Pollock Conservation Cooperative Research Center
2008 Awarded Research Projects
Project Title: Combining genetics and population dynamics to improve management of Pacific ocean perch (Sebastes alutus)
Principal Investigator: A. J. Gharrett and T. J. Quinn
Award: $79,234 (year 2), $81,892 (year 3) - continuation
Estimated Completion: January 31, 2011
Abstract
In this PCCRC project, we propose to develop and apply quantitative models to examine the influence of population subdivision on population dynamics models that are used to evaluate sustained production of exploited resources. The project arose because an assumption made for many marine species, which have pelagic larvae and apparently mobile adults, is that their populations extend over very broad reaches, possibly including much of their natural ranges. However, this may often not be the case. Recently, our genetic studies of Pacific ocean perch (POP) population structure demonstrated that relatively strong divergence occurs between collections that were sampled at locations spaced about 400 km apart along the GOA and BS continental slopes. The degree of divergence that we observed indicates that, although population structure is not defined by geographic or oceanographic boundaries, the limited net dispersal of both pelagic larvae and adults results in limiting the spatial scale of POP production. These limited areas are determined by the average distance moved between birth and reproduction; and are called “neighborhoods”. The spatial scale of neighborhoods (productivity units) is the geographic scale on which management should be focused.
From the results of research conducted in our laboratory, we will be able to estimate the maximum extent of substantial dispersal, and should be able to make preliminary estimates of neighborhood size. The questions that we will address are the effects that harvest patterns exert on the production and genetic structure of POP and, by extension, other species for which limited dispersal results in a neighborhood models for population structure, and for which the neighborhoods are much smaller than the management areas. To evaluate these effects, we will develop quantitative models that include information about dispersal, population dynamics, and exploitation and, with simulations, test the effects of different spatially-based harvesting strategies, which will range from harvesting over the entire management area to harvests in a few limited locations within the area.
Since the inception of the PCCRC project in July 2007, K. Palof has conducted a number of analyses of our POP data that we did not originally plan to include in her M.S. thesis. However, because those analyses were a necessary portion of this project and because they would enhance her thesis, we delayed completion of her program and included those analyses in her thesis, which was successfully defended on 4 December. We also have made the first steps toward developing our quantitative models by reexamine the quality of the data from which the parameters on population structure and dispersal are estimated. One of the potential challenges of microsatellite data is that biases may occur in their estimation as a result of the existence of so called “null” alleles. We have conducted a series of simulations to evaluate the potential effects and are conducting laboratory investigations to quantify the null allele incidence in our data. In this report we provide the background information for potential influences of null alleles on data interpretations, describe our simulations and their results, and present our approach to laboratory detection and estimation of null allele frequencies as well as preliminary results of the laboratory investigations.
We conclude from the results of our simulations and preliminary laboratory observations that microsatellite null alleles have very low frequencies at most of the loci that we apply and have had little influence on our analyses of population genetics structure or on the estimation of parameters derived from the data.
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