, which underlines the importance of obtaining occurrence data from a wide range of locations when trying to map potential distributions.

The occurrence of sugar maple in novel locations raises a number of important general considerations. First, there is the issue of cultivars-that is, cultivated plant varieties that have been selected for a particular combination of desirable attributes. There are more than 10 sugar maple cultivars on the market, and some of these varieties have been selected specifically for heat tolerance (e.g., A. saccharum 'Astis') and cold tolerance (e.g., A. saccharum 'Unity'). This intervarietal variation with respect to climatic tolerances may explain some of the outlying data points in the current work. Ultimately, we would like to produce models for each cultivar, although considerably more data will need to be collected before maps can be produced at this level of taxonomic detail. Another consideration is that outlying data records, particularly in the north, may come from a location with a microclimate that is not representative of the surrounding area. For example, a planting site within an urban heat island or on the lee side of a building may experience temperatures that are several degrees warmer than the general surroundings. Although we do request information on proximity to buildings for the observations submitted through the Internet, there are currently too few observations to address this issue adequately for most species. Users may wish to exercise some caution when interpreting the models for planting outside the area defined by the core range, especially for species with very few observations. Finally, although contributions have been modest to date, the Internet mechanism allows for a more detailed quantification of climate tolerances than the typical approach of developing climate envelopes from digital range maps when such maps are available (e.g., Shafer et al. 2001). Information on successful growth from new locations will be valuable in a climatically changing world, providing both evidence of change and options for future planting efforts ( McLachlan et al. 2007).

Beyond traditional plant hardiness zones

The potential distribution model for sugar maple covers much of the eastern United States and southeastern Canada when both temperature and precipitation requirements are considered ( figure 3a), and it covers nearly all of the United States and southern Canada when only temperature is taken into account ( figure 3b). Both scenarios indicate a much broader potential range than sugar maple naturally occupies (cf. Little's [1971] range in figure 2). This is not surprising, given that climate envelopes identify all locations with suitable climatic conditions for growth, but do not take into account other factors, such as competition and historical dispersal limitations, that have restricted each species to only a subset of its distributional potential ( Peterson 2003, Soberon and Peterson 2005). Incorporating available soil moisture variables into our models (as opposed to precipitation variables only) may also act to reduce the predicted potential ranges. In both scenarios, the core range follows the general pattern of the full climatic range but, of course, occupies only a subset of this geographic space. This subset may be considered a climatically safer planting area within which a species would be expected to exhibit higher rates of survival and growth.

On the basis of the traditional plant hardiness approach, sugar maple is designated to grow in USDA zones 4 through 8 ( Mityga 2005). Figure 4 shows a comparison between this area and that defined by the climate envelopes for the irrigated and nonirrigated scenarios. The climate habitat identified by the nonirrigated scenario is considerably more restricted than that defined by the traditional approach ( figure 4a), particularly in the western half of the continent. However, when precipitation is not a consideration (e.g., when irrigation is present), the climate envelope for sugar maple extends dramatically to the west, covering much of the area defined by zones 4 through 8 ( figure 4b). The drastic difference between the irrigation scenarios suggests that much of the western half of the continent has favorable temperatures, but perhaps inadequate rainfall, to optimally support sugar maple. This distinction could be useful information for those interested in growing this species, but is not immediately apparent from the traditional plant hardiness zone approach.

Both irrigation scenarios identify extensive areas of potential habitat in Canada that lie well north of USDA zone 4 ( figure 4). This pattern is driven by verified distribution records for sugar maple from locations in northern Ontario, southern Manitoba, and central British Columbia ( figure 2). Generally, the northern range limits provided by the traditional zone approach tend to be conservative (i.e., err on the southern side). This is probably because most published references (e.g., books and magazines) are from southern locations with limited experience, and limited reader market, in truly cold winter regions (John Valleau, Valleybrook Gardens/Heritage Perennials, Niagara-on-the-Lake, Ontario, Canada, personal communication, 14 November 2006). That said, there are a number of ongoing hardiness trials that do gather useful data on plant survival at higher latitudes, such as the Regional Woody Plant Test Project in Alberta ( Murray and Seymour 2005). We are endeavoring to make data agreements with any relevant and interested agencies to increase the number of observations and hence improve the veracity of models in all regions.

Incorporating climate change

With the climate-envelope approach, it is relatively straightforward to incorporate predicted impacts related to climate change. On the basis of projections from CGCM2, we mapped where the current climate envelope for sugar maple would be expected to occur by the end of this century ( figure 5). The future climate habitat for sugar maple shows a significant northward shift of nearly 1000 km and overlaps the current habitat only in the northern portion of the current range ( figure 5).

Clearly, this potential range is an unlikely outcome as a future natural distribution, given that suitable soil conditions are not likely to occur in much of this region by the end of this century. Furthermore, the magnitude of the predicted shift is well beyond generally accepted tree migration rates ( Neilson et al. 2005). However, if climate warming progresses as projected, maps such as those shown in figure 5 may become increasingly important for identifying potential planting areas for negatively affected plant species.

A caveat is that, as atmospheric carbon levels increase, the water-use efficiency of plants may also increase ( Wullschleger et al. 2002). This would mean that, in the future, plants may be able to persist in areas currently considered too dry. In this context, our models may be a conservative estimate of future range limits, particularly along southern and western edges that are strongly affected by precipitation.

Conclusions

Here we present the idea of using climate envelopes to map the potential range of North American plant species. This approach has several advantages over traditional plant hardiness zones, including (a) species-specific maps, (b) a more precise identification of potential growing space than that afforded by the traditional zone approach, (c) the ease of alternative interpretations (wild and cultivated), and (d) a transparent and repeatable method for defining and quickly updating the potential distribution of a species, requiring only georeferenced occurrence observations. The method also allows potential climate-change impacts to be incorporated in a relatively simple way. In the long run, we hope the monitoring and data-acquisition aspects of this effort will provide insights into the impacts of climate change, migration rates, and intervention and adaptation strategies for native, nonnative, and invasive plant species.

The prospect of detailed laboratory studies to determine the climatic tolerances of large numbers of plant species seems unlikely. As an alternative, the approach presented here makes use of geographically widespread distribution data to map the climate habitat of plants. More than 1500 plant species have been modeled to date. This work in progress is intended to engage both experts and the general public on the topic of plant biogeography. The Internet provides a practical mechanism to enhance this participation. Those interested in supporting the project by submitting plant observations are encouraged to do so at the Web site http://planthardiness.gc.ca/.

Acknowledgements

Funding for this project was provided by Natural Resources Canada. The authors would like to thank Louis Iverson, Bill Meades, Lisa Venier, Denys Yemshanov, A. Townsend Peterson, and two anonymous referees for insightful comments on an earlier version of this manuscript. Thanks also to Pia Papadopal for assistance in generating the climate surfaces used in this work. We are indebted to the many individuals and agencies that have provided plant observation data.

References cited

Author notes

# data[].attributes.attachment_link.title
Beyond Traditional Hardiness Zones: Using Climate Envelopes to Map Plant Range Limits

# data[].attributes.attachment_link.description
Abstract. Traditional plant hardiness zone maps identify areas that are relatively homogeneous with respect to climatic conditions that affect plant survival. P

# data[].attributes.headline
article: Fire Severity Filters Regeneration Traits to Shape Community Assembly in Alaska’s Boreal Forest

# data[].attributes.subject
article: Fire Severity Filters Regeneration Traits to Shape Community Assembly in Alaska’s Boreal Forest

# data[].attributes.body
Abstract: Disturbance can both initiate and shape patterns of secondary succession by affecting processes of community assembly. Thus, understanding assembly rules is a key element of predicting ecological responses to changing disturbance regimes. We measured the composition and trait characteristics of plant communities early after widespread wildfires in Alaska to assess how variations in disturbance characteristics influenced the relative success of different plant regeneration strategies. We compared patterns of post-fire community composition and abundance of regeneration traits across a range of fire severities within a single pre-fire forest type– black spruce forests of Interior Alaska. Patterns of community composition, as captured by multivariate ordination with nonmetric multidimensional scaling, were primarily related to gradients in fire severity (biomass combustion and residual vegetation) and secondarily to gradients in soil pH and regional climate. This pattern was apparent in both the full dataset (n = 87 sites) and for a reduced subset of sites (n = 49) that minimized the correlation between site moisture and fire severity. Changes in community composition across the fire-severity gradient in Alaska were strongly correlated to variations in plant regeneration strategy and rooting depth. The tight coupling of fire severity with regeneration traits and vegetation composition after fire supports the hypothesis that disturbance characteristics influence patterns of community assembly by affecting the relative success of different regeneration strategies. This study further demonstrated that variations in disturbance characteristics can dominate over environmental constraints in determining early patterns of community assembly. By affecting the success of regeneration traits, changes in fire regime directly shape the outcomes of community assembly, and thus may override the effects of slower environmental change on boreal forest composition.

# data[].attributes.body
The discussion of community assembly in this chapter reminds me of the study of broad biodiversity patterns (ex: latitudinal biodiversity gradient). In the first case they look at the causal environmental factors ("filters") that explain community composition at a local scale. In the latter case biodiversity scientists study the causal environmental factors ("drivers") that explain total number of species at a regional scale. I wonder if these two research areas could be unified under a single framework.

# data[].attributes.body
While reading chapter two, I quickly realized the focus was filters. I found it interesting that the number of filters isn’t as significant as the importance of the filter in that specific community. When looking for an article, I stumbled upon one that brought up a valid point, can filters give rise to competition that follows those patterns. In that case, there is some validity to that question and if competition does increase due to filters, how can the community sustain stability? - Jantele Wilford

# data[].attributes.body
Chapter 2 was very informative in describing many major filters, and their predicted power level. As I was searching for an article, I found it very interesting that there is a major debate in ecology about the term "ecological filter". Its also a frequently bought up topic in the book. On the section about drought, there is a part that talks about how the filter for a specific species varied depending on the species pool used. I wonder if there will ever be a consensus for how to determine a filter's strength. For the article, I chose this one that talks about how wild fires will continue to filter out koala populations, and how climate change will affect their distributions. -Melanie

# data[].attributes.body
Chapter 2 of A Framework in Community Ecology was focused on filters, which I find to be a very interesting topic, and believed each discussion regarding every filter to be very informative. I liked how Keddy and Laughlin took note of how not all filters work similarly and how each has a differing amount of severity to a population. Sounds obvious, but nuances like this can get lost when discussing filters in broad sense. Attached is an article about the Bubonic Plague which is one of the most famous diseases that acted as huge filter on the human population in the 1300s.

# data[].attributes.attachment_link.readable_text
Abstract

The medieval Black Death ( c. 1347-1351) was one of the most devastating epidemics in human history. It killed tens of millions of Europeans, and recent analyses have shown that the disease targeted elderly adults and individuals who had been previously exposed to physiological stressors. Following the epidemic, there were improvements in standards of living, particularly in dietary quality for all socioeconomic strata. This study investigates whether the combination of the selective mortality of the Black Death and post-epidemic improvements in standards of living had detectable effects on survival and mortality in London. Samples are drawn from several pre- and post-Black Death London cemeteries. The pre-Black Death sample comes from the Guildhall Yard (n = 75) and St. Nicholas Shambles (n = 246) cemeteries, which date to the 11 th-12 th centuries, and from two phases within the St. Mary Spital cemetery, which date to between 1120-1300 (n = 143). The St. Mary Graces cemetery (n = 133) was in use from 1350-1538 and thus represents post-epidemic demographic conditions. By applying Kaplan-Meier analysis and the Gompertz hazard model to transition analysis age estimates, and controlling for changes in birth rates, this study examines differences in survivorship and mortality risk between the pre- and post-Black Death populations of London. The results indicate that there are significant differences in survival and mortality risk, but not birth rates, between the two time periods, which suggest improvements in health following the Black Death, despite repeated outbreaks of plague in the centuries after the Black Death.

Introduction

The Black Death was one of the most devastating epidemics in human history. It was the first outbreak of medieval plague in Europe, and it killed tens of millions of people, an estimated 30-50 percent of the European population, between 1347-1351 [1]-[3]. This massive, extremely rapid depopulation event initiated or enhanced social, demographic, and economic changes throughout Europe, and thus has attracted the interest of a variety of researchers for decades [2], [4], [5]. Previous bioarchaeological research, using individuals buried in the East Smithfield Black Death cemetery from London, examined the selectivity of Black Death mortality, i.e. whether the medieval epidemic targeted particular individuals or whether, as is often assumed given its very high mortality levels, it killed indiscriminately. The results of this research indicated that people varied in their risks of dying during the Black Death [6]-[8]. In particular, older adults appear to have been more likely to die during the epidemic than their younger peers. Analysis of skeletal markers of physiological stress (short adult stature, enamel hypoplasia, tibial periosteal lesions, cribra orbitalia and porotic hyperostosis), which have been shown under conditions of normal, non-epidemic medieval morality to be associated with elevated risks of mortality [8], [9], revealed that people of all ages (not just the elderly) who were already in poor health (i.e. those who had been exposed to physiological stressors and had developed skeletal stress markers as a result) before the Black Death subsequently faced higher risks of death during the epidemic than their healthier peers. Thus, despite its incredibly high levels of mortality, the Black Death was, like most normal causes of death, a selective killer.

Given that the mortality associated with the Black Death was extraordinarily high and selective, the medieval epidemic might have powerfully shaped patterns of health and demography in the surviving population, producing a post-Black Death population that differed in many significant ways, at least over the short term, from the population that existed just before the epidemic. By targeting frail people of all ages, and killing them by the hundreds of thousands within an extremely short period of time, the Black Death might have represented a strong force of natural selection and removed the weakest individuals on a very broad scale within Europe. In particular, given that reproductive-aged individuals with relatively high frailty (i.e. an individual's risk of death relative to other members of the population [10]) were more likely to die during the Black Death than their age-peers with lower frailty, the epidemic might have affected genetic variation with respect to disease susceptibility or immune competence and thus, acted to reduce average levels of frailty in the surviving population. This might explain why, according to historical documents, medieval plague mortality declined steeply between the initial outbreak in 1347-1351 and the second outbreak in 1361 and why mortality levels remained lower in subsequent plague outbreaks throughout the medieval and early modern periods [11]-[13]. Perhaps people who survived the Black Death and their descendants were generally less frail and less likely to die from a variety of causes (including plague) compared to the pre-epidemic population because of heightened immune responses or reduced disease susceptibility, i.e. traits that were selectively favored during the epidemic. If this was the case, it is possible that the Black Death had positive (though perhaps short-lived) effects on population-level patterns of health and survival.

Observed decreases in mortality levels during medieval plague epidemics after the Black Death might reflect molecular changes in the pathogen responsible for the epidemic, and subsequent plague outbreaks, that rendered it less virulent rather than reflecting changes in health and susceptibility within the human host population. Recent molecular analyses of bone and tooth samples of people who died during the Black Death have yielded DNA from the causative pathogen of the medieval epidemic, Yersinia pestis (which continues to affect human populations today by causing bubonic plague) [14]-[19]. By comparing the genome of ancient Y. pestis to that of modern strains of the bacterium, such molecular investigations have the potential to reveal the genetic determinants of epidemiological changes in the disease over time. However, a recently published draft genome of 14 th-century Y. pestis did not reveal any unique derived positions in the ancient strain compared to a modern reference strain in regions of the genome associated with virulence [16]. Further analyses of medieval Y. pestis may yet reveal functionally significant differences between historic and modern plague. However, given that pathogen virulence is the result of host-pathogen interaction and not simply a characteristic of the pathogen itself, the current lack of evidence of such genetic differences might mean that factors other than, or in addition to, evolution of the pathogen itself were at play in the changing epidemiology of plague [20], [21].

In addition to its potential as a selective agent operating upon intrinsic biological factors, the Black Death might also have shaped population patterns by severely altering exogenous factors that affected health and demography. Historical documents from the post-Black Death period indicated that standards of living improved after the epidemic, at least in some areas of Europe such as England. These changes in standards of living resulted in large part from the massive depopulation caused by the Black Death, which reversed the pre-epidemic conditions of an excess population relative to resources [22]. After the Black Death, there was a severe shortage of laborers, effectively ending the medieval system of serfdom, and consequently wages improved dramatically while prices for food, goods, and housing fell [23]. These changes represented a major redistribution of wealth. Real wages rose to levels that were not exceeded until the 19 th century, which allowed for improvements in housing and diet for people of all social status levels [1], [24]-[28]. In England, for example, grain prices dropped steeply after 1375 and generally remained low for almost a century and a half thereafter [29]. Though it took several years for real wages to rise in England in the aftermath of the Black Death (in fact, they may have actually dropped in the period immediately after the epidemic), by the late 14 th century real wages had risen sharply to their medieval peak [30]. By the late 15 th century, real wages were at least three times higher than they had been at the beginning of the 14 th century [29]. The shortage of labor presented new freedoms to workers and placed new pressures on employers. Given that the number of workers was not only smaller than had existed before the Black Death, but that they had new opportunities for mobility and alternative employment if they found existing conditions unsatisfactory, employers increased not only wages but also payments in kind, such as extra food and clothing, to attract workers [23].

Improvements in diet after the Black Death, and particularly decreases in social inequities in diet that presumably benefitted the majority of the lower status population of England, might have acted to reduce average levels of frailty in the population, perhaps more than any other factor associated with improvements in standards of living. Changes in diet can lead to changes in health because nutritional status strongly influences immune competence [31]. Following the Black Death, the amount of money spent per capita on food increased, and people ate higher quantities of relatively high-quality wheat bread, meat, and fish, much of which was consumed fresh rather than salted as had been common prior to the epidemic [29]. Such changes probably improved the nutritional quality of the diet [29], and given that the diet of lower classes became more similar to that of high status individuals, a greater proportion of the post-Black Death English population was consuming a nutritious diet than had been true before the epidemic.

This study examines whether the selective mortality of the Black Death, combined with consequent rising standards of living after the epidemic, resulted in a healthier post-epidemic population in London compared to the pre-Black Death population. Given that death is the ultimate outcome of poor health, and that life-expectancy and mortality levels are commonly used indicators of the general health of living populations, this study examines the temporal changes, from the pre- to the post-Black Death periods (1000-1300 vs. 1350-1538), in survival and the hazard of mortality (as proxies for health) in London. If people were less frail (healthier) on average after the Black Death than before epidemic, a higher proportion of the post-Black Death population should have survived to older ages compared to the pre-Black Death population. This study also examines temporal changes in overall risks of mortality; if the post-Black Death population was healthier, risks of mortality overall should have declined from the pre- to post-Black Death periods. Previous studies using historical data have examined the demographic consequences of the Black Death; but the results have been mixed, with some finding tentative evidence of improvements in survival and mortality and others finding that survival declined in the centuries following the epidemic [12], [32], [33]. However, as described in the Discussion, these studies suffer from limitations that render any conclusions about population-wide patterns premature; these limitations include pre-Black Death samples that potentially do not fully represent typical pre-Black Death demographic patterns and restriction of analysis primarily to wealthy males.

The bioarchaeological data used in this study allow for assessment of individuals not typically included in medieval documentary data (e.g. the poor and women and children). Though previous modeling work has shown that demographic perturbations, such as the Black Death, can have effects on age-at-death distributions that last for several decades [34], [35], substantial effects are relatively short-lived (i.e. up to 50 years). Given the relatively long time period considered in this study, such potential effects of temporary perturbations in demographic patterns (e.g. as a result of severe famine or other crisis mortality events) are likely not strong enough to affect the conclusions I have drawn in this paper.

Materials and Methods

2.1 Skeletal Samples

Ethics statement

All skeletal samples for this study come from medieval London cemeteries and are curated at the Museum of London Centre for Human Bioarchaeology. Site codes and context numbers for each individual used in this study are provided in the supporting information, Table S1. There is no identifying information associated with any of the individuals (i.e. these cemeteries do not have burial records or coffin plates that identify the interred by name), and thus this research does not constitute any risk to living descendants.

Restriction to a single geographic location means that differences between the pre- and post-Black Death samples can be attributed to the effects of the Black Death and changes in standards of living following the epidemic (note, however, that London was a tremendous draw for migrants in this period; the possible effects of migration on the results of this study are addressed in the Discussion).

Pre-Black Death Samples: St. Mary Spital, Guildhall Yard, and St. Nicholas Shambles

The pre-Black Death sample comes from three cemeteries, St. Mary Spital, Guildhall Yard, and St. Nicholas Shambles. Guildhall Yard and St. Nicholas Shambles date to the 11-12 th centuries, based on stratigraphic and documentary data and artifacts [36]. The main cemetery associated with the hospital and priory of St. Mary Spital has been divided into four periods using Bayesian radiocarbon dating: Period 14 (c. 1120-1200), Period 15 (c. 1200-1250), Period 16 (c. 1250-1400) and Period 17 (c. 1400-1539), and there are both single and multiple burials in each period [37]. A combined random sample of 143 individuals was selected from among the single inhumations (burial type A) from Periods 14 and 15, both of which are believed to predate the use of the cemetery for infirmary burials [37]. This study uses a combined sample of 464 individuals from these three cemeteries; this sample represents all of the individuals in the St. Nicholas Shambles and Guildhall Yard cemeteries that were preserved well enough to provide data on age using the methods described below, as well as the random sample from St. Mary Spital (who were similarly well preserved). The pre-Black Death sample contains a mixture of lower status individuals, high status individuals, and members of religious communities and thus provides an appropriate comparison sample for the post-Black Death sample from St. Mary Graces [37], [38].

Post-Black Death Sample: St. Mary Graces

The post-Black Death sample comes from the cemetery associated with the Cistercian Abbey of St. Mary Graces, which was established in London in 1350, soon after the Black Death ended, and it was in use until the Reformation in 1538 [39], [40]. Burials in St. Mary Graces include both high and low status individuals and monks [41]. This study uses a sample of 133 individuals from St. Mary Graces who were preserved well enough to provide data on age using the methods described below.

2.2 Age Estimation

To avoid the problems associated with traditional methods of age estimate, such as age mimicry and poor estimates for older adults [42], [43], ages for this study were estimated using the method of transition analysis described by Boldsen et al. [44]. Transition analysis avoids the biases associated with traditional methods of age estimation and provides point estimates of age even for older adults rather than the broad terminal adult age category (e.g. 50+) typical of traditional methods. This latter feature is particularly useful in the current context, as point estimates for older adults allows for the estimation of important trends that may have occurred at the latest adult ages.

In transition analysis, data from a known-age reference collection are used to obtain the conditional probability, Pr(cj|a), that a skeleton will exhibit a particular age indicator stage or suite of age indicator stages given the individual's known age. This conditional probability is combined, using Bayes' theorem, with a prior distribution of ages at death to determine the posterior probability that a skeleton in the cemetery sample died at a certain age given that it displays particular age indicator stages. In transition analysis, the prior distribution of ages at death can either be an informative prior based on documentary data or a uniform prior. By combining the conditional probability, Pr(cj|a), from a known-age reference sample, with a uniform or informed prior distribution of ages at death, transition analysis avoids imposing the age distribution of the reference sample on the target sample, and is thus preferable to traditional methods of age estimation [44]. For this study, transition analysis was applied to skeletal age indicators on the pubic symphysis and the iliac auricular surface and to cranial suture closure as described by Boldsen et al. [44], and the ADBOU (Anthropological Database, Odense University) Age Estimation software was used to determine individual ages-at-death and the standard errors associated with those point estimates. The ADBOU program uses a conditional probability estimated from the Smithsonian Institution's Terry Collection and an informative prior distribution of ages at death based on data from 17th-century Danish rural parish records.

Ages for subadult individuals (i.e. those individuals for whom all epiphyses had not yet fused) were estimated based on epiphyseal fusion, and dental development and eruption [45]-[50].

2.3 Kaplan Meier Survival Analysis

The effect of time period (pre-Black Death = 0, post-Black Death = 1) on survival was assessed using Kaplan-Meier survival analysis with a log-rank test and using pooled data on age from both time periods. Analysis was performed using SPSS version 21.

2.4 Hazard Model

The differences in risks of mortality between the pre- and post-Black Death periods were assessed by pooling the adult data from all cemeteries to estimate the Gompertz hazard of mortality and modeling time period (pre-Black Death = 0, post-Black Death = 1) as a covariate affecting the baseline Gompertz hazard of adult mortality. Juvenile mortality is not included in the hazard analysis given the relatively small sample sizes of individuals below the age of 10. The typical human pattern of juvenile mortality (which can be modeled using the negative Gompertz component of the Siler model) starts very high at birth and then declines very rapidly within the first two years of life, and it is difficult to obtain reliable estimates of this mortality pattern without large samples of infants and young children [51]-[53]. The Gompertz hazard is a parsimonious two-parameter model of the typical human pattern of an increasing senescent risk of mortality [51], [54]: . The time period covariate effect was modeled using the proportional hazard specification where h( ti) is the baseline Gompertz hazard, ti is the age of the i th skeleton in years, xi is the time period covariate, and ρ is the parameter representing the effect of the covariate on the baseline hazard. The advantage of using a hazard model is that it requires the estimation of just a few parameters and thus makes efficient use of the available skeletal data. The model can be applied to relatively small samples, as it smoothes random variation in mortality data without imposing any particular age pattern on the data [54].

Parameters were estimated using maximum likelihood analysis with the program mle[55]. A positive or negative estimate for the parameter representing the effect of the time period covariate on the hazard would suggest that those who died in the period following the Black Death faced increased or decreased risk of death, respectively, compared to those who died before the epidemic. The fit of the full model with the time period covariate compared to a reduced model in which the value of the parameter representing the covariate was set equal to 0 was assessed using a likelihood ratio test (LRT). The LRT tests the null hypothesis that time period was not associated with elevated nor decreased risks of death. The LRT was computed as follows: LRT = −2[ln( Lreduced) - ln( Lfull)], where LRT approximates a χ ² distribution with df = 1. Previous research has shown that maximum likelihood estimates are increasingly biased with decreasing sample sizes [56], [57], though maximum likelihood estimation can perform satisfactorily with samples of less than 100 for models with relatively few parameters, such as the Gompertz model used in this study [58]. However, given that the sample sizes used in this study, particularly the post-Black Death sample, are not extremely large, the parameter estimates shown here are useful in so far as they reveal a general trend, but should not be viewed as completely unbiased estimates of the true underlying values.

2.5 Fertility Proxy

Given the relatively long period of time across which trends in survival and mortality are examined, this study controls for potential changes in fertility. This is necessary because changes in fertility can alter age-at-death distributions even if age-specific mortality does not change. In fact, cemetery age-at-death distributions are more sensitive to fertility than to mortality, i.e. a change in fertility of a particular magnitude will more dramatically affect age-at-death distributions than will a change in mortality of the same magnitude [59]-[61]. For example, if fertility increases in a population, more children will be born each year than the year before, thus increasing the number of children who die each year, even if age-specific mortality rates remain constant over time. Assuming those children are buried in the same cemetery as the rest of the population, the ever-increasing numbers of young children dying and entering the cemetery will comprise a growing proportion of the associated cemetery over time. Cemeteries are used only for limited periods, such that each birth cohort does not progress completely through the life span before use of the cemetery ceases and thus each birth cohort does not contribute equally to each age interval present in the cemetery. Ultimately, under these circumstances, the resulting cemetery assemblage from this growing population will contain an excess of young individuals relative to older individuals. This phenomenon makes it difficult to infer mortality patterns directly from age-at-death data from cemetery samples [62].

To control for fertility, this study uses the number of the individuals above the age of 30 divided by the number of individuals above the age of 5, i.e. D30+/D5+. Buikstra et al. [63] found that there is a strong (negative) relationship between D30+/D5+ and birth rate, and comparison of the 95% comparison intervals across samples reveals whether birth rates differ significantly among them.

Results

The pre- and post-Black Death age-at-death distributions, for all ages, are shown in Figure 1 ; the two distributions are significantly different (Kolmogorov-Smirnov p<0.001). In particular, there is a higher proportion of adults above the age of 50 in the post-Black Death sample. The Kaplan-Meier survival curves are shown in Figure 2 ; the survival functions reveal enhanced survival among those within the post-Black Death sample compared to the pre-Black Death sample (Mantel-Cox p<0.001).

The results of the hazard analysis are shown in Table 1 . The estimated value of the parameter representing the effect of time period of burial is negative, and the results of the likelihood ratio test indicate that inclusion of the covariate improves the fit of the model. These results indicate reduced risks of mortality across all ages after the Black Death compared to the pre-Black Death population.

Table 1

Parameter

estimate (s.e.)

−2LLR

α

0.0096 (0.001)

β

0.034 (0.002)

Time period covariate

−0.57 (0.1)

26.8 ( p<0.001)

The fertility proxies, i.e. the D ₃₀₊/D ₅₊ values and their 95% comparison intervals, are shown in Table 2 . The the D ₃₀₊/D ₅₊ value for the post-Black Death sample is higher than that of the pre-Black Death sample, which might indicate that birth rates declined following the epidemic. This is consistent with the modeling results found by Paine [34], which indicated a drop in crude birth rates for over 50 years following a catastrophic mortality event. However, as seen in Table 2 , the comparison intervals for the two periods overlap, which indicates a lack of a significant difference in birth rates between the two periods [63].

Table 2

D ₃₀₊/D ₅₊

95% CI

Pre-Black Death

0.44

0.36-0.51

Post-Black Death

0.54

0.45-0.62

Discussion

The age-at-death distributions from the pre- and post-Black Death samples suggest that survival improved following the Black Death, as the post-Black Death sample has a higher proportion of older adults. This is confirmed by the Kaplan-Meier survivorship analysis, which reveals a higher survivorship function for the post-Black Death sample. The results of hazard analysis indicate that overall, risks of mortality were lower in the post-Black Death population than they were before the epidemic. The D ₃₀₊/D ₅₊ values indicate that the observed changes in age-at-death distributions and mortality and survival functions are not an artifact of changes in birth rates between the pre- and post-Black Death periods. Together, these results indicate enhanced survival and improvements in mortality after the Black Death, and by inference, improved health at least at some ages in the post-Black Death population.

The results of this study are consistent with those from some previous studies that focused primarily on documentary evidence. Russell ([12]: p 465) compared post-Black Death, late 14 th-century age patterns derived from British and Spanish documentary data (tax records and inquisitions post mortem) to the "normal distribution of medieval society by age and sex" derived from skeletal data from 77 medieval burial grounds located primarily in central eastern Europe. His findings suggested that the ratio of individuals above the age of 60 relative to those between ages 20-60 increased after the Black Death in some areas, which would indicate that survival improved following the epidemic. However, there are several limitations to his approach. The dating of the cemeteries included in his study is not clear, but according to Russell, the pooled cemetery sample is more representative of the early medieval period, though it does include the later medieval period. Thus, it appears that he does not have an exclusively pre-Black Death baseline from the skeletal data for comparison with the post-Black Death mortality patterns. The skeletal data were pooled from cemeteries located within a broad geographic region, and thus potentially do not accurately represent normal pre-Black Death mortality patterns for any one particular locale. Further, the geographic areas represented by the skeletal data are mostly eastern and central Europe, and it is not clear whether England and Spain (i.e. the areas for which he had documentary data) are represented at all. This lack of total (or even partial) correspondence in the geographic origin of the skeletal and documentary datasets raises the possibility that the differences observed by Russell reflect population differences rather than the effects of the Black Death. Lastly, both datasets are subject to different but equally problematic sources of bias. The documentary data is biased towards adult males; the skeletal ages were estimated using traditional, biased methods that tend to underestimate older adult ages and overestimate younger adult ages [42]. Because of these limitations, it is possible that the tentative evidence observed by Russell does not truly reflect population-wide patterns resulting from the effects of the Black Death.

To date, the few studies of the demographic effects of the Black Death that have focused exclusively on historical documentary evidence have yielded conflicting results. Nightingale [32] examined records of deaths of creditors (primarily relatively wealthy men) in England from 1300-1530, and found an apparent decrease in "background mortality" following the Black Death. However, these data exclude many members of the population and they begin very soon before the Black Death during a period of severe famine, including the Great Famine 1315-1322 and the resulting Great Bovine Pestilence [64]. Thus, her data might not reflect true baseline, normal pre-Black Death mortality (i.e. they are not an ideal baseline for comparison with the longer post-Black Death period dataset). In contrast to the observed decreases in mortality among wealthy creditors following the Black Death, other studies, of monastic inhabitants, have found that mortality increased for monks in Christ Church Canterbury, Westminster Abbey, and Durham in the 15 th-century [32], [33]. The patterns observed by Nightingale for wealthy male creditors might more accurately reflect those of the general population than do those of monastic inhabitants, but the fact remains that documentary evidence excludes much of the medieval population, thereby making it difficult to generalize the results of these studies.

This study resolves many of the issues associated with previous studies based on documentary evidence. The bioarchaeological data used in this study allow for the assessment of men, women, and children of various socioeconomic status levels, most of whom are typically missing from many historical documents, and thus the results here are more representative of population-wide demographic patterns. The samples used here are also derived from a period before the Black Death that better represents normal pre-epidemic mortality patterns, for comparison with the post-Black Death data, than the early 14 th-century data used by Nightingale [32]. The large skeletal samples are from one circumscribed area, thereby reducing the possibility that the observed results reflect population differences rather than the effects of the epidemic. The application of unbiased skeletal age estimation methods means that more nuanced and accurate trends in mortality can be estimated than was possible with the cemetery data in Russell's study [12]. Finally, the use of hazard analysis, which smooths the random variation in data that is inherent in relatively small sample sizes without imposing any particular pattern on the data, allows for evaluation of patterns that are otherwise inaccessible [62]. It should be emphasized that the results of this study should not be viewed as convincing simply because they conform to what one might expect given the evidence available from some historical documents. The bioarchaeological approach should not be considered valuable only in terms of filling in the gaps that exist in historical documents, but rather because it allows us to use data that reflect the experiences of a wider segment of the population to test inferences derived from historical sources [65].

The results of this study are particularly striking given that the Black Death was just the first outbreak of medieval plague, and the period after the epidemic was characterized by repeated crisis mortality resulting in particular from repeated outbreaks of plague. These subsequent outbreaks of medieval plague might have prevented population recovery following the Black Death [5]. Given that catastrophic plague outbreaks were characteristic of the post-Black Death period, but not of the pre-Black Death period considered here, one might reasonably assume that health and survival declined following the Black Death. In London, during the latter half of the 14 th century, for example, evidence from wills reveals spikes in mortality associated with plague epidemics in 1361, 1368, 1375, 1382, and 1390 [13]. It should be noted that these sources "exclude married women, children, household servants and apprentices, labourers and paupers" ([13]: p 179), as is true of most medieval sources, so these peaks in mortality might not reflect patterns within the general population if there was any variation by age, sex, or social status. Nonetheless, for at least one segment of the population, the post-Black Death period was characterized by periodic crisis mortality. However, despite repeated plague outbreaks, and other episodes of crisis mortality caused by factors such as famines, the results of this study indicate that the general population enjoyed a period of at least 200 years during which mortality and survival overall improved compared to the pre-Black Death conditions.

The evidence from this study that survival and mortality were affected in positive ways by the Black Death raise the question of what was the proximate cause of these changes. Were the demographic changes a direct result of the selectivity of the epidemic, i.e. the selecting out of frail individuals such that the surviving population was less frail because of inherent biological factors? Or were improvements in diet and other standards of living more important causal factors in these demographic trends? These two possibilities are not necessarily mutually exclusive, as both were ultimately the results of the massive mortality caused by the Black Death. Nonetheless, the disentanglement of intrinsic versus extrinsic factors that could have led to changes in survival and mortality is relevant to an understanding, more generally, of the effects of disease on human biology and social, political, and economic conditions. Such a disentanglement, however, requires further study, such as an analysis of stable isotopes and skeletal stress markers that are associated with nutritional deficiencies before and after the Black Death to determine if diet changed in substantial ways that are discernible from the skeleton. Analysis of nitrogen isotope values, for example, might reveal whether people in general consumed substantially more animal protein following the Black Death than was true before the epidemic [66].

The results of this study also raise questions about the possible effects of migration, i.e. whether the pre- and post-Black Death patterns are artifacts of migration into the London after the epidemic rather than, or in addition to, reflecting the effects of the Black Death itself. The improvements in mortality and survivorship observed in the post-Black Death sample might indicate that migration into the city after the epidemic introduced a large number of healthy people. According to Dyer [29] migration likely increased after Black Death as an expression of resistance against restrictions enacted under labor laws in England, such as attempts to prevent increases in wages after 1349. However, London drew substantial numbers of migrants from throughout England and beyond throughout the medieval period, both before and after the Black Death [67], [68]. Famine, in particular, spurred migration before the Black Death, and London attracted large numbers of rural residents in search of work and charity [67]. The existence of migration throughout the medieval period means that both the pre-Black Death and post-Black Death assemblages likely contained a mixture of immigrants and native Londoners, so differences between the two cannot be attributed only to the effects of migration. Furthermore, the possibility of increased immigration to London following the Black Death does not explain the differences in age-at-death distributions observed between the two time periods, the most striking of which is the higher proportion of older adults in the post-Black Death sample. The differences in the age-at-death distributions would reflect the effects of migration alone only if the majority of migrants were older adults; however, it is more likely that most migrants were late teens or young adults [32], [69]. Lastly, the Black Death spared few regions within Europe [70]. Therefore, the selective mortality that operated during the epidemic would have shaped health and demography throughout the continent. Migrants into London in the post-Black Death period would have included survivors of the epidemic and their descendants (and the health and demographic characteristics thereof) to the same or similar extent as did the native population of London. In summary, the existence of migration into London following the Black Death does not necessarily undermine the conclusions made here about positive changes in mortality and, by inference, health following and resulting from the Black Death, thought it is certainly an important issue worthy of further study.

Conclusions

The results of this study indicate that mortality and survivorship improved in the generations following the Black Death, and that the patterns observed are not simply an artifact of temporal changes in fertility. These results highlight the power that infectious diseases have to shape population-wide patterns of health and demography over both the short- and long-term.

Supporting Information

Table S1

Site codes and contexts for all individuals included in the study. All individuals are curated at the Museum of London Centre for Human Bioarchaeology.

(DOCX)

Acknowledgments

I am grateful to Jelena Bekvalac and Rebecca Redfern at the Museum of London Centre for Human Bioarchaeology for providing access to the skeletal samples used in this study and for generously providing the physical facilities for this work. I also thank Dr. Eric Jones, Dr. Andrew Noymer, and three anonymous reviewers for providing comments on this manuscript.

Funding Statement

Data collection was supported by funding from NSF ( www.nsf.gov; BCS-1261682), the Wenner-Gren Foundation ( wwww.wennergren.org; grant #8247), and the American Association of Physical Anthropologists ( www.physanth.org). Preliminary analyses were conducted during a summer fellowship at the School for Advanced Research, sponsored by the Ethel-Jane Westfeldt Bunting Foundation ( www.sarweb.org). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

2. Cohn SK (2002) The Black Death transformed: disease and culture in early Renaissance Europe. London: Arnold. [ PubMed]

4. Twigg G (1984) The Black Death: a biological reappraisal. London: Batsford Academic and Educational.

5. Hinde A (2003) England's population: a history since the domesday survey. London: Hodder & Arnold.

9. Usher BM (2000) A multistate model of health and mortality for paleodemography: Tirup cemetery. PhD Dissertation: Pennsylvania State University, 2000.

11. Cohn SK Jr (2008) Epidemiology of the Black Death and successive waves of plague. Med Hist Suppl 27: 74-100. [ PMC free article] [ PubMed]

14. Drancourt M, Aboudharam G, Signoli M, Dutour O, Raoult D (1998) Detection of 400-year-old [[ Yersinia pestis DNA in human dental pulp: an approach to the diagnosis of ancient septicemia. Proc Natl Acad Sci U S A 95: 12637-12640. PubMed] PMC free article] [ Google Scholar]

15. Raoult D, Aboudharam G, Crubezy E, Larrouy G, Ludes B, et al. (2000) Molecular identification by "suicide PCR" of [[ Yersinia pestis as the agent of medieval black death. Proc Natl Acad Sci U S A 97: 12800-12803. PubMed] PMC free article] [ Google Scholar]

16. Bos K, Schuenemann V, Golding G, Burbano H, Waglechner N, et al. (2011) A draft genome of [[ Yersinia pestis from victims of the Black Death. Nature 478: 506-510. PubMed] PMC free article] [ Google Scholar]

25. Hatcher J (1977) Plague, population, and the English economy, 1348-1530. London: Macmillan.

30. Munro JH (2004) Before and after the Black Death: money, prices, and wages in fourteenth-century England. In: New Approaches to the History of Late Medieval and Early Modern Europe: Selected Proceedings of Two International Conferences at The Royal Danish Academy of Sciences and Letters in Copenhagen, Historisk-filosofiske Meddelser, Vol. 104, (February 2009): pp. 335-364.

35. Weiss KM (1975) Demographic Disturbance and the Use of Life Tables in Anthropology. Memoirs of the Society for American Archaeology No 30, Population Studies in Archaeology and Biological Anthropology: A Symposium: 46-56.

36. White WJ (1988) Skeletal Remains from the Cemetery of St Nicholas Shambles, City of London. London: London and Middlesex Archaeological Society.

37. Connell B, Jones A, Redfern R, Walker D (2012) A bioarchaeological study of medieval burials on the site of St. Mary Spital. London: Museum of London Archaeology.

40. Grainger I, Hawkins D, Cowal L, Mikulski R (2008) The Black Death cemetery, East Smithfield, London. Museum of London Archaeology Service Monograph 43. London: Museum of London Archaeology Service.

41. Grainger I, Phillpotts C (2011) The Cistercian abbey of St Mary Graces, East Smithfield, London. MoLA Monograph 44. London: Museum of London Archaeology.

49. Smith BH (1991) Standards of human tooth formation and dental age assessment. In: Kelley M, Larsen CS, editors. Advances in Dental Anthropology. New York: Wiley-Liss. pp. 143-168.

52. O'Connor KA (1995) The age-pattern of mortality: a micro-analysis of Tipu and a meta-analysis of twenty-nine paleodemographic samples. PhD Dissertation. Albany, NY: University at Albany, SUNY.

55. Holman DJ (2005) mle: A programming language for building likelihood models. Version 2.1 ed. Seattle, WA.

62. Milner GR, Wood JW, Boldsen JL (2008) Paleodemography. In: Katzenberg M, Saunders S, editors. Biological Anthropology of the Human Skeleton. NY: Wiley-Liss. pp. 561-600.

Articles from PLOS ONE are provided here courtesy of PLOS

# data[].attributes.attachment_link.title
Mortality Risk and Survival in the Aftermath of the Medieval Black Death

# data[].attributes.attachment_link.description
The medieval Black Death (c. 1347-1351) was one of the most devastating epidemics in human history. It killed tens of millions of Europeans, and recent analyses have shown that the disease targeted elderly adults and individuals who had been previously ...

# data[].attributes.body
While reading the chapter and reading all the filters that were mentioned what i kept thinking about was, are we ourselves considered filters too? What if studying the environment and the way of life of these species are affecting their norm and creating a change we ourselves don’t know about. Also proposition 2 is what i te rested me the most because it says “ In any habitat, only a small number of filters are likely to be important”, but how can the important filters be identified if we don’t know all of them? What if the ones that are deemed the most important filters not actually the most important ones?
-Jacquelyn Hernandez

# data[].attributes.attachment_caption
Rana sylvatica during a period of hibernation (or brumation) to conserve energy and survive the colder months.

# data[].attributes.headline
Traits developed to withstand the power of strong filters

# data[].attributes.subject
Traits developed to withstand the power of strong filters

# data[].attributes.body
While reading this chapter about filters, two major thoughts came to mind; filters have a higher influence in different communities, and the more intense the power of the filter, the more unorthodox traits seem to develop under those conditions. The cold was a major filter that was described in the chapter and one would generally only think about animals more commonly found in colder climates. When the topic of frogs came around, it made me remember a post I read about certain species of frogs being able to "freeze" themselves and come out unscathed during the warmer months.

In general, frogs aren't an animal one would think can survive in the cold, but because of the traits that were developed to adapt to that climate, the cold was no longer a viable filter to prevent this species of frog from surviving, even if the cold is considered a strong filter in most cases.

https://journals.biologists.com/jeb/article/216/18/3461/11609/Hibernation-physiology-freezing-adaptation-and

# data[].attributes.body
Meanwhile reading a chapter I noticed how the author focused on factors (filters) that species community may be affected by not one but simultaneously a couple of factors that depend on the intensity. The author also mentioned how not the amount of filters that matters but rather the importance and priority of the filters. In this article the author focused on how factors can affect species richness, he introduced two factors such as seeds from local species pool and fire, which happened to be the most important factor affecting the species richness to increase due to immigration meanwhile decreasing competition and predation already existing within the pool as a biotic filter that also plays an important role however fire happened to be the most influential filter.  The article also mentioned how we can use filters to change and help the environment. "Our experiment provides insights into how dispersal-assembly mechanisms may interact with niche-assembly mechanisms in space (spatial variation in disturbance) and time (temporal variation in resource availability) to structure high-diversity communities and can help guide conservation of threatened longleaf pine ecosystems in the face of habitat fragmentation and environmental change . " - Myers, J.
- Julia K

# data[].attributes.body
This chapter did a really good job at explaining these filters that affect community and ecosystems, and how some are large and small, and can have short, or lasting impact such as drought and hypoxic conditions on water, and plants. I found it especially interesting to explore the top-down affect of predators on managing the ecosystems and I have been aware of the misunderstanding of sharks's perception, on their critical role in ecosystem survivals. This article, which data conducted over almost 35 years, really explores the topic in great depth and I am rejoiced to see that the images of sharks, are reinforced with sensible research and conservation. Hopefully we as a society can come to embrace the necessary component of sharks in the stream of life that is the ocean.

# data[].attributes.headline
Stress from cold and drought as drivers of functional trait spectra in North American angiosperm tree assemblages

# data[].attributes.subject
Stress from cold and drought as drivers of functional trait spectra in North American angiosperm tree assemblages

# data[].attributes.body
- Shania

# data[].attributes.body
I found the sections focusing on fires in this chapter very interesting. While fires are terrible natural disasters that don't discriminate against anything in its path, they also bring benefits to the land once the fire has been extinguished. Some people believe that the use of fires to aid biodiversity, especially the biodiversity of predators, is a good method which should be explored and studied. Others believe it is a dangerous practice which can easily go unchecked. The paper above touches on this subject:

# data[].attributes.body
I found this chapter to be much more interesting and easier to understand than the previous chapter. One thing that I really enjoyed was that there were examples for each filter that was presented. Each example was detailed, and the conditions were well described which made it easy to visualize and understand the scenario. The filter that I found the most interesting was the one about hypoxic environments. Figure 2.4 was a great summary of the timeline of the effects of decreasing and increasing water levels. Other than the eventual death or migration of some fish in hypoxic conditions, I wonder how things such as their social behaviors and foraging skills are affected. It would have also been interesting if anthropogenic factors were considered such as eutrophication caused by nutrient runoff.

# data[].attributes.headline
The scale-dependent effect of environmental filters on species turnover and nestedness in an estuarine benthic community.

# data[].attributes.subject
The scale-dependent effect of environmental filters on species turnover and nestedness in an estuarine benthic community.

# data[].attributes.body
Salinity and sediment properties are looked at as the casual factors on the beta-diversity within a macro benthic community. They concluded that salinity is correlated to turnover (species replacement), and sediment to nestedness (species loss). They also consider the spatial scale of investigation.

# data[].attributes.body
The authors mentioned the seven main casual factors (5 abiotic and 2 biotic) and it was nice to see formulas used to calculate power and hardiness zones, however, I wonder if there is a formula for the "combined power" of the factors. I have a feeling there would be, which could be used to decide which two or more factors are in fact more important, but there also was no mention of one within the chapter. Deciding which filters are most important and acknowledging how they work together appears to be a known issue within the scientific community, but I also feel like there could be a reasonable formula used to come up with the cumulative power of multiple factors on a species pool which would then answer the big question.

# data[].attributes.body
In this chapter, I felt like predation was overlooked as being one of the most important filters. With that being said, humans rather than animals are some of the worst predators. In fact, we cut down trees, hunt animals, and ruin habitats while only considering the benefits for us. Humans tend to neglect the consequences that their actions bring for animals. In other words, many times humans hunt animals to the point where they become endangered and many even become extinct. It is mentioned on page 64 that alligators are hunted to make leather out of their skin. It is upsetting that alligators are hunted near-extinction not because they are a huge threat to humans but instead because humans rather use them to make clothing.
With that being said, I have found an article titled "The Unique Ecology of Human Predators" which further addresses how humans play a role in leading to a larger ecological effect when they hunt animals whereas there is somewhat of a balance in between animals who act as predators and their prey. This article also discusses that oftentimes human behavior is ignored hence why they continue to overexploit.

~Kiranjit Kaur

# data[].attributes.body
The text explains how certain filters have more power than others, the greater the difference between community vector and species pool, the more powerful the filter is. To illustrate, we have an example of the dry and arid soils in Ontario, Canada. These dry soils act as a filter because of the lack of nutrients. The filter power in this area is 96%

In contrast, we have hypoxic conditions as a filter in fish respiration. It is reported that 40 of 120 species of fish can survive hypoxic conditions. The filter power in this case is 67% (1-40/120) meaning that the filter is not as powerful due to the small difference between P and C.

Furthermore, just as hypoxic conditions can act as filters on fish survival, so also osmolarity, osmosis and diffusion can act as filters. The chapter points out that the gills allow the fish to take in oxygen and eliminate Co₂, but what it does not say is that the gills can also allow the passage of water in and out of the fish. This can create water imbalance depending on the solute concentration outside and inside the fish taking into consideration that osmolarity is lower inside the fish compared to the osmolarity of the water.

# data[].attributes.headline
Seed Dispersers, Seed Predators, and Browsers 

# data[].attributes.subject
Seed Dispersers, Seed Predators, and Browsers 

# data[].attributes.body
For the herbivores as filter, the book focused on cases where herbivory has significant effects. But the book didn't tell us in what other ways herbivores can act as filters. I found this article helpful to know a little more about herbivory filters. I found seed predators interesting. Also, how they synergistically create landscape is fascinating.

# data[].attributes.headline
Regional and Local Filters for Trait Dispersal

# data[].attributes.subject
Regional and Local Filters for Trait Dispersal

# data[].attributes.body
degrees of filtration: local and regional. More specifically, I would like to know if there are documented instances of two filters having contradictory effects. I think this paper might explore that.

- Gracia B.

# data[].attributes.body
It seems to me that the importance of filters, particularly, biotic ones is often discovered in hindsight; only after the land has been modified or one species overpopulate.

I find the neutrality of filters to be rather intriguing. We they are both beneficial and destructive (as Adrian mentions about floods).

- Gracia B.

# data[].attributes.attachment_caption
More species but all do the same contras

# data[].attributes.headline
Effect of Flooding on Ground Beetle Diversity 

# data[].attributes.subject
Effect of Flooding on Ground Beetle Diversity 

# data[].attributes.body
This article looks at the effect of habitat disturbance on community diversity by sampling Ground Beetles in the grasslands of the Elbe River located in Germany where periodic flooding occurs. They found that species richness increases with high flooding. During high flooding however, species functionality diversity decreases. Overall, I think this article does a good job of iterating the effect of powerful filters (like flooding) on the variation of species in a community.

# data[].attributes.body
I liked how this chapter touched on the idea of uncertainty of whether certain filters are truly responsible for the occurrence of species in a community. Sometimes we focus too much on the little details and not enough about the bigger picture. Sometimes those little details are negligible and don't matter at all. I think the science community as a whole can learn from all of this