INTRODUCTION
Predicting when and where the species are located gained relevance under the current threats to biodiversity characterizing the Anthropocene. However, despite the means currently available, producing reliable predictions on species distributions is not an easy task, especially in highly biodiverse regions, due to inadequate funding levels (Boakes et al., 2010; Gallo-Cajiao et al., 2018; Waldron et al., 2013), and the bias, low quality and insufficient availability of primary occurrence data (Cayuela et al., 2009; Hortal et al., 2015; Loiselle et al., 2008). Therefore, efforts to generate, gather and standardizing species distribution data, useful to predict the effects of threats to the environment and determining critical areas are crucial in conservation management nowadays (Handley et al., 2021; Ramos et al., 2018; Sánchez de Dios et al., 2017).
These critical areas often correspond to biodiversity ’hotspots’, detected by stacking individual species distribution maps. To this end, species distribution modelling (hereafter SDMs) constitutes a reliable alternative to overcome the limitations of more traditional methods such as the widely implemented “Extents of Occurrence” (Elith & Leathwick, 2009; Mainali et al., 2020; Syfert et al., 2014). These modern techniques use computer algorithms, occurrence data and environmental information to obtain models of the probabilistic distribution of a species in space or environment while reducing both false negatives and false positives errors (Jiménez-Valverde, 2012; Mendes et al., 2020).
Compared to other bird groups, raptors constitute a paradox since they are comparatively under-studied, given their low reproductive rates and abundances, while exercising great appeal for the financial support given their ecological roles as top predators, symbolism and threat levels (Donázar et al., 2016). Among raptors, true owls (family Strigidae) are reliable bioindicators of environmental quality (dal Pizzol et al., 2020; Dayananda et al., 2016; Fröhlich & Ciach, 2018, 2019), suitable conservation areas prioritization given their world diversity (> 220 species; Gill et al. 2021), and interspecific variation in size, distribution ranges, habitat specialization, and responses to habitat structure (Barros & Cintra, 2009; Burgas et al., 2014; Sergio et al., 2005) and alteration (Enríquez, 2017; Rullman & Marzluff, 2014).
However, given their nocturnal habits, owls can be overlooked in fauna inventories (H. G. de Silva & Medellín, 2001), limiting the knowledge on their biology and, probably, the underrepresentation in the official lists of threatened fauna of a megadiverse country such as Brazil (J. C. Motta-Junior et al., 2017; J. C. Motta-Junior & Braga, 2012). Even the information on their distribution is sparse, anecdotal, insufficiently detailed (J. C. Motta-Junior & Braga, 2012) and probably incomplete.
Brazil harbors some 21 recognized species of Strigidae (Gill et al., 2021), with the endemic Pernambuco pygmy owl (Glaucidium mooreorum ) being critically endangered (BirdLife International, 2019) or even extinct (G. A. Pereira, 2010), while others including the East Brazilian pygmy owl (Glaucidium minutissimum ), the black-capped screech owl (Megascops atricapilla ), the long-tufted screech-owl (M. sanctaecatarinae ), the tawny-browed owl (Pulsatrix koeniswaldiana ) and the rusty-barred owl (Strix hylophila ) are ”near-endemic” (J. C. Motta-Junior et al., 2017). However, such biodiversity is greatly underestimated, as suggested by the proposition of new owl species for Brazil based on molecular and bioacoustics traits by Dantas et al. (2013).
The need to complete our understanding of the biology, ecology and distribution of owls in Brazil, especially under scenarios of vast and rapid environmental transformations (Escobar, 2020; Sonter et al., 2017), places their SDMs as a challenging and urgent scientific and conservation task. Here, we: (1) generated SDMs for each species and subspecies based on a maximum entropy approach; (2) evaluated niche similarities between conspecific subspecies; (3) created species richness maps for mainland Brazil; (4) determined the biodiversity hotspots; and (5) identified priority conservation areas contrasting them against the existing network of strictly protected areas.