Data from: Assessing species richness trends: declines of bees and bumblebees in the Netherlands since 1945.
DOI10.5281/zenodo.4069102Zenodo4069102MaRDI QIDQ6708821
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 27 September 2020
Estimating and predicting temporal trends in species richness is of general importance, but notably difficult because detection probabilities of species are imperfect and many datasets were collected in an opportunistic manner. We need to improve our capabilities to assess richness trends using datasets collected in unstandardized procedures with potential collection bias. Two methods are proposed and applied to estimate richness change, which both incorporate models for sampling effects and detection probability: (1) non-linear species accumulation curves with an error variance model and (2) Pradel capture-recapture models. The methods are used to assess nationwide temporal trends (1945-2018) in the species richness of wild bees in the Netherlands. Previously, a decelerating decline in wild bee species richness was inferred for part of this dataset. Among the species accumulation curves, those with non-constant changes in species richness are preferred. However, when analysing data subsets, constant changes became selected for non-Bombus bees (for samples in collections) and bumblebees (for spatial grid cells sampled in three periods). Smaller richness declines are predicted for non-Bombus bees than bumblebees. However, when relative losses are calculated from confidence intervals limits, they overlap and touch zero loss. Capture-recapture analysis applied to species encounter histories infers a constant colonization rate per year and constant local species survival for bumblebees and other bees. This approach predicts a 6% reduction in non-Bombus species richness from 1945 to 2018 and a significant 19% reduction for bumblebees. Statistical modelling to detect species richness time trends should be systematically complemented with model checking and simulations to interpret the results. Data inspection, assessing model selection bias and comparisons of trends in data subsets were essential model checking strategies in this analysis. Opportunistic data will not satisfy the assumptions of most models and this should be kept in mind throughout.
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