Using a self-organizing map to predict invasive species: Sensitivity to data errors and a comparison with expert opinion

Journal of Applied Ecology

Volumn 47, Issue 2, 2010, Pages 290-298

  Paini, Dean R ^a,b   Worner, Susan P ^a,c   Cook, David C ^a,b,d   De Barro, Paul J ^a,b   Thomas, Matthew B ^a,b,e  

^a Australia Cooperative Research Centre for National Plant Biosecurity   (Australia)

^b CSIRO   (Australia)

^c LINCOLN UNIVERSITY   (New Zealand)

^d AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^e PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Artificial neural networks; Australia; Biosecurity; Establishment; Exotic pest; Invasion; Non indigenous species; Species assemblages

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BIOSAFETY; CROP PEST; DATA SET; ESTIMATION METHOD; INVASIBILITY; INVASIVE SPECIES; LOCAL GOVERNMENT; PREDICTION; RESEARCH AND DEVELOPMENT; SELF ORGANIZATION; SENSITIVITY ANALYSIS; SPECIES DIVERSITY; STAKEHOLDER;

AUSTRALIA;

HEXAPODA;

EID: 77951188109     PISSN: 00218901     EISSN: 13652664     Source Type: Journal    
DOI: 10.1111/j.1365-2664.2010.01782.x     Document Type: Article

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