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Since biodiversity often increases ecosystem functioning, changes in tree species diversity could substantially influence terrestrial carbon cycling. Yet much less is known about the relationships between forest structural diversity (i.e., the number and physical arrangement of vegetation elements in a forest) and carbon cycling, and the factors that mediate these relationships. We capitalize on spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) and on-the-ground forest inventory and analysis (FIA) data from 1796 plots across the contiguous United States to assess relationships among the structural and species diversity of live trees and aboveground carbon storage. We found that carbon storage was more strongly correlated with structural diversity than with species diversity, for both forest inventory-based metrics of structural diversity (e.g., height and DBH diversity) and GEDI-based canopy metrics (i.e., foliage height diversity (FHD)). However, the strength of diversity‑carbon storage relationships was mediated by forest origin and forest types. For both plot-based and GEDI-based metrics, the relationship between structural diversity (i.e., height diversity, DBH diversity, and FHD) and carbon storage was positive in natural forests for all forest types (broadleaf, mixed, conifer). For planted forests, structural diversity showed positive relationships in planted conifer forests but not in planted mixed forests. Species diversity did not show strong associations with carbon storage in natural forests but showed a positive relationship in mixed coniferous-broadleaf planted forests. Although plot-based structural diversity metrics refine our understanding of drivers of forest carbon balances at the plot scale, remotely sensed metrics such as those from GEDI can help extend that understanding to regional/national scales in a spatially continuous manner. Carbon storage showed stronger associations with plot-based structural diversity than with stand age, soil variables, or climate variables. Incorporating structural diversity into management and restoration strategies could help guide efforts to increase carbon storage and mitigate climate change as nature-based solutions. • Structural explained carbon storage better than did species diversity. • Structural diversity outperformed stand age, soil, and climate variables. • Influences of diversity on carbon storage vary across natural and planted forests. • Plot-based structural diversity showed stronger correlations than GEDI-based metrics. • GEDI-based metrics of forest structure will help enable large scale carbon mapping. [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]

• 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]