Abstract
Defining the species pool of a community is crucial for many types of ecological analyses, providing a foundation to metacommunity, null modelling or dark diversity frameworks. It is a challenge to derive the species pool empirically from large and heterogeneous databases. Here, we propose a method to define a site‐specific species pool (SSSP), i.e. the probabilistic set of species that may co‐occur with the species of a target community. Using large databases with geo‐referenced records that comprise full plant community surveys, our approach characterizes each site by its own species pool without requiring a pre‐defined habitat classification. We calculate the probabilities of each species in the database to occur in the target community using Beals’ index of sociological favourability, and then build sample‐based rarefaction curves from neighbouring records with similar species composition to estimate the asymptotic species pool size. A corresponding number of species is then selected among the species having the highest occurrence probability, thus defining both size and composition of the species pool. We tested the robustness of this approach by comparing SSSPs obtained with different spatial extents and dissimilarity thresholds, fitting different models to the rarefaction curves, and comparing the results obtained when using Beals co‐occurrence probabilities or presence/absence data. As an example application, we calculated the SSSPs for all calcareous grassland records in the German Vegetation Reference Database, and show how our method could be used to 1) produce grain‐dependent estimations of species richness across plots, 2) derive scalable maps of species richness and 3) define the full list of species composing the SSSP of each target site. By deriving the species pool exclusively from community characteristics, the SSSP framework presented here provides a robust approach to bridge biodiversity estimations across spatial scales.
Highlights
The difference in Akaike information criterion (AIC) values becomes more prominent in plots with large species pools, which are those with high AIC values (Supplementary material Appendix 1 Fig. A2)
This study proposes an approach to empirically define site-specific species pools on large databases containing community data
Using Beals occurrence probabilities allows to assess the composition of the species pool if the pool size is larger than the total number of species sampled in the surrounding of a target plot, which is often the case, for instance, when using Chao’s (1984) nonparametric richness estimator
Summary
The concept of a species pool, i.e. the set of all species available to colonize a focal site (Eriksson 1993, Srivastava 1999), plays a key role in community ecology theory (Cornell and Harrison 2014, Jiménez-Alfaro et al 2018), restoration applications (Ladouceur et al 2018), and the integration of large- and small-scale processes. By focusing on the species of the pool that do not occur in a target community, dark diversity has been used as a measure of community completeness in basic (Pärtel et al 2013) and applied (Suding 2011) ecology
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