globalchange  > 影响、适应和脆弱性
DOI: 10.1111/gcb.13038
论文题名:
Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling
作者: Mainali K.P.; Warren D.L.; Dhileepan K.; Mcconnachie A.; Strathie L.; Hassan G.; Karki D.; Shrestha B.B.; Parmesan C.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2015
卷: 21, 期:12
起始页码: 4464
结束页码: 4480
语种: 英语
英文关键词: Parthenium hysterophorus ; AUC ; Boosted regression trees ; Generalized additive models ; Generalized linear models ; Invasive species ; Model evaluation ; Nonequilibrium distribution ; Random forests ; Species distribution modeling
Scopus关键词: angiosperm ; invasive species ; numerical model ; regression analysis ; spatial distribution ; Australia ; Nepal ; Asteraceae ; Parthenium ; Parthenium hysterophorus ; Asteraceae ; biological model ; ecology ; environmental protection ; introduced species ; physiology ; plant dispersal ; procedures ; Asteraceae ; Conservation of Natural Resources ; Ecology ; Introduced Species ; Models, Biological ; Plant Dispersal
英文摘要: Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our 'best' model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. © 2015 John Wiley & Sons Ltd.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/61664
Appears in Collections:影响、适应和脆弱性

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作者单位: Department of Integrative Biology, The University of Texas at Austin, mail code C0930, Austin, TX, United States; Department of Biological Sciences, Macquarie University, Bldg. E8B, Sydney, NSW, Australia; Department of Agriculture and Fisheries, Ecosciences Precinct, Biosecurity Queensland, GPO Box 267, Brisbane, QLD, Australia; Agricultural Research Council-Plant Protection Research Institute, Private Bag X6006, Hilton, South Africa; Weed Research Unit, Biosecurity, NSW Department of Primary Industries, Locked Bag 6006, Orange, NSW, Australia; Department of Weed Science, NWFP Agricultural University, Peshawar, Pakistan; College of Applied Sciences Nepal, Anamnagar, Kathmandu, Nepal; Central Department of Botany, Tribhuvan University, Kirtipur, Kathmandu, Nepal; Marine Institute, Plymouth University, Marine Bldg. rm 305, Drakes Circus, Plymouth, United Kingdom; Department of Geological Sciences, The University of Texas at Austin, mail code C9000, Austin, TX, United States

Recommended Citation:
Mainali K.P.,Warren D.L.,Dhileepan K.,et al. Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling[J]. Global Change Biology,2015-01-01,21(12)
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