1 BI3063 H10 J. Mork H10 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment The development of a "modern",

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BI3063 H10 J. Mork H10

Genetic and biologic stock management

Hallerman Ch. 13Genetic 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.

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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 (Tåning) 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.

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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

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Genetic stock ID studies in the Atlantic codThe 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.

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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 PanI (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 Cytb 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).

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Cod sampling sites (of Mork et al. 1985)

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UPGMA dendrogram, and ’isolation by distance’ for cod (Mork et al. 1985)

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Wright’s Fst for cod compared to some other species

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GSI: Genetic Stock Identification studies in the Atlantic herringNot 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 bytheir 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 isolaion, 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 degreee 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).

http://images.google.no/imgres?imgurl=http://gs.alesund.kommune.no/stafsethneset/fisk/fiskebilder/sild.jpeg&imgrefurl=http://gs.alesund.kommune.no/stafsethneset/fisk/Sild.htm&h=185&w=567&sz=9&hl=no&start=5&usg=__ntkTwzvm8m03nS7bkwmUYAK5ln4=&tbnid=f_AlTKLj6mgTNM:&tbnh=44&tbnw=134&prev=/images%3Fq%3Dsild%26gbv%3D2%26hl%3Dno%26sa%3DG

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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 geotypic 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.

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Measures of genetic differences (the most commonly used)• Genetic Identities and Distances (Masatoshi Nei 1972)• FST of Sewall Wright (or θ of Weir & Cockerham), GST of M. nei• Cavalli-Sforza chord distance• Hierarchical genetic analyses

Cluster analysis and dendrogram drawing• UPGMA / WPGMA• Various algoritms 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.

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Stock management and ConservationStock 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 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 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 most important keys to structuring is the possibility of a gene flow between populations within species (next slides).populations within species (next slides).

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Genetic and biologic stock managementHallerman Ch. 13

Genetic Stock Identification and Risk Assessment

The relationship between the species' general biology and the degree of The relationship between the species' general biology and the degree of differentiation between its populationsdifferentiation 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 consistentlydepending on whether the actual fish species is:

1. Limnic2. Anadromous, or3. 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 wellits biology allows for a gene flow between populations, and to which degree its way of life reults in a genetic adaptation to local environmental factors. These are factors that obviously differ between limnic, anadromous and marine species (examples on next slides).

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Purpursnegl (Nucella sp.)

Utbredt over hele nord-Atlanteren. Svært store populasjoner medrelativt like miljøbetingelser. Kan foreta lange migrasjoner, men viserikke noen nøyaktig homing. Pelagiske egg- og larvestadier som kanvare i flere måneder. Trives i kalde og tempererte strøk. Omfattendegenetiske studier har vist relativ lite genetisk differensiering.

Utbredt over hele nord-Atlanteren. Anadrom (gyter i ferskvann). Relativt små elvepopulasjoner og betydelige forskjeller i miljø-faktorer mellom hver elv. Bentiske egg-, larve- og yngelstadier. Trives i kalde og tempererte strøk. Kan foreta lange migrasjoner, men viser ekstremt nøyaktig ”homing”. Omfattende studier av genetiskstruktur har indikert en moderat grad av genetisk differensiering.

N. lamellosa er utbredt på USAs østkyst. Relativt små gytegrupper.Lever i fjæresonen (flodmålet), der det kan være svært store mikro-geografiske forskjeller i habitat og miljø. Liten bevegelsesevne, menviser tendenser til ”homing”. Bentiske egg-kapsler der larven utvikler seg til et ”mikro-individ” før de kommer ut. Ingen pelagiskestadia. Trives i kalde og tempererte strøk. Studier av genetisk struktur har påvist betydelig grad av genetisk differensiering både på liten og stor geografisk skala.

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Utbredelse og vandringer for torsk, laks og Nucella sp.

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Table II. Two marine and one anadromous species; biological traits relevant for the relative strength of the two evolutionary forces gene flow and selection (local adaptation).

Atlantic cod Atlantic salmon Dog whelk

Pop. size Large Small Small

Stationary No Yes Yes

Milieu tolerance High High High

Fecundity High Medium Low/medium

Homing No (?) Yes Yes

Pelagic/benthic life Both Both Benthic

Pelagic eggs or larvae Pelagic Benthic Benthic

Migrations Large Large Small

Marine/anadrom. Marine Anadromous Marine



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References to the litterature from which the data were taken:

Cod:Mork, J., Ryman, N., Ståhl, G., Utter, F., & Sundnes, G. 1985. Genetic variation in Atlantic cod(Gadus morhua) throughout its range . Can. J. Fish. Aquat. Sci. 42 (10): 1580-1587.Salmon:Ståhl, G. & Hindar, K. 1988. Genetisk struktur hos norsk laks: status og perspektiver (Geneticstructure in Norwegian salmon: status and perspectives). (In Norwegian). Direktoratet forNaturforvaltning. Rapport fra Fiskeforskningen No1, 1988. 57 pp.Dog whelk:Grant, W.S. & Utter, F.M. 1988. Genetic heterogeneity on different geographic scales in Nucellalamellosa (Prosobranchia, Thaididae). Malacologia 28(1-2): 275-287.

Level of genetic differentiation in marine species with different biologies.The geographic areas sampled are comparable for the three species.

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D E C O M P O S I N G T H E T O T A L G E N E T I C V A R I A B I L I T Y .

T h e b e t w e e n c o m p o n e n t c o r r e s p o n d s t o F s t . I n g e n e r a l , a h i g h b e t w e e n c o m p o n e n t i n d i c a t e s r e s t r i c t i o n s t o t h e g e n e f l o w b e t w e e n

i n t r a - s p e c i f i c g r o u p i n g s .

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D e c o m p o s e d g e n e t i c v a r i a t i o n ( F s t )( W i t h i n a n d b e t w e e n c o m p o n e n t )

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Hallerman Ch. 13Hallerman Ch. 13Genetic Stock Genetic Stock Identification and Identification and Risk AssessmentRisk Assessment

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Hierarchical Genetic Analysis

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 supporrt the idea of "isolation by distance".

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Hierarchical Genetic Analysis cont'd

Among population geneticists, suxh 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)

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Mixed-Stock Fishery Analysis (MSA)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.


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Mixed-Stock Fishery Analysis (MSA) cont'dMixed-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 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.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".

While the first approaches used Maximum Likelihood estimates (in the "isozyme age" of the 1990ies), there is evidence that Bayesian methods perform better when using microsatellites.

The next slide show a treatment of this topic downloaded from the web.

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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".

While the first approaches used Maximum Likelihood estimates (in the "isozyme age" of the 1990ies), there is evidence that Bayesian methods perform better when using microsatellites.

The next slide show a treatment of this topic downloaded from the web.

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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 themixstock packageBen BolkerJuly 8, 2008

http://cran.r-project.org/web/packages/mixstock/vignettes/mixstock.pdf

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Box 13.3

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Documents

1 BI3063 H10 J. Mork H10 Genetic and biologic stock management Hallerman Ch. 13 Genetic Stock Identification and Risk Assessment The development of a "modern",