Dispersal–niche continuum index: a new quantitative metric for assessing the relative importance of dispersal versus niche processes in community assembly. Issue 3 (21st December 2020)

• Record Type: Journal Article
• Authors: Vilmi, Annika; Gibert, Corentin; Escarguel, Gilles; Happonen, Konsta; Heino, Jani; Jamoneau, Aurélien; Passy, Sophia I.; Picazo, Félix; Soininen, Janne; Tison‐Rosebery, Juliette; Wang, Jianjun
• Is Part Of: Ecography. Volume 44:Issue 3(2021)
• Page Start: 370
• Page End: 379
• Publication Date: 2020-12-21
• Subjects: assembly processes -- determinism -- dispersal -- environmental filtering -- niche processes -- null models -- PER-SIMPER -- stochasticity; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.05356
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : Patterns in community composition are scale‐dependent and generally difficult to distinguish. Therefore, quantifying the main assembly processes in various systems and across different datasets has remained challenging. Building on the PER‐SIMPER method, we propose a new metric, the dispersal–niche continuum index (DNCI), which estimates whether dispersal or niche processes dominate community assembly and facilitates the comparisons of processes among datasets. The DNCI was tested for robustness using simulations and applied to observational datasets comprising organismal groups with different trophic level and dispersal potential. Based on the robustness tests, the DNCI discriminated the respective contribution of niche and dispersal processes in pairwise comparisons of site groups with less than 40% and 30% differences in their taxa and site numbers, respectively. In the observational datasets, the DNCI suggested that dispersal rather than niche assembly was the dominant assembly process which, however, varied in intensity among organismal groups and study contexts, including spatial scale and ecosystem types. The proposed DNCI measures the relative strength of community assembly processes in a way that is simple, easily quantifiable and comparable across datasets. We discuss the strengths and weaknesses of the DNCI and provide perspectives for future research.