Genetic structure of invasive ctenophore Mnemiopsis leidyi populations in temperate northern European waters supports the southern North Sea overwintering refuge

doi:10.21203/rs.3.rs-4309373/v1

The ctenophore Mnemiopsis leidyi is native to the Atlantic coasts of the Americas and is considered a threat to biodiversity and food webs in introduced ecosystems. Most of these invasive ctenophores perish during the cold winter in temperate northern European waters (North and Baltic Seas), but spread again every summer/autumn. We collected ctenophores from the southern North Sea, inner Danish waters, and western Baltic Sea in 2017 and 2018, and sequenced genomic DNA obtained from tissue samples. We obtained sequences of internal transcribed spacer (ITS) 1 and 2, and the intervening 5.8S fragment and partial fragments of the flanking 18S rRNA and 28S rRNA genes from nuclear DNA (nDNA). These sequences were compared with ones archived in databases from specimens in its native (Northwest Atlantic) and other invaded habitats (Caspian Sea, Mediterranean Sea, central-western Atlantic Ocean). The comparison revealed no significant difference in genetic internal variation among temperate northern European sampling locations but showed variation when compared with native and other invaded habitats. No significant genetic difference was identified between specimens collected in the temperate northern European waters in the two consecutive years 2017 and 2018. The very low interregional and interannual genetic variations in M. leidyi specimens in the Northeast Atlantic indicate that its distribution in temperate northern European waters can be regarded as one single panmictic population and that the annual dispersal from the southeastern North Sea into the western Baltic Sea can be traced back to the same population, which most likely originates every spring from the Dutch Wadden Sea and the English Channel.

comb jelly

Ctenophora

introduced species

non-indigenous species

population genetic

Sanger sequencing

The introduction and spreading of marine species to exotic seas can have tremendous impacts on native biodiversity and food webs (Molnar et al. 2008; Simberloff et al. 2013; Sherpa and Després 2021). Semi-enclosed seas tend to be more sensitive to the impacts of newly introduced species than open ocean sites (Caddy 1993). Further, non-indigenous species (NIS) were suggested to have the highest success rate of establishing self-sustaining populations in areas of intermediate salinity due to a minimum of native species richness (Paavola et al. 2005). NIS are suspected to affect native species by competing for food and space, they can become predators or parasites on native species, or disperse parasites, and in the end, affect the original biodiversity of the area (Behrens et al. 2017; Simberloff 2003; Sherpa and Després 2020; Katsanevakis et al. 2023). In a more severe scenario, NIS can affect human economies and disrupt wildlife conservation plans (Behrens et al. 2017; Simberloff 2003; Sherpa and Després 2020). Europe’s annual costs associated with the consequences of introduced species amount to more than one billion Euros, and costs are not diminishing (Haubrock et al. 2021). Many NIS that become invasive in exotic ecosystems belong to the group of gelatinous zooplankton (cnidarians, ctenophores, pelagic tunicates), for instance, some ctenophore species representing Beroe (Shiganova and Abyzova 2022), the hydromedusa Blackfordia spp. (Iida et al. 2021), or the freshwater hydrozoan Craspedacusta sowerbii (Lüskow et al. 2021).

Among NIS, the ctenophore Mnemiopsis leidyi is perceived to be one of the most invasive worldwide, given its broad ecological and physiological tolerances (Fuentes et al. 2010; Haraldsson et al. 2013) and flexible planktonic diet (e.g., Costello et al. 2012). Its native distribution range extends along the western Atlantic Ocean from Massachusetts to southern Argentina (Ghabooli et al. 2011; Bayha et al. 2015). Mnemiopsis leidyi is a holoplanktonic, simultaneous hermaphrodite capable of self-fertilisation with high fecundity and short generation times (Baker and Reeve 1974; Sasson and Ryan 2016; Edgar et al. 2022). Its life span ranges from several months to one year (Pianka 1974). Today, this species has accidentally been introduced with ship ballast water and subsequently spread with currents to many northeastern Atlantic ecosystems from the English Channel to Denmark, the Norwegian west coast, and the western Baltic Sea (e.g., Jaspers et al. 2018a). Additional southern Eurasian introduced areas include the Mediterranean, Black, and Caspian Seas (e.g., Ivanov et al. 2000; Shiganova et al. 2001a; Fuentes et al. 2010). Recent attempts to monitor the distribution and seasonal shifts of M. leidyi via the detection of environmental DNA in water samples have been successful and may be possible to use as an instrument for an early warning system (Créach et al. 2022; Knudsen et al. 2022).

In temperate northern European waters, M. leidyi was first observed in August–November 2006 (southwestern North Sea; Faasse and Bayha 2006), in November–December 2006 (southeastern North Sea; Boersma et al. 2007), and in October 2006 (southwestern Baltic Sea; Javidpour et al. 2006). Since then, M. leidyi populations have become an annually recurrent phenomenon in many areas with locally extremely high abundances, e.g., in the Limfjord: 867 ± 121 ind. m^− 3 (Riisgård et al. 2007) and half-life times of copepods of only a few days, indicating strong predation pressure, which led to mesozooplankton organisms becoming virtually absent (e.g., Riisgård et al. 2015). This species has been shown to have detrimental effects, when abundant, on lower trophic levels (ichthyo- and zooplankton; Marchessaux et al. 2021), competing zooplanktivorous species (e.g., Granhag et al. 2011; Hamer et al. 2011), changing microbial abundance and community composition through excretion of mucus and dissolved compounds (Dinasquet et al. 2012), and causing trophic cascades that can locally lead to phytoplankton blooms and restructuring of pelagic food webs (e.g., Riisgård et al. 2012; Tiselius and Møller 2017; but see Stibor et al. 2004). Today, trophic interactions of M. leidyi are among gelatinous zooplankton species in the Baltic Sea well studied and are only surpassed by knowledge of the common jellyfish Aurelia aurita (Stoltenberg et al. 2021). Large-scale impacts of M. leidyi infestations were reported in other Eurasian seas (Shiganova et al. 2001a; Bilio and Niermann 2004) that could, in part, only be managed after the introduction of another (gelativore) ctenophore, Beroe ovata (e.g., Shiganova et al. 2001b).

Molecular evidence from mitochondrial and nuclear markers, as well as whole-genome analyses, is accumulating and shows that M. leidyi populations in temperate northern European waters (North and Baltic Seas) were introduced from native northern populations (Reusch et al. 2010; Ghabooli et al. 2011; Bayha et al. 2015; Jaspers et al. 2021). Here, more detailed intrapopulation differences (nucleotide diversity) are displayed in the northern non-native habitats compared with native populations along the northern Atlantic coast of North America, which is indicative of recurrent translocations and likely initial small founding populations (Jaspers et al. 2021). However, a recent spatial high-resolution analysis within the North Sea showed no metapopulation structure (southwestern part versus Skagerrak/Kattegat), indicating substantial gene flow and population connectivity at this geographic scale (Verwimp et al. 2020), which suggests that the southern North Sea serves as an overwintering refuge for M. leidyi.

Genetic variability can be indicative of the physiological response in a population towards seasonal and climate change-related environmental alternations such as in Aurelia aurita polyps (van Walraven et al. 2016). A lack of genetic variation among sampled individuals in the North and Baltic Seas (Reusch et al. 2010) has been keyed to recolonisation from a single source, and ocean connectivity and currents are the driving elements for the spread and reoccurrence of M. leidyi in the North and Baltic Seas (Jaspers et al. 2018a). Had the genetic variation among M. leidyi been large, this would have been indicative of separated and perhaps also isolated populations that are unlikely to mix and breed and would suggest that multiple founding events had taken place. Low genetic variation among North and Baltic Seas populations could also suggest the ITS marker used (Reusch et al. 2010) had an insufficient genetic variation for inferring population genetic variation or point to the Northeast Atlantic population of M. leidyi having experienced a population bottleneck, where only very few individuals have survived to be able to establish the subsequent populations.

A more likely background for the boom-and-bust population succession of M. leidyi in temperate northern European waters (i.e., North Sea, Limfjord, Skagerrak, Kattegat, Belt Sea, Baltic Sea) assumes an annual re-invasion from the Dutch Wadden Sea and the English Channel (here: southern North Sea; Riisgård 2017). Ocean current and population connectivity modelling suggest the southern North Sea as a winter refuge for the ctenophore (van der Molen et al. 2015; Jaspers et al. 2018a). Such overwintering populations have been proposed to be important in maintaining M. leidyi biomass also in its native range that will ultimately contribute to the bloom formation during the following summer (Costello et al. 2006; Marchessaux et al. 2020). Indeed, late summer/autumn population explosions are repeatedly documented in, e.g., the Limfjord, Denmark (Riisgård et al. 2007, 2015). Despite such source-sink dynamics being studied via ocean connectivity modelling and are evidenced by annual late summer/autumn observations, there is still no high-resolution proof of whether North and western Baltic Seas populations are genetically distinct. If population structure and genetic variation are absent (or highly limited) among M. leidyi within the North Sea-Baltic Sea transitional zone (encompassing the inner Danish waters) sampled over several years, the idea of overwintering in the southern North Sea would gain novel support. Modelled population dynamics in coastal Dutch waters were shown to enable a self-sustaining population year-round (van der Molen et al. 2015).

By comparing the variation in a short genetic fragment obtained from samples of M. leidyi collected over two years and various locations around Denmark and Germany, this study aims to identify temporal and spatial genetic population structures to clarify whether genetic variation in a single nuclear marker can lend support to the idea of M. leidyi using the southern North Sea as a refuge for overwintering. Based on the modelled interregional population connectivity, the annual boom-and-bust population succession, and previous population structure results, we expected to find no differences in genetic variation among Danish and North and western Baltic Seas populations between years.

Collection of specimens

In 2017 (May to November) and 2018 (February to August), we collected 59 specimens of Mnemiopsis leidyi at 10 sampling locations in the inner Danish waters and adjacent regions of the North and Baltic Seas (Table 1, Fig. 1). All specimens were smoothly collected by hand or dip net from near the surface in harbours and along the shores. Specimens were stored individually in plastic tubes with 96% ethanol to allow for the later extraction of DNA in the laboratory. Some of the collection locations were revisited in the second study year, to allow for the identification of any possible genetic variation related to sampling time (Table 1).

Table 1

Estimates of diversity and neutrality for *Mnemiopsis leidyi* populations in analysed regions. Abbreviations above columns: Number of haplotypes per population (N_h), haplotypic diversity (h), the standard deviation for haplotypic diversity (h.sd), nucleotide diversity (nd), the standard deviation for nucleotide diversity (nd.sd), and probability of Tajima’s D (p.TD). Sampled locations are abbreviated: Baltic Sea (B); Caspian Sea (CS); central-western Atlantic (CWA); Funen, Bogense (FBo); Funen, Kerteminde (FKe); Germany, Büsum (GBu); North Sea, Helgoland Roads (GHe); Germany, Kiel Fjord (GKi); Germany, Wismar Bight (GWi); Jutland, Limfjord (JLi); Jutland, Mariager Fjord (JMa); Mediterranean Sea (M); Northeast Atlantic (NEA); Northwest Atlantic (NWA); Samsøe, Ballen (SBa); Sealand, Skovshoved (SSk). Additional sequences were obtained from *M. leidyi* from the National Center for Biotechnology Information (NBCI) GenBank as indicated by accession numbers. Tajima’s D and Fu’s F are measures of the observed nucleotide diversity and the population mutation rate. Both measures approach zero if there is no selection or no demographic change (Fu 1997; Ramos-Onsins and Rozas 2006; Bolton et al. 2018)
Population	Coordinates	Sample size	N_h	h	h.sd	nd	nd.sd	Tajima’s D	p.TD	Fu’s F	Year	Month	Sample ID
B	55.183; 15.533	4	1	0.38	0.02	0.00	0.00	-2.10	0.04	-2.70	2010		HM147258-HM147261
CS	36.800; 53.120	20	14	0.94	0.00	0.07	0.00	-3.24	0.00	-3.17	2009		HM147276-HM147277, JQ742005, JX112766-JX112770, JX266248-JX266259
CWA	11.033; -74.850	13	7	0.90	0.00	0.02	0.00	0.52	0.60	-3.27	2009		GU062750-GU062762
FBo	55.608; 10.046	3	2	0.67	0.07	0.01	0.00	0.00	0.00	0.20	2017	Nov	PP669743-PP669745
FKe	55.450; 10.747	11	5	0.71	0.02	0.31	0.03	0.67	0.51	7.12	2017	May	PP669775-PP669777, PP669759, PP669756, PP669751-PP669754
GBu	54.135; 8.840	1	1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	2018	Jul	PP669731
GHe	54.198; 7.891	10	6	0.38	0.03	0.00	0.00	-3.23	0.00	-10.84	2010		HM147262, HM147263, HM147264, HM147265, PP669721.PP669723, PP669718-PP669720
GKi	54.472; 10.260	11	7	0.80	0.01	0.01	0.00	-3.10	0.00	-6.07	2010		HM147266-HM147271, PP669750, PP669746-PP669749
GWi	53.950; 11.343	8	1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	2018	Aug	PP669755, PP669724-PP669730
JLi	56.975; 9.213	10	5	0.80	0.01	0.00	0.00	-3.84	0.00	-7.65	2017		PP669770-PP669774, PP669732-PP669736
JMa	56.715; 10.406	5	2	0.40	0.06	0.01	0.00	-0.97	0.33	1.04	2017	Sep	PP669760-PP669764
M	43.417; 9.283	11	9	0.75	0.00	0.06	0.00	-2.76	0.01	-17.68	2015		HM007193-HM007195, JQ071529, JQ071530, KF435100-KF435104, KJ754164
NEA	56.580; 9.120	5	5	1.00	0.01	0.00	0.00	-6.74	0.00	-10.01	2022		EF175463, KY204082, OM368662, OU817777-OU817778
NWA	38.530; -70.600	8	7	0.96	0.00	0.27	0.02	-1.02	0.31	-0.25	2001		AF293700, EF175464-EF175465, HM147257, HM147272-HM147275
SBa	55.817; 10.652	6	2	0.53	0.02	0.00	0.00	-3.91	0.00	0.00	2018	Aug	PP669740-PP669742, PP669737-PP669739
SSk	55.761; 12.612	5	4	0.90	0.02	0.01	0.00	-4.54	0.00	-2.75	2017	Sep	PP669765-PP669769
Total		131	45	0.85	0.00	0.07	0.00	-3.05	0.00	-29.74

Extraction of genomic DNA and sequencing

The tissue was subsampled by cutting it into smaller pieces before lysing the tissue overnight with the Qiagene Blood and Tissue Kit (cat. no. 69506) following the supplied protocol. Concentrations of resulting extracted genomic DNA were measured with Qubit Fluorometer v.2.0. To obtain sufficient target copies of a fragment of the gene region in the nuclear DNA (nDNA) that comprises a partial fragment of the downstream and the 18S rRNA gene region (18S), plus the complete internal spacer 1 (ITS1), the complete 5.8S rRNA gene, and the complete internal spacer 2 (ITS2), and the partial upstream end of the 28S rRNA gene, collectively referred to as ‘ITS1-2’ region in the following. We used the primer set comprising the forward primer: ‘1400F’ (5’-TGYACACACCGCCCGTC-3’) and the reverse primer ‘5_28Sr’ (5’-CTTAAGTTCAGCGGGTAGTCTCG-3’) (Podar et al. 2001) in a polymerase chain reaction (PCR) setup to obtain the ITS1-2 region, which results in a fragment of around 857 bp. Reagents were mixed in a master tube and then subdivided into individual reactions of 25 µL total reaction volumes that each comprised 1.25 µL of the forward primer in a concentration of 10 µM and 1.25 µL of the reverse primer in a concentration of 10 µM, 14.70 µL ddH₂O, 2.50 µL ×10 buffer, 5.00 µL MgCl₂ in 25 mM concentration, 2.50 µL dNTP in concentrations of 2 mM per dNTP, 0.20 µL AmpliTaq Gold polymerase (ThermoFisher), and with 2.00 µL extracted genomic DNA added as a template. Thermocycler conditions were set to have an initial preheat at 95°C for 5 minutes, followed by 38 cycles of (denaturation at 95°C for 30 s, annealing at 45°C for 30 s, and extension at 72°C for 90 s), with a final extension step at 72°C for 10 minutes, and storage in the machine at 10°C until removed from the thermocycler. The resulting products were visualised after electrophoresis under UV light on a 2% agarose gel, stained with GelRed, and sent to the Macrogen Sequencing service (Amsterdam, the Netherlands) for purification and bidirectional Sanger sequencing with primers in concentrations of 5 µM per primer. Forward and reverse sequences were assembled in Geneious vR7 and inspected manually for ambiguities. Consensus sequences obtained from de novo assembly of bidirectional sequences were then used in the following alignments. Additionally, we made a few initial attempts with PCR amplification of 13 samples and sequencing of the mitochondrial cytochrome oxidase 1 region, using previously published primers (Folmer et al. 1994). This, together with sequences obtained from the National Center for Biotechnology Information (NCBI) GenBank (KF435105–KF435121), returned sequences without any intraspecific variation, and further sequencing of this fragment was abandoned.

Comparison of sequences and geographical distribution of genetic variance

The resulting ITS1-2 sequences were trimmed for the primer binding regions and aligned using Geneious vR7 (Kearse et al. 2012) and MAFFT v6.822 (Katoh and Toh 2010) and deposited on the NCBI GenBank (accession number: PP669718-PP669777). Using a code written in Python v3.10, the following sequences were obtained: AF293700 (Podar et al. 2001), EF175463–EF175465 (Faasse and Bayha 2006), GU062750–GU062762, KF435100–KF435104 (Ghabooli et al. 2011), HM007193–HM007195 (Fuentes et al. 2010), HM147257–HM147277 (Reusch et al. 2010), KJ754164 (Simion et al. 2015), KY204082 (Jaspers et al. 2018a), OM368662 (Knudsen et al. 2022), and JQ071529–JQ071530, JQ742005, JX112766–JX112770, JX266248–JX266259, OU817777–OU817778 (sequences from the NCBI GenBank associated with unpublished studies; Table 1). Additional sequences from M. leidyi already deposited on the NCBI GenBank were imported into Geneious vR7 to include these sequences from previous studies in the alignment. Alignments were trimmed using an R code previously prepared (Stewart et al. 2021) resulting in an alignment with more than 90% coverage of nucleotide positions in the matrix, to ensure unknown nucleotide positions had a reduced impact on the comparison of sequences. Trimmed alignments were then exported as fasta files and used in an additional analysis performed using R v4.3.2 (R Core Team 2023) in RStudio v2021.9.0.351 (RStudio Team 2021). Two versions of the alignment were used for further analysis. One alignment included all sequences of nDNA ITS1-2 region from samples from Denmark and Germany and obtained from NCBI GenBank, and a second version of the alignment only comprised the Danish and German samples. The R code (provided together with input data in the GitHub repository) allowed us to read and export fasta files using the package ‘ips’ (Heibl 2008), identify haplotypes, and prepare haplotype networks for sampling locations and sampling year. The comparison of sampling years was limited to the Danish and German samples collected in 2017 and 2018. We also estimated the difference between sampling localities by estimating ‘Φ_ST’ values. All calculations of genetic difference were done with code prepared for R and the packages: ‘ape’ (Paradis and Schliep 2019), ‘hierfstat’ (Goudet and Jombart 2021), ‘ggplot2’ (Wickham 2016), ‘pegas’ (Paradis 2010), prepared geographical distribution of haplotypes using the packages ‘rnaturalearth’ (South 2017), and ‘pals’ (Wright 2021), and ‘dartR’ (Gruber et al. 2018) to prepare principal component analysis (PCA) plots, and ‘MASS’ (Venables and Ripley 2002) for creating non-metric multidimensional scaling (NMDS) plots and performing an analysis of similarity (ANOSIM). We used the package ‘phangorn v2.10.0’ (Schliep 2011) and the GTR + gamma model as nucleotide substitution model for preparing a maximum likelihood (ML) topology, with 100 bootstrap replicates. Trees were then visualised using ‘ggtree’ (Yu et al. 2017) with the alignment added.

Samples collected and sequences obtained

Specimens of Mnemiopsis leidyi were collected in the North Sea, the inner Danish waters, and the western Baltic Sea (Fig. 1) in 2017 and 2018 (Table 1). The extractions of genomic DNA (nDNA) from the tissue of M. leidyi specimens yielded levels of concentrations that ranged from 1 to 200 ng µL^− 1. The polymerase chain reaction (PCR) amplification of nDNA ITS1-2 fragment was successful for more than 70% of the samples. For the samples that were successfully amplified in PCR, 90% of the sequenced products returned sequence reads that could be assembled for both directions. Trimming the alignment of the nDNA ITS1-2 region to ensure 90% coverage of nucleotide positions, gave a total of 131 sequences with 511 nucleotide positions, and 6.7% missing nucleotides. This first alignment comprised own sequences obtained from Denmark and Germany plus the sequences obtained from NCBI GenBank. A second version of this alignment comprised only own sequences obtained from Denmark and Germany, and included 60 sequences and 511 nucleotide positions, and had 1.4% missing nucleotides. Both alignments showed that sequences had very few differences, suggesting genetic homogeneity across sampled locations. The samples from Funen, Kerteminde (FKe) were the most variable, compared to all other sequences (Fig. 2). This variation was most pronounced in the ITS1 region in the 3’-end and the ITS2 region in the 5’-end of the obtained fragment. The intervening 5.8S did not display any variation.

Genetic variation in Mnemiopsis leidyi populations

The haplotype networks prepared for sampling locations (Fig. 3a) across the two sampling years (Fig. 3b) showed variation from 1 to 45 bases in the 857 bp region obtained. Among the haplotypes obtained from the inner Danish waters, a large proportion (haplotype 3) clustered (Fig. 3), reflecting they are identical. The most different sampled location is the haplotype 1 and 2 collected at Funen, Kerteminde. Identification of sampling years (2017 and 2018) across the same haplotype network (Fig. 3b) did not show segregation in samples that reflected the year sampled. Examining the variation among sequences in a Maximum Likelihood (ML) tree (Fig. 2) also indicates that these sequences have very few nucleotide differences across sampled locations and that the most different samples were the ones collected at Funen, Kerteminde (Figs. 2 and 3a). The comparison of Danish and German haplotypes with haplotypes from the Mediterranean Sea, Caspian Sea, central-western Atlantic and northwestern Atlantic (Fig. 4) shows that there are few nucleotide differences between the different haplotypes. The haplotype network suggests that M. leidyi displays genetic homogeneity for the ITS1-2 region across the Atlantic Ocean to the Mediterranean and Caspian Seas, with haplotypes 1, 10, 16, 19, 20, and 51 being the most common, and the Danish and German haplotypes mainly being represented by haplotype 20 (Fig. 4).

Mapping the haplotype variation within the study area (Fig. 5) and including the NCBI GenBank sequences from various locations around the North Atlantic (Fig. 6) revealed that the temperate northern European haplotypes are more different than the haplotypes from the Mediterranean Sea. The haplotype from the Netherlands (haplotype: 42, accession number: EF175463) was identical to haplotypes of samples from Woods Hole, USA, and to samples, we obtained from Kiel Fjord. The Mediterranean haplotypes had the highest resemblance with haplotypes from Kerteminde (Funen) and the Caspian Sea (Tables 2 and 3). There was very limited or no internal variation among haplotypes obtained from the North Sea, Danish waters, or the western Baltic Sea (Figs. 2 and 4). Comparing levels of Φ_ST (Table 3) did not indicate any major differences in genetic variation between the different sampled locations and years in temperate northern European waters. However, differences become apparent when the genetic structure of these samples is compared to samples from the Mediterranean Sea, Caspian Sea, and central-western Atlantic. The haplotypes found in temperate northern European waters bear a higher resemblance to samples along the Northwest Atlantic coast, and the sequence from the Netherlands (Fig. 6) bears a resemblance to the haplotypes found in the central-western Atlantic north off Columbia (Fig. 6). The sequences obtained from the Mediterranean Sea bear a closer resemblance with previous samples collected in the Caspian Sea (Tables 2 and 3, Fig. 6).

Table 2

Haplotype frequency per population (temperate northern European waters and other locations). Abbreviations for sampling locations are explained in Table 1
Haplotype	CWA	GKi	NWA	NEA	M	CS	FKe	JMa	SSk	B	FBo	GBu	GHe	GWi	JLi	SBa
1	3	1	1
2				1
3				1
4					1
5					1
6					1
7					1
8					1
9					1
10	2					5	1	1	1
11						1
12						1
13						2
14						1
15						1
16	2					1
17						2
18						1
19	2	1				1
20						1
21						1
22					1
23					3
24					1
25	1
26	1
27	1
28	1	5	1				6	4	2	2	2	1	5	8	4	4
29		1	2			1
30						1
31			1
32		1	1
33		1
34													1
35													1
36													1
37													1
38										1
39										1
40			1
41			1
42				1
43				1
44				1
45							1
46							2
47		1
48															1
49							1
50													1
51															2	2
52									1
53															1
54									1
55											1
56															1
57															1
Total	13	11	8	5	11	20	11	5	5	4	3	1	10	8	10	6

Table 3 Comparison of Φ_ST for Mnemiopsis leidyi obtained from sequences of nDNA ITS1-2 for Danish and German sampling locations plus the locations for sequences obtained from the National Center for Biotechnology Information (NCBI) GenBank. The lower triangle shows the Φ_ST values, the corresponding probability values are in the upper triangle. Sequences of nDNA ITS1-2 obtained from the NCBI GenBank were assigned a sampling location as inferred from the year the sequence was deposited on the NCBI GenBank. A low Φ_ST index indicates no variation among the populations sampled and as the Φ_ST value approaches one, it indicates separation of populations. The colour gradient ranges from yellow for low Φ_ST values to dark blue for high Φ_ST values. NA = data not available

	B	CS	CWA	FBo	FKe	GBu	GHe	GKi	GWi	JLi	JMa	M	NEA	NWA	SBa	SSk
B	NA	0.150	0.150	0.410	0.000	1.000	1.000	1.000	1.000	1.000	1.000	0.070	1.000	0.000	1.000	1.000
CS	0.004	NA	0.000	0.850	0.000	1.000	0.000	0.000	0.000	0.000	0.040	0.000	0.180	0.000	0.010	0.040
CWA	0.003	0.008	NA	0.670	0.000	1.000	0.150	0.150	0.150	0.150	0.410	0.350	0.150	0.000	0.150	0.410
FBo	0.000	-0.004	-0.001	NA	0.780	1.000	0.410	1.000	0.410	0.480	0.550	0.420	1.000	1.000	0.410	0.550
FKe	0.006	0.052	0.025	-0.001	NA	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000
GBu	0.000	-0.046	-0.004	-0.004	-0.073	NA	1.000	1.000	1.000	1.000	1.000	0.890	1.000	1.000	1.000	1.000
GHe	0.000	0.012	0.004	0.001	0.020	0.000	NA	0.150	1.000	1.000	0.330	0.000	1.000	0.000	1.000	0.330
GKi	-0.000	0.010	0.000	-0.001	0.021	-0.004	0.001	NA	0.150	0.550	0.820	0.080	1.000	0.000	0.150	1.000
GWi	0.000	0.012	0.004	0.001	0.018	0.000	0.000	0.000	NA	1.000	0.330	0.020	1.000	0.000	1.000	0.330
JLi	-0.000	0.012	0.003	0.001	0.020	-0.002	-0.000	0.000	-0.000	NA	1.000	0.060	1.000	0.000	1.000	1.000
JMa	-0.000	0.005	0.002	0.000	0.011	-0.003	0.000	-0.001	0.000	-0.000	NA	0.130	1.000	0.000	0.330	1.000
M	0.008	0.010	0.002	0.002	0.027	-0.004	0.010	0.005	0.010	0.009	0.007	NA	0.050	0.000	0.040	0.130
NEA	0.000	0.003	0.002	-0.001	0.006	0.000	0.000	-0.001	0.000	-0.000	-0.000	0.007	NA	0.140	1.000	1.000
NWA	0.002	0.040	0.014	-0.008	-0.014	-0.072	0.015	0.013	0.013	0.015	0.006	0.014	0.002	NA	0.000	0.000
SBa	0.000	0.009	0.004	0.001	0.014	0.000	0.000	0.000	0.000	-0.000	0.000	0.009	0.000	0.010	NA	0.330
SSk	-0.000	0.005	0.001	0.000	0.011	-0.003	0.000	-0.001	0.000	-0.000	-0.001	0.007	-0.000	0.006	0.000	NA

In a principal component analysis (PCA) based on genetic variation, most of the sampled locations clustered, with samples from the Caspian Sea (CS) being set aside from the other populations sampled, and the samples from Funen Kerteminde (FKe) grouped closer together with the northwestern Atlantic samples (NWA; Fig. S1). The sampled locations were insufficiently variable to infer specific groupings among haplotypes (Fig. 6). ANOSIM of the genetic sequence data showed that the sampled populations in temperate northern European waters are similar (R = 0.86, P = 0.001, Fig. S2), but differences are more pronounced when samples from locations representing the northwestern Atlantic, Mediterranean Sea, and Caspian Sea are included (R = 0.79, P = 0.001, Fig. S3). When samples representing only the northeastern Atlantic were collected across several years compared there are fewer differences (R = 0.89, P = 0.001, Figs. S4 and S5), as opposed to when northeastern Atlantic, northwestern Atlantic, Mediterranean samples and samples from the Caspian Sea representing several years are compared (R = 0.43, P = 0.001, Fig. S6). Comparing the pairwise genetic difference between sampled locations (Fig. 7) and years (Fig. S7) in a non-metric multidimensional scaling (NMDS) analysis did not sort out the different sampled populations, but grouped the samples from temperate northern European waters, away from other sampled locations (i.e., Caspian and Mediterranean Sea populations).

We did not find distinct genetic variations among the Mnemiopsis leidyi specimens collected in the southern North Sea, inner Danish waters, and western Baltic Seas. This is not unexpected and confirms what previous studies found with a much lower spatial resolution (Bayha et al. 2015) or in adjacent regions (Verwimp et al. 2020). These findings point towards a single founding event with the same genetic origin, which gives rise to an annual re-invasion of the region. The major ocean currents support the spreading and re-introduction of southern North Sea specimens to the inner Danish and subsequent western Baltic Sea waters (Riisgård 2017; Jaspers et al. 2018a). Specimens are likely transported via northward-flowing coastal currents from the English Channel along the Dutch, German, and Danish west coasts, and eventually, reach the Limfjord and Skagerrak. Here, they are transported further toward the Swedish west coast. A minor part of the more saline North Sea water, which mixes with Atlantic water in Kattegat, flows southwards below the less saline outflowing Baltic Sea water (Riisgård 2017), replenishing populations in the inner Danish and adjacent Baltic Sea waters. The introduction of M. leidyi through the Kiel Canal (the third connection between the North and Baltic Seas) is likely of subordinate importance as an invasion corridor because of its low salinity (Jaspers et al. 2018b). The resemblance between samples from the Kiel Fjord, sequences obtained from the Netherlands, and its native range points towards the Kiel Fjord as being a point of entry for M. leidyi before spreading into adjacent waters.

Mnemiopsis leidyi occurs in high numbers during late summer and autumn in the southeastern North and western Baltic Seas (and their transition zone), given their high fecundity and short generation times (Baker and Reeve 1974; Sasson and Ryan 2016; Edgar et al. 2022). However, once the conditions become less favourable (i.e., seasonal decrease of temperature and shortage of zooplankton prey), their numbers rapidly drop and usually cease by mid-winter. Some individuals may however survive the winter in the Kattegat and Skagerrak areas (Køhler et al. 2022). For overwintering, M. leidyi were shown to migrate to greater depths with temperatures ranging from 4 to 8°C in the Bornholm Basin, central Baltic Sea (Kube et al. 2007). In a French Mediterranean lagoon, the M. leidyi population was shown to seasonally vanish when temperatures fall below 6°C (Marchessaux et al. 2020). Overwintering populations are important in maintaining biomass that will ultimately contribute to the bloom formation during the following summer (Costello et al. 2006). Such depths albeit do not exist in the southeastern North Sea, inner Danish waters, and western Baltic Sea (e.g., the average depth of the Kattegat is 23 m and only 13 m in the Belt Sea). Similarly, the lobate ctenophore Bolinopsis infundibulum migrates into deeper waters (> 50–800 m) during autumn and winter to find refuge, while virtually disappearing from the surface (Båmstedt and Martinussen 2015). Overwintering in these areas seems therefore highly unlikely, requiring an alternative explanation for the species' annual re-occurrence. The seasonal disappearance of M. leidyi from the inner Danish waters has also been supported by monitoring of environmental DNA (eDNA) from harbours sampled in early summer and autumn across the country (Knudsen et al. 2022). Monitoring of eDNA for M. leidyi in seawater might even allow for individual-level haplotyping, and subsequent inference of population genetic variation, as has been performed on whale sharks (Dugal et al. 2022).

Physiological constraints, e.g., low temperatures, and bottom-up forcing, i.e., mismatch between food requirements and availability, are only part of the potential reasons why M. leidyi seasonally disappears from temperate northern European waters, but survives year-round, for instance, in the Scheldt estuaries, the Netherlands (van der Molen et al. 2015). In the Limfjord, ctenophores typically do not reach large size due to high intraspecific food competition, but remain small (< 15 mm; Riisgård et al. 2007, 2015). Even at a small size, M. leidyi can endure some time (a matter of days) of unfavourable conditions, shrink and continue egg production (Jaspers et al. 2015). However, after energy allocation to reproduction, senescence will eventually lead to death and cessation of the population. Top-down control is the fourth dimension of seasonal population vanishment, however, regionally less well-studied (Stoltenberg et al. 2021). Despite some studies investigating the effects of parasites on M. leidyi in invaded temperate northern European waters (Selander et al. 2009) and intraguild predation being likely considerable (Hosia and Titelman 2011), there is a lack of knowledge of other predator-prey interactions.

Overwintering of M. leidyi in the southern North Sea appears to be the most logical explanation for the annual re-appearance of the invasive ctenophore and receives support from field observations (Riisgård et al. 2007, 2012), population connectivity modelling (van der Molen et al. 2015; Jaspers et al. 2018a), eDNA monitoring (Knudsen et al. 2022), and genetic population structure analysis (Verwimp et al. 2020), like results of this study. The lack of population differentiation in M. leidyi, and more generally, in marine invertebrates, may be attributed to large population sizes (Deagle et al. 2015) and high population connectivity due to limited physical barriers/oceanographic boundaries (Cowen and Sponaugle 2009) as encountered in the North Sea/western Baltic Sea system. Secondary spread via ocean currents and ship traffic (ballast water) from and to some of the world’s busiest freight ports likely contributes to an indistinguishable M. leidyi population across the study area. The recent extinction of a genetically distinct M. leidyi population in the Baltic Sea and the subsequent re-opened niche likely contributed to the vastly homogenous population we investigated in 2017 and 2018 (Bolte et al. 2013; Jaspers et al. 2018a).

Despite the picture presented in the current study, with the genetic variation being absent among M. leidyi in temperate northern European waters, some limitations need to be addressed. It may be that our analysis missed identifying some genetic population structural differences, which would have been detected using high-density markers (Verwimp et al. 2020) or a whole-genome approach (Jaspers et al. 2021), or higher numbers of collected specimens per sampling location. However, given the concordance of our results with the findings presented by Verwimp et al. (2020), we do not see any reason to question our findings. For future studies, a set of several molecular markers complementing each other is recommended in the unavailability of whole-genome techniques. Furthermore, the annual re-sampling was limited in this study to two years. In contrast, Bolte et al. (2013) sampled three consecutive years (2008–2010) with one sampling location in the North and two sampling locations in the Baltic Sea. Instead of performing several short-term investigations, which are partly not comparable, we recommend defining a balanced number of sampling locations in the North and Baltic Seas and inner Danish waters. These locations should be sampled annually following a standardised protocol as part of a long-term monitoring programme, allowing for more sophisticated analyses of the metapopulation structure. At some of the sampling locations used in the present study (i.e., Helgoland Roads, North Sea, and Kiel Bight, Baltic Sea), such monitoring programmes are already in place.

One way to approach continuous seasonal monitoring could be to make use of eDNA from M. leidyi in the water (Créach et al. 2022; Knudsen et al. 2022; Leerhøi et al. 2024) and engage the public in a ‘community science’ project (Tøttrup et al. 2021; Agersnap et al. 2022; Knudsen et al. 2023; Leerhøi et al. 2024) with water samples collected in the spring and early autumn. If sampling is performed every year within the same time intervals at the same time of the day, a large part of the variation in levels of eDNA arising from diurnal (Jensen et al. 2022) and yearly (Sigsgaard et al. 2017) variation can be avoided. Coupling such surveillance with automated sampling is another way of continuous eDNA monitoring (Hansen et al. 2020). The possibilities of regular monitoring of M. leidyi from eDNA are many, and the absence of population genetic variation across its distribution in temperate northern European waters ensures that minor genetic variants are hard to go unnoticed when eDNA samples are analysed.

The genetic variability within temperate northern European water Mnemiopsis leidyi populations is low for all samples collected in 2017 and 2018. Very few differences were observed for specimens collected in the southern North Sea, the inner Danish waters, and the western Baltic Sea, which supports the idea of annual re-invasion from the Dutch Wadden Sea and the English Channel. Despite their relatively homogenous haplotypes, genetic diversity may be regionally higher than reported in the present study. It is possible that had monitoring been continuous for all succeeding years, the haplotype variation in temperate northern European waters might have looked different, allowing for more phenotypic plasticity regarding environmental change.

Funding

This study was partially supported by the Innovation Fund Denmark (Grant J.nr. 104-2012-1) and by the Danish Environmental Agency (Miljøstyrelsen).

Authors’ contributions

FL and SWK conceived the ideas; FL and SWK designed the study; FL and SWK collected samples and produced experimental data; SWK analysed the data; FL and SWK wrote the first draft of the manuscript; FL and SWK finalised and approved the manuscript for submission.

Acknowledgements

We thank the Danish Environmental Agency and the Innovation Fund Denmark, who covered the costs of the initial sampling and sequence analysis. We also thank Peter R. Møller and Xupeng Chi for supplying additional specimens, as well as Ciaran Murray for advice on tables in R. We gratefully appreciate the laboratory staff at the modern genetic GLOBE laboratory in Bartholinsgade associated with the Natural History Museum of Denmark for help and advice on laboratory protocols. This study is a result of the OBAMA-NEXT (Observing and Mapping Marine Ecosystems – Next Generation Tools) project, funded by the European Union under the Horizon Europe program (grant agreement no. 101081642), www.obama-next.eu. We appreciate comments made by Pablo J. López-González, Guillaume Marchessaux, and Hans Ulrik Riisgård on an earlier version of the manuscript.

Availability of data and materials

All data and code for data analysis can be obtained by git cloning this GitHub data repository: https://github.com/monis4567/Mnemiopsis_leidyi_in_NE_Atlantic.git

Conflict of interest

The authors declare that they have no conflict of interest.

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BiolInvPopgenMnemSupplementaryMaterialv042024apr24.doc

Genetic structure of invasive ctenophore Mnemiopsis leidyi populations in temperate northern European waters supports the southern North Sea overwintering refuge

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Conclusion

Declarations