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Spatial pattern and determinants of global invasion risk of an invasive species, sharpbelly Hemiculter leucisculus (Basilesky, 1855)
Dong, Xianghong1,2,3,4; Ju, Tao6; Grenouillet, Gael3,5; Laffaille, Pascal4; Lek, Sovan3; Liu, Jiashou1
2020-04-01
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
Volume711Issue:1Pages:9
Abstract

Invasive species have imposed huge negative impacts on worldwide aquatic ecosystems and are generally difficult or impossible to be eradicated once established. Consequently, it becomes particularly important to ascertain their invasion risk and its determinants since such information can help us formulate more effective preventive or management actions and direct these measures to those areas where they are truly needed so as to ease regulatory burdens. Here, we examined the global invasion risk and its determinants of sharpbelly (Hemiculter leucisculus), one freshwater fish which has a high invasive potential, by using species distribution models (SDMs) and a layer overlay method. Specifically, first an ensemble species distribution model and its basal models (developed from seven machine learning algorithms) were explored to forecast the global habitat-suitability and variables importance for this species, and then a global invasion risk map was created by combining habitat-suitability with a proxy for introduction likelihood (entailing propagule pressure and dispersal constraints) of exotic sharpbelly. The results revealed that (1) the ensemble model had the highest predictive power in forecasting sharpbelly's global habitat-suitability; (2) areas with high invasion risk by sharpbelly patchily spread over the world except Antarctica; and (3) the Human Influence Index (HII), rather than any of the bioclimatic variables, was the most important factor influencing sharpbelly' future invasion. Based on these results, the present study also attempted to propose a series of prevention and management strategies to eliminate or alleviate the adverse effects caused by this species' further expansion. (C) 2019 Elsevier B.V. All rights reserved.

KeywordAquatic invasive species Species distribution models Ensemble predicting Habitat-suitability Invasion risk Management strategies
DOI10.1016/j.scitotenv.2019.134661
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000509344700119
WOS KeywordFRESH-WATER FISHES ; PSEUDORASBORA-PARVA ; HABITAT SUITABILITY ; DISTRIBUTIONS ; NICHE ; PREDICTION ; MODELS ; IDENTIFICATION ; OCCURRENCES ; BASILEWSKY
PublisherELSEVIER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/342005/35097
Collection淡水生态学研究中心_期刊论文
Corresponding AuthorLiu, Jiashou
Affiliation1.Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.CNRS, Lab Evolut & Diversite Biol EDB, UMR5174, IRD,UPS, 118 Route Narbonne, F-31062 Toulouse 9, France
4.Univ Toulouse, Ecolab, CNRS, INPT,UPS, F-31062 Toulouse, France
5.Inst Univ France, Paris, France
6.Chinese Acad Fishery Sci, Yangtze River Fisheries Res Inst, Minist Agr & Rural Affairs China, Key Lab Freshwater Biodivers Conservat, Wuhan 430223, Peoples R China
Recommended Citation
GB/T 7714
Dong, Xianghong,Ju, Tao,Grenouillet, Gael,et al. Spatial pattern and determinants of global invasion risk of an invasive species, sharpbelly Hemiculter leucisculus (Basilesky, 1855)[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2020,711(1):9.
APA Dong, Xianghong,Ju, Tao,Grenouillet, Gael,Laffaille, Pascal,Lek, Sovan,&Liu, Jiashou.(2020).Spatial pattern and determinants of global invasion risk of an invasive species, sharpbelly Hemiculter leucisculus (Basilesky, 1855).SCIENCE OF THE TOTAL ENVIRONMENT,711(1),9.
MLA Dong, Xianghong,et al."Spatial pattern and determinants of global invasion risk of an invasive species, sharpbelly Hemiculter leucisculus (Basilesky, 1855)".SCIENCE OF THE TOTAL ENVIRONMENT 711.1(2020):9.
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