Search Results (163)

With its millennia-long agricultural history, Olive (Olea europaea L.) is one of the most strategic crops of the Mediterranean basin and a key component of the Turkish economy. This study assessed the effects of climate change on the potential distribution of olive in Türkiye using machine learning-based species distribution models (SDMs). Analyses were conducted using the 1970–2000 reference period and future projections for 2041–2060 and 2081–2100 under the SSP2-–4.5 and SSP5–8.5 scenarios, incorporating bioclimatic variables as well as topographic factors such as elevation, slope, and aspect. The model showed strong predictive performance (AUC = 0.93; TSS = 0.77) and identified elevation, winter precipitation (Bio19), and mean temperature of driest quarter (Bio9) as the primary variables influencing the distribution of olive trees. Model results predict a significant shift in suitable areas for olive cultivation, both northward—from the traditional Aegean and Mediterranean coastal belt toward the Marmara and Black Sea regions—and upward in elevation into higher-altitude inland areas. High-suitability areas, which accounted for 4.4% of Türkiye’s land area during the reference period, are projected to decline to 0.2% by the end of the century under the SSP5–8.5 scenario. UNEP Aridity Index analyses indicate increasing aridity pressure on olive habitats. While 87.2% of suitable habitats were classified as sub-humid in the reference period, projections for 2081–2100 under SSP5–8.5 suggest that 40.1% of these areas will shift to dry sub-humid and 26.4% to semi-arid conditions. Full article

The Silver pheasant, a species in the Phasianidae family, is a widely distributed ground-dwelling bird in southern China and a national Class II protected animal. Global climate change and land use and land cover change are recognized as major drivers of wildlife habitat loss and shift, especially for species with low dispersal ability, like the Silver pheasant. However, no studies have assessed the influence of climate change and land-cover change on suitable habitat at the regional scale. In this study, we aim to assess the combined effects of climate change and land cover change on the habitat of the Silver pheasant. We used an ensemble species distribution model to predict habitat suitability for the Silver pheasant under the current scenario and three shared socioeconomic pathways (SSPs) across two time periods (2041–2070, 2071–2100). The results demonstrate that the ensemble model outperforms individual models in predicting suitable habitat for the Silver pheasant. The ensemble model achieved an area under the receiver’s operating curve (AUC) of 0.90, a true skill statistic (TSS) of 0.61, and a Boyce index of 0.96, indicating excellent predictive performance. Currently, suitable habitat for the Silver pheasant in China, covering 4.33 × 10⁵ km², primarily exists in eight provinces (Sichuan, Yunan, Jiangxi, Fujian, Guangdong, Guangxi, Zhejiang, and Hunan). Land cover emerged as the most influential variable (43%), followed by annual precipitation amount (36%), elevation (9%), and slope (6%). Silver pheasant is projected to experience large habitat expansion (24.24% to 63.08%) depending on different scenarios and time. The centroid of suitable habitat is expected to shift northeastward, but with short distances (<39 km). Our results provide accurate predictions of the distribution patterns of the Silver pheasant habitat in China under current and future environmental change scenarios, which is the fundamental basis for scientific-based management and conservation decisions at the national level. Full article

Pohlia cruda (Hedw.) Lindb. is a cryophilous moss species with a boreo-arctic montane distribution. As global temperatures continue to rise, high-mountain plant species are increasingly forced to migrate to higher elevations to remain within their ecological and physiological tolerance limits. In this study, we applied ensemble species-distribution modeling (SDM) to evaluate the future niche availability of P. cruda in Italy under two greenhouse gas-emission scenarios and two time periods (2050 and 2090). Projections under the intermediate emission scenario (SSP2-4.5) indicate a habitat loss ranging from −24.1% to −46.7%, whereas predictions under the very high emission of greenhouse gases (SSP5-8.5) suggest even greater losses, between −28.1% and −59.9%. These findings point to a substantial reduction, fragmentation, and potential disappearance of suitable habitats for P. cruda in the coming decades. This study represents a pioneering application of bryophyte-distribution modeling for the territory of Italy and provides a foundation for integrating such approaches into conservation decisions aimed at preserving biodiversity. Full article

Ecosystems worldwide are being influenced by climate change in complex ways, leading to profound impacts on biodiversity. While these shifts may constrain the adaptive capacity of some species, they may simultaneously promote the expansion of others, including certain rodents. The Funambulus pennantii (five-striped northern palm squirrel) and Funambulus palmarum (three-striped palm squirrel) demonstrate exceptional adaptability across diverse habitats from rural plantations to densely populated urban areas. Their close association with humans ranges from causing crop damage to becoming urban nuisances, leading to their broad classification as agricultural pests. Hence, this study utilizes ensemble species distribution models to identify the climatic niches of these two species and delineate overlapping suitable habitats within agricultural zones in current and different future climate scenarios. The ensemble model identified 215,748 km² of suitable habitat for F. pennantii and 39,578 km² for F. palmarum under current climatic conditions in India. The future projections indicate habitat increases of about 20.6% for F. pennantii and 48.1% for F. palmarum. The agricultural overlap is also projected to rise by up to 45% and 48%, respectively, compared with present overlaps of 215,595 km² and 39,073 km². The most substantial expansions are projected in the Gangetic Plains, Central Highlands, and parts of the Western and Eastern Ghats and Deccan Plateau. These findings suggest that both species possess high resilience to environmental change and may pose increasing risks to agriculture in a warming climate. The study underscores the need for proactive, ecologically sustainable management strategies to mitigate potential human–squirrel interactions and agricultural losses, offering a foundation for targeted regional interventions. Full article

Understanding the alignment between fisher behavior and habitat dynamics is essential for data-driven fisheries management. This study analyzed high-resolution Automatic Identification System (AIS) and Vessel Monitoring System (VMS) data, integrated with logbooks from 10 stick-held dipnet vessels targeting Pacific saury (Cololabis saira) in the North Pacific high seas. We developed an optimized CNN-LSTM-SE model to classify vessel trajectories into eight operational states, achieving 91% accuracy. This model generated a high-confidence presence dataset, addressing spatiotemporal data limitations in pelagic species research. A dynamic Ensemble Species Distribution Model (ESDM) mapped habitat suitability index (HSI) for the primary fishing seasons (June–September) of 2023–2024, revealing seasonal northward migrations and an interannual eastward shift in core habitats, primarily driven by sea surface temperature (SST: 6.4–19.1 °C), chlorophyll-a (CHL: 0.2–2.0 mg/m³), mixed layer depth (MLD: 14–30 m), and dissolved oxygen (DO: 220–290 mmol/m³). Receiver operating characteristic (ROC) sensitivity analysis identified an HSI threshold of ≥0.4 for suitable habitats, where 98.4% of fishing effort was concentrated. Kaplan–Meier survival analysis demonstrated that vessels in high-quality habitats (HSI ≥ 0.8) exhibited significantly longer fishing bout durations and lower cessation probabilities (log-rank test, χ² = 20.9, p < 0.001), providing empirical evidence for the Marginal Value Theorem and Optimal Foraging Theory. Although HSI showed a weak direct correlation with catch rates (R² = 0.007), it effectively delineated high-potential fishing grounds (>90% of high-catch days > 30 tonnes in HSI ≥ 0.6). By demonstrating that fishers’ spatial decisions appear to reflect environmental gradients, suggesting that fishing effort may indirectly act as an ecological indicator, this integrated framework bridges fisher behavior with ecological theory, supporting dynamic ocean management in climate-variable fisheries. Full article

Fishery resources of tuna serve as a vital source of global protein. This study investigates the key environmental drivers influencing the spatial distribution of yellowfin tuna (Thunnus albacares) in the western tropical Pacific Ocean. A comprehensive dataset was constructed by linking the catch per unit effort (CPUE) from 43 Chinese longline fishing vessels (2008–2019) with 24 multi-source environmental variables. To accurately model this complex relationship, a total of 16 machine learning regression models, including advanced ensemble methods like Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting Regressor (CatBoost), were evaluated and compared using multiple performance metrics (e.g., Coefficient of Determination [R²], Root Mean Squared Error [RMSE]). The results indicated that the Light Gradient Boosting Machine (LightGBM) model achieved superior performance, demonstrating excellent nonlinear fitting capabilities and generalization ability. For robust feature interpretation, the study employed both the model’s internal feature importance metrics and the SHapley Additive exPlanations (SHAP) method. Both approaches yielded highly consistent results, identifying temporal (month), spatial (longitude, latitude), and key seawater temperature indicators at intermediate depths (T450, T300, T150) as the most critical predictors. This highlights significant spatiotemporal heterogeneity in the distribution of Thunnus albacares. The analysis suggests that mid-layer ocean temperatures directly influence catch rates by governing the species’ vertical and horizontal movements. In contrast, large-scale climate indices such as the Oceanic Niño Index (ONI) exert indirect effects by modulating ocean thermal structures. This research confirms the dominance of spatiotemporal and thermal variables in predicting yellowfin tuna distribution and provides a reliable, data-driven framework for supporting sustainable fishery management, resource assessment, and operational forecasting. Full article

The red-crowned crane (Grus japonensis) is a flagship species for wetland biodiversity in East Asia. The Songnen Plain is a vital wetland and habitat for rare and endangered birds in Northeast China. However, rapid land use changes have raised urgent concerns about habitat loss and the survival of these populations. We combined 30 years (1990–2020) of field surveys with ensemble species distribution models (SDMs) to analyze the spatio-temporal changes in suitable habitats for all three key life stages—spring migration, breeding, and autumn migration—across the Songnen Plain. We also assessed how well the current protected-area (PA) network covers suitable habitats and identified conservation gaps. Land use type was the most significant predictor of habitat suitability. Over this period, suitable habitats decreased sharply by 60% (spring migration), 72% (breeding), and 76% (autumn migration), with severe fragmentation and a clear northward shift. Core suitable areas are now mainly found within a few nature reserves, including Zhalong, Wuyu’er River, and Xianghai. We identified three significant conservation gaps: Lindian–Anda, Tailai–Dumeng, and Meilisi Daur–Fuyu. Our results show widespread habitat reduction and demonstrate the inadequacy of the current PA network in supporting the long-term survival of red-crowned crane populations. We recommend expanding protections and restoring wetland connectivity within these gaps to maintain critical habitats and improve landscape resilience for this endangered species. Full article

Hamatocaulis vernicosus is a pleurocarpous moss of conservation concern, listed in Annex II of the EU Habitats Directive due to its significant and ongoing decline across Europe. H. vernicosus is also listed as ‘Vulnerable’ on the Red List of Romanian Bryophytes. Despite its protected status, the species remains under-recorded in Romania, where many potentially suitable habitats have yet to be surveyed. The ecosystems, classified as Transition mire and quaking bog (NATURA 2000 code: 7140), are wet peatlands with oligo- to mesotrophic conditions and a pH of 5.0–7.5 H. vernicosus is recorded in 58 Romanian locations (10 confirmed by us, 5 new), spanning the Continental and Alpine bioregions. Models showed good performance (AUC 0.79–0.83; TSS 0.54–0.59), with distribution mainly shaped by mean annual temperature and temperature range, and secondarily by precipitation. The species favors cold, stable climates with high seasonal rainfall. Even though the number of localities reported for this species has increased in recent years, this does not indicate an improvement in its conservation status, but rather is an effect of recent recording efforts. To support targeted conservation planning, an ensemble species distribution model was developed in order to predict the suitable habitats of H. vernicosus across Romania. Both climate models project major range losses for the varnished hook-moss: ~30% by 2050 and ~40–60% by 2100, depending on the scenario. Losses are gradual under SSP245 but more abrupt under SSP585, with increased fragmentation, especially between the Eastern and Southern Carpathians. By integrating field observations with predictive climate change modeling, our study brings critical insights applicable to the conservation of H. vernicosus and the unique peatland ecosystems it relies on. Full article

Understanding how climate change may reshape species distributions and affect the associated ecosystem services is critical for sustainable agricultural planning. In this study, we integrated dietary DNA metabarcoding with ensemble species distribution modeling to assess the current and future ecological roles of Miniopterus fuliginosus, a widespread insectivorous bat species in East Asia known for preying on nocturnal agricultural pests. Fecal samples were collected in 2023 from three biogeographically distinct regions of China—Central China (Henan Province) and Southwest China (Guizhou and Yunnan provinces). DNA metabarcoding based on COI gene amplification and Illumina sequencing revealed a consistent dietary dominance of Lepidoptera, particularly families comprising major agricultural pest species such as Noctuidae, Crambidae, and Geometridae. This trophic consistency suggests that M. fuliginosus functions as a moth-specialized generalist predator. Species distribution models were constructed using occurrence records from field surveys, the literature, and the GBIF database, integrating multiple algorithms (GLM, GBM, MaxEnt, RF, and FDA) within an ensemble modeling framework. Habitat suitability was then estimated under current climatic conditions and projected for future distributions under two contrasting climate scenarios (SSP1–2.6 and SSP5–8.5) for the 2050s and 2070s. While the total suitable area may remain stable or even expand, future projections indicate a progressive poleward shift in range centroids and a divergence in habitat structure. Specifically, SSP1–2.6 is associated with greater spatial cohesion (25.34–31.11%), whereas SSP5–8.5 leads to increased habitat fragmentation and isolation of suitable patches (27.12–33.28%). Overlaying the potential for pest control with habitat projections highlights emerging spatial mismatches between ecological function and climatic suitability, particularly under high-emission trajectories. Our findings underscore the importance of identifying ecological refugia and maintaining landscape connectivity to sustain bat-mediated pest control. This spatially explicit framework offers new insights for integrating biodiversity-based pest management into climate-resilient agricultural strategies. Full article

Alexandrium spp., globally recognized as harmful algal bloom (HAB) species, pose severe threats to marine ecosystems, fisheries, and public health. Based on 469 occurrence records and 24 marine environmental variables, this study employed the Biomod2 ensemble modeling framework to predict the potential distribution of Alexandrium spp. under current and future climate scenarios, and to assess the role of key environmental factors and the spatiotemporal dynamics of habitat centroid shifts. The results revealed that (1) the ensemble model outperformed single models (AUC = 0.998, TSS = 0.977, Kappa = 0.978), providing higher robustness and reliability in prediction; (2) salinity range (bio18, 19.1%) and mean salinity (bio16, 5.8%) were the dominant factors, while minimum temperature (bio23) also showed strong constraints, indicating that salinity determines “whether persistence is possible,” while temperature influences “whether blooms occur”; (3) under present conditions, high-suitability habitats are concentrated in Bohai Bay, the Yangtze River estuary to the Fujian coast, and parts of Guangdong; (4) climate change is predicted to drive a southward shift of suitable habitats, with the most pronounced expansion under the high-emission scenario (RCP8.5), leading to the emergence of new high-risk areas in the South China coast and adjacent South China Sea; (5) centroid analysis further indicated a pronounced southward migration under RCP8.5 by 2100, highlighting a regional reconfiguration of ecological risks. Collectively, salinity and temperature are identified as the core drivers shaping the ecological niche of Alexandrium spp., and future warming is likely to exacerbate HAB risks in southern China. This study delineates key prevention regions and proposes a shift from reactive to proactive management strategies, providing scientific support for HAB monitoring and marine ecological security in China’s coastal waters. Full article

Climate change is reshaping plant distribution and ecological adaptation worldwide. Ficus tikoua Bur., a perennial resource plant native to Southwest and South China, has not been systematically assessed for its future cultivation potential. In this study, we used the Biomod2 ensemble modeling framework, integrating 12 algorithms with 469 occurrence records and 16 environmental variables, to predict the potential distribution and niche dynamics of F. tikoua under current and future climate scenarios (SSP126, SSP370, and SSP585). The ensemble model achieved high predictive accuracy based on multiple algorithms and cross-validation. The minimum temperature of the coldest month (bio6, 43.5%), maximum temperature of the warmest month (bio5, 25.0%), and annual precipitation (bio12, 10.3%) were identified as the dominant factors shaping its distribution. Model projections suggest that suitable habitats will generally expand northwestward, while contracting in the southeast. Core areas, such as the Yunnan–Guizhou Plateau and the Sichuan Basin, are predicted to remain highly stable. In contrast, southeastern marginal regions are likely to experience a decline in suitability due to intensified heat stress. Niche analyses further revealed strong niche conservatism (overlap D = 0.83–0.94), suggesting that the species maintains stable climatic tolerance and adapts primarily through range shifts rather than evolutionary change. This finding suggests limited adaptive flexibility in response to rapid warming. Overall, climate warming may enhance cultivation opportunities for F. tikoua at higher latitudes and elevations, while emphasizing the importance of protecting stable core habitats, planning climate adaptation corridors, and integrating this species into climate-resilient agroforestry strategies. These findings provide practical guidance for biodiversity conservation and land-use planning, offering a scientific basis for regional policy formulation under future climate change. Full article

Raptors, being at top of the food chain, serve as important models to study the impact of changing climate, as they are more vulnerable due to their unique ecology. They are vulnerable to extinction, with 52% species declining population and 18% are threatened globally. The effect of climate change on raptors is poorly studied in the Eastern Himalayan region. The present study offers a complete investigation of climate change effects on the raptors in the northeast region of the Eastern Himalayas, employing ensemble species distribution modeling. The future predictions were employed to model the climate change across two socioeconomic pathways (SSP) i.e. SSP245 and SSP585 for the periods 2041–2060 and 2061–2080. Specifically, five algorithms were employed for the ensemble model, viz. boosted regression tree (BRT), generalized linear model (GLM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt) and random forest (RF). The study highlights worrying results, as only 10.5% area of the NE region is presently suitable for Falco severus, 11.4% for the critically endangered Gyps tenuirostris, and a mere 6.9% area is presently suitable for the endangered Haliaeetus leucoryphus. The most influential covariates were precipitation of the driest quarter, precipitation of the wettest month, and temperature seasonality. Future projection revealed reduction of 33–41% in suitable habitats for F. severus, G. tenuirostris is expected to lose 53–96% of its suitable habitats, and H. leucoryphus has lost nearly 94–99% of its suitable habitats. Such decline indicates apparent habitat fragmentation, with shrinking habitat patches. Full article

Species distribution models (SDMs) have become an important tool to inform conservation and pest surveillance programs about the potential biological invasion of insect pests. Nonetheless, to be operational, SDMs need to incorporate multiple environmental covariates and a representative number of occurrence points depicting the species’ ecological niche. The algorithm of choice, model of choice, and comparison can also have a great effect on the final prediction output. We created a dataset based on previously published records, plus 36 new occurrences and 37 environmental predictors, to generate the first global ensemble distribution model for Aulacaspis yasumatsui. We employed a strategy that aggregates SDMs with the best performance (i.e., greater accuracy) from six different algorithms, resulting in an averaged and weighted model, i.e., the ensemble model. We then selected models from algorithms whose true skill statistic (TSS) was above 0.5 in order to map the potential global distribution of A. yasumatsui. Our results suggest that covariate selection and the individual model algorithms used in the ensemble may be more important for achieving an accurate SDM than the number of occurrence points. Full article

Global climate change poses a significant management challenge for vital transboundary resources like the Pacific saury (Cololabis saira). To address this, we developed an innovative framework that uses high-resolution Automatic Identification System (AIS) data and deep learning to define species distribution, which then informs a robust Ensemble Species Distribution Model (ESDM). The model (TSS > 0.89, AUC > 0.97) identifies sea surface temperature (SST) and chlorophyll-a (CHL) as key habitat drivers. Projections under future climate scenarios reveal two critical threats: (1) a continuous northeastward migration of the habitat’s centroid, exceeding 400 km by 2100 under a high-emission SSP5-8.5 scenario, and (2) a drastic contraction of highly suitable habitat (suitability > 0.8), shrinking by up to 94% under the high-emission SSP3-7.0 scenario. By directly linking key oceanographic features to these climate-driven risks, this study delivers an essential scientific decision-support tool for management bodies like the North Pacific Fisheries Commission (NPFC) to develop climate-adaptive strategies. Full article

Climate change poses a serious threat to biodiversity, particularly for woody species with limited dispersal capacity such as Prunus avium L. (wild cherry). In this study, we assessed potential shifts in its suitable distribution range (SDR) across Türkiye by applying an ensemble modeling framework that combined Generalized Additive Models (GAM), Maximum Entropy (MaxEnt), and Random Forest (RF). We used updated occurrence data (including GBIF and EUFORGEN records) and 11 ecologically relevant bioclimatic variables under SSP2-4.5 and SSP5-8.5 scenarios. Model performance was validated using AUC (Area Under the ROC Curve) and TSS (True Skill Statistic) metrics. Results suggest that while 60–70% of current SDRs remain stable by 2100, approximately 10% may be lost, with 20–23% new expansions. Temperature seasonality (Bio4) and seasonal precipitation (Bio15) were consistently identified as dominant predictors across models. Notably, newly suitable habitats are expected to be spatially isolated, limiting natural colonization. Our findings highlight the necessity of proactive conservation planning, including assisted migration and drought-resistant genotype selection, to ensure long-term persistence of wild cherry under changing climates. These results offer actionable insights for adaptive forest management and biodiversity conservation in Mediterranean-type ecosystems. Full article