Species distribution models (SDMs) are often used to understand changes to species' distributions and their habitats under different land-use scenarios, enabling decision-makers to prioritize areas for management efforts and balance environmental conservation with socio-economic demands on the landscape. However, application of SDMs in land-use planning and Environmental Impact Assessments (EIAs) remains limited due to challenges in interpreting and communicating continuous predictions resulting from these SDMs. Although different binarization methods have been used to overcome such challenges, the choice of threshold can profoundly alter the resulting binary habitat map, and most methods lack simplicity and require access to underlying species occurrence and environmental data used to develop the SDMs. Hence, there is a demand for testing simple alternative binarization methods to enable in-house application of SDMs by practitioners and to facilitate interpretation and communication. Using SDMs of 103 boreal bird species in Alberta, Canada, we transform species relative abundance predictions of SDMs into direct estimates of habitat area, a proxy for habitat suitability, using four simple and three complex thresholding methods. We compare the performance of the binarized models for each bird species and between forest specialists vs. generalists under land-use change scenarios. We found that thresholded models reflect losses in suitable habitat under industrial disturbance scenarios more realistically compared to continuous relative abundance models. Notably, simple thresholding methods, particularly the mean predicted relative abundance, performed similarly to complex thresholding methods in predicting suitable habitat areas, and as indicated by model evaluations using the area under the curve. These findings suggest that using the mean as a binarization threshold can effectively bridge the gap between complex SDMs and their application in policy and planning, without sacrificing predictive accuracy. We conclude that simple threshold binarization methods, such as the mean, can leverage the strong predictive power of SDMs to provide insights into future changes in species' habitat during land-use planning scenarios, account for their uncertainties, and expand their utility to facilitate interpretation for science-informed decision-making in multifunctional landscapes.

Funding provided by: N/ACrossref Funder Registry ID: 0