Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships
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 distribution...
| Main Authors: | , , |
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| Format: | Journal Article |
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Oxford University Press 2009
2009
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| Online Access: | http://icesjms.oxfordjournals.org/content/66/9/2068.full.pdf http://hdl.handle.net/20.500.11937/21924 |
| _version_ | 1848750726842417152 |
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| author | Moore, Cordelia Harvey, Euan Van Niel, K. |
| author_facet | Moore, Cordelia Harvey, Euan Van Niel, K. |
| author_sort | Moore, Cordelia |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 fourdemersal 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. |
| first_indexed | 2025-11-14T07:41:25Z |
| format | Journal Article |
| id | curtin-20.500.11937-21924 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:41:25Z |
| publishDate | 2009 |
| publisher | Oxford University Press 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-219242017-01-30T12:28:14Z Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships Moore, Cordelia Harvey, Euan Van Niel, K. classification trees spatial ecology species distribution models generalized additive models 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 fourdemersal 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. 2009 Journal Article http://hdl.handle.net/20.500.11937/21924 http://icesjms.oxfordjournals.org/content/66/9/2068.full.pdf Oxford University Press 2009 restricted |
| spellingShingle | classification trees spatial ecology species distribution models generalized additive models Moore, Cordelia Harvey, Euan Van Niel, K. Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships |
| title | Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships |
| title_full | Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships |
| title_fullStr | Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships |
| title_full_unstemmed | Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships |
| title_short | Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships |
| title_sort | spatial prediction of demersal fish distributions: enhancing our understanding of species-environment relationships |
| topic | classification trees spatial ecology species distribution models generalized additive models |
| url | http://icesjms.oxfordjournals.org/content/66/9/2068.full.pdf http://hdl.handle.net/20.500.11937/21924 |