Divergent post-breeding spatial habitat use of Laysan and black-footed albatross

Abstract

Understanding the at-sea movements of wide-ranging seabird species throughout their annual cycle is essential for their conservation and management. Habitat use and resource partitioning of Laysan (Phoebastria immutabilis) and black-footed (Phoebastria nigripes) albatross are well-described during the breeding period but are less understood during the post-breeding period, which represents ~40% of their annual cycle. Resource partitioning may be reduced during post-breeding, when birds are not constrained to return to the nest site regularly and can disperse to reduce competitive pressure. We assessed the degree of spatial segregation in the post-breeding distributions of Laysan (n = 82) and black-footed albatrosses (n = 61) using geolocator tags between 2008 and 2012 from two large breeding colonies in the Northwestern Hawaiian Islands, Midway Atoll, and Tern Island. We characterized the species-and colony-specific foraging and focal distributions (represented by the 95 and 50th density contours, respectively) and quantified segregation in at-sea habitat use between species and colonies. Laysan and black-footed albatross showed consistent and significant at-sea segregation in focal areas across colonies, indicating that resource partitioning persists during post-breeding. Within breeding colonies, segregation of foraging areas between the two species was more evident for birds breeding at Tern Island. Spatial segregation decreased as the post-breeding season progressed, when spatial distributions of both species became more dispersed. In contrast to studies conducted on breeding Laysan and black-footed albatross, we found that sea surface temperature distinguished post-breeding habitats of black-footed albatrosses between colonies, with black-footed albatrosses from Midway Atoll occurring in cooler waters (3.6°C cooler on average). Our results reveal marked at-sea segregation between Laysan and black-footed albatross breeding at two colonies during a critical but understudied phase in their annual cycle. The observed variation in species-environment relationships underscores the importance of sampling multiple colonies and temporal periods to more thoroughly understand the spatial distributions of pelagic seabirds.

Unseen overlap between fishing vessels and top predators in the northeast Pacific

“…Boosted regression tree models ( 66 ) were built using extensive telemetry datasets ( 18 – 20 ) combined with daily dynamic environmental data from Copernicus Marine Environmental Monitoring Service (CMEMS). Species included blue ( Prionace glauca ), mako ( Isurus oxyrinchus ), salmon ( Lamna ditropis ), and white ( Carcharodon carcharias ) sharks; albacore ( Thunnus alalunga ), yellowfin ( Thunnus albacares ), and bluefin ( T. orientalis ) tunas; black-footed ( Phoebastria nigripes ) and Laysan ( Phoebastria immutabilis ) albatrosses; sooty shearwaters ( Ardenna grisea ); blue whales ( Balaenoptera musculus ); California sea lions ( Zalophus californianus ); elephant seals ( Mirounga angustirostris ); and leatherback turtles ( Dermochelys coriacea ).…”

“…Unseen ( 16 ) and observed ( 6 ) fishing vessel activity were overlaid with the predicted distributions of 14 top predator species to quantify and map human-wildlife risk (i.e., overlap) from 2017 to 2022. Species distribution models ( 17 ) were derived from extensive telemetry datasets ( 18 – 20 ) for tunas, sharks, seabirds, mammals, and leatherback turtles. Of the 14 predators investigated, nine are listed as near-threatened, vulnerable, endangered, or critically endangered by the International Union for the Conservation of Nature (IUCN), with blue whales and leatherback turtles also listed endangered under the Endangered Species Act (ESA) (table S1).…”

Peri‐urban systems alter trophic niche size and overlap in sympatric coastal bird species

“…To account for individual differences and variability in the length of GPS deployments, we averaged the kernel utilization distribution estimation across individuals of each species within each colony. We used the 95% and 50% UDOI and BA to represent the spatial overlap between species over most of their foraging range and in their core foraging areas, respectively (Berlincourt & Arnould, 2015; Jordan et al, 2022; Reisinger et al, 2020). The approximate area (in square kilometers) of most of the foraging range and core foraging areas was also calculated and compared between species and colonies.…”