Search Results (367)

This study explored the application of carbon stable isotopes for tracing the geographical origin of Monochamus alternatus, an insect vector responsible for spreading pine wilt disease. The primary vector of pine wilt disease, an aggressive disease caused by the pine wood nematode and affecting pine forests, is Monochamus alternatus. Samples of Monochamus alternatus were collected from 12 provinces across China, and their carbon isotope ratios (δ¹³C) were measured. By analyzing the correlation between these ratios and various environmental factors, including latitude, longitude, altitude, and bioclimatic conditions, it was found that precipitation seasonality and solar radiation were the most important factors influencing the carbon isotope ratio of Monochamus alternatus. The spatial distribution of Monochamus alternatus carbon isotopes in China was predicted using the co-Kriging interpolation method, incorporating these two environmental variables. The findings revealed a gradient in the carbon isotope ratio of Monochamus alternatus, which could help differentiate the species across various geographical regions in China. Full article

An association between long-term climatic stability and endemism has been suggested, but it has been tested in plants and vertebrates rather than invertebrates. Using high-resolution paleoclimate reconstructions (CHELSA-TraCE21k; 21,000 BP–present), we tested whether non-cave localities of endemic beetles in the western Balkans are non-randomly associated with local climatic stability. For four bioclimatic variables, we quantified temporal variability using three metrics (SD, range, detrended SD) and defined stability islands as cells in the most stable quartile relative to their neighbourhood at three spatial scales (3 × 3, 5 × 5, 9 × 9). We tested whether 578 endemic-locality cells were enriched in stability islands, against elevation-matched null models. Annual mean temperature produced the highest raw frequency of endemic localities in stability islands, but this pattern was not significant after elevation control. In contrast, endemic localities showed a modest but consistent enrichment in annual precipitation stability islands (observed 9.7–10.7% vs. null 7.3–8.5%; p = 0.01–0.03) across neighbourhood sizes. At the 3 × 3 scale, 60 endemic localities fell within precipitation-stability islands; of them, 20 were outside current protected areas—indicating conservation gaps where minor boundary revisions could enable protection of endemic beetles’ habitats in precipitation-stable sites. Full article

Hippophae tibetana Schltdl. is a cold-tolerant deciduous shrub endemic to the Tibetan Plateau, playing a vital ecological role in high-altitude environments. This study utilized the Biomod2 platform to model its current and future potential distribution under climate change, integrating 34 environmental variables across bioclimatic, topographic, edaphic, anthropogenic, and ultraviolet (UV) dimensions. Among ten candidate species distribution models (SDMs), the random forest (RF) algorithm exhibited the highest predictive accuracy and stability. An ensemble model (EM) combining RF, GBM, MARS, and FDA further improved predictive performance (ROC = 0.992, TSS = 0.923, and Kappa = 0.886). Key determinants of habitat suitability included altitude, temperature, UV radiation, and biodiversity, with RF response curves revealing distinct nonlinear thresholds. Optimal suitability occurred at around a 4000 m elevation, decreasing beyond this range, while temperature and UV exhibited similar unimodal responses. Under the SSP2-4.5 climate scenario, the suitable habitat is projected to expand from the 2050s to the 2090s, particularly in eastern Qinghai, southwestern Gansu, northwestern Sichuan, and central–southern Tibet. The species’ distribution centroid is anticipated to shift southwestward toward Qinghai Province, with more rapid migration projected after the 2050s. These findings underscore the complex interplay of environmental factors shaping H. tibetana distribution and offer valuable insights for conservation planning in the ecologically fragile Tibetan Plateau. Full article

Old-growth forests play a vital role in the conservation of terrestrial biodiversity, though they are rare and increasingly threatened worldwide. The Mediterranean region hosts notable examples of these ecosystems, but information about their location, structure, and biodiversity is still largely incomplete. In this work, we tested the hypothesis that the region of Tuscany (Italy) harbors forest sites with old-growth characteristics in light of the EU indicators and the Italian ministerial guidelines. Accordingly, data on stand structural and plant diversity variables were collected in 27 plots located in pre-selected sites across different forest types of the region. As a result, 12 sites were inventoried that can be proposed as candidates for the national network of old-growth forests. These were largely unknown, ca. 10–300 ha in surface and encompassing five main forest types across 14 Natura2000 habitats. All stands have reached the mature or nearly senescent stage thanks to natural dynamic processes for over 70 years after the cessation of substantial anthropogenic disturbances. The structural heterogeneity index (SHI), based on living and deadwood biomass variables, was relatively high (66.2–84%). However, structural variables depended on forest type, thus on bioclimatic context and dominant tree species. Stands with beech and mountain conifers showed more pronounced old-growth characteristics than Mediterranean stands due to a faster recovery dynamic after cessation of disturbance. As many as 193 vascular plant taxa were recorded, with 16 species occurring with trees ≥ 50 cm in diameter. Forest specialist taxa, either woody or herbaceous, were prevalent, but numerous generalists also occurred in the gaps. Ancient forest species were also well represented, supporting the long temporal continuity of the forests. This work advances knowledge about forest sites with old-growth characteristics in southern Europe, contributing to the implementation of the national network and the EU Biodiversity Strategy 2030. Strict protection of these sites is necessary to allow the forest stands to fully reach the old-growth stage in the next decades, despite the negative influence of climate change. Full article

Climate change is reshaping climatic regimes worldwide, with direct consequences for species distributions and ecosystem services, including those provided by wild edible fungi. In Mexico, these fungi represent a resource of ecological, cultural, and economic importance, yet their vulnerability to future climate scenarios remains poorly understood. This study evaluated projected changes in the potential distributions of ten frequently consumed edible fungal species in central Mexico under current and future climate scenarios (2061–2080 and 2081–2100). Ecological niche models were performed using Maxent with 19 bioclimatic variables, spatial block cross-validation, and model tuning based on the AICc and partial ROC curves. Additionally, associations between species suitability and land use and vegetation variables were assessed through multivariate analyses. The most influential predictors were the mean temperature of the warmest quarter (71.929%), temperature seasonality (47.589%), and annual precipitation (41.962%). Current models identify high environmental suitability primarily within the TMVB, Sierra Madre Occidental, and southern mountainous regions such as Chiapas. Future projections revealed heterogeneous, species-specific responses. Suitability gains were projected for Cantharellus cibarius (21–50%), Infundibulicybe gibba (20–34%), Lactarius deliciosus (13–48%), and Lyophyllum decastes (8–141%), whereas Helvella crispa (1–99%), Agaricus campestris (2–88%), and Russula brevipes (74–100%) showed marked contractions under high-emission scenarios. These contrasting patterns suggest that climate change may restructure the spatial availability of edible fungi in Mexico, potentially affecting forest sustainability and the biocultural practices of communities that depend on these resources. Integrating species-specific climatic sensitivity into conservation and sustainable management strategies will be essential under future climate conditions. Full article

Aconitum carmichaelii Debeaux has been a traditional medicinal resource in China for over two millennia. However, sustainable utilization and preservation strategies for A. carmichaelii require a thorough understanding of environmental factors influencing its distribution. An optimized MaxEnt model was constructed using the ENMeval package based on 185 quality-controlled occurrence records and 10 selected environmental variables (bioclimatic, edaphic, topographic, and anthropogenic). The optimized model demonstrated reliable predictive accuracy, with an area under curve (AUC) value of 0.896. Soil moisture (37.7% contribution), human footprint (HFP) (23.9%), and July solar radiation (11.1%) were the primary variables determining A. carmichaelii distribution. The suitable thresholds were defined as soil moisture > 87.34 mm, HFP > 10.69, and July solar radiation < 19,125.72 kJ m⁻² day⁻¹. At present, highly suitable habitat covers approximately 8.243 × 10⁵ km², predominantly located in the Sichuan Basin and surrounding regions, including Sichuan, Chongqing, Guizhou, and northeastern Yunnan. Future predictions under all Shared Socioeconomic Pathway (SSP) scenarios indicate a significant reduction in highly suitable habitat, with losses of 63.01% (2041–2060, SSP126), 62.62% (2041–2060, SSP245), 61.35% (2041–2060, SSP370), and 61.99% (2061–2080, SSP585). Habitat contraction mainly occurs toward higher altitudes and southwestern areas, with a maximum displacement distance of 50.56 km under the SSP585 scenario. This study enhances our understanding of environmental factors affecting the distribution of A. carmichaelii and offers guidance for its sustainable management and cultivation amid global climate change. Full article

Olive cultivation is widespread throughout the Mediterranean basin, where the world’s main producing countries are located. Regions such as Extremadura are considered to be at high risk from the effects of climate change in the near future. In particular, olive cultivation is highly sensitive to climate change and can suffer profound effects on phenology and yield. This crop depends directly on variables such as maximum and minimum temperatures and rainfall. In this study, we have analysed how olive cultivation could be affected by calculating two bioclimatic indices, the Dryness Index (DI) and the Cool Night Index (CI), for three future periods. The methodology used projected ten combinations of climate models in two scenarios, RCP 4.5 and RCP 8.5. The results showed significant variations in the bioclimatic indices over the periods, which were used to calculate the water stress and extreme temperatures that these crops could suffer. They indicate that most of Extremadura will continue to be suitable for cultivation in the near future (2006–2035), while by the middle of the century (2036–2065) 67% of the area will remain temperate, where 72% of the olive groves are located, with a Dryness Index of 18% in the very dry category. By the end of the century (2066–2095), the zone will be 60–34% warm and very dry, with a Dryness Index of 72%. These results show that it will probably be necessary to create new areas suitable for olive cultivation and new varieties. Full article

Cryphalus dilutus is an emerging invasive pest of tropical and subtropical regions, with Mangifera indica and Ficus carica being its primary host plants. Larval damage caused by this insect can lead to severe tree wilting, posing a direct threat to agricultural production and ecological security. Native to South Asia, C. dilutus has established introduced populations in the Near East, Mexico, and other areas. In recent years, it has invaded multiple regions, including southern China and southern Italy. Given the widespread global distribution of host plants and the intensification of climate change, their distribution ranges are expected to expand. However, research assessing the potential global geographical distribution of this pest under climate change is lacking. In this study, we used the Random Forest model to predict the potential distribution range of C. dilutus. Under historical climatic conditions between 1970 and 2000, suitable climatic regions for C. dilutus were primarily distributed across southern China, southeastern Brazil, southeastern Mexico, the Congo Basin periphery, and the Iberian Peninsula, with a total area of 12,192.42 × 10⁴ km². The Temperature Annual Range and Precipitation of Warmest Quarter were identified as key environmental determinants that shaped its distribution. Under the future RCP4.5 climate scenario projected for the 2050s, the total suitable area for C. dilutus is projected to contract. Specifically, high-, medium-, and low-suitability areas are projected to decline by 52.77%, 62.39%, and 24.02%, respectively. While the total area of the very low zones is expected to increase, the total area of the suitable region has been reduced to 11,891.17 ×10⁴ km². Future climate change is expected to drive the distribution northward to high-altitude areas and inland areas. Model projections indicate a poleward expansion of the fundamental climatic niche, with climatic suitability increasing in high-latitude and high-altitude regions, such as Northern Europe and western North America. Conversely, current core tropical habitats in the Indian subcontinent and the Amazon Basin are projected to face significant habitat degradation due to thermal stress. Agricultural regions previously considered relatively safe due to climatic constraints, such as northern China, the midwestern United States, and Eastern Europe, may face new challenges from pest infestation. These findings underscore the importance of proactive monitoring and implementation of preventive measures. This provides crucial decision support for countries and regions to formulate precise pest control strategies and offers a theoretical basis for early monitoring and prevention of cross-border invasions on a global scale. Full article

This research assessed current and future climatic suitability for Crocus sativus L. cultivation across Italy, using species distribution models. A dataset of 721 georeferenced points from sites consistently producing top-quality saffron was combined with bioclimatic variables from the CHELSA v2.1 database. Habitat suitability was modelled with MaxEnt and projected under current (2025) climatic conditions and future scenarios for mid-century (2055) and late-century (2085), based on the GFDL-ESM4 model and the SSP3-7.0 emission scenario. The MaxEnt model showed moderate predictive performance (AUC = 0.73 ± 0.02; TSS = 0.37 ± 0.03), which is consistent with the broad ecological tolerance of C. sativus. Current suitable areas (90,049 km²) are mainly in central and northern Italy, especially along the hilly Apennines and much of the Po Plain. Response curves indicate that optimal saffron cultivation occurs mainly under moderately continental conditions, with moderate to high temperature seasonality (6.5–7.5 °C), cool winter temperatures (mean of the driest quarter 0–3.5 °C), and relatively high precipitation during the wettest month (150–250 mm). Future projections show an expansion of suitable areas (124,552 km² in 2055; 123,868 km² in 2085) and a spatial shift from lowlands and coasts toward hilly and mountain regions of the Apennines, the Alps, and the main islands. These findings can support farmers, land managers, and policy-makers in informed planning and sustainable management of saffron cultivation under climate change. Full article

Climate variability represents a growing challenge for livestock systems; however, its indirect economic effects remain insufficiently understood, particularly in data-scarce contexts. This study evaluates whether satellite-derived bioclimatic indices propagate into short-term variability of livestock-related sales from a digital agriculture perspective. Weekly commercial records from two geographically proximate livestock branches in Ecuador were integrated with meteorological data provided from NASA POWER to compute the Temperature Humidity Index (THI). A basal temperature index, defined as a four-week moving average of THI, and a corresponding thermal anomaly were derived in order to represent both cumulative and short-term thermal conditions. Linear time series models incorporating exogenous variables (ARIMAX) and a non-linear machine learning approach (Random Forest) were employed using lagged climatic and economic features. The results showed that linear models had limited explanatory capacity, indicating that short-term sales variability was primarily driven by market dynamics and logistical processes rather than direct climatic forcing. While the Random Forest model achieved better predictive performance, this was mainly due to its ability to capture systemic inertia and autoregressive structure in the sales series; climatic variables only provided a secondary, indirect signal. These findings highlight the value of artificial intelligence in identifying weak and delayed climate-related patterns in aggregated commercial indicators and support of satellite-based climate data in market-level decision making in livestock supply chains where animal-level measurements are unavailable. Full article

Viticulture is a vital sector of agriculture and economy exhibiting susceptibility to climate change, particularly in the Mediterranean regions. The present investigation examines the climatic suitability for vineyards development in Greece by exploiting geomorphological and bioclimatic data for the reference climatic period 1970–2000. The data is sourced from the ERA5-Land dataset and analyzed with R. The objective is to create a specific crop suitability map based on a simple, transparent model implemented through coding. This map identifies the climatically suitable areas for grapevine cultivation during the reference period. Results demonstrate that the model is highly adaptable, as both variable thresholds and areas of interest can be modified, while incorporating future climate scenarios can be performed, allowing for dynamic reconfiguration. According to the mapped climatic suitability, 55.1% of Greece is rated 3.5–4.0, and 12.9% is rated 4.0–4.5. The total suitability over Greece is calculated with a score of 3.5–4.0 for the 50.9% of total area, and for a score of 4.0–4.5, the covered area is 12.9%. Considering the Corine Land Cover classification as the reference land cover dataset, the false-negative areas (the model indicates that an area with vines is not suitable) are only 1.5% of the areas defined as viticultural. By providing clear and accurate spatial information, the model supports informed decision-making and the development of adaptation strategies, enhancing, therefore, the resilience and sustainability of viticulture in the context of climate change. Full article

Dasiops saltans is a small insect pest associated with pitahaya cultivation, whose occurrence is strongly influenced by specific environmental conditions. This study examined where this species could live in the Amazonas region by using models that identify areas with favourable conditions. With this approach, the current and future distribution of the insect was estimated, considering possible changes in climate. The results show that the places with the best conditions for the species may decrease slightly in the coming decades, while most of the region will continue to be unfavorable for its presence. The study also identified which environmental factors most influence where the insect can survive, highlighting the role of the terrain, soil characteristics and climate conditions related to temperature and moisture. These findings help us better understand the environmental limits of Dasiops saltans and provide useful information for decision-makers, farmers and local authorities, who can use this knowledge to improve management, monitoring and prevention strategies in agricultural areas. Full article

Arctic regions are warming rapidly, elevating extinction risks and accelerating ecosystem change, yet widely used bioclimatic datasets rarely represent polar-specific ecological constraints. Here we present BioClimPolar_2300 v1.0, a raster bioclimatic dataset designed for terrestrial Arctic biodiversity research under climate change. The dataset includes 33 gridded bioclimatic layers at a 10 km spatial resolution, covering seven discrete temporal intervals from 2010 to 2300 AD. In addition to conventional variables used globally, BioClimPolar_2300 incorporates three polar-relevant constraint domains: (1) polar day–night phenomena (PDNs), including degree-day metrics during polar night and polar day; (2) temperature-defined seasonal cycles (TSCs), including seasonal temperature, precipitation, aridity, and season length; (3) hot/cold stresses (HCSs), capturing indices of extreme summer heat and winter cold. Precipitation during snow-melting days (P_melting) is also included due to its relevance for species depending on subnivean habitats. Climate fields were extracted from CMIP6 models and statistically downscaled to 10 km using a change-factor approach under a polar projection. Monthly fields were linearly interpolated to derive daily grids, enabling the computation of variables that require daily inputs. Validation against observations from 30 Arctic weather stations indicates performance suitable for biodiversity applications, and two exemplar range shift case studies (one animal and one plant) illustrate biological relevance and provide practical guidance for data extraction and use. BioClimPolar_2300 fills a key gap in Arctic bioclimatic resources and supports more realistic biodiversity assessments and conservation planning through 2300. Full article

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