666 R. Horion et al.


FIG. 1 Niokolo-Koba National Park in Senegal, with the survey area where we conducted camera trapping during March–June 2021.


(Fonteyn et al., 2021). We calculated RAI as the number of independent images for each species divided by the total number of trap-days multiplied by 100 (O’Brien et al., 2003). Although RAIs do not incorporate detection hetero- geneity between species, they can be useful for species-level comparisons within single surveys (Royle & Nichols, 2003). We pooled our data into a relative abundance matrix for each species, thereafter fitting the nMDS with 10,000 ran- dom starts using the Bray–Curtis distance dissimilarity measure. We used covariates, which we selected based on a priori hypotheses (Table 1). We also included all mammal species (with a body mass $0.5 kg) detected, to determine similarities between carnivores and the terrestrial mammal community of the Park. In this ordination, the closer two points are, the more similar the corresponding species are with respect to the covariates (derived at the camera-station level) used in the nMDS plot. We checked nMDS distortion using the stress value, with values ,0.3 indicating that the ordination is arbitrary (Legendre & Legendre, 1991). We conducted the nMDS calculations using the vegan package (Oksanen et al., 2022)in R 4.1.1 (R Core Team, 2022).


Occupancy We fitted single-season, single-species occu- pancy models (MacKenzie et al., 2002) using the R package


unmarked (Fiske & Chandler, 2011) to investigate the pat- terns of habitat use in the Park for each large carnivore (lion, leopard, spotted hyaena and wild dog). Occupancy models utilize binary detection/non-detection data (a site-by-occasion matrix, where 1 represents a presence and 0 an absence) to estimate the probability of detection (ρ) and occupancy (ψ). In this study, because all target species have home ranges larger than our grid cells, we use the term ‘site use’ rather than occupancy (Choki et al., 2023), which represents the percentage of the study area used by the spe- cies (Tobler et al., 2015). We employed a data-driven ap- proach to mitigate zero inflation and improve model fit, as suggested previously (Broekhuis et al., 2022). To enhance modelling accuracy, we pooled detection histories into 9-day sampling occasions for leopard and spotted hyaena and 11-day occasions for wild dog and lion. After testing various durations (7–15 days), we selected the best-fit pool- ing duration for each species. Detection probability (ρ) and occupancy (ψ) can be modelled as functions of site-specific covariates (MacKenzie et al., 2002), and we used the same covariates as for the nMDS (Table 1) and standardized them to z-scores. We used the Spearman correlation coeffi- cient (r) to assess multicollinearity amongst chosen covari- ates, and removed covariates with the least explanatory


Oryx, 2024, 58(5), 664–675 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323001746


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