Conservation of Vieira’s titi monkey 839


FIG. 1 The Tapajós–Xingu interfluve, southern Amazonia, Brazil, showing the location of fires, illegal mining, hydroelectric dams as of 2020 (RAISG, 2020), protected areas and Indigenous lands (MMA, 2018).


municipalities of Cláudia and Sinop, Mato Grosso State, Brazil, c. 50 km from the the type locality of P. vieirai.


Data analysis


We used the occurrence records, and environmental variables from WorldClim 2.1 (Fick & Hijmans, 2017) and CliMond (Kriticos et al., 2012), to model habitat suitability and delimit the geographical distribution of P. vieirai.We selected only spatially independent records (n = 33) from our dataset (n = 99 records), applying a threshold of 10 km between records. We eliminated autocorrelated environ- mental variables to avoid model overfitting (Pearson’s correlation test r.0.80,P,0.05; Supplementary Table 1; Callegari-Jacques, 2003; Mateo et al., 2013). We converted the 11 environmental variables selected to a 2.5-min scale using the raster package (Hijmans & Etten, 2012)in R 4.1 (R Core Team, 2018). We modelled habitat suitability using four algorithms adequate for the type of data available


(presence and pseudo-absence records), using the Biomod2 package (Thuiller et al., 2016)in R: artificial neural networks (Ripley, 1996), generalized boosted models (Friedman, 2001), random forest (Breiman, 2001) and max- imum entropy (Phillips et al., 2006). We selected these four algorithms to avoid model overfitting that could result from the use of algorithms suitable for presence-only and pres- ence and true absence records from long-term surveys (Andrade et al., 2020; Silva et al., 2020). For each algorithm we established five datasets, each composed of 10,000 back- ground records randomly distributed throughout the study area. We then used 70% of the records for training and 30% for evaluating model fitting. In total we performed 200 runs (four algorithms, 10 runs of cross-validation, five sets of random background points), with 1,000 iterations each. To check the accuracy of models we used true skill statistical (TSS) analysis (Allouche et al., 2006) and the area under the curve value (AUC) of the receiver operating characteris- tic curve, incorporating a binomial probability as a null model (Phillips et al., 2006). The AUC and TSS values


Oryx, 2022, 56(6), 837–845 © The Author(s), 2022. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S003060532100171X


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