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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. 

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 Ecologists—especially those new to the field—are tasked with finding relevant literature matching their research interests and deciding upon a suitable venue for the publication of their work. To provide a roadmap for early career researchers to identify journals aligned with their interests, we analyzed major research themes found across the top 30 ecology journals and three high‐impact multi‐disciplinary journals (Nature, PNAS,andScience), utilizing an automated content analysis (ACA) of 84,841 article abstracts, titles, and author keywords published over the last four decades. Journals clustered into 10 distinct groups based on 46 research themes identified byACA. We examined the frequency of ecological themes in each of these journal groups to identify the journals most associated with each theme. We found three themes (anthropogenic impacts, disease,andtraits) that occurred at a high frequency in the high‐impact multi‐disciplinary journal group containingNature, PNAS,andScience. Themes that increased in frequency over the last four decades, such asclimate change, traits, anthropogenic, andcellular biology, were found more often in journals with higher impact factors, indicating that emerging research themes in ecology will likely become of interest to a broader readership over time. Our study provides a thematic review as a potential roadmap for junior ecologists to browse and publish journal articles.