How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land use planning as inputs to describe biodiversity patterns. SDMs can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting and evaluation all alter the resulting predictions. Commonly, the true distribution of species is not known, nor is there independent data to verify which SDM variant to choose. Such model uncertainty is concerning to planners. We analysed how 11 routine decisions about model complexity, predictors, bias treatment and thresholding of predicted values altered conservation priority patterns across 25 species. While all SDM variants had good model performance (AUC>0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not deal with bias or to apply binary thresholds to predicted values, where on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively), while using fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modelling choices, priority areas overlapped as little as 10-20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When the truth about the best SDM approach and conservation plan is not known, solving uncertainty or spending time considering alterative options is most important for those decisions that change plans the most. Article impact statement: Methodological decisions on how species distribution models (SDMs) are built can alter locations of priority areas up to 90%. This article is protected by copyright. All rights reserved.