Faculty of Science Department of Biology
Research group Marine Biology
Academic year 2011-2012
The spatial and temporal distribution of gelatinous zooplankton in the Belgian part of the North Sea
Tina VAN REGENMORTEL
©Karl Van Ginderdeuren, 2010
Supervisors: Prof. Dr. Magda VINCX1 Thesis submitted to obtain Dr. Kris HOSTENS2 the degree of Master in Biology. Tutor: Drs. Lies VANSTEENBRUGGE1,2
Faculty of Science - Research group Marine Biology
All rights reserved. This thesis contains confidential information and confidential research results that are property of UGent. The contents of this master thesis may under no circumstances be made public, nor complete or partial, without the explicit and preceding permission of the UGent representative, i.e. the supervisor. The thesis may under no circumstances be copied or duplicated in any form, unless permission granted in written form. Any violation of the confidential nature of this thesis may impose irreparable damage to the UGent. In case of a dispute that may arise within the context of this declaration, the Judicial Court of Gent only is competent to be notified.
Deze masterproef bevat vertrouwelijke informatieve en vertrouwelijke onderzoeksresultaten die toebehoren aan de UGent. De inhoud van de masterproef mag onder geen enkele manier publiek gemaakt worden, noch geheel noch gedeeltelijk zonder de uitdrukkelijke schriftelijke voorafgaandelijke toestemming van de UGent-vertegenwoordiger, in casus de promotor. Zo is het nemen van kopieën of het op eender welke wijze dupliceren van het eindwerk verboden, tenzij met schriftelijke toestemming. Het niet respecteren van de confidentiële aard van het eindwerk veroorzaakt onherstelbare schade aan de UGent. Ingeval een geschil zou ontstaan in het kader van deze verklaring, zijn de rechtbanken van het arrondissement Gent uitsluitend bevoegd daarvan kennis te nemen.
© Karl Van Ginderdeuren, 2010 Some of our smaller jellyfish species: clockwise starting left: Beroe cucumis, Amphinema dinema, Bougainvillia sp. and Mnemiopsis leidyi.
1 Ghent University, Biology Department, Marine Biology Section, Krijgslaan 281 S8, 9000 Gent
2 Institute for Agricultural and Fisheries Research (ILVO), Animal Science Unit, Fisheries, Bio- environmental Research, Ankerstraat 1, 8400 Oostende
Table of content Abstract ...... 7 Introduction ...... 8 Aims ...... 10 Material and methods...... 11 Study area ...... 11 Data collection ...... 11 Sample and data analyses ...... 12 Statistical analyses ...... 13 Results ...... 15 Replicate comparison ...... 16 Zooplankton diversity ...... 17 Species diversity analyses ...... 21 Spatial analyses in diversity indices ...... 21 Temporal analyses in diversity indices ...... 25 Environmental variables ...... 29 Community analyses ...... 31 Analysing the community on a spatial scale ...... 31 Analysing the community on a temporal scale ...... 33 Abiotic and biotic variables influencing the temporal variation ...... 35 Discussion...... 40 Replicate comparison ...... 40 Zooplankton diversity ...... 41 Species diversity analyses ...... 47 Spatial analyses in diversity indices ...... 47 Temporal analyses in diversity indices ...... 47 Community analyses ...... 48 Analysing the community on a spatial scale ...... 48 Analysing the community on a temporal scale ...... 49 Abiotic and biotic variables influencing the temporal variation ...... 50 Conclusions ...... 54 Summary ...... 56 Samenvatting ...... 59 Acknowledgements ...... 62 Table of Figures ...... 63 Table of Tables ...... 65 Reference list ...... 66 Appendix ...... 76
The spatial and temporal distribution of gelatinous zooplankton in the Belgian part of the North Sea.
Tina VAN REGENMORTEL
Ghent University, Marine Biology Section, Krijgslaan 281/S8, 9000 Ghent, Belgium
Abstract This study focuses on spatial and temporal behaviour of gelatinous zooplankton in the Belgian part of the North Sea. Zooplankton was sampled monthly at three stations, along a nearshore to offshore transect, from March 2011 till February 2012. A total of 37 gelatinous taxa were collected that showed densities between <1 and 69 ind. m-3. One Hydrozoa species was recorded for the first time in Belgium. However, its exact identity will be confirmed by genetic analyses outside the scope of this study. Univariate (diversity indices) and multivariate (MDS, DISTLM, dbRDA) analyses were conducted to examine both the spatial and temporal patterns. Replicate analyses showed high similarity coefficients (70-90%), indicating that samples analysed in this study explained the spatial and temporal patterns well. However, absent time constraints, more replicates would have been worked out to improve the accuracy of the results. Univariate and MDS analyses showed no obvious spatial distribution pattern in gelatinous zooplankton. This contributed to the assumption that the Belgian part of the North Sea shares one diverse gelatinous zooplankton community. On the other hand, our analyses showed a clear difference in temporal distribution. Two bloom periods in gelatinous zooplankton were recorded over the year. The first occurred in Spring and followed a bloom in non-gelatinous zooplankton, indicating a possible direct biological link between both groups. The second bloom occurred in Autumn and began earlier than, or coincided with, a second bloom in non-gelatinous zooplankton. This latter bloom might have been more influenced by environmental factors. Furthermore, gelatinous zooplankton reached higher densities than non-gelatinous zooplankton within the zooplankton assemblage during blooms. More research (i.e. experimental) will have to be conducted to determine the role of gelatinous zooplankton within the zooplankton assemblage. DISTLM and dbRDA analyses showed that temporal variability in the gelatinous zooplankton distribution was best explained by a combination of temperature, oxygen, salinity and the density of non-gelatinous zooplankton. These factors explained only 40% of the total observed variation, indicating that other factors such as currents, predation or competition also have a considerable influence on the gelatinous zooplankton assemblage.
Keywords: gelatinous zooplankton, zooplankton community, spatial and temporal distribution, Belgian part of the North Sea (BPNS)
Tina Van Regenmortel 7 Introduction
In recent years, various authors have suggested that we are heading towards a more gelatinous future (Mills, 2001; Purcell, 2005; Purcell et al, 2007). Some examples can be found in East Asian waters, where blooms of Aurelia aurita were recorded (Uye et al., 2003) and unusual population explosions of the jellyfish Nemopilema nomurai threaten fisheries (Kawahara et al., 2006). Examples can also be found closer to home, such as the population explosion of Mnemiopsis leidyi in the Black Sea in the 1980s (Vinogradov et al., 1989) and the large numbers of Physalia physalis off the coast of Spain in 2010 (©BBC News, 2010). Possible causes that are put forward for this increase in jellyfish populations include climate change (Purcell, 2005), overfishing (Parsons & Lalli, 2002; Daskalov et al., 2007), eutrophication (Arai, 2001), translocation (Mills, 2001; Purcell et al., 2001; Graham & Bayha, 2007) and habitat modification (Richardson et al., 2009). Some authors however claim that gelatinous zooplankton blooms have ancient origins and are not a new phenomenon (Condon et al., 2012). Others argue that due to lack of sufficient long term jellyfish data sets, trends in population increase are not yet clear (Purcell, 2012). All we know for certain is that jellyfish populations are nowadays changing, and it is therefore important to monitor their distribution.
According to the Belgian Register of Marine Species (BeRMS1), 39 jellyfish species (Ctenophora and Cnidaria) have been found in the Belgian part of the North Sea (BPNS). This data is based on both literature and qualitative research (Vandepitte et al., 2010; Van Ginderdeuren et al., 2012b). Three species of Ctenophora can be found in the BPNS, including one non-indigenous species: Mnemiopsis leidyi A. Agassiz, 1865. This species was first observed in the Westerschelde estuary in 2006 (Faasse & Bayha, 2006) and the BPNS in 2007 (Dumoulin, 2007). Its presence causes great concern because of its potential impact on the ecosystem. In the Black Sea, its blooms coincided with the reduction in the fishery-yields, including the collapse of the anchovy Engraulis encrasicolus fishery yields in the 80s (Kideys, 1994). Monitoring the distribution and assessing the ecology and impact of this species in the North Sea to avoid similar disasters is thus of major concern. Amongst the Cnidaria, six species of Scyphozoa and 30 species of Hydrozoa are found. Six hydromedusae species are vagrant, whereas 24 hydromedusae species are permanent residents. Amongst the hydromedusae, one non-indigenous species is present: Nemopsis bachei L. Agassiz, 1849. This species naturally occurs along the East coast of North America (Kramp, 1959), and was first observed in the BPNS in 1996 (Dumoulin, 1997 as referred in Faasse & Ates, 1998). There is however some discussion on its non-native status. Faasse and Ates (1998) claim to take
1 http://www.marinespecies.org/berms/
8 into consideration that even though the first description of the species was from the East Atlantic and it was only later discovered in Europe, it does not prove its absence in Europe before.
This master dissertation focuses on gelatinous zooplankton, which includes for the purpose of this study, the planktonic members of the phylum Ctenophora and the medusae of the phylum Cnidaria (hydromedusae and scyphomedusae). The majority of Ctenophores are planktonic, ranging from surface waters to depths of 3000 meters (Brusca & Brusca, 2003). Amongst the Cnidaria, Scyphozoa are largely coastal, and are usually the specimens found washed up on shores. However, they can also be found at great depths (Arai, 1997). Hydrozoa are generally much smaller and can be a prominent component of marine zooplankton communities in both coastal and oceanic environments (Raymont, 1983 as referred in Gibbons & Richardson, 2009). As no quantitative research has been done before on a regular basis for the BPNS, it is not known when, where and in which densities these gelatinous zooplankton species occur. Therefore, the aim of this study is to monitor the spatial and temporal distribution of the gelatinous zooplankton of the BPNS.
This thesis contributes to the cooperation between the Marine Biology Research Group at the University of Ghent and the Biological Environmental Research Group at the Institute for Agricultural and Fisheries Research (ILVO). Both research groups have a history in benthic community research, with a focus on macrobenthos, epibenthos, hyperbenthos, as well as the demersal fish fauna. Recently, research has taken off at the level of the pelagic community, as many benthic organisms have a larval pelagic stage (bentho-pelagic coupling). This thesis studies the pelagic zooplankton community.
The present study is also part of the MEMO project (Mnemiopsis Ecology and Modelling: Observation of an invasive comb jelly in the North Sea). This project is framed within the Interreg ІVA “2 seas” program, a European Union funding program which supports cross border cooperation between the UK, France, the Netherlands and Belgium. Project coordinator is the Institute for Agricultural and Fisheries Research (ILVO, Belgium). Other project partners are IFREMER (Institut Français de Recherche pour l'Exploration de la Mer), ULCO-LOG (Université du Littoral Côte d’Opale – Laboratoire d’Océanologie et de Géosciences), CEFAS (Centre for Environment, Fisheries and Aquaculture Science) and Stichting Deltares.
Tina Van Regenmortel 9 Aims
The aims of this study are: 1. to assess the spatial distribution of gelatinous zooplankton in the BPNS by sampling at three different locations on a nearshore to offshore transect, 2. to determine the temporal distribution of gelatinous zooplankton in the BPNS by sampling on a monthly basis during one year, 3. to discuss the position of gelatinous zooplankton within the entire zooplankton assemblage of the BPNS.
The proposed hypotheses are: 1. gelatinous zooplankton shows an obvious pattern in spatial distribution in the Belgian part of the North Sea, 2. gelatinous zooplankton shows a uniform temporal distribution throughout the year, 3. gelatinous zooplankton is never the most abundant component within the total zooplankton community.
10 Material and methods
Study area
In order to determine the spatial and temporal distribution of gelatinous zooplankton in the Belgian part of the North Sea, monthly samples were taken from March 2011 to February 2012 from the research vessel ‘Zeeleeuw’. Three locations were chosen on a nearshore to offshore transect (Figure 1). Station W04 is situated near ‘Vlakte van de Raan’, off the coast of Zeebrugge. Station W07 is located at the ‘Thornton’ sand bank. The W09 station is positioned north of the ‘Hinderbanken’.
Figure 1. Sampling locations in the Belgian part of the North Sea.
Data collection
Gelatinous and non-gelatinous zooplankton were sampled using a WP 3 net (ø 100 cm, mesh size 1 mm). This net was trawled at a speed of two to three knots, making two or three undulating movements from sea surface to sea bottom. By making use of an attached volumetric flow meter, the volume of water passing through the net was determined. Three replicates were taken at each sampling point. Due to a huge Cyanea lamarckii bloom and limited ship time, only one replicate was taken at sampling location W09 in March and no samples were taken at location W09 in May. All macroscopically visible ctenophores and scyphozoans were identified and measured (oral- aboral and umbrella diameter respectively) on board. Ctenophores do not preserve well in
Tina Van Regenmortel 11 standard preservation solution, hence the onboard analyses. The remaining content of the three replicates was preserved at 4% buffered formaldehyde solution, for further examination in the laboratory. Physical and chemical environmental parameters of water are accurately estimated by means of one replicate per sampling locations (Kramer et al., 1994). At each sampling location the transparency of the water column was determined using a Secchi disc as proxy for turbidity. CTD-sensors (type SBE19plusV2) were used to measure the temperature, oxygen concentrations, turbidity and salinity at different depths. Most environmental data were measured on board RV Zeeleeuw. However, missing data were completed by the database ODAS (generated on board from RV Belgica) hosted by the Management Unit of the North Sea Mathematical Models (MUMM). Satellites measured the reflection of chlorophyll a in the water column, as a proxy for the primary production by phytoplankton. This satellite data is also hosted by MUMM.
Sample and data analyses
Samples were analysed in the Microscopy lab at the Institute for Agricultural and Fisheries Research (ILVO). In total, 49 samples were analysed. First, one replicate from each month at each location was analysed. Using a stereo microscope, the gelatinous zooplankton present in the samples was identified to the lowest taxonomic level possible. Identification keys used for identification were: The medusae of the British Isles (Russel, 1953) and the Marine Species Identification Portal2. The exact scientific names were checked with the World Register of Marine Species (WoRMS) (Appeltans et al., 2011). All non-gelatinous zooplankton species were counted and classified on a higher level of classification (e.g. Amphipoda, Chaetognatha, Pisces larvae, etc.), this to have an idea of the prevailing zooplankton community. However, it should be noted that because the focus of this study was on gelatinous zooplankton, and therefore a WP3 net was used, the small non-gelatinous zooplankton (<1 mm) could have escaped the net. Therefore, the view that we obtain of the zooplankton community through this study does not exactly reflect reality. It is however still relevant to get a broader overall view of the zooplankton community and to better frame the gelatinous zooplankton community.
To check if replicates, taken during the sampling campaign, were not significantly different from each other, minimum one extra replicate from four selected months was analysed. These four months were chosen, based on the analyses of the first replicates. They represented bloom
2 http://species-identification.org
12 months in both gelatinous and non-gelatinous zooplankton. For these replicates, both gelatinous and non-gelatinous species were counted and classified on a higher level of classification. For all samples, densities ( , individuals per m3) were calculated using the following formula:
.
Statistical analyses
Univariate and multivariate analyses were carried out using Primer version 6, Permanova+ software (Clarke & Gorley, 2006; Anderson et al., 2008) and Statistica version 10.
A number of univariate diversity indices were calculated by means of the Primer module DIVERSE. This was done for the total zooplankton dataset, the gelatinous zooplankton dataset as well as the non-gelatinous zooplankton dataset for each sampling location. Furthermore, these indices were calculated per season for all datasets. The total number of species, S, and Margalef’s index, d, measuring the species richness (Margalef, 1958), were calculated. Pielou’s evenness index, J’, was calculated and represents how evenly the individuals are distributed among the different species (Pielou, 1966). Shannon’s index, H’, was determined with log to base e and measures the species diversity (Shannon, 1948). Simpson’s index, λ’, measures the probability that two individuals randomly chosen from a sample will belong to the same species (Simpson, 1949). Because of formula-constraints (dividing by zero) and meaning of the index (no negative densities), Margalef’s index, Pielou’s evenness and Simpson’s index were not calculated for the gelatinous and non-gelatinous zooplankton dataset. To test if the calculated indices were significantly different between locations and seasons, a non-parametrical Kruskal-Wallis test was conducted in Statistica version 10. When significant differences were present, a Mann-Whitney U test was conducted to examine between which locations or seasons the difference could be observed. A Bonferonni correction was made for multiple pairwise tests.
The data was also analysed in a multivariate way. A non-parametric multidimensional scaling (MDS) analysis was conducted for all zooplankton data, where gelatinous zooplankton taxa were combined to the higher level of Ctenophora, Hydrozoa or Scyphozoa. Furthermore, a MDS analysis on the gelatinous zooplankton dataset was done. Both analyses were executed on the spatial as well as the temporal scale. To determine whether environmental factors (temperature, salinity, oxygen concentration, turbidity, Secchi depth and chlorophyll a) had an influence on the seasonal changes in
Tina Van Regenmortel 13 zooplankton density, a Distance-based Linear Model (DISTLM) and distance-based Redundancy analysis (dbRDA) were conducted in Permanova+. To make sure that no multi-collinearity between the environmental variables was present, a Draftsman plot was created. Variables which showed right-skewness have to be transformed (e.g. log transformation). The cut-off value of 0.8 was used to delete variables, which still showed too much intercorrelation, from further analyses. To find the most parsimonious model and explain the variation in zooplankton data, the ‘Best’ selection procedure was used in DISTLM. This procedure examines the value of the selection criterion for all possible combinations of predictor variables, and provides the overall ten best models in its output. As selection criterion, both AIC (An Information Criterion) and BIC (Bayesian Information Criterion) were used and compared. To find the best variables, a scatter plot for the top ten AIC and BIC models was created. With this information, the most parsimonious model could be chosen and a dbRDA plot was made for this model.
To check the adequacy of the model in the constrained dbRDA plot, it was compared with an unconstrained Principal Coordinates analyses (PCO) plot.
The same procedure was carried out for the gelatinous zooplankton dataset. Here, the variable ‘zooplankton density’ was added to the analyses, to check whether gelatinous zooplankton variation could be explained by variation in non-gelatinous zooplankton densities. A Draftsman plot was created and log transformation was carried out when necessary. Variables which showed more correlation than the allowed cut-off value of 0.8 were deleted. The ‘Best’ selection procedure was used to find the most parsimonious model to explain the variation in gelatinous zooplankton data. Both AIC and BIC were used as selection criteria. The DISTLM analysis revealed the most parsimonious model and the corresponding dbRDA plot was made. This dbRDA plot was again compared with a PCO plot.
Replicates data were analysed by means of a MDS and a SIMPER-test with Bray-Curtis similarity measure, which made it possible to define similarities between the replicates.
14 Results In this study, three different datasets were assembled and used for analyses. (1) The total zooplankton dataset contains all non-gelatinous zooplankton taxa (Figure 2, red) and the gelatinous zooplankton taxa at a higher level of classification (Figure 2, green). The latter was done to avoid that gelatinous zooplankton taxa had a higher influence on the outcome of the results, compared to the non-gelatinous zooplankton taxa. (2) The gelatinous zooplankton dataset contains all the gelatinous zooplankton taxa (Figure 2, orange). (3) The non-gelatinous zooplankton dataset contains all non-gelatinous zooplankton taxa at a low level of classification (Figure 2, red). An overview of all species present in all three datasets, is given in the Appendix (Table 12, Table 13)
Figure 2. Overview of the three datasets for analyses: (1) total zooplankton dataset (red and green), (2) gelatinous zooplankton dataset (orange) and (3) non-gelatinous zooplankton dataset (red).
Tina Van Regenmortel 15 Replicate comparison
Literature on zooplankton sampling recommends taking three replicates for biological data (Kramer et al., 1994). Due to lack of time, not all three replicates could be examined. Therefore, minimum one extra replicate for four chosen months distributed over the year were examined for this master dissertation. Figure 3 shows a Multidimensional Scaling (MDS) analysis of all examined replicates. Clustering between the replicates can be seen.
Figure 3. MDS of all examined replicates.
All corresponding similarity coefficients can be found in Table 1. Samples: replicates Average similarity March: W04a, W04b, W04c 80.4 March: W07a, W07b, W07c 79.2 July: W04a, W04b 85.6 July: W07a, W07b, W07c 58.0 July: W09a, W09b 89.5 November: W04a, W04b, W04c 89.0 November: W07a, W07b, W07c 69.7 November: W09a, W09b 75.2 December: W04a, W04b 74.9 December: W07a, W07b 77.8 December: W09a, W09b 90.7
Table 1. Similarity coefficients (%) corresponding to all examined replicates.
16 Zooplankton diversity
The zooplankton community is dominated by non-gelatinous zooplankton throughout the year, with exceptions in July (nearshore) and October-November (near to offshore) when gelatinous zooplankton reaches higher densities (Figure 4).
Figure 4. Densities of non-gelatinous and gelatinous zooplankton from March 2011 to February 2012 at three sampling locations in the BPNS. Twenty-five taxa of non-gelatinous zooplankton were identified (Table 12, Appendix). Blooms in non-gelatinous zooplankton were recorded in Summer (June-August) and Winter (December- January) (Figure 4). During the Summer bloom, the bulk of non-gelatinous species was represented by Decapoda larvae and Brachyura zoea and megalopa larvae. However, an average of eleven other non-gelatinous species could be found during this bloom period. The winter bloom was also quite high in species richness (average of 14 non-gelatinous species), but showed an uneven composition, with an average of 89% Copepoda.
This study revealed 37 gelatinous zooplankton taxa, of which 25 were identified to species level. (Table 13, Appendix). Gelatinous zooplankton blooms were recorded in July and November (near- and midshore) and October and December (offshore) (Figure 4). In July, Eucheilota maculata bloomed nearshore, while Codonium proliferum peaked at the midshore location, representing 67% and 63 % of the gelatinous zooplankton densities respectively. Blooms from October to December were dominated by Clytia hemisphaerica.
Tina Van Regenmortel 17 Table 2 shows average and maximum density (ind. m-3) of the five most dominant and five rarest non-gelatinous and gelatinous zooplankton species found in this study. Additional info on densities and the spatial and temporal occurrence of all taxa can be found on the CD attached at the end.
Taxon/Species Average density Maximum density Copepoda sp. 42 596 Brachyura zoea 5 65 Tunicata sp. (pelagic) 4 74 Decapoda larva 3 20 Mysida sp. 3 51 Euphausiacea sp. <0.1 <0.1 Phtisica marina <0.1 <0.1 Cephalopoda sp. <0.1 <0.1
Non-gelatinous species Non-gelatinous Tomopteris helgolandica <0.1 <0.1 Rissoides desmaresti <0.1 <0.1
Clytia hemisphaerica 6 52 Eucheilota maculata 2 69 Pleurobrachia pileus 2 23 Beroe gracilis <1 10 Codonium proliferum <1 11 Eirene viridula <0.1 <0.1 Eirenidae sp. <0.1 <0.1
Gelatinous species Gelatinous Aequorea vitrina <0.1 <0.1 Gossea corynetes <0.1 <0.1 Leuckartiara octona <0.1 <0.1 Table 2. Average density (ind. m-3) and maximum density (ind. m-3) of some dominant and rare zooplankton species found in this study from March 2011 to February 2012.
The occurrence of gelatinous species shows a clear seasonal pattern, with different optima. Some species, such as Pleurobrachia pileus and Clytia hemisphaerica, occur throughout the year and show one or more peaks in density in the BPNS. Other species, such as Mnemiopsis leidyi and Obelia sp., only occur during certain months and are absent the rest of the year in the BPNS. This is demonstrated in Figure 5.
18
Pleurobrachia pileus Beroe gracilis
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Figure 5. Seasonal occurrence of some gelatinous zooplankton species at three sampling locations in the BPNS. Vertical axes at different scales.
Tina Van Regenmortel 19 Each season shows its specific composition of species. During Spring, Copepoda, Mysida and Brachyura were the main non-gelatinous species and Pleurobrachia pileus and Cyanea lamarckii were the main gelatinous species present. Summer was characterized by Brachyura zoea, Eucheilota maculata, Clytia hemisphaerica and Codonium proliferum. Copepods and Clytia hemisphaerica dominated Autumn. During Winter Mysida, C. lamarckii and Pisces larvae were the main species present. Some species only appeared in a certain season. For example, Bougainvillia sp., Margelopsis haeckeli and Chrysaora hysoscella were only recorded in Summer, while Ectopleura dumortieri and Eirene viridula were only observed in Autumn.
This study revealed a new species of gelatinous zooplankton for Belgium. Morphological identification points in the direction of Eucheilota menoni or Lovenella assimilis, both members of the Lovenellidae family. New fresh specimens will be subject to genetic identification, which should be conclusive. The species was found from September to December 2011 and was recorded from all examined stations, however in very low numbers (average of 0.03 ind. m-3).
Sample analyses also revealed new recordings of the larval stages of Rissoides desmaresti (Risso, 1816) since the early 1900’s (Vansteenbrugge et al., in prep.). These Stomatopod larvae were found in August and September at midshore and offshore sampling locations. Adults from this species are known from the British coasts to the East Atlantic Ocean and the Mediterranean Sea (Abelló et al., 1993-1994; Van der Baan & Holthuis, 1966; Verwey, 1966; Ramsay & Holt, 2001; Griffin et al., 2011).
20 Species diversity analyses
Spatial analyses in diversity indices
The univariate analyses for all zooplankton data are shown in Table 3.
# species (S) Total individuals (N) Species richness (d) Av. St. Dev. Av. St. Dev. Av. St. Dev. W04 14.6 3.1 62.7 70.3 4.9 2.9 W07 16.3 2.8 111.5 220.8 6.5 5.1 W09 15.8 2.3 57.6 92.6 6.4 3.7
Pielou's evenness (J') Shannon's index (H') Simpson's index (1-λ') Av. St. Dev. Av. St. Dev. Av. St. Dev. W04 0.5 0.2 1.4 0.6 0.6 0.3 W07 0.5 0.2 1.4 0.6 0.7 0.5 W09 0.5 0.2 1.3 0.5 0.6 0.3 Table 3. Diversity indices for three sampling locations in the BPNS for the total zooplankton dataset, combining the density data for all samples averaged per sampling location.
The average number of species (S) combined over the three sampling locations is about 15, but did not differ significantly between locations (p=0.270) . This index should be interpreted with care, as in this dataset taxa have different levels of classification, which might bias the outcome. However, no bias is found between the outputs per sampling locations, as the same classification was used for every location. A maximum of 20 taxa were found at the midshore location in May (Figure 6). The total number of individuals per m3 was also not significantly different between locations (p=0.777). The highest number of individuals was recorded at the midshore location in December. A total of 790 ind. m-3 were recorded, 75% of these individuals belonging to Copepoda sp..
Tina Van Regenmortel 21 Tunicata sp. Species composition Copepoda sp. Brachyura zoea W07 - May Decapoda larve Cumacea sp. Pisces egg Polychaeta sp. 12% 5% Amphipoda sp. 13% Scyphozoa 4% Mollusca sp. 4% Ctenophora 5% Decapoda adult 17% Ophiura sp. Pisces larva Brachyura megalopa Mysida sp. Hyperia galba 38% Pariambus typicus Chaetognatha sp. Hydrozoa Figure 6. Species composition at the midshore location in May 2011.
Margalef’s species richness and Pielou’s evenness did not significantly differ between locations (d: p=0.49; J’: p=0.818 ). A maximum Margalef’s species richness of 20.4 was recorded at the midshore location in April. The highest J’ of 0.8 was found at the nearshore sampling location in September (Figure 7). Shannon’s and Simpson’s index were also not significantly different between sampling sites (H’: p=0.932 and λ’: p=0.909), adding to the assumption of a similar diversity at the different sampling locations.
Copepoda sp. Species composition Ctenophora Mysida sp. W04 - September Hydrozoa Brachyura zoea Chaetognatha sp. 10% Decapoda larva 8% Polychaeta sp. 12% 5% Amphipoda sp. 4% Brachyura megalopa 9% Pisces larva 14% Decapoda adult Syngathus rostellatus Cumacea sp. 17% 19% Pariambus typicus Tomopteris helgolandica Scyphozoa
Figure 7. Species composition at the nearshore location in May 2011.
22 Table 4 shows the univariate analyses for the gelatinous zooplankton dataset.
# species (S) Total individuals (N) Pielou's evenness (J') Shannon's index (H') Av. St. Dev. Av. St. Dev. Av. St. Dev. Av. St. Dev. W04 6.5 3.2 18.0 30.9 0.5 0.2 0.9 0.5 W07 6.7 4.8 9.1 13.8 0.4 0.2 0.6 0.5 W09 4.5 2.5 7.5 11.5 0.3 0.4 0.4 0.5
Table 4. Diversity indices for gelatinous zooplankton for each sampling location combining the density data for all samples averaged per sampling location.
The average number of gelatinous zooplankton species and their densities seemed higher at near- and midshore locations in comparison to the offshore location, though not significantly (S: p=0.352; N: p=0.328). The highest species number of 15 gelatinous taxa was found at the W07 station in July (Figure 8).
Codonium proliferum Species composition Clytia hemisphaerica W07 - July Corynidae sp. Eucheilota maculata Pleurobrachia pileus 19% Hydractinia carnea Laodicea undulata 6% Leptomedusa sp. Anthomedusa sp. 12% Eutima gracilis Hydromedusa sp. Aequorea sp. 63% Obelia sp. Cyanea lamarckii Margelopsis haeckeli
Figure 8. Gelatinous zooplankton species composition at the midshore sampling location in July 2011.
Pielou’s evenness did also not differ significantly between sampling locations (p=0.887). The highest evenness in gelatinous zooplankton was found at the nearshore location in June (J’= 1), when only five species were found which showed approximately the same percentage in abundance (Figure 9). Shannon’s index seemed higher close to the coast, although not significantly, p=0.052.
Tina Van Regenmortel 23 Species composition W04 - June
20%
Pleurobrachia pileus 35% Beroe gracilis Aurelia aurita 15% Clytia hemisphaerica Nemopsis bachei
15% 15%
Figure 9. Gelatinous zooplankton species composition at the nearshore sampling location in June 2011.
Finally, univariate diversity indices for the non-gelatinous zooplankton dataset are shown in Table 5.
# species (S) Total individuals (N) Pielou's evenness (J') Shannon's index (H') Av. St. Dev. Av. St. Dev. Av. St. Dev. Av. St. Dev. W04 12.8 2.7 94.5 158.4 0.6 0.2 1.5 0.5 W07 11.5 3.5 26.9 55.8 0.5 0.3 1.2 0.6 W09 13.6 3.1 49.0 95.4 0.5 0.2 1.3 0.5 Table 5. Diversity indices for the non-gelatinous zooplankton dataset for each sampling location, combining the density data for all samples averaged per sampling location.
The average number of non-gelatinous zooplankton species seemed higher at the offshore location, however the number of species was not significantly different between locations (p=0.215). The highest species number at the offshore location was 16 species in February (Figure 10). Average densities seemed higher at the nearshore sampling location. However, maximum total density was recorded from the midshore location in December (780 ind. m-3). This index (N) was also not significantly different between locations (p=0.110).
24 Pisces larva Species composition Amphipoda sp. Pisces egg W09 - February Decapoda larva Mysida sp. Copepoda sp. 16% 7% Brachyura zoea 7% Cumacea sp. 4% Chaetognatha sp. Tunicata sp. Polychaeta sp. 20% 11% Mollusca sp. Euphausiacea sp. Ophiura sp. Decapoda adult 35% Brachyura megalopa Hyperia galba
Figure 10. Non-gelatinous species composition at the offshore sampling location in February 2012.
Both Pielou’s evenness and Shannon’s index seemed higher at the nearshore location, though again not significant (J’: p=0.437; H’: p=0.467). Highest J’ (0.8) and H’ (1.9) at this location were both recorded in April.
Temporal analyses in diversity indices
Table 6 presents the diversity indices for the total zooplankton dataset for each season.
# species (S) Total individuals (N) Species richness (d) Av. St. Dev. Av. St. Dev. Av. St. Dev. Spring 14.9 3.8 29.6 25.1 7.3 6.2 Summer 16.4 1.3 57.8 57.7 4.4 1.0 Autumn 15.7 2.4 166.8 256.2 4.2 2.0 Winter 15.2 3.3 51.8 72.2 7.8 4.0
Pielou's evenness (J')Shannon's index (H')Simpson's index (1-λ') Av. St. Dev. Av. St. Dev. Av. St. Dev. Spring 0.6 0.1 1.5 0.3 0.8 0.4 Summer 0.6 0.1 1.7 0.3 0.8 0.1 Autumn 0.3 0.2 0.8 0.5 0.4 0.3 Winter 0.5 0.2 1.3 0.6 0.7 0.4
Table 6. Diversity indices for each season for the total zooplankton dataset combining the density data for all samples averaged per season.
The average number of species (S) combined over the seasons is again about 15. However, we should be careful in interpreting these data, as different levels of classification were used in the
Tina Van Regenmortel 25 dataset, which could bias the outcome. The number of species, total number of individuals per cubic meter and Margalef’s species richness were not significantly different between seasons (S: p=0.747; N: p=0.1850; d: p=0.218). Pielou’s evenness, Shannon’s index and Simpson’s index did show a significant difference between seasons (J’: p=0.016; H’: p=0.016; λ’: p=0.041). Mann Whitney U-tests were conclusive to find out between which seasons significant differences were present. Table 7 shows the obtained p-values. J' Spring Summer Autumn Winter H' Spring Summer Autumn Winter Spring Spring Summer 0.370 Summer 0.093 Autumn 0.006 0.003 Autumn 0.015 0.003 Winter 0.815 0.436 0.077 Winter 0.888 0.387 0.094 λ' Spring Summer Autumn Winter Spring Summer 0.743 Autumn 0.011 0.006 Winter 1.000 0.730 0.094
Table 7. Overview of p-values of Mann Whitney U-tests for Pielou's evenness (J'), Shannon's index (H') and Simpson's index (λ') for the total zooplankton dataset. P-values in red are still significant after Bonferroni correction.
A correction had to be made for multiple pairwise tests (Bonferroni correction: p-values < 0.05/6 tests = 0.008). Therefore, a significant difference in Pielou’s index can be observed between Autumn and Spring (p=0.006) and Autumn and Summer (p=0.003). For both Shannon’s and Simpson’s index, a significant difference was observed between Autumn and Summer (p=0.003 and p=0.006 respectively).
Diversity indices for the gelatinous zooplankton dataset are shown in Table 8.
# species (S) Total individuals (N) Pielou's evennes (J') Shannon's index (H') Av. St.Dev. Av. St.Dev. Av. St.Dev. Av. St.Dev. Spring 2.50 1.51 1.12 2.09 0.58 0.29 0.45 0.54 Summer 8.22 4.12 19.39 32.19 0.48 0.25 0.95 0.45 Autumn 8.33 2.40 23.35 18.60 0.22 0.24 0.46 0.50 Winter 4.33 2.18 1.44 1.38 0.50 0.28 0.70 0.50
Table 8. Diversity indices for gelatinous zooplankton for each season, averaged per season.
The average number of gelatinous zooplankton species and the total number of individuals are significantly different between seasons (S: p=0.0003; N: p=0.0003). Mann Whitney U-tests were conclusive to find between which seasons the significant differences were present (Table 8Table 9).
26 S Spring Summer Autumn Winter N Spring Summer Autumn Winter Spring Spring Summer 0.001 Summer 0.006 Autumn 0.0001 0.863 Autumn 0.001 0.190 Winter 0.074 0.031 0.002 Winter 0.200 0.014 0.0003
J' Spring Summer Autumn Winter Spring Summer 0.074 Autumn 0.002 0.161 Winter 0.277 0.666 0.0503
Table 9. Overview of p-values of Mann Whitney U-tests for number of species (S), total number of individuals (N) and Pielou's evenness (J') for the gelatinous zooplankton dataset. P-values in red indicate those still significant after Bonferroni correction.
After Bonferroni correction, a significant difference in total number of species and total number of individuals can be found between Summer and Spring (S: p=0.001; N: p=0.006) , Autumn and Spring (S: p=0.0001; N: p=0.001) and Autumn and Winter (S: p=0.002; N: p=0.0003). A total number of 26 different species were found during Autumn (Figure 11, left), while eight different species were found during Spring (Figure 11, right), and ten during Winter (Figure 12, left). A total of 27 different gelatinous zooplankton species were found during Summer (Figure 12, right).
Autumn gelatinous Spring gelatinous zooplankton composition zooplankton composition 2% 3% 1% 5% 3% 5% Clytia hemisphaerica Pleurobrachia pileus 22% Beroe gracilis Cyanea lamarckii Obelia sp. Beroe gracilis Eucheilota maculata 22 other species 5 other species 70%
89%
Figure 11. Gelatinous zooplankton species composition during Autumn (October-December) (left) and Spring (April-June) (right).
Tina Van Regenmortel 27 Winter gelatinous Summer gelatinous zooplankton composition zooplankton composition 8% 6% 6% Eucheilota maculata 16% Pleurobrachia pileus Clytia hemisphaerica 43% Cyanea lamarckii 21% Pleurobrachia pileus Clytia hemisphaerica 16% 62% Codonium proliferum 7 other species 23 other species
22%
Figure 12. Gelatinous zooplankton species composition during Winter (January-March) (left) and Summer (July-September) (right).
Pielou’s evenness (p=0.021) was significantly different between Spring and Autumn (p=0.002). J’ showed a maximum of 0.95 during Spring. Shannon’s index seemed highest throughout Summer (though not significantly, p=0.120) with a maximum of 1.4.
Finally, diversity indices for non-gelatinous zooplankton for all seasons are shown in Table 10.
# species (S) Total individuals (N) Pielou's evennes (J') Shannon's index (H') Av. St.Dev. Av. St.Dev. Av. St.Dev. Av. St.Dev. Spring 2.5 1.5 1.1 2.1 0.6 0.3 0.5 0.5 Summer 8.2 4.1 19.4 32.2 0.5 0.2 0.9 0.4 Autumn 8.3 2.4 23.3 18.6 0.2 0.2 0.5 0.5 Winter 4.3 2.2 1.4 1.4 0.5 0.3 0.7 0.5
Table 10. Diversity indices for non-gelatinous zooplankton for each season, averaged for all samples per season.
Total number of species and total number of individuals were not significantly different between seasons (p=0.053 and p=0.258 respectively). Both Pielou’s evenness and the Shannon’s index were significantly different between seasons (p=0.003 and p=0.006 respectively). Table 11 shows corresponding p-values of Mann Whitney U-tests. J' Spring Summer Autumn Winter H' Spring Summer Autumn Winter Spring Spring Summer 0.236 Summer 0.370 Autumn 0.200 1.000 Autumn 0.673 0.161 Winter 0.002 0.004 0.004 Winter 0.006 0.004 0.003
Table 11. Overview of p-values of Mann Whitney U-tests for Pielou's evenness (J') and Shannon's index (H') for the non- gelatinous zooplankton dataset. P-values in red indicate those still significant after Bonferroni correction.
After Bonferroni correction a significant difference for both Pielou’s evenness and Shannon’s index could be observed between Winter and the three other seasons (Table 11)
28 Environmental variables
Sea water temperature varied from a minimum of 2.4 °C in Winter to a maximum of 18.6°C in Summer at the nearshore sampling location. The midshore and offshore locations showed less variation in sea water temperatures (5.6 – 17.8 °C) (Figure 13a). Salinity showed a nearshore to offshore increase. Average salinity in nearshore, midshore and offshore sampling locations were 32.4, 34.2 and 35.0 PSU respectively (Figure 13b). Oxygen concentrations were highest in Spring (15.8 mg/l) and lowest in Autumn (6 mg/l). No large variations were found along the near to offshore transect (Figure 13c). Turbidity was recorded in NTU (Nephelometric Turbidity Units), which shows a linear relationship with the amount of particles in the water. Highest turbidity and fluctuations in turbidity were recorded at the nearshore station (10–62 NTU). Turbidity in mid and offshore stations was much lower and did not fluctuate a lot during the year (2-6 NTU) (Figure 13d). Secchi depth was measured as proxy for the turbidity. As a consequence, Secchi depth was lowest nearshore, and showed higher – and thus more transparent -values at mid- and offshore locations (Figure 13e). Chlorophyll a concentrations were recorded as proxy for the primary production by phytoplankton. Chlorophyll a concentrations were highest in all three stations in Spring, and were lowest in Autumn (Figure 13f). It is important to note that the chlorophyll a records were obtained from satellite data. Here, a high error was observed, as two different satellite data (MERIS and MODIS) showed different values of chlorophyll a for the same location and time period. For this study, the data from satellite MODIS was used. This because of missing data for satellite MERIS for some months during the year.
Tina Van Regenmortel 29
Figure 13. Environmental variables at three sampling locations during the period March 2011 to February 2012.
30 Community analyses
Analysing the community on a spatial scale
Multivariate analysis was done on all zooplankton data, taking one replica of every month into account. All taxa including gelatinous zooplankton was combined at a higher taxonomic level of e.g. Hydrozoa, Scyphozoa or Ctenophora. To display the potential spatial distribution of zooplankton, a factor ‘distance to shore’ was added to the analysis. The MDS analysis shows no clear spatial pattern (Figure 14).
Figure 14. MDS analysis with factor ‘distance to shore’ of all zooplankton data.
The same analysis was also executed on the gelatinous zooplankton dataset separately, excluding the replicates. After adding the factor ‘distance to shore’, a MDS was made, but no clear spatial pattern could be found (Figure 15).
Tina Van Regenmortel 31
Figure 15. MDS analysis using the gelatinous zooplankton data adding a factor ‘distance to shore’.
32 Analysing the community on a temporal scale
Analysis was done on all zooplankton data, taking one replica of every month into account. The temporal distribution of zooplankton was displayed by adding a factor ‘season’ to the analyses. Figure 16 shows the corresponding MDS.
Figure 16. MDS with factor ‘season’ for all zooplankton data.
January and February of 2012 were warmer than the previous year, with exception of February 2011 at the W04 location (Table 14, Appendix). Therefore, data of these two months were added to the data of Spring 2011 in a second MDS (Figure 17).
Figure 17. MDS of the temporal distribution of zooplankton. Clustering by factor ‘season’ can be observed. January and February 2012 are added to the Spring-group because of the high temperature conditions.
Tina Van Regenmortel 33 To display the temporal distribution of gelatinous zooplankton, a factor ‘season’ was added to the analysis. As January and February of 2012 were warmer than the previous year (except for W04, February 2011, Table 14), data of these two months were added to the data of Spring 2011 in this analysis as well. Figure 18 shows the corresponding MDS, with a more pronounced clustering.
Figure 18. MDS of the temporal distribution of gelatinous zooplankton. January and February 2012 are added to the Spring-group because of the high temperature conditions
34 Abiotic and biotic variables influencing the temporal variation
As a pattern in the temporal distribution was observed for both the total zooplankton and the gelatinous zooplankton dataset, a DISTLM and dbRDA analysis were conducted to examine which abiotic or biotic variables explained this temporal variation. First, analyses were done on the total zooplankton dataset. After examining the initial Draftsman plot (Figure 19), which checks for multi-collinearity, the variables Secchi-depth and turbidity were log-transformed. The Draftsman plot of the transformed environmental variables is shown in the Appendix (Figure 25).
Figure 19. Draftsman plots with the untransformed environmental variables. Secchi-depth and turbidity show right-skewness. Therefore, these variables have to be transformed.
The corresponding correlation matrix is shown in Figure 20. Some variables showed strong correlations. The cut-off value of 0.8 was reached for the variable ‘Secchi depth’, therefore this variable was no longer incorporated in further analyses.
Figure 20. Correlation matrix with the transformed environmental variables. The cut-off value of 0.8 was reached for the variable ‘Secchi depth’.
Tina Van Regenmortel 35
A DISTLM analysis was executed on the zooplankton data with the selected and transformed environmental variabless. The marginal tests indicated that all variables, when considered alone, had a significant relationship with the zooplankton data-cloud (p <0.05, Figure 26, Appendix), with exception of chlorophyll a (p=0.212).
To find the most parsimonious model and explain the variation in zooplankton data, the ‘Best’ selection procedure was used in DISTLM. As selection criterion, both AIC (An Information Criterion) and BIC (Bayesian Information Criterion) were used and compared (Figure 27, Appendix). A scatterplot of the top ten models can be found in the Appendix (Figure 28). A dbRDA plot was made for the parsimonious model chosen above, with variables temperature, log(turbidity) and salinity (Figure 21).
Figure 21. dbRDA of the zooplankton data from the most parsimonious model.
The output file (Figure 29, Appendix) shows that the first two dbRDA axes explain 83% of the fitted variation. As this is higher than 70%, we can assume that the patterns in the model were correctly plotted (Anderson et al., 2008). Furthermore, the variable temperature alone explains nearly 17% of the variability in the data cloud and log(turbidity) also individually explains
36 almost 11%. All of the dbRDA axes together explain 100% of the fitted variation and 34% of the total variation.
A PCO analysis was executed to compare with the output of the dbRDA analysis (Figure 22). Considerable difference in the position of the points in the data cloud can be observed between the constrained dbRDA plot and the unconstrained PCO plot.
Figure 22. PCO analysis of all zooplankton data.
DISTLM and dbRDA analyses were also executed for the gelatinous zooplankton dataset separately. The variable ‘zooplankton density’ was added to the analyses, to check whether gelatinous zooplankton variation could be explained by variation in non-gelatinous zooplankton densities. A Draftsman plot indicated that Secchi-depth, turbidity and zooplankton density had to be log transformed. The Draftsman plots of the untransformed and transformed environmental variables are shown in the Appendix (Figure 30 and Figure 31). The corresponding correlation matrix is shown in Table 15 (Appendix). This indicated strong correlation between some variables. The cut-off value of 0.8 was reached for Secchi depth, which was therefore no longer incorporated in further analyses.
Tina Van Regenmortel 37
To find the most parsimonious model and explain the variation in zooplankton data, the ‘Best’ selection procedure was used in DISTLM with selection criterion AIC and BIC (Figure 32, Appendix). A scatterplot of the top ten models can also be found in the Appendix (Figure 33). The DISTLM analysis revealed the most parsimonious model with following variables: temperature, salinity, oxygen and log(zooplankton density). The corresponding dbRDA plot can be found in Figure 23.
The output file (Figure 34, Appendix) shows that the first two dbRDA axes explain 92% of the fitted variation. We can thus assume that the patterns in the model were correctly plotted (>70%, Anderson et al., 2008 ). Furthermore, the variable oxygen alone explains 27% of the variability in the data cloud and salinity also individually explains almost 10%. All of the dbRDA axes together explain 100% of the fitted variation and 40% of the total variation.
Figure 23. dbRDA of gelatinous zooplankton data from the most parsimonious model.
38 A PCO analysis was done to compare with the output of the dbRDA analysis (Figure 24). Considerable difference in the position of the points in the data cloud can again be observed between the constrained dbRDA plot and the unconstrained PCO plot.
Figure 24. PCO analysis of gelatinous zooplankton data.
Tina Van Regenmortel 39 Discussion
Replicate comparison
Due to time constraints, not all three replicates taken during the sampling campaigns could be fully analysed. First, one replicate from each month at each location was analysed. Based on these analyses, minimum one extra replicate was examined from four months to evaluate that replicates did not differ significantly from each other. These chosen months represented bloom months in both gelatinous and non-gelatinous zooplankton. According to the MDS and corresponding similarity coefficients, the average similarity between the replicates was quite high (70-90 %). The similarity coefficient for the replicates of July however was low (58%). In July, one replicate was worked out completely, whereas a subsample was examined from the other two replicates. The first replicate showed much higher numbers in Brachyura zoea, Hydrozoa, Decapoda larva, Pisces larvae, … than the latter two replicates. The low similarity coefficient could thus be explained by (1) a higher aggregation of organisms while sampling the first replicate, (2) an error by taking only subsamples in the latter two replicates, or (3) it might be ‘normal’ variation between replicates. The second option is however not very likely, as other samples were analysed in the same way and showed high similarity coefficients (i.e. July (W09a,b): 89%). Option one and three are more likely, as aggregation is possible due to physical factors (i.e. currents, turbulence, temperature) (Graham et al., 2001) and normal variation is always present. As most replicates showed high similarity coefficients, we assume that the samples worked out in this study do a good job of explaining both the spatial and temporal distribution in gelatinous zooplankton. However, if time constraints were absent, more replicates would have been worked out to further lower the observed error and thus to obtain more accurate results.
40 Zooplankton diversity
Overall, 37 gelatinous zooplankton taxa, of which one new species for Belgium, were found. Furthermore, 25 non-gelatinous zooplankton taxa were recorded.
Blooms in zooplankton are seasonal and usually follow blooms in phytoplankton (Bode et al., 2005; Freund et al., 2006). The seasonal distribution of copepod biomass in the Oosterschelde estuary (Southern North Sea) for example, showed peak concentrations in Spring. A significant correlation was found between time of development of the larval stages of the copepods and phytoplankton (Bakker & Van Rijswijk, 1987). There is however some variation in the seasonal timing of zooplankton blooms. They are not exactly repetitive from year to year (Mackas et al., 2012). Patterns in gelatinous zooplankton abundance are also known to be strongly seasonal. Gibbons and Richardson (2009) examined patterns in the North Atlantic. Abundances of gelatinous zooplankton in oceanic areas showed a mid-year peak, while shelf populations showed a later peak. Abundances of the latter increased during Spring, peaked during Summer and declined slowly during Autumn.
In this study, two bloom periods in both non-gelatinous and gelatinous zooplankton were observed at the three sampling locations throughout the year. The first bloom in gelatinous zooplankton followed that of non-gelatinous zooplankton. Many gelatinous zooplankton species feed on non-gelatinous zooplankton (e.g. Purcell, 1985; Greene et al., 1986; Daan, 1989). For example, Clytia hemisphaerica is known to predate on copepods (Matsakis & Nival, 1989; Matsakis, 1993), little non-crustacean and crustacean larvae (McCormick, 1969; Mills, 1981) and eggs of both euphausiids (Larson, 1987) and copepods (Daan, 1989). The second bloom in gelatinous zooplankton began earlier or coincided with the second bloom in non-gelatinous zooplankton. This could indicate that the second bloom is more controlled by other variables such as temperature and salinity. This shows resemblance with patterns of jellyfish abundance in the North Atlantic, where peaks of gelatinous shelf populations mirror changes in sea surface temperature (Gibbons & Richardson, 2009).
During blooms in gelatinous zooplankton, the gelatinous zooplankton species reached higher densities than non-gelatinous zooplankton. This therefore rejects our third hypothesis: gelatinous zooplankton are never the most abundant component within the total zooplankton community. More research (i.e. experimental) should however be conducted to determine the role of gelatinous zooplankton within the zooplankton community.
Tina Van Regenmortel 41 A good example of dominant densities is given by the species composition at the nearshore sampling location in July 2011. Here, 62% of the total density consisted of gelatinous species, with dominance of Eucheilota maculata (42%).
Furthermore, during these gelatinous blooms, some species showed higher abundances (Table 2). In order to discover the reason behind this, the food preferences, predation, reproductive strategy and invasiveness are discussed in the following paragraphs. Food preferences and predation affect both the abundances of other zooplankton species as well as those of the predator. Reproductive strategy is important to secure a viable population within the community. Invasiveness can affect the community if the invasive species can dominate a niche by rapid growth and reproduction or high tolerance to environmental conditions. Therefore, knowing the biology of the most abundant species is relevant to better understand the position of these species in the community. It makes it possible to frame our second and third hypothesis: better understanding the influence of these species during temporal blooms. These species represent most dominant ctenophores (P. pileus, B. gracilis), an invasive ctenophore (M. leidyi), a dominant scyphozoan (C. lamarckii), most dominant hydrozoans (C. hemisphaerica, E. maculata, C. proliferum and Obelia sp.) and the newly recorded species (E. menoni or L. assimilis, page 19). The distribution of these species throughout the year and at the three sampling locations was shown in Figure 5.
Pleurobrachia pileus O.F. Müller, 1776
The sea gooseberry, Pleurobrachia pileus, is an important resident in coastal waters of the West- Atlantic (Mayer, 1912; Lucas et al., 1995), the North-East Atlantic (Båmstedt, 1998; Frost et al., 2012) and the Black Sea (Mutlu & Bingel, 1999). The species is exclusively carnivorous (Haddock, 2007). It is an ambush predator, using its long tentacles, and displays strong prey selectivity as a consequence of different susceptibility and swimming rate among different types of prey (Greene et al., 1986). Presence of the ctenophore in the BPNS was demonstrated for coastal regions (De Blauwe, 2003) as well as mid and offshore regions (Van Ginderdeuren et al., 2012b). In this study, P. pileus was found nearshore throughout the year, with peak densities of 24 ind. m-3 in Summer (July-September). This is in contrast with data from 2009-2010 (Van Ginderdeuren, 2012b), where a peak of 79 ind. m-3 occurred in Spring (March-May). This time delay is presumably caused by lower temperatures in Winter 2010, leading to a shift in appearance (Schlüter et al., 2010). Numbers were much lower mid and offshore, and the species wasn’t found all year round at these locations. This contributes to the fact that Pleurobrachia pileus is most common in shallow coastal waters (Haddock, 2004).
42 Beroe gracilis Künne, 1939
Beroe gracilis is a species known from the North-East Atlantic Ocean (Greve, 1975). The ctenophore employs a raptorial feeding mode while predating on gelatinous organisms (Swanberg, 1974 as referred in Haddock, 2007). It is a known predator of Pleurobrachia pileus (Greve, 1981). In this study, peaks in B. gracilis coincided with peaks in P. pileus, which could demonstrate its predatory impact. Beroe gracilis was mainly found at the nearshore sampling location, which is in accordance with earlier studies (Van Ginderdeuren et al., 2012b). The largest peak in abundance occurred in December, with 10 ind. m-3. This is much later than in 2009-2010, when the highest peak was recorded June. Furthermore, densities in 2010 were much higher, with a maximum density of 139 ind. m-3 (Van Ginderdeuren et al., 2012b). Little is known about factors that affect the sexual reproduction and population abundances of this species, perhaps due to their fragility (Purcell, 2005). Therefore, we can only guess that environmental factors such as temperature were the reason for the later peak and lower abundances in this species, as was already shown for other species (Purcell, 2005).
Mnemiopsis leidyi A. Agassiz 1865
The American ctenophore, Mnemiopsis leidyi is a gluttonous predator which preys on zooplankton, fish eggs and fish larvae (Kremer, 1979; Purcell, 1985; Colin et al., 2010). It can tolerate a wide range of temperature and salinity (Ivanov et al., 2000). It can swiftly colonize a new habitat and bloom, because it is a rapidly reproducing, self-fertilizing hermaphrodite (Shiganova, 1998). In the early 1980s, M. leidyi was introduced into the Black Sea (Mutlu, 1999). It is assumed that this translocation happened by ballast water coming from the western Atlantic coastal regions (Vinogradov et al., 1989; Reusch et al., 2010 ), the native range of M. leidyi (Ivanov et al., 2000). In 1989, blooms of M. leidyi coincided with the reduction in the fishery-yields in this area, including the collapse of the anchovy Engraulis encrasicolus fishery (Kideys, 1994; Graham & Bayha, 2007). It was thought that M. leidyi caused the enormous downfall in E. encrasicolus yield through competition with adults and predation (direct feeding) on larvae and anchovy eggs (Graham & Bayha, 2007; Colin et al., 2010). However it is now presumed that fish stocks were already declining due to overfishing and eutrophication before M. leidyi was introduced (Shiganova, 1998; Gucu, 2002; Daskalov et al., 2007). As a result, the introduction of the ctenophore had a potentially greater influence on the ecosystem. Since its introduction into the Black Sea, M. leidyi has been spreading to adjacent waters. It migrated north to the Sea of Azov in the late eighties (Studenikina, 1991 as referred in
Tina Van Regenmortel 43 Shiganova et al., 2001) and south to the Sea of Marmara in the early nineties (Shiganova, 1993 as referred in Shiganova, 1998). Mnemiopsis leidyi reached the east of the Mediterranean basin (Aegean Sea and coasts of Syria and Turkey) and the Caspian Sea in the mid-nineties (Shiganova, 1993; Kideys & Niermann, 1994; Ivanov et al., 2000; Shiganova et al., 2001; Siapatis et al., 2008). Since 2006, it has been observed in the Baltic Sea (Javidpour et al., 2006; Oliveira, 2007), the coasts of Denmark (Boersma et al., 2007), the western part of the Mediterranean Sea (Fuentes et al., 2010) and the coastal waters of the Netherlands (Faasse & Bayha, 2006). The first sighting of M. leidyi in Belgian waters was in 2007, in the port of Zeebrugge (Dumoulin, 2007). Furthermore, in 2009 it was observed for the first time offshore (Van Ginderdeuren et al., 2012a). In this study, Mnemiopsis leidyi was recorded from nearshore (W04) and midshore (W07) stations. No individuals of M. leidyi were caught in the offshore station W09. Nearshore, the species was present from September to December, with a maximum density of 0.84 ind. m-3 recorded in September with. Midshore, however, the occurrence was negligible with 0.008 ind. m-3 in November only. At present, M. leidyi numbers in the BPNS are nowhere near those found in the Black Sea, where a peak abundance of 304 ind. m-3 was recorded in 1989 (Vinogradov et al., 1989). It is therefore presumed that the ctenophore has no large impact on the zooplankton yet. However, the range of the species in the BPNS could extend in the future, and therefore it is important to keep monitoring its distribution.
Cyanea lamarckii Péron & Lesueur, 1810
The blue jellyfish, Cyanea lamarckii, is one of the most abundant scyphozoan species found in the northern coastal zones (Helmholz et al., 2007). It has a wide diet, but is an enormous predator on ichtyoplankton prey as well as other gelatinous and crustacean prey (Purcell & Arai, 2001). Cyanea lamarckii is the most frequently observed scyphozoan in this study, with maximum densities of 1.4 ind. m-3. Its occurrence is in accordance with other studies in the Southern North
Sea (Barz & Hirche, 2007). This study reveals similar densities nearshore and midshore, and lowest densities offshore. This is in contrast to previous years (Van Ginderdueren et al., 2012b), where the species reached its highest densities offshore instead of nearshore. This could be influenced by the direction of winds and currents that move the species towards the shoreline in this study, as was observed for scyphozoans in other studies (Shenker, 1984; Larson, 1990)
44 Clytia hemisphaerica Linnaeus, 1767
A lot of recent literature on Clytia hemisphaerica can be found on genetic aspects of the species (Chevalier et al., 2006; Chiori et al., 2009), its reproduction (Carré & Carré, 2000; Verlhac et al., 2010) or enzymes (Denker et al., 2008). Literature on the distribution and ecology of C. hemisphaerica is on a rise. The species seems to have a near-cosmopolitan distribution in coastal waters. It is known from the East Atlantic, from the Mediterranean to the coasts of Iceland and Norway (Russel, 1953; Matsakis, 1993; Lucas et al., 1995; Carré & Carré, 2000). Furthermore, it has been reported from the West Atlantic (Calder, 1991; Kelmo & Attril, 2003), the Indian Ocean and the East Pacific (Mammen, 1995 and Fraser, 1948 as reffered in Kelmo & Attril, 2003). Clytia hemisphaerica is known to predate on copepods (Matsakis & Nival, 1989; Matsakis, 1993), little non-crustacean and crustacean larvae (McCormick, 1969; Mills, 1981) and eggs of euphausiids (Larson, 1987) and copepods (Daan, 1989). This study revealed Clytia hemisphaerica as a common species, found throughout the year, with exception of May. Densities were highest during Summer and Autumn, with a highest peak of 52 ind. m-3 in November at the nearshore sampling location and 48 ind. m-3 at the midshore sampling location.
Eucheilota maculata Hartlaub, 1894
Literature on Eucheilota maculata is scarce. The species seems to have a limited distribution, as it has been reported from the Southern North Sea and the West Baltic Sea (Russel, 1953; Kramp, 1961). In this study, E. maculata was present from July until December. Peak densities (69 ind. m-3) were found in July, at the nearshore station. Recent research in the BPNS showed very low numbers of this species, as only two specimens were found at W07 Summer of 2010 (Van Ginderdeuren et al., 2012b). This however, could be due to misidentification, as the species largely resembles Clytia hemisphaerica.
Codonium proliferum Forbes 1848
This hydrozoan is reported from the East Atlantic (Sanderson, 1930; Russel, 1953; Schuchert, 2001; Greve et al., 2004; Nawrocki et al., 2009), the Mediterranean (Boero, 1993; Bouillon et al., 2004) and the Black Sea (Thiel 1935 as referred in Russel, 1953). Apart from sexual reproduction, C. proliferum also shows asexual budding from the marginal tentacle bulbs (Russel, 1953). This characteristic was first described by Forbes (1848), who had never seen such a thing, which led to the following vivid description. ‘What strange and wondrous changes!’ he wrote, ‘Fancy an elephant with a number of little elephants sprouting
Tina Van Regenmortel 45 from his shoulders and thighs, bunches of tusked monsters hanging epaulette fashion from his flanks in every stage of advancement!’. This type of reproductive strategy allows quick increase in numbers, and might explain the higher abundance of C. proliferum as reported in this study. In this study, C. proliferum was found in July at the mid- and offshore sampling station. The occurrence of the species is in accordance with other studies (Russel, 1953; Schuchert, 2001), however its occurrence in the BPNS is much more limited in time.
Obelia sp. Péron & Lesueur, 1810
Obelia has a cosmopolitan distribution, and can be found at both coastal and offshore locations (Kramp, 1961). Obelia medusae differ from other medusae in their swimming mode, as movement is achieved by umbrella flapping and does not involve jet propulsion. Furthermore, in contrast to other Hydromedusae which are macrophagous, Obelia is a filter feeding medusa with a microphagous feeding strategy (Boero et al., 2007). It feeds on phytoplankton and bacteria by creating water currents via umbrella pulsations, which bring the food towards the mouth (Boero & Sarà, 1987; Boero et al., 2007).
Four species of Obelia can be found in the BPNS: O. bidentata, O. dichotoma, O. geniculata and O. longissima. No description of these species was made by Russel (1953), because they are very small and identification is very difficult. Also in this study they were not distinguished from each other. Obelia sp. was mainly found in the midshore station, where it peaked in December, with 6 ind. m-3. The species was never found nearshore, and numbers offshore were very low. This is in contrast to studies in 2009-2010 (Van Ginderdeuren et al., 2012b), when highest abundances were recorded nearshore, with maximum density at 104 ind. m-3. However, this is probably the cause of different sampling methods in both studies. The previous study used a sampling net with mesh size 200 µm, whereas in this study a sampling net with mesh size 1mm was used. Therefore, the small Obelia specimens would have collected more easily in the former net in comparison with the latter net, where specimens were more easily lost during sampling.
Eucheilota menoni Kramp 1959 or Lovenella assimilis Browne, 1905
As was mentioned before, a new species of gelatinous zooplankton was found during this study. Morphological identification point in the direction of Eucheilota menoni and Lovenella assimilis. Eucheilota menoni is a neretic species characteristic for Indo-Pacific waters, where it is widely distributed (Navas-Pereira & Vannucci, 1991; Santhakumari, 1993; Santhakumari & Nair, 1999;
46 Segura-Puertas et al., 2010). The species was first reported from the Atlantic in 2007, where it was found at the Basque coast in Spain. This introduction was presumably caused by polyps fouling on ships (Altuna, 2009). Lovenella assimilis is also typically found in the Indo-Pacific (Navas-Pereira & Vannuci, 1991). The species has not been reported for the Atlantic Ocean. Specimens in this study were preserved in formaldehyde, therefore no genetic analyses could be done. Genetic identification of the species will only be possible when fresh specimens are collected.
Species diversity analyses
To assess the species diversity at the different sampling locations, as well as during the different seasons, species diversity indices were calculated.
Spatial analyses in diversity indices
Diversity indices did not differ significantly between sampling locations for the total zooplankton dataset, the gelatinous zooplankton dataset nor the non-gelatinous zooplankton dataset. This indicates that the zooplankton assemblages found at each location are quite similar. The reason for this will be discussed in a following chapter (Community analyses), since these further analyses will provide the required insight to interpret these results.
Temporal analyses in diversity indices
The diversity indices calculated for the different seasons did show some significant differences. For the total zooplankton dataset, Pielou’s evenness, Shannon’s and Simpson’s index significantly differed between seasons. These three indices were highest during Spring and Summer, indicating that the zooplankton species diversity was highest during these seasons. This implies an indirect link with environmental factors such as temperature, oxygen or salinity. Furthermore, the individuals found during these seasons were more evenly distributed among the different species. These significant differences in diversity indices indicate that the zooplankton assemblages found each season do differ from each other. This pattern in temporal distribution will likely be controlled by biotic and abiotic variables. In a following chapter (Abiotic and Biotic variables influencing the temporal variation), the variables which explain this pattern will be discussed.
Tina Van Regenmortel 47 The diversity indices for gelatinous zooplankton for each season also showed significant differences, except for Shannon’s index. Number of species (S) was highest during Summer and Autumn, with an average of approximately eight gelatinous species. This again indicates a link between number of species and environmental factors e.g. temperature. The highest number of species in the North Sea was observed during Summer in a previous study (Van Ginderdeuren et al., 2012b). The total number of individuals was highest during Autumn, which is in accordance with other studies (Júnior et al., 2010). Pielou’s evenness was significantly higher during Spring, which was demonstrated in Figure 9. Again, this pattern in temporal distribution, shown by the diversity indices of gelatinous zooplankton will likely be controlled by biotic and abiotic variables.
Non-gelatinous zooplankton diversity indices for each season showed significant differences for Pielou’s evenness index and Shannon’s index. These indices were significantly lower during Winter, indicating a link with environmental variables.
Community analyses
To analyse whether clustering of samples occurred due to a spatial factor ‘distance’ or a temporal factor ‘season’, multivariate MDS analyses were conducted. Where clustering was observed, a DISTLM and dbRDA analysis were executed to determine whether biotic or abiotic factors could explain this clustering.
Analysing the community on a spatial scale
To assess the spatial distribution of gelatinous zooplankton in the Belgian part of the North Sea, samples were taken at three different locations on a nearshore to offshore transect. The hypothesis was that gelatinous zooplankton shows an obvious pattern in spatial distribution.
The MDS analysis rejects our first hypothesis and shows no clear pattern in spatial distribution of the gelatinous zooplankton dataset. (Figure 15). This is in accordance with recent work that examined the zooplankton community of the Belgian part of the North Sea on a nearshore- midshore-offshore gradient (Van Ginderdeuren K., pers. com.). The study took into account two of Morin’s methods (1999, as referred in Van Hoey et al., 2004) to delineate communities: (1) taxonomically, by the identification of a dominant indicator species and (2) statistically, by patterns of assemblages among species. It was observed that most species were found along the entire gradient. Furthermore, copepod species were most important contributors to similarity for nearshore, midshore and offshore sample clusters. Therefore, well defined communities are
48 not present in the BPNS, but mesozooplankton in the BPNS all belongs to one coastal, well-mixed zooplankton assemblage. Environmental variables (temperature, salinity, oxygen, turbidity) do not differ a lot throughout the small BPNS, which suggests they cannot influence the spatial distribution of gelatinous zooplankton and create clear cut communities. Other studies which examine (1) extensive study areas, where a difference in environmental factors is observed, or (2) study areas that show abrupt boundaries in environmental variables (e.g. due to river or sewage outlets), show higher variation in environmental variables on a spatial scale. This could thus influence the spatial distribution of species and therefore create different communities.
This research also showed that the same results are valid for the total zooplankton dataset, as no spatial pattern could be found. However, as was highlighted in section ‘Sample and data- analyses’, it should be noted that due to data collection by WP3 net, the total zooplankton dataset does not exactly represent the smaller zooplankton present in the water column. Further research, with different nets (smaller net size), should therefore be done to better validate the total zooplankton distribution and confirm the research by K. Van Ginderdeuren.
Analysing the community on a temporal scale
To explore the temporal distribution of gelatinous zooplankton in the BPNS, monthly sampling was done from March 2011 to February 2012. It was hypothesised that the gelatinous zooplankton shows no pattern in its temporal distribution. This study showed that the gelatinous zooplankton peaks twice a year (Figure 4), once during Summer and once during Autumn. The first bloom follows a peak in smaller zooplankton, indicating a direct biological link, as many gelatinous zooplankton species feed on non-gelatinous zooplankton. The second bloom period however, is earlier than or coincides with the second peak in smaller zooplankton. This second bloom may therefore be more controlled by abiotic variables such as temperature and salinity. Our second hypothesis is therefore rejected, as a clear pattern in temporal distribution was observed.
MDS analyses on the gelatinous zooplankton and total zooplankton assemblage (Figure 18 and Figure 17 respectively) showed clustering due to a factor ‘season’. This means that within every season, samples show a similar zooplankton species composition. Most of the zooplankton species of the North Sea show seasonal variations in their abundance (Fransz & Gonzalez, 2001; Halsband & Hirche, 2001; Barz & Hirche, 2007; Siegel et al., 2008). This might be expected for a temperate sea, with seasonal cycles in temperature and food supply in the form of
Tina Van Regenmortel 49 phytoplankton. Species do differ in the pattern of their seasonal cycle, which involves variations in cycle amplitude, timing and duration of the productive period (Fransz et al., 1991).
Abiotic and biotic variables influencing the temporal variation
MDS analyses showed clustering due to a factor ‘season’, and thus a pattern in the temporal distribution for both the total zooplankton and the gelatinous zooplankton dataset. Therefore a DISTLM and dbRDA analysis were conducted to examine which abiotic or biotic variables explained this temporal variation.
Considerable research has been done on the role of environmental factors in defining temporal distributions of zooplankton (Gaughan & Potter, 1995; Purcell et al., 1999; Greve et al., 2001; Hall & Burns, 2003; Lynam et al., 2004). Most however focus only on the effects of temperature and salinity on the occurrence of zooplankton.
In this study, DISTLM and dbRDA analyses were conducted to determine whether abiotic (environmental) or biotic factors had an influence on the seasonal changes in zooplankton distribution. Prior to the analyses, Draftsman plots indicated high correlation between certain variables. Secchi depth was strongly correlated with the variable ‘turbidity’. This relationship is logical, as Secchi depth is measured as proxy for turbidity. Furthermore, Secchi depth was also correlated with the variable ‘salinity’. Research has shown, that the higher the salinity, the greater the aggregation of suspended particles, and thus the settling velocity of these particles. A higher salinity therefore corresponds with higher clarity of the water and thus greater Secchi depth (Håkanson, 2006). At the end, ‘Secchi depth’ was removed from further analyses, because the measurement of this variable is more subjective than the measurement of turbidity and salinity by a CTD sensor.
The analyses on the total zooplankton dataset showed that three environmental factors could best explain its variation. A combination of the variables temperature, salinity and turbidity explained 34% of the total observed variation in the zooplankton community during the year. No link with biotic variables (chlorophyll a, as proxy for primary production by phytoplankton) was found. Many studies have shown that temperatures are correlated with the seasonality of many zooplankton species. An increase in temperature can result in an increase in zooplankton densities (Gaughan & Potter, 1995; ). Furthermore, temperature is able to modify the beginning, end, length and intensity of the seasonality of zooplankton populations (Greve et al., 2001). It is
50 also known that temperature influences a lot of aspects of zooplankton physiology such as assimilation, growth, reproduction, excretion and respiration (Carlotti, 2000 as referred in Greve, 2001). Some research has been done on the influence of salinity on zooplankton densities (Gaughan & Potter, 1995). A change in salinity is able to change the dominance of species to those species with more or less tolerance to higher or lower salinity ranges (Hall & Burns, 2003). The influence of turbidity on zooplankton occurrence could be twofold. (1) Higher turbidity means less light in the water column for phytoplankton to grow (Cloern, 1987; Oliver et al., 1999). This would therefore have a negative effect on the occurrence of zooplankton, as the latter would have no food resources. (2) Turbidity was measured in NTU, which shows a linear relationship with the amount of particles in the water column, this could also include phytoplankton particles. A higher turbidity and thus more phytoplankton could therefore have a positive effect on the occurrence of zooplankton, as the latter would have enough food resources. As our results indicate higher zooplankton densities during periods with higher turbidity values, we would likely agree with the second option. However, as we don’t know for certain what the turbidity measurements actually represent, we cannot be certain that the second option is valid. We can only guess that this option explains the relation between turbidity and total zooplankton distribution. Although many studies indicate a relationship between the distribution of zooplankton and the occurrence of phytoplankton (∼chlorophyll a) (Bode et al., 2005; Freund et al., 2006), no relationship between these factors was found in this study. However, as indicated in the section ‘Environmental variables, chlorophyll a data showed a large error, as different satellites showed different values for the same location and time period. This could explain the fact that no relationship between temporal variation in zooplankton distribution and variation in chlorophyll a concentrations was found.
Analysis of the gelatinous zooplankton showed that three abiotic (temperature, salinity and oxygen) and one biotic factor (non-gelatinous zooplankton density) best explained its variation. A combination of these variables explained 40% of the total observed variation in gelatinous zooplankton. For a number of gelatinous species, research has proven that temperature has an effect on their occurrence (Purcell et al., 1999; Woodmansee, 1958). This was explained by Russell in 1953 by asexual reproduction in medusa (Hydrozoa and Scyphozoa) being affected by temperature fluctuations. At the end of a cold period, numbers of medusae are high, as a rise in temperature stimulates the budding of their polyps. As water temperatures increase, sexual reproduction is
Tina Van Regenmortel 51 stimulated, which contributes to the presence of new polyps and the disappearance of the medusae. By the end of the warm period, water temperatures fall and an optimum for medusae production is again reached (Petrova et al., 2011). Little research has been done on the effect of salinity on the occurrence of gelatinous zooplankton. Purcell et al. (1999) however found that salinity significantly affects the production of ephyrae and polyps of Scyphozoa. This production was lower at both low and high salinities in comparison to intermediate salinities. The link between variation in gelatinous zooplankton and smaller zooplankton density is also easily explained, as a lot of gelatinous species predate on non-gelatinous zooplankton (Purcell, 1985; Greene et al., 1986; Daan, 1989). Little literature was found on the effect of oxygen on the temporal variation of gelatinous zooplankton. Research on Chrysaora quinquecirrha examined the effect of low Dissolved Oxygen (DO) on the survival and asexual reproduction of its polyps (Condon et al., 2001). Results showed that polyps could survive and asexually propagate even during hypoxic conditions, which would indicate that the temporal distribution of this species is not affected by oxygen concentrations. An indirect effect of low DO (<2 mg/l) are changes in behaviour (Decker et al., 2004). Gelatinous and non-gelatinous species differ in their physiology and oxygen demand, which affects their predatory or escape capabilities at low DO differently. Therefore, it is possible that gelatinous zooplankton may increase their prey consumption, as the changes in prey behaviour make the latter more susceptible to predation. Decker et al. (2004) showed that predation on copepods by large M. leidyi was increased at low DO. Breitburg et al. (1994) reported greater predation by Chrysaora quinquecirrha medusae feeding on fish larvae at low DO. Although gelatinous zooplankton densities recorded in this study were higher in months with lower oxygen concentrations (6-10 mg/l), the low oxygen concentrations as were recorded for previous studies (Decker et al, 2004; Breitburg et al, 1994: < 2mg/l) were never measured in this study. We should therefore not presume that this phenomenon is also applicable to our dataset, and would consequently explain the temporal variation found in the present study. We should be aware of this phenomenon in the future, as oxygen concentrations will presumable decrease due to global warming (Kennedy, 1990 as referred in Decker et al., 2004; Shaffer et al., 2009) and excess nutrient loadings (Boesch et al., 2001; Rabalais et al., 2002; Diaz, 2008). To conclude, we are not sure what the effect of oxygen on the temporal variation of gelatinous species is. It could be a phenomenon as described above, an effect which is not yet described or examined in research, or a factor that correlates with oxygen, but was not measured during this study.
52 To check the overall adequacy of the models generated in this study and described above, PCO plots for gelatinous and total zooplankton dataset (Figure 22 and Figure 24, respectively) were made to compare with the dbRDA plots (Figure 21 and Figure 23, respectively). It is clear that the patterns shown in the dbRDA and PCO plots are quite different, which indicates that the dbRDA models are not explaining the most salient patterns of variation across the data clouds. Therefore, there are likely to be some structuring factors which were not measured, but do play an important role in explaining variation. These could include currents, but also biological variables such as predation and competition. For example, currents produced by the North Atlantic Oscillation (NAO), are known to influence the inter-annual variability in gelatinous zooplankton abundance (Lynam et al., 2004; Purcell, 2005; Hays et al., 2005). The NAO is a phenomenon in the North Atlantic Ocean which is controlled by differences in atmospheric pressure between the low-pressure system above Iceland, and the high-pressure system above the Azores. This pressure difference is defined in an NAO-index and controls the mean wind speed and direction (and thus currents) over the Atlantic (Hurrel et al, 2003). A high index (NAO+) leads to increased Westerlies (prevailing winds between 30 and 60° latitude blowing from west to east) and thus currents to the east of the Atlantic. A low index (NAO-) suppresses these Westerlies (Hurrel et al., 2001; Hurrel et al., 2003). The NAO has significantly been related to changes in sea level pressure, wave heights, sea surface temperature, surface winds and current influxes into the North Sea (Beaugrand, 2003). These hydroclimatic changes can alter the timing of spring phytoplankton and zooplankton blooms, which can affect gelatinous zooplankton abundances (Lynam et al., 2004). However, there is disagreement on the correlation between the NAO-index and gelatinous zooplankton abundance, as some studies report a positive correlation (Attril et al., 2007) while others report a reverse relationship (Lynam et al., 2004; Purcell & Decker, 2005).
Tina Van Regenmortel 53 Conclusions
This study presents a quantitative research on gelatinous zooplankton in the Belgian part of the North Sea. The aims of this study were (1) to assess the spatial distribution of gelatinous zooplankton in the BPNS, (2) to determine the temporal distribution of gelatinous zooplankton in the BPNS and (3) to discuss the position of gelatinous zooplankton within the entire zooplankton assemblage found in the BPNS. Hypotheses put forward are: (1) gelatinous zooplankton shows an obvious pattern in spatial distribution in the BPNS, (2) gelatinous zooplankton shows a uniform temporal distribution throughout the year (3) gelatinous zooplankton are never the most abundant component within the total zooplankton community. During this study, 37 gelatinous zooplankton taxa of which 25 identified to species level were found at three different locations in the BPNS. A new species of gelatinous zooplankton for Belgium was recorded. Morphological identifications point in the direction of Eucheilota menoni and Lovenella assimilis, both members of the Lovenellidae family. Genetic analysis will be conclusive and confirm the identification to one of these two species.
A spatial pattern in the gelatinous zooplankton distribution could not be determined. This rejects our first hypothesis and contributes to the idea of one, well-mixed zooplankton assemblage in the BPNS. Gelatinous zooplankton did show a pattern in temporal distribution. Two bloom periods were recorded during the year, in Summer and in Autumn. During those seasons gelatinous zooplankton played was dominant within the total zooplankton assemblage. The first bloom period followed a bloom in non-gelatinous zooplankton. This could indicate a direct biological link, as gelatinous zooplankton are known to predate on non-gelatinous zooplankton species. The second bloom began earlier or coincided with a second bloom in non-gelatinous zooplankton. This bloom might therefore be linked to other biotic or abiotic factors. This temporal distribution therefore rejects our second hypothesis – that gelatinous zooplankton shows a uniform temporal distribution throughout the year. Our third hypothesis – that gelatinous zooplankton are never the most abundant component within the total zooplankton community– is rejected. However, more research (i.e. experimental) will have to be conducted to determine the role of gelatinous zooplankton within the zooplankton assemblage. Multivariate analyses were done to examine which factors best explained the temporal variation in the total zooplankton and the gelatinous zooplankton community. A combination of temperature, salinity and turbidity explained 34% of the variation in total zooplankton distribution. Temperature is known to modify the seasonality of zooplankton populations and
54 salinity can change the dominant species within a community. The influence of turbidity can be twofold. It’s influence could be indirect or direct, depending on either the amount of suspended particles or phytoplankton in the water column. A combination of temperature, oxygen, salinity and smaller zooplankton density explained 40% of the observed temporal variation in gelatinous zooplankton. Temperature and salinity are known to influence the reproduction in gelatinous zooplankton. Smaller non-gelatinous zooplankton is a food source for gelatinous zooplankton, which easily explains their link. The influence of oxygen was not clear, and could indirectly be affected by the mobility of the prey gelatinous zooplankton or due to a factor which correlates with oxygen, but was not measured during this study. The above models only explained a low percentage of the variation in both the total and the gelatinous zooplankton datasets. Therefore, there are presumably some structuring factors which were not measured, but do play an important role in explaining variation, which could include currents, but also biological variables such as predation and competition.
During this study, the spatial distribution of gelatinous zooplankton was shown to be homogeneous. Two blooms throughout the year were identified with partially known causes and the gelatinous zooplankton was found to be the most abundant component within the total zooplankton population during these blooms.
Tina Van Regenmortel 55 Summary
Recently, various authors have suggested that we are heading toward a more gelatinous future. The possible causes are human-mediated and include overfishing, eutrophication, translocation, habitat modification and climate change. Other authors however claim that blooms are not a new phenomenon but have ancient origins. Despite both visions, we do know for certain that jellyfish populations are changing, and therefore it is important to monitor their distribution. For this study, the term ‘gelatinous zooplankton’ is used to refer to the planktonic members of the phylum Ctenophora and the medusae of the phylum Cnidaria (hydromedusae and scyphomedusae. The Belgian Register of Marine Species (BeRMS3) reports 39 different gelatinous zooplankton species the Belgian part of the North Sea (BPNS). This includes three species of Ctenophora, six species of Scyphozoa and 30 species of Hydrozoa. Limited quantitative research on gelatinous zooplankton has been done before on a regular basis for the BPNS. Therefore, it is not known when, where and in which densities these gelatinous zooplankton species occur. The aim of this study was to monitor the spatial and temporal distribution of these gelatinous zooplankton species in the BPNS. The proposed hypotheses were: (1) gelatinous zooplankton shows an obvious pattern in spatial distribution in the BPNS, (2) gelatinous zooplankton shows a uniform temporal distribution throughout the year (3) gelatinous zooplankton are never the most abundant component within the total zooplankton community. To determine both the spatial and the temporal distribution of the gelatinous zooplankton, monthly samples were taken from March 2011 to February 2012 on board research vessel ‘Zeeleeuw’. Three locations, on a nearshore to offshore transect, were sampled: station W04 near ‘Vlakte van de Raan’, station W07 at the ‘Thornton’ sand bank and station W09 north of the ‘Hinderbanken’. Gelatinous zooplankton was sampled using a WP3 net. The macroscopically visible Scyphozoa and Ctenophora were identified and measured on board. The remaining content of the replicates was preserved at 4% buffered formaldehyde solution, for further examination at the laboratory. At each sampling location the transparency was determined by means of a Secchi disc. CTD- sensors were used to measure temperature, oxygen, salinity and turbidity at different depths. By means of satellites, the chlorophyll a concentrations were determined, as proxy for the primary production by phytoplankton.
Using a stereo microscope, the gelatinous zooplankton was identified to the lowest taxonomic level possible. Furthermore, all non-gelatinous zooplankton species found in the samples were counted and classified on a higher level of classification.
3 http://www.marinespecies.org/berms/
56
This study revealed 37 gelatinous zooplankton taxa (25 identified to species level) and 25 taxa of non-gelatinous zooplankton were found. A new species of gelatinous zooplankton for Belgium was recorded. Morphological identifications point in the direction of Eucheilota menoni or Lovenella assimilis, both members of the Lovenellidae family. However, the identity of the species could not be determined during this study based on morphology, due to high resemblance. Genetic analysis will be conclusive and confirm the identification to one of these two species. In addition, sample analyses also revealed new recordings of the larval stages of Rissoides desmaresti since the early 1900’s.
Replicate analyses showed high similarity coefficients (70-90%), indicating that samples analysed in this study explained the spatial and temporal patterns well. However, absent time constraints, more replicates would have been worked out to improve the accuracy of the results. Further analyses were done to examine spatial and temporal distributions. Univariate and multivariate analyses showed no obvious pattern in spatial distribution, contributing to the assumption that the small Belgian part of the North Sea shares one zooplankton community. This therefore rejects our first hypothesis, that the gelatinous zooplankton of the BPNS show an obvious pattern in its spatial distribution. A pattern could be observed in the temporal distribution for the gelatinous zooplankton. Two blooms were recorded: in July and November (near- and midshore) and October and December (offshore). During these blooms, gelatinous zooplankton was a dominant component within the zooplankton community, reaching higher densities (up to 103 ind. m-3). The first bloom followed a peak in smaller zooplankton, indicating a direct biological link, as many gelatinous zooplankton species feed on non-gelatinous zooplankton. The second bloom period however, was earlier than, or coincided with, the second peak in smaller zooplankton. This second bloom may therefore be more controlled by abiotic variables as temperature and salinity. Our second hypothesis - that gelatinous zooplankton shows a uniform temporal distribution throughout the year - is therefore rejected. Our third hypothesis - gelatinous zooplankton are never the most abundant component within the total zooplankton community - is rejected. However, more research (i.e. experimental) should be done to determine the role of gelatinous zooplankton within the zooplankton assemblage.
Furthermore, a Distance-based Linear Model and distance-based Redundancy Analysis was done to examine which abiotic and biotic variables explained this temporal variation in both the total zooplankton and the gelatinous zooplankton distribution. Output of these analyses showed that a combination of temperature, salinity and turbidity explained 34% of the variation in total
Tina Van Regenmortel 57 zooplankton distribution. Temperature is known to modify the seasonality of zooplankton populations and salinity can change the dominant species within a community. The influence of turbidity can be twofold. It’s influence could be indirect or direct, dependent on whether the measurement represented the amount of suspended particles or phytoplankton in the water column. A combination of temperature, oxygen, salinity and smaller zooplankton density explained 40% of the observed temporal variation in gelatinous zooplankton. Temperature and salinity influence the reproduction in gelatinous zooplankton. Smaller zooplankton is a food source for gelatinous zooplankton, which easily explains their link. The influence of oxygen was not clear, it could be indirect by affecting the mobility of its prey or due to a factor that correlates with oxygen, but was not measured during this study. The above models only explained a low percentage of the variation in both the total and the gelatinous zooplankton datasets. Therefore, there are likely some structuring factors which were not measured, but do play an important role in explaining variation, which could include currents, but also biological variables such as predation and competition.
58 Samenvatting
Kwallen nemen wereldwijd toe, wat er toe leidt dat verschillende auteurs de voorbije jaren aankondigden dat onze toekomst er “geleiachtig” zal uitzien. Mogelijke oorzaken hiervan lijken door de mens geïnitieerd te zijn (e.g. overbevissing, eutroficatie, translocatie, habitat modificatie en klimaatsverandering). Anderen echter beweren dat bloeien van kwallen geen nieuw fenomeen zijn, maar dat ze een historische oorsprong hebben. Ondanks deze beide visies is wel zeker dat kwallen populaties momenteel aan het veranderen zijn, en daarom is het belangrijk dat we hun verspreiding onderzoeken. Voor deze studie wordt de term ‘gelatineus zoöplankton’ gebruikt als referentie naar de planktonische vertegenwoordigers van het phylum Ctenophora en de medusen (kwallenstadia) van het phylum Cnidaria (hydromedusae en scyphomedusae). Het Belgische Register voor Mariene Soorten (BeRMS4) omschrijft 39 verschillende gelatineuze zoöplankton soorten in het Belgische deel van de Noordzee. Deze omvatten drie soorten Ctenophora, zes soorten Scyphozoa en 30 soorten Hydrozoa. Voor het BDNS was kwantitatief onderzoek naar het gelatineuze zooplankton beperkt. Het doel van dit onderzoek is om de ruimtelijke en temporele verspreiding van het gelatineus zoöplankton in het Belgische deel van de Noordzee (BDNZ) na te gaan. De vooropgestelde hypotheses zijn: (1) het gelatineus zoöplankton vertoont een duidelijk patroon in ruimtelijke verspreiding in het BDNZ, (2) het gelatineus zoöplankton vertoont een uniforme temporele verspreiding tijdens het jaar, (3) het gelatineus zoöplankton is nooit het meest abundant deel van de totale zooplankton gemeenschap. Om zowel de ruimtelijke als temporele verspreiding van het gelatineus zoöplankton na te gaan werden maandelijkse staalnames uitgevoerd van maart 2011 tot februari 2012. Drie locaties, op een transect van kust tot offshore, werden bemonsterd: station W04 aan de Vlakte van de Raan, station W07 bij de Thornton zandbank en station W09 gelegen ten noorden van de Hinderbanken. Bemonsteringen werden uitgevoerd vanop het onderzoeksschip ‘De Zeeleeuw’. Gelatineus zoöplankton werd bemonsterd m.b.v. een WP3 net. De macroscopisch zichtbare Scyphozoa en Ctenophora werden aan boord geïdentificeerd, omwille van hun slechte preservatie in standaard preservatie middel. De resterende inhoud van de replicaten werd gepreserveerd op 4% gebufferde formaldehyde oplossing, voor verdere analyse in het lab. Op elke locatie werd de transparantie van de waterkolom bepaald m.b.v. een Secchi schijf. CTD- sensoren bepaalden de temperatuur, zuurstof concentratie, turbiditeit en saliniteit op
4 http://www.marinespecies.org/berms/
Tina Van Regenmortel 59 verschillende dieptes. Verder werd a.d.h.v. satelliet data de chlorofyl a concentraties bepaald als maat voor de primaire productie door fytoplankton. Met behulp van een stereo microscoop werd het gelatineus zoöplankton geïdentificeerd tot op het laagst mogelijke taxonomische niveau. Verder werd ook het non-gelatineus zoöplankton geteld en geclassificeerd tot op een hoger niveau van classificatie.
Tijdens dit onderzoek werden 37 gelatineuze zoöplankton taxa gevonden, waarvan 25 geïdentificeerd konden worden tot op soort niveau. Verder werden ook 25 non-gelatineus zoöplankton taxa uit diezelfde stalen geïdentificeerd. Deze studie onthulde een nieuwe soort gelatineus zoöplankton voor België. Morfologische identificatie stuurt ons in de richting van Eucheilota menoni of Lovenella assimilis, beide leden van de familie Lovenellidae. De identiteit van de soort kon tijdens deze studie echter niet bepaald worden omdat de morfologische verschillen tussen beide soorten moeilijk te onderscheiden zijn, en geen vers materiaal aanwezig was voor genetisch onderzoek. Verder onthulden de analyses de herontdekking van de larvale stadia van Rissoides desmaresti, hier niet meer waargenomen sinds het begin van de 20e eeuw. Analyses van de replicaten toonden hoge similariteitscoëfficienten (70-90%), wat aantoont dat de stalen die geanalyseerd werden tijdens deze studie de ruimtelijke en temporele patronen goed weergeven. Echter, als tijd geen limitatie was geweest, zouden meer replicaten uitgewerkt zijn om de accuraatheid van de resultaten de verbeteren. Verdere analyses werden uitgevoerd om ruimtelijke en temporele verspreidingen na te gaan. Univariate en multivariate analyses toonden geen duidelijk patroon in ruimtelijke verspreiding aan, bijdragend aan de assumptie dat dit kleine gedeelte van de Noordzee één zoöplankton gemeenschap deelt. Hierbij is onze eerste hypothese, dat het gelatineus zoöplankton vertoont een duidelijke ruimtelijke verspreiding in het BDNZ, dus verworpen. Een patroon in de temporele verspreiding van gelatineus zoöplankton kon wel worden waargenomen. Twee bloeien in gelatineus zoöplankton werden waargenomen: in juli en november t.h.v. de kust en de ‘midshore’ en in oktober en december op de offshore locatie. Tijdens deze bloeien was het gelatineus zoöplankton zeer dominant in de zoöplankton gemeenschap, met hoge densiteiten (tot 103 ind. m-3). De eerste bloeiperiode volgde op een piek in kleiner zooplankton, wat een directe biologische link suggereert, aangezien veel gelatineuze zooplankton soorten zich voeden met non-gelatineus zooplankton. De tweede bloei periode daarentegen, begon vroeger dan, of viel samen met, de tweede piek in kleiner zooplankton. Deze tweede bloei periode zal daardoor eerder gecontroleerd worden door abiotische variabelen zoals temperatuur en saliniteit. Onze tweede hypothese, dat het gelatineus zoöplankton een uniforme temporele verspreiding tijdens het jaar vertoont, is hierbij dus verworpen. Onze derde
60 hypothese, dat het gelatineus zoöplankton is nooit het meest abundant binnen de totale zooplankton gemeenschap, is hierbij verworpen. Meer onderzoek (o.a. experimenteel) zal echter uitgevoerd moeten worden om de rol van het gelatineus zooplankton binnen de zooplankton gemeenschap te achterhalen.
Verder werd een “Distance-based Linear Model” (DISTLM) en “distance-based Redundancy Analysis” (dbRDA) uitgevoerd om te bepalen welke abiotische of biotische variabelen de variatie in de verspreiding van gelatineus en non-gelatineus zoöplankton kon verklaren. Deze analyses toonden aan dat een combinatie van temperatuur, saliniteit en turbiditeit 34% van de variatie in totale zoöplankton distributie kon verklaren. Temperatuur kan de seizoenaliteit van zoöplankton aanpassen en saliniteit kan de dominante soorten binnen een gemeenschap veranderen. Turbiditeit zou de verspreiding op twee manieren kunnen verklaren, op een direct of directe manier, afhankelijk van de metingen van de hoeveelheid opgelost materiaal of fytoplankton in de waterkolom. Een combinatie van temperatuur, zuurstof, saliniteit en de densiteit aan kleiner zoöplankton konden 40% van de totaal geobserveerde variatie in gelatineus zoöplankton verklaren. Temperatuur en saliniteit beïnvloeden de reproductie in gelatineus zoöplankton. Er is een directe link tussen gelatineus en kleiner zoöplankton, want laatstgenoemde is een voedselbron voor het eerste. De invloed van zuurstof is niet duidelijk, het kan indirect zijn doordat het de beweeglijkheid van prooisoorten beïnvloedt of ten gevolge van een factor gecorreleerd aan zuurstof, maar die niet gemeten werd tijdens deze studie. Er dient opgemerkt te worden dat de modellen hierboven maar een klein percent van de totaal geobserveerde variatie in zowel de totale als de gelatineuze zoöplankton dataset konden verklaren. Daarom lijkt het waarschijnlijk dat andere structurerende factoren, die niet gemeten werden, ook een belangrijke rol spelen om de variatie te verklaren, zoals stromingen, maar ook biologische variabelen zoals predatie en competitie.
Tina Van Regenmortel 61 Acknowledgements
This research was carried out at the Institute for Agricultural and Fisheries Research in Ostend. I want to show my gratitude for getting the opportunity to work as part of their team. I would also like to express my thanks to all who helped collecting data and who offered their assistance. I thank my supervisors, Prof. Dr. Magda Vincx and Dr. Kris Hostens, for their guidance throughout the year, for critical advice and for the opportunity to work in a marine research facility. Special thanks goes out to my tutor, Lies Vansteenbrugge, for her endless enthusiasm, advice, help in the lab, reviewing the manuscript during its preparation, and much more. I thank Karl Van Ginderdeuren for his advice and for providing the image on the front page, and Quinten Vanhellemont (MUMM) for all his help during the search for environmental data. Last, but certainly not least, my gratitude goes out to my boyfriend, Maarten Laukens, for his interest in the topic, his assistance, constructive comments on earlier drafts of this manuscript and his everlasting patience.
62 Table of Figures
Figure 1. Sampling locations in the Belgian part of the North Sea...... 11 Figure 2. Overview of the three datasets for analyses: (1) total zooplankton dataset (red and green), (2) gelatinous zooplankton dataset (orange) and (3) non-gelatinous zooplankton dataset (red)...... 15 Figure 24. MDS of all examined replicates...... 16 Figure 3. Densities of non-gelatinous and gelatinous zooplankton from March 2011 to February 2012 at three sampling locations in the BPNS...... 17 Figure 4. Seasonal occurrence of some gelatinous zooplankton species at three sampling locations in the BPNS. Vertical axes at different scales...... 19 Figure 5. Species composition at the midshore location in May 2011...... 22 Figure 6. Species composition at the nearshore location in May 2011...... 22 Figure 7. Gelatinous zooplankton species composition at the midshore sampling location in July 2011...... 23 Figure 8. Gelatinous zooplankton species composition at the nearshore sampling location in June 2011...... 24 Figure 9. Non-gelatinous species composition at the offshore sampling location in February 2012...... 25 Figure 10. Gelatinous zooplankton species composition during Autumn (October-December) (left) and Spring (April-June) (right)...... 27 Figure 11. Gelatinous zooplankton species composition during Winter (January-March) (left) and Summer (July-September) (right)...... 28 Figure 12. Environmental variables at three sampling locations during the period March 2011 to February 2012...... 30 Figure 13. MDS analysis with factor ‘distance to shore’ of all zooplankton data...... 31 Figure 14. MDS analysis using the gelatinous zooplankton data adding a factor ‘distance to shore’...... 32 Figure 15. MDS with factor ‘season’ for all zooplankton data...... 33 Figure 16. MDS of the temporal distribution of zooplankton. Clustering by factor ‘season’ can be observed. January and February 2012 are added to the Spring-group because of the high temperature conditions...... 33 Figure 17. MDS of the temporal distribution of gelatinous zooplankton. January and February 2012 are added to the Spring-group because of the high temperature conditions ...... 34 Figure 18. Draftsman plots with the untransformed environmental variables. Secchi-depth and turbidity show right-skewness. Therefore, these variables have to be transformed...... 35 Figure 19. Correlation matrix with the transformed environmental variables. The cut-off value of 0.8 was reached for the variable ‘Secchi depth’...... 35 Figure 20. dbRDA of the zooplankton data from the most parsimonious model...... 36 Figure 21. PCO analysis of all zooplankton data...... 37 Figure 22. dbRDA of gelatinous zooplankton data from the most parsimonious model...... 38 Figure 23. PCO analysis of gelatinous zooplankton data...... 39 Figure 25. Draftsman plot with the transformed environmental variables. Secchi-depth and turbidity were log-transformed and show no more right-skewness...... 78 Figure 26. Marginal test results of DISTLM for zooplankton data using transformed environmental variables...... 78
Tina Van Regenmortel 63 Figure 27. Results of DISTLM for total zooplankton data using the 'Best' selection procedure based on the AIC and BIC selection criteria...... 79 Figure 28. Scatterplot which shows the top ten models of the AIC and BIC values for the total zooplankton dataset...... 79 Figure 29. Output file from the dbRDA analysis on the total zooplankton dataset...... 79 Figure 30. Draftsman plots for the untransformed enviromental variables that may have an influence on the temporal variation in gelatinous zooplankton. Secchi depth, turbidity and non- gelatinous zooplankton density show right-skewness and therefore have to be transformed. .... 80 Figure 31. Draftsman plots for the transformed environmental variables that may have an influence on the temporal variation in gelatinous zooplankton. Secchi depth, turbidity and non- gelatinous zooplankton density are log-transformed...... 80 Figure 32. Results of DISTLM for gelatinous zooplankton data using the 'Best' selection procedure based on the AIC and BIC selection criteria...... 81 Figure 33. Scatterplot which shows the top ten models of the AIC and BIC values for the gelatinous zooplankton dataset...... 82 Figure 34. Output file from the dbRDA analysis on the gelatinous zooplankton dataset...... 82
64 Table of Tables
Table 11. Similarity coefficients (%) corresponding to all examined replicates...... 16 Table 1. Average density (ind. m-3) and maximum density (ind. m-3) of some dominant and rare zooplankton species found in this study from March 2011 to February 2012...... 18 Table 2. Diversity indices for three sampling locations in the BPNS for the total zooplankton dataset, combining the density data for all samples averaged per sampling location...... 21 Table 3. Diversity indices for gelatinous zooplankton for each sampling location combining the density data for all samples averaged per sampling location...... 23 Table 4. Diversity indices for the non-gelatinous zooplankton dataset for each sampling location, combining the density data for all samples averaged per sampling location...... 24 Table 5. Diversity indices for each season for the total zooplankton dataset combining the density data for all samples averaged per season...... 25 Table 6. Overview of p-values of Mann Whitney U-tests for Pielou's evenness (J'), Shannon's index (H') and Simpson's index (λ') for the total zooplankton dataset. P-values in red are still significant after Bonferroni correction...... 26 Table 7. Diversity indices for gelatinous zooplankton for each season, averaged per season...... 26 Table 8. Overview of p-values of Mann Whitney U-tests for number of species (S), total number of individuals (N) and Pielou's evenness (J') for the gelatinous zooplankton dataset. P-values in red indicate those still significant after Bonferroni correction...... 27 Table 9. Diversity indices for non-gelatinous zooplankton for each season, averaged for all samples per season...... 28 Table 10. Overview of p-values of Mann Whitney U-tests for Pielou's evenness (J') and Shannon's index (H') for the non-gelatinous zooplankton dataset. P-values in red indicate those still significant after Bonferroni correction...... 28 Table 12. List of non-gelatinous taxa found in the Belgian part of the North Sea in the period March 2011 - February 2012...... 76 Table 13. List of 37 gelatinous zooplankton taxa found in the Belgian part of the North Sea in the period March 2011 - February 2012...... 77 Table 14. Resemblance of temperatures (°C) during January and February of 2011 and 2012. All sampling stations show higher temperatures in 2012, with exception of sampling station W04 in February...... 78 Table 15. Correlation matrix for the transformed environmental variables which may influence the temporal variation gelatinous zooplankton...... 81
Tina Van Regenmortel 65 Reference list
Abelló, P., Pretus, J.L., Corbera, J. (1993-1994) Occurrence and distribution of some Stomatopod crustaceans in the Western Mediterranean. The Zoology Miscellany 17: 107-113
Anderson, M.J., Gorley, R.N., Clarke, K.R. (2008) PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. PRMER-E Ltd, Plymouth, United Kingdom. 214 pp.
Appeltans, W., Bouchet, P., Boxshall, G.A., Fauchald, K., Gordon, D.P., Hoeksema, B.W., Poore, G.C.B., van Soest, R.W.M., Stöhr, S., Walter, T.C., Costello, M.J. (Eds.) (2011) World Register of Marine Species. (Internet address: http://www.marinespecies.org)
Altuna, A. (2009) Eucheilota menoni Kramp 1959 (Cnidaria: Hydrozoa: Lovenellidae), an Indo- Pacific species new to the Atlantic fauna from the Bay of Biscay (north of Spain). Aquatic Invastions 4 (2): 353-356
Aria, M.N. (1997) A functional biology of Scyphozoa. Chapman & Hall, Londen. 317 pp.
Arai, M.N. (2001) Pelagic coelenterates and eutrophication: a review. Hydrobiologia 451: 69-87
Attril, M., Wright, J., Edwards, M. (2007) Climate-related increases in jellyfish frequency suggest a more gelatinous future for the North Sea. Limnology and Oceanography 52 (1): 480-485
Bakker, C. & Van Rijswijk, P. (1987) Development time and growth rate of the marine calanoid copepod Temora longicornis as related to food conditions in the Oosterschelde estuary (Southern North Sea). Netherlands Journal of Sea Research 21 (2): 125-141
Båmstedt, U. (1998) Trophodynamics of Pleurobrachia pileus (Ctenophora, Cydippida) and ctenophore summer occurrence off the Norwegian north-west coast. Sarsia 83: 169-181
Barz, K. & Hirche, H.J. (2007) Abundance, distribution and prey composition of scyphomedusae in the Southern North Sea. Marine Biology, 151 (3): 1021-1033.
Boesch, D.F., Brinsfield, R.B., Magnien, R. (2001) Chesapeake Bay eutrophication: scientific understanding, ecosystem restoration, and challenges for agriculture. Journal of Environmental Quality 30: 303-320
Bode, A., Álvarez-Ossorio, M.T., González, N., Lorenzo, J., Rodríguez, C., Varela, M., Varela, M.M. (2005) Seasonal variability of plankton blooms in the Ria de Ferrol (NW Spain): ΙΙ. Plankton abundance, composition and biomass. Estuarine, Coastal and Shelf Science 63: 285-300
Boero, F. (1993) Zoogeography and life cycle patterns of Mediterranean hydromedusae (Cnidaria). Biological Journal of the Linnean Society 48: 239-266
Boero, F. & Sarà, M. (1987) Motile sexual stages and evolution of Leptomedusae (Cnidaria) Bollettino di Zoologia 54: 131-139
Boero, F., Bucci, C., Colucci, A.M.R., Gravili, C., Stabili, L. (2007) Obelia (Cnidaria, Hydrozoa, Campanulariidae): a microphagous, filter-feeding medusa. Marine Ecology 28 (1): 178-183
66 Boersma, M., Malzahn, A.M., Greve, W., Javidpour, J. (2007) The first occurrence of the ctenophore Mnemiopsis leidyi in the North Sea. Helgoland Marine Research 61: 153-155.
Bouillon, J., Medel, M.D., Pagès, R., Gili, J., Boero, F., Gravili, C. (2004) Fauna of the Mediterranean Hydrozoa. Scientia Marina 68 (2): 5-438
Breitburg, D.L., Steinberg, N., DuBeau, S., Cooksey, C., Houde, E.D. (1994) Effects of low dissolved oxygen on predation on estuarine fish larvae. Marine Ecology Progress Series 104: 235-246
Brusca, R.C. & Brusca, G.J. (2003) Invertebrates, Second Edition. Sinauer Associates, Inc. Massachusetts, United States. 895 pp.
Beaugrand, G. (2003) Long-term changes in copepod abundance and diversity in the north-east Atlantic in relation to fluctuations in the hydroclimatic environment. Fisheries Oceanography 12 (4): 1-14
Calder, D.R. (1991) Abundance and distribution of hydroids in a mangrove ecosystem at Twin Cays, Belize, Central America. Hydrobiologia 216/217: 221-228
Carloti, R. (2000) Modeling zooplankton dynamics. – In: Harris, R.P., Wiebe, P.H., Lens, J., Skjoldal, H.R., Huntley, M. (Eds.) Zooplankton methodology manuals: 571-669; San Diego (Academic Press).
Carré, D. & Carré, C. (2000) Origin of germ cells, sex determination, and sex inversion in medusae of the genus Clytia (Hydrozoa, Leptomedusae): the influence of temperature. Journal of experimental zoology 287: 233-242
Chevalier, S., Martin, A., Leclère, L., Amiel, A., Houliston, E. (2006) Polarised expression of FoxB and FoxQ2 genes during development of the hydrozoan Clytia hemisphaerica. Development Genes and Evolution 216(11): 709-720
Chiori, R., Jager ,M., Denker, E., Wincker, P., Da Silva, C., Le Guyader, H., Manuel, M., Quéinnec, E. (2009) Are Hox genes ancestrally involved in axial patterning? Evidence from the Hydrozoan Clytia hemisphaerica (Cnidaria). PLoS ONE 4 (1): 1-14
Clarke, K.R. & Gorley, R.N. (2006) PRIMER v6: User Manual/Tutorial. PRIMER-E LTD, Plymouth, United Kingdom. 190 pp.
Cloern, J.E. (1987) Turbidity as a control on phytoplankton biomass and productivity in estuaries. Continental Shelf Research 6: 1367)1381
Colin, S.P., Costello, K.H., Hansson, L.J., Titelman, J., Dabiri, J.O. (2010) Stealth predation and the predatory success of the invasive ctenophore Mnemiopsis leidyi. Proceedings of the National Academy of Sciences 107: 17223-17227
Condon, R.H., Decker, M.B., Purcell, J.E. (2001) Effects of low dissolved oxygen on survival and asexual reproduction of scyphozoan polyps (Chrysaora quinquecirrha). Hydrobiologia 451: 89- 95
Condon, R.H., Graham, W.M., Duarte, C.M., Pitt, K.A., Lucas, C.H., Haddock, S.H.D., Sutherland, K.R., Robinson, K.L., Dawson, M.N., Decker, M.B., Mills, C.E., Purcell, J.E., Malej, A., Mianzan, H., Uye, S.,
Tina Van Regenmortel 67 Gelcich, S., Madin, L.P. (2012) Questioning the rise of gelatinous zooplankton in the world’s oceans. Bioscience 62 (2): 160-169
Daan, R. (1989) Factors controlling the summer development of copepod populations in the Southern Bight of the North Sea. Netherlands Journal of Sea Research 23 (3): 305-322
Daskalov, G.M., Crishin, A.N., Rodlonov, S., Mihneva, V. (2007) Trophic cascades triggered by overfishing reveal possible mechanisms of ecosystem regime shifts. Proceedings of the National Academy of Sciences 104: 10518-10523
De Blauwe, H. (2003) Ribkwallen (Ctenophora), schijfkwallen en medusevormende hydroïden (Cnidaria: Scyphozoa, Hydrozoa) te Zeebrugge, resultaten van 5 jaar waarnemingen (1999- 2003). De Strandvlo 23 (3): 80-125
Decker, M.B., Breitburg, D.L.n Purcell, J.E. (2004) Effects of low dissolved oxygen on zooplankton predation by the ctenophore Mnemiopsis leidyi. Marine Ecology Progress Series 280: 163-172
Denker, E., Chatonnet, A., Rabet, N. (2008) Acetylcholinesterase activity in Clytia hemisphaerica (Cnidaria). Chemico-Biological Interactions 175: 125-128
Diaz, R.J. & Rosenberg, R. (2008) Spreading dead zones and consequences for marine ecosystems. Science 321: 921-929
Dumoulin, E. (1997) Het invasieachtig voorkomen in de zuidelijke Noordzee van de hydromedusen Nemopsis bachei L. Agassiz, 1849 en Eucheilota maculata Hartlaub, 1894 in augustus-september 1996 (met aanvullende data voor 1997) (Hydrozoa:Athecata, Thecata). Strandvlo 17 (4): 102-126
Dumoulin, E (2007) De Leidy's ribkwal (Mnemiopsis leidyi A. Agassiz, 1865) al massaal in het havengebied Zeebrugge-Brugge, of: exoten als de spiegel van al tè menselijk handelen. De Strandvlo 27 (2): 44-60
Faasse, M., Ates, R. (1998) Het kwalletje Nemopsis bachei (L. Agassiz, 1849), terug van (nooit?) weggeweest. Het Zeepaard 58 (3): 72-81
Faasse, M.A. & Bayha, K.M. (2006) The ctenophore Mnemiopsis leidyi A. Agassiz 1865 in coastal waters of the Netherlands: an unrecognized invasion? Aquatic Invasions 1 (4): 270-277
Forbes, E. (1848) A monograph of the British naked-eye Medusae. Ray Society, London, pp. 104, plates 1-13.
Fransz, H.G., Colebrook, J.M., Gamble, J.C., Krause, M. (1991) The zooplankton of the North Sea. Netherlands Journal of Sea Research 28 (1/2): 1-52
Fransz, H.G. & Gonzalez, S.R. (2001) Seasonal and meridional trends in zooplankton diversity of the Central North Sea. Senckenbergiana maritime 31 (2): 255-261
Fraser, C.M. (1947) Hydroids of the 1939 Allan Hancock Caribbean Sea Expedition. Allan Hancock Atlantic Expeditions. 4: 179-335
68 Freund, J.A., Mieruch, S., Scholze, B., Wiltshire, K., Feudel, U. (2006) Bloom dynamics in a seasonally forced phytoplankton – zooplankton model: Trigger mechanisms and timing effects. Ecological complexity 3: 129-139
Frost, J.R., Denda, A., Fox, C.J., Jacoby, C.A., Koppelmann, R., Holtegaard Nielson, M., Youngbluth, M.J. (2012) Marine Biology 159: 239-253
Fuentes, V.L., Angel, D.L., Bayha, K.M., Atienza, D., Edelist, D., Bordehore, C., Gili, J., Purcell, J.E. (2010) Blooms of the invasive ctenophore, Mnemiopsis leidyi, span the Mediterranean Sea in 2009. Hydrobiologia 645: 23-37
Gamito, S. (2010) Caution is needed when applying Margalef diversity index. Ecological Indicators 10: 550-551
Gaughan, D.J. & Potter, I.C. (1995) Composition, distribution and seasonal abundance of zooplankton in a shallow, seasonally closed estuary in temperate Australia. Estuarine, Coastal and Shelf Science 41: 117-135
Gibbons, M.J. & Richardson, A.J. (2009) Patterns of jellyfish abundance in the North Atlantic. Hydrobiologia 616: 51-65
Graham, W.M., Pagès, F., Hamner, W.M. (2001) A physical context for gelatinous zooplankton aggregations: a review. Hydrobiologia 451: 199-212
Graham, W.M. & Bayha, K.M. (2007) Biological invasions by marine jellyfish. Ecological Studies 193(5):239-255
Greene, C.H., Landry, M.R., Monger, B.C. (1986) Foraging behavior and prey selection by the ambush entangling predator Pleurobrachia bachei. Ecology 67 (6): 1493-1501
Greve, W. (1975) Ctenophora. Fiches d'Identification du Zooplancton 146. Conseil International pour l'Exploration de la Mer, Kopenhagen.
Greve, W. (1981). Invertebrate predator control in a coastal marine ecosystem: the significance of Beroe gracilis (Ctenophora). - In: Rheinheimer, G. et al. (Ed.) (1981). Lower Organisms and their Role in the Food Web: Proceedings of the 15th European Marine Biology Symposium, Kiel, Damp 2000, Federal Republic of Germany (September 29-October 3, 1980). Kieler Meeresforschungen. Sonderheft, 5: pp. 211-217
Greve, W., Lange, U., Reiners, F., Nast, J. (2001) Predicting the seasonality of North Sea zooplankton. Senckenbergiana maritime 31 (2): 263-268
Greve, W., Reiners, F., Nast, J., Hoffman, S. (2004) Helgoland Roads meso- and macrozooplankton time-series 1974 to 2004: lessons from 30 years of single spot, high frequency, sampling at the only off-shore island of the North Sea. Helgoland Marine Research 58: 274-288
Griffin, R., Herbert, R.J.H., Pearce, B. (2011) New UK recordings of the mantis shripmp, Rissoides desmaresti (Crustacea: Stomatopoda), provided by broad-scale mapping projects. Marine Biodiversity Records 4: 1-4
Tina Van Regenmortel 69 Gucu, A.C. (2002) Can overfishing be responsible for the successful establishment of Mnemiopsis leidyi in the Black Sea? Estuarine, Coastal and Shelf Science 54: 439-451.
Haddock, S.H.D. (2004) A golden age of gelata: past and future research on planktonic ctenophores and cnidarians. Hydrobiologia 530/531: 549-556
Haddock, S.H.D. (2007) Comparative feeding behavior of planktonic ctenophores. Integrative and Comparative Biology 47 (6): 847-853
Håkanson, L. (2006) The relationship between salinity, suspended particulate matter and water clarity in aquatic systems. Ecological Research 21: 75-90
Hall, C.J. & Burns, C.W. (2003) Responses of crustacean zooplankton to seasonal and tidal salinity changes in the coastal Lake Waihola, New Zealand. New Zealand Journal of Marine and Freshwater Research 37 (1): 31-34
Halsband, C., & Hirche, H.J. (2001) Reproductive cycles of dominant calanoid copepods in the North Sea. Marine ecology progress series 209: 219-229
Hays, G.C., Richardson, A.J., Robinson, C. (2005) Climate change and marine plankton. Trends in Ecology and Evolution 20 (6): 337-344
Helmholz, H., Ruhnau, C., Schütt, C., Prange, A. (2007) Comparative study on the cell toxicity and enzymatic activity of two northern scyphozoan species Cyanea capillata (L.) and Cyanea lamarckii (Péron & Léslieur). Toxicon 50: 53-64
Hill, M.O. (1973) Diversity and Evenness: a unifying notation and its consequences. Ecology 54 (2): 427-432
Hurrell, J.W., Kushnir, Y., Visbeck, M. (2001) The North Atlantic Oscillation. Science 291: 603-605
Hurrell, J.W., Kushnir, Y., Ottersen, G., Visbeck, M. (2003) An overview of the North Atlantic Oscillation. - In: Hurrell, J.W., Kushnir, Y., Ottersen, G., Visbeck, M. (Eds.) The North Atlantic Oscillation: Climatic Significance and Environmental Impact. Geophysical Monograph Series 134: 279 pp. AGU, Washington, D.C.
Ivanov, V.P., Kamakin, A.M., Ushivtzev, V.B., Shiganova, T., Zhukova, O., Aladin, N., Wilson, S.I., Harbison, G.R., Dumont, H.J. (2000) Invasion of the Caspian Sea by the comb jellyfish Mnemiopsis leidyi (Ctenophora). Biological Invasions 2: 255-258
Javidpour, J., Sommer, U., Shiganova, T. (2006) First record of Mnemiopsis leidyi A. Agassiz 1865 in the Baltic Sea. Aquatic Invasions 1(4): 299-302
Júnior, M.N., Nagata, R.M., Haddad, M.A. (2010) Seasonal variation of macromedusae (Cnidaria) at North Bay, Florianópolis, southern Brazil. Zoologia 27 (3): 377-386
Kawahara, M., Uye, S., Ohtsu, K., Iizumi, H. (2006) Unusual population explosion of the giant jellyfish Nemopilema nomurai (Scyphozoa: Rhizostomeae) in East Asian waters. Marine Ecology Progress Series 307: 161-173
Kelmo, F. & Attrill, M.J. (2003) Shallow-water Campanulariidae (Hydrozoa, Leptothecatae) from Northern Bahía, Brazil. Revista de Biología Tropical 51 (1): 123-146
70 Kennedy, V.S. (1990) Anticipated effects of climate change on estuarine and coastal fisheries. Fisheries 15 (6): 16-24
Kideys, A.E. (1994) Recent dramatic changes in the Black Sea ecosystem: the reasons for the sharp decline in Turkish anchovy fisheries. J. Mar. Syst. 5 (2): 171-181
Kideys, A.E. & Niermann, U. (1994) Occurrence of Mnemiopsis along the Turkish coasts (from northeastern Mediterranean to Istanbul). ICES Journal of Marine Science 51, 423-427
Kramer, K.J.M., Brockmann, U.H., Warwick R.M. (1994) Tidal estuaries. Manual of sampling and analytical procedures. A.A. Balkema Publishers, USA. 313 pp.
Kramp, P.L. (1959) The hydromedusae of the Atlantic Ocean and adjacent waters. The Carlsberg Foundation's oceanographical expedition round the World 1928-30 and previous Dana- expeditions under the leadership of the late Professor Johannes Schmidt. Andr. Fred. Høst & Søn: Copenhagen. 283 pp.
Kramp, P.L. (1965) The hydromedusae of the Pacific and Indian Oceans. The Carlsberg Foundation's oceanographical expedition round the World 1928-30 and previous Dana- expeditions under the leadership of the late Professor Johannes Schmidt. Andr. Fred. Høst & Søn: Copenhagen. 166 pp.
Kremer, P. (1979) Predation by the ctenophore Mnemiopsis leidyi in Narragansett Bay, Rhode Island. Estuaries 2(2): 97-105
Larson, R.J. (1987) Trophic ecology of planktonic gelatinous predators in Saanich Inlet, British Columbia: Diets and prey selection. Journal of Plankton Research 9; 811-820
Larson, R.J. (1990) Scyphomedusae and Cubomedusae from the Eastern Pacific. Bulletin of Marine Science 47 (2): 546-556
Lucas, C.H., Williams, D.W., Williams, J.A., Sheader, M. (1995) Seasonal dynamics and production of the hydromedusan Clytia hemisphaerica (Hydromedusa: Leptomedusa) in Southhampton Water. Estuaries 18 (2): 362-372
Lynam, C.P., Hay, S.J., Brierley, A.S. (2004) Interannual variability in abundance of North Sea jellyfish and links to the North Atlantic Oscillation. Limnology and Oceanography 49 (3): 637- 643
Mackas, D.L., Greve, W., Edwards, M., Chiba, S., Tadokoro, K., Eloire, D., Mazzocchi, M.G., Batten, S., Richardson, A.J., Johnson, C., Head, E., Conversi, A., Peluso, T. (2010) Changing zooplankton seasonality in a changing ocean: Comparing time series of zooplankton phenology. Progress in Oceanography 97-100:31-62
Mamman, T.A. (1965) On a collection of hydroids from south India. ΙΙΙ. Family Plumulariidae. Journal of Marine Biological Association of India 7: 291-324
Margalef, R. (1958) Information theory in ecology. General Systems 3: 36-71
Tina Van Regenmortel 71 Matsakis, S. & Nival, P. (1989) Elemental composition and food intake of Phialidium hydromedusae in the laboratory. Journal of Experimental Marine Biology and Ecology 130: 277- 290
Matsakis, S. (1993) Growth of Clytia spp. hydromedusae (Cnidaria, Thecata): effects of temperature and food availability. Journal of Experimental Marine Biology and Ecology 171: 107-118
Mayer, A.G. (1912) Ctenophores of the Atlantic coast of North America. J.B. Lippencott Company, Philadelphia. 58 pp.
McCormick, J.M. (1969) Trophic relationships of hydromedusae in Yaquina Bay, Oregon. Northwest Science 43: 207-214
Mills, C.E. (1981) Diversity of swimming behaviors in hydromedusae as related to feeding and utilization of space. Marine Biology 64: 185-189
Mills, C.E. (2001) Jellyfish blooms: are populations increasing globally in response to changing ocean conditions? Hydrobiologia 451: 55-68
Morin, P.J. (1999) Community Ecology. Blackwell Science Ltd., England, 424 pp.
Mutlu, E. (1999) Distribution and abundance of ctenophores and their zooplankton food in the Black Sea. II. Mnemiopsis leidyi. Marine Biology 135: 603-613.
Mutlu, E. & Bingel, F. (1999) Distribution and abundance of ctenophores, and their zooplankton food in the Black Sea. I. Pleurobrachia pileus. Marine Biology 135: 589-601
Nawrocki, A.M., Schuchert, P., Cartwright, P. (2009) Phylogenetics and evolution of Capitata (Cnidaria: Hydrozoa), and the systematics of Corynidae. Zoologica Scripta 39 (3): 290-304
Navas-Pereira, D. & Vannucci, M. (1991) The hydromedusae and water masses of the Indian Ocean. Brazilian Journal of Oceanography 39 (1): 25-60
Oliveira, O.M.P. (2007) The presence of the ctenophore Mnemiopsis leidyi in the Oslofjorden and the considerations on the initial invasion pathways to the North and Baltic Seas. Aquatic Invasions 2(3): 185-189
Oliver, R.L., Hart, B.T., Olley, J., Grace, M., Rees, C., Caitcheon, G. (2000) The Darling River: algal growth and the cycling and sources of nutrients. Report to the Murray-Darling Basin Commission (Project M386). CRC for Freshwater Ecology and CSIRO Land and Water, Canberra, ACT, Australia
Parsons, T.R. & Lalli, C.M. (2002) Jellyfish population explosions: revising a hypothesis of possible causes. La mer 40: 111-121
Petrova, E.A., Dautova, T.N., Shkoldina, L.S. (2011) Species composition, seasonal dynamics of quantities and spatial distribution of Hydromedusae (Cnidaria: Hydrozoa) in Vostok Bay of the Sea of Japan. Russian Journal of Marine Biology 37 (2): 111-122
Pielou, E.C. (1966) The measurement of diversity in different types of biological collections. Journal of Theoretical Biology 13: 131-144
72 Purcell, J.E. (1985) Predation on fish eggs and larvae by pelagic cnidarians and ctenophores. Bulletin of Marine Science 37(2): 739-755
Purcell, J.E. (2005) Climate effects on formation of jellyfish and ctenophore blooms: a review. Journal of Marine Biology Association 85: 461-476
Purcell, J.E. (2012) Jellyfish and ctenophore blooms coincide with human proliferations and environmental perturbations. Annual Review of Marine Science 4: 209-235
Purcell, J.E., White, J.R., Nemazie, D.A., Wright, D.A. (1999) Temperature, salinity and food effects on asexual reproduction and abundance of the scyphozoan Chrysaora quinquecirrha. Marine Ecology Progress Series 180: 187-196
Purcell, J.E. & Arai, M.N. (2001) Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia 451: 27-44
Purcell, J.E., Shiganova, T.A., Decker, M.B., Houde, E.D. (2001) The ctenophore Mnemiopsis in native and exotic habitats: U.S. estuaries versus the Black Sea basin. Hydrobiologia 451: 145-176
Purcell, J.E. & Decker, M.B. (2005) Effects of climate on relative predation by scyphomedusae and ctenophores on copepods in Chesapeake Bay during 1987-2000. Limnology and Oceanography 50 (1): 376-387
Purcell, J.E., Uye, S., Lo, W. (2007) Anthropogenic causes of jellyfish blooms and their direct consequences for humans: a review. Marine Ecology Progress Series 350: 153-174
Rabalais, N.N., Turner, R.E., Wiseman, W.J. Jr., Boesch, D.F. (1991) A brief summary of hypoxia on the northern Gulf of Mexico continental shelf: 1985-1988. - In: Tyson, R.V., Person, T.H. (Eds) Modern and ancient continental shelf anoxia. Geological Society, London, 35-47
Ramsay, K. & Holt, R.H.F. (2001) Mantis shrimps Rissoides desmaresti in Tremadog Bay, North Wales. Journal of the Marine Biological Association of the United Kingdom 81: 3846/1-2
Raymont, J.E.G. (1983) Plankton and productivity in the oceans, Zooplankton. Permagon Press, Oxford. 700 pp.
Reusch, T.B.H., Bolte, S., Sparwel, M., Moss, A.G., Javidpour, J. (2010) Microsatellites reveal origin and genetic diversity of Eurasian invasions by one of the world’s most notorious marine invader, Mnemiopsis leidyi (Ctenophora). Molecular Ecology 19: 2690-2699
Richardson, A.J., Bakun, A., Hays, G.C., Gibbons, M.J. (2009) The jellyfish joyride: causes, consequences and management responses to a more gelatinous future. Trends in Ecology and Evolution 24 (6): 312-322
Russel, F.S. (1953) The medusae of the British Isles. Anthomedusae, Leptomedusae, Limnomedusae, Trachymedusae and Nacromedusae. Cambridge Univsersity Press, Londen. 522 pp.
Sanderson, A.R., (1930) The coelenterate plankton of the Northumbrian Coast during the year 1924. Journal of Marine Biological Association of the United Kindom 17 (1): 219-232
Tina Van Regenmortel 73 Santhakumari, V. (1993) A study of medusae from Andaman and Nicobar waters. Journal of the Zoological Society of Kerala 3: 37-43
Santhakumari, V. & Nair, V.R. (1999) Distribution of hydromedusae from the exclusive economic zone of the west and east coasts of India. Indian Journal of Marine Sciences 28: 150-157
Schlüter, M.H., Merico, A., Reginatto, M., Boersma, M., Wiltshire, K.H., Greves, W. (2010) Phenological shifts of three interacting zooplankton groups in relation to climate change. Global Change Biology 16: 3144-3153
Schuchert, P. (2001) Survey of the family Corynidae (Cnidaria, Hydrozoa). Revue suisse de Zoologie 108 (4): 739-878
Segura-Puertas, L., Franco-Gordo, C., Suarez-Morales, E., Gasca, R., Godinez-Dominguez, E. (2010) Summer composition and distribution of the jellyfish (Cnidaria:Medusozoa) in the shelf area off the central Mexican Pacific. Revista Mexicana de Biodiversidad 81: 103-112
Shaffer, G., Olsen, S.M., Pedersen, J.O.P. (2009) Long-term ocean oxygen depletion in response to carbon dioxide emissions from fossil fuels. Nature Geoscience 2: 105-109
Shannon, C.E. (1948) A mathematical theory of communication. The Bell System Technical Journal 27: 379-423, 623-656
SHenker, J.M. (1984) Scyphomedusae in surface waters near the Oregon coast, May-August, 1981. Estuarine, Coastal and Shelf Science 19: 619-632
Shiganova, T.A. (1993) Ctenophore Mnemiopsis leidyi and ichthyoplankton in the Sea of Marmara in October 1992. Okeanologiya 33 (6): 900-903
Shiganova, T.A. (1998) Invasion of the Black Sea by the ctenophore Mnemiopsis leidyi and recent changes in pelagic community structure. Fisheries Oceanography 7 (3:4): 305-310
Shiganova, T.A., Mirzoyan, Z.A., Studenikina, E.A., Volovik, S.P., Sioukou-Frangou, I., Zervoudaki, S., Christou, E.D., Skirta, A.Y., Dumont, H.J. (2001) Population development of the invader ctenophore Mnemiopsis leidyi, in the Black Sea and in other seas of the Mediterranean basin. Marine Biology 139: 431-445.
Siapatis, A., Giannoulaki, M., Valavanis, V.D., Palialexis, A., Schimenou, E., Machias, A., Somarakis, S. (2008) Modelling potential habitat of the invasive ctenophore Mnemiopsis leidyi in Aegean Sea. Hydrobiologia 612: 281-295
Siegel, V., Damm, U., Neudecker, T. (2008) Seks-ratio, seasonality and long-term variation in maturation and spawning of the brown shrimp Crangon crangon (L.) in the German Bight (North Sea). Helgoland Marine Research 62: 339-349
Simpson, E.H. (1949) Measurement of diversity. Nature 163: 688
Studeninina, E.I., Volovik, S.P., Miryozan, I.A., Luts, G.I. (1991) The ctenophore Mnemiopsis leidyi in the Sea of Azov. Oceanology 3: 722-725
Swanberg, N. (1974) The feeding behavior of Beroe ovata. Marine Biology 24: 69-76
74 Thiel, M.E. (1935) Zur kenntnis der Hydromedusenfauna des Schwarzen Meeres. Zoologiche Anzeiger Leipzig, BD. CXΙ, Heft 7/8, pp. 161-174
Uye, S., Fujii, N., Takeoka, H. (2003) Unusual aggregations of the scyphomedusa Aurelia aurita in coastal waters along western Shikoku, Japan. Plankton Biology and of Marine Species, compiled and validated by the VLIZ Belgian Marine Species Consortium. VLIZ Special Publication, 46. Vlaams Instituut voor de Zee (VLIZ): Oostende, Belgium. 78 pp.
Vandepitte, L., Decock, W., Mees, J. (2010) Belgian Register of Marine Species, compiled and validated by the VLIZ Belgian Marine Species Consortium. VLIZ Special Publication, 46. Vlaams Instituut voor de Zee (VLIZ): Oostende, Belgium. 78 pp.
Van der Baan, S.M. & Holthuis, L.B., (1966) On the occurrence of Stomatopoda in the North Sea, with special reference to larvae from the surface plankton near the lightship ‘Texel’. Netherlands Journal of Sea Research 3(1): 1-12
Van Ginderdeuren, K., Hostens, K., Hoffman, S., Vansteenbrugge, L., Soenen, K., De Blauwe, H., Robbens, J., Vincx, M. (2012a) Distribution of the invasive ctenophore Mnemiopsis leidyi in the Belgian part of the North Sea. Aquatic Invasions 7: 163-166
Van Ginderdeuren, K., Fiers, F., De Backer, A., Vincx, M., Hostens, K. (2012b) Updating the zooplankton species list in the Belgian part of the North Sea. Belgian Journal of Zoology 142 (1): 3-23
Van Hoey, G., Degraer, S., Vincx, M. (2004) Macrobenthic community structure of soft-bottom sediments at the Belgian Continental Shelf. Estuarine, Coastal and Shelf Science 59 (4): 599-613
Vansteenbrugge, L., Van Ginderdeuren, K., Van Regenmortel, T., Hostens, K., Vincx, M. Recent recordings of larval Rissoides desmaresti (Risso, 1816) in the Belgian part of the North Sea. In prep.
Verlhac, M. & Villeneuve, A. (2010) Clytia hemisphaerica: a Cnidarian model for studying oogenesis in Oogenesis, the Universal Process. John Wiley and Sons, Oxford, UK. 516 pp
Verwey, J. (1966) The origin of the Stomatopod larvae of the Southern North Sea. Netherlands Journal of Sea Research 3(1): 13-20
Vinogradov, M.YE., Shushkina, E.A., Musayeva, E.I., Sorokin, P.YU. (1989) A newly acclimated species in the Black Sea: the ctenophore Mnemiopsis leidyi (Ctenophora: Lobata). Oceanology 29 (2): 220-224
Woodmansee, R.A. (1958) The seasonal distribution of the zooplankton off Chicken Key in Biscanye Bay, Florida. Ecology 39 (2): 247-262
© BBC News, 2010: http://www.bbc.co.uk/news/world-europe-10948267
Tina Van Regenmortel 75 Appendix
Table 12. List of non-gelatinous taxa found in the Belgian part of the North Sea in the period March 2011 - February 2012.
Phylum Arthropoda Class Polychaeta Subphylum Crustacea Polychaeta sp. Class Malacostraca Tomopteris helgolandica Order Decapoda Class Maxillopoda Brachyura zoea Copepoda sp. Brachyura megalopa Decapoda larva (Caridae zoea, Caridae Phylum Chaetognatha megalopa, Hermit megalopa, Porcellana Chaetognatha sp. zoea, Anomura sp.) Decapoda adult (Crangon crangon) Phylum Chordata Order Cumacea Superclass Pisces Cumacea sp. Pisces larva Order Amphipoda Pisces egg Hyperia galba Syngathus rostellatus Pariambus typicus Phtisica marina Phylum Echinodermata Amphipod sp. Order Ophiurida Order Isopoda Ophiura sp. Isopoda sp. Subphylum Tunicata Order Mysida Tunicata sp. Mysida sp. Order Cumacea Phylum Mollusca Cumacea sp. Cephalochordata sp. Order Euphausiacea Mollusca sp. Euphausiacea sp. Order Stomatopoda Rissoides desmaresti
76 Table 13. List of 37 gelatinous zooplankton taxa found in the Belgian part of the North Sea in the period March 2011 - February 2012.
Cnidaria Order Leptothecata Scyphozoa Leptomedusa sp. Order Semaeostomeae Family Campanulariidae Ephyra sp. Clytia hemisphaerica (Linnaeus, 1767) Family Cyaneidae Obelia sp. Cyanea lamarckii Péron & Lesueur, 1810 Campanulariidae sp. Family Ulmaridae Family Lovenellidae Aurelia aurita (Linnaeus, 1758) Eucheilota maculata Hartlaub, 1894 Family Pelagiidae Eucheilota menoni A. Agassiz, 1862 Chrysaora hysoscella (Linnaeus, 1767) Family Eirenidae Eutima gracilis (Forbes & Goodsir, 1853) Cnidaria Eutima gegenbauri (Haeckel, 1864) Hydrozoa Eutima sp. Hydromedusa sp. Eutonina indicans (Romanes, 1876) Subclass Hydroidolina Eirene viridula (Péron & Lesueur, 1809) Order Anthoathecata Eirenidae sp. Anthomedusa sp. Family Laodiceidae Family Pandeidae Laodicea undulata (Forbes & Goodsir, 1853) Amphinema dinema (Péron & Lesueur, 1810) Family Aequoreidae Leuckartiara octona (Fleming, 1823) Aequorea vitrina Gosse, 1853 Family Bougainvilliidae Aequorea sp. Nemopsis bachei L. Agassiz, 184 Family Phialellidae Bougainvilla sp. Phialella quadrata (Forbes, 1848) Family Margelopsidae Margelopsis haeckeli (Hartlaub, 1897) Subclass Trachylinea Family Rathkeidae Order Limnomedusae Rathkea octopunctata (M. Sars, 1835) Family Olindiasidae Family Corynidae Gossea corynetes (Gosse, 1853) Codonium proliferum (Forbes, 1848) Cnidaria Corynidae sp. Ctenophora Family Hydractiniidae Order Beroida Hydractinia carnea (M. Sars, 1846) Family Beroidae Family Tubulariidae Beroe gracilis (Künne, 1939) Ectopleura dumortierii (Van Beneden, 1844) Family Bolinopsidae Family Corymorphidae Mnemiopsis leidyi (A. Agassiz, 1865) Euphysa sp. Family Pleurobrachiidae Pleurobrachia pileus (O. F. Müller, 1776)
Tina Van Regenmortel 77 W04 W07 W09 January 2011 3.4 4.3 6.4 January 2012 7.5 8.59 9.26
February 2011 4 4.6 5.8 February 2012 2.4 5.6 7.11
Table 14. Resemblance of temperatures (°C) during January and February of 2011 and 2012. All sampling stations show higher temperatures in 2012, with exception of sampling station W04 in February.
Figure 25. Draftsman plot with the transformed environmental variables. Secchi-depth and turbidity were log- transformed and show no more right-skewness.
Figure 26. Marginal test results of DISTLM for zooplankton data using transformed environmental variables.
78
Figure 27. Results of DISTLM for total zooplankton data using the 'Best' selection procedure based on the AIC and BIC selection criteria.
Figure 28. Scatterplot which shows the top ten models of the AIC and BIC values for the total zooplankton dataset.
Figure 29. Output file from the dbRDA analysis on the total zooplankton dataset.
Tina Van Regenmortel 79
Figure 30. Draftsman plots for the untransformed enviromental variables that may have an influence on the temporal variation in gelatinous zooplankton. Secchi depth, turbidity and non-gelatinous zooplankton density show right-skewness and therefore have to be transformed.
Figure 31. Draftsman plots for the transformed environmental variables that may have an influence on the temporal variation in gelatinous zooplankton. Secchi depth, turbidity and non-gelatinous zooplankton density are log- transformed.
80 Table 15. Correlation matrix for the transformed environmental variables which may influence the temporal variation gelatinous zooplankton.
Figure 32. Results of DISTLM for gelatinous zooplankton data using the 'Best' selection procedure based on the AIC and BIC selection criteria.
Tina Van Regenmortel 81
Figure 33. Scatterplot which shows the top ten models of the AIC and BIC values for the gelatinous zooplankton dataset.
Figure 34. Output file from the dbRDA analysis on the gelatinous zooplankton dataset.
82