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1. Assessing trait‐based scaling theory in tropical forests spanning a broad temperature gradient

   https://doi.org/10.1111/geb.12645  

   Enquist, Brian J. ; Bentley, Lisa Patrick ; Shenkin, Alexander ; Maitner, Brian ; Savage, Van ; Michaletz, Sean ; Blonder, Benjamin ; Buzzard, Vanessa ; Espinoza, Tatiana Erika Boza ; Farfan‐Rios, William ; et al ( October 2017 , Global Ecology and Biogeography)

   Tropical elevation gradients are natural laboratories to assess how changing climate can influence tropical forests. However, there is a need for theory and integrated data collection to scale from traits to ecosystems. We assess predictions of a novel trait‐based scaling theory, including whether observed shifts in forest traits across a broad tropical temperature gradient are consistent with local phenotypic optima and adaptive compensation for temperature.

   An elevation gradient spanning 3,300 m and consisting of thousands of tropical tree trait measures taken from 16 1‐ha tropical forest plots in southern Perú, where gross and net primary productivity (GPP and NPP) were measured.

   April to November 2013.

   Plants; tropical trees.

   We developed theory to scale from traits to communities and ecosystems and tested several predictions. We assessed the covariation between climate, traits, biomass and GPP and NPP. We measured multiple traits linked to variation in tree growth and assessed their frequency distributions within and across the elevation gradient. We paired these trait measures across individuals within 16 forests with simultaneous measures of ecosystem net and gross primary productivity.

   Consistent with theory, variation in forest NPP and GPP primarily scaled with forest biomass, but the secondary effect of temperature on productivity was much less than expected. This weak temperature dependence appears to reflect directional shifts in several mean community traits that underlie tree growth with decreases in site temperature.

   The observed shift in traits of trees that dominate in more cold environments is consistent with an ‘adaptive/acclimatory’ compensation for the kinetic effects of temperature on leaf photosynthesis and tree growth. Forest trait distributions across the gradient showed overly peaked and skewed distributions, consistent with the importance of local filtering of optimal growth traits and recent shifts in species composition and dominance attributable to warming from climate change. Trait‐based scaling theory provides a basis to predict how shifts in climate have and will influence the trait composition and ecosystem functioning of tropical forests.

    

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2. 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)

   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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3. Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments

   https://doi.org/10.1002/ecm.1488  

   Cavender‐Bares, Jeannine ; Schweiger, Anna K. ; Gamon, John A. ; Gholizadeh, Hamed ; Helzer, Kimberly ; Lapadat, Cathleen ; Madritch, Michael D. ; Townsend, Philip A. ; Wang, Zhihui ; Hobbie, Sarah E. ( November 2021 , Ecological Monographs)

   Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic‐functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic‐functional community composition of vegetation. We examined the relationships between the image‐derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.

    

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4. Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

   https://doi.org/10.1111/geb.13516  

   Kamoske, Aaron G. ; Dahlin, Kyla M. ; Read, Quentin D. ; Record, Sydne ; Stark, Scott C. ; Serbin, Shawn P. ; Zarnetske, Phoebe L. ( May 2022 , Global Ecology and Biogeography)

   Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).

   Eastern USA.

   2017–2018.

   Trees.

   With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.

   Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.

   We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

    

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5. Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study

   https://doi.org/10.1111/gcb.16916  

   Wang, Ran ; Springer, Kyle R. ; Gamon, John A. ( August 2023 , Global Change Biology)

   Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance‐based remotely sensed optical signals at northern latitude or high‐altitude regions are readily confounded by snow coverage, hampering applications of satellite‐based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness‐based indices and ecosystem productivity of many evergreen‐dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global‐scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

    

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