Search Results (91)

Accurate estimation of above-ground biomass (AGB) is vital for carbon accounting, biodiversity conservation, and sustainable forest management, especially in tropical regions under strong anthropogenic pressure. This study estimated and mapped AGB in the Atacora Mountain Chain, Togo, using a multi-source remote sensing approach within Google Earth Engine (GEE). Field data from 421 plots of the 2021 National Forest Inventory were combined with Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, bioclimatic variables from WorldClim, and topographic data. A Random Forest regression model evaluated the predictive capacity of different variable combinations. The best model, integrating SAR, optical, and climatic variables (S1S2allBio), achieved R² = 0.90, MAE = 13.42 Mg/ha, and RMSE = 22.54 Mg/ha, outperforming models without climate data. Dense forests stored the highest biomass (124.2 Mg/ha), while tree/shrub savannas had the lowest (25.38 Mg/ha). Spatially, ~60% of the area had biomass ≤ 50 Mg/ha. Precipitation correlated positively with AGB (r = 0.55), whereas temperature showed negative correlations. This work demonstrates the effectiveness of integrating multi-sensor satellite data with climatic predictors for accurate biomass mapping in complex tropical landscapes. The approach supports national forest monitoring, REDD+ programs, and ecosystem restoration, contributing to SDGs 13, 15, and 12 and offering a scalable method for other tropical regions. Full article

In sub-Saharan Africa, land degradation and climate change continue to undermine agricultural productivity by reducing soil productivity and water availability. This review identifies soil and water conservation technologies successfully applied in climatically analogous regions of sub-Saharan Africa with the aim of informing effective technology transfer to Senegal, particularly Sédhiou and Tambacounda. Using K-means clustering on WorldClim bioclimatic variables, 35 comparable countries were identified, of which 17 met inclusion criteria based on data availability and ≥60% climatic similarity. Eighty-five technologies were documented and assessed for their compatibility across rainfall patterns, land gradients, and uses, with 12 emerging as consistently effective. Quantitative evidence shows that zai/tassa pits, stone bunds, and half-moons increase crop yields by 50–200%, while stone bunds and mulching reduce runoff by up to 80% and improve soil moisture retention. Terracing and tied-ridging were also linked to higher water-use efficiency, with tied-ridging increasing soil moisture by 13%. Burkina Faso, Kenya, and Malawi lead in adoption and diversity, whereas Senegal lags due to institutional gaps, limited funding, and weak extension systems. These technologies offer a readily available, evidence-based toolkit for building agricultural resilience in Senegal. However, their successful adoption requires stronger policy integration, stakeholder empowerment, cross-border learning, and private-sector engagement. Full article

This study presents a geospatial assessment and modeling of the water–energy–food (WEF) nexus to enrich the sustainable paddy cultivation of the North Central Province (NCP) of Sri Lanka in the Dry Zone. Increasing climatic variability and limited resources have raised concerns about the need for efficient resource management to restore food security globally. The study analyzed the three components of the WEF nexus for their synergies and trade-offs using GIS and remote sensing applications. The food productivity potential was derived using the Normalized Difference Vegetation Index (NDVI), Soil Organic Carbon (SOC), soil type, and land use, whereas water availability was assessed using the Normalized Difference Water Index (NDWI), Soil Moisture Index (SMI), and rainfall data. Energy potential was mapped using WorldClim 2.1 datasets on solar radiation and wind speed and the proximity to the national grid. Scenario modeling was conducted through raster overlay analysis to identify zones of WEF constraints and synergies such as low food–low water areas and high energy–low productivity areas. To ensure the accuracy of the created model, Pearson correlation analysis was used to internally validate between hotspot layers (representing extracted data) and scenario layers (representing modeled outputs). The results revealed a strong positive correlation (r = 0.737), a moderate positive correlation for energy (r = 0.582), and a positive correlation for food (r = 0.273). Those values were statistically significant at p > 0.001. These results confirm the internal validity and accuracy of the model. This study further calculated the total greenhouse gas (GHG) emissions from paddy cultivation in NCP as 1,070,800 tCO_2eq yr⁻¹, which results in an emission intensity of 5.35 tCO_2eq ha⁻¹ yr⁻¹, with CH₄ contributing around 89% and N₂O 11%. This highlights the importance of sustainable cultivation in mitigating agricultural emissions that contribute to climate change. Overall, this study demonstrates a robust framework for identifying areas of resource stress or potential synergy under the WEF nexus for policy implementation, to promote climate resilience and sustainable paddy cultivation, to enhance the food security of the country. This model can be adapted to implement similar research work in the future as well. Full article

In this study, we investigated the current and future potential distribution of three parasitoid species of Drosophila suzukii, which represent promising candidates for the biological control of this pest: Leptopilina japonica (Hymenoptera, Figitidae), Pachycrepoideus vindemmiae (Hymenoptera, Pteromalidae), and Trichopria drosophilae (Hymenoptera, Diapriidae). To this end, we employed Ecological Niche Modeling using the Random Forest algorithm and climatic data from WorldClim v. 2.1 under climate change scenarios (SSP2-4.5 and SSP5-8.5), analyzing the spatial overlap between the pest and its natural enemies. The results indicate that the parasitoids exhibit distinct geographic distributions, although most species show higher suitability for temperate regions of the Northern Hemisphere. Species such as T. drosophilae and L. japonica stand out for their broad distribution and high overlap with the pest, whereas P. vindemmiae and display more restrictive climatic ranges and lower control efficiency. With ongoing climate change, all parasitoids tend to migrate toward higher latitudes, with significant range contractions in tropical regions. Thus, our results demonstrate the usefulness of Ecological Niche Modeling in the selection of biological control agents by considering host-specific preferences and environmental requirements in the development of management strategies adapted to future scenarios. Full article

Invasive alien species pose escalating threats to global biodiversity and ecosystems, which may be exacerbated by climate change, potentially leading to range expansions and intensified impacts. In China, Mikania micrantha Kunth, a fast-growing tropical vine listed among the world’s 100 worst invasive species, has proliferated since its introduction in the mid-20th century, causing severe ecological damage through the smothering of vegetation, suppression of allelopathy, and economic losses in agriculture and forestry. This study aimed to predict its current and future distributions to guide management. Using 205 stringently filtered occurrence records from databases, surveys, and literature, combined with bioclimatic variables from WorldClim and MaxEnt modeling—optimized via ENMeval and evaluated by AUC (>0.97)—projected habitats under current (1970–2000) conditions and future SSP1-2.6, SSP2-4.5, and SSP3-7.0 scenarios for the 2050s and 2070s via the BCC-CSM2-HR model. Temperature factors dominated predictions, with current excellent suitability (3.6 × 10⁴ km²) concentrated in Hainan and southern Guangdong, expanding to good and moderate zones in Guangxi, Fujian, and Yunnan. Future averages showed expansions in excellent (21.3%), good (10.0%), and moderate (14.0%) habitats, with some northward shifts into Jiangxi and Hunan under higher emissions. In situ augmentation of habitat suitability and spatial containment overshadows the northward range expansion. The high-emission scenario is projected to lead to temperature overshoots, which will dampen habitat suitability. The findings underscore M. micrantha’s resilience to warming, necessitating integrated strategies such as guarding critical biodiversity sites, early detection, biocontrol, and habitat restoration to mitigate risks in both core and emerging zones. Full article

Sweet cherry (Prunus avium L.), a temperate fruit species highly sensitive to thermal and hydric stress, faces increasing cultivation challenges in semi-arid regions such as Fez-Meknes (Morocco) due to climate change. This study aims to identify ecologically suitable zones for sweet cherry cultivation by modeling its current potential distribution using the MaxEnt (Maximum Entropy) approach. A total of 1151 georeferenced occurrence records were collected through field surveys and validated with satellite imagery. Nineteen bioclimatic variables from the WorldClim database were initially considered, and a subset with low multicollinearity (|r| < 0.7) was retained for analysis. Model performance, evaluated using the area under the ROC curve (AUC), yielded a high mean value of 0.960 ± 0.014, indicating excellent predictive accuracy. Elevation, annual precipitation (BIO12), and precipitation seasonality (BIO15) emerged as key drivers of the species’ distribution, as confirmed by both Jackknife and SPCPI analyses. Spatial prediction maps highlighted high-suitability zones in the provinces of Ifrane, El Hajeb, Azrou, and Sefrou, aligning with known agro-climatic production areas. In contrast, lower suitability was observed in more arid or heat-prone provinces such as Boulemane and Midelt. These findings provide a robust bioclimatic framework for agroecological planning, supporting adaptive varietal zoning and long-term planning for climate-resilient horticulture. Full article

Olive cultivation constitutes a fundamental Mediterranean rural activity in Greece, as it primarily accounts for the country’s substantial socio-economic development. Although the olive tree is one of the best acclimated species, its overall performance may be significantly impacted by changes in the climate. Thus, by considering the lack of scientific research on the climate suitability evaluation of olive groves over the entire Greek territory, a study between the geomorphological parameter mapping of Greece (altitude, aspect, slope, and terrain roughness) and the respective required atmospheric conditions for the olive crop’s growth (temperature, precipitation, and frost days) was performed. Every parameter is reclassified to translate its value into a score, and the final suitability map is the outcome of the aggregation of all score maps. Individually, the overall suitability for olive cultivation is high in Greece, given its extensive area, resulting in a high score (8–10); geomorphological and climatic conditions (34.44% and 59.40%, respectively); and overall suitability conditions (42.00%) for olive cultivation. Over the identified olive grove areas, the model gives a high score (8–10) for 91.59% of the cases. The model may be characterized by its simplicity, usability, flexibility, and efficiency. The current modelling procedure may serve as a means for identifying suitable areas for the sustainable and productive development of olive cultivation. Full article

This study investigated the application of Species Distribution Models (SDMs), based on Boosted Regression Tree (BRT) and Random Forest (RF), to predict the distribution of citrus crops in a Mediterranean climate by comparing climate data from WorldClim with those from the Regional Territorial Information System of Sicily (S.I.T.R.). To this aim, 19 bioclimatic variables were calculated from monthly temperature and precipitation data in the period 2003–2021 by using the biovars package in R software version 2023.12.0+369. Soil properties, terrain elevation, slope, and soil water retention capacity were considered to adequately simulate pedoclimatic conditions in the Syracuse area in Sicily (Italy). The SDM algorithms performed well (AUC: 0.84–0.93; TSS: 0.51–0.69), and Random Forest was selected to compare global and local outcomes. Using data from local meteorological stations increased the model’s reliability, resulting in a difference of approximately ~800 ha in the predicted citrus distribution compared to WorldClim data. This approach also provided a more accurate representation of precipitation patterns, for instance, in the municipality of Augusta, where WorldClim underestimated the average annual rainfall by 284 mm. These findings emphasise the importance of incorporating local environmental data into SDMs to improve prediction accuracy and inform future hybrid approaches to enhance model robustness in the context of climate change. Finally, the results contribute to expanding knowledge of citrus soil and climate conditions, with potential implications for land-use planning. Full article

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

This study developed a preliminary screening framework for identifying candidate Magnolia species potentially resistant to drought and cold conditions, using open access plant specimens and climate data. Based on 969 specimens, a distribution database was constructed to map 35 Magnolia species in China. Nonparametric variance analysis revealed significant interspecific differences in precipitation of the driest quarter (PDQ) and minimum temperature of the coldest month (MTCM). Using the updated climatic thresholds, nine candidate species each were identified as having drought resistance (PDQ < 60.5 mm) and cold tolerance (MTCM < 0.925 °C). In conclusion, the proposed method integrates geocoded specimen information with climate data, providing preliminary candidate species for future physiological validation, conservation planning, and further botanical research. However, the primary focus on climate data and lack of consideration of non-climatic factors warrant cautious interpretation of the results and comprehensive investigations for validation of the present study results. Full article

Background: The relationship between medicinal efficacy and the geographical environment in Thesium chinense Turcz. (T. chinense Turcz.), a traditional Chinese herb, remains systematically unexplored. This study integrates metabolomics, machine learning, and ecological factor analysis to elucidate the geographical variation patterns and regulatory mechanisms of secondary metabolites in T. chinense Turcz. from Anhui, Henan, and Shanxi Provinces. Methods: Metabolomic profiling was conducted on T. chinense Turcz. samples collected from three geographical origins across Anhui, Henan, and Shanxi Provinces. Machine learning algorithms (Random Forest, LASSO regression) identified region-specific biomarkers through intersection analysis. Metabolic pathway enrichment employed MetaboAnalyst 5.0 with target prediction. Antioxidant activity (DPPH/hydroxyl radical scavenging) was quantified spectrophotometrically. Environmental correlation analysis incorporated 19 WorldClim variables using redundancy analysis, Mantel tests, and Pearson correlations. Results: We identified 43 geographical marker compounds (primarily flavonoids and alkaloids). Random forest and LASSO regression algorithms determined core markers for each production area: Anhui (4 markers), Henan (6 markers), and Shanxi (3 markers). Metabolic pathway enrichment analysis revealed these markers exert pharmacological effects through neuroactive ligand–receptor interaction and PI3K-Akt signaling pathways. Redundancy analysis demonstrated Anhui samples exhibited significantly higher antioxidant activity (DPPH and hydroxyl radical scavenging rates) than other regions, strongly correlating with stable low-temperature environments (annual mean temperature) and precipitation patterns. Conclusions: This study established the first geo-specific molecular marker system for T. chinense Turcz., demonstrating that the geographical environment critically influences metabolic profiles and bioactivity. Findings provide a scientific basis for quality control standards of geo-authentic herbs and offer insights into plant–environment interactions for sustainable cultivation practices. Full article

Cotton is the most widely consumed natural fiber globally and emits fewer greenhouse gases compared to synthetic alternatives. Brazil is currently the largest cotton exporter, and understanding its potential for sustainable expansion is crucial. This study developed agroclimatic zoning maps for cotton (Gossypium hirsutum L.) across Brazil under current and future climate conditions using data from the World-Clim and MapBiomas platforms. Four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) were assessed over multiple time periods. Results showed that rising temperatures and reduced rainfall will likely reduce cotton suitability in traditional producing regions such as Bahia. However, areas with potential for cotton cultivation, especially in Mato Grosso, which currently accounts for 90% of national production, remain extensive, with agroclimatic conditions indicating a theoretical expansion potential of up to 40 times the current cultivated area. This projection must be interpreted with caution, as it does not account for economic, logistical, or social constraints. Notably, Brazilian cotton is cultivated with minimal irrigation, low fertilizer input, and high adoption of no-till systems, making it one of the least carbon-intensive globally. Full article

Deforestation and forest degradation are important drivers of global warming, yet their implications on regional temperature and precipitation patterns are more elusive. In the Yucatán Peninsula, forest cover loss and deterioration has been rapidly advancing over the past decades. We applied local indicators of spatial association (LISA) cluster analysis and spatial autoregressive models (SAR) to evaluate the spatial relationships between tree cover and regional temperature and precipitation. We integrated NASA’s Global Forest Cover Change (GFCC) and WorldClim’s historical monthly weather datasets (2000–2015) to assess the effects of deforested, degraded, and dense forest land cover on temperature and precipitation distributions on the Yucatán Peninsula. LISA cluster analyses show warmer and drier conditions geographically coincide with deforested and degraded tree cover, but outliers allude to the potential influence of forest cover impacts on regional climate. Controlling spatial dependencies and including covariates, SAR models indicate that deforestation is associated with higher annual mean temperatures and minimum temperatures during dry and wet seasons, and decreased precipitation in the dry season. Degraded tree cover was related to higher maximum temperatures but did not relate to precipitation variability. We highlight the complex interactions between forest cover and climate and emphasize the importance of forest conservation for mitigating regional climate change. Full article

The article addresses the current and future potential distribution of Drosophila suzukii (Diptera: Drosophilidae), commonly known as spotted wing Drosophila (SWD). This invasive pest affects various fruit crops worldwide. Native to Southeast Asia, the species has rapidly expanded due to its high adaptability to climates and ability to infest ripe fruits. SWD occurrence data were collected from multiple databases, pseudo-absences were selected from the background area, and climatic variables were downloaded from WorldClim. The Random Forest algorithm was employed to model the current distribution and project future scenarios, categorizing environmental suitability into high, moderate, and low levels. The analysis of bioclimatic variables indicated that factors such as isothermality, maximum temperature of the warmest month, and precipitation of the driest month are the most significant for pest distribution. The results revealed high climatic suitability for the species in North America, Europe, and Asia, with projections indicating expansion under climate change scenarios in the Northern Hemisphere, including new areas in Europe and North America. Regions with higher suitability are expected to require management and monitoring strategies, particularly in vulnerable agricultural areas. Furthermore, the study underscores the importance of climatic data in predicting pest distribution and formulating effective control and mitigation policies. Full article

Understanding the response of species to past climate change provides great opportunities to know their adaptive capacity for resilience under future climate change. Since the Late Pleistocene, dramatic climate fluctuations have significantly impacted the distribution of giant pandas (Ailuropoda melanoleuca). However, how the spatial distribution and climatic niche of giant pandas shifted in response to past climate change remain poorly understood. Based on the known distribution records (fossil and present day) and the most updated climate projections for the Last Interglacial (LIG; ~120 ka), Last Glacial Maximum (LGM; ~22 ka), Mid-Holocene (MH; ~6 ka), and the present day, we predicted and compared the distribution and climatic niche of giant pandas. The results show that giant pandas have undergone a considerable range contraction (a 28.27% reduction) followed by a marked range expansion (a 75.8% increase) during the LIG–LGM–MH period, while its climatic niche remained relatively stable. However, from the MH to the current, both the distribution area and climatic niche of giant pandas have undergone significant changes. Our findings suggest that the giant panda may adjust its distribution to track stable climatic niches in response to future climate change. Future conservation planning should designate accessible areas for giant pandas and adjust priority conservation areas as needed. Full article