Holland
Marika M.
Holland
Marika M.
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ArticleThe call of the emperor penguin: legal responses to species threatened by climate change(Wiley, 2021-08-03) Jenouvrier, Stephanie ; Che-Castaldo, Judy ; Wolf, Shaye ; Holland, Marika M. ; Labrousse, Sara ; LaRue, Michelle ; Wienecke, Barbara ; Fretwell, Peter T. ; Barbraud, Christophe ; Greenwald, Noah ; Stroeve, Julienne ; Trathan, Phil N.Species extinction risk is accelerating due to anthropogenic climate change, making it urgent to protect vulnerable species through legal frameworks in order to facilitate conservation actions that help mitigate risk. Here, we discuss fundamental concepts for assessing climate change risks to species using the example of the emperor penguin (Aptenodytes forsteri), currently being considered for protection under the US Endangered Species Act (ESA). This species forms colonies on Antarctic sea ice, which is projected to significantly decline due to ongoing greenhouse gas (GHG) emissions. We project the dynamics of all known emperor penguin colonies under different GHG emission scenarios using a climate-dependent meta-population model including the effects of extreme climate events based on the observational satellite record of colonies. Assessments for listing species under the ESA require information about how species resiliency, redundancy and representation (3Rs) will be affected by threats within the foreseeable future. Our results show that if sea ice declines at the rate projected by climate models under current energy system trends and policies, the 3Rs would be dramatically reduced and almost all colonies would become quasi-extinct by 2100. We conclude that the species should be listed as threatened under the ESA.
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ArticleDetecting climate signals in populations across life histories(Wiley, 2021-12-20) Jenouvrier, Stephanie ; Long, Matthew C. ; Coste, Christophe F. D. ; Holland, Marika M. ; Gamelon, Marlène ; Yoccoz, Nigel G. ; Saether, Bernt-ErikClimate impacts are not always easily discerned in wild populations as detecting climate change signals in populations is challenged by stochastic noise associated with natural climate variability, variability in biotic and abiotic processes, and observation error in demographic rates. Detection of the impact of climate change on populations requires making a formal distinction between signals in the population associated with long-term climate trends from those generated by stochastic noise. The time of emergence (ToE) identifies when the signal of anthropogenic climate change can be quantitatively distinguished from natural climate variability. This concept has been applied extensively in the climate sciences, but has not been explored in the context of population dynamics. Here, we outline an approach to detecting climate-driven signals in populations based on an assessment of when climate change drives population dynamics beyond the envelope characteristic of stochastic variations in an unperturbed state. Specifically, we present a theoretical assessment of the time of emergence of climate-driven signals in population dynamics (ToEpop). We identify the dependence of (ToEpop)on the magnitude of both trends and variability in climate and also explore the effect of intrinsic demographic controls on (ToEpop). We demonstrate that different life histories (fast species vs. slow species), demographic processes (survival, reproduction), and the relationships between climate and demographic rates yield population dynamics that filter climate trends and variability differently. We illustrate empirically how to detect the point in time when anthropogenic signals in populations emerge from stochastic noise for a species threatened by climate change: the emperor penguin. Finally, we propose six testable hypotheses and a road map for future research.
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ArticleEffects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice mean state(American Geophysical Union, 2022-09-29) Smith, Madison M. ; Holland, Marika M. ; Petty, Alek A. ; Light, Bonnie ; Bailey, David A.Many modern sea ice models used in global climate models represent the subgrid‐scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea‐ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard 5 ice thickness categories and 15 ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year‐round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice volume. The spatial resolution of the ICESat‐2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat‐2; 2018–2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.
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ArticleThe Arctic freshwater system : changes and impacts(American Geophysical Union, 2007-11-20) White, Daniel ; Hinzman, Larry ; Alessa, Lilian ; Cassano, John ; Chambers, Molly ; Falkner, Kelly ; Francis, Jennifer ; Gutowski, William J. ; Holland, Marika M. ; Holmes, Robert M. ; Huntington, Henry ; Kane, Douglas ; Kliskey, Andrew ; Lee, Craig M. ; McClelland, James W. ; Peterson, Bruce J. ; Rupp, T. Scott ; Straneo, Fiamma ; Steele, Michael ; Woodgate, Rebecca ; Yang, Daqing ; Yoshikawa, Kenji ; Zhang, TingjunDramatic changes have been observed in the Arctic over the last century. Many of these involve the storage and cycling of fresh water. On land, precipitation and river discharge, lake abundance and size, glacier area and volume, soil moisture, and a variety of permafrost characteristics have changed. In the ocean, sea ice thickness and areal coverage have decreased and water mass circulation patterns have shifted, changing freshwater pathways and sea ice cover dynamics. Precipitation onto the ocean surface has also changed. Such changes are expected to continue, and perhaps accelerate, in the coming century, enhanced by complex feedbacks between the oceanic, atmospheric, and terrestrial freshwater systems. Change to the arctic freshwater system heralds changes for our global physical and ecological environment as well as human activities in the Arctic. In this paper we review observed changes in the arctic freshwater system over the last century in terrestrial, atmospheric, and oceanic systems.
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ArticleThe Community Climate System Model version 4(American Meteorological Society, 2011-10-01) Gent, Peter R. ; Danabasoglu, Gokhan ; Donner, Leo J. ; Holland, Marika M. ; Hunke, Elizabeth C. ; Jayne, Steven R. ; Lawrence, David M. ; Neale, Richard B. ; Rasch, Philip J. ; Vertenstein, Mariana ; Worley, Patrick H. ; Yang, Zong-Liang ; Zhang, MinghuaThe fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.
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PreprintEffects of climate change on an emperor penguin population : analysis of coupled demographic and climate models( 2012-06-21) Jenouvrier, Stephanie ; Holland, Marika M. ; Stroeve, Julienne ; Barbraud, Christophe ; Weimerskirch, Henri ; Serreze, Mark ; Caswell, HalSea 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.
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ArticlePan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise(Nature Publishing Group, 2017-10-10) Che-Castaldo, Christian ; Jenouvrier, Stephanie ; Youngflesh, Casey ; Shoemaker, Kevin T. ; Humphries, Grant ; McDowall, Philip ; Landrum, Laura ; Holland, Marika M. ; Li, Yun ; Ji, Rubao ; Lynch, Heather J.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.
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ArticleSpatio-temporal transferability of environmentally-dependent population models: Insights from the intrinsic predictabilities of Adélie penguin abundance time series(Elsevier, 2023-04-19) Şen, Bilgecan ; Che-Castaldo, Christian ; Krumhardt, Kristen M. ; Landrum, Laura ; Holland, Marika M. ; LaRue, Michelle A. ; Long, Matthew C. ; Jenouvrier, Stéphanie ; Lynch, Heather J.Ecological predictions are necessary for testing whether processes hypothesized to regulate species population dynamics are generalizable across time and space. In order to demonstrate generalizability, model predictions should be transferable in one or more dimensions, where transferability is the successful prediction of responses outside of the model data bounds. While much is known as to what makes spatially-oriented models transferable, there is no general consensus as to the spatio-temporal transferability of ecological time series models. Here, we examine whether the intrinsic predictability of a time series, as measured by its complexity, could limit such transferability using an exceptional long-term dataset of Adélie penguin breeding abundance time series collected at 24 colonies around Antarctica. For each colony, we select a suite of environmental variables from the Community Earth System Model, version 2 to predict population growth rates, before assessing how well these environmentally-dependent population models transfer temporally and how reliably temporal signals replicate through space. We show that weighted permutation entropy (WPE), a model-free measure of intrinsic predictability recently introduced to ecology, varies spatially across Adélie penguin populations, perhaps in response to stochastic environmental events. We demonstrate that WPE can strongly limit temporal predictive performance, although this relationship could be weakened if intrinsic predictability is not constant over time. Lastly, we show that WPE can also limit spatial forecast horizon, which we define as the decay in spatial predictive performance with respect to the physical distance between focal colony and predicted colony. Irrespective of intrinsic predictability, spatial forecast horizons for all Adélie penguin breeding colonies included in this study are surprisingly short and our population models often have similar temporal and spatial predictive performance compared to null models based on long-term average growth rates. For cases where time series are complex, as measured by WPE, and the transferability of biologically-motivated mechanistic models are poor, we advise that null models should instead be used for prediction. These models are likely better at capturing more generalizable relationships between average growth rates and long-term environmental conditions. Lastly, we recommend that WPE can provide valuable insights when evaluating model performance, designing sampling or monitoring programs, or assessing the appropriateness of preexisting datasets for making conservation management decisions in response to environmental change.