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Ecotones are the transition zones between ecosystems and can exhibit steep gradients in ecosystem properties controlling flows of energy and organisms between them. Ecotones are understood to be sensitive to climate and environmental changes, but the potential for spatiotemporal dynamics of ecotones to act as indicators of such changes is limited by methodological and logistical constraints. Here, we use a novel combination of satellite remote sensing and analyses of spatial synchrony to identify the tropical dry forest–rainforest ecotone in Area de Conservación Guanacaste, Costa Rica. We further examine how climate and topography influence the spatiotemporal dynamics of the ecotone, showing that ecotone is most prevalent at mid‐elevations where the topography leads to moisture accumulation and that climatic moisture availability influences up and downslope interannual variation in ecotone location. We found some evidence for long‐term (22 year) trends toward upslope or downslope ecotone shifts, but stronger evidence that regional climate mediates topographic controls on ecotone properties. Our findings suggest the ecotone boundary on the dry forest side may be less resilient to future precipitation reductions and that if drought frequency increases, ecotone reductions are more likely to occur along the dry forest boundary. [ABSTRACT FROM AUTHOR]

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 pressurewashing-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. Key words: defoliation, experiment, herbivory, canopy, disturbance. La defoliation du couvert forestier est une source importante de perturbation dans les ecosystemes forestiers qui a rarement ete etudiee dans des experiences de manipulation a grande echelle. Une defoliation experimentale transposable de l'echelle de la cime a celle du couvert forestier est necessaire pour distinguer les effets de la variation de l'intensite, du moment et de la frequence sur la structure, la fonction et la mortalite de la foret. Nous presentons une nouvelle methode de defoliation fondee sur le lavage a la pression qui peut etre : appliquee a l'echelle du couvert forestier, a tout le volume du couvert forestier, appliquee a des arbres ou des feuilles individuellement et realisee a l'interieur d'une periode de quelques heures a quelques jours. Le lavage a la pression a reussi a produire une defoliation constante a l'echelle des feuilles et de l'ensemble du couvert forestier avec une reduction de 10-20 % de l'indice de surface foliaire et l'elimination d'une surface foliaire constante parmi les branches et les especes. Cette methode permet d'experimenter a l'echelle du peuplement avec les perturbations causees par une defoliation dans les ecosystemes forestiers et pourrait avoir un vaste champ d'application. Des etudes utilisant cette methode standardisee pourraient faciliter la comprehension mecaniste des effets de la defoliation sur les fonctions et la structure des ecosystemes ainsi que la comprehension synthetique des types forestiers, des ecoregions et des sources de defoliation. [Traduit par la Redaction] Mots-cles: defoliation, experimentation, broutage, couvert forestier, perturbation., 1. Introduction Large-scale experimental manipulations have been essential to advancing knowledge about ecosystem processes (e.g., Ainsworth and Long 2005; Templer et al. 2017). For example, experiments emulating variable disturbance severity, [...]

Intermediate disturbances are an important component of many forest disturbance regimes, with effects on canopy structure and related functions that are highly dependent on the nature and intensity of the perturbation. Ice storms are an important disturbance mechanism in temperate forests that often result in moderate-severity, diffuse canopy damage. However, it has not previously been possible to distinguish the specific effect of ice storm intensity (as ice accretion) from predisturbance stand characteristics and physiographic factors. In this study, we utilized a novel experimental ice storm treatment to evaluate the effects of variable ice accretion levels on forest canopy structure. Our results verified significant impacts of ice storm disturbance on near-term canopy structural reorganization. Canopy openness, light transmission, and complexity increased significantly relative to predisturbance baselines and undisturbed controls. We documented variable impacts with disturbance intensity, as significant canopy changes largely occurred with ice accretion levels of [greater than or equal to]12.7 mm. Repeated ice storm disturbance (two consecutive years) had marginal, rather than compounding, effects on forest canopy structure. Our findings are relevant to understanding how ice storms can affect near-term forest canopy structural reorganization and ecosystem processes and add to a growing base of knowledge on the effects of intermediate disturbances on canopy structure. Key words: intermediate disturbance, canopy structure, complexity, ecosystem function. Les perturbations intermediaires sont une composante importante de plusieurs regimes de perturbation des forets qui ont des effets sur la structure du couvert forestier et les fonctions qui y sont reliees lesquels dependent fortement de la nature et de l'intensite de la perturbation. Les tempetes de verglas qui causent des dommages diffus et moderement severes dans le couvert forestier constituent un mecanisme important de perturbation dans les forets temperees. Cependant, il n'a pas precedemment ete possible de distinguer l'effet specifique de l'intensite d'une tempete de verglas (sous forme d'accumulation de glace) des facteurs physiographiques et des caracteristiques du peuplement avant d'etre perturbe. Dans cette etude, nous avons utilise un nouveau traitement experimental qui reproduit une tempete de verglas pour evaluer les effets de differents niveaux d'accumulation de verglas sur la structure du couvert forestier. Nos resultats ont permis de constater les impacts importants de la perturbation due a une tempete de verglas sur la reorganisation structurale a court terme du couvert forestier. L'ouverture, la transmission de la lumiere et la complexite du couvert forestier ont significativement augmente par rapport a la situation anterieure a la perturbation et aux temoins non perturbes. Nous avons observe des impacts variables selon l'intensite de la perturbation alors que des changements importants dans le couvert forestier sont surtout survenus avec des niveaux d'accumulation de verglas [greater than or equal to] 12,7 mm. Des perturbations repetees (deux annees consecutives) dues a une tempete de verglas ont eu des effets marginaux plutot que conjugues sur la structure du couvert forestier. Nos resultats sont pertinents pour comprendre de quelle facon les tempetes de verglas peuvent avoir un impact a court terme sur la reorganisation structurale du couvert forestier et alterer les processus de l'ecosysteme. Ils contribuent au developpement de la base de connaissances sur la structure du couvert forestier. [Traduit par la Redaction] Mots-cles : perturbation intermediaire, structure du couvert forestier, complexite, fonction de l'ecosysteme., Introduction Moderate-severity disturbances are an important driver of ecosystem functioning, structural development, and successional change in forest ecosystems (Frelich 2002; Cohen et al. 2016). Disturbances that result in damage to [...]

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]

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]

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]

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]

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management. [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]

The fortedata R package is an open data notebook from the Forest Resilience Threshold Experiment (FoRTE) – a modeling and manipulative field experiment that tests the effects of disturbance severity and disturbance type on carbon cycling dynamics in a temperate forest. Package data consist of measurements of carbon pools and fluxes and ancillary measurements to help analyze and interpret carbon cycling over time. Currently the package includes data and metadata from the first three FoRTE field seasons, serves as a central, updatable resource for the FoRTE project team, and is intended as a resource for external users over the course of the experiment and in perpetuity. Further, it supports all associated FoRTE publications, analyses, and modeling efforts. This increases efficiency, consistency, compatibility, and productivity while minimizing duplicated effort and error propagation that can arise as a function of a large, distributed and collaborative effort. More broadly, fortedata represents an innovative, collaborative way of approaching science that unites and expedites the delivery of complementary datasets to the broader scientific community, increasing transparency and reproducibility of taxpayer-funded science. The fortedata package is available via GitHub: https://github.com/FoRTExperiment/fortedata (last access: 19 February 2021), and detailed documentation on the access, used, and applications of fortedata are available at https://fortexperiment.github.io/fortedata/ (last access: 19 February 2021). The first public release, version 1.0.1 is also archived at 10.5281/zenodo.4399601 (Atkins et al., 2020b). All data products are also available outside of the package as.csv files: 10.6084/m9.figshare.13499148.v1 (Atkins et al., 2020c). [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]

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 fortedata R package is an open data notebook from the Forest Resilience Threshold Experiment (FoRTE) – a modeling and manipulative field experiment that tests the effects of disturbance severity and disturbance type on carbon cycling dynamics in a temperate forest. Package data consists of measurements of carbon pools and fluxes and ancillary measurements to help users analyse and interpret carbon cycling over time. Currently the package includes data and metadata from the first two years of FoRTE, and serves as a central, updatable resource for the FoRTE project team and is intended as a resource for external users over the course of the experiment and in perpetuity. Further, it supports all associated FoRTE publications, analyses, and modeling efforts. This increases efficiency, consistency, compatibility, and productivity, while minimizing duplicated effort and error propagation that can arise as a function of a large, distributed and collaborative effort. More broadly, fortedata represents an innovative, collaborative way of approaching science that unites and expedites the delivery of complementary datasets in near real time to the broader scientific community, increasing transparency and reproducibility of taxpayer-funded science. fortedata is available via GitHub: https://github.com/FoRTExperiment/fortedata and detailed documentation on the access, used, and applications of fortedata are available at: https://fortexperiment.github.io/fortedata/. The first public release, version 1.0.1 is also archived at: https://doi.org/10.5281/zenodo.3936146 (Atkins et al., 2020b). All level one data products are also available outside of the package as .csv files: https://doi.org/10.6084/m9.figshare.12292490.v3 (Atkins et al. 2020c). [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]

In the past several decades, a trend in forestry and silviculture has been toward promoting complexity in forest ecosystems, but how complexity is conceived and described has shifted over time as new ideas and terminology have been introduced. Historically, ecologically-focused silviculture has focused largely on manipulation of structural complexity, but often with the functional role of features in mind. Recently there has been a shift toward viewing complexity in an “adaptive” or “resilience” context, with a focus on understanding forests as complex adaptive systems. As new concepts and terminology are introduced it will be essential that silviculture researchers understand their dissemination into silviculture research, experimental design, and treatment implementation. With this goal in mind we set out to better understand: (1) how complexity terminology and ideas have shifted over time in silviculture, (2) how different conceptions of complexity have been incorporated into silviculture experiments and treatments, and (3) how various complexity concepts are being reconciled with each other in practice. We conducted a multi-stage review of the silvicultural literature for the time period 1992–2017 that included: (1) a broad keyword analysis, (2) a detailed review of a narrower subset of publications, and (3) a thorough review of a set of silvicultural experiments that included a focus on complexity in their design. We also developed a set of case studies that illustrate shifts in complexity conceptions in silvicultural experiment design and analysis. Our analysis indicates considerable lags in incorporation of complexity-focused terminology and ideas into silvicultural research and experimental treatment design. Very few silviculture-focused studies have incorporated adaptive complexity concepts explicitly into design or analysis, even though these concepts were introduced nearly a decade ago and are widely discussed in the literature. However, in our case studies we document how silviculture experiments and research programs that were not designed explicitly around complexity concepts have begun to incorporate these ideas into analysis of treatment outcomes. Silviculture researchers should focus on reconciling conceptions of complexity through analysis of existing experiments and with modeling studies, as well as attempting to better understand mechanistic relationships among structural, functional, and adaptive conceptions of complexity. [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]

• Honeysuckle removal caused immediate declines in canopy leaf area and changes in spatial leaf distribution that resulted in increased canopy structural complexity that continued to accrue more than a decade after removal. • The long-term structural changes were dependent on initial site conditions prior to disturbance, whereby increased structural complexity only occurred in areas with initially large honeysuckle abundance and low native tree density. • Visual assessments of honeysuckle cover may be used to rapidly quantify areas that would benefit most from honeysuckle removal due to the strong correlation with native tree canopy structure, while honeysuckle stem count and basal area are poor candidates, due to a lack of correlation with canopy structural metrics and the time consuming and labor-intensive nature of collecting this data. • In application, our results suggest that honeysuckle removal has the potential to increase the long-term canopy structural complexity and increase understory openness; but initial stand conditions should be considered during forest management aimed at restoring structural complexity of forests. Riparian forests across the continental United States are heavily invaded by Amur honeysuckle (Lonicera maackii (Rupr.)), an invasive shrub which suppresses native plants, homogenizes community structure and composition, and alters ecosystem processes. However, no studies have quantified the impacts of honeysuckle removal on forest canopy structure across the first decade of restoration. In this study we used a portable canopy LiDAR (PCL) to characterize the immediate (<1 year), short-term (1–2 years) and long-term (>10 years) impacts of honeysuckle removal on the horizontal and vertical complexity of canopy structure in 5 heavily invaded riparian forests in Ohio. Within two years of removal, forest canopies had a 40% reduction in canopy leaf volume, a greater average height of maximum canopy density, and increased aggregation of the remaining leaf area around trees than pre-removal conditions. Honeysuckle removal also prompted long-term (>10 years) increases in canopy structural complexity, but only in areas with initially high honeysuckle abundance and low native tree density. Honeysuckle cover had a much stronger influence on canopy structure than either its basal area or stem density. Our results suggest that removing honeysuckle from heavily-invaded stands can promote complex canopy structure over the long-term that is beyond the short-term accrual immediately following disturbance, but might depend on initial stand conditions. [ABSTRACT FROM AUTHOR]

• Disturbance effects on forest canopy structural complexity are poorly understood. • We synthesized the results of three forest disturbance manipulations. • Disturbance had variable effects on the temporal dynamics of structural complexity. • Moderate severity disturbance sometimes increased structural complexity. • Disturbance severity, source, and scale constrain temporal changes in complexity. The temporal dynamics of forest canopy structure are influenced by disturbances that alter vegetation quantity and distribution. While canopy structural indicators such as leaf area index (LAI), canopy cover, and canopy height have been widely studied in the context of disturbance, the post-disturbance temporal dynamics of structural complexity, which summarizes the heterogeneity of vegetation arrangement, are poorly understood. With the goal of advancing conceptual and empirical understanding of the temporal dynamics of structural complexity following disturbance, we synthesized results from three large-scale disturbance manipulation experiments at the University of Michigan Biological Station (UMBS): the 4-year Forest Resilience Threshold Experiment (FoRTE) manipulating levels of disturbance severity; the decade-long Forest Accelerated Succession Experiment (FASET), in which all early successional tree species were stem-girdled within 39 ha in the same landscape; and forest chronosequences established following clear-cut harvesting. We found that the temporal dynamics of canopy structure following disturbance were dependent upon three factors: (1) the source and severity of disturbance; (2) the spatial and temporal scales of analysis; and (3) the measure of structure assessed. Unlike vegetation area index and canopy cover, which initially decreased in response to disturbance, structural complexity measures such as canopy and top rugosity did not consistently respond to moderate levels of disturbance severity. Over multi-decadal timescales, structural complexity increased to a maximum, regardless of whether fire occurred at the time of stand establishment, but intervening low-to-moderate severity disturbance in regrown century-old forests altered trajectories of canopy rugosity. We conclude that structural complexity indicators display a more nuanced temporal and directional response to disturbance than conventional leaf area and cover indexes. Predicting what disturbance conditions modify trajectories of structural complexity remains critical to disturbance characterization and the inference of ecosystem functioning. [ABSTRACT FROM AUTHOR]

• Open-source software usage is underrepresented in forestry and forest ecology research. • We found 83 R packages with specific utility for the forest research. • We present worked examples focusing on specific forestry research areas. • Our findings were collected into an R Shiny-based metapackage, ForestryAnalysisInR. Forestry and forest ecology research potentially lags behind related fields such as ecology, biodiversity, and conservation research in the employment of open-source software solutions, specifically the R programming language. A direct comparison of the last decade of published research literature from the top 20 ecology and forestry journals shows that R is utilized in over 30% of the literature for ecology, yet in less than 10% of the forestry literature. Open-source computing environments, such as R, Python, and Julia, increase the visibility and reproducibility of scientific research and foster collaborations through the removal of proprietary software restrictions. The lag in adoption of open-source software in forestry and forest ecology could be hindering collaboration, data sharing, and reproducibility. Here we survey the available packages in the R programming language with specific utility for forest-related research. We found more than 100 available packages which we systematically categorized by research category: community analysis; dendrochronology; forest mensuration and inventory; hydrology; informatics/IoT; modeling; phenology; and remote sensing. We present worked examples for a subgroup of R software packages for each category to demonstrate their potential and utility. In these examples we used open-source data sets of our own selection. Additionally, we collected this information into an R metapackage, ForestAnalysisInR , an R Shiny-based solution that allows users to query the R packages we have identified to find those best suited for their analysis needs in a quick and efficient way. [ABSTRACT FROM AUTHOR]

Carbon (C) cycling processes are particularly dynamic following disturbance, with initial responses often indicative of longer-term change. In northern Michigan, USA, we initiated the Forest Resilience Threshold Experiment (FoRTE) to identify the processes that sustain or lead to the decline of C cycling rates across multiple levels (0, 45, 65 and 85% targeted gross leaf area index loss) of disturbance severity and, in response, to separate disturbance types preferentially targeting large or small diameter trees. Simulating the effects of boring insects, we stem girdled > 3600 trees below diameter at breast height (DBH), immediately and permanently disrupting the phloem. Weekly DBH measurements of girdled and otherwise healthy trees (n > 700) revealed small but significant increases in daily aboveground wood net primary production (ANPPw) in the 65 and 85% disturbance severity treatments that emerged six weeks after girdling. However, we observed minimal change in end-of-season leaf area index and no significant differences in annual ANPPw among disturbance severities or between disturbance types, suggesting continued C fixation by girdled trees sustained stand-scale wood production in the first growing season after disturbance. We hypothesized higher disturbance severities would favor the growth of early successional species but observed no significant difference between early and middle to late successional species' contributions to ANPPw across the disturbance severity gradient. We conclude that ANPPw stability immediately following phloem disruption is dependent on the continued, but inevitably temporary, growth of phloem-disrupted trees. Our findings provide insight into the tree-to-ecosystem mechanisms supporting stand-scale wood production stability in the first growing season following a phloem-disrupting disturbance. [ABSTRACT FROM AUTHOR]

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 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]

Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy LiDAR system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10 s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales. [ABSTRACT FROM AUTHOR]

Forest spatial structure describes the relationships among different species in the same forest community. Automation in the monitoring of the structural forest changes and forest mapping is one of the main utilities of applications of modern geoinformatics methods. The obtaining objective information requires the use of spatial data derived from photogrammetry and remote sensing. This paper investigates the possibility of applying light detection and ranging (LiDAR) point clouds and geographic information system (GIS) analyses for automated mapping and detection changes in vegetation structure during a year of study. The research was conducted in an area of the Ourense Province (NWSpain). The airborne laser scanning (ALS) data, acquired in August 2019 and June of 2020, reveal detailed changes in forest structure. Based on ALS data the vegetation parameters will be analysed.To study the structural behaviour of the tree vegetation, the following parameters are used in each one of the sampling areas: (1) Relationship between the tree species present and their stratification; (2) Vegetation classification in fuel types; (3) Biomass (Gi); (4) Number of individuals per area; and (5) Canopy cover fraction (CCF). Besides, the results were compared with the ground truth data recollected in the study area.The development of a quantitative structural model based on Aerial Laser Scanning (ALS) point clouds was proposed to accurately estimate tree attributes automatically and to detect changes in forest structure. Results of statistical analysis of point cloud show the possibility to use UAV LiDAR data to characterize changes in the structure of vegetation. [ABSTRACT FROM AUTHOR]