Unravelling Forest Complexity: Resource Use Efficiency, Disturbance, and the Elusive Structure-Function Relationship

Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived structural complexity (SC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes in temperate forests with measurements of SC to establish which SC metrics were influential in predicting GPP, and tested potential mediators of the relationship using structural equation modeling. Data came from the 2019 Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) intensive field campaign, where a high density of flux towers was deployed across a 10x10km study domain in Northern Wisconsin. By using this dataset, we were able to separate variability in climate, soil fertility, and forest functional types from structural controls on productivity, allowing for a more representative physiological understanding than has been previously demonstrated. Mechanistic relationships were inspected at multiple spatial resolutions to determine whether relationships persisted with scale. Vertical complexity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity and a long history of management. Variability in the intensity of disturbance legacies resulted in differences in SC metric values and GPP. The structure-function relationship was not scale dependent, and was mediated by resource use efficiency, with water use efficiency a stronger predictor of GPP. These findings allow us to improve mechanistic representation in ecosystem models of how SC impacts light and water-sensitive processes, and consequently GPP. Ultimately, improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and adaptation strategies.