Framework for Eco-Genetic Modeling

The framework for eco-genetic modeling offers flexible tools for exploring the course and rates of multi-trait life-history evolution in natural populations.

© Ulf Dieckmann, IIASA

© Ulf Dieckmann, IIASA

This framework builds on existing modeling approaches by combining features that facilitate studying the ecological and evolutionary dynamics of realistically structured populations. In particular, the joint consideration of age and size structure enables the analysis of phenotypically plastic populations with more than a single growth trajectory, and ecological feedback is readily included in the form of density dependence and frequency dependence. Stochasticity and life-history trade-offs can also be implemented. Critically, eco-genetic models permit the incorporation of salient genetic detail such as a population’s genetic variances and covariances and the corresponding heritabilities, as well as the probabilistic inheritance and phenotypic expression of quantitative traits. These inclusions are crucial for predicting rates of evolutionary change on both contemporary and longer timescales. An eco-genetic model can be tightly coupled with empirical data and therefore may have considerable practical relevance, in terms of generating testable predictions and evaluating alternative management measures.

Related references:

Dunlop ES, Baskett ML, Heino M, & Dieckmann U (2009). Propensity of marine reserves to reduce the evolutionary impacts of fishing in a migratory species. Evolutionary Applications 2: 371-393.

Dunlop ES, Shuter BJ, & Dieckmann U (2007). The demographic and evolutionary consequences of selective mortality: Predictions from an eco-genetic model of the smallmouth bass. Transactions of the American Fisheries Society 136: 749-765.

Eikeset AM, Dunlop ES, Heino M, Storvik G, Stenseth NC, & Dieckmann U (2016). Roles of density-dependent growth and life history evolution in accounting for fisheries-induced trait changes. Proceedings of the National Academy of Sciences of the USA 113: 15030-15035.

Eikeset AM, Richter A, Dunlop ES, Dieckmann U, & Stenseth NC (2013). Economic repercussions of fisheries-induced evolution. Proceedings of the National Academy of Sciences of the USA 110: 12259-12264.

Enberg K, Dunlop ES, Jørgensen C, Heino M, & Dieckmann U (2009). Implications of fisheries-induced evolution for stock rebuilding and recovery. Evolutionary Applications 2: 394-414.

Mollet FM, Dieckmann U, & Rijnsdorp AD (2016). Reconstructing the effects of fishing on life history evolution in North Sea plaice (Pleuronectes platessa). Marine Ecology Progress Series 542: 195-208.

Mollet FM, Poos JJ, Dieckmann U, & Rijnsdorp AD (2016). Evolutionary impact assessment of the North Sea plaice fishery. Canadian Journal of Fisheries and Aquatic Sciences 73: 1126-1137.

Okamoto KW, Whitlock R, Magnan P, & Dieckmann U (2009). Mitigating fisheries-induced evolution in lacustrine brook charr (Salvelinus fontinalis) in southern Quebec, Canada. Evolutionary Applications 2: 415-437.

Thériault V, Dunlop ES, Dieckmann U, Bernatchez L, & Dodson JJ (2008). The impact of fishing-induced mortality on the evolution of alternative life-history tactics in brook charr. Evolutionary Applications 1: 409-423.

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Last edited: 25 April 2018


Ulf Dieckmann

Program Director

Evolution and Ecology


Capacity Building and Academic Training

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International Institute for Applied Systems Analysis (IIASA)
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