398 E. K. Madsen and F. Broekhuis
bushes, and closed habitat is predominantly riparian. Closed and semi-closed habitat were merged, as the proportions of closed habitat were relatively low (see Klaassen & Broekhuis, 2018, for details). The proportions of open and closed/ semi-closed habitat were then calculated for each site.
Distance to protected areas Per site, the mean Euclidean distance of the site to the protected areas was calculated.
Distance to rivers Per site, the mean Euclidian distance of the site to the nearest river was calculated.
All spatial calculations were performed in ArcGIS and all environmental variables, except those that are proportions, were standardized using a z-score transformation with a mean of 0 and a standard deviation of 1.
Site-occupancy modelling
Only interviews thatwere conducted outside protected areas were analysed, using a single-season occupancy model. All analyses were performed with R 3.4.0 (R Development Core Team, 2016) using the unmarked package (Fiske & Chandler, 2011). The study area was divided into 5 × 5 km sites, as this was a sufficiently fine scale to provide useful in- formation for planning conservancies and corridors (Fig. 1). However, as 25km2 is smaller than the mean home ranges of the species being assessed, this violates the assumption of closure, and therefore ψ was interpreted as the probability of site use rather than the probability of occupancy (Zeller et al., 2011; Alexander et al., 2016). Other assumptions of oc- cupancy modelling, such as no false positives and no mod- elled heterogeneity, are accounted for in our models. We randomly selected a maximum of 10 interviews per site to minimize variance and aid model convergence (Petracca et al., 2018) and each interview within a site was treated as a repeat survey. The potential for false positives was ac- counted for by introducing a binary variable, designating 1 as equal to or greater than the mean number of surveys (6) and 0 as less than the mean, because the probability of false positives is expected to increase with the number of surveys per site (Royle & Link, 2006; Petracca et al., 2018). The fol- lowing model was used:
L(p,c|y)/ R
i=1 + Pyi 1Pyi 11(1−P11)T−yi 10(1−P10)aT−yi c (1−c)
where P10 = false detection probability, P11 = true detection probability, R = number of sites, yi = number of detections at site i and T = total survey number at site i. To create detection histories for each site, daily and week-
ly sightings were considered as presence (1) and all other sightings as absence (0). However, for wild dogs, monthly
sightings were also used as presence because the species is uncommon. This distinction was employed as we wished to identify sites with the highest levels of use, and daily and weekly sightings are likely to indicate an animal incor- porates the site as part of its home range, whereas less fre- quent sightings may indicate an animal is transient. A respondent’s occupation, the proportion of open habi-
tat, or a combination of the two, could influence the detec- tion probability and account for heterogeneity, so each model was run separately and the variable(s) in the model with the lowest Akaike Information Criterion (AIC; Burnham & Anderson, 2002) were used in the subsequent analysis. For the human disturbance and habitat categories, univariate models were run and the AIC values were used to determine which variable within each category best pre- dicted site use per species. Pearson’s correlation tests were run on the variables selected in the univariate analysis stage, with a threshold of |r|.0.6 indicating correlation (Dormann et al., 2013). Uncorrelated variables were then used in the multivariate models, which included the top variables in the human presence and habitat categories and distances to the protected areas and nearest river. A priori candidatemodels were ranked using AIC and relative support was assessed using the ΔAIC and AIC weights. If the top model AIC weight was,0.9 then the probability of site usewas averaged using aweighted method for all the models with ΔAIC,2 (Burnham & Anderson, 2002). For models and model comparison statistics see Supplementary Material 1. The results from the top models were used to pre- dict the probability of site use (ψ) for sites without inter- views using the following equation:
c= 1+exp[/+(b×D1)+(b×D2)···(b×D5)] exp[/+(b×D1)+(b×D2)···(b×D5)]
where D1−5 = site use covariates and β1−5 = estimated coeffi- cients. The averaged probabilities of site use were mapped
individually for each species and then summed to generate a combined map.
Results
In total 648 interviews were conducted outside the protected areas in 67 of 139 sites (1–10 interviews per site; Fig. 1). Only data where species were correctly identified were used, re- sulting in varying sample sizes of interviews used per spe- cies: cheetahs n = 584, hyaenas n = 642, leopards n = 577, wild dogs n = 598, lions n = 648, and elephants n = 648. Pearson correlation tests indicated that none of the variables in the different univariate analysis groups were correlated (|r|,0.6). The false positive model was used for all species except the hyaena, for which the single-season model was used because the false positive model did not converge. Additionally, of the two models with a ΔAIC ,2 for
Oryx, 2020, 54(3), 395–404 © 2018 Fauna & Flora International doi:10.1017/S0030605318000297
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