The evolutionary potential of a population is closely related to two key population genetic parameters, namely the effective population size ( N e ) and migration rate ( m ). Furthermore, knowledge of these parameters is required in order to assess potential constraints on local adaptation and for the development of biologically sound management strategies. We addressed these key issues by investigating the temporal and spatial genetic structure of over 2000 adult Atlantic salmon ( Salmo salar ) collected from 17 sites in the Teno and Näätämö rivers in northernmost Europe with up to five time points spanning temporal intervals up to 24 years (∼4 generations). In all cases except one, local populations were found to be temporally stable within the river system. Estimates of N e were generally a magnitude larger for the mainstem and headwater populations (MS+HW, N e ∼340–1200) than for the tributary populations ( N e ∼35–160), thus explaining the higher genetic diversity and lower divergence of the MS+HW populations compared to tributaries. The overall migration rates to tributaries were low, and in some cases, low enough for local adaptations to potentially evolve, despite their lower N e . Signs of a population bottleneck and natural recruitment from nearby populations were detected in one local population. This highlights a fact which is relevant for the conservation and management of highly substructured population systems in general: that even when the overall census size is large, local populations can be vulnerable to perturbations. To preserve the current and to regain the historical distribution of salmon within the river system, we propose that the status of the total population complex should be evaluated at the local population level rather than from descriptive statistics at the system level.
CITATION STYLE
Vähä, J., Erkinaro, J., Niemelä, E., & Primmer, C. R. (2008). Temporally stable genetic structure and low migration in an Atlantic salmon population complex: implications for conservation and management. Evolutionary Applications, 1(1), 137–154. https://doi.org/10.1111/j.1752-4571.2007.00007.x
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