Occupancy Modeling for Improved Accuracy and Understanding of Pathogen Prevalence and Dynamics

<div><p>Most pathogen detection tests are imperfect, with a sensitivity < 100%, thereby resulting in the potential for a false negative, where a pathogen is present but not detected. False negatives in a sample inflate the number of non-detections, negatively biasing estimates of pathogen prevalence. Histological examination of tissues as a diagnostic test can be advantageous as multiple pathogens can be examined and providing important information on associated pathological changes to the host. However, it is usually less sensitive than molecular or microbiological tests for specific pathogens. Our study objectives were to 1) develop a hierarchical occupancy model to examine pathogen prevalence in spring Chinook salmon <i>Oncorhynchus tshawytscha</i> and their distribution among host tissues 2) use the model to estimate pathogen-specific test sensitivities and infection rates, and 3) illustrate the effect of using replicate within host sampling on sample sizes required to detect a pathogen. We examined histological sections of replicate tissue samples from spring Chinook salmon <i>O. tshawytscha</i> collected after spawning for common pathogens seen in this population: <i>Apophallus/</i>echinostome metacercariae, <i>Parvicapsula minibicornis, Nanophyetus salmincola/</i> metacercariae, and <i>Renibacterium salmoninarum</i>. A hierarchical occupancy model was developed to estimate pathogen and tissue-specific test sensitivities and unbiased estimation of host- and organ-level infection rates. Model estimated sensitivities and host- and organ-level infections rates varied among pathogens and model estimated infection rate was higher than prevalence unadjusted for test sensitivity, confirming that prevalence unadjusted for test sensitivity was negatively biased. The modeling approach provided an analytical approach for using hierarchically structured pathogen detection data from lower sensitivity diagnostic tests, such as histology, to obtain unbiased pathogen prevalence estimates with associated uncertainties. Accounting for test sensitivity using within host replicate samples also required fewer individual fish to be sampled. This approach is useful for evaluating pathogen or microbe community dynamics when test sensitivity is <100%.</p></div

Comparison of host-level model estimated (Ψ) and unadjusted prevalence (α) for pathogens detected.

<p>Unadjusted prevalence (α) represents estimates for each replicate (3 per pathogen, 12 estimates total). The dotted line denotes a 1:1 relationship. A small amount of random noise was added due to overplotting. Vertical lines denote 95% credible intervals.</p

Estimated organ-specific sensitivity (<i>s</i>) and 95% credible intervals for the occupancy model fit to spring Chinook collected from Willamette Hatchery, Oregon.

<p>Estimates reported for tissues where pathogens were detected.</p><p>Estimated organ-specific sensitivity (<i>s</i>) and 95% credible intervals for the occupancy model fit to spring Chinook collected from Willamette Hatchery, Oregon.</p

Fish and organ-level prevalence (unadjusted for test sensitivity; <i>a</i>) for replicated tissue-level detection/non-detection data.

<p>Fish level prevalence represents the aggregation of among organ detections.</p><p>^a Gill and kidney represent the highest level of detection for <i>Apophallus/</i>echinostome metacercariae and <i>P</i>. <i>minibicornis</i> and therefore fish level prevalence is equal to these values.</p><p>Fish and organ-level prevalence (unadjusted for test sensitivity; <i>a</i>) for replicated tissue-level detection/non-detection data.</p

Fish and organ-level infection rates (Ψ, φ) and prevalence estimates (<i>P</i>) and credible intervals for the occupancy model fit to spring Chinook collected from Willamette Hatchery, Oakridge, Oregon.

<p>Fish and organ-level infection rates (Ψ, φ) and prevalence estimates (<i>P</i>) and credible intervals for the occupancy model fit to spring Chinook collected from Willamette Hatchery, Oakridge, Oregon.</p

Frequency of detection combinations for the 26 fish sampled.

<p>Non-detections represent three replicate pathogen non-detections (i.e., 000). Imperfect detections represent possible combinations of replicate pathogen detections and non-detections (i.e., 100, 010, 001, 110, 101, 011). Perfect detections represent three replicate pathogen detections (i.e., 111).</p><p>Frequency of detection combinations for the 26 fish sampled.</p

Number of host samples needed to detect a pathogen 80% of the time for varying host-level infection rates (Ψ).

<p>Panel rows illustrate the effect of pathogens being searched for in multiple organs and the columns illustrate the effect of varying sensitivity and organ-level infection rate (φ). Organ-level infection rate (φ) and sensitivity (<i>s</i>) assumed to be constant among organs in simulations. Sampling sizes for the top left panel required more than 60 and therefore the panel represents the few combinations where the success criteria was met.</p

Appendix A. A figure showing an example length frequency histogram for mottled sculpin in Coweeta Creek and a map of the Coweeta drainage depicting the spatial locations of the study sites.

A figure showing an example length frequency histogram for mottled sculpin in Coweeta Creek and a map of the Coweeta drainage depicting the spatial locations of the study sites