416 E. Rios et al.


by using both the length function within the geometry group from the field calculator tool in QGIS and the v.dis- tance tool available in the GRASS plugin. We assumed that forest patches in a landscape with more and nearer rivers represent a higher habitat quality for red-billed curassows, because they were frequently recorded in forests in close proximity to rivers (Sick, 1997; IBAMA 2004; Bernardo et al., 2011). (3) Forest patch density (i.e. number of forest patches divided by the area within the buffer), by using Land Cover Statistics (LecoS)in QGIS (Jung, 2016). We as- sumed that, in an already fragmented but still well-forested region, a higher density of patches contributes to habitat quality at the landscape scale because it maintains higher connectivity, which is important for recolonization dynam- ics (Tambosi et al., 2014). (4) Distance from the edge of the focal forest patch to the nearest patch within the buffers (SOSMata Atlântica, 2012), assuming nearby forest patches improve connectivity and contribute to habitat quality at the landscape scale (Tambosi et al., 2014; Supplementary Table 1). We obtained this variable by using the v.distance tool available in the GRASS plugin. The variables related to hunting pressure were: (1) Pro-


portion of cacao agroforests and farmland within each buffer, which we obtained by using Land Cover Statistics (LecoS)in QGIS (Jung, 2016). (2) Total area occupied by set- tlements within both focal forest patch and buffers. We ob- tained this variable by creating a shapefile to draw polygons that corresponded to settlements within a Google Maps sat- ellite image visualized with the OpenLayers plugin. (3)Mean distance from the edge of the focal patch to settlements, using the v.distance tool available in the GRASS plugin. (4) Total length of all unpaved roads (hereafter roads) with- in the patch and the buffers, using the length function within the geometry group from the field calculator tool in QGIS; we used a shapefile available from DNIT (2015)and a shapefile we created to draw lines that corresponded to roads within a Google Maps satellite image visualized with the OpenLayers plugin. (5) Distance from the focal forest patch to the nearest road within the buffers, using the v.distance tool available in the GRASS plugin. We assumed that all these parameters are associated with higher numbers of and increased access by hunters and domestic dogs (Canale et al., 2012;Thorntonet al., 2012; Cassano et al., 2014; Supplementary Table 1). We showed the map of unpaved roads in the relevant forest patch to key interviewees, to confirm that the network was essentially unchanged for at least 20 years.


Data analysis


We first selected the appropriate spatial scale to be used in further analyses, by comparing the determination coeffi- cients (R2) of habitat quality and hunting pressure vari- ables as a function of forest cover at multiple scales (i.e.


considering the amount of forest cover within the focal for- est patch and at 500 m, 1 km and 2 km from its edge). The spatial scale with the highest R2 values was thus established as the standard for subsequent analysis. We checked for multicollinearity among the five vari-


ables related to habitat quality, and among the five variables related to hunting pressure, and selected those with values of thevarianceinflation factor ,5 (O’Brien, 2007). Then we ran all possible combinations of variables related to habitat quality (hereafter referred to as habitat quality submodels), and applied the same procedure for variables relating to hunting pressure (hereafter referred to as hunting pres- sure submodels). Weselected the best combinations of vari- ables based on the lowest Akaike information criterion (AIC; Burnham & Anderson, 2004) values, with ΔAIC ,2. Finally, we built a model containing variables presented in the best submodels of habitat quality and hunting pressure, following Thomas (2017). We selected the best model based on AIC values for the variables presented in the final model, as for the submodels. We excluded collinear variables from the final model. Because of the ordinal nature of the response variable,


we conducted ordinal logistic regression to evaluate which landscape variables were associated with the persistence levels of red-billed curassows. Ordinal logistic regression preserves information about the ordering (e.g. high, medi- um, low), whereas for other types of logistic regression (e.g. binomial models) information must be binary (i.e. 0 = absence, 1 = presence), which would oversimplify the analysis for our research question (Harrell, 2015). We calcu- lated values of variance inflation and performed ordinal logistic regression using the package rms (Frank & Harrell, 2017)in R 3.1.1 (R Development Core Team, 2015).


Results


The 2 km buffer radius best explained most of the habitat quality and hunting pressure variables, as indicated by the highest determination coefficients (Supplementary Table 2). The best combination of habitat quality variables resulted in two submodels with relatively low explanatory power (R2 = 0.22–0.26). The hunting pressure variables resulted in two well-fitted submodels with higher explanatory power (R2 = 0.54–0.56; Table 2). Four final best models containing both habitat quality


and hunting pressure variables had a high explanatory power (R2 = 0.36–0.63), which comprised four hunting pres- sure variables and two habitat quality variables (Table 2). Distance from the focal forest patch to settlements occurred in all final models, and area occupied by settlements in three models. In contrast, proportion of native forest cover and distance from the nearest forest patch occurred in one final model (Table 2). Specifically, curassow populations


Oryx, 2021, 55(3), 412–420 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319000711


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