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

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]

Ecosystem "hot spots" and "hot moments"—respectively, places and times of disproportionately high biogeochemical activity—are an important and often invoked concept in ecosystem science. Despite the popularity of the concept, there is no standard approach to quantifying hot spots and hot moments, hindering progress in understanding the phenomenon. For example, lack of a standard quantitative approach hinders advances arising from synthesis across datasets and studies potentially representing different processes, ecosystem types, places, and times. We present an approach to quantifying hot spots and hot moments based on the skewness and kurtosis of data distributions. Our approach explicitly tests for the presence of hot spots and/or hot moments, as well as identifies observations regarded as hot spots and/or hot moments. We apply our method to three case studies representing different ecosystem contexts and focal variables: soil pore space CO2 concentrations in a humid temperate watershed; dissolved oxygen saturation in a hypereutrophic, shallow lake; and seagrass metabolism on the coast of a tropical island. A rarefaction-based sensitivity analysis showed how detection of HSHM using our methods can be sensitive to changes in spatial and temporal sampling regimes, depending on the behavior of the system. To facilitate adoption of our approach, we provide the R package "hotspomoments" to implement the method. Adoption of a standard quantitative approach to hot spots and hot moments would advance ecosystem science. [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]

• 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]

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]

Soil respiration (Rs) is the largest outward flux of carbon (C) from terrestrial ecosystems, accounting for more than half of total temperate forest C loss. Evaluating the drivers of this globally important flux, as well as identifying autotrophic (Ra) and heterotrophic (Rh) responses, is critical in the era of rapid global change because small changes could result in disproportionally large impacts to ecosystem C balance. We assessed four years of Rs and Rh from the Forest Resilience Threshold Experiment (FoRTE) to better understand how soil C fluxes respond to a disturbance simulating phloem-disrupting insects. This replicated experiment spanning multiple landscape ecosystems contains four disturbance severities of 0, 45, 65 and 85% gross defoliation as well as two disturbance types targeting the upper and lower canopy. We found an immediate and sustained decline in Rs following phloem disruption that persisted for three years and was proportional to severity. Proportional declines in basal soil respiration and fine-root production with increasing disturbance severity and stable Rh lead us to conclude that Ra drove the suppression of Rs into the third year following disturbance. These responses were conserved across four landscape ecosystems, suggesting the mechanisms causing Rs to decline following phloem disruption were similar despite large differences in composition and productivity. The 3-year reduction of C losses through Rs and, contrastingly, sustained C storage through wood production suggests ecosystem C balance may have remained relatively stable in the first few years following disturbance, even at the highest severity. [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]