Ornithogenic sedimentary profiles of n-alkanes and PAHs constrain breeding penguin population dynamics at Cape Bird, Ross Island, Antarctica, over the past 1,500 years

Ornithogenic sediment may contain important paleoecological information concerning past bird population dynamics. In this paper, we analyse the distribution of n-alkanes and polycyclic aromatic hydrocarbons (PAHs) in two sedimentary profiles collected from abandoned penguin colonies at Cape Bird, Ross Island, Antarctica. The geochronology of the sediment profiles was determined using ²¹⁰Pb and AMS ¹⁴C dating techniques, and spans the past 1500 years. We observe low levels of n-alkanes (0.93–2.67 μg g⁻¹) and PAHs (20.5–46.8 ng g⁻¹) concentrations dominated by short-chain n-alkanes, low-molecular-weight and alkyl PAHs. Principal Component Analysis (PCA) shows that the PC 1 scores for n-alkanes and PAHs are significantly correlated with the input intensity of penguin guano and penguin bioelements. Using these proxies in combination with Generalized Additive Model (GAM), we reveal that breeding penguin population growth reaches a historical peak during the Medieval Climate Anomaly (MCA) prior to 1550 CE. This growth in population size corresponds to the extent of sea ice cover and the activity of the local atmospheric-ocean circulation. Warming induced by El Niño and the positive phase of the Southern Annular Mode, along with the expansion of ice-free zones and increased oceanic nutrient and food availability, are considered primary factors contributing to the growth of breeding penguin populations. Additionally, variations in sea ice extent and Amundsen Sea Low significantly influence penguin population dynamics. Our study suggests that n-alkanes and PAHs may be valuable organic proxies for reconstructing historical changes in breeding penguin populations, with the local sea ice extent and atmospheric-oceanic circulation exerting a major influence on Antarctic penguin population dynamics. Copyright © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.