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

Jalali, M. Ali, Ierodiaconou, Daniel, Gorfine, Harry, Monk, Jacquomo and Rattray, Alex 2015, Exploring spatiotemporal trends in commercial fishing effort of an abalone fishing zone: a GIS-based hotspot model, PLoS One, vol. 10, no. 5, Article Number : e0122995, pp. 1-20, doi: 10.1371/journal.pone.0122995.

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Title Exploring spatiotemporal trends in commercial fishing effort of an abalone fishing zone: a GIS-based hotspot model
Author(s) Jalali, M. Ali
Ierodiaconou, DanielORCID iD for Ierodiaconou, Daniel orcid.org/0000-0002-7832-4801
Gorfine, Harry
Monk, JacquomoORCID iD for Monk, Jacquomo orcid.org/0000-0002-1874-0619
Rattray, Alex
Journal name PLoS One
Volume number 10
Issue number 5
Season Article Number : e0122995
Start page 1
End page 20
Total pages 20
Publisher Public Library of Science (PLOS)
Place of publication San Francisco, Calif.
Publication date 2015
ISSN 1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
SPATIAL-DISTRIBUTION
FISHERIES MANAGEMENT
HABITAT SUITABILITY
BATHYMETRIC LIDAR
HALIOTIS-RUBRA
VMS DATA
REEF
VICTORIA
AUSTRALIA
RESOURCES
Summary 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.
Language eng
DOI 10.1371/journal.pone.0122995
Field of Research 060205 Marine and Estuarine Ecology (incl Marine Ichthyology)
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, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079939

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.