Abstract

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 toward 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.

Highlights

  • A rapid accumulation of species occurrence data over the past several decades have enabled researchers to carry out large-scale and multitaxa studies to develop methods for inferring present and future species distributions (Soberón & Peterson, 2004)

  • The potential consequences of using spatially biased data could be more detrimental in less-studied regions, often characterized by high biodiversity and serious risks of human threats, as conservation and management actions could be misdirected (Bini, Diniz-Filho, Rangel, Bastos, & Pinto, 2006)

  • The presence of records was negatively associated with changes in temperature and precipitation (Figure 5), indicating that records are scarce in areas with predicted increases in temperature and precipitation

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Summary

Introduction

A rapid accumulation of species occurrence data over the past several decades have enabled researchers to carry out large-scale and multitaxa studies to develop methods for inferring present and future species distributions (Soberón & Peterson, 2004). Spatial data biases can result in an uneven coverage of environmental conditions, such as biomes or climatic zones, where a species could occur (Kadmon, Farber, & Danin, 2004; Loiselle et al, 2007) Such biases can lead to deriving erroneous associations of species with environmental variables (Phillips et al, 2009; Yang, Ma, & Kreft, 2013), inferring incorrect species' absences (Bystriakova, Peregrym, Erkens, Bezsmertna, & Schneider, 2012), misidentifying areas that have been sampled intensively as species-rich (Petřík, Pergl, & Wild, 2010) and building spurious ecological hypothesis and concepts (Hortal et al, 2015). If threatened species are less surveyed, the uneven distribution of data over species can lead to the underestimation of species extinction risk in the taxon (Bland, Collen, Orme, & Bielby, 2015)

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