Abstract. 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 proportion of physiological, morphological, and anatomical parameters were matched to measured plant
traits, with estimates extracted from multiple databases and averaged at the required taxonomic level. Estimates for non-observable key parameters
were obtained using meta-modeling and calibration exercises. Missing values were addressed using imputation procedures based on trait covariation,
taxonomic averages or both. The model properly simulated observed historical changes in basal area, with a performance similar to an empirical model
trained for the same region. While strong efforts are still required to parameterize trait-enabled models for multiple taxa, and to incorporate
intra-specific trait variability, estimation procedures such as those presented here can be progressively refined, transferred to other regions or
models and iterated following data source changes by employing automated workflows. We advocate for the adoption of trait-enabled and
population-structured models for regional-level projections of forest function and dynamics.
<p>Grasslands cover ca. 7% (2,100,000 km2) of the African continent. They provide a wide range of ecosystem services (e.g., forage, water, recreational spaces, carbon sequestration), and host large wildlife communities. Despite their importance, African grasslands are reported to be suffering from degradation and, perhaps more worryingly, have received little consideration within international policies (e.g., United Nations Sustainable Development Goals). A key issue at present is widespread woody plant encroachment (WPE), which it is shifting African grassland from a grassy- to a (less palatable) woody-dominated biome. However, the way climatic (e.g., precipitation, soil moisture) and non-climatic disturbances (e.g., fire, population density) affect WPE is still poorly understood, particularly at large spatiotemporal scales. Here we identified grasslands in sub-Saharan Africa according to the ESA Climate Change Initiative (CCI) land cover product and use vegetation optical depth (VOD) from passive microwave observations as a proxy for woody vegetation change between 1992 and 2011. We then use independent climatic (precipitation and soil moisture) and non-climatic (burn intensity, population change) data to assess how both spatiotemporal variations and interactions between climatic and non-climatic drivers controlled rates of VOD increase during 1992-2011. We consider not only annual precipitation, soil moisture, fire, and population data, but also integrated and lagged precipitation data (both up to five years ahead of VOD) in these models. Preliminary results reveal a large overall increase in woody vegetation in sub-Saharan Africa grasslands as well as considerable spatiotemporal variation in VOD change that is not due to climatic factors alone.</p>
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