Ecogeographical variables
A set of criteria was developed to predict raptor habitat suitability which included choosing variables that were potentially useful for raptors’ distribution. The Worldclim database version 2.1 (Fick and Hijmans, 2017; https://www.worldclim.com/current) provided 19 bioclimatic variables. Because of their direct effects on species distribution, climate variables are commonly employed in habitat modeling . The Shuttle Radar Topographic Mission’s digital elevation model (SRTM DEM, opendata.rcmrd.org/datasets/) was used to extract digital elevation data for Kenya, slope and aspect were derived from the DEM, and the topographic roughness index calculated as the surface area ratio, which is also derived from the DEM . Data for the Normalized Difference Vegetation Index (NDVI) (2013-2020) were obtained from NOAA, as were observations from the Advanced Very High Resolution Radiometer (AVHRR) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellites (https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_browseByCountry.php). The Human Influence Index (HII), which best represents anthropogenic impacts spanning the years 1995 to 2004, was derived from Last of the Wild v2 (https://sedac.ciesin.columbia.edu/data/collection/wildareas-v2/). ArcGIS was used to rasterize all predictions at a spatial extent n of about 1 km (Version 10.5). We used the ‘usdm ’ package in R to carry out a variance inflation factor stepwise procedure to decrease multicollinearity in predictor variables . Variables with variance inflation factors greater than 10 were eliminated. As a result, we only kept the 12 best-fitting covariates based on the raptors’ ecological requirements from an initial set of 25 variables.