AbstractKnowledge of habitat suitability (HS) is required for effective conservation planning, yet it can be difficult to obtain. In the absence of local HS information, managers often use studies from other areas to guide their decisions, typically without local validation. We tested the hypothesis that HS is consistent across a species range, and thus reliably extrapolated to understudied sites. We focused on the puma, Puma concolor, a widely distributed large carnivore of conservation and management importance. We conducted a meta-analysis of 41 studies to calculate a mean effect size for six of the most common predictor variables used in mammal HS research. Using the estimated effect sizes in a regression model which included all six variables, we created a new HS model for pumas in an understudied site, the Tumbesian Region (TR). We contrasted predictions from this range-wide model with those from three more regionally specific HS models: a model developed in the Caatinga, Brazil (Caatinga model), a model considering only tropical studies (tropical model), and a model using only studies from the United States and Canada (temperate model). We used puma detection rates from camera trap surveys across the TR to validate model predictions. Although mean effect sizes of habitat predictor variables varied across puma range, all models provided useful predictions of HS for pumas in the TR (area under the receiver operating characteristic curve [AUC] > 0.64). Unexpectedly, the temperate model was best at predicting puma HS in the TR (AUC: 0.77; rs = 0.3), followed by the range-wide model (AUC = 0.73; rs = 0.29). The tropical and Caatinga models had lower predictive accuracy (AUC = 0.68; rs = 0.28 and AUC = 0.64; rs = 0.23, respectively). The accuracy of the tropical model improved when the area of potential recent puma extirpation was excluded from the validation data set. These results highlight that although HS for P. concolor varies across the species range, information collected across a wide range of sites may be better than only locally or regionally specific information for informing HS in understudied sites (e.g., for habitat protection, restoration areas). Given the pressing need for actions to address widespread biodiversity declines, existing knowledge can be used to predict HS to data-poor regions and inform conservation planning while also motivating model validations and targeted data collection.