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1 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment The development of a "modern", science-based fisheries biology early in the 1900s soon lead to the recognition that unless the fishery management was administred to the real reproduction units, any management measure (like quotae, mesh size regulations, season closures, minimum fish size) would be unprecise and might have unpredictable effects. Hence, much effort was laid down in identifying those reproduction units, or "stocks" as they were (and still are) rather unprecisively called. The classical tools (like tagging and recapture, monitoring of the location of the fishing fleet, landing statistics) were of course used for the purpose but in addition, genetic traits assumed to be population characteres were employed. (This was in the days prior to modern laboratory techniques like electrophoresis and DNA technology, and even prior to much of the population genetic theory). One methodology that was extensively applied in the 1920ies and 1930ies was the use of frequencies of meristic characteres; i.e. characteres like individual number of vertebrae, fin rays, or gill rakers. It was observed that many stocks differ in their average values for such traits. Certainly, the variability of such traits have a genetic basis. Hence, large studies were devoted to plotting mean values for meristic characters in different fish stocks. Slide 2 2 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment A pioneer in this new science of meristics was Johannes Schmidt (left) at the Carlsberg Laboratory in Denmark, the same man who after a massive work 1905 -1930 had established the early life history of the European and American freshwater eels (picture to the right). Armed with the tools of meristics, Schmidt and contemporaries explored the stock structure of many of our commercial fish species, e.g. the atlantic herring and the atlantic cod. Schmidt's opinion of the genetic structure of the cod was published in the 1930ies in form of tables of mean number of vertebrae and fin rays in cod samples from different parts of its distribution area in the Atlantic, and maps connecting groups with similar characteristics. The "stock map" produced in this way showed a very tight correlation with ocean temperatures, and it was later shown experimentally (Tning) that the environmental temperature during embryogenesis affected the number of vertebrae and fin rays to develop in the individual. Hence the "stock map" might reflect temperature ecotypes rather than genetically distinct stocks or populations. Meristics was soon discontinued as a key to genetic population structure. Slide 3 3 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Types of markers in modern population genetics studies: the various marker types have their advantages and disadvantages. Serum protein loci Isozymes mtDNA mini- and microsatellites (VNTR) nuclear and mtDNA sequences SNPs Slide 4 4 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Genetic stock ID studies in the Atlantic cod The first extensive use of electrophoresis and protein genetics was published in Nature in 1961 by the danish population geneticist Knud Sick. He used allele frequencies at a simple haemoglobin polymorphisms ( HbI ) in cod (and whiting) to demonstrate the existence of different populations with very different allele frequencies. His studies included the entire north Atlantic ocean. Actually, the stock structure indicated by the HbI locus was remarkably similar to that resulting from the previous meristic studies by Schmidt. After having been used for several decades in cod management, it was realised that allele frequencies at the HbI locus was influenced by environmental tempereratures, and therefore could not be used as signs of reproductive isolation. An attempt to use blood group techniques for stock identification i cod was terminated quite quickly. The genetic basis for the blood group variation was never established. Thereafter, studies using isozymes, mtDNA haplotypes, microsatellites, nuclear DNA RFLP, and SNP have succeeded each other but still, many questions are unclear about the genetic population structure of the Atlantic cod. Slide 5 5 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment GSI: Genetic Stock Identification studies in the Atlantic cod, cont'd Various types of genetic markers have given very different estimates of the general level of ifferentiation in cod. While markers like haemoglobins, one microsatellite and the Pan I (a nuclear RFLP marker) indicate substantial differentiation and some very abrupt changes in allele frequencies over short geographical distances, serum proteins, isozymes, most microsatellie, most nuclear RFLPs as well as "silent" substitutions of mt Cyt b sequences do not indiate substantial differentiation at at. Rather, the latter support an "isolation by distance" model of differentiation. The figures on the next slides show results from a distribution-wide study of genetic differentiation in cod based on 13 polymorphic isozymes loci (Mork et al. 1985). The results are quite similar to those obtained based on a set of nuclear RFLPs. A trait that seems to be common in many studies, is that the Baltic Sea cod stand out as being the genetically most differentiated among current stocks. Also, the western and eastern Atlantic stocks appear to form subgroups with some degree of genetic differentiation (only at common population level though). Slide 6 6 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Cod sampling sites (of Mork et al. 1985) Slide 7 7 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment UPGMA dendrogram, and isolation by distance for cod (Mork et al. 1985) Slide 8 8 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Wrights Fst for cod compared to some other species Slide 9 9 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment GSI: Genetic Stock Identification studies in the Atlantic herring Not being quite as controversial as that of the cod, the genetic population structure of the Atlantic herring show some traits which are not much disputed: 1 There are numerous smaller stocks tied by their life history to local areas like fjords, coastal areas and brackish oceanic areas (White Sea, Baltic Sea). 2 The genetic differentiatiation across the Atlantic is actually smaller than what can often be found between two neighbouring Norwegian fjords. 3 Previous keys to population isolation, like being spring- or autumn spawners, are not valid. Spring- and autumn spawners may be found in one and the same local population. Of course, the realization of this fact led to a simplification of previous models of stock structure. 4 In general, the herring appears, to a much higher degree than many other fishes, to be characterised by a so-called metapopulation structure (local populations are transient; they come and go depending on environmental conditions, general abundance etc). Slide 10 10 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Statistical analyses A typical population genetic study proceeds through three phases. The first stage involves testing the validity of using allele frequencies to describe the genotypic variation within samples or populations. These are genotypic analyses. The second stage usually involves exploring data for patterns. During this stage, scientists employ a variety of taxonomic type analyses, which can provide quantitative, objective descriptions of the pattern(s) of differences among groups. The third stage focuses on hypothesis-based analyses, whereby one hypothesizes that genetic differences are structured in a specific way, and then try to quantify or statistically test for those differences. Almost always, the structuring that is suspected and can be tested is geographic. Slide 11 11 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Measures of genetic differences (the most commonly used) Genetic Identities and Distances (Masatoshi Nei 1972) F ST of Sewall Wright (or of Weir & Cockerham), G ST of M. Nei Cavalli-Sforza chord distance Hierarchical genetic analyses (Amova) Cluster analysis and dendrogram drawing UPGMA / WPGMA Various algorithms and tree analyses Statistical tests The most basic are the chi-squared tests for Hardy-Weinberg genotypic proportions and RxC contingency table tests of homogeneity. Slide 12 12 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Stock management and Conservation Establishig a Genetic Inventory is a necessary starting point for a meaningful, genetically based management or conservation program. Usually, this is best achieved by performing initial studies that include the entire distribution range of a species. Depending on the findings in an inventory study, the principles for management can be layed down. Various fish species differ greatly in how their total gene pools are structured within and between poulations. It has been convincingly showed that one of the most important keys to structuring is the possibility of a gene flow between populations within species (next slides). Slide 13 13 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment The relationship between the species' general biology and the degree of differentiation between its populations Several review studies have shown that in fishes, the degree of genetic structuring (i.e. the genetic differences between populations within species) is consistently depending on whether the actual fish species is: 1.Limnic 2.Anadromous, or 3.Marine It is very likely that this pattern is a result of the general biology of the species. For example, to which degree it tends to develope local adaptations, how well its biology allows for a gene flow between populations, and to which degree its way of life results in a genetic adaptation to local environmental factors. These are factors that obviously differ between limnic, anadromous and marine species (examples on next slides). Slide 14 14 Nucella sp. Cod ( Gadus morhua ). Found in the entire North Atlantic. Very large populations with relatively similar environmental conditions. Can undertake extensive migrations, but do not show particulary accurate homing. Pelagic egg- and larvae stages which can last for several months. Thrives at cold and temperate latitudes. Extensive genetic investigations have shown limited genetic differentiation. Salmon ( Salmo salar ). Distributed over most of the North Atlantic. Anadromous; spawns in fresh water. Relatively small river populations which can differ considerably environmentally. Benthic egg and fry stages. Thrives at cold and temperate latitudes. Can undertake extensive migrations, and show extremely accurate homing. Extensive genetic studies have indicated a moderate level of genetic differentiation. N. lamellosa is distributed on the US West Coast. Relatively small populations. Lives in the littoral, and hence under very variable micro-geographic habitat and milieu conditions. Not very mobile, but shows a clear tendency for homing. Benthic egg capsules where the larvae develop into a "micro-individual" before hatching. No pelagic stages. Thrives in cold and temperate latitudes. Studies of genetic structure have shown a substantial degree of genetic differentiation on both a small and a large geographic scale. Salmo salar Gadus morhua Slide 15 15 Distribution and migrations of cod, salmon and Nucella sp. North east Arctic CodCod stocks and currents Atlantic salmon Nucella sp. Slide 16 16 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 17 17 Slide 18 18 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 19 19 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 20 20 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Hierarchical Genetic Analysis (often called Amova; Analysis of Molecular Variance) The genetic structure of a species may include several hierarchical levels, e.g.: Regions Areas within regions Drainages within areas Rivers within drainages Populations within rivers With hierarchical genetic analysis the amount of genetic differentiation connected with each level can be estimated. Often, but not always, analysis show that the largest differentiation is connected to the highest levels; i.e. that genetic differences are larger between regions than between lower levels in the hierarchy. Since the hierarchy usually is geographical, this would support the idea of "isolation by distance". Slide 21 21 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Hierarchical Genetic Analysis cont'd Among population geneticists, such hierarchical analysis is usually referred to as "Amova": Analysis of molecular variance. This kind of analysis is very computing-intensive, and is usually performed with computers. The most widely used software for such analysis is the "Arlequin" package. The software is free for download from the web. [ http://lgb.unige.ch/arlequin/ ] (see next slide) Slide 22 22 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 23 23 BI 3063 J. Mork H08 Genetic and biologic stock management Mixed-Stock Fishery Analysis (MSA) Pacific salmon species have posed special challenges to management, because in the saltwater phase they may co-occur in specific areas where they are exploited jointly by the fishing fleet. In order to manage the fishery so that no one river stock is over-exploitated, the composition over rivers stocks in the mixed- fishery must be known. Genetic analyses called MSA (Mixed-Stock-Analysis) have been developed for, and employed in such situations. The principle is to first create a detailed baseline of knowledge of the genetic characteristics of the potentially represented populations, by sampling them in their "home river". The more loci included, the better. Then, by sampling the mixed-fishery and analysing each individual for its multi- locus genotype, maximum-likely methods (MLY) can be employed to sort each individual to its most propable home population, and then estimate the relative proportions of the various river stocks in the mixed fisheries. Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 24 24 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Mixed-Stock Fishery Analysis (MSA) cont'd While MSA has worked well on Pacific salmon species, its efficiency in Atlantic salmon is less impressing. This is mainly due to the fact that the Atlantic salmon is less genetically structured than e.g. the chinook salmon. Hence, the sorting of individuals to their "home populations" is much less accurate, and mis-sortings occur more frequently. In fact, mis-sorting is the one most common problem with MSA, in that small river stocks tend to be over-represented in the mixed fisheries, and vice versa. Slide 25 25 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment There has been some discussion about what type of statistical procedures and algoritms to use in MSA. This discussion emerged with the introduction of new type of genetic markers; i.e. with the transition from isozyme markers to microsatellites which have a higher "resolution" because of higher mutation rates. While the first approaches used Maximum Likelihood estimates (in the "isozyme age" of the 1990ies), there is evidence that Bayesian methods perform better for microsatellites. The next slide show a treatment of this topic downloaded from the web. Slide 26 26 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 27 27 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Software for mixed-stock analysis is available for fre download from the Internet. For those who masters R, an application of MSA can be found at this site: http://cran.r-project.org/web/packages/mixstock/vignettes/mixstock.pdf Mixed stock analysis in R: getting started with the mixstock package Ben Bolker July 8, 2008 Slide 28 28 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Box 13.3 Slide 29 29 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 30 30 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 31 31 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment Slide 32 32 BI 3063 J. Mork H08 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment