Appendix S1. Addressing Spatial Autocorrelation Introduction A common challenge in interpreting species distribution patterns deals with spatial autocorrelation. Spatial autocorrelation (SA), resulting from spatial dependence between species presences or as a response to a spatially autocorrelated environmental factor, can lead an increased risk of a type I error (i.e. falsely reject null hypothesis) (Lichstein et al., 2002; Dormann et al., 2007). Various analytical techniques are currently available to test and to account for the occurrence of SAR in ecological data. Most of such techniques, however, require continuous data or parametric error estimates of species environment relations to assess the effect of SAR (Moran’s I test, Conditional Autoregressive model s, Simultaneous Autoregressive models, etc) (Rangel et al.; Legendre, 1998; Lichstein et al., 2002; Dormann et al., 2007; Allouche et al., 2008). Yet, when species absence data is not available and species-environment relations are predicted by using presence-only data, such techniques are not applicable. Hence, to investigate whether elephant presence data used in this study and the resulting niche model was affected by spatial dependence between elephant observations or spatially autocorrelated environmental predictors, we used an alternative approach. Methods First, to investigate whether the observed pattern of elephant occurrences in the north of Aceh were clustered in space, Ripley’s L function was used (Ripley, 1977; Wiegand & Moloney, 2004). This method compares the observed pattern of species presences in space to the pattern of points as expected based on a complete spatial randomness over a range of distances (Wiegand & Moloney, 2004). If species occurrences are not completely random, the observed pattern could either be a result of (1) spatial dependence of localities (i.e. presences are more likely to be observed in adjacent transects) or (2) a response of the elephants to a spatially autocorrelated environment (Legendre, 1998). On the other hand, if the observed pattern of elephant presences does not deviate from the expected pattern of complete spatial randomness, the observed distribution of elephant presences is accepted to be a result of a random process and hence can be considered to encompass a spatially independent data sample. In other words, in absence of spatial clustering, the observed presence of elephants at one transect is unlikely to be a result of elephant presence in any adjacent transect. The Ripley’s L statistic was calculated several times for increasing distance classes incrementing by 100 meters up to a maximum of 5000 meters, corresponding to the maximum distance between sample plots. In order to produce unbiased estimates of point clustering within and between sample plots the extent of the analysis was constrained by the outlines of the sampled area. Significance intervals of the obtained statistic were determined by means of 99 random Monte Carlo simulations. For each trial, a number of points corresponding to the number of observations within the original dataset, were randomly positioned to the analysis window and Ripley’s L statistic was calculated. The point pattern of elephant presences was considered to be random if the corresponding L-statistic fell within the 95% confidence envelope of the simulated trials. Secondly, to assess whether the predictor variables used for elephant niche model construction were spatial autocorrelated, possibly affecting the results of the ENFA analysis, spatial correlograms were calculated (Rangel et al.; Rossi et al., 1992; Bellehumeur & Legendre, 1998). The average similarity between elephant presence points was calculated by means of the Moran’s I statistic (and 95% confidence interval) and plotted over 10 classes of increasing between-point distance intervals (500m increments). As such, this method allows determining at what distance spatial autocorrelation exists and pseudo sampling of species distributions might occur (Anselin, 1995). Finally, to assess whether spatial autocorrelation in the predictor variables influenced the niche model predictions, the ENFA analysis was repeated separately using two pruned datasets. Therefore, elephant presence points occurring within 500 or 1000 meters of any adjacent presence point were omitted, resulting in two datasets relating to plot and site level presence observations. Data analysis was conducted using the R statistical computation environment version 2.11.1 (packages: adehabitat, maptools and spatstat). Results and conclusions Of all possible point-pair combinations within the extent of the analysis (i.e. 5000m from a focal point) 42 (15%) of all point-pairs were within 500m and 65 (23%) point-pairs were within 1000m distance of each other. The results of the point pattern analysis showed that the observed pattern of elephant presences across the north of Aceh did not differ significantly from a pattern expected based on complete spatial randomness. Therefore observed pattern of elephant presences can be considered spatially independent (Figure S1). The analysis of spatial autocorrelation in the predictor variables used to describe the elephants’ niche in the north of Aceh showed that all variable except for the variable Landscape curvature over 500m were significantly affected by spatial autocorrelation (Figure S2). One variable (Landscape curvature over 5000m) showed significant spatial autocorrelation at a 500 meter interval, two variables (NDVI, Slope) were autocorrelated at distances up to 1000 meter and 4 variables (Elevation, Ruggedness, Road density and Forest cover) were autocorrelated at distances of more than 1000 meter. Figure S1 Plot of the observed point clustering (black dots) and the 95% confidence interval of 99 Monte Carlo trials of complete spatial randomness (black lines) against the between-points distances Figure S2 Correllograms for each of the eight ecogeographical predictors included in the ENFA analysis. Each diagram shows the relative spatial autocorrelation indicated by the Morans I statistic for over ten distance intervals incrementing by 500 meters (0-5000 meters). Error bars indicate the 95% confidence interval around the average value. Since the model predicted by the ENFA analysis showed high correlation with both Landscape curvature over 5000m as well as the predictors: NDVI, Road density and Forest cover, the results could potentially be biased due to pseudo replication. Yet, since elephant presence observations showed no apparent pattern of spatial dependence between points, any inference about elephant habitat preferences in response to the environment is expected to solely reflect habitat selection processes. This hypothesis is confirmed by the results from the additional ENFA analysis. The elephants’ niche model appears to produce consistent results at different spatial scales. Hence, predictors which most strongly correlated to the elephants’ niche under the initial model were also the predictor variables shaping the elephants’ niche using the datasets in which adjacent points were omitted (Figure S3). Model performance was good (AUC=0.830.02, N=48) under the 500m dataset and reasonable under the 1000m dataset (AUC=0.780.02, N=41). Since no significant clustering was found between elephant presence points and the fact that the ENFA analysis produced consistent results even when the predictor variable were spatially autocorrelated in some cases, the niche model presented is believed to be valid. Our results have shown that the survey design used to collect invaluable data on elephant habitat produces robust results on elephant habitat selection and habitat suitability, also at different spatial scales. Figure S3. Bi-plots of the first two factors extracted by the ENFA analysis showing the marginality scores on the x-axis and the first specialization factor scores on the y-axis. The background environment of the whole study area is indicated by light grey polygons and the area used by elephants is indicated by dark grey polygons. The relative correlation between the two factors (marginality and specialisation) and each of the eight predictor variables are indicated by arrows. The niche model predicted by the ENFA analysis using the elephant presences pruned at 500m distance (A) does not deviate from the niche model predicted based elephant presences pruned at 1000m (B) A B References Allouche, O., Steinitz, O., Rotem, D., Rosenfeld, A. & Kadmon, R. (2008) Incorporating distance constraints into species distribution models. Journal of Applied Ecology, 45, 599-609. Anselin, L. (1995) Local indicators of spatial association - LISA. Geographical Analysis, 27, 93-115. Bellehumeur, C. & Legendre, P. (1998) Multiscale sources of variation in ecological variables: modeling spatial dispersion, elaborating sampling designs. Landscape Ecology, 13, 15-25. 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