DNA barcode analyses improve accuracy in fungal species distribution models

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