MSc. Thesis - Lang'at[1].pdf

Assessment of Forest Structure, Regeneration and Biomass 

Accumulation of Replanted Mangroves in Kenya 

Joseph Kipkorir Sigi LANG’AT 

A Thesis submitted to Graduate School in Partial Fulfillment for the 

Award of Master of Science Degree in Natural Resources Management of 

Egerton University 

Egerton University 

September 2008

DECLARATION 

I hereby declare that this thesis is my original work and has not been presented for the 

award of a degree in any university and that all the sources used herein have been 

acknowledged. 

Joseph Kipkorir Sigi Lang’at Reg. No. NM11/1066/03 

APPROVAL BY SUPERVISORS 

ii 

Signature……………………… 

Date…………………………… 

This research thesis has been submitted for examination with my approval as 

University Supervisor 

Dr. M. Karachi, Signature………………………. 

Department of Natural Resources, 

Egerton University, 

P. O. Box 536, 

Njoro, Kenya. Date…………………………….. 

Dr. J. G. Kairo, Signature………………………. 

Kenya Marine and Fisheries Research Institute, 

P. O. Box 81651, 

Mombasa, Kenya. Date……………………………..

COPYRIGHT 

All rights reserved. No part of this thesis may be reproduced, stored or transmitted in 

any form or by any means without prior permission of either the author or Egerton 

University. 

iii

ACKNOWLEDGEMENT 

I am very grateful to my supervisors; Dr. Moses Karachi (Egerton University) and Dr. 

James G. Kairo (Kenya Marine and Fisheries Research Institute) who offered insights 

in my field work as well as the preparation of this thesis. 

I am also obliged to thank Mr. Bernard Kivyatu (Kenya Forest Service) for his 

technical support during data collection. The student fraternity at KMFRI’s Gazi Sub- 

station; Mr. Geoffrey Bundotich, Mr. Fredrick Tamooh (MSc. Students, Egerton 

University) and, Dr. Bernard Kirui (Napier University, Scotland) are thanked for their 

moral support and encouragement. 

This study was supported by Alcoa Foundation’s Conservation and Sustainability 

Fellowship Program for which my supervisor Dr James Kairo was a Practitioner 

Fellow (2006). Additional support to this project was solicited from Earthwatch’s 

Mangrove of Kenya Project through my supervisor Dr James Kairo. I am grateful to 

Dr. Mark Huxham (Napier University, UK) and Dr. Martin Skov (University of 

Southampton, UK) for accepting me to work in the Earthwatch Project. 

I would like to thank my parents and family members for their moral and financial 

support that has enabled me to accomplish this study. Finally yet importantly, I am 

very grateful to the members of the of Gazi village for the warm welcome and 

cooperation they accorded to me. 

iv

ABSTRACT 

With the realization of the consequences of mangrove degradation, many reforestation 

projects have been initiated in some parts of the world; in Kenya reforestation of 

degraded mangrove areas has been going on since 1991. The established plantations 

now provide a great opportunity to assess forest structural development and biomass 

allocation with forest age. The objective of this study was to assess forest structure; 

regeneration and biomass accumulation in reforested Rhizophora mucronata (Lamk.) 

and Bruguiera gymnorrhiza (Lam) stands that were established in 1994 at Gazi Bay 

and Ramisi respectively. Tree height and stem diameter measured at 1.3 m above the 

ground (also referred to as DBH or D130) were determined for all trees with stem 

diameter ≥ 2.5 cm in 22 and 10 plots of 10 x 10 m 2 each for R. mucronata and B. 

gymnorrhiza respectively. Juveniles of all species in 5 x 5 m 2 (inside 10 x 10 m 2 

plots) were identified, and counted. Fifty R. mucronata and sixteen B. gymnorrhiza 

trees were harvested at Gazi and Ramisi respectively. Fresh weights of all the plant 

components were determined in the field and representative samples were taken to the 

laboratory to determine wet-dry weight conversion factors. Merchantable (stem) and 

non-merchantable (branches and stilt roots) volume were determined using the 

Smalian’s formula and volume-weight conversion factors respectively. From the 

harvested trees allometric equations to estimate standing biomass and volume were 

developed using either DBH alone or in combination with height. Belowground 

biomass for R. mucronata was estimated using modified coring technique. 

Rhizophora mucronata and B. gymnorrhiza plantations had a stand density of 5,132 

and 4,600 stems per hectare respectively. The mean canopy height and stem diameter 

were: R. mucronata; 8.37 ± 1.09 m and 6.24 ± 1.87 cm and B. gymnorrhiza; 4.69 ± 

1.06 m and 3.60 ± 0.82 cm respectively. The total biomass for R. mucronata was 

131.6 t/ha; giving a biomass accumulation of 10.96 t/ha/yr. The aboveground biomass 

for B. gymnorrhiza was estimated to be 16.65 t/ha, with a biomass accumulation of 

1.38 t/ha/yr. The stand volume was 100.44 and 14.81 m 3 /ha for R. mucronata and B. 

gymnorrhiza respectively. Rhizophora mucronata plantation had 4,886 juveniles/ha, 

while that of B. gymnorrhiza had 18,030 juveniles/ha. These results indicate the 

potential use of mangrove rehabilitation as a management tool of the system in 

Kenya. Information generated from this study has a wide application among Forest 

sector in Kenya as well other stakeholders interested in mangrove management. 

v

TABLE OF CONTENTS 

TITLE………………………………………………………………………………….i 

DECLARATION .......................................................................................................... ii 

APPROVAL BY SUPERVISORS ............................................................................... ii 

COPYRIGHT ............................................................................................................... iii 

ACKNOWLEDGEMENT ........................................................................................... iv 

ABSTRACT ...................................................................................................................v 

TABLE OF CONTENTS ............................................................................................. vi 

LIST OF TABLES ....................................................................................................... ix 

LIST OF FIGURES .......................................................................................................x 

ABBREVIATIONS ..................................................................................................... xi 

DEFINITION OF TERMS ......................................................................................... xii 

1.0 INTRODUCTION .............................................................................................1 

1.1 Background Information ....................................................................................1 

1.2 Statement of the Problem ...................................................................................2 

1.3 Overall Objective ...............................................................................................2 

1.4 Specific Objectives ............................................................................................2 

1.5 Research Questions ............................................................................................2 

1.6 Justification ........................................................................................................2 

2.0 LITERATURE REVIEW ..................................................................................4 

2.1 Mangrove Biogeography ...................................................................................4 

2.2 Mangrove Environment .....................................................................................5 

2.2.1 Climate .......................................................................................................5 

2.2.2 Sediment Characteristics ............................................................................5 

2.2.3 Tidal Inundation .........................................................................................6 

2.2.4 Salinity .......................................................................................................6 

2.2.5 Shelter from Waves....................................................................................7 

2.3 Valuation of Mangroves ....................................................................................7 

2.4 Threats to Mangroves ........................................................................................8 

2.5 Sustainable Management of Mangrove Forests .................................................9 

2.6 History of Reforestation and Management of Mangroves ...............................10 

2.7 Mangrove Reforestation in Kenya ...................................................................10 

vi

3.0 MATERIALS AND METHODS .....................................................................11 

3.1 Study Area .......................................................................................................11 

3.1.1 Gazi Bay...................................................................................................12 

3.1.2 Ramisi ......................................................................................................15 

3.2 Study Design ....................................................................................................15 

3.2.1 Soil Factors ..............................................................................................16 

3.2.2 Forest Structure ........................................................................................16 

3.2.3 Forest Biomass .........................................................................................17 

3.2.4 Stand Volume...........................................................................................19 

3.2.5 Natural Regeneration ...............................................................................19 

3.2.6 Primary Production ..................................................................................20 

3.3 Data Analyses ..................................................................................................20 

4.0 RESULTS ........................................................................................................21 

4.1 Soil Factors ......................................................................................................21 

4.2 Forest Structure ................................................................................................21 

4.3 Forest Biomass .................................................................................................24 

4.3.1 Aboveground Biomass .............................................................................24 

4.3.2 Belowground Biomass .............................................................................26 

4.3.3 Biomass Partitioning ................................................................................26 

4.3.4 Biomass Accumulation ............................................................................27 

4.4 Stand Volume...................................................................................................28 

4.5 Natural Regeneration .......................................................................................29 

4.6 Primary Production ..........................................................................................30 

5.0 DISCUSSIONS ................................................................................................31 

5.1 Soil Factors ......................................................................................................31 

5.2 Forest Structure ................................................................................................31 

5.3 Forest Biomass .................................................................................................33 

5.3.1 Aboveground Biomass .............................................................................33 

5.3.2 Belowground Biomass .............................................................................34 

5.3.3 Biomass Partitioning ................................................................................35 

5.3.4 Biomass Accumulation ............................................................................36 

5.4 Stand Volume...................................................................................................37 

vii

5.5 Natural Regeneration .......................................................................................38 

5.6 Primary Production ..........................................................................................39 

6.0 CONCLUSIONS AND RECOMMENDATIONS ..........................................40 

7.0 REFERENCES ................................................................................................41 

8.0 APPENDICES .................................................................................................50 

8.1 Appendix 1. Data used to develop allometric equations for R. mucronata .....50 

viii

LIST OF TABLES 

Table 1 Mangrove species found in Kenya and their uses.................................................11 

Table 2. Soil physical-chemical characteristics in reforested and un-forested sites ..........21 

Table 3. Structural characteristics for R. mucronata and B. gymnorrhiza 

plantations ..............................................................................................................22 

Table 4. Stand table for R. mucronata and B. gymnorrhiza plantations ............................24 

Table 5. Different allometric models derived from different independent variables 

for biomass estimation in reforested mangrove stands ..........................................25 

Table 6. Allometric equations for estimating biomass for R. mucronata and B. 

gymnorrhiza plantations.........................................................................................25 

Table 7. Allometric equations for estimating wood volume for R. mucronata and 

B. gymnorrhiza plantations ....................................................................................28 

Table 8. Juvenile densities in R. mucronata and B. gymnorrhiza plantations at 

Gazi and Ramisi .....................................................................................................29 

Table 9. Quality classes for mangrove poles at Gazi Bay and Ramisi ..............................33 

Table 10. Biomass table for replanted R. mucronata stand at Gazi bay ............................34 

Table 11. Volume (m 3 ) table for replanted R. mucronata stand at Gazi bay .....................38 

ix

LIST OF FIGURES 

Figure 1. Map of Gazi bay ................................................................................................13 

Figure 2. Map of the Kenyan south coast showing the study site: Ramisi ........................15 

Figure 3. Size class distribution for R. mucronata and B. gymnorrhiza plantations .........22 

Figure 4. Height-diameter distributions for R. mucronata and B. gymnorrhiza 

plantations ..............................................................................................................23 

Figure 5. Belowground root biomass distribution for R. mucronata plantation at 

Gazi ........................................................................................................................26 

Figure 6. AGB distribution amongst tree components for R. mucronata plantation .........27 

Figure 7. Biomass partitioning in relation to tree size in R. mucronata plantation ...........27 

Figure 8. BEF-Stem volume relationship for Rhizophora plantation at Gazi bay .............29 

Figure 9. Variability of AGB allocation in relation to tree size for Rhizophora 

plantation................................................................................................................36 

x

AGB Aboveground biomass 

BGB Belowground biomass 

DAS 

D130 

ABBREVIATIONS 

Diameter above the highest stilt for Rhizophora species 

Tree diameter at breast height (measured at about1.3 m above ground) 

FAO Food and Agriculture Organization of the United Nations 

KFS Kenya Forest Service 

ha Hectares 

ITCZ Intertropical Convergence Zone 

IV Importance Value 

LAI Leaf area index 

RC Regeneration Class 

UNEP United Nations Environment Program 

WRI World Resource Institute 

WWF Worldwide Fund for Nature 

xi

DEFINITION OF TERMS 

Allometric equation: refers to an equation derived from the relationship between size 

and shape of an organism. 

Billet: A portion of pole or log subdivided to a convenient length so as to reduce 

effects of tapering in stem volume estimation. 

Juveniles: refers to young mangrove trees that have not reached exploitable stage 

(usually

1.1 Background Information 

1.0 INTRODUCTION 

Mangrove forests constitute rich and complex ecosystems, which provide a range of 

important goods and services. However, they have been subjected to high rates of 

destruction around the world, and will be one of the first ecosystems to be affected by 

predicted sea level rise (Valiela et al., 2001; Saenger, 2002; McLeod and Salm, 2006). 

The mangrove forests of Africa have never been the object of any large-scale forest 

management. They have been mostly overlooked in national laws, and no appropriate 

use or protection methods have been applied to them (CEC, 1992). 

Poor management practices in general have caused severe widespread degradation of 

mangroves around the world (Choudhury, 1997). In most countries with mangrove 

formations no systematic silviculture or management is applied to the mangrove 

forest resources, with exception of Asia, yet they are being harvested and used 

extensively (Hussain, 1995). Probably because of lack of legal framework and 

appropriate management, human pressure on mangrove for fuel wood and timber is 

considerably increasing (CEC, 1992). Rhizophora mucronata is the most heavily used 

mangrove species because it is heavy and fine-grained and, with its short seasoning 

period, can be used as little as one to two months after cutting (CEC, 1992; Abuodha 

and Kairo, 2001). Major problems facing mangroves of Kenya are lack of appropriate 

management plans, inadequate knowledge of mangrove silviculture, of multiple use 

potentials of resources, and of techniques of natural regeneration (Abuodha and 

Kairo, 2001). 

Efforts have been made in Kenya at rehabilitating degraded mangrove areas. A 

program of replanting mangroves was initiated at Gazi Bay, in 1991. Five principal 

species; R. mucronata, Ceriops tagal (Perr.) C. B. Robinson, Sonneratia alba Sm., 

Avicennia marina (Forsks) Vierh. and Bruguiera gymnorrhiza (L) Lamk., were 

planted as mono stands in their natural areas (Kairo, 1995). Assessment of the 

reforested mangroves at Gazi bay has been restricted to early development and growth 

performance, floral and faunal secondary succession (Bosire et al., 2003; 2004; 2006), 

and estimation of aboveground biomass (Kairo, 2001). Very limited work has been 

carried out to assess the yield of these forests based on their structural attributes and 

biomass increment. The focus of this study was, therefore, to investigate how stand 

1

structure and biomass develop with increasing age in R. mucronata and B. 

gymnorrhiza plantations established in 1994 at Gazi bay and Ramisi respectively. 

1.2 Statement of the Problem 

Unlike the terrestrial tropical forestry, mangrove forestry has received little attention 

in Kenya and in the East African region. As a result, there is limited quantitative 

information on production of mangrove plantations. This study was carried out to 

generate information on structural development and biomass accumulation of 

replanted mangrove forests. In addition, this study aimed at assessing soil physico- 

chemical factors in the forested and non-forested sites. 

1.3 Overall Objective 

Assess structural characteristics, natural regeneration and biomass of reforested R. 

mucronata and B. gymnorrhiza mangrove stands at Gazi bay and Ramisi respectively 

and the influence of reforestation on soil factors. 

1.4 Specific Objectives 

1. Examine how reforestation has improved soil physico-chemical factors. 

2. Construct local stand and volume tables for R. mucronata and B. gymnorrhiza 

plantation at Gazi bay and Ramisi, Kenya. 

3. Quantify standing biomass and volume as an estimation of sequestered carbon. 

4. Assess stand composition of natural regeneration pattern of reforested stands. 

1.5 Research Questions 

1. Has mangrove reforestation contributed to improvement of soil physico-chemical 

factors? 

2. How are size-classes distributed in reforested mangrove stands? 

3. What is the potential role of replanted mangroves in carbon sequestration? 

4. How has mangrove reforestation influenced stand composition and natural 

regeneration pattern? 

1.6 Justification 

Managing mangrove ecosystem requires information to understand and predict 

changes in ecosystem structure and function. With the upsurge of reforestation 

2

programs worldwide (Field, 1998) there is need to examine structural development of 

replanted mangroves. Reliable estimates of biomass and production are essential for 

describing the current states of forests, for assessing the yield of commercial products 

from forests, and for the development of sound silvicultural practices, for predicting 

the consequences of forest changes (e.g. in age-size structure). Accurate 

quantification of carbon stocks in forests is very crucial in understanding the role of 

forests in mitigating climate change through carbon sequestration (Brown, 2002) for 

national and international carbon accounting and monitoring requirements (Comley 

and McGuiness, 2005). 

Forest biomass has been estimated conventionally by allometric relationships between 

standing biomass and structural variables such as D130 and height (Clough and Scott, 

1989; Ong et al., 2004; Soares and Scaeffer-Novelli, 2005; Kirui et al., 2006). A lot 

of published studies on mangrove biomass estimates exist for natural forests 

(Saintilan, 1997; Ross et al., 2001; Sherman et al., 2003; Comley and McGuiness, 

2005; Soares and Scaeffer-Novelli, 2005) and for managed plantations in the South 

East Asia region (Putz and Chan, 1986; Ong et al., 1995; 2004); but information on 

biomass and production for East African mangrove plantations is scanty. Considering 

the important role played by mangroves, it is important to assess the structure of 

replanted stands. The R. mucronata and B. gymnorrhiza plantations in Kenyan south 

coast provide an opportunity to evaluate biomass accumulation in reforested 

mangrove stands. 

Considering that mangrove forests provide about 70 % of wood requirement by the 

local people along the Kenyan coast (Wass, 1995), results from this study 

demonstrates the value of reforestation in increasing the wood volume. Based on the 

projection of wood supply and demand, Kenya forest sector is expected to experience 

a deficit of 6.8 million m 3 by the year 2020 (KFMP, 1994); therefore, in order to 

offset this scenario reforestation of degraded areas especially for mangroves need to 

be seriously undertaken. 

3

2.0 LITERATURE REVIEW 

Mangroves are plant formations of tropical and subtropical coastlines, usually 

between 25 o N and 25 o S latitude(Tomlinson, 1986). However, as an exception to this, 

they are found as far north as the southern Japan (32 o N) and as far south as southern 

Australia and New Zealand (38 o S) (Macnae, 1968). Local environmental factors such 

as temperature patterns; both sea surface and air temperatures, warm sea current, 

frost, salinity stress, wave action, among others, determine the occurrence of 

mangroves beyond these latitudinal limits (Duke, 1992; Ricklefs and Latham, 1993). 

The term ‘mangrove’ describes both the ecosystem and the plant families that have 

developed specialized adaptations to live in this tidal environment (Tomlinson, 1986). 

Mangrove forests consist of 50–75 species in 20–26 genera in 16–20 families 

(Tomlinson, 1986; Duke, 1992; Kathiresan and Bingham, 2001). They occur over a 

diversity of geomorphological settings; river-dominated, wave-dominated, river and 

wave-dominated, tide-dominated, drowned bedrock valleys and carbonate 

setting(Thom, 1967, 1982), giving rise to regional variation in structure and 

productivity (Twilley, 1995). 

Mangrove forests are estimated to have occupied 75 % of the tropical and subtropical 

coastlines (Farnsworth and Ellison, 1997), but due to widespread degradation their 

coverage has reduced to 50 % (Spalding et al., 1997). The total estimated area of 

mangroves worldwide varies between 18 and 20 million ha (Spalding et al., 1997). 

However, recent estimates suggest that mangrove coverage area is about 15.2 million 

ha, down from 18.8 million ha in 1980 (FAO, 2007). 

2.1 Mangrove Biogeography 

Globally mangroves are divided broadly into two main regions; the Indo-West Pacific 

(IWP) and the Atlantic Caribbean and East Pacific (ACEP) or the New World-West 

Africa regions. The former region comprises of East Africa, the Red Sea, India, South 

East Asia, Southern Japan, the Philippines, Australia, New Zealand and the Pacific 

islands east to Samoa. The latter region is composed of the Atlantic coast of Africa 

and the Americas, the Gulf of Mexico, the Pacific coast of tropical America, and the 

Galapagos Islands (Duke, 1992). Over a third of the total area of world’s mangroves 

occurs in South East Asia. It is in this region that mangroves are richest in species 

4

composition and most luxuriant in growth. Overall species richness of mangroves 

declines from a peak of 30 species in the South East Asia to less than 10 in the ACEP 

region (Duke, 1992). 

Kenya has about 54,000 ha of mangroves distributed along the coastline (Doute et al., 

1981). Lamu and the Tana River Districts hold 70 % of mangrove forests in Kenya. 

Less extensive mangrove areas occur in Kilifi, Mida creek, Mtwapa in the north and 

Mombasa, Gazi and Vanga - Funzi areas in the south. 

2.2 Mangrove Environment 

Mangrove ecosystems vary greatly regionally in response to environmental factors. 

The most important environmental factors that influence mangrove growth and 

distribution include: 

2.2.1 Climate 

Mangroves attain climax growth only under tropical conditions where atmospheric 

temperature in the coldest month is higher than 20 o C and seasonal fluctuations do not 

exceed 5 o C (Kathiresan and Qasim, 2005). Richest mangrove communities occur in 

latitudes where sea surface temperature average is 24 o C (Agrawala et al., 2003). Any 

further increase in temperature may lead to proliferation of some species, provided 

that the direction of ocean currents facilitates seed dispersal (Kathiresan and Qasim, 

2005). 

Availability of freshwater is an important factor in growth and development of 

mangrove forests. Freshwater supply has often been indicated by the ratio of rainfall 

to evapotranspiration. Under humid conditions, where the ratio exceeds 1, the 

mangroves grow luxuriantly. However, in arid climates, where it falls below 1, 

mangroves are stunted (Kathiresan and Qasim, 2005). High rainfall in humid areas 

leaches out residual salts from mangrove soil and thus encourages the growth of 

mangroves (Jimenez, 1992). 

2.2.2 Sediment Characteristics 

Although the hardier species can grow on rocky areas such exceptions are few and 

generally soft fine-grained substrates are necessary for proper mangrove development. 

5

Consequently, the best growth and development of mangroves takes place in alluvial 

soils and muddy substrate which are formed by the deposition of water-borne soil 

particles (Macnae, 1968; Chapman, 1976). The species composition and growth of 

mangroves is directly affected by the physical composition of mangrove soils. The 

proportions of clay, silt and sand dictate the permeability 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 generally higher in nutrients than 

sandy soils (English et al., 1997). Nutrient concentration is one of the important 

factors in mangrove habitats. Two major elements, nitrogen and phosphorus, are of 

great significance for the growth of mangroves (Kathiresan and Qasim, 2005). 

2.2.3 Tidal Inundation 

Mangroves grow in the intertidal areas of protected coastlines. The tidal range in 

association with the intertidal slope determines potential extent of mangroves. A 

greater tidal range increases the intertidal area and depending on the slope of the 

substrate, which if smooth, encourages the growth of mangroves. The topography of a 

mangrove swamp is normally smooth and flat. However, any rise or fall in the land 

can greatly influence the direction and rate of flow of the water in the system 

(Kathiresan and Qasim, 2005). Horizontal distribution of the species is related to the 

duration of tidal flooding and the tidal range and many species of mangroves respond 

differently to different tidal regimes (Chapman, 1976). Tides also are important for 

the exchange of nutrients and materials between mangrove areas and other adjacent 

marine ecosystems. In addition, seeds and propagules are dispersed by water, 

consequently their distribution is greatly influenced by tides (Hussain, 1995). 

2.2.4 Salinity 

Salinity plays a vital role in the distribution of species, their productivity and growth 

(Twilley and Chen, 1998). Changes in salinity are normally controlled by climate, 

hydrology, rainfall, topography and tidal flooding (Kathiresan and Qasim, 2005). 

Mangroves generally tolerate higher salinity than non-mangrove plants, but they are 

poor competitors under non-saline areas where freshwater marsh plants grow better. 

Nevertheless, tolerance to saline conditions varies among mangroves. For example, R. 

mucronata seedlings do better in salinities of 30 ppt, while R. apiculata flourishes 

6

etter at 15 ppt. In general, mangrove vegetation is more luxuriant in low salinities 

(Kathiresan and Qasim, 2005). 

2.2.5 Shelter from Waves 

Mangroves grow only in sheltered shores. Protection can be provided by reefs of coral 

lying along the edge of the continental shelf. A chain of islands protects other 

shorelines, as winds tend to run parallel to the shore. As such the most extensive 

growth of mangroves can be seen in estuaries of rivers and protected lagoons and bays 

(Chapman, 1976). The soils in mangrove areas are characterized by salt and water, 

low oxygen and high hydrogen sulphide contents and high proportion of humus 

(Macnae, 1968). Therefore, these soils are very anoxic except for the surface layer in 

which roots spread. As a result, mangroves generally have shallow root systems and 

therefore cannot withstand strong wind. Thus, they grow better in a sheltered habitats 

(Hussain, 1995). 

2.3 Valuation of Mangroves 

For centuries, mangrove ecosystems have provided goods and services to the people 

at community, national and global levels (Hamilton and Snedaker, 1984; Rönnbäck, 

1999; Dahdouh-Guebas et al., 2000). They provide the local people with wood 

products including timber, poles, posts, fish traps, firewood and charcoal (Saenger, 

2002; FAO, 2007). The annual economic value of mangroves has estimated to range 

from $200,000 – 900,000 per km 2 (Costanza et al., 1997). Mangrove forests act as 

natural barriers against strong waves and other natural oceanic catastrophes. They 

have been observed to attenuate waves thereby reducing their impacts considerably 

(Othman, 1994). During the 2004 tsunami strike, shorelines with healthy mangroves 

suffered relatively less loss of human lives and property than those denuded of 

mangroves (Dahdouh-Guebas et al., 2005). Much of the fisheries production largely 

depend the mangrove ecosystems (Rönnbäck, 1999). Thus mangroves form nursery 

and feeding grounds for commercial and artisanal fisheries, and are important habitats 

and feeding grounds for a range of benthic and pelagic marine animals and bird 

species (Macnae, 1968; FAO, 1994; Saenger, 2002; FAO, 2007). In addition, they 

enhance environmental quality by reducing coastal erosion, trapping of sediments and 

other pollutants from activities upstream thereby maintaining water quality. 

Mangroves also act as sources and sinks of carbon within the tropical coastal zones 

7

(Gong and Ong, 1990; Twilley et al., 1992; Ong, 1993; Ong et al., 1995) and 

therefore, play an important role in mitigating effects of climate change. 

2.4 Threats to Mangroves 

Mangrove forests have been estimated to have covered 75 % of the tropical coasts 

worldwide (Farnsworth and Ellison, 1997), but human pressure has reduced their 

global range to less than 50 % of the total original cover (Saenger et al., 1983; 

Spalding et al., 1997; Valiela et al., 2001). In most areas of the world, they are over- 

exploited for timber and fuelwood production due to increased human population 

(Hussain, 1995). In eastern Africa, small sized mangrove poles of 2.5 to 14 cm 

diameter are extensively used for house construction (Dahdouh-Guebas et al., 2000; 

Taylor et al., 2003). This operation has continued through selection of trees that need 

market specifications, without due silvicultural consideration (CEC, 1992; Hussain, 

1995). 

Mangrove areas have been converted to other land use activities such as agriculture 

and aquaculture, salt works and urban development (Primavera, 1995; Abuodha and 

Kairo, 2001; Kairo et al., 2001). Shrimp aquaculture is the greatest threat to 

mangroves and accounts for the loss of 20 to 50 % of mangroves worldwide 

(Primavera, 1995). Other threats include pollution, sedimentation, mining and 

damming of rivers that alter water salinity levels (FAO, 1994). Rise in sea level due to 

global warming also threaten mangrove forests (FAO, 1994; McLeod and Salm, 2006; 

Gilman et al., 2008). 

Major threats to mangroves of Kenya are over-exploitation, conversion of mangrove 

areas to other land uses and oil pollution. Over cutting has seriously depleted the 

availability of quality poles from most mangrove areas (Abuodha and Kairo, 2001). 

However, the forest areas are still allocated to concessionaires for building poles. 

There is also widespread small scale wood cutting by the local people to meet their 

needs (Kairo, 1995). 

Along the northern Kenyan coast, conversion of mangrove areas for pond culture is 

localized in Ngomeni. Mangrove forests have also been converted to saltworks; e.g. 

between Ngomeni and Karawa (Yap and Landoy, 1986), which is responsible for 

8

underground seepage of high saline water that seriously affects the neighbouring 

mangroves (Abuodha and Kairo, 2001). 

During the decade between 1983 and 1993, the port of Mombasa and its adjacent 

waters have experienced five tanker accidents spilling a total of 391,680 metric tons 

of oil. Large expanse of intertidal mangroves, seagrass beds, algae and associated 

invertebrates were covered with oil (Abuodha and Kairo, 2001). The saturated 

sediments act as long-term reservoirs of oil and are a major factor in continued re- 

oiling of the Makupa Creek in Mombasa and thus impeding the recovery of 

mangroves. A time period of up to 20 years or longer is required for deep mud coastal 

habitats to recover from the toxic impact of catastrophic oil spills (Burns et al., 1993). 

2.5 Sustainable Management of Mangrove Forests 

Mangrove ecosystems are complex systems composed of various inter-related 

elements in the land-sea interface zone, which are linked with other natural systems of 

the coastal region such as the corals, sea grass, coastal fisheries and beach vegetation. 

The concept of mangrove management has been highlighted with better understanding 

of these formations. Instead of simple management on stand basis, it is now realized 

that the whole ecosystem must be considered. It has been also realized that due to the 

diversity of mangrove formations, specific regulations are essential. Therefore, a new 

dimension has been added to mangrove silviculture; that of sustainable management 

of the ecosystem as a whole or an integrated management of the resource (Choudhury, 

1997). 

Integrated management of mangrove forests ensures that the multiple use potential of 

the ecosystem is sustainable since its major objective is to have sustainable 

management of the resource to yield wood, fish, recreation, and non-wood products. 

Sustainable management of mangroves provides employment to the local people, 

mangrove wood products for timber and charcoal, protection against coastal erosion, 

and breeding grounds for fish (Chan, 1996). Despite the availability of various 

examples of mangrove management in Asia, sustainable mangrove management in 

East Africa is yet to be effected. 

9

2.6 History of Reforestation and Management of Mangroves 

Reforestation and management of mangroves has been practiced in Southeast Asia 

for decades; mostly to produce forest products such as wood, fuelwood and thatching 

materials (Watson, 1928; Choudhury, 1997). The longest recorded history of 

mangrove management involving 600,000 ha of mangrove forests occur in the 

Sundarbans region of India and Bangladesh. This forest has been managed since 1769 

mostly for timber production and detailed work-plans prepared between 1893-1894 

(Choudhury, 1997). Similarly, the mangrove forests of Matang, that cover 40,000 ha, 

have been managed for fuelwood production since 1902 (Watson, 1928; Chan, 1996). 

More recently mangroves have been managed for fish production (Primavera, 1995) 

and eco-tourism (Bacon, 1987), erosion control in Florida (Teas, 1977), experimental 

analysis of mangrove biology in Panama and Kenya (Rabinowitz, 1978; Kairo et al., 

2001) and restoration of forests damaged by oil spills (FAO, 1994). With the 

realization of ecological roles of mangroves (Odum and Heald, 1975) and the passage 

of laws protecting them from destruction, many small reforestation and afforestation 

projects for mitigating environmental damage have been carried out in several 

countries like Hawaii, Burma and Fiji (Hamilton and Snedaker, 1984). 

2.7 Mangrove Reforestation in Kenya 

Information on earlier mangrove reforestation in East Africa is scanty (Kairo et al., 

2001). The earliest record of mangrove reforestation in Kenya date back to 1918 in 

which Smith and McKenzie Company undertook mangrove planting at Mobore in 

Lamu, after the forest was clear-felled during the First World War (Rawlins, 1957). 

The Kenya Marine and Fisheries Research Institute (KMFRI) initiated mangrove 

planting program trials at Gazi bay in 1990. This program got momentum in 1993, 

when aid was received from Biodiversity Support Program - a USAID funded 

consortium of World Wildlife Fund (WWF), the Nature Conservancy (NC) and World 

Resource Institute (WRI) (Kairo, 1995; Kairo et al., 2001). Planting was carried out 

in 1 x 1 m 2 and 2 x 2 m 2 matrices for propagules and saplings respectively (Kairo, 

1995). The present study is based on 7.0 ha of R. mucronata and 3.0 ha of B. 

gymnorrhiza plantations that were established in 1994 at Gazi and Ramisi 

respectively (Kairo 1995). 

10

3.1 Study Area 

3.0 MATERIALS AND METHODS 

Mangrove forests in Kenya , consisting of nine true mangrove species (Table 1), are 

found in tidal estuaries, creeks and protected bays scattered all along the coastline, 

between latitudes 1° 40’S and 4° 25’S and longitudes 41° 34’E and 39 o 17’E. The 

most extensive mangrove forests occur in Lamu and the Tana River districts, while 

the less extensive mangroves are found in Mida, Kilifi, Mombasa and Gazi-Funzi 

area, close to the Kenya-Tanzania border. Broadly, mangroves in Kenya may be 

divided into two blocks; areas north and south of Tana River. Mangroves north of 

Tana river are structurally more complex than those in the south largely due to the 

influence of Tana river as well as the East African Coastal Currents (Kairo, 2001). 

Table 1 Mangrove species found in Kenya and their uses 

Species Name Local Name Uses 

Rhizophora mucronata (Lam) Mkoko Timber, firewood, charcoal 

Bruguiera gymnorrhiza (L) Lam. Muia Timber and firewood 

Ceriops tagal (Perr) C. B. Robinson Mkandaa Timber and firewood 

Sonneratia alba (Sm) Mlilana Timber and firewood 

Avicennia marina (Forsks) Vierh. Mchu Firewood and fencing 

Lumnitzera racemosa (Willd) Kikandaa Firewood, Ribs for boats 

Xylocarpus granatum (Koen) Mkomafi Timber, firewood and carving 

Xylocarpus moluccensis (Lam.) Roem. Mkomafi dume Firewood and fencing 

Heritiera littoralis Dryand in Aint Msindukazi Poles, timber, boat mast 

Source: Kairo, (2001). 

The climate along the Kenyan coast is determined by the seasonal movement of the 

Intertropical Convergence zone (ITCZ), resulting in seasonal shifts in wind direction 

(McClanahan, 1988). The long rains occur from April to July and are associated with 

the southeastern monsoon winds while the short rains (associated with the northeast 

monsoon winds) occur from October to November. Mean annual precipitation ranges 

from 500 to 1600 mm (Mutai and Ward, 2000). It is normally hot and humid with an 

average annual air temperature of about 28 o C with little seasonal variation. Relative 

humidity is about 95 % due to the close proximity to the sea. The soils of the coastal 

11

area are predominantly unconsolidated collarine, with poor water holding capacity 

and extreme alkalinity (UNEP, 1998). 

The study was carried out in Gazi bay (4 o 25’S and 4 0 27’ S; 39 o 50’E and 39 0 50’ E) 

and Ramisi estuary (4 o 30’ S and 4 o 39’S) in the Kenyan south coast. The mangroves 

of Gazi bay are mainly fringe mangroves, with the major driving environmental 

factors being tidal, while those of Ramisi are riverine. In both systems past human 

disturbances have been considered as the most limiting factors in mangrove structural 

and biomass development (Abuodha and Kairo, 2001). 

3.1.1 Gazi Bay 

Gazi bay (Figure 1), which is about 55 km south of Mombasa, is a coastal lagoon with 

a total area of 700 ha mangrove forest (UNEP, 2001), dominated by R. mucronata, C. 

tagal and A. marina. The forest is sheltered from strong wave action by the presence 

of the Chale peninsula to the east and a fringing coral reef to the south. There are two 

major creeks penetrating the forest; the western creek is in the mouth of river 

Kidogoweni, a seasonal river, while the eastern one, Kinondo, is a tidal creek. Gazi 

bay has a semi-diurnal tidal regime with amplitude varying between 2.90 m at spring 

tide and 0.70 m at neap tide (Hemminga et al., 1994). 

The mangroves are not continuously under direct influence of fresh water because the 

two rivers (Kidogoweni in the north and Mkurumuji in the south) discharging in to the 

bay, are seasonal and temporal depending on the amount of rainfall inland. Ground 

seepage is also restricted to a few points (Tack and Polk, 1999). The freshwater influx 

via rivers and direct rainfall accounts for a volume of 305 000 m 3 per year, of which 

20 % is lost due to evapotranspiration. The evaporation is responsible for a salinity 

maximum zone of 38 ppt in the upper region of the bay covered by mangroves 

(Kitheka, 1997). High tidal flushing rates are coupled with short residence times (3–4 

h), which are a function of wide shallow entrance, lack of topographic controls and 

the orientation of the bay with respect to dominant water circulation patterns. River 

discharge is important during the wet season, which enhances weak stratification in 

the upper parts of Kidogoweni, whereas in the dry season, well mixed homogenous 

water is found in most regions of the bay (Kitheka, 1997). 

12

KEY: 

Mangrove forest 

Mudflat 

Figure 1. Map of Gazi bay (adapted from Bosire et al., 2003) 

13 

Study site

All the nine true mangrove species recorded in Kenya occur in Gazi bay (Kairo, 

2001). The general pattern of mangrove communities at Gazi appears to be similar to 

that of other parts in Kenya. Sonneratia alba, the most important pioneer species 

along open coast, occupies the lowest zone. The next zone is characterized by mixed 

vegetation: Rhizophora-Avicennia-Bruguiera community; followed by Ceriops- 

Avicennia community and Lumnitzera, Xylocarpus and Heritiera to the land ward side 

(Van Speybroeck, 1992; Kairo, 2001). 

Gazi village has a resident population of 1000 people (Kairo, 2001). Fishing is the 

primary occupation, while mangrove cutting is regarded as secondary. Approximately 

90 % of the men are involved in fisheries, while majority of the women earn their 

income from either weaving makuti (roof thatches made from coconut fronds), mats 

or from collection of firewood, mollusks and crustaceans, and from preparing and 

selling food items (Crona, 2006). About 90 % of the mangrove wood products are 

used for building and cooking (Dahdouh-Guebas et al., 2000). Rhizophora mucronata 

and C. tagal are highly rated for wood and firewood as they are easily available, 

straight, and strong, have high calorific values and emit little smoke (Dahdouh- 

Guebas et al., 2000). 

The mangrove forests of Gazi have been exploited for many years, especially for 

industrial wood fuel and building poles (Kairo, 1995; Dahdouh-Guebas et al., 2000), 

which has left some areas along the coastline completely denuded (Bosire et al., 2003; 

Dahdouh-Guebas et al., 2004). Mangrove reforestation program to rehabilitate 

degraded mangrove areas, restock denuded mudflats and transform disturbed forests 

into uniform stands of higher productivity was initiated at Gazi bay in October 1991 

(Kairo, 1995). In 1994 close to 70,000 R. mucronata propagules were planted in 6.74 

ha in the site located on the eastern creek of the bay bordering Kinondo village. The 

sites which were replanted had been clear-felled in the 1970s and did not show any 

natural regeneration almost 25 years later (Kairo, 1995). Subsequent development of 

the reforested areas has been monitored by comparing height and diameter increment 

of the planted trees, studying floral and faunal secondary succession (Bosire et al., 

2003; 2004) and by estimating the aboveground tree biomass for mangrove 

plantations (Kairo, 2001). 

14

3.1.2 Ramisi 

Mangrove plantation at Ramisi is situated on the Ramisi river bank at Funzi Bay, 70 

km from Mombasa city. Unlike the fringing mangroves of Gazi, mangroves of Ramisi 

are riverine, dominated by: A. marina, B. gymnorrhiza, C. tagal and R. mucronata. 

Human induced stresses of the mangroves in Ramisi are similar to those in Gazi. 

Clear felling operations of the 1970’s created huge contiguous blank areas with no 

natural regeneration to date. Some of the degraded mangrove areas in Ramisi have 

been recolonized by the giant mangrove fern; Acrostechum aureum (Lin.) (Kairo 

1995). 

Mangrove planting at Ramisi was carried out in 1994. At least 13,000 propagules of 

B. gymnorrhiza were planted (Kairo, 1995). Unlike Gazi, no monitoring has been 

carried out in the plantations at Ramisi. The present study investigated structural 

development and aboveground biomass of both plantations. 

Key 

Tanzania 

Mangrove forest 

Figure 2. Map of the Kenyan south coast showing the study site: Ramisi 

3.2 Study Design 

Ramisi 

In a ca. 7.0 ha reforested R. mucronata stand at Gazi, six (6) transects were 

established perpendicular to the waterline, along which a total of 22 plots of 10 m x 

10 m were marked. The distance from one transect to another was about 40 m, while 

15

the distance from one plot to another along each transect ranged from 20 to 30 m 

depending on vegetation characteristics and landscape. For the B. gymnorrhiza 

plantation 2 transects were laid and a total of 10 plots were established along them. A 

total of 40 plots were randomly established in a non-reforested site at Gazi to serve as 

control for soil characteristics. 

3.2.1 Soil Factors 

Each plot in the three sites was divided in to four quarters and a subsample was 

randomly scooped at 1-cm depth from each quarter using a 10-cm long 6 cm x 6 cm 

D-corer. The subsamples from each plot were reconstituted as one sample and put in 

labeled airtight containers and taken to the laboratory for grain size and sedimentary 

organic carbon analysis. In the laboratory the samples were weighed and oven-dried 

for 24 hours at 80 o C after which they were weighed again to determine the soil 

moisture (SOM) content. Twenty five (25) grams oven-dry weight of each sample was 

treated with 10 ml of aqueous sodium hexametaphosphate ((NaPO3)6) in a labeled 

beaker and subjected to a series of sieves; ranging from 63 to 500 µm mesh-size, to 

determine the portion of different grain sizes. Ten (10) grams of the remaining portion 

of each oven-dried sample was accurately weighed and combusted at 450 o C for four 

hours in order to determine % soil organic matter (SOM). SOM generally contains 

approximately 56 % organic carbon (Brady, 1990). Therefore, % soil organic carbon 

(SOC) was estimated following Brady (1990): 

% SOC = % SOM x 0.56 

3.2.2 Forest Structure 

Methods for vegetation structural assessment as outlined by Cintron and Schaeffer- 

Novelli (1984) were used. Within each plot, tree height and stem diameter (D130) at 

1.3 m above the ground (Brokaw and Thompson, 2000) for all the trees with D130 ≥ 

2.5 cm were determined. Tree height was estimated using a graduated pole, while D130 

was measured using a forester caliper. For R. mucronata trees diameter was measured 

at 30 cm above the highest stilt root. From the data obtained basal area, absolute 

density and frequency were calculated. Relative derivatives of density, dominance and 

frequency were then calculated from which the importance value (IV) of the stand 

was calculated. Basal area (m 2 ) was calculated as; BA = 0.00007854 D130 2 , while 

16

Importance value (IV) for each species was calculated as the sum of the relative 

derivatives of density, basal area (dominance) and frequency (Cintron and Schaeffer- 

Novelli, 1984). To evaluate quality of standing poles in the forest, a tree was 

arbitrarily assigned quality classes (Qc); Qc1 represented straight stems of minimum 

height of 2.5 m (≈ 8 feet); Qc2 were stems which could be slightly modified for 

construction and Qc3 represented crooked stems unsuitable for building (Kairo, 

2001). All the trees were separated in to utilization classes as follows; Fito (≤ 4.0 cm), 

Pau (4.1-6.0 cm), Mazio (6.1-9.0 cm) and Boriti (9.1-13.0 cm). 

3.2.3 Forest Biomass 

3.2.3.1 Aboveground Biomass 

Fifty R. mucronata and 15 B. gymnorrhiza trees of stem diameter ≥ 2.5 cm were 

harvested at Gazi and Ramisi respectively in order to develop allometric equations for 

estimating aboveground biomass. The D130 or Das (diameter above the highest stilt 

root for R. mucronata) and total height of each harvested tree were accurately 

measured in the field. For the R. mucronata plantation the harvested tree was then 

partitioned in to the stem (trunk), branches, leaves and stilt roots, and the fresh weight 

of each component was weighed in the field. Only the stem weight of each harvested 

tree was determined for B. gymnorrhiza plantation. 

A sample of each tree component was weighed and oven-dried at 85 o C to constant 

weight in order to obtain wet to dry weight ratios (conversion factors). The wet 

weight of each tree component was converted to dry weight using the corresponding 

conversion factor and summed up to obtain the total dry weight of the tree. 

Correlations between the structural variables (D130 and height) and the dry weight of 

each component and hence total dry weight of the tree were computed to derive 

allometric equations. The significance of the allometric equations was assessed by the 

correlation coefficient (r 2 ). The standard error of estimate (SEE) was used to identify 

the best biomass estimator for biomass of each component and total aboveground 

biomass (AGB). The standing AGB, expressed in tons dry weight per hectare, was 

then estimated by applying these equations to all the individuals in the plots as 

outlined in Clough and Scott (1989). 

17

3.2.3.2 Belowground Biomass 

Belowground biomass (BGB) was estimated for Gazi site only. BGB beneath and 

between trees was estimated using a modified core method of Saintilan (1997). 

Within each plot four trees were used for root sampling. Three cores, one at trunk 

base, middle and edge of the canopy, were made in three replicates at an 

approximately 120 o from each other. The roots were sampled in three 20-cm-depth 

profiles (0-20 cm, 20-40 cm and 40-60 cm) and washed of sediment and debris 

through a 1-mm mesh. Live and dead roots were separated and live roots were sorted 

into diameter classes (40 

mm). Fresh materials from each root class were oven-dried at 80 o C to a constant 

weight. 

For root biomass beneath the trees, the basal area of the trees (t, in m 2 ) within the 10 x 

10 m 2 plot was determined from the formula (Saintilan, 1997); 

t = ∑�(D130/2)2 π �, 

10000 

summed over all trees within each plot. Since the area occupied by a single core was 

0.0154 m 2 (14 cm in diameter) the biomass in the core was therefore multiplied by the 

factor; F = t/0.0154 to obtain the total biomass beneath trees within each plot. Root 

biomass between trees (A, m 2 ) was given by the equation A= 100-t. And so to 

estimate between-tree biomass, the core sample was multiplied by F2, where; 

F2, = 100-t/ (0.0154 x 100) 

The estimates for beneath- and between-tree root biomass were then summed to 

obtain an estimate of total below-ground biomass for each plot. Results obtained were 

pooled to obtain root biomass per unit ground area. 

In order to estimate total plant biomass in the R. mucronata plantation both above- 

and belowground biomass was estimated. The AGB and BGB were summed to give 

the total biomass of the forest. Biomass accumulation in t ha -1 yr -1 was estimated by 

dividing the total biomass by the age of the stand. 

18

3.2.4 Stand Volume 

The harvested trees used for biomass estimation were also used for the estimation of 

stand volume. Both merchantable and non-merchantable volume was estimated for R. 

mucronata plantation, while only merchantable volume was quantified for B. 

gymnorrhiza plantation. The merchantable stem was divided into 1-m long billet till a 

top diameter of 2.5 cm was reached. The diameters of both ends of each billet were 

measured to the nearest 0.1 cm and the volume of each billet was calculated using the 

Smalian’s formula (FAO, 1994): 

V = (D1 2 + D 2 2 ) ∕ 2 x π ∕ 4 x L; 

Where; V is volume; D1 and D2 are bottom and top diameters of the billet 

respectively; L is the billet length and π = 3.14. 

The volume of the stem section above the top 2.5 cm diameter (stem tip) was 

calculated using the formula for a cone (Teshome, 2005) as; ⅓AL, where A and L are 

bottom end cross-sectional area and length of the stem tip respectively. 

Samples of known weight of stilts and branches were submerged in water so as to 

determine their volumes. Thereafter specific gravity of stilts and branches were 

calculated using weight-volume ratios, which were then used to estimate their 

respective volumes. The entire woody components other than the merchantable stem 

were considered as non-merchantable wood as these are normally used by the local 

people as firewood. Volume regression equations were then developed so as to 

estimate the merchantable and non-merchantable volume in case of R. mucronata. 

3.2.5 Natural Regeneration 

Linear regeneration sampling (LRS) (Sukardjo, 1987) was used to assess the 

composition and densities of juveniles. Within 5 x 5 m 2 plots (inside the 10 x 10 m 2 ) 

the species and abundance of juveniles were identified, recorded and grouped into 

three regeneration classes (RC) based on height: RCI (≤ 40 cm), RCII (40-150 cm), 

RCII (1.5-3.0 m with diameter

3.2.6 Primary Production 

Leaf area index was determined from 50 leaf samples collected randomly from the R. 

mucronata plantation. Fresh weight of each leaf sample was accurately measured and 

its area was determined by square grid method. Correlation of leaf area and leaf 

weight was then computed and the regression equation obtained was used to estimate 

the leaf area index. Net canopy photosynthesis was then estimated following English 

et al. (1997): 

PN = A x d x L; 

where; A is the average rate of photosynthesis (g C m -2 leaf area hr -1 ) for all leaves in 

the canopy, d is the day length (hr) and L is the total leaf area index of the canopy 

above the ground. 

The average rate of photosynthesis (A) for Rhizophora species has been found to vary 

between the dry (A = 0.216 g C m 2 hour -1 ; salinities greater than 35 ppt) and wet 

seasons (A = 0.648 g C m 2 hour -1 ; low salinities) (Clough and Sim, 1989). Alongi et 

al. (2004) used A values of 0.26, 0.38 and 0.43 g C m 2 leaf area hour -1 for the 5-, 18- 

and 85-year-old forests respectively in Malaysia. Therefore, assuming that the average 

rate of photosynthesis for East African mangroves is similar to that of the mangroves 

of Southeast Asia and Australia, the empirical value of A used for the R. mucronata 

stand at Gazi, was 0.32 g C m 2 hour -1 . 

3.3 Data Analyses 

All data analyses were done using MINITAB 14.0 software package. The sediment 

characteristics for non-reforested and reforested sites were compared using ANOVA. 

Graphical illustrations of structural attributes were made using EXCEL spreadsheets 

and STATISTICA 7.0. Using the allometric equations derived biomass and volume 

tables were constructed based on height and diameter. Mean annual increments in 

height, D130, volume and biomass were calculated by dividing each attribute by the 

age of the stand. 

20

4.1 Soil Factors 

4.0 RESULTS 

The soil characteristics of the different sites are given in Table 2. Ramisi site had 

significantly higher proportions of silt-clay fraction than Gazi reforested and non- 

reforested sites (p < 0.001 for both cases). On the other hand Gazi reforested site had 

significantly higher soil moisture content and organic carbon than Gazi non-forested 

(p < 0.001 for both SOM and SOC) and Ramisi sites (p = 0.002; 0.0001, for SOM and 

SOC respectively). Contrary to what is normally expected, Ramisi plantation had a 

significantly lower proportions of soil organic carbon than the non-reforested site at 

Gazi (p = 0.0001). The proportions of fine sand were significantly higher in the non- 

reforested than reforested sites, however, coarse sand did not significantly differ 

between Gazi reforested and non-reforested sites (p = 0.08). 

Table 2. Soil physical-chemical characteristics in reforested and un-forested sites 

Variable* Gazi plantation Ramisi plantation Gazi non-reforested site 

Organic matter 31.0 ± 6.9 a 7.6 ± 1.8 c 22.2 ± 6.8 b 

Organic carbon 17.4 ± 3.9 a 4.2 ± 1.0 c 12.4 ± 3.8 b 

Moisture content 54.6 ± 4.9 a 37.79 ± 5.1 b 8.2 ± 2.4 c 

Silt-clay (

ate for R. mucronata was 0.5 ± 0.2 cm/yr. The mean height and stem diameter for B. 

gymnorrhiza plantation were 4.7 ± 1.1 m and 3.6 ± 0.8 cm and thus it had height and 

diameter mean annual growth increment of 0.4 ± 0.1 m/yr and 0.3 ± 0.1 cm/yr 

respectively. The overall basal area for R. mucronata plantation was 17.1 ± 3.8 m 2 /ha. 

Only B. gymnorrhiza was encountered in the plot studies for adult trees in the B. 

gymnorrhiza plantation; with basal area of 4.9 ± 2.0 m 2 /ha. 

Table 3. Structural characteristics for R. mucronata and B. gymnorrhiza plantations 

Mean height 

Basal 

area 

Site Species (m) (x+sd) (m 2 Relative values (%) 

/ha) Frequency Dominance Density IV 

Gazi B. gymnorrhiza 6.9 ± 1.7 0.1 13.6 0.4 1.0 15.0 

C. tagal 6.6 ± 0.9 0.2 13.6 1.0 2.8 17.4 

R. mucronata 8.5 ± 1.0 16.5 50.0 96.5 94.8 241.3 

S. alba 7.4 ± 1.6 0.2 11.4 0.9 0.7 13.0 

X. granatum 6.4 ± 1.8 0.2 11.4 1.1 0.8 13.3 

Ramisi B. gymnorrhiza 4.7 ± 1.1 5.0 100 100 100 300 

The size class distribution for R. mucronata plantation followed normal general 

distribution curve, which is expected for an even-aged forest (FAO, 1994), while that 

of B. gymnorrhiza plantation tended towards a reversed J-shaped curve typical of 

natural stands (Figure 3). Size class 6-7 cm had the highest stand density (1059 

stems/ha) for R. mucronata, while this was true for 3-4 cm (2200 stems/ha) for B. 

gymnorrhiza. 

Tees/ha 

Trees/ha 

Rhizophora 

1200 

800 

400 

0 

Bruguiera 

2500 

2000 

1500 

1000 

500 

0 

2.5-3 3.1-4 4.1-5 5.1-6 6.1-7 7.1-8 8.1-9 9.1-10 10.1-11 >11 

2.5-3 3.1 -4 4.1-5 5.1-6 6.1 -7 7.1-8.0 8.1-9 9.1-10 10.1-11 >11 

Diameter class (cm) 

Figure 3. Size class distribution for R. mucronata and B. gymnorrhiza plantations 

22

Figure 4. Height-diameter distributions for R. mucronata and B. gymnorrhiza 

plantations 

Figure 4 shows scattergram for each site; 50 % of trees in R. mucronata plantation 

ranged from 5-7 cm in diameter, while that in B. gymnorrhiza plantation ranged from 

3.8-4.8 cm. 

The stand densities for the different utilization classes for both plantations are given 

in Table 4. For R. mucronata plantation, 47 % of the total stand had Mazio sized 

poles, while 32 % were Pau sized poles. This shows that most of the poles in 

23

eforested R. mucronata stand are of the preferred classes for construction. On the 

contrary 78 % of the B. gymnorrhiza plantation had Fito sized stems. 

Table 4. Stand table for R. mucronata and B. gymnorrhiza plantations 

Plantation Variables 

Fito* Pau Mazio Boriti 

R. mucronata Stems/ha 559 1586 2391 327 4864±1477 

24 

Diameter classes (cm) 

≤ 4.0 4.1-6.0 6.1-9.0 9.1-13 Total 

§ Merchantable volume (m 3 /ha) 1.4 11.0 36.4 9.4 58.2±13.0 

§ Non-merchantable volume (m 3 /ha) 40.3±9.8 

§ Aboveground biomass (t/ha) 2.4 18.6 66.4 19.4 106.7±24.0 

Belowground biomass (t/ha) 24.9±11.4 

B. gymnorrhiza Stems/ha 3570 960 70 - 4600±1887 

§ Merchantable volume (m 3 /ha) 8.5 5.1 1.2 - 14.8±7.0 

§ Aboveground biomass (t/ha) 10.1 5.6 1.0 - 16.7±8.7 

*Fito, Pau, Mazio and Boriti are the local names for the different utilization classes 

§ Values were obtained from equations given in Tables 6 and 7. 

4.3 Forest Biomass 

4.3.1 Aboveground Biomass 

Aboveground biomass of tree components and total aboveground biomass (AGB) 

were regressed against different variables relating to D130 and height in order to 

determine the best allometric models that could be used to estimate biomass (Table 5). 

The models were of the form; y = ax 2 + bx +c; where; y = biomass, x = D130 alone or 

in combination with height, and a, b and c are constants. For R. mucronata all the 

models derived showed a high correlation (r 2 > 0.95), while only models A and B had 

r 2 > 0.90 for B. gymnorrhiza. 

Since Model A, with D130 2 H as the independent variable, had the highest r 2 and the 

smallest SEE it was used to estimate the aboveground biomass for R. mucronata. On 

the other hand model B, with D130H as the independent variable had the highest r 2 and 

the smallest SEE for B. gymnorrhiza (Table 5). The best biomass estimator models for 

each plantation are given in Table 6.

Table 5. Different allometric models derived from different independent variables for 

biomass estimation in reforested mangrove stands 

Plantation Model X a b c r 2 SEE n 

R. mucronata A D130 2 H 1.6E-05 0.0454 0.4951 0.98 1.98 35 

B D130H 0.0089 -0.2123 5.1448 0.97 2.53 35 

C D130 2 0.0012 0.392 -1.7334 0.97 2.29 35 

D D130 0.8113 -3.7898 7.0903 0.97 2.17 35 

B. gymnorrhiza A D130 2 H 1.5E-04 0.033 0.655 0.92 0.69 12 

B D130H 0.0066 0.059 0.39 0.94 0.59 12 

C D130 2 0.215 -0.216 -0.218 0.85 0.94 12 

D D130 0.277 0.103 -0.155 0.78 1.16 12 

Table 6. Allometric equations for estimating biomass for R. mucronata and B. 

gymnorrhiza plantations 

Plantation Component X a b c r 2 SEE 

R. mucronata Total AGB D130 2 H 1.6E-05 0.0454 0.4951 0.98 1.98 

Stem D130 2 H 1.4E-05 0.0156 0.70 0.93 1.16 

Stilt roots D130 2 H 2.0E-05 0.0054 0.76 0.91 1.69 

Branches D130 2 H 8.0E-06 0.005 0.32 0.93 0.79 

Leaves D130 2 H -3.0E-07 0.052 0.12 0.89 0.38 

B. gymnorrhiza Stem D130H 0.0066 0.059 0.39 0.94 0.59 

The total AGB ranged from 65.33 to 144.62 t/ha; with a mean of 106.67 ± 23.95 t/ha, 

for R. mucronata; while stem biomass ranged from 8.54 to 36.96 t/ha; with a mean of 

16.65 ± 8.73 t/ha, for B. gymnorrhiza (Table 4). Size class ≤ 4.0 cm (Fito) contributed 

more than 60% of the total biomass, while the highest size class (Mazio; 6.1-9.0 cm) 

contributed just only 0.05 % for B. gymnorrhiza plantation. The reverse was true for 

the R. mucronata plantation; in which Mazio contributed over 60 %, while Fito 

consisted merely 0.01% of the total AGB. 

25

4.3.2 Belowground Biomass 

The belowground biomass (BGB) for the R. mucronata plantation was 24.89 ± 11.4 

t/ha (Table 4); of which 44 %, 35 % and 21 % were within 0-20, 20-40 and 40-60 cm 

depth respectively. Root biomass distribution by size class along the depth profile is 

shown in Figure 5. In the first 20 cm most of the root biomass was in the 10-20 and 

20-30 mm diameter classes while the 5-10 mm size class had the lowest. The fine root 

biomass (< 5 mm) was almost evenly distributed along all the depth profiles. Except 

for the fine roots and 5-10 mm size class, root biomass decreased with increase in 

depth. 

Root biomass (t/ha) 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0-20cm 20-40cm 40-60cm 

Depth (cm) 

26 

Figure 6. AGB distribution amongst tree components for R. mucronata plantation 

Figure 7. Biomass partitioning in relation to tree size in R. mucronata plantation 

4.3.4 Biomass Accumulation 

The total biomass for R. mucronata plantation at Gazi was 131.56 t/ha, with 18.9 % 

being belowground biomass. The aboveground and belowground biomass 

accumulation for this plantation was 8.89 and 2.07 t/ha/yr respectively; giving a total 

biomass accumulation of 10.96 t/ha/yr. 

27

4.4 Stand Volume 

The mean green wood density for stem, stilt roots and branches were 1241.12 ± 

150.85, 1180.37 ± 34.04 and 1201.16 ± 62.01 kg/m 3 respectively for R. mucronata at 

Gazi. On the other hand B. gymnorrhiza at Ramisi had a mean green stem density of 

1166.28 ± 62.46 kg/m 3 . 

Volumes of the stem, stilt and branches were estimated using equations given in Table 

7. The stem volume for R. mucronata plantation was 58.16 ±13.00 m 3 /ha with a 

higher proportion represented in the Mazio sized poles, while that of non- 

merchantable (stem tip, stilts and branches) was 40.25 ± 9.81 m 3 /ha (Table 4). The 

overall standing volume for the R. mucronata plantation was, therefore, 100.44 ± 

22.53 m 3 /ha, with an annual volume increment of 8.4 m 3 /ha/yr. Bruguiera 

gymnorrhiza plantation had a stem volume of 14.81 ± 6.96 m 3 /ha.; giving an annual 

volume increment of 1.23 m 3 /ha/yr. 

Table 7. Allometric equations for estimating wood volume for R. mucronata and B. 

gymnorrhiza plantations 

Plantation Component X a b c r 2 SEE 

R. mucronata Total volume D130 2 H 1.39E-08 4.76E-05 8.54E-06 0.99 0.0021 

Stem D130 2 H 4.0E-10 3.0E-05 2.0E-05 0.99 0.0006 

Branches D130 2 H 6.0E-09 5.0E-06 9.0E-05 0.99 0.0004 

Stilt roots D130 2 H 2.0E-08 8.0E-07 6.0E-04 0.99 0.0006 

B. gymnorrhiza Stem D130 2 H 1.0 E-07 3E-05 6.0E-04 0.91 0.0005 

The biomass expansion factor (BEF), which is a ratio of aboveground biomass to stem 

volume, for R. mucronata plantation varied from 1.4 to 4.38 t m -3 , with a mean of 

2.04 ± 0.74 m -3 . This value decreased exponentially as the volume increased tending 

towards a constant value at high volume (Figure 8), which is expected for tropical 

forests (Brown, 2002). 

28

BEF (t/m 3 ) 

5 

4 

3 

2 

1 

0 

29 

y = 0.67x -0.22 

r 2 = 0.60 

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 

Volume (m 3 ) 

Figure 8. BEF-Stem volume relationship for Rhizophora plantation at Gazi bay 

4.5 Natural Regeneration 

Table 8 shows the composition of juveniles in R. mucronata and B. gymnorrhiza 

plantations at Gazi Bay and Ramisi respectively. Five juvenile species were 

encountered in R. mucronata plantation. The overall density was 4,886 juveniles/ha; 

most of which were R. mucronata, and the least were S. alba. RCI had the highest 

juvenile density; while RCIII had the least. The regeneration ratio; RCI: RCII: RCIII 

for this plantation was 5:3:1. 

Table 8. Juvenile densities in R. mucronata and B. gymnorrhiza plantations at Gazi 

and Ramisi 

*Regeneration classes 

Plantation Species 

I II III Total/ha 

R. mucronata R. mucronata 2527 (89) 964 (62) 350 (66) 3841 (79) 

B. gymnorrhiza 155 (6) 195 (13) 68 (13) 418 (9) 

C. tagal 105 (4) 186 (12) 86 (16) 377 (8) 

X. granatum 23 (1) 200 (13) 23(4) 245 (5) 

S. alba 0 (0) 0 (0) 5(1) 5 (

Two juvenile species were encountered in the B. gymnorrhiza plantation with an 

overall juvenile density of 18,030 juveniles/ha, of which B. gymnorrhiza constituted 

the highest percentage, while X. granatum represented only 1%. In this plantation 

RCII had the highest juvenile density, while RCI constituted the least. The 

regeneration ratio; RCI: RCII: RCIII, was 1:2:2. 

4.6 Primary Production 

The leaf area of the R. mucronata was linearly correlated to leaf weight using simple 

relation of y = 1.69x; where x = leaf dry weight, y = leaf area, r 2 = 0.92 and n = 50. 

From this regression equation the leaf area index (LAI) was 3.99. Using the derived 

LAI and the average rate of photosynthesis, 0.32 g C/m2/hr, (assumed to be similar to 

that of the South East Asian mangroves; Clough and Sim, 1989; Alongi et al., 2004) 

the net canopy photosynthetic production (PN) was estimated as: 

30 

= 0.32 g C/m 2 /hour x 3.99 x 12 hours 

= 15.4 g C/m 2 /hour 

= 56 t C/ha/yr.

5.1 Soil Factors 

5.0 DISCUSSIONS 

Soil physical-chemical characteristics in the reforested site were within the range of 

those observed in East African mangroves (Bosire et al., 2003; Lyimo and Mushi, 

2005). The improved soil conditions in mangrove forests have been attributed to the 

trapping and binding of sediments by mangrove roots (Thom, 1982; Krauss et al., 

2003). The soil organic matter obtained in for R. mucronata plantation was higher 

than that obtained earlier by Bosire et al. (2003) for the same site. This confirms the 

view that mangroves are not only influenced by chemical and physical conditions of 

their environment, but they usually help to create those conditions (Kathiresan and 

Qasim, 2005) thereby producing suitable habitats (Kraus et al., 2003) which would 

favour secondary faunal as well as floral colonization of the reforested areas. The high 

silt-clay fractions in the B. gymnorrhiza plantation at Ramisi could be attributed to 

riverine sediment supply along with land use practices in the hinterlands. 

5.2 Forest Structure 

The stand density of the R. mucronata plantation was higher than that reported by 

Bosire et al. (2006) for the adjacent natural stand of R. mucronata (1796 stems/ha); 

however, it had a smaller basal area (17.12 m 2 /ha) than that of the natural stand (21.1 

m 2 /ha). This difference in stand density between the replanted and natural forests was 

expected because of the planting density (7,500 – 10,000 saplings/ha), survival of 

saplings and probably recruitment of juveniles at early stages of forest development. 

A decline in stand density and an increase in basal area are typical for a developing 

forest (Twilley, 1995); thus, for high stand density the basal area is expected to be 

small. The structural attributes for the R. mucronata plantation were comparable to 

those of Rhizophora species of similar age in managed plantations in Malaysia 

(Saenger, 2002). For instance a 12-year old R. apiculata in Matang Mangrove 

Reserve had a stem density of 4181 stems/ha, mean D130 and height of 5.50 cm and 

10.96 m, respectively (Srivasatava et al., 1988). The size structure of the R. 

mucronata stand was typical of even-aged forest (FAO, 1994), with most stems being 

between 6 and 9 cm, which are among the preferred poles for construction in Kenya 

(Dahdouh-Guebas et al., 2000; Kairo et al., 2002a). 

31

In Kenyan mangrove forests, B. gymnorrhiza normally occurs in stands dominated by 

other species such as C. tagal and R. mucronata (Van Speybroeck, 1992; Kairo, 2001) 

and therefore, information of structural attributes of this species as a dominant 

component of the forest is limited. However, its structural attributes were similar to 

those of Bruguiera species elsewhere. For example B. gymnorrhiza in Bangladesh had 

a mean D130 and height of 3.5 cm and 3.4 m, respectively (Saenger and Siddiqi, 

1993). The tendency of size structure towards natural forest type in the B. 

gymnorrhiza plantation could probably be attributed to high recruitment of saplings in 

to adult phase due to high density of established regeneration (RCII and RCIII). 

The growth increments in both height and D130 for R. mucronata plantation were 

comparable to those obtained for mangrove forests in Southeast Asia. Ong et al. 

(1995) reported height increment of 1.05 m for 20-year-old R. apiculata forest in 

Malaysia, while Devoe and Cole (1998) obtained a diameter growth rate of 0.37 

cm/yr for R. mucronata (average diameter 11 cm) in Micronesia. The diameter growth 

rate of B. gymnorrhiza (0.30 cm/yr) reported here was similar to that of the same 

species reported elsewhere; e.g. 0.26 to 0.35 cm/yr in Micronesia and Bangladesh 

(Saenger and Siddiqi, 1993; Devoe and Cole, 1998; Cole et al., 1999). Diameter 

growth rates of mangrove species in natural forests have been shown to range from 

0.17 to 1.05 cm/yr (Watson, 1931; Durant, 1941; Putz and Chan, 1986; 

UNDP/UNESCO, 1991; Devoe and Cole, 1998; Cole et al., 1999). The growth in 

diameter for most mangrove species is influenced by factors such as size class, 

disturbance, site conditions and suppression by the crown dominant species. 

Quality class I poles represented over 95 % of the stand density in both plantations 

(Table 9) compared to 32.28 % of class 1 poles in some of the most pristine mangrove 

stands in Kenya (Kairo et al., 2002a; b). This was expected in forest plantations where 

the regular and closer spacing induce competitive interaction that may be responsible 

for straightness of the main stem. The quality of poles also depends on site conditions 

and silvicultural treatments. Both plantations were pruned at the age of 5 years. 

32

Table 9. Quality classes for mangrove poles at Gazi Bay and Ramisi 

Plantation Species Quality Classes Total/ha 

I II III 

R. mucronata B. gymnorrhiza 50 0 0 50 

C. tagal 141 0 0 141 

R. mucronata 4727 127 9 4864 

S, alba 31 5 0 36 

X. granatum 5 27 9 41 

B. gymnorrhiza B. gymnorrhiza 4430 90 80 4600 

5.3 Forest Biomass 

5.3.1 Aboveground Biomass 

The total aboveground biomass and biomass of different tree components were best 

estimated by quadratic function having D130 2 H as the independent variable for R. 

mucronata. Except for the leaves, the correlation between the variables was high; r 2 > 

0.90. The allometric relationships between leaf biomass and tree measurable 

parameters are generally less robust than those for total AGB or biomass of other 

components because leaves are more easily broken off the trees by strong winds. Leaf 

biomass may also vary seasonally (Clough, 1992). The correlation coefficient for total 

AGB was very high (r 2 = 0.98), indicating that the total aboveground biomass can be 

estimated with confidence from measurements of D130 and height. The correlation 

coefficient for stem biomass in B. gymnorrhiza plantation was also high (r 2 = 0.94). 

Considering the age of the forest, the total AGB for R. mucronata plantation was 

lower than AGB estimates for Rhizophora species in Asia and the Pacific. Estimates 

of AGB ranging from 106 to 576 t/ha have been reported for 5 to 85 years old R. 

apiculata in Malaysia (Ong et al., 1995; Alongi et al., 2004). The highest published 

estimates of AGB in old-aged mangrove forests in Asia and the Pacific are in the 

order of 500-550 t/ha (Paijmans and Rollet, 1977; Putz and Chan, 1986). The highest 

AGB has been estimated to be up to 700 t/ha for un-disturbed and un-managed forests 

in Australia (Clough, 1992); while the lowest reported value is 7.9 t/ha for R. mangle 

in Florida (Lugo and Snedaker, 1974). Secondary forests or areas under concession 

have been reported to have AGB less than 100 t/ha (Komiyama et al., 2008). 

33

Generally aboveground biomass in mangroves is higher in lower altitudes than in 

higher altitudes and may be related to different climatic conditions, such as 

temperature, solar radiation, precipitation, and frequency of storms (Komiyama et al., 

2008). 

A biomass table for R. mucronata at Gazi was constructed based on tree height (m) 

(range 4 – 11 m) and D130 (cm) (range 3 – 13 cm) that now allows rapid estimation of 

aboveground tree biomass (Table 10). 

Table 10. Biomass table for replanted R. mucronata stand at Gazi bay 

4 5 6 

Height (m) 

7 8 9 10 11 

3 *2.72 4.01 5.63 7.60 9.95 

4 3.48 5.24 7.45 10.16 13.43 17.30 

5 6.48 9.32 12.82 17.07 22.16 28.20 

6 7.76 11.23 15.57 20.88 27.28 34.94 

7 9.05 13.21 18.42 24.85 32.67 42.08 53.30 

8 15.23 21.37 28.98 38.31 49.62 63.18 

9 33.28 44.22 57.56 73.65 

10 37.74 50.39 65.90 84.71 

11 42.37 56.82 74.64 96.35 

12 63.52 83.78 108.58 

13 93.32 121.40 

*Values (Kg) were obtained using the equation: 

D130 (cm) 

Biomass = 0.00002 (D130 2 H) 2 + 0.0454 D130 2 H + 0.495. 

5.3.2 Belowground Biomass 

The proportion of the belowground biomass to the total biomass (18.92 %) was within 

the range of the values reported in other studies. Comely and McGuiness (2005) 

reported that the BGB contributed 32 % of total biomass for R. stylosa, while Ong et 

al (1995) reported just only 5 % of the total biomass for the 20-year-old R. apiculata 

in Malaysia. Similarly, Ong et al (2004) found that belowground root biomass 

proportion of the total biomass ranged from 5 % to 20 % for R. apiculata. The evenly 

distribution of fine roots along the depth profiles might be due to their nutrient 

absorption functions (Tomlinson, 1986). 

34

5.3.3 Biomass Partitioning 

Most of the aboveground biomass was allocated to the trunk and stilt roots than 

branches and leaves. Since the stem and stilts are relatively long-lived, stable 

structures, the accumulation of biomass in each provides at least some clue to the way 

in which carbon is partitioned between them as the tree grows (Clough, 1992). As the 

diameter increased the biomass apportioned to the stem declined while there was a 

marked increase in allocation to stilt roots. Biomass allocated to the branches 

increased slightly but that allocated to the leaves appeared to decline or remained 

constant with increase in tree size. This was similar to trend for the same stand at 5 

years of age (Kairo, 2001) and for R. apiculata and R. stylosa in Australia (Clough 

and Scott, 1989; Clough, 1992). The stilts form the support structures for other 

aboveground tree components for Rhizophora species (Tomlinson, 1986), therefore, 

they require more biomass investment as the tree grows. Likewise, the branches and 

to some extent twigs play an important role in leaf canopy expansion (Clough, 1992), 

as such biomass allocated to them tends to be higher than that allocated to the leaves. 

There was a higher variability in allocation of biomass to the aboveground tree 

components especially for trees below 8.5 cm D130 (Figure 9). This was more 

pronounced in stilt and least in leaves. This would imply that there is a high plasticity 

in AGB partitioning for small trees. This compares well with the observation made by 

Ong et al. (2004), in that variability in biomass partitioning was greatest for trees 

below about 25 cm GBH (girth at breast height; 25 cm GBH ≈ 8 cm D130). This 

indicates that allocation of biomass to plant components in young trees varies in 

response to growth and development, but as they mature allocation of biomass 

appears to stabilize. Leaves are the most metabolically components of the tree as a 

result they are least variable in terms of biomass partitioning (Ong et al., 2004). 

35

Figure 9. Variability of AGB allocation in relation to tree size for Rhizophora 

plantation 

The overall biomass partitioning reported here for R. mucronata was different from 

that of the 20-year-old R. apiculata forest in Malaysia; in which 74% of the biomass 

was in the trunk, 15 % in the roots (10 % in stilts and 5 % belowground roots), and 

10. 6 % in the canopy (only 2.6 % in leaves and 8 % in branches, twigs, fruits and 

propagules) (Ong et al., 1995). Ong et al. (2004) found that the proportion of 

belowground biomass declined with increase in tree size. By comparing the 

proportions of root biomass (for both stilt and underground roots) for different sites 

and Rhizophora species they also demonstrated that the differences in allocation of 

biomass to the root system might be species specific and/or influenced by 

environmental parameters. Other factors such as age of the forest, structural and 

architectural functions of the plant part are also thought to influence biomass 

allocation to different tree components (Tomlinson, 1986). 

5.3.4 Biomass Accumulation 

The biomass accumulation for R. mucronata plantation is lower than those observed 

for managed plantations in Southeast Asia, where annual biomass increment values 

ranging from 14 to 34 t/ha/yr for plantations of Rhizophora species (Aksornkoae, 

36

1976; Ong et al., 1984; Ong et al., 1995). Biomass accumulation ranging from 6.3 to 

45.4 t/ha/yr has been observed for Australian mangroves (Clough, 1992). The 

hydrodynamics of mangrove swamps, climatic conditions, nutrient limitation and soil 

factors are thought to influence biomass accumulation; even though the complexity of 

interactions between these factors and forest structure and growth often make it 

difficult to identify the main factors influencing biomass accumulation in any given 

site (Clough, 1992). 

5.4 Stand Volume 

The stand volume for R. mucronata plantation was within the range of values reported 

for Rhizophora species in other parts of the world. Other studies have shown that 

stand volume of Rhizophora species range from 3 (for 3 year-old young plantations) 

to over 280 m 3 /ha, with yield ranging from 0.1 to 18 m 3 /ha/yr (Da Silva et al., 1993; 

FAO, 1993; Devoe and Cole, 1998). Natural stands of R. mucronata in mangrove 

forests presumed to be pristine in Kenya, have been found to have standing volume 

ranging from 28 to 700 m 3 /ha (Kairo et al., 2002b). The stem volume and annual 

volume increment for B. gymnorrhiza plantation was higher than that of the same 

species in Bangladesh (5.5 m 3 /ha and 0.6 m 3 /ha/yr) (Saenger and Siddiqi, 1993). 

The BEF values obtained for R. mucronata plantation at Gazi were similar to those 

reported for tropical forests in other studies. For example Segura and Kaninen (2005) 

reported BEF value of 1.4 to 1.9 t/m 3 (mean = 1.6 t/m 3 ) for tropical humid forest. The 

discrepancy between their values and those of this study may be due to the sizes of the 

tree sampled. The largest tree sampled in this study was of D130 11.5 cm while in their 

sampling there were trees of D130 up to 90 cm. However, the values obtained in this 

study were within the range of BEF values for tropical forests (Brown, 2002). The r 2 

(0.60) for the fitted regression line indicated that there was a relationship between 

BEF and stem volume. Therefore, from a given estimate of volume of R. mucronata 

the aboveground biomass may be estimated. As is expected for tropical forests 

(Brown, 2002), the BEF values in this study decreased exponentially with increase in 

volume and tended to a constant value as the volume increased further. 

37

To allow quick estimation of wood volume a local volume table was prepared for the 

R. mucronata plantation using 50 trees (Table 11). The biomass and volume tables 

(Tables 10 and 11) are most suitable for trees in the diameter range of 2.5 to 13 cm. 

Table 11. Volume (m 3 ) table for replanted R. mucronata stand at Gazi bay 

D130 (cm) 

Height (m) 

4 5 6 7 8 9 10 11 

3 *0.001 0.002 0.003 0.004 0.006 

4 0.002 0.003 0.004 0.006 0.008 0.010 

5 0.004 0.005 0.007 0.010 0.012 0.015 

6 0.005 0.007 0.009 0.012 0.015 0.018 

7 0.005 0.008 0.010 0.014 0.017 0.021 0.026 

8 0.009 0.012 0.015 0.020 0.024 0.029 

9 0.017 0.022 0.027 0.033 

10 0.019 0.025 0.030 0.037 

11 0.021 0.027 0.034 0.041 

12 0.030 0.037 0.044 

13 0.040 0.048 

*The equation used was y = 0.000000000395x 2 +0.0000311x+0.0000231: 

where; y = stem volume, and x = D130 2 H 

5.5 Natural Regeneration 

Natural regeneration potential in the reforested R. mucronata stand has improved 

when compared to earlier assessments in the same plantation. At 5 years age there 

were 700 juveniles/ha (Bosire et al., 2003) and 2048 juveniles/ha at 8 years old 

(Bosire et al., 2006). Up to 7000 juveniles/ha for the adjacent natural stand of the 

same species has been reported (Bosire et al., 2006). In the Bosire et al. (2003) study, 

B. gymnorrhiza was the predominant juvenile species (400 juveniles/ha) in R. 

mucronata plantation. But results from this study showed that recruitment of non- 

planted species appears to be declining at the advantage of R. mucronata. At 8 years 

old there were 1036 RCI juveniles/ha of non-planted species (Bosire et al., 2006) 

compared to 283 juveniles/ha for this study. Propagule predation has been identified 

as one of the factors influencing establishment of mangrove juveniles in a stand 

(Dahdouh-Guebas et al 1997). Bosire et al. (2005) demonstrated that predation of 

propagules appeared to regulate individual species colonization in the reforested R. 

mucronata stand at Gazi whereby R. mucronata was preyed upon less than C. tagal 

and B. gymnorrhiza. Therefore, predation coupled with the advantage of being the 

38

crown species, might have influenced the shift in juvenile dominance to R. 

mucronata. 

Juvenile densities in the B. gymnorrhiza plantation were higher than those observed 

for natural mangrove forests in northern Kenya; 7,000-11,000 juveniles/ha (Kairo et 

al., 2002b), but lower than those of Mida Creek; over 200,000 juveniles/ha (Kairo et 

al., 2002a). The density of RCI seedlings in this plantation was low probably due to 

development of closed canopy system as more established regeneration were recruited 

to adult stage. This would imply that fewer seedlings would be recruited to the next 

stage than it was case previously. Generally mangrove forests lack understory 

vegetation and few attempts have been made to explain this phenomenon (Janzen, 

1985). 

The regeneration ratio observed in this study was smaller than that reported for 

pristine forests (Kairo et al., 2002a). A possible explanation of these differences is 

attributed to the fact that in natural forests the stand density tends to be low thereby 

minimizing the effects of closed canopy on development and distribution of juveniles. 

5.6 Primary Production 

The leaf area index (LAI) reported in this study is within the range of LAI values 

reported for mangrove forests in other parts of the world. Clough (1998) reported LAI 

of 3.1 for R. apiculata stand in Australia, while Alongi et al. (2004) estimated LAI of 

4.8, 7.0 and 8.2 for 5-, 18-, and 85-year old R. apiculata forest respectively in 

Malaysia. The LAI value observed in this study was lower than that of a younger 

plantation in Malaysia. This might be due to site history since the mangroves of 

Malaysia have been under management for over a century (Watson, 1928) while those 

of Kenya have been subjected to degradation with no mangrove management plans to 

date. The day time photosynthetic production value was within the range reported for 

mangroves in other regions; Alongi et al (2004) reported day time photosynthetic 

production for 5-, 18- and 85-year-old R. apiculata stands as 13, 21 and 35 g C/m 2 

ground/day (equivalent to 47, 76 and 127 t C/ha/yr) respectively. This indicates that 

photosynthetic production increases with forest age. 

39

6.0 CONCLUSIONS AND RECOMMENDATIONS 

This study has come up with stand, biomass and volume tables for reforested 

mangroves in Kenya, which provide valuable information for mangrove reforestation 

and management. Along with these, it has also developed allometric equations that 

would be used to assess productivity of restored mangrove forests of similar age. 

Consequently, this study has demonstrated that reforestation can be used as a 

management tool in rehabilitation of degraded mangrove areas. Reforestation of 

degraded mangrove areas has the potential to increase mangrove yield and contribute 

to national grid for forest products. This is seen particularly in the structural 

development of the reforested mangroves. In less than 15 years R. mucronata 

plantation is able to yield high percentage of good quality poles that would be used 

for construction. Quantitative information on production of reforested mangroves 

provided here is consistent with the role of reforestation in mitigation of climate 

change through carbon sequestration. This is useful in gauging carbon budgets for 

mangroves. Therefore, the results provided are useful to the sustainable management 

of mangroves in Kenya and the East African region. 

This study recommends that some sections of the reforested stands be set aside and 

appropriate silvicultural treatments such as thinning and pruning be applied to them in 

order to enhance forest development. This would provide a baseline for further 

research on the effects of these treatments on the productivity and natural regeneration 

of the restored forests. It also recommends that monitoring program be initiated by the 

relevant institutions in order to assess the development and functionality of restored 

mangroves as the forest matures. Further more, subsequent study, especially on 

production of restored mangroves, would improve the allometric equations, biomass 

and volume tables to allow for inclusion of larger size classes in estimation of forest 

productivity. 

40

7.0 REFERENCES 

Abuodha, P. and Kairo, J. G. (2001). Human-induced stresses on mangrove swamps 

along Kenya coast. Hydrobiologia, 458: 255-265. 

Agrawala, S., Ota, T., Risbey, J., Hagenstad, M., Smith, J., Aalst, M. V., Koshy, K. 

and Prasad, B. (2003). Development and Climate Change in Fiji: Focus on 

Coastal Mangroves. Report No. 4, Environment Directorate and Development 

Cooperation Directorate, Organisation for Economic Cooperation and 

Development (OECD), Paris. 56 pp. 

Aksornkoae, S. (1976). Mangrove plantation productivity at Amphoe Khlung, 

Changwat Chanturi. Paper presented at the First National Seminar on 

Mangrove Ecology, Phuket, Thailand, 

Alongi, D. M., Sasekumar, A., Chong, V. C., Pfitzner, J., Trott, L. A., Tirendi, F., 

Dixon, P. and Brunskill, G. J. (2004). Sediment accumulation and organic 

material flux in a managed mangrove ecosystem: estimates of land – ocean – 

atmosphere exchange in peninsular Malaysia. Marine Geology, 208: 383-402. 

Bacon, P. R. (1987). Use of wetlands for tourism in the insular Caribbean. Annals of 

Tourism Research, 14: 104-117. 

Bosire, J. O., Dahdouh-Guebas, F., Kairo, J. G. and Koedam, N. (2003). Colonization 

of non-planted mangrove species into restored mangrove stands in Gazi Bay, 

Kenya. Aquatic Botany, 76: 267-279. 

Bosire, J. O., Dahdouh-Guebas, F., Kairo, J. G., Cannicci, S. and Koedam, N. (2004). 

Spatial variations in macrobenthic fauna recolonisation in a tropical mangrove 

bay. Biodiversity and Conservation, 13: 1059-1074. 

Bosire, J. O., Kairo, J. G., Kazungu, J., Koedam, N. and Dahdouh-Guebas, F. (2005). 

Predation on propagules regulates regeneration in a high-density reforested 

mangrove plantation. Marine Ecology Progress Series, 299: 149-155. 

Bosire, J. O., Dahdouh-Guebas, F., Kairo, J. G., Wartel, S., Kazungu, J. and Koedam, 

N. (2006). Success rates of recruited tree species and their contribution to the 

structural development of reforested mangrove stands. Marine Ecology 

Progress Series, 325: 81-91. 

Brady, N. C. (1990). The Nature and Properties of Soils, 10th ed. MacMillan Books, 

New York. 621 pp. 

41

Brokaw, N. and Thompson, J. (2000). The H for DBH. Forest Ecology and 

Management, 129: 89-91. 

Brown, S. (2002). Measuring carbon in forests: current status and future challenges. 

Environmental Pollution, 116: 363-372. 

Burns, K. A., Garrity, S. D. and Levings, S. A. (1993). How many years until 

mangrove ecosystems recover from catastrophic oil spills? Marine Pollution 

Bulletin, 26: 239-248. 

CEC. (1992). Mangroves of Africa and Madagascar. Office for Official Publications 

of the European Communities, Brussels. 

Chan, H. T. (1996). Mangrove restoration in Peninsular Malaysia. In C. D. Field 

[ed.], Restoration of Mangrove Ecosystems. International Society for 

Mangrove Ecosystems. p. 64-75. 

Chapman, V. J. (1976). Mangrove vegetation. J. Cramer, Vaduz, Liechtenstein. pp. 

74-105. 

Choudhury, J. K. (1997). Mangroves and other Coastal Forests. Sustainable 

Management of Mangroves, Forest Development and Social Needs. 

Proceedings of the XI World Forestry Congress, Antalya, Turkey. 

Cintron, G. and Schaeffer-Novelli, Y. (1984). Methods for studying mangrove 

structure. In S. C. Snedaker and J. G. Snedaker [eds.], The Mangrove 

Ecosystem: Research Methods. p. pp. 91 -113. 

Clough, B. F. and Sim, R. G. (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. (1992). Primary productivity and growth of mangrove forests. In A. I. 

Robertson and D. M. Alongi [eds.], Tropical mangrove Ecosystems. America 

Geophysical Union. p. 225-249. 

Clough, J. and Scott, K. (1989). Allometric relationships for estimating above ground 

biomass in six mangrove species. Forest Ecology and Management, 27: 117- 

127. 

Cole, T. G., Ewel, K. C. and Devoe, N. N. (1999). Structure of mangrove trees and 

forests in Micronesia. Forest Ecology and Management, 117: 95-109. 

Comley, B. W. T. and McGuiness, K. A. (2005). Above- and below-ground biomass, 

and allometry, of four common northern Australian mangroves. Australian 

Journal of Botany, 53: 431-436. 

42

Costanza, R., d'Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, 

K., Naeem, S., O'Neill, R. V., Paruelo, J., Raskin, R. G., Sutton, P. and van 

den Belt, M. (1997). The value of the world's ecosystem services and natural 

capital. Nature, 387: 253-260. 

Crona, B. (2006). Of Mangroves and Middlemen: A Study of Social and Ecological 

Linkages in a Coastal Community. Doctoral Thesis, Department of Systems 

Ecology, Stockholm University, Stockholm, Sweden. 

Da Silva, J. A. A., de Melo, M. R. C. S. and Borders, B. E. (1993). A volume equation 

for mangrove trees in northeast Brazil. Forest Ecology and Management, 58: 

129-136. 

Dahdouh-Guebas, F., Mathenge, C., Kairo, J. G. and Koedam, N. (2000). Utilization 

of mangrove wood products around Mida Creek (Kenya) amongst subsistence 

and commercial users. Economic Botany, 54: 513-527. 

Dahdouh-Guebas, F., Van Pottelbergh, I., Kairo, J. G., Cannicci, S. and Koedam, N. 

(2004). Human-impacted mangroves in Gazi (Kenya): predicting future 

vegetation based on retrospective remote sensing, social surveys, and 

distribution of trees. Marine Ecology Progress Series, 272: 77-92. 

Dahdouh-Guebas, F., L. P. Jayatissa, D. Di Nitto, J. O. Bosire, Seen, D. L. and 

Koedam., N. (2005). How effective were mangroves as a defense against the 

recent tsunami? Current Biology, Vol. 15 

Devoe, N. N. and Cole, T. G. (1998). Growth and yield in mangrove forests of the 

Federated States of Micronesia. Forest Ecology and Management, 103: 33-48. 

Doute, R. N., Ochanda, N. and Epp, H. (1981). A forest inventory using remote 

sensing technique. Series No. 30, Kenya Rangelands Ecological Monitoring 

Unit, Department of Remote Sensing, Nairobi. 

Duke, N. C. (1992). Mangrove floristics and biogeography. In A. I. Robertson and D. 

M. Alongi [eds.], Tropical Mangrove Ecosystems. American Geophysical 

Union. p. pp. 63-100. 

Durant, C. L. (1941). The growth of mangrove species in Malaya. Malayan Forester, 

10: 3-15. 

English, S., Wilkinson, C. and Baker, V. [eds.]. (1997). Survey Manual for Tropical 

Marine Resources, 2nd ed. Australian Institute of Marine Science, Townsville. 

390. 

43

FAO. (1993). Mangrove for Production and Protection. Field Document No. 43, 

FAO, Bangkok, Thailand. 57 pp. 

FAO. (1994). Mangrove Forest Management Guidelines. FAO Forestry Paper 117, 

FAO, Rome. 350 pp. 

FAO. (2007). The World's Mangroves 1980-2005. Forestry Paper 153, FAO, Rome. 

ix + 77 pp. 

Farnsworth, E. J. and Ellison, A. M. (1997). The global conservation status of 

mangroves. Ambio 26: 328-334. 

Field, C. (1998). Rationales and practices of mangrove afforestation. Marine and 

Freshwater Research, 19: 353-358. 

Gilman, E. L., Ellison, J., Duke, N. C. and Field, C. (2008). Threats to mangroves 

from climate change and adaptation options: A review. Aquatic Botany, 89: 

237-250. 

Gong, W. and Ong, J. (1990). Plant biomass and nutrient flux in a managed mangrove 

forest in Malaysia. Estuarine, Coastal and Shelf Science, 31: 519-530. 

Hamilton, L. S. and Snedaker, S. C. [eds.]. (1984). Handbook for mangrove area 

management. Environment and Policy Institute, East-West Center, Honolulu, 

Hawaii. 123 pp. 

Hemminga, M. A., Slim, F. J., Kazungu, J., Ganssen, G. M., Nieuwenhuize, J. and 

Kruyt, N. M. (1994). Carbon outwelling from a mangrove forest with adjacent 

seagrass beds and coral reefs at Gazi bay, Kenya. Marine Ecology Progress 

Series, 106: 291-301. 

Hussain, M. Z. (1995). Silviculture of mangroves. Unasylva, 46: 36-42. 

Janzen, D. H. (1985). Mangroves: where's the understory? Journal of Tropical 

Ecology, 1: 89-92. 

Jimenez, J. A. (1992). Mangrove forests of the Pacific coast of Central America. In U. 

Seeliger [ed.], Coastal Plant Communities of Latin America. Academic Press. 

p. 259-267. 

Kairo, J. G. (1995). Community Participatory Forestry for the Rehabilitation of 

Deforested Mangrove Forests of Gazi Bay (Kenya). Biodiversity Support 

Program/WWF, Washington, D. C. 

Kairo, J. G. (2001). Ecology and Restoration of Mangrove Systems in Kenya. Ph.D. 

Thesis, Laboratory of Plant Sciences and Nature Management, University of 

Brussels (VUB), Brussels, Belgium. 150 pp. 

44

Kairo, J. G., Dahdouh-Guebas, F., Bosire, J. and Koedam, N. (2001). Restoration and 

management of mangrove systems - a lesson for and from the East African 

region. South African Journal of Botany, 67: 383-389. 

Kairo, J. G., Dahdouh-Guebas, F., Gwada, P. O., Ochieng, C. and Koedam, N. 

(2002a). Regeneration Status of Mangrove Forests in Mida Creek, Kenya: A 

compromised or Secured Future? Ambio, 31: 562-568. 

Kairo, J. G., Kivyatu, B. and Koedam, N. (2002b). Application of remote sensing and 

GIS in the management of mangrove forests within and adjacent to Kiunga 

Marine Protected Area, Lamu, Kenya. Environment, Development and 

Sustainability, 4: 153-166. 

Kathiresan, K. and Bingham, B. L. (2001). Biology of mangroves and mangrove 

ecosystems. Advance Marine Biology, 40: 84-251. 

Kathiresan, K. and Qasim, S. Z. (2005). Biodiversity of Mangrove Ecosystem. 

Hindustan Publishing Corporation, New Delhi, India. 251 pp. 

KFMP (Kenya Forest Master Plan) (1994). Kenya Forest Master Plan; Development 

Programmes. Forest Department, Ministry of Environment and Natural 

Resources, Nairobi, Kenya. 422 pp. 

Kirui, B., Kairo, J. G. and Karachi, M. (2006). Allometric equations for estimating 

above ground biomass of Rhizophora mucronata Lamk. (Rhizophoraceae) 

mangroves at Gazi Bay, Kenya. Western Indian Ocean Journal of Marine 

Science, 5: 27-34. 

Kitheka, J. U. (1997). Coastal tidally-driven circulation and the role of water 

exchange in the linkage between tropical ecosystems. Estuarine, Coastal and 

Shelf Science, 45: 177-187. 

Komiyama, A., Ong, J. E. and Poungparn, S. (2008). Allometry, biomass and 

productivity of mangrove forests: a review. Aquatic Botany, 89: 128-137. 

Krauss, K. W., Allen, J. A. and Cahoon, D. R. (2003). Differential rates of vertical 

accretion and elevation change among aerial root types in Micronesian 

mangrove forests. Estuarine Coastal and Shelf Science, 56: 251-259. 

Lugo, A. E. and Snedaker, S. C. (1974). The ecology of mangroves. Annual Review of 

Ecology and Systematics, 5 39-64. 

Lyimo, T. J. and Mushi, D. (2005). Sulfide concentration and redox potential patterns 

in mangrove forests of Dar es Salaam: Effects of Avicennia marina and 

45

Rhizophora mucronata seedling establishment. Western Indian Ocean Journal 

of Marine Science, 4: 163-173. 

Macnae, W. (1968). Geneneral account of the fauna and flora of mangrove swamps 

and forests in the Indo-West-Pacific region. In F. S. Russel and M. Yonge 

[eds.], Advances in Marine Biology. p. pp 74-165. 

McClanahan, T. R. (1988). Seasonality in East Africa’s coastal waters. Marine 

Ecology Progress Series, 44: 191-199. 

McLeod, E. and Salm, R. V. (2006). Managing Mangroves for Resilience to Climate 

Change. IUCN, Gland, Switzerland. 64 pp. 

Mutai, C. and Ward, N. (2000). East African rainfall and the tropical 

circulation/convection on intraseasonal to interannual timescales. Journal of 

Climate, 13: 3915-3939. 

Odum, W. E. and Heald, E. J. (1975). The detritus food web of an estuarine mangrove 

community. In L. Cronin [ed.], Estuarine Research. Academic Press. p. 265- 

286. 

Ong, J., Gong, W. K., Wong, C. H. and Dhanarajan, G. (1984). Contribution of 

aquatic productivity in managed mangrove ecosystems in Malaysia. 

Proceedings of the Asian Symposium on Mangrove Environment Research 

and Management, University of Malaya, Kuala Lumpur. pp. 209-215. 

Ong, J. (1993). Mangroves - A carbon source and sink. Chemosphere, 27: 1097-1107. 

Ong, J., Gong, W. and Clough, B. F. (1995). Structure and productivity of a 20-year 

old stand of Rhizophora apiculata Bl. Mangrove forest. Journal of 

Biogeography, 22: 417-424. 

Ong, J., Gong, W. and Wong, C. (2004). Allometry and partitioning of the mangrove, 

Rhizophora apiculata. Forest Ecology and Management, 188: 395-408. 

Othman, M. A. (1994). Values of mangroves in coastal protection. Hydrobiologia, 

285: 277-282. 

Paijmans, K. and Rollet, B. (1977). The mangroves of Galley Reach, Papua New 

Guinea. Forest Ecology and Management, 1: 141-147. 

Primavera, J. H. (1995). Mangroves and brackish water pond culture in the Philipines. 

Hydrobiologia, 295: 303-309. 

Putz, F. and Chan, H. T. (1986). Tree growth, dynamics, and productivity in a mature 

mangrove forest in Malaysia. Forest Ecology and Management, 17: 211-230. 

46

Rabinowitz, D. (1978). Early growth of mangrove seedlings in Panama, and a 

hypothesis concerning the relationship of dispersal and zonation. Journal of 

Biogeography, 5: 113-133. 

Rawlins, S. (1957). The East African Mangrove Trade. Unpublished typescript in the 

East Africa Herbarium, National Museums of Kenya, Nairobi. 

Ricklefs, R. E. and Latham, R. E. (1993). Global patterns of diversity in mangrove 

floras. In R. E. Ricklefs and D. Schlutter [eds.], Species Diversity in 

Ecological Communities. Historical and Geographical Perspectives. 

University of Chicago Press. p. 215-233. 

Rönnbäck, P. (1999). The ecological basis for economic value of seafood production 

supported by mangrove ecosystems. Ecological Economics, 29: 235-252. 

Ross, M. S., Ruiz, P. L., Telesnicki, G. J. and Meeder, J. F. (2001). Aboveground 

Biomass and Production in Mangrove Communities of Biscayne National 

Park, Florida (USA). Wetlands Ecology and Management, 9: 27-37. 

Saenger, P., Hegerl, E. J. and Davies, J. D. S. (1983). Global status of mangrove 

ecosystem. Environmentalist, 3: 1-88. 

Saenger, P. and Siddiqi, N. A. (1993). Land from sea: the mangrove afforestation 

program of Bangladesh. Ocean and Coastal Management, 20: 23-39. 

Saenger, P. (2002). Mangrove Ecology, Silviculture and Conservation. Kluwer 

Academic Publishers, Dordecht, The Netherlands. 350 pp. 

Saintilan, N. (1997). Above- and below-ground biomasses of two species of 

mangrove on the Hawkesbury River estuary, New South Wales. Marine 

Freshwater Research, 48: 147-152. 

Sherman, R. E., Fahey, T. J. and Martinez, P. (2003). Spatial patterns of biomass and 

aboveground net primary productivity of mangrove ecosystem in Dominican 

Republic. Ecosystems, 6: 384-398. 

Soares, M. L. G. and Scaeffer-Novelli, Y. (2005). Aboveground biomass of mangrove 

species. I. Analysis of models. Estuarine, Coastal and Shelf Science, 65: 1-18. 

Spalding, M., Blasco, F. and Field, C. (1997). World Mangrove Atlas. International 

Society for Mangrove Ecosystems, Okinawa, Japan. 178 pp. 

Srivasatava, P. B. L., Guan, S. L. and Muktar, A. (1988). Progress of crop in some 

Rhizophora stands before first thinning in Matang Mangrove Reserve of 

Peninsular Malaysia. Pertanika, 11: 365-374. 

47

Sukardjo, S. (1987). Natural regeneration status of commercial mangrove species 

(Rhizophora apiculata and Bruguiera gymnorrhiza) in the mangrove forest of 

Tanjung Bungin, Banyuasin District, South Sumatra. Forest Ecology and 

Management, 20: 233-252. 

Tack, J. F. and Polk, P. (1999). The influence of tropical catchments upon coastal 

zone: Modeling the links between groundwater and mangrove losses in Kenya, 

India and Florida. In D. Harper and T. Brown [eds.], Sustainable 

Management in Tropical Catchments. John Willey and sons Ltd. p. 359-372. 

Taylor, M., Ravilious, C. and Green, E. P. (2003). Mangroves of East Africa. UNEP- 

WCMC, Cambridge, UK. 25. 

Teas, H. T. (1977). Ecology and restoration of mangrove shorelines in Florida. 

Environmental Conservation, 4: 51-58. 

Teshome, T. (2005). A ratio method for predicting stem merchantable volume and 

associated taper equations for Cupressus lusitanica, Ethiopia. Forest Ecology 

and Management, 204: 171-179. 

Thom, B. G. (1967). Mangrove ecology and deltaic geomorphology, Tabasco, 

Mexico. Journal of Ecology, 55: 301-343. 

Thom, B. G. (1982). Mangrove ecology - a geormophological perspective. In B. 

Clough [ed.], Mangrove ecosystems in Australia. Australian National 

University Press. p. 3-17. 

Tomlinson, C. B. (1986). The Botany of Mangroves. Cambridge University Press, 

Cambridge, New York, U. S. A. 419. 

Twilley, R. R., Chen, R. and and Hargis, T. (1992). Carbon sinks in mangroves and 

their implications to carbon budget of tropical mangrove ecosystems. Water 

Air Soil Pollution, 64: 265-288. 

Twilley, R. R. (1995). Properties of mangrove ecosystems related to energy 

signatures of coastal environments. In C. A. S. Hall [ed.], Maximum Power. 

University of Colorado Press. p. 43-62. 

Twilley, R. R. and Chen, R. (1998). A water budget and hydrology model of a basin 

mangrove forest in Rookery Bay, Florida. Marine and Freshwater Research, 

49: 309-323. 

UNDP/UNESCO. (1991). Final Report of the Integrated Multidisciplinary Survey 

and Research Programme of Ranong Mangrove Ecosystem. RAS/86/120, 

Bangok. 183 pp. 

48

UNEP. (1998). Eastern Africa Atlas of Coastal Resources 1: Kenya. (EAF-14) UNEP, 

119 pp. 

UNEP. (2001). The East African Coastal Resources Atlas 1: Kenya. , 

Valiela, I., Bowen, J. L. and York, J. K. (2001). Mangrove forests: one of the world’s 

threatened major tropical environments. BioScience, 51: 807-815. 

Van Speybroeck, D. (1992). Regeneration strategy of mangroves along the Kenyan 

coast: A first approach. Hydrobiologia, 247: 243-251. 

Wass, P. [ed.]. (1995). Kenya's Indigenous Forests: Status, Management and 

Conservation. IUCN, Grand, Switzerland and Cambridge, UK. Xii + 250 pp. 

252-8317-0292-8315 

Watson, J. G. (1928). Mangrove forests of the Malay Peninsula. Fraser and Neave, 

Singapore. 275 pp. 

Watson, J. G. (1931). The growth of mangrove species. Malayan Forester, 1: 217- 

218. 

Yap, W. G. and Landoy, R. J. (1986). Report of Survey of Coastal Area of Kenya for 

Shrimp Farm Development. FAO, Rome. 

49

8.0 APPENDICES 

8.1 Appendix 1. Data used to develop allometric equations for R. mucronata 

D130 

(cm) Ht (m) 

Above-ground dry biomass (kg) Total 

Stilts Leaves Stem Branches 

2.9 5.74 0.61 0.65 1.67 0.32 3.24 

3.2 4.93 1.01 0.39 0.81 0.49 2.70 

3.2 5.53 1.05 0.36 1.48 0.21 3.10 

3.7 4.19 0.60 0.50 1.81 0.48 3.38 

3.7 5.61 0.63 0.32 1.70 1.05 3.70 

3.9 5.05 2.01 0.32 2.23 0.39 4.95 

4.0 6.44 1.38 0.71 2.58 0.95 5.62 

4.5 6.06 0.95 0.64 2.49 0.93 5.01 

4.6 5.82 1.84 0.95 2.16 1.15 6.10 

4.6 6.62 2.15 0.73 2.77 1.05 6.70 

4.7 7.96 1.52 1.04 6.03 1.23 9.83 

4.8 7.77 1.41 1.00 4.24 2.77 9.43 

4.8 8.28 2.00 0.67 5.62 1.08 9.38 

5.3 8.06 1.89 1.00 5.37 1.83 10.08 

5.7 6.99 3.09 2.08 4.83 2.38 12.39 

5.7 7.37 4.09 1.76 5.73 3.93 15.52 

5.7 7.42 1.79 1.66 5.60 3.14 12.19 

5.8 6.32 3.68 0.68 4.06 0.83 9.26 

5.8 7.49 6.23 1.59 5.86 2.28 15.96 

5.9 6.04 3.51 1.54 4.59 2.03 11.66 

6.0 6.94 2.91 1.12 5.80 1.90 11.74 

6.4 6.73 2.93 1.89 7.25 5.42 17.48 

6.4 7.28 4.51 1.42 5.06 2.37 13.36 

6.5 6.27 6.37 1.11 4.90 1.28 13.66 

6.5 7.57 4.90 1.81 6.07 3.57 16.35 

6.6 7.24 4.50 1.11 6.30 1.67 13.58 

6.7 8.01 5.28 1.30 7.28 3.22 17.08 

7.3 7.53 10.18 2.88 6.65 3.56 23.26 

7.3 8.31 6.87 2.12 9.99 2.38 21.36 

7.3 8.35 2.90 2.83 10.68 5.55 21.96 

7.7 8.21 6.52 1.86 11.63 3.84 23.85 

7.8 8.87 6.78 2.24 13.66 4.40 27.09 

8.0 8.89 6.73 3.54 17.99 5.05 33.30 

9.2 8.22 17.30 3.35 16.45 8.67 45.78 

11.5 8.40 29.78 5.47 17.70 15.95 68.90 

50

MSc. Thesis - Lang'at[1].pdf

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?