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.