This study was focused on conservation genetics of threatened Hippocampus guttulatus on the Atlantic coast of NW Iberian Peninsula. Information about spatial structure and temporal stability of wild populations was obtained based on microsatellite markers, and used for monitoring a captive breeding program firstly initiated in this zone at the facilities of the Institute of Marine Research (Vigo, Spain). No significant major genetic structure was observed regarding the biogeographical barrier of Cape Finisterre. However, two management units under continuous gene flow are proposed based on the allelic differentiation between South-Atlantic and Cantabrian subpopulations, with small to moderate contemporary effective size based on single-sample methods. Temporal stability was observed in South-Atlantic population samples of H. guttulatus for the six-year period studied, suggesting large enough effective population size to buffer the effects of genetic drift within the time frame of three generations. Genetic analysis of wild breeders and offspring in captivity since 2009 allowed us to monitor the breeding program founded in 2006 in NW Spain for this species. Similar genetic diversity in the renewed and founder broodstock, regarding the wild population of origin, supports suitable renewal and rearing processes to maintain genetic variation in captivity. Genetic parentage proved single-brood monogamy in the wild and in captivity, but flexible short- and long-term mating system under captive conditions, from strict monogamy to polygamy within and/or among breeding seasons. Family analysis showed high reproductive success in captivity under genetic management assisted by molecular relatedness estimates to avoid inbreeding. This study provides genetic information about H. guttulatus in the wild and captivity within an uncovered geographical range for this data deficient species, to be taken into account for management and conservation purposes.
Citation: López A, Vera M, Planas M, Bouza C (2015) Conservation Genetics of Threatened Hippocampus guttulatus in Vulnerable Habitats in NW Spain: Temporal and Spatial Stability of Wild Populations with Flexible Polygamous Mating System in Captivity. PLoS ONE 10(2): e0117538. https://doi.org/10.1371/journal.pone.0117538
Academic Editor: Hanping Wang, The Ohio State University, UNITED STATES
Received: September 17, 2014; Accepted: December 26, 2014; Published: February 3, 2015
Copyright: © 2015 López et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by the Spanish Government (CGL2005-05927-C03-01/-03; CGL2009-08386) and the Regional Government of Galicia (09MDS022402PR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Seahorses (Hippocampus spp.) are endangered species due to population decline of many wild populations by means of direct overexploitation, incidental captures, habitat destruction and other anthropogenic perturbations [1–3]. The entire genus Hippocampus was listed as threatened by the Convention on International Trade in Endangered Species . The fact that the conservation status of so many seahorse species is Data Deficient  demonstrates the challenges in conducting robust conservation assessments .
The European long-snouted seahorse, Hippocampus guttulatus (Cuvier 1829) is present along the Northeastern Atlantic coast, the Mediterranean and Black Sea. Regionally, it is listed as near threatened, vulnerable or endangered in different European countries . Moreover, it is also included in the list of threatened species and habitats by the Convention for the Protection of the Marine Environment of the North-East Atlantic , together with Cymodocea meadows and Zostera beds, commonly inhabited by seahorses, which reinforces the conservation value of these vulnerable marine ecosystems. Similarly, Spanish legislation has recently included the Mediterranean and Atlantic Iberian populations of H. guttulatus, C. nodosa and Z. marina into the list of wild species under special protection .
Despite the huge conservation interest, scarce information is available on population status and trends of European Atlantic seahorses . The major threat to H. guttulatus is habitat degradation and anthropogenic disturbance, such as destructive fishing practices, coastal development and pollution [3, 6]. On the Galician coast (NW Spain), some commercial fisheries directed at fish, clams or scallops can cause considerable habitat damage, especially to seagrass beds, and it may impact non-targeted species such as seahorses . Field data revealed small census and densities in wild populations of NW Iberian coasts, in agreement with the observation from fishermen and scuba diving associations about the decrease of seahorse populations in the last years . In such context, actions for the development of rearing techniques and conservation of this species have been recently undertaken in NW Spain [9–14].
The primary goal of species conservation is the preservation of viable wild populations in their original habitats. Genetic monitoring is particularly warranted for threatened species that live in vulnerable habitats. The ability to detect genetic differences in time and space for wild populations is critical in species management and conservation of genetic variation . However, investigations of the temporal and spatial genetic structure of marine fish are relatively scarce . Knowledge of seahorse population structure may improve conservation efforts by identifying evolutionary management units and sources of diversity to be used for conservation breeding programs . Previous studies in seahorses based on mitochondrial and microsatellite markers have reported either small genetic differentiation or moderate but significant structure regarding biogeographical discontinuities [17–21]. Geographical structure has also been suggested for Atlantic populations of H. guttulatus and H. hippocampus, with barriers to gene flow identified at Cape Finisterre based on mitochondrial DNA [22, 23].
As part of overall conservation strategies, captive breeding programs may be necessary to improve the survival of threatened species that are in vulnerable circumstances . Among the priorities of the conservation breeding are to capture and retain the maximum possible genetic diversity in a limited population and to minimize any deleterious effects, such as artificial selection and disease risks . Genealogical traceability and knowledge about relationships between breeders are crucial data for ensuring successful management of captive stocks, avoiding pairings between closely related individuals [26, 27]. In addition, captive breeding may benefit from incorporating knowledge about the natural breeding system and mate choice, as relevant evolutionary components of population genetic diversity . The knowledge on genetic and social mating systems in seahorses represents essential information for planning conservation mechanisms and strategies in these species [29, 30]. Microsatellite markers have proven to be useful to ascertain the mating system and to investigate parentage and kinship of wild and captive populations of some seahorse species [31–34], including H. guttulatus [30, 35]. Genetic analysis of different populations of this latter species revealed single maternity per brood [30, 35], and serial monogamy in captivity .
This study is included into a multidisciplinary conservation approach on wild populations’ recovery and rearing development of a flagship marine species, the long-snouted seahorse, on the Atlantic Iberian coast (NW Spain). A captive breeding program has been firstly initiated in Europe at the facilities of the Institute of Marine Research (IIM, Vigo, Spain) for research and conservation purposes in this species [9, 10, 13]. In such context, the purpose of the present study was to investigate the spatial and temporal stability of wild populations of this species based on microsatellite markers, and to use such information for the genetic monitoring of the captive broodstock. The specific aims were: i) to assess the genetic structure of wild populations of H. guttulatus in NW Spain flanking the biogeographical barrier of Cape Finisterre, ii) to evaluate the temporal stability and the effective population size of Atlantic population samples over the six-year period analyzed, iii) to monitor the genetic diversity of the renewed captive broodstock respect to the founder breeders and the wild population of origin, iv) to trace genealogical relationships to evaluate the reproductive success and genetic mating system at short- and long-term (within brood, within season and among seasons) in the conservation program.
Materials and Methods
All locations were sampled under specific permission by Marine Authorities from the local Government Xunta de Galicia (Consellería de Pesca e Asuntos Marítimos). Non-lethal sampling was carried out from small tissue pieces (dorsal fin or skin filaments) of live specimens for this protected species in the Cantabrian and South Atlantic estuaries under study. Collection, sampling methods, animal maintenance and manipulation practices were conducted in compliance with all bioethics standards of the Spanish Government and approved by the CSIC Bioethics Committee.
Biological samples and DNA extraction
From 2006 until 2012, scuba diving (510 hours) was performed for evaluating wild seahorse resources on Galician coasts (NW Spain). Non-lethal samples of dorsal fin or skin filaments  were collected in situ from 225 live specimens of H. guttulatus. They came from Cantabrian (Betanzos 42.1N/8.5W) to South-Atlantic (Arousa 42.3N/8.6W, Pontevedra 42.2N/8.5W, and Vigo 42.1N/8.5W) estuaries, to the north and to the south of Cape Finisterre (Fig. 1). Each estuary comprised 52, 73, 48 and 52 samples, respectively, in the period under study. Temporal samples were available within South-Atlantic estuaries and were distributed in four groups (2006, 2009, 2010 and 2011; Table 1), based on the onset of breeding season of H. guttulatus in NW Spain (March; [9, 10]).
The situation of the captive breeding program at the Institute of Marine Research (IIM Center) is also included.
Sample size (by sex in brackets; males:females) is indicated for each geographical (see Fig. 1) and temporal sample.
Fifty-three out of the wild seahorses sampled were moved to the captive breeding program (Stock09; see Broodstock genetic analysis section) at the Institute of Marine Research (IIM, CSIC, Vigo, Spain; Fig. 1; ), under permission of local Government Xunta de Galicia. Since 2009, 814 out of the total 13,948 offspring (6%) obtained from 39 batches of newborn seahorses from the renewed broodstock (mean: 16.8 offspring per batch) were sampled at the IIM facilities for molecular parentage analysis. Among them, 12 and 27 batches were from wild-caught male broods and captive breeding events, respectively. Genomic DNA from non-lethal samples of wild seahorses and from dead young offspring was isolated using NucleoSpin Tissue XS kit (Macherey-Nagel) and Chelex procedure , respectively.
A novel set of 13 microsatellites from those previously reported in the genus Hippocampus was selected for population analysis following technical and polymorphism criteria (Table 2; [37–39]). Two multiplex PCR using multiple primer sets were assayed for amplifying and genotyping these microsatellite loci. A first set of six loci useful for parentage analysis in H. guttulatus was amplified in a single PCR for all wild seahorses and offspring (Hgu-USC5, Hgu-USC6, Hgu-USC7, Hgu-USC8, Hgut4, Hgut6) following López et al. . A second PCR multiplex with the remaining seven loci was amplified for wild seahorses in a volume of 10 μL with 1X Qiagen Multiplex PCR Master Mix, 50 ng of DNA template, and 0.08, 0.10, 0.10, 0.08, 0.15, 0.15, 0.10 μM of each primer for Hgu-USC2, Hgu-USC4, Hgu-USC9, Hgu-USC12, Hgu-USC13, Hcaμ25, Hcaμ36, respectively, using NED, 6-FAM, VIC, VIC, 6-FAM, PET, PET as 5’ fluorescent label for their respective forward primer. PCR conditions consisted of an initial denaturation at 95°C for 15 min, followed by 25 cycles at 94°C for 30 s, 57°C for 90 s and 72°C for 60 s, and a final extension at 60°C for 30 min. PCR products were run on an ABI 3730xl DNA Analyzer (Applied Biosystem), using GeneMapper v4.0 (Applied Biosystems) for genotyping.
Genetic diversity and demography
Allele frequencies were obtained from genotype data (S1 Table) using Fstat v18.104.22.168 . Conformance to Hardy-Weinberg (HW) expectations and linkage disequilibrium were checked using exact tests implemented in Genepop v4.0 , applying Bonferroni correction for multiple tests. The presence of null alleles and scoring errors (allele dropout, stuttering) was investigated using Micro-Checker v2.2.3 . Allelic richness (AR) was obtained using Fstat. Allele number per locus (A), and observed and expected heterozygosity (Ho, He) were estimated using Cervus v3.0 .
Contemporary effective population size (Ne) was computed using single point and temporal methods . i) Ne in single spatial samples caught in the same year from Cantabrian (CS06) and South-Atlantic (SA06) estuaries was estimated using ONeSAMP v1.2 [45, 46]. Ne prior intervals of 2 to 500 were applied based on the small and unequal population density observed across locations during underwater surveys , and assuming that sampling size (37–52) could represent around 10% of the population size . ii) Starting from the most distant temporal samples from South-Atlantic estuaries (SA06 vs. SA11; Table 1), a temporal moment-based estimate of Ne was obtained using NeEstimator v1.3 , assuming a generation time of 1.8 years .
Signatures of genetic bottlenecks were investigated using the M-value method implemented in M_P_Val . Mean M-values over loci and their significance (10,000 replicates) were calculated assuming a proportion of 0.10 for multi-step mutations with mean size of 3.5. A wide range of possible scenarios was covered (θ = 4Neμ), from small (θ = 0.1) to large (θ = 10) pre-bottleneck Ne in natural populations, and also a long-term Ne estimate for each sample derived from He (Ne = [(1/(1-He))2–1]/(8μ); μ: mutation rate ) [50, 51], assuming equilibrium and stepwise mutation model (SMM) [49, 52, 53].
Allelic differentiation AST between pairs of population samples was estimated using Metapop v2.0.a1 . The statistics Jost’s  DEST and the standardized Hedrick’s  G”ST were estimated using GenAlEx v6.5  (999 permutations, 999 bootstraps), since they are not influenced by intrapopulation diversity. G”ST is further corrected for bias when number of populations is small . Analysis of molecular variance (AMOVA; ) was performed using Arlequin v3.11  to assess the distribution of the genetic variation among and within estuaries, and between temporal samples under a priori grouping of samples according to their estuarine location (Betanzos, Arousa, Pontevedra and Vigo; see Table 1 and Fig. 1).
Isolation-by-distance over the studied distribution area was evaluated using the correlation of Rousset’s distance measure  based on G”ST against the logarithm of the geographical distances among the four estuaries of origin. Mantel test (30,000 replicates) was conducted to assess the relationship between genetic and geographical distances, using the software IBDWS .
Number of population units (K) was inferred using the Bayesian MCMC approach implemented in Structure v2.3.1 . Analysis of the whole sampling data was carried out under the admixture ancestral model with correlated allele frequencies , without prior population information and also using prior population information since it could aid to detect cryptic structure under small genetic differentiation  (burn-in: 50,000; MCMC: 200,000). Ten independent runs were conducted for the Ks tested (1 to 13) and the mean of Ln probabilities of data (Ln Pr(X|K)) across runs were calculated. Posterior probability of each K was computed from Bayes’ Rule . The most likely value of K was also estimated according to the Evanno’s ΔK statistic using Structure Harvester v0.6.94 [65, 66]. Finally, the Structure output for the best ΔK was summarized to correct the variance across runs using Clumpp v1.1.2 , and graphically displayed using Distruct v1.1 .
Despite the departure from HW expectations at one locus and the possible presence of null alleles at very low frequency in another one (see Results), all population structure analyses were based on all loci. The relative coefficients of genetic and allelic differentiation together with the Structure analyses were recalculated excluding these loci and rendered very similar results (see below; S2 Table; S1 Fig.).
Broodstock genetic analysis
The conditions for the maintenance of the individuals in captivity were reported by Planas et al. [9, 12]. Briefly, adult seahorses were kept under temperature (15°C in winter to 19°C in summer) and natural photoperiod (16L:8D in June-July; 10L:14D in December-January) regimes and fed ad libitum twice daily on enriched adult Artemia (EG, Inve, Spain), supplemented with captured Mysidacea (Leptomysis sp. and Siriella sp.).
Genetic diversity in the renewed broodstock since 2009 (Stock09) was compared with the Galician wild population based on the complete set of 13 loci, and with the stock founded in 2006 (Stock06; ) using the six most polymorphic loci . Departure from HW expectations and linkage disequilibrium were checked using exact tests implemented in Genepop, applying Bonferroni correction for multiple tests. Genetic diversity estimators (A, AR, He), together with theoretical probabilities for exclusion (Excl1 and Excl2, when the other parent is unknown and known, respectively) and for sibling identity (SI) were estimated using Cervus. DEST and G”ST between Stock09 and South-Atlantic wild population samples, as well as between Stock09 and Stock06 were estimated using GenAlEx (999 permutations, 999 bootstraps).
Relatedness (r) between all pairs of breeders in the renewed Stock09 was computed using Wang estimator  (SPAGeDi v1.2; ). The midpoints between the expected r-values for unrelated (UR; r = 0), half-sibs (HS; r = 0.25) and full-sibs (FS; r = 0.5) kinships were used as thresholds to classify individuals (UR≤0.125<HS<0.375≤FS; [35, 71]).
Parentage analysis of wild and captive progeny
The renewed broodstock (28 M-males and 25 F-females) was distributed in five aquaria (A to E) under unbiased sex-ratio treatment (5–7 M:5–7 F) to improve reproductive success based on previous data [9, 35]. Temporal changes in some aquaria were recorded, either towards male-biased (4–7 M:3–5 F) or female-biased (2–4 M:5–6 F) sex-ratios (Table 3), which were associated with new incorporations from wild populations and eliminations by death or disease. The distribution of breeders would provide 22 possible mating pairs under strict monogamy assumption. Twelve wild-caught males carrying young inside their brood pouch when collected were kept separately until newborn seahorses were released (wild batches); three of these males were sampled in the vicinity of a female during the field survey. Twenty-seven batches of newborn seahorses were obtained from reproductive events in captivity (captive batches). Parentage assignment for all offspring genotyped (S1 Table) in each aquarium was assessed using the exclusion-based method (FAP v3.6; ), including all sampled females for parentage analysis of batches from wild-pregnant males. When necessary, maternal genotypes were reconstructed from progeny and paternal arrays using gerud 2.0 , and the resulting inferred maternal genotypes were compared with each other and with the population genotype collection for adult females using Cervus.
Genetic diversity of wild population samples
Spatial variation on either side of Cape Finisterre discontinuity
Samples of H. guttulatus collected in 2006 from Cantabrian Sea (CS06) and South-Atlantic coasts (SA06; Table 1; Fig. 1) were analyzed for spatial comparison northward and southward of the Cape Finisterre, respectively. A single locus showed significant deviation from HW equilibrium after Bonferroni correction (heterozygote excess at Hgu-USC2 in CS06). It was not associated to genotyping errors using Micro-Checker, which only showed signs of null alleles at Hgut6 at low frequency (0.03) in CS06. There was no evidence of linkage disequilibrium between all pairs of loci tested. There was no indication of spatial change in genetic diversity between CS06 and SA06, with no significant differences in number of alleles, allelic richness and expected heterozygosity (Table 2; P = 0.056, P = 0.327 and P = 0.084, respectively; Wilcoxon tests). The spatial samples showed M-ratios around 0.7 which is a diagnostic value of genetic bottlenecks  (M = 0.679 and 0.697 in CS06 and SA06, respectively). The M-ratio obtained in the whole dataset (M = 0.737 in CS06+SA06 under continuous gene flow; see below) was also lower than the cutoff value of 0.82 reported for stable natural populations [49, 53]. Most M-values reached statistical significance or were marginally significant (under very large pre-bottleneck Ne; S3 Table), suggesting that they were not strongly affected by the choice of prior Ne scenario, and, thus, that they represent a reliable evidence of genetic bottleneck . Point estimates of contemporary effective population size (Ne) based on single spatial samples were 862 in CS06 (95% CL: 474, 2307) and 200 in SA06 (95% CL: 136, 430).
Small and non-significantly different from zero DEST (0.003; 95% CI: -0.003, 0.011) and standardized G”ST (0.005; 95% CI: -0.006, 0.016) values were obtained between CS06 and SA06. Allelic differentiation between both population samples was significantly different from zero at 0.05 level (AST = 0.124; 95% CI: 0.020, 0.182), with 26 private alleles although mostly rare at the most variable loci (only three with a frequency above 0.05). Similar results were observed when the only locus deviating from HW (Hgu-USC2) was excluded (S2 Table). Heterogeneity of allelic frequencies per locus was only significant at Hgut4 (P = 0.0003) with two alternative alleles in CS06 (*158; freq. 0.09) and SA06 (*164; freq. 0.07).
Spatial and temporal variation along northwestern Iberian coasts
Results from Structure analyses for the global dataset (all geographical and temporal samples; Table 1) revealed one population unit as the most probable number of clusters (P(K = 1) = 1; P(K = 2–13)≈0). These same posterior probabilities of K were observed in the spatial analyses from the same year (CS06 and SA06). The highest ΔK was observed for K = 6 and K = 2 in the global and spatial analyses, respectively, using Structure Harvester, but none of them was consistent with a temporal or spatial clustering. Thus, the proportion of the sample assigned to each cluster was roughly symmetric (1/K in each population) and most individuals were fairly admixed (S1 Fig.), indicative signs of absence of population structure . This could be due to limitations of ΔK, which cannot find the best K if K = 1 . Similar results were observed when Hgu-USC2 (with HW deviations) and Hgut6 (signs of null alleles at low frequency) were excluded from the global and spatial analysis (highest P-value for K = 1; best ΔK for K = 2 and K = 3, respectively, with clear signs of genetic homogeneity; S1 Fig.). AMOVA results allocated practically all genetic variation (99.80%) to differences within samples (Variance component = 3.49363; P = 0.100). Only 0.47% was assigned to differences among temporal replicates within estuary (Variance component = 0.01651; P = 0.112) and no differences were found among estuaries (-0.27% of genetic variation; Variance component = -0.00937; P = 0.885). Correlation between genetic and geographical distances among estuaries was not significant (Mantel test; r = 0.297; P = 0.294). These data support geographical and temporal genetic stability (i.e. one panmictic unit) of H. guttulatus in the area under study.
Accordingly, South-Atlantic estuarine samples were grouped in four temporal samples for population monitoring along six-year period (SA06–SA09–SA10–SA11; Table 2). No signs of genotyping errors but of null alleles at Hgu-USC7 in SA09 and Hgu-USC5 in SA11 (frequency: 0.09 and 0.07, respectively) were found using Micro-Checker. No significant deviations from HW expectations were detected after Bonferroni correction within each of the four temporal samples. Concordance to HW was observed when they were pooled. There were no indications of linkage disequilibrium between all pair of loci tested within temporal samples. No significant differences in genetic diversity were observed for allelic richness and heterozygosity estimators among the four temporal SA samples (Table 2; Wilcoxon tests; P>0.05). Neither significant differences in genetic diversity by sex nor heterogeneity of allele frequencies between sexes within temporal samples have been detected. The temporal moment-based estimate of Ne was 1,602 (95% CI: 210, ∞) in the South-Atlantic population sample of H. guttulatus (SA06 vs. SA11).
According to the global structure analysis, a single population cluster including all temporal samples was also the most probable alternative using the Bayesian analysis (P≈1; K = 2–4, P≈0). The highest ΔK was observed for K = 2 using Structure Harvester, but with signs of genetic homogeneity (symmetric sample proportion 1/K assigned to each cluster and all individuals fairly admixed; S1 Fig.). Small and non-significantly different from zero estimates of genetic differentiation among South-Atlantic temporal samples were observed, both global (DEST: 0.004, 95% CI: -0.002, 0.014; G”ST: 0.006, 95% CI: -0.004, 0.019) and between pairs of samples (DEST and G”ST ranges: from -0.004 to 0.009 and from -0.007 to 0.015, respectively; minimum for SA06-SA11 and maximum for SA09-SA11).
Genetic diversity of the captive population
No significant deviations from HW expectations and absence of linkage disequilibrium were detected in the renewed broodstock comprising 53 wild H. guttulatus (Stock09). No significant differences in genetic diversity were observed between the Stock09 (AR: 9.8, He: 0.612; Table 2) and the wild population samples of origin (SA06–09–10–11; mean AR: 9.6, mean He: 0.607) (P>0.05; Wilcoxon and Kruskal-Wallis tests). Similar genetic diversity was detected in the renewed Stock09 (mean AR: 19.3; mean He: 0.869; Table 2) and the founder Stock06 based on the six most polymorphic loci (P>0.05; Wilcoxon test; ). These six microsatellite markers provided a useful tool for parentage analysis based on their high combined exclusion probabilities for a false parent when the other parent was unknown (Excl1: 0.9985) or known (Excl2: 0.9999), and for sibling identity (SI: 0.9988). Small and non-significantly different from zero estimates of genetic differentiation were observed between the renewed Stock09 and the founder Stock06 (G''ST = -0.001, 95% CI: -0.026, 0.024, P = 0.47; DEST = -0.001, 95% CI: -0.024, 0.022, P = 0.471), as well as between the renewed Stock09 and the four temporal South-Atlantic wild population samples (G”ST<0.002; DEST<0.001; P>0.05; S4 Table).
Pairwise relatedness coefficients (r) among breeders within the renewed broodstock of H. guttulatus ranged from -0.269 to 0.550 (mean r: 0.001). Relatedness estimates were low for most pairwise dyads (83%, r≤0.125; 72% of them with r<0), and high for only a few comparisons between breeders (0.9%, r≥0.375). Two of these latter corresponded to specimens of similar size collected from the same estuary and sampling date, thus suggesting real cases of highly-related individuals, beyond inherent error on individual pairwise r estimations .
Parentage of wild and captive progeny
Thirty-nine batches of newborn seahorses were analyzed for parentage; 12 of them from wild origin and the remaining obtained from mating events in captivity (Table 3). Wild batches were observed in all estuarine areas since 2010 (Table 3). Captive batches were evenly distributed along the period studied (8, 11 and 8 in 2010, 2011 and 2012, respectively) and across sex-ratio treatments (10, 7 and 10 from unbiased, female- and male-biased, respectively; Table 3).
Each of the 27 captive batches was unambiguously assigned to one parent pair from the captive broodstock. A single mating pair was also assigned in the 12 wild batches (Table 3): i) six of them confirmed either the candidate female in the field (B4, B21, B28) or female genotypes from the same sampling site (B5, B8, B41); ii) in the others, which could not be assigned to any known female in this study, a unique maternal genotype per brood was inferred (coded as F1 to F6; Table 3).
The parentage analysis of the 39 batches revealed 30 full-sib families (FS), since some captive batches were assigned to the same mating pair (Table 3; Fig. 2). Three captive mating pairs had a single reproductive event along the period under study (16%; FS1, FS3 and FS21), whereas six pairs (31%) were mated monogamously within (FS2, FS13, FS17, FS28 and FS29) and/or among (FS8 and FS17) breeding seasons. Most reproductive units in captivity (53%) showed temporal substitutions of the mating pair within and/or among breeding seasons (6 females and 7 males). This mating behavior determined groups of half-sibs (HS), which shared either sire (7 sHS) or dam (6 dHS) parents (Table 3; Fig. 2). A total of 38 breeders (22 males and 16 females) out of the renewed Stock09, plus six inferred maternal genotypes of wild origin (F1-F6), contributed to all FS and HS families obtained in captivity.
First reproductive event of male parents in the wild (w), either sampled with one known or unknown female (see Table 3) in the field surveys. Polygynous and polyandrous breeders among breeding seasons were marked in grey.
Variable mating behavior was observed from wild to captive conditions (Table 3; Fig. 2). Eight out of 12 male parents of wild batches did not further reproduce under captive conditions (67%). A single male maintained the female partner from the wild (FS4), whereas the others had breeding events in captivity in presence (G171) or not (G114, G118) of the wild-caught female, and even switched the partnership among breeding seasons. Similarly, four known mothers assigned to the wild batches did not further reproduce in captivity (67%); the others showed either monogamous (G97) or polyandrous (G176) mating behavior under captive conditions.
Relatedness between effective mating pairs showed similar ranges in captive (-0.157 for FS30 to 0.315 for FS17) than wild (-0.144 for FS9 to 0.312 for FS6) reproductive events, with lower average in captivity (0.041, SD: 0.152) than in nature (0.075, SD: 0.156). Global relatedness between all pairs of reproductively successful breeders was 0.055 (SD: 0.152), in accordance with the management under low relatedness aquaria and natural mating system.
Genetic diversity and demography
Average microsatellite diversity over 13 loci in Cantabrian (A: 10.8; He: 0.59) and South-Atlantic (A: 9.5; He: 0.62) population samples of H. guttulatus in NW Spain was not significantly different to that described over five loci in other European populations of the species  (U Mann-Whitney test; P>0.05). These values were within the range reported for other seahorse species, such as the endangered H. capensis (A: 10.3; He: 0.63; ) or H. hippocampus (A: 8.0; He: 0.53; ), but lower than reported in marine fish (A = 20.6; He = 0.79; ).
Genetic diversity in H. guttulatus in this study could be related with population demography, based on the decline reported in some coastal areas of Galicia . In Spain, it seems that H. guttulatus does not suffer fishing pressure for international trade, and its major threat is related with the vulnerability of its habitat, often degraded by anthropogenic actions, such as coastal development, effect of fishing gear and pollution [3, 6, 76]. Evidence of reduction in population size of the Cantabrian and South-Atlantic contemporary population samples was observed using M-ratio tests . Although we did not find signs of severe losses of genetic diversity in the population samples, these signals might be indicative of demographic declines that should not be unnoted .
The estimation of contemporary effective population size (Ne) can aid to understand the population status of threatened seahorse species, such as H. guttulatus. Despite the necessity of being cautious of the accuracy of Ne estimation , small to moderate contemporary single-sample Ne was observed in South-Atlantic and Cantabrian subpopulations of H. guttulatus. A larger but less precise Ne estimate was observed in the South-Atlantic subpopulation, with an infinite upper confidence limit. The lower limit that could be an indicator of the lowest possible level of Ne  would also alert about small local Ne in such sample. Discrepancy between temporal and single-sample estimates suggests accuracy dependence on the sampling design, and this uncertainty should be taken into account for further management actions . Under continuous gene flow among weakly differentiated Cantabrian and South-Atlantic subpopulations (see below), the Ne estimates would be closer to the metapopulation size of contemporary generations . It could partly explain the higher Ne estimates than local census observed in the field surveys . The low census may be an indication of smaller local population sizes, although certain underestimation due to seahorses’ capacity to camouflage, depth constraints and segregated distributions [18, 78] cannot be ruled out. Regardless of the precise Ne value, cautions about the population persistence on South-Atlantic and Cantabrian coasts should be considered from a conservation perspective in vulnerable habitats, to insure maintenance of genetic variation at the long-term .
Gene flow across the Cape Finisterre barrier
In general, genetic divergence in marine fishes is low due to the high dispersive capabilities through drift of planktonic phases or active adult migration, favored by the lack of geographical barriers . However, several oceanographic features such as ocean current patterns, sea floor topology and thermal gradients, as well as species-specific life-history characteristics may provide some opportunity for separation of populations [23, 81, 82]. The latter may be the case of seahorses, with short juvenile planktonic stage and with low mobility and site fidelity of adults [1, 48], although some evidence suggested occasional long-distance dispersal for some species [1, 23, 83, 84]. Understanding the population structure in seahorses is important for establishing appropriate management units of H. guttulatus and ensuring best practices for seahorse captive breeding programs.
The Cape Finisterre has been described as a biogeographical boundary associated with the complex Portugal Current System, with different current flows affecting the shores to the north and the south of this cape . This barrier to gene flow has been proposed in different marine species [82, 86], including seahorses H. guttulatus and H. hippocampus based on mitochondrial markers [22, 23]. Sufficient gene flow across short to medium distances (tens of km) to homogenize populations has been suggested in seahorses, but across distances >200 km gene flow could be limited . In accordance, in this study analyzing populations of H. guttulatus from both sides of Cape Finisterre, with a distance about 200 km, no significant genetic structure using microsatellite markers was observed regarding this biogeographical barrier to gene flow (K = 1, P≈1; G”ST: 0.005, 95% CI: -0.006, 0.016; see also AMOVA and IBD results). Other biogeographical barriers have been suggested eastern (Gulf of Biscay; ) or southernmost Cape Finisterre (Estremadura Promontory; [88, 89]). They may be responsible of the differentiation previously detected in H. guttulatus in absence of samples surrounding Cape Finisterre , although discordance among the markers assayed cannot be ruled out. Further mitochondrial and microsatellite analysis in northern Portuguese and eastern Cantabrian populations will aid in defining the management units of H. guttulatus along the Atlantic Iberian coasts.
This study supports the existence of a single panmictic population of H. guttulatus throughout Cantabrian and South-Atlantic Galician coasts. However, two management units under continuous gene flow would be proposed based on the allelic distinctiveness (AST) detected between Cantabrian and South-Atlantic subpopulations. This parameter could be a key point in conservation programs because it gives idea of the potentiality for evolution among subpopulations, since allelic diversity variables are better predictors of long-term adaptation than gene frequency variables .
Temporal stability in South-Atlantic population
Genetic diversity was temporary stable in the South-Atlantic population of H. guttulatus for the six-year period studied (2006–2012). It suggests that effective population size (Ne) could be large enough to buffer the effects of genetic drift, within the time frame of 2–3 generations, although the confidence intervals also alert about the smallest possible Ne in such population (see above). Continuous gene flow could lead homogenization avoiding the loss of genetic variation in the metapopulation of H. guttulatus. It was associated with low exposure coastal habitats in the underwater surveys, including bays, estuaries and harbors, as other seahorse species . Seahorses in NW Spain were observed on all sediment types, as macroalgae and seagrass, and also on artificial structures [9, 91]. Noteworthy is the stable local population located at the Ribeira harbor in the Arousa Estuary, which reinforces the interest of artificial habitats in shallow waters for conservation of seahorse species [92–94].
Genetic monitoring of captive breeding
The International Union for Conservation of Nature (IUCN) recognizes the importance of the ex situ maintenance for species that are prone to effects of human activities or are likely to become endangered in a very short time . Captive breeding has also made contributions to conservation other than just demographic supplementation, such as research, professional training and public education . Since 2006, a captive breeding program has been established for H. guttulatus in NW Spain with conservation and research purposes . The genetic diversity of the renewed Stock09 was similar to that of the wild population of origin in this study, and to that of the founder Stock06 . It suggests suitable processes of renewal and maintenance to ensure the genetic representativeness of wild populations of H. guttulatus in captivity, a key point to guarantee the long-term adaptive potential in conservation programs .
The high exclusion and sibling identity probabilities (>0.99) for the most polymorphic loci in the renewed Stock09 allowed the assignment to a single parent pair for all wild and captive batches of newborn seahorses. Each wild batch was compatible with a unique maternal genotype, suggesting different wild female parents (F1 to F6; Table 3; Fig. 2) which would have not been sighted during the field survey. This may point out the existence of cryptic individuals which could partly explain the small local census size observed.
Within-brood monogamy in the wild and captivity
Interpopulation variation in mating behavior had been reported, including fish , and also suggested in seahorses . Parentage analysis in this study proved the single-brood genetic monogamy of wild-caught pregnant males from Galician populations of H. guttulatus for the first time in the wild. These results are in accordance with those described in Portuguese populations of this species irrespective of the density and sex-ratios .
In this study, within-brood monogamy was also observed under captive conditions, confirming previous observations in the species [30, 35]. Captive batches were obtained under female-biased (26%) and unbiased (37%) sex-ratio aquaria, as reported in the founder population , but also under male-biased treatment (37%; this study). It suggests that sex ratios do not affect the occurrence of mating events in captivity, as observed for this species in the wild .
Temporal polygamy within and among breeding seasons in captivity
Mate swapping across successive male broods in captivity was observed in the population under study, both within and among breeding seasons. Partner switching was also stated for most wild-caught effective mating pairs (75%) when further reproduced under captive conditions. Mate swapping was observed among different breeding seasons in this study (8%), mostly of females (13%; 5% of males), as reported in this species . However, the previously noted monogamous mating within seasons (; serial monogamy) was not the rule in the population under study. Indeed, more regular mate swapping across broods within a single breeding season (global 21%; 25% of females and 18% of males) was observed (Fig. 2). In seahorses, mate switching within breeding seasons could be harmful for both sexes due to the time cost involved in establishing a new breeding pair , being the synchronized cycle with a monogamous mate optimal for reproductive success . Nevertheless, individual’s fecundity also depends on the mating habits, and larger potential fitness benefits can arise from having multiple sexual partners, such as better genes for progeny, higher genetic diversity and improved offspring viability . Our data showed that short and long-term genetic polygamy does exist for socially promiscuous populations of H. guttulatus in captivity . Geographical variation in male mating behavior has been reported for other syngnathids, related to ecological factors, such as water temperature, adult sex ratio and seagrass biomass [100, 101]. In our study, mate switching was more frequent under male-biased and unbiased (50% and 37.5%, respectively) than female-biased (12.5%) aquaria, suggesting the possible influence of sex ratios on mate switching for the studied population, particularly when there are more males than females.
Naud et al.  have pointed out that females in the field may have more chance of reproducing successfully if they mate monogamously, whereas polygynous males could increase their reproductive success. Results in this study did not suggest significant differences in mate switching between males and females under captive conditions, with 32% polygynous males and 38% polyandrous females within the breeders contributing to the offspring (Fig. 2).
The mating systems may have large effects on effective population size (Ne). Multiple mating and temporal polygamy can have similar effects on increasing Ne and improving the maintainance of genetic diverstity in a conservation context [103, 104]. The importance of such effects is greatest when population size is small, increasing genetic variation at short term . Also, polygamy could help purge inbreeding depression and decrease extinction rate, compared with a monogamous system in scenarios with small Ne . In this study, a temporal polygamous mating system was observed in H. guttulatus in captivity, that should be taken into account for management practices with conservation purposes.
Reproductive success in captivity
Genetic data indicate a flexible mating system for the captive population of H. guttulatus studied, ranging from strict monogamy to polygamy within and among the three breeding seasons studied (Fig. 2). This could provide an advantage to ensure the reproductive success of the broodstock, allowing the management of breeders under different treatments. Natural mating systems represent an important component of the diversity of populations, recommended to be preserved in captive programs for conservation purposes because of its fundamental influence on breeding success . Molecular monitoring of broodstock management in absence of pedigree data for wild breeders also contributes to preserve genetic diversity and to avoid inbreeding within conservation breeding programs . Low relatedness was observed among most breeders in the renewed Stock09, according to their wild origin. A few dyads with high relatedness estimates (0.9%) could point to pairs of first-degree relatives, which should be held in different aquaria for managing the captive broodstock under minimum inbreeding criteria. Such broodstock organization could explain the lower average kinship observed for mating pairs established in captivity than in the wild (Table 3).
Family data showed higher breeder contribution in the renewed (72%) than in the founder (56%; ) broodstock, suggesting an improvement on reproductive success of the captive program under genetic management. This could be also linked to the advances in the phases of nutrition and rearing in captivity [10–14]. The limited effective population size of this captive population based on the reproductively successful breeders (Ne = 37) will maintain short-term genetic diversity (approximately 98% in one generation), but suggests caution about population management in captivity with the aim of ensuring its long-term adaptive potential [79, 95].
This study adds new genetic information about the conservation status of northwestern Iberian population of Hippocampus guttulatus in the wild and under captive conditions, within an uncovered geographical range for this data deficient species . Genetic diversity and demographic inferences on this metapopulation will be useful to be considered in management programs, ideally in combination with evaluation and remediation of underlying causes of associate habitats declines. Supplementation breeding programs to mitigate losses in declining or threatened populations from human activities and/or environmental changes [106, 107] could be considered for further demonstration actions within conservation programs of the long-snouted seahorse in NW Spain. Furthermore, genetic and proactive husbandry research will benefit conservation of wild populations (e.g., methods, life history information ) for threatened seahorses that are in vulnerable habitats before they become highly endangered. It will also contribute to stimulate actions on the management of captive populations in Aquarium institutions to be part of wider conservation strategies. The biological and cultural uniqueness of seahorses as flagship species could promote public awareness and change in human behavior regarding shallow coastal waters and marine biodiversity which share the same habitats or are vulnerable to the same threats. The occupancy of artificial habitats could be managed as an opportunity for seahorse conservation accompanied with public education to develop appreciation of the biological richness of coastal ecosystems.
S1 Fig. Structure plots.
Consensus plots across ten Structure’s runs. A) Global analysis based on the set of 13 loci. B) Global analysis excluding Hgu-USC2; C) Spatial analysis based on the set of 13 loci. D) Spatial analysis excluding Hgu-USC2 and Hgut6. E) Temporal analysis based on the set of 13 loci.
S1 Table. Genotypes of wild seahorses and offspring analyzed in this study.
S2 Table. Pairwise estimates of population differentiation based on the set of 13 loci and excluding Hgu-USC2.
S3 Table. Bottleneck analyses of Hippocampus guttulatus in NW Spain using M-ratios.
We thank Alexandro Chamorro, Patricia Quintas, Sonia Valladares, Andreu Blanco and Antonio Vilar for supplying wild and reared seahorse samples under collaboration with Institute of Marine Research (CSIC, Vigo, Spain) and Aquarium Finisterrae (A Coruña, Spain). Authors are grateful to two anonymous reviewers for valuable comments that improved the manuscript and to Christina Caballero for editing advice. Thanks to María López for technical support.
Conceived and designed the experiments: CB AL. Performed the experiments: AL. Analyzed the data: AL MV CB. Contributed reagents/materials/analysis tools: MP. Wrote the paper: AL CB MV.
- 1. Foster SJ, Vincent ACJ (2004) Life history and ecology of seahorses: implications for conservation and management. J Fish Biol 65: 1–61.
- 2. Martin-Smith KM, Samoilus MA, Meeuwig JJ, Vincent ACJ (2004) Collaborative development of management options for an artisanal fishery for seahorses in the central Philippines. Ocean Coast Manag 47: 165–193.
- 3. IUCN (2014) The IUCN Red List of Threatened Species. Version 2014.1. http://www.iucnredlist.org.
- 4. CITES (2004) Convention on International Trade in Endangered Species of Wild Fauna and Flora. Thirteenth meeting of the Conference of the Parties, Bangkok (Thailand), 2–14 October 2004.
- 5. Vincent ACJ, Foster SJ, Koldewey HJ (2011) Conservation and management of seahorses and other Syngnathidae. J Fish Biol 78: 1681–1724. pmid:21651523
- 6. OSPAR (2008) Case reports for the OSPAR List of threatened and/or declining species and habitats. OSPAR Commission, Biodiversity Series.
- 7. BOE (2011) Real Decreto 139/2011, de 4 de Febrero, para el desarrollo del Listado de Especies Silvestres en Régimen de Protección Especial y del Catálogo Español de Especies Amenazadas. Boletín Oficial del Estado 46 (23/02/2011): 20912–20951. pmid:25194435
- 8. Kaiser MJ, Collie JS, Hall SJ, Jennings S, Poiner IR (2002) Modification of marine habitats by trawling activities: prognosis and solutions. Fish Fish 3: 14–136.
- 9. Planas M, Chamorro A, Quintas P, Vilar A (2008) Establishment and maintenance of threatened long-snouted seahorse, Hippocampus guttulatus, broodstock in captivity. Aquaculture 283: 19–28.
- 10. Planas M, Quintas P, Chamorro A, Silva C (2010) Female maturation, egg characteristics and fatty acids profile in the seahorse Hippocampus guttulatus. Anim Reprod Sci 122: 66–73. pmid:20727689
- 11. Planas M, Blanco A, Chamorro A, Valladares S, Pintado J (2012) Temperature-induced changes of growth and survival in the early development of the seahorse Hippocampus guttulatus. J Exp Mar Bio Ecol 438: 154–162.
- 12. Planas M, Chamorro A, Quintas P (2013) Effect of photoperiod and temperature on female maturation in the seahorse Hippocampus guttulatus. Aquaculture 388–391: 147–152.
- 13. Olivotto I, Planas M, Simões N, Holt GJ, Avella MA, et al. (2011) Advances in breeding and rearing marine ornamentals. J World Aquac Soc 42: 135–166.
- 14. Blanco A, Chamorro A, Planas M (2014) Implications of physical key factors in the early rearing of the long-snouted seahorse Hippocampus guttulatus. Aquaculture 433: 214–222.
- 15. Reiss H, Hoarau G, Dickey-Collas M, Wolff WJ (2009) Genetic population structure of marine fish: mismatch between biological and fisheries management units. Fish Fish 10: 361–395.
- 16. Larsson LC, Laikre L, Andre C, Dahlgren TG, Ryman N (2010) Temporally stable genetic structure of heavily exploited Atlantic herring (Clupea harengus) in Swedish waters. Heredity 104: 40–51. pmid:19654606
- 17. Saarman NP, Louie KD, Hamilton H (2010) Genetic differentiation across eastern Pacific oceanographic barriers in the treatened seahorse Hippocampus ingens. Conserv Genet 11: 1989–2000.
- 18. Teske PR, Cherry MI, Matthee CA (2003) Population genetics of the endangered Knysna seahorse, Hippocampus capensis. Mol Ecol 12: 1703–1715. pmid:12803625
- 19. Panithanarak T, Karuwancharoen R, Na-Nakorn U, Nguyen TTT (2010) Population genetics of the spotted seahorse (Hippocampus kuda) in Thai waters: Implications for conservation. Zool Stud 49: 564–576.
- 20. Thangaraj M, Lipton AP, John L, Gopalakrishnan A (2012) Genetic diversity of three spotted seahorse, Hippocampus trimaculatus (Leach, 1814) in India using four microsatellite loci. Notulae Scientia Biologicae 4: 7–13.
- 21. Nickel J, Cursons R (2012) Genetic diversity and population structure of the pot-belly seahorse Hippocampus abdominalis in New Zealand. N Z J Mar Freshwater Res 46: 207–218.
- 22. Woodall LC (2009) Population genetics and mating systems of European seahorses Hippocampus guttulatus and Hippocampus hippocampus. PhD Thesis, University of London, UK.
- 23. Woodall LC, Koldewey HJ, Shaw PW (2011) Historical and contemporary population genetic connectivity of the European short-snouted seahorse Hippocampus hippocampus and implications for management. J Fish Biol 78: 1738–1756. pmid:21651525
- 24. IUCN (2011) Review and Reconsideration of the IUCN Technical Guidelines on the Management of Ex-situ populations for Conservation. Why, when (and how) to establish an ex situ population. CBSG News vol 22 January 2011.
- 25. Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conservation genetics. Cambridge: Cambridge University Press. 617 p. pmid:25057650
- 26. Montgomery ME, Ballou JD, Nurthen RK, England PR, Briscoe DA, et al. (1997) Minimizing kinship in captive breeding programs. Zoo Biol 16: 377–389.
- 27. Pino-Querido A, Hermida M, Vilariño M, Bouza C, Martínez P (2010) Statistical properties and performance of pairwise relatedness estimators using turbot (Scophthalmus maximus L.) family data. Aquac Res 41: 528–534.
- 28. Wedekind C (2002) Sexual selection and life-history decisions: Implications for supportive breeding and management of captive populations. Conserv Biol 16: 1204–1211.
- 29. Avise JC, Jones AG, Walker D, DeWoody JA (2002) Genetic mating systems and reproductive natural histories of fishes: Lessons for ecology and evolution. Annu Rev Genet 36: 19–45. pmid:12429685
- 30. Woodall LC, Koldewey HJ, Shaw PW (2011) Serial monogamy in the European long-snouted seahorse Hippocampus guttulatus. Conserv Genet 12: 1645–1649.
- 31. Jones AG, Kvarnemo C, Moore GI, Simmons LW, Avise JC (1998) Microsatellite evidence for monogamy and sex-biased recombinated in the Western Australian seahorse Hippocampus angustus. Mol Ecol 7: 1497–1505. pmid:9819904
- 32. Jones AG, Moore GI, Kvarnemo C, Walker D, Avise JC (2003) Sympatric speciation as a consequence of male pregnancy in seahorses. Proc Natl Acad Sci USA 100: 6598–6603. pmid:12732712
- 33. Kvarnemo C, Moore GI, Jones AG, Nelson WS, Avise JC (2000) Monogamous pair bonds and mate switching in the Western Australian seahorse Hippocampus subelongatus. J Evol Biol 13: 882–888.
- 34. Wilson AB, Martin-Smith M (2007) Genetic monogamy despite social promiscuity in the pot-bellied seahorse (Hippocampus abdominalis). Mol Ecol 16: 2345–2352. pmid:17561895
- 35. López A, Pardo BG, Planas M, Quintas P, Martínez P, et al. (2012) A microsatellite panel for mating system analysis and broodstock management of captive long-snouted seahorse Hippocampus guttulatus. Aquaculture 356–357: 153–157.
- 36. Estoup A, Largiadèr CR, Perrot E, Chourrout D (1996) Rapid one-tube DNA extraction for reliable PCR detection of fish polymorphic markers and transgenes. Mol Mar Biol Biotechnol 5: 295–298.
- 37. Galbusera PH, Gillemot S, Jouk P, Teske PR, Hellemans B, et al. (2007) Isolation of microsatellite markers for the endangered Knysna seahorse Hippocampus capensis and their use in the detection of a genetic bottleneck. Mol Ecol Notes 7: 638–640.
- 38. Pardo BG, López A, Martínez P, Bouza C (2007) Novel microsatellite loci in the threatened European long-snouted seahorse (Hippocampus guttulatus) for genetic diversity and parentage analysis. Conserv Genet 8: 1243–1245.
- 39. van de Vliet MS, Diekmann OE, Serrão ETA (2009) Highly polymorphic microsatellite markers for the short-snouted seahorse (Hippocampus hippocampus), including markers from a closely related species the long-snouted seahorse (Hippocampus guttulatus). Conserv Genet Resour 1: 93–96.
- 40. Goudet J (2001) Fstat, a program to estimate and test gene diversities and fixation indices (version 2.9.3).
- 41. Rousset F (2008) Genepop’007: A complete re-implementation of the Genepop software for Windows and Linux. Mol Ecol Resour 8: 103–106. pmid:21585727
- 42. van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) Micro-checker: Software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4: 535–538.
- 43. Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the computer program Cervus accommodates genotyping error increases success in paternity assignment. Mol Ecol 16: 1099–1106. pmid:17305863
- 44. Hare MP, Nunney L, Schwartz MK, Ruzzante DE, Burford M, et al. (2011) Understanding and estimating effective population size for practical application in marine species management. Conserv Biol 25: 438–49. pmid:21284731
- 45. Tallmon DA, Koyuk A, Luikart G, Beaumont MA (2008) ONeSAMP: A program to estimate effective population size using approximate Bayesian computation. Mol Ecol Resour 8: 299–301. pmid:21585773
- 46. 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. Conserv Genet 11: 355–373.
- 47. Peel D Ovenden JR, Peel SL (2004) NeEstimator: software for estimating effective population size, Version 1.3. Queensland Government, Department of Primary Industries and Fisheries.
- 48. Curtis JMR, Vincent ACJ (2006) Life history of an unusual marine fish: survival, growth and movement patterns of Hippocampus guttulatus Cuvier 1829. J Fish Biol 68: 707–733.
- 49. Garza JC, Williamson EG (2001) Detection of reduction in population size using data from microsatellite loci. Mol Ecol 10: 305–318. pmid:11298947
- 50. Ohta T, Kimura M (1973) The model of mutation appropriate to estimate the number of electrophoretically detectable alleles in a genetic population. Genet Res 22: 201–204. pmid:4777279
- 51. Nei M (1987) Molecular Evolutionary Genetics. New York: Columbia University Press. 512 p. pmid:25032476
- 52. Guinand B, Scribner KT, Page KS, Burnham-Curtis MK (2003) Genetic variation over space and time: analyses of extinct and remnant lake trout populations in the Upper Great Lakes. Proc R Soc Lond B 270: 425–433.
- 53. Riccioni G, Landi M, Ferrara G, Milano I, Cariani A et al. (2010) Spatio-temporal population structuring and genetic diversity retention in depleted Atlantic bluefin tuna of the Mediterranean Sea. Proc Natl Acad Sci U S A 107: 2102–2107. pmid:20080643
- 54. Pérez-Figueroa A, Saura M, Fernández J, Toro MA, Caballero A (2009) metapop–A software for the management and analysis of subdivided populations in conservation programs. Conserv Genet 10: 1097–1099.
- 55. Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17: 4015–4026. pmid:19238703
- 56. Hedrick PW (2005) Genetics of Populations. Massachusetts: Jones & Bartlett Publishers. 737 p. pmid:25275211
- 57. Peakall R, Smouse PE (2012) GenAlEx6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28: 2537–2539. pmid:22820204
- 58. Excoffier L, Smouse P, Quattro J (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131: 479–491. pmid:1644282
- 59. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol Bioinform Online 1: 47–50.
- 60. Rousset F (1997) Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145: 1219–1228. pmid:9093870
- 61. Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance, web service. BMC Genet 6: 13. pmid:15760479
- 62. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959. pmid:10835412
- 63. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164: 1567–1587. pmid:12930761
- 64. Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9: 1322–1332. pmid:21564903
- 65. Evanno G, Regnaut S, Goudet J. (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14: 2611–2620. pmid:15969739
- 66. Earl DA, vonHoldt BM (2012) structure harvester: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4: 359–361.
- 67. Jakobsson M, Rosenberg NA (2007) clumpp: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23: 1801–1806. pmid:17485429
- 68. Rosenberg NA (2004) distruct: a program for the graphical display of population structure. Mol Ecol Notes 4: 137–138.
- 69. Wang JL (2002) An estimator for pairwise relatedness using molecular markers. Genetics 160: 1203–1215. pmid:11901134
- 70. Hardy OJ, Vekemans X (2002) SPAGeDi: A versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2: 618–620.
- 71. Martínez P, Fernández J (2008) Estimating parentage relationships using molecular markers in aquaculture. New York: Nova Science Publishers. 81p. pmid:25506952
- 72. Taggart JB (2007) fap: An exclusion-based parental assignment program with enhanced predictive functions. Mol Ecol Notes 7: 412–415.
- 73. Jones AG (2005) gerud2.0: A computer program for the reconstruction of parental genotypes from half-sib progeny arrays with known or unknown parents. Mol Ecol Notes 5: 708–711.
- 74. López A, Vera M, Otero-Ferrer F, Pardo BG, Martínez P, et al. (2010) Species identification and genetic structure of threatened seahorses in Gran Canaria Island (Spain) using mitochondrial and microsatellite markers. Conserv Genet 11: 2431–2436.
- 75. DeWoody JA, Avise JC (2000) Microsatellite variation in marine, freshwater and anadromous fishes compared with other animals. J Fish Biol 56: 461–473.
- 76. CITES (2002) Convention on International Trade in Endangered Species of Wild Fauna and Flora. Twelfth meeting of the Conference of the Parties, Santiago (Chile), 3–15 November 2002.
- 77. 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? Mol Ecol 17: 3428–3447. pmid:19160474
- 78. Curtis JMR, Moreau M- A, Marsden D, Bell E, Martin-Smith K, et al. (2004) Underwater visual census for seahorse population assessments. Project Seahorse Technical Report Series 8, version 1.0.
- 79. Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Massachusetts: Blackwell Publishing. 642 p. https://doi.org/10.1093/jxb/erm028 pmid:25506957
- 80. Waples RS (1998) Separating the wheat from the chaff: Patterns of genetics differentiation in high gene flow species. J Hered 80: 438–450.
- 81. Lundy CJ, Morán P, Rico C, Milner RS, Hewitt GM (1999) Macrogeographical population differentiation in oceanic environments: a case study of European hake (Merluccius merluccius), a commercially important fish. Mol Ecol 8: 1889–1898. pmid:10620232
- 82. Piñeira J, Quesada H, Rolán-Alvarez E, Caballero A (2008) Genetic discontinuity associated with an environmentally induced barrier to gene exchange in the marine snail Littorina saxatilis. Mar Ecol Prog Ser 357: 175–184.
- 83. Lourie SA, Green DM, Vincent ACJ (2005) Dispersal, habitat differences, and comparative phylogeography of Southeast Asian seahorses (Syngnathidae: Hippocampus). Mol Ecol 14: 1073–1094. pmid:15773937
- 84. Teske PR, Hamilton H, Palsbøll PJ, Choo CK, Gabr H, et al. (2005) Molecular evidence for long-distance colonization in an Indo-Pacific seahorse lineage. Mar Ecol Prog Ser 286: 249–260.
- 85. López-Jamar E, Cal RM, González G, Hanson RB, Rey J, et al. (1992) Upwelling and outwelling effects on the benthic regime of the continental-shelf off Galicia, NW Spain. J Mar Res 50: 465–488.
- 86. Abaunza P, Murta AG, Campbell N, Cimmaruta R, Comesaña AS, et al. (2008) Stock identity of horse mackerel (Trachurus trachurus) in the Northeast Atlantic and Mediterranean Sea: Integrating the results from different stock identification approaches. Fish Res 89: 196–209.
- 87. Zarraonaindia I, Pardo MA, Iriondo M, Manzano C, Estonba A (2009) Microsatellite variability in European anchovy (Engraulis encrasicolus) calls for further investigation of its genetic structure and biogeography. ICES J Mar Sci 66: 2176–2182.
- 88. Diekmann OE, Coyer JA, Ferreira J, Olsen JL, Stam WT, et al. (2005) Population genetics of Zostera noltii along the west Iberian coast: consequences of small population size, habitat discontinuity and near-shore currents. Mar Ecol Prog Ser 290: 89–96.
- 89. Pascoal S, Creer S, Taylor MI, Queiroga H, Carvalho G, et al. (2009) Development and application of microsatellites in Carcinus maenas: Genetic differentiation between northern and central Portuguese populations. PLoS ONE 4: e7268. pmid:19789651
- 90. Caballero A, García-Dorado A (2013) Allelic diversity and its implications for the rate of adaptation. Genetics 195: 1373–1384. pmid:24121776
- 91. Valladares S, Quintas P, Blanco A, Chamorro A, Hermelo T, et al. (2011) Description of Hippocampus guttulatus populations in Punta Cabalo (Ría de Arousa, NW Spain). The First Syngnathid Biology International Symposium. Fiskebäckskil, Sweeden. April 25–29th.
- 92. Clynick BG (2008) Harbour swimming nets: a novel habitat for seahorses. Aquat Conserv 18: 483–492.
- 93. Hellyer CB, Harasti D, Poore AGB (2011) Manipulating artificial habitats to benefit seahorses in Sydney Harbour, Australia. Aquat Conserv 21: 582–589.
- 94. Correia M, Palma J, Koldewey H, Andrade JP (2013) Can artificial holdfast units work as a habitat restoration tool for long-snouted seahorse (Hippocampus guttulatus Cuvier)? J Exp Mar Bio Ecol 448: 258–264.
- 95. Caballero A, Rodríguez-Ramilo ST, Ávila V, Fernández J (2010) Management of genetic diversity of subdivided populations in conservation programmes. Conserv Genet 11: 409–419.
- 96. Kelly CD, Godin A-GJ, Wright JM (1999) Geographical variation in multiple paternity within natural populations of the guppy (Poecilia reticulata). Proc R Soc Lond B 266: 2403–2408.
- 97. Jones AG, Avise JC (2001) Mating systems and sexual selection in male-pregnant pipefishes and seahorses: Insights from microsatellite-based studies of maternity. J Hered 92: 150–158. pmid:11396573
- 98. Sogabe A, Matsumoto K, Yanagisawa Y (2007) Mate change reduces the reproductive rate of males in a monogamous pipefish Corythoichthys haematopterus. Ethology 113: 764–771.
- 99. Avise JC, Liu J- X (2011) Multiple mating and its relationship to brood size in pregnant fishes versus pregnant mammals and other viviparous vertebrates. Proc Natl Acad Sci U S A 108: 7091–7095. pmid:21482777
- 100. Mobley KB, Jones AG (2007) Geographical variation in the mating system of the dusky pipefish (Syngnathus floridae). Mol Ecol 16: 2596–2606. pmid:17561916
- 101. Mobley KB, Jones AG (2009) Environmental, demographic, and genetic mating system variation among five geographically distinct dusky pipefish (Syngnathus floridae) populations. Mol Ecol 18: 1476–1490. pmid:19368649
- 102. Naud M-J, Curtis JMR, Woodall LC, Gaspar MB (2009) Mate choice, operational sex ratio, and social promiscuity in a wild population of the long-snouted seahorse Hippocampus guttulatus. Behav Ecol 20: 160–164.
- 103. Fiumera AC, Porter BA, Looney G, Asmussen MA, Avise JC (2004) Maximizing offspring production while maintaining genetic diversity in supplemental breeding programs of highly fecund managed species. Conserv Biol 18: 94–101.
- 104. Pearse DE, Anderson EC (2009) Multiple paternity increases effective population size. Mol Ecol 18: 3124–3127. pmid:19555411
- 105. Plesnar-Bielak A, Skrzynecka AM, Prokop ZM, Radwan J (2013) Mating system affects population performance and extinction risk under environmental challenge. Proc R Soc B 279: 4661–4667.
- 106. Waples RS, Adams PB, Bohnsack J, Taylor BL (2007) A biological framework for evaluating whether a species is threatened or endangered in a significant portion of its range. Conserv Biol 21: 964–974. pmid:17650247
- 107. Fraser DJ, Cook AM, Eddington JD, Bentzen P, Hutchings JA (2008) Mixed evidence for reduced local adaptation in wild salmon resulting from interbreeding with escaped farmed salmon: complexities in hybrid fitness. Evol Appl 1: 501–512.