Search Results (197)

Viola ucriana Erben & Raimondo is a rare and endangered taxon, endemic to a limited area on Mount Pizzuta in northwestern Sicily, Italy. Its population is significantly threatened by anthropogenic activities, including fires, overgrazing, and habitat alterations. Temporary immersion systems (TISs) have proven effective for large-scale propagation in various protected species, offering potential for ex situ conservation and population reinforcement of V. ucriana. This study aimed to establish a bioreactor-based micropropagation protocol for shoot multiplication and compare the efficacy of a TIS with that of conventional solid culture medium (SCM). Three different plant growth regulators (PGRs) were also compared: 6-benzylaminopurine (BA), zeatin, and meta-topolin-9-riboside (mTR). The starting material originated from seeds collected from mother plants in their natural environment. The best growth outcomes (in terms of shoot multiplication, shoot length, and relative growth rate) were achieved using THE RITA® TIS, with BA (0.2 mg/L) and mTR (0.5 or 0.8 mg/L) outperforming SCM. Anomalous or hyperhydric shoots were observed with all zeatin treatments (especially with 0.8 mg/L) in both the TIS and SCM, suggesting that this cytokinin is unsuitable for V. ucriana biomass production. The rooting phase was significantly improved by transferring propagules onto rockwool cubes fertilized with Hoagland solution. This approach yielded more robust roots in terms of number and length compared to the conventional agar-based medium supplemented with indole-3-butyric acid (IBA). Flow cytometry analysis confirmed the genetic fidelity of the regenerants from the optimal PGR treatments, showing that all plantlets maintained the diploid ploidy level of their maternal plants. Over 90% of the in vitro derived plantlets were successfully acclimatized to greenhouse conditions. This paper represents the first report of V. ucriana biomass multiplication using a RITA^® bioreactor. The stability of the regenerants, confirmed by nuclei quantification via cytofluorimetry, provides guidance in establishing a true-to-type ex situ population, supporting conservation and future reinforcement efforts. Full article

The Yellow River Basin serves as a critical ecological barrier in China. However, it has increasingly faced severe ecological and environmental challenges, with soil erosion and overgrazing being particularly prominent issues. As an important region in the middle and lower reaches of the Yellow River, the Jinan section of the Yellow River Basin is similarly affected by these problems, posing significant threats to the stability and sustainability of its ecosystems. To scientifically identify areas severely impacted by soil erosion and systematically quantify the effects of climate change on vegetation coverage within the Yellow River Basin, this study focuses on the Jinan section. By analyzing the spatio-temporal evolution patterns of the Normalized Difference Vegetation Index (NDVI), this research aims to explore the driving mechanisms behind these changes and further predict the future spatial distribution of NDVI, providing theoretical support and practical guidance for regional ecological conservation and sustainable development. This study employed the slope trend analysis method to examine the spatio-temporal variation characteristics of NDVI in the Jinan section of the Yellow River Basin from 2008 to 2022 and utilized the FLUS model to predict the spatial distribution of NDVI in 2025. The Optimal Parameters-based Geographical Detector (OPGD) model was applied to systematically analyze the impacts of four key driving factors—precipitation (PRE), temperature (TEM), population density (POP), and gross domestic product (GDP) on vegetation coverage. Finally, correlation and lag effect analyses were conducted to investigate the relationships between NDVI and TEM as well as NDVI and PRE. The research results indicate the following: (1) from 2008 to 2022, the NDVI values during the growing season in the Jinan section of the Yellow River Basin exhibited a significant increasing trend. This growth suggests a continuous improvement in regional vegetation coverage, likely influenced by the combined effects of natural and anthropogenic factors. (2) The FLUS model predicts that, by 2025, the proportion of high-density NDVI areas will rise to 55.35%, reflecting the potential for further optimization of vegetation coverage under appropriate management. (3) POP had a particularly significant impact on vegetation coverage, and its interaction with TEM, PRE, and GDP generated an amplified combined effect, indicating the dominant role of the synergy between socioeconomic and climatic factors in regional vegetation dynamics. (4) NDVI exhibited a significant positive correlation with both temperature and precipitation, further demonstrating that climatic conditions were key drivers of vegetation coverage changes. (5) In urban areas, NDVI showed a certain time lag in response to changes in precipitation and temperature, whereas this lag effect was not significant in suburban and mountainous areas, highlighting the regulatory role of human activities and land use patterns on vegetation dynamics in different regions. These findings not only reveal the driving mechanisms and influencing factors behind vegetation coverage changes but also provide critical data support for ecological protection and economic development planning in the Yellow River Basin, contributing to the coordinated advancement of ecological environment construction and economic growth. Full article

The “ruined landscapes” of the Mediterranean littoral are a consequence of millennia of human impact and include abandoned agricultural lands, deforested areas, and degraded coastal areas. One of the drivers is the historical pattern of land use, which has resulted in the clearing of vegetation, soil erosion, and overgrazing. These have caused significant damage to natural ecosystems and landscapes leading to soil degradation, loss of biodiversity, and the destruction of habitats. The UN Sustainable Development Goal 15 “Life on Land” recommends a substantial increase in afforestation (SDG 15.2). Whilst this goal is certainly necessary in places, it should be implemented with caution. The general perception that certain ecosystems, such as forests, are inherently more valuable than grasslands and shrublands contributes to afforestation drives prioritising quick and visible results. This, however, increases the possibility of misguided afforestation, particularly in areas that never supported forests under the present climatic conditions. We argue that in areas that have not supported forest ecosystems, targeted reinforcement of existing populations and recreation of historical ones is preferable to wholesale ecosystem modification disguised as afforestation. We present a possible strategy for targeted reinforcement in areas that never supported forests and that would still achieve the goals of SDGs 15.5 and 15.8. Full article

Being located in the middle of Southern Europe, and thus likely representing a particularly dynamic member of Mediterranean Europe, Italy has experienced a sudden increase in early desertification risk because of multiple factors of change. Long-term research initiatives have provided relatively well-known examples of the continuous assessment of the desertification risk carried out via multiple exercises from different academic and practitioner stakeholders, frequently using the Environmentally Sensitive Area Index (ESAI). This composite index based on a large number of elementary variables and individual indicators—spanning from the climate to soil quality and from vegetation cover to land-use intensity—facilitated the comprehensive, long-term monitoring of the early desertification risk at disaggregated spatial scales, being of some relevance for policy implementation. The present study summarizes the main evidence of environmental monitoring in Italy by analyzing a relatively long time series of ESAI scores using administrative boundaries for a better representation of the biophysical and socioeconomic trends of interest for early desertification monitoring. The descriptive analysis of the ESAI scores offers a refined representation of economic spaces in the country during past (1960–2010 on a decadal basis), present (2020), and future (2030, exploring four different scenarios, S1–S4) times. Taken as a proxy of the early desertification risk in advanced economies, the ESAI scores increased over time as a result of worse climate regimes (namely, drier and warmer conditions), landscape change, and rising human pressure that exacerbated related processes, such as soil erosion, salinization, compaction, sealing, water scarcity, wildfires, and overgrazing. Full article

This study provides new insights into the oviposition strategy of Eriogaster catax (Linnaeus, 1758) (Lepidoptera, Lasiocampidae), an endangered species of moth found in semi-natural habitats within agricultural landscapes. Protected under various European directives and listed as Data Deficient by the IUCN, E. catax inhabits warmer regions of the Western Palearctic. Despite noted geographic variations in its ecological preferences, few studies have statistically significant data on its ecology. Our six-year study, conducted within the largest known population of E. catax. in Romania, reveals critical data on its oviposition preferences, including the species’ tendency to utilize Prunus spinosa L. and Crataegus monogyna Jacq. shrubs at an average height of 80.48 ± 34.3 cm, with most nests placed within the 41–80 cm range and containing an average of 186 ± 22 eggs. The study also addresses the species’ vulnerability to human activities such as bush trimming, agricultural burning, and uncontrolled grazing, particularly due to its low oviposition height. These findings underscore the negative impact of overgrazing and burning practices, particularly when conducted on a large scale, on the conservation of E. catax. The detailed ecological requirements identified in this study are essential for developing effective conservation strategies and habitat management practices. Furthermore, the study highlights the importance of local community involvement and public education in raising awareness about biodiversity and the conservation of endangered species. Full article

The sustainable management of protected areas has increasingly become difficult due to the lack of updated information on land use and land cover transformations caused by anthropogenic pressures. This study investigates the spatiotemporal dynamics of the Dinderesso and Peni classified forests in Burkina Faso from 1986 to 2022. First, a data driven method was adopted to investigate these forests degradation dynamics. Hence, relevant Landsat images data were collected, segmented, and analyzed using QGIS SCP plugin Random Forest algorithm. Ninety percent of the overall adjusted classification accuracies were obtained. The analysis also showed significant degradation and deforestation with high wooded vegetation classes such as clear forest and wooded savannah (i.e., tree savannah) converging to lower vegetation classes like shrub savannah and agroforestry parks. A second investigation carried out through surveys and field trips revealed key anthropogenic drivers including agricultural expansion, demographic pressure, bad management, wood cutting abuse, overexploitation, overgrazing, charcoal production, and bushfires. These findings highlight the critical need for better management to improve these protected areas. Full article

As a vital component of the terrestrial ecosystem, grassland accounts for one-third of the global vegetation system. Grassland degradation has been exacerbated due to extreme overgrazing in China’s Inner Mongolia Autonomous Region (IMAR). While conservation was carried out via the Ecological Subsidy and Award Program (ESAP) to mitigate grassland degradation, little is known about its effectiveness in improving the biophysical conditions of grassland. This paper integrates the conceptual frameworks of total socio-environmental systems (TSESs) to assess how ecological systems respond to the ESAP, investigate the spatial heterogeneity of the ESAP, and explore the meddling effects of socio-environmental interactions on the ESAP. We integrated ecological, climate, and socioeconomic data and developed several hierarchical linear mixed models (HLMMs) to investigate how these factors interact with the ESAP in the IMAR. Our findings prove that the above-ground biomass between 2011 and 2015 responds significantly to variations in socioeconomic conditions and ecological communities. Available land resources, hospital and medical facilities, and net farmer and herdsman income are the most critical factors positively related to grassland productivity. Primary industries like mining, total consumer retail value, farming, forestry, animal husbandry, fishery productions, and GDP are the most damaging factors affecting biomass. Our study recommends a regionally or locally tailored ecological recovery policy, instead of a generalized one, in future efforts to conserve grassland. Full article

This study aims to predict the carbon sequestration capacity of Chinese grasslands to address climate change and achieve carbon neutrality goals. Grassland carbon sequestration is a crucial part of the global carbon cycle. However, its capacity is significantly impacted by climate change and human activities, making its dynamic changes complex and challenging to predict. This study adopts a fractional-order accumulation grey model, using 11 provinces in China as samples, to analyze and forecast grassland carbon sequestration. The study finds significant differences in grassland carbon sequestration trends across the sample regions. The carbon sequestration capacity of the grasslands in Xizang (Tibet) and Heilongjiang province is increasing, while it is decreasing in other provinces. The varying prediction results are influenced not only by regional climatic and natural conditions, but also by human interventions such as overgrazing, irrational reclamation, excessive mineral resource exploitation, and increased tourism development. Therefore, more region-specific grassland management and protection strategies should be formulated to enhance the carbon sequestration capacity of grasslands and promote the sustainable development of ecosystems. The significance of this study lies not only in providing scientific guidance for the protection and sustainable management of Chinese grasslands, but also in contributing theoretical and practical insights into global carbon sequestration strategies. 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

Monitoring the status and dynamics of desertification is one of the most important parts of combating it. In this study, 30 m high-resolution information on land desertification and restoration in the Heihe River basin (HRB) was extracted from the land cover database. The results indicate that land desertification coexists with land restoration in the HRB. In different periods, the area of land restoration was much larger than the area of land desertification in the HRB, and the HRB has undergone land restoration. Upstream of the HRB, there is a continuing trend of increasing land desertification associated with overgrazing in a context where climate change favors desertification reversal. In the middle and lower reaches, although climate variability and human activities favor land desertification, land desertification is still being reversed, and land restoration dominates. Implementing the eco-environmental protection project and desertification control measures, especially the Ecological Water Distribution Project (EWDP), contributes to the reversal of desertification in the middle and lower reaches of the HRB. However, the EWDP has indirectly led to the lowering of the water table in the middle reaches, resulting in local vegetation degradation. Therefore, there is an urgent need to transform the economic structure of the middle reaches to cope with water scarcity and land desertification. Full article

The Arabian Peninsula, with its rugged mountains, wadis, alluvial plains, sand dune deserts, and diverse coastlines, spans over 3 million km². The Peninsula is situated at the crossroads of Africa and Asia and is a meeting point for diverse biogeographic realms, including the Palearctic, Afrotropical, and Indomalayan regions. This convergence of biogeographic zones has resulted in a remarkably diverse flora and fauna, which is adapted to the harsh and varied climates found throughout the Peninsula. Each of the countries of the Arabian Peninsula are biologically diverse and unique in their own right, but Yemen, Saudi Arabia, and Oman are the most diverse in terms of their landforms and biological diversity. The mountainous regions support a cooler and more moderate climate compared to the surrounding lowlands, thus forming unique ecosystems that function as refugia for plant and animal species, and have a high endemism of plant species. The desert ecosystems support a variety of lifeforms that are specially adapted to an extreme arid climate. Due to its long history of human habitation and subsistence agriculture, particularly in the mountainous areas, the Arabian Peninsula possesses unique crop varieties adapted to extreme arid climates, making them important genetic resources for the future in the face of climate change. The Arabian Peninsula, though rich and diverse in its biological diversity, has been greatly affected by human activities, especially in the last 50 years, including urbanization, habitat destruction, overgrazing, and climate change, which pose significant threats to the biodiversity of the region. This review presents the biogeography and background of conservation efforts made in the countries in the Arabian Peninsula and gives the progress made in botanical research and conservation practices throughout the Peninsula. Full article

Argania spinosa is among the most important species of the Moroccan forest in terms of ecological, environmental, and socio-economic aspects. However, it faces a delicate balance between regeneration and degradation in its natural habitat. Hence, the efforts to preserve and regenerate argan forests are crucial for biodiversity, soil quality, and local livelihoods, yet they face challenges like overgrazing and climate change. Sustainable management practices, including reforestation and community engagement, are vital for mitigating degradation. Similarly, exploiting the argan tree’s rhizosphere can enhance soil quality by leveraging its rich microbial diversity. This approach not only improves crop growth but also maintains ecosystem balance, ultimately benefiting both agriculture and the environment. This enrichment can be achieved by different factors: mycorrhizae, plant extracts, algae extracts, and plant growth-promoting rhizobacteria (PGPR). The benefits provided by PGPR may include increased nutrient availability, phytohormone production, shoot, root development, protection against several plant pathogens, and disease reduction. In this study, the effect of rhizobacteria isolated from the Agran rhizosphere was evaluated on germination percentage and radicle length for Argania spinosa in vitro tests, growth, collar diameter, and branching number under greenhouse conditions. One hundred and twenty (120) bacteria were isolated from the argan rhizosphere and evaluated for their capacity for phosphate solubilization and indole acetic acid production. The results showed that 52 isolates could solubilize phosphorus, with the diameters of the solubilization halos varying from 0.56 ± 0.14 to 2.9 ± 0.08 cm. Among 52 isolates, 25 were found to be positive for indole acetic acid production. These 25 isolates were first tested on maize growth to select the most performant ones. The results showed that 14 isolates from 25 tested stimulated maize growth significantly, and 3 of them by 28% (CN005, CN006, and CN009) compared to the control. Eight isolates (CN005, CN006, CN004, CN007, CN008, CN009, CN010, and CN011) that showed plant growth of more than 19% were selected to evaluate their effect on argan germination rate and radicle length and were subjected to DNA extraction and conventional Sanger sequencing. The 8 selected isolates were identified as: Brevundimonas naejangsanensis sp2, Alcaligenes faecalis, Brevundimonas naejangsanensis sp3, Brevundimonas naejangsanensis sp4, Leucobacter aridicollis sp1, Leucobacter aridicollis sp2, Brevundimonas naejangsanensis sp1, and Staphylococcus saprophyticus. The results showed that Leucobacter aridicollis sp2 significantly increased the germination rate by 95.83%, and the radicle length with a value of 2.71 cm compared to the control (1.60 cm), followed by Brevundimonas naejangsanensis sp3 and Leucobacter aridicollis sp1 (2.42 cm and 2.11 cm, respectively). Under greenhouse conditions, the results showed that the height growth increased significantly for Leucobacter aridicollis sp1 (42.07%) and Leucobacter aridicollis sp2 (39.99%). The isolates Brevundimonas naejangsanensis sp3 and Leucobacter aridicollis sp1 increased the gain of collar diameter by 41.56 and 41.21%, respectively, followed by Leucobacter aridicollis sp2 and Staphyloccocus saprophyticus (38.68 and 22.79%). Leucobacter aridicollis sp1 increased the ramification number per plant to 12 compared to the control, which had 6 ramifications per plant. The use of these isolates represents a viable alternative in sustainable agriculture by improving the germination rate and root development of the argan tree, as well as its development, while increasing the availability of nutrients in the soil and consequently improving fertilization. Full article

Accurate information about the amount of standing biomass is important in pasture management for monitoring forage growth patterns, minimizing the risk of overgrazing, and ensuring the necessary feed requirements of livestock. The morphological features of plants, like crop height and density, have been proven to be prominent predictors of crop yield. The objective of this study was to evaluate the effectiveness of stereovision-based crop height and vegetation coverage measurements in predicting the aboveground biomass yield of bermudagrass (Cynodon dactylon) in a pasture. Data were collected from 136 experimental plots within a 0.81 ha bermudagrass pasture using an RGB-depth camera mounted on a ground rover. The crop height was determined based on the disparity between images captured by two stereo cameras of the depth camera. The vegetation coverage was extracted from the RGB images using a machine learning algorithm by segmenting vegetative and non-vegetative pixels. After camera measurements, the plots were harvested and sub-sampled to measure the wet and dry biomass yields for each plot. The wet biomass yield prediction function based on crop height and vegetation coverage was generated using a linear regression analysis. The results indicated that the combination of crop height and vegetation coverage showed a promising correlation with aboveground wet biomass yield. However, the prediction function based only on the crop height showed less residuals at the extremes compared to the combined prediction function (crop height and vegetation coverage) and was thus declared the recommended approach (R² = 0.91; SeY= 1824 kg-wet/ha). The crop height-based prediction function was used to estimate the dry biomass yield using the mean dry matter fraction. Full article

As an important ecosystem, the wild fruit forest in the Tianshan Mountains is one of the origins of many fruit trees in the world. The wild fruit forest in Emin County, Xinjiang, China, was taken as the research area, the spatial and temporal distribution of the wild fruit forest was inverted using random forest and PLUS models, and the 2027 distribution pattern of the wild fruit forest was simulated and predicted. From 2007 to 2013, damage to the wild fruit forest from tourism and overgrazing was very serious, and the area occupied by the wild fruit forest decreased rapidly from 9.59 km² to 7.66 km². From 2013 to 2020, suitable temperatures and reasonable tourism management provided strong conditions for the rejuvenation of wild fruit forests. The distance of the center of gravity of the wild fruit forest increased, and the density of distribution of the wild fruit forest in the northwest direction of the study area also increased. It is predicted that the wild fruit forest in the study area will show a steady and slowly increasing trend in places far away from tourist areas and with more complex terrain. It is suggested that non-permanent fences be set up as buffer zones between wild fruit forests, ensuring basic maintenance of wild fruit forests, limiting human disturbance such as overgrazing, and reducing the risk of soil erosion. 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