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Background: Although the loss of Melipona beecheii colonies in meliponaries suggests insufficient availability of melliferous blooming plants, there is limited knowledge about the diversity and conditions of the surrounding vegetation. Question: What is the diversity, structure, and composition of the melliferous and non-melliferous vegetation surrounding meliponaries, and how does it affect the availability of food sources for bees? Study site and dates: The vegetation surrounding nine meliponaries, three in each political state of the Yucatán Peninsula encompassing the main vegetation types, was studied in 2022 and 2023. Methods: Four 150-meter-long transects with the point-centered quadrants method were traced in each meliponary to estimate plant species composition, the availability of melliferous and blooming plants (IVI), diversity parameters (Hill-numbers), tree density, and diameter per strata. Results: 312 taxa, 250 genera, and 73 plant families were recorded. In five meliponaries, blooming melliferous plants accounted for less than 9 % of the IVI, with one meliponary having no blooming species. The highest diversity was found in a meliponary surrounded by semi-evergreen forest. The high stratum had a mean tree height of 5.5 (SD ± 2.9) meters and 3,390 (SD ± 2,702) trees/hectare across vegetation types. The tree diameter was lowest in meliponaries located in the semi-deciduous forest. Conclusions: The meliponaries are surrounded by young secondary vegetation with high density of small trees and predominance of the low stratum. We found a similar vegetation diversity among meliponaries and scarce blooming melliferous plants. Human activity seems to impact plant diversity and food availability for M. beecheii.

Tropical forests hold large stocks of carbon in biomass and face pressures from changing climate and anthropogenic disturbance. Forests' capacity to store biomass under future conditions and accumulate biomass during regrowth after clearance are major knowledge gaps. Here we use chronosequence data, satellite observations and a C-cycle model to diagnose woody C dynamics in two dry forest ecotypes (semi-deciduous and semi-evergreen) in Yucatán, Mexico. Woody biomass differences between mature semi-deciduous (90 MgC ha−1) and semi-evergreen (175 MgC ha−1) forest landscapes are mostly explained by differences in climate (c. 60%), particularly temperature, humidity and soil moisture effects on production. Functional variation in foliar phenology, woody allocation, and wood turnover rate explained c. 40% of biomass differences between ecotypes. Modeling experiments explored varied forest clearance and regrowth cycles, under a range of climate and CO2 change scenarios to 2100. Production and steady state biomass in both ecotypes were reduced by forecast warming and drying (mean biomass 2021–2100 reduced 16–19% compared to 2001–2020), but compensated by fertilisation from rising CO2. Functional analysis indicates that trait adjustments amplify biomass losses by 70%. Experiments with disturbance and recovery across historically reported levels indicate reductions to mean forest biomass stocks over 2021–2100 similar in magnitude to climate impacts (10–19% reductions for disturbance with recovery). Forest disturbance without regrowth amplifies biomass loss by three- or four-fold. We conclude that vegetation functional differences across the Yucatán climate gradient have developed to limit climate risks. Climate change will therefore lead to functional adjustments for all forest types. These adjustments are likely to magnify biomass reductions caused directly by climate change over the coming century. However, the range of impacts of land use and land use change are as, or more, substantive than the totality of direct and indirect climate impacts. Thus the carbon storage of Yucatan's forests is highly vulnerable both to climate and land use and land use change. Our results here should be used to test and enhance land surface models use for dry forest carbon cycle assessment regionally and globally. A single plant functional type approach for modeling Yucatán's forests is not justified.

Litter decomposition is a central process in forest ecosystems because of its role in carbon and nutrient cycling and maintaining soil fertility. Decomposition is affected by plant traits, soil and microenvironmental conditions, topography, and vegetation structure, which varies with successional age. However, it is unclear how all these factors affect leaf decomposition of dominant tree species in tropical dry forests (TDFs). The objective of this study was to compare the decomposition rates of six dominant tree species: three legumes (Caesalpinia gaumeri, Lysiloma latisiliquum, Piscidia piscipula) and three non-legumes (Bursera simaruba, Gymnopodium floribundum, Neomillspaughia emarginata) in five successional age categories (8–10, 15–22, 23–30, 65–84, > 85 years-old) and two topographic conditions (flat and sloping sites) in a TDF, and to analyze the association with leaf traits (toughness, N, C and total phenols content) soil properties (bulk density, organic carbon, pH, clay), microenvironmental (litter and soil moisture, leaf area index), and vegetation variables (basal area, aboveground biomass, tree diameter, tree height). Litterbags were placed in 30–400 m2 circular plots distributed in forests of the Yucatan, Mexico, and collected on six occasions spread over 230 days (540 samples per species). L. latisiliquum and C. gaumeri had the highest decomposition rates (as well as leaf nitrogen concentration and the lowest leaf toughness). Conversely, G. floribundum had the lowest decay rate. Decomposition rate reached high values at intermediate successional ages, suggesting that soil fertility recovers rapidly after disturbance, although only L. latisiliquum showed significant differences among stand age categories. Decomposition rate was consistently higher at flat sites than on slopes but the difference was significant only for L. latisiliquum. The soil, vegetation structure and microenvironmental variables that contributed most to explaining variation in decay rates varied among species. Decomposition tended to increase with soil moisture and clay content, and to decrease with soil organic carbon and pH suggesting susceptibility to climate change and soil erosion, particularly in sloping areas. Our results highlight the importance of analyzing species-specific responses, especially for dominant species, which likely contribute most to leaf litter decomposition, and to consider key ecological factors that influence this key process.

Abstract Spatial information on the timing of forest cover loss is important to identify and map stand age, which is a key factor driving the recovery of carbon pools and can also be used to estimate aboveground biomass (AGB) based on its relationship with stand age. Here, we estimated the spatial distribution of stand age and AGB of young forest (

The Puuc Biocultural State Reserve (PBSR) is a unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on maintaining low-impact productive activities coordinated by multiple communal and private landowners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and their relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000–2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. 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 changed over the years after the creation of the PBSR in 2011. However, an emergent hotspot analysis shows that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012–2021 period was less common than prior to 2011, and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, the results show that landowners outside the PBSR often clear forests with lower carbon density and species diversity than those inside the PBSR. This suggests that, compared to landowners outside the PBSR, landowners within the PBSR might be practicing longer fallow periods 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 acted as a stabilizing forest management scheme during the 2012–2021 period, minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend continuing efforts to provide alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. Combining these models with continuous change detection datasets will allow for underlying ecological processes to be revealed and the generation of information better adapted to forest governance scales.

Abstract 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.

Efforts to assess and understand changes in plant diversity and ecosystem functioning focus on the analysis of taxonomic diversity. However, the resilience of ecosystems depends not only on species richness but also on the functions (responses and effects) of species within communities and ecosystems. Therefore, a functional approach is required to estimate functional diversity through functional traits and to model its changes in space and time. This study aims to: (i) assess the accuracy of estimates of species richness and tree functional richness obtained from field data and Sentinel-2 imagery in tropical dry forests of the Yucatan Peninsula; (ii) map and analyze the relationships between these two variables. We calculated species richness and functional richness (from six functional traits) of trees from 87 plots of the National Forest Inventory in a semi-deciduous tropical forest and 107 in a semi-evergreen tropical forest. Species richness and functional richness were mapped using reflectance values, vegetation indices, and texture measurements from Sentinel-2 imagery as explanatory variables. Validation of the models to map these two variables yielded a coefficient of determination (R2) of 0.43 and 0.50, and a mean squared relative error of 25.4% and 48.8%, for tree species richness and functional richness, respectively. For both response variables, the most important explanatory variables were Sentinel-2 texture measurements and spectral bands. Tree species richness and functional richness were positively correlated in both forest types. Bivariate maps showed that 44.9% and 26.5% of the forests studied had high species richness and functional richness values. Our findings highlight the importance of integrating field data and remotely sensed variables for estimating tree species richness and functional richness. In addition, the combination of species richness and functional richness maps presented here is potentially valuable for planning, conservation, and restoration strategies by identifying areas that maximize ecosystem service provisioning, carbon storage, and biodiversity conservation.

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 (2) of 0.28 and 0.31 and a relative mean square error of 38.5% and 33.0% for aboveground biomass and species richness, respectively, at pixel level. Estimates of carbon density were influenced mostly by radar backscatter and climatic data, while those of species richness were influenced mostly by radar texture and climatic variables. Correlation between carbon density and species richness was positive in 79.3% of the peninsula, while bivariate maps showed that 39.6% of the area in the peninsula had high carbon stocks and species richness. Our results highlight the importance of combining carbon and diversity maps to identify areas that are critical—both for maintaining carbon stocks and for conserving biodiversity.

Secondary tropical forests are largely dominated by small-diameter trees. Their contribution to the total stand productivity may be determinant for ecosystem functioning. However, the biomass contained in small-size trees is frequently ignored because they are rarely included in forest inventories, as well as due to the lack of biomass equations for such trees. The aim of this study was to generate specific biomass equations by tree structural components and total-tree biomass for 22 species (diameter at breast height-DBH ≤ 10 cm diameter) structurally important of secondary tropical forests in southeast Yucatán peninsula. The biomass equations were generated using two fitting approaches. First, using the additive approach where the biomass equations by structural tree components (stem, branches and foliage) were simultaneously fitted. Second, the tree-level approach where the total-tree biomass was independently fitted. We used DBH, total height (TH) and wood density (WD) as predictor variables during the fitting process. The performance of each total-tree biomass equation was compared to generalized biomass equations developed in other tropical forests. The variance explained of observed biomass across tree species were stem, 64% to 99%; branch, 24% to 95%; foliage, 35% to 94% for equations by structural tree components; while the explained variance for total tree biomass equations was between 94 to 99 %. The inclusion of TH and WD as predictor variables in the equation structure, significantly improved the goodness of fit statistics. These are the first equations developed for small-size tree species in secondary forests of Yucatán peninsula. We observed that generalized equations developed for other tropical forests, in some cases overestimated up to ~ 44.2% and in other cases underestimated up to ~ 48.4% the mean total-tree biomass. When we applied our equations to forest inventory data, they showed a better predicting performance than generalized equations. Therefore, they are reliable for estimating biomass of small-size tree species in similar forest types. Our equations could be used for biomass-carbon estimation in tropical secondary forests composed by high densities of small-size tree species; therefore, they are relevant within climate change contexts.

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 Agustín Ejido, Yucatán, México, 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(D2HPw)) for trees < 10 cm DBH and AGB = exp(-9.171+1.591lnD+3.902lnH+0.496lnPw) for trees ? 10 cm DBH (R2 = 0.85 and R2 = 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.

The Mayans of the Yucatán Peninsula, together with their environment, conform social-ecological systems with adaptation and resilience to natural, political and economic disturbances. In this study, we use the framework of social-ecological systems for describing a mechanism that allows a Mayan community and its natural environment to respond to disturbances over time. We describe (1) the activities that members of the community develop as part of their strategy for managing natural resource management, and (2) the history of the social-ecological system focusing on meaningful events: changes in their institutional body, landscape and/or practices for exploiting natural resources, and interactions between these changes. Through both semi-structured and in-depth interviews, historical narratives and participant observation, we found that managers use the environmental heterogeneity to diversify the exploitation of species, manage the secondary vegetation and protect mature vegetation. Formal and informal institutions of access, regulation and administration of natural resources regulate productive activities and management practices. These institutions operate differentially at the levels of environmental units (prohibition of the exploitation of the natural resources of mature tropical forests) and species (protection of the jaguar and cougar). Diversification of productive activities, management of environmental heterogeneity and the presence of flexible institutions enable responses in the social-ecological systems that have the potential to contribute to its long-term maintenance. Comprehensive studies like this might help to understand adaptive capacity and social-ecological resilience.

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.

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.

El análisis de los cambios de cobertura y uso del suelo (ccus) en relación con las actividades humanas es esencial para entender y predecir la dinámica del paisaje. Se analizan los ccus en Solferino y San Ángel, dos comunidades rurales del noroeste de Quintana Roo, a partir de fotografías aéreas de 1979 y 2000, recorridos de campo y entrevistas para documentar la historia de uso del suelo. Contrario a la mayoría de estudios, se registró un aumento en la cobertura de selvas, proveniente principalmente de la regeneración de áreas quemadas, lo cual resalta el papel de los incendios como agentes de cambio de cobertura y la capacidad de recuperación de las selvas. El cambio en el uso del suelo favoreció ligeramente la deforestación en Solferino, debido en parte a una mayor disponibilidad de selvas para usos agropecuarios en las cercanías del poblado. En San Ángel se observó una mayor regeneración de selvas, debido en parte a una mayor deforestación previa, asociada a un gran aumento poblacional y a la implementación de programas federales de fomento a la ganadería. Se discuten los escenarios de cambio y las posibles repercusiones de las actuales tendencias institucionales, socioeconómicas y climáticas sobre los futuros ccus.

Seasonally dry tropical forests (SDTF) are located in regions with alternating wet and dry seasons, with dry seasons that last several months or more. By the end of the 21st century, climate models predict substantial changes in rainfall regimes across these regions, but little is known about how individuals, species, and communities in SDTF will cope with the hotter, drier conditions predicted by climate models. In this review, we explore different rainfall scenarios that may result in ecological drought in SDTF through the lens of two alternative hypotheses: 1) these forests will be sensitive to drought because they are already limited by water and close to climatic thresholds, or 2) they will be resistant/resilient to intra- and inter-annual changes in rainfall because they are adapted to predictable, seasonal drought. In our review of literature that spans microbial to ecosystem processes, a majority of the available studies suggests that increasing frequency and intensity of droughts in SDTF will likely alter species distributions and ecosystem processes. Though we conclude that SDTF will be sensitive to altered rainfall regimes, many gaps in the literature remain. Future research should focus on geographically comparative studies and well-replicated drought experiments that can provide empirical evidence to improve simulation models used to forecast SDTF responses to future climate change at coarser spatial and temporal scales.

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.

Two main theories have attempted to explain variation in plant species composition (β-diversity). Niche theory proposes that most of the variation is related to environment (environmental filtering), whereas neutral theory posits that dispersal limitation is the main driver of β-diversity. In this study, we first explored how α- and β-diversity of plant functional groups defined by growth form (trees, shrubs and lianas, which represent different strategies of resource partitioning), and dispersal syndrome (autochory, anemochory and zoochory, which represent differences in dispersal limitation) vary with successional age and topographic position in a tropical dry forest. Second, we examined the effects of environmental, spatial, and spatially-structured environmental factors on β-diversity of functional groups; we used the spatial structure of sampling sites as a proxy for dispersal limitation, and elevation, soil properties and forest stand age as indicators of environmental filtering. We recorded 200 species and 22,245 individuals in 276 plots; 120 species were trees, 41 shrubs and 39 lianas. We found that β-diversity was highest for shrubs, intermediate for lianas and lowest for trees, and was slightly higher for zoochorous than for autochorous and anemochorous species. All three dispersal syndromes, trees and shrubs varied in composition among vegetation classes (successional age and topographic position), whilst lianas did not. β-diversity was influenced mostly by proxies of environmental filtering, except for shrubs, for which the influence of dispersal limitation was more important. Stand age and topography significantly influenced α-diversity across functional groups, but showed a low influence on β-diversity -possibly due to the counterbalancing effect of resprouting on plant distribution and composition. Our results show that considering different plant functional groups reveals important differences in both α- and β-diversity patterns and correlates that are not apparent when focusing on overall woody plant diversity, and that have important implications for ecological theory and biodiversity conservation.

El análisis de los cambios de cobertura y uso del suelo (ccus) en relación con las actividades humanas es esencial para entender y predecir la dinámica del paisaje. Se analizan los ccus en Solferino y San Ángel, dos comunidades rurales del noroeste de Quintana Roo, a partir de fotografías aéreas de 1979 y 2000, recorridos de campo y entrevistas para documentar la historia de uso del suelo. Contrario a la mayoría de estudios, se registró un aumento en la cobertura de selvas, proveniente principalmente de la regeneración de áreas quemadas, lo cual resalta el papel de los incendios como agentes de cambio de cobertura y la capacidad de recuperación de las selvas. El cambio en el uso del suelo favoreció ligeramente la deforestación en Solferino, debido en parte a una mayor disponibilidad de selvas para usos agropecuarios en las cercanías del poblado. En San Ángel se observó una mayor regeneración de selvas, debido en parte a una mayor deforestación previa, asociada a un gran aumento poblacional y a la implementación de programas federales de fomento a la ganadería. Se discuten los escenarios de cambio y las posibles repercusiones de las actuales tendencias institucionales, socioeconómicas y climáticas sobre los futuros ccus.