DNA barcode analyses improve accuracy in fungal species distribution models

Abstract

Abstract Species distribution models based on environmental predictors are useful to explain a species geographic range. For many groups of organisms, including fungi, the increase in occurrence data sets has generalized their use. However, fungal species are not always easy to distinguish, and taxonomy of many groups is not completely settled. This study explores the effect of taxonomic uncertainty in databases used for modeling fungal distributions. We analyze distribution models for three morphospecies from the corticioid genus Xylodon (Hymenochaetales, Basidiomycota), comparing models based on species names on vouchers specimens with models derived from species identified by DNA barcode. Differences in the contribution of predictors driving the distribution of each modeled taxon and the extent of their ranges were studied. Records under Xylodon paradoxus, X. flaviporus, and X. raduloides were obtained from fungarium collections and GenBank repository. Two grouping criteria were used: (a) specimens were grouped by their collection or sequence voucher names and (b) specimens were grouped following molecular identification using ITS sequences through barcoding gap species recognition (BGSR). Climatic, geographic, and biotic variables were used to predict the potential distribution of each taxon through MaxEnt algorithm. From the three morphospecies selected according to voucher names, up to 19 species candidates were detected using BGSR. Climatic variables were the most important predictors in distribution models made from names on voucher specimens, but their importance decreased when BGSR was applied. In general, the extent of species distributions was more restricted for taxa under BGSR. Our results show that taxonomic uncertainty has a strong effect in Xylodon species distribution models. Misleading results can be obtained when cryptic species or identification errors mask the actual diversity of the presence records. Preserved specimens in natural history collections offer the possibility to assess whether the species name on labels matches the current species recognition criteria.

Keywords

• ITS
• MaxEnt
• natural history collections
• species range
• species recognition
• Xylodon