South Fork Tolt River
Seattle City Light (SCL) has multiple ongoing monitoring programs in the South Fork Tolt River, including summer steelhead life history studies, steelhead spawning surveys, stream temperature monitoring, and large woody debris monitoring. General analyses of these multi-year datasets have been conducted by SCL staff, and SLC required statistical support with the more intense statistical questions associated with this long-term monitoring program.
Kleinschmidt and TerraStat Consulting were engaged to develop a size cutoff criteria for estimating the age of juvenile summer steelhead captured during previous sampling efforts, based on fork length or weight. These cutoff criteria would then be used to improve accuracy assigning individual juvenile steelhead to brood years. This will allow greater certainty of age assignments when tracking PIT tagged fish both individually and as part of a cohort. Having greater certainty of age can provide greater confidence answering multiple study questions including outmigration and return age distribution, to assist in survival estimates, to estimate uncertainty in age dependent analysis, and to help SCL to develop sampling plans for future scale-age sampling efforts.
The fork length data collected over 10 years of sampling was well-fit by a normal mixture model – a mixture of three to four overlapping normal distributions, each representing an age class. Using maximum-likelihood methods and R statistical packages, our team estimated the parameters of these overlapping distributions, including prior probabilities for each age class and means and variances. For each fish in the existing database, we were then able to provide the probabilities that the fish belonged to each age class. These probabilities are part of the SCL database and can be used as flexible cutoffs for age class determination depending on the requirements for a particular analysis. SCL was also able to use this information to determine the subset of juvenile steelhead that should be scale-aged to determine age class with more precision for future sampling.
Kleinschmidt | R2 statistical support in improving accuracy and estimating precision in age class assignments important for other steelhead analyses needed for quantitative project objectives.