Spatial distribution models
We gathered information on the distribution (= occurrences) of the
Strigidae in Brazil from: (1) skin specimens deposited in several
museums according to the Global Biodiversity Information Facility
(GBIF.org, 2019); (2) more than 164 publications in peer-reviewed
literature regarding taxonomic assessments, fauna inventories or owl
biology; and (3) field records from the bioacoustics database
www.xeno-canto.org. We provide the citations of these sources in
Supplementary material A, Appendix 2. The quality of the geographic
coordinates varied from GPS recordings until those of the nearest town
listed on the specimens’ labels. We corroborated the localities through
an ornithological gazetteer specific for Brazil (Paynter & Traylor,
1991) and online (www.geonames.org).
There are no records for the buff-fronted owl (Aegolius harrisii )
in Northern Brazil, but in the nearby Northern border at both Cerro de
la Neblina (Willard et al., 1991) and Roraima Tepui (Braun et al.,
2003). Similarly, most of the records for the foothill screech owl
(Megascops roraimae ) come from outside Brazil in Cerro Urutaní
(Dickerman & Phelps, 1982), Cerro de la Neblina (Willard et al., 1991),
Acary Mountains (Robbins et al., 2007), and Roraima Tepui (Milensky et
al., 2016). In both cases, we included these records in our analyzes by
reassigning coordinates within their respective closest Brazilian
territory. The Pernambuco pygmy-owl is known from two localities (J. M.
C. da Silva et al., 2002), to which we added eight random points located
within a polygon resulting from two merged circles, each centered in one
of the known localities and radius equaling the distance between both,
clipped by the neighbor coastline. We excluded a record of the
short-eared owl (Asio flammeus ) in the Roraima State
(wikiaves.com.br; consulted on April 10, 2021), likely belonging to the
subspecies A. f. pallidicaudus from “Venezuela, Guyana and
Suriname” (Gill et al., 2021).
The geographical and environmental clustering of field surveys, known as
spatial autocorrelation (Araújo & Guisan, 2006; Loiselle et al., 2008),
can negatively affect the performance of the SDMs (Veloz, 2009).
Consequently, some authors remove those records under the same
environmental conditions within an arbitrary distance (Delgado-Jaramillo
et al., 2020). Thus, we created
two datasets for each species, one including all the records and another
excluding those closer than 25 km, and computed empirical entrograms for
both using “elsa” (B. Naimi et al., 2019),
comparing the entropy-based local
indicators of spatial association for both categorical or continuous
environmental covariates. Entrograms are variogram-like graphs
quantifying the spatial association of geographical covariates based on
information entropy concepts (B. Naimi, 2015).
We used “ENMeval” (Muscarella et al., 2014), a package based on Maxent
(Phillips et al., 2006, 2017, 2004), that automatically splits data into
training/test subsets, performs SDMs across a range of settings, and
calculates diverse evaluation metrics. For each taxon, we ran 10 models,
each one after partitioning occurrences in testing and training bins
using a 10-fold cross-validation scheme (Fielding & Bell, 1997). For
each run, we created 10 000 pseudoabsence points distributed randomly
throughout Brazil and selected the model with the lowest Akaike
information criterion corrected for small samples sizes
(ΔAICc =0) as the best one, since it reflects both
model goodness-of-fit and complexity (Burnham & Anderson, 2002; Warren
& Seifert, 2011) and less overfitting (Muscarella et al., 2014).
Different habitat suitability thresholds may disagree in terms of
suitable areas and omission errors (Bean et al., 2012; Liu et al., 2016;
Nenzén & Araújo, 2011). Thus, for each taxon, we plotted the extension
of the predicted area (in number of pixels) against the number of
omissions and compared across taxa the performance of the different
thresholds, keeping the one that consistently provided the lowest values
for both measurements. The final binary models combined the best models
(ΔAICc = 0) and the threshold with the lower
number of omissions within the smallest predicted area. We stacked these
binary distributions to create two maps of taxa richness for (A) the 21
species evaluated (hereafter sensu stricto map), (B) the 12
monotypic species (including polytypic ones represented by only one
subspecies in Brazil) and 21 subspecies (henceforth sensu latomap). We overlapped the protected areas distributions corresponding to
IUCN’s categories I to IV (according to Protected Planet 2021) on each
richness map.