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Exploring spatiotemporal trends in commercial fishing effort of an abalone fishing zone: a GIS-based hotspot model

Citation

Jalali, MA and Ierodiaconou, D and Gorfine, H and Monk, J and Rattray, A, Exploring spatiotemporal trends in commercial fishing effort of an abalone fishing zone: a GIS-based hotspot model, PLoS ONE, 10, (5) Article e0122995. ISSN 1932-6203 (2015) [Refereed Article]


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Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1371/journal.pone.0122995

Abstract

Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics.

Item Details

Item Type:Refereed Article
Keywords:LiDAR, abalone, commercial fishing, suitable habitat
Research Division:Agricultural and Veterinary Sciences
Research Group:Fisheries Sciences
Research Field:Aquatic Ecosystem Studies and Stock Assessment
Objective Division:Environment
Objective Group:Flora, Fauna and Biodiversity
Objective Field:Marine Flora, Fauna and Biodiversity
UTAS Author:Monk, J (Dr Jacquomo Monk)
ID Code:100808
Year Published:2015
Web of Science® Times Cited:11
Deposited By:IMAS Research and Education Centre
Deposited On:2015-05-29
Last Modified:2017-11-04
Downloads:184 View Download Statistics

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