Geoffrey G. Parker

Accurate estimates of carbon stocks from plant biomass are crucial for climate change mitigation. Temporal changes in biomass must also be quantified to accurately assess forest carbon emissions and absorption. LiDAR remote sensing is the primary method for mapping forest aboveground biomass (AGB) and its changes, as it can penetrate the canopy to create detailed three-dimensional point clouds. This study developed statistical models to estimate AGB and its changes in a tropical dry forest in the Yucatan Peninsula for 2013 and 2021. The models utilized biomass data from field measurements as the dependent variable and LiDAR-derived metrics as explanatory variables. Data were collected using airborne LiDAR in 2013 and ground-based LiDAR in 2021, allowing for a comparison of accuracy from different perspectives. The results indicated strong model performance, with coefficients of determination (R²) of at least 0.89 for AGB estimates in both years. Cross-validation errors ranged from 24% to 29%. For models estimating AGB increases, R² values reached approximately 0.92, with errors under 27% when differences in metrics from both years were analyzed. These results demonstrate the good performance of the models for estimating aboveground biomass and its increases using LiDAR and highlight the feasibility and importance of integrating field data and remotely sensed data in biomass estimates and its increases in tropical dry forests.

Summary: Tree size shapes forest carbon dynamics and determines how trees interact with their environment, including a changing climate. Here, we conduct the first global analysis of among‐site differences in how aboveground biomass stocks and fluxes are distributed with tree size. We analyzed repeat tree censuses from 25 large‐scale (4–52 ha) forest plots spanning a broad climatic range over five continents to characterize how aboveground biomass, woody productivity, and woody mortality vary with tree diameter. We examined how the median, dispersion, and skewness of these size‐related distributions vary with mean annual temperature and precipitation. In warmer forests, aboveground biomass, woody productivity, and woody mortality were more broadly distributed with respect to tree size. In warmer and wetter forests, aboveground biomass and woody productivity were more right skewed, with a long tail towards large trees. Small trees (1–10 cm diameter) contributed more to productivity and mortality than to biomass, highlighting the importance of including these trees in analyses of forest dynamics. Our findings provide an improved characterization of climate‐driven forest differences in the size structure of aboveground biomass and dynamics of that biomass, as well as refined benchmarks for capturing climate influences in vegetation demographic models.

Ecological release from herbivory due to chemical novelty is commonly predicted to facilitate biological invasions by plants, but has not been tested on a community scale. We used metabolomics based on mass spectrometry molecular networks to assess the novelty of foliar secondary chemistry of 15 invasive plant species compared to 46 native species at a site in eastern North America. Locally, invasive species were more chemically distinctive than natives. Among the 15 invasive species, the more chemically distinct were less preferred by insect herbivores and less browsed by deer. Finally, an assessment of invasion frequency in 2,505 forest plots in the Atlantic coastal plain revealed that, regionally, invasive species that were less preferred by insect herbivores, less browsed by white‐tailed deer, and chemically distinct relative to the native plant community occurred more frequently in survey plots. Our results suggest that chemically mediated release from herbivores contributes to many successful invasions.

Hülsmann and Hartig suggest that ecological mechanisms other than specialized natural enemies or intraspecific competition contribute to our estimates of conspecific negative density dependence (CNDD). To address their concern, we show that our results are not the result of a methodological artifact and present a null-model analysis that demonstrates that our original findings—(i) stronger CNDD at tropical relative to temperate latitudes and (ii) a latitudinal shift in the relationship between CNDD and species abundance—persist even after controlling for other processes that might influence spatial relationships between adults and recruits.

Chisholm and Fung claim that our method of estimating conspecific negative density dependence (CNDD) in recruitment is systematically biased, and present an alternative method that shows no latitudinal pattern in CNDD. We demonstrate that their approach produces strongly biased estimates of CNDD, explaining why they do not detect a latitudinal pattern. We also address their methodological concerns using an alternative distance-weighted approach, which supports our original findings of a latitudinal gradient in CNDD and a latitudinal shift in the relationship between CNDD and species abundance.

Maintaining tree diversity: Negative interaction among plant species is known as conspecific negative density dependence (CNDD). This ecological pattern is thought to maintain higher species diversity in the tropics. LaManna et al. tested this hypothesis by comparing how tree species diversity changes with the intensity of local biotic interactions in tropical and temperate latitudes (see the Perspective by Comita). Stronger local specialized biotic interactions seem to prevent erosion of biodiversity in tropical forests, not only by limiting populations of common species, but also by strongly stabilizing populations of rare species, which tend to show higher CNDD in the tropics. Science , this issue p. 1389 ; see also p. 1328

Remote sensing of the structural and spectral traits of vegetation is being transformed by structure from motion (SFM) algorithms that combine overlapping images to produce three-dimensional (3D) red-green-blue (RGB) point clouds. However, much remains unknown about how these point clouds are used to observe vegetation, limiting the understanding of the results and future applications. Here, we examine the content and quality of SFM point cloud 3D-RGB fusion observations. An SFM algorithm using the Scale Invariant Feature Transform (SIFT) feature detector was applied to create the 3D-RGB point clouds of a single tree and forest patches. The fusion quality was evaluated using targets placed within the tree and was compared to fusion measurements from terrestrial LIDAR (TLS). K-means clustering and manual classification were used to evaluate the semantic content of SIFT features. When targets were fully visible in the images, SFM assigned color in the correct place with a high accuracy (93%). The accuracy was lower when targets were shadowed or obscured (29%). Clustering and classification revealed that the SIFT features highlighted areas that were brighter or darker than their surroundings, showing little correspondence with canopy objects like leaves or branches, though the features showed some relationship to landscape context (e.g., canopy, pavement). Therefore, the results suggest that feature detectors play a critical role in determining how vegetation is sampled by SFM. Future research should consider developing feature detectors that are optimized for vegetation mapping, including extracting elements like leaves and flowers. Features should be considered the fundamental unit of SFM mapping, like the pixel in optical imaging and the laser pulse of LIDAR. Under optimal conditions, SFM fusion accuracy exceeded that of TLS, and the two systems produced similar representations of the overall tree shape. SFM is the lower-cost solution for obtaining accurate 3D-RGB fusion measurements of the outer surfaces of vegetation, the critical zone of interaction between vegetation, light, and the atmosphere from leaf to canopy scales.