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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.

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.

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 (

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.

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.

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.

Questions Is the accuracy of predictions of above-ground biomass (AGB) and plant species richness of tropical dry forests from LIDAR data compromised during leaf-off canopy period, when most of the vegetation is leafless, compared to the leaf-on period? How does topographic position affect prediction accuracy of AGB for leaf-off and leaf-on canopy conditions? Location Tropical dry forest, Yucatan Peninsula, Mexico. Methods We evaluated the accuracy of predictions using both leaf-on and leaf-off LIDAR estimates of biomass and species richness, and assessed the adequacy of both LIDAR data sets for characterizing these vegetation attributes in tropical dry forests using multiple regression analysis and ANOVA. The performance of the models was assessed by leave-one-out cross-validation. We also investigated differences in vegetation structure between two topographic conditions using PCA and ANOSIM. Finally, we evaluated the influence of topography on the accuracy of biomass estimates from LIDAR using multiple regression analysis and ANOVA. Results A higher overall accuracy was obtained with leaf-on vs leaf-off conditions for AGB (root mean square error (RMSE) = 21.6 vs 25.7 ton·ha−1), as well as for species richness (RMSE = 5.5 vs 5.8 species, respectively). However, no significant differences in mean dissimilarities between biomass estimates from LIDAR and in situ biomass estimates comparing the two canopy conditions were found (F1,39 = 0.03, P = 0.87). In addition, no significant differences in dissimilarities of AGB estimation were found between flat and hilly areas (F1,39 = 1.36, P = 0.25). Conclusions Our results suggest that estimates of species richness and AGB from LIDAR are not significantly influenced by canopy conditions or slope, indicating that both leaf-on and leaf-off models are appropriate for these variables regardless of topographic position in these tropical dry forests.

Functional Diversity is considered an important driver of community assembly in environmental and successional gradients. To understand tree assembly processes in a semideciduous tropical forest, we analyzed the variation of Functional Richness (FRic), Functional Divergence (FDiv), and Functional Evenness (FEve) of small vs. large trees in relation to fallow age after slash-and-burn agriculture and topographical position (flat sites vs. hills). FRic of small trees was lower than null model predicted values across the successional gradient, and decreased unexpectedly in older successional ages. FRic of large trees was higher than null model predictions early in succession and lower in late-successional stands on hills. Dominant species were more similar (low FDiv) in early and intermediate successional stands for small trees, and on hills for large trees, suggesting that species that are best adapted to harsh conditions share similar traits. We also found evidence of competitive exclusion among similar species (high FEve) for small trees in early successional stands. Overall, our results indicate that community assembly of small trees is strongly affected by the changing biotic and abiotic conditions along the successional and topographical gradient. For large trees, hills may represent the most stressful conditions in this landscape.

Question What is the relative importance of forest successional age, environmental heterogeneity, landscape structure and spatial structure of sampling sites on β-diversity of tropical dry forests (TDF)? How do the magnitude of β-diversity and the relative influence of factors, processes and mechanisms driving β-diversity differ at different spatial grains? What are the effects of stand age on β-diversity? Location Yucatan Peninsula, Mexico. Methods Floristic composition was obtained from a hierarchical survey performed in 276 sites distributed across 23 sampling landscapes (SL) (12 sites per SL). Land-cover classes were derived from the classification of multi-spectral SPOT 5 satellite imagery. We calculated landscape metrics of patch type for each SL, and characterized local soil conditions for each sampling site. A principal coordinates of neighbour matrices (PCNM) analysis was performed to estimate spatial variables, and partial redundancy analysis was used to decompose variation into spatial, stand age and landscape structure components. Results The magnitude of β-diversity varied with spatial scale (grain size), and was larger at the local than at the landscape grain. The magnitude of β-diversity also decreased slightly but significantly with successional age. There were significant differences in species composition among vegetation classes. Environmental factors (local soil conditions, as well as landscape structure) and spatial structure both contributed to woody plant β-diversity in our TDF landscape, but their relative importance was scale-dependent. At the local grain size, both the environment (mainly soil conditions) and the spatial structure strongly affected β-diversity, while at the landscape grain, environmental factors (variation in soil conditions, as well as landscape configuration) played a more prominent role. Conclusions The magnitude of β-diversity decreased with increasing spatial grain and successional age, while the relative importance of mechanisms influencing β-diversity was scale-dependent: both niche partitioning and dispersal limitation affect β-diversity at the local grain size, while niche partitioning prevails at the landscape grain.

Research on tropical dry forest (TDF) succession i0s needed for effective conservation and management of this threatened and understudied ecosystem. We used a highly replicated chronosequence within a 37,242-ha TDF landscape to investigate successional patterns by plant size class and to evaluate the influence of stand age, topographic position, soil properties and spatial autocorrelation on forest structure and composition. We used a SPOT5 satellite image to obtain a land-cover thematic map, and sampled woody vegetation (adults: >5 cm diam; saplings: 1–5 cm) and soil properties in 168 plots distributed among four vegetation classes: VC1 (3–8-yr-old forest), VC2 (9–15-yr-old forest), VC3 (>15-yr-old forest on flat areas), VC4 (>15-yr-old forest on hills). Stem density decreased with stand age and was lowest in VC3, while height, basal area and species density increased with age and were higher in older than in younger forests. Topographic position also influenced forest structure and composition. Basal area and height were largely determined by stand age, whereas stem and species density, and composition were influenced mostly by soil variables associated with fertility, and by spatial autocorrelation. Adults and saplings showed contrasting patterns and correlates of community structure, but similar patterns and correlates of composition, possibly due to the prevalence of coppicing. Our results show that our sampling approach can overcome several limitations of chronosequence studies, and provide insights in the patterns and drivers of succession, as well as guidelines for forest management and conservation. Abstract in Spanish is available at http://www.blackwell-synergy.com/loi/btp.

Three central related issues in ecology are to identify spatial variation of ecological processes, to understand the relative influence of environmental and spatial variables, and to investigate the response of environmental variables at different spatial scales. These issues are particularly important for tropical dry forests, which have been comparatively less studied and are more threatened than other terrestrial ecosystems. This study aims to characterize relationships between community structure and landscape configuration and habitat type (stand age) considering different spatial scales for a tropical dry forest in Yucatan. Species density and above ground biomass were calculated from 276 sampling sites, while land cover classes were obtained from multi-spectral classification of a Spot 5 satellite imagery. Species density and biomass were related to stand age, landscape metrics of patch types (area, edge, shape, similarity and contrast) and principal coordinate of neighbor matrices (PCNM) variables using regression analysis. PCNM analysis was performed to interpret results in terms of spatial scales as well as to decompose variation into spatial, stand age and landscape structure components. Stand age was the most important variable for biomass, whereas landscape structure and spatial dependence had a comparable or even stronger influence on species density than stand age. At the very broad scale (8,000–10,500 m), stand age contributed most to biomass and landscape structure to species density. At the broad scale (2,000–8,000 m), stand age was the most important variable predicting both species density and biomass. Our results shed light on which landscape configurations could enhance plant diversity and above ground biomass.

We investigated relationships between population dynamics and microclimate, physiology, and the degree of mycorrhizal colonization, for three species (Piscidia piscipula L.(Sarg.)) (Fabaceae), Bunchosia swartziana Griseb. (Malpighiaceae) and Psidium sartorianum (Bergius) Nied. (Myrtaceae)) of a tropical sub deciduous forest in Yucatan, Mexico that were growing in plots of different successional ages. We hypothesized that abundance and persistence were related to increased plasticity in CO2 assimilation. We found that Piscidia piscipula had greater abundance in intermediate plots (18 to 21 years), presented higher levels of plasticity in CO2 assimilation (greater variability among individuals, plots, and seasons), presented the highest CO2 assimilation rates, and presented greater drought resistance (higher water potentials and capacitance). Conversely, Psidium sartorianum had greater abundance in older plots (more than 50 years of secondary succession), lower assimilation rates, and low levels of plasticity in CO2 assimilation. Bunchosia had intermediate values. Locally, the degree of mycorrhizal colonization was consistent with abundance across plots. Regionally (but not locally), plasticity in CO2 assimilation was consistent with abundance. We found differences in microclimates among plots and within plots among species. Physiological adjustments appeared to play an important role in the capacity to regenerate and in the successional persistence of these species in this tropical dry forest.

https://www.scopus.com/inward/record.url?eid=2-s2.0-84939570316&partnerID=40&md5=fcadae8e6c274e8b7efca96099304a7c Successional gradients are ubiquitous in nature, yet few studies have systematically examined the evolutionary origins of taxa that specialize at different successional stages. Here we quantify successional habitat specialization in Neotropical forest trees and evaluate its evolutionary lability along a precipitation gradient. Theoretically, successional habitat specialization should be more evolutionarily conserved in wet forests than in dry forests due to more extreme microenvironmental differentiation between early and late-successional stages in wet forest. We applied a robust multinomial classification model to samples of primary and secondary forest trees from 14 Neotropical lowland forest sites spanning a precipitation gradient from 788 to 4000 mm annual rainfall, identifying species that are old-growth specialists and secondary forest specialists in each site. We constructed phylogenies for the classified taxa at each site and for the entire set of classified taxa and tested whether successional habitat specialization is phylogenetically conserved. We further investigated differences in the functional traits of species specializing in secondary vs. old-growth forest along the precipitation gradient, expecting different trait associations with secondary forest specialists in wet vs. dry forests since water availability is more limiting in dry forests and light availability more limiting in wet forests. Successional habitat specialization is non-randomly distributed in the angiosperm phylogeny, with a tendency towards phylogenetic conservatism overall and a trend towards stronger conservatism in wet forests than in dry forests. However, the specialists come from all the major branches of the angiosperm phylogeny, and very few functional traits showed any consistent relationships with successional habitat specialization in either wet or dry forests. Synthesis. The niche conservatism evident in the habitat specialization of Neotropical trees suggests a role for radiation into different successional habitats in the evolution of species-rich genera, though the diversity of functional traits that lead to success in different successional habitats complicates analyses at the community scale. Examining the distribution of particular lineages with respect to successional gradients may provide more insight into the role of successional habitat specialization in the evolution of species-rich taxa.

Long-term human disturbance of tropical forests may favor generalist plant species leading to biotic homogenization. We aimed to a) assess if generalist species dominate across different successional ages and topographical positions in a tropical dry forest with a long history of human disturbance, b) to characterize functional traits associated with generalist and specialist species, and c) to assess if a predominance of generalists leads to a homogeneous functional structure across the landscape. We used a multinomial model of relative abundances to classify 118 woody species according to their successional/topographic habitat. Three species were classified as secondary-forest specialists, five as mature-forest specialists, 35 as generalists, and 75 as too rare to classify. According to topography, six species were hill specialists, eight flat-site specialists, 35 generalists, and 70 too rare. Generalists dominated across the landscape. Analysis of 14 functional traits from 65 dominant species indicated that generalists varied from acquisitive strategies of light and water early in succession to conservative strategies in older forests and on hills. Long-term human disturbance may have favored generalist species, but this did not result in functional homogenization. Further analyses considering other functional traits, and temporal and fine-scale microenvironmental variation are needed to better understand community assembly.

The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.

The carbon balance of secondary dry tropical forests of Mexico’s Yucatan Peninsula is sensitive to human and natural disturbances and climate change. The spatially explicit process model Forest-DeNitrification-DeComposition (DNDC) was used to estimate forest carbon dynamics in this region, including the effects of disturbance on carbon stocks. Model evaluation using observations from 276 sample plots in a tropical dry forest in the Yucatan Peninsula indicated that Forest-DNDC can be used to simulate carbon stocks for this forest with good model performance efficiency. The simulated spatial variability in carbon stocks was large, ranging from 5 to 115 Mg carbon (C) ha−1, with a mean of 56.6 Mg C ha−1. Carbon stocks in the forest were largely influenced by human disturbances between 1985 and 2010. Based on a comparison of the simulations with and without disturbances, carbon storage in the year 2012 with disturbance was 3.2 Mg C ha−1, lower on average than without disturbance. The difference over the whole study area was 154.7 Gg C, or an 8.5 % decrease. There were substantial differences in carbon stocks simulated at individual sample plots, compared to spatially modeled outputs (200 m2 plots vs. polygon simulation units) at some locations due to differences in vegetation class, stand age, and soil conditions at different resolutions. However, the difference in the regional mean of carbon stocks between plot-level simulation and spatial output was small. Soil CO2 and N2O fluxes varied spatially; both fluxes increased with increasing precipitation, and soil CO2 also increased with an increase in biomass. The modeled spatial variability in CH4 uptake by soils was small, and the flux was not correlated with precipitation. The net ecosystem exchange (NEE) and net primary production (NPP) were nonlinearly correlated with stand age. Similar to the carbon stock simulations, different resolutions resulted in some differences in NEE and NPP, but the spatial means were similar.

Previous studies have emphasized the importance of including not only the potential and costs of different land use/land cover alternatives on carbon sequestration but that also there is a need to study the impact of the resulting land cover changes on biodiversity. Tropical forests are undergoing rapid transformation as the result of human activities, which have created more than 600 million ha of secondary vegetation. In particular, tropical dry forests (TDF) are under great pressure caused by conversion to agriculture and other land uses, resulting in a heterogeneous landscape mosaic of secondary forest in different stages of succession or small forest remnants embedded in a matrix of agriculture. Changes in the landscape mosaic affect patterns of animal species abundance and distribution and, consequently, influence community composition. Despite the prevalence of successional forests, few studies have examined their influence on higher trophic levels such as bird communities. The aim of this study was to examine the relative influence of successional age, vegetation structure, and landscape structure on bird guild composition in a TDF region in the Yucatan Peninsula, an important area for migratory birds characterized by high avian endemism. Species composition of different bird feeding guilds was calculated for 274 plots of bird point counts, and vegetation structure was obtained from a vegetation survey in the same plots. We used a land cover thematic map, derived from a supervised classification of SPOT5 satellite imagery, to calculate landscape pattern metrics. Species composition of birds was related to structure of vegetation, landscape metrics of patch types, and principal coordinates of neighbor matrices (PCNM) variables using canonical correspondence analysis (CCA). Overall, bird feeding guilds were influenced by stand age, vegetation structure, and spatial structure of sampled data, and marginally by landscape composition and configuration, but varied in their response and susceptibility to habitat changes. Sound conservation and management should take into account forest specialist species, which require pristine or late secondary forests to persist, and should consider a possible decline in species that may occur in secondary forests but would otherwise use mature forests, as well as declines in species which may feed in a variety of habitats but may not necessarily reproduce in all habitat types.

Expression of crassulacean acid metabolism (CAM) is characterized by extreme variability within and between taxa and its sensitivity to environmental variation. In this study, we determined seasonal fluctuations in CAM photosynthesis with measurements of nocturnal tissue acidification and carbon isotopic composition (δ(13)C) of bulk tissue and extracted sugars in three plant communities along a precipitation gradient (500, 700, and 1,000 mm year(-1)) on the Yucatan Peninsula. We also related the degree of CAM to light habitat and relative abundance of species in the three sites. For all species, the greatest tissue acid accumulation occurred during the rainy season. In the 500 mm site, tissue acidification was greater for the species growing at 30% of daily total photon flux density (PFD) than species growing at 80% PFD. Whereas in the two wetter sites, the species growing at 80% total PFD had greater tissue acidification. All species had values of bulk tissue δ(13)C less negative than -20‰, indicating strong CAM activity. The bulk tissue δ(13)C values in plants from the 500 mm site were 2‰ less negative than in plants from the wetter sites, and the only species growing in the three communities, Acanthocereus tetragonus (Cactaceae), showed a significant negative relationship between both bulk tissue and sugar δ(13)C values and annual rainfall, consistent with greater CO(2) assimilation through the CAM pathway with decreasing water availability. Overall, variation in the use of CAM photosynthesis was related to water and light availability and CAM appeared to be more ecologically important in the tropical dry forests than in the coastal dune.

Climate change may increase the intensity of hurricanes (Emanuel 1987, 2003), and thus the size of disturbance in tropical forests. As a consequence, disturbance-specialist plants, such as lianas, may increase in abundance there (Phillips & Gentry 1994). Putz (1984) hypothesized that lianas create larger treefall gaps by connecting trees together and pulling down multiple trees during storms. This positive-feedback cycle may increase the prevalence of lianas in disturbed tropical forests (Schnitzer & Bongers 2002, Schnitzer & Carson 2001). Alternatively, Putz (1984) proposed that lianas tie and stabilize canopies together, resulting in less disturbance. Forest age may determine the role of lianas during disturbance because liana abundance and composition vary through secondary succession (De Waltet al. 2000, Schnitzeret al. 2000). To test the two hypotheses of Putz (1984), we evaluated the effect of liana cutting between forests of different successional ages on tree damage by hurricane Wilma.