Table_2_A Method to Project Future Impacts From Threats and Conservation on the Probability of Extinction for North American Migratory Monarch (Danaus.DOCX (204.97 kB)
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Table_2_A Method to Project Future Impacts From Threats and Conservation on the Probability of Extinction for North American Migratory Monarch (Danaus plexippus) Populations.DOCX

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posted on 07.11.2019, 06:57 authored by Kristen J. Voorhies, Jennifer Szymanski, Kelly R. Nail, Mason Fidino

The United States Fish and Wildlife Service is currently evaluating the monarch butterfly (Danaus plexippus) for listing under the Endangered Species Act and using the Species Status Assessment (SSA) framework to estimate and forecast drivers that impact the species' risk of extinction. To evaluate eastern and western monarch populations the monarch SSA built on a foundation of published population models and other literature to identify current growth rates and information on threats and conservation efforts. Here we present the resulting methodology, which aimed to explore the magnitude of monarch population responses to the aggregation of multiple drivers under various scenarios. Our methodology differs from previous research by developing a series of functional cause and effect relationships that link monarch population-specific responses to threats or conservation actions. We incorporated these population-specific responses into stochastic geometric growth models for both eastern and western populations to estimate the probability of quasi-extinction in 50 years. Our models were parameterized using previously estimated population-specific trend data (growth rates and environmental variability) and expert elicited estimates of population responses to multiple drivers (i.e., amount of available breeding and overwintering habitat, insecticide use, migration resource availability, and climate change). We explored plausible future scenarios with realistic place-holder data to evaluate how changes in these drivers influenced monarch quasi-extinction risk for each population. In addition, we captured uncertainty in quasi-extinction risk by calculating cumulative quasi-extinction risk over a full range of quasi-extinction threshold values which were sampled from a uniform distribution bounded by expert-elicited estimates. In both populations, our baseline for comparison was the “current” condition defined by population-specific growth rate and environmental stochasticity from previous research. The result of the methodology presented here is a novel and comprehensive tool that incorporates the impact of future stressors into projections of population numbers over time. The approach provides a tractable and updatable tool that includes multiple types of information and the associated uncertainty of drivers, population impacts, and risk of extinction. For monarchs, this tool will be critical for incorporating the best scientific and commercial information available in the upcoming listing decision.

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