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Inferring community assembly processes from macroscopic patterns using dynamic eco-evolutionary models and Approximate Bayesian Computation (ABC)


Pontarp, Mikael; Brännström, Åke; Petchey, Owen L (2019). Inferring community assembly processes from macroscopic patterns using dynamic eco-evolutionary models and Approximate Bayesian Computation (ABC). Methods in Ecology and Evolution, 10(4):450-460.

Abstract

Statistical techniques exist for inferring community assembly processes from community patterns. Habitat filtering, competition, and biogeographical effects have, for example, been inferred from signals in phenotypic and phylogenetic data. The usefulness of current inference techniques is, however, debated as a mechanistic and causal link between process and pattern is often lacking, and evolutionary processes and trophic interactions are ignored.
Here, we revisit the current knowledge on community assembly across scales and, in line with several reviews that have outlined challenges associated with current inference techniques, we identify a discrepancy between the current paradigm of eco‐evolutionary community assembly and current inference techniques that focus mainly on competition and habitat filtering. We argue that trait‐based dynamic eco‐evolutionary models in combination with recently developed model fitting and model evaluation techniques can provide avenues for more accurate, reliable, and inclusive inference. To exemplify, we implement a trait‐based, spatially explicit eco‐evolutionary model and discuss steps of model modification, fitting, and evaluation as an iterative approach enabling inference from diverse data sources.
Through a case study on inference of prey and predator niche width in an eco‐evolutionary context, we demonstrate how inclusive and mechanistic approaches—eco‐evolutionary modelling and Approximate Bayesian Computation (ABC)—can enable inference of assembly processes that have been largely neglected by traditional techniques despite the ubiquity of such processes.
Much literature points to the limitations of current inference techniques, but concrete solutions to such limitations are few. Many of the challenges associated with novel inference techniques are, however, already to some extent resolved in other fields and thus ready to be put into action in a more formal way for inferring processes of community assembly from signals in various data sources.

Abstract

Statistical techniques exist for inferring community assembly processes from community patterns. Habitat filtering, competition, and biogeographical effects have, for example, been inferred from signals in phenotypic and phylogenetic data. The usefulness of current inference techniques is, however, debated as a mechanistic and causal link between process and pattern is often lacking, and evolutionary processes and trophic interactions are ignored.
Here, we revisit the current knowledge on community assembly across scales and, in line with several reviews that have outlined challenges associated with current inference techniques, we identify a discrepancy between the current paradigm of eco‐evolutionary community assembly and current inference techniques that focus mainly on competition and habitat filtering. We argue that trait‐based dynamic eco‐evolutionary models in combination with recently developed model fitting and model evaluation techniques can provide avenues for more accurate, reliable, and inclusive inference. To exemplify, we implement a trait‐based, spatially explicit eco‐evolutionary model and discuss steps of model modification, fitting, and evaluation as an iterative approach enabling inference from diverse data sources.
Through a case study on inference of prey and predator niche width in an eco‐evolutionary context, we demonstrate how inclusive and mechanistic approaches—eco‐evolutionary modelling and Approximate Bayesian Computation (ABC)—can enable inference of assembly processes that have been largely neglected by traditional techniques despite the ubiquity of such processes.
Much literature points to the limitations of current inference techniques, but concrete solutions to such limitations are few. Many of the challenges associated with novel inference techniques are, however, already to some extent resolved in other fields and thus ready to be put into action in a more formal way for inferring processes of community assembly from signals in various data sources.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Uncontrolled Keywords:Ecological Modelling, Ecology, Evolution, Behavior and Systematics
Language:English
Date:1 April 2019
Deposited On:14 Mar 2019 11:13
Last Modified:03 Apr 2019 01:04
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:2041-210X
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1111/2041-210x.13129

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