Interpreting beta‐diversity components over time to conserve metacommunities in highly dynamic ecosystems. Issue 6 (29th May 2017)

• Record Type: Journal Article
• Authors: Ruhí, Albert; Datry, Thibault; Sabo, John L.
• Is Part Of: Conservation biology. Volume 31:Issue 6(2017:Dec.)
• Page Start: 1459
• Page End: 1468
• Publication Date: 2017-05-29
• Subjects: beta‐diversity partitioning -- conservation planning -- dispersal -- metacommunity ecology -- source–sink dynamics -- statistics -- dinámica de fuente‐sumidero -- dispersión -- ecología de metacomunidades -- estadística -- partición de la beta diversidad -- planificación de la conservación; Conservation biology -- Periodicals; 333.9516
• DOI: 10.1111/cobi.12906
• Languages: English
• ISSNs: 0888-8892

Abstract

Abstract: The concept of metacommunity (i.e., a set of local communities linked by dispersal) has gained great popularity among community ecologists. However, metacommunity research mostly addresses questions on spatial patterns of biodiversity at the regional scale, whereas conservation planning requires quantifying temporal variation in those metacommunities and the contributions that individual (local) sites make to regional dynamics. We propose that recent advances in diversity‐partitioning methods may allow for a better understanding of metacommunity dynamics and the identification of keystone sites. We used time series of the 2 components of beta diversity (richness and replacement) and the contributions of local sites to these components to examine which sites controlled source‐sink dynamics in a highly dynamic model system (an intermittent river). The relative importance of the richness and replacement components of beta diversity fluctuated over time, and sample aggregation led to underestimation of beta diversity by up to 35%. Our literature review revealed that research on intermittent rivers would benefit greatly from examination of beta‐diversity components over time. Adequately appraising spatiotemporal variability in community composition and identifying sites that are pivotal for maintaining biodiversity at the landscape scale are key needs for conservation prioritization and planning. Thus, our framework may be used to guide conservation actions in highly dynamic ecosystems when time‐series data describing biodiversity across sites connected by dispersal are available.