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 collected in the CHEESEHEAD19 field campaign with drone LiDAR measurements of SC to establish which SC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at four metric calculation resolutions 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. SC metrics included in the structure-function relationship as well as the strength of drivers 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 SC impacts light and water-sensitive processes, and ultimately GPP. 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 strategies.

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 collected in the CHEESEHEAD19 field campaign with drone LiDAR measurements of SC to establish which SC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at four metric calculation resolutions 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. SC metrics included in the structure-function relationship as well as the strength of drivers 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 SC impacts light and water-sensitive processes, and ultimately GPP. 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 strategies.

Climate change is intensifying the hydrologic cycle and altering ecosystem function, including water flux to the atmosphere through evapotranspiration (ET). ET is made up of evaporation (E) via non-stomatal surfaces, and transpiration (T) through plant stomata which are impacted by global changes in different ways. E and T are difficult to measure independently at the ecosystem scale, especially across sites that represent different land use and land management strategies. To address this gap in understanding, we applied flux variance similarity to quantify how E and T differ across 12 different ecosystems measured using eddy covariance in a 10 × 10 km2 area from the CHEESEHEAD19 experiment in northern Wisconsin, USA. The study sites included seven deciduous broadleaf forests, three evergreen needleleaf forests, and two wetlands. Net radiation explained on average 68% of the variance of half-hourly T, which decreased from summer to autumn. Average T/ET for the study period was 55% in forested sites and 46% in wetlands. Deciduous and evergreen forests showed similar E trajectories over time despite differences in vegetation phenology. E increased dramatically after large precipitation events in loam soils but the response in sandy soils was more muted, consistent with the notion that lower infiltration rates temporarily enhance E. Results suggest that E and T partitioning methods are promising for comparing ecosystem hydrology across multiple sites to improve our process-based understanding of ecosystem water flux.

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple time scales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study (ChEAS) Ameriflux core site cluster in the upper Great Lakes region of the USA from 1997-2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying exposure and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower CHEESEHEAD19 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became decoupled with time. NEE at the tall tower transitioned from carbon source to sink to a more variable period over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. A declining snowpack offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. No direct CO2 fertilization trend was noted in gross primary productivity, but influenced maximum net assimilation. Direct upscaling of stand-scale sites led to a larger net sink than the landscape tower. These results highlight the value of clustered, long-term carbon flux observations for understanding the diverse links between carbon and climate and the challenges of upscaling observations.