High-accuracy aboveground biomass maps are essential for describing tropical dry forests (TDFs), guiding sustainable management, and enhancing conservation efforts. In this study, an aboveground biomass density (AGBD) map was generated through a two-stage approach. In the first stage, AGBD was estimated at the footprint level using the National Forest Inventory (NFI) and GEDI LiDAR metrics between 2019 and 2020. In addition, NFI data were corrected to include small trees and to account for temporal differences between the years of field data collection and GEDI data acquisition. In the second stage, the footprint-level AGBD estimates were linked with Sentinel-1 and Sentinel-2 imagery to produce a continuous biomass map across the Yucatan Peninsula. The results show that the corrections improved the AGBD estimates (R2 = 0.38 and % RMSE = 34.8) compared to the uncorrected data and also were superior (R2 = 0.41, %RMSE = 36.4) compared to the GEDI L4A product (R2 = 0.07, %RMSE = 87.9). In the second stage, the validation model showed good accuracy, with an R2 of 0.52 and %RMSE of 24.1, outperforming other studies that report R2 values between 0.26 and 0.28, and %RMSE between 30.79 and 62.02. This study presents an approach that improves AGBD maps in tropical dry forests. It highlights the value of ecological knowledge in correcting errors in AGBD estimation at the plot level and in addressing discrepancies between field data and remote sensing, as well as the use of GEDI data to increase sample size and model accuracy for AGBD.

Forest inventories are fundamental instruments for estimating the aboveground biomass density (AGBD) of forests and for assessing their contribution to climate change mitigation. However, inventories may entail errors that generate uncertainty in the estimates, the magnitude of which varies according to vegetation type. Few studies have addressed the sources of error and bias in AGBD estimates based on forest inventories in drylands. In this study, three major sources of error or bias were analyzed: the selection of inadequate dendrometric variables during sampling, the omission of small trees and shrubs due to inclusion criteria, and the lack of allometric equations for some of the most abundant species with contrasting growth form, such as stem-succulent columnar cacti. The results reveal an alarming level of underestimation due to the omission of multi-stemmed trees, shrubs and small individuals in xeric shrubland and tropical dry forest, whose contribution can increase the average AGBD value by up to 307

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.

The identification of Priority Areas for Conservation (PAC) can guide strategic actions in regions with high tree taxonomic diversity, high compositional distinctiveness, or ecological vulnerability. Delineating such areas is essential to safeguard ecosystems. Here, we related Hill numbers and a DCA-derived metric of compositional differentiation (beta), calculated from National Forest Inventory data, to remote sensing variables to estimate the spatial distribution of tree diversity metrics and identify PAC across the Yucatan Peninsula. We hypothesized that spectral and structural remote sensing variables capture complementary dimensions of tree diversity. Spectral data were expected to relate more strongly to species composition, represented by beta, whereas texture and backscatter were expected to better explain variation in Hill numbers linked to abundance and diversity. Our results support this hypothesis, showing that model performance improved mainly for Hill numbers when integrating multiple data sources, while beta was largely explained by Sentinel-2 spectral variables. Contrary to expectations, no consistent positive associations were found between tree diversity metrics and texture measures, possibly due to differences in metric sensitivity. After model calibration and validation, we generated spatial maps of Hill numbers and beta, which allowed us to identify areas with high tree taxonomic diversity and high compositional differentiation, and to propose a PAC map for the peninsula. The proposed priority areas overlapped 39% with existing Protected Natural Areas (PNA), whereas 61% (2,525,633 ha) of the proposed priority areas remained outside current protection boundaries, underscoring the need to expand and strengthen the current conservation network. Integrating field-based metrics with satellite data provides a useful approximation for conservation planning, highlighting unprotected regions of high tree diversity that should be considered in future conservation and management strategies.

Plant diversity plays a fundamental role in ecosystem functioning and is essential for sustaining ecosystem services. National forest inventories are key instruments for assessing floristic diversity. However, their measurement protocols may introduce bias by omitting smaller individuals because of the stem diameter criterion used or the minimum plant size threshold applied. Such bias is exacerbated in dryland ecosystems where small-statured plants with low-branching stems are particularly abundant. In this study, we evaluated the effects of using basal diameter (BD) instead of diameter at breast height, and of sampling small individuals (BD >= 2.5 cm), on the estimation of abundance, alpha and gamma diversity and community composition in different vegetation types in NW Mexico. We found substantial underestimation due to the omission of smaller individuals in xeric shrubland and tropical dry forest, where gamma diversity may be underestimated by up to 209% and 139%, respectively. Broadleaf forest also showed strong underestimation (133%), whereas mixed conifer-broadleaf forests were unaffected. We discuss these differential effects and propose a methodology to attenuate this underestimation and achieve more accurate floristic diversity estimates from national forest inventories in dryland vegetation, which encompasses roughly one-third of the Earth's surface and more than half of Mexico's territory.

Tropical dry forests are among the most threatened ecosystems globally, facing extensive degradation from land-use change. Understanding how biodiversity responds during forest regeneration is critical for conservation and sustainable land management. We assessed the community composition and functional diversity of dung beetles (Scarabaeinae) across a forest recovery chronosequence (1–100 years) in the Yucatán Peninsula. The study area is characterized by traditional Mayan agroforestry systems that shape the landscape. 90 pitfall traps were set up across six forest age classes and were collected 6,605 individuals from 23 species and 13 genera. Dung beetle species richness, biomass, and abundance were significantly associated with forest structural, and diversity metrics. Forest Shannon entropy ( H ′), inverse Simpson concentration ( IF ₀, ₂), and aboveground biomass emerged as strong predictors of community attributes. Abundance and biomass responses varied by functional group: small diurnal rollers (SRD) increased with land-use intensity, while large nocturnal rollers (LRN), large diurnal tunnellers (LTD), and small nocturnal tunnellers (STN) declined sharply from mature forests to early successional stages and agricultural areas. Species richness ( 0 D ) peaked in early to intermediate successional stages (5–20 years), whereas dominant species diversity ( 2 D ) was highest in mixed-use forests under moderate disturbance. Distance-based redundancy analysis (db-RDA) and multi-model inference revealed that forest attributes—including DBH, aboveground biomass, canopy openness, and litter depth—jointly explained 48.7% of the variation in dung beetle assemblage structure (p < 0.001). Litter volume was positively correlated with species richness (adj. R 2 = 0.76), and IF₀,₂ was a key predictor of biomass (adj. R 2 = 0.62). Our findings reveal threshold-based and trait-mediated responses of dung beetle assemblages to forest succession, highlighting the ecological importance of bioculturally managed landscapes. These results underscore the role of secondary forests in maintaining biodiversity and ecosystem functions, supporting their conservation as vital components of tropical dry forest recovery.

Trees can differ enormously in their crown architectural traits, such as the scaling relationships between tree height, crown width and stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of terrestrial ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from 374,888 globally distributed trees, we explore how climate, disturbance, competition, functional traits, and evolutionary history constrain the height and crown width scaling relationships of 1914 tree species. We find that variation in height-diameter scaling relationships is primarily controlled by water availability and light competition. Conversely, crown width is predominantly shaped by exposure to wind and fire, while also covarying with functional traits related to mechanical stability and photosynthesis. Additionally, we identify several plant lineages with highly distinctive stem and crown forms, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legume trees in African savannas. Our study charts the global spectrum of tree crown architecture and pinpoints the processes that shape the 3D structure of woody ecosystems.

Accurate assessment of forest aboveground biomass density (AGBD) is essential for understanding the role of vegetation in climate change mitigation and developing forest management and environmental policies at national and regional levels. The Global Ecosystem Dynamics Investigation (GEDI) uses full-waveform LiDAR and provides a valuable tool for estimating AGBD. Calibrating GEDI biomass products with local field data is vital for improving model accuracy, as current estimates rely on global datasets. Additionally, evaluating key factors that influence biomass estimation is essential to refine GEDI-based models. In this research, we calibrated linear models with field AGBD as the dependent variable and GEDI metrics as independent variables, and compared the performance against the GEDI L4A product across forest types. Additionally, we evaluated the effects of terrain slope, forest structural complexity, and forest type on the accuracy of the models. Finally, we mapped AGBD in Mexico by aggregating footprint-level estimates with local models and compared it with the GEDI AGBD map (L4B product). Model validation showed R 2 values from 0.35 to 0.46 across forest types, with most models having %RMSE below 52.0. Errors were 32.7 to 34.2% lower than GEDI L4A, highlighting a notable accuracy improvement. The total carbon stocks in Mexico estimated here are approximately 1.78 Gt, aligning closely with official FAO estimates, whereas GEDI estimates are 33.5% higher than the official estimate. Biomass estimation with GEDI is most accurate in areas with moderate slopes and low forest structural complexity. Coniferous and tropical forests showed the lowest errors in estimating AGBD with GEDI (46.7 and 47.3 of %RMSE, respectively) likely due to the widespread presence of uniformly structured coniferous trees and the moderate terrain slopes found in tropical forests. Our findings highlight the importance of calibrating local AGBD data with GEDI forest structure metrics to improve biomass estimations at the footprint and national levels.

Mangrove ecosystems are a priority for conservation. They provide diverse ecosystem services and are key in the life cycle of many species. However, they are threatened by various productive activities and natural phenomena such as hurricanes, which impact the coasts causing damage to the vegetation. Monitoring the effects of hurricane impact on mangroves is a complex task since many resources are needed to access the devastated sites and conduct evaluations over large areas. Therefore, remote sensing products represent tools with great potential to assess the most vulnerable areas. In this study, the impact of Hurricane Lorena which hit the archipelago of Espiritu Santo, BCS, in the summer of 2019 was evaluated. Two Sentinel-2 satellite images taken before (09/09/2019) and after (09/24/2019) the hurricane passed were used. Four vegetation indices (VI) related to photosynthetic activity and canopy moisture content were calculated. In addition, Delta VI was calculated for each index, which represents the proportional reduction of the VI value after the impact. The results showed a general increase in the values of the four VI throughout the study area. The category of hurricane Lorena, which confers a lower wind speed and the precipitation associated with this meteor, could explain the increase in the values of the VI. The differentiated response among the four VI shows the importance of using more than one indicator in studies assessing the impact of natural phenomena on coastal vegetation.

AimSuccessional changes in functional diversity provide insights into community assembly by indicating how species are filtered into local communities based on their traits. Here, we assess successional changes in taxonomic and functional richness, evenness and redundancy along gradients of climate, soil pH and forest cover.LocationNeotropics.Time periodLast 0-100 years.Major taxa studiedTrees.MethodsWe used 22 forest chronosequence studies and 676 plots across the Neotropics to analyse successional changes in Hill's taxonomic and functional diversity of trees, and how these successional changes vary with continental-scale gradients in precipitation, soil pH and surrounding forest cover.ResultsTaxonomic and functional richness and functional redundancy increased, while taxonomic and functional evenness decreased over time. Functional richness and evenness changed strongly when not accounting for taxonomic richness, but changed more weakly after statistically accounting for taxonomic richness, indicating that changes in functional diversity are largely driven by taxonomic richness. Nevertheless, the successional increases in functional richness when correcting for taxonomic richness may indicate that environmental heterogeneity and limiting similarity increase during succession. The taxonomically-independent successional decreases in functional evenness may indicate that stronger filtering and competition select for dominant species with similar trait values, while many rare species and traits are added to the community. Such filtering and competition may also lead to increased functional redundancy. The changes in taxonomically-independent functional diversity varied with resource availability and were stronger in harsh, resource-poor environments, but weak in benign, productive environments. Hence, in resource-poor environments, environmental filtering and facilitation are important, whereas in productive environments, weaker abiotic filtering allows for high initial functional diversity and weak successional changes.Main conclusionWe found that taxonomic and functional richness and functional redundancy increased and taxonomic and functional evenness decreased during succession, mainly caused by the increasing number of rare species and traits due to the arrival of new species and due to changing (a)biotic filters.

Los bosques nativos de Uruguay brindan importantes servicios ecosistémicos. A pesar de esto, son escasos los mapas con la distribución espacial de atributos de la vegetación en el país. El objetivo de este estudio fue obtener mapas con la distribución espacial de la biomasa aérea y la riqueza de especies que muestren zonas con altas concentraciones de ambas variables, fundamentales para la mitigación del cambio climático y la conservación la biodiversidad. El área de estudio comprende la ecorregión Cuenca Sedimentaria Gondwánica. Para la estimación de la biomasa aérea y la riqueza de especies se utilizaron Modelos Lineales Generalizados, donde las variables de respuesta fueron calculadas utilizando datos de campo del Inventario Forestal Nacional. Las variables explicativas en el modelo se obtuvieron con información espectral, de retrodispersión y de textura derivada de Sentinel-2, y ALOS PALSAR; así como de datos ambientales, de topografía y clima. El modelo para la estimación de biomasa presentó una devianza explicada (D2) de 0,25, mientras que el de riqueza de especies la D2 fue 0,19. Para evaluar ambos modelos se realizaron validaciones cruzadas, obteniendo un R2 de 0,25 para biomasa y de 0,20 para riqueza de especies, con un error cuadrático medio relativo de 45,8 % y de 32,5 %, respectivamente. El mapa bivariado con la distribución conjunta de la riqueza de especies y la biomasa aérea muestra que existe una correlación positiva entre ambas variables en el 63,8 % de la superficie de bosque nativo de la ecorregión. Los resultados de este trabajo podrían ser utilizados tanto para el mantenimiento de los almacenes de carbono, como para la conservación de la biodiversidad.

Plant species diversity is key to ecosystem functioning, but in recent decades anthropogenic activities have prompted an alarming decline in this community trait. Thus, developing strategies to understand diversity dynamics based on affordable and efficient remote sensing monitoring is essential, as well as examining the relevance of spatial scale and vegetation structural complexity to these dynamics. Here, we used two mathematical approaches to assess the relationship between tropical woody species diversity and spectral diversity in a human-modified landscape in two vegetation types differing in their degree of complexity. Vegetation complexity was measured through the fraction of species that concentrate different proportions of the cumulative importance value index. Species diversity was assessed using Hill numbers at three spatial scales, and metrics of spectral heterogeneity, vegetation indices, as well as raw data from Landsat 9 and Sentinel-2 sensors were calculated and analysed through general linear models (GLM) and Random Forest. Vegetation complexity emerged as an important variable in modelling species from remote sensing metrics, indicating the need to model species diversity by vegetation type rather than region. Hill numbers showed different relationships with remotely sensed metrics, in consistency with the scale-dependency of ecological processes on species diversity. Contrary to multiple previous reports, in our study, GLMs produced the best fits between Hill numbers of all orders and remotely sensed metrics. If we are to meet the need of conducting efficient and speedy woody species diversity monitoring globally, we propose modelling this diversity from remotely-sensed variables as an attractive strategy, so long as the intrinsic properties of each vegetation type are acknowledged to avoid under- or overestimation biases.

Coffee is one of the most important agricultural commodities. Agroforestry systems (AFS) are increasingly used in coffee cultivation because of environmental benefits, adaptability of the systems, and economic profits. However, identifying the spatial distribution of AFS through remote sensing continues to be challenging. The current systematic review focuses on the accuracies obtained and the computational methods and satellite data used in mapping coffee AFS between 2000 and 2020. To facilitate the analysis, we ordered the mapped AFS into five classes according to their density and species composition of shade trees. The Kruskal-Wallis test was applied to evaluate significative differences among classes. Both shade-tree densities and species composition affected the accuracy level. The worst results were obtained in AFS retaining many woody species from the original forest and high tree density (user accuracy <0.5). About the methods, maximum likelihood was the most widely used with very variable results; some non-parametric methods such as CART, ISODATA, RF, SMA, and SVM presented consistently high accuracy (>0.75). High spatial resolution multispectral imagery was suitable for mapping AFS; very few studies were found with radar imagery, so it would be desirable to increase its use combined with optical data.

Background: Maps of disturbed forests help to identify impacts on biodiversity and ecosystem services. Methods using only spectral data to detect disturbance at the regional level have limitations, but expert knowledge and fragmentation analysis can improve estimation. Questions: What is the distribution of disturbed forests in a region of high biodiversity, and which vegetation types and regions are most affected? Data description: SPOT 2015, Sentinel -2 satellite imagery from 2019. Vegetation data were collected at 653 sites. In addition, herbarium, agricultural census, and National Forest Inventory data were used. Study site and dates: Chiapas State, during 2018-2022. Methods: We elaborated a hybrid map of vegetation types, emphasizing identifying secondary forests. Also, we carried out a fragmentation analysis and calculated the woody biomass per forest type. Results: 40 % of the State still maintains tree cover, but only 18 % is undisturbed; most undisturbed forests are in three regions: Selva Lacandona, Sierra Madre, and Gulf Plain. Overall, the biomass of disturbed forests is significantly lower than that of their mature counterparts. Conclusions: In Chiapas, the distribution of forests with good conservation status is restricted; almost half of them are outside NPAs, so it is imperative to promote additional strategies for their conservation and management.

Los manglares son ecosistemas prioritarios para la conservación. Proveen diversos servicios ecosistémicos y son clave para el ciclo vital de muchas especies. Sin embargo, se encuentran amenazados por diversas actividades productivas y por fenómenos naturales como los huracanes, que impactan las costas causando daños en la vegetación. Monitorear los efectos del impacto de huracanes en las zonas de manglar es una tarea compleja, ya que son necesarios muchos recursos para acceder a los sitios devastados y para realizar evaluaciones en grandes superficies. Por ello, los datos de sensores remotos representan herramientas con un gran potencial para el estudio de las zonas más vulnerables. El objetivo principal de este estudio fue evaluar el impacto producido por el huracán Lorena que golpeó el archipiélago de Espíritu Santo, localizado en Baja California Sur, México, en el verano del año 2019. Se utilizaron imágenes del satélite Sentinel-2 tomadas antes (09/09/2019) y después (24/09/2019) del paso del huracán, con las cuales se calcularon cuatro índices de vegetación (IV) relacionados con la actividad fotosintética y el contenido de humedad del dosel. De igual forma, se calculó ΔIV para cada índice, que representa la reducción proporcional del valor del IV después del impacto. Los resultados generales mostraron un incremento de los valores de los cuatro IV en el área de estudio, lo cual podría explicarse por la velocidad del viento relativamente baja y el aporte de agua por la precipitación asociada al huracán Lorena. Sin embargo, los IV utilizados tuvieron una respuesta diferenciada, lo que demuestra la importancia de utilizar más de un indicador en los estudios que evalúen el impacto de fenómenos naturales en la vegetación costera.

Antecedentes: Los mapas de bosques perturbados son útiles para identificar afectaciones sobre la biodiversidad y los servicios ecosistémicos. Los métodos que emplean únicamente datos espectrales para detectar las perturbaciones a nivel regional tienen limitaciones. El conocimiento de expertos y el análisis de fragmentación puede mejorar la estimación. Preguntas: ¿Cuál es la distribución de los bosques perturbados en una región de alta biodiversidad? ¿Qué tipos de vegetación y regiones son las más afectadas? Descripción de los datos: imágenes satelitales SPOT 2015, Sentinel-2 de 2019. Se colectó información de la vegetación en 653 sitios. Además, se usaron datos de herbario, censos agrícolas y del Inventario Nacional Forestal. Lugar y fecha del estudio: Estado de Chiapas, durante 2018-2022. Métodos: Se elaboró un mapa híbrido de los tipos de vegetación enfatizando la identificación de bosques secundarios, también se realizó un análisis de fragmentación y se calculó la biomasa leñosa por tipo de bosque. Resultados: El 40 % de la superficie del Estado mantiene una cobertura arbórea; pero solo en el 12 % no se aprecia perturbación; la mayor parte de los bosques no perturbados se encuentran en tres regiones: Selva Lacandona, Sierra Madre y Planicie del Golfo. En general la biomasa de los bosques perturbados es significativamente menor que la de su contraparte madura. Conclusiones: En Chiapas la distribución de los bosques en buen estado de conservación está restringida; casi la mitad de ellos se encuentran fuera de las ANP, por lo que es imperativo promover estrategias adicionales para su manejo y conservación.

The native forests of Uruguay provide important ecosystem services. Despite this, there are few maps with the spatial distribution of vegetation attributes in the country. The objective of this study was to obtain maps with the spatial distribution of aboveground biomass and species richness that show areas with high concentrations of both variables, essential for climate change mitigation and biodiversity conservation. The study area includes the Gondwanan Sedimentary Basin ecoregion. Generalized Linear Models were used to estimate aboveground biomass and tree species richness, where the response variables were calculated using field data from the National Forest Inventory. Whereas, the predictor variables were obtained with spectral and texture information derived from Sentinel-2, and ALOS PALSAR; as well as environmental, topography and climate data. The biomass estimation model presented an explained deviance (D2) of 0,25, while in the species richness model, the D2 was 0,19. To evaluate both models, cross-validations were carried out, obtaining an R2 of 0.25 for aboveground biomass and 0,19 for species richness, with a relative mean square error of 45,8 % and 32,5 % respectively. The bivariate map with the joint distribution of species richness and aboveground biomass shows that there is a positive correlation between both variables in 63,8 % of the native forest area of the ecoregion. The results of this work could be used for the maintenance of carbon stocks and for the conservation of biodiversity.