816 I. C. Avila et al.


FIG. 1 The Colombian Caribbean Sea, showing the bathymetry of the study area and the locations of the Archipelago of San Andrés, Old Providence and Saint Catherine (the latter two islands labelled Providencia), the Gulf of Urabá, the Gulf of Darién, and the rivers Atrato, Sinú, Magdalena and Ranchería.


We created one random point per 4.5 × 4.5 km grid cell for the surface model and one random point per 9.2 × 9.2 km grid cell for the models at various depth levels, using the randomPoints function in the Dismo package (Hijmans, 2017)in R 3.6.3 (R Core Team, 2020) over the study area defined for each model (Supplementary Material 1). Environmental variables used in the modelling were se-


lected based on information in Baumgartner et al. (2001), Tobeña et al. (2016) and Barragán-Barrera et al. (2019). As the sperm whale is a deep diving species, we included environmental variables from different levels of the water column: on the surface, at c. 0.5mdepth (Level 1), c. 500m (Level 2), c. 1,000m (Level 3), c. 1,500m (Level 4) and c. 2,000m (Level 5) (Supplementary Fig. 2). Source, types of environmental data available, spatial resolution and time span of sea surface data differ from those of data for various depths, and therefore we chose data that we consid- ered to be equivalent.One exceptionwas the inclusion of the ocean mixed layer thickness data in the models at different depth levels, which was not included as a surface layer. We analysed variable importance and selected uncorrelated environmental variables following the method proposed by Dormann et al. (2013), implemented by Zurell et al. (2020). Firstly, we examined the importance of variables for the surface and different depths using a simple general- ized linear model for each potential predictor, and ranked


variable importance with Akaike’s information criterion. We then inspected correlations between environmental layers to identify all pairs of variables that had a Spearman correlation coefficient .0.7, removing the less important variable from further analyses (Supplementary Material 1, Supplementary Tables 1–3). From a total of 111 environ- mental layer candidates, we selected 22, including dynamic (ocean mixed layer thickness, salinity, temperature, total chlorophyll a and phytoplankton) and static (bathymetry, distance to shore, seafloor aspect and slope) variables. We extracted predictor values for each occurrence, and back- ground points, using the function Fun_Extract (Derville et al., 2018), which returns the closest values for empty cells. To summarize the environmental conditions at the surface and depth levels, and to describe the environmental heterogeneity of thewater column,we conducted a principal component analysis (PCA) for the 22 selected layers, using the function rasterPCA in the Rstoolbox package (Leutner & Horning, 2016)in R (Supplementary Table 4). Maxent model settings were defined through the ENMevaluate function of the ENMeval 0.3.0 package (Muscarella et al., 2014)in R, which provides species-specific settings such as feature classes and regularization multi- pliers to generate models (see Supplementary Material 2 for details). Model performance and cross-validation pre- dictions were estimated using a series of adapted functions


Oryx, 2022, 56(6), 814–824 © The Author(s), 2022. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605321001113


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