Structural diversity as a reliable and novel predictor for ecosystem productivity

Abstract

The physical structure of vegetation is thought to be closely related to ecosystem function, but little is known of its pertinence across geographic regions. Here, we used data from over three million trees in continental North America to evaluate structural diversity – the volumetric capacity and physical arrangement of biotic components in ecosystems – as a predictor of productivity. We show that structural diversity is a robust predictor of forest productivity and consistently outperforms the traditional measure – species diversity – across climate conditions in North America. Moreover, structural diversity appears to be a better surrogate of niche occupancy because it captures variation in size that can be used to measure realized niche space. Structural diversity offers an easily measured metric to direct restoration and management decision making to maximize ecosystem productivity and carbon sequestration.

Snow damage in a Himalayan forest maintains the dominance of evergreen oaks

“…Canopy gaps resulting from the heavy snowfall will promote uneven‐aged populations, important for enhancing structural diversity. Structural diversity contributes significantly to sustaining productivity and species diversity (LaRue et al, 2023).…”

Tree diversity increases productivity through enhancing structural complexity across mycorrhizal types

“…In forests, stand structural complexity is caused by a high variation in tree size and a high dissimilarity in the spatial arrangement of tree crowns. It has been quantified with several indices, often based on one-or two-dimensional stand structural attributes such as variation in tree diameter and tree height or stand density (9,10). Exploring the role of forest structural complexity in regulating species interactions and stand productivity, however, requires an 2 accurate quantification of the three-dimensional (3D) morphology of individual trees as well as the space occupied by growing trees (11).…”

The control of external and internal canopy structural heterogeneity on diversity and productivity relationship in a subtropical forest

“…Similarly, partitioning of canopy space may also explain BEF relationships through structural diversity. Morphological and physiological differences enable interacting neighbors to differentiate horizontal and vertical positions within a canopy space, resulting in a unique occupancy of niche axes, such as light capture (LaRue et al, 2023). Therefore, a community with higher species diversity is predicted to occupy more niche space and a more complicated canopy structure, which in turn, elevates light acquisition and light-use e ciency and enhances ecosystem function (Gough et al, 2019;LaRue et al, 2023).…”

“…Therefore, a community with higher species diversity is predicted to occupy more niche space and a more complicated canopy structure, which in turn, elevates light acquisition and light-use e ciency and enhances ecosystem function (Gough et al, 2019;LaRue et al, 2023). Canopy structural diversity captures the 3-D variations in vegetation size and structure for a forest canopy, and provides a direct measure of realized niche space (Ehbrecht et al, 2017;LaRue et al, 2023). Approaches to quantifying canopy structural diversity are mainly based on the heterogeneity of fractal dimensions within the canopy, such as tree size differentiation (e.g., coe cient of variation or Gini coe cient) (Zhang et al, 2015), Shannon diversity index (Ali et al, 2016), size distributions (Bohn and Huth, 2017) and combinations of several structural attributes, such as a crown complementarity index (Williams et al, 2017;LaRue et al, 2023).…”

“…Canopy structural diversity captures the 3-D variations in vegetation size and structure for a forest canopy, and provides a direct measure of realized niche space (Ehbrecht et al, 2017;LaRue et al, 2023). Approaches to quantifying canopy structural diversity are mainly based on the heterogeneity of fractal dimensions within the canopy, such as tree size differentiation (e.g., coe cient of variation or Gini coe cient) (Zhang et al, 2015), Shannon diversity index (Ali et al, 2016), size distributions (Bohn and Huth, 2017) and combinations of several structural attributes, such as a crown complementarity index (Williams et al, 2017;LaRue et al, 2023). However, results about the mediation effects of structural diversity on species diversity-forest productivity relationship (DPR) ranged from positive (Williams et al, 2017;Xu et al, 2022) to negative (Bohn and Huth, 2017), including neutral (Ali et al, 2016).…”