Data from: Opportunistic data reveal widespread species turnover in Enallagma damselflies at biogeographical scales
Bried, Jason T.; Siepielski, Adam M. (2017), Data from: Opportunistic data reveal widespread species turnover in Enallagma damselflies at biogeographical scales, Dryad, Dataset, https://doi.org/10.5061/dryad.m2187
An information tradeoff exists between systematic presence/absence surveys and purely opportunistic (presence-only) records for investigating the geography of community structure. Opportunistic species occurrence data may be of relatively limited quality, but typically involves numerous observations and species. Given the quality-quantity tradeoff, what can opportunistic data reveal about spatial patterns in community structure? Here we explore opportunistic data in describing geographic patterns of species composition, using over 4,600 occurrence records of Enallagma damselflies in the United States. We tested phylogenetic scale (genus level, Enallagma major clades, Enallagma subclades) and spatial extent (U.S. vs. watershed regions), hypothesizing that nonrandom structure is more likely at larger spatial extents. We also used three sets of systematic presence/absence surveys as a benchmark for validating opportunistic presence-only records. Null model analysis of matrix coherence and species replacements showed many cases of nonrandom structure and widespread species turnover. This outcome was repeated across spatial and environmental gradients and community composition scenarios. Turnover dominated across the U.S. and two watersheds spanning biogeographic boundaries, but random assemblages were prevalent in a third watershed with limited longitudinal extent. Turnover also pervaded each level of phylogeny. Opportunistic presence-only datasets showed identical patterns as systematic presence/absence datasets. These results indicate that extensive opportunistic data can be used to detect species turnover, especially at geographic scales where range margins are crossed.
National Science Foundation, Award: 1620046