quantitative assessment of the intertidal environment of kuwait ii: controlling factors

Journal of Environmental Management (1997) 51, 333–341

Quantitative Assessment of the Intertidal Environment of Kuwait II:Controlling Factors

D. Al Bakri∗, A. Khuraibet† and M. Behbehani‡

∗Orange Agricultural College, The University of Sydney, PO Box 883, Orange,NSW 2800, Australia. †Department of Environmental Health, College of HealthSciences, Public Authority of Applied Education, Shuwaikh, Kuwait.‡Department of Zoology, University of Kuwait, PO Box 5969, Safat 13060,Kuwait.

Received 3 March 1996; accepted 16 June 1997

Multivariate analysis of variance, canonical correlation and canonicaldiscriminate analysis were performed on environmental data collected from theKuwaiti intertidal zone to determine the statistical relationships between thesediment, chemical, biological, spatial and temporal variables.

The quantitative analysis showed that the sediment type was the paramountfactor influencing the distribution and composition of the intertidal benthicfauna. The muddy, sandy and rocky shores support three distinct faunalcommunities. The seasonal variation in the benthic fauna community was notstatistically significant but variation in biological data due to differences intransect location and tidal level were significant. This spatial variation has beenattributed to differences in sediment and substrate types. The physico-chemicalparameters were found to correlate positively with the mud content of thesediments but were unimportant in the overall composition of the intertidalfauna. This paper complements the results reported in Al Bakri et al.(1997–Part I) in establishing a valuable benchmark for assessing environmentalimpacts and for developing sustainable coastal zone management in Kuwait.The integrated approach outlined here could be adopted to develop a soundbasis for the protection and management of coastal environments andresources in similar areas. 1997 Academic Press Limited

Keywords: coastal management, statistical analysis, benthic fauna, sediment,intertidal ecosystem, integrated assessment.

1. Introduction

This paper builds on and extends the work reported in Al Bakri et al. (1997–Part I).The aim of this collective effort was to undertake a comprehensive and rigorousenvironmental assessment of the intertidal zone of Kuwait in order to facilitate thedevelopment of several environmental related policies. By employing the cluster analysis

0301–4797/97/120333+09 $25.00/0/ev970153 1997 Academic Press Limited

Intertidal Environment of Kuwait II334

technique in Part I of the paper, it was possible to classify the intertidal zone of Kuwaitinto five distinct sub-environments. The classification was estabished by determiningthe resemblance between sampling stations on the basis of their bio-physical data, ingeneral, and number of taxa and density of benthic fauna, in particular. Cluster analysisdid not, however, provide a rigorous assessment of all environmental factors relatedto the the intertidal benthic ecology. To achieve a holistic and quantitative assessmentof the intertidal environment, this paper (Part II) employed multivariate analyses totest and verify the inter-relationships of the bio-physical parameters that emerged inPart I and to determine the effects of the spatial and temporal variations on theintertidal benthic ecosystem. In other words, this paper attempts to define the mostimportant environmental factors governing the type and distribution of intertidal faunalcommunities that should be considered when assessing the coastal ecosystem of Kuwait.

The main interrelationships of the biological, physical, chemical, spatial and temporalfactors are discussed here, their effects on the distribution of the benthic fauna aredescribed, and implication for monitoring and management of the coastal system areoutlined.

2. Methods and materials

The field study involved collecting samples and undertaking measurements from 35transects along the intertidal area of Kuwait (Figure 1) to determine the sediment/substrate type, the benthic fauna and the physico-chemical characteristics. Details ofthe field survey, sampling program, laboratory results and related information arereported in Al Bakri et al. (1997) and Al Bakri et al. (1985).

Multivariate analyses were used to determine the statistical relevance of relationshipsbetween the physical, chemical, spatial, and temporal parameter, on one hand, and thebiological communities found in the intertidal zone on the other. To examine therelationship between more than one dependent variable and more than one independentvariable, three types of multivariate analysis were performed using the statistical analysissystem (SAS) group of programs (SAS Institute, 1982). A multivariate analysis ofvariance (MANOVA) technique was used to examine the relationship between threedependent variables measured on the interval scale (total species count, number oftaxa, and species evenness) and three independent predictor variables (season, transect,or station) measured on the nominal scale. The canonical correlation analysis wasapplied to examine the multivariate relationships between the sediment data (the sixgrain size categories: mud, fine sand, medium sand, coarse sand, gravel and rock), thebiological (total species count) and the chemical data. The canonical discriminantanalysis was used to relate the biological and chemical data to the substrates (muddy,sandy and rocky shores).

3. Results

3.1. (ANOVA) (MANOVA)

The null hypotheses (Ho) tested by ANOVA were:

(1) There is no difference in evenness due to transect or season.(2) There is no difference in numbers of taxa due to transect or season.(3) There is no difference in mean density due to transect or season.

D. Al Bakri, A. Khuraibet and M. Behbehani 335

Kuwait Bay

IRANIRAQ

Shatt Al Arab

Memlaha KuwaitCity

Shuwaikh Port

Shuaiba

Arabian Gulf

Khiran

Nuwaisib

N

Bubiyan

IRAN

Zogros rangeArabian Gulf

SaudiArabia

IRA

Q

200 km

Sand spit

KUWAIT

SAUDI ARABIA

SulaibikhatBayDoha

Failaka

Khor

Sublya

A

B

CE

D

F

G

H

KL

M

N

OP Q

RST

UV

WX

YAI

AA

ZABAC

AF AD

AH

AE

AG0 15

Kms

J

Figure 1. Coastal area of Kuwait and location of studied transects (A, B. C, . . . AG).

(4) There is no difference in evenness due to station height (tidal level) or season.(5) There is no difference in numbers of taxa due to station or season.(6) There is no difference in mean density due to station or season.

The null hypotheses tested by MANOVA were:


(1) There are no overal transect or seasonal effects on biological variables.(2) There are no overall station or seasonal effects on biological variables.

The results of the MANOVA run for the 10 transects samples in all four seasonsshowed a significant overall effect of transect (spatial or geographical location) onbiological variables, and no significant overall seasonal effect. The ANOVA tests showed:

(1) No significant difference in evenness due to transect (PR>F=0·1040) or season(PR>F=0·7246).

(2) A significant difference in numbers of taxa due to transect (PR>F=0·0001), andno significant difference due to season (PR>F=0·8962).

(3) A significant difference in mean density due to transect (PR>F=0·0001), and nosignificant difference due to season (PR>F=0·0549). The season effect was stat-istically significant, but at the lower PR>F=0·1 level.

The results of MANOVA for all 35 transects samples only in winter and summer hadvirtually the same results (i.e. a significant overall transect effect on biological variables)and no significant overall seasonal effect. The ANOVA tests showed:

(1) No significant difference in evenness due to transect (PR>F=0·0711) or season(PR>F=0·0547). There was, however, a significant difference due to both transectand season at the PR>F=0·1 level.

(2) A significant difference in numbers of taxa due to transect (PR>F=0·0001), andno significant difference due to season (PR>F=0·2735).

(3) A significant difference in mean density due to transect (PR>F=0·0001), and nosignificant difference due to season (PR>F=0·5229).

MANOVA results for the 35 transects indicated that there was an overall effect ofstation height on the biological variables. The results of ANOVA showed:

(1) There were significant differences in evenness due to station height (PR>F=0·0001),and no significant differences due to season (PR>F=0·8287).

(2) There were significant differences in number of taxa due to station height (PR>F=0·0001) and no significant differences due to seasons (PR>F=0·3673).

(3) There were significant differences in mean density due to station height (PR>F=0·0001) and season (PR>F=0·0348).

(4) Within individual stations, season was important in determining differences inevenness (PR>F=0·0001), numbers of taxa (PR>F=0·0001), and mean density(PR>F=0·0001).

Although the ANOVA resulted in significant differences for evenness, numbers oftaxa, and mean density due to stations and season, the R2 values for these models werelow, (0·24, 0·25, 0·33, respectively), therefore a large part of the variation in thesebiological variables (67–76%) was due to replication error. This indicates that a largernumber of sample replicates is necessary to reduce this source of variance. This resultshould be considered in the design of future studies.

In contrast, a much greater percentage of the variation in the biological variablesis explained by the models for transect difference, particularly for mean density andnumber of taxa. The ANOVA test for the 10 transects produced R2 values of 0·51 forevenness, 0·80 for mean density, and 0·81 for number of taxa. The ANOVA tests forthe 35 transects produced similar results. The R2 values were 0·57 for evenness, 0·79for mean density, and 0·77 for number of taxa. This means that between 51 and 57%


of the variation in evenness, 70 and 80% of the variation in mean density, and 77 and81% of the variation in number of taxa was due to transect, season, and transect–seasoninteraction. The remainder of the variation was caused by replication error. Sincesignificant differences (PR>F=0·05) were found only for mean density and number oftaxa due to transect, the conclusion is that transect or geographical location along theKuwait coastline was most important in determining these two biological variables.

3.2.

The canonical correlation analysis related the six grain size categories as the “X”variables to five chemical variables (pH, temperature, salinity, water content anddissolved sulphide) and the log transformed mean density for 91 intertidal species asthe “Y” variables. The 91 species were those that appear in five or more sampledstations. A selection criteria of “occurrence at five or more stations” was chosen, sincea species appearing in only a few stations would not be important in explaining thevariance of the data.

The analysis yielded two significant combinations of canonical variables at the a=0·05 level. Both canonical variables have large R2s (>0·97), indicating a strong re-lationship between the “Y” and “X” variables. PR>F=0·0001 for canonical variable1 and 0·0012 for canonical variable 2. Canonical variable 1 is positively correlated withrock (r=0·91) and negatively correlated with mud (r=−0·58) and fine sand (r=−0·43).Those biological variables that are positively correlated with canonical variable 1 areassociated with rock. The species most correlated with rock were gastropoda of thespecies Cronia margariticola (r=0·54) and Cerithium caeruleum (r=0·44), polychaetaspecies Pomatoleios kraussi (r=0·50), echniodermata species Asterina burtoni (r=0·45),and bivalvia species Lithophaga malaccana (r=0·43). These five species either attachthemselves to rocks or bore into them. Chemical variables did not correlate with therock substrates.

The biological and chemical variables negatively correlated wth canonical variable1 are associated with the mud flats. The most important species are chordata speciesof mudskippers (r=−0·52), and crustacea of the species Macrophthalmus pectinipes(r=0·50) and Eurycarcinus orientalis (r=−0·44), all are mobile fish and crab species.The three chemical variables of water content, temperature, and salinity are lesscorrelated with mud flats (r=−0·24 to−0·31). The fine sand sediment type is negativelycorrelated with canonical variable 1 since it is a main constituent of mud flats.

Canonical variable 1 describes the difference between rock and mud species. It doesnot describe the difference in mean species count for the rock-sand and mud-sandspecies. Canonical variable 2 is positively correlated with find sand (r=0·65), mediumsand (r=0·33), and coarse sand (r=0·36) and negatively correlated with mud (r=−0·71). The species and chemical variables negatively correlated with canonical variable2 are intimately associated with mud tidal flats. The most important are Macrophthalmuspectinipes (r=−0·57), mudskippers (r=−0·50), and water content (r=−0·51).

There are only two species positively correlated with canonical variable 2, crustaceaEmerita holthuisi (r=0·24) and gastropoda Umbonium vestiarium (r=0·26). The bio-logical community of the sand is not as distinct as the biological communities of therock and mud sediments. The differences between the mud biological species and rockand sand biological species differ in both count and type. The difference in the biologicalcommunities of the rock and sand is not due to type of species but to number of species(taxa) and total count. More species exist on rock substrates than on sand, even though


the sand species is a sub-set of the rock species. The results of the canonical correlationanalysis indicate that the six sand clusters identified by the cluster analysis in Al Bakriet al. (1997–Part I) should be grouped together for the canonical discriminate analysis.The differences between the species types of the different sand clusters were not distinctto justify six separate sand groups.

3.3.

Based on the results of the canonical correlation analysis, it was decided to use threesediment/substrate clusters: sand; rock; and mud. The sand group comprises 52% ofthe total stations sampled; the rock and mud groups contain 26 and 22%, respectively.The purpose of the canonical discriminant analysis was to create two canonical variables(since there are only three analysis groups, only two variables could be formed) thatwould explain the differences between the groups. Mean densities plus the chemicalvariables were employed to create the discriminating variables.

The analysis yielded two significant discriminant functions (PR>F=0·0001 forfunction 1 and 0·0003 for function 2). Both functions have a strong relationship betweenthe set of discriminating variables and discriminant function. The smallest canonicalR2 is 0·92 for function two. Function one is positively correlated with rock andnegatively correlated with mud. Function two is positively correlated with mud andnegatively correlated with sand. A plot of the function scores for canonical discriminantfunction one v. the function scores of canonical function two clearly shows that thethree groups have distinct biological communities. The sand community (group 1) loadsnegatively on both functions 1 and 2. The rock community (group 2) loads positivelyon both functions. The mud community (group 3) loads positively on function twoand negatively on function one.

Echniodermata of the species Asterrina burtoni (r=0·48), anthropoda of the speciesBalanus amphitrite (r=0·45), bivalvia species Lithophaga malaccana (r=0·45), poly-chaeta species Pomatoleios kraussi (r=0·50), and chordata species of tunicates (r=0·40) are all correlated with rock. They attach directly to rock because they requirehard substrates for their existence. The snails (gastropoda) Cerithium caeruleum (r=0·46) and Strombus persicus (r=0·38) crawl over and feed off rock surfaces or prey ontaxa associated with rock. These taxa probably also require firm substrates on whichto lay their eggs. Gastropoda of the species Cronia margariticola (r=0·56) was thesingle individual species most highly correlated with rock.

Gastropoda Cerithium cingulata (r=−0·21), crustacea of the species Eurycarcinusorientalis (r=–0·43), Macrophthalmus pectinipes (r=−0·53) and Scopimera scabricauda(r=−0·32), echiurida Ikeda taeninoides (r=−0·36), chordata species of mudskippers(r=0·54), and polychaeta of the species Nephtyidae spp. (r=−0·30), were all correlatedwith function one and associated with mud and sandy mud sediments. The same mudspecies were important in function two, which explains the difference between mud andsand sediment groups.

There were only four species correlated with the sandy sediment group: Crustaceaof the species Emerita holthuisi (r=−0·27) and Uca annulipes (r=−0·20), and gastro-poda of the species Nassarius plicatus (r=−0·22) and Umbonium vestiarium (r=−0·23).Crustacea species Pagurus spp. (r=+0·27 for function one, r=−0·27 for functiontwo) was correlated with both the rock and sand sediment type.


4. Discussion

4.1.

Both the multivariate analysis carried out in this paper and the cluster analysis reportedin Al Bakri et al. (1997) have shown that sediment type was the primary environmentalparameter controlling the distribution and abundance of the intertidal macrofaunacommunity. The canonical correlation and canonical discriminant analysis were con-clusive in identifying three distinct biological communities corresponding with muddy,rocky, and sandy shores. The mud group correlates significantly with 11 species. Themost highly correlated species were: mudskipper, M. pectinipes, E. orientalise, Ikedataenioides, and S. scabricauda. The mud group was the only sediment type that correlatedwith the physico-chemical parameters. Mud was moderately well correlated with watercontent and weakly with temperature, pH, and dissolved sulphide.

The rocky tidal flat has a greater number of taxa or variety of life than other intertidalhabitats. The rock group was significantly correlated with 39 species; of these, C. mar-gariticola, B. amphitrite, L. malaccana, P. kraussi, C. caeruleum, A. burtoni, S. persicus,and tunicates had the highest correlation. Rocky substrates provide the hard surfacesnecessary for suspension feeders to attach themselves and their eggs. Other benthic fauna,such as snails, crawl over the hard surfaces and prey on the fauna attached to rocks(Wagner and Togt, 1972). This may explain why the highest number of taxa and meandensity of benthic organisms was found in the rocky shores. The biological communityof the sandy sediment group is not as distinct as those of the rock and mud groups. Infact, there was a great overlap in community species composition between rock and sand,the difference is due to the number of taxa rather than the type of species. Only five specieswere found to correlate significantly with the sand group: E. holthuisi, U. vestiarium,Pagurus spp., N. plicatus, and U. annulipes. The subgroups of the sand sediments (fine,medium, and coarse sands) did not show distinct communities.

On the basis of these results, it can be concluded that changes in sediment type,particularly when the change is made from one main sediment group to another (e.g.from mud and sand), would have significant impacts on the composition of the faunalcommunity of a given area. This study, however, did not indicate conclusively whetherchanges within the sand group (e.g. from medium to coarse sand) would have asignificant impact on the faunal community. Further studies may need to be performedto provide more elaboration on this point.

4.2.

ANOVA and MANOVA tests showed that the greatest differences in communitystructure, principally number of taxa and mean density, were explained by differencesbetween transects (i.e. geographical location), and that tidal height (station) wasimportant but subordinate. It is obvious that the effects of transect and stations areindirectly related to the influence of sediment/substrate type. Changes from one transectto another or from one tidal level to another will most likely be accompanied bychanges of substrate. The ANOVA/MANOVA tests did not determine, however, wherethese differences occurred.

4.3.

The ANOVA/MANOVA tests showed that seasonal differences with respect to faunalcomposition are unimportant compared to the transect and station differences, but


seasonal differences within the individual stations were found to be moderately sig-nificant. It is believed that local movements of the mobile benthic fauna up and downthe transects from season to season, is responsible for this difference. This movementcould be related to the seasonal changes in the width of the areas uncovered, whichmeans variation in exposure time to sun radiation (heat) and amount of water contentin the sediments. It is recommended, therefore, that future monitoring or assessmentstudies of the overall composition of the macrofauna community of the intertidal zoneshould not emphasize temporal variation, but rather the variation across and along thetidal flat (spatial variation).

4.4. -

The annual mean of the physico-chemical parameters recorded in this study (tem-perature, salinity, pH, water content, dissolved sulphide, and TOC) were not determiningfactors in the overall distribution or composition of the intertidal organisms. This isconsistent with the findings of the cluster analysis carried out by Al Bakri et al. (1997).

Close examination of the individual stations with abnormal values revealed thatextreme high salinity (55–75 0/00) and high temperature in the upper intertidal zone ofnorthern Kuwait Bay, appears to have had some negative impact on the number oftaxa and abundance of some species, but only in the immediate area. It was interestingto note that the macrofauna community was relatively undisturbed by the highconcentrations of TOC and dissolved sulphide. Comparing areas with maximumconcentrations of these two parameters (relatively polluted sites) such as SulaibikhatBay, Kuwait City and the Shuaiba area, with areas of similar nature but relativelyclean, revealed no significant degree of degradation in the faunal communities. It seems,therefore, that the benthic macrofauna have adapted well to these conditions anddeveloped a certain tolerance to that level of oil and organic pollution. Continuingwith the activities that cause this pollution, however, may lead to serious damage tothe coastal ecosystem. It is recommended, therefore, that current disposal practices becontrolled and closely monitored. It is also essential to assess the effect of the GulfWar’s oil spill on the benthic ecology of the intertidal zone.

4.5.

Some of the major environmental hazards facing the study area are the filling anddredging activities associated with land reclamation. The impacts of these reclamationactivities not only include losing all or part of the upper intertidal flats, but also themodification of the physical nature of the adjacent tidal flats. Such a modificationwould naturally be accompanied by loss of the ecosystem and death or migration ofthe inhabitants of the affected areas. Details on the impact of land reclamation of thecoastal ecosystem in Kuwait are given in Al Bakri (1996). As far as possible, controlshould be introduced, to limit the damage being caused by land reclamation, particularlyin areas where the tidal flat is narrow.

5. Conclusions

This paper together with Part I (Al Bakri et al., 1997) provides an integrated en-vironmental baseline, that can be used for the development of environmental policiesfor the protection and management of the coastal zone in Kuwait. Since this effort was


based on a survey completed prior to the Gulf War, the results provide an excellentbenchmark for evaluating the extent and damage caused by the war. There is an urgentneed to undertake a follow up investigation, of this work, in order to improve theunderstanding of the long-term impact of the war on the coastal zone of Kuwait.Approaches similar to those described in these two papers have been successfullyapplied elsewhere to assess environmental impacts and to use benthic fauna as aguide to protection and management of coastal zones (e.g. Jan and Chang, 1991;Mastrantuono, 1986; Reddy and Rao, 1991).

In addition to confirming the validity of the environmental classification of theintertidal zone described in Part I, this paper extends our knowledge about the intertidalecosystems by identifying the principal factors influencing the intertidal benthic faunaand by providing insight into the relevant bio-physical processes. In order to carryout appropriate assessment of the Kuwaiti coastal environment in the future, it isrecommended that the findings of the study be carefully considered in the design andimplementation of related investigations and monitoring.

The study has re-enforced the need for adopting an integrated approach whendealing with complex environmental systems such as the coastal ecosystem. Theapproach developed in this investigation provides an effective tool to undertake aholistic and integrated bio-physical assessment which is fundamental for assessing thetotal health of the ecosystem and understanding the natural constraints of the coastalenvironment. Given the serious environmental problems and land use conflicts facingcoastal managers and planners worldwide, this approach provides a good frameworkfor the development of environmentally sound decision-making process regarding themanagement and monitoring of the coastal systems in the Arabian Gulf and ArabianSea, in particular, and other parts of the world, in general.

This paper is based on data obtained from a research project funded by the EnvironmentProtection Council of Kuwait and carried out at the Kuwait Institute for Scientific Research.Special note of thanks are extended to the team members of the project EES-35 for their valuablecontributions throughout the study. The authors are inbedted to the Environmental Science andEngineering Inc., of Gainsville, Florida for their assistance with the statistical analysis.

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quantitative assessment of the intertidal environment of kuwait ii: controlling factors

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