Swimming depth of sea trout

1. Introduction

The Scottish Government has a target for 100% of the Scottish demand for electricity to be met from renewable resources by 2020 by creating a balanced portfolio of both onshore and offshore technologies. To ensure that the developments are ecologically sustainable, there is a need for data on relevant ecological matters, including the behaviour of fish in the vicinity of proposed offshore developments. The recent Annex IV 2016 State of the Science Report: Environmental Effects of Marine Renewable Energy Development Around the World review (available at http://tethys.pnnl.gov/sites/default/files/attachments/SoS-Report-Public_Review-Draft_highres.pdf) points out that "the possibility of marine animals colliding with dynamic components of MRE (marine renewable energy) devices is the greatest challenge to siting and permitting". While this concern is most strongly expressed in relation to marine mammals, similar risks apply to marine fish, for example diadromous species. These risks to fish species are partly dependent on their depth distribution at sea. Fish which predominantly occupy space close to the surface will be at less risk from bottom-mounted tidal turbines than fish that have a bentho-pelagic lifestyle.

The sea trout Salmo trutta (L.) is one of the fish species which occurs in areas with renewable energy potential, and it is of high conservation, commercial and recreational fishing importance. However, information on swimming depths of sea trout is scarce and not in a suitable format for direct incorporation into collision models for tidal turbines (see, for example Band [2015]), which require depth frequency information for different swimming depth bands. The need for information led to the author of this report being contracted by Marine Scotland to work on data he had collected in various studies on sea trout during their sea migration in Icelandic waters. The work consisted of compiling and analysing the raw data on swimming depth into a format suitable for use in risk assessments. The data presented give detailed insight into the observed main patterns in vertical distribution of sea trout.