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Predicting the distributions of marine organisms at the global scale
Ready, J.; Kaschner, K.; South, A.B.; Eastwood, P.D.; Rees, T.; Rius, J.; Agbayani, E.; Kullander, S.; Froese, R. (2010). Predicting the distributions of marine organisms at the global scale. Ecol. Model. 221(3): 467-478. dx.doi.org/10.1016/j.ecolmodel.2009.10.025
In: Ecological Modelling. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0304-3800, more
Peer reviewed article  

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Keyword
    Marine
Author keywords
    Species distribution modelling; Range maps; Global marine biodiversity;Trawl surveys; Expert review; Model comparison

Authors  Top 
  • Ready, J.
  • Kaschner, K.
  • South, A.B.
  • Eastwood, P.D.
  • Rees, T.
  • Rius, J.
  • Agbayani, E.
  • Kullander, S.
  • Froese, R.

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.

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