Skip to main content
SLU publication database (SLUpub)
Research article - Peer-reviewed, 2018

Integrating genetic analysis of mixed populations with a spatially explicit population dynamics model

Whitlock, Rebecca; Mantyniemi, Samu; Palm, Stefan; Koljonen, Marja-Liisa; Dannewitz, Johan; Ostergren, Johan


1. Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones.2. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement, akin to an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed.3. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. Use of a population dynamics model allowed robust interpolation of expected catch composition at areas and times with no genetic observations. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses: incorporation of prior information about stock abundance and movement resulted in more plausible and precise estimates of mixture compositions in time and space.4. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.


Baltic salmon; Bayesian approach; genetic mixed stock analysis; integrated population models; spatial models

Published in

Methods in Ecology and Evolution
2018, Volume: 9, number: 4, pages: 1017-1035
Publisher: WILEY