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1. Forest structural diversity is linked to soil microbial diversity

   https://doi.org/10.1002/ecs2.4702  

   Lang, Ashley K.; LaRue, Elizabeth A.; Kivlin, Stephanie N.; Edwards, Joseph D.; Phillips, Richard P.; Gallion, Joey; Kong, Nicole; Parker, John D.; McCormick, Melissa K.; Domke, Grant; et al (November 2023, Ecosphere)

   Abstract Efforts to catalog global biodiversity have often focused on aboveground taxonomic diversity, with limited consideration of belowground communities. However, diversity aboveground may influence the diversity of belowground communities and vice versa. In addition to taxonomic diversity, the structural diversity of plant communities may be related to the diversity of soil bacterial and fungal communities, which drive important ecosystem processes but are difficult to characterize across broad spatial scales. In forests, canopy structural diversity may influence soil microorganisms through its effects on ecosystem productivity and root architecture, and via associations between canopy structure, stand age, and species richness. Given that structural diversity is one of the few types of diversity that can be readily measured remotely (e.g., using light detection and ranging—LiDAR), establishing links between structural and microbial diversity could facilitate the detection of belowground biodiversity hotspots. We investigated the potential for using remotely sensed information about forest structural diversity as a predictor of soil microbial community richness and composition. We calculated LiDAR‐derived metrics of structural diversity as well as a suite of stand and soil properties from 38 forested plots across the central hardwoods region of Indiana, USA, to test whether forest canopy structure is linked with the community richness and diversity of four key soil microbial groups: bacteria, fungi, arbuscular mycorrhizal (AM) fungi, and ectomycorrhizal (EM) fungi. We found that the density of canopy vegetation is positively associated with the taxonomic richness (alpha diversity) of EM fungi, independent of changes in plant taxonomic richness. Further, structural diversity metrics were significantly correlated with the overall community composition of bacteria, EM, and total fungal communities. However, soil properties were the strongest predictors of variation in the taxonomic richness and community composition of microbial communities in comparison with structural diversity and tree species diversity. As remote sensing tools and algorithms are rapidly advancing, these results may have important implications for the use of remote sensing of vegetation structural diversity for management and restoration practices aimed at preserving belowground biodiversity. 

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2. Compatibility of Aerial and Terrestrial LiDAR for Quantifying Forest Structural Diversity

   https://doi.org/10.3390/rs12091407  

   LaRue, Elizabeth A.; Wagner, Franklin W.; Fei, Songlin; Atkins, Jeff W.; Fahey, Robert T.; Gough, Christopher M.; Hardiman, Brady S. (May 2020, Remote Sensing)

   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. 

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3. Seasonal structural stability promoted by forest diversity and composition explains overyielding

   https://doi.org/10.1002/ecy.70055  

   Guzmán_Q, J Antonio; Park, Maria H; Williams, Laura J; Cavender‐Bares, Jeannine (March 2025, Ecology)

   Abstract The stability of forest productivity is a widely studied phenomenon often associated with tree species diversity. Yet, drivers of stability in forest structure and its consequences for forest productivity remain poorly understood. Using a large (10 ha) young tree diversity experiment, we evaluated how forest structure and multiple dimensions of diversity and composition are related to remotely sensed structural metrics and their stability through the growing season. We then examined whether structural stability (SS) across the growing season (April–October) could explain overyielding (i.e., the net biodiversity effect, NBE) in annual wood productivity. Using Uncrewed Aerial Vehicle‐Light Detecting and Ranging (UAV‐LiDAR), we surveyed experimental tree communities eight times at regular intervals from before bud break to after leaf senescence to derive metrics associated with canopy height heterogeneity, fractional plant cover, and forest structural complexity (based on fractal geometry). The inverse coefficients of variation for each of these three metrics through the season were used as measures of SS. These metrics were then coupled with annual tree inventories to evaluate their relationships with the NBE. Our findings indicate that wood volume and, to some extent, multiple dimensions of diversity and composition (i.e., taxonomic, phylogenetic, and functional) explain remotely sensed metrics of forest structure and their SS. Increases in wood volume as well as functional and phylogenetic diversity and variability (a measure of diversity independent of species richness) were linked to higher SS of forest complexity and canopy height heterogeneity. We further found that higher SS of forest complexity and fractional plant cover were associated with increased overyielding, which was mostly attributable to the complementarity effect. Structural equation models indicate that the stability of structural complexity explains more variation in NBE among plots than dimensions of diversity or variability, highlighting its value as an informative metric that likely integrates multiple drivers associated with overyielding. This study highlights the potential to integrate remote sensing and ecology to disentangle the role of forest SS in shaping ecological processes. 

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4. Diversity – volume relationships: adding structural arrangement and volume to species – area relationships across forest macrosystems

   https://doi.org/10.1111/ecog.06723  

   LaRue, Elizabeth A.; Downing, Anna G.; Saucedo, Sheila; Rocha, Arturo; Zesati, Sergio A.; Mata‐Silva, Vicente; Harvey, Michael G. (October 2023, Ecography)

   The species – area relationship (SAR) is a common pattern in which diversity increases with the area sampled, but ecosystems are three‐dimensional (3D) and diversity – volume relationships (DVRs) may exist in ecosystems that vary substantially in their vegetation volume. We tested whether forest vegetation volume, as a 3D extension of area in SARs, was a significant predictor of taxonomic (species) and structural (arrangement) diversity in five groups of organisms across the National Ecological Observatory Network (NEON). Vegetation volume and four structural arrangement metrics within the area of NEON plots were measured using NEON's discrete return lidar. Species richness was measured as the number of species within the respective NEON plot sampling area for understory plants, trees, breeding land birds, small mammals, and ground beetles. We found that volume negatively predicted understory plants and positively predicted tree and beetle species richness across the USA forest macrosystem, but not bird and small mammal species richness. Furthermore, volume was a significant predictor of several metrics that describe the internal and external heterogeneity of vegetation in forests (structural arrangement) within the ecosystem across the USA forest macrosystem. There were several significant within site‐level relationships, but not at all sites, between volume and species richness or structural arrangement in organism groups. Our study indicates that previous work that has focused on a 2D conceptualization of habitat can be expanded to 3D habitat space, but that the strength and the positive or negative direction of DVRs may vary taxonomically or geographically. 

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5. Combining Open-Source Machine Learning and Publicly Available Aerial Data (NAIP and NEON) to Achieve High-Resolution High-Accuracy Remote Sensing of Grass–Shrub–Tree Mosaics

   https://doi.org/10.3390/rs17132224  

   Noble, Brynn; Ratajczak, Zak (July 2025, Remote Sensing)

   Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience. 

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