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\begin{document}

\title{Evaluating the Brazilian system of strictly protected areas using owls
as an indicator group}



\author[1]{Paolo Ramoni Perazzi}%
\author[1]{Marcelo Passamani}%
\author[2]{Dirk Thielen}%
\author[3]{Marco Arizapana-Almonacid}%
\affil[1]{Universidade Federal de Lavras}%
\affil[2]{Venezuelan Institute for Scientific Research}%
\affil[3]{National Autonomous University of Huanta}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
AIM To set or assess conservation and management efforts based on the
knowledge of the total biodiversity is unrealistic. For such a reason,
the identification of priority areas based on biodiversity hotspots
determined through indicator groups has become a common approach. This
requires a crystal-clear knowledge of the taxonomy and distribution of
such indicator groups, which in the Tropics can be troublesome,
especially for rare or secretive taxa. Thus, we assessed the potential
distribution of 21 species and 21 subspecies of Brazilian Strigidae
through Species Distribution Modelling (SDM) based on a Maximum Entropy
approach. LOCATION Brazil. METHODS We (1) gathered and filtered
occurrences data for Brazilian Strigidae, (2) generated SDMs for each
species and subspecies, (3) evaluated the niche similarity among
subspecies, (4) built up species' richness maps, and (5) contrasted such
information to the strict protection areas in Brazil. RESULTS With 81\%
of the Brazilian species recorded, both the Atlantic Forest and the
Cerrado have the highest richness, followed by the Amazonia (67\%),
Pampa (62\%), Caatinga (57\%) and Pantanal (48\%). However, the
comparison of the recorded and predicted richness suggests overall
incomplete inventories, especially in the Caatinga and Pantanal. On the
other hand, subspecies showed marked niches divergencies, suggesting
that the recognized Strigidae species richness is underestimated in
Brazil. Cerrado and Atlantic forest are the most threatened biomes, with
preservation areas relatively small and sparse. MAIN CONCLUSIONS We
demonstrated that the situation of Brazilian Strigidae involves an
underestimated species richness, within an inadequate framework of
protected areas, in a megadiverse Country characterized by high rates of
habitat transformations. Thus, our study is a hurrying call to explore
owl lineage diversification in Brazil to improve biodiversity-related
conservation efforts.%
\end{abstract}%



\sloppy


\textbf{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; S\selectlanguage{ngerman}ánchez
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 (\textgreater{} 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 (\emph{Glaucidium
mooreorum} ) being critically endangered (BirdLife International, 2019)
or even extinct (G. A. Pereira, 2010), while others including the East
Brazilian pygmy owl (\emph{Glaucidium minutissimum} ), the black-capped
screech owl (\emph{Megascops atricapilla} ), the long-tufted screech-owl
(\emph{M. sanctaecatarinae} ), the tawny-browed owl (\emph{Pulsatrix
koeniswaldiana} ) and the rusty-barred owl (\emph{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.

\textbf{METHODS}

\textbf{Study area}

Brazil is a continent-sized country of contrasting topographic features
(plain landscapes in the Amazon basin, or mass of ridges and mountain
ranges in the southeast) and a latitudinal extent exceeding the Equator
to the North and the Tropic of Capricorn to the South. Concomitantly,
according to the Köppen classification system, there are three major
climatic types (Alvares et al., 2013): humid subtropical, tropical rainy
and tropical dry. Also, six major terrestrial biomes: Amazonia, Atlantic
Forest, Caatinga, Cerrado, Pampa and Pantanal (IBGE, 2019).

Despite the aforementioned contrasts, and acknowledging the importance
of areas accessibility via dispersal in SDMs (Barve et al., 2011), we
considered Brazil as an orographic continuum for flying species based on
two facts. First, Brazil lacks massive and steeply orographic barriers.
Second, the Brazilian system of rivers includes wide ones, but their
course and level underwent dramatic changes throughout their recent
geological history, limiting their influence in shaping bird species
distributions (Fluck et al., 2020; Santorelli et al., 2018).

\textbf{Species and subspecies account}

We followed the International Ornithological Committee v. 11.1 (Gill et
al., 2021), which considers 21 valid species and 21 conspecific
subspecies. To model the distributions of the subspecies separately, and
given the lack of more comprehensive sources, we split the occurrences
based on Gill et al. (2021), and the maps from www.xeno-canto.org (Table
1).

\textbf{Environmental covariates}

Bioclimatic covariates are successfully used in SMDs since their
beginning (Booth et al., 2014), and perhaps reflect physiological
constraints. Soil type and geology have been used in SDMs for
Neotropical flying vertebrates (Ramoni-Perazzi et al., 2012, 2017, 2020)
likely echoing deeper ecological and historical constraints. Soil
characteristics modulate aspects of vegetation as functional traits,
biodiversity, and speciation, among others (Hulshof \& Spasojevic, 2020;
Le Stradic et al., 2015; Mori et al., 2021; Nunes et al., 2015;
Rajakaruna, 2018). Besides, plant species distributions can shape
primary (Freeman \& Mason, 2015) and secondary (Sanín \& Anderson, 2018)
consumers distributions. Furthermore, geology underpins several soil
characteristics (Bockheim et al., 2014), as well as topography and
geological events that can influence biological diversification
processes over short timespans and over regional or local scales
(Antonelli et al., 2018; Gillespie \& Roderick, 2014; S. L. Pereira \&
Baker, 2004).

We used an \emph{ad hoc} database of bioclimatic covariates for
Brazilian mainland (Ramoni-Perazzi et al., in press), information on
elevation (GMTED2010; Danielson and Gesch 2011), geological substrate
(hereafter geology; Gómez Tapias et al. 2019), and soil type (Hengl et
al. 2017). All variables were used at (or resampled to) 30 arc seconds
resolution.

We performed all the analyses using R 3.6.3 (R Core Team, 2020). To
remove collinearity, we reduced the number of continuous (bioclimatic +
elevation) covariables through a principal component analysis using
`RStoolbox' (Leutner et al., 2018), keeping the first four components,
whose eigenvalues were higher than one and explained 90.4\% of the
variance (Supplementary material A, Appendix 1, Fig. A). The first
component (PC1) can be interpreted as a contrast between the temperature
during the most extreme conditions and its seasonality/variability
(Supplementary material A, Appendix 1, Fig. B1). Similarly, the second
component (PC2) contrasts the precipitation during extreme conditions
and its seasonality. The third component (PC3) involves the effect of
water availability, since involves temperatures under extreme
conditions/seasonality and precipitation during the warmest quarter.
Finally, the fourth component (PC4) has a weak negative association
between Mean Diurnal Temperature Range (Bio 02) and elevation.

\textbf{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 (\emph{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
(\emph{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 (\emph{Asio flammeus} ) in the Roraima State
(wikiaves.com.br; consulted on April 10, 2021), likely belonging to the
subspecies \emph{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
(\selectlanguage{greek}Δ\selectlanguage{english}AIC\emph{\textsubscript{c}} =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;
Nenz\selectlanguage{ngerman}én \& 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
(\selectlanguage{greek}Δ\selectlanguage{english}AIC\emph{\textsubscript{c}} = 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 \emph{sensu stricto} map), (B) the 12
monotypic species (including polytypic ones represented by only one
subspecies in Brazil) and 21 subspecies (henceforth \emph{sensu
lato}map). We overlapped the protected areas distributions corresponding
to IUCN's categories I to IV (according to Protected Planet 2021) on
each richness map.

\textbf{Subspecies niche analysis}

We used `phyloclim' (Heibl \& Calenge, 2018) to quantify the niche
overlap measurements for each pair of conspecific subspecies based on
predictions (Warren et al., 2008): \emph{D} (Schoener, 1968) and\emph{I}
(van der Vaart, 1998). Both report whether the average agreement of
environmental requirements calculated between subspecies pairs is
greater or smaller than expected if such subspecies were independent of
each other, ranging from zero (no overlap) to one (identical niches).
Then, we assessed the null hypothesis of niche identity, evaluating the
equivalency between pairs of predictions, comparing the respective
observed values of \emph{D} and \emph{I}against those generated through
100 pseudoreplicates, assigning the occurrence points for both
subspecies to one lineage or the other at random to simulate the
potential overlap of a group of points occurring across a given
geographic space (Warren et al. 2008).

Moreover, we used background randomization tests to contrast the
observed niche overlap values against a null distribution of 100 overlap
values resulting from contrasting the predictions of one subspecies
against those created from points taken at random from the geographic
space of the another subspecies (Warren et al., 2008). We considered
that: (1) there was niche conservation when the backgrounds overlapped
and niche distance was not significantly different from zero; (2) there
was niche divergence when the distance between both subspecies was
significantly different from zero, independently of backgrounds'
overlap.

\textbf{RESULTS}

\textbf{The occurrences}

We gathered 2600 occurrence localities, after removing redundant ones
(Supplementary Material B). Most occurrences were from the Atlantic
Forest (48\%), followed by the Amazonia (21\%), Cerrado (17\%), Caatinga
(8\%), Pampa (4\%), and Pantanal (3\%; Fig. 1; Table 2). The
Southeastern Region, \textasciitilde{}11\% of the Brazilian territory,
was represented the best with 34\% of the records, while the Northern
region, 45\%\textless{} of the Brazilian territory, only provided 25\%
of the records. Both the Atlantic Forest and Cerrado were the most
diverse biomes harboring records for \textasciitilde{}81\% of the
species each, followed by the Amazonia (67\%), Pampa (76\%), Caatinga
(57\%), and Pantanal (48\%).

For several species, we found records far outside the geographical or
ecological limits traditionally reported in the literature. For example,
the spectacled owl (\emph{Pulsatrix perspicillata} ) is frequently split
into an Amazonian population and another from the Atlantic Forest, but
there are records for all the remnant biomes except the Pampa.

\textbf{Spatial distribution models}

Excluding the case of the striped owl (\emph{Asio clamator} ), the
empirical entrograms showed no substantial variations when excluding
those localities closer than 25 km (Supplementary Material A, Appendix
1. Fig. C). Thus, we performed the SDMs including all the occurrences,
except for the aforementioned species.

We provide detailed information of the models in Supplementary material
A, Appendix 3, Table A. In concordance with Liu et al. (2005, 2016), we
found that thresholds based on sensitivity-specificity outperformed the
remnant ones (Supplementary material Appendix 1, Fig. D). For such a
reason, we used the sensitivity-specificity equality threshold to create
the binary maps.

The climatic variables were usually the most important predictors of
suitability (Table 3). Temperature-based PC1 and, especially, PC4 ranked
highest for percentage contribution for 27 of the taxa studied, followed
by precipitation-based PC3 (nine taxa), geology (four), and both soil
and PC2 (one each).

The predictions of the monotypic species fitted the best their
traditionally reported distributions and had lower omission percentages
(median 13\%, ranging between 0\% for \emph{G. mooreorum} , to 31\%,
for\emph{Lophostrix cristata} ; Supplementary material A, Appendix 1,
Fig. E) compared to the polytypic ones (median 34\%, from 24\%
for\emph{Strix virgata} to 42\% for \emph{Megascops choliba} ). These
omissions usually felt outside the corresponding most represented biome
(often, outside the Atlantic Forest). Besides, we found a general
tendency towards fitting improvements after running models based on
occurrences of their respective subspecies (median 15\% of omissions,
from 4\% in \emph{Athene cunicularia cunicularia} and, exceptionally,
66\% for \emph{Strix huhula huhula} ). For the endemic and probably
extinct Pernambuco pygmy-owl, our models predicted a very restricted
range around both known localities, but also two additional separated
spots, one located in the protected area of Manguezais da Foz do Rio
Mamanguape, and the other in the mouth of the Sergipe river: unassessed
areas from the ornithological point of view.

According to the \emph{sensu stricto} map (Fig. 2A), the Atlantic Forest
hosts the highest potential richness (ca. 15 species), especially within
the Dense Ombrophylous Forest range (around the littoral and mountainous
areas of the Southeastern region). Scattered areas along the Amazonas
river lowlands, notably in the belt of siliciclastic sedimentary rocks
north of the river and around its mouth are also highly diverse.
Conversely, wide coldspots (around zero predicted species) characterize
more open environments such as Cerrado, Caatinga or Pampa, as well as
broad areas in the Amazonia. Moreover, the \emph{sensu lato} map (Fig.
2B) keeps the same areas of high biodiversity (over 15 taxa) but
reducing the extension of the coldspots in the Amazonia, keeping only
some areas in the Rond\selectlanguage{ngerman}ônia State. Thus, both approaches indicate that
the Atlantic Forest, which harbors the highest richness, is poorly
covered by strictly protected areas since these become substantially
smaller and sparser within a gradient from Northwest to Southeast
Brazil. However, by comparing the number of species (\emph{sensu
stricto} ) recorded against those predicted, we found that all biomes
are under-sampled (Table 2), especially the Pantanal and the Caatinga.

\textbf{Subspecies' niche analysis}

The results of the similarity tests between conspecific subspecies are
summarized in Supplementary material A, Appendix 1, Fig. F. We found
niche overlap values significantly lower than expected from a null model
distribution for all the pairwise comparisons. Consequently, we reject
the null hypothesis of niche identity for all pairs, suggesting that in
all cases niches are not identical to one another.

Our background tests yielded a more complex scenario (Supplementary
material A, Appendix, 1 Fig. G). The observed values of \emph{D}
and\emph{I} usually lied below the corresponding null distributions;
hence, differences are bigger than expected by chance. However, we were
failed to reject the null hypotheses of the background similarity tests
when contrasting \emph{A. c. cunicularia} \selectlanguage{english}- \emph{A. c. grallaria} ,
\emph{B. v. nacurutu} \selectlanguage{english}- \emph{B. v. deserti} , \emph{M. c. choliba} \selectlanguage{english}-
\emph{M. c. decussatus} , \emph{M. c. choliba} \selectlanguage{english}- \emph{M. c. uruguaii} ,
and \emph{M. w. watsonii} \selectlanguage{english}- \emph{M. w. usta} , with arrows indicating
the directionality of the comparison: the first subspecies against a
randomized background derived from the second one. However, the
\emph{D}and \emph{I} metrics of the respective complementary comparisons
fell below their corresponding null distributions, indicating that these
were more different than expected by chance, hence leading us to reject
the null hypothesis that their niches are similar.

\textbf{DISCUSSION}

In general, our results show that the situation of Brazilian Strigidae
is complicated, involving an underestimated richness, within an
inadequate framework of protected areas, in a megadiverse Country
characterized by high rates of habitat transformations.

\textbf{Sampling biases, distribution patterns and taxonomic knowledge}

Brazil harbors a rich owl diversity, distributed unequally throughout
its territory. Indeed, our models suggest a markedly variable richness
across the Country, ranging between zero and 16, averaging three,
species per 30 arcsec * 30 arcsec pixel. Since 69\% of the occurrences
are from Atlantic Forest and Amazonia, their higher predicted richness
can partially respond to sampling biases. The Atlantic Forest
exemplifies those situations where sampling efforts are favored by
neighboring established research centers and major urban areas (Brito et
al., 2009; Moerman \& Estabrook, 2006; G. V. T. Ribeiro et al., 2016).
The Amazonia replicates the pattern already described for botanical and
ornithological records (Vale \& Jenkins, 2012), probably prompted by
local facilities or recurrent bird-oriented inventories.

However, all biomes are under-sampled, as suggested by the difference
between the number of recorded and predicted species, especially in the
Caatinga and Pantanal. Hence, our results agree with Silva (1995) and
Fernandez-Arellano et al. (2021) regarding the need for research efforts
addressed to unexplored areas and periods, less detectable species, in
neglected biomes. It is reasonable to expect that such efforts oriented
to nocturnal birds would increase the respective species lists for all
biomes, especially in Caatinga and Pantanal.

Excluding the possibility of factors shaping the distributions of the
Brazilian Strigidae, not considered in the present study, several not
mutually exclusive possibilities can explain the overall elevated number
of omissions for the models. First, misidentifications. For example,
(Rocha \& Lopez-Baucells, 2014b) reported a young \emph{Lophostrix
cristata} that was indeed a \emph{Strix virgata} (Rocha \&
Lopez-Baucells, 2014a). Thus, similar unnoticed mistakes, especially
those involving species more alike, can result in false occurrences
reported far from the actual range.

Second, vagrancy, a phenomenon frequently reported in the ornithological
literature at least since Grinnell (1922), including cases of owls
impacting native fauna in remote islands (Bried, 2003), and prompted by
factors as diverse as expanding populations, weather conditions,
geography, age, and genetics (Kalwij et al., 2019; Ralph \& Wolfe, 2018;
Veit, 2000). According to the `Exodus Hypothesis' (Flade \& Lachmann,
2008), vagrancy can be triggered by severe habitat loss, and individuals
occasionally can settle in available but less adequate habitats
affecting their fitness and population permanence (Part et al., 2007;
Robertson \& Hutto, 2006). Here, most omissions corresponded to taxa
from the Atlantic Forest recorded in open areas such as the Cerrado.
Currently, the Atlantic Forest covers less than 16\% of the original
extent, and more than 80\% of the fragments are smaller than 50 ha
(Ribeiro et al. 2009; but see Rezende et al. 2018), a dramatic situation
that could have triggered the translocation of owls toward the
comparatively less impacted Cerrado areas in the past.

Third, range expansions, as frequently reported for Strigiformes in
well-monitored areas in North America (Livezey, 2009a), Europe (Bashta,
2009; Mysterud, 2016), and Oceania (Hyde et al., 2009), occasionally
mediated by human activities. For example, Livezey (2009b) postulated
that the forest expansions after the arrival of European settlers (who
excluded fires set by Native Americans, planted trees, and overhunted
bison, elks, deers and beavers) allowed barred owls (\emph{Strix varia}
) to overcome the historical lack of trees in the Great Plains of North
America granting their expansion westward. According Haywood (2010), the
plantations of Tasmanian Blue Gum (\emph{Eucalyptus globulus} )
facilitated the range expansion of the powerful owl (\emph{Ninox
strenua} ) in South Australia. Similar situations could be shaping the
distributions of some Brazilian owl species, such as the
tree-nester\emph{A. harrissi} , whose omissions mostly correspond to
records from the last two decades in open biomes such as Cerrado and
Caatinga (A. F. T. da Silva et al., 2021) or Pampa (Muller Rebelato et
al., 2011). That is, these new records correspond to biomes anthropized
the most during the recent decades according to MAPBIOMAS
(https://plataforma.brasil.mapbiomas.org/, consulted April 14, 2021),
with steady increases in planted tree surfaces
(http://atlasagropecuario.imaflora.org/, consulted April 16, 2021),
perhaps offering new more suitable areas for these owls.

Notwithstanding, a more likely option is that the taxonomy of the
Brazilian owls is far from being completely understood, with local
populations/morphs representing in many cases valid species, as recently
demonstrated by Dantas et al. (2021). In these cases, it is reasonable
to expect SDMs biased toward the best-represented subspecies, failing to
predict regions suitable for the remnant ones. Our models for the
conspecific subspecies and our results on niche equivalency, even if
assigning the occurrences in an approximative way, reinforced this
possibility.

Our analyses agree with Peterson et al. (1999) that the speciation
process involves geographic dimensions first and then ecological
aspects. Environmental parameter variations may result in niche
divergences (Pyron et al., 2015; Ramoni-Perazzi et al., 2020), which may
explain to some extent most of the speciation patterns within the
Brazilian Strigidae since our results imply that the niches of the
subspecies under comparison can be more dissimilar than expected, in
most cases obligated by dissimilarities of their respective backgrounds.
This situation may also involve species considered as monotypic. For
example, the high omission rates of over 26\% in the case of the
Amazonian pygmy owl (\emph{Glaucidium hardyi} ) may suggest a species
complex since the central portion of the Amazon River basin is a
secondary contact zone for taxa isolated in the main Amazonian
sub-basins during the Mid- and Late Pleistocene (Thom et al., 2020).

All the aforementioned options deserve further evaluations, especially
involving morphological, molecular and bioacoustics approaches.

\textbf{Conservation-related issues}

The combination of sampling biases, plus ecologic and taxonomic
knowledge gaps, can lead to misguided conservation decisions. For
example, similarly to what had already reported for bats
(Delgado-Jaramillo et al., 2020), most of the coldspots in
our\emph{sensu stricto} map (Fig. 2A) are placed along areas severely
deforested during the last decades, particularly along the contact zone
between Amazonia and Cerrado in the so-called ``arch of deforestation''
(Becker, 2005). Such coldspots could erroneously be interpreted as
regions of lower conservation interest, instead of originated by the
absence of information after local extinctions prompted by anthropic
impacts (Delgado-Jaramillo et al., 2020).

The greater diversity of Strigidae within the Atlantic forest
corresponds to an extensively degraded biome with small-sized and sparse
protected areas, whose efficiency is lower than other regions, such as
the Amazonia (Sobral-Souza et al., 2018). Worse yet, the buffer zones
around these protected areas in the economically prosperous Brazilian
regions tend to be as degraded as the surrounding unprotected ones
(Almeida-Rocha \& Peres, 2021). Bird species were already extinct and
others were extirpated, from large parts of the Atlantic Forest,
especially the Pernambuco Centre of Endemism in northeast Brazil
(Develey \& Phalan, 2021). Besides the probably extinct Pernambuco
pygmy-owl (\emph{G. mooreorum} ), the spectacled owl (\emph{Pulsatrix
perspicillata pulsatrix} ) may be extinct in most of its range (Leal \&
Assis, 1993). Thus, we agree with Oliveira et al. (2017) who highlighted
the deficiencies of the Brazilian network of protected areas given the
combination of the scarce knowledge on their biodiversity and an
inadequate spatial disposition which offers limited or no protection to
most species and even evolutionary lineages. We also agree with Jenkins
et al. (2015), regarding the need to substantially improve the network
of protected areas in Brazil, with an emphasis in the Atlantic forest.

\textbf{Final remarks}

The frequently adopted biological concept has been criticized (Hauser,
1987) since many biological species and subspecies seems to be
unsuitable for practical or theoretical purposes. Thus, it is likely
that differentiation processes reported across several groups of
Neotropical birds (Harvey et al., 2017; McGuire et al., 2014) are being
overlooked. The Anthropocene, with its widely recognized adverse impacts
on biodiversity, driven by the current trends of wide and quick habitat
conversion (Gaston et al., 2003), and climatic change (Devictor et al.,
2008; Urban, 2015) worsens this scenario. In consequence, as indicated
by Mishler (2021), the adoption of less restrictive concepts, as the
phylogenetic one, will allow abandoning the biases and restrictions
imposed by the use of the species rank, improving the evolutive and
ecological research, and ultimately influencing positively areas of
public interest such as conservation.

The traditional use of single umbrella species for conservation purposes
received critics since a single species can hardly encapsulate the
environmental requirements and geographical distribution of the
remaining species to be protected (Fourcade et al., 2013). Consequently,
alternative multi-species approaches appeared, balancing the number of
taxa involved and their sensitivity to area/resources requirements
(including connectivity and natural processes) to establish realistic,
concrete and quantitative landscape design criteria (Lambeck, 1997;
Roberge \& Angelstam, 2004). As top predators with variable size, and
differential environmental sensitivity and requirements, owls are good
candidates to be considered for a multi-taxa approach, for which our
study serves as a hurrying call to explore in-depth owl lineage
diversification in Brazil to improve biodiversity-related efforts.

\textbf{Acknowledgements}

We are grateful to Ivo Rohling Ghizoni Jr, Gerald Mayr, Martin Flade and
Thadeu Sobral for providing valuable information. P.R.P. also thanks to
the Organization of American States, through its Partnerships Program
for Education and Training of the Coimbra Group of Brazilian
Universities (OAS/PAEC/GCUB).

\textbf{Data availability statement}

The following supplementary material is available online:

Supplementary Material A DOI 10.4121/15121137

Supplementary Material B DOI 10.4121/15121188

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Table 1. Account of the species and subspecies occurring in Brazil,
according to the International Ornithological Committee v. 11.1 (Gill et
al. 2021). ``Distribution'' refers to the range described in the
aforementioned source, used jointly to biomes, major hydrographic basins
and reinforced by other sources such www.xeno-canto.org, to proximately
assign the occurrences to the different subspecies (our
``interpretation''), to create the occurrences databases used to model
the subspecies distributions.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
English name & Scientific name & Distribution &
Interpretation\tabularnewline
\midrule
\endhead
Buff-fronted Owl & \emph{Aegolius harrisii} & &\tabularnewline
& \emph{A}. \emph{h}. \emph{iheringi} & ``Paraguay, SE Brazil, Uruguay
and NE Argentina'' &\tabularnewline
Striped Owl & \emph{Asio clamator} & &\tabularnewline
& \emph{A. c. clamator} & ``Colombia and Venezuela to E Peru and C, NE
Brazil'' & Caatinga and Amazonia\tabularnewline
& \emph{A. c. midas} & ``E Bolivia to Paraguay, S Brazil, Uruguay and N
Argentina'' & Cerrado, Atlantic Forest and Pampa\tabularnewline
Short-eared Owl & \emph{Asio flammeus} & &\tabularnewline
& \emph{A. f. suinda} & ``S Peru and S Brazil to Tierra del Fuego''
&\tabularnewline
Stygian Owl & \emph{Asio stygius} & &\tabularnewline
Burrowing Owl & \emph{Athene cunicularia} & &\tabularnewline
& \emph{A. c. minor} & ``C, SE Venezuela, S Guyana and N Brazil'' &
Amazonia\tabularnewline
& \emph{A. c. cunicularia} & ``S Bolivia, Paraguay and S Brazil to
Tierra del Fuego'' & Pampa\tabularnewline
& \emph{A. c. grallaria} & ``E and C Brazil'' & Remnant
biomes\tabularnewline
Great Horned Owl & \emph{Bubo virginianus} & &\tabularnewline
& \emph{B. v. deserti} & ``NE Brazil (NC Bahia)'' &
Caatinga\tabularnewline
& \emph{B. v. nacurutu} & ``E Colombia through the Guianas to N, E
Brazil, Argentina, Bolivia and C Peru'' & Remnant biomes\tabularnewline
Ferruginous Pygmy Owl & \emph{Glaucidium brasilianum} & &\tabularnewline
& \emph{G. b. ucayalae} & ``Amazonia'' & Amazonia\tabularnewline
& \emph{G. b. brasilianum} & ``E Brazil to NE Argentina'' & Remnant
biomes\tabularnewline
Amazonian Pygmy Owl & \emph{Glaucidium hardyi} & &\tabularnewline
East Brazilian Pygmy Owl & \emph{Glaucidium minutissimum} &
&\tabularnewline
Pernambuco Pygmy Owl & \emph{Glaucidium mooreorum} & &\tabularnewline
Crested Owl & \emph{Lophostrix cristata} & &\tabularnewline
Black-capped Screech Owl & \emph{Megascops atricapilla} &
&\tabularnewline
Tropical Screech Owl & \emph{Megascops choliba} & &\tabularnewline
& \emph{M. c. choliba} & ``S Brazil and E Paraguay'' & Paran\selectlanguage{ngerman}á river
basin\tabularnewline
& \emph{M. c. cruciger} & ``E Colombia and E Peru through Venezuela, the
Guianas to NE Brazil'' & Amazonia\tabularnewline
& \emph{M. c. uruguaii} & ``SE Brazil, Uruguay and NE Argentina'' &
Pampa\tabularnewline
& \emph{M. c. decussatus} & ``C, E Brazil'' & Remnant
areas\tabularnewline
Foothill Screech Owl & \emph{Megascops roraimae} & &\tabularnewline
Long-tufted Screech Owl & \emph{Megascops sanctaecatarinae} &
&\tabularnewline
Tawny-bellied Screech Owl & \emph{Megascops watsonii} & &\tabularnewline
& \emph{M. w. watsonii} & ``N of the Amazon'' & N of the Amazon
river\tabularnewline
& \emph{M. w. usta} & ``S of the Amazon'' & S of the Amazon
river\tabularnewline
Tawny-browed Owl & \emph{Pulsatrix koeniswaldiana} & &\tabularnewline
Spectacled Owl & \emph{Pulsatrix perspicillata} & &\tabularnewline
& \emph{P. p. perspicillata} & ``E Colombia through the Guianas and
Amazonia'' & Amazonia\tabularnewline
& \emph{P. p. pulsatrix} & ``Paraguay, E Brazil and NE Argentina'' &
Atlantic Forest\tabularnewline
Black-banded Owl & \emph{Strix huhula} & &\tabularnewline
& \emph{S. h. huhula} & ``Colombia, Venezuela and the Guianas S to E
Peru, E Bolivia, NW Argentina and EC Brazil'' & Amazonia and
Cerrado\tabularnewline
& \emph{S. h. albomarginata} & ``SE Brazil, Paraguay and NE Argentina''
& Atlantic Forest\tabularnewline
Mottled Owl & \emph{Strix virgata} & &\tabularnewline
& \emph{S. v. superciliaris} & ``NC, NE Brazil'' &
Amazonia\tabularnewline
& \emph{S. v. borelliana} & ``S Brazil, Paraguay and NE Argentina'' &
Atlantic Forest\tabularnewline
\bottomrule
\end{longtable}

Table 2. Numbers of records, omissions, number of species (\emph{sensu
stricto} ) recorded and predicted and their differences for the
different Brazilian biomes. Numbers in parentheses represent percentages
based on the total of 2608 occurrences (2600 from collections/literature
+ eight generated for \emph{Glaucidium mooreorum} ) or the 21 species.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Biome & Records & Omissions & Richness\emph{\textsubscript{rec}} &
Richness\emph{\textsubscript{pred}} & Difference\tabularnewline
\midrule
\endhead
Amazonia & 540 (21) & 284 & 14 (67) & 16 (76) & 2\tabularnewline
Atlantic Forest & 1249 (48) & 84 & 17 (81) & 21 (100) & 4\tabularnewline
Caatinga & 209 (8) & 129 & 12 (57) & 19 (90) & 7\tabularnewline
Cerrado & 438 (17) & 210 & 17 (81) & 20 (95) & 3\tabularnewline
Pampa & 104 (4) & 14 & 13 (62) & 16 (76) & 3\tabularnewline
Pantanal & 68 (3) & 9 & 10 (48) & 15 (71) & 5\tabularnewline
\bottomrule
\end{longtable}

Table 3. The mean \selectlanguage{ngerman}± standard deviation of the percent of contribution
and permutation importance of the explanatory variables to the SDMs of
each species and subspecies. The percent of contribution is the gain of
the model by including a given environmental covariate at each step of
the Maxent algorithm. The permutation importance is the influence of
each environmental covariate on the final model, measured through the
random permutation of the values of such covariate among the training
points and measuring the resulting decrease in the training area under
the curve.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
& Percent of contribution & Percent of contribution & Percent of
contribution & Percent of contribution & Percent of contribution &
Percent of contribution & Permutation importance & Permutation
importance & Permutation importance & Permutation importance &
Permutation importance & Permutation importance\tabularnewline
\midrule
\endhead
Species/subspecies & PC1 & PC2 & PC3 & PC4 & Geology & Soil & PC1 & PC2
& PC3 & PC4 & Geology & Soil\tabularnewline
\emph{Athene cunicularia} & 6.4 \selectlanguage{ngerman}± 3.2 & 3.2 \selectlanguage{ngerman}± 1 & 6.2 \selectlanguage{ngerman}± 8.6 & 64 \selectlanguage{ngerman}± 9.5 &
10.1 \selectlanguage{ngerman}± 1.1 & 10.1 \selectlanguage{ngerman}± 2.4 & 0.9 \selectlanguage{ngerman}± 1.3 & 3.1 \selectlanguage{ngerman}± 0 & 9.2 \selectlanguage{ngerman}± 2.3 & 70.4 \selectlanguage{ngerman}± 3.8 &
6.6 \selectlanguage{ngerman}± 1.5 & 4.5 \selectlanguage{ngerman}± 1.3\tabularnewline
\emph{Athene cunicularia cunicularia} & 14.1 \selectlanguage{ngerman}± 4 & 0.5 \selectlanguage{ngerman}± 0.4 & 2.9 \selectlanguage{ngerman}± 1.7
& 51.7 \selectlanguage{ngerman}± 4.4 & 10.3 \selectlanguage{ngerman}± 1.2 & 20.4 \selectlanguage{ngerman}± 1 & 0.8 \selectlanguage{ngerman}± 0.6 & 1.4 \selectlanguage{ngerman}± 0 & 5.2 \selectlanguage{ngerman}± 2.8 &
78.6 \selectlanguage{ngerman}± 4 & 5.6 \selectlanguage{ngerman}± 1.1 & 5.4 \selectlanguage{ngerman}± 1.1\tabularnewline
\emph{Athene cunicularia grallaria} & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 100 \selectlanguage{ngerman}± 0 &
0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 100 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}±
0\tabularnewline
\emph{Bubo virginianus} & 3.2 \selectlanguage{ngerman}± 4.2 & 0.6 \selectlanguage{ngerman}± 0.4 & 1.4 \selectlanguage{ngerman}± 2.6 & 59.4 \selectlanguage{ngerman}± 6.7
& 35.1 \selectlanguage{ngerman}± 3.3 & 0.3 \selectlanguage{ngerman}± 0.6 & 0.9 \selectlanguage{ngerman}± 0.7 & 0.9 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 60 \selectlanguage{ngerman}± 5.8 & 37.5
\selectlanguage{ngerman}± 5.6 & 0.5 \selectlanguage{ngerman}± 0.7\tabularnewline
\emph{Bubo virginianus nacurutu} & 2.9 \selectlanguage{ngerman}± 2.5 & 0.4 \selectlanguage{ngerman}± 0.7 & 3.9 \selectlanguage{ngerman}± 3.1 &
61.3 \selectlanguage{ngerman}± 4 & 31.4 \selectlanguage{ngerman}± 1.3 & 0.1 \selectlanguage{ngerman}± 0.2 & 2.5 \selectlanguage{ngerman}± 1.8 & 1.4 \selectlanguage{ngerman}± 0 & 0.5 \selectlanguage{ngerman}± 1.2 &
54.6 \selectlanguage{ngerman}± 4.3 & 40.8 \selectlanguage{ngerman}± 4.2 & 0.2 \selectlanguage{ngerman}± 0.5\tabularnewline
\emph{Glaucidium brasilianum} & 1.9 \selectlanguage{ngerman}± 2 & 1.9 \selectlanguage{ngerman}± 0.9 & 10.1 \selectlanguage{ngerman}± 12 & 54.7 \selectlanguage{ngerman}±
11.7 & 22.3 \selectlanguage{ngerman}± 2.6 & 9.2 \selectlanguage{ngerman}± 2.3 & 4.1 \selectlanguage{ngerman}± 1.1 & 1.4 \selectlanguage{ngerman}± 0 & 1.6 \selectlanguage{ngerman}± 1.6 & 77.4 \selectlanguage{ngerman}±
2.8 & 11 \selectlanguage{ngerman}± 2 & 2.6 \selectlanguage{ngerman}± 0.4\tabularnewline
\emph{Glaucidium brasilianum brasilianum} & 2.9 \selectlanguage{ngerman}± 1.8 & 1.7 \selectlanguage{ngerman}± 0.7 & 12.7
\selectlanguage{ngerman}± 12.8 & 51.7 \selectlanguage{ngerman}± 12 & 16.6 \selectlanguage{ngerman}± 2.4 & 14.4 \selectlanguage{ngerman}± 2.1 & 4.3 \selectlanguage{ngerman}± 0.9 & 1.8 \selectlanguage{ngerman}± 0 & 1.8
\selectlanguage{ngerman}± 1.3 & 78 \selectlanguage{ngerman}± 3.4 & 7.8 \selectlanguage{ngerman}± 1 & 2.9 \selectlanguage{ngerman}± 0.9\tabularnewline
\emph{Glaucidium hardyi} & 2 \selectlanguage{ngerman}± 2.1 & 33.9 \selectlanguage{ngerman}± 3.2 & 5.6 \selectlanguage{ngerman}± 0.5 & 34.8 \selectlanguage{ngerman}± 3.4
& 12.2 \selectlanguage{ngerman}± 1.1 & 11.6 \selectlanguage{ngerman}± 1.5 & 0.8 \selectlanguage{ngerman}± 1.9 & 3.4 \selectlanguage{ngerman}± 0 & 13.7 \selectlanguage{ngerman}± 3.1 & 49.2 \selectlanguage{ngerman}± 4
& 7.7 \selectlanguage{ngerman}± 2.3 & 7.9 \selectlanguage{ngerman}± 2.7\tabularnewline
\emph{Lophostrix cristata} & 1.2 \selectlanguage{ngerman}± 1.1 & 48.5 \selectlanguage{ngerman}± 0.9 & 2.2 \selectlanguage{ngerman}± 0.6 & 42.9 \selectlanguage{ngerman}±
1.7 & 0.1 \selectlanguage{ngerman}± 0.1 & 5.2 \selectlanguage{ngerman}± 0.7 & 2 \selectlanguage{ngerman}± 1.9 & 4.2 \selectlanguage{ngerman}± 0 & 7 \selectlanguage{ngerman}± 1.8 & 54.7 \selectlanguage{ngerman}± 4.6 &
0 \selectlanguage{ngerman}± 0 & 9.1 \selectlanguage{ngerman}± 2.8\tabularnewline
\emph{Megascops atricapilla} & 10 \selectlanguage{ngerman}± 5.8 & 17.4 \selectlanguage{ngerman}± 1 & 10 \selectlanguage{ngerman}± 5.8 & 33.8 \selectlanguage{ngerman}±
7.8 & 12.5 \selectlanguage{ngerman}± 1.2 & 16.4 \selectlanguage{ngerman}± 2.8 & 3.6 \selectlanguage{ngerman}± 1.7 & 4.1 \selectlanguage{ngerman}± 0 & 3 \selectlanguage{ngerman}± 4.2 & 60.5 \selectlanguage{ngerman}±
6.5 & 3.7 \selectlanguage{ngerman}± 0.9 & 3.8 \selectlanguage{ngerman}± 0.9\tabularnewline
\emph{Megascops choliba} & 16.4 \selectlanguage{ngerman}± 4.7 & 16.1 \selectlanguage{ngerman}± 1.1 & 17.5 \selectlanguage{ngerman}± 2.6 & 21.3 \selectlanguage{ngerman}±
2.7 & 15.5 \selectlanguage{ngerman}± 1.9 & 13.2 \selectlanguage{ngerman}± 1 & 2.4 \selectlanguage{ngerman}± 1.3 & 1.9 \selectlanguage{ngerman}± 0 & 7.2 \selectlanguage{ngerman}± 1.8 & 42.8 \selectlanguage{ngerman}±
2.4 & 10.6 \selectlanguage{ngerman}± 1.3 & 4.2 \selectlanguage{ngerman}± 0.9\tabularnewline
\emph{Megascops choliba choliba} & 24.1 \selectlanguage{ngerman}± 17.7 & 1.1 \selectlanguage{ngerman}± 0.8 & 15.3 \selectlanguage{ngerman}± 16.2
& 37.4 \selectlanguage{ngerman}± 7 & 13.4 \selectlanguage{ngerman}± 2.9 & 8.7 \selectlanguage{ngerman}± 2 & 14.3 \selectlanguage{ngerman}± 3 & 1 \selectlanguage{ngerman}± 0 & 1.2 \selectlanguage{ngerman}± 1.5 & 69.8
\selectlanguage{ngerman}± 3.5 & 10.6 \selectlanguage{ngerman}± 1.6 & 2.7 \selectlanguage{ngerman}± 0.7\tabularnewline
\emph{Megascops choliba cruciger} & 7.4 \selectlanguage{ngerman}± 2.7 & 47.7 \selectlanguage{ngerman}± 3.9 & 4.2 \selectlanguage{ngerman}± 1.4 &
26.1 \selectlanguage{ngerman}± 3.8 & 14.4 \selectlanguage{ngerman}± 3.4 & 0.3 \selectlanguage{ngerman}± 0.3 & 20.9 \selectlanguage{ngerman}± 4.9 & 6.7 \selectlanguage{ngerman}± 0 & 9.6 \selectlanguage{ngerman}± 3.7 &
25.8 \selectlanguage{ngerman}± 6.3 & 7.5 \selectlanguage{ngerman}± 2.4 & 0.1 \selectlanguage{ngerman}± 0.1\tabularnewline
\emph{Megascops choliba decussatus} & 13 \selectlanguage{ngerman}± 2.5 & 17.2 \selectlanguage{ngerman}± 1.2 & 5.7 \selectlanguage{ngerman}± 0.7
& 22.9 \selectlanguage{ngerman}± 1.2 & 24.5 \selectlanguage{ngerman}± 2 & 16.7 \selectlanguage{ngerman}± 1.5 & 0.1 \selectlanguage{ngerman}± 0.2 & 1.6 \selectlanguage{ngerman}± 0 & 13.8 \selectlanguage{ngerman}± 3.8
& 43.3 \selectlanguage{ngerman}± 2.7 & 13.2 \selectlanguage{ngerman}± 3.1 & 3 \selectlanguage{ngerman}± 0.9\tabularnewline
\emph{Megascops roraimae} & 23 \selectlanguage{ngerman}± 1.3 & 0 \selectlanguage{ngerman}± 0 & 36.2 \selectlanguage{ngerman}± 3 & 31.8 \selectlanguage{ngerman}± 2.4 & 9
\selectlanguage{ngerman}± 0.5 & 0 \selectlanguage{ngerman}± 0 & 32.2 \selectlanguage{ngerman}± 7.2 & 0 \selectlanguage{ngerman}± 0 & 23.5 \selectlanguage{ngerman}± 9.1 & 42.3 \selectlanguage{ngerman}± 12.7 & 2 \selectlanguage{ngerman}± 1.6
& 0 \selectlanguage{ngerman}± 0\tabularnewline
\emph{Megascops watsonii} & 5 \selectlanguage{ngerman}± 6 & 3.5 \selectlanguage{ngerman}± 4.4 & 1.3 \selectlanguage{ngerman}± 2.8 & 41.4 \selectlanguage{ngerman}± 12.4
& 47.9 \selectlanguage{ngerman}± 5.7 & 0.9 \selectlanguage{ngerman}± 0.9 & 2.2 \selectlanguage{ngerman}± 2.4 & 1.7 \selectlanguage{ngerman}± 0 & 1.2 \selectlanguage{ngerman}± 1.4 & 54.4 \selectlanguage{ngerman}± 6.4
& 37.9 \selectlanguage{ngerman}± 6 & 2.3 \selectlanguage{ngerman}± 1.1\tabularnewline
\emph{Megascops watsonii usta} & 4.5 \selectlanguage{ngerman}± 2.6 & 3.7 \selectlanguage{ngerman}± 2 & 11.8 \selectlanguage{ngerman}± 4.2 & 28 \selectlanguage{ngerman}±
4.7 & 45.5 \selectlanguage{ngerman}± 1.4 & 6.5 \selectlanguage{ngerman}± 1.6 & 10.5 \selectlanguage{ngerman}± 5.7 & 1.1 \selectlanguage{ngerman}± 0 & 14.3 \selectlanguage{ngerman}± 4 & 41.6 \selectlanguage{ngerman}±
5.2 & 23.7 \selectlanguage{ngerman}± 6.1 & 7.7 \selectlanguage{ngerman}± 1.9\tabularnewline
\emph{Pulsatrix koeniswaldiana} & 36.4 \selectlanguage{ngerman}± 5.3 & 7.9 \selectlanguage{ngerman}± 0.9 & 10.3 \selectlanguage{ngerman}± 4.6 &
17.1 \selectlanguage{ngerman}± 1.4 & 12.7 \selectlanguage{ngerman}± 1.1 & 15.6 \selectlanguage{ngerman}± 2.6 & 10.5 \selectlanguage{ngerman}± 5.1 & 5 \selectlanguage{ngerman}± 0 & 31.6 \selectlanguage{ngerman}± 8 &
38 \selectlanguage{ngerman}± 8.9 & 3.3 \selectlanguage{ngerman}± 1.1 & 4.3 \selectlanguage{ngerman}± 1\tabularnewline
\emph{Pulsatrix perspicillata perspicillata} & 0.3 \selectlanguage{ngerman}± 0.6 & 44.4 \selectlanguage{ngerman}± 5.1 &
1.7 \selectlanguage{ngerman}± 0.9 & 34 \selectlanguage{ngerman}± 3.2 & 13.6 \selectlanguage{ngerman}± 2.4 & 6.1 \selectlanguage{ngerman}± 1.6 & 0 \selectlanguage{ngerman}± 0 & 5.5 \selectlanguage{ngerman}± 0 & 6 \selectlanguage{ngerman}±
1.9 & 50 \selectlanguage{ngerman}± 6.7 & 7.5 \selectlanguage{ngerman}± 3.3 & 3.2 \selectlanguage{ngerman}± 1.5\tabularnewline
\emph{Pulsatrix perspicillata pulsatrix} & 4.9 \selectlanguage{ngerman}± 3.2 & 58.3 \selectlanguage{ngerman}± 4.7 & 9.4
\selectlanguage{ngerman}± 2.7 & 14.6 \selectlanguage{ngerman}± 1.8 & 9.5 \selectlanguage{ngerman}± 0.8 & 3.2 \selectlanguage{ngerman}± 0.9 & 0.2 \selectlanguage{ngerman}± 0.2 & 3.3 \selectlanguage{ngerman}± 0 & 2.9 \selectlanguage{ngerman}±
1.7 & 36.2 \selectlanguage{ngerman}± 2.5 & 1.3 \selectlanguage{ngerman}± 0.9 & 1.1 \selectlanguage{ngerman}± 0.9\tabularnewline
\emph{Strix virgata borelliana} & 37.9 \selectlanguage{ngerman}± 4.8 & 19.7 \selectlanguage{ngerman}± 0.7 & 5.6 \selectlanguage{ngerman}± 1.5 &
13.8 \selectlanguage{ngerman}± 2.1 & 11.1 \selectlanguage{ngerman}± 1.8 & 11.8 \selectlanguage{ngerman}± 2.4 & 19.4 \selectlanguage{ngerman}± 6 & 4.8 \selectlanguage{ngerman}± 0 & 7.2 \selectlanguage{ngerman}± 5.1 &
33 \selectlanguage{ngerman}± 8.9 & 2.7 \selectlanguage{ngerman}± 0.7 & 2.2 \selectlanguage{ngerman}± 1.2\tabularnewline
\emph{Strix virgata superciliaris} & 0 \selectlanguage{ngerman}± 0 & 15.2 \selectlanguage{ngerman}± 1.3 & 0 \selectlanguage{ngerman}± 0 & 84.8 \selectlanguage{ngerman}±
1.3 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 8.6 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 84.6 \selectlanguage{ngerman}± 8.6 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}±
0\tabularnewline
\emph{Asio clamator} & 21.3 \selectlanguage{ngerman}± 3.9 & 12.6 \selectlanguage{ngerman}± 0.8 & 18.5 \selectlanguage{ngerman}± 3.7 & 34.1 \selectlanguage{ngerman}± 5.9
& 8.9 \selectlanguage{ngerman}± 1.4 & 4.5 \selectlanguage{ngerman}± 0.8 & 6 \selectlanguage{ngerman}± 1 & 2.8 \selectlanguage{ngerman}± 0 & 57.2 \selectlanguage{ngerman}± 4.5 & 9.7 \selectlanguage{ngerman}± 2.6 &
10.3 \selectlanguage{ngerman}± 2.2 & 2.1 \selectlanguage{ngerman}± 0.8\tabularnewline
\emph{Asio clamator midas} & 48.1 \selectlanguage{ngerman}± 5.4 & 8.7 \selectlanguage{ngerman}± 0.8 & 16.1 \selectlanguage{ngerman}± 5.4 & 14.3
\selectlanguage{ngerman}± 0.8 & 6.2 \selectlanguage{ngerman}± 1.5 & 6.6 \selectlanguage{ngerman}± 1.3 & 12.6 \selectlanguage{ngerman}± 4 & 1 \selectlanguage{ngerman}± 0 & 67.1 \selectlanguage{ngerman}± 4.9 & 13.6 \selectlanguage{ngerman}±
1.7 & 1.9 \selectlanguage{ngerman}± 1.2 & 1.6 \selectlanguage{ngerman}± 0.7\tabularnewline
\emph{Athene cunicularia midas} & 0.7 \selectlanguage{ngerman}± 1.5 & 4.4 \selectlanguage{ngerman}± 1.7 & 13.3 \selectlanguage{ngerman}± 4.2 &
74.4 \selectlanguage{ngerman}± 4.5 & 7 \selectlanguage{ngerman}± 1.6 & 0.3 \selectlanguage{ngerman}± 0.5 & 0.1 \selectlanguage{ngerman}± 0.1 & 0 \selectlanguage{ngerman}± 0 & 99.8 \selectlanguage{ngerman}± 0.2 & 0.1
\selectlanguage{ngerman}± 0.2 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0\tabularnewline
\emph{Glaucidium minutissimum} & 10.2 \selectlanguage{ngerman}± 3.6 & 18.2 \selectlanguage{ngerman}± 1.6 & 17.1 \selectlanguage{ngerman}± 3.7 &
15.5 \selectlanguage{ngerman}± 3.3 & 12.8 \selectlanguage{ngerman}± 1.2 & 26.1 \selectlanguage{ngerman}± 1.2 & 2 \selectlanguage{ngerman}± 1.3 & 2 \selectlanguage{ngerman}± 0 & 48.2 \selectlanguage{ngerman}± 8.8 &
23.7 \selectlanguage{ngerman}± 5.3 & 4.1 \selectlanguage{ngerman}± 1.8 & 5.3 \selectlanguage{ngerman}± 2\tabularnewline
\emph{Megascops choliba uruguaii} & 8.3 \selectlanguage{ngerman}± 2.3 & 3.6 \selectlanguage{ngerman}± 0.8 & 13.5 \selectlanguage{ngerman}± 4.8 &
71.9 \selectlanguage{ngerman}± 4 & 0.3 \selectlanguage{ngerman}± 0.2 & 2.4 \selectlanguage{ngerman}± 1 & 0.2 \selectlanguage{ngerman}± 0.1 & 0.1 \selectlanguage{ngerman}± 0 & 98.9 \selectlanguage{ngerman}± 0.3 & 0 \selectlanguage{ngerman}±
0 & 0.2 \selectlanguage{ngerman}± 0.2 & 0.6 \selectlanguage{ngerman}± 0.2\tabularnewline
\emph{Megascops sanctaecatarinae} & 41.9 \selectlanguage{ngerman}± 7 & 5 \selectlanguage{ngerman}± 1 & 5 \selectlanguage{ngerman}± 2.7 & 40.2 \selectlanguage{ngerman}±
5.6 & 1.1 \selectlanguage{ngerman}± 0.6 & 6.7 \selectlanguage{ngerman}± 1 & 11.1 \selectlanguage{ngerman}± 4.7 & 0 \selectlanguage{ngerman}± 0 & 79.8 \selectlanguage{ngerman}± 8.5 & 6 \selectlanguage{ngerman}± 5 &
0.5 \selectlanguage{ngerman}± 0.3 & 2.5 \selectlanguage{ngerman}± 1.6\tabularnewline
\emph{Megascops watsonii watsonii} & 0 \selectlanguage{ngerman}± 0 & 46.5 \selectlanguage{ngerman}± 2.7 & 36.7 \selectlanguage{ngerman}± 3.1 & 0
\selectlanguage{ngerman}± 0 & 10.9 \selectlanguage{ngerman}± 3.1 & 5.9 \selectlanguage{ngerman}± 0.9 & 0 \selectlanguage{ngerman}± 0 & 5.7 \selectlanguage{ngerman}± 0 & 32.7 \selectlanguage{ngerman}± 6 & 0 \selectlanguage{ngerman}± 0 & 4.9
\selectlanguage{ngerman}± 2.8 & 4.2 \selectlanguage{ngerman}± 1\tabularnewline
\emph{Strix huhula} & 2.3 \selectlanguage{ngerman}± 0.4 & 12.7 \selectlanguage{ngerman}± 1.9 & 15.2 \selectlanguage{ngerman}± 1.5 & 10.8 \selectlanguage{ngerman}± 2 &
24.3 \selectlanguage{ngerman}± 1.6 & 34.8 \selectlanguage{ngerman}± 3 & 5.6 \selectlanguage{ngerman}± 1.2 & 4.7 \selectlanguage{ngerman}± 0 & 30 \selectlanguage{ngerman}± 4.4 & 13.7 \selectlanguage{ngerman}± 3.7 & 19
\selectlanguage{ngerman}± 3.6 & 14.8 \selectlanguage{ngerman}± 3\tabularnewline
\emph{Strix huhula albomarginata} & 17.3 \selectlanguage{ngerman}± 7.5 & 16.4 \selectlanguage{ngerman}± 1.5 & 12.6 \selectlanguage{ngerman}± 4.8
& 14.1 \selectlanguage{ngerman}± 6.8 & 17.5 \selectlanguage{ngerman}± 0.7 & 21.9 \selectlanguage{ngerman}± 2.9 & 0.4 \selectlanguage{ngerman}± 0.9 & 6.6 \selectlanguage{ngerman}± 0 & 44.2 \selectlanguage{ngerman}±
12.9 & 7.9 \selectlanguage{ngerman}± 4.7 & 10.9 \selectlanguage{ngerman}± 3.3 & 19.8 \selectlanguage{ngerman}± 8.1\tabularnewline
\emph{Aegolius harrisii} & 30.8 \selectlanguage{ngerman}± 4.1 & 0 \selectlanguage{ngerman}± 0.1 & 1 \selectlanguage{ngerman}± 0.3 & 8.1 \selectlanguage{ngerman}± 0.9 &
35.1 \selectlanguage{ngerman}± 1.3 & 24.9 \selectlanguage{ngerman}± 4.5 & 54.7 \selectlanguage{ngerman}± 4.3 & 1.1 \selectlanguage{ngerman}± 0 & 2.5 \selectlanguage{ngerman}± 1.9 & 6.5 \selectlanguage{ngerman}± 2.6 &
19.6 \selectlanguage{ngerman}± 5.6 & 16.3 \selectlanguage{ngerman}± 2.6\tabularnewline
\emph{Asio flammeus} & 85.3 \selectlanguage{ngerman}± 4.7 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0.9 \selectlanguage{ngerman}± 0.8 & 6.4 \selectlanguage{ngerman}±
1.2 & 7.4 \selectlanguage{ngerman}± 2.8 & 89 \selectlanguage{ngerman}± 4 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 2.8 \selectlanguage{ngerman}± 1.4 & 4.1 \selectlanguage{ngerman}± 2.1 & 4.1 \selectlanguage{ngerman}±
2\tabularnewline
\emph{Asio stygius} & 47.1 \selectlanguage{ngerman}± 5.9 & 5.9 \selectlanguage{ngerman}± 1.3 & 0.5 \selectlanguage{ngerman}± 0.3 & 1.9 \selectlanguage{ngerman}± 2.1 &
34.4 \selectlanguage{ngerman}± 2.9 & 10.2 \selectlanguage{ngerman}± 3.4 & 35 \selectlanguage{ngerman}± 7.6 & 3 \selectlanguage{ngerman}± 0 & 1.9 \selectlanguage{ngerman}± 2.7 & 15.7 \selectlanguage{ngerman}± 10.9 &
24.1 \selectlanguage{ngerman}± 7.1 & 12.2 \selectlanguage{ngerman}± 2.9\tabularnewline
\emph{Strix hylophila} & 69.7 \selectlanguage{ngerman}± 2.6 & 0.2 \selectlanguage{ngerman}± 0.2 & 0.8 \selectlanguage{ngerman}± 1.3 & 4.3 \selectlanguage{ngerman}± 2.1
& 11.2 \selectlanguage{ngerman}± 3.1 & 13.8 \selectlanguage{ngerman}± 2.4 & 94.3 \selectlanguage{ngerman}± 3.9 & 0.4 \selectlanguage{ngerman}± 0 & 0.2 \selectlanguage{ngerman}± 0.2 & 3 \selectlanguage{ngerman}± 1.9 &
1.3 \selectlanguage{ngerman}± 1.4 & 1.1 \selectlanguage{ngerman}± 0.3\tabularnewline
\emph{Strix virgata} & 31.7 \selectlanguage{ngerman}± 5 & 31.2 \selectlanguage{ngerman}± 3.3 & 4.4 \selectlanguage{ngerman}± 0.6 & 7.5 \selectlanguage{ngerman}± 0.7 &
12.4 \selectlanguage{ngerman}± 3.4 & 12.8 \selectlanguage{ngerman}± 4.7 & 29.1 \selectlanguage{ngerman}± 5.6 & 4 \selectlanguage{ngerman}± 0 & 15.4 \selectlanguage{ngerman}± 3.3 & 8.7 \selectlanguage{ngerman}± 3.9 &
4.6 \selectlanguage{ngerman}± 2.9 & 2.6 \selectlanguage{ngerman}± 2.6\tabularnewline
\emph{Asio clamator clamator} & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 29.4 \selectlanguage{ngerman}± 0.7 &
58.4 \selectlanguage{ngerman}± 1.4 & 12.2 \selectlanguage{ngerman}± 1.1 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 32 \selectlanguage{ngerman}± 18.1 & 49.7 \selectlanguage{ngerman}±
11.3 & 18.3 \selectlanguage{ngerman}± 9.7\tabularnewline
\emph{Glaucidium brasilianum ucayalae} & 0.1 \selectlanguage{ngerman}± 0.2 & 0.2 \selectlanguage{ngerman}± 0.3 & 0 \selectlanguage{ngerman}± 0 &
26.1 \selectlanguage{ngerman}± 10.2 & 49.4 \selectlanguage{ngerman}± 5.6 & 24.1 \selectlanguage{ngerman}± 4.9 & 0.3 \selectlanguage{ngerman}± 0.7 & 1.5 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 &
41.9 \selectlanguage{ngerman}± 12.6 & 45.2 \selectlanguage{ngerman}± 11.3 & 11.9 \selectlanguage{ngerman}± 4.6\tabularnewline
\emph{Pulsatrix perspicillata} & 2.8 \selectlanguage{ngerman}± 1.4 & 37.3 \selectlanguage{ngerman}± 2.2 & 3.4 \selectlanguage{ngerman}± 1.8 &
18.3 \selectlanguage{ngerman}± 1.4 & 28.2 \selectlanguage{ngerman}± 1 & 10.1 \selectlanguage{ngerman}± 2 & 10.4 \selectlanguage{ngerman}± 5.1 & 8.1 \selectlanguage{ngerman}± 0 & 9 \selectlanguage{ngerman}± 5.2 & 19.1
\selectlanguage{ngerman}± 5.4 & 23.6 \selectlanguage{ngerman}± 5.7 & 4.5 \selectlanguage{ngerman}± 1.3\tabularnewline
\emph{Strix huhula huhula} & 7.2 \selectlanguage{ngerman}± 11.4 & 0 \selectlanguage{ngerman}± 0 & 0.3 \selectlanguage{ngerman}± 0.5 & 14.3 \selectlanguage{ngerman}± 9.9
& 40.1 \selectlanguage{ngerman}± 2.3 & 38 \selectlanguage{ngerman}± 1.6 & 12 \selectlanguage{ngerman}± 17.2 & 0.2 \selectlanguage{ngerman}± 0 & 4.9 \selectlanguage{ngerman}± 4.4 & 17.2 \selectlanguage{ngerman}± 12.1
& 34.4 \selectlanguage{ngerman}± 9.3 & 31.4 \selectlanguage{ngerman}± 8.4\tabularnewline
\emph{Bubo virginianus deserti} & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 82.3 \selectlanguage{ngerman}±
1.6 & 17.7 \selectlanguage{ngerman}± 1.6 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 0 \selectlanguage{ngerman}± 0 & 41.6 \selectlanguage{ngerman}± 7.5 & 58.4 \selectlanguage{ngerman}±
7.5\tabularnewline
\emph{Glaucidium mooreorum} & 0.2 \selectlanguage{ngerman}± 0.1 & 73.0 \selectlanguage{ngerman}± 4.5 & 0 \selectlanguage{ngerman}± 0 & 0.2 \selectlanguage{ngerman}± 0.1
& 25.6 \selectlanguage{ngerman}± 4.7 & 0.9 \selectlanguage{ngerman}± 0.3 & 0.1 \selectlanguage{ngerman}± 0.1 & 99.4 \selectlanguage{ngerman}± 0.2 & 0 \selectlanguage{ngerman}± 0 & 0.4 \selectlanguage{ngerman}± 0.2 &
0.1 \selectlanguage{ngerman}± 0.1 & 0.2 \selectlanguage{ngerman}± 0.1\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Records of Brazilian Strigidae used in the modeling process.
Biome colors correlates with the abundance of records (the darkest the
most abundant). State and countries acronyms follow ISO 3166-2 and ISO
3166-1 alpha-2, respectively.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Predicted Strigidae species richness patterns in Brazil based
on (A) species \emph{sensu stricto} ; and (B) \emph{sensu
lato}(including subspecies). Dashed polygons represent the strict
protected areas. Biome limits in dark gray lines.

\selectlanguage{english}
\FloatBarrier
\end{document}

