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Predicting the distributions of marine organisms at the global scale

Author

Listed:
  • Ready, Jonathan
  • Kaschner, Kristin
  • South, Andy B.
  • Eastwood, Paul D.
  • Rees, Tony
  • Rius, Josephine
  • Agbayani, Eli
  • Kullander, Sven
  • Froese, Rainer

Abstract

We present and evaluate AquaMaps, a presence-only species distribution modelling system that allows the incorporation of expert knowledge about habitat usage and was designed for maximum output of standardized species range maps at the global scale. In the marine environment there is a significant challenge to the production of range maps due to large biases in the amount and location of occurrence data for most species. AquaMaps is compared with traditional presence-only species distribution modelling methods to determine the quality of outputs under equivalently automated conditions. The effect of the inclusion of expert knowledge to AquaMaps is also investigated. Model outputs were tested internally, through data partitioning, and externally against independent survey data to determine the ability of models to predict presence versus absence. Models were also tested externally by assessing correlation with independent survey estimates of relative species abundance. AquaMaps outputs compare well to the existing methods tested, and inclusion of expert knowledge results in a general improvement in model outputs. The transparency, speed and adaptability of the AquaMaps system, as well as the existing online framework which allows expert review to compensate for sampling biases and thus improve model predictions are proposed as additional benefits for public and research use alike.

Suggested Citation

  • Ready, Jonathan & Kaschner, Kristin & South, Andy B. & Eastwood, Paul D. & Rees, Tony & Rius, Josephine & Agbayani, Eli & Kullander, Sven & Froese, Rainer, 2010. "Predicting the distributions of marine organisms at the global scale," Ecological Modelling, Elsevier, vol. 221(3), pages 467-478.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:3:p:467-478
    DOI: 10.1016/j.ecolmodel.2009.10.025
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    Citations

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    Cited by:

    1. Simon J Pittman & Kerry A Brown, 2011. "Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-12, May.
    2. 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.
    3. 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.
    4. Jones, Miranda C. & Dye, Stephen R. & Pinnegar, John K. & Warren, Rachel & Cheung, William W.L., 2012. "Modelling commercial fish distributions: Prediction and assessment using different approaches," Ecological Modelling, Elsevier, vol. 225(C), pages 133-145.
    5. Coro, Gianpaolo & Magliozzi, Chiara & Ellenbroek, Anton & Pagano, Pasquale, 2015. "Improving data quality to build a robust distribution model for Architeuthis dux," Ecological Modelling, Elsevier, vol. 305(C), pages 29-39.
    6. Cheung, William W.L. & Jones, Miranda C. & Reygondeau, Gabriel & Stock, Charles A. & Lam, Vicky W.Y. & Frölicher, Thomas L., 2016. "Structural uncertainty in projecting global fisheries catches under climate change," Ecological Modelling, Elsevier, vol. 325(C), pages 57-66.
    7. Rodrigues, Lucas dos Santos & Daudt, Nicholas Winterle & Cardoso, Luis Gustavo & Kinas, Paul Gerhard & Conesa, David & Pennino, Maria Grazia, 2023. "Species distribution modelling in the Southwestern Atlantic Ocean: A systematic review and trends," Ecological Modelling, Elsevier, vol. 486(C).
    8. Gormley, Kate S.G. & Hull, Angela D. & Porter, Joanne S. & Bell, Michael C. & Sanderson, William G., 2015. "Adaptive management, international co-operation and planning for marine conservation hotspots in a changing climate," Marine Policy, Elsevier, vol. 53(C), pages 54-66.
    9. Pontin, D.R. & Schliebs, S. & Worner, S.P. & Watts, M.J., 2011. "Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms," Ecological Modelling, Elsevier, vol. 222(10), pages 1657-1665.
    10. Melo-Merino, Sara M. & Reyes-Bonilla, Héctor & Lira-Noriega, Andrés, 2020. "Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence," Ecological Modelling, Elsevier, vol. 415(C).
    11. Coro, Gianpaolo & Pagano, Pasquale & Ellenbroek, Anton, 2013. "Combining simulated expert knowledge with Neural Networks to produce Ecological Niche Models for Latimeria chalumnae," Ecological Modelling, Elsevier, vol. 268(C), pages 55-63.
    12. Martín-García, Laura & González-Lorenzo, Gustavo & Brito-Izquierdo, Isabel T. & Barquín-Diez, Jacinto, 2013. "Use of topographic predictors for macrobenthic community mapping in the Marine Reserve of La Palma (Canary Islands, Spain)," Ecological Modelling, Elsevier, vol. 263(C), pages 19-31.

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