Morphological Discrimination of Genetically Distinct Chinook Salmon Populations: an Example from California's Central Valley

Abstract

We compared a length‐at‐date growth model to truss network (morphometric) models for classifying emigrating juvenile (age 0 through age 1) California Central Valley Chinook Salmon Oncorhynchus tshawytscha to three genetically identified races at upstream (salmon generally younger and smaller) and downstream (salmon generally older and larger) monitoring stations. Morphometric models including capture date and head to standard length ratio performed better than the growth model at distinguishing genetically assigned fall‐run and late fall‐run juveniles; prediction accuracy increased from 54.6% to 63.8% for upstream salmon and from 17% to 73% for downstream salmon. The growth model may over inflate downstream estimates of federally listed Chinook Salmon by misidentifying fall and late‐fall runs (nonlisted) as winter and spring runs (both listed) as much as 83% of the time. Morphometric models did not improve run assignment for the listed runs; the growth model outperformed morphometric models at downstream stations. Morphometric models including head shape and sample date had similar accuracy measures to models, including multiple fish measurement ratios, suggesting head shape is the strongest predictor of the juvenile Central Valley Chinook Salmon race. Our results indicate morphometric modeling can improve identification of nonlisted juvenile Central Valley Chinook Salmon from federally listed runs, potentially benefitting monitoring, water management, and protection of sensitive species. Received May 5, 2014; accepted August 13, 2014

Advanced Techniques for Morphometric Analysis in Fish

“…A sufficient degree of isolation may result in notable morphological, meristic, and shape differentiation among stocks of a species which may be recognizable as a basis for identifying the stocks. The characteristics may be more applicable for studying short-term, environmentally induced disparities, and the findings can be effectively used for improved fisheries management [18][19][20][21]. The truss network system can effectively be used to distinguish between the hatchery and wild stocks.…”

Juvenile Chinook Salmon Weight Prediction Using Image‐Based Morphometrics

“…Image-based morphometric analysis on juvenile salmon has been around since 1984, but has primarily been a lab-based exercise (Winans 1984;Beeman et al 1994) or more recently with anesthesia protocols (Tiffan and Connor 2011;Billman et al 2014;Merz et al 2014). Field wet weight measurement on small live…”

“…Image‐based morphometric analysis on juvenile salmon has been around since 1984, but it has been primarily a lab‐based exercise (Winans 1984; Beeman et al 1994) or, more recently, it has been conducted with anesthesia protocols (Tiffan and Connor 2011; Billman et al 2014; Merz et al 2014). Measuring field wet weight on small, live fish is inherently difficult and subject to error through many factors, including wind, drops of water that consist of a relatively high percentage of a fish’s weight or transcription error.…”

“…Morphometrics using image‐derived landmarks have been used previously on juvenile Chinook Salmon in the Pacific Northwest for fish stock analysis (Tiffan et al 2000; Tiffan and Connor 2011; Merz et al 2014), phenotypic analysis (Hard et al 1999; Billman et al 2014), feeding regime effects on morphology (Currens et al 1989), and the assessment of physiological change during smoltification (Beeman et al 1994). These studies were conducted with anesthetized or euthanized specimens, generally with the fins pinned out to improve the image quality and the accuracy of landmark placement.…”