258
M.L. Cancela et al.
future genome scans will be imbedded in a framework of landscape or “seascape”
genetics (Galindo et al. 2006, Joost et al. 2007), i.e. where the patterns of selection observed are statistically tested for associations with particular environmental
variables. Obvious candidate drivers of selection and local adaptation are temperature, salinity and oxygen content. However, a number of other factors such as
pollution, infections and predator/prey interactions are also very likely to be driving
local adaptation.
The identification of differential selection among populations does not only rely
on assessment of variation in the genome, but can also be evaluated through expression phenotypes. Investigating differences among populations in gene expression,
by conducting quantitative PCR or microarray studies under controlled conditions,
can provide good indications of genetically based gene expression differences
among populations. For example, Lucassen et al. (2006) investigated the effect
of cold acclimation on RNA expression of mtDNA genes in two populations of
Atlantic cod maintained at identical conditions, but experiencing different temperature conditions in their native habitat. They found clear differences among
populations, which were ascribed to genetic variation at functional sites between
the two populations. Finally, gene expression profiles can also be used to detect
evolutionary changes in hatchery-reared fish compared to their wild conspecifics.
Roberge et al. (2006) found strong differences in gene expression between wild and
farmed salmon using a microarray with 3,557 genes, but patterns of parallel evolution between two farmed populations. Consequently, this highlights the need for
avoiding escapes of hatchery-reared fish into the wild, since hybridisation can have
large and unexpected effects on gene expression (Roberge et al. 2008).
7.7.2.3 Tracing Natural Populations for Fisheries Enforcement
and Traceability
Probably the most serious obstacle for obtaining sustainable fisheries is illegal,
unreported and unregulated (IUU) fisheries. Therefore, there is a huge demand for
methods which can establish the species and area of origin of fish in all links of
the chain, i.e. from catch fisheries or aquaculture to the plate of the consumer. This
information can be used for forensic purposes, allowing genetic evidence to be presented in court-cases relating to illegal fishing and or mislabeling of fish products.
A number of methods are available which can provide information on catch origin such as lipid composition (Falch et al. 2006), isotope analysis (Guelinckx et al.
2008) and elemental analysis (Campana and Thorrold 2001). As DNA is found in all
cells it allows analysis from all types of tissue in fish, even partly degraded samples
such as processed food (Rasmussen and Morrissey 2008) and historical samples
(Nielsen and Hansen 2008). Accordingly, genetic methods are generally considered the most versatile means for classification of fish to species or local population
origin (e.g. Hansen et al. 2001). Species identification is relatively straightforward
commonly requiring sequencing of just one gene (see also Section 9.5.5). An example is the COI (Cytochrome Oxidase I) gene, which is the chosen marker for the
M.L. Cancela et al.
future genome scans will be imbedded in a framework of landscape or “seascape”
genetics (Galindo et al. 2006, Joost et al. 2007), i.e. where the patterns of selection observed are statistically tested for associations with particular environmental
variables. Obvious candidate drivers of selection and local adaptation are temperature, salinity and oxygen content. However, a number of other factors such as
pollution, infections and predator/prey interactions are also very likely to be driving
local adaptation.
The identification of differential selection among populations does not only rely
on assessment of variation in the genome, but can also be evaluated through expression phenotypes. Investigating differences among populations in gene expression,
by conducting quantitative PCR or microarray studies under controlled conditions,
can provide good indications of genetically based gene expression differences
among populations. For example, Lucassen et al. (2006) investigated the effect
of cold acclimation on RNA expression of mtDNA genes in two populations of
Atlantic cod maintained at identical conditions, but experiencing different temperature conditions in their native habitat. They found clear differences among
populations, which were ascribed to genetic variation at functional sites between
the two populations. Finally, gene expression profiles can also be used to detect
evolutionary changes in hatchery-reared fish compared to their wild conspecifics.
Roberge et al. (2006) found strong differences in gene expression between wild and
farmed salmon using a microarray with 3,557 genes, but patterns of parallel evolution between two farmed populations. Consequently, this highlights the need for
avoiding escapes of hatchery-reared fish into the wild, since hybridisation can have
large and unexpected effects on gene expression (Roberge et al. 2008).
7.7.2.3 Tracing Natural Populations for Fisheries Enforcement
and Traceability
Probably the most serious obstacle for obtaining sustainable fisheries is illegal,
unreported and unregulated (IUU) fisheries. Therefore, there is a huge demand for
methods which can establish the species and area of origin of fish in all links of
the chain, i.e. from catch fisheries or aquaculture to the plate of the consumer. This
information can be used for forensic purposes, allowing genetic evidence to be presented in court-cases relating to illegal fishing and or mislabeling of fish products.
A number of methods are available which can provide information on catch origin such as lipid composition (Falch et al. 2006), isotope analysis (Guelinckx et al.
2008) and elemental analysis (Campana and Thorrold 2001). As DNA is found in all
cells it allows analysis from all types of tissue in fish, even partly degraded samples
such as processed food (Rasmussen and Morrissey 2008) and historical samples
(Nielsen and Hansen 2008). Accordingly, genetic methods are generally considered the most versatile means for classification of fish to species or local population
origin (e.g. Hansen et al. 2001). Species identification is relatively straightforward
commonly requiring sequencing of just one gene (see also Section 9.5.5). An example is the COI (Cytochrome Oxidase I) gene, which is the chosen marker for the