Project: Antarctic Marine Predators in a Dynamic Climate

Project

Antarctic Marine Predators in a Dynamic Climate

September 2023 - December 2024

Personnel

Christian Che-Castaldo, Co-Principal Investigator

Participating Agencies

The accelerating pace of global change creates urgency to understand and predict climate impacts on ecosystems. In the Antarctic region, sea ice is a fundamental feature of the environment; projected sea ice loss has the potential to alter all levels of the food chain, from primary and secondary productivity to seabirds and marine mammals. A predictive capacity for ecosystems is critical to enable decision-making for conservation and resource management; such a capacity requires clear understanding of ecosystem function in the context of dynamic environmental conditions.

This project is a collaboration between the USGS, NASA the University of Wisconsin - Madison and the Woods Hole Oceanographic Institute. We use a comprehensive dataset on the abundance and distribution of four Antarctic key marine predators, made possible via satellite imagery, to captures the spatial and temporal population dynamics at the pan-Antarctic scale. We use this to evaluate the conditions sustaining marine “hotspots” -regions where biomass is exceptionally concentrated- which will contribute to our capacity to forecast the long-term resilience of Antarctic marine predators.

Research Publications

Research Publications	Publication Date
Bilgecan Şen, Che‐Castaldo, C., Lynch, H.J., Ventura, F., LaRue, M.A. and Stéphanie Jenouvrier (2024). Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability. Methods in Ecology and Evolution. doi:https://doi.org/10.1111/2041-210x.14423. ‌ \| Abstract \| Download \| Publisher Website	September 2024	1. Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise.<br><br>2. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie (<i>Pygoscelis adeliae</i>) and emperor penguin (<i>Aptenodytes forsteri</i>) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species.<br><br>3. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%).<br><br>4. rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance.
Bilgecan Şen, Che‐Castaldo, C. and H. Reşit Akçakaya (2024). The potential for species distribution models to distinguish source populations from sinks. Journal of Animal Ecology. doi:https://doi.org/10.1111/1365-2656.14201. ‌ \| Abstract \| Download \| Publisher Website	September 2024	1. While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this knowledge gap is criti- cal for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics.<br><br>2. Using linear regression, we associated previously published population level es- timates of intrinsic growth and abundance derived from a Bayesian analysis of mark-recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive per- formance. Each SDM was built with presence points obtained from eBird (a web- available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species.<br><br>3. We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suit- ability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks.<br><br>4. Habitat suitability have the potential to be a useful measure to indicate a popula- tion's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpreta- tion of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.

Presentations

Presentations	Presentation Date
Şen, B., S. Jenouvrier and C. Che-Castaldo. 2024. Quantifying the short- and long-term predictability of Adélie penguin populations. American Geophysical Union Conference, Washington DC.	December 2024
DuVivier, A.K., K. Krumhardt, L. Landrum, Z. Sylvester, C. Che-Castaldo, A. Eparvier, M. M. Holland, S. Jenouvrier, S. Labrousse, M. LaRue, C. Nissen, B. Şen and C. M. Brooks. 2025. Quantifying the value of Antarctic polynyas on the ecosystem from phytoplankton to penguins. American Meteorological Society Denver Summit, Denver, CO.	May 2025

Cooperative Fish and Wildlife Research Units Program: Education, Research and Technical Assistance for Managing Our Natural Resources

Project

Antarctic Marine Predators in a Dynamic Climate

September 2023 - December 2024

Personnel

Participating Agencies

Cooperative Fish and Wildlife Research Units Program:
Education, Research and Technical Assistance for Managing Our Natural Resources