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Increasing food production and mitigating climate change are two primary but seemingly contradictory objectives for tropical landscapes. This special feature examines synergies and trade-offs among these objectives. Four themes emerge from the papers: the important roles of both forest and agriculture sectors for climate mitigation in tropical countries; the minor contribution from deforestation-related agricultural expansion to overall food production at global and continental scales; the opportunities for synergies between improved food production and reductions in greenhouse gas emissions through diversion of agricultural expansion to already-cleared lands, improved soil, crop, and livestock management, and agroforestry; and the need for targeted policy and management interventions to make these synergistic opportunities a reality. We conclude that agricultural intensification is a key factor to meet dual objectives of food production and climate mitigation, but there is no single panacea for balancing these objectives in all tropical landscapes. Place-specific strategies for sustainable land use emerge from assessments of current land use, demographics, and other biophysical and socioeconomic characteristics, using a whole-landscape, multisector perspective.

Expanding croplands to meet the needs of a growing population, changing diets, and biofuel production comes at the cost of reduced carbon stocks in natural vegetation and soils. Here, we present a spatially explicit global analysis of tradeoffs between carbon stocks and current crop yields. The difference among regions is striking. For example, for each unit of land cleared, the tropics lose nearly two times as much carbon (∼120 tons·ha⁻¹ vs. ∼63 tons·ha⁻¹) and produce less than one-half the annual crop yield compared with temperate regions (1.71 tons·ha⁻¹·y⁻¹ vs. 3.84 tons·ha⁻¹·y⁻¹). Therefore, newly cleared land in the tropics releases nearly 3 tons of carbon for every 1 ton of annual crop yield compared with a similar area cleared in the temperate zone. By factoring crop yield into the analysis, we specify the tradeoff between carbon stocks and crops for all areas where crops are currently grown and thereby, substantially enhance the spatial resolution relative to previous regional estimates. Particularly in the tropics, emphasis should be placed on increasing yields on existing croplands rather than clearing new lands. Our high-resolution approach can be used to determine the net effect of local land use decisions.

1. Agricultural expansion and intensification are major threats to tropical biodiversity. In addition to the direct removal of native vegetation, agricultural expansion often elicits other human-induced disturbances, many of which are poorly addressed by existing environmental legislation and conservation programmes. This is particularly true for tropical freshwater systems, where there is considerable uncertainty about whether a legislative focus on protecting riparian vegetation is sufficient to conserve stream fauna. 2. To assess the extent to which stream fish are being effectively conserved in agricultural landscapes, we examined the spatial distribution of assemblages in river basins to identify the relative importance of human impacts at instream, riparian and catchment scales, in shaping observed patterns. We used an extensive dataset on the ecological condition of 83 low-order streams distributed in three river basins in the eastern Brazilian Amazon. 3. We collected and identified 24,420 individual fish from 134 species. Multiplicative diversity partitioning revealed high levels of compositional dissimilarity (DS) among stream sites (DS = 0.74 to 0.83) and river basins (DS = 0.82), due mainly to turnover (77.8% to 81.8%) rather than nestedness. The highly heterogeneous fish faunas in small Amazonian streams underscore the vital importance of enacting measures to protect forests on private lands outside of public protected areas. 4. Instream habitat features explained more variability in fish assemblages (15%-19%) than riparian (2%-12%), catchment (4%-13%) or natural covariates (4%-11%). Although grouping species into functional guilds allowed us to explain up to 31% of their abundance (i.e. for nektonic herbivores), individual riparian - and catchment - scale predictor variables that are commonly a focus of environmental legislation explained very little of the observed variation (partial R² values mostly <5%). 5. Policy implications. Current rates of agricultural intensification and mechanization tropical landscapes are unprecedented, yet the existing legislative frameworks focusing on protecting riparian vegetation seem insufficient to conserve stream environments and their fish assemblages. To safeguard the species-rich freshwater biota of small Amazonian streams, conservation actions must shift towards managing whole basins and drainage networks, as well as agricultural practices in already-cleared land.

Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall‐to‐wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM‐X data, simplifications are necessary to estimate canopy height with semi‐empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR). We trained the semi‐empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM‐X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation. Our results indicated that the empirical parameters substantially improved the estimation from a root‐mean‐square‐error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land‐use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals. We demonstrate the potential of the combined exploitation of the TanDEM‐X and GEDI missions for a wall‐to‐wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation canopy height in tropical landscapes and on large scales with spaceborne laser and SAR data.

We estimate the potential reductions in methane and carbon dioxide emissions from several livestock and pasture management options in the mixed and rangeland-based production systems in the tropics. The impacts of adoption of improved pastures, intensifying ruminant diets, changes in land-use practices, and changing breeds of large ruminants on the production of methane and carbon dioxide are calculated for two levels of adoption: complete adoption, to estimate the upper limit to reductions in these greenhouse gases (GHGs), and optimistic but plausible adoption rates taken from the literature, where these exist. Results are expressed both in GHG per ton of livestock product and in Gt CO₂-eq. We estimate that the maximum mitigation potential of these options in the land-based livestock systems in the tropics amounts to approximately 7% of the global agricultural mitigation potential to 2030. Using historical adoption rates from the literature, the plausible mitigation potential of these options could contribute approximately 4% of global agricultural GHG mitigation. This could be worth on the order of $1.3 billion per year at a price of $20 per t CO₂-eq. The household-level and sociocultural impacts of some of these options warrant further study, however, because livestock have multiple roles in tropical systems that often go far beyond their productive utility.

The distribution of different vegetation types is important information for landscape management, especially in the context of tackling global environmental change. Vegetation types can be mapped using satellite and airborne passive remote sensing. However, spectrally similar yet structurally different vegetation types, like different tree-dominated land covers, are often challenging to map using spectral information alone. We examined the potential of vertical vegetation structure acquired in the global ecosystem dynamics investigation (GEDI) mission that harnesses a space-borne waveform lidar sensor in vegetation mapping across a heterogeneous tropical landscape in Cambodia. We extracted 121 waveform metrics from Level-1B and Level-2A data products at 1062 locations across five key vegetation types. After reducing the relative height variables’ dimensionality through simple linear regressions, we developed a Random Forest classifier to predict vegetation classes based on 23 GEDI metrics. We then used this model to classify the vegetation types across more than 77 000 GEDI footprints in the study area. GEDI metrics alone were useful in identifying vegetation types with 81% accuracy. Cropland/grassland class had the highest prediction accuracy (user’s accuracy [UA] = 89%; producer’s accuracy [PA] = 91%), while dry deciduous forest had the lowest accuracy (UA = 73%; PA = 69%). By comparing the GEDI-only classification with an optical-radar map, we found that structural and topographic information from GEDI Level-1B and Level-2A can complement the spectral information in assessing natural habitats that neighbor other vegetation types in a heterogeneous landscape. The highest classification accuracy at the footprint scale was obtained from the combination of GEDI, Sentinel-1, and Sentinel-2 (88.3%). We also demonstrated how wall-to-wall vegetation mapping is possible by combining the three data sources. These findings expand the potential use of GEDI waveform lidar data in supporting the development of policy-relevant maps that depict the distribution of forests together with other vegetation types.

The U-net is nowadays among the most popular deep learning algorithms for land use/land cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar (SAR) and multispectral (MS) imagery. On the other hand, the discrimination between plantations and forests in LULC maps has been emphasized, especially for tropical areas, due to their differences in biodiversity and ecosystem services provision. In this study, we trained a U-net using different imagery inputs from Sentinel-1 and Sentinel-2 satellites, MS, SAR and a combination of both (MS + SAR); while a random forests algorithm (RF) with the MS + SAR input was also trained to evaluate the difference in algorithm selection. The classification system included ten classes, including old-growth and secondary forests, as well as old-growth and young plantations. The most accurate results were obtained with the MS + SAR U-net, where the highest overall accuracy (0.76) and average F1-score (0.58) were achieved. Although MS + SAR and MS U-nets gave similar results for almost all of the classes, for old-growth plantations and secondary forest, the addition of the SAR band caused an F1-score increment of 0.08–0.11 (0.62 vs. 0.54 and 0.45 vs. 0.34, respectively). Consecutively, in comparison with the MS + SAR RF, the MS + SAR U-net obtained higher F1-scores for almost all the classes. Our results show that using the U-net with a combined input of SAR and MS images enabled a higher F1-score and accuracy for a detailed LULC map, in comparison with other evaluated methods.

Habitat loss and land-use change in tropical forests have modified the composition and configuration of natural landscapes, driving biodiversity loss. Through studies covering different approaches to diversity and functional traits, a more holistic comprehension may be drawn regarding the effects of habitat transformation. In this study, we evaluated how the forest cover and landscape heterogeneity shape the taxonomic and functional diversity and biomass of dung beetles. Dung beetles were sampled in 16 landscape units of the “Los Tuxtlas” Biosphere Reserve (Mexico). We collected a total of 2396 individuals of 25 species in 14 genera. Taxonomic and functional diversity and biomass of dung beetles were positively related to the amount of primary forest and negatively related to increased landscape heterogeneity. These results indicate that tropical rainforests are highly sensitive to landscape transformation, which jeopardizes the different aspects of biodiversity. By showing the importance of evaluating different facets of biodiversity we conclude that implementing different landscape descriptors and different diversity components is a complementary and efficient approach to assessing the effects of landscape composition on dung beetles’ assemblages in tropical rainforests.Implications for insect conservationLandscape composition plays a pivotal role in elucidating the various components that define the dung beetle community in tropical forests. Furthermore, it is of utmost significance to encompass a diverse array of biodiversity components, alongside species biomass, in order to comprehensively evaluate the impact of human-induced landscape transformation.

Theoretical and empirical studies demonstrate that the total amount of forest and the size and connectivity of fragments have nonlinear effects on species survival. We tested how habitat amount and configuration affect understory bird species richness and abundance. We used mist nets (almost 34,000 net hours) to sample birds in 53 Atlantic Forest fragments in southeastern Brazil. Fragments were distributed among 3 10,800-ha landscapes. The remaining forest in these landscapes was below (10% forest cover), similar to (30%), and above (50%) the theoretical fragmentation threshold (approximately 30%) below which the effects of fragmentation should be intensified. Species-richness estimates were significantly higher (F = 3715, p = 0.00) where 50% of the forest remained, which suggests a species occurrence threshold of 30—50% forest, which is higher than usually occurs (<30%). Relations between forest cover and species richness differed depending on species sensitivity to forest conversion and fragmentation. For less sensitive species, species richness decreased as forest cover increased, whereas for highly sensitive species the opposite occurred. For sensitive species, species richness and the amount of forest cover were positively related, particularly when forest cover was 30—50%. Fragment size and connectivity were related to species richness and abundance in all landscapes, not just below the 30% threshold. Where 10% of the forest remained, fragment size was more related to species richness and abundance than connectivity. However, the relation between connectivity and species richness and abundance was stronger where 30% of the landscape was forested. Where 50% of the landscape was forested, fragment size and connectivity were both related to species richness and abundance. Our results demonstrated a rapid loss of species at relatively high levels of forest cover (30—50%). Highly sensitive species were 3-4 times more common above the 30—50% threshold than below it; however, our results do not support a unique fragmentation threshold. Estudios teóricos y empíricos demuestran que la cantidad total de bosque y el tamaño y la conectividad de los fragmentos tienen efectos no lineales sobre la supervivencia de especies. Probamos el efecto de la cantidad y configuración de hábitat sobre la riqueza y abundancia de especies de aves de sotobosque. Utilizamos redes de niebla (casi 34,000 horas red) para muestrear aves en 53 fragmentos del Bosque del Atlántico en el sureste de Brasil. Los fragmentos se distribuyeron en tres paisajes de 10,800 ha. El bosque remanente en estos paisajes era menor (10% cobertura forestal), similar (30%) y superior (50%) al umbral de fragmentación teórico (aproximadamente 30%) por debajo del cual se intensifican los efectos de la fragmentación. Las estimaciones de riqueza de especies fueron significativamente mayores (F = 3715, p = 0.00) donde permanecía 50% del bosque, lo que sugiere un umbral de ocurrencia de especies de 30—50% que es mayor a lo que ocurre usualmente (<30%). Las relaciones entre la cobertura de bosque y la riqueza de especies difirieron dependiendo de la sensibilidad de la especie a la conversión y fragmentación del bosque. Para especies menos sensibles, la riqueza de especies decreció a medida que incrementó la cobertura de bosque, mientras que ocurrió lo contrario para especies altamente sensibles. Para especies sensibles, la riqueza de especies y la cantidad de cobertura de bosque estuvieron relacionados positivamente, particularmente cuando la cobertura del bosque fue 30—50%. El tamaño del fragmento y la conectividad se relacionaron con la riqueza y abundancia de especies en todos los paisajes, no solo debajo del umbral de 30%. Cuando permanecía 10% del bosque, el tamaño del fragmento estaba más relacionado con la riqueza y abundancia de especies que la conectividad. Sin embargo, la relación entre la conectividad y la riqueza y abundancia de especies fue mayor donde 30% del paisaje tenía bosques. Donde 50% del paisaje tenía bosques, el tamaño del fragmento y la conectividad se relacionaron con la riqueza y abundancia de especies. Nuestros resultados demostraron una pérdida rápida de especies en niveles relativamente altos de cobertura forestal (30—50%). Especies altamente sensibles fueron 3—4 veces más comunes por encima del umbral de 30—50% que por debajo de él; sin embargo, nuestros resultados no sustentan un umbral de fragmentación único.

ContextIsolated pasture trees play an important role in forest recovery within fragmented tropical landscapes by attracting seed dispersers and facilitating seedling growth. However, studies with conflicting results have led to confusion about what drives variation in zoochorous-dispersed seed rain patterns under isolated tree canopies.ObjectivesTo assess the role of landscape and biological factors impacting zoochorous-dispersed seed rain under isolated pasture trees across three tropical regions of the world.MethodsWe measured seed dispersal under 144 isolated pasture trees found in 12 fragmented tropical and sub-tropical landscapes in Australia, Colombia and Nigeria. Using linear mixed effect models, we modeled seed diversity, abundance, richness and evenness as functions of the biological features and landscape context of isolated trees.ResultsThroughout all regions, the amount of woody vegetation surrounding trees in pastures was negatively related to rainforest seed diversity, evenness and abundance. Seed diversity and evenness increased significantly with the distance of isolated trees to forest fragments in the Australian sub-tropics, but elsewhere, seed diversity and evenness tended to decline with distance to forest, though not significantly.ConclusionsOur results suggest that the tree composition of landscapes surrounding isolated pasture trees is important for influencing zoochorous-dispersed seed rain, regardless of the region studied. Our study highlights the prominent role of landscape-scale, rather than local-scale factors on seed dispersal to isolated pasture trees, while providing strong evidence that early stage successional processes involving isolated pasture trees are similar throughout global tropical regions.