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Despite decades of progress, much remains unknown about successional trajectories of carbon (C) cycling in north temperate forests. Drivers and mechanisms of these changes, including the role of different types of disturbances, are particularly elusive. To address this gap, we synthesized decades of data from experimental chronosequences and long-term monitoring at a well-studied, regionally representative field site in northern Michigan, USA. Our study provides a comprehensive assessment of changes in above- and belowground ecosystem components over two centuries of succession, links temporal dynamics in C pools and fluxes with underlying drivers, and offers several conceptual insights to the field of forest ecology. Our first advance shows how temporal dynamics in some ecosystem components are consistent across severe disturbances that reset succession and partial disturbances that slightly modify it: both of these disturbance types increase soil N availability, alter fungal community composition, and alter growth and competitive interactions between short-lived pioneer and longer-lived tree taxa. These changes in turn affect soil C stocks, respiratory emissions, and other belowground processes. Second, we show that some other ecosystem components have effects on C cycling that are not consistent over the course of succession. For example, canopy structure does not influence C uptake early in succession, but becomes important as stands develop, and the importance of individual structural properties changes over the course of two centuries of stand development. Third, we show that in recent decades, climate change is masking or overriding the influence of community composition on C uptake, while respiratory emissions are sensitive to both climatic and compositional change. In synthesis, we emphasize that time is not a driver of C cycling; it is a dimension within which ecosystem drivers such as canopy structure, tree and microbial community composition change. Changes in those drivers, not in forest age, are what control forest C trajectories, and those changes can happen quickly or slowly, through natural processes or deliberate intervention. Stemming from this view and a whole-ecosystem perspective on forest succession, we offer management applications from this work and assess its broader relevance to understanding long-term change in other north temperate forest ecosystems. 

Abstract Cooling energy demand is sensitive to urban form and socioeconomic characteristics of cities. Climate change will impact how these characteristics influence cooling demand. We use random forest machine learning methods to analyze the sensitivity of cooling demand in Chicago, IL, to weather, vegetation, building type, socioeconomic, and control variables by dividing census tracts of the city into four groups: below-Q1 income–hot days; above-Q1 income–hot days; below-Q1 income–regular days; and above-Q1 income–regular days. Below-Q1 census tracts experienced an increase in cooling demand on hot days while above-Q1 census tracts did not see an increase in demand. Weather (i.e. heat index and wind speed) and control variables (i.e. month of year, holidays and weekends) unsurprisingly had the most influence on cooling demand. Among the variables of interest, vegetation was associated with reduced cooling demand for below-Q1 income on hot days and increased cooling demand for below-Q1 income on regular days. In above-Q1 income census tracts building type was the most closely associated non-weather or control variable with cooling demand. The sensitivity of cooling demand for below-Q1 income census tracts to vegetation on hot days suggests vegetation could become more important for keeping cities cool for low-income populations as global temperatures increase. This result further highlights the importance of considering environmental justice in urban design. 

Structural diversity, characterizing the volumetric capacity and physical arrangement of biotic components in an ecosystem, controls critical ecosystem functions like light interception, hydrology, and microclimate. This product generates structural diversity metrics for the NEON sites, sourced from the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform (DP1.30003.001; collected in March 2023). Using R programming, we computed the metrics detailing height, heterogeneity, and density at 30 m, aligned to the Landsat grids, for 243 site years in 57 NEON sites from 2013 to 2022. 

Canopy defoliation is an important source of disturbance in forest ecosystems that has rarely been represented in large-scale manipulation experiments. Scalable crown to canopy level experimental defoliation is needed to disentangle the effects of variable intensity, timing, and frequency on forest structure, function, and mortality. We present a novel pressure-washing-based defoliation method that can be implemented at the canopy-scale, throughout the canopy volume, targeted to individual leaves or trees, and completed within a timeframe of hours or days. Pressure washing proved successful at producing consistent leaf-level and whole-canopy defoliation, with 10%–20% reduction in leaf area index and consistent leaf surface area removal across branches and species. This method allows for stand-scale experimentation on defoliation disturbance in forested ecosystems and has the potential for broad application. Studies utilizing this standardized method could promote mechanistic understanding of defoliation effects on ecosystem structure and function and development of synthetic understanding across forest types, ecoregions, and defoliation sources. 

The three‐dimensional (3D) physical aspects of ecosystems are intrinsically linked to ecological processes. Here, we describe structural diversity as the volumetric capacity, physical arrangement, and identity/traits of biotic components in an ecosystem. Despite being recognized in earlier ecological studies, structural diversity has been largely overlooked due to an absence of not only a theoretical foundation but also effective measurement tools. We present a framework for conceptualizing structural diversity and suggest how to facilitate its broader incorporation into ecological theory and practice. We also discuss how the interplay of genetic and environmental factors underpin structural diversity, allowing for a potentially unique synthetic approach to explain ecosystem function. A practical approach is then proposed in which scientists can test the ecological role of structural diversity at biotic–environmental interfaces, along with examples of structural diversity research and future directions for integrating structural diversity into ecological theory and management across scales. 

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that described heterogeneity of the outermost layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicated that aerial LiDAR could be of use in quantifying broad-scale variation in structural diversity across macroscales. 

Abstract Biodiversity is believed to be closely related to ecosystem functions. However, the ability of existing biodiversity measures, such as species richness and phylogenetic diversity, to predict ecosystem functions remains elusive. Here, we propose a new vector of diversity metrics, structural diversity, which directly incorporates niche space in measuring ecosystem structure. We hypothesize that structural diversity will provide better predictive ability of key ecosystem functions than traditional biodiversity measures. Using the new lidar-derived canopy structural diversity metrics on 19 National Ecological Observation Network forested sites across the USA, we show that structural diversity is a better predictor of key ecosystem functions, such as productivity, energy, and nutrient dynamics than existing biodiversity measures (i.e. species richness and phylogenetic diversity). Similar to existing biodiversity measures, we found that the relationships between structural diversity and ecosystem functions are sensitive to environmental context. Our study indicates that structural diversity may be as good or a better predictor of ecosystem functions than species richness and phylogenetic diversity.