Bridging the gap between forest planning and ecology in biodiversity forecasts: A review

Including biodiversity indicators into forest planning is increasing in importance as it is a supporting service for other ecosystem services. To forecast biodiversity potential, forest planners use models that simulate forest growth and other biological and ecological processes. As models are simplifications of reality, they may ignore components of biodiversity’s multi-scales and multi-facets. To address this issue, we explored if current models used in forest planning can characterize biodiversity in a similar way as it is defined in ecology. We performed a narrative review of ecological papers to identify the main aspects of biodiversity defined in ecology. We then reviewed 64 forest planning articles to identify the indicators they use and what aspects of biodiversity they represent. We compared the aspects identified in ecology and forest planning to evaluate the discrepancies between the two fields and suggest improvements for future biodiversity studies in forest planning.We identified spatial and temporal connectivity, structure, and abiotic factors as the main biodiversity drivers defined in ecology and genetic, species, and functional diversity as the main responses. Based on this classification, we found that biodiversity models used in forest planning mainly focus on structure and species elements, with minor focus on connectivity and functions and none on genetic diversity. We found that most studies base their choice of biodiversity indicators on the outputs available from traditional forest simulators. Additionally, many studies do not frame biodiversity rigorously or acknowledge its complexity. This trend is explained by the traditional focus of forest planning on the economic value of the forest and maximization of timber volumes rather than its ecological value and the presence of diverse habitats. Our results describe and quantify the importance given to the different biodiversity aspects in forest planning studies and highlight the current limitations. We anticipate that improvements can be achieved through the inclusion of connectivity and we suggest paths to improve future biodiversity models.