Dataset Information

Global potential distribution prediction of Xanthium italicum based on Maxent model.

ABSTRACT: Alien invasive plants pose a threat to global biodiversity and the cost of control continues to rise. Early detection and prediction of potential risk areas are essential to minimize ecological and socio-economic costs. In this study, the Maxent model was used to predict current and future climatic conditions to estimate the potential global distribution of the invasive plant Xanthium italicum. The model consists of 366 occurrence records (10 repeats, 75% for calibration and 25% for verification) and 10 climate prediction variables. According to the model forecast, the distribution of X. italicum was expected to shrink in future climate scenarios with human intervention, which may be mainly caused by the rise in global average annual temperature. The ROC curve showed that the AUC values of the training set and the test set are 0.965 and 0.906, respectively, indicating that the prediction result of this model was excellent. The contribution rates of annual mean temperature, monthly mean diurnal temperature range, standard deviation of temperature seasonal change and annual average precipitation to the geographical distribution of X. italicum were 65.3%, 11.2%, 9.0%, and 7.7%, respectively, and the total contribution rate was 93.2%. These four variables are the dominant environmental factors affecting the potential distribution of X. italicum, and the influence of temperature is greater than that of precipitation. Through our study on the potential distribution prediction of X. italicum under the future climatic conditions, it has contribution for all countries to strengthen its monitoring, prevention and control, including early warning.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC8368065 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCitrus huanglongbing (HLB) is a destructive disease of citrus and a major threat to the citrus industry around the world. This disease accounts for substantial economic losses in China every year. Diaphorina citri Kuwayama is one of the major vectors by which citrus HLB is spread under natural conditions in China. Research is needed to identify the geographic distribution of D. citri and its major areas of occurrence and to formulate measures for early warning, monitoring, and control of this pest and citrus HLB.MethodsIn this study, the ecological niche modelling software MaxEnt (maximum entropy model) was combined with ArcGIS (a geographic information system) to predict the potential geographic distribution of D. citri in China. Key environmental factors and the appropriate ranges of their values were also investigated.ResultsOur results show that the training data provided a good forecast (AUCmean = 0.988). The highly suitable areas for D. citri in China are mainly concentrated to the south of the Yangtze River, and the total area is 139.83 × 104 km2. The area of the moderately suitable areas is 27.71 × 104 km2, with a narrower distribution than that of the highly suitable area. The important environmental factors affecting the distribution of D. citri were min temperature of coldest month, mean temperature of coldest quarter, precipitation of wettest quarter, mean temperature of warmest quarter, precipitation of warmest quarter, max temperature of warmest month, and temperature seasonality. These results provide a valuable theoretical basis for risk assessments and control of D. citri.DiscussionThe predicted results showed that there were highly suitable areas for D. citri in Chongqing, Hubei, Anhui, and Jiangsu. Therefore, the possibility exists for the further spread of D. citri in China in the future. Extreme temperature variables, especially the min temperature of the coldest month, play an important role in the distribution of D. citri and are most closely related to the distribution of D. citri.

Project description:BACKGROUND:The ecosystems across Tibetan Plateau are changing rapidly under the influence of climate warming, which has caused substantial changes in spatial and temporal environmental patterns. Stipa purpurea, as a dominant herbsage resource in alpine steppe, has a great influence on animal husbandry in the Tibetan Plateau. Global warming has been forecasted to continue in the future (2050s, 2070s), questioning the future distribution of S. purpurea and its response to climate change. The maximum entropy (MaxEnt) modeling, due to its multiple advantages (e.g. uses presence-only data, performs well with incomplete data, and requires small sample sizes and gaps), has been used to understand species environment relationships and predict species distributions across locations that have not been sampled. RESULTS:Annual mean temperature, annual precipitation, temperature seasonality, altitude, and precipitation during the driest month, significantly affected the distribution of S. purpurea. Only 0.70% of the Tibetan Plateau area included a very highly suitable habitat (habitat suitability [HS] = 0.8-1.0). Highly suitable habitat (HS = 0.6-0.8), moderately suitable habitat (HS = 0.4-0.6), and unsuitable habitat (HS = 0.2-0.4) occupied 6.20, 14.30 and 22.40% of the Tibetan Plateau area, respectively, and the majority (56.40%) of the Tibetan Plateau area constituted a highly unsuitable habitat (HS = 0-0.2). In addition, the response curves of species ecological suitability simulated by generalized additive model nearly corresponded with the response curves generated by the MaxEnt model. CONCLUSIONS:At a temporal scale, the habitat suitability of S. purpurea tends to increase from the 1990s to 2050s, but decline from the 2050s to 2070s. At a spatial scale, the future distribution of S. purpurea will not exhibit sweeping changes and will remain in the central and southeastern regions of the Tibetan Plateau. These results benefit the local animal husbandry and provide evidence for establishing reasonable management practices.

Project description:BackgroundSchistosomiasis is a snail-borne parasitic disease and is endemic in many tropical and subtropical countries. Biomphalaria straminea, an intermediate host for Schistosoma mansoni, is native to the southeastern part of South America and has established in other regions of South America, Central America and southern China during the last decades. S. mansoni is endemic in Africa, the Middle East, South America and the Caribbean. Knowledge of the potential global distribution of this snail is essential for risk assessment, monitoring, disease prevention and control.Methods and findingsA comprehensive database of cross-continental occurrence for B. straminea was compiled to construct ecological models. We used several approaches to investigate the distribution of B. straminea, including direct comparison of climatic conditions, principal component analysis and niche overlap analyses to detect niche shifts. We also investigated the impacts of bioclimatic and human factors, and then used the bioclimatic and footprint layers to predict the potential distribution of B. straminea at global scale. We detected niche shifts accompanying the invasions of B. straminea in the Americas and China. The introduced populations had enlarged its habitats to subtropical regions where annual mean temperature is relatively low. Annual mean temperature, isothermality and temperature seasonality were identified as most important climatic features for the occurrence of B. straminea. Additionally, human factors improved the model prediction (P<0.001). Our model showed that under current climate conditions the snail should mostly be confined to the tropic and subtropic regions, including South America, Central America, Sub-Saharan Africa and Southeast Asia.ConclusionsOur results confirmed that niche shifts took place in the invasions of B. straminea, in which bioclimatic and human factors played an important role. Our model predicted the global distribution of B. straminea based on habitat suitability, which would help for prioritizing monitoring and management efforts for B. straminea control in the context of ongoing climate change and human disturbances.

Project description:Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

Dataset Information

Global potential distribution prediction of Xanthium italicum based on Maxent model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure