Age Structure, Changing Demography and Effective Population Size in Atlantic Salmon (Salmo salar)

Abstract

Effective population size (N e ) is a central evolutionary concept, but its genetic estimation can be significantly complicated by age structure. Here we investigate N e in Atlantic salmon (Salmo salar) populations that have undergone changes in demography and population dynamics, applying four different genetic estimators. For this purpose we use genetic data (14 microsatellite markers) from archived scale samples collected between 1951 and 2004. Through life table simulations we assess the genetic consequences of life history variation on N e . Although variation in reproductive contribution by mature parr affects age structure, we find that its effect on N e estimation may be relatively minor. A comparison of estimator models suggests that even low iteroparity may upwardly bias N e estimates when ignored (semelparity assumed) and should thus empirically be accounted for. Our results indicate that N e may have changed over time in relatively small populations, but otherwise remained stable. Our ability to detect changes in N e in larger populations was, however, likely hindered by sampling limitations. An evaluation of N e estimates in a demographic context suggests that life history diversity, density-dependent factors, and metapopulation dynamics may all affect the genetic stability of these populations.

Genetic monitoring reveals temporal stability over 30 years in a small, lake-resident brown trout population

“…We have not monitored census size (N C ), but we do have one estimate from a mark recapture study conducted in 2009 when N C was estimated as 576 (95% confidence interval 493-706) for fish with a total body length of X19 cm (Charlier et al, 2011). This estimate translates into an N e /N C ratio of 0.13, which is in line with observations from many other species including salmonids (Frankham, 1995;Palstra and Ruzzante, 2008;Palstra et al, 2009). Assuming that our mean catch per effort reflects an increase of N C , the ratio might have decreased over the study period.…”

“…This observation suggests that monitoring of N b can be used for detecting fluctuations of census size in brown trout (see Palstra et al, 2009 andOsborne et al, 2010 for similar observations in other freshwater fishes).…”

“…Thus, although the LD approach applied to a sample of mixed ages results in an estimate of N e that is very similar to that obtained when correcting for overlapping generations using the temporal method, the LD method results in a substantial downward bias (B45%) when sampling a single cohort and a gross overestimate (4200%) when using the GN b approximation. Palstra et al (2009) reported similar results for Atlantic salmon (Salmo salar; iteroparous) and inferred that GN b is generally not a good estimator of N e in iteroparous species.…”

“…Studies that monitor genetic variability patterns over time are needed, but most monitoring efforts are restricted to a few years of sampling or single samples separated over relatively long periods of time (Nielsen et al, 1997;Heath et al, 2002;Hansen et al, 2002;Poulsen et al, 2006;Borrell et al, 2008;Hansen et al, 2009;Gomaa et al, 2011). Detailed temporal genetic studies that investigate the genetic composition over microevolutionary time scales-that is, from year to year, cohort to cohort and generation to generation-are accumulating, however (Palm et al, 2003;Dowling et al, 2005;Araki et al, 2007;Palstra et al, 2009;Osborne et al, 2010;Skrbinšek et al, 2012). The representativeness of single samples with respect to genetic composition and level of population variation is also poorly understood.…”

Demographic and genetic factors shaping contemporary metapopulation effective size and its empirical estimation in salmonid fish

“…All scales were stored in dry paper envelopes from collection until molecular analyses. DNA was extracted from these samples and genetically characterized for 13 microsatellite marker loci, previously found to be selectively neutral, unlinked and free of technical artefacts in allele scoring for populations in this region (see Palstra et al (2007Palstra et al ( , 2009) for details; locus SSsp1605 was omitted from the present study due to allele scoring difficulties).…”