Research agenda of EvoFish

Fishing is the dominant source of mortality in most commercially exploited fish stocks. This means that benefits of living long and growing large are strongly reduced as very few individuals survive to a late age. Life on the "slow lane" is disadvantaged, while "fast track" life histories gain advantage. This is well known from studies on exploited fish communities. However, during the last few years, evidence has started to accumulate suggesting that also within a species, "fast track" variants are gaining ground - exactly as life history theoreticians have predicted years ago. Thus, fishing might well be the most influential single factor driving evolution in contemporary fish populations!

Fisheries-induced evolution is the most prominent research topic in EvoFish. Ultimately, the research in EvoFish revolves around two main questions: Does fisheries-induced evolution have ecological consequences? Are the long-term societal benefits of fishing affected by fisheries-induced evolution? And if the answer to any of these two questions is affirmative, a third question naturally follows: How should management of our common renewable resources respond to these challenges posed by fisheries-induced evolution?

At a more detailed level, we plan to address the following questions that will contribute to our synthesis on ecological and societal dimensions of fisheries-induced evolution:

How extensive are the evolutionary changes that have already taken place?
Which traits evolve most readily in response to fishing? What kinds of species are most prone to undergo fisheries-induced evolution?
What evolutionary changes can we expect in the future when fishing interacts with other anthropogenic influences?
How can fish farmers delay the maturation of reared fish?
What are the ecological consequences of changing behaviour and life histories?
How are characteristics of harvest, such as average yield and average size of fish in the catch, affected by exploitation-induced evolution?
What are the economic and societal costs of not responding to the evolutionary dimensions of harvesting our fish resources?
Can harvesting strategies be designed to minimize unwanted evolutionary consequences of exploitation, given that the socio-economic benefits from fishing are to be maintained?

This broad array of questions require a suite of methods. We will simultaneously utilize a broad set of approaches and tools: analysing observational data, conducting experiments designed to address specific questions, and modelling to facilitate conceptualisation and interpretation of observations, and to conduct virtual experiments:

Statistical analyses of fish life history data from the wild and from aquaculture. Statistical methods to analyse data (both from research surveys and from sampling of commercial landings) include the established statistical toolbox, comprising both classic methods and modern recent developments such as generalized linear models, mixed models and geo-statistics. These methods will be complemented by more specialised, recently developed tools for estimating reaction norms for age and size at maturation from various types of (often incomplete) fisheries data, partly relying on computation-intensive methods such as bootstrapping and randomisation.
Otolith analyses. Otoliths are natural data storage tags of fish: thickness and chemical composition of layers depends on growth rate and ambient conditions. This offers exciting possibilities for extracting life history information from historic data collections, possibly also from pre-historic archeological material. At the Department of Biology, we have top expertise on these methods through Prof. Arild Folkvord and Prof. Audrey Geffen.
Models of fisheries-induced evolution. The art of modelling lies in finding the best compromise between simplicity and realism. While all important aspects of biological reality should ideally be included, models including too much detail become intractable. The best compromise will, naturally, depend on the questions being addressed, as well as on availability of data to parameterise and calibrate the model. We are taking advantage the following suite of modelling approaches: quantitative genetics models, adaptive dynamics models, eco-genetic models and evolutionary energy allocation models.
Oceanographic models. General circulation models are now producing reliable flow fields of oceanic currents, and these can be utilized to assess dispersal and environmental exposure of eggs and larvae. Oceanography and larval ecology place important constraints on life history and migration strategies of marine fish, which can be revealed in coupled bio-physical models.
Bio-economic analyses. Bioeconomic analysis integrates biological models with economic models of price formation, cost structures and industry behaviour. In this connection the biological input to the analysis would be models of population response to continued exploitation that is selective in terms of fish size and possibly other characteristics. The economic component is twofold. First, changes in population structure such as size and age composition have economic consequences, adverse or beneficial, as the price of fish may vary with size and other characteristics of fish populations. Second, such price variability may be a driving force for selective fishing and thereby have genetic consequences. It is of obvious interest to integrate this economic aspect with the genetic analysis.
Experimental systems. We are also using experimental laboratory systems. We are particularly interested in trade-offs among life history, behavioural and physiological traits, and how harvesting will influence these, either directly or through genetic or mechanistic correlations. We are also planning to test the performance of the so-called reaction norm approach to detecting life history evolution. We are working with two model systems. Guppies (Poecilia reticulata) are guinea pigs of life-history evolution, and we have started a harvesting experiment based on wild guppies from Trinidad. Here we benefit from the experise of Dr. David Reznick. In addition, we are collaborating with the scientists at BIO experienced in working with waterfleas (Daphnia), a homage to the seminal work by Edley & Law from 1988 [MT Edley & R Law 1988: Evolution of life histories and yields in experimental populations of Daphnia magna. Biol. J. Linn. Soc. 34:309-326]. While obviously not fish, Daphnia are quite fish-like in their life history and allow addressing questions that would not be feasible with fish because of issues of scale and time.