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Summary Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed.Here, we analyzedR_darkvariability and its associations withV_cmaxand other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative.We found that leaf magnesium and calcium concentrations were more significant in explaining cross‐siteR_darkthan commonly used traits like LMA, N and P concentrations, but univariate trait–R_darkrelationships were always weak (r² ≤ 0.15) and forest‐specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait–R_darkrelationships, accurately predicted cross‐siteR_dark(r² = 0.65) and pinpointed the factors contributing toR_darkvariability.Our findings reveal a few novel traits with greater cross‐site scalability regardingR_dark, challenging the use of empirical trait–R_darkrelationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimatingR_dark, which could ultimately improve process modeling of terrestrial plant respiration. 

Abstract Tropical tree communities are among the most diverse in the world. A small number of genera often disproportionately contribute to this diversity. How so many species from a single genus can co‐occur represents a major outstanding question in biology. Niche differences are likely to play a major role in promoting congeneric diversity, but the mechanisms of interest are often not well‐characterized by the set of functional traits generally measured by ecologists.To address this knowledge gap, we used a functional genomic approach to investigate the mechanisms of co‐occurrence in the hyper‐diverse genusFicus. Our study focused on over 800 genes related to drought and defence, providing detailed information on how these genes may contribute to the diversity ofFicusspecies.We find widespread and consistent evidence of the importance of defence gene dissimilarity in co‐occurring species, providing genetic support for what would be expected under the Janzen‐Connell mechanism. We also find that drought‐related gene sequence similarity is related toFicusco‐occurrence, indicating that similar responses to drought promote co‐occurrence.Synthesis. We provide the first detailed functional genomic evidence of how drought‐ and defence‐related genes simultaneously contribute to the local co‐occurrence in a hyper‐diverse genus. Our results demonstrate the potential of community transcriptomics to identify the drivers of species co‐occurrence in hyper‐diverse tropical tree genera. 

Abstract All species must partition resources among the processes that underly growth, survival, and reproduction. The resulting demographic trade‐offs constrain the range of viable life‐history strategies and are hypothesized to promote local coexistence. Tropical forests pose ideal systems to study demographic trade‐offs as they have a high diversity of coexisting tree species whose life‐history strategies tend to align along two orthogonal axes of variation: a growth–survival trade‐off that separates species with fast growth from species with high survival and a stature–recruitment trade‐off that separates species that achieve large stature from species with high recruitment. As these trade‐offs have typically been explored for trees ≥1 cm dbh, it is unclear how species' growth and survival during earliest seedling stages are related to the trade‐offs for trees ≥1 cm dbh. Here, we used principal components and correlation analyses to (1) determine the main demographic trade‐offs among seed‐to‐seedling transition rates and growth and survival rates from the seedling to overstory size classes of 1188 tree species from large‐scale forest dynamics plots in Panama, Puerto Rico, Ecuador, Taiwan, and Malaysia and (2) quantify the predictive power of maximum dbh, wood density, seed mass, and specific leaf area for species' position along these demographic trade‐off gradients. In four out of five forests, the growth–survival trade‐off was the most important demographic trade‐off and encompassed growth and survival of both seedlings and trees ≥1 cm dbh. The second most important trade‐off separated species with relatively fast growth and high survival at the seedling stage from species with relatively fast growth and high survival ≥1 cm dbh. The relationship between seed‐to‐seedling transition rates and these two trade‐off aces differed between sites. All four traits were significant predictors for species' position along the two trade‐off gradients, albeit with varying importance. We concluded that, after accounting for the species' position along the growth–survival trade‐off, tree species tend to trade off growth and survival at the seedling with later life stages. This ontogenetic trade‐off offers a mechanistic explanation for the stature–recruitment trade‐off that constitutes an additional ontogenetic dimension of life‐history variation in species‐rich ecosystems. 

Abstract Forest trees provide critical ecosystem services for humanity that are under threat due to ongoing global change. Measuring and characterizing genetic diversity are key to understanding adaptive potential and developing strategies to mitigate negative consequences arising from climate change. In the area of forest genetic diversity, genetic divergence caused by large-scale changes at the chromosomal level has been largely understudied. In this study, we used the RNA-seq data of 20 co-occurring forest trees species from genera including Acer, Alnus, Amelanchier, Betula, Cornus, Corylus, Dirca, Fraxinus, Ostrya, Populus, Prunus, Quercus, Ribes, Tilia, and Ulmus sampled from Upper Peninsula of Michigan. These data were used to infer the origin and maintenance of gene family variation, species divergence time, as well as gene family expansion and contraction. We identified a signal of common whole genome duplication events shared by core eudicots. We also found rapid evolution, namely fast expansion or fast contraction of gene families, in plant–pathogen interaction genes amongst the studied diploid species. Finally, the results lay the foundation for further research on the genetic diversity and adaptive capacity of forest trees, which will inform forest management and conservation policies. 

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km²shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling. 

# Individual Tree Predictions for 100 million trees in the National Ecological Observatory Network Preprint: https://www.biorxiv.org/content/10.1101/2023.10.25.563626v1 ## Manuscript Abstract The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales allows an unprecedented view of forest ecosystems, forest restoration and responses to disturbance. To create detailed maps of tree species, airborne remote sensing can cover areas containing millions of trees at high spatial resolution. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual tree species using ground truthed data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees for 24 sites in the National Ecological Observatory Network. Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1km^2 shapefiles with individual tree species prediction, as well as crown location, crown area and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of six species per site, ranging from 3 to 15 species. All predictions were uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. These data can be used to study forest macro-ecology, functional ecology, and responses to anthropogenic change. ## Data Summary Each NEON site is a single zip archive with tree predictions for all available data. For site abbreviations see: https://www.neonscience.org/field-sites/explore-field-sites. For each site, there is a .zip and .csv. The .zip is a set 1km .shp tiles. The .csv is all trees in a single file. ## Prediction metadata *Geometry* A four pointed bounding box location in utm coordinates. *indiv_id* A unique crown identifier that combines the year, site and geoindex of the NEON airborne tile (e.g. 732000_4707000) is the utm coordinate of the top left of the tile.  *sci_name* The full latin name of predicted species aligned with NEON's taxonomic nomenclature.  *ens_score* The confidence score of the species prediction. This score is the output of the multi-temporal model for the ensemble hierarchical model.  *bleaf_taxa* Highest predicted category for the broadleaf submodel *bleaf_score* The confidence score for the broadleaf taxa submodel  *oak_taxa* Highest predicted category for the oak model  *dead_label* A two class alive/dead classification based on the RGB data. 0=Alive/1=Dead. *dead_score* The confidence score of the Alive/Dead prediction.  *site_id* The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations. *conif_taxa* Highest predicted category for the conifer model *conif_score* The confidence score for the conifer taxa submodel *dom_taxa* Highest predicted category for the dominant taxa mode submodel *dom_score* The confidence score for the dominant taxa submodel ## Training data The crops.zip contains pre-cropped files. 369 band hyperspectral files are numpy arrays. RGB crops are .tif files. Naming format is __, for example. "NEON.PLA.D07.GRSM.00583_2022_RGB.tif" is RGB crop of the predicted crown of NEON data from Great Smoky Mountain National Park (GRSM), flown in 2022.Along with the crops are .csv files for various train-test split experiments for the manuscript. ### Crop metadata There are 30,042 individuals in the annotations.csv file. We keep all data, but we recommend a filtering step of atleast 20 records per species to reduce chance of taxonomic or data cleaning errors. This leaves 132 species. *score* This was the DeepForest crown score for the crop. *taxonID*For letter species code, see NEON plant taxonomy for scientific name: https://data.neonscience.org/taxonomic-lists *individual*unique individual identifier for a given field record and crown crop *siteID*The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations. *plotID* NEON plot ID within the site. For more information on NEON sampling see: https://www.neonscience.org/data-samples/data-collection/observational-sampling/site-level-sampling-design *CHM_height* The LiDAR derived height for the field sampling point. *image_path* Relative pathname for the hyperspectral array, can be read by numpy.load -> format of 369 bands * Height * Weight *tile_year*  Flight year of the sensor data *RGB_image_path* Relative pathname for the RGB array, can be read by rasterio.open() # Code repository The predictions were made using the DeepTreeAttention repo: https://github.com/weecology/DeepTreeAttentionKey files include model definition for a [single year model](https://github.com/weecology/DeepTreeAttention/blob/main/src/models/Hang2020.py) and [Data preprocessing](https://github.com/weecology/DeepTreeAttention/blob/cae13f1e4271b5386e2379068f8239de3033ec40/src/utils.py#L59). 

Abstract Metabolomics provides an unprecedented window into diverse plant secondary metabolites that represent a potentially critical niche dimension in tropical forests underlying species coexistence. Here, we used untargeted metabolomics to evaluate chemical composition of 358 tree species and its relationship with phylogeny and variation in light environment, soil nutrients, and insect herbivore leaf damage in a tropical rainforest plot. We report no phylogenetic signal in most compound classes, indicating rapid diversification in tree metabolomes. We found that locally co‐occurring species were more chemically dissimilar than random and that local chemical dispersion and metabolite diversity were associated with lower herbivory, especially that of specialist insect herbivores. Our results highlight the role of secondary metabolites in mediating plant–herbivore interactions and their potential to facilitate niche differentiation in a manner that contributes to species coexistence. Furthermore, our findings suggest that specialist herbivore pressure is an important mechanism promoting phytochemical diversity in tropical forests.