Search

Abstract Northern regions are warming faster than the rest of the globe. It is difficult to predict ecosystem responses to warming because the thermal sensitivity of their biophysical components varies. Here, we present an analysis of the authors’ expert judgment regarding the sensitivity of six ecosystem components – permafrost, peatlands, lakes, snowpack, vegetation, and endothermic vertebrates – across northern landscapes ranging from boreal to polar biomes. We identified 28 discontinuous component states across a 3700 km latitudinal gradient in northeastern North America and quantified sensitivity as the transition time from an initial to a contrasting state following a theoretical step change increase in mean annual air temperature of 5 °C. We infer that multiple interconnected state shifts are likely to occur within a narrow subarctic latitudinal band at timescales of 10 to more than 100 years, and response times decrease with latitude. Response times differ between components and across latitudes, which is likely to impair the integrity of ecosystems.

The Arctic tundra has a relatively low biodiversity but species living there have unique adaptations and are exposed to unprecedented rates of climate warming. Monitoring changes in Arctic biodiversity and identifying the driving forces is thus a pressing issue. Bylot Island in the Canadian Arctic has one of the longest and most comprehensive monitoring programs of the tundra food web, spanning four decades. We provide a historical overview of ecological studies on Bylot Island, summarize their key scientific contributions, show their impacts, and present the ingredients for the success of the program and the main challenges encountered. Some major contributions include demonstrating the key role of predation in structuring the tundra food web, the importance of exchanges between ecosystems for the persistence of top predators and their cascading effects on trophic interactions, the apparent resistance of the vertebrate biota to climate warming, the need to consider multiple hypotheses to explain northward range expansion of species and the benefits of integrating scientific data and local knowledge into ecological monitoring. The program has produced >250 journal articles and >80 graduate student theses, which generated >7,700 citations in the scientific literature. A high proportion (65%) of the articles had more citations than comparable publications in their field. The longevity and success of the program can be attributed to several factors, including a researcher-driven (i.e. bottom-up) approach to design the monitoring; long-term commitment of a small number of dedicated researchers and the strong participation of graduate students; the adoption of a food web rather than a single species perspective; extensive presence in the field; the combination of several methodological approaches; and the use of multiple spatial scales adapted to research questions of interest. Challenges encountered include funding issues, transfer of expertise over time, limited spatial replication, statistical design and maintaining partnerships. Robust monitoring is essential to provide sound baseline to detect future changes, and lessons learned from our program could improve future monitoring schemes in the Arctic. Paradoxically, we believe that ecological monitoring on Bylot Island has been successful in large part because it was not originally designed as a monitoring program per se.

Although decades of research suggest that higher species richness improves ecosystem functioning and stability, planted forests are predominantly monocultures. To determine whether diversification of plantations would enhance aboveground carbon storage, we systematically reviewed over 11,360 publications, and acquired data from a global network of tree diversity experiments. We compiled a maximum dataset of 79 monoculture to mixed comparisons from 21 sites with all variables needed for a meta-analysis. We assessed aboveground carbon stocks in mixed-species planted forests vs. (a) the average of monocultures, (b) the best monoculture, and (c) commercial species monocultures, and examined potential mechanisms driving differences in carbon stocks between mixtures and monocultures. On average, we found that aboveground carbon stocks in mixed planted forests were 70% higher than the average monoculture, 77% higher than commercial monocultures, and 25% higher than the best performing monocultures, although the latter was not statistically significant. Overyielding was highest in four-species mixtures (richness range 2–6 species), but otherwise none of the potential mechanisms we examined (nitrogen-fixer present vs. absent; native vs. non-native/mixed origin; tree diversity experiment vs. forestry plantation) consistently explained variation in the diversity effects. Our results, predominantly from young stands, thus suggest that diversification could be a very promising solution for increasing the carbon sequestration of planted forests and represent a call to action for more data to increase confidence in these results and elucidate methods to overcome any operational challenges and costs associated with diversification.

Food webs are complex ecological networks whose structure is both ecologically and statistically constrained, with many network properties being correlated with each other. Despite the recognition of these invariable relationships in food webs, the use of the principle of maximum entropy (MaxEnt) in network ecology is still rare. This is surprising considering that MaxEnt is a statistical tool precisely designed for understanding and predicting many types of constrained systems. This principle asserts that the least-biased probability distribution of a system's property, constrained by prior knowledge about that system, is the one with maximum information entropy. MaxEnt has been proven useful in many ecological modeling problems, but its application in food webs and other ecological networks is limited. Here we show how MaxEnt can be used to derive many food-web properties both analytically and heuristically. First, we show how the joint degree distribution (the joint probability distribution of the numbers of prey and predators for each species in the network) can be derived analytically using the number of species and the number of interactions in food webs. Second, we present a heuristic and flexible approach of finding a network's adjacency matrix (the network's representation in matrix format) based on simulated annealing and SVD entropy. We built two heuristic models using the connectance and the joint degree sequence as statistical constraints, respectively. We compared both models' predictions against corresponding null and neutral models commonly used in network ecology using open access data of terrestrial and aquatic food webs sampled globally (N = 257). We found that the heuristic model constrained by the joint degree sequence was a good predictor of many measures of food-web structure, especially the nestedness and motifs distribution. Specifically, our results suggest that the structure of terrestrial and aquatic food webs is mainly driven by their joint degree distribution.

Food webs represent the energy fluxes and the nutrient cycling between interacting species that underpin several ecosystem functions. Whether and how interactions vary across environmental gradients is still largely unknown. We reviewed the literature searching for systematic relationships between structural food-web properties and environmental gradients. Temperature and biotic factors are amongst the most frequently addressed drivers of food-web structure. We also assessed the degree to which food-web ecology has accomplished a mechanistic understanding of ecosystem functioning. We found that most studies are one-off descriptions of local food webs making it difficult to achieve an understanding of the response to human or environmental gradients. The lack of a consistent theory predicting how food webs change across environmental gradients, the diversity of objectives in food-web studies, and the absence of a standardized methodology for analysing them severely limit progress in the field. Moving forward requires the establishment of a core set of testable predictions, agreed standards for data collection and analysis, and the development of geographically distributed experimental studies of food-web dynamics.

Seasonal migrants transport energy, nutrients, contaminants, parasites and diseases, while also connecting distant food webs between communities and ecosystems, which contributes to structuring meta-communities and meta-ecosystems. However, we currently lack a framework to characterize the structure of the spatial connections maintained by all migratory species reproducing or wintering in a given community. Here, we use a network approach to represent and characterize migratory pathways at the community level and provide an empirical description of this pattern from a High-Arctic terrestrial community. We define community migration networks as multipartite networks representing different biogeographic regions connected with a focal community through the seasonal movements of its migratory species. We focus on the Bylot Island High-Arctic terrestrial community, a summer breeding ground for several migratory species. We define the non-breeding range of each species using tracking devices, or range maps refined by flyways and habitat types. We show that the migratory species breeding on Bylot Island are found across hundreds of ecoregions on several continents during the non-breeding period and present a low spatial overlap. The migratory species are divided into groups associated with different sets of ecoregions. The non-random structure observed in our empirical community migration network suggests evolutionary and geographic constraints as well as ecological factors act to shape migrations at the community level. Overall, our study provides a simple and generalizable framework as a starting point to better integrate migrations at the community level. Our framework is a far-reaching tool that could be adapted to address the seasonal transport of energy, contaminants, parasites and diseases in ecosystems, as well as trophic interactions in communities with migratory species.

Habitat fragmentation and eutrophication have strong impacts on biodiversity but there is limited understanding of their cumulative impacts. This study presents simulations of meta-food-webs and provides a mechanistic explanation of how landscape heterogeneity promotes biodiversity through rescue and drainage effects.

Summary: Critical transition theory suggests that complex systems should experience increased temporal variability just before abrupt state changes. We tested this hypothesis in 763 patients on long-term hemodialysis, using 11 biomarkers collected every two weeks and all-cause mortality as a proxy for critical transitions. We find that variability—measured by coefficients of variation (CVs)—increases before death for all 11 clinical biomarkers, and is strikingly synchronized across all biomarkers: the first axis of a principal component analysis on all CVs explains 49% of the variance. This axis then generates powerful predictions of mortality (HR95 = 9.7, p

The dynamics of ecological communities depends on interactions between species as well as those between species and their environment, however the effects of the latter are poorly understood. Here, Yeakel et al. reveal how species that modify their environment (ecosystem engineers) impact community dynamics and the risk of extinction.

Abstract As of 2020, the world has an estimated 290 million ha of planted forests and this number is continuously increasing. Of these, 131 million ha are monospecific planted forests under intensive management. Although monospecific planted forests are important in providing timber, they harbor less biodiversity and are potentially more susceptible to disturbances than natural or diverse planted forests. Here, we point out the increasing scientific evidence for increased resilience and ecosystem service provision of functionally and species diverse planted forests (hereafter referred to as diverse planted forests) compared to monospecific ones. Furthermore, we propose five concrete steps to foster the adoption of diverse planted forests: (1) improve awareness of benefits and practical options of diverse planted forests among land‐owners, managers, and investors; (2) incentivize tree species diversity in public funding of afforestation and programs to diversify current maladapted planted forests of low diversity; (3) develop new wood‐based products that can be derived from many different tree species not yet in use; (4) invest in research to assess landscape benefits of diverse planted forests for functional connectivity and resilience to global‐change threats; and (5) improve the evidence base on diverse planted forests, in particular in currently under‐represented regions, where new options could be tested.

Weather and land use can significantly impact mosquito abundance and presence, and by consequence, mosquito-borne disease (MBD) dynamics. Knowledge of vector ecology and mosquito species response to these drivers will help us better predict risk from MBD. In this study, we evaluated and compared the independent and combined effects of weather and land use on mosquito species occurrence and abundance in Eastern Ontario, Canada. Data on occurrence and abundance (245,591 individuals) of 30 mosquito species were obtained from mosquito capture at 85 field sites in 2017 and 2018. Environmental variables were extracted from weather and land use datasets in a 1-km buffer around trapping sites. The relative importance of weather and land use on mosquito abundance (for common species) or occurrence (for all species) was evaluated using multivariate hierarchical statistical models. Models incorporating both weather and land use performed better than models that include weather only for approximately half of species (59% for occurrence model and 50% for abundance model). Mosquito occurrence was mainly associated with temperature whereas abundance was associated with precipitation and temperature combined. Land use was more often associated with abundance than occurrence. For most species, occurrence and abundance were positively associated with forest cover but for some there was a negative association. Occurrence and abundance of some species (47% for occurrence model and 88% for abundance model) were positively associated with wetlands, but negatively associated with urban (Culiseta melanura and Anopheles walkeri) and agriculture (An. quadrimaculatus, Cs. minnesotae and An. walkeri) environments. This study provides predictive relationships between weather, land use and mosquito occurrence and abundance for a wide range of species including those that are currently uncommon, yet known as arboviruses vectors. Elucidation of these relationships has the potential to contribute to better prediction of MBD risk, and thus more efficiently targeted prevention and control measures.

The functional response is at the core of any predator-prey interactions as it establishes the link between trophic levels. The use of inaccurate functional response can profoundly affect the outcomes of population and community models. Yet most functional responses are evaluated using phenomenological models which often fail to discriminate among functional response shapes and cannot identify the proximate mechanisms regulating predator acquisition rates. Using a combination of behavioral, demographic, and experimental data collected over 20 years, we develop a mechanistic model based on species traits and behavior to assess the functional response of a generalist mammalian predator, the arctic fox (Vulpes lagopus), to various tundra prey species (lemmings and the nests of geese, passerines, and sandpipers). Predator acquisition rates derived from the mechanistic model were consistent with field observations. Although acquisition rates slightly decrease at high goose nest and lemming densities, none of our simulations resulted in a saturating response in all prey species. Our results highlight the importance of predator searching components in predator-prey interactions, especially predator speed, while predator acquisition rates were not limited by handling processes. By combining theory with field observations, our study provides support that the predator acquisition rate is not systematically limited at the highest prey densities observed in a natural system. Our study also illustrates how mechanistic models based on empirical estimates of the main components of predation can generate functional response shapes specific to the range of prey densities observed in the wild. Such models are needed to fully untangle proximate drivers of predator-prey population dynamics and to improve our understanding of predator-mediated interactions in natural communities.

There is an increasingly widespread use of biomarkers in network physiology to evaluate an organism’s physiological state. A recent study showed that albumin variability increases before death in chronic hemodialysis patients. We hypothesized that a multivariate statistical approach would better allow us to capture signals of impending physiological collapse/death. We proposed a Moving Multivariate Distance (MMD), based on the Mahalanobis distance, to quantify the variability of the multivariate biomarker profile as a whole from one visit to the next. Biomarker profiles from a visit were used as the reference to calculate MMD at the subsequent visit. We selected 16 biomarkers (of which 11 are measured every 2 weeks) from blood samples of 763 chronic kidney disease patients hemodialyzed at the CHUS hospital in Quebec, who visited the hospital regularly (∼every 2 weeks) to perform routine blood tests. MMD tended to increase markedly preceding death, indicating an increasing intraindividual multivariate variability presaging a critical transition. In survival analysis, the hazard ratio between the 97.5th percentile and the 2.5th percentile of MMD reached as high as 21.1 [95% CI: 14.3, 31.2], showing that higher variability indicates substantially higher mortality risk. Multivariate approaches to early warning signs of critical transitions hold substantial clinical promise to identify early signs of critical transitions, such as risk of death in hemodialysis patients; future work should also explore whether the MMD approach works in other complex systems (i.e., ecosystems, economies), and should compare it to other multivariate approaches to quantify system variability.

The St. Lawrence is a vast and complex socio-ecological system providing a wealth of services that sustain numerous economic sectors. This ecosystem is subject to significant human pressures that overlap and potentially interact with climate-driven environmental changes. Our objective in this paper was to systematically characterize the distribution and intensity of drivers of environmental change (hereafter, drivers) in the St. Lawrence System. We gathered data-based indicators for 22 coastal, climate, fisheries, and marine traffic drivers through collaborations, existing environmental initiatives and open data portals. We show that few areas of the St. Lawrence are free of cumulative exposure. The Estuary, Anticosti Gyre, and coastal areas are particularly exposed, especially in the vicinity of urban centers. We identified six distinct clusters with similar suites of co-occurring drivers and show that certain driver combinations are inherent to different regions of the St. Lawrence and that coastal areas are exposed to all driver types. Of particular concern are two clusters capturing most exposure hotspots and that show the convergence of contrasting cumulative exposure profiles at the head of the Laurentian Channel. Sharing knowledge of drivers emerged as a priority to facilitate future environmental assessment efforts. We thus launch eDrivers, an open knowledge platform gathering experts committed to structuring, standardizing and sharing knowledge on drivers of environmental change in support of holistic science and management. eDrivers was built on a series of guiding principles upholding existing data management and open science standards. We therefore expect it to evolve through time to address knowledge gaps and refine current driver layers. Ultimately, we believe that eDrivers represents a much needed solution that could radically influence broad scale research and management practices by increasing knowledge accessibility and interoperability.

Classical biomonitoring techniques have focused primarily on measures linked to various biodiversity metrics and indicator species. Next-generation biomonitoring (NGB) describes a suite of tools and approaches that allow the examination of a broader spectrum of organizational levels—from genes to entire ecosystems. Here, we frame 10 key questions that we envisage will drive the field of NGB over the next decade. While not exhaustive, this list covers most of the key challenges facing NGB, and provides the basis of the next steps for research and implementation in this field. These questions have been grouped into current- and outlook-related categories, corresponding to the organization of this paper.

The structure of ecological networks can vary dramatically, yet there may be common features across networks from different ecosystem types. Here, Bramon Mora et al. use network alignment to demonstrate that there is a common backbone of interactions underlying empirical food webs.

The grand ambition of theorists studying ecology and evolution is to discover the logical and mathematical rules driving the world's biodiversity at every level from genetic diversity within species to differences between populations, communities, and ecosystems. This ambition has been difficult to realize in great part because of the complexity of biodiversity. Theoretical work has led to a complex web of theories, each having non-obvious consequences for other theories. Case in point, the recent realization that genetic diversity involves a great deal of temporal and spatial stochasticity forces theoretical population genetics to consider abiotic and biotic factors generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved this problem by using formal languages based on logic to manage theorems. However, theories in ecology and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial Intelligence in bridging logic and probability theory offers the opportunity to build rich knowledge bases that combine logic's ability to represent complex mathematical ideas with probability theory's ability to model uncertainty. We describe these hybrid languages and explore how they could be used to build a unified knowledge base of theories for ecology and evolution.

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases with respect to both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These ecological and statistical issues directly affect ecologists’ abilities to accurately construct ecological networks. However, statistical biases introduced by sampling are difficult to quantify in the absence of full knowledge of the underlying ecological network’s structure. To explore properties of large-scale ecological networks, we developed the software EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different mathematical sampling designs that correspond to methods used in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties depends strongly both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, modules with nested structure were the easiest to detect, regardless of the sampling design used. Sampling a network starting with any species that had a high degree (e.g., abundant generalist species) was consistently found to be the most accurate strategy to estimate network structure. Because high-degree species tend to be generalists, abundant in natural communities relative to specialists, and connected to each other, sampling by degree may therefore be common but unintentional in empirical sampling of networks. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. To reduce biases introduced by sampling methods, we recommend that these findings be incorporated into field design considerations for projects aiming to characterize large species interaction networks.

Temperature is the most significant environmental gradient at the global scale, impacting the distributions of species and their ecological interactions. It is now established that temperature affects several biological rates and body mass, and can, in turn, alter interaction strength. Latitudinal variation in the strength of interactions has been observed for trophic and competitive interactions and many studies support that biotic interactions are more intense at low latitudes. Nevertheless, the mechanisms underlying the temperature dependence of trophic regulation, the effect of consumers on their preys, remain unclear. The aim of our study is to get better insights on the effects of temperature on trophic regulation. We used a consumer-resource model and considered that organisms' biological rates present a unimodal thermal response and that body mass decreases with temperature. We compared three measures of interaction strength: per capita, per population and net interaction strength. Our results demonstrate that the effect of temperature on interaction strength is contingent upon which species' biological rates are temperature dependent. When all biological rates are temperature dependent, the thermal response of interaction strength is hump-shaped following the scaling of search rate, whilst it is monotonically decreasing when only mortality rates vary with temperature. Finally, we show that temperature can indirectly impact trophic interaction strength through the temperature-size rule. A decrease in organisms' body size due to temperature induces a decrease in per capita and per population interaction strength and tend to decrease net interaction strength, depending on which trophic level follows the temperature-size rule. Our analysis gives an overview of how temperature, through various effects, may impact different measures of interaction strength.

The mechanisms allowing highly biodiverse ecosystems to remain stable are poorly understood. Here, Gravel and colleagues provide theoretical evidence that dispersal of organisms and material modifies species interactions and thus enables highly biodiverse communities to exist.

A long-standing ecological hypothesis is that complexity should decrease stability in food webs. Here, Jacquet and colleagues analyse over 100 real-world food webs and show that complexity does not decrease stability, but that a high frequency of weak species interactions stabilizes complex food webs.

Assessing the perception of key stakeholders within the forest sector is critical to evaluating their readiness to engage in adapting to climate change. Here, we report the results of the most comprehensive survey carried out in the Canadian forestry sector to date regarding perceptions of climate change. A total of 1158 individuals, representing a wide range of stakeholders across the five most important forestry provinces in Canada, were asked about climate change, its impact on forest ecosystems, and the suitability of current forest management for addressing future impacts. Overall, we found that respondents were more concerned about climate change than the general population. More than 90% of respondents agreed with the anthropogenic origins of climate change, and > 50% considered it a direct threat to their welfare. Political view was the main driver of general beliefs about the causes of climate change and its future consequences, while the province of origin proved to be the best predictor of perceived current impacts on forest ecosystems and its associated risks; and type of stakeholder was the main driver of perceived need for adaptation. Industrial stakeholders were the most skeptical about the anthropogenic cause(s) of climate change (18% disagreed with this statement, compared to an average of 8% in the other stakeholders), its impacts on forest ecosystems (28% for industry vs. 10% for other respondents), and the need for new management practices (18% vs. 7%). Although the degree of awareness and the willingness to implement adaptive practices were high even for the most skeptical groups, our study identified priority sectors or areas for action when designing awareness campaigns. We suggest that the design of a strategic framework for implementing climate adaptation within the Canadian forest sector should focus on the relationship between climate change and changes in disturbance regimes, and above all on the economic consequences of these changes, but it should also take into account the positions shown by each of the actors in each province.

Scientists, policy makers, and journalists are three key, interconnected players involved in prioritizing and implementing solutions to mitigate the consequences of anthropogenic pressures on the environment. The way in which information is framed and expertise is communicated by the media is crucial for political decisions and for the integrated management of environmental issues. Here we present a comparative study of scientific literature and press articles addressing climate change and biodiversity. We extensively scrutinized the scientific literature, research funding, and press articles from the USA, Canada, and United Kingdom addressing climate change and biodiversity issues between 1991 and 2016. We found that media coverage of climate change was up to eight times higher compared to biodiversity. This discrepancy could not be explained by different scientific output between the two issues. Moreover, climate change media coverage was often related to specific events whereas no such indication of a connection was found in the case of biodiversity. An international communication strategy is urgently required to raise public awareness on biodiversity issues. We discussed several initiatives that scientists could undertake to better communicate major discoveries to the public and policy makers.

Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with a supervised machine learning technique. Recommenders are algorithms developed for companies like Netflix to predict whether a customer will like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized for ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

Measures of alpha diversity are more frequently used to detect environmental changes and subsequent impacts on biodiversity, while measures based on variability (beta diversity) are said to be more appropriate for detecting those impacts. Theory predicts that beta diversity should increase with disturbance frequency in patchy communities. Our objective in this study was to experimentally determine the effect of high and low disturbance regimes, frequency and intensity combined, on marine benthic alpha and beta diversity. The experiment was conducted in a rock pool system of the St. Lawrence estuary, Canada. Rock pools were disturbed by (1) nutrient enrichment and (2) draining according to three disturbance regimes (none, low, high). Disturbance regimes had little or no effect on alpha diversity of benthic algae and sessile animals. However, the low regime of nutrient enrichment induced greater within-group beta diversity than the reference rock pools, while the high disturbance regime induced equal or even smaller within-group beta diversity compared to the reference. Draining had an opposite effect on benthic beta diversity, with a greater variability of the community structure under the high regime of disturbance. Taking into account the effect of disturbance regimes on beta diversity could provide a useful diagnostic for disturbed benthic communities.

The metacommunity perspective appears as an appropriate conceptual framework to make ecology more predictive. It is particularly relevant to limnology, where exchanges of organisms and nutrients affect community and ecosystem properties from the local to the regional scales. The recent development of neutral theory appears as a step back in that direction because of the assumption of ecological equivalence and the absence of any effect of the environment on community organization. A remarkable strength of neutral theory is nonetheless to provide a general theory of diversity that accounts for a wide range of empirical observations. In this paper, we argue that neutral theory can be useful to understand the impact of dispersal on community assembly in landscapes of various complexities. Our analysis focus on spatially explicit landscapes conceptualized as networks of local communities (e.g., lakes) connected to each other by dispersal channels (e.g., rivers). The main objective of the paper is to use neutral theory to stress the importance of landscape structure on the distribution of diversity. We refer to the landscape organization as a spatial contingency that could potentially affect the coexistence mechanisms at play. We briefly review the main approaches to describe spatial networks and describe three simple toy models of metacommunity dynamics. We take this opportunity to review their assumptions and main predictions. We then conduct simulations of these models to reveal with simple examples the impact of spatial network structure on diversity distribution. The simulation results show that competitive interactions buffer the potential impact of landscape structure. The strongest relationship between node position in the landscape and species richness was observed for the patch dynamics model without any interactions. On the other hand, strong and unequal competitive interactions minimized the effect of node position. We conclude that the neutral model is a useful tool to understand the joint effects of dispersal and ecological interactions. Our analysis shows that limnologists must now integrate more realistic landscapes when analyzing community assembly from a metacommunity perspective.

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

n/a

The correspondence between species distribution and the environment depends on species' ability to track favorable environmental conditions (via dispersal) and to maintain competitive hierarchy against the constant influx of migrants (mass effect) and demographic stochasticity (ecological drift). Here we report a simulation study of the influence of landscape structure on species distribution. We consider lottery competition for space in a spatially heterogeneous environment, where the landscape is represented as a network of localities connected by dispersal. We quantified the contribution of neutrality and species sorting to their spatial distribution. We found that neutrality increases and the strength of species-sorting decreases with the centrality of a community in the landscape when the average dispersal among communities is low, whereas the opposite was found at elevated dispersal. We also found that the strength of species-sorting increases with environmental heterogeneity. Our results illustrate that spatial structure of the environment and of dispersal must be taken into account for understanding species distribution. We stress the importance of spatial geographic structure on the relative importance of niche vs. neutral processes in controlling community dynamics.

Interaction networks are central elements of ecological systems and have very complex structures. Historically, much effort has focused on niche-mediated processes to explain these structures, while an emerging consensus posits that both niche and neutral mechanisms simultaneously shape many features of ecological communities. However, the study of interaction networks still lacks a comprehensive neutral theory. Here we present a neutral model of predator-prey interactions and analyze the structural characteristics of the simulated networks. We find that connectance values (complexity) and complexity-diversity relationships of neutral networks are close to those observed in empirical bipartite networks. High nestedness and low modularity values observed in neutral networks fall in the range of those from empirical antagonist bipartite networks. Our results suggest that, as an alternative to niche-mediated processes that induce incompatibility between species ("niche forbidden links"), neutral processes create "neutral forbidden links" due to uneven species abundance distributions and the low probability of interaction between rare species. Neutral trophic networks must be seen as the missing endpoint of a continuum from niche to purely stochastic approaches of community organization.

We are interested in understanding if metacommunity dynamics contribute to the persistence of complex spatial food webs subject to colonization-extinction dynamics. We study persistence as a measure of stability of communities within discrete patches, and ask how do species diversity, connectance, and topology influence it in spatially structured food webs.We answer this question first by identifying two general mechanisms linking topology of simple food web modules and persistence at the regional scale. We then assess the robustness of these mechanisms to more complex food webs with simulations based on randomly created and empirical webs found in the literature. We find that linkage proximity to primary producers and food web diversity generate a positive relationship between complexity and persistence in spatial food webs. The comparison between empirical and randomly created food webs reveal that the most important element for food web persistence under spatial colonization-extinction dynamics is the degree distribution: the number of prey species per consumer is more important than their identity.With a simple set of rules governing patch colonization and extinction, we have predicted that diversity and connectance promote persistence at the regional scale. The strength of our approach is that it reconciles the effect of complexity on stability at the local and the regional scale. Even if complex food webs are locally prone to extinction, we have shown their complexity could also promote their persistence through regional dynamics. The framework we presented here offers a novel and simple approach to understand the complexity of spatial food webs.