Modelling abundance hotspots for data-poor Irish Sea rays

• Published in: Ecological modelling Vol. 312; pp. 77 - 90
• Main Authors: Dedman, Simon, Officer, Rick, Brophy, Deirdre, Clarke, Maurice, Reid, David G.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2015
• Subjects: Abundance; Boosted regression trees; Data poor; Elasmobranch; Fish; Fishing; Marine; Mathematical models; Modelling; Rays; Sand; Skates; Teleostei; ANE, Irish Sea; Skates; Boosted regression trees; Elasmobranch; Data poor; Rays
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2015.05.010

•Spatial abundance predictions produced from limited data.•Abundance predictions convert easily to hotspots for MPA candidates.•Method reveals possible refuges and assesses prediction quality.•Boosted regression tree results align with stakeholder knowledge and other studies.•Protecting the most vulnerable species will benefit those less vulnerable. Skates and rays represent one of the most vulnerable components of fish communities in temperate demersal fisheries such as the Irish Sea. They also tend to be data poor in comparison to commercially exploited teleost fish. Spatial management has been suggested as an important tool to protect these species, but requires an understanding of the abundance distribution, and the relationship the abundance distribution has with the environment at both adult and juvenile life history stages. Here we modelled bottom trawl survey data using delta log-normal boosted regression trees to derive rays’ spatial abundance, and environmental links. The modelling approach allowed the development of high resolution predictive maps of abundance of four skate and ray species targeted by fishing activity: thornback, spotted, cuckoo and blonde rays. The distributions of these species were driven by a general preference for sand and coarser substrates as well as higher salinities, temperatures and currents speeds. Spatial comparisons between abundance distributions and locations of skate and ray commercial landings indicated that the main hotspots for the investigated species are outside of the main commercial fishing areas and overlap with potential MPAs proposed for wider ecosystem protection. The method offers a useful tool for selecting potential MPAs to assist the management and conservation of data-poor species.