Explore the vast range of titles available.

Background Anopheles stephensi is native to Southeast Asia and the Arabian Peninsula and has emerged as an effective and invasive malaria vector. Since invasion was reported in Djibouti in 2012, the global invasion range of An. stephensi has been expanding, and its high adaptability to the environment and the ongoing development of drug resistance have created new challenges for malaria control. Climate change is an important factor affecting the distribution and transfer of species, and understanding the distribution of An. stephensi is an important part of malaria control measures, including vector control. Methods In this study, we collected existing distribution data for An. stephensi , and based on the SSP1-2.6 future climate data, we used the Biomod2 package in R Studio through the use of multiple different model methods such as maximum entropy models (MAXENT) and random forest (RF) in this study to map the predicted global An. stephensi climatically suitable areas. Results According to the predictions of this study, some areas where there are no current records of An. stephensi , showed significant areas of climatically suitable for An. stephensi . In addition, the global climatically suitability areas for An. stephensi are expanding with global climate change, with some areas changing from unsuitable to suitable, suggesting a greater risk of invasion of An. stephensi in these areas, with the attendant possibility of a resurgence of malaria, as has been the case in Djibouti. Conclusions This study provides evidence for the possible invasion and expansion of An. stephensi and serves as a reference for the optimization of targeted monitoring and control strategies for this malaria vector in potential invasion risk areas.

In recent decades, ecological niche models (ENMs) have been widely used to predict suitable habitats for species. However, for invasive organisms, the prediction accuracy is unclear. In this study, we employed the most widely used maximum entropy (MaxEnt) model and ensemble model (EM) Biomod2 and verified the practical effectiveness of the ENM in predicting the distribution areas of invasive insects based on the true occurrence of Hyphantria cunea in China. The results showed that when only limited data of invasive areas were used, the two ENMs could not effectively predict the distribution of suitable habitats of H. cunea, although the use of global data can greatly improve the prediction accuracy of ENMs. When analyzing the same data, Biomod2's prediction accuracy was significantly better than that of MaxEnt. For long‐term predictions, the area of suitable habitat predicted by the ENMs was much greater than the occurrence area; for short‐term predictions, the accuracy of the predicted area was significantly improved. Under the current conditions, the area of suitable habitat for H. cunea in China is 118 × 104 km2, of which 59.32% is moderately or highly suitable habitat. Future climate change could significantly increase the suitable habitat area of H. cunea in China, and the predicted area of suitable habitats in all climate scenarios exceeded 355 × 104 km2, accounting for 36.98% of the total land area in China. This study demonstrates the use of ENMs to study invasive insects and provides a reference for the management of H. cunea in China. This study verifies the accuracy of ENMs in predicting the distribution area of invasive insects. The comprehensiveness of species distribution points is the key to determining whether the results are accurate. The prediction performance of the EM is better than that of the single model.

Alpinia officinarum , commonly known as Galangal, is not only widely used as a medicinal plant but also holds significant ornamental value in horticulture and landscape design due to its unique plant structure and floral aesthetics in China. This study evaluates the impact of current and future climate change scenarios (ssp126, ssp245, ssp370, and ssp585) on the suitable habitats for A. officinarum in China. A total of 73 reliable distribution points for A. officinarum were collected, and 11 key environmental variables were selected. The ENMeval package was used to optimize the Maxent model, and the potential suitable areas for A. officinarum were predicted in combination with Biomod2. The results show that the optimized Maxent model accurately predicted the potential distribution of A. officinarum in China. Under low emission scenarios (ssp126 and ssp245), the suitable habitat area increased and expanded towards higher latitudes. However, under high emission scenarios (ssp370 and ssp585), the suitable habitat area significantly decreased, with the species distribution range shrinking by approximately 3.7% and 19.8%, respectively. Through Multivariate environmental similarity surface (MESS) and most dissimilar variable (MoD) analyses revealed that increased climate variability under high emission scenarios, especially in ssp585, led to large-scale habitat contraction due to rising temperatures and unstable precipitation patterns. Changes in the center of suitability location showed that the current center of A. officinarum ’s suitable habitat is located in Guangxi, China. Under low emission scenarios, the center of suitability gradually shifts northwest, while under high emission scenarios, this shift becomes more pronounced. These findings provide a scientific basis for the conservation of A. officinarum germplasm resources and the management strategies in response to climate change.

Ceracris kiangsu (Orthoptera: Arcypteridae), is greatly affected by climatic factors and exhibits strong adaptability, posing a serious threat to the ecological environment. Therefore, predicting its potential suitable habitat distribution provides a proactive theoretical basis for pest control. This study using the Biomod2 package of R simulated and predicted the current and future potential distribution, area changes, changes in the center points of suitable habitats, and niche shifts of C. kiangsu under two different greenhouse gas emission scenarios, SSP1-26 and SSP5-85. The results show that: (1) Currently, the high suitability areas for C. kiangsu are mainly distributed in Yunnan, Jiangxi, Hunan provinces in southern China and phongsaly province in northern Laos. In the future, the center of the suitable habitat distribution pattern of C. kiangsu will remain unchanged, primarily expanding outward from medium and high suitability areas. Additionally, significant suitable habitats for C. kiangsu were discovered in Southeast Asian countries without previous pest records. (2) Compared to the present, the overall suitable habitat area for C. kiangsu is expected to expand, particularly under the SSP5-85 climate change scenario. (3) In the SSP1-26 and SSP5-85 climate scenarios, the geometric center of the suitable habitat generally shows a trend of gradually shifting northeast. (4) Under different climate scenarios, the suitable habitat of C. kiangsu has highly overlapping, indicating that the suitable habitat of C. kiangsu in the invaded areas is broader than in its native regions. In conclusion, the research findings represent a breakthrough in identifying the potential distribution areas of C. kiangsu , which is of great practical significance for the monitoring and control of C. kiangsu pest infestation in China and Southeast Asian countries.

Accurate identification of forest and grassland fire-prone areas is essential for effective fire management and ecosystem protection. This study aimed to evaluate the performance of two species distribution models, MaxEnt and Biomod2, in predicting forest grassland fire risks in Sichuan Province, and to accurately identify regions with high fire risk. Using fire occurrence data from 2011 to 2020 and relevant environmental variables, both models were applied to generate fire risk maps and identify key influencing factors. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and the true skill statistics (TSS). Results showed that the Biomod2 model outperformed MaxEnt, with the ensemble model EMwmean achieving the highest accuracy (AUC = 0.93, TSS = 0.71). Mean temperature and precipitation were the most influential variables, with human activity also playing a significant role. High-fire-risk areas were concentrated in southwestern Sichuan, the western populated zones, and the northern Sichuan Basin. We recommended that the MaxEnt model be considered when both model prediction accuracies are adequate for subsequent applications, or when only local fire point data are available and the global distribution must be predicted. Otherwise, the Biomod2 model is to be preferred.

Modelling dolphin distribution is key for understanding their ecology and for their conservation and management. Information on the distribution and preferred habitats of southern Australian bottlenose dolphins Tursiops sp. is lacking, particularly in metropolitan areas where the species is under threat from anthropogenic activities. Here, we used boat-based surveys and an ensemble modelling approach that combined results from 6 modelling techniques (generalised additive models, generalised boosted models, classification tree analysis, flexible discriminant analysis, random forest and maximum entropy) to identify areas of high probability of southern Australian bottlenose dolphin occurrence along the metropolitan coast of Adelaide, South Australia. We used kernel density estimation to identify core and representative areas according to behaviour and investigated the importance and potential ecological function of areas of high dolphin occurrence. The ensemble predictions of dolphin distribution performed better than the corresponding single models. Results indicate that depth, benthic habitat type and slope influenced dolphin occurrence along Adelaide’s coast. Dolphins favoured shallow nearshore areas and temperate reefs in summer, shallow nearshore areas in autumn and deep waters further offshore in winter. In comparison to other observed behaviours, core feeding areas overlapped considerably with areas of high probability of dolphin occurrence. Thus, we suggest that prey availability is an important driver influencing the seasonal variation in dolphin distribution along Adelaide’s metropolitan coast. Our predictions identify priority areas for dolphin conservation and for the implementation of boating and fishing regulations. Continued monitoring is needed to assess potential changes in preferred habitat under increasing anthropogenic pressures.

The performance of species distribution models is influenced by model algorithms, and the form of occurrence/non-occurrence data. Therefore, selecting an appropriate approach based on the objective and type/size of the modeling data is essential for reducing model uncertainty. In this study, we used a range of algorithm-based single models to predict the habitat suitability of Corythucha marmorata (chrysanthemum lace bug) worldwide and developed ensemble models using different methods, including mean, median, committee averaging, and weighted mean, so that they could be further applied to a specific region (South Korea). In addition, we tested the pseudo-absence data generation methods (random, surface range envelope, and Disk) using a combination of ensemble modeling methods in terms of model performance. Among the three methods, the TSS of the committee averaging algorithm and the weighted mean algorithm with the surface range envelope method were the highest at 0.980 and 0.977, respectively. These models were used to predict the potential distribution of C. marmorata in South Korea, showing a high probability of occurrence throughout the country, except on the southernmost island. Through this study, we expected to provide insights into the methodological use of species distribution modeling by incorporating various algorithm-based models, ensemble methods, and data preprocessing techniques.

Invasive alien plants pose a significant threat to biodiversity and the agricultural economy. The invasive weed ( Ammannia coccinea ) competes with rice in paddy fields, potentially threatening rice production. Despite the crucial need to estimate the global geographical distribution and ecological niche dynamics of A. coccinea for effective early warning, control strategies, and global rice security, relevant research remains scarce. This study utilized the Biomod2 platform, which integrates multiple single models into ensemble model, incorporating environmental and species data to analyze the distribution range shifts of A. coccinea under current and future climate scenarios. It also quantified and analyzed shifts in the species’ ecological niche across these climate scenarios. The results indicated that the potential suitable areas for A. coccinea were mainly in Southern North America, northern and south-eastern South America, south-western Europe, the Middle East, central Africa, western Asia, south-eastern Asia, with a gradual increase in mid-high suitability habitat over time and radiation levels. While the overall ecological niche of A. coccinea remains stable, minor shifts are expected under future conditions. Temperature, precipitation, and the human impact index were the key factors influencing the future distribution of A. coccinea . Climate change contributes to the expansion of A. coccinea 's highly suitable areas and shifts its ecological niche. Organizations efforts should focus on preventing the spread of A. coccinea in regions where its potential distribution overlaps with key rice production areas. The findings of this study provide critical insights into the global distribution and ecological niche dynamics of A. coccinea , aiding in the development of early warning and control strategies to mitigate its impact on biodiversity, agriculture, and particularly rice production under future climate scenarios.

Moso bamboo (Phyllostachys edulis), a non-timber plant resource in China, possesses significant ecological and economic value. However, human activities and climate change have degraded its natural habitat, posing a significant threat to its widespread distribution. To address this, we used species distribution models based on 115 occurrence data and 10 ecological variables to predict the potential suitable areas and spatial change trends of moso bamboo in present and future periods in China. We also analyzed areas with environmental anomalies and the drivers of its geographical variations under climate change. The results showed that the biomod2 ensemble model, consisting of eleven integrated models, exhibited significantly improved accuracy compared to single models. Key environmental factors limiting its distribution were mean diurnal temperature range (bio2), minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and elevation. Currently, the potential suitable habitat covers 152.74 × 104 km2, mainly from south of the Qinling-Huaihe River to north of the Tropic of Cancer. However, under future climate scenarios, these habitats will considerably shrink, especially in highly suitable areas. The moderately suitable habitat will fragment, and the low-suitability boundary will move northward. With the deepening impact of climate change, the entire distribution range will move towards higher latitudes. Hunan, Guizhou, Zhejiang, and western Jiangxi emerge as future climate refuges for moso bamboo, necessitating critical population protection. In summary, our research deepens our insight of how climate change drives the geographic distribution of moso bamboo and offers valuable theoretical support for its cultivation and conservation.

Bioclimatic envelope models are commonly used to assess the influence of climate change on species' distributions and biodiversity patterns. Understanding how methodological choices influence these models is critical for a comprehensive evaluation of the estimated impacts. Here we systematically assess the performance of bioclimatic envelope models in relation to the selection of predictors, modeling technique, and pseudo‐absences. We considered (a) five different predictor sets, (b) seven commonly used modeling techniques and an ensemble model, and (c) three sets of pseudo‐absences (1,000 pseudo‐absences, 10,000 pseudo‐absences, and the same as the number of presences). For each combination of predictor set, modeling technique, and pseudo‐absence set, we fitted bioclimatic envelope models for 300 species of mammals, amphibians, and freshwater fish, and evaluated the predictive performance of the models using the true skill statistic (TSS), based on a spatially independent test set as well as cross‐validation. On average across the species, model performance was mostly influenced by the choice of predictor set, followed by the choice of modeling technique. The number of the pseudo‐absences did not have a strong effect on the model performance. Based on spatially independent testing, ensemble models based on species‐specific nonredundant predictor sets revealed the highest predictive performance. In contrast, the Random Forest technique yielded the highest model performance in cross‐validation but had the largest decrease in model performance when transferred to a different spatial context, thus highlighting the need for spatially independent model evaluation. We recommend building bioclimatic envelope models according to an ensemble modeling approach based on a nonredundant set of bioclimatic predictors, preferably selected for each modeled species. Bioclimatic envelope models are commonly used to assess the influence of climate change on species' distributions and biodiversity patterns. We systematically evaluated the performance of bioclimatic envelope models in relation to the selection of predictors, modelling technique, and pseudo‐absences. Model performance was mostly influenced by the choice of predictor set, followed by the choice of modelling technique, and based on our results, we recommend building bioclimatic envelope models according to an ensemble modelling approach based on a nonredundant set of bioclimatic predictors, preferably selected for each modelled species.