We searched the literature for modelling studies of the 80 extant non-human primate genera in the IUCN Red List of Threatened Species v. 2020.3 (IUCN 2020) using the broadest academic database, Google Scholar (Gusenbauer 2019). Search terms included the genus name, “species distribution model” and “ecological niche model”, linked with Boolean operators. Where we could find no relevant literature for a genus, we refined the taxonomic search term (e.g. using alternative genus names or adding the rest of the binomial) and finally widened our search using the search term “climate change model”. 
For each genus we sorted the resulting papers by relevance and selected up to four for detailed analysis (the modal number of relevant hits per genus was 2). Where a genus had more than four relevant papers (which affected the great apes, Hylobates, Macaca, Rhinopithecus, Cercopithecus, Ateles, Aotus, Alouatta, Saguinus and Sapajus), we read abstracts and methods of all we found and selected a representative sample of four that included (1) as many species as possible; (2) a range of approaches, if several had been used; and (3) predictive and/or palaeoclimate studies if they had been done. This meant, for instance, that for the best-studied genera we excluded some very small-scale studies (especially where they modelled a single population or a region rather than a species’ full range). We also sometimes found Masters theses and then papers deriving from the same models (we retained the peer-reviewed papers) or research teams which had produced a series of papers each considering a different species but using the same method and scenario (here we chose a representative example or, if it existed, a recent synthesis covering multiple species). For our final literature sample, we then performed a content analysis by extracting information on climate change scenario(s) and date(s) modelled, modelling approaches, focus and scale (taxon-specific, regional or larger), and the main aims and findings.