Aim: Field surveys are necessary to overcome Wallacean shortfalls. The task is even more important when human pressure on tropical – megadiverse – ecosystems is considered. However, due to financial constraints, spatial and temporal prioritization is required. Here we used the concept of environmental complementarity to identify non-surveyed regions for bats that are environmentally different from other already surveyed regions. We highlighted regions in Brazil where field inventories could be conducted to locate new occurrences or even new bat species.

Location: Brazil.

Methods: We based our analysis on environmental characterization aiming to identify dissimilar regions to those already sampled for bats in Brazil. We used 21 environmental variables to characterize 1,531 unique localities where bats occur. Then, we applied the parameters of a generalised linear model (GLM) to extrapolate the expected values of the environmental variables for the entire country. We compared the predicted values of localities with newly described bat species occurrence against the values for other bat species.

Results: We found that sites from which recently discovered species were described are environmentally distinct from the sites where previously described species occur. Therefore, new occurrences and even new species could be found in regions that are environmentally dissimilar from those already surveyed. By crossing the model with a human footprint map, we defined temporal priorities for field inventories. Regions such as the Northern Cerrado and Western Caatinga should be surveyed first. Similar approaches could be undertaken for other biological groups or regions, allowing the identification of spatial congruence and the development of a comprehensive national programme for biological field inventories. 

Main conclusion: Newly described species occurred in environments dissimilar to those previously identified, showing that environmental complementarity analysis is a valid approach to define priority regions for new bat inventories.

We compiled a dataset on Brazilian bat species occurrences based on an extensive literature revision, scientific collections belonging to our institutions and from the Global Biodiversity Facility - GBIF. The list has a total of 181 Brazilian bat species, including the species cited by Nogueira et al. (2018), plus four additional species recently described: Eumops chimaera Gregorin, Moras, Acosta, Vasconcellos, Poma, Santos & Paca 2016, Histiotus diaphanopterus Feijó, Rocha & Althoff 2015, Lonchophylla inexpectata Moratelli & Dias 2015, and Pteronotus alitonus Pavan, Bobrowieck & Percequillo 2018. The database was primarily organized by LMSA and complemented with records provided by MRJP, MZ and from online databases (Global Information Facility – GBIF). We used the package rangeBuilder in R to apply a spatial filter to eliminate all duplicated coordinates and keeping just one-point location.

The dataset is available in a csv format composed by four columns: species (scientific name), source, long, and lat. The source field can have the values “lmsa” (literature and field data compiled by Ludmilla M.S. Aguiar), “mjrp” (field data compiled by Maria João Ramos Pereira), “mz” (literature and field data compiled by Marlon Zortéa), and “gbif” (data obtained from the Global Biodiversity Information Facility – GBIF) (https://doi.org/10.15468/dl.9rg5ue). Geographical coordinates are represented in decimal degrees, WGS84 datum.