Spatial prediction of demersal fish distributions: enhancing our understanding of species–environment relationships
Author(s) -
Cordelia H. Moore,
Euan S. Harvey,
Kimberly P. Van Niel
Publication year - 2009
Publication title -
ices journal of marine science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.348
H-Index - 117
eISSN - 1095-9289
pISSN - 1054-3139
DOI - 10.1093/icesjms/fsp205
Subject(s) - demersal zone , demersal fish , habitat , species distribution , fish <actinopterygii> , environmental science , spatial distribution , ecology , fishery , environmental data , benthic zone , geography , biology , remote sensing
Moore, C. H., Harvey, E. S., and Van Niel, K. P. 2009. Spatial prediction of demersal fish distributions: enhancing our understanding of species-environment relationships. - ICES Journal of Marine Science, 66: 2068-2075.We used species distribution modelling to identify key environmental variables influencing the spatial distribution of demersal fish and to assess the potential of these species-environment relationships to predict fish distributions accurately. In the past, predictive modelling of fish distributions has been limited, because detailed habitat maps of deeper water (>10 m) have not been available. However, recent advances in mapping deeper marine environments using hydroacoustic surveys have redressed this limitation. At Cape Howe Marine National Park in southeastern Australia, previously modelled benthic habitats based on hydroacoustic and towed video data were used to investigate the spatial ecology of demersal fish. To establish the influence of environmental variables on the distributions of this important group of marine fish, classification trees (CTs) and generalized additive models (GAMs) were developed for four demersal fish species. Contrasting advantages were observed between the two approaches. CTs provided greater explained variation for three of the four species and revealed a better ability to model species distributions with complex environmental interactions. However, the predictive accuracy of the GAMs was greater for three of the four species. Both these modelling techniques provided a detailed understanding of demersal fish distributions and landscape linkages and an accurate method for predicting species distributions across unsampled locations where continuous spatial benthic data are available. Information of this nature will permit more-targeted fisheries management and more-effective planning and monitoring of marine protected areas.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom