Range Maps and Occurrence Data Support Conflicting Biogeographical Hypotheses About Australian Amphibians

ABSTRACT Aim Understanding patterns and drivers of biodiversity is a primary focus of ecological research. While all biodiversity data contain biases, most recent studies have used range maps derived from experts or species distribution models to test for controls on observed species richness patterns. Less attention has been given to other data sources, such as occurrence records from museum specimens or community science initiatives, which are heavily biased by sampling effort. However, this problem can be resolved using richness extrapolators to estimate ‘true’ richness from counts of observations, which allows for undetected or undescribed species to be considered. Location Australia. Time Period Recent. Major Taxa Studied Amphibians. Methods We apply four richness extrapolation methods while evaluating their performance. We analyse occurrence records and compare how richness estimated from range maps, museum specimens and community science affects support for biodiversity hypotheses. These are compared using spatial autoregressive models at two resolutions. We also include sensitivity analyses using zero‐richness cells and human population density. Results The geometric series index (GSI) distribution performed best on the museum occurrence datasets and estimated higher richness than using range maps. Fitting the geometric Weibull (GW) distribution yielded similar performance. We find that occurrence‐ and range map‐based models show support for the energy and habitat heterogeneity hypotheses, although models explain much less variation in occurrence estimates. Including zero‐richness cells sometimes improved model performance. However, extrapolating richness from community science occurrences failed to detect missing richness or recover the latitudinal diversity gradient present in the other two datasets. Main Conclusions Richness estimated from different data sources may support different biodiversity hypotheses, and these differences may be influenced by underlying biases. We conclude that occurrence records can be informative for testing biodiversity hypotheses when biases are appropriately considered, and that future studies should take advantage of multiple data types when available.