Search Results (62)

Savanna systems are characterised by a community of large mammal herbivores with up to 30 species; coexistence is based on resource partitioning. In this paper we analyse the features of the landscape and plant structure which lead herbivores to use particular locations, a key to resource partitioning. The processes involved, top-down versus bottom-up, are well known for large species and small ones but not for medium-sized ones. We use two resident, medium-sized species, topi (Damaliscus lunatus jimela) and kongoni, (Alcelaphus buselaphus cokei) in the central woodlands of the Serengeti; selection of habitat by the residents is important for predator-prey interactions and for interactions among the grazers. Using Principal Component Analysis and Multiple Regression we develop highly predictive models which show that resource availability is the critical determinant of habitat selection in the dry season; and reduction in predation risk appears to be important in the wet season. These results show for the first time that habitat selection by the medium-sized herbivores is driven by different strategies in the two seasons. This contributes to understanding the processes involved in the dynamics of this globally important savanna system, a necessary foundation for management. Full article

Garvin Heights Park in southeastern Minnesota, USA, is a 12 ha mosaic of bluff prairie, oak savanna, and oak–hickory woodland co-owned by the City of Winona and Winona State University, with a 40+ year history of encroachment by non-native woody invasives, especially buckthorn (Rhamnus cathartica) and honeysuckles (Lonicera spp.). Habitat restoration was initiated in the early 1990s, but management gaps and a seedbank of invasives compromised initial efforts. More consistent and sustainable restoration activities since 2016 have included cutting and chemical treatment of invasives, managed goat browsing, targeted reseeding and plug planting with native species, and more regular prescribed fires. Throughout the restoration process, we assessed changes in buckthorn densities in response to various management practices, assessed the restored savanna tree community, and documented the presence of blooming plants across all park habitats. Manual clearing of woody invasives and repeated goat browsing significantly reduced buckthorn and honeysuckle abundance in prairies and savannas. Park plant communities responded to the combination of management strategies with reduced densities of woody invasives and expanding diversity (currently >220 species present) of forbs and grasses, including a large and growing population of state-threatened Great Indian Plantain (Arnoglossum reniforme). Prescribed fires have benefitted prairies but have done little to improve savanna plant communities, due largely to excessive tree canopy coverage causing a lack of burnable fuels (i.e., dry forbs and grasses). Improved partnerships between landowners and dedicated volunteers are working to expand restoration efforts to include other portions of the park and adjacent woodlands. Full article

The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to its intricate vegetation mosaics and the limited availability of reliable ground reference data. Accurate land cover maps are urgently needed to support conservation and sustainable land-use planning in this frontier region, especially for distinguishing critical vegetation types such as Amazon rainforest, Cerradão (dense woodland), and Savanna. In this study, we produce the first high-resolution land cover map of the CAT by integrating PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data within a U-net deep learning framework. This data fusion approach enables improved discrimination of ecologically similar vegetation types across heterogeneous landscapes. We systematically compare classification performance across single-sensor and fused datasets, demonstrating that multi-source fusion significantly outperforms single-source inputs. The highest overall accuracy was achieved using the fusion of PlanetScope, Sentinel-2, and Sentinel-1 (F1 = 0.85). Class-wise F1 scores for the best-performing model were 0.91 for Amazon Forest, 0.76 for Cerradão, and 0.76 for Savanna, indicating robust model performance in distinguishing ecologically important vegetation types. According to the best-performing model, 50.3% of the study area remains covered by natural vegetation. Cerradão, although ecologically important, covers only 8.4% of the landscape and appears highly fragmented, underscoring its vulnerability. These findings highlight the power of deep learning and multi-sensor integration for fine-scale land cover mapping in complex tropical ecotones and provide a critical spatial baseline for monitoring ecological changes in the CAT region. Full article

In protected areas of the Brazilian savannas (Cerrado), Land Surface Phenology (LSP) is influenced by both precipitation and fire, but the nature of these relationships remains unexplored. Here, we assessed the impacts of precipitation and fire on LSP metrics derived from the Normalized Difference Vegetation Index (NDVI) at Emas National Park (ENP). Using TIMESAT, along with the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and 30-m Harmonized Landsat Sentinel (HLS) products, we investigated these effects in both grassland and woodland areas. To evaluate the effects of precipitation, we identified the driest and wettest seasonal cycles between 2002 and 2023 and analyzed the relationships between accumulated rainfall during the rainy season and each of the 13 TIMESAT metrics. To assess the effects of fire, three major events were examined: 1 September 2005 (affecting 45% of the park’s area), 12 August 2010 (90%), and 10 July 2021 (21%). The burned grassland area and the subsequent vegetation recovery following the 2021 event were analyzed in detail using a non-burned control site and LSP metrics extracted from the HLS product, covering both pre- and post-disturbance cycles. The results indicated that the metrics most positively correlated to precipitation were Amplitude (AMP), End of Season (EOS), Large and Small Seasonal Integrals (LSI and SSI), and Rate of Increase at the Beginning of the Season (RIBS). The highest correlation coefficients were found in woodland areas, which were less affected by fire disturbance than grassland areas. Similar trends were observed in the behavior of AMP, EOS, and SSI in response to both precipitation and fire, with fire exerting a stronger influence. By decoupling the fire effects from rainfall influence using the control site, we identified Base Level (BL), SSI, EOS, AMP, and Values at the End and Start of the Season (VES and VSS), as the metrics most sensitive to fire and subsequent vegetation recovery in burned areas. The effects of fire were evident for most metrics, both during the disturbance cycle and in the post-fire cycle. Our study underscores the importance of combining MODIS and HLS time series to understand vegetation phenology in the Cerrado. Full article

Historically protected by cultural traditions, sacred forests are now increasingly threatened by anthropogenic pressures, particularly in West Africa, where natural areas and wildlife populations have dwindled as human populations have increased exponentially. Residents in the vicinity of sacred forests play critical roles in conservation success or failure, but few studies have investigated their views. We surveyed 281 residents representing ~100% of households surrounding the sacred forest of Nakpadjoak, a 50-hectare remnant of Sudan-Guinea woodland savanna in northern Togo that is now surrounded by human-dominated landscapes. The majority of residents believe that the sacred forest should be protected (92%) and that access to the forest should be prohibited (55%). Most residents own livestock (93%) and reported that the forest has become a pasture for domestic animals (70%) while wildlife populations have declined (79%). Two-thirds of residents (64%) reported that the forest has changed due to wood cutting, a practice that occurs despite being banned. Most (96%) residents use wood as their primary source of domestic energy, but 90% would switch to alternative fuels, such as natural gas, if available. Unfortunately, despite residents’ desire to protect the forest and external funding for its protection and restoration, Nakpadjoak forest has become increasingly degraded due to ongoing exploitation and conflicts of interest surrounding its use. We recommend bolstering local prohibitions on sacred forest exploitation as well as government interventions such as subsidizing natural gas as an alternative to wood fuel to support the conservation of this and other protected areas in the region, which may otherwise be destroyed. Full article

Many ecosystems have been altered since European colonization, resulting in the loss of historical ecosystems along with information about historical ecosystems. Tree density estimation from historical land surveys with alignment to expert classifications of historical vegetation strengthen reconstructions of vegetation history through research triangulation. For the midwestern United States, we extended historical tree density estimates (≥12.7 cm in diameter) to contextualize expert classifications of vegetation types in Illinois and Minnesota, part of the historical Great Plains grasslands with very frequent fire exposure, and Indiana and southern Michigan, which were more protected from fire. We also identified a tree density threshold between grasslands and savannas and contrasted density estimates with two alternate density estimates. After refining expert-classified vegetation types, out of 14 major historical ecosystems in this region, 11 were grasslands, savannas, or woodlands. The three additional ecosystems were American beech (Fagus grandifolia) closed woodlands and forests in Indiana and American beech-oak (Quercus) closed woodlands and forests and tamarack (Larix laricina) and ash (Fraxinus) swamp forests in southern Michigan. Because tree densities in the grasslands of Illinois and Minnesota did not exceed 4 trees/ha and tree densities in the savannas of Indiana, Michigan, and Minnesota ranged from 23 trees/ha to 78 trees/ha, around 15 trees/ha may be a reasonable threshold between grasslands and savannas. Density estimates generally matched with two other sources of density estimates, despite using different approaches, supporting the reliability of density estimation. Anchoring density estimates from land surveys to other sources of historical vegetation establishes the validity of density estimation, while supplementing expert-classified ecosystems. Full article

Soil organic matter plays a crucial role in the global carbon cycle, yet the magnitude and direction of changes in soil carbon content following vegetation shifts in the tropics remain highly debated. Most studies have focused on short-term changes, typically spanning only a few months or years. In this study, we investigated the medium-term dynamics of organic matter at a site where savanna, protected from fire for 58 years, has gradually transitioned to woodland vegetation. Natural ¹³C abundance analysis combined with particle-size fractionation was used to characterize the changes in SOM over time. While carbon content remains relatively stable, δ¹³C exhibits a distinct shift, particularly in the surface layers, reflecting the gradual replacement of savanna-derived carbon with tree-derived carbon. All fractions were influenced by the inputs and outputs of carbon from both savanna and tree sources. In the coarse fractions, most of the carbon originates from trees; however, a significant proportion of savanna-derived carbon (ranging from 10% to 40%, depending on the fraction, depth, and patch) persists, likely in the form of black carbon. In the fine fractions, nearly half of the carbon (40% to 50%) remains derived from the savanna, highlighting the greater stability of organic matter that is physically bound to clays and protected within microaggregates. Full article

The relative influence of climate and Indigenous cultural burning on past forest composition in southern New England, US, remains debated. Employing varied analyses, this study compared data on Indigenous settlements from over 5000 years before present (YBP) with relative tree abundances estimated from pollen and land survey records. Results suggested that fire-tolerant vegetation, mainly oak (Quercus spp.), was more abundant near Indigenous settlements from 4955 to 205 YBP (i.e., 86–91% fire-tolerant trees), and significantly (p < 0.05) higher from 3205 to 205 YBP; fire-tolerant vegetation was less abundant away from settlements, where it also experienced greater fluctuations. Correlative models showed that warmer temperatures and distance to Indigenous settlement, which are both indicators of fire, were important predictors in the 17th–18th centuries of fire-tolerant tree abundance; soil variables were less important and their relationships with vegetation were unclear. A marked increase in oak abundance occurred above 8 °C mean annual temperature and within 16 km of major Indigenous settlements. Pyrophilic vegetation was most correlated with distance to Indigenous villages in areas with 7–9 °C mean annual temperature, typical of higher latitudes and elevations that usually supported northern hardwoods. Widespread burning in warmer areas potentially weakened relationships between distance and pyrophilic abundance. Indigenous land use imprinted upon warmer areas conducive to burning created patterns in fire-tolerant vegetation in southern New England, plausibly affecting most low-elevation areas. Results imply that restoration of fire-dependent species and of barrens, savannas, and woodlands of oak in southern New England benefit from cultural burning. Full article

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands. Full article

The Cerrado has high plant and vertebrate diversity and is an important biome for conserving species and provisioning ecosystem services. Volume equations in this biome are scarce because of their size and physiognomic diversity. This study was conducted to develop specific volumetric models for the phytophysiognomies Gallery Forest, Dry Forest, Forest Savannah, and Savannah Woodland, a generic model and a model for Cerrado forest formation. Twelve 10 m × 10 m (100 m²) (National Forest Inventory) plots were used for each phytophysiognomy at different sites (regions) of the Federal District (FD) where trees had a diameter at breast height (DBH; 1.30 m) ≥5 cm in forest formations and a diameter at base height (Db; 0.30 m) ≥5 cm in savanna formations. Their diameters and heights were measured, they were cut and cubed, and the volume of each tree was obtained according to the Smalian methodology. Linear and nonlinear models were adjusted. Criteria for the selection of models were determined using correlation coefficients, the standard error of the estimates, and a graphical analysis of the residues. They were later validated by the chi-square test. The resultant models indicated that fit by specific phytophysiognomy was ideal; however, the generic and forest formation models exhibited similar performance to specific models and could be used in extensive areas of the Cerrado, where they represent a high potential for generalization. To further increase our understanding, similar research is recommended for the development of specific and generic models of the total volume in Cerrado areas. Full article

The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our work aimed to evaluate the seasonal and interannual variability of the surface energy balance in a woodland savanna (Cerrado) ecosystem in southeastern Brazil over a period of 19 years, from 2001 to 2019. Using field micrometeorological measurements, we examined the variation in soil moisture and studied its impact on the temporal pattern of energy fluxes to distinguish the effects during rainy years compared to a severe drought spell. The soil moisture measures used two independent instruments, cosmic ray neutron sensor CRNS, and FDR at different depths. The measures were taken at the Pé de Gigante (PEG) site, in a region of well-defined seasonality with the dry season in winter and a hot/humid season in summer. We gap-filled the energy flux measurements with a calibrated biophysical model (SiB2). The long-term averages for air temperature and precipitation were 22.5 °C and 1309 mm/year, respectively. The net radiation (Rn) was 142 W/m², the evapotranspiration (ET) and sensible heat flux (H) were 3.4 mm/d and 52 W/m², respectively. Soil moisture was marked by a pronounced negative anomaly in the 2014 year, which caused an increase in the Bowen ratio and a decrease in Evaporative fraction, that lasted until the following year 2015 during the dry season, despite the severe meteorological drought of 2013/2014 already ending, which was corroborated by the two independent measurements. The results showed the remarkable influence of precipitation and soil moisture on the interannual variability of the energy balance in this Cerrado ecosystem, aiding in understanding how it responds to strong climate disturbances. Full article

Climate and land cover changes are key factors in river basins’ management. This study investigates on the one hand 60-year (1960 to 2019) rainfall and temperature variability using station data combined with gridded data, and on the other hand land cover changes for the years 1990, 2005, and 2020 in the Sota catchment (13,410 km², North Benin, West Africa). The climate period is different from the chosen land use change period due to the unavailability of satellite images. Standardized anomaly index, break points, trend analysis, and Thiessen’s polygon were applied. Satellite images were processed and ground truthing was carried out to assess land cover changes. The analyses revealed a wet period from 1960 to 1972, a dry period from 1973 to 1987, and another wet period from 1988 to 2019. The annual rainfall decreases from the south to the north of the catchment. In addition, rainfall showed a non-significant trend over the study period, and no significant changes were identified between the two normals (1960–1989 and 1990–2019) at catchment scale, although some individual stations exhibited significant trends. Temperatures, in contrast, showed a significant increasing trend over the study period at catchment scale, with significant break points in 1978, 1990, and 2004 for Tmax, and 1989 for Tmin. An increase of 0.4 °C and 1.2 °C is noted, respectively, for Tmax and Tmin between the two normals. The study also revealed increases in agricultural areas (212.1%), settlements (76.6%), waterbodies (2.9%), and baresoil (52%) against decreases in woodland (49.6%), dense forest (42.2%), gallery forest (21.2%), and savanna (31.9%) from 1990 to 2020. These changes in climate and land cover will have implications for the region. Appropriate adaptation measures, including Integrated Water Resources Management and afforestation, are required. Full article

Key leaf functional traits, such as chlorophyll and carotenoids content (C_ab and C_xc), equivalent water thickness (EWT), and leaf mass per area (LMA), are essential to the characterization and monitoring of ecosystem function. Spectroscopy provides access to these four leaf traits by relying on their specific spectral absorptions over the 0.4–2.5 µm domain. In this study, we compare the performance of three categories of estimation methods to retrieve these four leaf traits from laboratory directional-hemispherical leaf reflectance and transmittance measurements: statistical, physical, and hybrid methods. To this aim, a dataset pooling samples from 114 deciduous and evergreen oak trees was collected on four sites in California (woodland savannas and mixed forests) over three seasons (spring, summer and fall) and was used to assess the performance of each method. Physical and hybrid methods were based on the PROSPECT leaf radiative transfer model. Physical methods included inversion of PROSPECT from iterative algorithms and look-up table (LUT)-based inversion. For LUT-based methods, two distance functions and two sampling schemes were tested. For statistical and hybrid methods, four distinct machine learning regression algorithms were compared: ridge, partial least squares regression (PLSR), Gaussian process regression (GPR), and random forest regression (RFR). In addition, we evaluated the transferability of statistical methods using an independent dataset (ANGERS Leaf optical properties database) to train the regression algorithms. Thus, a total of 17 estimations were compared. Firstly, we studied the PROSPECT leaf structural parameter N retrieved by iterative inversions and its distribution over our oak-specific dataset. N showed a more pronounced seasonal dependency for the deciduous species than for the evergreen species. For the four traits, the statistical methods trained on our dataset outperformed the PROSPECT-based methods. More particularly, statistical methods using GPR yielded the most accurate estimates (RMSE = 5.0 µg·cm⁻²; 1.3 µg·cm⁻²; 0.0009 cm; and 0.0009 g·cm⁻² for C_ab, C_xc, EWT, and LMA, respectively). Among the PROSPECT-based methods, the iterative inversion of this model led to the most accurate results for C_ab, C_xc, and EWT (RMSE = 7.8 µg·cm⁻²; 2.0 µg·cm⁻²; and 0.0035 cm, respectively), while for LMA, a hybrid method with RFR (RMSE = 0.0030 g·cm⁻²) was the most accurate. These results showed that estimation accuracy is independent of the season. Considering the transferability of statistical methods, for the four leaf traits, estimation performance was inferior for estimators built on the ANGERS database compared to estimators built exclusively on our dataset. However, for EWT and LMA, we demonstrated that these types of statistical methods lead to better estimation accuracy than PROSPECT-based methods (RMSE = 0.0016 cm and 0.0013 g·cm⁻² respectively). Finally, our results showed that more differences were observed between plant functional types than between species or seasons. Full article

While many tree species occur across the Coastal Plain of the southeastern United States, longleaf pine (Pinus palustris C. Lawson) savannas and woodlands once dominated this region. To quantify longleaf pine’s past primacy and trends in the Coastal Plain, we combined seven studies consisting of 255,000 trees from land surveys, conducted between 1810 and 1860 with other descriptions of historical forests, including change to the present day. Our synthesis found support that Pinus palustris predominantly constituted 77% of historical Coastal Plain trees and upland oaks (Quercus) contributed another 8%. While Pinus still dominates these forests today (58% of all trees), most are now either planted loblolly (Pinus taeda L.) or slash (Pinus elliottii Engelm.) pines. Water oak (Quercus nigra L.), live oak (Quercus virginiana Mill.), sweetgum (Liquidambar styraciflua L.), and red maple (Acer rubrum L.) have increased their proportions compared to historical surveys; both longleaf pine and upland oaks have declined to ≤5% of all trees. Our work also supports previous estimates that longleaf pine originally dominated over 25–30 million ha of Coastal Plain forests. As late as the early 1900s, longleaf pine may still have covered 20 million ha, but declined to 7.1 million ha by 1935 and dropped to 4.9 million ha by 1955. Longleaf pine’s regression continued into the mid-1990s, reaching a low of about 1.3 million ha; since then, restoration efforts have produced a modest recovery to 2.3 million ha. Two centuries of overcutting, land clearing, turpentining for chemicals, fire exclusion followed by forest densification by fire-sensitive species, and other silvicultural influences, including widespread loblolly and slash pine plantations, have greatly diminished the Coastal Plain’s once extensive open longleaf pine forests. Full article

Woody plant encroachment in grassy ecosystems is a widely reported phenomenon associated with negative impacts on ecosystem functions. Most studies of this phenomenon have been carried out in arid and semi-arid grasslands. Therefore, studies in tropical regions, particularly savannas, which are composed of grassland and woodland mosaics, are needed. Our objective was to evaluate the accuracy of woody encroachment classification in the Brazilian Cerrado, a tropical savanna. We acquired dry and wet season unmanned aerial vehicle (UAV) images using RGB and multispectral cameras that were processed by the support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers. We also compared two validation methods: the orthomosaic and in situ methods. We targeted two native woody species: Baccharis retusa and Trembleya parviflora. Identification of these two species was statistically (p < 0.05) most accurate in the wet season RGB images classified by the RF algorithm, with an overall accuracy (OA) of 92.7%. Relating to validation assessments, the in situ method was more susceptible to underfitting scenarios, especially using an RF classifier. The OA was higher in grassland than in woodland formations. Our results show that woody encroachment classification in a tropical savanna is possible using UAV images and field surveys and is suggested to be conducted during the wet season. It is challenging to classify UAV images in highly diverse ecosystems such as the Cerrado; therefore, whenever possible, researchers should use multiple accuracy assessment methods. In the case of using in situ accuracy assessment, we suggest a minimum of 40 training samples per class and to use multiple classifiers (e.g., RF and DT). Our findings contribute to the generation of tools that optimize time and cost for the monitoring and management of woody encroachment in tropical savannas. Full article