Spatial distribution models
Excluding the case of the striped owl (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 G. mooreorum , to 31%, forLophostrix cristata ; Supplementary material A, Appendix 1, Fig.
E) compared to the polytypic ones (median 34%, from 24% forStrix virgata to 42% for 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 Athene cunicularia cunicularia and, exceptionally,
66% for 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 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 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 Rondônia 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 (sensu
stricto ) recorded against those predicted, we found that all biomes are
under-sampled (Table 2), especially the Pantanal and the Caatinga.