Botanical Studies (2007) 48: 419-433.

*

Corresponding author: E-mail: tmlee@mail.nsysu.edu.tw.

INTRODUCTION

Coral reefs are the most diverse marine ecosystems

with the highest productivity on earth. Macroalgae, one

of the components of the coral reef ecosystem, are usually

inconspicuous on well developed reefs where nutrient

concentrations are low and grazing pressure is high. In

the past few years, several lines of evidence have shown

that many coral reefs in tropical coastal waters of the

western and central Pacific Ocean, the Indian Ocean, and

the western Atlantic Ocean have undergone shifts from

coral to macroalgal dominance (Littler et al., 1992; Naim,

1993; Hughes, 1994; Lapointe, 1997). The shift of coral

reefs to algal domination causes a dramatic decline in

the reef ecosystem¡¦s biodiversity (Hughes, 1994; Andres

and Witman, 1995). Thus, understanding the macroalgal

abundance and the factors influencing species structure

is an important aspect of the ecological, environmental,

aesthetic, and socio-economic value of reefs.

Nutrients, temperature, and salinity as primary factors

influencing the temporal dynamics of macroalgal

abundance and assemblage structure on a reef of Du-

Lang Bay in Taitung in southeastern Taiwan

I-Chi CHUNG

1

, Ray-Lien HWANG

1

, Sin-Haw LIN

1

, Tzure-Meng WU

1

, Jing-Ying WU

1

, Shih-Wei

SU

1

, Chung-Sin CHEN

2

, and Tse-Min LEE

1,

*

1

Institute of Marine Biology, National Sun Yat-sen University, Kaohsiung, Taiwan

2

Department of Aquaculture, National Taiwan Ocean University, Keelung, Taiwan

(Received May 8, 2006; Accepted April 24, 2007)

ABSTRACT.

Temporal dynamics (2001-2003) of macroalgal abundance and assemblage structure in

relation to environmental variables were studied on a reef in Du-Lang Bay in southeastern Taiwan. Sixty-six

species were identified, with rhodophytes as the abundant species. Both the areal wet weight and areal dry

weight biomass of total macroalgae increased as time advanced and reached the maximum in the winter of

2003 mainly due to the blooms of Gracilaria coronopifolia and Ceratodictyon/Haliclona, a red alga-sponge

symbiose. Macroalgal cover varied temporally, % cover in 2001 and 2002 was low in spring but high in

summer while that in 2003 was high in winter, spring, and summer and low in autumn. Species richness (species

number), diversity (H¡¦) and evenness (J¡¦) increased, peaked in the winter in 2001, stabilized in 2002, and then

decreased in 2003. The data of hierarchical cluster analysis and non-metric multidimensional scaling ordination

of species similarities between different sampling times and the results of an analysis of similarity (ANOSIM)

showed that the macroalgal assemblage is structured primarily by year and secondarily by season. Although

H¡¦ and J¡¦ showed fewer changes, the k-dominance curve and a decrease in species number as time advanced

suggest a switch of species structure from a highly diversified community to a less diversified one. The

similarity percentage breakdown procedure (SIMPER) analysis shows that G. coronopifolia and Ceratodictyon/

Haliclona are the species contributing to year-over-year and seasonal differences in species structure. The

comparison of macroalgal compositions with environmental variables indicates that decreasing soluble-reactive

phosphorus (SRP) concentrations and increasing salinity are the best combination of environment variables to

explain the yearly changes in algal compositions. Seasonal variations in species structure were associated with

temporal variations in temperature, precipitation, salinity, and NH

4

+

. In conclusion, the nearshore macroalgal

assemblage in Du-Lang Bay in Taitung in southeastern Taiwan during 2001-2003 became less diversified over

time; the structure is modified yearly by increased nitrogen/phosphorus levels, and salinity and is also affected

seasonally by fluctuating temperature and precipitation.

Keywords: Assemblage; Macroalgae; Nutrient; Salinity; Temperature; Temporal variation.

Abbreviation: ANOSIM, analysis of similarity; DIN, dissolved inorganic nitrogen; d. wt., dry weight;

MDS, multidimensional scaling; SIMPER, similarity percentage breakdown procedure; SRP, soluble reactive

phosphorus; w. wt, wet weight.

phySIOLOgy

420

Botanical Studies, Vol. 48, 2007

Nutrient enrichment is considered to be a factor behind

macroalgal blooms. In the mid 1970s, several studies

on coral reefs in Hawaii¡¦s Kaneohe Bay revealed the

impact of anthropogenic nutrient inputs on the bloom

o f Dictyosphaeria cavernosa (Banner, 1974; Smith

et al., 1981). After that, the effects of anthropogenic

nutrient enrichment on macroalgal blooms have been

studied worldwide, for example, in the coastal waters of

Reunion Island in the Indian Ocean (Cuet et al., 1988)

and the Caribbean and Florida regions (Lapointe and

O¡¦Connell, 1989; Bell, 1992; Lapointe et al., 1994). The

changes in macroalgal assemblages are considered to be a

consequence of habitat modification mediated by physical

and/or anthropogenic disturbances (Banner, 1974; Smith

et al., 1981). It has been suggested that increasing water

column nutrient concentrations play a role in macroalgal

blooming at Reunion Island (Cuet et al., 1988) and in

the Caribbean and Florida coastal regions (Lapointe and

O¡¦Connell, 1989; Bell, 1992). Apparently, the growth

of macroalgae overwhelms coral after changes driven

by natural disturbance or human activities (McCook,

1999; Schaffelke, 1999; McCook et al., 2001), and highly

abundant macroalgae, especially erect fleshy algae, are a

sign of reef degradation (Done, 1992; Hughes, 1994).

In the past five years, our laboratory has initiated a

series of surveys on temporal and spatial changes in

benthic macroalgal compositions around Taiwan and its

adjunct islands, where anthropogenic activities along

the coastal regions have increased significantly over

the past ten years. We also plan to determine the factors

affecting the macroalgal assemblage structure for the

setup of marine protected area (MPA). Our studies on the

coral reefs in Hengchun Peninsula in southern Taiwan

have recently been published (Tsai et al., 2004; Hwang

et al., 2004). It was found that temperature is a primary

factor restricting macroalgal growth in Taiwan, and the

blooms of macroalgae like Gracilaria coronopifolia,

Laurencia papillosa, and Sargassum spp., in coral reefs

in southern Taiwan are attributable to high nutrient

loading (Hwang et al., 2004). It reflects the impact of

increasing anthropogenic activities on the nearshore

benthic community. Similarly, the nearshore reefs of Du-

Lang Bay in Taitung in southeastern Taiwan (Figure 1)

have faced increasing tourism pressure over the past

ten years. Because macroalgae tend to integrate the

effects of long-term exposure to adverse conditions, the

macroalgal assemblages are widely used to characterize

and monitor benthic communities. A 3-year quantitative

investigation on the influence of natural and anthropogenic

disturbances on macroalgal abundance and species

compositions was conducted on a reef (GPS: 22o03¡¦43¡¦¡¦

N; 121o32¡¦18¡¦¡¦ E) of Du-Lang Bay over 2001-2003. A

non-metric multidimensional scaling (nMDS) method

and analysis of similarity (ANOSIM) were used to

compare the macroalgal assemblage compositions

between sampling times using the Plymouth Routines in

Multivariate Ecological Research (PRIMER) statistical

software package, v. 6 (Clarke and Warwick, 1994). The

comparison of temporal variations in macroalgal structure

and environmental factors by using a "forward selection

backward elimination" algorithm (BVSTEP) was made

to extract the factors showing the best combination of

environmental variables to algal compositions. The

macroalgal species responsible for differences in the

macroalgal assemblage structure between years and

between seasons were identified using the similarity

percentage breakdown procedure (SIMPER).

MATERIALS AND METhODS

Study site and environmental characteristics

The study site has a horizontal width of 652 m with an

intertidal region approximately 20-435 m long, a subtidal

macroalgal region approximately 17-28 m long, and a

depth of 0-18 m (below MHWS) on a seaward gradient.

The 30-year climate records (1971-2000) of Taitung

obtained from the Central Weather Bureau of the Republic

of China show that the mean annual air temperature in

Orchard is 22.6oC; the mean monthly air temperature is

low (18.4oC) in January and high (26.2oC) in July (Figure

2). The annual mean relative humidity is 78% and the

annual cumulative precipitation is 3081.3 mm, which

mostly occurs in May-September. Typhoons usually occur

in May-September, and the prevailing northeasterly winds

occur in November-February.

During the surveys (2001-2003), mean monthly air

temperature was 23.9oC, annual precipitation was 3136.7

mm, and annual cumulative irradiance was 5881.9 MJ/m

2

(Figure 2). Temporal variations in mean monthly air

temperature, mean monthly maximum air temperature,

Figure 1. The map of sampling site.

CHUNG et al. ¡X Salinity, temperature, nutrients, and macroalgal assemblage structure

421

mean monthly minimum air temperature, annual

cumulative precipitation, and annual cumulative irradiance

were significant (Friedman¡¦s test, p < 0.001); no year-to-

year differences were found for these climate parameters,

except that irradiance was highest in 2003. Precipitation

not only showed seasonality with a low value in winter

and high value in spring-autumn, but also showed year-

to-year differences, such as being low in 2002 relative to

2001 and 2003. Four typhoons passed through Taitung in

both 2001 and 2003, but none occurred in 2002.

Estimation of macroalgal cover, biomass and

species composition

To characterize the spatial changes in macroalgal

assemblage compositions, two 10¡Ñ10 m

2

blocks (as the

effect of habitat) with a 10-m interval were set in the

subtidal regions with 1-3 m water depth (MHWS), and at

each block four random stations were set up to estimate

species abundance, in terms of percentage cover, which

was calculated as the sampling surface covered in vertical

projection by the species using a 50¡Ñ50 cm quadrat, and

total macroalgal cover was the sum of all species cover

values. The macroalgal cover in different vegetation layers

(erect layer, encrusting layer and turf) was recorded,

and total macroalgae in each 50¡Ñ50 cm quadrat (there

were four quadrats in each block, with each quadrat

as a replicated sample) were scraped for estimation of

macroalgal compositions and biomass, and the species

were identified using a microscope. Temporal changes in

macroalgal cover and biomass were determined in April,

July, and October of 2001, in February, May, July, and

October of 2002, and in January, May, July, and September

of 2003 for the analysis of both year-to-year and seasonal

(January-February as winter, April-May as spring, July as

summer, and October-September as autumn) changes in

macroalgal assemblage structure and their relationships to

environmental variables.

Determination of turbidity, seawater tempera-

ture, salinity, and nutrient concentrations

Seawater temperature, salinity, and seawater nutrient

concentrations were determined randomly in four sampling

stations for each block. Near-bottom (20 cm above the

bottom) seawater samples were collected at each sampling

station, and one part was immediately subjected to

sedimentation detection while another part was transported

to the laboratory under low temperature within 24 h. These

water samples were stored at -70

o

C until analysis. Before

nutrient determination, frozen samples were thawed on

ice in the dark. The determination of dissolved inorganic

phosphorus (SRP) was modified from the method of

Murphy and Riley (1962). Colour reagent was prepared

by mixing 1 ml of 3% ammonium molybdate solution

and 0.75 ml of 5

n

H

2

SO

4

and after 10 min of incubation

at room temperature, 0.9 ml of 1

m

ascorbic acid (freshly

prepared) and 0.08 ml of 2% potassium antimonyl-tartrate

were added and held at room temperature for a further

10 min. Then, 50

£g

l of colour reagent was added in 0.5

ml of seawater, and after 10 min of incubation at room

temperature, the absorbance was read at 882 nm within

15 min by a Hitach spectrophotometer (model U-2000,

Hitachi, Tokyo, Japan). The detection limit of SRP

concentration was 0.02

£g

m

.

Seawater NO

2

-

and NO

3

-

concentrations were

determined according to Strickland and Parsons (1972).

NH

4

+

concentrations were determined according to

Parsons et al. (1984). The detection limits for seawater

NO

2

-

, NO

3

-

and NH

4

+

concentrations were 0.2, 0.2 and 0.3

£g

m

, respectively. The NO

3

-

, NO

2

-

, and NH

4

+

concentrations

were summed as the concentration of dissolved inorganic

nitrogen (DIN).

Data analysis

Statistical evaluation was performed using SAS

statistical software package v 8.0 (SAS Ltd., NC, USA).

All summary statistics were expressed as mean and

standard deviation (SD). The normality of environmental

factors (mean monthly air temperature, mean monthly

maximum air temperature, mean monthly minimum air

temperature, annual cumulative precipitation, annual

cumulative irradiance, temperature, salinity, and seawater

Figure 2. Climate data from 1971-2000 and during the survey

(2001-2003).

422

Botanical Studies, Vol. 48, 2007

nutrient concentrations) and of biotic variables (total

macroalgal cover, total macroalgal biomass (areal wet

weight and areal dry weight), species number, and areal

wet weight of Ceratodictyon/Haliclona association, and

Gracilaria coronopifolia) was analyzed by the Shapiro-

Wilk W Test (p > 0.05). All parameters, which did not

fit normality after data transformation, were subjected to

non-parametrical analysis by Friedman¡¦s test for two-way

(seasonal and year-to year changes) layout data (Siegel

and Castellan, 1988). Homogeneity of variance was

determined using the F

max

test (Sokal and Rohlf, 1981).

Because all data did not show habitat difference (p > 0.05),

only temporal variations (seasonal and year-to-year) were

tested.

To measure attributes of community structure between

sampling times, several univariate indices¡Xincluding the

number of species, the Shannon-Wiener species diversity

index, H¡¦ (Shannon and Weaver, 1949) (by log e in the

calculation) and evenness, Pielou¡¦s J¡¦ (Pielou, 1975) (by

log e in the calculation)¡Xwere calculated.

A multivariate analysis was done to compare the

macroalgal assemblage compositions between stations

and between seasons using the Plymouth Routines in

Multivariate Ecological Research (PRIMER) statistical

software package (v. 5) (Clarke and Warwick, 1994). For

each sampling time, the average data of eight replicates

(the data collected on each quadrat) was used for analysis.

The similarity matrix of species compositions (areal wet

weight without data transformation) was classified by

hierarchical agglomerative clustering using the unweighted

pair group mean arithmetic (UPGMA) linkage method and

was ordinated using non-metric multidimensional scaling

(nMDS) analysis. Macroalgal assemblages were compared

among stations by means of hierarchical agglomerative

cluster analysis and MDS (Kruskal and Wish, 1978) of

species areal wet weight using the Bray-Curtis similarity

measure (Bray and Curtis, 1957). Diversity profiles

were also drawn using k-dominance curves to extract

information on patterns of relative species abundance

and dominance (Lambshead et al., 1983). The difference

of macroalgal assemblage structure between seasons and

between years was tested using ANOSIM (analysis of

similarity) (Clarke and Warwick, 1994), and the species

mainly responsible for differences between years were

determined by the similarity percentage breakdown

procedure, SIMPER (Clarke, 1993). The "forward

selection backward elimination" algorithm analysis

(BVSTEP) was used to determine the environmental

factors that best explain the observed patterns of

macroalgal assemblage structures.

RESULTS

Environmental factors

During the surveys, mean seawater temperature and

salinity were 27.14 ¡Ó 2.99oC and 31.37 ¡Ó 4.77 psu,

respectively (Figure 3). Seawater temperature showed

marked year-to-year (Friedman¡¦s test, p < 0.0001) (2003 >

2001, 2002) and seasonal (F= 82.00, p < 0.0001) (summer

> spring > autumn > winter) variations and significant

year-to-year and seasonal interaction (F

3,87

= 30.25, p

< 0.0001) (Table 1). Salinity did not show seasonal

variations (Friedman¡¦s test, F

2,87

= 0.77, p = 0.5138)

(summer = autumn > spring > winter) but showed year-to-

year variations (F

6,87

= 69.96, p < 0.0001) (Table 1).

Mean DIN, NO

3

-

, NO

2

-

, NH

4

+

, and SRP concentrations

during the survey were 4.35 ¡Ó 3.52, 2.05 ¡Ó 3.39, 0.06 ¡Ó

0.10, 2.37 ¡Ó 1.88, and 0.47 ¡Ó 0.48

£g

m

, respectively (Figure

3). DIN concentrations showed year-to year (F

2,87

= 3.21,

p = 0.0468) (2002 = 2003 > 2001) and seasonal (F

3,87

=

14.88, p < 0.0001) (winter > summer = spring > autumn)

variations and the year-to year and seasonal interaction

was significant (F

6,87

= 17.07, p < 0.0001) (Table 1).

NO

3

-

concentrations did not show seasonal variations

(Friedman¡¦s test, F

2,87

= 2.26, p = 0.0570) but did show

year-to-year variations (F

2,87

= 15.27, p < 0.0001) and the

year-to-year and seasonal interaction was significant (F

6,87

= 17.67, p < 0.0001). NO

2

-

concentrations showed seasonal

(Friedman¡¦s test, F

3,87

= 9.19, p < 0.0001) and year (F

2,87

= 117.25, p < 0.0001) variations, and the year and season

interaction was significant (F

6,87

= 3.33, p = 0.0096) (Table

1). NH

4

+

concentrations showed both seasonal (F

3,87

=

6.49, p = 0.0006) (spring = summer > winter > autumn)

Figure 3. Variations of seawater temperature, salinity, DIN,

NO

3

-

, NO

2

-

, NH

4

+

, and SRP from 2001-2003. Data are present as

mean ¡Ó SD (n=8).

CHUNG et al. ¡X Salinity, temperature, nutrients, and macroalgal assemblage structure

423

and year (Friedman¡¦s test, F

2,87

= 11.30, p < 0.0001)

(2002 > 2001 = 2003) variations but the year and season

interaction was not significant (F

6,87

= 1.80, p = 0.1241)

(Table 1). SRP concentrations also showed seasonal (F

3,87

= 14.54, p < 0.0001) (summer > autumn > spring > winter)

and year variations (Friedman¡¦s test, F

2,87

= 129.26, p <

0.0001) (2001 > 2002 > 2003) (Table 1), in which SRP

concentrations were ¡Ù 0.6

£g

m

in 2001 and February of

2002, and then decreased gradually as time advanced, even

below the detection limit (0.2

£g

m

) in 2003.

Macroalgal abundance

Sixty-six species were recorded during the surveys: 21

Chlorophyta, 8 Phaeophyta, and 39 Rhodophyta (Table 2).

Because the data did not show habitat difference, the data

of eight sampling stations from two blocks (four random

samples from each block) were pooled and averaged for

analysis to give the overall picture of seasonal changes in

macroalgal abundance (Figure 4). Mean species numbers

per m

2

were affected by season (Friedman¡¦s test, F

3,87

=

11.30, p = 0.0003) (winter > spring > autumn > summer)

and year (F

2,87

= 11.26, p < 0.0001) (2002 > 2001 = 2003),

and the interaction of year and season was significant (F

6,87

= 5.33, p = 0.0004 and Table 3). Mean species numbers

per m

2

were highest in October 2001 mainly due to the

appearance of several chlorophytes and rhodophytes.

During the survey, erect algae were more abundant than

encrusting and turf algae.

Total macroalgal % cover showed seasonal (Friedman¡¦s

test, F

3,87

= 10.35, p < 0.0001) (summer = winter > autumn

> spring) and year (F

2,87

= 4.22, p = 0.0189) (2003 > 2002

> 2001) variations, and the interaction of year and season

was significant (F

6,87

= 4.43, p = 0.0015) (Figure 4 and

Table 3). Total macroalgal wet weight biomass showed

seasonal (Friedman¡¦s test, F

3,87

= 9.48, p < 0.0001) (winter

> summer > spring > autumn) and year (F

2,87

= 19.84,

p < 0.0001) (2003 > 2002 > 2001) variations, and the

interaction of year and season was significant (F

6,87

= 3.34,

p = 0.0093) (Figure 4 and Table 3). Total macroalgal dry

Table 1. Friedman test for environment factors.

F

p Tukey¡¦s test

1

NH

4

+

Season

6.49 0.0006 Spr

2a

=

Sum

a

> Win

ab

> Aut

b

Year

11.3 <0.0001 2002

a

> 2003

b

= 2001

b

Season*year 1.8 0.1241

NO

2

-

Season

9.19 <0.0001 Spr

a

>

Sum

ab

> Win

b

= Aut

b

Year

117.25 <0.0001 2003

a

> 2002

b

= 2001

b

Season*year 3.33 0.0096

NO

3

-

Season

2.63 0.057

Year

15.27 <0.0001 2003

a

> 2001

b

= 2002

b

Season*year 17.67 <0.0001

DIN

Season

14.88 <0.0001 Win

a

>

Sum

b

= Spr

b

> Aut

c

Year

3.21 0.0468 2003

a

= 2002

a

> 2001

b

Season*year 6.73 <0.0001

SRP

Season

14.54 <0.0001 Sum

a

>

Aut

ab

> Spr

bc

> Win

c

Year

129.26 <0.0001 2001

a

> 2002

b

> 2003

c

Season*year 2.17 0.0675

Seawater temperature

Season

82 <0.0001 Sum

a

>

Spr

b

> Aut

c

> Win

d

Year

23.26 <0.0001 2003

a

> 2002

b

= 2001

b

Season*year 30.25 <0.0001

Salinity

Season

0.77 0.5138

Year

69.96 <0.0001 2002

a

= 2003

a

> 2001

b

Season*year 3.45 0.0079

1

Different symbol indicates significance at p < 0.05.

2

Spr, spring; Sum, summer; Aut, autumn; Win, winter.

Figure 4. Total macroalgal cover (A), areal wet weight (B), areal

dry weight (C), species number (D), H¡¦ (E), and J¡¦ (F). Data are

present as mean

¡Ó

SD (n=8).

424

Botanical Studies, Vol. 48, 2007

Table 2. Macroalgal species list from 2001-2003.

Family

Species

2001

2002

2003

Spr

1

Sum Aut Win Spr Sum Aut Win Spr Sum Aut

Total species number

2 18 21 23 21 11 14 13 17 11 2

CHLOROPHYTA

Ulotrichaceae Ulothrix flacca (Dillwyn) Thurer

+ +

Monostromataceae Monostroma nitidum Wittrock

+

Ulvaceae

Enteromorpha linza (Linnaeus) J. Agardh

+

Enteromorpha prolifeta (Muller) J. Agardh

+ + + + + + + +

Ulva conglobata Kjellman

+

Ulva lactuca Linnaeus

+ + + + +

Anadymene wrightii Harvey ex Gray

+ + +

Cladophoraceae Chaetomorpha antennina (Bory) Kutzing

+

Chaetomorpha crassa (C. Agardh) Kutzing

+

Chaetomorpha linum (Muller) Kutzing

+ + + + + + +

Boergesenia forbesii (Harvey) Feldmann

+ + + + + +

Boodleaceae Boodlea ngulate (Harvey et Hooker) Brand

+ +

Dictyosphaeria cavernosa (Forsskal) Borgesen

Valonia aegagropila C. Agardh

+ + +

Caulerpaceae Caulerpa brachypus f. parvifolia (Harvey) Cribb

+ +

Caulerpa racemosa v. laete-virens (Mont.) W.-v. Bosse

Caulerpa racemosa v. clavifera f. Macrophysa (K.) W.-v. Bosse

+

Udoteaceae

Chlorodesmis caespitosa J. Agardh

+ + + + +

Chlorodesmis fastigiata (C. Agardh) Ducker

Halimeda macroloba Decaisne

+

Codiaceae

Codium formosanum Yamada

+

PHAEOPHYTA

+ + + +

Ectocarpaceae Ectocarpus confervoides Le Jolis

Dictyotaceae Dictyota cervicornis Kutzing

+ +

Lobophora ngulate (Lamouroux) Womersley

+ + + + + + +

Padina australis Hauck

+ +

Zonaria diesingiana J. Agardh

+ +

Scytosiphonaceae Colpomenia sinuosa (Mertens ex Roth) Derbes et Solier +

Hydroclathrus clathratus (C. Agardh) Howe

+

Sargassaceae Sargassum siliguosum J. Agardh

+

RHODOPHYTA

Dermonemataceae Dermonema virens (J. Agardh) Pedroche et Vila Orth

+

Yamadaella cenomvce (Decaisne) Abbott

+ +

Galaxauraceae Galaxaura marginata (Ellis et Solander) Lamouroux

+ + +

Galaxaura oblongata (Solander) Lamouroux

+

Liagoraceae

Helminthocladia ngulate Harvey

Gelidiaceae

Gelidiella acerosa (Forsskal) Feldmann et Hamel

Gelidium sp.

+

Caulacanthaceae Caulacanthus okamurae Yamada

Halymeniaceae Carpopeltis maillardii (Montagne et Millardet) Chiang

+

Grateloupia filicina (Wulfen) C. Agardh

+

CHUNG et al. ¡X Salinity, temperature, nutrients, and macroalgal assemblage structure

425

weight biomass showed seasonal (F

3,87

= 81.26, p < 0.0001)

(winter > spring > summer > autumn) and year (F

2,87

=

59.18, p < 0.0001) (2003 > 2002 > 2001) variations, and

the interaction of year and season was significant (F

6,87

= 7.94, p < 0.0001) (Figure 4 and Table 3). Macroalgal

biomass increased gradually as time advanced and peaked

in January 2003 while macroalgal % cover only showed

a drop, with cover < 50% in April 2001, May 2002, and

September 2003.

Macroalgal assemblage structure

The univariate indices¡Xthe Shannon-Wiener species

diversity index, the H¡¦ and evenness, and Pielou¡¦s J¡¦¡X

showed significant temporal variations that H¡¦ values had

Family

Species

2001

2002

2003

Spr

1

Sum Aut Wi n Spr Sum Aut Win Spr Sum Aut

Hypneaceae

Hypnea cervicornis J. Agardh

+

Hypnea charoides Lamoruoux

+ + + + + + + + +

Hypnea pannosa J. Agardh

Phyllophoraceae Ahnfeltia plicata (Hudson) Fries

+

Ahnfeltiopsis flabelliformis (Harvey) Masuda

+

Rhizophyllidaceae Portieria hornemannii (Lyngbye) P.C. Silva

+ +

Sarcodiaceae Sarcodia montagneana (Hooker et Harvey) J. Agardh

+

Solieriaceae

Eucheuma serra J. Agardh

+ + +

Corallinaceae Amphiroa fragilissima (Linnaeus) Lamouroux

+

Jania adhaerens Lamouroux

+ + + + + +

Jania ngulate (Yendo) Yendo

+

Mastophora rosea (C. Agardh) Setchell

+ +

Gracilariaceae Gracilaria arcuata Zanardini

+ +

Gracilaria coronopifolia J. Agardh

+

Gracilaria eucheumoides Harvey

+ + + + + + + +

Gracilaria sordica (Suringar) Hariot

+

Gracilaria sp.

+

Rhodymeniaceae Ceratodictyon spongiosum Zanardini

+ + + + + + + + + +

Gelidiopsis repens (Kutzing) Weber-van Bosse

+

Ceramiaceae Ceramium sp.

Rhodomelaceae Acanthophora spicifera (Vahl) Borgesen

+ + +

Acrocystis nana Zanardini

+

Bostrychia tenella (Lamouroux) J. Agardh

+ +

Laurencia brongniartii J. Agardh

+

Laurencia intermedia Yamada

+

Laurencia okamurae Yamada

Laurencia pinnata Yamada

+ +

Laurencia papillosa (C. Agardh) Greville

+ + + + + + + + +

Melanamansia glomerata (C. Agardh) Norris

+

1

Spr, spring; Sum, summer; Aut, autumn; Win, winter.

Table 2. (Continued.)

year-over-year variations (Friedman¡¦s test, F

2,87

= 3.15, p =

0.0491) while J¡¦ values showed seasonal variations (F

3,87

=

4.83, p = 0.0042) (Figure 4 and Table 3).

Based on the value of each block, the results from

cluster analysis and MDS ordination analysis of species

areal wet weight (without data transformation) using

the Bray Curtis similarity measures showed that three

groups were discerned to correspond to 2001, 2002, and

2003 groups (Figure 5A and B). The k-dominance curves

showed that species diversity was lower in 2002 and

2003 as compared to 2001 (Figure 5C), and this is mainly

due to the high abundance of the rhodophyte Gracilaria

coronopifolia and Ceratodictyon/Haliclona association in

2001 (Figure 6).

426

Botanical Studies, Vol. 48, 2007

Macroalgal assemblage is primarily structured by year

(group 2001, group 2002, and group 2003) and secondarily

by season. A two-way cross ANOSIM test showed that

macroalgal assemblage was yearly (R = 0.569, p =

0.001) and seasonally (R = 0.469, p = 0.001) significant

(Table 4). The SIMPER analysis of species contributing

to year-over-year difference showed that the species

responsible for differences in structures between years was

Gracilaria coronopifolia, the Ceratodictyon/Haliclona

association, Gracilaria sp., Gracilaria eucheumoides, and

Hypnea charoides (Table 5). Gracilaria coronopifolia

and Ceratodictyon/Haliclona association were the main

species corresponding to the difference in macroalgal

assemblage structure between 2001 and 2002, between

2001 and 2003, and also between 2002 and 2003.

Gracilaria coronopifolia biomass increased gradually

from a low level in 2001 (areal wet weight was 159.36

g w. wt./m

2

) to 786.39 g w. wt./m

2

in 2002 and futher

to 1577.83 g w. wt./m

2

in 2003 (Figure 6 and Table 5).

The red alga/sponge Galaxaura oblongata showed a

Table 3. Friedman test for macroalgal abundance, H¡¦, and J¡¦.

F p

Tukey's test

1

Wet weight biomass

Season

9.48 <0.0001 Win

2a

> Sum

ab

> Spr

bc

> Aut

c

Year

19.84 <0.0001 2003

a

> 2002

b

> 2001

c

Season*Year 3.34 0.0093

Dry weight biomass

Season

9.83 <0.0001 Win

a

> Spr

ab

> Sum

bc

> Aut

c

Year

59.18 <0.0001 2003

a

> 2002

b

> 2001

c

Season*Year 7.94 <0.0001

% Cover

Season 10.35 <0.0001 Win

a

=Sum

a

> Aut

ab

> Spr

b

Year

4.22 0.0189 2003

a

> 2002

b

> 2001

c

Season*Year 4.43 0.0015

Species number

Season

7.27 0.0003 Win

a

>

Spr

ab

> Sum

b

=Aut

b

Year

11.26 <0.0001 2002

a

> 2001

b

= 2003

b

Season*Year 5.33 0.0004

H¡¦

Season

0.85 0.4696

Year

3.15 0.0491 2002

a

> 2001

ab

= 2003

ab

Season*Year 4.15 0.0024

J¡¦

Season

0.57 0.5691

Year

4.83 0.0042 2002

a

> 2001

b

= 2003

b

Season*Year 5.45 0.0003

1

Different s ymbol indicates significance at p < 0.05;

2

Spr,

spring; Sum, summer; Aut, autumn; Win, winter.

Figure 5. Clustering group (A), multidimensional scaling (MDS)

ordination (B) and k-dominance curve (C) of samples taken on

each sampling time during 2001-2003.

Table 4. Two-way ANOSIM of macroalgal assemblage.

R statistic Significance level Permutation

Year

0.569

0.001**

999

2001-2002

0.603

0.001**

999

2001-2003

0.908

0.001**

999

2002-2003

0.532

0.001**

999

Season

0.495

0.001**

999

Spring-Summer 0.451

0.001**

999

Spring-Autumn 0.589

0.002**

999

Spring-Winter 0.581

0.001**

999

Summer-Autumn 0.556

0.007**

999

Summer-Winter 0.411

0.001**

999

Autumn-Winter 0.440

0.001**

999

**p < 0.01.

CHUNG et al. ¡X Salinity, temperature, nutrients, and macroalgal assemblage structure

427

DISCUSSION

Temporal variations in macroalgal assemblage

compositions in Du-Lang Bay have been monitored in

2001-2003. Sixty-six species have been identified, in

which erect algae were more abundant than encrusting

and turf algae. The red alga Gracilaria coronopifolia and

the Ceratodictyon/Haliclona association are the dominant

algae, with yearly biomass advances which peaked in

2003. Total macroalgal wet weight and dry weight biomass

also increased as time advanced due to the blooms of

the Ceratodictyon/Haliclona association and Gracilaria

coronopifolia.

The macroalgal assemblage structure showed year-

over-year variation. k-Dominance curve analysis

demonstrates a continuous decrease in species diversity

from 2001 to 2003, and also shows a shift of macroalgal

assemblage compositions after 2001, in which the 2001

assemblage with less abundant Ceratodictyon/Haliclona

and Gracilaria coronopifolia changes to a assemblage

dominated by them in 2002/2003. The shift of macroalgal

assemblage structure from 2001 to 2002 is also reflected

by decreased Gracilaria eucheumoides abundance in 2002.

The blooms of Gracilaria coronopifolia and identified

Gracilaria species were accompanied by reduction in

Gracilaria eucheumoides. Gracilaria coronopifolia and

an identified Gracilaria species seem to compete with it.

After 2002, the dominance of Ceratodictyon/Haliclona

and Gracilaria coronopifolia further increased and caused

a shift of macroalgal compositions to an assemblage of

low diversity in 2003. MDS analysis confirms this change

in the macroalgal community, in which 2003 samples in

the MDS plot were closer to each other than to those of

2002, and samples in 2002 were closer to each other than

to those of 2001.

Low DIN/SRP ratios in 2001 and then increasing

DIN/SRP ratios as time advanced indicate N limitation

during 2001 and P limitation during 2002 and 2003 in Du-

Lang Bay. The type and severity of nutrient limitation

vary among habitats, species, and time (Lapointe, 1987;

Lapointe et al., 1987). In a nearshore coral reef in the

southern tip of Taiwan (Nanwan Bay), the growth of G.

coronopifolia was P-limited as indicated by decreased

tissue P contents, a marked drop in tissue P contents below

the subsistence level, and increased alkaline phosphatase

activity in mid-September and December 1999 (Tsai et

al., 2005). The P-limitation of macroalgal productivity

was also demonstrated by Lapointe (1997) on carbonate-

rich reefs in Discovery Bay, Jamaica, that are enriched by

nitrate in the submarine groundwater. However, Lapointe

(1997) found that macroalgae were more N-limited on

the siliiclastic reefs of southeast Florida, where the water

column was more enriched in soluble reactive phosphorus

(SRP). The data from the comparison of water-column

inorganic nitrogen:phosphorus (N:P) ratios to algal

tissue N:P ratios and the results of nutrient enrichment

experiments also indicate that the productivity of algae

Figure 6. Temporal variations in areal wet weight of dominant

algae during 2001-2003. Data are present as mean

¡Ó

SD (n=8).

similar pattern in that the biomass increased from 2.82 g

w. wt./m

2

in 2001 to 421.02 and 1536.73 g w. wt./m

2

in

2002 and 2003, respectively (Figure 6 and Table 5). The

biomass of Hypnea charoides showed an approximately

4-fold increase in 2002 over 2001 (Table 5). In contrast,

the biomass of Gracilaria eucheumoides disappeared in

2002 (Table 5). Shown in table 5, the SIMPER analysis of

species contributing to seasonal difference revealed that

Gracilaria coronopifolia and the Ceratodictyon/Haliclona

association, the most important species separating winter

assemblages and other assemblages in spring, summer,

and autumn, was abundant in winter with 2088.55 and

1305.87g w. wt./m

2

, respectively.

Environmental factors determining structure

difference

To elucidate environmental factors in regulating

temporal variations in macroalgal assemblage, the

BVSTP analysis was used for determination of the best

combinations of the eleven environmental variables (mean

monthly air temperature, mean monthly maximum air

temperature, mean monthly minimum air temperature,

monthly cumulative irradiance, monthly cumulative

precipitation, seawater temperature, salinity, and DIN,

NO

3

-

+NO

2

-

, NH

4

+

, and SRP concentrations) producing the

largest matches of changes in macroalgal structure and

environmental variables over 2001-2003. SRP and salinity

were the best variable combination responsible for year

variations in macroalgal assemblage, its Spearman rank

correlation (£l) was 0.484 (Table 6). The best combination

of environmental variables producing the largest

matches of seasonal changes in macroalgal structure and

environment variables in each year was also analyzed.

As can be seen in Table 6, temperature, irradiance, and

precipitation were the factors determining the seasonality

in 2001 and 2003 while seasonal variations in species

structure in 2002 were attributable to variations in NH

4

+

concentrations and salinity.

428

Botanical Studies, Vol. 48, 2007

Table 5. Result of SIMPER test on percentage contributions of species.

Species

Mean abundance (g wet wt./m

2

) Contribution (%) Cumulative contribution (%)

A. between years

2001

2002

Gracilaria coronopifolia

159.36

786.39

28.80

28.80

Ceratodictyon spongiosum

2.82

421.02

14.86

43.66

Gracilaria sp.

0

114.92

8.19

51.85

Gracilaria eucheumoides

298.31

0

5.72

57.57

Hypnea charoides

26.86

104.08

4.59

62.16

2001

2003

Ceratodictyon spongiosum

2.82

1536.73

37.51

37.51

Gracilaria coronopifolia

159.36

1577.83

30.85

68.36

2002

2003

Ceratodictyon spongiosum

421.02

1536.73

37.48

37.48

Gracilaria coronopifolia

786.39

1577.83

32.45

69.93

Species

Mean abundance

Contribution (%)

Cumulative

B. between seasons

Spr

1

Sum

Gracilaria coronopifolia

480.55

888.74

26.39

26.39

Ceratodictyon spongiosum

704.15

553.59

23.84

50.23

Gracilaria sp.

209.56

0

7.59

57.81

Gracilaria eucheumoides

0

223.73

7.53

65.34

Spr

Aut

Ceratodictyon spongiosum

704.15

548.43

31.99

31.99

Gracilaria coronopifolia

480.55

480.36

24.99

56.99

Gracilaria sp.

209.56

0

9.89

66.88

Spr

Win

Gracilaria coronopifolia

480.55

2088.55

38.37

38.37

Ceratodictyon spongiosum

704.15

1305.87

27.12

65.48

Gracilaria sp.

209.56

0

6.22

71.71

Sum

Aut

Gracilaria coronopifolia

888.74

480.36

28.60

28.60

Ceratodictyon spongiosum

553.59

548.43

26.04

54.64

Gracilaria eucheumoides

223.73

0

8.44

63.08

Sum

Win

Gracilaria coronopifolia

888.74

2088.55

34.91

34.91

Ceratodictyon spongiosum

553.59

1305.87

26.27

61.18

Gracilaria eucheumoides

223.73

0

6.05

67.23

Aut

Win

Gracilaria coronopifolia

480.36

2088.55

40.28

40.28

Ceratodictyon spongiosum

548.43

1305.87

30.14

70.43

1

Spr, spring; Sum, summer; Aut, autumn; Win, winter.

CHUNG et al. ¡X Salinity, temperature, nutrients, and macroalgal assemblage structure

429

in Kaneohe Bay, Hawaii is limited by N instead of P

(Larned, 1998). Evidently, the type and severity of nutrient

limitation are variable among habitats, species, and time

(Lapointe, 1987; Lapointe et al., 1987). It is clear that the

nutrient status in the nearshore reefs of Du-Lang Bay in

Taitung shifts over the 2001-2003 period, with a change

toward P limitation after 2001.

Nutrients are linked to the temporal variations in

macroalgal abundance and assemblage structure. Because

Gracilaria is known as the species that could accumulate

high N and P reserves in response to high nutrient

environments (DeBoer et al., 1978; Costanzo et al., 2000),

the blooming of Gracilaria coronopifolia in Du-Lang

Bay can be considered a sign of eutrophication. Although

algal blooms on coral reefs are associated with enhanced

nutrient availability (Bell, 1992; Tsai et al., 2004;

Hwang et al., 2004; Tsai et al., 2005), the productivity of

macroalgae on coral reefs is still limited by nitrogen (N)

and/or phosphorus (P) (Lapointe, 1987; Lapointe et al.,

1987; Littler et al., 1991; Lapointe, 1997; Larned, 1998).

Our previous study using a continuous flow-through

outdoor laboratory tank culture system showed that the

nutrient threshold for the maximum growth of Garcilaria

coronopifolia is 16/8/1.2 £gM NO

3

-

/NH

4

+

/PO

4

3-

(Tsai et

al., 2005). The seawater nutrient concentrations were

below the threshold. It is therefore clear that the growth of

Garcilaria coronopifolia was still under nutrient limitation.

If nutrient concentrations increase further, the biomass

o f Gracilaria coronopifolia will increase. The type and

severity of nutrient limitation still need to be determined.

A positive correlation between Ceratodictyon/Haliclona

biomass and decreased SRP concentrations suggests that

the blooms of Ceratodictyon/Haliclona are associated

with decreased P availability. By tracing the stable isotope

of

15

N and feeding experiments, it is proposed that N

sources from grazing on ultraplankton by the sponge

partner of Ceratodictyon/Haliclona symboses (Pile et al.,

2003) and subsequent waste ammonium excretion to the

rhodophyte partner (Davy et al., 2002) are essential for

the growth of Ceratodictyon in the nutrient-poor waters

of the Great Barrier Reef. However, a positive correlation

of Ceratodictyon/Haliclona biomass with seawater DIN

concentrations reflects that the blooming of Ceratodictyon/

Haliclona in Du-Lang Bay might not be due to the need

to meet the N requirement of an algal partner. We propose

that the association of Ceratodictyon with Haliclona

enables the alga to obtain P from Haliclona under

P-limited conditions (2002 and 2003). However, the role

of P status on regulating the growth of the Ceratodictyon/

Haliclona association needs further work. A study carried

out in One Tree Lagoon in the southern Great Barrier Reef

of Australia has shown that Ceratodictyon/Haliclona is

absent in regions that lack hard substrata for attachment

of propagules or fragments (Trautman et al., 2000, 2003).

The habitats in the present study site are changing.

Ecosystems in coastal areas of islands around Taiwan

have faced threats in the past ten years. We observed the

release of significant urban sewage wastes into this studied

Table 6. The best combinations of 11 environmental variables producing the largest matches of changes in macroalgal assemblages

and environme ntal variables over 2001-2003. Environmental variables are m onthly mea n tempera ture, monthly maximum

temperature, monthly minimum temperature, monthly cumulative irradiance, monthly cumulative precipitation, seawater

temperature, salinity, DIN, SRP, NO

3

-

+ NO

2

-

, and NH

4

+

.

Number of variable

Spearman rank correlation (£l)

Best variable combination

A. 2001-2003

2

0.484

SRP, Salinity

B. each year

2001

2

0.757

Seawater temperature, Monthly cumulative irradiance

1

0.701

Monthly minimum temperature

2002

2

0.295

NH

4

+

, Salinity

1

0.288

Monthly maximum temperature

2003

3

0.451

Seawater temperature, Monthly cumulative precipitation, Monthly

cumulative irradiance

1

0.440

Monthly maximum temperature

1

0.419

Seawater temperature

430

Botanical Studies, Vol. 48, 2007

reef in 2001. The sampling site in this study is shallow,

1-3 m deep, which explains why the salinities in the

upper regions of the subtidal reef at the study site (1-3 m

depth) are low in 2001, and the concentrations of nutrient,

especially P, are high in 2001. However, we observed

that the release of urban sewage was reduced after June

2002, allowing the salinities to recover. Because salinity

is known to affect macroalgal growth, one could expect a

shift of salinity to influence macroalgal compositions and

their growth in the studied reef in southeastern Taiwan.

The effects of salinity on macroalgal growth have been

documented in many studies (Dawes et al., 1998; Israel et

al., 1999; Eriksson and Bergstrom, 2005; Larsen and Sand-

Jensen, 2006). Kramer and Fong (2000) demonstrated that

the populations of E. intestinalis in coastal estuaries may

suffer from freshwater inputs if salinity conditions are

persistently reduced. This work shows an increase of both

Gracilaria coronopifolia and Ceratodictyon/Haliclona

biomass after salinity increased to normal levels (35 psu)

in 2002 and 2003, and this suggests that the growth of both

species might be inhibited under low salinity conditions.

The growth of Gracilaria spp. (Gracilariales, Rhodophyta)

from Japan, Malaysia, and India was optimal at a normal

seawater salinity, 35 psu (Raikar et al., 2001). Possibly the

growth of Gracilaria coronopifolia at the reef in Du-Lang

Bay was restricted by low salinities in 2001. However,

as we know, the responses of Ceratodictyon/Haliclona

to salinity fluctuations have not been reported, and its

reponse to decreasing salinity is unclear.

Present evidence has suggested that temperature was a

primary factor influencing the seasonal variations of the

macroalgal assemblage structure in both 2001 and 2003.

Temperature has been recognized as principal ecological

factor affecting macroalgal growth and morphology,

geographical distribution, and seasonal changes in growth

pattern (Garbary, 1979; Luning, 1984; Van den Hoek,

1984; Breeman, 1988; Pakker et al., 1994; Davison and

Pearson, 1996; Lee et al., 1999). Studies carried out on

lagoons and nearshore waters in the Gulf of Mexico

have shown that seawater temperature is pivotal in the

seasonality of benthic algae in tropical waters (Conover,

1964; Earle, 1969). Temperature has also been found

to influence the seasonality in vegetation growth and

reproduction of Sargassum in Hawaii (De Wreede,

1976) and the British Isles (Jephson and Gray, 1977).

In Currimao, Ilocos Norte, in the northern Philippines,

slight seasonal variations of seawater temperature were

positively correlated with the biomass of Sargassum from

subtidal zones (Hurtado and Ragaza, 1999). Our previous

studies have identified the impact of temperature on the

growth of Gracilaria tenuistipitata in Taiwan (Lee et al.,

1999). Laboratory and field experiments from those studies

also showed that the seasonal abundance of Gracilaria

coronopifolia from southern Taiwan was determined by

seasonal variations in seawater temperatures and nutrient

concentrations as well as by different physiological

growth strategies (Hwang et al., 2004), so the seasonality

of biomass of Gracilaria coronopifolia from Du-Lang

Bay in Taitung in southeastern Taiwan was influenced by

temperature fluctuations, especially in 2001 and 2003.

However, the response of Ceratodictyon/Haliclona growth

to temperature fluctuations remains unclear.

In conclusion, this study indicates that the nearshore

macroalgal assemblage in Du-Lang Bay in southeastern

Taiwan is structured primarily by year and secondarily by

season and the blooms of Ceratodictyon/Haliclona and

Gracilaria coronopifolia in 2002-2003 contribute to year-

over-year variations of the algal assemblage structure.

Nutrients and salinity are the factors governing the year-

over-year variations in the structure and abundance of

tropical benthic macroalgal assemblages in southeastern

Taiwan. Temperature is a factor influencing seasonality. To

provide the proper management of benthic communities on

coral reefs in southeastern Taiwan, it will be necessary to

ascertain the relative importance of abiotic and biotic (such

as herbivores) factors affecting the growth of macroalgae.

Acknowledgements. This work is dedicated to memory

of Prof. Chung-Sin Chen, one of its authors. We are

indebted to Prof. Kuo-Tien Lee, the President of the

National Taiwan Ocean University, Keelung, Taiwan, and

Prof. Kwang-Tsao Shao, Research Fellow in the Center for

Biodiversity, Academia Sinica, Taipei, Taiwan, for their

assistance and valuable suggestions in this study. This

study was supported by grants from the National Science

Council (No. 89-2621-Z-110-1 and No. 90-2621-Z-110-1)

and the Council of Agriculture (No. 90AS-1.4.5-

FA-F1[22], 91AS-2.5.1-F1[13], 91AS-2.1.4-FC-R2,

92AS-9.1.1-FA-F1[1], 93AS-9.1.1-FA-F1[19], 93AS-

9.1.1-FA-F2[1], and 94AS-9.1.1-FA-F2[1]), Executive

Yuan, Taiwan.

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