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Time matters: genetic composition and evaluation of effective population size in temperate coastal fish species
Volume: 8
DOI 10.7717/peerj.9098
Abstract

BackgroundExtensive knowledge on the genetic characterization of marine organisms has been assembled, mainly concerning the spatial distribution and structuring of populations. Temporal monitoring assesses not only the stability in genetic composition but also its trajectory over time, providing critical information for the accurate forecast of changes in genetic diversity of marine populations, particularly important for both fisheries and endangered species management. We assessed fluctuations in genetic composition among different sampling periods in the western Portuguese shore in three fish species.MethodsWhite seabream Diplodus sargus, sand smelt Atherina presbyter and shanny Lipophrys pholis were chosen, because of their genetic patterns in distinct ecological environments, insight into historical and contemporary factors influencing population effective size (Ne), and degree of commercial exploitation. Samples were obtained near Lisbon between 2003 and 2014 and screened for genetic variation with mitochondrial and nuclear markers. Analyses included genealogies, genetic diversities, temporal structures and contemporary Ne.ResultsFor mtDNA no temporal structure was detected, while for nDNA significant differences were recorded between some sampling periods for the shanny and the sand smelt. Haplotype networks revealed deep genealogies, with various levels of diversification. The shanny revealed a smaller Ne/generation when compared to the other species, which, in turn, revealed no evidence of genetic drift for most study periods. These results highlight the fact that temporal variations in genetic pool composition should be considered when evaluating the population structure of fish species with long distance dispersal, which are more vulnerable to recruitment fluctuations.

Keywords
Francisco, Robalo, and Esteban: Time matters: genetic composition and evaluation of effective population size in temperate coastal fish species

Introduction

Over the last four decades, we have assembled extensive knowledge on the genetic characterization of marine organisms, mainly concerning the spatial distribution and structuring of populations (e.g., Knutsen et al., 2007; Plank et al., 2010; Robalo et al., 2013; Riginos et al., 2019; Verry et al., 2020). With this information, it is now possible and relevant to understand how these patterns behave through time. Temporal monitoring assesses the genetic composition’s stability and trajectories over time, providing critical information for the accurate forecast of changes in the genetic diversity of marine populations. This is increasingly important for both endangered and commercially exploited species, particularly in a context of severe broad-based anthropogenic influences and climate change (Schwartz, Luikart & Waples, 2007; Cuveliers et al., 2011; Goldstien et al., 2013).

Temporally replicated sampling also allows the estimate of Ne—effective population size, one of the most important parameters in both conservation and evolutionary biology (Waples & Do, 2010; Hare et al., 2011). From a genetic perspective, Ne is defined as the size of an ideal population that has the same rate of change in allele frequencies and heterozygosity as the observed population (Wright, 1931). Estimates of Ne may be used to assess the loss of genetic variation, increases in inbreeding, accumulation of mutations, and effectiveness of selection (Waples & Do, 2010; Hare et al., 2011). In the short to medium term, potential reductions in Ne may lead to inbreeding depression and/or drastic loss of genetic diversity (Frankham, Bradshaw & Brook, 2014), particularly in commercially overexploited populations, populations inhabiting highly degraded ecosystems or located near the margins of the species distribution. Loss of genetic diversity may take a long time to recover through mutation, thus limiting or impeding the adaptive response to environmental changes (Lynch & Lande, 1998).

Several approaches have been developed in the past decades to overcome difficulties in directly measuring Ne in the marine realm (e.g., Wang, 2005; Luikart et al., 2010; Hare et al., 2011; Wang, Santiago & Caballero, 2016 for a review). Traditional demographic estimators based on Capture-Mark-Recapture (e.g., Jolly-Seber Model (Jolly, 1965; Seber, 1965), and multinomial approach in open populations (Crosbie & Manly, 1985)) have been complemented with genetic methods: single-sample Ne estimators (e.g., linkage disequilibrium method (Hill, 1981), heterozygote-excess method (Pudovkin, Zaykin & Hedgecock, 1996), sibship frequency (Wang, 2009), molecular coancestry (Nomura, 2008)) and two-sample Ne estimators (temporal change in allele frequencies (Waples, 1989; Wang, 2001)).

A frequently used genetic approach to measure contemporary changes in Ne has been the Temporal Method (Nei & Tajima, 1981). This method depends on estimates of allelic frequencies in two or more points in time, assuming that the observed genetic drift between two sampling moments will be more pronounced in small populations. Jorde & Ryman (1995) adjusted the method to incorporate the effects of age structure, demonstrating that the magnitude of changes in allelic frequencies was also determined by age-specific birth and survival rates, in addition to the actual size and the sampling interval. Later, these authors presented a direct extension of the previous model, allowing its application to temporal samplings of single cohorts born within a given number of years (Jorde & Ryman, 2007; Jorde, 2012).

Temporal genetic changes in coastal species have been linked to the stochastic nature of oceanographic conditions, including upwelling systems, fronts, eddies and sharp discontinuities in physicochemical variables (Selkoe et al., 2006; White et al., 2010). Moreover, the stability of allele frequencies in natural populations can be deeply influenced by extreme events (anthropogenic and/or natural disturbing factors) (Allison et al., 2003). Additionally, intrinsic biological and ecological factors associated with the species life cycle, also shape the genetic pattern of marine organisms, namely inshore-offshore spawning, pelagic larval duration and other early life-history traits (Riginos & Victor, 2001; Galarza et al., 2009).

Genetic monitoring through time, specifically, the temporal method, has been successfully employed to evaluate the temporal stability and estimate Ne in several species, including some stocks with high economic significance such as the European hake Merluccius merluccius (Pita et al., 2017), the brown trout Salmo trutta (Serbezov et al., 2012) and the Atlantic cod Gadus morhua (Therkildsen et al., 2010). In the present study, three coastal fishes were chosen to cover a wide range of contrasting environmental and ecological traits: the white seabream Diplodus sargus L. 1758 (Pisces: Sparidae); the sand-smelt Atherina presbyter Cuvier 1829 (Pisces: Atherinidae); and the shanny Lipophrys pholis L. 1758 (Pisces: Blenniidae).

Although they are not threatened—the three species are listed as least concerned—their population trend is unknown, and they can be locally vulnerable due to various reasons. The white-seabream is a commercially important species throughout European shores, wide-spread and locally abundant (Pollard et al., 2014) and, although no major threats have been identified, some local overfishing may occur and lead to reduction in population size. The sand-smelt is commercially exploited as life bait for tuna and local coastal development can be a threat (Gon, 2015). In the long-term, climate change could be a problem for this temperate species. Finally, no major threats are found across the shanny’s distribution range (Williams & Craig, 2014). This species can be used as an indicator for pollution monitoring due to several of its characteristics, including restricted home range and high sensitiveness to organic contaminants (Lima et al., 2008).

These target species provide crucial insight into historical and present factors influencing effective population sizes and genetic patterns in distinct ecological environments and degrees of commercial interest. In this study, we assessed the changes in genetic composition in different points in time in the western Portuguese shore, using a mitochondrial and a nuclear marker (chosen due to their extensive use in the past decade). The specific objectives were: (1) to compare the inter-annual variation in genetic composition and structure of coastal species with contrasting traits, and (2) to test the temporal model and its potential for Ne estimation in populations with overlapping generations.

Materials & Methods

Target species

The white seabream Diplodus sargus is a coastal species in the north-eastern Atlantic ranging from Senegal to the Bay of Biscay, including the archipelagos of Canaries, Madeira and Azores, Mediterranean and Black Sea (Bauchot & Hureau, 1986). Eggs are planktonic, hatching after 3 days at 15–17 °C (Morato et al., 2003). Larvae are also planktonic, settling after a pelagic larval duration (PLD) of 14–19 days (Di Franco et al., 2011). Adults have considerable swimming ability and tend to remain near the coast. This is a commercially exploited fish, with increasing landings per year in the last 40 years (FAO, 2019). Some degree of overfishing can be empirically inferred, even if stocks show no evidence of decline, since larger D. sargus are mainly spotted within Marine Protected Areas. Previous studies on the genetic structure of the white seabream revealed isolation-by-distance (IBD), suggesting genetic isolation over large geographic distances (e.g., Domingues et al., 2007). In contrast, allozyme studies reported significant divergences in cohorts sampled over a 6-month period in a Mediterranean population (Lenfant & Planes, 2002; Planes & Lenfant, 2002).

The sand smelt Atherina presbyter is an inshore marine species, also present in estuaries and coastal lagoons. Its distribution area comprises the British Isles and southern North Sea to the Canary Islands and Mauritania (Quignard & Pras, 1986), and has also been reported in the Azores (Santos, Porteiro & Barreiros, 1997). This species spawns in shallow waters, the eggs are demersal and attached to vegetation, and larvae hatch after 15–16 days with 6.7–7.5 mm TL at a temperature of 15 °C (Bamber, Henderson & Turnpenny, 1985). The larval stage is very short (the hatching larvae are well developed and ready to start exogenous feeding), which likely restricts passive dispersal. Migratory movements of adult sand-smelts along exposed shores are probably difficult, although they are active swimmers in the water column. Our current understanding of gene flow in A. presbyter indicates a structured population across its range, also showing IBD (Francisco et al., 2009). A previous mtDNA study revealed temporal stability in the genetic composition of the sand-smelt from the Portuguese west coast (Francisco & Robalo, 2015).

The shanny Lipophrys pholis is a rocky intertidal resident fish, very common in western European shores, ranging from Norway to Mauritania and from the Azores and Madeira to the entrance of the Mediterranean (Zander, 1986). The eggs are demersal, guarded by the male, and hatch after 16 days with 5.0 mm total length at a temperature of 17 °C (Almada et al., 1992; Faria et al., 2002). The larvae hatch in a well-developed stage and settle at 13–14 mm TL after a PLD of ca. 29 days at a temperature of 15.5–17.5 °C (Faria et al., 2002). Juveniles and adults show weak swimming capabilities and, consequently, restricted movements within the same rocky stretch (Faria, Almada & Goncalves, 1996). Previous molecular work on the shanny, using both nuclear and mitochondrial markers, strongly suggests panmixia across its distribution (Francisco, Vieira & Almada, 2006; Stefanni et al., 2006; Francisco et al., 2011). Contrary to A. presbyter, the mtDNA study of L. pholis detected significant genetic differentiation between some sampling years (Francisco & Robalo, 2015).

Sampling scheme

Individuals of L. pholis, D. sargus and A. presbyter were sampled near Lisbon, in S. Pedro do Estoril (38°42′N, 9°22′W) and Fonte-da-Telha (38°34′N, 9°11′W) (Table 1) between 2003 and 2014. Both sampling locations are in the vicinity of marine protected areas and present heterogeneous rocky habitats mixed with sandy patches (e.g., Henriques, Gonçalves & Almada, 1999; Faria & Almada, 2001). They are known to harbour a relevant number of post-larvae and juveniles for several taxa, being important settlement and recruitment areas (e.g., Borges et al., 2009; Vinagre et al., 2018). Juveniles of each year were collected in intertidal rocky pools (L. pholis) and beach channels (D. sargus and A. presbyter). A small sample of fin clip was collected, preserved in 96° ethanol and deposited in ISPA-IU/MARE collections. All sampling and handling of fish were conducted according to established animal welfare guidelines (ORBEA-ISPA, Animal Welfare Body) and following the relevant legislation, as none of the sampled species are endangered or protected in Portugal.

Table 1
Diversity measures for sampling periods of Diplodus sargus, Atherina presbyter and Lipophrys pholis based on the control region of the mitochondria: number of sequences (N), number of haplotypes (Nh), percentage of private haplotypes (%Ph), haplotype richness (R), private allelic richness (pR), haplotype diversity (h), nucleotide diversity (π) and mean number of pairwise differences (k).
SpeciesSampling periodNNh%PhRpRhπk
Diplodus sargus2006201968.4220.00016.0080.9950.03413.058
2009393070.0016.44111.550.9740.03212.093
2011272259.0915.3799.4760.9830.03312.479
2014957379.4516.43111.6430.9930.02911.135
All1811200.9900.03111.690
Atherina presbyter2005342968.9620.23512.4230.9860.0228.012
2012916369.8417.6409.3520.9840.0217.780
2013614868.0817.9739.3960.9920.0217.866
2014955662.5016.5417.9220.9750.0228.054
All2811550.9840.0217.931
Lipophrys pholis2003302669.2321.63413.2110.9910.03111.614
2013977387.6123.70815.2910.9880.02810.844
2014998893.1826.24018.0340.9970.03111.977
All2261710.9950.03011.522

DNA extraction, amplification and sequencing

Total genomic DNA was extracted from about 20 mg of tissue with REDEXtract-N-mp kit (Sigma-Aldrich) following manufacturer’s instructions. Polymerase Chain Reaction (PCR) amplification was performed for two fragments, the mitochondrial control region (CR) and an intron of the nuclear S7 ribosomal protein gene (S7), with the primer pairs: LPro1 (5′-ACT CTC ACC CCT AGC TCC CAA AG-3′) and HDL1 (5′-CCT GAA GTA GGA ACC AGA TGC CAG-3′) (CR for the three species) (Ostellari et al., 1996), S7RPEX1F (5′-TGG CCT CTT CCT TGG CCG TC-3′) and S7RPEX2R (5′-AAC TCG TCT GGC TTT TCG CC-3′) (first intron of the S7 for the shanny and the white seabream), S7RPEX2F (5′-AGC GCC AAA ATA GTG AAG CC-3′) and S7RPEX3R (5′-GCC TTC AGG TCA GAG TTC AT-3′) (second intron of the S7 for the sand-smelt) (Chow & Hazama, 1998). The PCR protocol was performed in a 20 µl total reaction volume with 10 µl of REDExtract-N-ampl PCR mix (Sigma-Aldrich), 0.8 µl of each primer (10 µM), 4.4 µl of Sigma water and 4 µl of template DNA. An initial denaturation at 94 °C for 3 min was followed by 35 cycles (denaturation at 94 °C for 30/45 s, annealing at 55/58 °C for 30/45 s, and extension at 72 °C for 1 min; values CR/S7, respectively) and a final extension at 72 °C for 10 min on a Bio-Rad MyCycler thermal cycler. The same primers were used for the sequencing reaction and PCR products were purified and sequenced at STABVIDA (Portugal, http://www.stabvida.net) and GATC (Germany, http://www.gatc-biotech.com).

Sequences were edited with Codon Code Aligner (http://www.codoncode.com/index.htm) and aligned with Clustal X2 (Larkin et al., 2007). For S7 both strands of the same specimen were recovered, whenever possible, following the approach of Sousa-Santos et al. (2005). This approach takes advantage of the presence of indels in each nuclear marker and uses them to accurately reconstruct the individual haplotypes without the need for probabilistic estimation. Sequences were deposited in GenBank (Accession numbers MG992598MG992888, MH024090MH024357, MH030878MH031272). Additional sequences from previous work (Francisco et al., 2006; Francisco, Vieira & Almada, 2006; Domingues et al., 2007; Francisco et al., 2008; Francisco & Robalo, 2015) were retrieved from GenBank (Tables S1S3 in Data S1).

DNA analyses

The appropriate model of sequence evolution for the CR and S7 of each species was determined using the software jModeltest v.2.1.10 (Guindon & Gascuel, 2003; Darriba et al., 2012), under the Akaike Information Criterion (AIC) (Nei & Kumar, 2000). Parsimony networks estimated with TCS version 1.21 (Clement, Posada & Crandall, 2000) were used to analyse relationships among haplotypes and to compute outgroup weights, based on parsimony methods (Templeton, Crandall & Sing, 1992). Visualization and network layout were edited with tcsBU (Múrias dos Santos et al., 2016).

ARLEQUIN software package v.3.5 (Excoffier & Lischer, 2010) was used to estimate the genetic diversity (k—mean number of pairwise differences (Tajima, 1983); π- nucleotide diversity; and h—haplotype diversity (Nei, 1987)) within each sampling period, to perform analyses of molecular variance (AMOVA) (Excoffier, Smouse & Quattro, 1992) and to compute pairwise FSTs. The χ2 test (Salicru et al., 1993) was used to access the significance of differences in haplotype diversity among temporal samples. The software HP-Rare (Kalinowski, 2005) was used to estimate allelic richness R and private allelic richness pR, using rarefaction to correct for sample-size bias. Principal Coordinate Analysis (PCoA) was performed with GenAlEx 6.5 (Peakall & Smouse, 2006; Peakall & Smouse, 2012) to visualize the patterns of temporal genetic structure in a bi-dimensional space. The analyses of the S7 intron were also run in ARLEQUIN, after allowing the program to reconstruct the haplotypes present, using the ELB algorithm (Excoffier, Laval & Balding, 2003). The same software was used to perform the exact probability tests for deviations from the Hardy–Weinberg equilibrium (HWE) (Guo & Thompson, 1992).

Contemporary effective population size (Ne) and genetic drift (Fs) were estimated using TempoFs (Jorde & Ryman, 2007), under the temporal method of allele frequency shifts. The program reports Fs′ (genetic drift corrected for sampling plan) and Ne per generation. For this approach, we used sampling plan II (individuals sampled before reproduction and not returned to the population; Waples, 1989) and a generation time of 2 yr for L. pholis (Milton, 1983; Faria, Almada & Goncalves, 1996), 2 yr for D. sargus (Morato et al., 2003) and 1 yr for A. presbyter (Turnpenny, Bamber & Henderson, 1981).

Results

Mitochondrial data

For D. sargus, a total of 181 CR sequences were obtained, comprising 120 haplotypes and 119 polymorphic sites (117 transitions, 15 transversions and two indels) (Table S1). For the seabream CRs, 16.67% of haplotypes were shared between sampling periods. For A. presbyter, 281 CR sequences were obtained corresponding to 155 haplotypes (368bp long fragment). Differences among haplotypes corresponded to 70 polymorphic sites (63 transitions, 19 transversions and six indels) (Table S2). For the sand-smelt, 16.13% of haplotypes were shared between sampling periods. A total of 226 shannies were sequenced for the CR (380 bp), corresponding to 171 distinct haplotypes (Table S3). A total of 108 polymorphic sites were found, corresponding to 105 transitions, 26 transversions and eight indels. Only 7.02% of haplotypes were shared among sampling periods. The generalized time-reversible (GTR) + invariable sites (I) (Tavare, 1986) was estimated as the optimal molecular evolutionary model for the CR of the three species.

The statistical parsimony networks constructed with the CR datasets revealed multiple levels of diversification and deep genealogies, seemingly without temporal structure (Fig. 1). The inferred ancestral haplotype for the CR of D. sargus included specimens collected in 2009, 2011 and 2014 (outgroup weight: 0.071). In this network, most of the haplotypes were not arranged in a star-like pattern, and some of the branches reached a maximum of 46 mutational steps, and 18 steps from the ancestral haplotype. For A. presbyter, the network built with the CR dataset presented several star-like patterns around haplotypes, including the haplotype inferred as ancestral (outgroup weight: 0.051), which comprised individuals from every sampling period. The network was dominated by two haplotypes shared by most sampling periods. In this network, also deep but with less diversification, some of the haplotypes differed 16 mutational steps from ancestor. For L. pholis, the CR network displayed several haplotypes, including the estimated ancestor, in the centre of star-like patterns. This ancestral haplotype included specimens collected in the three sampling periods (outgroup weight: 0.053), and some haplotypes differed as many as 17 mutations (Fig. 1). Interestingly, for all three species, haplotypes that initially had been merely inferred were sampled in more recent periods.

Haplotype network for the CR of (A) Diplodus sargus, (B) Atherina presbyter and (C) Lipophrys pholis.
Figure 1
The haplotype with the highest out group probability is displayed as a square, other haplotypes as circles. The area of the circles is proportional to each haplotype frequency. Colours refer to the year of sampling. In the case where haplotypes are shared among sampling periods, shading is proportional to the frequency of the haplotype in each period.Haplotype network for the CR of (A) Diplodus sargus, (B) Atherina presbyter and (C) Lipophrys pholis.

For the CR, genetic diversity indices were generally high and similar across years (Table 1). Salicru tests revealed non-significantly different levels of haplotype diversity among sampling years: χ2 = 1.557 (p = 0.816), χ2 = 4.342 (p = 0.362) and χ2 = 3.022 (p = 0.388), for D. sargus, A. presbyter and L. pholis respectively. The CR allelic richness and private allelic richness yielded distinct results for the three species, with no pattern for the seabream, increasing over sampling time in the shanny, and decreasing in the sand-smelt (Table 1).

Global genetic differentiation among sampling years was not significant for CR in any of the analysed species—AMOVA results (white seabream: FST = 0.003, p = 0.250; sand-smelt: FST = 0.002, p = 0.312; shanny: FST = 0.009, p = 0.074). Comparisons between periods were not significant in D. sargus and A. presbyter. However, for L. pholis, significant differentiation was detected between 2013 and 2014 samples (FST = 0.013, p = 0.028). No species showed marked patterns of temporal genetic structure in CR and, therefore, the PCoA was not performed.

Estimates of effective population size based on the CR of the three species are given on Table 2, after correction for haploid data. L. pholis revealed a smaller Ne/generation when compared to the other species and A. presbyter showed no evidence of genetic drift for some of the study periods (negative values of Fs′), which resulted in much higher Ne/generation (∼inf).

Table 2
Mitochondrial control region (CR), nuclear S7 ribosomal protein gene (S7), estimated drift (Fs′), estimated effective population size per generation (Negen).
Estimates of contemporary effective population size and genetic drift for Diplodus sargus, Atherina presbyter and Lipophrys pholis.
SpeciesMarkerSampling intervalFsNegen
Diplodus sargusCR2006–20090.029102
2009–2011−0.005
2011–20140.005426
S72006–20090.02268
2009–2011−0.008
2011–2014−0.005
Atherina presbyterCR2005–2012−0.001
2012–2013−0.001
2013–20140.0011,282
S72005–20120.35210
2012–20130.01534
2013–2014−0.006
Lipophrys pholisCR2003–20130.008612
2013–20140.01094
S72003–20130.5345
2013–20140.01053

Nuclear data

For D. sargus, 302 sequences of S7 were obtained with 308bp (corresponding to 151 individuals), comprising 99 haplotypes and 50 polymorphic sites (29 transitions, 21 transversions and 2 indels) (Table S1). Only 12.12% of haplotypes of S7 were shared between sampling periods for the seabream. The amplification of S7 (N = 286,572 sequences) in A. presbyter resulted in a 201bp fragment, corresponding to 35 haplotypes (Table S2). A total of 25 polymorphic sites (6 transitions, 12 transversions and 7 indels) were found, and 57.14% of the haplotypes were shared between sampling periods. For the S7 of the shanny, a total of 360 sequences (180 individuals) were obtained (576 bp), comprising 24 distinct haplotypes (26.09% shared between sampling periods) (Table S3). The fragment yielded 20 polymorphic sites, corresponding to 9 transitions, 8 transversions and 3 indels. The unequal transitional substitution (TIM3) + invariable sites (I) + rate variation among sites (G) (Posada, 2003) was estimated as the optimal molecular evolutionary model for the S7 of the seabream. For the S7 of the sand-smelt, the selected model was the equal transitional substitution model (TIM1ef) + invariable sites (I) (Posada, 2003), and for the shanny was the unequal transitional substitution model (TIM3) + invariable sites (I) (Posada, 2003).

The S7 haplotype networks of the three species revealed shallower genealogies comparing to the CR (Fig. 2). The networks showed star-like patterns dominated by 2–3 very frequent haplotypes and no temporal structure was evident. The inferred ancestral haplotypes were shared among sampling periods, yielding outgroup weights of 0.067, 0.117 and 0.190, for D. sargus, A. presbyter and L. pholis, respectively.

Haplotype network for the S7 of (A) Diplodus sargus, (B) Atherina presbyter and (C) Lipophrys pholis.
Figure 2
The haplotype with the highest outgroup probability is displayed as a square, other haplotypes as circles. The area of the circles is proportional to each haplotype frequency. Colours refer to the year of sampling. In the case where haplotypes are shared among sampling periods, shading is proportional to the frequency of the haplotype in each period.Haplotype network for the S7 of (A) Diplodus sargus, (B) Atherina presbyter and (C) Lipophrys pholis.

The S7 genetic diversity indices of the three species are summarized in Table 3. The pattern of haplotypic diversity stability across years registered for the CR was only recovered for the S7 of D. sargus (χ2 = 1.289, p = 0.863). Salicru tests yielded significant differences for the haplotype diversity between sampling year for both A. presbyter (χ2 = 73.080, p < 0.001) and L. pholis (χ2 = 17.864, p < 0.001). Similarly, the Markov chain tests yielded significant deviations from the HWE for some years of the sand-smelt and shanny datasets (p < 0.001) (Table 3).

Table 3
Diversity measures for sampling periods of Diplodus sargus, Atherina presbyter and Lipophrys pholis based on the S7: number of sequences (N), number of haplotypes (Nh), percentage of private haplotypes (%Ph), haplotype richness (R), private allelic richness (pR), haplotype diversity (h), nucleotide diversity (π), mean number of pairwise differences (k) and test of deviations from the Hardy–Weinberg equilibrium observed/expected heterozygosity (Ho/He).
SpeciesSampling periodNNh%PhRpRhπkHo/He
Diplodus sargus2006202090.0024.96115.4610.9630.0123.7091.000/0.963
2009353369.7020.5838.0810.9420.0133.9680.943/0.942
2011191978.9519.0008.0520.9530.0123.8630.895/0.953
2014776786.5724.36811.3010.9610.0134.0060.961/0.962
All1511250.9810.0133.948
Atherina presbyter200538450.004.0002.0700.2420.0020.3800.034/0.033 *
2012901717.6514.3342.0820.8160.0011.9900.129/0.142*
2013641921.0516.2234.2620.7430.0091.7720.099/0.099
2014942626.9217.8304.6050.7700.0102.0630.102/0.115
All286420.7340.0091.779
Lipophrys pholis200320751.717.0004.6370.3600.0021.1210.400/0.360
2013761353.857.1622.6080.7570.0042.5340.750/0.757*
2014841250.006.2251.4890.7210.0042.3270.655/0.721*
All180230.7340.0042.380
Notes.
* Significant values of probability p.

For the S7 of the white seabream, the AMOVA results revealed no significant temporal structure among sampling years (FST = 0.003, p = 0.204). Pairwise FST comparisons were also non-significant. A different pattern was recovered for the S7 of the sand-smelt and the shanny, as significant global genetic differentiation was yielded (FST = 0.020 p < 0.001 and FST = 0.067 p < 0.001, respectively). For A. presbyter significant differences were found between 2005 and every other period (p < 0.001). For L. pholis pairwise FST was significant between 2003 and the more recent sampling times (p < 0.001). The PCoA corroborated these patterns of temporal genetic structure, with the first axis explaining 94% and 99% of the observed variance, for the S7 of A. presbyter and L. pholis, respectively, and suggesting two groups associated with older vs recent sampling periods (Fig. 3).

Principal Coordinate Analysis for the S7 of (A) Atherina presbyter and (B) Lipophrys pholis.
Figure 3
Principal Coordinate Analysis for the S7 of (A) Atherina presbyter and (B) Lipophrys pholis.

For the S7 fragment, TempoFs yielded lower Ne/generation estimates for the shanny (Table 2). Both the white seabream and the sand-smelt revealed negative values of Fs′ for some of the more recent sampling intervals, i.e., no evidence of genetic drift and, conversely, very high Ne/generation values (∼inf) (Table 2).

Discussion

Temporal genetic stability

Traditionally population genetic and phylogeographic studies have disregarded the temporal dimension, pooling samples based on their geographical origin and ignoring their collecting period (Viñas, Alvarado-Bremer & Pla, 2004; Charrier et al., 2006; Luttikhuizen et al., 2008; Gonzalez-Wanguemert et al., 2011; Stefanni et al., 2015). One of our main result is the subsequent appearance of inferred haplotypes, clearly reinforcing the importance of temporal evaluations. Similar results were also found using mitochondrial markers in several other species (e.g., Genypterus capensis (Henriques et al., 2017), Prionace glauca (Veríssimo et al., 2017), Seriola lalandi in the Pacific Ocean (Sepúlveda & González, 2017)).

Another important finding was the stability across years in these three ecologically distinct species, reinforcing the preliminary work by Francisco & Robalo (2015). This high degree of similarity was found in (a) the high genetic diversity, with little variation between sampling periods, especially true for the mitochondrial data (Table 1); (b) the deep genealogies with several diversification levels (Fig. 2); and (c) the absence of temporal structure for the mitochondrial data. In contrast, we detected temporal structure and some marked differences among sampling years in both the shanny and the sand-smelt for S7 (Fig. 3). In fact, this differentiation between sampling periods was also detected for the CR of L. pholis, notwithstanding the absence of temporal genetic structure.

Stochasticity in larval survival and transport, due to variation in oceanographic conditions, and genetic differentiation among temporal samples are broadly documented in the literature (e.g., Selkoe et al., 2006; Selkoe et al., 2010). Lipohrys pholis lays fewer eggs and has a longer planktonic larval stage when compared to both A. presbyter and D. sargus. Therefore, while its male-guarding behaviour can promote larval retention near rocky coasts, this is easily disrupted by atypical or severe events, when larvae may be transported very large distances if storm conditions prevail. Thus, the shanny’s higher differentiation (marked differences among sampling periods in both markers and temporal structure for S7) might be closely related with its greater vulnerability to recruitment fluctuations. Conversely, the white seabream revealed a greater degree of temporal stability for both molecular markers. In contrast, allozyme studies in D. sargus in the Mediterranean found rapid genetic change within a population, assumed to be driven by genetic drift (Lenfant & Planes, 2002; Planes & Lenfant, 2002). We suggest that these apparently contradictory findings may be due to the type of molecular marker. Planes & Lenfant (2002) associated the large variation in the white seabream’s reproductive success with linkage disequilibrium and genetic relatedness shown in their allozyme data. In our study (mtDNA and nDNA) the absence of temporal structure is probably associated with D. sargus’ higher fecundity (Goncalves & Erzini, 2000; Mouine et al., 2007), longer spawning season (Dias et al., 2016) and relatively short PLD (Di Franco et al., 2011). Temporal stability has been described in several other marine species, such as Solea solea (Cuveliers et al., 2011), Pagrus auratus (Bernal-Ramírez et al., 2003), Meganyctiphanes norvegica (Papetti et al., 2005) and Ammodytes marinus (Jiménez-Mena et al., 2019).

Also noteworthy is the significant deviation from HWE in the sand-smelt and the shanny, the two species with most contrasting early life-history traits. Although unexpected for a relatively long PLD species like L. pholis, this result was previously reported in other marine organisms (e.g., Chapman, Ball & Mash, 2002; Karlsson & Mork, 2005; Pérez-Portela et al., 2019). Departure from HWE has been ascribed to natural selection, migration, Wahlund effect, null alleles, inbreeding and/or phenotypic assortative mating (e.g., Addison & Hart, 2005; Karlsson & Mork, 2005; Gonzalez, Beerli & Zardoya, 2008; Garnier-Géré & Chikhi, 2013). Although we were not able to explain these results, we are aware that HW disequilibrium violates the assumptions behind temporal Ne estimation (e.g., Nei & Tajima, 1981; Serbezov et al., 2012; Wang, Santiago & Caballero, 2016).

Estimates of contemporary effective population size

In the present work, the estimated population effective size per generation was smaller for the shanny than the other two species, corroborating preliminary findings by Francisco & Robalo (2015). The negative Fs′ found for some of the more recent sampling intervals, for both white seabream and sand-smelt, evidenced no genetic drift (CR and S7). Conversely, the Ne/generation values for these species were very high (∼inf) (Table 2). Genetic stochasticity is most likely weaker in marine organisms with such large Ne (Palstra & Ruzzante, 2008), as found in fish species like S. solea (Cuveliers et al., 2011) and G. morhua (Therkildsen et al., 2010) (see Marandel et al. (2019) for a review). Lower Ne/generation was found in fish species such as the herring Clupea harengus (Larsson et al., 2010), the silver seabream P. auratus (Hauser et al., 2002) and the thornback ray Raja clavata (Chevolot et al., 2008). The extremely low Ne/generation found in the first sampling interval of L. pholis and A. presbyter (S7) (Table 2) might be an underestimation due to statistical artefacts, immigration from other populations, or other factors (e.g., Palstra & Ruzzante, 2008).

Considering the target species and sampling scheme, the adjusted temporal method was, in our opinion, the best choice for estimating contemporary Ne. Not only is this method considered the least biased for species with overlapping generations (e.g., Jorde & Ryman, 2007; Waples & Yokota, 2007), the analysis of consecutive cohorts is also the best way to reduce Ne estimation bias (e.g., Jorde & Ryman, 1995; Luikart et al., 2010). Nevertheless, several factors can confound Ne estimates, including the aforementioned departure from HWE. For instance, temporal estimates of Ne may be biased by potential migratory movements within metapopulations that can override the effects of genetic drift (e.g., Palstra & Ruzzante, 2008; Waples & Do, 2010; Ryman et al., 2014). In fact, the assumption of complete isolation of the study population is frequently violated, and the resulting bias is generally of unknown magnitude (e.g., Ryman et al., 2014). In this study, this might result in biased Ne estimates for the sand-smelt and white seabream, for which IBD patterns have been recorded (Domingues et al., 2007; Francisco et al., 2009). Sampling strategy is another potential source of bias (e.g., Jorde & Ryman, 2007; Waples & Yokota, 2007; Luikart et al., 2010), including low sample size and sex-biased or age-biased sampling. In the present work, uneven sample size between years (considerably lower N for the first sampling point) likely affected our Ne estimates. Species life-history and reproductive strategy can also hamper interpretation of Ne estimates (e.g., Waples & Yokota, 2007). Sequential hermaphrodites, such like the protandrous D. sargus (Bauchot & Hureau, 1986), present sex ratios skewed towards the initial sex (Charnov & Bull, 1989). According to standard fixed-sex theory, this results in reduced Ne (Wright, 1938) due to greater genetic drift (Charlesworth, 2009). The very high Ne/generation values obtained for the white seabream seemingly contradict this prediction (Table 2). This expected lower Ne was also challenged in recent studies with protogynous species over historical timescales (Coscia et al., 2016), and in analysis of an eco-evolutionary model with ten hypothetical species (Waples, Mariani & Benvenuto, 2018).

Recently, the reliability of contemporary estimates of Ne has been challenged and questioned (e.g., Wang, Santiago & Caballero, 2016; Marandel et al., 2019). However, these reviews focused on single-point estimation approaches (Linkage-Disequilibrium methods), assuming discrete generations and, implicitly, reduced population sizes. Marandel et al. (2019) found significantly biased estimates in simulations of large populations of fish and therefore a need for extra-large sampling sizes (impracticable in most sampling schemes). Unlike the criticized studies, our work used a temporal method comprising extensions to estimate Ne in age-structured species (Jorde, 2012).

While the present study used only one geographical location and two genetic markers, these apparent limitations allowed a rigorous comparison with data from several species collected in the same area over the past fifteen years and using the same two markers (mitochondrial control region and nuclear S7) (e.g., Domingues et al., 2006; Almada et al., 2012; Robalo et al., 2013; Francisco et al., 2014; Stefanni et al., 2015; Almada et al., 2017; Pappalardo et al., 2017), which facilitated comparison and calibration of genetic diversity results. Thus, it was possible to observe the high genetic diversity pattern found in several coastal fish populations in western Portugal (e.g., D. vulgaris (Stefanni et al., 2015) and Labrus bergylta (Almada et al., 2017)).

Conclusions

Genetic monitoring through time seeks to disentangle life-trait patterns affecting marine organisms, while deepening our ability to forecast changes in both genetic composition and diversity. This study assessed fluctuations in genetic composition among different sampling periods in the western Portuguese shore using a comparative approach in three species with distinct ecological and life-history characteristics. No temporal structure was detected for the mitochondrial marker, while for nDNA significant differences were recorded among some sampling periods for the shanny and the sand-smelt. The haplotype networks revealed deep genealogies, and one of our major findings was the repeated appearance of previously inferred haplotypes. The shanny revealed a smaller Ne/generation when compared to the other species but revealed no evidence of genetic drift for most study periods. These results clearly underline that temporal variation in gene pool composition should be considered when evaluating population structure of long larval dispersion fish species, which are more vulnerable to recruitment fluctuations. Comparison between the commercially and non-commercially explored species yielded no conclusive results.

Previous authors suggested increasing the number of time-point estimates (e.g., from pre-harvest and postharvest times around an overexploitation event) to circumvent difficulties in short-term prediction of Ne (Pita et al., 2017). Future studies with additional time-points covering a longer time span, additional sampling locations covering the species geographical distribution and additional markers would greatly improve the reliability of the present paper’s results. Available tools include a combination of methods of demographic inference and large genomic datasets generated with RAD-seq (restriction site-associated DNA sequencing) (e.g., Barbato et al., 2015), as recently reported in critically endangered species (e.g., Carcharias taurus in Reid-Anderson, Bilgmann & Stow, 2019) and commercially important species (e.g., Oncorhynchus kisutch in Barría et al., 2019; Solea solea in Le Moan et al., 2019). These genomic approaches, with their high-throughput sequencing methods, will likely improve our understanding of recent population demography (Waples, 2016). Applying and extending this framework to species with distinct features (e.g., life-history traits), conservation status and commercial importance would be of paramount importance to detect global patterns and predict the ability of species to adapt to future changes.

Acknowledgements

We thank Frederico Almada, Ana Faria, Ana Lopes and Pedro Duarte Coelho for their help with field work. We are grateful to Per Erik Jorde for helpful suggestions on the temporal data analysis. We also thank André Levy for his valuable language editing.

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Sara M. Francisco conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.
Joana I. Robalo conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

Animal Ethics

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

All sampling (fin clip) and handling of fish were conducted according to established animal welfare guidelines (ORBEA-ISPA, Animal Welfare Body—declaration 01/2019) and in accordance with the relevant legislation, as none of the sampled species is endangered or protected in Portugal.

Field Study Permissions

The following information was supplied relating to field study approvals (i.e., approving body and any reference numbers):

The sampling took place in accordance with the relevant legislation, as none of the sampled species is endangered or protected in Portugal.

DNA Deposition

The following information was supplied regarding the deposition of DNA sequences:

Sequences are available in GenBank: MG992598MG992888, MH024090MH024357, MH030878MH031272.

Data Availability

The following information was supplied regarding data availability:

The complete list of specimens, sampling year, haplotype number and GenBank accession number for the mitochondrial control region and nuclear S7 ribosomal protein gene (intron 1 and 2) of the three target species (the white seabream Diplodus sargus, the sand-smelt Atherina presbyter and the shanny Lipophrys pholis) are available in the Supplemental File.

References

Addison & Hart (2005) 

    Addison JA, Hart MW 2005. . Spawning, copulation and inbreeding coefficients in marine invertebrates. Biology Letters 1: , pp.450-453, doi: 10.1098/rsbl.2005.0353

Allison et al. (2003) 

    Allison GW, Gaines SD, Lubchenco J, Possingham HP 2003. . Ensuring persistence of marine reserves: catastrophes require adopting an insurance factor. Ecological Applications 13: , pp.8-24, doi: 10.1890/1051-0761(2003)013[0008:EPOMRC]2.0.CO;2

Almada et al. (2012) 

    Almada VC, Almada F, Francisco SM, Castilho R, Robalo JI 2012. . Unexpected high genetic diversity at the extreme northern geographic limit of Taurulus bubalis (Euphrasen, 1786). PLOS ONE 7: e44404, doi: 10.1371/journal.pone.0044404

Almada et al. (2017) 

    Almada F, Francisco SM, Lima CS, Fitzgerald R, Mirimin L, Villegas-Ríos D, Saborido-Rey F, Afonso P, Morato T, Bexiga S, Robalo JI 2017. . Historical gene flow constraints in a northeastern Atlantic fish: phylogeography of the ballan wrasse Labrus bergylta across its distribution range. Royal Society Open Science 4: , pp.160773, doi: 10.1098/rsos.160773

Almada et al. (1992) 

    Almada VC, Goncalves EJ, Oliveira RF, Barata EN 1992. . Some features of the territories in the breeding males of the intertidal blenny Lipophrys pholis (Pisces: Blenniidae). Journal of the Marine Biological Association of the United Kingdom 72: , pp.187-197, doi: 10.1017/S0025315400048876

Bamber, Henderson & Turnpenny (1985) 

    Bamber RN, Henderson PA, Turnpenny AWH 1985. . The early life history of the sand smelt (Atherina Presbyter). Journal of the Marine Biological Association of the United Kingdom 65: , pp.697-706, doi: 10.1017/S002531540005253X

Barbato et al. (2015) 

    Barbato M, Orozco-terWengel P, Tapio M, Bruford MW 2015. . SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Frontiers in Genetics 6: , pp.109, doi: 10.3389/fgene.2015.00109

Barría et al. (2019) 

    Barría A, Christensen KA, Yoshida G, Jedlicki A, Leong JS, Rondeau EB, Lhorente JP, Koop BF, Davidson WS, Yáñez JM 2019. . Whole genome linkage disequilibrium and effective population size in a coho salmon (Oncorhynchus kisutch) breeding population using a high-density SNP array. Frontiers in Genetics 10: , pp.498, doi: 10.3389/fgene.2019.00498

Bauchot & Hureau (1986) 

    Bauchot M, Hureau J 1986. . Sparidae. Fishes of the North-Eastern Atlantic and Mediterranean UNESCOParis Whitehead P, Bauchot M, Hureau J, Nielsen J, Tortonese E, pp.883-907

Bernal-Ramírez et al. (2003) 

    Bernal-Ramírez JH, Adcock GJ, Hauser L, Carvalho GR, Smith PJ 2003. . Temporal stability of genetic population structure in the New Zealand snapper, Pagrus auratus, and relationship to coastal currents. Marine Biology 142: , pp.567-574, doi: 10.1007/s00227-002-0972-9

Borges et al. (2009) 

    Borges R, Vaz J, Serrão EA, Gonçalves EJ 2009. . Short-term temporal fluctuation of very-nearshore larval fish assemblages at the Arrábida Marine Park (Portugal). Journal of Coastal Research 56: , pp.376-380

Chapman, Ball & Mash (2002) 

    Chapman R, Ball A, Mash L 2002. . Spatial homogeneity and temporal heterogeneity of red drum (Sciaenops ocellatus) microsatellites: effective population sizes and management implications. Marine Biotechnology 4: , pp.589-603, doi: 10.1007/s10126-002-0038-5

Charlesworth (2009) 

    Charlesworth B 2009. . Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics 10: , pp.195-205, doi: 10.1038/nrg2526

Charnov & Bull (1989) 

    Charnov EL, Bull JJ 1989. . Non-fisherian sex ratios with sex change and environmental sex determination. Nature 338: , pp.148-150, doi: 10.1038/338148a0

Charrier et al. (2006) 

    Charrier G, Chenel T, Durand JD, Girard M, Quiniou L, Laroche J 2006. . Discrepancies in phylogeographical patterns of two European anglerfishes (Lophius budegassa and Lophius piscatorius). Molecular Phylogenetics and Evolution 38: , pp.742-754, doi: 10.1016/j.ympev.2005.08.002

Chevolot et al. (2008) 

    Chevolot M, Ellis JR, Rijnsdorp AD, Stam WT, Olsen JL 2008. . Temporal changes in allele frequencies but stable genetic diversity over the past 40 years in the Irish Sea population of thornback ray, Raja clavata. Heredity 101: , pp.120-126, doi: 10.1038/hdy.2008.36

Chow & Hazama (1998) 

    Chow S, Hazama K 1998. . Universal PCR primers for S7 ribosomal protein gene introns in fish. Molecular Ecology 7: , pp.1247-1263

Clement, Posada & Crandall (2000) 

    Clement M, Posada D, Crandall KA 2000. . TCS: a computer program to estimate gene genealogies. Molecular Ecology 9: , pp.1657-1659

Coscia et al. (2016) 

    Coscia I, Chopelet J, Waples RS, Mann BQ, Mariani S 2016. . Sex change and effective population size: implications for population genetic studies in marine fish. Heredity 117: , pp.251-258, doi: 10.1038/hdy.2016.50

Crosbie & Manly (1985) 

    Crosbie SF, Manly BFJ 1985. . Parsimonious modelling of capture-mark-recapture studies. Biometrics 41: , pp.385-398, doi: 10.2307/2530864

Cuveliers et al. (2011) 

    Cuveliers EL, Volckaert FAM, Rijnsdorp AD, Larmuseau MHD, Maes GE 2011. . Temporal genetic stability and high effective population size despite fisheries-induced life-history trait evolution in the North Sea sole. Molecular Ecology 20: , pp.3555-3568, doi: 10.1111/j.1365-294X.2011.05196.x

Darriba et al. (2012) 

    Darriba D, Taboada GL, Doallo R, Posada D 2012. . jModelTest 2: more models, new heuristics and parallel computing. Nature Methods 9: , pp.772-772, doi: 10.1038/nmeth.2109

Di Franco et al. (2011) 

    Di Franco A, De Benedetto G, De Rinaldis G, Raventos N, Sahyoun R, Guidetti P 2011. . Large scale-variability in otolith microstructure and microchemistry: the case study of Diplodus sargus sargus (Pisces: Sparidae) in the Mediterranean Sea. Italian Journal of Zoology 78: , pp.182-192, doi: 10.1080/11250003.2011.566227

Dias et al. (2016) 

    Dias M, Roma J, Fonseca C, Pinto M, Cabral HN, Silva A, Vinagre C 2016. . Intertidal pools as alternative nursery habitats for coastal fishes. Marine Biological Research 12: , pp.331-344, doi: 10.1080/17451000.2016.1143106

Domingues et al. (2006) 

    Domingues VS, Almada VC, Santos RS, Brito A, Bernardi G 2006. . Phylogeography and evolution of the triplefin Tripterygion delaisi (Pisces, Blennioidei). Marine Biology 150: , pp.509-519, doi: 10.1007/s00227-006-0367-4

Domingues et al. (2007) 

    Domingues V, Santos R, Brito A, Alexandrou M, Almada V 2007. . Mitochondrial and nuclear markers reveal isolation by distance and effects of Pleistocene glaciations in the northeastern Atlantic and Mediterranean populations of the white seabream (Diplodus sargus, L.). Journal of Experimental Marine Biology and Ecology 346: , pp.102-113, doi: 10.1016/j.jembe.2007.03.002

Excoffier, Laval & Balding (2003) 

    Excoffier L, Laval G, Balding D 2003. . Gametic phase estimation over large genomic regions using an adaptive window approach. Human Genomics 1: , pp.7, doi: 10.1186/1479-7364-1-1-7

Excoffier & Lischer (2010) 

    Excoffier L, Lischer HEL 2010. . Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10: , pp.564-567, doi: 10.1111/j.1755-0998.2010.02847.x

Excoffier, Smouse & Quattro (1992) 

    Excoffier L, Smouse PE, Quattro JM 1992. . Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131: , pp.479-491

FAO (2019) 

Faria & Almada (2001) 

    Faria C, Almada V. Microhabitat segregation in three rocky intertidal fish species in Portugal: does it reflect interspecific competition?. Journal of Fish Biology 2001. 58: , pp.145-159, doi: 10.1111/j.1095-8649.2001.tb00504.x

Faria, Almada & Goncalves (1996) 

    Faria C, Almada VC, Goncalves EJ 1996. . Juvenile recruitment, growth and maturation of Lipophrys pholis (Pisces: Blenniidae), from the west coast of Portugal. Journal of Fish Biology 49: , pp.727-730, doi: 10.1111/j.1095-8649.1996.tb00068.x

Faria et al. (2002) 

    Faria C, Borges R, Gil F, Almada VC, Goncalves EJ 2002. . Embryonic and larval development of Lipophrys pholis (Pisces: Blenniidae). Scientia Marina 66: , pp.21-26, doi: 10.3989/scimar.2002.66n121

Francisco et al. (2014) 

    Francisco SM, Almada VC, Faria C, Velasco EM, Robalo JI 2014. . Phylogeographic pattern and glacial refugia of a rocky shore species with limited dispersal capability: the case of Montagu’s blenny (Coryphoblennius galerita, Blenniidae). Marine Biology 161: , pp.2509-2520, doi: 10.1007/s00227-014-2523-6

Francisco et al. (2006) 

    Francisco SM, Cabral H, Vieira MN, Almada VC 2006. . Contrasts in genetic structure and historical demography of marine and riverine populations of Atherina at similar geographical scales. Estuarine Coastal and Shelf Science 69: , pp.655-661, doi: 10.1016/j.ecss.2006.05.017

Francisco et al. (2009) 

    Francisco SM, Castilho R, Soares M, Congiu L, Brito A, Vieira MN, Almada VC 2009. . Phylogeography and demographic history of Atherina presbyter (Pisces: Atherinidae) in the North-eastern Atlantic based on mitochondrial DNA. Marine Biology 156: , pp.1421-1432, doi: 10.1007/s00227-009-1182-5

Francisco et al. (2008) 

    Francisco SM, Congiu L, Stefanni S, Castilho R, Brito A, Ivanova PP, Levy A, Cabral H, Kilias G, Doadrio I, Almada VC 2008. . Phylogenetic relationships of the North-eastern Atlantic and Mediterranean forms of Atherina (Pisces, Atherinidae). Molecular Phylogenetics and Evolution 48: , pp.782-788, doi: 10.1016/j.ympev.2007.12.009

Francisco et al. (2011) 

    Francisco SM, Faria C, Lengkeek W, Vieira MN, Velasco EM, Almada VC 2011. . Phylogeography of the shanny Lipophrys pholis (Pisces: Blenniidae) in the NE Atlantic records signs of major expansion event older than the last glaciation. Journal of Experimental Marine Biology and Ecology 403: , pp.14-20, doi: 10.1016/j.jembe.2011.03.020

Francisco & Robalo (2015) 

    Francisco SM, Robalo J 2015. . Genetic structure and effective population size through time: a tale on two coastal marine species with contrasting life-history patterns. Journal of Phylogenetics and Evolutionary Biology 3: , pp.155, doi: 10.4172/2329-9002.1000155

Francisco, Vieira & Almada (2006) 

    Francisco SM, Vieira MN, Almada VC 2006. . Genetic structure and historical demography of the shanny Lipophrys pholis in the Portuguese coast based on mitochondrial DNA analysis. Molecular Phylogenetics and Evolution 39: , pp.288-292, doi: 10.1016/j.ympev.2005.12.009

Frankham, Bradshaw & Brook (2014) 

    Frankham R, Bradshaw CJA, Brook BW 2014. . Genetics in conservation management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biological Conservation 170: , pp.56-63, doi: 10.1016/j.biocon.2013.12.036

Galarza et al. (2009) 

    Galarza JA, Carreras-Carbonell J, Macpherson E, Pascual M, Roques S, Turner GF, Rico C 2009. . The influence of oceanographic fronts and early-life-history traits on connectivity among littoral fish species. Proceedings of the National Academy of Sciences of the United States of America 106: , pp.1473-1478, doi: 10.1073/pnas.0806804106

Garnier-Géré & Chikhi (2013) 

    Garnier-Géré P, Chikhi L 2013. . Population subdivision, Hardy–Weinberg equilibrium and the Wahlund effect. Encyclopedia of Life Sciences Hoboken John Wiley & Sons, Ltd (Ed.), doi: 10.1002/9780470015902.a0005446.pub3

Goldstien et al. (2013) 

    Goldstien SJ, Inglis GJ, Schiel DR, Gemmell NJ 2013. . Using temporal sampling to improve attribution of source populations for invasive species. PLOS ONE 8: e65656, doi: 10.1371/journal.pone.0065656

Gon (2015) 

Goncalves & Erzini (2000) 

    Goncalves JMS, Erzini K 2000. . The reproductive biology of the two-banded sea bream (Diplodus vulgaris) from the southwest coast of Portugal. Journal of Applied Ichthyology 16: , pp.110-116, doi: 10.1046/j.1439-0426.2000.00232.x

Gonzalez, Beerli & Zardoya (2008) 

    Gonzalez EG, Beerli P, Zardoya R 2008. . Genetic structuring and migration patterns of Atlantic bigeye tuna, Thunnus obesus (Lowe, 1839). BMC Evolutionary Biology 8: , pp.252, doi: 10.1186/1471-2148-8-252

Gonzalez-Wanguemert et al. (2011) 

    Gonzalez-Wanguemert M, Froufe E, Perez-Ruzafa A, Alexandrino P 2011. . Phylogeographical history of the white seabream Diplodus sargus (Sparidae): implications for insularity. Marine Biology Research 7: , pp.250-260, doi: 10.1080/17451000.2010.499438

Guindon & Gascuel (2003) 

    Guindon S, Gascuel O 2003. . A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Systematics Biology 52: , pp.696-704, doi: 10.1080/10635150390235520

Guo & Thompson (1992) 

    Guo SW, Thompson EA 1992. . Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48: , pp.361-372, doi: 10.2307/2532296

Hare et al. (2011) 

    Hare MP, Nunney L, Schwartz MK, Ruzzante DE, Burford M, Waples RS, Ruegg K, Palstra F 2011. . Understanding and estimating effective population size for practical application in marine species management. Conservation Biology 25: , pp.438-449, doi: 10.1111/j.1523-1739.2010.01637.x

Hauser et al. (2002) 

    Hauser L, Adcock GJ, Smith PJ, Bernal-Ramirez JH, Carvalho GR 2002. . Loss of microsatellite diversity and low effective population size in an overexploited population of New Zealand snapper (Pagrus auratus). Proceedings of the National Academy of Sciences of the United States of America 99: , pp.11742-11747, doi: 10.1073/pnas.172242899

Henriques, Gonçalves & Almada (1999) 

    Henriques M, Gonçalves EJ, Almada VC 1999. . The conservation of littoral fish communities: a case study at Arrábida coast (Portugal). Behaviour and conservation of littoral fishes ISPALisbon Almada VC, Oliveira RF, Gonçalves EJ, pp.473-519

Henriques et al. (2017) 

    Henriques R, Nielsen ES, Durholtz D, Japp D, Von der Heyden S 2017. . Genetic population sub-structuring of kingklip (Genypterus capensis—Ophidiidiae), a commercially exploited demersal fish off South Africa. Fisheries Research 187: , pp.86-95, doi: 10.1016/j.fishres.2016.11.007

Hill (1981) 

    Hill WG 1981. . Estimation of effective population size from data on linkage disequilibrium. Genetic Research 38: , pp.209-216, doi: 10.1017/S0016672300020553

Jiménez-Mena et al. (2019) 

    Jiménez-Mena B, Le Moan A, Christensen A, Van Deurs M, Mosegaard H, Hemmer-Hansen J, Bekkevold D 2019. . Weak genetic structure despite strong genomic signal in lesser sandeel in the North Sea. Evolutionary Applications 13: , pp.376-387, doi: 10.1111/eva.12875

Jolly (1965) 

    Jolly GM 1965. . Explicit estimates from capture-recapture data with both death and immigration-stochastic model. Biometrika 52: , pp.225-248, doi: 10.2307/2333826

Jorde (2012) 

    Jorde PE 2012. . Allele frequency covariance among cohorts and its use in estimating effective size of age-structured populations. Molecular Ecology Resources 12: , pp.476-480, doi: 10.1111/j.1755-0998.2011.03111.x

Jorde & Ryman (1995) 

    Jorde PE, Ryman N 1995. . Temporal allele frequency change and estimation of effective size in populations with overlapping generations. Genetics 139: , pp.1077-1090

Jorde & Ryman (2007) 

    Jorde PE, Ryman N 2007. . Unbiased estimator for genetic drift and effective population size. Genetics 177: , pp.927-935, doi: 10.1534/genetics.107.075481

Kalinowski (2005) 

    Kalinowski ST 2005. . hp-rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Molecular Ecology Notes 5: , pp.187-189, doi: 10.1111/j.1471-8286.2004.00845.x

Karlsson & Mork (2005) 

    Karlsson S, Mork J 2005. . Deviation from HardyeWeinberg equilibrium, and temporal instability in allele frequencies at microsatellite loci in a local population of Atlantic cod. ICES Journal of Marine Science 62: , pp.1588-1596, doi: 10.1016/j.icesjms.2005.05.009

Knutsen et al. (2007) 

    Knutsen H, Jorde PE, Albert OT, Hoelzel AR, Stenseth NC 2007. . Population genetic structure in the North Atlantic Greenland halibut (Reinhardtius hippoglossoides): influenced by oceanic current systems?. Cannadian Journal of Fisheries and Aquatic Sciences 64: , pp.857-866, doi: 10.1139/F07-070

Larkin et al. (2007) 

    Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG 2007. . Clustal W and Clustal X version 2.0. Bioinformatics 23: , pp.2947-2948, doi: 10.1093/bioinformatics/btm404

Larsson et al. (2010) 

    Larsson LC, Laikre L, André C, Dahlgren TG, Ryman N 2010. . Temporally stable genetic structure of heavily exploited Atlantic herring (Clupea harengus) in Swedish waters. Heredity 104: , pp.40-51, doi: 10.1038/hdy.2009.98

Le Moan et al. (2019) 

    Le Moan A, Jímenez-Mena B, Bekkevold D, Hemmer-Hanse J 2019. . Fine scale population structure linked to neutral divergence in the common sole (Solea solea), a marine fish with high dispersal capacity. BioRxiv , doi: 10.1101/662619

Lenfant & Planes (2002) 

    Lenfant P, Planes S 2002. . Temporal genetic changes between cohorts in a natural population of a marine fish, Diplodus sargus. Biological Journal of the Linnean Society 76: , pp.9-20, doi: 10.1046/j.1095-8312.2002.00041.x

Lima et al. (2008) 

    Lima D, Santos MM, Ferreira AM, Micaelo C, Reis-Henriques MA 2008. . The use of the shanny Lipophrys pholis for pollution monitoring: a new sentinel species for the northwestern European marine ecosystems. Environment International 34: , pp.94-101, doi: 10.1016/J.ENVINT.2007.07.007

Luikart et al. (2010) 

    Luikart G, Ryman N, Tallmon DA, Schwartz MK, Allendorf FW 2010. . Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches. Conservation Genetics 11: , pp.355-373, doi: 10.1007/s10592-010-0050-7

Luttikhuizen et al. (2008) 

    Luttikhuizen PC, Campos J, Van Bleijswijk J, Peijnenburg KTCA, Van der Veer HW 2008. . Phylogeography of the common shrimp, Crangon crangon (L.) across its distribution range. Molecular Phylogenetics and Evolution 46: , pp.1015-1030, doi: 10.1016/j.ympev.2007.11.011

Lynch & Lande (1998) 

Marandel et al. (2019) 

    Marandel F, Lorance P, Berthelé O, Trenkel VM, Waples RS, Lamy J-B 2019. . Estimating effective population size of large marine populations, is it feasible?. Fish and Fisheries 20: , pp.189-198, doi: 10.1111/faf.12338

Milton (1983) 

    Milton P 1983. . Biology of littoral blenniid fishes on the coast of South-West England. Journal of the Marine Biological Association of the United Kingdom 63: , pp.223-237, doi: 10.1017/S0025315400049912

Morato et al. (2003) 

    Morato T, Afonso P, Lourinho P, Nash RDM, Santos RS 2003. . Reproductive biology and recruitment of the white sea bream in the Azores. Journal of Fish Biology 63: , pp.59-72, doi: 10.1046/j.1095-8649.2003.00129.x

Mouine et al. (2007) 

    Mouine N, Francour P, Ktari M-H, Chakroun-Marzouk N 2007. . The reproductive biology of Diplodus sargus sargus; in the Gulf of Tunis (central Mediterranean). Scientia Marina 71: , pp.461-469, doi: 10.3989/scimar.2007.71n3461

Múrias dos Santos et al. (2016) 

    Múrias dos Santos A, Cabezas MP, Tavares AI, Xavier R, Branco M 2016. . tcsBU: a tool to extend TCS network layout and visualization. Bioinformatics 32: , pp.627-628, doi: 10.1093/bioinformatics/btv636

Nei (1987) 

    Nei M 1987. Molecular evolutionary genetics Columbia University PressNew York

Nei & Kumar (2000) 

    Nei M, Kumar S 2000. Molecular evolution and phylogenetics Oxford University PressOxford

Nei & Tajima (1981) 

    Nei M, Tajima F 1981. . Genetic drift and estimation of effective population size. Genetics 98: , pp.625-640

Nomura (2008) 

    Nomura T 2008. . Estimation of effective number of breeders from molecular coancestry of single cohort sample. Evolutionary Applications 1: , pp.462-474, doi: 10.1111/j.1752-4571.2008.00015.x

Ostellari et al. (1996) 

    Ostellari L, Bargelloni L, Penzo E, Patarnello P, Patarnello T 1996. . Optimization of single-strand conformation polymorphism and sequence analysis of the mitochondrial control region in Pagellus bogaraveo (Sparidae, Teleostei): rationalized tools in fish population biology. Animal Genetics 27: , pp.423-427, doi: 10.1111/j.1365-2052.1996.tb00510.x

Palstra & Ruzzante (2008) 

    Palstra FP, Ruzzante DE 2008. . Genetic estimates of contemporary effective population size: What can they tell us about the importance of genetic stochasticity for wild population persistence?. Molecular Ecology 17: , pp.3428-3447, doi: 10.1111/j.1365-294X.2008.03842.x

Papetti et al. (2005) 

    Papetti C, Zane L, Bortolotto E, Bucklin A, Patarnello T 2005. . Genetic differentiation and local temporal stability of population structure in the euphausiid Meganyctiphanes norvegica. Marine Ecology Progress Series 289: , pp.225-235, doi: 10.3354/meps289225

Pappalardo et al. (2017) 

    Pappalardo AM, Francisco SM, Fruciano C, Lima CS, Pulvirenti V, Tigano C, Robalo JI, Ferrito V 2017. . Mitochondrial and nuclear intraspecific variation in the rusty blenny (Parablennius sanguinolentus, Blenniidae). Hydrobiologia 802: , pp.141-154, doi: 10.1007/s10750-017-3248-6

Peakall & Smouse (2006) 

    Peakall R, Smouse PE 2006. . Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: , pp.288-295, doi: 10.1111/j.1471-8286.2005.01155.x

Peakall & Smouse (2012) 

    Peakall R, Smouse PE 2012. . GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28: , pp.2537-2539, doi: 10.1093/bioinformatics/bts460

Pérez-Portela et al. (2019) 

    Pérez-Portela R, Wangensteen OS, Garcia-Cisneros A, Valero-Jiménez C, Palacín C, Turon X 2019. . Spatio-temporal patterns of genetic variation in Arbacia lixula, a thermophilous sea urchin in expansion in the Mediterranean. Heredity 122: , pp.244-259, doi: 10.1038/s41437-018-0098-6

Pita et al. (2017) 

    Pita A, Pérez M, Velasco F, Presa P 2017. . Trends of the genetic effective population size in the Southern stock of the European hake. Fisheries Research 191: , pp.108-119, doi: 10.1016/j.fishres.2017.02.022

Planes & Lenfant (2002) 

    Planes S, Lenfant P 2002. . Temporal change in the genetic structure between and within cohorts of a marine fish, Diplodus sargus, induced by a large variance in individual reproductive success. Molecular Ecology 11: , pp.1515-1524, doi: 10.1046/j.1365-294X.2002.01521.x

Plank et al. (2010) 

    Plank SM, Lowe CG, Feldheim KA, Wilson RR, Brusslan JA 2010. . Population genetic structure of the round stingray Urobatis halleri (Elasmobranchii: Rajiformes) in southern California and the Gulf of California. Journal of Fish Biology 77: , pp.329-340, doi: 10.1111/j.1095-8649.2010.02677.x

Pollard et al. (2014) 

    Pollard D, Russel B, Carpenter KE, Iwatsuki Y, Vega-Cendejas M, Jassim-Kawari A, Hartmann S, Alnazry H, Abdulqader E, Alam S, Bishop J, Hassan-Al-Khalf K, Kaymaram F 2014. . Diplodus sargus. The IUCN Red List of Threatened Species 2014 , doi: 10.2305/IUCN.UK.2014-3.RLTS.T170155A42736975.en

Posada (2003) 

    Posada D 2003. . Using MODELTEST and PAUP* to select a model of nucleotide substitution. Current Protocols in Bioinformatics 00: , pp.6.5.1-6.5.14, doi: 10.1002/0471250953.bi0605s00

Pudovkin, Zaykin & Hedgecock (1996) 

    Pudovkin AI, Zaykin DV, Hedgecock D 1996. . On the potential for estimating the effective number of breeders from heterozygote-excess in progeny. Genetics 144: , pp.383-387

Quignard & Pras (1986) 

    Quignard J-P, Pras A 1986. . Atherinidae. Fishes of the North-Eastern Atlantic and Mediterranean UNESCOParis Whitehead P, Bauchot M, Hureau J, Nielsen J, Tortonese E, pp.1207-1210

Reid-Anderson, Bilgmann & Stow (2019) 

    Reid-Anderson S, Bilgmann K, Stow A 2019. . Effective population size of the critically endangered east Australian grey nurse shark Carcharias taurus. Marine Ecology Progress Series 610: , pp.137-148, doi: 10.3354/meps12850

Riginos et al. (2019) 

    Riginos C, Hock K, Matias AM, Mumby PJ, Van Oppen MJH, Lukoschek V 2019. . Asymmetric dispersal is a critical element of concordance between biophysical dispersal models and spatial genetic structure in Great Barrier Reef corals. Diversety and Distributions 25: , pp.1684-1696, doi: 10.1111/ddi.12969

Riginos & Victor (2001) 

    Riginos C, Victor BC 2001. . Larval spatial distributions and other early life-history characteristics predict genetic differentiation in eastern Pacific blennioid fishes. Proceedings of the Royal Society B 268: , pp.1931-1936, doi: 10.1098/rspb.2001.1748

Robalo et al. (2013) 

    Robalo JI, Lima CS, Francisco SM, Almada F, Bañon R, Villegas-ríos D, Almada VC 2013. . Monitoring climate change impact on the genetic population structure: the case of the fivebeard rockling (Ciliata mustela, Linnaeus, 1758) in its southern limit of distribution. Journal of Phylogenetics and Evolutionary Biology 1: , pp.1-4, doi: 10.4172/2329-9002.1000123

Ryman et al. (2014) 

    Ryman N, Allendorf FW, Jorde PE, Laikre L, Hössjer O 2014. . Samples from subdivided populations yield biased estimates of effective size that overestimate the rate of loss of genetic variation. Molecular Ecology Resources 14: , pp.87-99, doi: 10.1111/1755-0998.12154

Salicru et al. (1993) 

    Salicru M, Menendez ML, Morales D, Pardo L 1993. . Asymptotic distribution of (hφ)-entropies. Communications in Statistics—Theory and Methods 22: , pp.2015-2031, doi: 10.1080/03610929308831131

Santos, Porteiro & Barreiros (1997) 

    Santos R, Porteiro F, Barreiros J 1997. . Marine fishes of the azores: annotated checklist and bibliography. Arquipelago—Life and Marine Sciences Suppl 1: , pp.1-268

Schwartz, Luikart & Waples (2007) 

    Schwartz MK, Luikart G, Waples RS 2007. . Genetic monitoring as a promising tool for conservation and management. Trends in Ecology and Evolution 22: , pp.25-33, doi: 10.1016/J.TREE.2006.08.009

Seber (1965) 

    Seber GAF 1965. . A note on the multiple-recapture census. Biometrika 52: , pp.249-259, doi: 10.2307/2333827

Selkoe et al. (2006) 

Selkoe et al. (2010) 

    Selkoe KA, Watson JR, White C, Horin TB, Iacchei M, Mitttarai S, Siegel DA, Gaines SD, Toonen RJ 2010. . Taking the chaos out of genetic patchiness: seascape genetics reveals ecological and oceanographic drivers of genetic patterns in three temperate reef species. Molecular Ecology 19: , pp.3708-3726, doi: 10.1111/j.1365-294X.2010.04658.x

Sepúlveda & González (2017) 

    Sepúlveda FA, González MT 2017. . Spatio-temporal patterns of genetic variations in populations of yellowtail kingfish Seriola lalandi from the south-eastern Pacific Ocean and potential implications for its fishery management. Journal of Fish Biology 90: , pp.249-264, doi: 10.1111/jfb.13179

Serbezov et al. (2012) 

    Serbezov D, Jorde PE, Bernatchez L, Olsen EM, Vøllestad LA 2012. . Short-term genetic changes: evaluating effective population size estimates in a comprehensively described brown trout (Salmo trutta) population. Genetics 191: , pp.579-592, doi: 10.1534/genetics.111.136580

Sousa-Santos et al. (2005) 

    Sousa-Santos C, Robalo JI, Collares-Pereira MJ, Almada VC 2005. . Heterozygous indels as useful tools in the reconstruction of DNA sequences and in the assessment of ploidy level and genomic constitution of hybrid organisms. DNA Sequence 16: , pp.462-467, doi: 10.1080/10425170500356065

Stefanni et al. (2015) 

    Stefanni S, Castilho R, Sala-Bozano M, Robalo JI, Francisco SM, Santos RS, Marques N, Brito A, Almada VC, Mariani S 2015. . Establishment of a coastal fish in the Azores: recent colonisation or sudden expansion of an ancient relict population?. Heredity 115: , pp.527-537, doi: 10.1038/hdy.2015.55

Stefanni et al. (2006) 

    Stefanni S, Domingues V, Bouton N, Santos RS, Almada F, Almada V 2006. . Phylogeny of the shanny, Lipophrys pholis, from the NE Atlantic using mitochondrial DNA markers. Molecular Phylogenetics and Evolution 39: , pp.282-287, doi: 10.1016/j.ympev.2005.07.001

Tajima (1983) 

    Tajima F 1983. . Evolutionary relationship of DNA sequences in finite populations. Genetics 105: , pp.437-460

Tavare (1986) 

    Tavare S 1986. . Some probabilistic and statistical problems in the analysis of DNA sequences. Lectures on Mathematics in the Life Sciences 17: , pp.57-86

Templeton, Crandall & Sing (1992) 

    Templeton AR, Crandall KA, Sing F 1992. . Cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. 111. Cladogram estimation. Genetics 132: , pp.619-633

Therkildsen et al. (2010) 

    Therkildsen NO, Nielsen EE, Swain DP, Pedersen JS 2010. . Large effective population size and temporal genetic stability in Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Canadian Journal of Fisheries and Aquatic Sciences 67: , pp.1585-1595, doi: 10.1139/F10-084

Turnpenny, Bamber & Henderson (1981) 

    Turnpenny AWH, Bamber RN, Henderson PA 1981. . Biology of the sand-smelt (Atherina presbyter Valenciennes) around Fawley power station. Journal of Fish Biology 18: , pp.417-427, doi: 10.1111/j.1095-8649.1981.tb03783.x

Veríssimo et al. (2017) 

    Veríssimo A, Sampaio Í, McDowell JR, Alexandrino P, Mucientes G, Queiroz N, Da Silva C, Jones CS, Noble LR 2017. . World without borders—genetic population structure of a highly migratory marine predator, the blue shark (Prionace glauca). Ecology and Evolution 7: , pp.4768-4781, doi: 10.1002/ece3.2987

Verry et al. (2020) 

    Verry AJF, Walton K, Tuck ID, Ritchie PA 2020. . Genetic structure and recent population expansion in the commercially harvested deep-sea decapod, Metanephrops challengeri (Crustacea: Decapoda). New Zealand Journal of Marine and Freshwater Research 54: , pp.251-270, doi: 10.1080/00288330.2019.1707696

Vinagre et al. (2018) 

    Vinagre C, Mendonça V, Cereja R, Abreu-Afonso F, Dias M, Mizrahi D, Flores AAV 2018. . Ecological traps in shallow coastal waters-Potential effect of heat-waves in tropical and temperate organisms. PLOS ONE 13: e0192700, doi: 10.1371/journal.pone.0192700

Viñas, Alvarado-Bremer & Pla (2004) 

    Viñas J, Alvarado-Bremer J, Pla C 2004. . Phylogeography of the Atlantic bonito (Sarda sarda) in the northern Mediterranean: the combined effects of historical vicariance, population expansion, secondary invasion, and isolation by distance. Molecular Phylogenetics and Evolution 33: , pp.32-42, doi: 10.1016/j.ympev.2004.04.009

Wang (2001) 

    Wang J 2001. . A pseudo-likelihood method for estimating effective population size from temporally spaced samples. Genetics Research 78: , pp.243-257, doi: 10.1017/S0016672301005286

Wang (2005) 

    Wang J 2005. . Estimation of effective population sizes from data on genetic markers. Philosophical Transactions of the Royal Society of London B 360: , pp.1395-1409, doi: 10.1098/rstb.2005.1682

Wang (2009) 

    Wang J 2009. . A new method for estimating effective population sizes from a single sample of multilocus genotypes. Molecular Ecology 18: , pp.2148-2164, doi: 10.1111/j.1365-294X.2009.04175.x

Wang, Santiago & Caballero (2016) 

    Wang J, Santiago E, Caballero A 2016. . Prediction and estimation of effective population size. Heredity 117: , pp.193-206, doi: 10.1038/hdy.2016.43

Waples (1989) 

    Waples RS 1989. . A generalized approach for estimating effective population size from temporal changes in allele frequency. Genetics 121: , pp.379-391

Waples (2016) 

    Waples RS 2016. . Life-history traits and effective population size in species with overlapping generations revisited: the importance of adult mortality. Heredity 117: , pp.241-250, doi: 10.1038/hdy.2016.29

Waples & Do (2010) 

    Waples RS, Do C 2010. . Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evolutionary Applications 3: , pp.244-262, doi: 10.1111/j.1752-4571.2009.00104.x

Waples, Mariani & Benvenuto (2018) 

    Waples RS, Mariani S, Benvenuto C 2018. . Consequences of sex change for effective population size. Proceedings of the Royal Society B 285: , pp.20181702, doi: 10.1098/rspb.2018.1702

Waples & Yokota (2007) 

    Waples RS, Yokota M 2007. . Temporal estimates of effective population size in species with overlapping generations. Genetics 175: , pp.219-233, doi: 10.1534/genetics.106.065300

White et al. (2010) 

    White C, Selkoe KA, Watson J, Siegel DA, Zacherl DC, Toonen RJ 2010. . Ocean currents help explain population genetic structure. Proceedings of the Royal Society B 277: , pp.1685-1694, doi: 10.1098/rspb.2009.2214

Williams & Craig (2014) 

Wright (1931) 

    Wright S 1931. . Evolution in mendelian populations. Genetics 16: , pp.97-159

Wright (1938) 

    Wright S 1938. . Size of population and breeding structure in relation to evolution. Science 87: , pp.430-431

Zander (1986) 

    Zander CD 1986. . Blenniidae. Fishes of the North-Eastern Atlantic and Mediterranean UNESCOParis Whitehead P, Bauchot M, Hureau J, Nielsen J, Tortonese E, pp.1096-1112
https://www.researchpad.co/tools/openurl?pubtype=article&doi=10.7717/peerj.9098&title=Time matters: genetic composition and evaluation of effective population size in temperate coastal fish species&author=Sara M. Francisco,Joana I. Robalo,María Ángeles Esteban,&keyword=Effective population size,mtDNA,nDNA,Temporal structure,Temporal stability,Temporal method,&subject=Aquaculture, Fisheries and Fish Science,Evolutionary Studies,Marine Biology,Population Biology,