Using null models to disentangle variation in community dissimilarity from variation in α‐diversity

By Jonathan M. Chase, Nathan J B Kraft, Kevin G. Smith, M. Vellend, B. Inouye. 2011

866 citations recorded by the source corpus.

β-diversity represents the compositional variation among communities from site-to-site, linking local (α-diversity) and regional (γ-diversity). Researchers often desire to compare values of β-diversity across localities or experimental treatments, and to use this comparison to infer possible mechanisms of community assembly. However, the majority of metrics used to estimate β-diversity, including most dissimilarity metrics (e.g., Jaccard's and Sorenson's dissimilarity index), can vary simply because of changes in the other two diversity components (α or γ-diversity). Here, we overview the utility of taking a null model approach that allows one to discern whether variation in the measured dissimilarity among communities results more from changes in the underlying structure by which communities vary, or instead simply due to difference in α-diversity among localities or experimental treatments. We illustrate one particular approach, originally developed by Raup and Crick (1979) in the paleontological literature, which creates a re-scaled probability metric ranging from −1 to 1, indicating whether local communities are more dissimilar (approaching 1), as dissimilar (approaching 0), or less dissimilar (approaching −1), than expected by random chance. The value of this metric provides some indication of the possible underlying mechanisms of community assembly, in particular the degree to which deterministic processes create communities that deviate from those based on stochastic (null) expectations. We demonstrate the utility of this metric when compared to analyses of Jaccard's dissimilarity index with case studies from disparate empirical systems (coral reefs and freshwater ponds) that differ in the degree to which disturbance altered α-diversity, as well as the selectivity by which disturbance acted on members of the community.

Subfields: Ecology, Nature and Landscape Conservation, Paleontology

View Paper: https://www.semanticscholar.org/paper/a4c3b3b6c63c7fffe8cc3cd15d29f4d3da3a93ec

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In Ecology

In Nature and Landscape Conservation

In Paleontology

Citations in the corpus

A general framework for quantitatively assessing ecological stochasticity (2019, methodology): “In null model PF, the probabilities of taxa occurrence are proportional to the observed occurrence frequencies, and taxon richness in each sample is fixed as observed (19).”
Stochastic and deterministic assembly processes in subsurface microbial communities (2012, background): “Doing so characterizes how the relative influences of stochastic and deterministic processes change with environmental conditions (Chase and Myers, 2011; Chase et al., 2011).”
What drives study‐dependent differences in distance–decay relationships of microbial communities? (2021, background): “The Raup– Crick index is less influenced by concurrent changes in species richness between communities, and as such, is a purer reflection of shifts in β- diversity (Chase et al., 2011).”
Forest disturbance accelerates thermophilization of understory plant communities (2015, background): “RC scores adjust for the positive relationship between alpha diversity and beta diversity by generating a null distribution of pairwise dissimilarities based on random draws from the regional species pool (Chase et al. 2011).”
Quantifying community assembly processes and identifying features that impose them (2013, methodology): “95 as significant departures from the degree of turnover expected when Drift acts alone (Chase et al., 2011).”
Undersampling and the measurement of beta diversity (2013, result): “Crucially to our topic, results are compared with null model simulations (related to rarefaction), which ensure that inference is robust to undersampling even if absolute measures of diversity components may be biased (Crist &Veech 2006; Chase et al. 2011).”
Estimating dark diversity and species pools: an empirical assessment of two methods (2016, background): “Most often, studies define the species pool subjectively or arbitrarily, using regional or local species inventories (Williams 1947; White & Hurlbert 2010), or by pooling local-scale observations (Belote, Sanders & Jones 2009; Chase et al. 2011; Kraft 2011).”
Estimating and mapping ecological processes influencing microbial community assembly (2015, methodology): “Vertically structured features were obtained from Bjornstad et al. (2009).”
Community assembly along a soil depth gradient: contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland (2012, neutral): “An effect size (ES) was calculated based on the probability for the observed value to be lower than expected by chance (i.e. the quantile of the null distribution in which the observed value is found; Kelt, Taper & Meserve 1995; Chase et al. 2011; see calculations in Appendix S1).”