Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model.
M Ali Jalali, Daniel Ierodiaconou, Harry Gorfine, Jacquomo Monk, Alex Rattray
Author Information
M Ali Jalali: Deakin University, Centre for Integrative Ecology, School of Life and Environmental Sciences, Faculty of Science, Engineering and Built Environment, Warrnambool, Victoria, Australia.
Daniel Ierodiaconou: Deakin University, Centre for Integrative Ecology, School of Life and Environmental Sciences, Faculty of Science, Engineering and Built Environment, Warrnambool, Victoria, Australia.
Harry Gorfine: Department of Environment and Primary Industries, DEPI Queenscliff Centre, Queenscliff, Victoria, Australia.
Jacquomo Monk: Deakin University, Centre for Integrative Ecology, School of Life and Environmental Sciences, Faculty of Science, Engineering and Built Environment, Warrnambool, Victoria, Australia; Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 49, Hobart, Tasmania, Australia.
Alex Rattray: Deakin University, Centre for Integrative Ecology, School of Life and Environmental Sciences, Faculty of Science, Engineering and Built Environment, Warrnambool, Victoria, Australia; Dipartimento di Biologia, Università di Pisa, Pisa, Italy.
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.
References
Philos Trans R Soc Lond B Biol Sci. 2010 Jul 27;365(1550):2177-85
[PMID: 20566495]
Philos Trans R Soc Lond B Biol Sci. 2010 Jul 27;365(1550):2157-62
[PMID: 20566493]
