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• W4295332152 endingPage "110099" @default.
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• W4295332152 abstract "Process-based Forest Landscape Models (FLMs) rely on first principles to simulate ecological patterns and processes, making them uniquely powerful for forecasting ecological dynamics under unprecedented climatic and disturbance regimes. Persistent challenges with any ecological forecasting model are calibration (“tuning” the model) and validation (“proofing” the model). As no actual future data exist from which to conduct a formal model validation, model credibility is established through numerous tests against empirical datasets and comparisons with other types of models. The purpose of this study was to establish more consistent and generalizable standards for calibrating and validating LANDIS-II, a widely used, open-source FLM. We reviewed methods gleaned from a wide variety of previous FLM studies and advance some new techniques for evaluating the credibility of the model outputs. We used publicly available data with full coverage for the United States (US) so that our methods will be generalizable to other landscapes in the US, and we developed an ecologically meaningful set of validation metrics for evaluating the credibility of new applications. We found that LANDIS-II could be calibrated to reliably simulate empirical vegetation-disturbance-climate dynamics in diverse, mountainous terrain and fire-prone landscapes of the eastern Cascade Mountains. We performed an inter-model validation between LANDIS-II and the Forest Vegetation Simulator (FVS), demonstrating consistent projections of biomass dynamics for all tree-dominated ecoregions in the study domain. Similarly, simulated fires reliably approximated the empirical fire event size and severity patch size distributions based on observed fire activity from 1984 to 2019. By establishing rigorous, transparent, and repeatable standards for calibrating and validating FLM dynamics, we sought to remove some of the barriers to adapting LANDIS-II to new landscapes and climates, facilitate further validation of existing models, and aid independent assessment of the credibility of forest landscape models." @default.
• W4295332152 created "2022-09-13" @default.
• W4295332152 creator A5016495279 @default.
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• W4295332152 date "2022-11-01" @default.
• W4295332152 modified "2023-10-12" @default.
• W4295332152 title "Predicting future patterns, processes, and their interactions: Benchmark calibration and validation procedures for forest landscape models" @default.
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• W4295332152 doi "https://doi.org/10.1016/j.ecolmodel.2022.110099" @default.
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