Predicting coexistence in experimental ecological communities

Maynard, Daniel S.; Miller, Zachary R.; Allesina, Stefano

doi:10.1038/s41559-019-1059-z

Cited by 58 publications

(83 citation statements)

References 37 publications

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“…In particular, determining the number of assembly endpoints, the presence of cycles, and the accessibility of states from bottom-up assembly would be key to determine the possibility of predicting assembly. Importantly, Maynard et al (2020) have shown how the nodes of the assembly graph for a pool of species can be identified by performing a limited number of experiments; further experiments and new statistical methods are needed to determine the presence and orientation of the edges. By fostering a dialogue between theoretical and experimental findings, we hope our contribution will kindle a renewed interest in the challenging study of assembly.…”

Section: Discussionmentioning

confidence: 99%

Tractable models of ecological assembly

Serván

Allesina

2021

Ecology Letters

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Ecological assembly is a fundamental and yet poorly understood process. Three main obstacles hinder the development of a theory of assembly, and when these issues are sidestepped by making strong assumptions, one can build an assembly graph in which nodes are ecological communities and edges are invasions shifting their composition. The graph can then be analysed directly, without the need to consider dynamics. To showcase this framework, we build and analyse assembly graphs for the competitive Lotka–Volterra model, showing that in these cases sequential assembly (in which species invade a community one at a time) can reach the same configurations found when starting the system with all species present at different initial conditions. We discuss how our results can advance our understanding of assembly both from an empirical and a theoretical point of view, informing the study of ecological restoration and the design of ecological communities.

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Section: Discussionmentioning

confidence: 99%

Tractable models of ecological assembly

Serván

Allesina

2021

Ecology Letters

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“…While a community with S species has 2 S ≠ 1 potential species combinations, it has been shown that only a small fraction of such combinations can persist (Angulo et al, 2020). Because of the sparsity of these persistent combinations, it can be possible to infer all persistent combinations using a minimum of S + 1 experiments in a community with S species (Maynard et al, 2020). Moreover, if one is only interested in bottom-up assembly, then persistent combinations that are unreachable via introduction of single species do not need to be mapped to construct the corresponding assembly graph.…”

Section: From Theory To Testable Hypothesesmentioning

confidence: 99%

Untangling the complexity of priority effects in multispecies communities

Song

Fukami

Saavedra

2021

Preprint

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Priority effects arise when the history of species arrival influences local species interactions, thereby affecting the composition of ecological communities. The outcome of some priority effects may be more difficult to predict than others, but this possibility remains to be fully investigated. Here, we provide a graph-based, non-parametric, theoretical framework to understand the classification of priority effects and the predictability of multi-species communities. We show that we can classify priority effects by decomposing them into four basic dynamical sources: the number of alternative stable states, the number of alternative transient paths, the length of composition cycles, and the interaction between alternative stable states and composition cycles. Although the number of alternative stable states has received most of the attention, we show that the other three sources can contribute more to the predictability of community assembly, especially in small communities. We discuss how this theoretical framework can guide new experimental studies.

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“…neutral) factors act together, and the interdependency between these processes shape communities (Leibold et al 2004, Brown et al 2017). However, the effects of environmental heterogeneity on niche construction are often mixed with the biotic effects (Kraft et al 2015, Thakur and Wright 2017), and the role of species interactions are frequently neglected in community assembly models (Thakur and Wright 2017, Wright et al 2017, Maynard et al 2020). Here, we examine the relative roles (and their interdependencies) of environmental variability, spatial constraints and biotic interactions on the community assembly at the seafloor.…”

Section: Introductionmentioning

confidence: 99%

Coupled effects of environment, space and ecological engineering on seafloor beta‐diversity

et al. 2021

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One of the challenges in modern ecology is reconciling how biotic interactions and abiotic factors are interwoven drivers of community assembly. β-diversity is a measure of the variation in species composition across space and time. The drivers of variability in β-diversity are thus responsible for dictating the trajectory of assembling communities. With data from multiscale surveys conducted in three marine intertidal sandflats, we aimed to determine the relative importance and interdependencies of biotic engineering, environment and spatial distances for β-diversity. In each sandflat, macrofauna and environmental properties were assessed in 400 samples collected at different spatial distances over 300 000 m 2 . The role of environmental variability in driving patterns in β-diversity and species turnover was dependent on the abundances of ecosystem engineers and spatial connectivity among seascapes as shown by the variance explained by the interaction terms in the variance partitioning. Our results highlight the interdependence between space, environment and species interactions in driving community assembly. Given that most models aiming to explain β-diversity variation only consider abiotic factors, our findings call for the incorporation of biotic interactions into these models and we argue that this is essential for understanding resilience to environmental change. The degree of interdependence between environment, space and biotic interactions in driving ecological assembly is pivotal to our understanding of how biodiversity will respond to predicted changes in habitats, environment and key species distributions.

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Predicting coexistence in experimental ecological communities

Cited by 58 publications

References 37 publications

Tractable models of ecological assembly

Tractable models of ecological assembly

Untangling the complexity of priority effects in multispecies communities

Coupled effects of environment, space and ecological engineering on seafloor beta‐diversity

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