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  • 1
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    Climate change threatens polar bear populations : a stochastic demographic analysis (2011)
    Ecological Society of America
    Details
    Publication Date: 2022-05-25
    Description: Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 91 (2010): 2883–2897, doi:10.1890/09-1641.1.
    Description: The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture–recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001–2003) and population decline in years with less ice coverage (2004–2005). LTRE (life table response experiment) analysis showed that the reduction in λ in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log λs, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log λs ≈ − 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with “business as usual” (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act.
    Description: We acknowledge primary funding for model development and analysis from the U.S. Geological Survey and additional funding from the National Science Foundation (DEB-0343820 and DEB-0816514), NOAA, the Ocean Life Institute and the Arctic Research Initiative at WHOI, and the Institute of Arctic Biology at the University of Alaska–Fairbanks. Funding for the capture–recapture effort in 2001–2006 was provided by the U.S. Geological Survey, the Canadian Wildlife Service, the Department of Environment and Natural Resources of the Government of the Northwest Territories, and the Polar Continental Shelf Project, Ottawa, Canada.
    Keywords: Climate change ; Demography ; IPCC ; LTRE analysis ; Matrix population models ; Polar bear ; Sea ice ; Stochastic growth rate ; Stochastic models ; Ursus maritimus
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
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    Mating behavior, population growth, and the operational sex ratio : a periodic two‐sex model approach (2010)
    University of Chicago Press
    Details
    Publication Date: 2022-05-25
    Description: Author Posting. © University of Chicago, 2010. This article is posted here by permission of University of Chicago for personal use, not for redistribution. The definitive version was published in American Naturalist 175 (2010): 739-752, doi:10.1086/652436.
    Description: We present a new approach to modeling two‐sex populations, using periodic, nonlinear two‐sex matrix models. The models project the population growth rate, the population structure, and any ratio of interest (e.g., operational sex ratio). The periodic formulation permits inclusion of highly seasonal behavioral events. A periodic product of the seasonal matrices describes annual population dynamics. The model is nonlinear because mating probability depends on the structure of the population. To study how the vital rates influence population growth rate, population structure, and operational sex ratio, we used sensitivity analysis of frequency‐dependent nonlinear models. In nonlinear two‐sex models the vital rates affect growth rate directly and also indirectly through effects on the population structure. The indirect effects can sometimes overwhelm the direct effects and are revealed only by nonlinear analysis. We find that the sensitivity of the population growth rate to female survival is negative for the emperor penguin, a species with highly seasonal breeding behavior. This result could not occur in linear models because changes in population structure have no effect on per capita reproduction. Our approach is applicable to ecological and evolutionary studies of any species in which males and females interact in a seasonal environment.
    Description: H.C. acknowledges support from the National Science Foundation (DEB-0343820 and DEB-0816514) and the Ocean Life Institute and the hospitality of the Max Planck Institute for Demographic Research.
    Keywords: Two‐sex periodic matrix model ; Population structure ; Population growth rate ; Mating systems ; Sex ratio ; Emperor penguin
    Repository Name: Woods Hole Open Access Server
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  • 3
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    The COMPADRE Plant Matrix Database : an open online repository for plant demography (2015)
    John Wiley & Sons
    Details
    Publication Date: 2022-05-25
    Description: © The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Ecology 103 (2015): 202–218, doi:10.1111/1365-2745.12334.
    Description: Schedules of survival, growth and reproduction are key life-history traits. Data on how these traits vary among species and populations are fundamental to our understanding of the ecological conditions that have shaped plant evolution. Because these demographic schedules determine population growth or decline, such data help us understand how different biomes shape plant ecology, how plant populations and communities respond to global change and how to develop successful management tools for endangered or invasive species. Matrix population models summarize the life cycle components of survival, growth and reproduction, while explicitly acknowledging heterogeneity among classes of individuals in the population. Matrix models have comparable structures, and their emergent measures of population dynamics, such as population growth rate or mean life expectancy, have direct biological interpretations, facilitating comparisons among populations and species. Thousands of plant matrix population models have been parameterized from empirical data, but they are largely dispersed through peer-reviewed and grey literature, and thus remain inaccessible for synthetic analysis. Here, we introduce the compadre Plant Matrix Database version 3.0, an open-source online repository containing 468 studies from 598 species world-wide (672 species hits, when accounting for species studied in more than one source), with a total of 5621 matrices. compadre also contains relevant ancillary information (e.g. ecoregion, growth form, taxonomy, phylogeny) that facilitates interpretation of the numerous demographic metrics that can be derived from the matrices. Large collections of data allow broad questions to be addressed at the global scale, for example, in genetics (genbank), functional plant ecology (try, bien, d3) and grassland community ecology (nutnet). Here, we present compadre, a similarly data-rich and ecologically relevant resource for plant demography. Open access to this information, its frequent updates and its integration with other online resources will allow researchers to address timely and important ecological and evolutionary questions.
    Keywords: Big data ; Comparative approach ; Elasticity ; Matrix population model ; Open access ; Plant population and community dynamics ; Population growth rate ; Sensitivity ; Transient dynamics
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 4
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    Effects of climate change on an emperor penguin population : analysis of coupled demographic and climate models (2012)
    Details
    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
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