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

• Published in: Fisheries research Vol. 169; pp. 26 - 36
• Main Authors: Jalali, M. Ali, Ierodiaconou, Daniel, Monk, Jacquomo, Gorfine, Harry, Rattray, Alex
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2015
• Subjects: Distribution modeling; Fishing grounds; GPS records; Haliotis; Haliotis rubra; LiDAR bathymetry; Marine; Australia; LiDAR bathymetry; GPS records; Distribution modeling; Fishing grounds; Haliotis rubra
• ISSN: 0165-7836; 1872-6763
• DOI: 10.1016/j.fishres.2015.04.009

•We model suitable fishing grounds for a commercial marine mollusc.•High resolution remotely sensed information from LiDAR and GPS were integrated.•Suitable grounds were predicted in shallow waters with complex reef structures.•Bathymetry and reef complexity had highest contribution in the predictive model.•Findings indicate a high overlap between diver-identified and predicted grounds.
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.