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Estimating absence locations of marine species from data of scientific surveys in OBIS

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  • Coro, Gianpaolo
  • Magliozzi, Chiara
  • Vanden Berghe, Edward
  • Bailly, Nicolas
  • Ellenbroek, Anton
  • Pagano, Pasquale

Abstract

Estimating absence locations of a species is important in conservation biology and conservation planning. For instance, using reliable absence as much as presence information, species distribution models can enhance their performance and produce more accurate predictions of the distribution of a species. Unfortunately, estimating reliable absence locations is difficult and often requires a deep knowledge of the species’ distribution and of its abiotic and biotic environmental preferences and tolerance. In this paper, we propose a methodology to reconstruct reliable absence information from presence-only information, and the conditions that those presence-only data have to meet to make this possible.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:323:y:2016:i:c:p:61-76
    DOI: 10.1016/j.ecolmodel.2015.12.008
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    Cited by:

    1. Zhiqiang Chen & Zhibiao Chen, 2018. "Effects of ecological restoration measures on the distribution of Dicranopteris dichotoma at the microscale in the red soil hilly region of China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-17, October.
    2. Coro, Gianpaolo & Vilas, Luis Gonzalez & Magliozzi, Chiara & Ellenbroek, Anton & Scarponi, Paolo & Pagano, Pasquale, 2018. "Forecasting the ongoing invasion of Lagocephalus sceleratus in the Mediterranean Sea," Ecological Modelling, Elsevier, vol. 371(C), pages 37-49.
    3. Gómez-Sanz, Valentín, 2019. "Site- scale ecological marginality: Evaluation model and application to a case study," Ecological Modelling, Elsevier, vol. 408(C), pages 1-1.
    4. Coro, Gianpaolo, 2020. "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate," Ecological Modelling, Elsevier, vol. 431(C).

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