COMPETITION AND INFORMATION AMONG BRITISH COLUMBIA SALMON PURSE SEINERS
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|Traditional fisheries models are based upon simple Poisson assumptions concerning fleet behavior. Fishing vessels are assumed to operate independently of one another and to sample the fish population in a random fashion. This dissertation uses field data on salmon purse seiners to present tests of hypotheses contained in the historical assumptions. Data pertaining to interference competition and information are analyzed. Since fisheries management usually entails assumptions concerning the form of exploitation rate responses to fishing effort (boat days, landings, sales slips, etc.), the consequences of non-random fisher behavior are explored. Alternative models of the fishing process are proposed and examined.
Published electronically with permission from Max Ledbetter, Ph.D. ISBN-10 0-315-34900-X (ISBN-13 978-0-315-34900-1)
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|Ledbetter, Max. 1986. Competition and information among British Columbia salmon purse seiners. Ph.D. diss., University of British Columbia, Vancouver, B.C.
In British Columbia, salmon purse seiners line up at fishing access points. These line-ups were measured multiple times during each weekly fishing opening in Johnstone Strait, British Columbia, using a one dimensional recording scale.
The distribution of effort was fit to theoretical truncated Poisson and truncated negative binomial distributions. Most data fit the negative binomial rather than the Poisson. Movement patterns and time series of catches were also non-random. Analysis of variance methods indicated that line-up lengths reflected catch rates.
Waiting times were quantified using functional and statistical models. Using the waiting times and the line-up data, the fleet set effort (the total number of sets made by the entire purse-seine fleet in Johnstone Strait at a given time) and the number of sets per boat were calculated. Although the fleet set effort was a near linear function of the number of boats in the area, interference competition produced an initial decrease in sets per vessel.
Two models were presented for exploitation rates in relation to queuing patterns. The overflight model was based upon the line-up distributions and fit the data well. The overflight model provided estimates of exploitation rates (estimates of the percentage of the vulnerable salmon population taken by the fleet during a fishing opening). Those exploitation rates reached 90 percent and then remained fairly constant at high effort, when 100 or more boats fished the area.
As an alternative model, the negative binomial distribution was used to estimate exploitation rates from catch per vessel distributions. It was assumed that salmon abundance did not affect the shape of the negative binomial/catch per vessel distributions. As effort increased, however, the distribution of catch per vessel (from sales slip data) was predicted to become more skewed to the origin. The parameter describing the shape of the distribution, k, should have tracked the fishing power of the fleet (decreasing as the distribution became more skewed).
After fitting the weekly distributions, it was found that the relative exploitation rates from the sales slip model did not saturate like the parameters of the overflight model. An alternative derivation indicated that the shape of catch per unit effort distributions responds to the size and aggregative properties of the fleet and to the magnitude of the catch. As the mean catch per set increases, the parameter k will increase. Salmon abundance, fleet numerical responses and vessel aggregations affect the skewness of the catch per unit effort (catch per boat) distributions.