Predicting the distribution of deep-sea vulnerable marine ecosystems using high-resolution data: Considerations and novel approaches

Anna M. Rengstorf, Christian Mohn, Colin Brown, Mary Wisz, Anthony J. Grehan

Research output: Contribution to journalJournal articleResearchpeer-review


Little is known about species distribution patterns in deep-sea environments, primarily because sampling surveys in the high seas are expensive and time consuming. The increasing need to manage and protect vulnerable marine ecosystems, such as cold-water corals, has motivated the use of predictive modelling tools, which produce continuous maps of potential species or habitat distribution from limited point observations and full coverage environmental data. Rapid advances in acoustic remote sensing, oceanographic modelling and sampling technology now provide high quality datasets, facilitating model development with high spatial detail. This paper provides a short overview of existing methodologies for predicting deep-sea benthic species distribution, and illustrates emerging issues related to spatial and thematic data resolution, and the use of transect-derived species distribution data. In order to enhance the ecological relevance and reliability of deep-sea species distribution models, novel techniques are presented based on a case study predicting the distribution of the cold-water coral Lophelia pertusa in three carbonate mound provinces in Irish waters. Specifically, the study evaluates (1) the capacity of newly developed high-resolution (250 m grid cell size) hydrodynamic variables to explain local scale cold-water coral distribution patterns, (2) the potential value of species occurrence proportion data to maintain semi-quantitative information of coral prevalence (i.e. coverage) and sampling effort per grid cell within the response variable, and (3) mixed effect modelling to deal with spatially grouped transect data. The study shows that predictive models using vertical and horizontal flow parameters perform significantly better than models based on terrain parameters only. Semiquantitative proportion data may decrease model uncertainty and increase model reliability, and provide a fruitful avenue of research for analysing large quantities of video data in a detailed yet time-efficient manner. The study concludes with an outlook of how species distribution models could improve our understanding of vulnerable marine ecosystem functioning and processes in the deep sea. (C) 2014 Elsevier Ltd. All rights reserved.
Original languageEnglish
JournalCurrent Biology
Issue number4
Pages (from-to)72-82
Publication statusPublished - 2014
Externally publishedYes


  • Cold-water corals
  • Generalized linear model
  • Hydrodynamic modelling
  • Lophelia pertusa
  • Species distribution modelling
  • Aquatic Science
  • Oceanography
  • Aquatic ecosystems
  • Fluid dynamics
  • Forecasting
  • Uncertainty analysis
  • Population distribution
  • carbonate mound province
  • deep-sea vulnerable marine ecosystem
  • ecological relevance
  • habitat distribution
  • high-resolution data
  • species distribution
  • thematic data resolution
  • Invertebrata Animalia (Animals, Invertebrates) - Cnidaria [41000] coral common Lophelia pertusa species
  • 00512, General biology - Conservation and resource management
  • 04500, Mathematical biology and statistical methods
  • 07502, Ecology: environmental biology - General and methods
  • 07508, Ecology: environmental biology - Animal
  • 07512, Ecology: environmental biology - Oceanography
  • 10515, Biophysics - Biocybernetics
  • 62800, Animal distribution
  • 64008, Invertebrata: comparative, experimental morphology, physiology and pathology - Cnidaria
  • Computational Biology
  • Ecology, Environmental Sciences
  • Population Studies
  • acoustic remote sensing applied and field techniques
  • generalized linear mixed model mathematical and computer techniques
  • mixed effect modeling mathematical and computer techniques
  • oceanographic modeling mathematical and computer techniques
  • Biogeography
  • Conservation
  • Marine Ecology
  • Models and Simulations


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