MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales

作者：Miquel De Cáceres, Roberto Molowny‐Horas, Antoine Cabon, Jordi Martínez‐Vilalta, Maurizio Mencuccini, Raúl García‐Valdés, Daniel Nadal‐Sala, Santiago Sabaté, Nicolas Martin‐StPaul, Xavier Morin, Francesco D’Adamo, Enric Batllori, Aitor Améztegui · 发表于：Geoscientific model development · 年份：2023 · DOI：10.5194/gmd-16-3165-2023 · 被引用次数：38 · 研究领域：Forest ecology and management、Plant Water Relations and Carbon Dynamics、Ecology and Vegetation Dynamics Studies

Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity, which requires moving away from broadly defined functional types. Different approaches have been adopted in the last years to incorporate a trait-based perspective into modeling exercises. A common parametrization strategy involves using trait data to represent functional variation between individuals while discarding taxonomic identity. However, this strategy ignores the phylogenetic signal of trait variation and cannot be employed when predictions for specific taxa are needed, such as in applications to inform forest management planning. An alternative strategy involves adapting the taxonomic resolution of model entities to that of the data source employed for large-scale initialization and estimating functional parameters from available plant trait databases, adopting diverse solutions for missing data and non-observable parameters. Here we report the advantages and limitations of this second strategy according to our experience in the development of MEDFATE (version 2.9.3), a novel cohort-based and trait-enabled model of forest dynamics, for its application over a region in the western Mediterranean Basin. First, 217 taxonomic entities were defined according to woody species codes of the Spanish National Forest Inventory. While forest inventory records were used to obtain some empirical parameter estimates, a large proporti...