796 R. Freire Filho and J. M. Palmeirim
TABLE 1 Predictive variables used in our analysis of the potential distribution of and priority conservation areas for the Caatinga howler monkey Alouatta ululata.
Variable
Maxent variables % tree cover
Aridity index Bio17
Bio15
Forest canopy height Ruggedness index
Constraint layers Anthropogenic areas
Influence of roads Population density Description Canopy closure for all vegetation .5 m height
Rainfall deficit for potential vegetative growth (higher values represent greater aridity) Precipitation of driest quarter
Precipitation seasonality (SD of monthly precipitation expressed as %) Global 1 km forest canopy height
Measurement of terrain heterogeneity generated in QGIS using SRTM3 data
Land cover map from GlobCover project; all categories (croplands & urban areas) were joined in a single class
Buffer of road influence 18 km either side; this distance was selected subjectively by visually analysing land cover along roads in the study area
GlobalRural–UrbanMapping Project,Version1 (GRUMPv1): Population Density Grid
Source
Global forest change 2000–2014; Hansen et al. (2013)
CGIAR Consortium for Spatial Information; Zomer et al. (2008) WorldClim 1.4; Hijmans et al. (2005) WorldClim 1.4; Hijmans et al. (2005)
SDAT (2011), Simard et al. (2011) USGS (2000), Riley et al. (1999)
ESA (2009) IBGE (2015) SEDAC (2011), Balk et al. (2006)
when the occurrence data include a moderate level of loca- tional error (Graham et al., 2008). Our initial database in- cluded 184 occurrences, 52 from our surveys and 132 from CPB/ICMBio. However, to minimize problems associated with spatial biases in sampling we used spatial filtering (Kramer-Schadt et al., 2013), reducing the number of occur- rences in oversampled areas by using only one within a ra- dius of 5 km. This filtering procedure reduced the number of occurrences used in the modelling to 117 (20 direct observa- tions and 70 reports from CPB/ICMBio, and one observa- tion and 26 reports from our surveys).
Modelling of potential distribution
We used Maxent to identify variables influencing the dis- tribution of A. ululata and generate a distribution map (Phillips et al., 2006). The choice of environmental variables was guided by the species’ biology; A. ululata is arboreal, feeds on leaves, fruits and other plant parts, and lives in a semi-arid region influenced by seasonal rainfall and high temperatures. We expected areas with higher precipitation to be more suitable during the most critical period of the year, the dry season. Furthermore, we hypothesized that tree cover and tree height would influence suitability, and that areas with rugged terrain would be more suitable be- cause terrain ruggedness tends to be an obstacle to habitat destruction and to provide some protection from hunting. With the help of a matrix of correlations between all candi- date variables, we selected a set of six predictors (Table 1)
that were not highly correlated (|r|,0.70; Rainho & Palmeirim, 2013) and that were biologically meaningful. Prior to running Maxent all layers were converted to the
WGS 1984 geographical coordinate system and to a cell size of 30 arc seconds (c. 1 km2), using IDRISI Selva (Eastman, 2012) and QGIS 2.8 (QGIS Development Team, 2016). In Maxent we used the following settings: convergence thresh- old (10–5), maximum iterations (500), regularization multi- plier (1), maximum number of background points (104), linear, quadratic, product and hinge features, random seed generation and 50 replicates. The resulting map is in logistic format, with the probability of presence for each cell being in the range 0–100% (Phillips, 2008). To select a suitability threshold for the potential distribution map we used the methodology described in Rainho & Palmeirim (2013), which facilitates the selection of the smallest area including most occurrences. The area selected was that corresponding to the Maxent suitability threshold 70%, which encom- passed 83% of the occurrences (Fig. 2); above this threshold the inclusion of more occurrences would force the addition of a disproportionally large area.
Prioritizing areas for conservation
We used Zonation (Moilanen et al., 2005) to prioritize areas for the conservation of A. ululata. Zonation generates a pri- ority map that can be used to inform decision-making. Cost efficiency can be considered in this prioritization through the inclusion of a cost layer. We assume that conservation
Oryx, 2020, 54(6), 794–802 © 2019 Fauna & Flora International doi:10.1017/S0030605318001084
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