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Using Predicted Patterns of 3D Prey Distribution to Map King Penguin Foraging Habitat

Frontiers in Marine Science 2021 6 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 30 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Roland Proud, Yan Ropert‐Coudert, Camille Le Guen, Norman Ratcliffe, Camille Le Guen, Yan Ropert‐Coudert, Andrew S. Brierley Simon Jarman, Richard B. Sherley, Richard B. Sherley, Akiko Kato, Yan Ropert‐Coudert, Norman Ratcliffe, Lars Boehme, Simon Jarman, Andrew S. Brierley Adam J. Wyness, Roland Proud, John P. Y. Arnould, Ryan A. Saunders, Paul G. Fernandes, Lars Boehme, Ryan A. Saunders, Ryan A. Saunders, Andrew S. Brierley Akiko Kato, John P. Y. Arnould, Richard B. Sherley, Yan Ropert‐Coudert, Andrew S. Brierley

Summary

Researchers combined acoustic surveys of prey distribution with GPS tracking of king penguin foraging behavior around South Georgia Island. Understanding penguin foraging habitat is relevant to assessing how plastic-contaminated prey could expose these birds to microplastics through their diet.

Body Systems
Study Type Environmental

King penguins ( Aptenodytes patagonicus ) are an iconic Southern Ocean species, but the prey distributions that underpin their at-sea foraging tracks and diving behaviour remain unclear. We conducted simultaneous acoustic surveys off South Georgia and tracking of king penguins breeding ashore there in Austral summer 2017 to gain insight into habitat use and foraging behaviour. Acoustic surveys revealed ubiquitous deep scattering layers (DSLs; acoustically detected layers of fish and other micronekton that inhabit the mesopelagic zone) at c. 500 m and shallower ephemeral fish schools. Based on DNA extracted from penguin faecal samples, these schools were likely comprised of lanternfish (an important component of king penguin diets), icefish ( Channichthyidae spp.) and painted noties ( Lepidonotothen larseni ). Penguins did not dive as deep as DSLs, but their prey-encounter depth-distributions, as revealed by biologging, overlapped at fine scale (10s of m) with depths of acoustically detected fish schools. We used neural networks to predict local scale (10 km) fish echo intensity and depth distribution at penguin dive locations based on environmental correlates, and developed models of habitat use. Habitat modelling revealed that king penguins preferentially foraged at locations predicted to have shallow and dense (high acoustic energy) fish schools associated with shallow and dense DSLs. These associations could be used to predict the distribution of king penguins from other colonies at South Georgia for which no tracking data are available, and to identify areas of potential ecological significance within the South Georgia and the South Sandwich Islands marine protected area.

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