Methods of Studying Mangroves

3.3. Methods of studying Mangroves

Prof. K. Kathiresan

Centre of Advanced Study in Marine Biology Annamalai University

his chapter deals with methods of studying mangroves, with some modifications of the internationally recognized scientific methods as

proposed by the Australian Institute of Marine Sciences (English et al., l997). While studying a mangrove area, baseline data on area map, area description, tidal amplitude, rainfall, evapo‐transpiration in the study area have to be collected.

3.3.1. Tidal flooding/inundation:

The frequency and duration of tidal flooding is important in determining the zonation, distribution and species composition of mangrove forests. Some workers have been content to subdivide mangrove areas into low‐, mid‐ and high‐intertidal areas. This is somewhat arbitrary division, hard to quantify and makes comparisons difficult between areas with differing tidal regimes. A more quantitative approach was used by Watson (1928), who divided mangrove areas into 5 inundation classes:

Class 1: inundated by all high tides

Class 2: inundated by medium high tides

Class 3: inundated by normal high tides

Class 4: inundated by spring tides; and

Class 5: occasionally inundated by exceptional or equinoctial tides

In many cases, these inundation classes may be quantified in terms of number of times per month that an area is inundated tidally. However, Watson’s classification scheme is still somewhat arbitrary and its use is restricted largely to the mangrove areas of Malaysia, for which it was originally devised. There is a need for more quantitative description of tidal inundation classes that is both ecologically and hydrologically meaningful across a wide range of tidal regimes.

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3.3.2. Forest structure of vegetation:

In spite of numerous papers on mangrove floristics, systematics, phytogeography and related topics, there is only little published information available on mangrove forest structure. The comparative value of many of the available studies is not as great as it could be; author’s differing research goals have led to the adoption of non‐uniform measurement techniques, yielding results that are often difficult to compare with others. The architecture of a forest is influenced by the magnitudes and periodicities of such forcing functions as tides, nutrients, hydroperiod, and stressors like hurricanes, drought, salt accumulation and frost. Because the action of these factors varies widely over geographic regions, mangrove stands exhibit wide regional and local variation in structural characteristics. Also, where species diversity is high, structural variation is even greater (English et al., 1997).

In 1974, Lugo and Snedaker developed a classification scheme for mangroves based on tidal and hydroperiod characteristics. Implicit in their scheme was the assumption that this environmental factor was the most important component of the energy signature of a mangrove forest (Lugo and Snedaker, l974). This classification system, further modified by Cintron et al. (1980), serves to identify some common patterns of mangrove response to varying environmental conditions and it remains a useful framework for the first‐approximation in classification of mangrove stands. As amended, it recognized three general forest types: riverine, fringe and overwash, and basin. Dwarf, scrub and hammock mangroves are recognized as special sub‐types responding to localized geologic of edaphic conditions. As a general rule, riverine forests exhibit the highest level of structural development, followed by basin, and finally by fringe and overwash types.

In 1973, researches began searching for the most meaningful ecosystem parameters to use in rapid characterization of mangrove stands over wide geographic areas; the methods and parameters selected needed to be simple, time‐cost effective and universally applicable. A survey of some twenty‐five mangrove stands in Florida, Puerto Rico and Costa Rica was undertaken to test the methods chosen (Pool et al., 1977). Since then similar techniques have been used to describe Puerto Rican mangroves (Martinez et al., 1979), mangrove stands in Brazil (Novelli et al., 1980) and Costa Rica (Jimenez, 1981). The structural data available as of 1980 have been reviewed by Cintron et al. (1980) and by Cintron and Novelli (1983). By analyzing the forest

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vegetation characteristics to calculate the following (Cintron & Novelli, 1984):

Complex Index: It denotes the diversity and abundance of flora within the forest community. It is calculated combining the number of species, stand density, basal area and height.

Importance value index (IVI): It indicates the structural importance of a species within a stand of mixed species. It is calculated by summing up the relative percentages of basal area, density and frequency, each weighed equally for each species, relative to the same dimensions for the entire stand.

Data to be collected and processed in the following steps:

• The study site will be selected using GPS

• Transact line is perpendicular to the shoreline. The length depends on the vegetation type (dense and sparse; zonation pattern)

• The plot dimension is 10 m x 10 m.

• Within each plot, counts are made for tree counts; and in 1 m x 1 m sub‐plot counts are made for seedlings and sapling.

• Counting the numbers of three class of maturity namely, trees, saplings and seedlings by species within the plot

• Trees more than 4 m are measured species wise for numbers and Girth at a breast height of 1.3 m

• Saplings between 1‐4 m and seedlings below 1 m are counted species wise for numbers

• Measure height of all the individuals species wise

• No. of benthic individuals of major groups in 0.25 x 0.25 m quadrates will be counted.

Data processing:

• Density is measured species wise and total in each plot as follows:


o Density of each species (no/ha)= no. x 10,000 m2 / area of plot in m2

o Total density of all species = sum of all species densities

• Basal area is measured species wise and total in each plot as follows:

o Basal area (m2) of each species = 0.005 x DBH

o Total basal area of all species (m2/ha)= sum of all species basal area / area of plot in m2 x 10,000 m2

• Relative density = no. of individuals of a species / total no. of individuals of all species x100

• Relative dominance = total basal area of a species / basal area of all species x 100

• Relative frequency = frequency of species/ total frequency of all species in different plots x 100

• Importance value of a species = relative density + relative dominance + relative frequency

• Complex Index = number of species + stand density + basal area + height.

• Species diversity is described according to the Shannon index (H) based on importance value of a species (Ni) and sum of importance value for all the species (N).

H = Ni/N log Ni/N

3.3.3. Forest leaf area index, net canopy photosynthesis and biomass

Measurements of light absorption by the forest canopy are used to estimate leaf area index (m2 leaf area m‐2 ground area). The leaf area index may then be converted to net canopy photosynthesis using the average rate of photosynthesis per unit leaf area. The method is useful for comparisons between forests over a wide range of forest types and distributions, and for monitoring changes in a particular forest. However, it does not provide a reliable estimate of the net primary productivity (English et al., 1997).

The method described by Bunt et al. (1979) has been used widely in recent years to estimate ‘potential’ primary productivity in mangrove forests. The method was based on that originally described by Kirita and Hozumi (1973) in studies of oak forests, where the amount of light

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absorbed by the mangrove canopy is related to the total canopy chlorophyll content. Canopy chlorophyll concentration was then multiplied by a rate of carbon fixation per unit of chlorophyll to give an estimate of ‘potential’ primary productivity. However, recent work suggests that figures calculated using the method of Bunt et al. (1979) significantly underestimate ‘potential’ primary productivity.

English et al., (1997) proposed uses of the same techniques to collect data, and the same primary dataset as the method of Bunt et al. (1979), but a different, and theoretically more robust, method of calculation to provide an estimate of canopy leaf area index. This index can be multiplied by the average rate of canopy photosynthesis (if it is known) to provide an estimate of net canopy according to the relationship:

I=Ioe‐kL

where: I = photon flux density beneath the canopy

Io = photon flux density incident on the top of the canopy (in this case at ground level in a fully exposed position outside the canopy)

L = leaf area index (m2 leaf area m‐2 ground area)

K = canopy light extinction coefficient that is determined

by the angle and spatial arrangement of the leaves

This relationship is used widely in agriculture and forestry. The equation can be rewritten as L= loge (I/Io) (m2 leaf area m‐2 ground area)

‐k

This equation allows the direct estimation of leaf area index from the ratio of light flux density below and above the canopy (I/Io), and the canopy light extinction coefficient, k.

The ratio, I/Io, is measured using the same techniques as that described by Bunt et al. (1979). However, English et al. (1997) recommends that the value of k used to obtain an estimate of leaf area index should be 0.5. This is based on a number of studies that have shown that k commonly lies between 0.4 and 0.65 in mangrove canopies, with an average about 0.5. Using a value for k of 0.5, and values for I/Io obtained from field measurements, it is possible to obtain an estimate of leaf area index (L.) Net canopy photosynthesis per unit leaf area (A) and day length values for leaf area index vary with species, soil salinity, and


climatic conditions: in harsh conditions (hot, dry, and cloudless climate and a high soil salinity of 25 ppt). A is usually about 0.216 g C m‐2 leaf hour‐1; under favourable conditions (cloudy and humid climate and a low soil salinity of 20 ppt) A may reach 1.0 g C m‐2 leaf hour‐1 (Andrews and Muller, 1985; Clough and Sim, 1989; Cheeseman et al., 1991). While it is desirable to measure the actual rate of photosynthesis at each site, approximate rates of 0.216 g C m‐2 leaf hour‐1 can be used for harsh conditions, and 0.648 g C m‐2 leaf hour‐1 for favourable conditions (English et al., 1997). Action:

Measure light intensity above canopy or in open space (Io) and under canopy (I) between 10 a.m – 2 p.m , using a lux meter to measure leaf area index (in unit uE/m2/sec)

Data processing:

Leaf area index and net canopy photosynthesis are calculated as follows:

Leaf area index = loge (I/Io) / ‐k m2 leaf area /m2 area of ground (where k value is 0.5)

Leaf area index correction = Leaf area index x Cos (O x 3.141593/180) (where 0 is zenith Angle of the sun for a given latitude, longitude, date and time of day from internet).

Net canopy photosynthesis = Leaf area x rate of photosynthesis (0.216 g C /m2) x day length

Measurements of Girth at Breast Height (GBH) or Diameter at Breast Height (DBH) can be used to calculate above ground biomass using allometric relationships between GBH (or DBH) and the biomass of individual plant parts (Ong et al., 1984; Putz and Chan, 1986; Clough and Scott, 1989). Coefficients for these allometric relationships for a number of species are summarized by Clough (1992). Recently a common allometric equation for estimating the tree weight of mangroves has been proposed (Komiyama et al., 2005) as follows.

Leaf weight = 0.126p (D2B)0.848

Above ground weight = 0.247p (D2)1.23

Root weight = 0.196p0.899 (D2)1.11

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Tree trunk weight = 0.687p (D2H)0.931

Where D – Trunk diameter at breast height at 30 cm above ground in Rhizophoraceae members.

DB – Trunk diameter at the lowest living branch

H – tree height

P –wood density of trunk

3.3.4. Mangrove Soil analysis

Soil characteristics are one of the most important environmental factors directly affecting mangrove productivity and structure. The major physical and chemical properties of the mangrove soils are pH (hydrogen ion concentration), Eh (Redox, potential), salinity and particle size.

The acidity of the soil influences the chemical transformation of most nutrients and their availability to plants. Most mangrove soils are well buffered, having a pH in the range of 6 to 7, but some have a pH as low as 5. Measurement of the acidity or alkalinity of soils using pH must be done with fresh samples to avoid oxidation of iron pyrites (a common constituent of mangrove soils) to sulphuric acid, thus giving a much lower value of pH than normally occurs in situ (English et al. 1997).

Since mangrove soils are typically waterlogged, and hence anaerobic, microbial decomposition takes place through a series of oxygen‐reduction (redox) processes. The redox potential (Eh) is a quantitative measure of reducing power which provides a diagnostic index of the degree of anaerobiosis or anoxia (Patrick and Delaune, 1977). Totally anoxic sediments have redox potentials below ‐200 mV, while typical oxygenated soils have potentials of above +300 mV. The measurement of Eh has been used as a rapid means of assessing the potential impact of additional organic input to marine sediment (Pearson and Stanley, 1979). Reliable measurements of redox require great care to minimize exposure of the soil sample to air (English et al., 1997).

The salinity of mangrove soils has a significant effect on the growth and zonation of mangrove forests. The majority of mangrove species grow best in low to moderate salinities (25 ppt), although there appear to be marked differences in the ability of species to tolerate very


high salinities. In the past, soil salinity was measured in pore water that drained into a hole made by removing a sediment core. This is not a reliable measure of soil salinity because of uncertainly about the source of water filling the core hole. The method, in which pore water is physically squeezed from the soil sample, is preferred (English et al., 1997).

Two methods are presented for the analysis of soil particle size: a ‘hydrometer method’ (after Bouyoucos, 1962) and ‘pipette method’ (after Buchanan, 1984). All soils and sediment (unconsolidated or ‘loose’ deposits) are composed of particles with a wide range of sizes. These are generally divided into 3 major groups: gravel (greater than 2 millimetres), sand (0.062 ‐ 2 millimetres) and mud (silt and clay). The mud fraction is further divided into coarse silt (62‐15.6 μm), fine silt (15.6‐3.9μm) and clay (less than 3.9 μm). A graded scheme for soils is given by the Wentworth Grade Scale (Folk, 1974). The species composition and growth of mangroves is directly affected by the physical composition of mangrove soils. The proportions of clay, silt and sand, together with the grain size, dictate the permeability (or hydraulic conductivity) of the soil to water, which influences soil salinity and water content. Nutrient status is also affected by the physical composition of the soil with clay soils, which are generally higher in nutrients than sandy soils (English et al., 1997). Action:

• If it is soft soil, a corer of 50 cm height and 5cm diameter is used.

• If it is hard soil, dig a hole at 50 cm using a crowbar.

• The soil at 2 depths namely 10 cm and 40 cm will be measured for temperature using a thermometer with 0.5° accuracy and for pH and redox potential using a pH/millivoltmeter with platinum electrode.

• The soil collected at the two depth will be strained using a 20 ml syringe to collect pore water for analysis of salinity using a refractometer.

• The soil samples collected at the two depth will be transferred to laboratory immediately in sterile polythene bags and analysed for moisture, nutrients, trace elements and microbial counts as well as soil composition analysis.

• For each site, three replicates from each sampling plot are used.

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References Bouyoucos, G.J., 1962. Hydrometer method improved for making particle size

analysis of soils. Agronomy J., 54: 464‐465.

Buchanan, J.B., 1984. Sediment analysis. In: Holme N.A. and A.D. McIntyre (eds.), Methods for the study of Marine Benthos. Blackwell Scientific Publictaions, London, pp. 41‐65.

Bunt, J.S., 1979. Studies on mangrove litter fall in tropical Australia. In: Clough, B.F. (ed.), Proc. Australian Natl. Mang. Workshop, Australian Institute of Marine Science, Cape Ferguson, 223‐237.

Cintron G., A.E. Lugo and R. Martinez, 1980. Structural and functional properties of mangrove forests. Symposium signaling the completion of the ‘Flora of Panama’. Panama City, University of Panama.

Cintron, G. and Y.S. Novelli, 1984. Methods for studying mangrove structure. In: Snedaker, S.C. and J.G. Snedaker (eds.), The Mangrove Ecosystem: Research Methods. Unesco, Paris, pp. 91‐113.

Cintron, G., A.E. Lugo and R. Martinez, 1980. Structural and Functional Properties of Mangrove Forests. Symposium Signalling the Completion of the ‘ Flora of Panama’. Panama City, University of Panama.

Clough, B.F. and B.G. Sim, 1989. Changes in gas exchange characteristics and water use efficiency of mangroves in response to salinity and vapour pressure deficit. Oecologia, 79: 38‐44.

Clough, B.F. and K. Scott, 1989. Allometric relationships for estimating above‐ground biomass in six mangrove species. Forest Ecol. Manage., 27: 117‐127.

Clough, B.F., 1992. Primary productivity and growth of mangrove forests. pp. 225‐249. In: Robertson, A.I. and D.M. Alongi (eds.). Trophical Mangrove Ecosystems. American Geophysical Union, Washington, DC. 329 p.

English, S., C. Wilkinson and V. Basker, 1997. Survey manual for tropical marine resources (2nd Ed). Australian Institute of Mar. Sci.. Townsville, pp.119‐195.

Folk, R.L., 1974. Petrology of sedimentary rocks. Hemphill Publishing Co., Austin, Texas, 182p.

Koch, M.S. and S.C. Snedaker, 1997. Factors influencing Rhizophora mangle L. seedling development in Everglades carbonate soils. Aqua. Bot., 59(1‐2): 87‐98

Komiyama, A., Sasitorn Ponungparn and Shogo Kato, 2005. A common allometric equation for estimating the tree weight of mangroves. J. Trop. Ecol., 21: 471‐477.

Lugo, A.E. and S.C. Snedaker, 1974. The ecology of mangroves. Annual Review of Ecology and Systematics, 5: 39‐63.


Martinez, R., G. Cintron and L.A. Encarnacion, 1979. Mangroves in Puerto Rico: A Structural Inventory. San Juan, Department of Natural Resources, (Final Report office of Coastal Zone Management, NOAA), 149 p.

Ong, J.E., W.K. Gong, C.H. Wong and G. Dhanarajan, 1984. Contribution of aquatic productivity in managed mangrove ecosystem in Malaysia. In: Soepadmo, E., A.N. Rao and D.J. Macintosh (eds.), Proc. Asian Symp. Mang. Env.: Res. & Manage. University of Malaya, Kuala Lumpur, pp. 209‐215.

Patrick, W.H. Jr. and R.D. Delaune, 1977. Chemical and biological redox systems affecting nutrient availability in the coastal wetlands. Geoscience and Man, 18: 131‐137.

Pearson, T.H. and S.O. Stanley, 1979. Comparative measurement of the redox potential of marine sediments as a rapid means of assessing the effect of organic pollution. Mar. Biol., 53: 371‐379.

Pool, D.J., S.C. Snedaker and A.E. Lugo, 1977. structure of mangrove forests in Florida, Puerto Rico, Mexico and Costa Rica. Biotropica, 9: 195‐212.

Putz, F.E. and H.T. Chan, 1986. Tree growth, dynamic and productivity in a mature mangrove forest in Malaysia. Forest Ecol. Manage., 17: 211‐230.

Methods of Studying Mangroves

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