DOI: 10.5061/dryad.qrfj6q5b3
title: Spatial and taxonomic biases in bat records: Drivers and conservation implications in a megadiverse country

description: Biases in data availability have serious consequences on scientific
 inferences that can be derived. The potential consequences of these biases
 could be more detrimental in the less-studied megadiverse regions, often
 characterized by high biodiversity and serious risks of human threats, as
 conservation and management actions could be misdirected. Here, focusing
 on 134 bat species in Mexico, we analyze spatial and taxonomic biases and
 their drivers in occurrence data; and identify priority areas for further
 data collection which are currently under-sampled or at future
 environmental risk. We collated a comprehensive database of 26,192
 presence-only bat records in Mexico to characterize taxonomic and spatial
 biases and relate them to species’ characteristics (range size and
 foraging behavior). Next, we examined variables related to accessibility,
 species richness and security to explain the spatial patterns in
 occurrence records. Finally, we compared the spatial distributions of
 existing data and future threats to these species to highlight those
 regions that are likely to experience an increased level of threats but
 are currently under-surveyed. We found taxonomic biases, where species
 with wider geographical ranges and narrow-space foragers (species easily
 captured with traditional methods) had more occurrence data. There was a
 significant oversampling towards tropical regions, and the presence and
 number of records was positively associated with areas of high topographic
 heterogeneity, road density, urban and protected areas, and negatively
 associated with areas which were predicted to have future increases in
 temperature and precipitation. Sampling efforts for Mexican bats appear to
 have focused disproportionately on easily-captured species, tropical
 regions, areas of high species richness and security; leading to
 under-sampling in areas of high future threats. These biases could
 substantially influence the assessment of current status of, and future
 anthropogenic impacts on, this diverse species group in a tropical
 megadiverse country.
description: We generated a presence-only database for all bat species that
 occur in Mexico using information compiled from the National Commission
 for Knowledge and Use of Biodiversity (CONABIO), Global Biodiversity
 Information Facility (GBIF), and Mammal Networked Information System
 (MaNIS). We searched for published literature in English and Spanish using
 Web of Science, Google Scholar, and Scielo to obtain additional
 information on Mexican bat occurrence data using the search words
 “chiroptera”, “M*xico”, “bat”, “bats”, “record*”, “occurrence”,
 “registro*”, “quiropter*”, “murci*lago*”. Additionally, we requested
 unpublished material from Mexican researchers. To
 determine range size, we counted the total number of grid cells in which
 the species is expected to occur using species range maps (IUCN, 2015). We
 used foraging space as a surrogate for sampling method. Three explanatory
 variables represent species richness: (i) bat species richness, that is,
 the number of all species that are expected to occur in each grid cell,
 estimated by overlaying the IUCN range maps of all the bat species
 distributed in Mexico (IUCN, 2015) and counting how many coincide in each
 cell, (ii) elevational heterogeneity was estimated as the difference in
 elevation within each grid cell  based on a digital elevation model at 60
 m resolution (INEGI, 2017) and (iii) the coverage of federal protected
 areas estimated as the percentage of the protected areas polygons within
 each grid cell (CONANP, 2016). Other three variables represent the
 accessibility of each cell: (i) road density estimated as the sum within
 each grid cell of roads length in km (INEGI, 2015), (ii) percentage of
 urban areas estimated as the percentage of the city polygons within each
 grid cell (INEGI, 2014) and (iii) human population density by municipality
 and estimated by assigning the value or average of values given by the map
 polygons that coincide in each cell (CONABIO, 2010). The remaining
 variable measures the security level of each grid cell, using reports of
 the Centro Nacional de Información, which aggregates information on
 average rate of homicides between 1996-2000 per state (SEGOB, 2016). All
 variables had a correlation less than 0.6 or -0.6.
description: Explanation on the metadata is contained within the excel
 file