Dispersal–niche continuum index: a new quantitative metric for assessing the relative importance of dispersal versus niche processes in community assembly
Metadatos
Mostrar el registro completo del ítemEditorial
Wiley
Materia
Assembly processes Determinism Dispersal
Fecha
2021Referencia bibliográfica
Vilmi et al. Ecography 44: 370-379, 2021 [http://dx.doi.org/10.1111/ecog.05356]
Patrocinador
National Natural Science Foundation of China (91851117); Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (2019QZKK0503); Chinese Academy of Sciences Key Research Program of Frontier Sciences (QYZDB-SSW-DQC043); National Natural Science Foundation of China (41871048, 41571058); Chinese Academy of Sciences President’s International Fellowship Initiative (2018PS0007); Project FRESHABIT LIFE IP (LIFE14/IPE/FI/023)Resumen
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 organis-
mal groups with different trophic level and dispersal potential. Based on the robustness
tests, the DNCI discriminated the respective contribution of niche and dispersal pro-
cesses 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 pro-
cess 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.





