A probabilistic time geographic approach to quantifying seabird-vessel interactions

Citation
Rutter JD, Borrelle SB, Bose S, et al (2024) A probabilistic time geographic approach to quantifying seabird-vessel interactions. Animal Conservation early view: https://doi.org/10.1111/acv.12961
Abstract

Accounting for uncertainty is essential for precautionary approaches to managing seabird bycatch in commercial fisheries. However, there is no existing mechanism to explicitly quantify the uncertainty of seabird-vessel interactions (i.e. co-occurrence in space and time). Here we develop a time geographic method to measure the probability of individual birds encountering (co-occurring within 30 km) and attending (within 5 km) individual fishing vessels. The approach involves creating voxel-based probabilistic space–time prisms (PSTPs) to model the movements of individual birds and vessels, with trajectory data from bird-borne GPS devices and vessel Automatic Identification Systems (AIS). We intersected these PSTPs to quantify the probability of interaction between bird-vessel pairs over time and space. We demonstrate the approach with a case study of interactions of Endangered Toroa (Antipodean Albatross; Diomedea antipodensis antipodensis) with pelagic longline vessels in part of the South Pacific high seas. We found 15 vessels within 150 km and 3 h of two birds, yet interaction occurred with only two of those vessels. We visualised the probability of encounter and attendance over time and space and determined that interactions lasted several hours each (up to 6.2–14.1 h attendance, 20.8–26.1 h encounter for one bird-vessel pair). Our time geographic approach adds to existing tools to quantify seabird bycatch risk by providing an explicit measure of uncertainty of seabird-vessel interactions. We provide a flexible methodological pathway and R scripts, the application of which would allow managers to estimate interaction probability for multiple marine species and fisheries, including those with lower-resolution positional datasets.