Search Results (115)

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

Sustainable Development Reserves are organized units in the Amazon that are essential for the proper use and sustainable management of the region’s natural resources and for the livelihoods and economy of the local communities. This study aims to provide a climatic characterization of the Madeira River Sustainable Development Reserve (MSDR), offering scientific support to efforts to assess the feasibility of implementing adaptation measures to increase the resilience of isolated Amazon communities in the face of extreme climate events. Significant statistical analyses based on time series of observational and reanalysis climate data were employed to obtain a detailed diagnosis of local climate variability. The results show that monthly mean two-meter temperatures vary from 26.5 °C in February, the coolest month, to 28 °C in August, the warmest month. Monthly precipitation averages approximately 250 mm during the rainy season, from December until May. July and August are the driest months, August and September are the warmest months, and September and October are the months with the lowest river level. Cold spells were identified in July, and warm spells were identified between July and September, making this period critical for public health. Heavy precipitation events detected by the R80, Rx1day, and Rx5days indices show an increasing trend in frequency and intensity in recent years. The analyses indicated that the MSDR has no potential for wind-energy generation; however, photovoltaic energy production is viable throughout the year. Regarding the two major commercial crops and their resilience to thermal stress, the region presents suitable conditions for açaí palm cultivation, but Brazil nut production may be adversely affected by extreme drought and heat events. The results of this study may support research on adaptation strategies that includethe preservation of local traditions and natural resources to ensure sustainable development. 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

Ostryopsis davidiana shrubs, widely distributed in northern China, have been significantly affected by global warming. Based on the current geographical distribution data of O. davidiana in China, this study used climate data, soil data, topographic data, human activity data, and the “biomod2” integrated model to conduct an integrated study on the suitable habitat of O. davidiana under the current scenario and three future climate scenarios (SSP126, SSP370, and SSP585). The results showed the following: (1) The suitable habitats of O. davidiana are mainly concentrated in the northwest and north China regions, accounting for about 9.09% of the national area, centered in Shanyin County, Shuozhou City, Shanxi Province. (2) The suitable habitats of O. davidiana are mainly influenced by temperature and precipitation, with precipitation of wettest quarter (Bio16), isothermality (Bio3), and maximum temperature of warmest month (Bio5) being the key driving factors, with contribution rates of 25.69%, 24.31%, and 14.45%, respectively. (3) Under the three future climate scenarios, the suitable habitats of O. davidiana are expected to contract significantly, with only the low suitability areas expanding, while the rest would be contracting, showing a trend of losing most of their original habitat. The centroid of the suitable habitat would be shifting westward, and the suitable habitats would be generally migrating to higher elevation areas. (4) Climate change reduces the aggregation of O. davidiana, leading to gradual habitat fragmentation. This study provides a theoretical basis for the conservation of O. davidiana. Full article

Understanding morphological descriptions of plants documented by ancient peoples over 1000 years ago and identifying the species they described are critical for reconstructing the natural geographic distribution of plant taxa, tracking taxonomic variations, and inferring historical climate dynamics. Analyzing shifts in plant communities and climatic conditions during this period is essential to unravel the interplay among floristic composition, climate fluctuations, and anthropogenic impacts. However, research in this field remains limited, with greater emphasis placed on plant taxa from hundreds of millions of years ago. Investigations into flora and climate during the last two millennia are sparse, and pre-millennial climatic conditions remain poorly characterized. In this study, a historical text written 1475 years ago was analyzed to compile plant names and morphological features, followed by taxonomic identification. The research identified three gymnosperm species (one in Pinaceae, two in Cupressaceae), 1 Tamaricaceae species (dicotyledon), and 19 dicotyledon species. However, three plant groups could only be identified at the genus level. Using textual analysis and woody plant coexistence methods, the climate of 1475 years ago in western Henan Province, located in the middle-lower Yellow River basin in East Asia, was reconstructed. Results indicate that the mean temperature of the coldest month (MTCM) was approximately 1.3 °C higher than modern values. In comparison, the mean temperature of the warmest month (MTWM) and mean annual temperature (MAT) were lower than present-day levels. This suggests slightly cooler overall conditions with milder seasonal extremes in ancient Luoyang—a finding supported by contemporaneous studies. Furthermore, annual precipitation (AP), precipitation of the warmest quarter (PWQ), and precipitation of the coldest quarter (PCQ) in the Luoyang region 1475 years ago exceeded modern measurements, despite the area’s monsoonal climate. This suggests significantly higher atmospheric moisture content in ancient air masses compared to today. This study provides floristic and climatic baseline data for advancing our understanding of global climate variability at millennial scales. Full article

Phytophthora cinnamomi is a globally distributed plant-pathogenic oomycete that threatens economically important crops, including Lauraceae, Bromeliaceae, Fabaceae, and Solanaceae. Utilizing species occurrence records and 35 environmental variables (|R| < 0.8), we employed the MaxEnt model and ArcGIS spatial analysis to systematically predict the potential geographical distribution of P. cinnamomi under current (1970–2000) and future (2030S, 2050S, 2070S, 2090S) climate scenarios across three Shared Socioeconomic Pathways (SSPs). The results indicate that currently suitable habitats cover the majority of China’s provinces (>50% of their areas), with only sporadic low-suitability zones in Qinghai, Tibet, and Xinjiang. The most influential environmental variables were the mean diurnal temperature range, mean temperature of the warmest quarter, annual precipitation, precipitation of the driest month, and elevation. Under future climate scenarios, new suitable habitats emerged in high-latitude regions, while the highly suitable area expanded significantly, with the distribution centroid shifting northeastward. This study employs predictive modeling to elucidate the future distribution patterns of P. cinnamomi in China, providing a theoretical foundation for establishing a regional-scale disease early warning system and formulating ecological management strategies. Full article

Camellia oleifera Abel (C. oleifera) is an evergreen shrub classified under the Camellia genus. It is an important oil species and has great economic benefits. At present, C. oleifera is widely cultivated in the Yangtze River Basin in South China, and its wild species are mainly distributed in the native forests of Hainan Province. Therefore, in the current study, we used the MaxEnt model to predict the suitable habitat for C. oleifera and different environmental factors affecting its current and future distribution. The AUC values exceeded 0.98, showing that the simulation of the model was good, and the TSS values were all above 0.96, indicating that the model was feasible. The results showed that C. oleifera was mainly distributed in Southern China, with a total area of 56.68 × 10⁴ km². The suitable habitats of Camellia oleifera are affected by the precipitation of the warmest quarter (bio18), human activity, soil available water content (awc_class), and minimum temperature of the coldest month and seasonal temperature (bio04). Furthermore, rainfall in the warmest quarter (bio18) was recognized as a crucial factor impacting its distribution. Under future climate conditions, the suitable habitat area of C. oleifera is projected to expand with a slight northward shift in its distribution center. Therefore, in addition to maintaining the current planting area of C. oleifera, the planting area can be appropriately expanded upward along the current area and along the Yangtze River Basin. Full article

Beetles of the family Mordellidae, important global pollinators, include Glipa, the third largest genus, which retains plesiomorphic traits related to pollination and is mainly found between 38° S–38° N. Existing studies on Glipa focus largely on taxonomy and systematics. The ecological response of Glipa to climate change remains poorly understood. Our objective was to investigate how the distribution of Glipa may respond to climate change using a species-level MaxEnt based model with 297 geographic distribution data points and seven bioclimatic environmental variables. The study showed that the MaxEnt model had a high predictive accuracy, with an Area Under the Curve (AUC) value of 0.963. The maximum temperature of the warmest month, mean annual precipitation, and mean precipitation of the driest quarter were the three most important factors affecting the distribution of Glipa. Currently, the suitable distribution areas of Glipa are mainly located in East Asia, Southeast Asia, eastern North America, South America, and central and western Africa. Under future climate scenarios, the area of suitable habitat is expected to increase gradually as global temperatures rise. Under the SSP585 scenario in the 2070s, the suitable habitat area is projected to expand by 53.89% compared to the present. Additionally, the centroid of suitable habitat is expected to shift northward. This study not only deepens the understanding of the distribution patterns of Glipa and their response to climate change but also provides important scientific evidence for the conservation of pollinator diversity. Full article

This study, conducted in Mersin, a Mediterranean sub-tropical area, examined the potential of avocado and pitaya to thrive under current and future climate conditions. Researchers utilized climate and soil data, initially selecting 14 parameters (mean annual temperature, mean minimum temperature of the coldest month, mean maximum temperature of the warmest month, mean annual precipitation, soil texture, soil depth, land use capability, soil pH, soil organic carbon, soil salinity, land cover, elevation, slope, and groundwater level) for analysis, which were narrowed down to 12 after correlation analysis. The potential distributions were projected using the MaxEnt model for current and future scenarios. Three global climate models—HadGEM3-GC31-LL, MPI-ESM1-2-HR, and GFDL-ESM4—were utilized under the SSP2-4.5 and SSP5-8.5 scenarios. Under SSP2-4.5, an average increase of 1.32%, 1.95%, and 4.02% in the “S1” class is expected. For SSP5-8.5, average gains of 1.33%, 1.58%, and 0.77% are projected. In Pitaya, the “S1” class in SSP2-4.5 is expected to increase by 0.96% compared to the first model and decrease by 7.06% and 5.71% compared to the other models, respectively. Under SSP5-8.5, the changes are determined to be 1.49%, −7.27%, and −7.28%, respectively. Our findings indicate that climate change poses a significant threat to the region; however, the application demonstrates that agricultural activities can remain sustainable despite climate change impacts. Full article

Luprops orientalis (Motschulsky, 1868) is an economically important pest in traditional Chinese medicines, widely distributed in East Asia. However, the primary limiting factors affecting its distribution, potential suitable areas, as well as its response to global warming, remain largely unknown. Utilizing 295 filtered distribution points and 10 environmental variables (9 climate variables and 1 land cover type), this study uses the MaxEnt model to predict the potential distribution of L. orientalis under near-current and future environmental change scenarios. The results indicated that precipitation of the warmest quarter (bio18), temperature seasonality (bio04), and precipitation of the wettest month (bio13) were the most significant environmental variables affecting the distribution of suitable habitats for L. orientalis, while the contribution of average variation in daytime temperature (bio2) was the smallest. Under the near-current climate, the areas of low, moderate, and high suitability for L. orientalis are approximately 1.02 × 10⁶ km², 1.65 × 10⁶ km², and 8.22 × 10⁵ km², respectively. The suitable areas are primarily located in North China, Central China, the Korean Peninsula, and Central and Southern Japan. Under future climate conditions, the potential suitable areas are expected to expand significantly, especially in Central China. However, the high-suitability areas in North China are predicted to experience a slight reduction. With the increase in carbon emission concentrations, the suitable area shows an increasing trend in the 2050s, followed by a declining trend in the 2090s. The centroids of suitable areas will shift to the northeast in the future. These findings enhance our understanding of how climate change affects the distribution of L. orientalis and will assist governments in formulating effective pest control strategies, including widespread monitoring and stringent quarantine measures. Full article

Sterculia foetida L., commonly known as the Java olive, is a tropical tree species native to regions of East Africa, tropical Asia, and northern Australia. This study employs species distribution modeling (SDM) to predict the potential geographic distribution of S. foetida under current and future climate scenarios. Using 1425 occurrence data and 19 environmental variables, we applied an ensemble modelling approach of three algorithms: Boosting Regression Trees (BRT), Generalized Linear Model (GLM), and Random Forests (RF), to generate distribution maps. Our models showed high accuracy (mean AUC = 0.98) to indicate that S. foetida has a broad ecological niche, with high suitability in tropical and subtropical regions of north Australia (New Guinea and Papua), Southeast Asia (India, Thailand, Myanmar, Taiwan, Philippines, Malaysia, Sri Lanka), Oman and Yemen in the southwest of Asia, Central Africa (Guinea, Ghana, Nigeria, Congo, Kenya and Tanzania), the Greater and Lesser Antilles, Mesoamerica, and the north of South America (Colombia, Panama, Venezuela, Ecuador and Brazil). Indeed, the probability of occurrence of S. foetida positively correlates with the Maximum temperature of warmest month (bio5), Mean temperature of wettest quarter (bio8) and Precipitation of wettest month (bio13). The model results showed a suitability area of 4,744,653 km², representing 37.86% of the total study area, classified into Low (14.12%), Moderate (8.71%), and High suitability (15.02%). Furthermore, the study found that habitat suitability for S. foetida showed similar trends under both near future climate scenarios (SSP1-2.6 and SSP5-8.5 for 2041–2060), with a slight loss in potential distribution (0.24% and 0.25%, respectively) and moderate gains (1.98% and 2.12%). In the far future (2061–2080), the low scenario (SSP1-2.6) indicated a 0.29% loss and a 2.52% gain, while the high scenario (SSP5-8.5) showed a more dramatic increase in both loss (0.6%) and gain areas (3.79%). These findings are crucial for conservation planning and management, particularly in regions where S. foetida is considered invasive and could become problematic. The study underscores the importance of incorporating climate change projections in SDM to better understand species invasiveness dynamics and inform biodiversity conservation strategies. Full article

Climate change significantly impacts species distribution and survival, particularly for habitat specialists with limited dispersal abilities. This study investigates the current and future distribution of Tylonycteris pachypus, one of the world’s smallest bats specialized in bamboo-dwelling, using ensemble modeling approaches. Based on comprehensive occurrence data and seven environmental variables, we developed an ensemble model using the Biomod2 platform, achieving high predictive accuracy (AUC: 0.981, TSS: 0.877). Three environmental variables were identified as crucial determinants: minimum temperature of the coldest month (40.90% contribution), maximum temperature of the warmest month (38.38%), and precipitation of the wettest quarter (11.09%). Currently, highly suitable habitats (291.893 × 10⁴ km²) are concentrated in three main regions: southern China and Indochina Peninsula, Myanmar–Bangladesh–northeastern India, and isolated areas in southwest India and Thailand. Under future climate scenarios, particularly SSP585, suitable habitats are projected to decrease substantially (64.4% reduction by 2090s), with a notable northward shift in distribution. However, the species’ limited dispersal ability, specific habitat requirements, and geographical barriers may constrain its capacity to track these climate-driven changes. Our findings highlight the vulnerability of T. pachypus to climate change and emphasize the need for targeted conservation strategies, including protecting climate-resilient habitats and maintaining bamboo forest corridors. This study provides a comprehensive framework for monitoring and conserving this specialized species under climate change, while considering its unique ecological constraints and dispersal limitations. Full article

(1) Background: The variability and trend in harvest dates of table and Pisco grapes have been scarcely studied. This can be closely influenced by bioclimatic indices since they account for the interactions between climatic factors and vine phenology. Understanding the environmental factors influencing harvest timing has become increasingly critical to perform specific viticultural practices. (2) Methods: The aim of this research was to evaluate the influence of bioclimatic indices on variability and trend of harvest date from the 2002–2003 to 2017–2018 seasons in Flame Seedless, Thompson Seedless, Muscat of Alexandria, and Moscatel Rosada growing in Northern Chile. (3) Results: The harvest date of Flame Seedless advanced significantly with an increasing Growing Season Temperature (GST) (from 1 October to 31 December), while Thompson Seedless showed a significant advancement in harvest date with rising the Maximum Springtime Temperature Summation SON_max (from 1 September to 30 November) values. Similarly, the harvest date of Muscat of Alexandria was significantly earlier with higher Heliothermal Index (HI) (from 1 July to 31 January and from 1 August to 30 April) values, whereas Moscatel Rosada exhibited a significant advancement in harvest date as the GST (from 1 July to 31 December and from 1 July to 31 January) increased. The trend in the harvest date of Thompson Seedless was statistically significant, reaching a coefficient of determination of 0.42. (4) Conclusions: Understanding the influence of bioclimatic indices on harvest date in long-term periods is critical in the context of climatic variability since producers can make more informed decisions to optimize grape quality and maintain sustainability in production systems. Full article

Understanding how mammals respond to climate change is critical for predicting future biogeographic shifts and implementing effective conservation strategies. In this study, we applied MaxEnt modeling to identify key determinants of the distribution of the Malabar slender loris (Loris lydekkerianus malabaricus), a nocturnal primate endemic to the Western Ghats of India. Using 416 slender loris sightings, spatially thinned at 0.5 km intervals to reduce spatial autocorrelation, we evaluated 19 present bioclimatic variables alongside 10 additional climatic variables. From these, 14 predictor variables with Pearson correlation values above 0.75 were selected for analysis. Future distribution models employed bioclimatic projections from the CNRM-CM5 global climate models under three Representative Concentration Pathways (RCPs): 2.6, 4.5, and 8.5. The current distribution models identified 23 km² as a suitable habitat for slender lorises, with 3 km² suitable for males and 12 km² for females. Projections for 2070 under RCP 2.6, 4.5, and 8.5 scenarios predict habitat reductions of 52%, 13%, and 8%, respectively, signaling significant vulnerability under changing climatic conditions. Precipitation of the warmest quarter, precipitation of the driest month, distance from roads, and elevation were identified as the most influential variables shaping the species’ distribution. This study underscores the pressing need for targeted conservation efforts to mitigate habitat loss and fragmentation, particularly in the context of climate change. By providing a detailed analysis of current and future habitat suitability, it lays the groundwork for similar predictive studies on nocturnal small mammals. As climate change accelerates, the integration of species–specific ecological insights and advanced modeling techniques will be vital in guiding conservation actions and preserving biodiversity in vulnerable ecosystems like the Western Ghats. Full article

This study employs the Biomod2 model, along with 22 bioclimatic variables, to predict the geographic distribution of the medicinal plant Epimedium acuminatum Franch. for the current period and three future timeframes (2050s, 2070s, and 2090s). Ultimately, 11 key environmental variables were identified as critical for assessing the habitat suitability of the medicinal plant. These include the annual mean temperature (Bio 1), isothermally (Bio 3), temperature seasonality (Bio 4), maximum temperature of the warmest month (Bio 5), minimum temperature of the coldest month (Bio 6), mean temperature of the driest quarter (Bio 9), mean temperature of the coldest quarter (Bio 11), precipitation of the driest quarter (Bio 17), elevation (Elev), aspect, and slope. The results indicate that the current high suitability areas are primarily distributed across Yunnan, Chongqing, Sichuan, Hunan, Guangxi, and Hubei provinces. In the future, the extent of high suitability areas is expected to increase. This study aims to provide a theoretical reference for the conservation of E. acuminatum genetic resources from a geographic distribution perspective. Full article