Unraveling Forest Complexity: Resource Use Efficiency, Disturbance, and the Structure‐Function Relationship. Issue 6 (6th June 2022)

• Record Type: Journal Article
• Authors: Murphy, Bailey A.; May, Jacob A.; Butterworth, Brian J.; Andresen, Christian G.; Desai, Ankur R.
• Is Part Of: Journal of geophysical research. Volume 127:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-06
• Subjects: forest complexity -- scaling -- structure‐function -- LiDAR -- forest management -- eddy covariance; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2021JG006748
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Structurally complex forests optimize resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)‐derived canopy structural complexity (CSC) 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 from nine forested sites within the 10 × 10 km CHEESEHEAD19 study domain in Northern Wisconsin, USA with drone LiDAR measurements of CSC to establish which CSC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at five resolutions (0.25, 2, 10, 25, and 50 m) to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. CSC metrics included in the structure‐function relationship as well as driver strength was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how CSC impacts light and water‐sensitive processes, and ultimately GPP. Improved models enhance our capacity to accurately simulate forest responses to management, furthering our ability to assess climate mitigation strategies. Plain Language Summary: The way that trees are arranged within a forest impacts the forest's ability to use light and water resources for photosynthesis. Forests that are arranged in more complex ways do a better job of using available resources, and have higher rates of photosynthesis, or productivity. By combining data that describes the complexity of the forest with data that describes how much photosynthesis is occurring, we can better understand which factors impact that relationship, and which types of forest complexity are the most important. We used data from nine temperate forest sites with a long history of management and found that vertical complexity was the most influential, and that the intensity of management had a large impact on the relationship between complexity and productivity. We also found that the relationship was controlled by how efficiently the forest used the available resources, and that the spatial resolution at which the data were examined changed the relationship. These findings will allow us to improve the mathematical models we use to test the impacts of forest management on forest productivity, which will enhance our ability to manage our resources in the face of climate change. Key Points: Vertical heterogeneity metrics are the most influential productivity drivers for heterogenous temperate forests The structure‐function relationship is mediated by resource use efficiency, and water use efficiency is a strong driver of productivity The mechanistic relationship between forest structure and function is dependent upon resolution used to calculate the structural metric