ENSO and anthropogenic impacts on phytoplankton diversity in tropical coastal waters

Progress in Oceanography 140 (2016) 1–13

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ENSO and anthropogenic impacts on phytoplankton diversity in tropical coastal waters Hai Doan-Nhu ⇑, Lam Nguyen-Ngoc, Chi-Thoi Nguyen Institute of Oceanography, Viet Nam Academy of Science and Technology, Viet Nam

a r t i c l e

i n f o

Article history: Received 23 June 2015 Received in revised form 22 October 2015 Accepted 22 October 2015 Available online 28 October 2015

a b s t r a c t 16-year phytoplankton data were analysed to assess ENSO and anthropogenic impacts on biodiversity and community structure at 3 locations (Nha-Trang and Phan-Thiet Bays and near Phu-Qui Island) in South Centre Viet Nam to understand (1) the primary scales of change in phytoplankton community structure, and traditional and taxonomic diversity indices; (2) the significance of environmental changes and/or climate variability on phytoplankton diversity; and (3) the usefulness of these long-term data for analysing future impacts of anthropogenic and climate changes. Traditional and taxonomic diversity indices were compared and tested in linkage with environmental conditions and ENSO. Nutrient data indicated stronger environmental impacts in Phan-Thiet Bay, milder in Nha-Trang Bay and less noticeable near Phu-Qui Island. There were measurable impacts of both anthropogenic and ENSO on phytoplankton at different locations in various parameters, e.g. species number, diversity and community structures. The lowest diversity was recorded in the most anthropogenically impacted site, Phan-Thiet Bay. Although a stronger impact on phytoplankton was recorded in ENSO year in Phan Thiet Bay, quantitative separation between anthropogenic and ENSO impacts using phytoplankton biodiversity indices was impossible. In the waters with less anthropogenic impacts, ENSO effects on taxonomic diversity was better indicated by negative phytoplankton responses to the ONI index (Nha-Trang Bay) and recovery of phytoplankton after the ENSO events (near Phu-Qui Island). Among the diversity indices, the taxonomic diversity indices (e.g. D+ and K+) better described impacts of ENSO than the traditional ones. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Phytoplankton are among the most frequently used targets for studying environmental impacts because of their fast uptake of dissolved substances and response to environmental changes. Some recent studies reported changes in phytoplankton size (Montes-Hugo et al., 2009; Morán et al., 2010; Lewandowska and Sommer, 2010) and/or community structure (Morán et al., 2010; Yvon-Durocher et al., 2011) under the influence of climate change. Marinov et al. (2010), using global Community Climate System Model, predicted that changes of both biomass and phytoplankton would be greatly different between north and south hemispheres at the end of 21 century. While many studies have used diatoms or phytoplankton as indicators for pollution assessment (e.g., Rimet, 2012 and references cited), diversity and/or taxonomic diversity of phytoplankton has rarely been used to assess climate impacts.

⇑ Corresponding author at: Institute of Oceanography, 01, Cau Da, Nha Trang, Viet Nam. E-mail address: [email protected] (H. Doan-Nhu). http://dx.doi.org/10.1016/j.pocean.2015.10.004 0079-6611/Ó 2015 Elsevier Ltd. All rights reserved.

Traditional diversity indices such as Simpson, Margalef, Shannon, Pielou, and Brillouin are widely used in ecological studies (Tramer, 1969; Harper and Hawksworth, 1994; Magnussen and Boyle, 1995; Stirling and Wilsey, 2001; Hill et al., 2003; Davidson et al., 2010; Cabrini et al., 2012; David et al., 2012). However, in many cases these indices seem to be disadvantageous for other assessments (Warwick and Clarke, 1995, 1998; Clarke and Warwick, 2001a; Warwick et al., 2002). Since taxonomic diversity indices were first introduced to measure species richness and to assess the role of environmental influences on diversity (Warwick and Clarke, 1995, 1998), these indices have been used widely to assess changes in terrestrial (Euler and Svensson, 2001; Baños-Picón et al., 2009) and aquatic systems (Hall and Greenstreet, 1998; Bates et al., 2005; Mouillot et al., 2005; Prato et al., 2009) in relation to environmental degradation, stress or climate variability. Environmental changes due to pollution or other factors (e.g., eutrophication, temperature, salinity, and acidity variations) would cause habitat changes and consequently changes in species composition, functional groups (Cloern, 1996; Richardson, 1997; Le Quéré et al., 2005; Breton et al., 2006; Follows et al., 2007; Bouvy et al., 2010) and food webs through bottom-up and top-down controls (Chesson, 2000;

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Barton et al., 2010; Prowe et al., 2012). As a result, scales of change in habitats might determine differences in species richness, traditional and taxonomic diversities of ecosystem in time and space (Warwick and Clarke, 1998; Leira et al., 2009; Salmaso et al., 2012). Studies of taxonomic diversity have been conducted in various communities, including those dominated by nematodes (Warwick and Clarke, 1998), macro-benthos (Warwick et al., 2002), macrophytes (Mouillot et al., 2005), periphytic diatoms (Izsak et al., 2002), benthic diatoms (Petrov et al., 2010), seaweeds (Bates et al., 2005), zooplankton (Badosa et al., 2007), and fish (Hall and Greenstreet, 1998). However, taxonomic diversity of phytoplankton has been less studied in temperate and tropical regions despite its importance role in marine ecosystems. Studies of the impacts of anthropogenic activities and climate variability on marine organisms in coastal waters of Viet Nam have received increasing attention in recent years. One reason is that Viet Nam is expected to be among the most impacted countries due to climate change. However, many studies lack the essential systematisation to quantify such changes. When assessing environmental impacts arising from infrastructure development within the Mekong delta, Hashimoto (2001) reported that assessments are ‘‘often hampered by the lack of adequate baseline and postimplementation monitoring”. South centre coast of Viet Nam is influenced strongly by ENSO. During the normal ENSO years, coastal upwelling appeared during southwest monsoon months (e.g. from May to September) supplying nutrients for phytoplankton growth and sometime causing algal blooms (Doan-Nhu et al., 2010). In El Niño years, the monsoon and coastal upwelling are weakened, sea surface temperatures are high and rainfall is low in the South Viet Nam. In La Niña years, however, strong trade wind causes high rainfall over the South Viet Nam. In 1996, marine environmental monitoring programs in Viet Nam began to include surveys in some coastal and offshore areas. Even though the sampling frequencies were low, these data probably cover the longest period in Viet Nam. This study uses long-term data on phytoplankton and selected environmental factors at three different locations to assess impacts of human activities and climate variations on the diversity of phytoplankton. We aimed to understand the following: (1) What are the primary scales of changes in phytoplankton species composition, structure, and traditional and taxonomic diversity indices? (2) How significant are the environmental changes and/or climate variability on phytoplankton diversity? and (3) Can this record be useful for analysing future impacts of anthropogenic and climate changes?

2. Materials and methods 2.1. Sampling location Nha Trang Bay is located in Khanh Hoa Province, south central Viet Nam (Fig. 1). Its area is about 250 km2 with a number of islands in the west–southwest. The Cai River and Cua Be (Dong Bo) River both feed into the Bay (Table 1). Average depth is 17 m, and the 50 and 100 m isobaths are about 13.5 and 24.5 km from shoreline, respectively, making the residence time of the bay short. Our monitoring station is near Chut Cape. Phan Thiet Bay is located in Binh Thuan Province (Fig. 1), about 250 km south of Nha Trang. The bay has freshwater input from two Ca Ty and Cai (Phan Thiet) Rivers. The depth of Phan Thiet bay is about 12 m. The sampling station is near Ca Ty river mouth. Phu Qui is an island in Binh Thuan Province, located 100 km southwest of Phan Thiet. The monitoring station in Phu Qui (Fig. 1) is at the south west of the island, 32 m deep.

The monitoring stations were selected based on several criteria: Nha Trang Bay has impacts from urban development, transportation, mariculture, fisheries and seafood industries; Phan Thiet Bay has impacts from substantial urban development, fisheries and seafood industry, and Phu Qui station is considered as an open waters station. Risk quotient calculated in 5-year average (2001– 2006) indicated pollution of nitrate and coliform bacteria in Nha Trang and Phan Thiet Bays, while Phu Qui remaining un-polluted (La, 2007). Sampling was initiated in 1996 in Nha Trang and Phu Qui and 2001 in Phan Thiet. 2.2. Sampling Sampling time, Oceanic Niño Index (ONI) index and rainfall in Nha Trang station were plotted in Fig. 2. Qualitative phytoplankton samples were collected using a conical, 20 lm net, hauling vertically from near bottom to the surface. Quantitative samples were taken from Niskin bottles at surface and near bottom at high and low tides in Nha Trang and Phan Thiet, and at surface, 10 m, 20 m and near bottom (ca. 32 m) in Phu Qui. Processing and analysis of samples followed standard methods (Sournia, 1978). Phytoplankton identification followed Taylor (1976), Round et al. (1990), Truong (1993), Tomas (1999), and Larsen and Nguyen-Ngoc (2004). Taxonomic analysis was performed by the same individuals with 5–15 years of experience in phytoplankton species identification in Viet Nam. Moreover, as a data quality control requirement of the monitoring programme, each sample was cross-checked between researchers and approved by a senior expert. Validation of scientific names followed Tomas (1999) and Guiry and Guiry (2015). Only taxa below class levels were used. Environmental data were provided by the Viet Nam environmental monitoring project (southern part). Nutrients were analysed using standard methods (APHA, 2005). 2.3. Data treatment Data were collected on phytoplankton abundance (cell-count) and presence (only found in qualitative samples) to species level at both high and low tides and different water layers (e.g., surface and bottom layers). Data of 399 phytoplankton samples were used including 176, 120 and 103 for Nha Trang Bay, Phan Thiet Bay and Phu Qui respectively. In Nha Trang and Phan Thiet Bay, sampling frequency was every three months during 1998–2004 and in 2010, and twice a year during 2005–2013. Data in Phan Thiet Bay was since 2001. At near Phu Qui Island, sampling frequency was twice a year during 1998–2008 and one in a year during 2009–2013 with missing data in 2012. The data in groups (e.g., diatoms Chaetoceros spp.) were not included in diversity calculations. Averages of high and/low tides abundances and/or different sampling depths was used to generate a month–year sampling data set. Yearly data was an average of all sampling in a year. The data, including presence data, were analysed using Primer 6.0 (Primer-E Ltd.) to calculate all diversity indices (Table 2), including three categories: (1) species richness: species number (S), Margalef’s species richness (d, Margalef, 1958); (2) non-parametric diversity indices (= traditional diversity indices): Brillouin (Pielou, 1969), Shannon diversity index H0 (Shannon, 1948), and Simpson diversity index (1  k0 ) (Simpson, 1949; Clarke and Warwick, 2001a,b); and (3) Taxonomic diversity indices (Clarke and Warwick, 2001a,b): taxonomic diversity (D), taxonomic distinctness (D⁄), average taxonomic distinctness (D+), total taxonomic distinctness (sD+), variation in taxonomic distinctness (K+),

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Fig. 1. Map showing Viet Nam and the sampling sites.

Table 1 Characteristics of three study locations (Khanh Hoa Statistics office, 2013; Binh Thuan Statistical Yearbook, 2012). ND = no data. Location

River

City/district Catchment area (km2)

Mean discharge (m3/s)

Climate Population

Waste water treatment

T(air) (°C)

Rainy season

Dry season

Nha Trang

Cai (NT) Quan Truong

2988 ca. 50

876 ND

Nha Trang city

392,279

None

26.9 (23.4–29.7)

September–December

January–August

Phan Thiet

Ca Ty Cai (PT)

775 800

9.9 9.2

Phan Thiet city

217,588

None

27.1 (24.4–29.9)

May–October

November–April

26,107

None

27.1 (24.4–29.9)

May–October

November–April

Phu Qui

Phu Qui

average phylogenetic diversity (U+) – averaged over number of species in sample, and total phylogenetic diversity (sU+). Statistical analyses were performed on non-parametric (for abundance data) and parametric (for secondary data, e.g. diversity indices) data sets. T-tests were used for comparing averages of

indices. For taxonomic diversity indices, step-length = 1 was used for weighting distances between hierarchical taxonomic levels (Clarke and Warwick, 1999, 2001b). All parametric analyses were performed from fourth-root transformed data using either Primer 6.0 or JMP 10 (SAS Institute Inc.). Similarity percentages (SIMPER, Primer 6.0) was performed on transformed abundance data with

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Fig. 2. Variation of ONI index (bars) with El Nino (positive values) and La Nina (negative values) episodes, monthly precipitation at Nha Trang station (shade area) and sampling time (dots).

one-way analysis using Bray-Curtis similarity matrix. TAXDTEST (Primer 6) using 1000 simulation subsamples for expected D+ from master list of phytoplankton in the study waters were performed to evaluate both single event (e.g. El Niño versus normal years) and/or to build 95% probability intervals for a ranges of sublist sizes (species number m = 10, 15, 20, . . .) to review real D+ values against species number (Clarke and Warwick, 2001a,b). The Oceanic Niño Index (ONI) is obtained from NOAA Center for Weather and Climate Prediction. The values for El Niño (positive) and La Niña (negative) were defined in three categories: weak (0.5–1), moderate (1–1.5) and strong (>1.5). To test the effects of ENSO (ONI index) on average taxonomic distinctness (D+), fit model of second degree polynomial regression was applied using JMP 10. Test on recovery of diversity of phytoplankton between two strong ENSO events in Phu Qui were performed using linear regression.

3. Results 3.1. Species composition A total of 419 phytoplankton taxa were recorded in seven groups: diatoms, dinoflagellates, chlorophytes, cyanobacteria, dictyochales, haptophytes, and euglenoids. Diatoms and dinoflagellates dominated in all locations (Table 3) with 271 and 134 species, respectively. The ratio of diatoms/dinoflagellates was about 2, but varied among locations. Phan Thiet and Phu Qui had ratios >2 (Table 3). Base on major dissolved nutrient data, Phan Thiet Bay was the most anthropogenically impacted site with more than 50% of sampling time had concentrations of PO4 and NO3 exceeding the ASEAN standard criteria for coastal waters (ASEAN, 2008). Milder and almost no anthropogenic impacts were found in Nha Trang Bay and near Phu Qui Island, respectively (Table 3).

SIMPER analysis for domination showed different responses of phytoplankton community structure to the rain fall in Nha Trang Bay. During months with low and no rain, few species (4) dominated up to 60% of the total phytoplankton abundance. However, in months with high rain, only diatoms Chaetoceros spp. contributed up to 60% of the abundance. Numbers of species/group species contributing to 60% of cell densities in El Niño, normal (no ENSO) and La Niña years were 4, 5 and 1, respectively. At Phan Thiet, the opposite trend was observed. Only diatoms Chaetoceros spp. and Thalassionema frauenfeldii dominated up to 60% of the total phytoplankton abundance in dry months while there were 6 dominant species/group species in the rainy months. However, the phytoplankton community structure varied among the rainy months with only 9% of similarity. In the dry months this community similarity was 21%. Number of dominated species was higher in ENSO years (5–6) than in normal years (1). In Phu Qui, both Chaetoceros spp. and Thalassionema frauenfeldii dominated over the entire study period. Chaetoceros spp. contributed 26.0%, 47.9%, and 40.4% and T. frauenfeldii provided 16.4%, 9.5%, 17.1% during El Niño, normal and La Niña years respectively. The El Niño years, similar to Phan Thiet, also had more dominant species. 3.2. Phytoplankton diversity Some characteristics of phytoplankton communities at the three locations are presented in Table 4. Monthly average of species numbers varied between 74 and 94 and was highest in Nha Trang (4 samples per month). However, yearly average of species number varied between 120 and 150. The traditional indices including Brillouin, H0 (ln) and 1  k0 indicated the highest diversity in Phu Qui, and no significant difference between Phan Thiet and Nha Trang. The taxonomic indices (sD+, K+, U+, and sU+), however, indicated highest diversity in Nha Trang as well as distinct

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H. Doan-Nhu et al. / Progress in Oceanography 140 (2016) 1–13 Table 2 Description of diversity indices. Groups of indices Species richness indices 1 Species number (S) 2 Margalef’s species richness (d) Non-parametric diversity indices 3 Shannon (H0 )

Description

References

Number of species in each sample Simplest measure of biodiversity; number of species in a given area; strongly dependent on sampling size and effort

Margalef (1958)

Assumption that individuals are randomly sampled from an infinitely large community, and that all species are represented in the sample Similar to H0 but where the randomness cannot be guaranteed Probability that any two individuals drawn at random from an infinitely large community belong to the same species; transformation of true Simpson diversity of order 2

5 Brillouin 5 Simpson diversity index (1  k0 ) = Gini–Simpson index Taxonomic diversity indices 6 Taxonomic diversity (D) 7

8

9 10 11 12

Simply the average path length between every pair of individuals in a sample; quantitative; independent of sample size, i.e. species number Remove some dependence of D on the species abundance distribution; more nearly a Taxonomic distinctness (D⁄) function of pure taxonomic relatedness of individuals; quantitative; independent of sample size + Average taxonomic distinctness (D ) Presence/absence species data; qualitative; independent of sample size and mean value on sampling effort; better use for comparison of historical data (e.g. when only a list of species was available) Total taxonomic distinctness (sD+) Total of D+, increasing at every species added Variation in taxonomic distinctness (K+) Variance of taxonomic distances between each pair of species about their mean value D+ Average phylogenetic diversity, PD (U+) Average amount of PD (branch length) contributed by a randomly chosen species to the total PD (sU+); negative correlation with species richness Total branch length in the taxonomic tree, increasing at every species added. Total phylogenetic diversity (sU+) Correlated to species number

Shannon (1948) Pielou (1969) Simpson (1949) and Clarke and Warwick (2001a,b)

Clarke and Warwick (2001a,b) Clarke and Warwick (2001a,b)

Clarke and Warwick (2001a,b)

Clarke and Warwick (2001a,b) Clarke and Warwick (2001a,b) Clarke and Warwick (2001a,b) Clarke and Warwick (2001a,b)

Table 3 Numbers of phytoplankton species and ratios of different groups, and number of surface water samples (per total surface water samples) exceeding the ASEAN standard criteria for coastal waters, at each station during 1998–2014. Numbers in parentheses are concentration range (lg L1). Groups

All locations

Nha Trang

Phan Thiet

Phu Quy

All phytoplankton Dinoflagellates Diatoms Centric diatoms Pennate diatoms Cyanobacteria Dictyochales Chlorophytes Haptophytes Euglenoids Diatoms/dinoflagellates Centric/Pennate diatoms No. samples exceed ASIAN criteria standard PO4 (>15 lg L1) NO3 (>60 lg L1)

419 134

361 119

266 72

305 92

193 78 4 2 6 1 1 2.0 2.5

166 64 4 2 5 0 1 1.9 2.6

140 45 3 2 3 1 0 2.6 3.1

147 60 3 2 1 0 0 2.3 2.5

14/98 (15.2–24.5) 22/98 (26–213)

25/60 (15.5–65.6) 36/60 (61–432)

2/35 (15.8–21.9) 4/35 (65–95)

Table 4 Monthly average values of phytoplankton diversity indices in three locations.

Phu Qui Nha Trang PhanThiet P(pq-nt) P(pq-pt) P(pt-nt)

S

d

J0

Brillouin

H0 (ln)

1  k0

D

D⁄

D+

sD+

K+

U+

sU+

80 93 75

9.2 10.1 6.9 ns

0.58 0.49 0.44

2.47 2.16 1.88

2.55 2.24 1.92

0.82 0.72 0.66

**

*

**

**

**

***

***

***

***

**

ns

447 468 407 ns ns

2999 3353 2935

***

ns

ns

***

***

ns

ns

ns

ns

ns

81.2 81.6 81.9 ns ns ns

38.4 36.4 39.6

ns

67.9 69.9 69.2 Ns Ns Ns

6495 7642 6173

**

56.52 50.77 46.29 ns

***

***

***

***

**

**

P, probability of t-test between two means indices at study sites. Ns = not significant; Bold numbers indicate the highest/lowest; pq = near Phu Qui island, pt = Phan Thiet Bay, nt = Nha Trang Bay. * P < 0.05, one tailed test. ** P < 0.05, two tailed tests. *** P < 0.001.

differences among the three locations. The monthly taxonomic diversity (D) showed significant differences (P < 0.05) between Phu Qui (58) and Phan Thiet (46.9), but the same taxonomic distinctness (D⁄ = 69.8–72.1) was found among all study locations (Table 4).

Variations in H0 , 1  k0 , and D indices were different among study sites with no clear trend (Fig. 3, 1  k0 and D are not shown). In Phu Qui, interannual variabilities of these indices were low compared to Nha Trang and Phan Thiet (Fig. 3 for H0 ). Taxonomic

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Fig. 3. Variation of diversity indices during 1998–2013. Dash lines are trend line analysis with probability of fitness (p) and R-squared correlation coefficients (R2) of linear regressions.

indices D+ and K+ showed no particular trend through time except in Phu Qui, where D+ significantly increased. However, the three indices D⁄, D+, and K+ were well reflected in particular periods associated with ENSO event (1998–2000 and 2010, Figs. 3 and 4). In Nha Trang Bay, D+ was well correlated with ONI index (Fig. 5).

Average taxonomic distinctness (D+) of the monthly average data plotted against species numbers is shown in Fig. 6. Those values of D+ below the average dash line and outside the continuous lines 95% probability limits for a single value indicated that they were not as expected for the whole communities in the study area.

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Fig. 4. Temporal trend analysis of average taxonomic distinctness (D+) and variations (K+) during 2002 and 2008 after disturbance by the 1998–2000 El Niño and La Niña events (dashed lines are linear regressions, p = 0.0025 and 0.0001 for D+ and K+, respectively).

Fig. 5. Correlation between average taxonomic distinctness index (D+) of phytoplankton in Nha Trang Bay and ONI index from covariant analysis. Solid circle is individual D+ value with corresponding ONI, solid line is regression line (p = 0.0035, ANOVA).

There was indication of stronger environmental impacts (including ENSO effect) on phytoplankton in Phan Thiet than in Phu Qui and Nha Trang. Low values of D+ were recorded in Phan Thiet even during a normal year. 41.2% of D+ values of normal years in Phan Thiet Bay lie below the 95% simulated D+ of all plankton communities compare to 34.8% in Nha Trang Bay and 36.4% in Phu Qui Island. In Nha Trang and Phu Qui, years with La Niña events had a stronger effect on phytoplankton diversity compared to both normal and El Niño years at each site (as K+ values were higher, Fig. 6). The observed average taxonomic distinctness (D+) was plotted over a simulated histogram of D+ to check for variations among locations within year 2006 (normal year) and 2010 (La Niña year) (Fig. 7). In 2006, 26 species were different between the two sites, both observed average taxonomic distinctness (D+) of phytoplankton in Nha Trang and Phu Qui were high and close to the simulated mean. However, much lower D+ compared to the simulated mean was observed in Phan Thiet. In 2010 both Phu Qui and Phan Thiet Bays showed significant reductions of taxonomic distinctness, with the lowest value in Phan Thiet. There was a slight increase in K+

and a decrease in D+ in a La Niña year (2010) compared to normal year (2006) in Phan Thiet Bay (Fig. 7). In Phu Qui a strong influence of ENSO was observed, especially during La Niña events (Fig. 7). The La Niña events were correlated with high rain fall in south central Viet Nam. Average taxonomic distinctness (D+) and monthly rainfall were negatively correlated (R2 = 0.22, p = 0.025). The amount of monthly rain was positively correlated to centric/pennate diatoms ratio (R2 = 0.43, p = 0.0004). Average taxonomic distinctness (D+) plotted against K+ at three locations over 999 simulations of m species (indicated by contours) is presented in Fig. 8. Month/year variations were observed that appeared to be linked with the ONI index. In Phan Thiet (Fig. 8c) D+ values were almost outside of the 95% contour of m = 200 species in all El Niño, La Niña and normal years. In Phu Qui (Fig. 8b), the low values of D+ and K+ were mainly during La Niña. In Nha Trang (Fig. 8a), both periods with strong La Niña and El Niño caused low diversity. Almost 32% of samples were within the contour line of m = 200 at 95% confidence level.

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Fig. 6. Average taxonomic distinctness of phytoplankton from 1998 to 2014, in different years with and without ENSO events, plotted against the species richness. The expected taxonomic distinctness (dashed line) and the 95% probability limits for a single value (continuous lines) in random subsamples of the 419 phytoplankton species included in the study. The outliers were labelled with specific time (month/year) corresponding to monthly ONI index indicating El Nino (E), La Nina (L), and normal (N) months.

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Fig. 7. Histograms of simulated D+, from 1000 sublists drawn randomly from a list of 419 phytoplankton species. Sublist sizes of normal year (2006, left column) and La Niña year (2010, right column) in Nha Trang Bay (top graphs), Phu Qui (middle graphs) and Phan Thiet Bay (bottom graphs). Measured species number (S), average taxonomic distinctness (D+) and average taxonomic variation (K+) at each location/sampling month–year are listed at each graph.

4. Discussion 4.1. Species richness The species number recorded during the last two decades at the three locations studied was 419. The species number is similar to other reports from Viet Nam, Nguyen-Van et al. (1995), Nguyen and Vu (2000), and Boonyapiwat (2000a), who found 342, 508

and 357 species, respectively. However, the latter two publications may be inaccurate because of imprecise species identifications. For example, the list of Chaetoceros species provided by Nguyen and Vu (2000), and Boonyapiwat (1999, 2000a, 2000b) should have removed at least two species (e.g., Chaetoceros laevis Leuduger – Fortmorel, and Chaetoceros weissflogii Schutt; Doan-Nhu et al., 2014). The species list provided by Nguyen and Vu (2000) also had other examples of overlapping species identifications. Recent

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Fig. 8. Average taxonomic distinctness (D+) and average taxonomic variation (K+) at Nha Trang (a), Phu Qui (b) and Phan Thiet (c) with indication of ENSO scales (ONI index). The 95% probability contours are shown for m species (100, 150 and 200 species) of 999 simulated from list of 419 species of the study waters.

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publications (Nguyen-Chi et al., 2011; Voss et al., 2013) reported 241 species in Vung Tau (data of 10 years) and 334 species in Mekong estuaries. Nha Trang Bay had the highest species number (361), whereas Phan Thiet and Phu Quy hosted 266 and 305 species, respectively. The yearly average species number of Nha Trang bay ranged from 119 to 177 (ca. 10 samples per year), slightly higher than the 115 species (13 samples) reported in Casirugan Bay, Philippines in 2013 (Angara et al., 2013) or the 121 species (16 samples) in coastal waters of Malaysia in 2012 (Mohammad-Noor et al., 2012). Variations in species number depend on both number of investigated samples and accuracy of species identification. Nha Trang Bay is located near one of the most rapidly developing cities in Viet Nam, Nha Trang. The risk quotient calculation for Nha Trang Bay suggests pollution with nitrate in dry seasons, and coliform bacteria in all seasons (La, 2007). Nha Trang’s population is ca. 392,000, but more than 3,000,000 tourists visited in 2013 (Khanh Hoa statistics office, 2013). This anthropogenic influence is reflected in estuarine phosphate concentration, which increased from about 0.4 to 0.8 lM in 10 years (2001–2011). More recent studies have confirmed high concentrations of ammonium (2070 lg/L) and coliform bacteria (46  105 MPN/100 mL) (Le et al., 2007). These high ammonium and coliform concentrations indicate that domestic waste was the pollution source. However, the nutrient concentrations in the bay, especially at the monitoring station, do not have a high risk of eutrophication or bacterial contamination due to extensive tidal flushing (Bui, 2002). The monitoring station near Cape Chut is influenced by water from the south part of the bay during high tide, and the open waters and Cai River during low tide (Bui, 2002). As such, the high material loading from the river is quickly diluted. This would also explain why Nha Trang Bay has the highest number of species among the study sites and that the species composition of the bay is mixed among riverine, coastal and oceanic communities. 4.2. Other factors influencing phytoplankton diversity In our present research, the phytoplankton biodiversity data were not aimed to quantify natural and anthropogenic effects, but rather to investigate where and when each environmental impact has become dominant relative to others. In Phan Thiet Bay phytoplankton community structure was correlated with rain, being markedly influenced in 2003 and slightly less in 2010 and 2002. While 2003 was normal year, the 2010 and 2002 were La Niña years. In Binh Thuan Province aquaculture activities (mainly shrimp pond culture), especially at the beginning of the season when soil was overturned, could lead to a massive release of suspended matter and nutrients to the adjacent waters (Le et al., 2012). Thus would have strong effect to the plankton communities. In both cases, the effect of climate (e.g., rain in normal and in La Niña years) would enhance material loads to coastal waters and thus causes changes in phytoplankton. The phytoplankton communities in Phu Qui coast have clear changes between months, with rainfall being responsible for about 60% of the observed differences. The Island is located 100 km from mainland, so nutrient enrichment would originate from the land itself. The major use of this island is agriculture, which would explain the differences in phytoplankton between dry and wet seasons. Evaluation impacts of environmental stresses on phytoplankton was often based on analysis of both community structures (e.g. Rimet, 2012) and diversity indices (e.g. David et al., 2012). Traditionally, Shannon index was the most widely used. The Shannon index calculation is based on both species presence and abundance at a site and the balance between them (Shannon, 1948). The more domination in communities, the lower Shannon index will

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recorded. However, the more resent taxonomic diversity indices were introduced and increasing applied in assessment of biodiversity (Warwick and Clarke, 1995; Desrochers and Anand, 2004). These indices (D, D⁄, D+, K+, and U+) are not only calculated basing on species presence/absence and abundance but also using distances between them at different taxonomical ranks (species, genus, family, order, class, and phylum). For example, a site with A species from the same genus is less diverse than other site with same A species number but from many different genera. With common assumption that closely related species would have more of less similar niches and that would reflect poorer diversity in niches and/or more stressful environmental condition, these taxonomic indices were expected to explained better effect of changes in environment to biodiversity. At all sites ENSO events influenced phytoplankton diversity. The traditional diversity indices (H0 and 1  k0 ) were low during years with El Niño (1998) and La Niña (2010), and these effects were more pronounced in Nha Trang Bay. Previously, these indices were also remarkably low during 1998, 2001–2002, 2008–2009 and high during 2005–2007 in northern Adriatic Sea (Cabrini et al., 2012). However, despite clear differences in D+ and K+, these traditional diversity indices and few other taxonomic indices (e.g., D) poorly discriminated the changes of composition and the influence of environmental impacts. Among the taxonomic indices, D+ and K+ are widely used, but mostly applied to benthic fauna. There are different opinions of using, applying and interpreting these indices. Analysis of environmental disturbances on marine benthic communities showed a clear and strong indication of both D+ and K+ from degraded to pristine locations (Clark and Warwick, 2001a,b; Warwick, 2008). However, applying same analysis for Mediterranean nematodes, no distinction could be discerned between perturbed and unperturbed sites (Bevilacquaa et al., 2012). Abellán et al. (2011) also found no correlation between three indices (D+, K+, and sD+) and anthropogenic impacts on aquatic beetles from the Iberian Peninsula. It was suggested that there were possible dependences of taxonomic distinctness on both phylogenetic structure of sampled communities and their evolutionary and ecological history. Previously, Marchant (2007) has shown the sensitivity of D+ and its ability to distinguish sites with different degrees of environmental disturbance. He also argued that studies in river systems would not find best responses of D+ to environmental disturbances because of either too few taxa or higher taxonomic levels being used. Similarly, Schweiger et al. (2008) conducted a test of different taxonomic/phylogenic diversity indices based on simulation of 38,000 artificial communities at different degree of diversity. Ten indices, computing in three categories, were compared using topological based discriminations and two types used distance based (minimum spanning path and pairwise) analyses. The authors recommended both D+ and sD+ together with species richness. However, while the D+ was used to compare distances (temporal and spatial) of communities with strong variations of species diversity, the sD+ was best in discriminating differences among interdependent communities that were controlled by species extinction and introduction. Similarly, our study revealed that D+ and K+ are the best indices indicating of environmental stress from both climate variation and anthropogenic impacts, e.g. better indication in time point analysis for stresses, or in evaluation of diversity recovery after a disturbance (Figs. 4–8, and see details in 3 following paragraphs). From our results, comparisons (e.g., statistics tests) among studied sites for the entire sampling period were not as definitive as other investigators have reported (e.g., Abellán et al., 2011; Bevilacquaa et al., 2012). However, the historical data from different sites that include different impacts (e.g., climate variation, anthropogenic, upwelling, etc.) and/or with opposite effects

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(e.g., rain in eutrophic vs. oligotrophic waters) would affect the test results. In the present study, despite some insignificant values among sites (e.g., in Table 4), the effects of climate variation and environmental pressures were observed. A 2 degree of correlation of D+ and ONI index, which indicates ENSO events, was found in Nha Trang Bay (Fig. 5, p = 0.0035, ANOVA). The regression line has downward parabolic shape with both La Niña (towards negative values) and El Niño (towards positive values) caused decreases in taxonomic distinctness in the phytoplankton. The highest value of diversity D+ is close to 0 of ONI index meaning normal years should have high diversity of phytoplankton. However, the anthropogenic impacts could not be separated from the ENSO impact. Comparison with normal year and current environmental data suggests that the anthropogenic impacts on phytoplankton in Nha Trang Bay were small, and La Niña impacts were stronger than those of El Niño due to the larger river discharge (Table 1). Further analysis of the trends of D+ and K+ with ENSO events at Phu Qui Island revealed a clear pattern of phytoplankton diversity that recovered after a disturbance. There were increases in the average taxonomic distinctness (D+) and decreases in variation (K+) indices during 2002–2008 after disturbance caused by the 1998–2000 El Niño and La Niña events (Fig. 4). In the same periods, however, the Shannon index (H0 ) decreased (R2 = 0.19) and the Simpson index (1-Lambda) had no temporal trend. We found that both D+ and K+ were useful indicators in different permutation tests. Comparison among the three study sites for effects of ENSO events with simulations of the whole phytoplankton species (419 species) showed a gradient of ENSO impacts from Nha Trang Bay to Phu Qui Island and Phan Thiet Bay (Fig. 6). The same trend was shown in the plots of D+ and K+ in a simulation of the 419 species contour lines (100, 150 and 200 species, Fig. 7). There were fewer samples within the contour of 100 species in Phan Thiet, implying heavier impacts from both ENSO events and other environmental stresses in this site compared to the other sites. Test on a single event using D+ and K+ showed a negative impact relative to a La Niña (year 2010) and a normal year (2007) in Nha Trang Bay and Phu Qui (Fig. 7). In Phan Thiet, a La Niña impacts were even stronger on D+ compared to a normal year. This indicates that during a La Niña, D+ decreased and cocontributed a negative impact with the other possible negative impacts. In general, the average taxonomic distinctness (D+) and average taxonomic variation (K+) indices of tropical coastal phytoplankton are good indicators for assessing the impacts of an ENSO event from either a single event or long-term data. These indices could also be used for prediction as in the case of Nha Trang Bay. In this regard, long-term data which include ENSO episodes are essential, but it also requires expertise in analysing time series data, especially taxonomical data. At the moment, such an assessment is difficult and has not yet been evaluated elsewhere. We also found that, as mentioned earlier by Bevilacqua and Terlizzi (2014), good approaches to quantifying biodiversity should rely on excellent taxonomy. Acknowledgments This work is funded by Viet Nam’s NAFOSTED Project No. 106.13-2011.16. Part of the data were obtained from environmental monitoring program for southern Viet Nam (Institute of Oceanography). We thank Prof. Walker O. Smith, King’s Professor at Gothenburg University (Sweden) and VIMS (USA), and Prof. Kam Tang at Swansea University (UK) for critical comments and language editing the manuscripts.

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