COS 62-3 - Combining continuous remote detections with traditional sampling to estimate the migratory flux of fish 

Tuesday, August 8, 2017: 2:10 PM
D129-130, Oregon Convention Center
Maria C. Dzul, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ, Charles B. Yackulic, Southwest Biological Science Center, US Geological Survey, Flagstaff, AZ and Josh Korman, Ecometric Research Inc., Vancouver, BC, Canada

Movement of individuals between habitats is central to the life history of many species and can couple the dynamics of spatially segregated ecosystems. In lotic systems, autonomous passive integrated transponder (PIT) arrays (hereafter PIT arrays) are often used to characterize movement patterns between rivers and can be useful tools for detecting rare movements. Although PIT arrays continuously detect individually marked organisms over long time periods, their utility is limited because the detection probability of unmarked individuals is zero and this shortcoming impedes quantification of movement fluxes. Here we develop a model that combines data from mark-recapture efforts (in a large river) and PIT array detections (in a smaller tributary) to quantify how many animals move between rivers. Specifically, we develop and apply a model to estimate abundance of non-native rainbow trout Oncorhynchus mykiss that move from the Colorado River to the Little Colorado River (LCR), the latter of which is important spawning and rearing habitat for federally-endangered humpback chub Gila cypha. Because rainbow trout negatively impact juvenile humpback chub, rainbow trout in the LCR pose a threat to the humpback chub population.


The LCR PIT array detected 38 unique rainbow trout entering the LCR between October 2013 and April 2014, and our modeling approach translated these 38 unique detections to a monthly movement probability of 1.2% and an estimate of 259 (95% CI: 134-451) rainbow trout entering the LCR during this period. This estimate accounts for the spatial mixing of marked and unmarked fish, the spatial variability of fish abundances, and the influence of spatial location on movement probability (e.g., fish that are located closer to the PIT array are more likely to move over it compared to fish located farther from the array). Our model exhibited minimal bias and was insensitive to most model parameters, with the exception of the monthly movement probability, thus highlighting that rare movements are difficult to estimate accurately and that even small uncertainties in low movement probabilities can greatly reduce precision. This study provides an example of how to combine mark-recapture data with PIT array detections and quantify movement rates, and we emphasize that further advancement in wildlife conservation science will require innovative statistical methods to integrate multiple data types into population models.