Abstract

AbstractWe demonstrate the process of synchronously combining multiple sources of available fishery information to estimate total abundance in data‐limited situations. The application is specific to semelparous populations, such as Pacific salmon, where only data for spawners and recruits are necessary to describe the dynamics of these populations. We apply this technique to summer chum salmon Oncorhynchus keta of the Kuskokwim and Yukon rivers of Alaska. Since 1997, low numbers of returning chum salmon to these rivers have resulted in low harvests, with significant negative economic and social impacts to rural residents of the region. The existing programs for assessing salmon stock in these river basins are inadequate for conventional estimates of total run abundance and the modeling of stock dynamics necessary to derive a quantitative assessment of the returns. Our approach was to utilize the pattern extraction qualities of principal components analysis (PCA) to estimate the underlying trend in escapement. We then combined this index with other available fishery data in a maximum likelihood statistical framework, weighting the data sets according to their quality. Using this methodology, we derived indices of chum salmon abundance and escapement for the Kuskokwim and Yukon rivers. Data sources included commercial catch and effort, escapement surveys, test fishery catch rates, and whole‐river sonar counts. Error estimates of the time series of abundance and escapement as well as of the model parameters were generated by bootstrap methods. We found that several parameters of the model were confounded without some independent measure of total abundance or escapement. We also determined that the escapement trend estimated by PCA was consistent over a large geographic area, suggesting that survival was predominantly influenced by conditions where the fish share a common environment. We suggest that our methodology may be appropriate to other regions and different semelparous species.

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