Nicholas A. Som

Flow variation and substrate type affect dislodgement of the freshwater polychaete, Manayunkia speciosa

Abstract.  We quantified microscale flow forces and their ability to entrain the freshwater polychaete, Manayunkia speciosa, the intermediate host for 2 myxozoan parasites (Ceratomyxa shasta and Parvicapsula minibicornis) that cause substantial mortalities in salmonid fishes in the Pacific Northwest. In a laboratory flume, we measured the shear stress associated with 2 mean flow velocities and 3 substrates and quantified associated dislodgement of polychaetes, evaluated survivorship of dislodged polychaetes, and observed behavioral responses of the polychaetes in response to increased flow. We used a generalized linear mixed model to estimate the probability of polychaete dislodgement for treatment combinations of velocity (mean flow velocity  =  55 cm/s with a shear velocity  =  3 cm/s, mean flow velocity  =  140 cm/s with a shear velocity  =  5 cm/s) and substrate type (depositional sediments and analogs of rock faces and the filamentous alga, Cladophora). Few polychaetes were dislodged at shear velocities <3 cm/s on any substrate. Above this level of shear, probability of dislodgement was strongly affected by both substrate type and velocity. After accounting for substrate, odds of dislodgement were 8× greater at the higher flow. After accounting for velocity, probability of dislodgement was greatest from fine sediments, intermediate from rock faces, and negligible from Cladophora. Survivorship of dislodged polychaetes was high. Polychaetes exhibited a variety of behaviors for avoiding increases in flow, including extrusion of mucus, burrowing into sediments, and movement to lower-flow microhabitats. Our findings suggest that polychaete populations probably exhibit high resilience to flow-mediated disturbances.

Integrating models to predict distribution of the invertebrate host of myxosporean parasites

Manayunkia speciosa, a freshwater polychaete, is the invertebrate host of myxosporean parasites that negatively affect salmonid populations in the Pacific Northwest of the USA. Factors that drive the distribution of M. speciosa are not well understood, which constrains our understanding of disease dynamics and the development of management solutions. We described the distribution of M. speciosa at 3 sites on the Klamath River, California, based on 2-dimensional hydraulic models (2DHMs) and a generalized linear mixed model (GLMM). 2DHMs were built to explain hydraulic variation at each site and used to stratify biological sampling effort along depth–velocity gradients and by substrate class. We assessed the presence/absence of M. speciosa at 362 georeferenced locations in July 2012 and built GLMMs to describe relationships between hydraulic and substrate variables and the distribution of M. speciosa. The best-fitting GLMMs demonstrated that M. speciosa distributions were associated with depth–velocity conditions and substrate size during base discharge (area under the receiver operating characteristic curve [AUC] = 0.88) and at peak discharge (AUC = 0.86). We evaluated the GLMMs with an independent data set collected in July 2013 (n = 280) and found that the top models predicted the distribution of M. speciosa with a high degree of accuracy (AUC = 0.90). These results support the conclusion that the summer distribution of M. speciosa is related to observed hydraulic and substrate conditions during base discharge (summer) and modeled hydraulic and substrate conditions during peak discharge (late winter to early spring). These results may have implications for the use of flow manipulation as a disease management tool. These results also illustrate the importance of examining species distribution data in the context of temporally disconnected environmental factors and demonstrate how models can fulfill this need.

Using Cure Models for Analyzing the Influence of Pathogens on Salmon Survival

AbstractParasites and pathogens influence the size and stability of wildlife populations, yet many population models ignore the population-level effects of pathogens. Standard survival analysis methods (e.g., accelerated failure time models) are used to assess how survival rates are influenced by disease. However, they assume that each individual is equally susceptible and will eventually experience the event of interest; this assumption is not typically satisfied with regard to pathogens of wildlife populations. In contrast, mixture cure models, which comprise logistic regression and survival analysis components, allow for different covariates to be entered into each part of the model and provide better predictions of survival when a fraction of the population is expected to survive a disease outbreak. We fitted mixture cure models to the host–pathogen dynamics of Chinook Salmon Oncorhynchus tshawytscha and Coho Salmon O. kisutch and the myxozoan parasite Ceratomyxa shasta. Total parasite concentration, ...