Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise

Abstract

Colonially-breeding seabirds have long served as indicator species for the health of the oceans on which they depend. Abundance and breeding data are repeatedly collected at fixed study sites in the hopes that changes in abundance and productivity may be useful for adaptive management of marine resources, but their suitability for this purpose is often unknown. To address this, we fit a Bayesian population dynamics model that includes process and observation error to all known Adélie penguin abundance data (1982–2015) in the Antarctic, covering >95% of their population globally. We find that process error exceeds observation error in this system, and that continent-wide “year effects” strongly influence population growth rates. Our findings have important implications for the use of Adélie penguins in Southern Ocean feedback management, and suggest that aggregating abundance across space provides the fastest reliable signal of true population change for species whose dynamics are driven by stochastic processes.

Density dependence forces divergent population growth rates and alters occupancy patterns of a central place foraging Antarctic seabird

“…Our demonstration of density-dependent population regulation for Adélie penguins in East Antarctic matches similar conclusions from studies in the Ross Sea and Antarctic Peninsula regions (Che-Castaldo et al, 2017;Lyver et al, 2014), suggesting that density-dependent processes may operate throughout the species' circumpolar distribution.…”

“…In response to growing concern for how a future changing environment will affect biota worldwide (Walther et al, ), there has been a strong focus by ecologists to develop quantitative models to predict the future trajectory and state of species' distributions and populations, with several studies focussing on Antarctic penguins (Ainley et al, ; Ballerini, Tavecchia, Pezzo, Jenouvrier, & Olmastroni, ; Che‐Castaldo et al, ; Cimino, Lynch, Saba, & Oliver, ; Jenouvrier et al, , , ). Our study highlights the importance of incorporating nonconstant species–environment relationships in predictive models.…”

“…In response to growing concern for how a future changing environment will affect biota worldwide (Walther et al, 2002), there has been a strong focus by ecologists to develop quantitative models to predict the future trajectory and state of species' distributions and populations, with several studies focussing on Antarctic penguins Ballerini, Tavecchia, Pezzo, Jenouvrier, & Olmastroni, 2015;Che-Castaldo et al, 2017;Cimino, Lynch, Saba, & Oliver, 2016;Jenouvrier et al, 2009Jenouvrier et al, , 2012Jenouvrier et al, , 2014 (Elith & Leathwick, 2009). Our finding that Adélie penguins breeding in mountain nunatak habitats occur not only at higher occupancy rates, but also on steeper slopes and greater distances inland that are unoccupied in other regional populations, demonstrates that occupancy and habitat use are density-dependent and can vary under certain conditions for this species.…”

Comparative population genomics reveals key barriers to dispersal in Southern Ocean penguins

“…For emperor penguins, there were six observations of colony establishment or relocation in a period of just five years, again providing direct evidence of dispersal (LaRue et al., ). Large fluctuations in abundance at individual colonies from year to year have been observed in both emperor (Kooyman & Ponganis, ) and Adélie penguins (Che‐Castaldo et al., ; Dugger, Ainley, Lyver, Barton, & Ballard, ), indicating either dispersal or a high incidence of skipped breeding. Overall, there is a growing body of evidence indicating that dispersal in many species of penguins is a regular occurrence.…”

Sea ice predicts long‐term trends in Adélie penguin population growth, but not annual fluctuations: Results from a range‐wide multiscale analysis

“…To generate the sea ice covariates for each Adélie penguin colony, we extracted preprocessed monthly sea ice concentration data from the National Snow and Ice Data Center (Cavalieri, Parkinson, Gloersen, & Zwally, 2018) using methods outlined in Che‐Castaldo et al (2017). Sea ice concentration (SIC) is directly observed using passive microwave radiation and is provided at grid cells of 25 × 25 km.…”