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Developing a basis for detecting and predicting long-term ecosystem changes
Jarre, A.; Moloney, C.L.; Shannon, L.J.; Fréon, P.; Van Der Lingen, C.D.; Verheye, H.M.; Hutchings, L.; Roux, J.-P.; Cury, P. (2006). Developing a basis for detecting and predicting long-term ecosystem changes, in: Shannon, V. et al. Benguela: predicting a large marine ecosystem. Large Marine Ecosystems Series, 14: pp. 239-272. hdl.handle.net/10.1016/S1570-0461(06)80016-9
In: Shannon, V. et al. (2006). Benguela: predicting a large marine ecosystem. Large Marine Ecosystems Series, 14. Elsevier: [s.l.]. ISBN 978-0-444-52759-2 . 3-410 pp., more
In: Sherman, K. Large Marine Ecosystems Series. Blackwell: London; New York. ISSN 1570-0461, more

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Keyword
    Marine

Authors  Top 
  • Jarre, A.
  • Moloney, C.L.
  • Shannon, L.J.
  • Fréon, P.
  • Van Der Lingen, C.D.
  • Verheye, H.M.
  • Hutchings, L.
  • Roux, J.-P.
  • Cury, P.

Abstract
    Long-term ecosystem changes in the Benguela region include species alternations and regime shifts, which are sometimes obscured by large intra- and inter- annual variability in the ecosystem. This chapter proposes that no single model or approach can resolve this variability and effectively detect and predict long-term ecosystem changes; a coherent, robust, transparent and reproducible synthesis framework is required. Indicators and models are described that can be used to identify some aspects of the current state of ecosystem structure and to detect and monitor long-term change. A short-term challenge is to synthesize these varied sources of multidisciplinary (and sometimes contradictory) information in a logical and consistent fashion. An expert system approach is proposed to do this, consolidating results of different indicators and models within a dynamic process that uses feedbacks to validate predictions of the expert system, and to improve it. It is suggested that such an approach should be initiated in the short term, even as models and indicators are being developed further. In parallel, multivariate statistical tools should be refined and applied to existing time series, to identify past periods of ecosystem change. Current data gaps should be filled, including time series of primary production and the abundance of gelatinous zooplankton. In the medium term, the expert system model should evolve to a point where its results can be used to inform various management groups about the state of the ecosystem. Part of this evolution requires that ecosystem indicators be presented with error estimates or formal assessments of quality.

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