Epilogue part Jarle. 1st draft
Required research and management tools
Summary of available research and management tools
For several fish species, particularly those of commercial value, long term data collections exist which document
the development of fish body size, age at maturation, and other phenotypic traits. Some of theses collections
span a hundred years and more. In some cases, relevant data collections are also available on ecosystem
parametres such as hydrography and food web dynamics.
Traditionally, such data bases have formed the basis for attempts to explore the effect of fisheries on the
demography and productivity of fish stocks. It has long been recognized that explanatory models can usually not
be based on point estimates; rather detectable trends over time is a prerequisite to filter out the noise due to e.g.
climate changes, density dependant growth and other variability typically inbuilt in marine ecosystems.
Where information on the actual fisheries (i.e. type of gear, number of vessels, areas, landings) also exist, the
traditional analytical approach has been to search for correlations in the data materials. For example, one might
explore possible correlations between stock characteristics (demography, size, recruitment, mortality) and
fishing intensity.
Using multiple regression analyses, several variables can be studied simultaneously and their relative effect on
the dependent variable can be assessed. However, as discussed by Trippel & Mork in in chapter B1, care must be
taken not to automatically interpret covariation as cause-effect relationships. There are many possibilities of
autocorrelations among life-history traits, and causal relationships may be difficult to establish. Likeways,
correlation and regression analyses are not in themselves particularly efficient in disentangling potential genetic
effect from phenotypic plasticity and short- or longterm environmental variability effects. Nevertheless, long
term data collections are in themselves prerequisites for any successful analysis of human impact on the gene
pools of exploited fish stock. They have proved invaluable for the application of modern types of analyses, e.g.
the study of reaction norms. Properly designed, reaction norm studies allow a separation of phenotypic plasticity
from real genetic changes in populations over time, and the trends can be correlated to exploitation patterns.
If exploitation can be shown to result in unwanted adaptive change in the populations, managemental remedies
may be called for. The traditional management tools available to regulate the qualitative and quantitative aspects
of fisheries are mesh size regulations, quota sizes, and temporal and spatial closures of fisheries (including
”boxes” where no fishery is allowed for shorter or longer periods of time). Substantial amounts of information
about the nature of the fishery induced selection regime, its strength and direction is required to apply these tools
with any efficiency.
Box 1. List of recommendations for future research.
Extensive time-series of relevant data will continue to be a prerequisite for exploring various factors
attributing to genetic changes in life-history traits in fish stocks. The recordings in the data bases should
include:

Individual body metrics (length, weight, age and sexual maturity)

Calcified tissues for age determinations (otholits, scales)

Catch landings data

Resource survey data (population and cohort sizes)

History of gear used in different fleet sectors and periods of time

Data on environmental changes

Abundance of predators, competitors and prey species
In order to properly utilize the time-series data in modelling, experiments must be conducted to get estimates
of trait heritabilities, genetic correlations between traits, and the selection differentials connected with
different types of exploitation. Since these entities may be species- and even stock-specific, it is clear that
there is a lot of work to do before the potential of existing data bases can be fully utilized.
Future challenge: genetic correlations within and between groups of life-history traits; counteracting selection
pressures require integrative evolutionary assessments.
Selection, natural as well as anthropogenically induced, act on the individual (the individual phenotype). Hence a
surviving and reproducing individual may carry with it life-history traits that were not directly involved in a
specific selection event. Genetic correlations propably exist between various life-history traits like growth, age at
maturation, survival, disease resistence, and certain behaviour traits. Hence, fishery-induced selection based on
individual fish size may affect several other traits as well. Presently, very little is known about these correlations
in most fishes. However, studies on sea-ranched salmon on Iceland have shown significant genetic correlations
between egg size and survival (return rate), and egg size and growth (see Friars & Smith Box 2).
The existence of genetic correlations makes it very difficult to predict the all effects from a specific selection
regime, e.g. one caused by mesh size regulations. Hence management measures to counteract selection pressures
is correspondingly difficult to define. The fact that the heritability of a trait is not a fixed entity but may vary in
time and space adds to the difficulties.
The only way to attack these problems is to increase our knowledge about the genetic basis of traits, their genetic
correlations and heritabilities in realistic situations, and the selection differentials connected with different gear
and fishing mortalities. The complexity of the possible genetic interactions is huge, and the use of advanced
computer modelling and simulations one will be necessary. Still the models will at least initially have to be
relatively simple; while one may obtain reliable estimates of input parametres for additive genetic factors
through experiments, epistatic factors may not be under control. Nevertheless, the integration of trait
heritabilities and genetic correlations together with selection differentials into computer models seems to be the
most powerful weapon to explore the nature of selection regimes and hence to define measures to counteract
unwanted evolutionary effects on the fish stocks. Currently, such work is only in its beginning.
Future challenge; genetic correlations within and between groups of life-history traits; counteracting selection
pressures requires integrative evolutionary assessments.