Basin characteristics and temperature improve abundance estimates from standard index netting of rainbow trout (Oncorhynchus mykiss) in small lakes

Abstract

Fisheries management often requires efficient methods for assessing populations across a variety of sampling units. We develop a model to assess the status of rainbow trout (Oncorhynchus mykiss) in lakes using a fixed length of multimesh gillnets. We use mark-recapture data to examine how gillnet capture efficiency varies with lake-basin characteristics. Once we accounted for variation in lake-size (net-effort density), we found that capture efficiency was best explained by surface water temperature at the time of gillnetting and lake-basin characteristics (proportion of littoral area). We use Bayesian techniques to fit our model to the data and explore how uncertainty in the estimated parameters leads to uncertainty in population estimates. Our analysis suggests that lake size, temperature and the proportion of littoral area can be used to predict population size using this index netting method. In addition to providing density estimates, our results help to better understand how fish ecology and behavior influences gillnet catches.