Comparative estimation of effective population sizes and temporal gene flow in two contrasting population systems

Abstract

Estimation of effective population sizes (N(e)) and temporal gene flow (N(e)m, m) has many implications for understanding population structure in evolutionary and conservation biology. However, comparative studies that gauge the relative performance of N(e), N(e)m or m methods are few. Using temporal genetic data from two salmonid fish population systems with disparate population structure, we (i) evaluated the congruence in estimates and precision of long- and short-term N(e), N(e)m and m from six methods; (ii) explored the effects of metapopulation structure on N(e) estimation in one system with spatiotemporally linked subpopulations, using three approaches; and (iii) determined to what degree interpopulation gene flow was asymmetric over time. We found that long-term N(e) estimates exceeded short-term N(e) within populations by 2-10 times; the two were correlated in the system with temporally stable structure (Atlantic salmon, Salmo salar) but not in the highly dynamic system (brown trout, Salmo trutta). Four temporal methods yielded short-term N(e) estimates within populations that were strongly correlated, and these were higher but more variable within salmon populations than within trout populations. In trout populations, however, these short-term N(e) estimates were always lower when assuming gene flow than when assuming no gene flow. Linkage disequilibrium data generally yielded short-term N(e) estimates of the same magnitude as temporal methods in both systems, but the two were uncorrelated. Correlations between long- and short-term geneflow estimates were inconsistent between methods, and their relative size varied up to eightfold within systems. While asymmetries in gene flow were common in both systems (58-63% of population-pair comparisons), they were only temporally stable in direction within certain salmon population pairs, suggesting that gene flow between particular populations is often intermittent and/or variable. Exploratory metapopulation N(e) analyses in trout demonstrated both the importance of spatial scale in estimating N(e) and the role of gene flow in maintaining genetic variability within subpopulations. Collectively, our results illustrate the utility of comparatively applying N(e), N(e)m and m to (i) tease apart processes implicated in population structure, (ii) assess the degree of continuity in patterns of connectivity between population pairs and (iii) gauge the relative performance of different approaches, such as the influence of population subdivision and gene flow on N(e) estimation. They further reiterate the importance of temporal sampling replication in population genetics, the value of interpreting N(e)or m in light of species biology, and the need to address long-standing assumptions of current N(e), N(e)m or m models more explicitly in future research.

Temporal changes in allele frequencies but stable genetic diversity over the past 40 years in the Irish Sea population of thornback ray, Raja clavata

“…This is because the change in allele frequencies is larger than expected by genetic drift alone. However, as reported by Fraser et al (2007), the bias on N est strongly depends on the extent of genetic differentiation between the source and the receiving populations. In cases where genetic differentiation between populations is low, as is the case here for thornback rays (global y in British waters ¼ 0.013; see Chevolot et al, 2006), N est could be overestimated if migration is ignored, as migration might counterbalance the effect of genetic drift.…”

“…However, both estimates are within the confidence intervals and thus are in the same range. Furthermore, because of the low genetic differentiation between our target and the source populations, the most relevant scale for our N est is probably the metapopulation scale rather than the discrete population (Fraser et al, 2007).…”

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

“…Morrissey and de Kerckhove (2009) showed that under conditions of hierarchical spatial structuring in dendritic systems, such as often seen in freshwater, asymmetrical gene flow can maintain high genetic diversity and thereby lead to high meta-N e . Studies on anadromous salmonid fish (for example, Hindar et al, 2004;Fraser et al, 2007;Kuparinen et al, 2010), however, report that metapopulation effective size can be much reduced compared with the sum of local deme effective sizes. Using the approach of Tufto and Hindar (2003), Kuparinen et al (2010) found that meta-N e was mainly determined by the population with the highest emigration rate (see also Hindar et al (2004)).…”

“…Spatial segregation, population connectivity and temporal stratification (that is, age structure) can all complicate the behaviour and estimation of effective population size (for example, Jorde and Ryman, 1995;Waples, 2010;Hare et al, 2011). The effects of incoming gene flow on local N e can be multifarious, depending on its relative magnitude, directional symmetry and the time frame of interest (Wang and Whitlock, 2003;Fraser et al, 2007;Palstra and Ruzzante, 2008).…”

“…At this level, the focus is on the effective size of an assemblage of populations interconnected by gene flow (hereafter referred to as meta-N e ; Fraser et al, 2007). This parameter has seen considerable theoretical treatment, starting with Wright (1943), who showed that under the restricted assumptions of his island model (that is, all demes of equal size and experiencing exactly the same level of symmetrical gene flow), population subdivision increases meta-N e beyond the sum of local or deme effective sizes (SN e(s) ).…”