How sensitive are climatic niche inferences to distribution data sampling? A comparison of Biota of North America Program (BONAP) and Global Biodiversity Information Facility (GBIF) datasets

Climatic niche modeling is widely used in modern macroecology and evolutionary biology to model species' distributions and ecological niches. Frequently, Global Biodiversity Information Facility (GBIF) distribution data are used as raw data for such models. Unfortunately, the accuracy of resulting niche estimates is difficult to assess, and GBIF users continue to call for a better understanding of GBIF data precision and uncertainty. Our research evaluates how GBIF data perform in comparison with curated, county-level species distributions from the Biota of North America Program (BONAP; www.bonap.org ) to estimate the climatic niche for Carex (Cyperaceae), one of the largest angiosperm genera in the temperate zone. In particular, we investigate the complementary strengths and weaknesses of the two datasets in the context of climatic niche estimation, namely (1) the incomplete sampling coverage of species distributions of GBIF data, and (2) elevation bias in climatic niche estimates resulting from the coarse resolution of the BONAP distribution dataset. To do so, we quantified climatic niches for 388 North American Carex species and calculated the distance of GBIF and BONAP climatic niche estimates in principal component space. We found little to no relationship between differences in climatic niche estimates and sampling coverage metrics, suggesting that sparse sampling coverage of GBIF data may have negligible average effects on mean climate estimates at the species level. However, elevation had a significant effect on differences in niche estimates, suggesting that using county-level distribution data-in our study, represented by BONAP-may introduce a bias in estimated climatic niche. To investigate if any such bias is phylogenetically structured, we examined the relationship between GBIF and BONAP climatic niche tip states using Phylogenetic Generalized Least Squares Regression (PGLS), estimating phylogenetic signal in the residuals of the regression by estimating Pagel's lambda simultaneously with other regression parameters. Estimates were tightly correlated, with little to no phylogenetic signal-low lambda-in the regression residuals, suggesting that any potential bias is more or less independent of phylogeny for the sedges of North America. Based on our results, we recommend a hybrid GBIF and BONAP data approach for the best understanding of species' climatic niche, relying on BONAP to fill in distributions primarily where collection records may be sparse. Our findings provide needed context and perspective on the implications of alternative distribution datasets for climatic niche estimation, especially in a macroevolutionary context.