Date: Thursday, September 18, 2025 at 1-2pm ET
On September 18, 2025, the National Institute of Statistical Sciences (NISS) and the Canadian Statistical Sciences Institute (CANSSI) hosted a session of the NISS-CANSSI Collaborative Data Science Webinar Series titled “In the Right Place at the Right Time: Modeling to Guide Ancient Murrelet Conservation.” The webinar featured presentations by Dr. Laura Cowen and Dr. Patrick O’Hara and examined how statistical methodology and data science could be leveraged to support conservation efforts for the Ancient Murrelet, a seabird species experiencing significant population declines.
The webinar opened with introductory remarks by Dr. Saman Muthukumarana, who framed the session within the broader goals of the NISS-CANSSI Collaborative Data Science initiative. He emphasized the value of interdisciplinary collaboration between statisticians and domain scientists, particularly in ecological and environmental applications where data are often sparse, complex, and subject to substantial uncertainty. Dr. Muthukumarana highlighted the Ancient Murrelet as an example of how statistical innovation could directly inform conservation planning and policy.
Ecological Framework Overview
Dr. Patrick O’Hara provided an overview of the ecological context for Ancient Murrelet conservation. He described the species’ life history, including its reliance on marine environments for foraging and island-based colonies for breeding. Dr. O’Hara outlined the primary threats facing the species, such as oil and plastic pollution, climate change, fisheries bycatch, introduced mammalian predators, and habitat loss. He noted that more than half of the global Ancient Murrelet population, estimated at approximately 265,000 breeding pairs, nested in the Haida Gwaii archipelago, underscoring the regional and global importance of effective conservation strategies in this area.
Precision – Spatiotemporal Modeling
Dr. O’Hara discussed the importance of spatiotemporal modeling in improving the precision of ecological inference. He explained how integrating spatial and temporal data allowed researchers to better understand when and where Ancient Murrelets were most vulnerable to environmental threats. Increased precision in these models enabled more accurate identification of high-risk areas and periods, supporting targeted mitigation strategies and more efficient allocation of conservation resources.
Precision – Population Dynamics and Trends
Continuing the theme of precision, Dr. O’Hara addressed the role of statistical modeling in estimating population dynamics and long-term trends. He described how improved estimates of survivorship and recruitment were critical for detecting changes in population trajectories that might otherwise go unnoticed. By increasing precision in these estimates, researchers were better positioned to assess whether conservation interventions were effective and to identify when additional measures were required.
Movement Modeling: Study 1 – Ancient Murrelet Foraging Behavior
Dr. Laura Cowen presented the first of two collaborative studies, which focused on Ancient Murrelet foraging behavior. She described how movement data were analyzed using hidden Markov models to identify behavioral states along foraging routes. This approach allowed researchers to distinguish between transit and foraging behaviors and to characterize key foraging habitats. Dr. Cowen emphasized that understanding where and how Ancient Murrelets foraged was essential for identifying habitat features critical to the species’ survival and for mitigating threats encountered at sea.
Study 2 – Ancient Murrelet Population Trends
Dr. Cowen then discussed a second study examining Ancient Murrelet population trends using multi-event and ecological modeling techniques. This work focused on estimating survival probabilities for both breeding adults and juvenile birds. She highlighted how these models accommodated uncertainty in observation processes and life-history transitions, resulting in more robust estimates of population vital rates. These findings contributed to a clearer understanding of population change over time and informed conservation decision-making.
Contributions to Ancient Murrelet Conservation
In closing, Dr. O’Hara summarized how the studies presented contributed to Ancient Murrelet conservation. He emphasized that integrating statistical methods with ecological knowledge improved understanding of the species as an indicator of broader environmental change driven by anthropogenic activity and climate change. The research demonstrated how rigorous statistical modeling could inform conservation policy, guide habitat protection efforts, and support evaluation of management actions.
Thanks & Acknowledgements
NISS and CANSSI thanked Dr. Laura Cowen and Dr. Patrick O’Hara for their presentations and for their ongoing collaborative contributions at the intersection of statistics, ecology, and conservation science. Appreciation was also extended to Dr. Saman Muthukumarana for moderating the session and to all participants who engaged in the discussion. The success of this webinar reflected the continued commitment of NISS and CANSSI to advancing interdisciplinary, data-driven approaches to complex environmental challenges.
Recordings of the webinar were made available online to support continued learning and engagement within the statistical and environmental research communities.
About the NISS-CANSSI Collaborative Data Science Web Series:
The NISS-CANSSI Collaborative Data Science initiative that the National Institute of Statistical Sciences (NISS) in collaboration with the Canadian Statistical Sciences Institute (CANSSI) brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.
Goals of the Initiative
The goal is to foster progress in:
- Developing new ideas for experimental and observational data-driven learning and discovery that address key questions at the cutting edge of science and scientific deduction;
- Quantifying and summarizing uncertainty in data-driven theories, as well as complex Data Science models, algorithms, and workflows; and
- Establishing new practices for scientific reproducibility and replicability through Data Science.
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