To show how network analyses can be used to select plants supporting a high diversity of insect pollinators and parasitoids of insect pests, but low diversity of herbivores. Further to this, we wanted to understand the trade- offs in ecosystem service provision associated with conventional management practices that focus on individual ecosystem services.

Two hypotheses: 

(a) Some plant species are more important than others in supporting the species richness of pollinators, parasitoids of pests and herbivores.

(b) There will be trade- offs in the provision of ecosystem services associated with different mixes of plants, that is, managing plant species for individual ecosystem services (e.g. increase pollination or parasitism services) will not support the provision of other ecosystem services and may incur ecosystem disservices (e.g. her-bivory).

Used a subset of the Norwood Farm network to create a multilayer network comprised of three animal groups interacting with shared plants (flower visitors, phytophagous herbivores and leaf- miner parasitoids) exhibiting both mutualistic and antago-nistic interactions.

 

Habitats were subject to replicated monthly sampling over 2 years (2007– 2008). Samples for each taxonomic group were collected from three to four randomly located transects per month. Interactions between different species were compiled from a range of methods including both observations and secondary literature.

Developed a method able to select mixes of plant species that both maximises the species richness of pollinators and parasitoids, while minimising the species richness of herbivores.

Firstly, species interaction data and subsequent analyses were conducted at the farm scale (125 ha) which incorporates multiple habitats (see Pocock et al., 2012a). As a result, it is assumed that organisms interacting with the non- crop plants across all habitats are

Secondly, Norwood Farm is an organic farm with a mixture of cropped and non- cropped habitats. Thus, the abundance, diversity and importance of weed species and other non- crop plants within ecological networks may be enhanced as a result of the management techniques (Norton et al., 2009). Similar analyses carried out across non- organic farms may not support the findings above illustrating the importance of non- crop, weed plant species. It is therefore important to collect species interaction data across other agricultural habitats, especially when looking to assess the effectiveness of management interventions

Thirdly, this study infers ecosystem service provision from species richness of organisms involved in key ecosystem processes (e.g. pollination).

It is apparent that the relationships between biodiversity and ecosystem functioning/services are highly variable (van der Plas, 2019). The method also does not account for a range of other important factors, such as trait distributions in plant mixes (e.g. flowering phenology, flower morphology and nectar provision), which may influence the importance of plants and the extent to which they contribute to ecosystem services within the environment .

Showed that ecological network approaches can be utilised at the heart of farm management and are ready to be developed and implemented in practice.

Predictive network ecology appears an effective tool for identifying mixes of non crop plant species that balance the trade offs between ecosystem services (pollination and parasitism of pest insects) and disservices (herbivory) in agroecosystems. 

The aim of the present study was to assess the interspecific competition between either C. taxifolia or C. racemosa and the seagrass Posidonia oceanica (L.)

B) Hypothesis

 Sampling, extracting, testing, 

C) Methods.

Sampling of C. taxifolia was performed every two months, from July 1999 to July 2000, at the Cap Martin site, at a depth of 10.0 m, and at the Antignano site for C. racemosa, at a depth of 2.0 m.  For each site, three levels of competition were considered:

Station 0—absence ofP. oceanica

Station 1—simple contact betweenP. oceanica and eitherC. taxifolia orC. racemosa (boundaries), in which the fronds ofCaulerpa just touch the sheath and leaves of P. oceanica.

Station 2—strong interaction between P. oceanica and either C. taxifolia or C. racemosa (mixed population), in which the fronds of Caulerpa penetrate inside foliar shoots and rhizomes, between sheaths and leaves of P. oceanica

The sampling stations at each site were within the same meadow and exhibited similar biological/physical and chemical characteristics. Five samples were taken from each station

D) Limitations or discussions. 

The seasonal variations in CYN concentrations observed in C. taxifolia confirm previous results (Amade et al., 1996). As pointed out by Lem´ee et al. (1997), these variations help to explain the maximum and minimum toxicities observed for C. taxifolia in summer/fall and in spring, respectively.

The time between experiments is way too short as to draw conclusions, so more time must be taken in order to obtain more varied results. In addition, there were only two places chosen for the investigation. It could be taken from more places to be able to do the experimentation.

E) Conclusiones.  

The lower the level of competition, the lower the growth and the higher the level of toxins. With more competition the toxins grow less, but the plants grow more.          

 The  objectives  of  this  study  are  twofold.  First,  our  aim  is  to evaluate the impact of wastewater discharges and non-point sources of contamination on the water quality of the three main water courses that flow  through  the  city  of  Ushuaia. Second we developed the fuegan water quality index as a tool for environmental monitoring in forested urban watersheds. ) F_WQI  aims  to  integrate  in  a  simple numeric  scale  the  chemical, physical and biological characteristics of the forested streams. 

B) Hypothesis: We expected to find reduced water quality in sampling sites downstream the city, as they would be more affected by the input of wastewater and runoff associated with urbanization. We explored this idea with a PCA analysis for each sampling year. We predicted the F_WQI to be lower in the sites nearby the city of Ushuaia. 

We calculated the area of each watershed and its percentage of urbanization through the QGIS 3.10. software. First, each watershed was modeled as polygons, using the “Rwatershed” tool. Then, we calculated the area of each polygon with the raster calculator tool. The percentage of urbanized area was calculated from the overlapping layers: the area of intersection between the satellite image of the city (updated, extracted from Google Earth) and the total area of each watershed. We sampled 12 sites, four belonging to each watershed: S1 (reference, not urbanized at the upper section), S2 (transitional section), S3 (urbanized at the middle-low section), and S4 (urbanized, close to the outlet)  (Fig.  1).  Due  to  that  watersheds  are  located  in  mountainous topography there is a marked altitude gradient between S1 and S4. 

Taking multiple samples during different time-periods in order to get accurate results regarding water quality. 

D) Limitations or discussions. 

Other possible variables or indices are discussed. 

E) Conclusions. 

The urbanization of Ushuaia in Tierra del Fuego negatively affected the water quality of urban forested streams that cross the city, particularly, in sites downstream. We detected very good water quality in all sections of the Pipo river watershed. Buena Esperanza stream and Arroyo Grande river showed water quality impairment: BES in the middle-low site (S3) and lower site (S4) and AGR in the lower site (S4). We encourage to use the F_WQI contemplating the inclusion of periphyton chlorophyll-a (Peri Chl-a) which proved to add sensitivity to the water quality index in temperate rivers and streams where this community is in general ubiquitou

 The  objectives  of  this  study  are  twofold.  First,  our  aim  is  to evaluate the impact of wastewater discharges and non-point sources of contamination on the water quality of the three main water courses that flow  through  the  city  of  Ushuaia. Second we developed the fuegan water quality index as a tool for environmental monitoring in forested urban watersheds. ) F_WQI  aims  to  integrate  in  a  simple numeric  scale  the  chemical, physical and biological characteristics of the forested streams. 

B) Hypothesis: We expected to find reduced water quality in sampling sites downstream the city, as they would be more affected by the input of wastewater and runoff associated with urbanization. We explored this idea with a PCA analysis for each sampling year. We predicted the F_WQI to be lower in the sites nearby the city of Ushuaia. 

We calculated the area of each watershed and its percentage of urbanization through the QGIS 3.10. software. First, each watershed was modeled as polygons, using the “Rwatershed” tool. Then, we calculated the area of each polygon with the raster calculator tool. The percentage of urbanized area was calculated from the overlapping layers: the area of intersection between the satellite image of the city (updated, extracted from Google Earth) and the total area of each watershed. We sampled 12 sites, four belonging to each watershed: S1 (reference, not urbanized at the upper section), S2 (transitional section), S3 (urbanized at the middle-low section), and S4 (urbanized, close to the outlet)  (Fig.  1).  Due  to  that  watersheds  are  located  in  mountainous topography there is a marked altitude gradient between S1 and S4. 

Taking multiple samples during different time-periods in order to get accurate results regarding water quality. 

D) Limitations or discussions. 

Other possible variables or indices are discussed. 

The urbanization of Ushuaia in Tierra del Fuego negatively affected the water quality of urban forested streams that cross the city, particularly, in sites downstream. We detected very good water quality in all sections of the Pipo river watershed. Buena Esperanza stream and Arroyo Grande river showed water quality impairment: BES in the middle-low site (S3) and lower site (S4) and AGR in the lower site (S4). We encourage to use the F_WQI contemplating the inclusion of periphyton chlorophyll-a (Peri Chl-a) which proved to add sensitivity to the water quality index in temperate rivers and streams where this community is in general ubiquitou

Hypothesis: Plant mixes designed solely for maximising pollinator species richness are not optimal for the provision of other ecosystem services and disservices (e.g. parasitism of insect pests and herbivory).

Methods: Habitats were subject to replicated monthly sampling over 2 years (2007–2008). Samples for each taxonomic group were collected from three to four randomly located transects per month.Interactions between different species were compiled from a range of methods including both observations and secondary literature. Interaction strengths for pairwise interactions were estimated fromdirect field measurements or abundance surveys in conjunction with methods detailed in Pocock et al. (2012a) and Pocock et al. (2012b).All data were up-scaled to provide a total per habitat (counts of or-ganisms per transect area multiplied up to total habitat area) and then summed across all available habitat to provide a farm-scale

abundance measure. The original networks consisted of plants and 11 groups of animals that interact with them: plant feeders (butterflies and other flower visitors, phytophagous insects, seed-feeding insects and granivorous birds and mammals) and their dependents (primary and secondary aphid parasitoids, leaf-miner parasitoids, seed-feeding insect parasitoids and rodent ectoparasites). More detail on the Norwood Farm network is available in previous publications (Evans et al., 2011, 2013; Pocock et al., 2012a).

Limitations: Here, using a network approach, we show that a series of trade-offs may exist when managing plant species to maximise ecosystem services and minimise ecosystem disservices

in agricultural environments. Specifically, managing plant species to promote individual ecological processes (e.g. pollination) may lead to the unintended maximisation or minimisation of other functions, such as parasitism or herbivory.

Conclusions: Predictive network ecology appears an effective tool for identifying mixes of non-crop plant species that balance the trade-offs between ecosystem services (pollination and parasitism of pest insects) and disservices (herbivory) in agroecosystems.