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Context: Land-use change, including agricultural expansion, is one of the major drivers of biodiversity loss globally. Given the rapid pace of land-use change, data-driven, strategic, and dynamic conservation planning is imperative. Objectives: We present an exemplar application of using existing data to inform conservation planning. Specifically, we developed a systematic approach for identifying areas of conservation concern due to cannabis cultivation in California, USA. Methods: We used three existing datasets: (1) camera trap data from ten projects (n = 1186); (2) the locations of cannabis cultivation sites eradicated by law enforcement (n = 834); and (3) the locations of cultivation licenses (n = 4366). We analyzed this data using multi-species occupancy models to estimate the occupancy and richness of 30 species, and maximum entropy models to estimate the risk of unlicensed and trespass cultivation. We then identified areas of overlap and determined the percent of suitable habitat potentially impacted by cannabis cultivation. Results: Cannabis cultivation was estimated to overlap 39–74% of suitable habitat for special status species. Private land cultivation tended to have a larger influence on generalist species whereas trespass cultivation had the largest potential influence on fisher (Pekania pennanti), a special status species. Conclusions: Our results can be used to prioritize eradication, restoration, and remediation activities; to target mitigation efforts; and to guide the placement of new, licensed cultivation. Our approach demonstrates the utility of aggregating existing biological and socioeconomic data to inform conservation planning and is broadly applicable to other data sources and ecological stressors. [ABSTRACT FROM AUTHOR]

• The proliferation of climate datasets is stimulating the use of climate-based species distribution models (CSDMs) in conservation planning and management. • We showed that the choice of climate dataset affects the performance of CSDMs. • The spatial resolution of climate data did not affect model performance in a consistent way. • High performance as measured by commonly used metrics did not guarantee realistic spatial predictions. • Model predictions often differed from historical range maps. Climate is a core aspect of a species' niche that is frequently incorporated into species distribution models (SDMs). The proliferation of readily accessible climate data is stimulating the increasing use of climate-based species distribution models (CSDMs) in conservation planning and management. However, it is uncertain how sensitive CSDMs are to the choice of climate data used. We compared the performance of maximum entropy-based CSDMs calibrated with seven different climate data sets (WorldClim, Chelsa, TerraClimate, Climate Research Unit Time-Series [CRU-TS], PRISM, StreamCat, and EarthEnv). For each species (four amphibians and two reptiles in California), we used a standardized, objective procedure to select climate predictors from each data set. We then assessed performance with standard metrics, calculated variable importance scores, and compared predicted distributions to historical range maps of the target species. The climate data set used affected CSDM performance, with models based on some data sets overpredicting and others underpredicting distributions compared with historical range maps of each of the species. Critically, for the six species we tested, no one climate data set always performed worst or best. These results indicate that the effects of different climate data sets should be incorporated into model evaluation when developing, applying, and interpreting climate-based SDMs. [Display omitted] [ABSTRACT FROM AUTHOR]