Evolution of dispersal in metacommunities of interacting species

Theoretical studies on the evolution of dispersal in metacommunities are rare despite empirical evidence suggesting that interspecific interactions can modify dispersal behaviour of organisms. To understand the role of species interactions for dispersal evolution, we utilize an individual‐based model of a metacommunity where local population dynamics follows a stochastic version of the Nicholson–Bailey model and dispersal probability is an evolving trait. Our results show that in comparison with a neutral system (commensalism), parasitism promotes dispersal of hosts and parasites, while mutualism tends to reduce dispersal in both partners. Search efficiency of guests (only in the case of parasitism), dispersal mortality and external extinction risk can influence the evolution of dispersal of all partners. In systems composed of two host and two guest species, lower dispersal probabilities evolve under parasitism as well as mutualism than in one host and one guest species systems. This is because of frequency‐dependent modulations of dispersal benefits emerging in such systems for all partners.     

Biotic interactions hold the key to understanding metacommunity organisation

Biotic interactions are fundamental drivers governing biodiversity locally, yet their effects on geographical variation in community composition (i.e. incidence-based) and community structure (i.e. abundance-based) at regional scales remain controversial. Ecologists have only recently started to integrate different types of biotic interactions into community assembly in a spatial context, a theme that merits further empirical quantification. Here, we applied partial correlation networks to infer the strength of spatial dependencies between pairs of organismal groups and mapped the imprints of biotic interactions on the assembly of pond metacommunities. To do this, we used a comprehensive empirical dataset from Mediterranean landscapes and adopted the perspective that community assembly is best represented as a network of interacting organismal groups. Our results revealed that the co-variation among the beta diversities of multiple organismal groups is primarily driven by biotic interactions and, to a lesser extent, by the abiotic environment. These results suggest that ignoring biotic interactions may undermine our understanding of assembly mechanisms in spatially extensive areas and decrease the accuracy and performance of predictive models. We further found strong spatial dependencies in our analyses which can be interpreted as functional relationships among several pairs of organismal groups (e.g. macrophytes–macroinvertebrates, fish–zooplankton). Perhaps more importantly, our results support the notion that biotic interactions make crucial contributions to the species sorting paradigm of metacommunity theory and raise the question of whether these biologically-driven signals have been equally underappreciated in other aquatic and terrestrial ecosystems. Although more research is still required to empirically capture the importance of biotic interactions across ecosystems and at different spatial resolutions and extents, our findings may allow decision makers to better foresee the main consequences of human-driven impacts on inland waters, particularly those associated with the addition or removal of key species.     

Meta-ecosystems: a theoretical framework for a spatial ecosystem ecology

This contribution proposes the meta‐ecosystem concept as a natural extension of the metapopulation and metacommunity concepts. A meta‐ecosystem is defined as a set of ecosystems connected by spatial flows of energy, materials and organisms across ecosystem boundaries. This concept provides a powerful theoretical tool to understand the emergent properties that arise from spatial coupling of local ecosystems, such as global source–sink constraints, diversity–productivity patterns, stabilization of ecosystem processes and indirect interactions at landscape or regional scales. The meta‐ecosystem perspective thereby has the potential to integrate the perspectives of community and landscape ecology, to provide novel fundamental insights into the dynamics and functioning of ecosystems from local to global scales, and to increase our ability to predict the consequences of land‐use changes on biodiversity and the provision of ecosystem services to human societies.     

Environmental and spatial controls of taxonomic versus trait composition of stream biota

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Spatial and environmental effects on a rock‐pool metacommunity depend on landscape setting and dispersal mode

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The community ecology of pathogens: coinfection,coexistence and community composition

Disease and community ecology share conceptual and theoretical lineages, and there has been a resurgence of interest in strengthening links between these fields. Building on recent syntheses focused on the effects of host community composition on single pathogen systems, we examine pathogen (microparasite) communities using a stochastic metacommunity model as a starting point to bridge community and disease ecology perspectives. Such models incorporate the effects of core community processes, such as ecological drift, selection and dispersal, but have not been extended to incorporate host–pathogen interactions, such as immunosuppression or synergistic mortality, that are central to disease ecology. We use a two‐pathogen susceptible‐infected (SI) model to fill these gaps in the metacommunity approach; however, SI models can be intractable for examining species‐diverse, spatially structured systems. By placing disease into a framework developed for community ecology, our synthesis highlights areas ripe for progress, including a theoretical framework that incorporates host dynamics, spatial structuring and evolutionary processes, as well as the data needed to test the predictions of such a model. Our synthesis points the way for this framework and demonstrates that a deeper understanding of pathogen community dynamics will emerge from approaches working at the interface of disease and community ecology.