Ecological niche modelling of the distribution of cold-water coral habitat using underwater remote sensing data |
| |
Affiliation: | 1. Department of Earth and Ocean Sciences, National University of Ireland, Galway, Ireland;2. Marine Institute, Rinville, Oranmore, Galway, Ireland;3. Geological Survey of Norway, 7491 Trondheim, Norway;1. Instituto Español de Oceanografía, Centro Oceanográfico de Santander, Spain;2. Instituto Español de Oceanografía, Centro Oceanográfico de La Coruña, Spain;3. Instituto Español de Oceanografía, Centro Oceanográfico de Gijón, Spain;4. Instituto Español de Oceanografía, Centro Oceanográfico de Madrid, Spain;5. Institut de Ciències del Mar de Barcelona, CSIC, Spain;1. Marine Biology & Ecology Research Centre, Marine Institute, Plymouth University, Plymouth PL4 8AA, UK;2. Centre for the Environment, Fisheries and Aquaculture Science (Cefas), Pakefield Road, Lowestoft NR33 0HT, UK;1. National Institute of Water and Atmospheric Research, Wellington, New Zealand;2. Marine Conservation Institute, Seattle, USA |
| |
Abstract: | Despite a growing appreciation of the need to protect sensitive deep sea ecosystems such as cold-water corals, efforts to map the extent of their distribution are limited by their remoteness. Here we develop ecological niche models to predict the likely distributions of cold-water corals based on occurrence records and data describing environmental parameters (e.g. seafloor terrain attributes and oceanographic conditions). This study has used bathymetric data derived from ship-borne multibeam swath systems, species occurrence data from remotely operated vehicle video surveys and oceanographic parameters from hydrodynamic models to predict coral locations in regions where there is a paucity of direct observations. Predictions of the locations of the scleractinian coral, Lophelia pertusa are based primarily upon ecological niche modelling using a genetic algorithm. Its accuracy has been quantified at local (~ 25 km2) and regional scales (~ 4000 km2) along the Irish continental slope using a variety of error assessment techniques and a comparison with another ecological niche modelling technique. With appropriate choices of parameters and scales of analyses, ecological niche modelling has been effective in predicting the distributions of species at local and regional scales. Refinements of this approach have the potential to be particularly useful for ocean management given the need to manage areas of sensitive habitat where survey data are often limited. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|