Spatio temporal and environmental factors influencing macroalgal β diversity in the Red Sea, Egypt

DOI 10.1515/bot-2013-0107 Botanica Marina 2014; 57(2): 99–110

Ahmed A. Issa, Awatief F. Hifney, Khayria M. Abdel-Gawad and Mohamed Gomaa*

Spatio temporal and environmental factors influencing macroalgal β diversity in the Red Sea, Egypt

Abstract: β Diversity is an ecological concept used to describe the turnover of species across a wide range of spatial and temporal scales, but such knowledge is lacking for macroalgal assemblages of the Red Sea. β Diversity pat-terns for Red Sea macroalgae were evaluated from different sites (Hurghada, Safaga, and Al-Quseir) and seasons across different environmental disturbances. β Diversity was studied by partitioning the total number of species (γ diversity) into additive components. Geographical hetero-geneity was more important than seasonal heterogeneity in structuring macroalgae both at species and functional group levels. Species replacement as a component of β diversity produced dissimilarity in species composition and taxonomic structure. Replacement of species between sites was responsible for the presence of new functional groups of macroalgae. High β diversity and taxonomic similarity values were characteristic of the macroalgae of the Red Sea. The occurrence of small macroalgal thalli that have short life cycles induced high species replacement and subsequently high β diversity, with spatial heteroge-neity and environmental gradient as drivers of β diversity. Anthropogenic disturbance at the Safaga site was sug-gested to induce variation of macroalgal assemblages and functional groups. Excluding rare species from the data set did not change the high values of β diversity.

Keywords: β diversity; functional group; rare species; SDR simplex; taxonomic similarity.

*Corresponding author: Mohamed Gomaa, Faculty of Science, Botany and Microbiology Department, Assiut University, 71516 Assiut, Egypt, e-mail: [email protected] A. Issa, Awatief F. Hifney and Khayria M. Abdel-Gawad: Faculty of Science, Botany and Microbiology Department, Assiut University, 71516 Assiut, Egypt

IntroductionThe Red Sea constitutes a landlocked quasi-unperturbed marine ecosystem that is located in one of the hottest

regions on the planet along a narrow basin separating the African and the Asian continents. It represents one of the most saline bodies of water in the world’s ocean (Morcos 1970). The flora of the Red Sea is essentially from the Indian Ocean, but the opening of the Suez Canal was responsible for migration of marine species either from or to the Mediterranean (Lipkin 1991). However, it con-tains a relatively high level of endemism, including 9% for macroalgae, but comparative studies with other areas are required to prove this endemism (Head 1987). Gen-erally, the Red Sea was considered as a remarkable and very important area, which exhibits a species and biotope diversity exceeding that of the world’s ocean and the Med-iterranean Sea (Sheppard 2000).

In recent years, an increase in the macroalgal biomass in the Red Sea has become apparent and has been mainly attributed to both nutrient enrichment from urban and aquaculture effluents, and decreased herbivory (Figueroa et al. 2009). The Red Sea is probably very susceptible to disturbances, and the accumulation of harmful materi-als could have negative consequences for this unique environment (Hoepner and Lattemann 2003). In the Red Sea of Egypt, there are two mainly anthropogenic sources of nutrients represented by sewage outfalls and spillage of phosphate dust from phosphate mining at the port of Safaga. Environmental disturbance can affect not only the number of species present but also the degree of turnover in species composition across space or time and conse-quently algal conservation and sustainability (Passy and Blanchet 2007, Mazariegos-Villarreal et al. 2012).

Species diversity is a measure of the complexity of structure in an ecological community and can be sepa-rated into different concepts such as alpha (α), beta (β), and gamma (γ) diversity (Whittaker 1972). α Diversity is the diversity in species at individual sites at a given time (i.e., the number of species present at a site or species richness), γ diversity is that of the whole region of interest for the study, and β diversity is a measure of the degree of similarity or dissimilarity in species composition among sites. In other words, β diversity examines the degree of species turnover from habitat to habitat, from community to community, or along any ecological gradient (Whittaker

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100 A.A. Issa et al.: Macroalgal β diversity in the Red Sea

1972). Turnover suffers from a plethora of definitions and formulations (discussed by Magurran 1988). Nested assemblages occur when species-poor sites possess subsets of assemblages supported by species-rich sites, and the degree of nestedness is a measure of the overlap in species composition between high- and low-diversity areas (Atmar and Patterson 1993). β Diversity may reflect two different phenomena, spatial turnover and the nested-ness, which result from two antithetic processes, namely, species replacement and species loss (Baselga 2010). The difference in richness between two assemblages is a com-ponent of species turnover, which is attributable only to a change in the number of species (Podani and Schmera 2011). Podani and Schmera (2011) considered this fraction as a contributor to nestedness. β Diversity indicates the extent to which different habitats have been partitioned by species and, along with α diversity, is a measure of biotic diversity of an area (Wilson and Shmida 1984). Measuring β diversity for macroalgae is important, not only to under-stand the functioning of the ecosystem better but also for the conservation and sustainability of the ecosystem, as anthropogenic disturbance can affect not only α but also β diversity (Passy and Blanchet 2007, Piazzi and Balata 2008).

Although β diversity has been considered less often than species richness in marine ecological studies, it rep-resents a fundamental aspect of the ecosystems and a good ecological indicator (Piazzi and Balata 2008). To our knowledge, there are no studies of β diversity for macroal-gae in the Red Sea. Few recent attempts have been made to study the assemblages and diversity of macroalgae of the Red Sea in Egypt (Abou-Aisha et al. 1995, 1997, El-Sharouny et al. 2001). Thus, the aims of this study were (1) to investi-gate the role of different spatial and temporal scales in the variation of species composition and functional groups of macroalgae, (2) to assess the role played by sites and seasons in changing β diversity, (3) to characterize the different macroalgal assemblages and their functional groups in relation to environmental disturbance, (4) to study the role played by rare species in changing patterns of β diversity measures, and (5) to broaden our knowledge of macroalgal distribution from the Red Sea of Egypt.

Materials and methods

Study area and sampling of macroalgae

Macroalgal sampling was undertaken seasonally between autumn 2012 and summer 2013 from the intertidal zone of

Hurghada (27°12′N, 33°50′E), Safaga (26°46′N, 33°56′E), and Al-Quseir (26°5′N, 34°17′E) along the coast of the Red Sea, Egypt. According to their distance from the phos-phate mining port of Safaga (26°41′55″N°33°56′10″E), Hurghada and Al-Quseir sites were considered to be rela-tively undisturbed sites, while the Safaga site was con-sidered to be a disturbed site. Each of the three studied sites was about 100 m2 wide and was characterized by the presence of rocky substrates. Safaga and Al-Quseir have intermixed rocky and sand substrates at about 1 m depth, whereas in Hurghada, only rocky substrates are present. The algae were collected by hand in polyethylene bags and put in an ice box until identified and examined in the laboratory. Algal identification at least to the genus level followed the keys and descriptions adapted from Abbott and Dawson (1978), Meñez and Mathieson (1981), Niza-muddin (1991), Aleem (1993), Jha et al. (2009). Macroal-gae were classified into functional groups according to Steneck and Dethier (1994), Rubal et al. (2011) and taxo-nomic classification followed Algaebase (Guiry and Guiry 2013). Algal sampling was semiquantitative to determine the dominance or rareness of each algal taxon with the help of the DAFOR scale (Dominant; Abundant; Frequent; Occasional; Rare), but rare species were focused on in the analysis.

Seawater analysis

Water temperature was measured in situ. Water samples were collected from each site and all variables were meas-ured in triplicate for each sample. Prior to seawater analy-sis, samples were vacuum filtered through Whatman glass microfiber filters GF/C (pore size 1.2 μm) (Grasshoff et al. 1999). Water pH and electrical conductivity (EC, mS.cm-1) were measured by pH and EC meters (pH, 211 Hanna instruments, USA; EC, YSI Model 35 yellow spring, OH, USA). Sodium and potassium were determined by flame photometry (Dr Lange Flame Photometer M 71 D type Nr/LPG 075). The complexometric titration method (Grasshoff et al. 1999) was employed for both Ca+2 and Mg+2 determi-nations. Chlorinity was estimated by titration with AgNO3 and subsequently was used for estimation of salinity (Grasshoff et al. 1999). A gravimetric method was used for determination of sulfate anions (Grasshoff et al. 1999). Phosphate was determined as described by Murphy and Riley (1958). Finally, nitrate was spectrophotometrically determined by chromotropic acid (1,8-dihydroxynaphtha-lene-3,6-disulphonic acid disodium salt) in concentrated sulfuric acid (detection limit 0.12 ppm) (Bulgariu and Bulgariu 2012).



A.A. Issa et al.: Macroalgal β diversity in the Red Sea 101

Data analysis

The contribution of geographical and seasonal variation to macroalgal assemblages was examined by permuta-tional multivariate analysis of variance (PERMANOVA) at species and functional group levels. The PERMANOVA design consisted of two factors, site (Si) with three levels (Hurghada, Safaga, and Al-Quseir) as fixed factor, and Season (Se) with four levels (autumn, winter, spring, and summer) as random factor. PERMANOVA analyses were done based on Jaccard similarity (SJac) using permuta-tion of residuals under a reduced model (999 permuta-tions). When PERMANOVA showed significant differences (p < 0.05), a pair-wise comparison (999 permutations) was done to explore differences among all pairs of levels of the selected factor.

A distance-based redundancy analysis (dbRDA) (McArdle and Anderson 2001) plot allowed the visualiza-tion of the samples’ ordination according to their macroal-gal assemblage and correlation to environmental factors. The analysis was based on Jaccard similarity between biological data and Euclidean distance for normalized environmental variables.

PERMANOVA and dbRDA were carried out using PERMANOVA+ in PRIMER v6 software (Anderson et al. 2008)

(α), (γ), and (β) diversity of macroalgae

α Diversity was defined as the total number of species present at an individual site at a given time (Magurran 2004) and γ diversity as the total number of species found at all three sites on the same sampling date or in all four seasons at the same site (Lande 1996).

Patterns of β-diversity components (species replace-ment, R, richness difference, D, and similarity, S) were analyzed using the conceptual and methodological frame-work provided by Podani and Schmera (2011), which is known as the SDR simplex approach. The SDR partitions γ-diversity into additive components through calculating three relativized similarity indices as follows (see Podani and Schmera 2011 for more details)1. Similarity (S) calculated as Jaccard index of similarity

(SJac = a/n), where a is the number of species shared by two sites or two seasons, and n is the total number of species, n = a+b+c, where b and c are number of species found only in the first and second sites/seasons, respectively.

2. Richness difference, D = |b-c|/n3. Species replacement, R = 2* min(b,c)/n.

From the above indices, β diversity can be calculated as β = R+D and nestedness, N = S+D, where S > 0.

The SDR values were calculated with the SDR-simplex program (Podani and Schmera 2011) and the ternary plots illustrating the values were drawn with Tri-plot version 4.1.2.

Two-way analysis of variance (ANOVA) was used to determine the possible spatial and temporal differences in α diversity (Zar 2009).

Taxonomic β diversity

The taxonomic similarity index (ΔS) was used to compute taxonomic β diversity (Izsak and Price 2001). ΔS is derived from taxonomic distance (TD), which is computed from presence/absence data. If the species were classified into genera, families, orders, etc. in different sites, then taxo-nomic distance is defined as the average minimum path length between any two species in two different sites (Izsak and Price 2001).

ΔS = 1-(TD/(L-1)), where L is the number of taxonomic levels used to classify the species. TD index was calcu-lated by PRIMER software, where it is the Gamma+ (Γ+) index in the program (Clarke and Gorley 2001). Taxonomic β diversity was defined as the average ΔS for all pair-wise comparisons of species recorded in the three sites during the same season or during different seasons in the same site.

In order to evaluate the relationship between different similarity matrices, the slope of linear regression equa-tions was calculated between all pair-wise comparisons of sites and seasons. The Mantel test with Pearson’s product-moment correlation was used to detect significant correla-tion, with p-values being based on 10,000 permutations using XLSTAT program v7.

Analyzing rare species

As the exclusion or inclusion of taxa in the data set may influence α, γ, or β diversity, the data sets with and without rare species were compared using Wilcoxon signed-rank tests. Taxa that had < 10 individuals were considered rare.

ResultsA total of 31 macroalgal species were identified (Table 1), including 9 taxa of Chlorophyta, 12 taxa of Rhodophyta




Table 1 List of macroalgae recorded in the study sites from the Red Sea, Egypt, and their assigned functional groups.

Algal name Functional group

Chlorophyta Caulerpa serrulata (Forsskål) J. Agardh Siphonous Chaetomorpha sp. Filamentous uniseriate Cladophora sp. 1 Filamentous uniseriate Cladophora sp. 2 Filamentous uniseriate Boergesenia forbesii (Harvey) Feldmann Siphonous Dictyosphaeria cavernosa (Forsskål) Børgesen Hollow with spherical or subspherical shape Rhizoclonium sp. Filamentous uniseriate Ulva lactuca Linnaeus Foliose Valonia aegagropila C. Agardh SiphonousRhodophyta Acanthophera spicifera (Vahl) Borgesen Corticated macrophytes Digenea simplex (Wulfen) C. Agardh Corticated macrophytes Gracilaria arcuata Zanardini Corticated macrophytes Heterosiphonia sp. Corticated filamentous Hypnea cornuata (Kützing) J. Agardh Corticated macrophytes Jania rubens (Linnaeus) J.V. Lamouroux Calcareous Jania sp. Calcareous Liagora viscida (Forsskål) C. Agardh Calcareous Laurencia sp. Corticated macrophytes Tricleocarpa fragilis (Linnaeus) Huisman et R.A. Townsend Calcareous Palisada perforata (Bory de Saint-Vincent) K.W. Nam Corticated macrophytes Palisada sp. Corticated macrophytesOchrophyta Dictyota pinnatifida Kützing Corticated foliose Dictyota sp. Corticated foliose Cystoseira myrica (S.G. Gmelin) C. Agardh Leathery macrophytes Cystoseira sp. Leathery macrophytes Colpomenia sinuosa (Mertens ex Roth) Derbès et Solier Hollow with spherical or subspherical shape Hydroclathrus clathratus (C. Agardh) M.A. Howe Hollow with spherical or subspherical shape Padina pavonica (Linnaeus) Thivy Corticated foliose Sargassum sp. 1 Leathery macrophytes Sargassum sp. 2 Leathery macrophytes Turbinaria decurrens Bory de Saint-Vincent Leathery macrophytes

and 10 taxa of Ochrophyta. The frequency of occurrence of macroalgae differed between sites and seasons, but Palisada perforata was dominant throughout the study in different seasons and sites. Liagora viscida (Rhodophyta, Liagoraceae) was recorded for the first time from Egyptian Red Sea coasts. Among the macroalgal phyla, Chloro-phyta represented the smallest fraction of the community ranging from none in Safaga (all seasons) to 29% in Al-Quseir (spring). The macroalgae collected were grouped into nine functional groups (Table 1). Corticated macro-phytes were the most highly represented groups (eight species), followed by leathery macrophytes (five species), while foliose and corticated filamentous algae were repre-sented by only one species each.

Two-way PERMANOVA of macroalgal species com-position showed significant spatial variation (p = 0.001, Table 2), with significant pair-wise differences between

Hurghada-Safaga and Safaga-Al-Quseir (p < 0.05). Sea-sonal variation in species composition was nonsignifi-cant (p > 0.05, Table 2). Similar results were obtained for the analysis of functional groups with significant differ-ences between sites (Table 2) and a significant pair-wise difference between Hurghada and Safaga only (p < 0.05). Although macroalgal composition and functional groups varied among sites using the Jaccard index, there was no significant variation of macroalgal species using taxonomic similarity (p > 0.05, Table 2). Neither sites nor seasons showed significant differences in the composition of dominant algal taxa (i.e., when rare taxa were excluded from the analysis, Table 2).

The dbRDA plots allowed the visualization of the rela-tionship between macroalgal species composition and environmental variables and highlighted the variability in species composition along the site factor using the Jaccard




Table 2 Results of two-way PERMANOVA tests [with site (Si) as fixed factor with three levels and season (Se) as random factor with four levels] considering both species and functional groups as aggregation level for macroalgal assemblages at the Red Sea, Egypt.

Source of variation df SS MS Pseudo-F P (Perm) Unique permutations

All taxaa

Si 2 16,613 8306.5 4.4113 0.001 999 Se 3 5583.1 1861 0.98835 0.517 996 Res 6 11,298 1883 Total 11 33,494 All taxab

Si 2 8381.2 4190.6 0.88269 0.721 993 Se 3 14,264 4754.6 1.0015 0.505 995 Res 6 28,485 4747.5 Total 11 51,130 Functional groupsa

Si 2 6145 3072.5 3.3526 0.013 998 Se 3 2931.1 977.02 1.0661 0.437 999 Res 6 5498.7 916.45 Total 11 14,575 Excluding rare speciesa

Si 2 8678.9 4339.5 1.6638 0.086 998 Se 3 8933.2 2977.7 1.1417 0.292 996 Res 6 15,649 2608.2 Total 11 33,261 4584.2 Excluding rare speciesb

Si 2 9168.4 4619 1.0105 0.501 998 Se 3 13,857 4536.8 1.0181 0.479 996 Res 6 27,221 Total 11 50,246

aBased on Jaccard similarity.bBased on taxonomic similarity.Two similarity matrices were used for the analysis: Jaccard similarity and taxonomic similarity. df, degrees of freedom; SS, sum of squares; MS, mean squares; Res, residuals. The interaction Si × Se was excluded due to absence of replication at the lowest level. Significant values are shown in bold.

similarity between macroalgal species (Figure 1A). Exclud-ing rare species reduced the discriminatory power of the analysis (graph not shown). Macroalgal assemblages from Safaga showed higher negative scores on the first dbRDA axis, while Al-Quseir showed higher positive scores on the second dbRDA axis. Autumn and summer samples at Hurghada showed higher scores on the second dbRDA axis than winter and spring samples. The environmental values that related negatively to the first dbRDA axis were water temperature, K+, Mg+2, phosphate and sulfate, while the others showed positive correlation. Water tempera-ture, K+, Mg+2, phosphate and sulfate variables had higher values for the seaweed assemblage of Safaga (Figure 1A). The tight clusters of sites formed by ordination of mac-roalgal assemblages based on similarity between species (Figure 1A) were not obvious using functional groups as aggregation level (Figure 1B). However, analysis includ-ing functional group explained a higher percentage of total variation than species level (Figure 1A,B). Leathery

macrophytes and corticated filamentous algae are charac-teristic of the macroalgal assemblage at Safaga (negatively related to the first dbRDA axis, Figure 1B).

α Diversity ranged from 4 to 9 species in Hurghada, 3 to 9 species in Safaga, and 4 to 17 species in Al-Quseir. Two-way ANOVA revealed no significant differences in α diversity for sites (F2,6 = 1.22, p = 0.36) or for seasons (F3,6 = 0.305, p = 0.82). Excluding rare species had a signifi-cant influence on the average species richness (α) values (Wilcoxon signed-rank test, Z = -2.36, p = 0.018) and γ diver-sity values (Z = -2.37, p = 0.018). The exclusion of rare species from the data set for all combinations had no significant influence on values of β diversity (Z = -0.019, p = 0.99) but necessarily had a strong negative influence on taxonomic similarity (Z = -3.6, p < 0.0001). Species nestedness was fre-quently low between seasons (seasonal scale) and high between sites (geographical scale) in comparison to β diversity (Table 3). Excluding rare species had no influ-ence on nestedness values (Z = -0.839, p = 0.4).




Leathery macrophytes

Corticated macrophytes

Corticated foliose

Filamentous uniserriate

Corticated filamentous

Calcareous algae

Q-Wi

H-Wi

H-Au

S-Au

S-Sp

H-Su

H-Sp

Q-Sp

Q-Au

S-Wi

20

0

dbR

DA

2 (2

9.4%

of f

itted

, 31.

2% o

f tot

al v

aria

tion)

-20

-40

-40 -20 0

dbRDA1 (45.1% of fitted, 47.9% of total variation)

20 40

-40-60 -20 0dbRDA1 (32.9% of fitted, 32.9% of total variation)

4020

40B

20

0

dbR

DA

2 (2

1.5%

of f

itted

, 21.

5% o

f tot

al v

aria

tion)

-40

-20

-60

40A

Q-Su

Transform: presence/absence

Hurghada

EI-QuseirSafaga

Site

Resemblance: S7 Jaccard

Hollow with spherical or subspherical shape

Foliose

Siphonous

Transform: presence/absence

Hurghada

EI-QuseirSafaga

Site

Resemblance: S7 Jaccard

Figure 1 Distance-based redundancy analysis (dbRDA). (A) Relationships between the ordination of the sites based on macroalgal species composition and environmental factors. (B) Relationships between the ordination of the sites based on macroalgal functional groups. Sites: H, Hurghada; S, Safaga; Q, Al-Quseir. Seasons: Au, autumn; Wi, winter; Sp, spring; Su, summer. Note: S-Su and S-Sp have exactly the same coordinates in (B).




Tabl

e 3

Mea

n va

lues

( ± st

anda

rd e

rror

) for

α , γ

div

ersi

ty, J

acca

rd s

imila

rity

(SJa

c), re

lativ

ized

rich

ness

diff

eren

ce (D

), re

lativ

ized

spe

cies

repl

acem

ent (

R), β

div

ersi

ty, n

este

dnes

s (N

), an

d ta

xo-

nom

ic s

imila

rity

(ΔS) b

etw

een

seas

ons

(with

site

s as

repl

icat

es fo

r int

ersi

te h

eter

ogen

eity

) and

site

s (w

ith s

easo

ns a

s re

plic

ates

for i

ntra

site

het

erog

enei

ty) f

or m

acro

alga

l ass

embl

ages

in th

e Re

d Se

a, E

gypt

.

Al

l tax

a Ex

clud

ing

rare

taxa

α γ

%S Ja

c %

D %

R %

β %

N %

ΔS

α γ

%S Ja

c %

D %

R %

β %

N %

ΔS

Seas

onal

sca

le

Autu

mn

5.6

7 ± 1.

2 13

18

.05 ±

7.7

27.3

1 ± 9.

3 54

.6 ± 1

6.4

81.9

4 ± 7.

8 45

.35 ±

16.4

96

.41 ±

0.5

3.67

± 0.3

3 8

22.2

2 ± 5.

5 11

.11 ±

5.6

66.7

± 0.0

77.

83 ± 5

.6

33.3

7 ± 0.

03

96.5

9 ± 0.

3

Win

ter

7.3

3 ± 2.

3 17

15

.4 ± 2

.65

45.1

4 ± 14

.0

39.7

± 13.

2 84

.86 ±

2.6

60.2

8 ± 13

.2

95.1

1 ± 0.

81

5.0 ±

1.5

10 2

5.27

± 6.4

46

.4 ± 1

8.2

28.3

3 ± 17

.4 7

4.73

± 6.5

71

.67 ±

17.4

95

.76 ±

1.1

Sp

ring

9.0

0 ± 4.

0 22

12

.3 ± 1

.78

41.0

5 ± 20

.5

46.6

4 ± 21

.3 8

7.70

± 1.8

53

.35 ±

21.3

95

.52 ±

0.26

3.

33 ± 1

.9

8 25

.6 ± 1

3.3

66.0

7 ± 10

.5

8.3 ±

8.3

74.4

± 12.

2 91

.67 ±

8.3

94.0

6 ± 1.

3

Sum

mer

7.3

3 ± 1.

7 16

21

.65 ±

3.46

30

.30 ±

15.2

48

.05 ±

11.7

78.

36 ± 3

.5

51.9

5 ± 11

.7

96.9

5 ± 0.

32 3

.33 ±

0.67

8

18.1

± 1.9

26

.67 ±

13.3

55

.23 ±

15.2

81

.9 ± 1

.9

44.7

7 ± 15

.2

95.0

4 ± 0.

77Ge

ogra

phic

al s

cale

Hu

rgha

da

6.5 ±

1.2

12

42.1

3 ± 6.

5 33

.35 ±

7.5

24.5

± 11.

2 57

.87 ±

6.5

75.4

8 ± 11

.1

97.1

3 ± 0.

62

4.0 ±

1.2

10 2

3.67

± 2.5

50

.4 ± 1

2.1

25.9

± 12.

1 76

.3 ± 1

2.1

74.1

± 12.

1 94

.37 ±

0.75

Sa

faga

5.

5 ± 1.

3 11

46

.03 ±

4.6

37.7

8 ± 8.

7 16

.18 ±

9.1

53.9

7 ± 4.

6 83

.82 ±

9.1

98.0

5 ± 0.

22

2.8 ±

0.48

6

46.0

3 ± 4.

6 37

.78 ±

8.7

16.1

8 ± 9.

1 53

.97 ±

4.6

83.8

2 ± 9.

1 97

.23 ±

0.45

Al

-Qus

eir

10

± 2.7

22

32.1

7 ± 4.

9 46

.22 ±

7.9

21.6

2 ± 4.

9 67

.83 ±

4.9

78.3

8 ± 4.

9 97

.2 ± 0

.26

4.5 ±

1.2

11 2

4.97

± 4.7

41

.43 ±

8.1

33.6

± 4.4

75.

03 ± 4

.7

66.4

± 4.4

94

.97 ±

0.87

All m

easu

res

wer

e ca

lcul

ated

for a

ll ta

xa a

nd e

xclu

ding

rare

taxa

.

SDR simplex analysis revealed that benthic macroal-gal communities from the Red Sea have high β diversity values, thus several pairs in the ternary diagram tend to be closer to the left (R and D) side (Figure 2). The closer the dot to the R apex, the more β diversity is resulting from species replacement instead of richness differences between the two samples. When site to site pairs were plotted separately for each season, the high values of β diversity were obvious with sharing of species replace-ment and richness difference (half of the pairs are close to R apex and the rest close to D apex, Figure 2A). Even when all combinations among sites and seasons were plotted, the points are closer to the R and D side (Figure 2C), indi-cating the dominance of richness difference and species replacement in determining data structure. A small shift from large β diversity toward similarity was observed when rare species were excluded from the analysis of site to site pairs for each season (Figure 2A,B). However, it was less conspicuous when comparing all sites and seasons (Figure 2C,D).

Increasing similarity in macroalgal structure was necessarily correlated with the presence of taxonomically close species (increasing taxonomic similarity; Mantel’s r = 0.808, p = 0.0001, Figure 3A). β Diversity patterns were negatively correlated with both species similarity, Sjac (r = -1.00, p = 0.0001), and taxonomic similarity, ΔS (r = -0.8, p = 0.0001). In other words, increasing species dissimi-larity leads to increasing β diversity. In order to quantify which component of β diversity (richness difference, D, or species replacement, R) was responsible for this result, correlations were calculated for D and R separately. A strong negative correlation was found between species replacement and both species similarity, SJac (r = -0.55, p = 0.0001, Figure 3B) and taxonomic similarity, ΔS (r = -0.38, p = 0.002, Figure 3C). In contrast, richness differ-ences were not significantly correlated with SJac (r = -0.01, p = 0.40) or ΔS (r = -0.09, p = 0.24). Thus, species replacement between temporal or spatial scales was the most impor-tant component of β diversity that was responsible for the observed variation of macroalgae in terms of dissimilar-ity in species composition and dissimilarity in taxonomic pattern. When species of poor sites (i.e., Safaga) were subsets from species of rich sites, nestedness increased, while β diversity decreased (r = -0.55, p = 0.0001) and taxo-nomic similarity increased (r = 0.4, p = 0.001).

Increasing species similarity was necessarily accom-panied by an increase in similarity between algal func-tional groups (Mantel’s r = 0.56, p = 0.0001, Figure 4A). Increasing replacement of species from site to site or from season to season had a greater influence on the pres-ence of new functional groups, resulting in decreasing




RA B

C D

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RSeasonsAutumnWinterSpringSummer

D SR

D S

R

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Figure 2 SDR simplex plots for seaweed communities at the Red Sea, Egypt. The abbreviations S, D, and R refer to relative similarity, richness difference, and species replacement, respectively. (A) Symbols represent pair-wise comparisons using all taxa between the three study sites in each season, (B) similar to (A), but excluding rare taxa, (C) symbols represent pair-wise comparisons using all taxa between all different sites and seasons, (D) similar to (C), but excluding rare taxa. Note: The same number of points are plotted when rare taxa were excluded as when all taxa were used, but some points overlapped.

functional group similarity, than differences in species richness (Mantel test between functional group similar-ity and species replacement, r = 0.36, p = 0.0001, Figure 4B; with richness difference, r = -0.035, p = 0.4). Decreas-ing functional group similarity was necessarily accompa-nied by reduced taxonomic similarity between species. In other words, the appearance of more taxonomically close species resulted in an increase in similarity of functional groups (r = 0.73, p = 0.0001, Figure 4C).

DiscussionBenthic macroalgae of the Red Sea in Egypt showed a dis-tinct spatial pattern with significant differences between Hurghada-Safaga and Safaga-Al-Quseir (Table 2). For instance, Chlorophycean macroalgae were completely lacking from Safaga. The foliose alga Ulva sp. was reported only at Al-Quseir, while the other three functional groups of green algae were found in Hurghada and Al-Quseir, but the composition of each functional group was different. The only siphonous alga at Hurghada was Valonia aega-gropila, while Caulerpa serrulata and Boergesenia forbesii

were found in Al-Quseir. Rhodophycean and Phaeophy-cean algae were grouped into three functional groups for each. Among the red algae classified as corticated mac-rophytes, Acanthophera spicifera, Hypnea cornuata, and Palisada sp. were found only at Al-Quseir, while Graci-laria arcuata and Laurencia sp. were found at Hurghada. Additionally, hollow spherical brown algae (Colpomenia sinuosa and Hydroclathrus clathratus) were recorded only from Al-Quseir. A possible explanation of the existence of significant differences between Safaga and the other two studied sites is the environmental disturbance caused by phosphate mining port at Safaga (26°41′55″N 33°56′10″E) that was very close to the study site (distance ∼7.8 km) but relatively far from the other two sites. dbRDA highlighted the importance of phosphorus inputs that were more evident in changing macroalgal structure in Safaga than in the other two sites (Figure 1A). Chemical disturbance by phosphorus may also contribute additional impacts with other agents that can cause a reduction in similarity of assemblages over time (e.g., increasing phosphate at Safaga was accompanied by increasing sulfate, magne-sium, and potassium in comparison to the other two sites; Figure 1A). This environmental disturbance induced fluc-tuation in the macroalgal abundance and diversity as well




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y=-7.841x+799.4R2=0.142Mantel test, p=0.002

Figure 3 Correlation and regression between different β diversity measures of macroalgae (calculated as percentages) at the Red Sea, Egypt. (A) Correlation between taxonomic similarity (%ΔS) and Jaccard similarity (%SJac), (B) correlation between relativized species replacement (%R) and Jaccard similarity (%SJac), (C) correlation between relativized species replacement (%R) and taxonomic simi-larity (%ΔS). Points represent pair-wise comparisons of combina-tions between all sites and seasons.

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Figure 4 Correlation and regression between functional group similarity and (A) Jaccard species similarity (%SJac), (B) relativ-ized species replacement (%R), (C) taxonomic similarity (%ΔS). Functional group similarity was calculated as Jaccard similarity of presence/absence of macroalgal functional groups. Points repre-sent pair-wise comparisons of combinations between all sites and seasons.

as chemical constituents (Abou-Aisha et al. 1995, 1997) and may have contributed to the lower species richness than in the other two study sites. Thus, the existence of an environmental disturbance gradient affected the turnover of species and functional groups between sites and was also reflected in the degree of similarity of species assem-blages between sites. Considering functional groups, leathery macrophytes were more frequent at Safaga, but siphonous, filamentous uniseriate, and hollow spherical algae were completely lacking. New functional groups were added by the replacement of species between highly

disturbed and less disturbed sites. This result contrasts with Phillips et al. (1997) who found that individual species were more sensitive in responding to a disturbance gradi-ent, but at the functional group level, there was simply replacement of species within a functional group.

Consistent spatial variation was found using species and functional group approaches (Table 2), but the spe-cies-level approach was better able to discriminate sites in contrast to the functional groups (Phillips et al. 1997). Many studies have found consistent results between func-tional groups and species level at different spatial scales (Díez et al. 2010, Rubal et al. 2011, Veiga et al. 2013). There-fore, functional groups could be an important surrogate in studying macroalgal variation in the Red Sea among




different sites. Particularly, corticated macrophytes were consistently collected from different study sites and seasons. In the less disturbed regions of Hurghada and Al-Quseir, four species of corticated macrophytes were recorded, while this was reduced to two species in Safaga. Despite limited data, the number of species of corticated macrophytes seemed to be influenced by environmental disturbance. Steneck and Dethier (1994) found that corti-cated macrophytes were abundant in environments with low disturbance. Moreover, in the studied sites, the cor-ticated macrophytes were all species of Rhodophyta, and Palisada perforata was found in all three sites. Although, the current data are limited, the species within the cor-ticated functional group could be a valuable indicator of environmental impacts because of their relatively con-stant presence and their sensitivity to environmental disturbance.

A characteristic feature of macroalgae of the Red Sea, Egypt, was the high β diversity values (Table 3), which had a mean value of 83.2 ± 2% dissimilarity among seasons. This could be the result of the small size ( < 20 cm length) and short life span ( < 1 year) of most algae in the region, so that more different species could be present in macroalgal assemblages depending on the availability of space, prop-agules, or spores (Gray 2000, Vroom et al. 2005, Mazarie-gos-Villarreal et al. 2012), eventually causing fast turnover (Shmida and Wilson 1985). Clear spatial effects were found to play an important role in determining species similar-ity, species replacement, and richness differences among sites. The spatial component may reflect niche processes (e.g., dispersal limitation and biotic interactions) and also the effects of some unmeasured environmental variables that are spatially structured (Legendre et al. 2005). High spatial turnover had been also found in macroalgae of the Mediterranean Sea (Balata et al. 2007a,b, Balata and Piazzi 2008). In these papers, variations in local envi-ronmental variables such as spatial heterogeneity, tem-perature, exposure, depth, and sediment are identified as key factors that influence macroalgal composition and turnover. Thus, β diversity seemed to be correlated with a number of factors that increase habitat complexity and heterogeneity.

β Diversity patterns are determined by two basic pro-cesses: the replacement of species and richness differ-ences. Assessing the relative role of each component in determining β diversity patterns is a fundamental task in biogeography, ecology, and conservation (Baselga 2007). However, species replacement and richness differences contributed to the high β diversity values (some points are close to the R apex, while others were close to the D apex in the SDR simplex plot, Figure 2). In our study,

species replacement was more important than differences in species richness in structuring macroalgal β diversity patterns, as the observed similarity patterns (Sjac and ΔS) were negatively related to species replacement and not to richness differences (Figure 3B,C). On the other hand, the appearance of new functional groups due to species replacement resulted in decreasing functional group simi-larity, which was necessarily accompanied by reduced taxonomic similarity between species (Figure 4). The presence of taxonomically close species that had similar functional groups was evident, as some functional groups were restricted to certain macroalgal phyla. For instance, leathery macrophytes were represented only by members of the Ochrophyta, siphonous algae and filamentous uni-seriate algae were restricted to the Chlorophyta, and corti-cated macrophytes and calcareous algae were found only in the Rhodophyta (Table 1).

When macroalgal assemblages of species-poor sites are subsets from rich sites, macroalgal assemblages tended to be more nested. The concept of nestedness is important in conservation biology because if nestedness values are high, the few species present in poor sites will be found elsewhere, thus, the richer sites contain more uncommon species that need preservation (Patterson 1987). β Diver-sity and nestedness are not independent features, and their joint evaluation is promising to reveal and explain ecological factors influencing community composition, structure, and functioning (Baselga 2010). Richness dif-ference is a component shared by β-diversity and nest-edness (Podani and Schmera 2011). In this investigation, richness difference did not contribute to the observed β diversity and nestedness patterns. However, the decline of species richness in areas subjected to high levels of natural or anthropogenic disturbance is a widespread pattern in marine ecosystems (Gray 1997). In the current study, the subtle difference in species richness between sites may be a consequence of unstable environmental disturbance, as severe environmental disturbance was not detected.

Rare species represent a challenge when analyzing ecological communities. This may be considered from either a biological or statistical viewpoint, and the latter was focused on to study the impacts of rare species on β diversity estimation. Rare species are perceived as impor-tant in aquatic systems and are usually retained for the analyses, as their preservation is often the ultimate aim of biological monitoring (Lenat and Resh 2001). The present results suggest that inclusion or exclusion of rare species in the analyses is not as important as the primary information included in the data matrices in the estima-tion of β diversity and nestedness using the SDR simplex approach (Figure 2B,D). Analyses including rare taxa had




slightly more discriminating power than those excluding rare species, but the fine discrimination of study sites was not improved notably (Table 2). Thus, to overcome this problem, the estimation of the importance of spatial scale in structuring macroalgae could be obtained using func-tional groups instead of species as aggregation level. On the other hand, estimation of α, γ diversity or taxonomic β diversity patterns was highly dependent on the number of taxa included in the analysis.

ConclusionTo conclude, any attempt to detect changes in community structure need to be conducted on the scale at which man-agement decisions are made. This study was conducted at different seasonal and geographical scales along with a disturbance gradient to detect community change and

β diversity pattern. Spatial heterogeneity was detected in species and functional groups. However, certain species and functional groups are found at both highly and less disturbed sites. This is possibly explained by the fact that certain species, over time, become so well adapted and have evolved survival strategies that enable them to with-stand environmental disturbance (Phillips et al. 1997). This can also explain why environmental disturbance did not result in variation in taxonomic similarity between sites. The presence of small thalli that have a short life cycle increased species replacement, which resulted in high β diversity, with spatial heterogeneity and environ-mental disturbance (changing physicochemical charac-teristics of seawater, especially phosphate) as drivers of β diversity.

Received 4 November, 2013; accepted 19 February, 2014; online first 21 March, 2014

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Spatio temporal and environmental factors influencing macroalgal β diversity in the Red Sea, Egypt

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