Species distribution and abundance models (SDMs) frequently inform land and population management actions, including conservation planning, forest management, impact assessment, and recovery planning for species at risk. Sampling effort varies across large heterogeneous landscapes, and accurate spatial and/or temporal extrapolation is challenging, so model reliability varies across space and time. Models that extrapolate poorly can still remain useful for applications within well-sampled geographic or environmental space, but the risks associated with errors will vary with application. Evaluations of model reliability are therefore most useful when they are both spatially explicit and application-specific. Quantitative evaluation methods can help identify areas of extrapolation and variation in model reliability. Qualitative evaluations of SDMs by people who know the land, species, and/or sampling limitations provide additional, complementary benefits. To date, the process for integrating qualitative knowledge into model evaluation and interpretation has been ad hoc, variable, and often inefficient. Our goals are to develop tools and guidance to enable context-specific model evaluation informed by diverse expertise, and to facilitate effective communication among modelers, evaluators, and model users with these tools. We present: a prototype sdmEvaluationTool R Shiny app that enables both quantitative and qualitative spatially-explicit review and evaluation of models; and complementary guidance on how to do application-specific evaluation that helps clarify what questions to ask evaluators and how to interpret answers. We will continue to develop the tool and guidance as we use them to evaluate national, provincial, and regional boreal bird models for various applications.

Keywords: species distribution and abundance models; application-specific spatial model evaluation; R shiny app; expert elicitation