Non-native and invasive species are among the leading causes of global biodiversity loss and could therefore compromise the recovery of native forests after disturbance, such as on abandoned agricultural lands. Here we evaluated how the relative density and richness of non-native woody species (NNS) change across secondary tropical forest succession, determined whether they vary between dry and moist forests and identified the underlying environmental and social drivers of these changes. We used data from 1,561 forest plots and 58 chronosequences from ten neotropical countries. We classified 3,735 woody species by origin and invasiveness. Our analyses and conclusions focus on NNS, whereas native (potentially) invasive groups were examined separately. NNS were widespread, occurring in 81% of the chronosequences and comprising 18% of dry and 41% of moist forest plots. We recorded 11 non-native invasive species, most of which were multifunctional trees associated with human activity. In early succession (the first 10-20 years), NNS reached high relative density and richness, accounting for 28% of stems and 22% of species in moist forests, and 9% of stems and species in dry forests. Both metrics declined considerably during the same period but were still present in late succession, mirroring the successional trajectory of native pioneer species, probably due to canopy closure and increased shading. Spatially, NNS richness increased with the Human Development Index. However, both density and richness were negatively affected by increasing surrounding forest cover, agricultural proximity and precipitation, while soil organic carbon generally favoured NNS retention. Our findings suggest that naturally regrowing forests and maintaining relatively intact forest landscapes provide nature-based solutions to control NNS, thereby protecting native biodiversity, ecosystem integrity and local livelihoods.

Abstract Light detection and ranging (lidar) technology has fundamentally advanced the way we measure forest structure, facilitating new insights into ecological processes. Lidar for forest ecology applications is deployed on multiple types of platforms that operate from the ground, air, or space, and each has associated strengths and limitations. Ideally, the choice of what kind of lidar to use in a particular study should be guided by the ecological question of interest; however, practical considerations of cost, data availability, and processing tools can be equally important. This synthesis is a practical introduction to how different lidar platforms characterize forest structure (e.g., tree size/location, wood volume, branching structure, aboveground biomass, leaf properties), designed for a general audience of ecologists (not remote sensing scientists) seeking an accessible introduction to the use of lidar. We also provide examples of novel ecological insights from recent lidar research and describe current limitations and areas of expected improvement. Last, we include an appendix of data collected from terrestrial, mobile, unoccupied aerial system, airplane, and satellite lidar platforms within a common temperate forest area, with associated code to allow new lidar users to visualize and manipulate data in R.

Old-growth tropical forests store vast amounts of carbon in their aboveground biomass (AGB), yet the relative roles of abiotic factors such as climate, soil, and topography in governing its spatial distribution remain poorly understood. In particular, the degree to which climate acts on AGB through forest structure is still poorly quantified at the pantropical scale. Using a pantropical dataset of more than 2,000 old-growth forest plots and a structure-explicit framework, we assess how climate influences AGB through its effects on four structural attributes: basal area, mean diameter, stem density, and basal area-weighted wood density. We find that climate shapes AGB primarily through its effects on forest structure. However, structural attributes respond to climate in opposite directions, so climate’s net effect on AGB largely cancels out, and no clear climate-AGB relationship emerges across tropical regions. Moreover, only wood density responds consistently, decreasing with annual precipitation and increasing with precipitation seasonality, whereas all other attributes respond to climate differently from one region to another. This geographical variation further obscures any global climatic signal on AGB and points to the role of biogeographic history in shaping forest structure. Our findings highlight the central role of the climate-structure nexus in explaining AGB variation, and call for structure-explicit models to improve carbon stock predictions and inform climate adaptation strategies.

Tropical forest restoration is a key natural climate solution, yet monitoring structural and carbon changes at regional scales remains challenging. Multi-temporal Airborne Laser Scanning (ALS) provides a powerful tool to capture these dynamics, though sensor inconsistencies can limit comparability, particularly in regenerating landscapes with subtle structural changes. Here, we present the first large-scale, high-resolution (30 m) assessment of tropical forest height and carbon change in passive restoration areas over six years. We developed a framework to correct inter-survey ALS biases arising from terrain model offsets and pulse density differences. A model calibrated with biome-specific field plots (RSE = 43 ± 11 %) converted ALS height changes into aboveground carbon density. Using lidar-derived growth rates, topography, and soil variables, we projected pasture restoration outcomes over 30 years along a known secondary succession gradient. We found that the lidar-measured net carbon accumulation rate of young forests (2.03 Mg C/ha/yr) was among the highest reported in multi-temporal lidar studies and approximately twice that reported for other temperate and tropical biomes. Restoration activities generated a net carbon gain of 45,000 Mg CO2/yr across 69 km2, equivalent to the annual footprint of 20,000 people. Forests younger than 20 years were projected to accumulate on average 1.61 ± 0.83 Mg C/ha/yr, with peak growth at 19 years (2.01 ± 0.92 Mg C/ha/yr). Simulated carbon uptake rates were below Neotropical and IPCC estimates but 45 % above the regional mean from a global chronosequence-based dataset, highlighting the importance of locally calibrated models for accurate carbon accounting. Restoring legally obligated pastures in the study region could sequester 191 Mg CO2/ha over 30 years, totalling ∼14,700,000 Mg CO2 across 770 km2. This study demonstrates that multi-temporal ALS delivers rapid, large-scale, and reliable data on forest structure change, enabling accurate carbon accounting and restoration forecasts. These outputs are central for compliance with carbon market standards and can directly support the planning, prioritisation, and scaling of tropical forest restoration projects.

We report on our excavation and radiocarbon dating program at the Lamar Mound and Village in the Ocmulgee River Basin of central Georgia. Based on its ceramic assemblage, Lamar has long thought to have been occupied intensively during the fifteenth and sixteenth centuries, and it is one of the regions that the De Soto entrada came through in 1540 as it made its way through what is now Georgia. Excavations in the 1930s revealed several houses and two mounds—one a pyramidal platform mound (Mound A) and the other a spiral mound (Mound B) with a flat summit—all of which were surrounded by a palisade wall. Subsequent work by Williams in 1996 provided further information on the construction history of Mound A and the spatial layout of the village. Our new radiocarbon dating program and Bayesian analysis of dates not only provides a more precise chronology, but also allows us to link events in the village with mound building and use of Mound A at what is likely the capital of the province of Ichisi.

Tropical forest restoration is an important nature-based solution that can sequester carbon as a means to mitigate climate change. Restoration approaches range from natural regeneration to intensive tree planting, but few studies have compared aboveground biomass (AGB) stocks over decadal time scales across multiple restoration treatments implemented at the same sites. Here we leverage data from a two-decade restoration experiment in southern Costa Rica; in 2004-2006, we established restoration plots representing a gradient of intervention intensity: natural regeneration (no planting), applied nucleation (planting tree clusters), and plantation (full planting). We compare 18-20 years of AGB across treatments of the four planted tree species and naturally recruited trees. We also examine the relationships between AGB pools and structural metrics derived from UAV-borne LiDAR data collected after 16-18 years. Because most AGB was in planted trees, AGB was about 6.5 times greater in plantations compared to natural regeneration, and double in plantations compared to applied nucleation after two decades, despite substantial mortality of planted trees. However, the plantation treatment suppressed naturally recruited AGB and accumulated only half the amount of the natural regeneration treatment. Natural recruits comprised four times the proportion of AGB in applied nucleation compared to plantation. LiDAR-quantified LAI was more tightly correlated with total AGB in natural regeneration plots, whereas canopy height was more strongly correlated with planted biomass in plantation and applied nucleation. Our results highlight that tree planting accelerates AGB accumulation at degraded sites and illustrate that practitioners should select species with complementary life history strategies that enable carbon to be sequestered beyond the first decade. Given the tradeoff between planted tree biomass and naturally recruited biomass, spatially patterned methods that plant fewer trees may better balance restoration goals beyond carbon accumulation, leading to more structurally and biologically diverse reforested systems.

Accurately monitoring aboveground biomass (AGB) and tree mortality is crucial for understanding forest health and carbon dynamics. LiDAR (Light Detection and Ranging) has emerged as a powerful tool for capturing forest structure across different spatial scales. However, the effectiveness of LiDAR for predicting AGB and tree mortality depends on the type of instrument, platform, and the resolution of the point cloud data. We evaluated the effectiveness of three distinct LiDAR-based approaches for predicting AGB and tree mortality in a 25.6 ha North American temperate forest. Specifically, we evaluated the following: GEDI-simulated waveforms from airborne laser scanning (ALS), grid-based structural metrics derived from unmanned aerial vehicle (UAV)-borne lidar data, and individual tree detection (ITD) from ALS data. Our results demonstrate varying levels of performance in the approaches, with ITD emerging as the most accurate for AGB modeling with a median R2 value of 0.52, followed by UAV (0.38) and GEDI (0.11). Our findings underscore the strengths of the ITD approach for fine-scale analysis, while grid-based forest metrics used to analyze the GEDI and UAV LiDAR showed promise for broader-scale monitoring, if more uncertainty is acceptable. Moreover, the complementary strengths across scales of each LiDAR method may offer valuable insights for forest management and conservation efforts, particularly in monitoring forest dynamics and informing strategic interventions aimed at preserving forest health and mitigating climate change impacts.

This study addresses the urgent need for effective methods to monitor and conserve Araucaria angustifolia, a critically endangered species of immense ecological and cultural significance in southern Brazil. Using high-resolution satellite images from Google Earth, we apply the YOLOv7x deep learning model to detect this species in two distinct urban contexts in Curitiba, Paraná: isolated trees across the urban landscape and A. angustifolia individuals within forest remnants. Data augmentation techniques, including image rotation, hue and saturation adjustments, and mosaic augmentation, were employed to increase the model’s accuracy and robustness. Through a 5-fold cross-validation, the model achieved a mean Average Precision (AP) of 90.79% and an F1-score of 88.68%. Results show higher detection accuracy in forest remnants, where the homogeneous background of natural landscapes facilitated the identification of trees, compared to urban areas where complex visual elements like building shadows presented challenges. To reduce false positives, especially misclassifications involving palm species, additional annotations were introduced, significantly enhancing performance in urban environments. These findings highlight the potential of integrating remote sensing with deep learning to automate large-scale forest inventories. Furthermore, the study highlights the broader applicability of the YOLOv7x model for urban forestry planning, offering a cost-effective solution for biodiversity monitoring. The integration of predictive data with urban forest maps reveals a spatial correlation between A. angustifolia density and the presence of forest fragments, suggesting that the preservation of these areas is vital for the species’ sustainability. The model’s scalability also opens the door for future applications in ecological monitoring across larger urban areas. As urban environments continue to expand, understanding and conserving key species like A. angustifolia is critical for enhancing biodiversity, resilience, and addressing climate change.

Tropical forests may be nearing critical temperatures, yet tree species may respond differently. Using high-resolution thermal, hyperspectral, and LiDAR imagery, we mapped 652 crowns of four Hawaiian tree species to study the effects of crown traits and abiotic conditions on species' temperatures at two scales (whole crown vs. sunlit leaves). We show scale-dependent, species-specific relationships with environmental fluctuations. Net radiation was consistently the dominant determinant of crown temperature deviations from air temperature (Tdiff), while vapor pressure deficit, wind speed, and crown traits (e.g., roughness) varied in importance by species and scale. Species explained 17% and 44% of Tdiff variation at the crown and leaf scales, respectively, after controlling for climatic factors. Findings suggest that leaf temperatures overestimate larger-scale temperature differences, while canopy-scale observations underestimate leaf heat stress. Because leaf and crown traits can have opposing effects on Tdiff, disentangling these can advance our understanding of species' thermoregulation under climate change.

Tamarins (Saguinus spp., Leontocebus spp.) have been characterized as tolerating or even preferring secondary growth and anthropogenically disturbed areas, and as performing critical seed dispersal in these areas. To test the hypothesis that tamarins prefer secondary growth, we segregated animal presence records by behavior and then used niche modeling to quantify the suitability of various microhabitats for emperor tamarins (Saguinus imperator) and saddleback tamarins (Leontocebus weddelli) over a 315 ha area in the southeastern Peruvian Amazon. Our analysis combines fine-scale maps of key environmental parameters derived from drone-borne lidar data with a behaviorally-sensitive niche modeling of animal movement data measured in the field. This combination allows us to define critical and non-critical areas and gain a new and detailed understanding of microhabitat choice. In saddleback tamarins, we find higher-than-expected use of primary forest for foraging activity. In emperor tamarins, conversely, we find a significant preference for secondary forest in sleeping and unexpectedly high presence in anthropogenically disturbed areas. More broadly, we show that behavioral data lends important nuance to niche modeling methods and that, in combination with fine-scale environmental data, this kind of modeling reveals forms of niche segregation not visible when studying presence alone.

Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals-reflectance across wavelengths-from liana-infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering 775 ha of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short-wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy-reflectance signal are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

Species' traits and environmental conditions determine the abundance of tree species across the globe. The extent to which traits of dominant and rare tree species differ remains untested across a broad environmental range, limiting our understanding of how species traits and the environment shape forest functional composition. We use a global dataset of tree composition of >22,000 forest plots and 11 traits of 1663 tree species to ask how locally dominant and rare species differ in their trait values, and how these differences are driven by climatic gradients in temperature and water availability in forest biomes across the globe. We find three consistent trait differences between locally dominant and rare species across all biomes; dominant species are taller, have softer wood and higher loading on the multivariate stem strategy axis (related to narrow tracheids and thick bark). The difference between traits of dominant and rare species is more strongly driven by temperature compared to water availability, as temperature might affect a larger number of traits. Therefore, climate change driven global temperature rise may have a strong effect on trait differences between dominant and rare tree species and may lead to changes in species abundances and therefore strong community reassembly.

Quantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. This study compares alternative methods for directly estimating basal area in three tree diameter classes that are relevant to timber operations and wildlife habitat planning in southern United States pine forests. Specifically, linear modeling, ensemble linear modeling (ELM) and ensemble general additive modeling (EGAM) were compared. The results showed that the EGAM method provided the highest r-squared values and the lowest RMSE, and the ELM method provided good interpretability and 30 times faster processing than the EGAM method. Both ensemble methods produced a spatially explicit standard error estimate output without additional steps, unlike the single linear model. In general, the estimation results of this study were comparable or improved over prior studies’ estimates of basal area by tree diameter class.

Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species. Data from Sentinel-2 optical imagery, ERA-5 climate data, SRTM-DEM imagery, and simulated GEDI data were selected for the characterization of diversity in four study areas. The integration of ancillary data can improve biodiversity metrics predictions. Random Forest (RF) regression models were suitable for estimating tree species diversity indices from remote sensing variables. From these models, we generated diversity index maps for the entire Cerrado using all GEDI data available in orbit. For all models, the structural metric Foliage Height Diversity (FHD) was selected; the Renormalized Difference Vegetation Index (RDVI) was also selected in all species diversity models. For the Shannon model, two GEDI variables were selected. Overall, the models indicated performances for species diversity ranging from (R2 = 0.24 to 0.56). In terms of RMSE%, the Shannon model had the lowest value among the diversity indices (31.98%). Our results suggested that the developed models are valuable tools for assessing species diversity in tropical savanna ecosystems, although each model can be chosen based on the objectives of a given study, the target amount of performance/error, and the availability of data.

Unraveling the mechanisms underlying the maintenance of species diversity is a central pursuit in ecology. It has been hypothesized that ectomycorrhizal (EcM) in contrast to arbuscular mycorrhizal fungi can reduce tree species diversity in local communities, which remains to be tested at the global scale. To address this gap, we analyzed global forest inventory data and revealed that the relationship between tree species richness and EcM tree proportion varied along environmental gradients. Specifically, the relationship is more negative at low latitudes and in moist conditions but is unimodal at high latitudes and in arid conditions. The negative association of EcM tree proportion on species diversity at low latitudes and in humid conditions is likely due to more negative plant-soil microbial interactions in these regions. These findings extend our knowledge on the mechanisms shaping global patterns in plant species diversity from a belowground view.

The frequency and intensity of flood events are increasing year by year as a result of climate change. This poses significant threats to human settlements and adversely affects biodiversity, agriculture, and infrastructure. One of the most prominent and traditional flood evacuation approaches is through the use of boats. Nonetheless, serious challenges exist with respect to determining the optimal deployment locations, routes, and timing. Given research advances in the Unmanned Aerial Vehicles (UAVs) sector—and their ability to offer real-time data and aerial monitoring services—we argue that their applications could help enhance boat-supported flood evacuation operations. In this opinion piece, we explore new opportunities for disaster management and underscore the advantages of integrating UAVs into flood evacuation methodologies, including areas of rapid field assessment, optimal route planning, and improved coordination between rescue boats. Notwithstanding the potential of UAVs, we emphasize several gaps to be explored in terms of large-scale data management/processing, regulatory limitations, and technological know-how. Furthermore, we provide recommendations for bolstering boat deployment protocols, disaster preparedness training programs, policy frameworks, and emergency response systems, which could maximize their efficacy in flood evacuation scenarios.

AimTo determine the relationships between the functional trait composition of forest communities and environmental gradients across scales and biomes and the role of species relative abundances in these relationships.LocationGlobal.Time periodRecent.Major taxa studiedTrees.MethodsWe integrated species abundance records from worldwide forest inventories and associated functional traits (wood density, specific leaf area and seed mass) to obtain a data set of 99,953 to 149,285 plots (depending on the trait) spanning all forested continents. We computed community-weighted and unweighted means of trait values for each plot and related them to three broad environmental gradients and their interactions (energy availability, precipitation and soil properties) at two scales (global and biomes).ResultsOur models explained up to 60% of the variance in trait distribution. At global scale, the energy gradient had the strongest influence on traits. However, within-biome models revealed different relationships among biomes. Notably, the functional composition of tropical forests was more influenced by precipitation and soil properties than energy availability, whereas temperate forests showed the opposite pattern. Depending on the trait studied, response to gradients was more variable and proportionally weaker in boreal forests. Community unweighted means were better predicted than weighted means for almost all models.Main conclusionsWorldwide, trees require a large amount of energy (following latitude) to produce dense wood and seeds, while leaves with large surface to weight ratios are concentrated in temperate forests. However, patterns of functional composition within-biome differ from global patterns due to biome specificities such as the presence of conifers or unique combinations of climatic and soil properties. We recommend assessing the sensitivity of tree functional traits to environmental changes in their geographic context. Furthermore, at a given site, the distribution of tree functional traits appears to be driven more by species presence than species abundance.

AimEcological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species?LocationGlobal.Time period1990-2017.Major taxa studiedTrees.MethodsWe used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated.ResultsCommunity dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large-scale land degradation.Main conclusionsBy linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large-scale management and conservation practices.