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
Fernández-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 & López-Baucells, 2014b) reported a young Lophostrix
cristata that was indeed a Strix virgata (Rocha &
López-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 (Pärt 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 (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 (Eucalyptus globulus )
facilitated the range expansion of the powerful owl (Ninox
strenua ) in South Australia. Similar situations could be shaping the
distributions of some Brazilian owl species, such as the tree-nesterA. 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 (Müller 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 (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.