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In the Neotropics, the integration of remotely sensed products to understand socioecological processes at local scales is limited by the physical difficulties and financial costs of collecting field data to train and validate these models. In this study, we used carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data and compared these products to a Landsat-based continuous change detection product for the 2000-2021 period. This was performed to evaluate forest clearing dynamics in and around the Puuc Biocultural State Reserve (PBSR) in Mexico. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (+19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000-2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing has changed over the years after the creation of the PBSR in 2011. The emerging hotspot analysis shows, however, that forest clearing spatiotemporal clustering (hotspots) during the 2012-2021 period was less widespread and mostly confined to areas outside the PBSR. In addition, the analysis shows forest clearing clustering is on a downward trend within the PBSR. After comparing forest clearing events to carbon density and tree species richness models, our data also suggests that land owners within the PBSR might be practicing longer barbecho (fallow) periods in contrast to land owners outside the PBSR allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acts as stabilizing forest management scheme that minimizes the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests. We recommend the continuation of efforts for providing alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. The combination of these models with continuous change detection datasets will allow to reveal underlying ecological processes and generate information better adapted to forest governance scales.

The recovery of soil conditions is crucial for successful ecosystem restoration and, hence, for achieving the goals of the UN Decade on Ecosystem Restoration. Here, we assess how soils resist forest conversion and agricultural land use, and how soils recover during subsequent tropical forest succession on abandoned agricultural fields. Our overarching question is how soil resistance and recovery depend on local conditions such as climate, soil type and land-use history. For 300 plots in 21 sites across the Neotropics, we used a chronosequence approach in which we sampled soils from two depths in old-growth forests, agricultural fields (i.e. crop fields and pastures), and secondary forests that differ in age (1-95 years) since abandonment. We measured six soil properties using a standardized sampling design and laboratory analyses. Soil resistance strongly depended on local conditions. Croplands and sites on high-activity clay (i.e. high fertility) show strong increases in bulk density and decreases in pH, carbon (C) and nitrogen (N) during deforestation and subsequent agricultural use. Resistance is lower in such sites probably because of a sharp decline in fine root biomass in croplands in the upper soil layers, and a decline in litter input from formerly productive old-growth forest (on high-activity clays). Soil recovery also strongly depended on local conditions. During forest succession, high-activity clays and croplands decreased most strongly in bulk density and increased in C and N, possibly because of strongly compacted soils with low C and N after cropland abandonment, and because of rapid vegetation recovery in high-activity clays leading to greater fine root growth and litter input. Furthermore, sites at low precipitation decreased in pH, whereas sites at high precipitation increased in N and decreased in C : N ratio. Extractable phosphorus (P) did not recover during succession, suggesting increased P limitation as forests age. These results indicate that no single solution exists for effective soil restoration and that local site conditions should determine the restoration strategies. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'., Philosophical Transactions of the Royal Society B: Biological Sciences, 378 (1867), ISSN:0962-8436, ISSN:1471-2970, ISSN:0080-4622

Information on the spatial distribution of forest aboveground biomass (AGB) and its uncertainty is important to evaluate management and conservation policies in tropical forests. However, the scarcity of field data and robust protocols to propagate uncertainty prevent a robust estimation through remote sensing. We upscaled AGB from field data to LiDAR, and to landscape scale using Sentinel-2 and ALOS-PALSAR through machine learning, propagated uncertainty using a Monte Carlo framework and explored the relative contributions of each sensor. Sentinel-2 outperformed ALOS-PALSAR (R2 = 0.66, vs 0.50), however, the combination provided the best fit (R2 = 0.70). The combined model explained 49% of the variation comparing against plots within the calibration area, and 17% outside, however, 94% of observations outside calibration area fell within the 95% confidence intervals. Finally, we partitioned the distribution of AGB in different management and conservation categories for evaluating the potential of different strategies for conserving carbon stock.

Funder: Agua Salud Project, Funder: U.S. Department of Energy; Id: http://dx.doi.org/10.13039/100000015, Funder: CAPES; Id: http://dx.doi.org/10.13039/501100002322, Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Accounting for small-size tree biomass is critical to improve total stand biomass estimates of secondary tropical forests, and is essential to quantify their vital role in mitigating climate change. However, owing to the scarcity of equations available for small-size trees, their contribution to total biomass is unknown. The objective of this study was to generate allometric equations to estimate total biomass of 22 tree species ≤ 10 cm in diameter at breast height (DBH), in the Yucatan peninsula, Mexico, by using two methods. First, the additive approach involved the development of biomass equations by tree component (stem, branch and foliage) with simultaneous fit. In the tree-level approach, total tree biomass equations were fit for multi-species and wood density groups. Further, we compared the performance of total tree biomass equations that we generated with multi-species equations of previous studies. Data of total and by tree component biomass were fitted from eight non-linear models as a function of DBH, total height (H) and wood density (ρ). Results showed that two models, identified as model I and II, best fitted our data. Model I has the form AGB = β0 (ρ·DBH2·H)β1 + e and model II: AGB = exp(-β0)(DBH2·H)β1 + e, where AGB is biomass (kg). Both models explained between 53% and 95% of the total observed variance in biomass, by tree-structural component and total tree biomass. The variance of total tree biomass explained by fit models related to wood density group was 96%-97%. Compared foreign equations showed between 30% and 45% mean error in total biomass estimation compared to 0.05%-0.36% error showed by equations developed in this study. At the local level, the biomass contribution of small trees based on foreign models was between 24.38 and 29.51 Mg ha-1, and model I was 35.97 Mg ha-1. Thus, from 6.5 up to 11.59 Mg ha-1 could be excluded when using foreign equations, which account for about 21.8% of the total stand biomass. Local equations provided more accurate biomass estimates with the inclusion of ρ and H as predictors variables and proved to be better than foreign equations. Therefore, our equations are suitable to improve the accuracy estimates of carbon forest stocks in the secondary forests of the Yucatan peninsula.

Tropical forest soils store a third of the global terrestrial carbon and control carbon dioxide (CO2) terrestrial effluxes to the atmosphere produced by root and microbial respiration. Soil CO2 efflux varies in time and space and is known to be strongly influenced by soil temperature and water content. However, little is known about the influence of seasonality on soil CO2 efflux, especially in tropical dry forests. This study evaluated soil CO2 efflux, soil temperature, and soil volumetric water content in a semideciduous tropical forest of the Yucatan Peninsula under two sites (flat areas close to and far from hills), and three seasons: dry, wet, and early dry (a transition between the rainy and dry seasons) throughout a year. Additionally, six 24-h periods of soil CO2 efflux were measured within these three seasons. The mean annual soil CO2 efflux was 4±2.2 μmol CO2 m-2 s-1, like the mean soil CO2 efflux during the early dry season. In all seasons, soil CO2 efflux increased linearly with soil moisture, which explained 45% of the spatial-temporal variation of soil CO2 efflux. Soil CO2 efflux was higher close to than far from hills in some months. The daily variation of soil CO2 efflux was less important than its spatial and seasonal variation likely due to small diel variations in temperature. Transition seasons are common in many tropical dry forests, and they should be taken into consideration to have a better understanding of the annual soil CO2 efflux, especially under future climate-change scenarios.

El manejo forestal comunitario sustentable favorece la economía de las comunidades rurales sin comprometer la capacidad de regeneración ni los servicios ecosistémicos que brindan las selvas, como el almacenamiento de carbono. Esta actividad ha sido ampliamente documentada en la Península de Yucatán, pero escasamente evaluada en el estado de Yucatán. En esta investigación se hizo una comparación de composición arbórea, estructura, diversidad y carbono almacenado en la biomasa aérea en tres áreas de selva mediana subcaducifolia con diferente tiempo de regeneración después de un aprovechamiento forestal en el ejido San Agustín (AAF1, AAF10 y AAF+50 años). En cada una se establecieron dos conglomerados conformados por cuatro parcelas circulares de 400 m2, en las que se identificaron y midieron (diámetro y altura) todos los árboles de diámetro mayor o igual a 7.5 cm. Se analizó la distribución de tamaños de los árboles, la diversidad de especies, su valor de importancia relativo y la biomasa aérea (a partir de ecuaciones alométricas). La especie más dominante fue Bursera simaruba y el AAF+50 presentó la menor dominancia. La distribución de clases diamétricas no varió entre las áreas de aprovechamiento y mostró un patrón de J invertida, lo cual sugiere un alto potencial de regeneración. La diversidad tampoco varió entre las áreas de aprovechamiento, mientras que el carbono almacenado en la biomasa aérea sí, siendo mayor en el AAF+50 (65.2 t /ha). Los resultados indican que el plan de manejo establecido por el ejido no ha afectado la diversidad arbórea ni su potencial de regeneración y permite un alto almacenamiento de carbono.

Knowledge of the spatial distribution of aboveground biomass (AGB) is crucial to guide forest conservation and management to maintain carbon stocks. LiDAR has been highly successful for this purpose, but has limited availability. Very-high resolution (

Background: Accurate estimations of aboveground biomass (AGB) based on allometric models are needed to implement climate-change mitigation strategies. However, allometry can change with tree size. Questions: Does allometry in a tropical dry forest change with tree size? Does combining different allometric equations provide better AGB estimates than using a single equation? Study site and dates: San Agustin Ejido, Yucatan, Mexico, 2016. Methods: Forty-seven trees of 18 species with 2.5 to 41.5 cm in diameter at breast height (DBH) were sampled. Stems and branches were sectioned, and samples were dried and weighed to estimate tree AGB. Segmented linear regression was used to evaluate changes in allometry between DBH, height and AGB. Different equations were tested for each size category identified, and the best models and model-combinations selected. Results: A shift in the AGB-height relationship was found, defining two tree-size categories (2.5-9.9 cm and ≥ 10 cm in DBH), with the inflection point corresponding to the average canopy height (12.2 m). The best models were AGB = exp(-2.769+0.937ln(D 2 HPw)) for trees < 10 cm DBH and AGB = exp(-9.171+1.591lnD+3.902lnH+0.496lnPw) for trees ≥ 10 cm DBH ( R 2 = 0.85 and R 2 = 0.92, respectively). The combination of these models produced more accurate AGB estimates than a single model or combinations involving regional models with larger sample sizes. Conclusions : These results highlight the importance of locally-developed models and suggest changes in allometry and resource allocation: towards height growth for small trees, thereby reducing the risk of suppression, versus towards AGB growth for larger trees, thereby maximizing stability and resource acquisition.

Tropical dry forests provide goods and ecosystem services that rely on their diversity and are vital for human wellbeing. However, they are among the most threatened ecosystems due mainly to conversion to agriculture. Accurate estimations of species diversity in tropical secondary forests are needed for effective conservation and forest management. We assessed the separate and combined performance of remotely-sensed surrogates of habitat heterogeneity and vegetation structure complexity for predicting and mapping woody plant species richness and diversity in tropical dry forests. Here, we used image texture measures to calculate spectral variability from RapidEye imagery as an indicator of habitat heterogeneity as well as height and cover metrics from LiDAR data as surrogates of the complexity of vegetation structure. Separately, image texture measures and LiDAR metrics were used to explain variation in species richness and exp Shannon diversity calculated in 48 plots using multiple regression analysis. We also evaluated the relative importance of two sets of indicators to estimate species diversity using variation partitioning analyses. Habitat heterogeneity (image texture metrics) contributed most to explain variation in species richness (R2: 0.72–0.87), whereas complexity of vegetation structure (LiDAR metrics) was more important for diversity (R2: 0.68–0.74). However, a large percentage of variance of richness and diversity (58%–67%) was jointly explained by both factors and using models that combine them provided similar or higher prediction accuracy (R2: 0.68–0.89). We conclude that using image texture of high resolution imagery as an indicator of habitat heterogeneity allows precise and cost-effective estimations of species richness, while LiDAR metrics as a surrogate of vegetation structure complexity allow better estimations of diversity and that combining image texture and LiDAR provides the best estimates of species richness and diversity.

Tropical forests are converted at an alarming rate for agricultural use and pastureland, but also regrow naturally through secondary succession. For successful forest restoration, it is essential to understand the mechanisms of secondary succession. These mechanisms may vary across forest types, but analyses across broad spatial scales are lacking. Here, we analyse forest recovery using 1,403 plots that differ in age since agricultural abandonment from 50 sites across the Neotropics. We analyse changes in community composition using species-specific stem wood density (WD), which is a key trait for plant growth, survival and forest carbon storage. In wet forest, succession proceeds from low towards high community WD (acquisitive towards conservative trait values), in line with standard successional theory. However, in dry forest, succession proceeds from high towards low community WD (conservative towards acquisitive trait values), probably because high WD reflects drought tolerance in harsh early successional environments. Dry season intensity drives WD recovery by influencing the start and trajectory of succession, resulting in convergence of the community WD over time as vegetation cover builds up. These ecological insights can be used to improve species selection for reforestation. Reforestation species selected to establish a first protective canopy layer should, among other criteria, ideally have a similar WD to the early successional communities that dominate under the prevailing macroclimatic conditions.

Leaf habit has been hypothesized to define a linkage between the slow-fast plant economic spectrum and the drought resistance-avoidance trade-off in tropical forests ('slow-safe vs fast-risky'). However, variation in hydraulic traits as a function of leaf habit has rarely been explored for a large number of species. We sampled leaf and branch functional traits of 97 tropical dry forest tree species from four sites to investigate whether patterns of trait variation varied consistently in relation to leaf habit along the 'slow-safe vs fast-risky' trade-off. Leaf habit explained from 0% to 43.69% of individual trait variation. We found that evergreen and semi-deciduous species differed in their location along the multivariate trait ordination when compared to deciduous species. While deciduous species showed consistent trait values, evergreen species trait values varied as a function of the site. Last, trait values varied in relation to the proportion of deciduous species in the plant community. We found that leaf habit describes the strategies that define drought avoidance and plant economics in tropical trees. However, leaf habit alone does not explain patterns of trait variation, which suggests quantifying site-specific or species-specific uncertainty in trait variation as the way forward.

Engineering resilience, a forest's ability to maintain its properties in the event of disturbance, comprises two components: resistance and recovery. In human-dominated landscapes, forest resilience depends mostly on recovery. Forest recovery largely depends on autogenic regulation, which entails a negative feedback loop between rates of change in forest state variables and state variables themselves. Hence community dynamics change in response to deviations from forest equilibrium state. Based on the premise that autogenic regulation is a key aspect of the recovery process, here we tested the hypothesis that combined old-growth forest (OGF) and secondary forest (SF) dynamics should show autogenic regulation in state variables, and thus convergence towards OGF-based reference points, indicating forest resilience. We integrated dynamic data for OGF (11-year monitoring) and SF (16-year monitoring) to analyse three key state variables (basal area, tree density and species richness), their annual rates of change and their underlying demographic processes (recruitment, growth and mortality). We examined autogenic regulation through generalized linear mixed-effects models (GLMMs) to quantify functional relationships between rates of change of state variables (and underlying demographic processes) and their respective state variables. State variables in OGF decreased moderately over time, against our prediction of OGF constancy. In turn, the three state variables analysed showed negative relationships with their respective rates of change, which allows the return of SF to OGF values after disturbance. In all cases, recruitment decreased with increasing values in state variables, while mortality increased. The observed negative relationships between state variables, their rates of change and their underlying demographic processes support our hypothesis of integrated OGF and SF dynamics showing autogenic regulation for state variables. Competition seems to be a major driver of autogenic regulation given its dependence on a resource availability that declines as forest structure develops. Synthesis. Based on a straightforward and comprehensive approach to quantify the extent to which tropical forest dynamics is self-regulated, this study highlights the importance of autogenic regulation for tropical dry forest as a basic component of its resilience. This approach is potentially valuable for a generalized assessment of engineering resilience of forests world-wide.

One-third of all Neotropical forests are secondary forests that regrow naturally after agricultural use through secondary succession. We need to understand better how and why succession varies across environmental gradients and broad geographic scales. Here, we analyze functional recovery using community data on seven plant characteristics (traits) of 1,016 forest plots from 30 chronosequence sites across the Neotropics. By analyzing communities in terms of their traits, we enhance understanding of the mechanisms of succession, assess ecosystem recovery, and use these insights to propose successful forest restoration strategies. Wet and dry forests diverged markedly for several traits that increase growth rate in wet forests but come at the expense of reduced drought tolerance, delay, or avoidance, which is important in seasonally dry forests. Dry and wet forests showed different successional pathways for several traits. In dry forests, species turnover is driven by drought tolerance traits that are important early in succession and in wet forests by shade tolerance traits that are important later in succession. In both forests, deciduous and compound-leaved trees decreased with forest age, probably because microclimatic conditions became less hot and dry. Our results suggest that climatic water availability drives functional recovery by influencing the start and trajectory of succession, resulting in a convergence of community trait values with forest age when vegetation cover builds up. Within plots, the range in functional trait values increased with age. Based on the observed successional trait changes, we indicate the consequences for carbon and nutrient cycling and propose an ecologically sound strategy to improve forest restoration success.

Litterfall production plays a fundamental role in the dynamics and function of tropical forest ecosystems, as it supplies 70&ndash, 80% of nutrients entering the soil. This process varies annually and seasonally, depending on multiple environmental factors. However, few studies spanning several years have addressed the combined effect of climate variables, successional age, topography, and vegetation structure in tropical dry forests. In this study, we evaluated monthly, seasonal, and annual litterfall production over a five-year period in semideciduous dry forests of different successional ages growing on contrasting topographic conditions (sloping or flat terrain) in Yucatan, Mexico. Its relationship with climate and vegetation structural variables were also analyzed using multiple linear regression and generalized linear models. Litterfall was measured monthly in 12 litterfall traps of 0.5 m2 in three sampling clusters (sets of four 400 m2 sampling plots) established in forests of five successional age classes, 3&ndash, 5, 10&ndash, 17, 18&ndash, 25, 60&ndash, 79, and &gt, 80 years (in the latter two classes either on slopping or on flat terrain), for a total of 15 sampling clusters and 180 litterfall traps. Litterfall production varied between years (negatively correlated with precipitation), seasons (positively correlated with wind speed and maximum temperature), and months (negatively correlated with relative humidity) and was higher in flat than in sloping sites. Litterfall production also increased with successional age until 18&ndash, 25 years after abandonment, when it attained values similar to those of mature forests. It was positively correlated with the aboveground biomass of deciduous species but negatively correlated with the basal area of evergreen species. Our results show a rapid recovery of litterfall production with successional age of these forests, which may increase with climate changes such as less precipitation, higher temperatures, and higher incidence of hurricanes.

Integrating information about the spatial distribution of carbon stocks and species diversity in tropical forests over large areas is fundamental for climate change mitigation and biodiversity conservation. In this study, spatial models showing the distribution of carbon stocks and the number of species were produced in order to identify areas that maximize carbon storage and biodiversity in the tropical forests of the Yucatan Peninsula, Mexico. We mapped carbon density and species richness of trees using L-band radar backscatter data as well as radar texture metrics, climatic and field data with the random forest regression algorithm. We reduced sources of errors in plot data of the national forest inventory by using correction factors to account for carbon stocks of small trees (

The objectives of this study were to develop and compare allometric equations of aboveground biomass (AGB) for a semi-deciduous tropical forest of the ejido (system of communal or cooperative farming) San Agustin, Yucatan, Mexico, and to compare them to existing equations. Forty seven trees of 18 different species with diameters between 2.5 cm and 41.5 cm were selected. The diameter of each tree was measured before felling, and the height was estimated before and measured after felling. Fresh weight of each component (stem, branches, leaves) was measured in the field, and samples were taken to determine dry weight in the laboratory and to extrapolate to the whole individual. Six allometric equations with different functional forms were explored, and tested using the Akaike information criterion (AIC), adjusted R2value and cross validation. The best equation yielded an AIC value of 48.36 and an adjusted R2 of 0.96. Cross-validation showed an adjusted R2 value of 0.72 and a coefficient of agreement of 0.56. Unexpectedly, this equation is less precise than others used in the forests of the Yucatan Peninsula, including equations developed locally and in other continents, with very contrasting sample sizes. However, the best equation obtained in this study allows adequate estimates of AGB of the semideciduous tropical forests of Yucatan, especially for the largest trees, which are the most influential.

Tropical forests are globally important for biodiversity conservation and climate change mitigation but are being converted to other land uses. Conversion of seasonally dry tropical forests (SDTF) is particularly high while their protection is low. Secondary succession allows forests to recover their structure, diversity and composition after conversion and subsequent abandonment and is influenced by demographic rates of the constituent species. However, how these rates vary between seasons for different plant sizes at different successional stages in SDTF is not known. The effect of seasonal drought may be more severe early in succession, when temperature and radiation are high, while competition and density-dependent processes may be more important at later stages, when vegetation is tall and dense. Besides, the effects of seasonality and successional stage may vary with plant size. Large plants can better compete with small plants for limiting resources and may also have a greater capacity to withstand stress. We asked how size-dependent density, species density, recruitment and mortality varied between seasons and successional stages in a SDTF. We monitored a chronosequence in Yucatan, Mexico, over six years in three 0.1 ha plots in each of three successional stages: early (3–5 years-old), intermediate (18–20 years-old) and advanced (>50 years-old). Recruitment, mortality and species gain and loss rates were calculated from wet and dry season censuses separately for large (diameter > 5 cm) and small (1–5 cm in diameter) plants. We used linear mixed-effects models to assess the effects of successional stage, seasonality and their changes through time on demographic rates and on plant and species density. Seasonality affected demographic rates and density of large plants, which exhibited high wet-season recruitment and species gain rates at the early stage and high wet-season mortality at the intermediate stage, resulting in an increase in plant and species density early in succession followed by a subsequent stabilization. Small plant density decreased steadily after only 5 years of land abandonment, whereas species density increased with successional stage. A decline in species dominance may be responsible for these contrasting patterns. Seasonality, successional stage and their changes through time had a stronger influence on large plants, likely because of large among-plot variation of small plants. Notwithstanding the short duration of our study, our results suggest that climate-change driven decreases in rainy season precipitation may have an influence on successional dynamics in our study forest as strong as, or even stronger than, prolonged or severe droughts during the dry season.

Background Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates. Results Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R2 = 0.44 vs R2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R2 = 0.26). Conclusions This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

Tropical secondary forests recover quickly (decades) in tree species richness but slowly (centuries) in species composition., Old-growth tropical forests harbor an immense diversity of tree species but are rapidly being cleared, while secondary forests that regrow on abandoned agricultural lands increase in extent. We assess how tree species richness and composition recover during secondary succession across gradients in environmental conditions and anthropogenic disturbance in an unprecedented multisite analysis for the Neotropics. Secondary forests recover remarkably fast in species richness but slowly in species composition. Secondary forests take a median time of five decades to recover the species richness of old-growth forest (80% recovery after 20 years) based on rarefaction analysis. Full recovery of species composition takes centuries (only 34% recovery after 20 years). A dual strategy that maintains both old-growth forests and species-rich secondary forests is therefore crucial for biodiversity conservation in human-modified tropical landscapes.

La selva mediana superennifolia es la comunidad más extendida y una de las más transformadas en la Península de Yucatán, sin embargo, existen pocos estudios sobre su proceso de recuperación. Este trabajo planteó analizar los patrones de recuperación de la estructura, diversidad y composición de especies en una selva de este tipo. El estudio se realizó en el área de conservación El Zapotal, en Yucatán, México, entre 2011 y 2012. Se muestreó la vegetación leñosa en una cronosecuencia de rodales, de 4 años a 60 años de abandono tras un uso ganadero, y en un remanente de selva conservada (madura). Los patrones de recuperación de la estructura y la diversidad con la edad sucesional se analizaron comparando diferentes modelos no lineales y los de composición mediante análisis de ordenación y de clasificación. Los atributos de la estructura y la diversidad aumentaron con la edad sucesional, excepto la densidad, que alcanzó un máximo en edades tempranas, seguido de una disminución. La diversidad alcanzó los valores observados en la selva conservada, pero la estructura no. La similitud florística con respecto a la selva madura aumentó con la edad de sucesión y se distinguieron tres grupos de especies que indican un recambio en la dominancia durante la sucesión. Los resultados sugieren una recuperación rápida de la diversidad, más lenta de la estructura, y un patrón sucesional de composición más semejante al de las selvas más húmedas que al de selvas más secas, con importantes implicaciones para la conservación, la restauración y la mitigación del cambio climático.

The magnitude of the carbon sink in second-growth forests is expected to vary with successional biomass dynamics resulting from tree growth, recruitment, and mortality, and with the effects of climate on these dynamics. We compare aboveground biomass dynamics of dry and wet Neotropical forests, based on monitoring data gathered over 3–16 years in forests covering the first 25 years of succession. We estimated standing biomass, annual biomass change, and contributions of tree growth, recruitment, and mortality. We also evaluated tree species’ contributions to biomass dynamics. Absolute rates of biomass change were lower in dry forests, 2.3 and 1.9 Mg ha−1 y−1, after 5–15 and 15–25 years after abandonment, respectively, than in wet forests, with 4.7 and 6.1 Mg ha−1 y−1, in the same age classes. Biomass change was largely driven by tree growth, accounting for at least 48% of biomass change across forest types and age classes. Mortality also contributed strongly to biomass change in wet forests of 5–15 years, whereas its contribution became important later in succession in dry forests. Biomass dynamics tended to be dominated by fewer species in early-successional dry than wet forests, but dominance was strong in both forest types. Overall, our results indicate that biomass dynamics during succession are faster in Neotropical wet than dry forests, with high tree mortality earlier in succession in the wet forests. Long-term monitoring of second-growth tropical forest plots is crucial for improving estimates of annual biomass change, and for enhancing understanding of the underlying mechanisms and demographic drivers.

Land-use change occurs nowhere more rapidly than in the tropics, where the imbalance between deforestation and forest regrowth has large consequences for the global carbon cycle(1). However, considerable uncertainty remains about the rate of biomass recovery in secondary forests, and how these rates are influenced by climate, landscape, and prior land use(2-4). Here we analyse aboveground biomass recovery during secondary succession in 45 forest sites and about 1,500 forest plots covering the major environmental gradients in the Neotropics. The studied secondary forests are highly productive and resilient. Aboveground biomass recovery after 20 years was on average 122 megagrams per hectare (Mg ha(-1)), corresponding to a net carbon uptake of 3.05 Mg C ha(-1) yr(-1), 11 times the uptake rate of old-growth forests. Aboveground biomass stocks took a median time of 66 years to recover to 90% of old-growth values. Aboveground biomass recovery after 20 years varied 11.3-fold ( from 20 to 225 Mg ha(-1)) across sites, and this recovery increased with water availability (higher local rainfall and lower climatic water deficit). We present a biomass recovery map of Latin America, which illustrates geographical and climatic variation in carbon sequestration potential during forest regrowth. The map will support policies to minimize forest loss in areas where biomass resilience is naturally low (such as seasonally dry forest regions) and promote forest regeneration and restoration in humid tropical lowland areas with high biomass resilience.

In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery.

El índice de área foliar (IAF) proporciona información acerca de la cantidad de superficie fotosintética que existe en relación con la superficie total del ecosistema y se relaciona con procesos vitales como la fotosíntesis, la respiración y la productividad. Por lo tanto, es importante contar con información sobre la distribución espacial del IAF a escala de paisaje. El método indirecto más utilizado para la estimación del IAF se basa en imágenes de satélite y consiste en asociarlo con características espectrales e índices de vegetación. Sin embargo, estos índices tienen una fuerte limitación debido a problemas de saturación, lo cual restringe la posibilidad de generar mapas precisos de IAF, particularmente en bosques con altos niveles de biomasa. En el presente trabajo se obtuvieron modelos para mapear el IAF en un bosque tropical seco de Yucatán durante las estaciones de lluvia y estiaje a partir de imágenes de alta resolución, utilizando un procedimiento de regresión combinado con kriging. Este procedimiento integra la relación del IAF, tanto con datos espectrales y de textura de las imágenes, como con la dependencia espacial de los residuales. Se obtuvieron valores de IAF por medio de fotografías hemisféricas con una precisión aceptable y valores medios significativamente diferentes entre la temporada de lluvias (3.37) y la de estiaje (2.49). Los valores de R2aj de los modelos de regresión múltiple fueron de 0.58 y 0.63 para la temporada de lluvias y estiaje, respectivamente. En general, los resultados demuestran que, al utilizar el análisis de textura, se pueden generar modelos aceptables para la estimación del IAF en bosques tropicales secos con altos niveles de biomasa.

Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m2, 400 m2 and 1000 m2) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m2 to 1000 m2 (R2 = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass.