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Forest canopy structural complexity (CSC), an emergent ecosystem property, plays a critical role in controlling ecosystem productivity, resource acquisition and resource use‐efficiency; yet is poorly characterized across broad geographic scales and is difficult to upscale from the plot to the landscape.Here, we show that the relationship between canopy height and CSC can be explained using power laws by analysing lidar‐derived CSC data from 17 temperate forest sites spanning over 17 degrees of latitude. Across three plant functional types (deciduous broadleaf, evergreen needleleaf and mixed forests), CSC increases as an approximate power law of forest height. In evergreen needleleaf forests, increases in canopy height do not result in increases in complexity to the same magnitude as in other forest types.We attribute differences in the slope of height:complexity relationships among forest types to: (a) the limited diversity of crown architectures among evergreen conifer trees relative to broadleaf species; (b) differences in how vertical forest layering develops with height; and (c) competitive exclusion by needleleaf species. We show support for these potential mechanisms with an analysis of 4,324 individual trees from across 18 National Ecological Observatory Network sites showing that crown geometry‐to‐tree height relationships differ consistently between broadleaf and needleleaf species.Power law relationships between forest height and CSC have broad implications for modelling, scaling and mapping forest structural attributes. Our results suggest that forest research and management should consider the nonlinearity in scaling between forest height and CSC and that the nature of these relationships may differ by forest type. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Aim: Canopy structural complexity, which describes the degree of heterogeneity in vegetation density, is strongly tied to a number of ecosystem functions, but the community and structural characteristics that give rise to variation in complexity at site to subcontinental scales are poorly defined. We investigated how woody plant taxonomic and phylogenetic diversity, maximum canopy height, and leaf area index (LAI) relate to canopy rugosity, a measure of canopy structural complexity that is correlated with primary production, light capture, and resource‐use efficiency. Location: Our analysis used 122 plots distributed across 10 ecologically and climatically variable forests spanning a > 1,500 km latitudinal gradient within the National Ecological Observatory Network (NEON) of the USA. Time period: 2016–2018. Taxa studied: Woody plants. Methods: We used univariate and multivariate modelling to examine relationships between canopy rugosity, and community and structural characteristics hypothesized to drive site and subcontinental variation in complexity. Results: Spatial variation in canopy rugosity within sites and across the subcontinent was strongly and positively related to maximum canopy height (r2 =.87 subcontinent‐wide), with the addition of species richness in a multivariate model resolving another 2% of the variation across the subcontinent. Individually, woody plant species richness and phylogenetic diversity (r2 =.17 to.44, respectively) and LAI (r2 =.16) were weakly to moderately correlated with canopy rugosity at the subcontinental scale, and inconsistently explained spatial variation in canopy rugosity within sites. Main conclusions: We conclude that maximum canopy height is a substantially stronger predictor of complexity than diversity or LAI within and across forests of eastern North America, suggesting that canopy volume places a primary constraint on the development of structural complexity. Management and land‐use practices that encourage and sustain tall temperate forest canopies may support greater complexity and associated increases in ecosystem functioning. [ABSTRACT FROM AUTHOR]

Vegetation canopy structure is a fundamental characteristic of terrestrial ecosystems that defines vegetation types and drives ecosystem functioning. We use the multivariate structural trait composition of vegetation canopies to classify ecosystems within a global canopy structure spectrum. Across the temperate forest sub‐set of this spectrum, we assess gradients in canopy structural traits, characterise canopy structural types (CST) and evaluate drivers and functional consequences of canopy structural variation. We derive CSTs from multivariate canopy structure data, illustrating variation along three primary structural axes and resolution into six largely distinct and functionally relevant CSTs. Our results illustrate that within‐ecosystem successional processes and disturbance legacies can produce variation in canopy structure similar to that associated with sub‐continental variation in forest types and eco‐climatic zones. The potential to classify ecosystems into CSTs based on suites of structural traits represents an important advance in understanding and modelling structure–function relationships in vegetated ecosystems. [ABSTRACT FROM AUTHOR]

Moderate severity disturbances, those that do not result in stand replacement, play an essential role in ecosystem dynamics. Despite the prevalence of moderate severity disturbances and the significant impacts they impose on forest functioning, little is known about their effects on forest canopy structure and how these effects differ over time across a range of disturbance severities and disturbance types.Using longitudinal data from the National Ecological Observatory Network project, we assessed the effects of three moderate severity press disturbances (beech bark disease, hemlock woolly adelgid and emerald ash borer, which are characterized by continuous disturbance and sustained mortality) and three moderate severity pulse disturbances (spring cankerworm moth, spongy moth and ground fire, which are associated with discrete and relatively short mortalities) on temperate forest canopy structure in eastern US. We studied (1) how light detection and ranging (LiDAR)‐derived metrics of canopy structure change in response to disturbance and (2) whether initial canopy complexity offsets impact of disturbances on canopy structure over time. We used a mixed‐effects modelling framework which included a non‐linear term for time to represent changes in canopy structure caused by disturbance, and interactions between time and both disturbance intensity and initial canopy complexity.We discovered that high intensity of both press and pulse disturbances inhibited canopy height growth while low intensity pulse disturbances facilitated it. In addition, high intensity pulse disturbances facilitated increases in the complexity of the canopy over time. Concerning the impact of initial canopy complexity, we found that the initial canopy complexity of disturbed plots altered the effects of moderate disturbances, indicating potential resilience effects.Synthesis. This study used repeated measurements of LiDAR data to examine the effects of moderate disturbances on various dimensions of forest canopy structure, including height, openness, density and complexity. Our study indicates that both press and pulse disturbances can inhibit canopy height growth over time. However, while the impact of press disturbances on other dimensions of canopy structure could not be clearly detected, likely because of compensatory growth, the impact of pulse disturbances over time was more readily apparent using multi‐temporal LiDAR data. Furthermore, our findings suggest that canopy complexity might help to mitigate the impact of moderate disturbances on canopy structures over time. Overall, our research highlights the usefulness of multi‐temporal LiDAR data for assessing the structural changes in forest canopies caused by moderate severity disturbances. [ABSTRACT FROM AUTHOR]

The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large‐scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass‐loss rates and stabilization factors of plant‐derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy‐to‐degrade components accumulate during early‐stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass‐loss rates and stabilization, notably in colder locations. Using TBI improved mass‐loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early‐stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models. [ABSTRACT FROM AUTHOR]

This article is a Commentary on Smith et al., 222: 1284–1297. [ABSTRACT FROM AUTHOR]

Structure–function relationships are central to many ecological paradigms. Chief among these is the linkage of net primary production (NPP) with species diversity and canopy structure. Using the National Ecological Observatory Network (NEON) as a subcontinental‐scale research platform, we examined how temperate‐forest NPP relates to several measures of site‐level canopy structure and tree species diversity. Novel multidimensional canopy traits describing structural complexity, most notably canopy rugosity, were more strongly related to site NPP than were species diversity measures and other commonly characterized canopy structural features. The amount of variation in site‐level NPP explained by canopy rugosity alone was 83%, which was substantially greater than that explained individually by vegetation area index (31%) or Shannon's index of species diversity (30%). Forests that were more structurally complex, had higher vegetation‐area indices, or were more diverse absorbed more light and used light more efficiently to power biomass production, but these relationships were most strongly tied to structural complexity. Implications for ecosystem modeling and management are wide ranging, suggesting structural complexity traits are broad, mechanistically robust indicators of NPP that, in application, could improve the prediction and management of temperate forest carbon sequestration. [ABSTRACT FROM AUTHOR]

Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi‐decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following stand‐replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub‐model–parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under‐predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant–soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED‐2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models. [ABSTRACT FROM AUTHOR]

Gradient and scale are two key concepts in ecology and evolution that are closely related but inherently distinct. While scale commonly refers to the dimensional space of a specific ecological/evolutionary (eco–evo) issue, gradient measures the range of a given variable. Gradient and scale can jointly and interactively influence eco–evo patterns. Extensive previous research investigated how changing scales may affect the observation and interpretation of eco–evo patterns; however, relatively little attention has been paid to the role of changing gradients. Here, synthesizing recent research progress, we suggest that the role of scale in the emergence of ecological patterns should be evaluated in conjunction with considering the underlying environmental gradients. This is important because, in most studies, the range of the gradient is often part of its full potential range. The difference between sampled (partial) versus potential (full) environmental gradients may profoundly impact observed eco–evo patterns and alter scale–gradient relationships. Based on observations from both field and experimental studies, we illustrate the underlying features of gradients and how they may affect observed patterns, along with the linkages of these features to scales. Since sampled gradients often do not cover their full potential ranges, we discuss how the breadth and the starting and ending positions of key gradients may affect research design and data interpretation. We then outline potential approaches and related perspectives to better integrate gradient with scale in future studies. [ABSTRACT FROM AUTHOR]