Mar Biodiv DOI 10.1007/s12526-015-0389-6
ORIGINAL PAPER
No divergent evolution, despite restricted connectivity, between Atlantic and Indian Ocean goby populations Eduard Drost 1 & Tirupathi Rao Golla 1 & Sophie von der Heyden 2 & Peter R. Teske 1
Received: 7 May 2015 / Revised: 1 July 2015 / Accepted: 23 August 2015 # Senckenberg Gesellschaft für Naturforschung and Springer-Verlag Berlin Heidelberg 2015
Abstract Southern Africa is a marine biodiversity hotspot that not only comprises faunal elements from the Atlantic and Indian Oceans, but also large numbers of endemic species. Using mitochondrial and nuclear DNA sequence data, we explored whether genetic structure in the endemic coastal goby Psammogobius knysnaensis, a species whose range straddles both biomes, is linked to the boundary between the two oceans. Subtle genetic structure was identified between Atlantic and Indian Ocean populations, with genetic diversity being lower in the Atlantic, and particularly on the west coast. Our results point to partial isolation between the populations associated with each biome that is most likely driven by the region’s oceanography, but unlike in other species, there is no evidence for distinct regional evolutionary lineages that are likely adapted to the environmental conditions prevalent in each region. The exclusive presence of P. knysnaensis in sheltered habitats (estuaries and lagoons) may protect this species from the severe impacts of cold water upwelling on the west coast.
Keywords Cryptic biodiversity . Genetic structure . Mitochondrial DNA COI gene . Nuclear DNA S72 intron . Psammogobius knysnaensis
Communicated by P. R. Teske * Peter R. Teske
[email protected] 1
Molecular Zoology Lab, Department of Zoology, University of Johannesburg, Auckland Park 2006, South Africa
2
Evolutionary Genomics Group, Department of Botany and Zoology, Stellenbosch University, Matieland 7602, South Africa
Introduction The southern African marine region is located at the contact zone between the Atlantic and Indian Ocean biomes. It not only comprises a mixture of taxa associated with each biome, but also hosts large numbers of endemics, making it a global marine biodiversity hotspot (Turpie et al. 2000; Griffiths et al. 2010). The marine environment of southern Africa, along with weather conditions and ecological processes, is greatly influenced by the region’s ocean currents (Brown et al. 2012). The Benguela Current off the west coast transports cold water from the poles up to the equator, while the warm Agulhas Current on the east coast flows in a southerly direction (Brown et al. 2012). Along the south-eastern coast of South Africa, the warmer water from the Agulhas Current and the colder water from the Benguela Current mix to different degrees to form several distinct marine biogeographical provinces (Griffiths et al. 2010; Teske et al. 2011). These provinces are separated by biogeographic disjunctions that are typically associated with changes in water temperature (Meadows 1985). The cool-temperate province on the west coast is separated from the warm-temperate south coast province by a transition zone on the south-west coast that is sometimes treated as a distinct biogeographic province (Griffiths et al. 2010), while the warm-temperate province is separated from the subtropical east coast by a transition zone on the south-east coast (Teske et al. 2011) (Fig. 1). These biogeographical provinces can also have a profound effect on the genetic structure of marine species that are present in more than one province, as genetic discontinuities (phylogeographic breaks) tend to be associated with the boundaries between the provinces (von der Heyden et al. 2008; Teske et al. 2011), although such patterns are not shared for all species examined to date (e.g., Wright et al. 2015). Some genetic lineages even have distributions that are confined to the transition zones separating the provinces
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Sampling and DNA extraction
Fig. 1 A map of southern Africa depicting the location of sampling sites 1–9 (see Table 1 for details) in the marine biogeographic regions associated with the Atlantic and Indian Ocean. W: west coast, SW: south-west coast; S: south coast; SE: south-east coast
Tissue samples of Knysna sandgobies were collected in estuaries and lagoons throughout the species’ range, from Langebaan Lagoon in the west to the Bulolo Estuary in the south-east (Fig 1, Table 1). Gobies were captured by means of a scoop net, and small fin clips from one of the pectoral fins were obtained before returning the fish to their habitat. The fin clips were stored in absolute ethanol until further processing. Genomic DNA was extracted using a Qiagen® Blood and Tissue Kit following the manufacturer’s instructions. The quality and quantity of the DNA extractions was assessed using a Nanodrop 1000 spectrophotometer, and in the case of samples whose quality was poor, isopropanol precipitation (Zeugin and Hartley 1985) was used to improve this. PCR and sequencing
(Teske et al. 2006; Papadopoulos and Teske 2014; Qhaji et al. 2015). In the present study, the genetic struture of the endemic Knysna sandgoby, Psammogobius knysnaensis, was investigated to determine whether this species is subdivided into evolutionary lineages that are associated with the boundary between the Atlantic and Indian Oceans. Previous studies found that these regions are often inhabited by distinct evolutionary lineages that are morphologically and physiologically distinct, and, thus, represent different species (Teske et al. 2006, 2008, 2009; von der Heyden et al. 2011; Dawson and Griffiths 2012; Papadopoulos and Teske 2014). We therefore hypothesised that the Knysna sandgoby comprises at least two regional lineages that may represent cryptic species.
Materials and methods Study organism The Knysna sandgoby is a numerically dominant estuarine species endemic to temperate southern Africa (Strydom 2015), with a distribution range that includes two marine biogeographic provinces (the cool-temperate west coast and the warm-temperate south coast), as well as two biogeographic transition zones on the south-west and south-east coast (Branch et al. 2010). Although the adults are sedentary, the species’ dispersal potential is likely high because gobies have pelagic larvae (Beldade et al. 2007) and because hundreds of thousands of individuals representing all life history stages may emigrate from individual estuaries (Beckley 1985; Whitfield 1989) and potentially settle in new habitats. Spawning occurs throughout the year and peaks in summer (Sutherland et al. 2012).
Polymerase chain reaction (PCR) amplifications of mitochondrial cytochrome oxidase c subunit I gene (mtDNA COI) and the second intron of the S7 ribosomal protein (nuDNA S72) were carried out using primers LCO1490 and HCO2198 (Folmer et al. 1994), and S7RPEX2F and S7RPEX3R (Chow and Hazama 1998), respectively. The PCR amplifications were carried in 20-μl volumes containing 100 to 150 ng of genomic DNA, 2 μl of 10× reaction buffer (Promega), 20 mM of mix containing all dTNPs, 0.5 μM of each forward and reverse primer, 1 μg of bovine serum albumin (BSA) and 0.5 units of Taq polymerase (Medox). The thermal cycling process consisted of denaturation at 94 °C for 3 min, 35–40 cycles of denaturation of DNA at 94 °C for 30 s to ensure only single strands were present to allow primers to anneal. Annealing at 50 °C/55 °C for 45 s and extension at 72 °C for 45 s was followed by a final extension of 72 °C for 20 min. Amplification success was assessed by running the DNA on a 2 % agarose gel in Tris/borate/EDTA (TBE) buffer. PCR products were then purified using a QIAquick PCR purification kit (Qiagen), and the cycle sequenced using BigDye Terminator 3.1 chemistry (Applied Biosystems 3730). Data analyses Sequences were aligned and edited using the program MEGA v. 6 (Tamura et al. 2013). The S72 sequences were phased using the program PHASE v. 2.1 (Stephens et al. 2001) with default settings (100 iterations, a thinning interval of 1 and a burn-in of 100 iterations, and a phase threshold of 90 %). The program was run three times with different starting seeds to ensure consistency of results. A single site could not be phased, and this did not improve when a lower phase threshold of 70 % was specified. Nucleotides at this site were consequently coded a missing character in all individuals.
Mar Biodiv Table 1 Sites at which individuals of Psammogobius knysnaensis were collected, number of individuals sequenced, and genetic diversity indices at each site Region
Atlantic Ocean
Indian Ocean
Site
1-W Langebaan 2 - SW Rooiels 3 - SW Klein 4 - SW Uilenkraals 5-S Klein Brak 6-S Swartkops 7-S Bushmans 8 - SE Nenga 9 - SE Bulolo
COI
S72
N
h
π
AR
PAR
N
h
π
AR
PAR
21
0.40 ±0.11 0.60 ±0.18 0.75 ±0.14 -
0.001 ±0.001 0.001 ±0.001 0.003 ±0.002 -
1.90
0.18
11
0.06
0.01
5
3.20
0.40
3.05
1.01
2
0.002 ±0.002 0.005 ±0.004 -
1.90
1.98
0.26 ±0.11 0.64 ±0.15 -
-
-
-
-
0
-
-
-
-
0.92 ±0.05 0.96 ±0.07 0.80 ±0.17 0.93 ±0.08 0.94 ±0.05
0.005 ±0.003 0.008 ±0.005 0.004 ±0.003 0.007 ±0.005 0.006±0.004
4.14
1.53
10
0.39
1.97
2
0.005 ±0.004 -
2.65
4.12
0.54 ±0.10 -
-
-
3.04
1.01
12
1.54
1.43
4
1.77
0.77
4.09
1.65
4
0.003 ±0.003 0.001 ±0.001 0.008 ±0.006
3.47
4.00
0.57 ±0.12 0.25 ±0.18 0.64 ±0.18
3.30
1.53
5 8 2 19 8 6 10 13
N number of samples, h haplotype diversity (± standard deviation (SD)), π nucleotide diversity (± SD), AR allelic richness, PAR private allelic richness, W west coast, SW south-west coast, S south coast, SE south-east coast. Rarefaction was applied to both AR and PAR, with sample sizes of five for COI data and eight (two sequences each for four individuals) for the S72 data. Sites at which fewer than five sequences were available were not analysed
The program Arlequin v.3.5 (Excoffier and Lischer 2010) was used to calculate haplotype and nucleotide diversity (Nei 1987), while allelic richness and private allelic richness were calculated in HP-Rare (Kalinowski 2005). This latter program applies rarefaction to correct for differences in sampling sites; we excluded sites for which less than five sequences were available, and specified the smallest sample size of the remaining sites for rarefaction. The genetic structure of Psammogobius knysnaensis was assessed by constructing haplotype networks in NETWORK v.4.613 (Bandelt et al. 1999) and by performing two analyses based on F-statistics (Wright 1950). First, ΦST values were calculated for pairs of site in Arlequin. To account for multiple comparisons, the false discovery rate method of Benjamini and Yekutieli (2001) was applied. Second, analysis of molecular variance (AMOVA; Excoffier et al. 1992) was used to identify groups of sites that maximised the statistic ΦCT. Sites were grouped on the basis of biogeography, and ΦCT was estimated for various assignments of sites into two, three and four clusters, with the latter representing the two biogeographic regions and two transition zone in which this species is represented (i.e., west, south-west, south and south-east coast). In all analyses, 1000 permutations were specified to test for significance. A matrix correlation analysis (Mantel test; Mantel 1967) permuting a matrix of genetic distances against a matrix of geographic distances was conducted in Arlequin to determine whether genetic structure in this species is explained by a
model of isolation by geographic distance. In this case, only the more informative cytochrome oxidase I (COI) sequences were used. The genetic distance matrices comprised pairwise Slatkin’s linearised FST values (Slatkin 1995), and the geographic distances matrices contained the corresponding shortest along-coast distances between sites, which were measured using GoogleEarth. Significance was based on 10,000 random permutations.
Results Sequence lengths of COI and S72 sequences were 411 and 217, respectively. A total number of 92 mtDNA COI sequences were generated, and 26 unique haplotypes were recovered. S72 sequences were generated for approximately half the data (Table 1). As each individual has two copies of this marker, the resulting data set comprised slightly more sequences than that of the maternally inherited COI gene (100 sequences), but only 14 haplotypes were recovered. All sequences were submitted to GenBank (COI sequences: accession numbers KT239736-KT239827; S72 sequences: accession numbers KT239828-KT239951). Genetic diversity indices of the COI sequences tended to be lower at sites in the Atlantic Ocean than at Indian Ocean sites, and particularly so at the two westernmost sites. No clear trend was found for the S72 data, except that private allelic richness was particularly low at the westernmost site (site 1).
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Fig. 2 Haplotype networks constructed from COI and S72 sequences of Psammogobius knysnaensis. Colours were used to depict in which of the four marine biogeographic provinces inhabited by this species a particular haplotype was found. The size of a particular circle reflects the frequency
of a haplotype. The smallest circles indicate that a particular haplotype was found in a single individual, and the lines represent the number of nucleotide differences between haplotypes, with the shortest connecting lines representing a single difference
Both haplotype networks recovered star-like phylogenies in which a common basal haplotype that is present throughout the species’ range has given rise to large numbers of comparatively rare, derived haplotypes (Fig. 2). In the COI haplotype network, the basal haplotype was particularly common at site 1, and only two additional haplotypes were found at this site, despite a comparatively large sample size. No clear regional trends were identified in the S72 haplotype network. For the COI data, significant pairwise ΦST values were found between site 1 and several others (Table 2), but the only significant ΦCT value was found for an AMOVA grouping comprising Atlantic Ocean (1–4) vs. Indian Ocean (5–9) sites (Table 3). In contrast, only a single pairwise ΦST value was significant on the basis of the S72 data (site 1 vs. 9), and none
of the ΦCT values were significant. The Mantel test identified a negative correlation between genetic and geographic distance matrices (rY1=−0.18), but this was not significant (P=0.88), suggesting that the genetic structure identified is not an artefact of isolation by geographic distance.
Table 2 Genetic structure among pairs of sites at which Psammogobius knysnaensis was sampled
Discussion and conclusion The goby Psammogobius knysnaensis is represented as a single evolutionary lineage throughout its temperate southern African range. However, lower genetic diversity in the Atlantic, and particularly on the genetically differentiated west coast, points to restricted connectivity between the
Marker
Site
1
COI
1 2 3
0.189 0.161
0.000
5
0.058
0.000
0.007
6 7 8 9 1 2 3 5 6 7 8 9
0.301 0.119 0.186 0.122
0.068 0.000 0.004 0.000 0.340
0.129 0.020 0.071 0.053 -
S72
0.026 0.079 0.000 0.000 0.024
2
3
5
6
7
8
9
0.068
0.043 0.730
0.005* 0.951 0.324
0.000** 0.222 0.061
0.074 0.691 0.318
0.000** 0.489 0.119
0.000** 0.521 0.139
0.925
0.438
0.794
0.162
0.112 0.840
0.146 0.877 0.984
0.000 0.000 0.000 0.013
-
0.166 0.037 0.000 0.000 0.000 0.089 0.936 0.048 0.017 0.013
0.064 0.065 0.064 -
0.000 0.000 0.940 0.539 0.113 0.000 0.154
0.000 0.924 0.480 0.294 0.999
0.017* 0.419 0.216 0.030 0.183
0.128
Site numbers correspond to those in Fig. 1. Sites excluded from this analysis because of low sample sizes are sites 3 and 6 (S72 data) and site 4 (both data sets); *P<0.05 (corrected for multiple comparisons using the method of Benjamini and Yekutieli (2001): P<0.018) and **0.01 (corrected: P<0.004) Below diagonal: ΦST values; above diagonal: P-values
Mar Biodiv Table 3 AMOVA results for various groups of sites using mtDNA COI and nuDNA S72 data of Psammogobius knysnaensis No. groups Grouping
COI ΦCT
S72 P
ΦCT
P
2
(1) (2,3,[4],5,6,7,8,9)
0.023 0.333
2
(1,2,3,[4]) (5,6,7,8,9)
0.033 0.045* 0.000 0.469
0.000 0.617
2 3
(1,2,3,[4],5,6,7) (8,9) (1) (2,3,[4],5,6,7) (8,9)
0.036 0.090 0.035 0.118
3
(1,2,3,[4]) (5,6,7) (8,9)
0.032 0.070
0.000 0.601
4
(1) (2,3,[4]) (5,6,7) (8,9) 0.025 0.161
0.000 0.792
0.000 0.499 0.000 0.660
Site numbers correspond to those in Fig. 1. Site 4 (in square brackets) was only available for the COI data
regions. A non-significant Mantel test indicates that the species’ dispersal potential is, nonetheless, very high, and that the genetic differentiation found is not the result of large geographic distances between sites, but of oceanographic features that limit dispersal in a portion of the range. The mitochondrial DNA data were considerably more informative than those of the more slowly evolving nuclear intron. Nonetheless, the fact that both markers identified single evolutionary lineages is important to confirm that the COI sequences were not affected by evolutionary artefacts (e.g., numts or mitochondrial capture). This congruence corroborates the idea that the same species is represented in both biomes, and indicates that contemporary environmental conditions are likely important in driving and maintaining the genetic structure identified. Drivers of genetic differentiation Oceanographic data suggest that larval dispersal in southwestern Africa mostly occurs in a westward direction (Shannon and Chapman 1983; Lutjeharms 2006), and this is also confirmed by analyses of gene flow (von der Heyden et al. 2008; Muller et al. 2012; Reynolds et al. 2014; Teske et al. 2014; Muteveri et al. 2015). Although rings and filaments that originate from the Agulhas Current and contain
Fig. 3 Examples of a warm and b cold sea surface temperatures recorded during the austral summer of 1999/2000 (Roy et al. 2001). Temperatures in °C are given in the middle panel. Reproduced with permission from the South African Journal of Science
warm-water species from the east coast are the main mechanism for the exchange of migrants between the two biomes, these are thought to rarely reach the west coast (Ducombe Rae 1991). Instead, the Atlantic coast is more likely to be seeded by migrants from the south coast that reach this area by means of surface water that slowly drifts westwards (Shannon and Chapman 1983). Species like P. knysnaensis, in which most spawning occurs during the summer months (Sutherland et al. 2012), are strongly affected by wind-driven upwelling that peaks during this period, and larvae originating from the south coast are not only expected to suffer mortalities due to low water temperatures, but settling success is further reduced by the offshore flow of surface water (Lutjeharms and Meeuwis 1987). Overall, the region’s oceanography thus represents a potent barrier to larval exchange. Occasional warm temperature anomalies resulting from cessation of strong southeasterly winds (Fig. 3a) during El Niño conditions (Dufois and Rouault 2012) represent a notable exception, and it is likely that the larvae primarily settle in estuaries west of Cape Agulhas during such events. Not only is the survival of warm-water species improved by the large-scale ocean warming, but shoreward wind-driven surface flows will keep planktonic larvae close to the coast (Lutjeharms 2006), aiding them in readily reaching suitable habitat for settlement. When conditions return to normal (Fig. 3b), the settlers are protected from cold water upwelling by maturing in relatively sheltered estuaries and lagoons where water temperatures are higher than those of the adjacent ocean (Smith et al. 2013). The observed lower genetic diversity of P. knysnaensis in the Atlantic Ocean is well explained by this scenario, and may be the result of a combination of: a) a small number of settlers that harbour only a sub-set of the genetic diversity present in their source habitat; b) small populations that are partially isolated from each other; and c) sporadic input of additional settlers from the Indian Ocean. This pattern of low genetic diversity on the west coast has also been observed for clinid fishes and molluscs (Wright et al. 2015), thus hinting at shared processes shaping the genetic structure in divergent taxonomic groups. In fact, multi-species analyses suggest that patterns of genetic diversity mirror those of overall species diversity and the number of endemic species, with the west coast being
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the most depauperate in all these variables (Wright et al. 2015).
Directions for future research The lack of genetic structure identified in some marine organisms across putative marine barriers that in sympatric species define highly distinct evolutionary lineages remains difficult to explain when species’ dispersal potential is similar (Dawson 2012). Genetic structure associated with the disjuntion between the Atlantic and Indian Oceans have been documented in numerous species whose ranges extend into both biomes (Teske et al. 2011). However, there is no clear link between genetic structure and dispersal potential (Teske et al. 2014; Wright et al. 2015). Even though all low-dispersal direct developers studied to date are genetically structured in this region, only about half of the high-dispersal planktonic developers are genetically homogeneous, even though larval exchange between the regions should, at times, be very high (Teske et al. 2014). In many cases, the two regions define evolutionary lineages that have diverged so long ago that they may constitute cryptic species that are adapted to the environmental conditions prevalent in each region (Teske et al. 2006, 2009). The fact that the extent of genetic divergence between geminate sister lineages may differ considerably among species suggests that congruent phylogeographic breaks evolved multiple times, and were potentially driven by climate oscillations (Teske et al. 2013). Given that mitochondrial DNA does not necessarily reflect near-contemporary demography, but usually carries a signature of demographic changes that occurred during the Pleistocene or earlier (Mmonwa et al. 2015; Muteveri et al. 2015), it is possible that very recently evolved genetic divergence is not yet evident on the basis of mitochondrial DNA, which may explain the lack of structure in some species. The comparatively subtle genetic differentiation in P. knysnaensis either suggests that limited contemporary gene flow has driven contrasting levels of genetic diversity, or that the regional populations of this species are in the very early stages of divergence. Disentangling these two scenarios requires more informative genetic data, making P. knysnaensis an interesting candidate species to explore the reasons for the conflicting genetic patterns in species with similar dispersal potential.
Acknowledgments We are grateful to Nadine Strydom for providing information on the life history of Psammogobius knysnaensis. This study was funded by the PADI Foundation (Grant No. 10981, awarded to P. Teske) and the University of Johannesburg. T.R. Golla gratefully acknowledges the National Research Foundation and the University of Johannesburg for financial support.
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