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Predictive mapping of abalone fishing grounds using remotely-sensed LiDAR and commercial catch data

Jalali, M. Ali, Ierodiaconou, Daniel, Monk, Jacquomo, Gorfine, Harry and Rattray, Alex 2015, Predictive mapping of abalone fishing grounds using remotely-sensed LiDAR and commercial catch data, Fisheries research, vol. 169, pp. 26-36, doi: 10.1016/j.fishres.2015.04.009.

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Title Predictive mapping of abalone fishing grounds using remotely-sensed LiDAR and commercial catch data
Author(s) Jalali, M. Ali
Ierodiaconou, DanielORCID iD for Ierodiaconou, Daniel orcid.org/0000-0002-7832-4801
Monk, JacquomoORCID iD for Monk, Jacquomo orcid.org/0000-0002-1874-0619
Gorfine, Harry
Rattray, Alex
Journal name Fisheries research
Volume number 169
Start page 26
End page 36
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-09-01
ISSN 0165-7836
1872-6763
Keyword(s) Distribution modeling
LiDAR bathymetry
GPS records
Fishing grounds
Haliotis rubra
Summary Defining the geographic extent of suitable fishing grounds at a scale relevant to resource exploitation for commercial benthic species can be problematic. Bathymetric light detection and ranging (LiDAR) systems provide an opportunity to enhance ecosystem-based fisheries management strategies for coastally distributed benthic fisheries. In this study we define the spatial extent of suitable fishing grounds for the blacklip abalone (Haliotis rubra) along 200 linear kilometers of coastal waters for the first time, demonstrating the potential for integration of remotely-sensed data with commercial catch information. Variables representing seafloor structure, generated from airborne bathymetric LiDAR were combined with spatially-explicit fishing event data, to characterize the geographic footprint of the western Victorian abalone fishery, in south-east Australia. A MaxEnt modeling approach determined that bathymetry, rugosity and complexity were the three most important predictors in defining suitable fishing grounds (AUC = 0.89). Suitable fishing grounds predicted by the model showed a good relationship with catch statistics within each sub-zone of the fishery, suggesting that model outputs may be a useful surrogate for potential catch.
Language eng
DOI 10.1016/j.fishres.2015.04.009
Field of Research 060205 Marine and Estuarine Ecology (incl Marine Ichthyology)
070402 Aquatic Ecosystem Studies and Stock Assessment
0704 Fisheries Sciences
0502 Environmental Science And Management
0602 Ecology
Socio Economic Objective 960507 Ecosystem Assessment and Management of Marine Environments
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075638

Document type: Journal Article
Collection: School of Life and Environmental Sciences
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Created: Mon, 30 Nov 2015, 14:51:38 EST

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