Search Results (550)

Colonizing populations at the leading edge of range expansion are expected to have a reduced genetic diversity and strong genetic structure caused by genetic drift and allele surfing. Until now, few studies have found the genetic signatures of allele surfing in expanding wild populations. Using mtDNA markers, we studied the genetic structure of the population of midday gerbils (Meriones meridianus) expanding their range to the west in Kalmykia (southern Russia) following the new cycle of desertification, re-colonizing areas abandoned in the mid-2010s. In the colonizing population, we found a reduced genetic diversity, the redistribution of haplotype frequencies—in particular, in favor of variants rare in the core population—and strong genetic structure combined with strong differentiation from the core population—patterns suggestive of allele surfing on the wave of expansion. In terms of genetic diversity and spatial structuration, the western edge population sampled in 2008 before its collapse in 2017 occupies the intermediate position between the current colonizing and core population. This suggests that reduced genetic diversity and increased genetic differentiation are general features of marginal populations, enhanced by the founder and allele-surfing effects at the leading edges of expanding ranges. Full article

Rangeland restoration in southern Africa faces complex challenges that require the involvement of diverse social groups to ensure both ecological and social sustainability. This systematic review focuses on the role of social inclusion in rangeland restoration across multiple countries in the region, specifically examining the engagement of marginalized groups such as women, youth, and indigenous communities. We conducted a comprehensive search using the PRISMA approach, utilizing Scopus and other literature sources. Initially, we found 853 articles published between 2000 and 2024, which were subsequently screened down to 20 studies that met stringent inclusion criteria. This review identifies key strategies and outcomes associated with social inclusion in restoration efforts. Our findings reveal that participatory planning, gender-inclusive strategies, indigenous engagement, and capacity building are crucial for gaining community support, promoting social equity, and enhancing ecological resilience. However, challenges such as power dynamics, cultural norms, and resource constraints often impede the full realization of these inclusive practices. Despite these barriers, integrating local and indigenous knowledge and empowering marginalized groups significantly strengthens governance structures and leads to more sustainable restoration outcomes. Our review highlights the necessity of adopting holistic and inclusive approaches in rangeland restoration where social inclusion is not just a component but a central pillar of successful ecological management. It emphasizes the importance of social inclusion in the restoration of communal rangelands in southern Africa and provides insights into the approaches used, challenges faced, and outcomes achieved in incorporating social inclusion in rangeland restoration efforts. Our findings underscore the significance of collaborative efforts and social inclusion among local communities, policymakers, and stakeholders to achieve the sustainable restoration of communal rangelands in southern Africa. Full article

Historical sheep farming in the Patagonian drylands has led to reduced grass cover, soil erosion, and shrub encroachment, compromising ecosystem function. Effective restoration requires managing shrub cover, bare soil, and patch connectivity through various strategies. This study evaluates rehabilitation interventions in a grass-steppe ecosystem, comparing grazed and ungrazed areas. Over three years, we tested the following: (a) mechanical shrub cutting with biomass redistribution, and (b) enhancing patch connectivity with Pinus spp. branch piles, alongside controls, in eighteen 5 m × 5 m plots invaded by Mulinum spinosum. Half of the plots were fenced to exclude grazing, resulting in six treatment combinations. We monitored soil properties, vegetation cover, and species composition. The treatments explained twice as much of the variation in community composition as the annual climatic variations (0.26 vs. 0.13). Livestock exclusion increased perennial grass cover more than the grazed plots did (2.14 vs. 1.42 times the initial measure). All treatments reduced the amount of bare soil except the grazed controls. Shrub cutting, especially with grazing, increased the lasting litter coverage by 5–10% and decreased the bare soil equivalently. Organic matter increased except in the non-intervened interpatches (0.95 times). The enclosures with cut shrubs trapped erodible particles, showing a 5% increase. Our study highlights that grazing destabilizes communities, while enclosures stabilize them, with interventions improving soil fertility and mitigating erosion. Full article

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being $Z_{VCI}$ and $Z_{WCI}$, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the $Z_{WCI}$ had a strong probability of informing in advance, where the negative $Z_{VCI}$ will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of $Z_{WCI}$ to aid the prediction of $Z_{VCI}$. During these months, using $Z_{WCI}$ as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón. Full article

Land degradation is a major threat to sub-Saharan Africa rangelands, which are crucial for livestock farming and the livelihood of millions of people in the region. This systematic review aims to provide a comprehensive understanding of the causes and effects of land degradation, as well as to evaluate the effectiveness of different mitigation strategies. Following the PRISMA guidelines, we identified, screened, and analyzed 25 peer-reviewed articles published before 30 June 2024 using Scopus. The review highlights key factors that drive land degradation, such as soil erosion, drought, deforestation, and climate change, as well as socio-economic factors like poverty, land tenure issues, population pressure, and economic instability. These factors have serious implications, as land degradation can lead to poor quality of forage, an increased incidence of diseases, higher mortality rates among livestock, and a significant decline in livestock productivity. The socio-economic consequences of this degradation are significant, leading to reduced household income, increased poverty, and heightened food insecurity. Furthermore, the review assesses various mitigation strategies and concludes that practices such as rotational grazing and soil conservation techniques demonstrate high effectiveness, while agroforestry practices show only moderate success. An integrated approach that combines environmental, economic, and policy measures is crucial to addressing the complex challenge of land degradation in sub-Saharan Africa. Strengthening land tenure, improving economic stability, and promoting sustainable agricultural practices are essential steps toward improving the health of rangelands, increasing livestock productivity, and supporting the resilience and well-being of rural communities. Full article

High-resolution remote sensing platforms are crucial to map land use/cover (LULC) types. Unmanned aerial vehicle (UAV) technology has been widely used in the northern hemisphere, addressing the challenges facing low- to medium-resolution satellite platforms. This study establishes the scalability of Sentinel-2 LULC classification with ground-linked UAV orthoimages to large African ecosystems, particularly the Burunge Wildlife Management Area in Tanzania. It involved UAV flights in 19 ground-surveyed plots followed by upscaling orthoimages to a 10 m × 10 m resolution to guide Sentinel-2 LULC classification. The results were compared with unguided Sentinel-2 using the best classifier (random forest, RFC) compared to support vector machines (SVMs) and maximum likelihood classification (MLC). The guided classification approach, with an overall accuracy (OA) of 94% and a kappa coefficient (k) of 0.92, outperformed the unguided classification approach (OA = 90%; k = 0.87). It registered grasslands (55.2%) as a major vegetated class, followed by woodlands (7.6%) and shrublands (4.7%). The unguided approach registered grasslands (43.3%), followed by shrublands (27.4%) and woodlands (1.7%). Powerful ground-linked UAV-based training samples and RFC improved the performance. The area size, heterogeneity, pre-UAV flight ground data, and UAV-based woody plant encroachment detection contribute to the study’s novelty. The findings are useful in conservation planning and rangelands management. Thus, they are recommended for similar conservation areas. Full article

Accurate livestock counts are essential for effective pastureland management. High spatial resolution remote sensing, coupled with deep learning, has shown promising results in livestock detection. However, challenges persist, particularly when the targets are small and in a heterogeneous environment, such as those in African rangelands. This study evaluated nine state-of-the-art object detection models, four variants each from YOLOv5 and YOLOv8, and Faster R-CNN, for detecting cattle in 10 cm resolution aerial RGB imagery in Kenya. The experiment involved 1039 images with 9641 labels for training from sites with varying land cover characteristics. The trained models were evaluated on 277 images and 2642 labels in the test dataset, and their performance was compared using Precision, Recall, and Average Precision (AP_0.5–0.95). The results indicated that reduced spatial resolution, dense shrub cover, and shadows diminish the model’s ability to distinguish cattle from the background. The YOLOv8m architecture achieved the best AP_0.5–0.95 accuracy of 39.6% with Precision and Recall of 91.0% and 83.4%, respectively. Despite its superior performance, YOLOv8m had the highest counting error of −8%. By contrast, YOLOv5m with AP_0.5–0.95 of 39.3% attained the most accurate cattle count with RMSE of 1.3 and R² of 0.98 for variable cattle herd densities. These results highlight that a model with high AP_0.5–0.95 detection accuracy may struggle with counting cattle accurately. Nevertheless, these findings suggest the potential to upscale aerial-imagery-trained object detection models to satellite imagery for conducting cattle censuses over large areas. In addition, accurate cattle counts will support sustainable pastureland management by ensuring stock numbers do not exceed the forage available for grazing, thereby mitigating overgrazing. Full article

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m⁻² day⁻¹), ANHP increased by 9.39 kg ha⁻¹, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha⁻¹ more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C₃ mid-grasses in taller structured ecosites compared to the C₄ short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R² values varied by ecosite ranging from 0.49 to 0.67, while RAP R² values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP. Full article

Restoring native plant species on degraded landscapes is challenging. Symbiotic partners in the plant rhizosphere can aid in nutrient acquisition, pathogen protection, stress tolerance, and many other processes. However, these microbes are often absent in altered landscapes and need to be re-integrated to improve restoration efforts. We evaluated, within a laboratory setting, the ability of commercial and indigenous rhizobia strains to form nodules on lupine species used for rangeland seedings in the Great Basin region of the Western United States and ascertained if these strains could be applied through a seed coating. We also evaluated if a compost amendment applied via seed coating could further enhance the performance of the rhizobia strains. Our analysis showed that successful nodulation could occur using commercial and wildland-collected indigenous strains through either a liquid culture applied to seedlings or as a dry seed coating. However, the number of root nodules and the presence of a pink color (indicating nitrogen fixation) were typically higher in the commercial product than in the indigenous strains. Compost did not improve nodulation or the performance of the nodules; however, this treatment alone improved shoot growth. Overall, these results suggest that commercial rhizobium may be more effective in improving plant growth, and future research with native rhizobia may want to consider identifying strains compatible with seed-coating delivery. Longer-term studies are now merited for assessing how the rhizobia strains evaluated in this study influence plant growth, particularly in a field setting. Full article

Understanding past landscape changes is crucial to promote agroecological landscape transitions. This study analyzes past land cover changes (LCCs) alongside subsequent degradation and improvements in the study area. The input land cover (LC) data were taken from ESRI’s ArcGIS Living Atlas of the World and then assessed for accuracy using ground truth data points randomly selected from high-resolution images on the Google Earth Engine. The LCC analyses were performed on QGIS 3.28.15 using the Semi-Automatic Classification Plugin (SCP) to generate LCC data. The degradation or improvement derived from the analyzed data was subsequently assessed using the UNCCD Good Practice Guidance to generate land cover degradation data. Using the Landscape Ecology Statistics (LecoS) plugin in QGIS, the input LC data were processed to provide landscape metrics. The data presented in this article show that the studied landscape is not static, even over a short-term time horizon (2017–2022). The transition from one LC class to another had an impact on the ecosystem and induced different states of degradation. For the three main LC classes (forest, crops, and rangeland) representing 98.9% of the total area in 2022, the landscape metrics, especially the number of patches, reflected a 105% increase in landscape fragmentation between 2017 and 2022. Full article

In the United States, rangelands comprise 30% of the total land cover and serve as a valuable resource for livestock, wildlife, water, and recreation. Rangelands vary in climate and are often subject to disturbances like drought and wildfire. Historic wildfire trends have indicated an increase in wildfire size and frequency, raising societal and ecological concerns about the management of these lands, both pre- and post-wildfire. While there has been investigation into the effects of grazing prior to a wildfire on fire severity and plant mortality, there is limited research related to grazing post-wildfire even though current management paradigms suggest deferring grazing rangeland for two years after a wildfire to avoid additional stress on native plant species. Based on the diversity found across rangeland ecotypes and history with wildfire, the two-year deferment recommendation may need to be reconsidered for some ecosystems. Species found in perennial bunchgrass rangelands like Pseudoroegneria spicata (bluebunch wheatgrass) and Festuca idahoensis (Idaho fescue) may be less susceptible to post-fire grazing than initially thought, necessitating the need for research into different rangeland ecosystems. Full article

Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region’s ecosystems and human communities. Full article

The remarkable adaptability and rapid proliferation of Prosopis juliflora have led to its invasive status in the rangelands of Kenya, detrimentally impacting native vegetation and biodiversity. Exacerbated by human activities such as overgrazing, deforestation, and land degradation, these conditions make the spread and management of this species a critical ecological concern. This study assesses the effectiveness of artificial intelligence (AI) and remote sensing in monitoring the invasion of Prosopis juliflora in Baringo County, Kenya. We investigated the environmental drivers, including weather conditions, land cover, and biophysical attributes, that influence its distinction from native vegetation. By analyzing data on the presence and absence of Prosopis juliflora, coupled with datasets on weather, land cover, and elevation, we identified key factors facilitating its detection. Our findings highlight the Decision Tree/Random Forest classifier as the most effective, achieving a 95% accuracy rate in instance classification. Key variables such as the Normalized Difference Vegetation Index (NDVI) for February, precipitation, land cover type, and elevation were significant in the accurate identification of Prosopis juliflora. Community insights reveal varied perspectives on the impact of Prosopis juliflora, with differing views based on professional experiences with the species. Integrating these technological advancements with local knowledge, this research contributes to developing sustainable management practices tailored to the unique ecological and social challenges posed by this invasive species. Our results highlight the contribution of advanced technologies for environmental management and conservation within rangeland ecosystems. Full article

This study examines the Riparian Buffer Zones (RBZs) in Türkiye, analyzing land cover changes from 2017 to 2023 to assess their effectiveness in ecological conservation and water resource management. The research employs Esri-Sentinel land cover data, Shuttle Radar Topography Mission digital elevation models, and RiverATLAS data to identify and delineate RBZs, classify land cover types, and evaluate trends and changes over seven years. The findings indicate that 44.8% of RBZs are covered by ‘Rangelands’, 32.2% by ‘Crops’, and 16.1% by ‘Trees’ classes. The study notes a considerable increase in built areas and agricultural fields, which may indicate ongoing urban expansion and its potential impact on the effectiveness of RBZs. Despite regulatory efforts, the substantial presence of agriculture within RBZs indicates a need for more robust environmental protection policies. The results indicate a pressing need for a policy reassessment to enhance forest cover within RBZs and reduce agricultural encroachment. This would improve water quality and facilitate the achievement of sustainability goals numbered 2, 3, 6, 11, 14 and 15. The findings thus call for a strategic enhancement of RBZ management to better protect water resources against the adverse effects of agricultural and urban activities. Full article