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Dryad

Data from: Estimating dark diversity and species pools: an empirical assessment of two methods

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Jul 13, 2016 version files 179.33 KB

Abstract

1. Species absent from a community, but with the potential to establish (dark diversity) are an important, yet rarely considered, component of habitat specific species pools. However, quantifying this component remains a challenge as dark diversity cannot be observed directly, and must be estimated. Here, we empirically test whether species ecological requirements or species co-occurrences provide accurate estimates of dark diversity. 2. We used two spatially nested independent datasets, one comprising 3033 samples of coastal grassland vegetation plots of 4m2 and 200m2 from Scotland, UK and another comprising 780 samples of forest vegetation plots of 30m2 and 500m2 from Switzerland. Dark diversity for each of the smaller scaled plots was estimated through investigating the degree of 1) similarity in ecological requirements (measured as Ellenberg values) and 2) co-occurrence likelihood of all species observed. Estimates were validated through comparing estimates with observed species at the larger spatial scale. Estimates were further validated using observations from all larger-scale plots surrounding a focal small-scale plot within a 2km (Scottish grassland) and 10km (Swiss forest) radius. 3. The co-occurrence method was shown to be more accurate and resulted in far fewer negative mismatches (i.e. species observed but not predicted), and higher proportions of observed and predicted species, relative to the Ellenberg approach. Of the species observed in the large-scale samples, 18% were estimated as part of the smaller-scale dark diversity via the co-occurrence approach relative to 8% for the Ellenberg method for both the Scottish and Swiss data respectively. These values increased to 67% & 60% and 32% & 35% respectively across all observations within a 2km (Scottish grasslands) and 10km (Swiss forests) radius. 4. The study demonstrates that dark diversity for a community can be successfully estimated using readily available data, through exploring species co-occurrence patterns. This work substantiates that; habitat-specific species pools, can be accurately quantified and should prove valuable for understanding underlying community processes and improving our knowledge of the mechanisms governing species co-existence.