| Popis: |
Many fish species reproduce by creating nests (redds) in alluvial stream gravels, which can be used to track population trends. However, temporal and spatial overlap across multiple redd-building species can hinder redd species classification. This is further complicated when the corresponding adult is not present. Spawning surveys on the Lower American River (LAR) have been conducted since 2003 to document fall-run Chinook Salmon and California Central Valley (CCV) steelhead spawning. Other fish species on the LAR have overlapping reproduction timing, including Pacific Lamprey. Prior to 2016, a redd observed during field surveys that was not associated with a fish observation was assigned species identity based on seasonal timing and professional judgement. However, this method has potential to misidentify the species that built the redd due to overlap in spawning season and similarity in redd dimensions among LAR fish species. To decrease subjectivity associated with unoccupied redd identification, we used occupied redd data to build a discriminant function analysis (DFA), which predicts redd species identity based on field-measured parameters that vary across species including time of year, redd dimensions, and ambient conditions. We compared model accuracy across 6 years in which additional “fish on” observations were added annually to the discriminant function to test whether adding observational data improved model accuracy. We also applied the discriminant function to historical redd data in which species identification was made based on professional judgement to compare the two approaches. DFA accuracy improved with additional years of data, and in the iteration that included the most observational data it was highly accurate in identifying fall-run Chinook Salmon and CCV steelhead (96% and 97%, respectively). Accuracies for Pacific Lamprey were slightly lower (91%) than salmonids due to the relatively low number of “fish-on” redd observations for Pacific Lamprey. Comparisons between the DFA and historical identification based on professional opinion were generally similar, but with up to 19.6% disagreement in some years. Our study demonstrates that physical and temporal metrics can support more accurate species identification, and field data can be used to support more robust population estimates and inform future habitat restoration decisions. |
