Accounting for temporal change in multiple biodiversity patterns improves the inference of metacommunity processes. Issue 6 (3rd May 2022)

• Record Type: Journal Article
• Authors: Guzman, Laura Melissa; Thompson, Patrick L.; Viana, Duarte S.; Vanschoenwinkel, Bram; Horváth, Zsófia; Ptacnik, Robert; Jeliazkov, Alienor; Gascón, Stéphanie; Lemmens, Pieter; Anton‐Pardo, Maria; Langenheder, Silke; De Meester, Luc; Chase, Jonathan M.
• Is Part Of: Ecology. Volume 103:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-03
• Subjects: metacommunity ecology -- random forests -- simulation study -- spatiotemporal dynamics -- summary statistics -- variation partitioning; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecy.3683
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract: In metacommunity ecology, a major focus has been on combining observational and analytical approaches to identify the role of critical assembly processes, such as dispersal limitation and environmental filtering, but this work has largely ignored temporal community dynamics. Here, we develop a "virtual ecologist" approach to evaluate assembly processes by simulating metacommunities varying in three main processes: density‐independent responses to abiotic conditions, density‐dependent biotic interactions, and dispersal. We then calculate a number of commonly used summary statistics of community structure in space and time and use random forests to evaluate their utility for inferring the strength of these three processes. We find that (i) both spatial and temporal data are necessary to disentangle metacommunity processes based on the summary statistics we test, and including statistics that are measured through time increases the explanatory power of random forests by up to 59% compared to cases where only spatial variation is considered; (ii) the three studied processes can be distinguished with different descriptors; and (iii) each summary statistic is differently sensitive to temporal and spatial sampling effort. Including repeated observations of metacommunities over time was essential for inferring the metacommunity processes, particularly dispersal. Some of the most useful statistics include the coefficient of variation of species abundances through time and metrics that incorporate variation in the relative abundances (evenness) of species. We conclude that a combination of methods and summary statistics is probably necessary to understand the processes that underlie metacommunity assembly through space and time, but we recognize that these results will be modified when other processes or summary statistics are used.