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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]

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 measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity—an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research. [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]

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. [ABSTRACT FROM AUTHOR]

Lidar‐derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed.We investigated the appropriate scale of inference for eight lidar‐derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)‐‐representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub‐boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric.We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests.These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar‐derived structural metrics for research and management applications. [ABSTRACT FROM AUTHOR]

Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR.The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open‐source web‐based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting‐edge analyses of properties related to forest ecology and management.We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post‐hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest.treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open‐source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function. [ABSTRACT FROM AUTHOR]

Determining when a disturbance has occurred, its severity, and when the system recovered, is important to numerous questions in the aquatic sciences. This problem can be conceptualized as the timing and degree of perturbation from a typical state, and when the system returns to that typical state. We present an algorithm for detecting disturbance and recovery designed for high‐frequency time series, e.g., data produced by automated sampling devices in instrumented buoys and flux towers. The algorithm quantifies differences in the empirical cumulative distribution functions of moving windows over reference and evaluation periods, and is sensitive to changes in the mean, variance, and higher statistical moments. Tests on simulated data show it accurately identifies disturbance and recovery. Three case studies illustrate the application of our algorithm in different empirical settings. A case study on dissolved oxygen in a Florida, USA estuary following a hurricane identified the disturbance and recovery 73 d later. A case study on air temperature and net ecosystem exchange in the Florida everglades identified cold snaps coinciding with periods of reduced carbon uptake. A case study on rotifer abundance following zebra mussel invasion in the Hudson River, NY showed rotifer collapse following invasion and recovery over a decade later. Methods such as ours can improve understanding response to disturbance and facilitate comparative and synthetic study of disturbance impacts across ecosystems. [ABSTRACT FROM AUTHOR]

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]

Many secondary deciduous forests of eastern North America are approaching a transition in which mature early‐successional trees are declining, resulting in an uncertain future for this century‐long carbon (C) sink. We initiated the Forest Accelerated Succession Experiment (FASET) at the University of Michigan Biological Station to examine the patterns and mechanisms underlying forest C cycling following the stem girdling‐induced mortality of >6,700 early‐successional Populus spp. (aspen) and Betula papyrifera (paper birch). Meteorological flux tower‐based C cycling observations from the 33‐ha treatment forest have been paired with those from a nearby unmanipulated forest since 2008. Following over a decade of observations, we revisit our core hypothesis: that net ecosystem production (NEP) would increase following the transition to mid‐late‐successional species dominance due to increased canopy structural complexity. Supporting our hypothesis, NEP was stable, briefly declined, and then increased relative to the control in the decade following disturbance; however, increasing NEP was not associated with rising structural complexity but rather with a rapid 1‐yr recovery of total leaf area index as mid‐late‐successional Acer, Quercus, and Pinus assumed canopy dominance. The transition to mid‐late‐successional species dominance improved carbon‐use efficiency (CUE = NEP/gross primary production) as ecosystem respiration declined. Similar soil respiration rates in control and treatment forests, along with species differences in leaf physiology and the rising relative growth rates of mid‐late‐successional species in the treatment forest, suggest changes in aboveground plant respiration and growth were primarily responsible for increases in NEP. We conclude that deciduous forests transitioning from early to middle succession are capable of sustained or increased NEP, even when experiencing extensive tree mortality. This adds to mounting evidence that aging deciduous forests in the region will function as C sinks for decades to come. [ABSTRACT FROM AUTHOR]

During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi‐resolution information for analysis. Climate change, land‐use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high‐density, and high‐resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) for Socio‐Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross‐site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio‐environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data. [ABSTRACT FROM AUTHOR]

Vegetation structural complexity and biodiversity tend to be positively correlated, but understanding of this relationship is limited in part by structural metrics tending to quantify only horizontal or vertical variation, and that do not reflect internal structure. We developed new metrics for quantifying internal vegetation structural complexity using terrestrial LiDAR scanning and applied them to 12 NEON forest plots across an elevational gradient in Great Smoky Mountains National Park, USA. We asked (1) How do our newly developed structure metrics compare to traditional metrics? (2) How does forest structure vary with elevation in a high‐biodiversity, high topographic complexity region? (3) How do forest structural metrics vary in the strength of their relationships with vascular plant biodiversity? Our new measures of canopy density (Depth) and structural complexity (σDepth), and their canopy height‐normalized counterparts, were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Forest structure varied widely across plots spanning the elevational range of GRSM, with taller, more structurally complex forests at lower elevation. Vascular plant biodiversity was negatively correlated with elevation and more strongly positively correlated with vegetation structure variables. The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms. [ABSTRACT FROM AUTHOR]

In an era of unprecedented human impacts on the planet, macrosystems biology (MSB) was developed to understand ecological patterns and processes within and across spatial and temporal scales. We used machine‐learning and qualitative literature review approaches to evaluate the thematic composition of MSB from articles published since the 2010 creation of the US National Science Foundation's MSB Program. The machine‐learning analyses revealed that MSB articles studied scale and human components similarly to six ecology subdisciplines, indicating that MSB has deep ecological roots. A comparison with 84,841 ecological studies demonstrated that MSB has extended the knowledge space of ecology by examining large‐scale patterns and processes alongside anthropogenic factors, which was also confirmed by the qualitative literature review approach. Our analyses indicated that MSB emphasizes large scales, has deep roots in ecological disciplines, and may emerge as a new research frontier, but this last point has yet to be proven. [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]

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]

The study of vegetation community and structural change has been central to ecology for over acentury, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs)with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical-based remote sensing platforms, which are often above-canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance. [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]

Abstract: Terrestrial LiDAR (light detection and ranging) technologies have created new means of quantifying forest canopy structure, allowing not only the estimation of biomass, but also descriptions of the position and variability in canopy elements in space. Such measures provide novel structural information broadly useful to ecologists. There is a growing need for both a detailed taxonomy of forest canopy structural complexity (CSC) and open, transparent, and flexible tools to quantify complexity in ways that will advance foundational ecological knowledge of structure‐function relationships. The CSC taxonomy we present groups structural descriptors into five categories: leaf area and density, canopy height, canopy arrangement, canopy openness, and canopy variability. This paper also introduces the r package forestr, the first open‐source r package for the calculation of CSC metrics from terrestrial LiDAR data. The r package forestr is an analysis toolbox that works with portable canopy LiDAR (PCL) data and other pixelated/voxelized point clouds derived from terrestrial LiDAR scanning (TLS) data to calculate CSC metrics of interest to ecologists, modellers, forest managers, and remote sensing scientists. [ABSTRACT FROM AUTHOR]

The role that microclimates play on soil decomposition is poorly understood. Though litter decomposition is controlled by climate and substrate quality at coarse spatial scales, at the watershed scale, microclimates mediated by forest structure and landscape position can influence decomposition rates and in turn affect nitrogen cycling. To evaluate the effects of landscape position and vegetation heterogeneity on decomposition, we employed a two-year litterbag study (2011–2013) using yellow birch leaf litter across the Weimer Run watershed, a cool, humid watershed located near Davis, West Virginia. From our results, we created a spatially explicit empirical model that we tested against both a single-pool and three-pool decomposition model, each based on climate and derived from the Long-Term Intersite Decomposition Experiment Team. Initial litter decomposition varied by elevation, with greater rates of decomposition at locations lower in the watershed. Decomposition rates differed by elevation, except during the first winter of the study. No differences in decomposition rates were seen among elevation levels when snowfall was below average for the first winter (2011–2012). During the second winter (2012–2013), elevation levels showed separation in decomposition rates, with higher elevations exhibiting lower decomposition rate. This suggests important controls on decomposition exerted by the presence or absence of snow, interannual climate variability, and the interaction of both with topography. Our empirical model showed greater rates of decomposition during early stages of decomposition (<12 months), but converged with the three-pool decomposition model after 20 months. Plant available nitrogen differed by vegetation cover, largely driven by greater availability of nitrate (NO3-) beneath areas of canopy closure in the forest. Controls on decomposition and nitrogen cycling within the Weimer Run watershed vary spatially by elevation and vegetation cover and are subject to complex interactions and differ from standard models of decomposition. The effect of the inter-annual variance of snow depth on litter decomposition is of note and an important consideration moving forward. Climate-based models of decomposition greatly underestimate initial rates of decomposition, potentially leading to under-accounting and compounded uncertainty. [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]

Globally, shrub expansion is an important driver of ecological change. However, most studies of shrub expansion have focused on dryland ecosystems (e.g., savannas, rangelands, grasslands), or in tundra regions (e.g., arctic, alpine). However, shrubs play a key role in other systems, such as the understory of temperate forests. In the Appalachian forests of the eastern United States, rhododendron (Rhododendron maximum) is the most prevalent species constituent of the understory evergreen shrub community and can affect forest diversity and structure by altering light and moisture regimes, and changing soil chemical and physical properties. We examine the spatial patterns and temporal dynamics of the evergreen shrub layer within a mid‐Appalachian forest over the period from 1986 to 2011 using Landsat TM data to explore how shrub expansion is related to landscape position (e.g., elevation, aspect, and distance‐to‐stream). We use a combination of remotely sensed vegetation indices (e.g., NDVI, tasseled cap indices) derived from snow‐free, leaf‐off Landsat 5 surface reflectance data from 1986 to 2011 paired with a Random Forest classification model to examine shrub dynamics in the Weimer Run watershed near Canaan Valley, West Virginia, a first‐order, high‐altitude watershed. We show extensive increases in winter greenness we attribute to expansion of the evergreen shrub community. From 1986 to 2011, there is a 0.14 increase in winter NDVI, with a 52% relative increase in shrub coverage over 25 yr. Shrub expansion is most notable at lower elevations, along streams, and on southerly oriented slopes. We argue that changes in shrub abundance are due to decreased moisture limitations driven by changing climate. There is an increased effort to reintroduce native conifers to this region, but changes in the shrub community jeopardize this effort, as rhododendron inhibits the growth and dispersal of these conifers (e.g., red spruce). Our results show that rhododendron shrubs are expanding into areas of the forest from which they have previously been restricted. Use of these remote sensing methods may allow better habitat suitability mapping, leading to better targeted restoration efforts and more‐informed ecosystem forecasts. [ABSTRACT FROM AUTHOR]