Refaat Et Al 2012- Mangrove Paper

8/3/2019 Refaat Et Al 2012- Mangrove Paper

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Environmental Research Jowml6 (1): 22-31, 2012ISSN: 1994-5396 Medwell Jowmls, 2012

Perennial Biomass Production in Arid Mangrove Systems on theRed Sea Coast of Saudi Arabia

1Refaat Atalla Ahmed Abohassan, 'Clement Akais Okia, 'Jacob Godfrey Agea,3James Munga Kimondo and 4Morag M. McDonald

'Faculty of Meteorology, Environment and Arid Land Agriculture,King Abdulaziz University, P.O. Bo x 80208 Jeddah, 21589, Saudi Arabia

2College of Agricultural and Environmental Sciences, Makerere University,P.O. Bo x 7062, Kampala, Uganda

3Kenya Forestry Research Institute, P.O. Bo x 20412-00200, Nairobi, Kenya4Bangor University, Bangor Gwynedd, LL57 2UW, UK

Abstract: Above and below biomass production were estimated in two Avicennia marina mangrove standsin Yanbu and Shuaiba regions on the Red sea coast of Saudi Arabia. Allometric equations were used to estimateabove grormd biomasses including stem, branches, leaves and total biomass while aerial and fine roots wereestimated using grmmd plots and random coring, respectively. Linear relationships on log-log scale with treeDBH and height as predictor parameters best described the biomass variations. The total abovegrmmd biomassin Shuaiba, (18.58 ha - 1) was significantly higher than that of Yanbu (1 0.77 t ha - 1) (p


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Environ. Res. J 6 (I): 22-31. 2012commmrity over a specified time. There are three mainmethods to estimate perennial biomass production (abovean d belowgrmmd) namely: tree clear cutting, mean-treebiomass and allometric equations. In the clear cut method,a tree is destructively sampled and thus frequentassessment of biomass increase is not possible. "While themean-tree biomass method requires even aged trees withhomogeneous tree size and thus cannot be applied innatural forest (Kamiyama et oZ . 2008). The allometricequations involve estimating the whole or partial weightof a tree from easily measured tree parameters such asDiameter at Breast Height (DBH). tree height. BasalDiameter (BD ). This method is considered robust andnon-destructive allowing estimation of temporal changesin forest biomass (Brovvn et al., 1989) thus commonlyused for perennial biomass production estimation.

Mangrove trees have relatively higher root to shootratio when compared to terrestrial forests, this ratioincreases in extreme (i.e., arid hyper saline and nutrientdeficient) environment (Iwasa and Roughgarden, 1984).Although, representing a significant biomass component,estimations of belowgrmmd biomass in mangroves arescarce. Most biomass studies on mangrove trees haveneglected estimating the belowground component mainlydue to several difficulties associated with quantitativesampling in intertidal habitats such as time wastage,equipment transportation and handling (Clough andAttiwill. 1982; Snedaker and Snedaker. 1984).

The environment of the Red sea is considered alimiting factor for the development and growth ofmangroves (Edwards and Head, 1987). The mangrovetrees are growing in hyper saline conditions reaching 41%salinity mainly as a result of low rates of rainfall and highevaporation. The area is also characterized by limitednutrient availability evident in the absence of riversestuaries and direct influx of water from the Indian ocean'Mangroves are also exposed to wide ranges of airtemperature. Shore air temperature is elevated to ratesthat are sometimes higher than desert temperatures(Edwards and Head. 1987). In addition. the Red seamangroves are growing on shallow sedimentation(averaging


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Environ. Res. J 6 (I): 22-31. 2012Tree measurement and sampling: In each site 120 treeswere randomly selected and measured for DBH, heightand density. I t should be noted that Avicennia marinatrees are well knmvn for their multi-stemmed and irregulargrowth characteristics (Clough et oZ 1997) (on average.there are 4-6 stems per tree). The following proceduredescribed by Snedaker and Snedaker (1984) andEnglish et ol. (1997) was used for measuring DBH ofindividual trees in such cases: if a tree forks at or belowbreast height, each forked stem is measured separately.However if a tree forks at or slightly above breast height,DBH at breast height is measured. On the other hand if anirregular grovvth or swelling is present at breast height,DBH is taken just above or below the irregularity.However, in the cwrent study, trees always forked atlevels lower than breast height. Hence, all stems within atree were measured for their DBH and then swnmed toobtain a DBH value per tree.Tree population density was estimated by cmmtingsingle trees within each of the study quadrates. Therequired nwnber of trees to be sampled for biomassestimation was determined following Stain's two stagesampling procedure (Hedayat and Sinha. 1991; Steel et oZ .1997) using DBH as an indicator for flie populationvariance and following the equation:

Where:nE'S't

Sample size(0.1 DBH" )'DBH varianceTabulated t value from flie t table at 0.05probability level

N Total nwnber of trees in the pre-sampledpopulation (120 tree)

From the pre-sampling, the estimated sample size was16 and 10 trees in Shuaiba andYanbu, respectively. Thesetrees were randomly sampled in each site, measured forDBH and height and then felled. Tree componentsincluding stem, branches and leaves were separated andweighed. Sub samples from each tree component weretaken for moisture content determination.Root biomass estimation: Weight and density of aerialroots were estimated for each site, 1 m2 quadrats wereplaced a t distances of one, 2 and 3 m away from mangrovetrees with a total of 108 quadrats were used in each sitefor aerial root estimation. All roots within quadrats werecu t at grmmd level, separated from dead roots, counted

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and weighted on site. Subsamples of roots were taken formoisture content determination which was later used toderive dry weight.

Fine roots biomass estimation was carried out usingrandom coring. Core samples were taken at 1 and2.5 m away from trees. For each distance, core sampleswere taken and sectioned by depflis into 0-10. 10-20.20-30. 30-40 and 40-50 em depflis. A total of 24 coresamples were taken from each site. Fine roots (


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Environ. Res. J., 6 (1): 22-31, 2012Table 2: Allometric models of above ground biomass components in Shuaiba and overall (Shuaiba+Yanbu) for Avicenniamarina mangroves grown on the

Red sea coast, Saudi ArabiaSites Components Equations r F pShuaiba Stern logbiomass=- 1.607+2.026logHt+0.552logDBH 0.57 8.54 **Branch logbiomass =- 1.621+0.542logDBH2Ht 0.36 8.02

Leaves logbiomass =- 2.841+ 1.585logHt+0.838logDBH 0.55 8.03 **Total logbiomass=- 1.145+1.793logHt+O. 777logDBH 0.54 7.76 **Overall Stern logbiomass =- 3.550+3.242logHt+O. 725logDBH 0.62 19.07 ***Branch Biomass=- 14.092+4.900Ht+0.506DBH 0.34 6.00 **

Leaves Biomass= 1.059+0.004DBff'Ht 0.47 21.66 ***Total Biomass=- 38.299+13.483Ht+l.242DBH 0.52 12.45 ****p =


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Environ. Res. J., 6 (1): 22-31, 2012Table 3: Aerial root density and biomass of Avicennia marina mangroves in Shuaiba and Yanbu regions, Saudi Arabia (standard deviations)

Shuaiba YanbuDistances (m) Density (m -2) Biomass (t ha - 1) Biomass (t ha-1)1 128.7821.41 23.54.42 133.9421.23 25.04.63 122.3617.54 22.64.0Mean 128.365.800 23. 71.2

8070

,...._60) 50i40

30! 20100

-10

96.42

IJ 0-10 emIJ 10-20 ema20-30cm30-40cm40-SOcm

[L_-1Shuaiba ..

39.12

....+-....1Yanbu

Fig. 2: Fine root biomass of Avicennia marina mangrovesin Shuaiba and Y anbu region, Saudi Arabia (errorbars are standard deviations; different lettersdenote significant differences (bracket values aretotal biomass in tha-I)

Aerial root biomass estimation: In Shuaiba, the numberof aerial roots was 129, 134 and 122 roots m - 2 at 1, 2 and3 m away from the trees, respectively. Their respectivebiomass was 23.5, 25 and 23 t ha -1, respectively. On theother hand, the number of aerial roots in Y anbu was141, 137 and 108 roots m-2 at 1, 2 and 3m away from thetrees, respectively and their respective biomass was 11.4,10 and 10 t ha -1 (Table 3). No significant differences werefound in aerial root density or biomass at any distance forany site (p>0.05). Generally, root density at Shuaiba(128.36 roots m-2) was not significantly different fromthat at Y anbu (128.54 roots m - 2) (p>O. 05). However, aerialroot biomass for Shuaiba (24 t ha-1) was significantlyhigher than that for Yanbu (1 0 t ha -1) (p>0 .001).Belowground biomass estimation: Almost all fine rootbiomass appeared at the top 30 em profile (Fig. 2). InShuaiba, 97% of fine roots were concentrated in thetop 30 em profile (93.47 t ha -1) with 52% of thatconcentrated in the top 10 em profile. In Y anbu, 98% ofroots were concentrated in the top 30 em profile(38.34 t ha-1) . However, 83% of that was concentrated inthe top 1 0 em profile. In addition, when the top 1 0 emprofile was compared between sites, it was found that fineroot biomass in Shuaiba was significantly higher than thatin Yanbu (p


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Environ. Res. J 6 (I): 22-31. 2012Table4: Com2arisons of aboveground biomass {t ha-'} of Avicenn ia manS!:ove ~ s t e m s arotmd the world {*mean values}

EnvironmetalSource SEecies Leaves {t ha-'} Branches {t ha-'} Stem {tha-'} Total {t ha-'} DBH range {em} H e i ~ t { m } condition Reg!onCurrent study A. manna 2.70 5.77 6.40Kairo et al. (2009) A. manna 1.38 4.20 6.10Medeiros and Sampio A. schauenana(2008)Saintilan (1997a, b) A. mannaTam et al. (1995) A. manna 0.62 4.90 2.90Mackey (1993) A. mannaWoodroffe (1985) A. mannaWoodroffe (1985) A. mannaMurray (1985) A. mannaDavie (1984) A. mannaClough and Attiwill A. manna(1975)Briggs {1977} A. manna

possibly contributed in limiting vertical tree grovvth.Moreover, the narrow growth space of the fr inge Y anbumangroves had possibly resulted in the denser treegrovvth and thus minimized diameter grovvth.Aboveground biomass estimationSite specific biomass estimation: As mentioned earlier,Avicennia marina trees were noticeably multi-stemmedand grovvth-irregular more in Yanbu than in Shuaiba andstems were branching at very low levels of the treetrunk. And in some cases may start below the soilsurface making it very difficult to differentiate stems frombranches. These characteristics could have caused errorsin estimating stem and branch biomass which may havecontributed to the insignificant prediction. Similar errorsin estimating A. marina abovegrmmd biomass using stemDBH were encmmtered in the literature (Tam et al., 1995;Clough et oZ.. 1997). Tam et oZ. (1995) working on stunted,irregular A. marina trees in China found no significantrelationships between the tree parameters (DBH andheight) and any biomass component. A more recent studyby Kairo et ol. (2009) studying aboveground biomass inGazi bay, Kenya of several species among which isA. marina has also found no simple relationships betweenDBH and any biomass component.

Although, no simple model describing the treebiomass for Y anbu was achieved, similar cases have beenreported in literature for Avicennia species (Tam et al.,1995; Kairo et oZ . 2009). In which a straightforwardrelationship between biomass and tree parameters was notfound or when compared to other species had lowcoefficient of determinations.

In Shuaiba site, stem biomass was the componentthat best predicted by allometric equations while leafbiomass was the least, this might be due to the fact thatleaves are susceptible to seasonal variations and thusmay cause variation in biomass sampling. Moreover,leaves are more vulnerable to environmental conditions

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14.77 3.3-20.2 2.5-3.8 Arid Saudi Arabia11.70 >5 Hot and humid Keny'2.76 3.4-10.2 3.1-7.5 Saline Brazil

56.10 0.5-2.0 Hyper saline Australia8.50 8.3-14.3 3.1-5.6 Humid China

341.00 1.4* Subtropical Australia6.80 >1 Temperate New Zeland

23.70 1-2.5 Temperate New Zeland21.70 4.4* 4.3* Humid Australia30.00 Temperate Australia86.00 Temperate Australia

128.00 58.1 * 7.34* Tem2erate Australia

such as wind and rain (Robertson and Alongi. 1992) andthus could lead to errors in biomass estimations. Inaddition such low leaf biomass prediction has beenfrequently reported in the literature (Kamiyama et al.,2000; Sherman et oZ . 2003; Ong et oZ . 2004; Soares andSchaeffer-Novelli. 2005; Smith and Whelan. 2006;Medeiros and Sampio. 2008).

I t should be noted that most of the publishedbiomass estimations in mangrove ecosystems were ofspecies other than A. marina particularly for species thatyield higher biomass such as Rhizophora mangle. Thismight be due to the low biomass of A. marina treescompared to other species. The multi-stemmed andgrowth irregularity features are other factors that couldhave made working with A. marina less attractive.

In similar cases where stems are forking close to thesurface level, it would be of great interest to useindividual stem diameter (per tree) just above the stemjunction and if present, the girth of common butts wherestems arose from. Clough et al. (1997) attained regressionequations for A. marina and Rhizophora stylosa usingindividual stem girths and common butts as biomasspredictors, the technique they used involved taking stemgirths at 10-15 em above stem junction and in case wherestems arose from a common butt at height of >20 emabove the ground, the butt girth was also recorded. Allstems, branches, leaves and total biomass were bestpredicted using these parameters with high significantcorrelation (r2 = 0.97 for total biomass). Anothe r study byComley and McGuinness (2005) working on A. marinaand following the same procedure as Clough et ol. (1997)also attained similar accuracy (r2 = 0.94 for total biomass).Moreover, crown diameter 1s sometime used mconjunction with DBH as other predictor for biomass(Ross et oZ.. 2001; Coronado-Molina et oZ.. 2004;Soares and Schaeffer-Novelli. 2005). In other studies.allometric equations of different species gave a betterprediction when wood specific gravity of each species


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Environ. Res. J 6 (I): 22-31. 2012was considered. Moreover, Kamiyama et al. (2005)reached a common allometric equation using DB H2Ht asbiomass predictor.

Overall aboveground biomass estimation: Although,reaching a model that can predict Yanbu's biomass wasno t achieved, a combined biomass (Table 4) of the twosites was predictable by allometric models which werecompared with biomass estimations of Avicennia speciesworldwide. According to Saenger and Snedaker (1993).the global estimations for mangrove biomass are between6.8 and 436 tha- 1. A. marina species fall in the lower halfof that wide range. The lowest reported estimation camefrom New Zealand (6.8 tha-1) and the highest estimationcame from Australia (341 t ha - 1). High biomassaccwnulations occur in tropical hwnid conditions wheretemperature and environmental conditions are favourable.In extreme conditions such as arid and temperateenvironments where temperature, salinity and nutrientemichment are limiting factors, few species can thrive andsuch areas are often mono-specific. Mangroves growingin such environments need to spend much of their energyproduction in mechanisms that help to cope with theenvironmental stresses reducing availability for biomassaccumulation (Robertson and Alongi. 1992). Suchmechanisms would include physiological adaptationssuch as salt filtration and extrusion, thick waxy leafsurfaces and morphological adaptations such as aerialand anchoring root systems.

The cment study of mangrove systems wasconducted in one of the most extreme environmentworldwide in fact the Red sea represent the northerngrowth limits of any mangrove species worldwide(For etol.. 1977; EEAA. 1998; Edwards and Head. 1987)thus A. marina species accmmts for 90% of mangroveson the Re d sea. The cment biomass estimations arecomparable to those estimations in extreme environments;the estimation of the Red sea mangrove of 14.8 t ha-1slightly higher than those reported in the closest regionof Gazi bay. Kenya (11.7 t ha-') (Kairo et oZ . 2009) andsometimes higher than other regions (8.5 t ha- 1 in Chinaand 6.8 t ha-' in New Zealand). To the best of theknowledge, this is the first study that has provided aquantitative estimation of aboveground biomass in theRed sea as previous research on mangrove productivityhas mainly focused on annual litterfall estimations andtree mensuration (Saifullah et oZ . 1989; Khafajiel ol.. 1991;Mandura. 1997. 1998). Therefore. the current biomassestimation can serve as baseline information that can beutilized when conducting biomass estimations in otherparts of the Red sea and for future comparisons.

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Belowground biomass estimationSite specific belowground biomass estimation: 1.1angrovebelowground biomass estimation is scarce and most ofthe biomass studies have neglected estimating thebelowground part of mangrove trees. This is mainly dueto several difficulties associated with quantitativesampling in intertidal habitats such as time conslllllption,equipment transportation and handling (Clough andAttiwill. 1982; Snedaker and Snedaker. 1984). In thepresent study, both Shuaiba and Yanbu had similar aerialroot densities, this might be related to the shallow andextensive underground cable root system of A. marina,this root system has to be very dense in order to not onlystabilize the tree bu t also by dispersing tree weight overa large area, to keep the trees from sinking into the mu d(Kamiyama et oZ.. 2008). Although. not different indensity, Shuaiba had a higher aerial root biomass thanY anbu, this might be attributed to the substrate,sedimentation and tree density of each site. Shuaiba'smangroves are basin with many in plantation lakes anddeep sedimentation (reaching approximately 1.8 m depth).This provides space and allow for higher root grovvth andbiomass. On the other hand, Yanbu's mangroves arefringe with higher tree density and shallow sedimentation(reaching approximately 60 em depth) offering very littlefor root biomass.

The top 1 0 em soil profile contains >50% of the fineroot biomass such high fine root biomass in top soilprofiles is commonly reported in literature. Lauff (1967)found that most of A. marina roots are concentrated atthe top 30 em below the ground level. Moreover,Tamooh et al. (2008) working also on A. marina in Kenyahas found that 65% of fine roots is concentrated in thetop 20 em soil profile. In addition, Kamiyama et ol. (2000)working on Ceriops tagal mangroves has found few rootspresent below that same depth. The high fine rootbiomass in the top 1 0 em profile obtained from the cmentstudy may be attributed to the mangrove adaptivemechanism for living in soft, saline and sometimes, ho tdry sediments (Briggs. 1977; Kamiyama et oZ 2008). Inaddition, the high root biomass in the upper profile mayalso be attributed to the anoxic environment that haltsroot growth into deeper soil profiles (Stafford-Deitsch,1996). The concent rated amount of roots in the top profilewould also facilitate efficient uptake of water andnutrients in the sediment layers which are characterizedby acclllllulated organic matter and relatively large amountof available nutrients as in terrestrial forests (Claus andGeorge. 2005).Overall belowground biomass estimation: Estimates offine root biomassin A. marina range globally from15-166 t ha-' (Table 5). As mentioned earlier. studies


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Environ. Res. J 6 (I): 22-31. 2012Table 5: A comparison ofAvicnmiam(U'ina fme root biomass (t ha- 1) from

various sourcesFine roots Environmental

Source biomass (t ha- 12 conditions LocationsCurrent shidy 67.77 Arid Saudi ArabiaTamooh et al. (2008) 41.4 Tropical KenyoSaintilan (1997a) 15-60 Hyper saline AustraliaSaintilan (1997b) 70.0-166 Subtropical Australia*Briggs (1977) 153.8 Temperate Australia*Mackey (1993) 118.6 Subtropical AustraliaAlongi (20022 21.2 Australia

of belowgrmmd biomass worldwide are limited and mostof reported studies were coming from Australia. Thevariations in the root biomass values reflect thedissimilarity in the environmental and regional conditionsat the different sites. Overall, the fine roo t biomass of thecwrent study was 67.8 t ha-l, close to the limits reportedin subtropical and hyper saline Australian environments(Saintilan, 1997a. b) and higher than those reported inKenya of 41.1 t ha-' (Tamooh et oZ . 2008).

I t should be noted that applying allometric equationsfor belowgrmmd biomass was not possible in the cwrentstudy due to the web spreading nature of the root systemwhich make assigning roots to specific trees impossible inaddition a complete extraction of the root system is adifficult and inapplicable process (Kamiyama et oZ.. 2008).The only estimate of A. marina belowground biomassusmg allometric equations was by Comley andMcGuinness (2005) who reported estimates of roots ataround 2 m radius since, it was impossible to trace rootsto their final destination. This study partitionedpercentage of common stem and common belowgroundbiomass according to relative stem diameter. However, thestudy reported poor relationships between DBH andbelowground root biomass owing to the limited rootestimate to the 2 m radius around the tree and whichunderestimate the true belowground biomass. Thus,studies on the allometric relationship of mangrove rootsare still needed due to the lack of study cases and to thedifferences in root extract ion methods.

In the cwrent study, the shoot to root ratio was 0.22which could be one of the smallest reported in theliterature. In his review research on mangrove biomassand productivity. Kamiyama et oZ. (2008) reported shootto root ratio of 12 mangrove stands ranging from 0.9-5with A. marina ranging from 0.9-2.8. Mangroves areknmvn to allocate a greater amount of their biomass to thebelowground root system in order to cope with theunstable, soft, anoxic, hypersaline and nutrient deficientsediments they grow on and to ensure stabilization andanchoring of the tree (Kamiyama et oZ.. 2008). Thisallocation of biomass into the root system can increaseswith aridity, light intensity and grazing rates (Iwasa andRoughgarden, 1984).

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CONCLUSIONThe regression equations developed in this study

would facilitate future estimation of abovegroundmangrove biomass in the Red sea. It is a valuable practicaltool that estimates biomass from easily measured treeparameters. However, applying these equations musthave the following considerations:

The regression equations are applicable when usedwithin the DBH and height range reported in this study.Site specific equations should be only applicable at thesame or similar sites only. The genera lized equation canbe used if an overall estimation of Red sea mangrovebiomass is desired.

When applicable, it would be of interest to useparameters that were reported good biomass predictorssuch as girt h at base, cro\Vll diameter, butt girth and wooddensity (in case of multiple species). Thus it is advisableto consider equation modification when necessary. Inaddition, the developed regression equation would aid inmonitoring annual biomass increment as a function of siteproductivity and health. This is specifically important forsites similar to Shuaiba in the Southern Red sea regionswhere A. marina grows bigger and are mixed with othermangrove species.

The cwrent investigation showed that A. marinabelowground biomass was greater than those estimatesobtained in East Africa and comparable to estimatesobtained in similar environmental conditions. Thus, thecwrent estimation will add a significant value to theregional estimates and to the global estimates of roots insimilar environments. Moreover, The cwrentbelowground biomass estimates are one of the very fewbelowground estimates done on A. marina trees.

RECOMMENDATIONTherefore, there is a great need for studies

addressing allometric relationships of roots due to thelack of reliable estimate and variations in the extractionmethods.

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Refaat Et Al 2012- Mangrove Paper