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Ecological Informatics for the Prediction and Management of Invasive Species

  • Susan P. WornerEmail author
  • Muriel GevreyEmail author
  • Takayoshi IkedaEmail author
  • Gwenaël LedayEmail author
  • Joel PittEmail author
  • Stefan SchliebsEmail author
  • Snjezana SolticEmail author
Part of the Springer Handbooks book series (SHB)

Abstract

Ecologists face rapidly accumulating environmental data form spatial studies and from large-scale field experiments such that many now specialize in information technology. Those scientists carry out interdisciplinary research in what is known as ecological informatics. Ecological informatics is defined as a discipline that brings together ecology and computer science to solve problems using biologically-inspired computation, information processing, and other computer science disciplines such as data management and visualization. Scientists working in the discipline have research interests that include ecological knowledge discovery, clustering, and forecasting, and simulation of ecological dynamics by individual-based or agent-based models, as well as hybrid models and artificial life. In this chapter, ecological informatics techniques are applied to answer questions about alien invasive species, in particular, species that pose a biosecurity threat in a terrestrial ecological setting. Biosecurity is defined as the protection of a regionʼs environment, flora and fauna, marine life, indigenous resources, and human and animal health. Because biological organisms can cause billions of dollars of impact in any country, good science, systems, and protocols that underpin a regulatory biosecurity system are required in order to facilitate international trade. The tools and techniques discussed in this chapter are designed to be used in a risk analysis procedure so that agencies in charge of biosecurity can prioritize scarce resources and effort and be better prepared to prevent unexpected incursions of dangerous invasive species. The methods are used to predict, (1) which species out of the many thousands might establish in a new area, (2) where those species might establish, and, (3) where they might spread over a realistic landscape so that their impact can be determined.

Keywords

Geographic Information System Gypsy Moth Relative Operating Characteristic Well Match Unit True Skill Statistic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

ANN

artificial neural network

DENFIS

dynamic neuro-fuzzy inference system

ECM

evolving clustering method

GIS

geographical information system

GNS

global navigation system

IBM

individual-based model

MDIG

modular dispersal in GIS

MLP

multilayer perceptron

NBC

naive Bayesian classifier

NNET

neural network

QiSNN

quantum-inspired spiking neural network

ROC

receiver operating characteristic

SDM

species distribution model

SOM

self-organizing map

SV

support vector

SVM

support vector machine

TSS

true skill statistic

ctree

conditional tree

lda

linear discriminant analysis

nb

naive Bayes

qda

quadratic discriminant analysis

svm

support vector machine

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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Bio-Protection Research CentreLincoln UniversityLincolnNew Zealand
  2. 2.Chesapeake Biological LaboratoryUniversity of MarylandSolomonsUSA
  3. 3.Deanʼs DepartmentUniversity of OtagoWellingtonNew Zealand
  4. 4.Department of MathematicsVrije Universiteit AmsterdamAmsterdamThe Netherlands
  5. 5.c/0 Bio-Protection Research CentreLincoln UniversityLincolnNew Zealand
  6. 6.School of Computing and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand
  7. 7.Engineering Centre of ExcellenceManukau Institute of TechnologyManukauNew Zealand

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