Motifs in bipartite ecological networks: uncovering indirect interactions

Indirect interactions play an essential role in governing population, community and coevolutionary dynamics across a diverse range of ecological communities. Such communities are widely represented as bipartite networks: graphs depicting interactions between two groups of species, such as plants and pollinators or hosts and parasites. For over thirty years, studies have used indices, such as connectance and species degree, to characterise the structure of these networks and the roles of their constituent species. However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. Given the large literature demonstrating the importance and ubiquity of indirect effects, many studies of network structure are likely missing a substantial piece of the ecological puzzle. Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. While this framework is a significant departure from the current way of thinking about bipartite ecological networks, we show that this shift is supported by analyses of simulated and empirical data. We use simulations to show how consideration of indirect interactions can highlight differences missed by the current index paradigm that may be ecologically important. We extend this finding to empirical plant–pollinator communities, showing how two bee species, with similar direct interactions, differ in how specialised their competitors are. These examples underscore the need to not rely solely on network‐ and species‐level indices for characterising the structure of bipartite ecological networks.     

Analyzing the functional type concept in arctic plants using a dynamic vegetation model

Grouping organisms into categories based on common traits has been a tool of ecological scientists for some time now. Defining groups of species, examples being life forms and functional types, is an operational procedure, conducted to answer a particular question. This becomes rather practical when performing analyses at coarse spatial scales, given data limitations and the potential for species redundancy. However, the implications of aggregating organisms for modeling purposes are still unclear. Does averaging the traits of several species into a functional type sufficiently represent the dynamics of the individual components? How much variability is lost when we aggregate species into groups? In an attempt to address these questions, we examined how the level of vegetation aggregation affected a variety of ecosystem properties using a regional‐scale model of arctic tundra ecosystems (ArcVeg).
We used four levels of aggregation: species (15 dominant ones), functional types (7), life forms (4) and vegetation type (1), in addition to two methods of aggregation (simple vs weighted means of plant parameter values). We found that the level of aggregation consistently affected community composition, total community biomass and ecosystem net primary production (NPP). Neither simple means nor weighted means of aggregated parameter values adequately captured the ecosystem properties simulated at lower levels of aggregation. Aggregation of vegetation (i.e. reduced parameter variability) using simple means underestimated total biomass, whereas aggregation using weighted means overestimated total biomass. Aggregation led to increases in NPP with both methods. These findings suggest that aggregating vegetation, particularly to levels less detailed than plant functional types, will have important implications for regional‐scale modeling of vegetation dynamics and carbon cycling.     

Disentangling the effect of hybrid interactions and of the constant effort hypothesis on ecological community stability

In the last years, a remarkable theoretical effort has been made in order to understand the relation between stability and complexity in ecological communities. Yet, what maintains species diversity in real ecological communities is still an open question. The non‐random structures of ecological interaction networks have been recognized as one key ingredient impacting the maximum number of coexisting species within the ecological community. However most of the earlier theoretical studies have considered communities with only one interaction type (either antagonistic, competitive or mutualistic). Recently, it has been proposed that multiple interaction types might stabilize ecosystems and that, in this hybrid case, increasing complexity increases stability. Here we show that these results depend on ad hoc hypothesis that the authors used in their model and we highlight the need to disentangle the role of multiple interaction types and constant interaction effort allocation on community stability. Indeed, we find that mixing of mutualistic and predator–prey interaction types does not stabilize the community dynamics and we demonstrate that a positive correlation between complexity and stability is observed only if a constant effort allocation is imposed in the ecological interactions. Synthesis In recent years a sparkling research has been devoted to the search of new theoretical mechanisms to explain way ecosystems may persist despite their complexity. Here we show that, contrary to what recently suggested (Mougi et al. 2012), the mismatch between theoretical results and empirical evidences on the stability of ecological community is still there also for communities with both mutualistic and antagonistic interactions, and the ‘complexity‐stability’ paradox is still alive. Indeed, we demonstrate that their results arise as an artifact of the peculiar rescaling of the interaction strengths they imposed. Our study suggests that further theoretical studies and experimental evidences are still needed to better understand the role of interaction strengths in real ecological communities.     

A process‐based metacommunity framework linking local and regional scale community ecology

The metacommunity concept has the potential to integrate local and regional dynamics within a general community ecology framework. To this end, the concept must move beyond the discrete archetypes that have largely defined it (e.g. neutral vs. species sorting) and better incorporate local scale species interactions and coexistence mechanisms. Here, we present a fundamental reconception of the framework that explicitly links local coexistence theory to the spatial processes inherent to metacommunity theory, allowing for a continuous range of competitive community dynamics. These dynamics emerge from the three underlying processes that shape ecological communities: (1) density‐independent responses to abiotic conditions, (2) density‐dependent biotic interactions and (3) dispersal. Stochasticity is incorporated in the demographic realisation of each of these processes. We formalise this framework using a simulation model that explores a wide range of competitive metacommunity dynamics by varying the strength of the underlying processes. Using this model and framework, we show how existing theories, including the traditional metacommunity archetypes, are linked by this common set of processes. We then use the model to generate new hypotheses about how the three processes combine to interactively shape diversity, functioning and stability within metacommunities.     

生态系统性状对宏生态研究的启示与挑战

功能性状在器官-物种-种群-群落-生态系统水平都具有其特定的适应或功能优化的意义,但目前对功能性状的测定和研究大都局限于器官或物种水平。然而,当前高速发展的宏生态新研究技术和方法(如遥感观测、通量观测、模型模拟)的研究对象都是在生态系统或区域尺度上,如何将传统功能性状与其相连结并服务于生态环境问题和全球变化问题是科学界的一大难题。为了解决传统性状与宏生态研究"尺度不统一"和"量纲不统一"的难题,研究人员最新发展了"生态系统性状(Ecosystem traits, ESTs)"概念体系,并从"理念-数据源-推导方法-应用"等多角度为后续研究提供了可借鉴案例。生态系统性状将传统性状研究从器官水平拓展到了群落和生态系统水平,以单位土地面积为基础构建了传统性状与宏生态研究(或地学研究)的桥梁,开启了性状研究从"器官到群落"、从"经典理论验证到宏观应用"的美好愿景,为多学科交叉提供了新思路。然而,它在方法学和数据源等方面还存在诸多问题与挑战;在此,我们呼吁相关专家从研究方法、概念体系和应用实践上赋予"生态系统性状"更强大的生命力,尤其从动物群落性状和微生物群落性状等角度。本文在深入解读先前生态性...     

Recent advances in ecological stoichiometry: insights for population and community ecology

Conventional theories of population and community dynamics are based on a single currency such as number of individuals, biomass, carbon or energy. However, organisms are constructed of multiple elements and often require them (in particular carbon, phosphorus and nitrogen) in different ratios than provided by their resources; this mismatch may constrain the net transfer of energy and elements through trophic levels. Ecological stoichiometry, the study of the balance of elements in ecological processes, offers a framework for exploring ecological effects of such constraints. We review recent theoretical and empirical studies that have considered how stoichiometry may affect population and community dynamics. These studies show that stoichiometric constraints can affect several properties of populations (e.g. stability, oscillations, consumer extinction) and communities (e.g. coexistence of competitors, competitive interactions between different guilds). We highlight gaps in general knowledge and focus on areas of population and community ecology where incorporation of stoichiometric constraints may be particularly fruitful, such as studies of demographic bottlenecks, spatial processes, and multi-species interactions. Finally, we suggest promising directions for new research by recommending potential study systems (terrestrial insects, detritivory-based webs, soil communities) to improve our understanding of populations and communities. Our conclusion is that a better integration of stoichiometric principles and other theoretical approaches in ecology may allow for a richer understanding of both population and community structure and dynamics.     

On the integration of biotic interaction and environmental constraints at the biogeographical scale

Biogeography is primarily concerned with the spatial distribution of biodiversity, including performing scenarios in a changing environment. The efforts deployed to develop species distribution models have resulted in predictive tools, but have mostly remained correlative and have largely ignored biotic interactions. Here we build upon the theory of island biogeography as a first approximation to the assembly dynamics of local communities embedded within a metacommunity context. We include all types of interactions and introduce environmental constraints on colonization and extinction dynamics. We develop a probabilistic framework based on Markov chains and derive probabilities for the realization of species assemblages, rather than single species occurrences. We consider the expected distribution of species richness under different types of ecological interactions. We also illustrate the potential of our framework by studying the interplay between different ecological requirements, interactions and the distribution of biodiversity along an environmental gradient. Our framework supports the idea that the future research in biogeography requires a coherent integration of several ecological concepts into a single theory in order to perform conceptual and methodological innovations, such as the switch from single‐species distribution to community distribution.     

How to make more out of community data? A conceptual framework and its implementation as models and software

Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non‐manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data‐driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species‐to‐species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R‐ and Matlab‐packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time‐series data. We illustrate the use of this framework through a series of diverse ecological examples.     

Local Ecological Knowledge and Institutional Dynamics for Ecosystem Management: A Study of Lake Racken Watershed, Sweden

The sustainable use of resources requires that management practices and institutions take into account the dynamics of the ecosystem. In this paper, we explore the role of local ecological knowledge and show how it is used in management practices by a local fishing association in a contemporary rural Swedish community. We focus on the local management of crayfish, a common-pool resource, and also address the way crayfish management is linked to institutions at different levels of Swedish society. Methods from the social sciences were used for information gathering, and the results were analyzed within the framework of ecosystem management. We found that the practices of local fishing association resemble an ecosystem approach to crayfish management. Our results indicate that local users have substantial knowledge of resource and ecosystem dynamics from the level of the individual crayfish to that of the watershed, as reflected in a variety of interrelated management practices embedded in and influenced by institutions at several levels. We propose that this policy of monitoring at several levels simultaneously, together with the interpretation of a bundle of indicators and associated management responses, enhances the possibility of building ecological resilience into the watershed. Furthermore, we found that flexibility and adaptation are required to avoid command-and-control pathways of resource management. We were able to trace the development of the local fishing association as a response to crisis, followed by the creation of an opportunity for reorganization and the recognition of slow ecosystem structuring variables, and also to define the role of knowledgeable individuals in the whole process. We discuss the key roles of adaptive capacity, institutional learning, and institutional memory for successful ecosystem management and conclude that scientific adaptive management could benefit from a more explicit collaboration with flexible community-based systems of resource management for the implementation of policies as experiments. Received 26 April 2000; accepted 13 October 2000.     

Metacommunity speciation models and their implications for diversification theory

The emergence of new frameworks combining evolutionary and ecological dynamics in communities opens new perspectives on the study of speciation. By acknowledging the relative contribution of local and regional dynamics in shaping the complexity of ecological communities, metacommunity theory sheds a new light on the mechanisms underlying the emergence of species. Three integrative frameworks have been proposed, involving neutral dynamics, niche theory, and life history trade‐offs respectively. Here, we review these frameworks of metacommunity theory to emphasise that: (1) studies on speciation and community ecology have converged towards similar general principles by acknowledging the central role of dispersal in metacommunities dynamics, (2) considering the conditions of emergence and maintenance of new species in communities has given rise to new models of speciation embedded in the metacommunity theory, (3) studies of diversification have shifted from relating phylogenetic patterns to landscapes spatial and ecological characteristics towards integrative approaches that explicitly consider speciation in a mechanistic ecological framework. We highlight several challenges, in particular the need for a better integration of the eco‐evolutionary consequences of dispersal and the need to increase our understanding on the relative rates of evolutionary and ecological changes in communities.     

A general multi-trait-based framework for studying the effects of biodiversity on ecosystem functioning

Environmental change is as multifaceted as are the species and communities that respond to these changes. Current theoretical approaches to modeling ecosystem response to environmental change often deal only with single environmental drivers or single species traits, simple ecological interactions, and/or steady states, leading to concern about how accurately these approaches will capture future responses to environmental change in real biological systems. To begin addressing this issue, we generalize a previous trait-based framework to incorporate aspects of frequency dependence, functional complementarity, and the dynamics of systems composed of species that are defined by multiple traits that are tied to multiple environmental drivers. The framework is particularly well suited for analyzing the role of temporal environmental fluctuations in maintaining trait variability and the resultant effects on community response to environmental change. Using this framework, we construct simple models to investigate two ecological problems. First, we show how complementary resource use can significantly enhance the nutrient uptake of plant communities through two different mechanisms related to increased productivity (over-yielding) and larger trait variability. Over-yielding is a hallmark of complementarity and increases the total biomass of the community and, thus, the total rate at which nutrients are consumed. Trait variability also increases due to the lower levels of competition associated with complementarity, thus speeding up the rate at which more efficient species emerge as conditions change. Second, we study systems in which multiple environmental drivers act on species defined by multiple, correlated traits. We show that correlations in these systems can increase trait variability within the community and again lead to faster responses to environmental change. The methodological advances provided here will apply to almost any function that relates species traits and environmental drivers to growth, and should prove useful for studying the effects of climate change on the dynamics of biota.     

Diversity in a complex ecological network with two interaction types

Most studies on ecological networks consider only a single interaction type (e.g. competitive, predatory or mutualistic), and try to developrules for system stability based exclusively on properties of this interaction type. However, the stability of ecological networks may be more dependent on the way different interaction types are combined in real communities. To address this issue, we start by compiling an ecological network in the Doñana Biological Reserve, southern Spain, with 390 species and 798 mu-tualistic and antagonistic interactions. We characterize network structure by looking at how mutualistic and antagonistic interactions are combined across all plant species. Both the ratio of mutualistic to antagonistic interactions per plant, and the number of basic modules with an antagonistic and a mutualistic interaction are very heterogeneous across plant species, with a few plant species showing very high values for these parameters. To assess the implications of these network patterns on species diversity, we study analytically and by simulation a model of this ecological network. We find that the observed correlation between strong interaction strengths and high mutualistic to antagonistic ratios in a few plant species significantly increases community diversity. Thus, to predict the persistence of biodiversity we need to understand how interaction strength and the architecture of ecological networks with different interaction types are combined.     

A plea for more ecology in phytoplankton ecology

When looking for a pattern of phytoplankton behaviour across trophic gradients, we need to cross the boundaries between different disciplinary areas, from autoecology to systems ecology, because eutrophication is a complex process which involves different time scales and different levels of community structure. Thus, we submit our observations to the muddled conceptual world of assemblage ecology. These inaccuracies arise, for example, from both species and community arguments; eutrophication as a fertilization or a metabolic phenomenon; and the notions frequently interwoven of pattern, process and rules. We suggest that it is advantageous to tackle this issue from the perspective of general ecology, rather than from a specifically planktonic orientation. In this way, useful general ecological tools, for example, time series and assembly-rule studies, can be used. Time-series study allows the dynamics of any variable to be described or to show that long term variable fluctuations may sometimes be unregulated, in response to some exogenous factor. Rules of assembly help us to resolve which traits are selectively involved during the eutrophication process. In this context, we advocate (1) the use of traits instead of morphospecies in phytoplankton studies, (2) looking for the dynamic patterns of phytoplankton with eutrophication, (3) the use of time series techniques to study phytoplankton trajectories, (4) the use of assembly rules to discern patterns in the formation of multispecies assemblages, (5) the consideration of the pelagic food-web in studies of phytoplankton dynamics and, as an overall suggestion, to borrow knowledge and inspiration from general ecology.     

Stage-structured ontogeny in resource populations generates non-additive stabilizing and de-stabilizing forces in populations and communities

Demographic heterogeneity influences how populations respond to density dependent intraspecific competition and trophic interactions. Distinct stages across an organism's development, or ontogeny, are an important example of demographic heterogeneity. In consumer populations, ontogenetic stage structure has been shown to produce categorical differences in population dynamics, community dynamics and even species coexistence compared to models lacking explicit ontogeny. The study of consumer–resource interactions must also consider the ontogenetic stage structure of the resource itself, particularly plants, given their fundamental role at the basis of terrestrial food webs. We incorporate distinct ontogenetic stages of plants into an adaptable multi-stage consumer–resource modeling framework that facilitates studying how stage specific consumers shape trophic dynamics at low trophic levels. We describe the role of density dependent demographic rates in mediating the dynamics of stage-structured plant populations. We then investigate how these demographic rates interact with consumer pressure to influence stability and coexistence in multiple stage-specific consumer–resource interactions. Results detail how density dependent effects across distinct ontogenetic stages in plant development produce non-additivity in the drivers of dynamic stability both in single populations and in consumer–resource settings, challenging the ubiquity of certain traditional ecological dynamic paradigms. We also find categorical differences in the population variability induced by herbivores consuming separate plant stages. Consumer–resource models, such as plant–herbivore interactions, often average out demographic heterogeneity in populations. Here, we show that explicitly including plant demographic heterogeneity through ontogeny yields distinct dynamic expectations for both plants and herbivores compared to traditional consumer–resource formulations. Our results indicate that efforts to understand the demographic effect of herbivores on plant populations may need to also consider the effects of plant demographics on herbivores and the reciprocal relationship between them.     

Using expert knowledge to incorporate uncertainty in cause‐of‐death assignments for modeling of cause‐specific mortality

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause‐specific mortality provide an example of implicit use of expert knowledge when causes‐of‐death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause‐specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause‐of‐death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event‐time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause‐of‐death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause‐of‐death assignment in modeling of cause‐specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause‐specific survival data for white‐tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause‐of‐death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.     

Quantifying the Adaptive Cycle

The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation) in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994–2011) data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and adaptation provides opportunities to cope with the intricacies and uncertainties associated with fast ecological change, driven by shifting system controls. Ultimately, combining traditional ecological paradigms with heuristics of complex system dynamics using quantitative approaches may help refine ecological theory and improve our understanding of the resilience of ecosystems.     

Using ecological stoichiometry to understand and predict infectious diseases

A key characteristic of host–parasite interactions is the theft of host nutrients by the parasite, yet we lack a general framework for understanding and predicting the interplay of host and parasite nutrition that applies across biological levels of organization. The elemental nutrients (C, N, P, Fe, etc.), and ecological stoichiometry provide a framework for understanding host–parasite interactions and their relation to ecosystem functioning. Here we use the ecological stoichiometry framework to develop hypotheses and predictions regarding the relationship between elemental nutrients and host–parasite interactions. We predict that a suite of host and parasite traits, stoichiometric homeostasis, host diet stoichiometry, and biogeochemical cycling are related to disease dynamics, host immunity and resistance, and bacterial growth form determination. We show that ecological stoichiometry is capable of expanding our understanding of host–parasite interactions, and complementing other approaches such as population and community ecology, and molecular biology, for studying infectious diseases.     

The method matters: A guide for indicator aggregation in ecological assessments

Ecological assessment requires the integration of many physical, chemical, and/or biological quality elements. The choice of the aggregation method of such partial assessments into an overall assessment can considerably affect the assessment outcome – an issue that has been controversially discussed within the scientific community for the last decade. Current practice often considers only two different aggregation methods, the weighted arithmetic mean (additive aggregation) and the one-out, all-out method (minimum aggregation). However, both have important drawbacks. Additive aggregation compensates a bad status of one quality element by a number of elements featuring good status. Minimum aggregation can lead to overly pessimistic assessment results, since only the quality element in the worst status is considered. Here, we introduce a toolbox containing current and new aggregation methods, demonstrate and discuss their properties with simple, didactical examples, and suggest in which situations best to use them. Then, we illustrate the consequences of selected aggregation schemes for ecological river assessment with the case study of the Swiss Modular Concept of stream assessment (SMC), which we apply to ten river reaches in the Mönchaltdorfer Aa catchment in Switzerland. To be able to do so, we used multi-criteria decision analysis, i.e., multi-attribute value theory, to arrange the SMC quality elements into an objectives hierarchy, and to translate their individual assessments into value functions. Our case study revealed that choosing the most appropriate aggregation method particularly matters, if objectives with significantly different qualities are aggregated. We argue that redundant objectives (i.e., quality elements), often found at the lower levels of the objectives hierarchy, should best be aggregated additively allowing for compensation to increase the statistical significance of the results. Further, we suggest that complementary sub-objectives that often occur at higher levels may be optimally aggregated with a mixture of additive and minimum aggregation. Such a mixed method will allow some compensation, but nevertheless penalize for very bad states. Since here we compare commonly used aggregation methods with some which we believe have never been discussed in an assessment context before, our study concurrently informs ecological assessment in theory and in practice.     

The effects of space and diversity of interaction types on the stability of complex ecological networks

The relationship between structure and stability in ecological networks and the effect of spatial dynamics on natural communities have both been major foci of ecological research for decades. Network research has traditionally focused on a single interaction type at a time (e.g. food webs, mutualistic networks). Networks comprising different types of interactions have recently started to be empirically characterized. Patterns observed in these networks and their implications for stability demand for further theoretical investigations. Here, we employed a spatially explicit model to disentangle the effects of mutualism/antagonism ratios in food web dynamics and stability. We found that increasing levels of plant-animal mutualistic interactions generally resulted in more stable communities. More importantly, increasing the proportion of mutualistic vs. antagonistic interactions at the base of the food web affects different aspects of ecological stability in different directions, although never negatively. Stability is either not influenced by increasing mutualism—for the cases of population stability and species’ spatial distributions—or is positively influenced by it—for spatial aggregation of species. Additionally, we observe that the relative increase of mutualistic relationships decreases the strength of biotic interactions in general within the ecological network. Our work highlights the importance of considering several dimensions of stability simultaneously to understand the dynamics of communities comprising multiple interaction types.     

Linking community and disease ecology: the impact of biodiversity on pathogen transmission

The increasing number of zoonotic diseases spilling over from a range of wild animal species represents a particular concern for public health, especially in light of the current dramatic trend of biodiversity loss. To understand the ecology of these multi-host pathogens and their response to environmental degradation and species extinctions, it is necessary to develop a theoretical framework that takes into account realistic community assemblages. Here, we present a multi-host species epidemiological model that includes empirically determined patterns of diversity and composition derived from community ecology studies. We use this framework to study the interaction between wildlife diversity and directly transmitted pathogen dynamics. First, we demonstrate that variability in community composition does not affect significantly the intensity of pathogen transmission. We also show that the consequences of community diversity can differentially impact the prevalence of pathogens and the number of infectious individuals. Finally, we show that ecological interactions among host species have a weaker influence on pathogen circulation than inter-species transmission rates. We conclude that integration of a community perspective to study wildlife pathogens is crucial, especially in the context of understanding and predicting infectious disease emergence events.