Combining tracking with at-sea surveys to improve occurrence and distribution estimates of two threatened seabirds in Peru. (21st November 2023) - Fischer, Johannes H.; Bose, Samhita; Romero, Cynthia; Charteris, Matt; Crowe, Patrick; Parker, Graham C.; Ray, Samantha; Rexer-huber, Kalinka; Sagar, Paul M.; Thompson, David R.; Bell, Elizabeth; Debski, Igor; et al.
Summary: Seabirds are highly threatened, including by fisheries bycatch. Accurate understanding of offshore distribution of seabirds is crucial to address this threat. Tracking technologies revolutionised insights into seabird distributions but tracking data may contain a variety of biases. We tracked two threatened seabirds (Salvin's Albatross Thalassarche salvini n = 60 and Black Petrel Procellaria parkinsoni n = 46) from their breeding colonies in Aotearoa (New Zealand) to their non-breeding grounds in South America, including Peru, while simultaneously completing seven surveys in Peruvian waters. We then used species distribution models to predict occurrence and distribution using either data source alone, and both data sources combined. Results showed seasonal differences between estimates of occurrence and distribution when using data sources independently. Combining data resulted in more balanced insights into occurrence and distributions, and reduced uncertainty. Most notably, both species were predicted to occur in Peruvian waters during all four annual quarters: the northern Humboldt upwelling system for Salvin's Albatross and northern continental shelf waters for Black Petrels. Our results highlighted that relying on a single data source may introduce biases into distribution estimates. Our tracking data might have contained ontological and/or colony-related biases (e.g. only breeding adults from one colony were tracked), while our survey data might have contained spatiotemporal biases (e.g. surveys were limited to waters <200 nm from the coast). We recommend combining data sources wherever possible to refine predictions of species distributions, which ultimately will improve fisheries bycatch management through better spatiotemporal understanding of risks.