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Classifying degrees of species commonness: North Sea fish as a case study

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  • Coro, Gianpaolo
  • Webb, Thomas J.
  • Appeltans, Ward
  • Bailly, Nicolas
  • Cattrijsse, André
  • Pagano, Pasquale

Abstract

Species commonness is often related to abundance and species conservation status. Intuitively, a “common species” is a species that is abundant in a certain area, widespread and at low risk of extinction. Analysing and classifying species commonness can help discovering indicators of ecosystem status and can prevent sudden changes in biodiversity. However, it is challenging to quantitatively define this concept. This paper presents a procedure to automatically characterize species commonness from biological surveys. Our approach uses clustering analysis techniques and is based on a number of numerical parameters extracted from an authoritative source of biodiversity data, i.e. the Ocean Biogeographic Information System. The analysis takes into account abundance, geographical and temporal aspects of species distributions. We apply our model to North Sea fish species and show that the classification agrees with independent expert opinion although sampling biases affect the data. Furthermore, we show that our approach is robust to noise in the data and is promising in classifying new species. Our method can be used in conservation biology, especially to reduce the effects of the sampling biases which affect large biodiversity collections.

Suggested Citation

  • Coro, Gianpaolo & Webb, Thomas J. & Appeltans, Ward & Bailly, Nicolas & Cattrijsse, André & Pagano, Pasquale, 2015. "Classifying degrees of species commonness: North Sea fish as a case study," Ecological Modelling, Elsevier, vol. 312(C), pages 272-280.
    • Handle: RePEc:eee:ecomod:v:312:y:2015:i:c:p:272-280
    DOI: 10.1016/j.ecolmodel.2015.05.033
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    References listed on IDEAS

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    1. Debeljak, Marko & Squire, Geoffrey R. & Kocev, Dragi & Hawes, Cathy & Young, Mark W. & Džeroski, Sašo, 2011. "Analysis of time series data on agroecosystem vegetation using predictive clustering trees," Ecological Modelling, Elsevier, vol. 222(14), pages 2524-2529.
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    3. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    4. Picard, Nicolas & Mortier, Frédéric & Rossi, Vivien & Gourlet-Fleury, Sylvie, 2010. "Clustering species using a model of population dynamics and aggregation theory," Ecological Modelling, Elsevier, vol. 221(2), pages 152-160.
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    Cited by:

    1. Coro, Gianpaolo & Magliozzi, Chiara & Vanden Berghe, Edward & Bailly, Nicolas & Ellenbroek, Anton & Pagano, Pasquale, 2016. "Estimating absence locations of marine species from data of scientific surveys in OBIS," Ecological Modelling, Elsevier, vol. 323(C), pages 61-76.

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