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Structural diversity predicts forest carbon storage.

Structural diversity predicts forest carbon storage.

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Structural diversity – the volumetric capacity (total, occupied, and unoccupied) and physical... View more

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Structural diversity – the volumetric capacity (total, occupied, and unoccupied) and physical arrangement of biotic components within ecosystems – has the potential to serve as an additional and possibly even superior predictor of productivity. Despite having roots in early ecology, the idea that diverse vegetation structure plays a crucial role in ecosystem function has been given surprisingly little consideration since its origin. In general, plants of varying sizes and structure are located across different horizontal and vertical spaces within an ecosystem, leading to the unique occupancy of niche axes such as light. The occupancy of more niche space, in turn, can be closely linked to essential ecosystem functions, such as an elevated capacity for ecosystem vegetation to convert more resources into growth.
Unlike species diversity, which measures the potential niche space that organisms might occupy , structural diversity offers a more direct measure of realized niche occupancy. Structural diversity captures variation in vegetation size and structure, and plants of different sizes – even those of the same species – can be functionally distinct in obtaining and utilizing resources. Therefore, structural diversity can provide estimations of not only theactual volumetric occupancy and arrangement of the niche space but also the total volumetric capacity of the niche space. Existing diversity measures, such as species diversity, cannot be used to directly measure niches filled by the presence of additional species or different- sized individuals of the same species.

We estimated the structural diversity of forest stands from metrics that measure horizontal, vertical, and structural richness using tree diameter and height size classes from forest inventory data. research the uncertainties of environmental and climate variables in predicting productivity or carbon stocks. It is also possible to consider introducing machine learning to scale the data.

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