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  • 1
    Publication Date: 2022-05-26
    Description: Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 18 (2012): 2756–2770, doi:10.1111/j.1365-2486.2012.02744.x.
    Description: Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adelie, Antarctica, by linking demographic models (stage-structured, seasonal, nonlinear, two-sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture-mark-recapture analysis, we find that seasonal sea ice concentration anomalies (SICa) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SICa, because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SICa. We identify an ensemble of 5 general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adelie. The output of this ensemble is used to produce stochastic forecasts of SICa, which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adelie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems.
    Description: MH acknowledges support through the National Science Foundation. HC acknowledges support from NSF Grant DEB-0816514, from the WHOI Arctic Research Initiative, and from the Alexander von Humboldt Foundation.
    Keywords: Stochastic matrix population model ; Stochastic climate forecast ; IPCC ; Uncertainties ; Sea ice ; Seabirds
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
    Format: application/pdf
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  • 2
    Publication Date: 2022-05-26
    Description: Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Philosophical Transactions of the Royal Society of London.Series B, Biological Sciences 372 (2017): 2016.0143, doi: 10.1098/rstb.2016.0143.
    Description: Climate changes include concurrent changes in environmental mean, variance and extremes, and it is challenging to understand their respective impact on wild populations, especially when contrasted age-dependent responses to climate occur. We assessed how changes in mean and standard deviation of sea surface temperature (SST), frequency and magnitude of warm SST extreme climatic events (ECE) influenced the stochastic population growth rate log(λs) and age structure of a black-browed albatross population. For changes in SST around historical levels observed since 1982, changes in standard deviation had a larger (threefold) and negative impact on log(λs) compared to changes in mean. By contrast, the mean had a positive impact on log(λs). The historical SST mean was lower than the optimal SST value for which log(λs) was maximized. Thus, a larger environmental mean increased the occurrence of SST close to this optimum that buffered the negative effect of ECE. This ‘climate safety margin’ (i.e. difference between optimal and historical climatic conditions) and the specific shape of the population growth rate response to climate for a species determine how ECE affect the population. For a wider range in SST, both the mean and standard deviation had negative impact on log(λs), with changes in the mean having a greater effect than the standard deviation. Furthermore, around SST historical levels increases in either mean or standard deviation of the SST distribution led to a younger population, with potentially important conservation implications for black-browed albatrosses.
    Description: Work carried out at Canyon des Sourcils Noirs was supported by Institut Paul Emile Victor (IPEV program no.109) and Terres Australes et Antarctiques Françaises. S.J. thanks support from NSF-Antarctic Sciences Division (project no. 1246407), the Grayce B. Kerr Fund and the Penzance Endowed Fund in Support of Assistant Scientists. D.P. PhD was supported by a grant from the French Research Minister CNRS-INEE.
    Keywords: Age ; Climate change ; IPCC ; Matrix population model ; Sensitivity analysis ; Survival
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
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