2020 ESA Annual Meeting (August 3 - 6)

COS 39 Abstract - Developing a species risk indicator based on financial metrics to inform conservation prioritization

Judy Che-Castaldo, Conservation and Science, Lincoln Park Zoo, Chicago, IL, David S. Matteson, Department of Statistics and Data Science, Cornell University, Ithica, NY, Mila Getmansky Sherman, Finance Department, Isenberg School of Management, UMASS Amherst and Deborah Sunter, Department of Mechanical Engineering, Tufts University
Background/Question/Methods

Due to the severity of the biodiversity crisis and resource limitations, it has become critical to prioritize species conservation efforts. Application of modern portfolio theory from the financial sector suggests that prioritization simply based on species diversity may not always maximize conservation returns; contributions of individual species to ecosystem services should also be considered. Rather than focusing on the benefits of individual species, it may also be possible to prioritize efforts based on each species’ contribution to overall biodiversity declines. We apply the metric of marginal contribution to risk (MCTR) to the Breeding Bird Survey data from 1993 to 2017 to evaluate the effectiveness of this metric for assessing the risk of biodiversity loss. We also compare MCTR values for threatened and non-threatened species across trophic levels and at different spatial scales.

Results/Conclusions

We find that bird species with declining population trends tend to have higher MCTR values than expected, meaning they contribute more to the volatility of total species abundance after controlling for correlations among species trends. We also show that threatened species have higher MCTR values than non-threatened species, and this pattern is consistent across trophic levels and spatial scales. This project is the first step in a multi-disciplinary effort to explore the complex interconnectedness among risks in human-natural systems. We will next apply machine learning methods and high-dimensional network modeling to quantify connections among critical risk indicators from the fields of hydrology, agriculture, ecology, energy, and finance to predict potential system-wide failures as well as to inform mitigation strategies.