Autotrophic nanoplankton dynamics is significant on the spatio-temporal variation of phytoplankton biomass size structure along a coastal trophic gradient

Regional Studies in Marine Science 33 (2020) 100920

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Autotrophic nanoplankton dynamics is significant on the spatio-temporal variation of phytoplankton biomass size structure along a coastal trophic gradient Esra Kocum Department of Biology, Ecology Section, Canakkale Onsekiz Mart University, 17100 Canakkale, Turkey

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Article history: Received 4 April 2019 Received in revised form 28 October 2019 Accepted 29 October 2019 Available online 31 October 2019 Keywords: Dardanelles Nutrients Phytoplankton Size-structure Nanoplankton Picoplankton

a b s t r a c t The biomass size structure is an intrinsic feature of phytoplankton communities that changes along the gradients of temperature, light intensity, nutrient concentrations, water column turbulence, as well as with changes in the size and the composition of the grazer community. The variations in it alter the trophic structuring in pelagic food webs leading to the quantitative changes in the flow of organic matter to the fisheries and to the deep ocean via the biological pump. The spatio-temporal dynamics of bulk and size-fractionated phytoplankton chl a biomass were studied in relation to the environmental factors at two coastal sites along the Dardanelles Strait, to test the suitability of its use as a nutrient enrichment indicator. The magnitude and the pattern of temporal change of the bulk phytoplankton biomass were very similar at two sampling sites, despite the significant variations in the spatial distributions of nutrients. Nevertheless, the spatial variation in the size fractionation of phytoplankton biomass, with significantly higher contributions of nano- and picoplankton at the nutrient-poor site reflected the nutrient gradient. Nanoplankton size fraction influenced the dynamics of the size and the size fractionation of the phytoplankton biomass, revealed by the statistically significant relations of the biomass and the relative abundance of it with each other and with the bulk phytoplankton biomass. Thus, the study altogether demonstrated; (i) the partitioning of bulk chlorophyll a biomass into size fractions may undergo changes without an accompanying change in its magnitude along the nutrient gradients, (ii) biomass size structure is a promising attribute of phytoplankton to be used as a nutrient enrichment indicator, (iii) identification and an in-depth analysis of factors influencing nanoplankton abundance are necessary for a through understanding of the spatio-temporal dynamics of the bulk and size-fractionated phytoplankton chl a biomass in the study area. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Autotrophs connect the abiotic and biotic components of all ecosystems by being able to synthesize organic matter from inorganic precursors. This makes them highly susceptible and responsive to changes in the abiotic dimensions of their niches probably more than the heterotrophs are. In pelagic ecosystems microbial autotrophs, the phytoplankton, have been frequently used as an indicator of environmental change (ex., Garmendia et al., 2013 and refs. therein) and bulk chlorophyll a (chl a) is an accepted index of phytoplankton biomass that is widely used as a metric for trophic status (ex.; Smith, 2006; Ferreira et al., 2011). However, factors like advection, mixing and/or grazing may prevent the accumulation of chl a, thus cause such a relation not to be seen on every spatio-temporal scale (Li et al., 2010). Also, depending on the level of nutrient enrichment and grazing capacity of the consumer community, phytoplankton growth rate and E-mail address: [email protected]. https://doi.org/10.1016/j.rsma.2019.100920 2352-4855/© 2019 Elsevier B.V. All rights reserved.

production may respond to nutrients on different spatio-temporal scales (Caperon et al., 1971; Malone et al., 1996). Therefore, other phytoplankton related indicators are necessary for the detection and the assessment of the nutrient enrichment related changes in coastal ecosystems (Cloern, 2001) which has been demanded by the Water Framework (Garmendia et al., 2013) and the Marine Strategic Framework Directives (Mangoni et al., 2017), too. The distribution of phytoplankton chl a biomass into micro-, nano and pico-plankton size groups, respectively encompassing cell sizes; between 20–200 µm, between 2–20 µm and smaller than 2 µm (Sieburth et al., 1978), is called the phytoplankton biomass size structure (PBSS). It varies along environmental conditions due to size-dependent differential abilities of phytoplankton for the exploitation of resources, namely nutrients and solar radiation as well as size-dependent susceptibility of phytoplankton to grazing and sinking. Small phytoplankton have higher affinities for nutrients and can achieve their maximum photosynthetic capacities at much lower solar irradiances, but they are more susceptible to predation due to short generation

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E. Kocum / Regional Studies in Marine Science 33 (2020) 100920

times of their grazers. On the other hand, large phytoplankton require higher levels of nutrients and solar irradiance, but less susceptible to grazing pressure due to longer generation times of their grazers (ex. Malone, 1971a,b; Kiørboe, 1993; Legendre and Rassoulzadegan, 1996). Therefore, the change in PBSS in response to variations in nutrients is predictable that picoplankton dominates the phytoplankton under nutrient-poor conditions, whereas with increasing nutrients the contribution of microplankton fraction increases (ex., Maranon et al., 2001; Mousing et al., 2018). This makes PBSS a potential phytoplankton community trait to be used as a nutrient enrichment indicator. Apart from being able to reflect the changes in their environment, the variations in PBSS have significant consequences on the trophic organization of pelagic food webs, hence on the fate of photosynthetically produced organic matter. In small phytoplankton dominated pelagic ecosystems the organic matter is degraded via microbial food webs which accounts for most of the respiration and nutrient recycling in the marine ecosystems. On the other hand in large phytoplankton dominated ecosystems a shorter herbivorous food chain forms and a higher trophic transfer efficiency and potential for downward flux of organic matter occur (ex., Azam et al., 1983; Legendre and Rassoulzadegan, 1996). Here, a presentation of spatio-temporal variations detected in the bulk and the sizefractionated phytoplankton chl a biomass (PBSS) was made and discussed in relation to environmental factors at two coastal sites along the Dardanelles Strait, in order to test whether the distribution of chl a biomass into micro-, nano- and picoplankton size fractions reflect the variations in nutrients, hence can be used as an nutrient enrichment indicator. The causes for conducting the study and for the choice of study area were; (i) the study of phytoplankton size structure in relation to nutrients is valuable in the sense that this can serve as a useful tool in the detection and assessment of nutrient enrichment, (ii) the changes in the phytoplankton size structure have repercussions on the trophic structuring of pelagic food webs and the cycling of carbon, thus help us to predict the functioning of future coastal ecosystems, (iii) there are relatively fewer amount of research on phytoplankton size structure that examines it in relation to nutrient enrichment problem (Garmendia et al., 2011), (iv) such studies are even rarer in the Mediterranean Sea (Pulina et al., 2017) and particularly in the Dardanelles Strait (Kocum and Sutcu, 2014). 2. Materials and methods 2.1. Description of study site and field work Two sampling sites were chosen along the Dardanelles Strait for the study. The Dardanelles Strait, the Sea of Marmara (SOM) and the Strait of Istanbul (SOI) are jointly called the ‘‘Turkish Straits System’’ (TSS) (Fig. 1). A bi-layered counterflow system formed by the inflow of brackish Black Sea water overlying saline Mediterranean water makes the TSS a hydrodynamically unique system and enables the exchange of water masses and matter between the Mediterranean Sea and the Black Sea (ex., Oguz and Sur, 1989). The chemical characteristics of surface water quality in the Dardanelles Strait are influenced by Black Sea water whose chemical properties is modified (Coban-Yildiz et al., 2000; Stashchuk and Hutter, 2001; Tzali et al., 2010) during its ca. 4–5 months-long journey through The SOI and The SOM (Oguz and Tugrul, 2010). In the Dardanelles Strait, the prevailing wind directions are N and NE and W and SE winds are frequent between October and March. The semi-diurnal tide regime is masked by the wind effect, hence has a minor influence on the sea level variations (Yuce, 1994; Alpar and Yuce, 1998). The first sampling site of the study, site 1 (S1) was located in an open harbour (40◦ 06′ 21.16′′ N–26◦ 22′ 41.21′′ E) ca. 220 m away from

the natural shoreline and accessed by walking on a dock. The other sampling site, site 2 (S2) was located on the shoreline, close to the mouth of the Kepez Stream (40◦ 06′ 12.04′′ N–26◦ 22′ 37.87′′ E) (Fig. 1). Kepez is 25.9 km long, a low-lying stream with a drainage area of 95.56 km2 (Erginal et al., 2002). Crop production is the dominant land use type in its catchment where there is also a residential development because of the urban sprawl from Canakkale town centre. The stream’s delta forms a 1.5 km long protrusion into the sea and has an area of 639.6 hectares. The most recent geological formation in the delta area is young alluvial soils (Akbulak et al., 2008; Erginal et al., 2002). Surface samples were collected in triplicate at each site, approximately at monthly intervals between May 2015 and June 2016 with a clean bucket tied to a rope and carried back to the laboratory in 5 l clean HDPE containers within less than an hour of sample collection. Samples were collected between 8.30 a.m. and 9.30 a.m. (GMT + 3) on all sampling occasions. Temperature and Secchi disc depth measurements were made at each site before sample collection. 2.2. Laboratory work The measurement of pH and salinity were done immediately upon returning to the laboratory with a pH metre (Hanna HI 8314, Romania) and an optical refractometer (Atago, S/Mill-E, Japan), respectively. For measurement of bulk phytoplankton chl a biomass (BPB), a known volume of sample (1–2 L, depending on the amount of the phytoplankton) were filtered through 0.7 µm pore sized glass fibre filters (GF/F, Whatman, UK) which were then used in bulk chl a analysis. The filtrates were transferred into 50 mL polyethylene (PE) bottles and stored at −20 ◦ C for nutrient analysis. The size fractionation of samples into micro, nano- and picoplankton size fractions (PSFs), was achieved by sequential filtration of samples through 20 µm nylon net filters (Millipore, Ireland), 2 µm and 0.2 µm nucleopore polycarbonate filters (Whatman, UK). Then all the filters were processed following the protocol described in Arar (1997) and chl a concentrations in the bulk and the size-fractionated samples were calculated by the trichromatic equation of Jeffrey and Humphrey (1975). The relative abundance (RA) of each PSF was estimated by calculating its per cent contribution to the total chl a (Tchl a) which is the sum of chl a concentrations contained in micro− , nano- and picoplankton fractions. Nitrate (NO− 3 ), nitrite (NO2 ), 3− ortho-phosphate (PO4 ) and silicic acid [Si(OH)4 ] were analysed according to Parsons et al. (1984). Ammonium (NH+ 4 ) analysis was done using the modified (Krom, 1980) indophenol blue method of Harwood and Kuhn (1970). Absorbance readings were done on a UV–VIS spectrophotometer (PG T+80 model, UK) for the analysis of chl a and nutrients. Unfiltered water samples, as well as samples filtered through 20 µm and 2 µm filters, were examined using an Olympus BX 51 model microscope on the day of sampling for a qualitative broad taxonomy of the plankton community. Additionally, on each sampling day fixed volumes of whole and size-fractionated samples were poured over glass microscope slides that were horizontally placed side by side in plastic containers of equal dimensions (15 cm × 15 cm) where they let to settle on to the slide surfaces. The slides were then examined under the microscope daily, though only the observations done within the first two days of the sampling dates were taken into consideration. For plankton identification (Tomas, 1997), illustrations and descriptions contained in the web pages of the Algaebase database (algaebase.com) and the Encyclopedia of Life Project (www.eol.org) were used.

E. Kocum / Regional Studies in Marine Science 33 (2020) 100920

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Fig. 1. Location of study sites. Source: Google Earth Pro 7.3.2.5776.

2.3. Data analysis The significances of temporal and spatial variations in the variables were tested by two-way ANOVA (GLM, Minitab 17.00), with site and time as the fixed factors. The relative amount of variation explained by each factor was determined by calculating the percentage of the total sum of squares. Pearson correlation coefficients were used to test the significance of the relation between the variable pairs (SPSS 20.00). Correlation and regression analysis were applied to site-specific as well as pooled (data from

two sites combined) data sets. Sigma Plot 10.0 (SPSS Inc., USA) software was used for producing the graphs and performing the regression analysis. The statistical analysis of data was carried out at a significance level of ≥95% (p ≤ 0.05) and the data transformations were made according to Zar (1984) to meet the assumptions of the statistical tests used. Size index (SI) formula of SI (µm) = [1 * (% picoplankton) + 5 * (% nanoplankton) + 50 * (% microphytoplankton)]/100 proposed by Bricaud et al. (2004) was utilized to make a general characterization of the size structure of phytoplankton community, i.e. the dominant size fraction, at

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E. Kocum / Regional Studies in Marine Science 33 (2020) 100920 Table 1 Descriptive statistics of variables measured at two sites. Variable

SITE I

Temperature (o C) Salinity pH Secchi disc depth (m) NH+ 4 (µM) NO− 3 (µM) PO34− (µM) Si(OH)4 (µM) DIN: PO34− DIN:Si(OH)4 sil Si(OH)4 :DIN Si(OH)4 :PO34− Bulk phytoplankton biomass (µg chl a L−1 ) Microplankton biomass (µg chl a L−1 ) Nanoplankton biomass (µg chl a L−1 ) Picoplankton biomass (µg chl a L−1 ) Relative abundance of microplankton (%) Relative abundance of nanoplankton (%) Relative abundance of picoplankton (%) SI (µm)

SITE II

Range

Average ± s.e.

CV (%)

N

Range

Average ± s.e.

CV (%)

N

8–25 18–34 7.87–8.43 2.45–10.3 < 0.05–9.63 0.15–2.62 < 0.01–0.24 0.13–26.44 6.11–48.48 0.16–8.22 0.15–6.31 3.57–310.06 0.43–2.48 0.05–1.13 0.067–1.30 0.02–0.60 17.64–47.78 25.49–62.57 3.59–46.57 12.15–25.50

16.57 ± 1.59 23.5 ± 1.06 8.15 ± 0.05 5.28 ± 0.59 4.45 ± 0.60 1.00 ± 0.012 0.13 ± 0.021 3.45 ± 1.01 28.80 ± 3.26 3.30 ± 0.47 1.01 ± 0.24 51.08 ± 18.80 1.06 ± 0.09 0.29 ± 0.04 0.41 ± 0.05 0.23 ± 0.02 29.79 ± 1.36 41.12 ± 2.09 29.08 ± 2.06 17.24 ± 0.64

36.02 17.00 2.39 41.52 78.24 84.09 86.16 190.08 58.82 93.25 157.56 191.24 60.05 94.09 85.98 63.17 29.58 32.96 46.00 24.13

14 14 14 14 33 42 27 42 27 42 42 27 42 42 42 42 42 42 42 42

8–25 16–31 7.75–8.37 1.3–2.1 8.09–144.01 0.88–131.10 0.82–11.24 1.55 −82.51 8.48–68.25 0.93–33.27 0.20–2.22 5.04–22.16 0.52–2.65 0.06–0.99 0.04–0.90 0.05–0.37 10.70–56.43 14.89–56.48 9.50–66.03 7.17–29.27

16.57 ± 1.59 22 ± 0.96 8.04 ± 0.05 1.71 ± 0.068 60.93 ± 7.01 39.29 ± 5.86 4.34 ± 0.53 36.62 ± 3.82 31.61 ± 3.58 5.37 ± 1.23 0.44 ± 0.08 11.18 ± 1.01 1.07 ± 0.11 0.28 ± 0.04 0.30 ± 0.04 0.15 ± 0.01 40.24 ± 1.93 37.22 ± 2.12 24.27 ± 2.57 22.21 ± 0.94

36.02 16.43 2.31 14.95 74.64 96.75 76.98 67.66 70.75 149.40 111.20 56.78 69.26 89.38 94.05 63.33 31.19 37 66.20 27.32

14 14 14 14 42 42 39 42 39 42 39 39 42 42 42 39 42 42 39 42

Table 2 Significant pairwise Pearson correlation coefficients among environmental and phytoplankton variables at two sampling sites. Site 1 (3) (1) (2) (3) (4) (5) (6) (8) (9) (10) (11) (12) (13) (16) (17)

(4)

(6)

(7)

.87** .76** .68** .83** .58* .68**

(10)

(11) (13) (14) (15)

−.56* .89** .84** .85** .90** .85** .93** −.69* .54*

−.68**

.85** .98** .90**

(18)

Site 2 (3)

(1) (2) (3) .54* (4) (5) (6) (8) (9) (10) (11) (12) (13) −.57* (16) .77** (17)

(5)

.72** .87**

(6)

(7)

.56*

(10) (11) (13) .56*

−.66* .66** −.71**

(14) (16)

(17)

(18)

.68* .54*

−.74** −.77** −.61* .53* .84** .78**

.61*

−.65* −.56*6*

.92**

−.55* .56* −.69** −.63* .73**

(1 = WPB, 2 = microplankton biomass, 3 = nanoplankton biomass, 4 = picoplankton biomass, 5 = RA of microplankton, 6 = RA of nanoplankton, 3− 3− 3− − 7 = RA of picoplankton, 8 = NH+ 4 , 9 = NO3 , 10 = PO4 , 11 = Si(OH)4 , 12 = DIN: PO4 , 13 = Si(OH)4 :DIN, 14 = Si(OH)4 : PO4 ,15 = secchi disc depth, 16 = salinity, 17 = pH, 18 = temperature, sample sizes and units of measurements are as shown in Table 1). *p<0.05. **p<0.01.

each site. SI values vary on a scale of 1–50 where the extreme values, respectively represent a phytoplankton community solely composed of picoplankton and only of microplankton (Bricaud et al., 2004). 3. Results 3.1. Physico-chemical data The same temperature values were measured at two sampling sites which varied between 8 ◦ C (May 2016) and 25 ◦ C (July and September 2015). The Secchi disc depth, salinity and pH values were significantly higher at S1 (Table 1). The pattern of temporal change in salinity was characterised by a period of increase over October–January, followed by a period of decrease between February and August at both sites. Temperature correlated to salinity and pH at both of the study sites (Table 2). 3.2. Nutrient data The temporal and spatial variations in all nutrient concentrations were significant and site-specific differences explained

>60% of the variations in all nutrients (Table 3). The nutrient concentrations at S2 were more than an order of magnitude higher than those at S1 (Table 1). The correlation analysis between the concentrations of each nutrient measured at two sampling sites yielded non-significant (p > 0.05) relations for all nutrients. The dominant form of dissolved inorganic nitrogen (DIN) species was NH+ 4 making up over more than 50% of the DIN pool at both sites, on average. NH+ 4 concentrations were above 100 µM in May 2015, November and May 2016 at S2 while they remained below 10 µM at S1, throughout the study period (Fig. 2a). There was a distinct seasonal pattern of change in NO− 3 concentrations marked by a period of a clear increase between October and February at S2 while they displayed a highly fluctuating non-seasonal change at S1 (Fig. 2b). On average, the per cent contribution of NO− 3 to DIN pool was 21.86 ± 3.33% and 32.93 ± 4.07% at S1 and S2, respectively. The surface waters were depleted in PO34− in January and during the March–June 2016 period and the concentrations were always <0.50 µM at S1 whereas much higher values were measured at S2 (Fig. 2c). Si(OH)4 concentrations were below 2 µM except for May 2015–September 2015 interval at S1 and fluctuated between 8.47 µM and 62.21 µM with no clear seasonal pattern at S2 (Fig. 2d). The correlations of NO− 3 with temperature, salinity, pH, PO34− and Si(OH)4 and that of PO34− to Si(OH)4 were

E. Kocum / Regional Studies in Marine Science 33 (2020) 100920

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3− − and (d) Si(OH)4 concentrations measured at two sampling sites (mean ± s.e., n = 3). Fig. 2. Temporal and spatial changes in (a) NH+ 4 (b) NO3 , (c) PO4

significant at S2 (p < 0.01 for all, Table 2). The DIN:PO34− ratios were similar at both sites (Tables 1 and 3), though there were 3− 3− significant differences between NO− and NH+ ratios 3 :PO4 4 :PO4 at both sampling sites (12.7:1 ± 1.97 and 25.08:1 ± 3.43 at S1, respectively; 21.46:1 ± 3.14 and 10.15:1 ± 1.77 at S2, respectively. p < 0.05 for all). The mean monthly DIN:PO34− ratios were above the Redfield N:P ratio of 16:1, except for in July 2015 (8.5:1), at S2 and in November 2015 (∼6:1), at S1 (Fig. 3a). The spatio-temporal variations in both the Si(OH)4:DIN and Si(OH)4 :PO34− ratios were significant and spatial changes were responsible for much of the variation in the latter (Table 3). At S1, except for the extremely high values of May 2015, Si(OH)4:DIN and Si(OH)4 :PO34− ratios ranged between 0.15:1–1.48:1 and 3.57:1–39.09:1, respectively. The Si(OH)4 :DIN ratios were below 1:1 barring July 2015 and Si(OH)4 :PO34− < 16:1 were common (10 out of 13 monthly means) at S2 (Fig. 3b, c). 3.3. Phytoplankton data The BPB values displayed non-significant spatial variation and varied in a similar range at both sampling sites (Tables 1 and 3). The temporal variation in BPB was significant and portrayed a common pattern of change at both sampling sites defined by two co-occurring major peaks in June 2015 and in May 2016, and a minor one in November (Fig. 4). The BPB values varied within a narrow range (0.52 ± 0.02–0.76 ±0.03 µg chl a/L, n = 3) between December 2015 and March 2016, at both sampling sites. The ranges of BPB values measured at both sites over the summer 2015 and spring 2016 (0.54 ± 0.20–2.65 ±0.27 µg chl a/L, avr. ± s.e. = 1.55 ± 0.12, n = 36) were much higher than the ones measured over the autumn and winter (0.52 ± 0.02– 1.15 ±0.51 µg chl a/L, avr. ± s.e. = 0.69 + 0.05 µg chl a/L, n =

36). BPB values were slightly higher at S2 between May 2015 and December 2015 while they were higher at S1 for the remaining part of the study (Fig. 4). Among physico-chemical variables, BPB correlated with SD at S1 and with PO34− and Si(OH)4:DIN at S2 (Table 2). There were significant spatio-temporal variations in the biomasses of nano- and picoplankton and in the relative abundances of all size fractions while the temporal variation in the microplankton biomass was significant, too (Table 3). The biomass size structure of the phytoplankton community described by the Bricaud size index yielded mean SI values of 17.24 ±0.64 µm (range: 12.15 µm–25.50 µm) and 22.21 ±0.94 µm (range: 7.17 µm–29.27 µm) for sites 1 and 2; respectively and displayed a significant spatio-temporal variation. A stable PBSS formed at S1 over the October–December interval when SI values varied within a narrow range (18.83 µm–19.08 µm). The magnitude of the spatial difference in SI values was relatively larger (with smaller values at S1) in August, September, November, March and June 2016. The SI values correlated to NO− 3 (r = .54, p < 0.05, n = 14), PO34− (r = −.67, p < 0.05, n = 9), Si(OH)4 :DIN (r = .54, p < 0.05, n = 12) and microplankton biomass at S1 (r = .55, p < 0.05, n = 14) and with picoplankton biomass (r = −.74, p < 0.01, n = 13), at S2. The microplankton biomass values were higher over the May– August 2015 and Spring 2016 periods and peaked in May 2015, at both sampling sites without a significant spatial variation. The RA of microplankton was significantly higher at S2, making up >40% of Tchl a for most of the study period (9 out of 14 monthly means), while it remained below <40%, except in May 2015 and February, at S1. The pattern of temporal change in the RA of microplankton differed from that in its biomass except for May 2015 when both increased to peak values at two

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E. Kocum / Regional Studies in Marine Science 33 (2020) 100920

Table 3 Two-way ANOVA of variables. The percentage of the total sum of squares (SS) explained by each factor within the ANOVA is given. Variable

Factor

% SS

F

p

NH+ 4 (µM)

Date Site D*S

11.86 72.25 9.51

8.19 498.99 6.57

<0.001 <0.001 <0.001

NO− 3 (µM)

Date Site D*S

20.27 64.26 14.16

66.61 2745.17 46.53

<0.001 <0.001 <0.001

PO34− (µM)

Date Site D*S

9.96 78.21 10.88

47.48 2982.82 51.90

<0.001 <0.001 <0.001

Si(OH)4 (µM)

Date Site D*S

24.38 64.17 10.06

75.57 25.85 31.18

<0.001 <0.001 <0.001

DIN (µM)

Date Site D*S

7.04 81.21 9.23

11.97 1796.63 15.70

<0.001 <0.001 <0.001

DIN:PO34−

Date Site D*S

28.44 0.19 30.67

3.14 0.17 3.39

0.008 0.685 0.005

Si(OH)4 :DIN

Date Site D*S

45.722 7.36 36.36

18.65 39.03 14.83

<0.001 <0.001 <0.001

Si(OH)4 :PO34−

Date Site D*S

29.11 46.12 10.95

9.48 120.16 3.57

<0.001 <0.001 0.004

Bulk phytoplankton biomass (µg chl a/L)

Date Site D*S

81.51 0.013 6.48

29.27 0.06 2.33

<0.001 0.809 0.015

Microplankton biomass (µg chl a/L)

Date Site D*S

91.32 0.037 3.46

76.01 0.40 2.88

<0.001 0.530 0.003

Nanoplankton biomass (µg chl a/L)

Date Site D*S

88.11 3.52 5.88

152.27 78.98 10.17

<0.001 <0.001 <0.001

Picoplankton biomass (µg chl a/L)

Date Site D*S

53.76 9.64 26.59

23.29 50.15 11.52

<0.001 <0.001 <0.001

Relative abundance of microplankton (%)

Date Site D*S

46.75 17.48 21.46

14.08 68.45 6.46

<0.001 <0.001 <0.001

Relative abundance of nanoplankton (%)

Date Site D*S

65.37 2.16 23.70

32.14 13.80 11.65

<0.001 0.012 <0.001

Relative abundance of picoplankton (%)

Date Site D*S

64.74 1.53 21.94

23.80 6.75 8.07

<0.001 <0.001 0.012

SI (µM)

Date Site D*S

48.37 14.75 19.96

12.39 45.35 5.11

<0.001 <0.001 <0.001

sites (Fig. 5a, d). Microplankton biomass correlated to Si(OH)4 , Si(OH)4 :DIN and Si(OH)4 :PO34− ratios while its RA correlated to PO34− and Si(OH)4 :DIN ratio, at S1 (Table 2). The correlation (r = 0.42, p < 0.05, n = 28) and the linear relation of microplankton biomass to BPB (log microplankton biomass = 0.027 + 0.25 log BPB, R2 = 0.17, p = 0.026, n = 28) were significant in the pooled data set. The dominant taxa observed in the microplankton of S1 were diatoms followed by dinoflagellates. Silicoflagellates (Dicytocha spp.) and filamentous cyanobacteria were rare in the microplankton of S1. The dominant diatom taxa were Cylindrotheca spp., Chaeteceros spp., Leptocylindrus sp., Rhizosolenia sp., Dactlyliosolen sp. They usually co-occurred and gained higher abundance in the winter phytoplankton, though Cylindrotheca spp. were observed throughout the study at both sites. Guinardia spp. and Ditylum sp. were rarer centric diatom members of the microplankton community. Navicula spp. and Nitzschia spp. were

Fig. 3. Molar ratios of (a) DIN:PO34− (b) Si(OH)4 :DIN and (c) Si(OH)4 :PO34− , calculated for two sampling sites. The horizontal lines display the corresponding Redfield ratios.

most frequently observed pennate diatoms at S2, whereas Licmophora sp. and Cocconeis sp. were the common pennate diatoms of S1. Throughout the study, the relative abundances of centric and pennate diatoms were higher in the diatom communities of sites 1 and 2, respectively. Ceratium spp. and Prorocentrum spp. were common microplanktonic dinoflagellate taxa at two sites though gained a higher abundance in the phytoplankton of S1. Dinophysis spp. and Gymnodinium spp. were occasionally observed in the microplankton of both study sites.

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Fig. 4. Temporal and spatial changes in the bulk chl a phytoplankton biomass values at two sampling sites (mean ± s.e., n = 3).

The contribution of nanoplankton to phytoplankton was significantly higher at S1 (Table 1). A fairly similar pattern of change was detected in the biomass and the RA of nanoplankton with higher values over June–July 2015 and April–May 2016 intervals, at both sites (Fig. 5b, e). The correspondence between the temporal dynamics of the biomass and the RA of nanoplankton was also reflected in the highly significant linear responses of the latter (Y) to the former (X), at S1 (logY = 1.47 + 0.92*logX, R2 = 0.46, p = 0.008), at S2 (logY = 1.42 + 1.29*logX, R2 = 0.44, p = 0.009) and in the pooled data set (logY = 1.44 + 1.09*logX, R2 = 0.45, p < 0.0001). Nanoplankton dominated (as both biomass and RA) the phytoplankton when BPB values exceeded 1 µg chl a/L and 2 µg chl a/L levels at sites 1 and 2, respectively. The biomass and RA of nanoplankton had positive significant relations with BPB at both sites as shown by correlation (Table 2) and regression analysis done for each study site (Fig. 6a, b) and also in the pooled data set (log nanoplankton biomass = −0.0056 + 0.60 log BPB, R2 = 0.62, p < 0.001 and log RA of nanoplankton = 1.34 + 0.80 log BPB, R2 = 0.40, p = 0.003; n = 28 for both). Among the nutrient variables, Si(OH)4 :DIN ratio correlated to nanoplankton biomass, at S2 (Table 2). Coccolithopores were common in the autotrophic nanoplankton between spring and mid-summer and dominated the phytoplankton community in June 2015 and May 2016, at both sampling sites. The other commonly observed nanoplankton taxa were small solitary diatoms (Chaeteceros spp., Navicula spp., Nitzschia spp.). They were more abundant when coccolithophores were rare and dominated the nanoplankton in their absence. Small dinoflagellates (Prorocentrum spp., Karlodinium sp.), Imantonia sp. looking like cells, Chrysochromulina spp. and filamentous cyanobacteria (Oscillatoriaceae) were rarely observed in the nanoplankton of both sites. Dinoflagellates were more common at S1 than at S2 while the opposite was true for filamentous cyanobacteria. There were significant spatio-temporal variations in the biomass and RA of picoplankton with generally higher values at S1, where it continuously dominated the phytoplankton between September and January (Fig. 5c). The picoplankton biomass had the smallest variability among the three PSF, while the opposite was true for its RA, at both sampling sites (Table 1). The most noticeable feature of the temporal pattern of change in picoplankton biomass was a period of decrease following the peak in May 2015 that lasted till October 2015 and a brief period of

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increase over March–May 2016, observed at S1. The mean seasonal picoplankton biomass values calculated at S1 showed that picoplankton biomass was highest (0.31 µg chl a/L) in summer followed by autumn, spring and winter (0.12 µg chl a/L). At the other sampling site, it displayed a highly fluctuating pattern of temporal change with higher values in May 2015, July, January and May 2016 (Fig. 5f). The RA of picoplankton had no significant linear relation with its own biomass or with BPB, at both study sites, though it (y) displayed a significantly negative trend when plotted against increasing BPB values (x) (log y = 1.65 − 0.89 log x, R2 = 0.19, p = 0.022, n = 27), in the pooled data set. The picoplankton biomass had correlations with temperature, Si(OH)4 , Si(OH)4 :DIN and Si(OH)4 :PO34− at S1 while its RA negatively correlated to micro- and nanoplankton biomasses at S2 (Table 2). Picocyanobacteria, mainly Synechococcus looking like cells, were most frequently observed autotrophic picoplankton and were more abundant in summer, at both sites. Additionally, there were other cells in picoplankton samples that could not be identified with the light microscopy. The heterotrophic component of plankton was dominated by heterotrophic nanoflagellates (HNF) (mainly Bodo-, Cafeteria-, Kathablepharis- looking like cells) at both sites though with a relatively higher occurrence at S1. Heterotrophic micro-flagellates were more frequent in the heterotrophic microbial community of S2 which had members of Ciliate ordo Holotrichia and Oligotrichia in July, August and September. 4. Discussion 4.1. Spatio-temporal dynamics of nutrients The significant spatial variation in the nutrients and salinity values indicated the formation of two distinct water bodies along the short coastal-offshore transect. At S2, freshwater inputs by Kepez Stream caused significantly lower salinity values and elevated nutrient concentrations compared to those observed at S1, where salinity and nutrient concentrations were in similar ranges measured in the surface layer of the Dardanelles Strait in other studies (ex., Polat and Tugrul, 1996; Tugrul et al., 2002; Turkoglu et al., 2004; Turkoglu, 2010). The lack of correlations between the concentrations of the same nutrient measured at two sampling sites confirmed that the stream’s influence on the nutrients was confined, i.e., only affecting those at S2, throughout the study. 3− The highly significant correlations of NO− with each 3 and PO4 other and with Si(OH)4 at S2 and not at S1 further signified the influence of the stream on the nutrient concentrations was local and did not extend into S1. In fact, salinity values were higher at both sites during winter which can be considered as unusual for a site where salinity values are expected to decrease due to a higher freshwater runoff. Therefore, the salinity and nutrient values observed at S2 could have been affected by the intrusion of water masses from S1, and not vice versa. The nature of nutrient imbalances inferred by comparing observed DIN:PO34− ratios with Redfield ratio signalled a potential Plimitation for both sampling sites. The comparison of Si(OH)4 :DIN:and Si(OH)4 :PO34− ratios with those required by diatoms (Brzezinski, 1985; Gilpin et al., 2004) pointed DIN and PO34− concentrations were in stoichiometric excess relative to Si(OH)4 , at S2 for most of the study period. This made S2 more enriched with anthropogenic nutrients, in particular with DIN species, creating a Si-limited condition for diatom growth. The enrichment of coastal ecosystems with nitrogen and phosphorus because of anthropogenic activities in their catchments is a global issue (ex., Cloern, 2001) and reported for other stream-influenced coastal locations along the Dardanelles Strait (Kocum and Dursun, 2007; Kocum and Akgul, 2009; Kocum and Sutcu, 2014). The

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Fig. 5. The biomass (symbols) and relative abundance (bars) of (a, d) microplankton, (b, e) nanoplankton and (c, f) picoplankton measured at two sampling sites (mean ± s.e., n = 3).

most noteworthy feature of nutrient conditions observed at S1 was a clear stoichiometric P-limitation denoted by DIN:PO34− and Si(OH)4 :PO34− ratios that were mostly above 16:1 as well as PO34− concentrations that were below the analytical detection limit during the growing season of the phytoplankton. At this site, average Si(OH)4 :DIN ratio (Table 1) was not indicative of a stoichiometric Si limitation, though it dropped to 0.60 ± 0.08 (n = 39) when the extremely large values measured in May 2015 were excluded. Besides the monthly average Si(OH)4 :DIN ratios < 1:1 were dominant (11 out of 14 values). These together with the fact that the actual Si(OH)4 concentrations were ≤2 µM between August 2015 and June 2016 made S1 a rather unsuitable location for diatom dominance (ex., Egge and Aksnes, 1992) or only suitable for lightly silicified diatom genera (Brzezinski, 1985; Pondaven et al., 1999). The frequent occurrence of members of Chaetoceros, Leptocylindrus, Cylindrotheca and Rhizosolenia, the lightly silicified diatom genera (Tett et al., 2008), at S1 was in favour of the latter possibility.

4.2. Spatio-temporal dynamics of the phytoplankton variables The range of bulk chl a concentrations classified the study area as mesotrophic for most of the year, and eutrophic during phytoplankton growing season, based on the trophic classes defined for the Aegean Sea by Ignatiades (2005). The bulk phytoplankton chl a values of this study were reasonably similar to those measured in different parts of the Dardanelles Strait (ex., Turkoglu, 2010; Yalcın et al., 2017. Whereas fairly similar and much higher levels of chl a concentrations were measured during the periods between 11.04.2006–12.04.2007 (Buyukates and Inanmaz, 2009) and 26.04.2005–25.04.2006 (Buyukates and Inanmaz, 2010), respectively at S1. The BPB values of S2 were well below the ones measured at a nearby coastal site located within the plume of another stream (Saricay), where over two orders of magnitude higher NO− 3 concentrations were measured (Kocum and Sutcu, 2014). The lack of significant spatial variation

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Fig. 6. Scatter plots with fitted linear regressions for the biomass and the relative abundance of nanoplankton on bulk phytoplankton biomass at two sampling sites.

in the BPB, despite significantly higher nutrient concentrations occurring at S2 hinted that a similar suit of environmental factors that either exclude nutrients or mask the response of BPB to nutrients were responsible for BPB dynamics. Bulk chl a is a frequently used variable in the assessment of marine phytoplankton response to nutrient enrichment (ex.; Smith, 2006; Ferreira et al., 2011), though such a response might not be seen on every spatio-temporal scale due to several factors including; local hydrodynamics, changes in phytoplankton or grazer community species composition (Li et al., 2010), increased grazing activity and climate change (Cloern, 2001). Although the study did not involve the measurement of the grazing activity and the loss rates due to sedimentation, the presence of a shallow water column at S2 might have facilitated sedimentation of cells while the frequent occurrence of larger sized heterotrophic protists could have exerted higher grazing pressure on nano- and picoplankton, here. The significant spatial variation in the RA of microplankton despite the presence of almost the same amount of microplankton biomass at two sampling sites (Tables 1 and 3) could also be a sign of higher grazing activity keeping smaller phytoplankton size fractions in check hence increasing the RA of microplankton without an increase in its biomass, at S2. The significant spatial variation in PBSS and the narrower ranges of variations in the RA of each size fraction than those in their corresponding biomass values (Tables 1 and 2), indicated that fairly stable size structures were retained by the phytoplankton communities of each sampling site. The statistical relations

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between the nanoplankton and BPB were significant and there was also significant relations between the biomass and RA of nanoplankton at both sampling sites. These indicated that the relative contribution of nanoplankton to the phytoplankton community was mainly influenced by changes in its biomass, i.e. self-driven. Hence, nanoplankton and factors affecting its abundance were most influential on the observed spatio-temporal dynamics of the magnitude and the size structure of the phytoplankton biomass in this study. Nano- and picoplankton can be significant in both coastal (Calvo-Diaz et al., 2008; Delpy et al., 2018; Kukrer and Buyukisik, 2010; Aytan et al., 2018) and in open ocean phytoplankton (Hunt et al., 2017) and the significance of the former in coastal phytoplankton has been recognised since the beginning of 1970s (Malone, 1971a). In the present study, coccolithophores were abundant in the nanoplankton and dominated the phytoplankton under bloom conditions (in June 2015, May 2016) and persisted through summer and autumn, at both sampling sites. Frequent occurrence of coccolithopore (Emiliania huxleyi) blooms over May–June period was observed in the Black Sea (Mikaelyan et al., 2015 and refs. therein) and they were reported to spread into the Dardanelles Strait (Turkoglu, 2016). The influence of Black Sea water extends into the North East Aegean Sea and reported to be a significant factor in the formation of coccolithophore assemblages there (Dimiza et al., 2015; Karatsolis et al., 2017), too. Consequently, it is possible that coccolithophore bloom formations in the Dardanelles Strait and other parts of the TSS are influenced by the intrusion of the Black Sea water to some degree and may have similar underpinning environmental factors with those in the Black Sea. The superior ability of E. huxleyi for dissolved organic nitrogen (Benner, 2008) and phosphorus (Riegmann et al., 2000) uptake can explain the recurring blooms of this species in the Dardanelles Strait. The Black Sea water brings higher amounts of organic nutrients relative to the inorganic nutrients to the Dardanelles Strait, due to preferential uptake of the latter by phytoplankton, during its ca. 4 months journey (Polat and Tugrul, 1996; Tugrul et al., 2002; Zeri et al., 2014). This also explains why E. huxleyi blooms usually follow those of diatoms which cause release of substantial amounts of dissolved organic matter into the water (Wetz and Wheeler, 2007 and refs. therein). Among several postulated causes for the formation of coccolithophore blooms in the Black Sea, is the deficiency of either phosphorus (Silkin et al., 2009) or nitrogen (Oguz and Merico, 2006; Silkin et al., 2014), i.e., both low and high DIN:PO34− ratios seem to give coccolithopores a competitive advantage over diatoms. The strong contrast in environmental preferences of coccolithophores and diatoms has been confirmed by a study of realized niches of 87 diatom and 40 coccolithophore taxa living in the ocean. It revealed that diatoms prosper under relatively lower light, temperature, salinity but higher silicate and nitrate concentrations while the opposite grades of light, salinity and nutrients define the realized niche of the coccolithophores (Brun et al., 2015). Thus, through their consumption of inorganic nutrients and release of dissolved organic matter into water, diatom blooms may help to create environmental ‘‘patches’’ suitable for coccolithophore succession (Lessard, 2005), providing that other relevant physico-chemical conditions (light, temperature, salinity, carbonate chemistry) are also in their preferred range. In the present study no diatom bloom was recorded, except that in November 2011 when they dominated the phytoplankton, though the sampling frequency may not be enough to capture the dynamics of changes in species composition of the phytoplankton. However considering that Si(OH)4 concentrations decreased by ca.87% from May 2015 to June 2015 (when a coccolithophore bloom observed), it can be speculated that a diatom bloom might have preceded that of coccolithophores at S1, within the period between May and June samplings. Although

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smaller, another notable decline in Si(OH)4 from a monthly average value of 2.04 µM in April 2016 to 1.43 µM in May 2016 was registered at the same site which coincided with the second coccolithophore bloom of the study. Apart from coccolithophores, diatoms were also important in the nanoplankton of both study sites. Most of the pennate diatom taxa observed in this study were nanoplanktonic (Navicula spp, Nitzschia spp), though they were observed in the microplankton samples examined in other similar studies carried out at different coastal locations along the Dardanelles Strait (ex.; Kocum and Sutcu, 2014; Sutcu and Kocum, 2017). Small centric diatoms (solitary Cheateceros sp. and other centric genera) were also common in the nanoplankton of S1. A diatom community dominated by small cells at the low Si:P supply ratio (20:1) was observed in a culture study of the natural plankton community (Grover, 1989). The Si:P ratios range at S2 (∼5:1–22:1) was low and can be a culprit for the presence of nanoplanktonic diatom cells in this study. Nanoplanktonic diatoms were also reported to be dominant members of winter– spring phytoplankton in a eutrophic coastal site in the Aegean Sea (Mihalatau and Moustaka-Gouni, 2002) and also in oligotrophic parts of the eastern Mediterranean Sea (LeBlanc et al., 2018). The picoplankton biomass values remained below 0.50 µg chl a/L (except for the monthly mean value of 0.60 µg chl a/L in May 2015, at S1) throughout the study, supporting the general notion that picoplankton makes a constant background in the marine phytoplankton despite the increases in bulk chl a (Chisholm, 1992). Temporal changes were important on the variation of picoplankton underlining the significance of seasonal changes in its dynamics. The positive correlation of picoplankton biomass to temperature at S1 pointed that temperature can be the cause of the seasonal change in it, at this site. Although the effect of temperature on picoplankton biomass cannot be disentangled from that of nutrients (Moran, 2010), it can still be important on the picoplankton biomass at S1, as it did not correlate with any of the nutrients measured here. At S2, the negative correlations of RA of picoplankton to the microplankton and nanoplankton biomasses suggested the other size fractions and/or grazing influenced the RA of picoplankton more than its biomass did. The lack of a statistically significant relation between the biomass and the RA of picoplankton at both sampling sites, further support and make this explanation possible for S1, too. The presence of a significantly higher amount of picoplankton biomass at S1 could be due to the inhibition of microplankton from being the dominant PSF by low nutrient concentrations. The lack of negative correlations between the picoplankton biomass and other PSF further support this possibility. Nutrient availability has been considered to have predominant influence on the marine phytoplankton size structure (Maranon et al., 2014, 2015; Mousing et al., 2018). Low nutrient concentrations do not cause a rise only in the share of picoplankton but also in the heterotroph to autotroph ratios, (Cermeno et al., 2006; Pugnetti et al., 2008 and refs. therein,). The fact that nutrient-poor S1 has been identified as a heterotrophic system by Buyukates et al. (2007) corroborates these studies. Picoplankton has been generally regarded as the major component of the open ocean phytoplankton (ex. Agawin et al., 2000), though it can also achieve high abundances in the coastal phytoplankton (ex. Moran, 2007; Paoli et al., 2007). On average, the RA of picoplankton values of this study were less than those measured in the northern and southern basins of the Aegean Sea (Ignatiades et al., 2002), the Maliakos Gulf, Greece (Kormas et al., 2002), Ebro shelf area, NW Mediterranean (Arin et al., 2005), the Bay of Biscay (Calvo-Diaz et al., 2008), in stations located in the southern exit of the Dardanelles Strait (Lagaria et al., 2017), the SE Black Sea (Agirbas et al., 2017) and the Mersin Bay, NE Mediterranean (Yucel, 2018). However, RA of picoplankton values of this study were fairly close to the ones measured in the

SOM (Lagaria et al., 2013), Iskenderun Bay, eastern Mediterranean (Polat, 2006) and the Central Cantabrian Sea (Calvo-Diaz et al., 2008), and were within the same range with the ones measured at two nearby (ca. 7 km north of the S2) coastal stations along the shores of the Dardanelles Strait (Kocum and Sutcu, 2014). 5. Conclusions The significant differences in the concentrations of nutrients and in salinity values of two sampling sites indicated the formation of two distinct water bodies along the short coastal-offshore transect. The presence of significant spatial variations in the fractionation of phytoplankton chl a biomass into size fractions without a significant difference in the BPB values measured at two sites was the interesting outcome of the study. Bulk chl a is a widely accepted nutrient enrichment indicator, though a response by chl a to changing nutrient concentrations may not always be seen due to the disrupting effect of the biological and/or the physical factors on the relationship between chl a and nutrient concentrations (see the discussions). As put forward by Camp et al. (2015), the highly dynamic nature of marine phytoplankton do not allow chl a to ‘‘temporally integrate the environmental changes’’. Therefore, there is a clear need for a phytoplankton trait that varies over long time scales hence affected by long term variations in the nutrient concentrations. PBSS and other size-dependent phytoplankton indices have been regarded as conserved characteristics of phytoplankton communities (Sabetta et al., 2005; Litchman et al., 2010) that are able to reflect the phytoplankton’s responses to the nutrients (ex., Garmendia et al., 2011; Sabetta et al., 2008; Garmendia et al., 2013; Vadrucci et al., 2013). Therefore PBSS can be a more effective phytoplankton trait than bulk chl a in ‘‘temporally integrating’’ the phytoplankton’s response to nutrients. This study showed the occurrence of fairly stable and significantly different PBSS at two sampling sites which reflected the differences in the spatial distribution of nutrients, hence supported the previous studies that encourage the use of PBSS as a nutrient enrichment indicator. The coastal nutrient enrichment problem has a global occurrence with adverse ecological and socio-economic impacts, hence finding useful indicators for the detection and assessment of this problem is important for the conservation of the coastal ecosystems and the services they provide. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding The study was funded by T.C. Canakkale Onsekiz Mart University, Turkey Scientific Projects Commission under the contract number FBA-2014-149. The preliminary findings of the project was presented as posters during the International Conference on Biological Sciences held in Konya, Turkey between 21-23/10/2016 and the International Conference on Advances in Natural and Applied Sciences held in Antalya, Turkey between 18-21/04/2017 and an extended abstract containing summary data on picoplankton was published in the proceedings of the latter conference.

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