Linking land change model evaluation to model objective for the assessment of land cover change impacts on biodiversity

• Published in: Landscape ecology 2021-09, Vol.36 (9), p.2707-2723
• Main Authors: Sangermano, Florencia, Pontius, Robert Gilmore, Chaitman, Jamieson, Meneghini, Aaron
• Format: Article
• Language: English
• Subjects: Assessments; Biodiversity; Biomedical and Life Sciences; Decomposition; Deforestation; Ecology; Environmental Management; Evaluation; Forest management; Grain size; Land cover; Landscape Ecology; Landscape/Regional and Urban Planning; Life Sciences; Nature Conservation; Particle size; Pixels; Research Article; Simulation; Species composition; Sustainable Development
• ISSN: 0921-2973; 1572-9761
• DOI: 10.1007/s10980-021-01251-5

Context Evaluation of land cover change (LCC) is commonly done at the pixel level; however, the model’s purpose may be relevant at a different grain size. Thus, the same model may be good for one purpose but inappropriate for another. For conservation applications, it is crucial to assess land change simulations at the grain relevant for the assessment of biodiversity impacts. Objectives Our objective is to evaluate land cover change scenarios in Bolivia, at the pixel-level and grain relevant to biodiversity, to inform LCC models for biodiversity assessments. Methods We created six deforestation simulations that varied deforestation allocation based on forest management units (national, province, and municipality), ecoregions, and carbon stocks. We evaluated the simulations at the pixel level, and the objective’s relevant grain size through stratified error decomposition. We assessed biodiversity impacts by comparing the quantity of reference and simulated deforestation within species ranges. Results The spatial allocation of deforestation differed across simulations; however, their pixel-level error were similar. The province and municipality land change simulations had the lowest allocation errors at the relevant grain despite their large pixel-level errors, and they showed the lowest biodiversity errors. The province simulation provided the best balance identifying both affected species composition and the area of impact. Conclusions This work presents evidence of the importance of incorporating information regarding the purpose of the simulation during model evaluation and selection. Error decomposition allowed ignoring irrelevant errors, translating into meaningful assessments of biodiversity impacts. As opposed to pixel-level metrics, stratified errors identified models that characterized biodiversity impacts best.