Simulating disease risk for juvenile salmonids using a mechanistic framework to model the spring density of the parasite Ceratonova shasta


SOCIETY FOR INTEGRATIVE AND COMPARATIVE BIOLOGY
2021 VIRTUAL ANNUAL MEETING (VAM)
January 3 – Febuary 28, 2021

Meeting Abstract


62-2  Sat Jan 2  Simulating disease risk for juvenile salmonids using a mechanistic framework to model the spring density of the parasite Ceratonova shasta Robinson, HE*; Alexander, JD; Bartholomew, JL; Hallett, SL; Hetrick, NJ; Perry, RW; Som, NA; Humboldt State University, Arcata CA; Oregon State University, Corvalis OR; Oregon State University, Corvalis OR; Oregon State University, Corvalis OR; US Fish and Wildlife Service, Arcata CA; US Geological Survey, Cook WA; US Fish and Wildlife Service and Humboldt State University, Arcata CA hr573@humboldt.edu

The myxozoan parasite Ceratonova shasta is linked to low survival rates of juvenile salmonids. This parasite is endemic to the Pacific Northwest, and alternates between salmonid and annelid hosts as waterborne spore phases. In the Klamath River (CA), dams have created an “infectious zone” of elevated parasite density by limiting upstream passage of anadromous fishes that concentrates spawning and co-occurs with habitat suitable for annelids. The density of C. shasta spores is typically highest in the spring (March–June) when juvenile salmonids outmigrate through the infectious zone. Management approaches aim to disrupt the parasite’s lifecycle as abundance of spores directly influences disease risk for juvenile salmonids. Predicting spore density can be used to assess the impact of proposed management actions and to support existing tools that estimate population dynamics and disease-induced mortality of outmigrating salmon. We developed a model to predict the spring density of the parasite using both mechanistic (process-based) and statistical (correlative) relationships. The model captures seasonal features of C. shasta such as the initial detection of the parasite in the spring, temporal variability, and peak density. Using a mechanistic framework encapsulates the complex lifecycle and transmission dynamics for this aquatic parasite, and includes environmental parameters that are sensitive to management decisions. The predictive model can be used to evaluate the impact of future scenarios such as dam removal and climate change on disease risk.

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