Biodivers Conserv (2017) 26:1275–1293 DOI 10.1007/s10531-017-1300-5 ORIGINAL PAPER

Conservation implications of significant population differentiation in an endangered estuarine seahorse T. K. Mkare1,2 • B. Jansen van Vuuren1 • P. R. Teske1

Received: 25 August 2016 / Revised: 11 January 2017 / Accepted: 24 January 2017 / Published online: 18 February 2017 Ó Springer Science+Business Media Dordrecht 2017

Abstract The spatial distribution of a species’ genetic diversity can provide insights into underlying evolutionary, ecological and environmental processes, and can contribute information towards the delineation of conservation units. The Knysna seahorse, Hippocampus capensis, is endangered and occurs in only three estuaries on the warm-temperate south coast of South Africa: Knsyna, Keurbooms and Swartvlei. Population sizes in the latter two estuaries have been very small for a prolonged period of time, and the populations residing in them may thus benefit from translocations as a means of increasing population sizes and possibly also genetic diversity. However, information on whether these three estuaries constitute distinct conservation units that warrant separate management is presently lacking. Here, we used genetic information from mitochondrial (control region) and nuclear microsatellite loci to assess the genetic diversity and spatial structure across the three estuaries, and also whether translocations should be included in the management plan for the Knysna seahorse. Although each population had a unique combination of alleles, and clustering methods identified the Swartvlei Estuary as being distinct from the others, levels of genetic admixture were high, and there was no evidence for reciprocal monophyly that would indicate that each estuary has a unique demographic

Communicated by Angus Jackson. This article belongs to the Topical Collection: Coastal and marine biodiversity. Electronic supplementary material The online version of this article (doi:10.1007/s10531-017-1300-5) contains supplementary material, which is available to authorized users. & P. R. Teske [email protected] 1

Molecular Zoology Laboratory and Centre for Ecological Genomics and Wildlife Conservation, Department of Zoology, University of Johannesburg, Auckland Park 2006, Johannesburg, South Africa

2

Present Address: Kenya Marine and Fisheries Research Institute, P.O. Box 81651–80100, Mombasa, Kenya

123

1276

Biodivers Conserv (2017) 26:1275–1293

history. On these grounds, we suggest recognising the three populations as a single evolutionarily significant unit (ESU), and encourage translocations between them to ensure the species’ long-term survival. Keywords Evolutionarily significant units (ESU)  Endangered estuarine fish  Population differentiation  Seahorse  Hippocampus capensis  South Africa

Introduction An increasing number of species are threatened by habitat degradation, habitat fragmentation and decreased population sizes (Hanski 1999; Johansson et al. 2007; Dixo et al. 2009). The spatial distribution of a species’ genetic diversity can provide useful insights into underlying evolutionary, ecological and environmental processes (Avise 2000), information that is useful to identify distinct genetic groups if these are present across the range (Allendorf and Luikart 2007). The delineation and designation of conservation units for populations of an endangered species is not only important for its long-term viability, but also for guiding the allocation and prioritisation of conservation efforts (Petit et al. 1998; Schwartz et al. 2007; Volkmann et al. 2014). The maintenance of conservation units in an endangered species, each of which may comprise geographically distinct populations with unique genetic attributes, continues to receive much support despite controversies concerning the definitions and criteria of such an approach. For instance, since the original definition proposed for an Evolutionarily Significant Unit (ESU; the identification of distinct populations or units based on a diverse range of information including spatial data: Ryder 1986), several alternative and more detailed definitions have been proposed, with only some consensus among them (e.g., Moritz 1994; Crandall et al. 2000; Fraser and Bernatchez 2001; Palsbøll et al. 2007). Endangered species are in most cases characterised by small, fragmented and declining populations. Because of reduced genetic diversity and the increased impact of demographic stochasticity, these small populations are at a high risk of extirpation (Fahrig 2003; Henle et al. 2004). Thus, augmenting such populations with immigrants from supposedly large and genetically stable populations appears the conservative choice, because the population size can be enhanced thus reducing the extent of demographic stochasticity (demographic rescue) as well as increasing the genetic diversity of the imperilled sink population (genetic rescue) (Carlson et al. 2015; Whiteley et al. 2015). Despite this, there is still reluctance among conservationists to augment populations of endangered species, which primarily results from the fear of mixing highly diverged populations that are genetically incompatible, dilution of the genetic diversity of the recipient population, and the introduction of diseases (Tallmon et al. 2004; Edmands and Timmerman 2003; Frankham et al. 2011; Miller et al. 2012). However, case studies exist where augmentation (genetic rescue) of genetically impoverished populations in the wild has been successful (Hogg et al. 2006; Bouzat et al. 2009; Benson et al. 2011; Heber et al. 2013), and it has recently been argued that the genetic rescue of a population at risk of extinction should not be subject to scientific restrictions, provided there are no foreseeable risks of outbreeding depression (Frankham et al. 2011; Frankham 2015). This is particularly true when different populations of a species comprise distinct management units (MUs) that differ merely on the basis of allele frequencies, and in which there is no genealogical signature of reciprocal monophyly that would suggest long-term isolation (Moritz 1994).

123

Biodivers Conserv (2017) 26:1275–1293

1277

In studies of endangered species where neutral DNA markers have been applied, the combination of the maternally-inherited mitochondrial DNA (mtDNA) control region (CR) and bi-parentally inherited microsatellite markers is suitable to study both historical and near-contemporary demographic processes (Wan et al. 2004; Arif and Khan 2009). Due to their comparatively high levels of variation, these markers are often informative for studying endangered species which typically have low genetic diversity and small effective population sizes (Hedrick and Hurt 2012; Volkmann et al. 2014). The Knysna seahorse, Hippocampus capensis Boulenger, 1900 is the world’s most endangered seahorse (Czembor and Bell 2012). This species is endemic to South Africa and occurs in only three estuaries on the warm-temperate south coast, namely Keurbooms, Knysna and Swartvlei (Lockyear et al. 2006). The Knysna Estuary is by far the largest of the three (Lockyear et al. 2006). It is unique in being the only estuarine bay on the south coast, and only one of four such systems in South Africa (Whitfield and Baliwe 2013). Environmental conditions are characterised by long-term stability because of the presence of a wide and deep estuary mouth that facilitates the rapid dissipation of river floods from the estuary and the re-establishment of tidal conditions as soon as the floodwaters have subsided (Largier et al. 2000). The mouth is kept permanently open by rocky headlands on both sides of the estuary, as well as the absence of a large marine sand supply from the coast immediately adjacent to the estuary (Tyson 1971; Largier et al. 2000). The estuary has a great diversity of aquatic habitats and, as a result, a greater species richness than any other estuary in southern Africa, including several species that are rare or endangered (Davies 1948; Allanson and Herbert 2005). Unlike the Knysna Estuary, the other two estuaries may experience substantial changes in population size similar to those reported for seahorse populations elsewhere (Martin-Smith and Vincent 2005; Caldwell and Vincent 2012). Although population sizes in the Keurbooms and Swartvlei estuaries can at times be much larger than in the Knysna Estuary, populations may undergo drastic crashes, especially during flooding (Russell 1994; Lockyear et al. 2006). The estuarine portion of the Swartvlei Lake system is intermittently open and often experiences episodes of low water quality (Russell 2015). Mass mortalities have been reported when the sand bar that most of the time separates this estuary from the adjacent ocean was breached and the water level dropped rapidly (Russell 1994). The Keurbooms Estuary is fed by two rivers (Keurbooms and Bitou) that cause considerable population reductions when they flood, as both fauna and flora may be flushed out of the estuary (Bell et al. 2003; Lockyear et al. 2006). It is currently unclear whether the seahorses residing in each of the three estuaries represent distinct populations with a long history of separation, or whether they are connected by gene flow (admixed), information that is crucial for the implementation of accurate management plans. A population genetic study that was conducted more than a decade ago, using haplotype frequency differences in mtDNA CR sequences, identified genetic differentiation among the three estuaries (Teske et al. 2003). It was also found that the Swartvlei population was genetically depauperate. As the previous study included only mtDNA data, a marker that is more informative at the historical than contemporary timescale (Wan et al. 2004), it remains unclear whether current estuarine populations warrant separate conservation status. Furthermore, mtDNA data cannot provide information on inbreeding, so it remains unclear whether the Swartvlei population would benefit from the introduction of genetic diversity from the other estuaries. Because of the limitations of mtDNA, the results from Teske et al. (2003) have not been incorporated into current management strategies, and translocations are not presently considered as a management tool.

123

1278

Biodivers Conserv (2017) 26:1275–1293

Fig. 1 Map of the south coast of South Africa showing the three sites where Hippocampus capensis was sampled

In the present study, we re-investigated the species’ genetic variation and population structure by using a combination of mtDNA CR data and six nuclear microsatellite loci. The latter are considered particularly useful to differentiate between historical and nearcontemporary demographic events, as new mutations often arise over a small number of generations (Wan et al. 2004; Selkoe and Toonen 2006). The objectives of the study were to determine (i) whether the populations from the three estuaries are genetically distinct, such that each should be managed as a separate conservation unit and (ii) whether the Swartvlei population (which was previously found to have lower genetic diversity than the other populations; Teske et al. 2003) remains genetically impoverished a decade later, and whether it suffers from inbreeding such that translocating individuals into this population should be considered. We hypothesised that the genetic data generated would provide tangible information useful to formulate management strategies that can improve the conservation success of the Knysna seahorse.

Methods Sample acquisition Sampling permits for this study were obtained from CapeNature (Keurbooms Estuary) and SANParks (South African National Parks; Knysna and Swartvlei estuaries). Given the high conservation status of this species, only small sample sizes were permitted. Seahorses were caught during the austral summer of 2014 in the Keurbooms (n = 33), Knysna (n = 46) and Swartvlei (n = 26) estuaries by means of snorkelling, SCUBA diving or wading in

123

Biodivers Conserv (2017) 26:1275–1293

1279

shallow water (Fig. 1). A small fin clip (\1 mm2) from the lower edge of the dorsal fin was obtained using sterilised fingernail scissors while the fish remained submerged, and fin clips were preserved in absolute ethanol. Seahorses were then immediately released at the location of capture.

Laboratory procedure Genomic DNA was extracted using the cetyltrimethyl ammonium bromide (CTAB) approach (Doyle and Doyle 1987, 1990) and stored at -20 °C prior to analyses. As previously collected DNA samples (Teske et al. 2003) were degraded, and the quality was not sufficient for microsatellite analyses, we generated genetic data for both mtDNA CR and microsatellites using only the samples from 2014. The mtDNA CR was amplified with forward primer HCAL2 (50 -CAC ACT TTC ATC GAC GCT T-30 ) and reverse primer HCAH2 (50 -TCT TCA GTG TTA TGC TTT A-30 ), with polymerase chain reaction (PCR) and thermal profiles as described in Teske et al. (2003). In addition to the mtDNA CR fragment, we attempted amplification for a total of 18 published microsatellite loci developed for seahorses (Jones et al. 1998; Galbusera et al. 2007; van de Vliet et al. 2009). However, the PCR profile for three loci (Hcal38 and Hcal43: Galbusera et al. 2007; Hgut6: van de Vliet et al. 2009) could not be optimised and these loci were excluded from the study. The 15 remaining loci (Table S1) were PCR amplified across all samples collected in the present study. Microsatellite amplification was conducted using three primers in a single PCR reaction tube (Schuelke 2000). To prevent adenylation of amplification products, which can result in problematic allele scoring, we added a PIG-tail to the 50 end of the reverse primers, following Brownstein et al. (1996). PCR conditions and thermal profiles for loci followed those of their original authors (Jones et al. 1998; Galbusera et al. 2007; van de Vliet et al. 2009), with slight modifications. A post-PCR multiplexing approach was used, where loci are first amplified separately and then pooled prior to genotyping. PCR amplicons were analysed on an ABI PRISM 3730 Genetic Analyzer (Thermo Fisher Scientific). GeneScanTM 500 LIZ (Thermo Fisher Scientific) was used as the internal size standard. Microsatellite alleles were scored using GENEIOUS version R 6.1.5 (Biomatters Ltd., http://www.geneious.com). Twenty-five percent (25%) of all individuals were randomly selected for re-amplification and genotyped again to verify consistency of results, and the scoring was independently verified by another experienced researcher.

Data treatment: genetic diversity MtDNA CR sequences were edited with GENEIOUS version R 6.1.5. Edited sequences were aligned using the ClustalW global alignment algorithm (Thompson et al. 1994) in MEGA version 7 (Kumar et al. 2016). Haplotype (h) and nucleotide (p) diversities were estimated for each estuary using ARLEQUIN version 3.5 (Excoffier and Lischer 2010). Previously generated mtDNA CR sequences (Teske et al. 2003) were not incorporated into the present study, as the previous and current samples were not collected at the same sites, and differences in genetic structure were evident (Global UST = 0.2025; P = 0.0000). However, mtDNA-based genetic diversity indices were compared (see Discussion). For the microsatellite data, the occurrence of genotyping errors, including null alleles, stuttering and large allele dropouts, were investigated using MICROCHECKER version 2.2.3 (van Oosterhout et al. 2004). Linkage disequilibrium (LD) and deviations from Hardy–Weinberg equilibrium (HWE) were investigated using GENEPOP version 44.2 on

123

1280

Biodivers Conserv (2017) 26:1275–1293

the Web (Raymond and Rousset 1995; Rousset 2008) available at http://genepop.curtin. edu.au/. HWE was investigated at locus and locality level, specifying 104 dememorizations, 103 batches and 104 iterations per batch. Deviations from HWE due to homozygote excess for neutral genes can result from a number of reasons other than selection, including genotyping errors and inbreeding; these pitfalls were investigated prior to further analyses. Inbreeding was investigated by estimating the inbreeding coefficient FIS using GENETIX version 4.05 (Belkhir et al. 2001). This program provides 95% confidence intervals (CIs), and when these include zero, then there is no evidence for inbreeding or outbreeding even when FIS itself is positive or negative. Summary statistics included the unbiased expected (HE) and observed heterozygosity (HO) (Nei 1987). These statistics were calculated using MICROSATELLITE TOOLKIT version 3.1 (Park 2001). The rarefaction method (Kalinowski 2004) implemented in HP-RARE version 1.1 (Kalinowski 2005) was used to estimate allelic richness (i.e., the number of alleles per population using a standardised sample size).

Population differentiation and admixture To test the null hypothesis of no genetic differentiation among estuaries, pairwise UST statistics (with uncorrected p-distances) as implemented in ARLEQUIN version 3.5 were calculated for the mtDNA CR data. Significance of the analysis was obtained through a nonparametric procedure with 104 permutations. For the microsatellite data, we first tested the power of each locus in detecting genetic differentiation. To achieve this, POWSIM version 4.1 (Ryman and Palm 2006) was used. POWSIM tests the power of the microsatellite loci to reject the null hypothesis of no differentiation based on FST (Wright 1951) between a pair of samples (sampled after a specified number of generations of drift process t), by performing a random number of simulations under the Wright-Fisher model, while assuming no migrations or mutations. To perform the analysis, we used default Markov chain process parameters, but we retained our actual number of sample sizes and loci in the analysis. Population differentiation among the three estuaries was investigated by calculating pairwise FST, G00 ST (Meirmans and Hedrick 2011) and DEST (Jost 2008) in GENALEX version 6.5 (Peakall and Smouse 2012). Significance was determined by specifying 9999 bootstrap replicates. The statistics G00 ST and DEST are considered particularly appropriate to measure differentiation in microsatellites because they account for these genetic markers’ high levels of heterozogosity (Jost 2008; Heller and Siegismund 2009). To infer the number of genetic clusters (K) present in the microsatellite dataset, three clustering methods were applied: a multivariate discriminant analysis of principal components (DAPC; Jombart et al. 2010), a non-Bayesian iterative reallocation method (Paetkau et al. 1995), and a Bayesian clustering approach (STRUCTURE; Pritchard et al. 2000). Unlike STRUCTURE, the first two analyses do not require conformity to LD or HWE (Jombart et al. 2010; Duchesne and Turgeon 2012). The DAPC analysis was performed using ADEGENET version 1.2.8 (Jombart 2008) in the R statistical environment R 3.2.2 (R Development Core Team 2015). The DAPC analysis first transforms and summarises the raw data by using principal components analysis (PCA). Thereafter, discriminant analysis (DA) is used to partition the transformed data into within-group and among-group genetic variation. The DA analysis minimises the genetic variation within groups, and maximises variation among groups at a given value of K (Jombart et al. 2010). The optimal number of genetic clusters describing the data was identified using Bayesian information criterion (BIC) scores, using the find.clusters function. In this analysis, the

123

Biodivers Conserv (2017) 26:1275–1293

1281

optimal K is expected to be associated with a low BIC, after which the BIC values either rise again, or further decrease is minimal (Jombart et al. 2010). Because retaining an incorrect number of principal components (PCs) in the analysis can lead to questionable results (i.e., affect the balance between power of discrimination and overfitting of the data), the optimal number of retained PCs was identified by performing a cross-validation analysis where the a-score approach (the proportion of successful reassignments of group membership of all individuals in the dataset corrected for the number of retained PCs) was used (Jombart and Collins 2015). Additionally, we generated a ‘‘compoplot’’ (i.e., a bar plot in which each individual is assigned to a particular cluster) using ADEGENET. This was done to illustrate membership probability and to identify admixed individuals. The admixed individuals were arbitrarily assumed to be those containing less than 90% of the probability of membership in a single genetic cluster (Jombart and Collins 2015). The iterative reallocation method implemented in the program FLOCK version 3.1 (Duchesne and Turgeon 2012) randomly partitions individuals into clusters and then repeatedly re-allocates them until homogeneity within clusters, and differentiation between clusters, are maximised. A total of 50 iterations and 100 runs for each K (with K ranging from 1 to 3) were specified. The stopping and estimation rules for the correct K followed the guidelines on the plateau sequence for each K (Duchesne and Turgeon 2012). The program STRUCTURE version 2.3.4 (Pritchard et al. 2000; Hubisz et al. 2009) was also used for the identification of the K clusters. This program can identify K by integrating admixture models (Falush et al. 2003), which is not the case with FLOCK, whose genetic clusters are identified without considering admixture or models that assume correlated allele frequencies (Anderson and Barry 2015). Because of a number of deviations from HWE observed for the Swartvlei population, and because of the comparatively large pairwise genetic differentiation with the other two populations (see Results), two datasets were created and analysed separately with STRUCTURE. The first dataset included samples from all three estuaries while the second included only samples from the genetically very similar Keurbooms and Knysna estuaries. The first dataset was analysed by selecting a model that included ‘admixture’ and ‘allele frequencies correlated’, with default parameters applied for advanced settings. Because of the absence of genetic differentiation observed between Keurbooms and Knysna in the first dataset (see Results), the second dataset was rather analysed by selecting the admixture model, but with sampling localities as priors. All other settings were identical to those of the three-population analysis. The model combination applied for the second dataset is considered particularly suitable for populations with minimal genetic divergence and when a limited number of loci are used (Falush et al. 2003; Hubisz et al. 2009). For each dataset, ten independent runs were performed for each K (ranging from 1 to 5), using a burnin period of 105 MCMC generations and 106 iterations. The best-supported value of K was identified using the DK method (Evanno et al.2005) implemented in STRUCTURE HARVESTER version 0.6.94 (Earl and vonHoldt 2012). Moreover, CLUMPP version 1.1.2 (Jakobsson and Rosenberg 2007) and DISTRUCT version 1.1 (Rosenberg 2004) were used to create barplots similar to those constructed using DAPC and FLOCK.

Genealogy reconstructions To infer genealogical relationship among CR sequences, a minimum spanning network (MSN: Bandelt et al. 1999) was reconstructed using POPART version 1.7 (Leigh and Bryant 2015). A network was also reconstructed for the microsatellite data using the POPPR package (Kamvar et al. 2014). The genetic distance of Bruvo et al. (2004), which

123

1282

Biodivers Conserv (2017) 26:1275–1293

takes into account stepwise mutational model (SMM; Kimura and Ohta 1978), was used for the analysis. The infinite allele model (IAM; Kimura and Crow 1964) was used to account for all unsampled alleles.

Results Mitochondrial DNA diversity The mtDNA CR data set included 370 bp for 99 individuals. These sequences collapsed into nine haplotypes defined by 6 polymorphic positions. The nine haplotypes were deposited in GenBank (accession numbers KY363235–KY363243). The Keurbooms and Swartvlei estuaries were characterised by three haplotypes each, while Knysna had eight (Table S2). The most common haplotype in the Swartvlei Estuary was present only in this estuary, whereas the three haplotypes found in the Keurbooms Estuary were all present and common in the Knysna Estuary. Of the eight haplotypes that were found in the Knysna Estuary, five were unique to this population. The Knysna population also had the highest haplotype diversity (h) and nucleotide diversity (p) (see Table S3). The Keurbooms population had a greater h than the Swartvlei population, while the inverse was true for p, which suggests that the latter is composed of haplotypes that are comparatively more distantly related to each other (Table S3).

Microsatellite diversity and inbreeding Out of the 15 loci that were genotyped, data from only six loci, including the five tetranucleotide loci Hhip1, Hhip3, Hhip4, Hhip7 (van de Vliet et al. 2009) and Hcal10 (Galbusera et al. 2007), and one di-nucleotide locus, Hcal8 (Galbusera et al. 2007), were retained for analyses. Data from the remaining nine loci were excluded because of unreliable scoring (see Supplementary information). No genotyping problems such as null alleles, stuttering or large allele dropouts were detected by MICROCHECKER for the six loci included. No linkage disequilibrium was detected in the dataset, and deviations from HWE were not consistent across loci or sampling sites (Table S4), indicating stochastic effects arising from a fairly small data set rather than departures of loci from the expectations of selective neutrality. Details of the genetic diversity indices at each microsatellite locus and all loci combined are indicated in Table S4. Observed (HO) and expected heterozygosity (HE) were generally higher in the Keurbooms Estuary than in the Knysna Estuary, and much lower in the Swartvlei Estuary. Allelic richness (NA) was highest in Knysna, followed by Keurbooms and lastly the Swartvlei Estuary. There was no evidence for inbreeding in any of the estuaries (FIS was never significantly greater than zero; Table S4). The Swartvlei Estuary had the lowest number of private alleles, whereas the Keurbooms and Knysna estuaries shared a particularly large number of alleles (Table S5).

Analyses of genetic differentiation Genetic diversity in the Knysna seahorse is significantly partitioned between the different estuaries based on both mtDNA (UST) and nuclear data (G}ST and DEST: Table 1; FST: Table S6). The six microsatellite loci used were all informative, with the POWSIM-

123

Biodivers Conserv (2017) 26:1275–1293

1283

Table 1 Pairwise genetic structure among populations; (a) pairwise UST values based on mtDNA CR data; (b) pairwise G}ST (below diagonal) and Jost’s DEST (above diagonal) for the microsatellite data Marker a

b

Locality Keurbooms

–

Knysna

0.0752**

–

Swartvlei

0.8869**

0.4056**

Keurbooms

–

0.0952**

0.2310**

Knysna

0.1213**

–

0.1647**

Swartvlei

0.3079**

0.2305**

–

–

Significance level: * P \ 0.05, ** P \ 0.01

estimated FST values across runs and loci (FST = 0.009) being within the threshold within which clustering methods are expected to distinguish populations (FST = 0.005–0.136: Waples and Gaggiotti 2006). The results of the three clustering approaches were more difficult to interpret. For the DAPC analyses, the output plot generated by the find.clusters function indicated the presence of two genetic clusters, as was indicated by a rapid decrease from K = 1 to K = 2, beyond which further decreases were minimal (see Figs. S1a, S1b). However, K = 3 had the lowest BIC value overall, and the interpretation of this plot is thus not straightforward. In the scatterplot constructed for K = 3 (Fig. S1c), the separation of populations along the first DA axis (x-axis) indicated a separation into Swartvlei versus Knysna and Keurbooms estuaries, and the second DA-axis (y-axis) then separated the genetically more similar Knysna and Keurbooms estuaries. These results confirm those based on pairwise G00 ST and DEST, where genetic differentiation was more pronounced between the Swartvlei Estuary and the other populations than between Knysna and Keurbooms. The DAPC barplot (compoplot, Fig. 2) depicted for K = 3 indicated that, even though individuals associated with cluster 1 were particularly common in the Keurbooms Estuary, those of cluster 2 in the Knysna population and those of cluster 3 in

Fig. 2 DAPC compoplot showing the assignment of individuals to genetic clusters. Each individual is represented by a vertical bar, and colours indicate the probability of an individual’s membership in one of three genetic clusters

123

1284

Biodivers Conserv (2017) 26:1275–1293

Fig. 3 STRUCTURE plot generated using the combined samples’ dataset showing the presence of two genetic clusters. Each vertical bar represents a single individual, and the presence of more than one colour in an individual indicates admixture

the Swartvlei population, there was considerable admixture among all three estuaries (Fig. 2). The iterative reallocation method of FLOCK identified three genetic clusters, but also confirmed that differentiation between Knysna and Keurbooms was minimal, whereas one of the clusters (Cluster 3) was mostly represented by individuals from the Swartvlei population (Fig. S2). Similarly, STRUCTURE indicated two clusters using the combined dataset (Figs. 3, S3a), and indicated that levels of admixture were high, as many individuals could not clearly be assigned to either cluster. However, when samples from the Swartvlei population were removed (because these samples deviated from HWE and were more genetically distinct from the other two populations), the remaining samples could also be separated into two clusters (but only when using localities as priors) associated with the Keurbooms and Knysna populations (Figs. S3a, S3b).

Genealogy reconstructions The genealogical relationships among CR haplotypes and microsatellite genotypes reconstructed using the haplotype networks collectively indicated clustering that is not clearly linked to geography. However, haplotypes/genotypes from the Swartvlei population generally clustered more distinct from those of the other populations than was the case between Keurbooms and Knysna (Fig. 4).

Discussion Delineating conservation units in an endangered species, each of which may harbour unique evolutionary attributes, is not only important to maintain evolutionary potential and adaptive gene complexes, but also for guiding the allocation and prioritisation of conservation efforts (Petit et al. 1998; Schwartz et al. 2007; Volkmann et al. 2014). However, a balance needs to be struck between delineating distinct conservation units based on minor genetic differences and managing each separately, and the risk that such a conserved strategy may exacerbate the negative effects of fragmentation in an already threatened species. Until now, the number of conservation units that could be considered for

123

Biodivers Conserv (2017) 26:1275–1293

1285

Fig. 4 Haplotype networks showing genealogical relationships based on a mtDNA CR sequences and b microsatellite data. In a haplotypes are represented by circles whose sizes are proportional to the number of individuals carrying that particular haplotype. Each perpendicular cross-bar represents one mutational step. In b each circle represents a unique multilocus genotype and their sizes are proportional to number of individuals having that particular genotype. Thickness of connecting lines between genotypes represents relatedness

Hippocampus capensis has remained speculative. This is not only because of the slow mutation rate of the mitochondrial results available prior to this study (Teske et al. 2003), but also because some of the criteria for designating conservation units require the joint analysis of mitochondrial and nuclear DNA markers (Moritz 1994). The present study was aimed at re-analysing the species’ genetic variation and population structure using a combination of mtDNA CR data and six nuclear microsatellite loci to generate information with conservation relevance. Based on our result, four findings are important for further consideration: (i) the presence of genetic differentiation among the three estuaries, with the Swartvlei population being most distinct, (ii) comparably low genetic diversity for the Swartvlei population, but no evidence for inbreeding, (iii) genetic admixture between the three estuaries that reflects recent gene flow, although unauthorised

123

1286

Biodivers Conserv (2017) 26:1275–1293

translocations cannot be ruled out, and (iv) no evidence for reciprocally monophyletic genetic clusters that would indicate periods of prolonged isolation.

Population differentiation The presence of mtDNA-based genetic differentiation among the three estuaries is consistent with the results of a previous study using the same marker, as is the finding that the Swartvlei population was genetically the most distinct (Teske et al. 2003). The latter pattern arises because the majority of Swartvlei individuals share haplotypes that are rare or absent in the other estuaries. Note, however, that the most common haplotype in the Swartvlei Estuary (no. 8 in Fig. 4a), which was exclusively found in this population in the present study, was also present at low frequency in the other two populations in Teske et al. (2003), a discrepancy that could be an artefact of lower sample sizes in the present study. Overall congruence of different studies conducted a decade apart and including samples from different sites within each estuary nonetheless confirms that differences in genetic diversity between estuaries cannot be attributed to sampling artefacts. The inclusion of rapidly mutating microsatellites in this study adds another dimension to the conservation genetics of this species because the nuclear genome is transmitted to the next generation by both parents, and microsatellites are more suitable to capture near-contemporary demographic processes (Wan et al. 2004; Arif and Khan 2009), making them more suitable to identify genetic units that can be used to develop conservation management strategies. While the number of microsatellites used in this study was comparatively low, their high information content (based on POWSIM analyses) nonetheless made them adequate to discriminate between populations. Previous studies have shown that a small number of microsatellites from a limited number of samples (*25 and 30 per locality) is often adequate for population genetic studies (Hale et al. 2012). Empirical information from a number of recent studies of marine organisms confirm that this is particularly true in species that, like H. capensis, have low dispersal potential and highly structured populations, including the seahorse H. abdominalis (four loci; Nickel and Cursons 2012), H. zosterae (four loci; Rose et al. 2014), H. guttulatus (five loci; Woodall et al. 2015), and the seadragon Phycodurus eques (six loci; Larson et al. 2014). There are also numerous examples of a small number of microsatellites being sufficient to identify genetic structure in species with much greater dispersal potential, including the Cape hakes Merluccius paradoxus and Merluccius capensis (two loci; Hoareau et al. 2015), the migratory eulachon fish, Thaleichthys pacificus (five loci; McLean and Taylor 2001) and eastern African corals (five and six loci; Montoya-Maya et al. 2016). The occurrence of significant genetic differentiation between Swartvlei and the other two populations, coupled with comparatively weak differentiation between Keurbooms and Knysna for the microsatellite data (irrespective of the method used) corroborates the results of the CR data. Moreover, the occurrence of Swartvlei as being the most distinct on the basis of the microsatellite data cannot simply be attributed to deviations from HWE, but also to the existence of a few private alleles, and the comparably low number of shared alleles between this population and the others (see Table S5). Generally, H. capensis is an example of a low dispersal species (which relies on passive drifting of larvae, likely associated with floating objects), and such species usually have high levels of genetic differentiation across small geographical distances (Cahill and Levinton 2016).

123

Biodivers Conserv (2017) 26:1275–1293

1287

Genetic diversity, inbreeding and admixture Interestingly, mtDNA-based genetic diversity indices were consistently lower in the two smaller estuaries (Keurbooms and Swartvlei) in the present study compared to values found a decade ago (Teske et al. 2003), whereas those for the larger Knysna estuary were similar. For example, haplotype diversity for the Keurbooms population was 0.75 previously, compared to 0.24 for the present study, whereas that for the Swartvlei population had declined from 0.48 to 0.16. To our knowledge, these are the lowest haplotype diversity indices so far recorded for seahorses (see Panithanarak et al. 2010; Lo´pez et al. 2015; Woodall et al. 2015). Similarly, nucleotide diversity had declined from 0.0046 to 0.0008 for the Keurbooms population and from 0.0046 to 0.0013 for the Swartvlei population. Additionally, the expected heterozygosity (microsatellite loci) for the Swartvlei was lower than those of both captive and wild populations of H. guttulatus (Lo´pez et al. 2015). Although it cannot be ruled out that this is a sampling artefact, given the small sample sizes that were permitted, it is possible that this is a result of severe population declines (Lockyear et al. 2006) that occurred subsequent to the fieldwork conducted for the previous genetic study. The Keurbooms Estuary had the highest observed heterozygosity, but this is less strongly affected by genetic bottlenecks that last for only a single generation (Allendorf 1986), whereas for the Swartvlei population, both mtDNA and microsatellite-based genetic diversity indices were noticeably low. This can be attributed to an almost decade-long reduction in population size. Monitoring of seahorse numbers (Ian Russell, SANParks, unpubl. data) following a population decline reported in 2002/2003 (Lockyear et al. 2006) indicated that numbers remained low following flooding in 2006, which was so devastating that no seahorses were found during surveys conducted during 2007 and 2012, and only a few individuals were found in 2013 (L. Claassens, per. comm.). Even during the fieldwork conducted for the present study in 2014, seahorses in the Swartvlei Estuary were confined to a single, extended seagrass bed, and the same was true in the Keurbooms Estuary. For that reason, it is interesting that no evidence for inbreeding was found in any of the populations, which may be a result of the avoidance of interbreeding with close kin, as has been reported for a number of teleosts (Frommen and Bakker 2006; Szulkin et al. 2016). In contrast to the populations in the two smaller estuaries, population densities in the Knysna Estuary are characterised by long-term stability (Lockyear et al. 2006). As a result, this population’s genetic diversity is more similar to that of other seahorse species with much larger ranges. For example, a population of the widespread Indo-Pacific species Hippocampus kuda in Thailand had a haplotype diversity of only 0.60 (Panithanarak et al. 2010), compared to 0.78 for the Knysna population a decade ago (Teske et al. 2003) and 0.75 in the present study.

Conclusions Conservation planning for endangered or threatened species requires a careful assessment of the available information, as data are often not clear-cut, and the implications of management actions far-reaching. Essentially, one needs to weigh up the recognition and thus management of distinct populations against the maintenance of diversity and increased overall population sizes (Weeks et al. 2011; Kronenberger et al. 2016). Although the present study confirms previous results that each of the estuaries inhabited by

123

1288

Biodivers Conserv (2017) 26:1275–1293

Hippocampus capensis harbours a distinct population on the basis of a unique combination of haplotypes or genotypes, whether these three populations warrant separate management requires more careful consideration. In particular, the possibility that the translocation of individuals between different estuaries might result in outbreeding depression (i.e. an overall reduction in fitness resulting from the mixing of distinct populations, each of which is uniquely adapted to its habitat; Whiteley et al. 2015) requires attention. Outbreeding depression resulting from the translocation of fish species has been reported in a number of studies, but in each case, it involved the translocation of highly distinct genetic lineages into habitats with clear environmental differences, e.g. different salinity regimes (Stearns and Sage 1980; Ludwig 2006). The augmentation of populations that are genetically and adaptively similar represents a more conservative approach (Frankham 2015; Whiteley et al. 2015). Although the number of microsatellites used in the present study was comparatively low, these additional data support the previous results based on mtDNA CR sequences, and reject the idea that the lack of reciprocal monophyly identified previously may have merely been a result of incomplete lineage sorting between populations that have long histories of diverging in isolation. On these grounds, an argument might be made for the combined management of all three estuaries. In addition, there is no evidence that environmental conditions in the three estuaries differ sufficiently to generate unique adaptations in each seahorse population. The Knysna Estuary displays a very wide range of environmental conditions along its length, from the marine-dominated mouth to the river-dominated head (Largier et al. 2000), and conditions in the smaller estuaries are most similar to those in the central lagoonal portion of the Knysna Estuary (Russell 1994, 1996). However, seahorses can be found throughout these systems as long as suitable plant holdfasts are present (Teske et al. 2007), as they have very wide tolerance ranges to temperature (Russell 1994) and salinity (they readily survive transferral from seawater to almost freshwater, Riley 1986). High genetic diversity is required for a population to adapt to ongoing or future environmental fluctuations, and at present, only the Knysna Estuary can ensure the long-term survival of the species because of its ability to buffer adverse environmental impacts, which results in a long-term stable population size (Lockyear et al. 2006). However, the genetic integrity of this population is not guaranteed. In recent times, alien introductions and poor water quality have been reported in this estuary (Knysna Basin Project, unpubl. data). Moreover, several sites at which the species was formerly abundant (Lockyear et al. 2006) are now devoid of seahorses (Teske, pers. obs.), although the situation is by no means as dire as in the other two estuaries. This suggests that management strategies aimed at conserving as much genetic diversity as possible need to be considered. One way to achieve this would be to translocate individuals from Knysna to the smaller estuaries, and in that way maximise genetic diversity in all three estuarine systems, such that the smaller estuaries can act as reservoirs of genetic diversity, should the Knysna population undergo any significant declines. Despite seahorses’ poor swimming abilities, these fish are believed to be particularly adept at establishing themselves in distant habitats using floating objects as holdfasts (Teske et al. 2005), and large numbers of juveniles may be flushed into the ocean (Whitfield 1989) and disperse to other estuaries. However, it is not known at what temporal scale such gene flow operates, and natural genetic exchange may be too slow to be of relevance for present-day management. Human-mediated translocations seem to be an appropriate conservation strategy given the level of genetic admixture observed between the different estuaries, and is particularly urgent in the case of the genetically impoverished Swartvlei population. Given that population sizes in the smaller estuaries are comparatively less stable and experience boom-and-bust cycles as a result of both natural

123

Biodivers Conserv (2017) 26:1275–1293

1289

and anthropogenic effects (Russell 1994; Lockyear et al. 2006), a translocation-based management strategy would require careful monitoring of population sizes and occasional re-stocking. Acknowledgements We are grateful to Louw Claassens (Knysna Basin Project), Zeen Weight, Fatima Daniels and Sophie Bader for assisting in the acquisition of samples from the Knysna and Keurbooms estuaries, and to an anonymous benefactor for providing free accommodation for the duration of the fieldwork. Sampling permits were granted by SANParks and CapeNature. This study received funding from the Rufford Foundation (small grant 14490-1 awarded to PR Teske), and from the University of Johannesburg (URC Grant). T. K. M. acknowledges the University of Johannesburg for awarding him a Global Excellence and Stature (GES) scholarship. Compliance with ethical standards Conflict of interest The authors have declared no conflict of interest. Ethical approval Ethical approval to collect genetic samples was granted by the Ethics Committee of the University of Johannesburg, South Africa. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

References Allanson B, Herbert DG (2005) A newly discovered population of the critically endangered false limpet Siphonaria compressa Allanson, 1958 (Pulmonata: Siphonariidae), with observations on its reproductive biology. S Afr J Sci 101:95–97 Allendorf FW (1986) Genetic drift and the loss of alleles versus heterozygosity. Zoo Biol 5:181–190 Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell Publishing, Malden Anderson EC, Barry PD (2015) Interpreting the FLOCK algorithm from a statistical perspective. Mol Ecol Resour 15:1020–1030 Arif IA, Khan HA (2009) Molecular markers for biodiversity analysis of wildlife animals: a brief review. Anim Biodivers Conserv 32:9–17 Avise JC (2000) Phylogeography: The history and formation of species. Harvard University Press, Cambridge Bandelt H, Forster P, Ro¨hl A (1999) Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol 16:37–48 Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2001) Genetix 4. 02, logiciel sous WindowsTM pour lagntique des populations. Laboratoire ge´nome, populations, interactions: CNRS UMR 5000, Universite´ de Montpellier II, Montpellier Bell EM, Lockyear JF, McPherson JM, Marsden AD, Vincent ACJ (2003) First field studies of an Endangered South African seahorse, Hippocampus capensis. Environ Biol Fishes 67:35–46 Benson JF, Hostetler JA, Onorato DP, Johnson WE, Roelke ME, O’Brien SJ, Jansen D, Oli MK (2011) Intentional genetic introgression influences survival of adults and subadults in a small, inbred felid population. J Anim Ecol 80:958–967 Bouzat JL, Johnson JA, Toepfer JE, Simpson SA, Esker TL, Westemeier RL (2009) Beyond the beneficial effects of trans-locations as an effective tool for the genetic restoration of isolated populations. Conserv Genet 10:191–201 Brownstein MJ, Carpten JD, Smith JR (1996) Modulation of non-templated nucleotide addition by Taq DNA polymerase: primer modifications that facilitate genotyping. Biotechniques 20:1004–1010 Bruvo RA, Michiels NK, D’Souza TG, Schulenburg H (2004) A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level. Mol Ecol 13:2101–2106 Cahill AE, Levinton JS (2016) Genetic differentiation and reduced genetic diversity at the northern range edge of two species with different dispersal modes. Mol Ecol 25:515–526 Caldwell IR, Vincent ACJ (2012) Revisiting two sympatric European seahorse species: apparent decline in the absence of exploitation. Aquat Conserv Mar Freshw Ecosyst 22:427–435 Carlson SM, Cunningham CJ, Westley PAH (2015) Evolutionary rescue in a changing world. Trends Ecol Evol 29:521–530

123

1290

Biodivers Conserv (2017) 26:1275–1293

Crandall KA, Bininda-Emonds ORP, Mace GM, Wayne RK (2000) Considering evolutionary processes in conservation biology. Trends Ecol Evol 15:290–295 Czembor CA, Bell EM (2012) Hippocampus capensis. The IUCN Red List of Threatened Species 2012:e.T10056A495994 Davies DH (1948) A new goby from the Knysna River. Ann Mag Nat Hist 1:357–376 Dixo M, Metzger JP, Morgante JS, Zamudio KR (2009) Habitat fragmentation reduces genetic diversity and connectivity among toad populations in the Brazilian Atlantic Coastal Forest. Biol Cons 142:1560–1569 Doyle JJ, Doyle JL (1987) A rapid DNA isolation procedure for small quatities of fresh leaf tissue. Phytochem Bull 19:11–15 Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13–15 Duchesne P, Turgeon J (2012) FLOCK provides reliable solutions to the ‘‘number of populations’’ problem. J Hered 103:734–743 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 Edmands S, Timmerman CC (2003) Modelling factors affecting the severity of outbreeding depression. Conserv Biol 17:883–892 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 Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567 Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Ann Rev Ecol Evol Syst 34:487–515 Falush D, Stephens M, Pritchard JK (2003) Inference of population structure: extensions to linked loci and correlated allele frequencies. Genetics 164:1567–1587 Frankham R (2015) Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow. Mol Ecol 24:2610–2618 Frankham R, Ballou JD, Eldridge MDB, Lacy RC, Ralls K, Dudash MR, Fenster CB (2011) Predicting the probability of outbreeding depression. Conserv Biol 25:465–475 Fraser DJ, Bernatchez L (2001) Adaptive evolutionary conservation: towards a unified concept for defining conservation units. Mol Ecol 10:2741–2752 Frommen JG, Bakker TCM (2006) Inbreeding avoidance through non-random mating in sticklebacks. Biol Lett 2:232–235 Galbusera PHA, Gillemot S, Jour P, Teske PR, Hellemans B, Volckaert FAMJ (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 Hale ML, Burg TM, Steeves TE (2012) Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS ONE 7(9):e45170 Hanski I (1999) Metapopulation ecology. Oxford University Press, Oxford Heber S, Varsani A, Kuhn S, Girg A, Kempenaers B, Briskie J (2013) The genetic rescue of two bottlenecked South Island robin populations using translocations of inbred donors. Proc R Soc B 280:2012–2228 Hedrick PW, Hurt CR (2012) Conservation genetics and evolution in an endangered species: research in Sonoran topminnows. Evol Appl 5:806–819 Heller R, Siegismund HR (2009) Relationship between three measures of genetic differentiation GST, DEST and G0 ST: how wrong have we been? Mol Ecol 18:2080–2083 Henle K, Lindenmayer DB, Margules CR, Saunders DA, Wissel C (2004) Species survival in fragmented landscapes: where are we now? Biodivers Conserv 13:1–8 Hoareau TB, Klopper AW, Dos Santos SMR, Oosthuizen CJ, Bloomer P (2015) Evaluating the resolution power of new microsatellites for species identi cation and stock delimitation in the Cape hakes Merluccius paradoxus and Merluccius capensis (Teleostei: Merlucciidae). J Fish Biol 86:1650–1657 Hogg JT, Forbes SH, Steele BM, Luikart G (2006) Genetic rescue of an insular population of large mammals. Proc R Soc B 273:1491–1499 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 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 Johansson M, Primmer CR, Merila J (2007) Does habitat fragmentation reduce fitness and adaptability? A case study of the common frog (Rana temporaria). Mol Ecol 16:2693–2700

123

Biodivers Conserv (2017) 26:1275–1293

1291

Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405 Jombart T, Collins C (2015) A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0.0. http://adegenet.r-forge.r-project.org/files/tutorial-dapc.pdf Accessed 23 Jun 2015 Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94 Jones AG, Kvarnemo C, Moore GI, Simmons LW, Avise JC (1998) Microsatellite evidence for monogamy and sex-biased recombination in the Western Australian seahorse Hippocampus angustus. Mol Ecol 7:1497–1505 Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17:4015–4026 Kalinowski ST (2004) Counting alleles with rarefaction: Private alleles and hierarchical sampling designs. Conserv Genet 5:539–543 Kalinowski ST (2005) HP-RARE 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5:187–189 Kamvar ZN, Tabima JF, Grunwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281 Kimura M, Crow JF (1964) The number of alleles that can be maintained in a finite population. Genetics 49:725–738 Kimura M, Ohta T (1978) Stepwise mutation model and distribution of allelic frequencies in finite population. Proc Natl Acad Sci USA 75:2868–2872 Kronenberger JA, Funk WC, Smith JW, Fitzpatrick SW, Angeloni LM, Broder ED, Ruell EW (2016) Testing the demographic effects of divergent immigrants on small populations of Trinidadian guppies. Anim Conserv. doi:10.1111/acv.12286 Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874 Largier JL, Attwood C, Harcourt-Baldwin JL (2000) The hydrographic character of the Knysna Estuary. Trans Roy Soc S Afr 55:107–122 Larson S, Ramsey C, Tinnemore D, Amemiya C (2014) Novel microsatellite loci variation and population genetics within Leafy Seadragons, Phycodurus eques. Diversity 6:33–42 Leigh JW, Bryant D (2015) popart: full-feature software for haplotype network construction. Methods Ecol Evol 6:1110–1116 Lockyear JF, Hecht T, Kaiser H, Teske PR (2006) The distribution and abundance of the endangered Knysna seahorse Hippocampus capensis (Pisces: Syngnathidae) in South African estuaries. Afr J Aquat Sci 31:275–283 Lo´pez 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:e0117538 Ludwig A (2006) A sturgeon view on conservation genetics. Eur J Wildl Res 52:3–8 Martin-Smith KM, Vincent ACJ (2005) Seahorse declines in the Derwent estuary, Tasmania in the absence of fishing pressure. Biol Cons 123:533–545 McLean J, Taylor E (2001) Resolution of population structure in a species with high gene flow: microsatellite variation in the eulachon (Osmeridae: Thaleichthys pacificus). Mar Biol 139:411–420 Meirmans PG, Hedrick PW (2011) Assessing population structure: FST and related measures. Mol Ecol Resour 11:5–18 Miller JM, Poissant J, Hogg JT, Coltman DW (2012) Genomic consequences of genetic rescue in an insular population of bighorn sheep (Ovis canadensis). Mol Ecol 21:1583–1596 Montoya-Maya PH, Schleyer MH, Macdonald AHH (2016) Limited ecologically relevant genetic connectivity in the south-east African coral populations calls for reef-level management. Mar Biol 163:171 Moritz C (1994) Defining ‘evolutionarily significant units’ for conservation. Trends Ecol Evol 9:373–375 Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New York Nickel J, Cursons R (2012) Genetic diversity and population structure of the pot-belly seahorse Hippocampus abdominalis in New Zealand. New Zeal J Mar Fresh 46:207–218 Paetkau D, Calvert W, Sterling I, Strobeck C (1995) Microsatellite analysis of population-structure in Canadian polar bears. Mol Ecol 4:347–354 Palsbøll PJ, Be´rube´ M, Allendorf FW (2007) Identification of management units using population genetic data. Trends Ecol Evol 22:11–16 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 Park SDE (2001) The Excel Microsatellite Toolkit (v3.1). Animal Genomics Laboratory, UCD, Ireland

123

1292

Biodivers Conserv (2017) 26:1275–1293

Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539 Petit RJ, Mousadik AE, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. Conserv Biol 12:844–855 Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959 R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/ Raymond M, Rousset F (1995) genepop, version 1.2: population genetics software for exact tests and ecumenicism. J Hered 86:248–249 Riley AK (1986) Aspekte van die soutgehalte toleransie van die Knysna seeperdjie, Hippocampus capensis (Boulenger, 1900) in die Knysna estuarium. Unpublished report. Grahamstown, Rhodes University Rose E, Small CM, Saucedo HA, Harper C, Jones AG (2014) Genetic evidence for monogamy in the dwarf seahorse, Hippocampus zosterae. J Hered 105:828–833 Rosenberg NA (2004) Distruct: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138 Rousset F (2008) Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour 8:103–106 Russell IA (1994) Mass mortality of marine and estuarine fish in the Swarlvlei and Wilderness lake systems, southern cape. Sth Afr J Aquat Sci 20:93–96 Russell IA (1996) Water quality in the Knysna estuary. Koedoe 39:1–8 Russell IA (2015) Spatio-temporal variability of five surface water quality parameters in the Swartvlei estuarine lake system, South Africa. Afr J Aquat Sci 40:119–131 Ryder OA (1986) Species conservation and systematics: the dilemma of subspecies. Trends Ecol Evol 1:9–10 Ryman N, Palm S (2006) POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Mol Ecol Notes 6:600–602 Schuelke M (2000) An economic method for the fluorescent labelling of PCR fragments. Nat Biotechnol 18:233–234 Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22:25–33 Selkoe KA, Toonen RJ (2006) Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol Lett 9:615–629 Stearns SC, Sage RD (1980) Maladaptation in a marginal population of the Mosquito fish, Gambusia affinis. Evolution 34:65–75 Szulkin M, Stopher KV, Pemberton JM, Reid JM (2016) Inbreeding avoidance, tolerance, or preference in animals? Trends Ecol Evol 28:205–211 Tallmon DA, Luikart G, Waples RS (2004) The alluring simplicity and complexity reality of genetic rescue. Trends Ecol Evol 19:489–496 Teske PR, Cherry MI, Matthee CA (2003) Population genetics of the endangered Knysna seahorse, Hippocampus capensis. Mol Ecol 12:1703–1715 Teske PR, Hamilton H, Palsboll PJ, Choo CK, Gabr H, Lourie SA, Santos M, Sreepada A, Cherry MI, Matthee CA (2005) Molecular evidence for long-distance colonization in an Indo-Pacific seahorse lineage. Mar Ecol Prog Ser 286:249–260 Teske PR, Lockyear JF, Hecht T, Kaiser H (2007) Does the endangered Knysna seahorse, Hippocampus capensis, have a preference for aquatic vegetation type, cover or height? Afr Zool 42:23–30 Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position–specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680 Tyson PD (1971) Outeniqualand: The George-Knysna area. The South African landscape, No. 2. South African Geographical Society, Braamfontein van de Vliet MS, Diekmann OE, Serra˜o 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 van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICROCHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535–538 Volkmann L, Martyn I, Moulton V, Spillner A, Mooers AO (2014) Prioritizing populations for conservation using phylogenetic networks. PLoS ONE 9:e88945 Wan QH, Wu H, Fujihara T, Fang SG (2004) Which genetic marker for which conservation genetics issue? Electrophoresis 25:2165–2176

123

Biodivers Conserv (2017) 26:1275–1293

1293

Waples RS, Gaggiotti O (2006) What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol Ecol 15:1419–1439 Weeks AR, Sgro CM, Young AG, Frankham R, Mitchell NJ, Miller KA, Byrne M, Coates DJ, Eldridge MDB, Sunnucks P, Breed MF, James EA, Hoffmann AA (2011) Assessing the benefits and risks of translocations in changing environments: a genetic perspective. Evol Appl 4:709–725 Whiteley AR, Fitzpatrick SW, Funk WC, Tallmon DA (2015) Genetic rescue to the rescue. Trends Ecol Evol 30:42–49 Whitfield AK (1989) Ichthyoplankton interchange in the mouth region of a southern African etuary. Mar Ecol Prog Ser 54:25–33 Whitfield AK, Baliwe NG (2013) A century of science in South African estuaries: Bibliography and review of research trends. SANCOR Occasional Report No. 7: 289 pp Woodall LC, Koldewey HJ, Boehm JT, Shaw PW (2015) Past and present drivers of population structure in a small coastal fish, the European long snouted seahorse Hippocampus guttulatus. Conserv Genet 16:1139–1153 Wright S (1951) The genetical structure of populations. Ann Eugenic 15:323–354

123