Climate models, niche models and the conservation of owls in Brazil

Abstract

The effective conservation and management of species or groups of them requires basic knowledge about their distribution, which is problematic in the case of highly mobile, ecologically diverse, little studied and nocturnal species such as owls (Strigidae). In addition, producing detailed distributions to megabiodiverse and continental-sized countries such as Brazil is difficult, especially when the base information is dispersed and non-standardized. Using Species Distribution Modeling (SDM) based on a maximum entropy approach, we evaluated the potential distribution of 21 species and 21 subspecies of owls recorded in Brazil. For this, we first evaluated the information on minimum and maximum temperatures, in addition to precipitation, provided by the Brazilian network of meteorological stations, comparing conventional and automatic stations. We found that both data sets have problems, but the evidence evaluated suggests that conventional stations provide slightly more reliable precipitation data, while automatic gauges are more consistent with regards to temperature information. Second, this information was used to evaluate the performance of the bioclimatic bases currently available. We found a better correspondence between the climate data measured in the field and the information provided by the Tropical Rainfall Measuring Mission (TRMM 3B43 v7), in the case of precipitation, and the surfaces provided by the National Oceanic and Atmospheric Administration (NOAA), in the case of temperatures, sources not traditionally used for SDMs. We gauge-calibrated these surfaces using the climatic information obtained in the field, through machine-learning algorithms (gradient boosting and random forest), and used these improved surfaces to create a base of bioclimatic variables we called BrazilClim. Third, we collected and filtered occurrence data for the Strigidae in Brazil, and generated particular SDMs for each species and subspecies, evaluating the similarity of niches between conspecific subspecies. Finally, we created maps of species richness, and contrasted this information with the areas of integral protection in Brazil. With 81% of the Brazilian species recorded, both the Atlantic Forest and the Cerrado have the highest richness, followed by the Amazon (67%), Pampa (62%), Caatinga (57%) and Pantanal (48%). However, the comparison of recorded and predicted richness suggests incomplete inventories, especially in the Caatinga and Pantanal. On the other hand, the subspecies presented marked divergences of niches, suggesting that the species richness of Strigidae is underestimated in Brazil. The Cerrado and the Atlantic Forest are the most threatened biomes, with relatively small and sparse preservation areas. Thus, our study is an urgent call to explore the diversification of owl strains in Brazil in order to improve efforts related to biodiversity conservation.