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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Predictive systems ecology.

Matthew R Evans1, Mike Bithell, Stephen J Cornell

  • 1School of Biological and Chemical Sciences, Queen Mary University of London, , Mile End Road, London E1 4NS, UK, Department of Geography, University of Cambridge, , Downing Place, Cambridge CB2 3EN, UK, Institute of Integrative Biology, University of Liverpool, , Liverpool L69 7ZB, UK, Centre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, , Cornwall Campus TR10 9EZ, UK, Instituto Multidisciplinario de BiologíaVegetal (CONICET-UNC) and FCEFyN, Universidad Nacional de Córdoba, , Casilla de Correo 495, Córdoba 5000, Argentina, Computational Science Laboratory, Microsoft Research, , 21 Station Road, Cambridge CB1 2FB, UK, IFREMER, Laboratorie Ressources Halieutiques, 150 quai Gambetta, BP 699, Boulogne-sur-Mer 62321, France, Helmhotz Center for Environmental Research, Department of Ecological Modelling, Permoserstrasse 15, Leipzig 04318, Germany, Earth and Biosphere Institute, University of Leeds, , Woodhouse Lane, Leeds LS2 9JT, UK, Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, , Darwin Building, Gower Street, London WC1E 6BT, UK, Natural England, , Cromwell House, Andover Road, Winchester SO23 7BT, UK, British Antarctic Survey, Madingley Road, High Cross, Cambridge CB3 0ET, UK, United Nations Environment Programme World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge CB3 0DL, UK, Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, The University of Reading, , Earley Gate, PO Box 237, Reading RG6 6AR, UK, Institute of Evolutionary Biology and Environmental Studies, University of Zurich, , Winterhurerstrasse 190, Zurich 8057, Switzerland, Institute of Biological and Environmental Sciences, Zoology Building, Tillydrone Avenue, Aberdeen AB24 2TZ, UK, School of Biology, University of Leeds, , Leeds LS2 9JT, UK.

Proceedings. Biological Sciences
|October 4, 2013
PubMed
Summary
This summary is machine-generated.

Predictive systems ecology aims to forecast ecosystem changes and guide management. Embracing predictive approaches will enhance ecological science and societal impact.

Keywords:
climate changeecosystem assessmentmodellingsystems ecology

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Area of Science:

  • Ecology
  • Environmental Science
  • Systems Biology

Background:

  • Human well-being is intrinsically linked to ecosystem health.
  • Ecosystems are undergoing rapid changes due to anthropogenic pressures.
  • Predicting future ecosystem states is crucial for effective environmental management.

Purpose of the Study:

  • To propose and develop the paradigm of predictive systems ecology.
  • To understand and forecast the properties and behavior of ecological systems.
  • To outline the essential features for predictive systems ecology models.

Main Methods:

  • Conceptual framework development for predictive systems ecology.
  • Review and summarization of existing terrestrial and marine predictive ecology examples.
  • Discussion of necessary and desirable characteristics of predictive models.

Main Results:

  • Identified existing practices in predictive systems ecology across various domains.
  • Highlighted key features required for robust predictive ecological models.
  • Acknowledged significant challenges that remain in the field.

Conclusions:

  • Predictive systems ecology offers a pathway to better understand and manage ecosystems.
  • Adoption of predictive approaches can significantly benefit ecological science.
  • Increased predictivity in ecology will enhance its societal relevance and impact.