ASSESMENT OF CARBON SEQUESTRATION AND BIOMASS 
ACCUMULATION OF MANAGED AND NATURAL MANGROVE 
PLANTATIONS OF MIDA CREEK, KILIFI COUNTY KENYA 

 

 

KEVIN OGOLLA OMOLLO 

 I56/37485/2017 

 

 

 

 

A thesis submitted in partial fulfillment of the requirements for the award of the 
degree of masters of sciences (Plant Ecology) in the school of pure and applied 
sciences of Kenyatta University 

 

 

 

 

 

 

 October 2021 


 DECLARATION 

 

This thesis is my original work and has not been presented for a degree in any other 
university or any other award 

Kevin Ogolla Omollo 

Reg. No. I56/37485/2017 

 

Signature ............................. Date: .................................... 

 

SUPPERVISORS 

We confirm that the work reported in this thesis was carried out by the candidate under 
our supervision 

Dr. Najma Dharani 

Department of Plant Sciences, 

 Kenyatta University 

 

Signature........................... .............................................................Date: ........................... 

Dr. Benards Okeyo 

Department of Environmental Sciences, 

Pwani University 

 

 

Signature...................................................................................Date................................... 


 DEDICATION 

 

This research thesis is dedicated to my dear parents; Mr. Noah Omollo Osuda, Mrs. 
Pamela Akinyi and my wife Belinda Adhiambo, who are my motivators and inspirers. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


 ACKNOWLEDGEMENTS 

I give my sincere gratitude to God the Father who has made my visions to be a reality 
by helping me to locate my destiny helpers throughout the study time. I also give my 
heart-felt gratitude to my supervisors; Dr Najma Dharani and Dr Benards Okeyo for 
their valuable guidance, encouragement during fieldwork and critical review starting 
from proposal development to the completion of this research work. I sincerely give 
thanks to my supervisor Dr Najma Dharani by giving me financial support for the 
success of my fieldwork. I also thank the chairperson of the mangrove reserve area at 
Mida Creek; Alex Toya Saidi for allowing me to conduct the research in the mangrove 
field peacefully and without demanding any pay or compensation. My gratitude also 
goes to my field assistant; Mr. Mohammed Ali who was always willing and ready to go 
with me to the muddy mangrove forests to collect samples. 

I give special thanks to my fellow student Mr. David Amakanga whom we were with in 
the field helping one another in developing ideas to deal with difficult matters that arose 
in the field. Special thanks to my father and mother for their support in form of finance, 
prayer, affection, and encouragement in all stages of my life. Thanks to my wife Mrs. 
Omollo Belinda for understanding me during my commitments and for standing with 
me in prayer. I give thanks to my friends Cecilia, Valentine, Beatrice, Okanda, Mugo, 
Peter, Onyancha and Dalmas whose love and encouragement in prayer and hard work 
has made me to continue being strong regardless of difficult situations here and there. 

Finally, am forever grateful to Kenyatta University for awarding me a scholarship to 
pay my full school fees for the two academic years to complete my study


 TABLE OF CONTENTS 
DECLARATION ............................................................................................................................. ii 
DEDICATION ............................................................................................................................... iii 
ACKNOWLEDGEMENTS ........................................................................................................... iv 
TABLE OF CONTENTS ................................................................................................................ v 
LIST OF TABLES ....................................................................................................................... viii 
LIST OF FIGURES ........................................................................................................................ ix 
LIST OF ABBREVIATIONS ........................................................................................................ xi 
DEFINITION OF TERMS ........................................................................................................... xiii 
ABSTRACT ................................................................................................................................ xiv 
CHAPTER ONE .............................................................................................................................. 1 
1 INTRODUCTION ....................................................................................................................... 1 
1.1 Background of the study ............................................................................................................ 1 
1.2 Statement of the problem ........................................................................................................... 3 
1.3 Justification of the study ............................................................................................................ 4 
1.4 Hypotheses ............................................................................................................................... 5 
1.5 Objectives ................................................................................................................................. 5 
1.5.1 General objectives ............................................................................................................... 5 
1.5.2 Specific objectives ........................................................................................................ 6 
1.6 Significance of the study ........................................................................................................... 6 
CHAPTER TWO ............................................................................................................................. 8 
2 LITERATURE REVIEW ............................................................................................................ 8 
2.1 Mangrove environment and dynamics ................................................................................... 8 
2.2 Importance of mangroves ...................................................................................................... 8 
2.3 Mangrove structure ................................................................................................................ 9 
2.4 Natural regeneration .............................................................................................................. 9 
2.5 Biomass accumulation ......................................................................................................... 10 
2.6 Threats to mangroves ........................................................................................................... 11 
2.7 Mangrove management in Kenya ........................................................................................ 12 
2.8 Mangroves in Kenya. ........................................................................................................... 13 
2.9 Mangrove Restoration and conservation measures in Kenya. ............................................. 13 
CHAPTER THREE ....................................................................................................................... 15 
3 RESEARCH METHODOLOGY ............................................................................................... 15 
3.1 Study site ................................................................................................................................ 15 
3.1.1 Managed mangrove forests of Mida Creek. ......................................................................... 16 
3.2 Study design ............................................................................................................................ 17 
3.3 Sampling procedures ............................................................................................................... 18 
3.3.1 Forest structure sampling .................................................................................................. 18 
3.3.2 Biomass and Carbon Stock accumulation estimates ......................................................... 19 
3.3.2.1 Aboveground Carbon ................................................................................................. 19 
3.3.2.2 Belowground biomass .................................................................................................... 19 
3.4 Data analysis ............................................................................................................................ 20 
3.4.1 Analysis of species structure and stand composition .................................................... 20 
3.4.2 Analysis on estimation of biomass and carbon stock accumulation ............................. 21 
3.4.3 Analysis for carbon sequestration for the managed mangroves .................................... 23 
3.4.4 Soil analysis .................................................................................................................. 24 
CHAPTER FOUR ......................................................................................................................... 28 
4 RESULTS ............................................................................................................................... 28 
4.1 Structural characteristics ...................................................................................................... 28 
4.2.1 Aboveground biomass ................................................................................................. 35 
4.2.2 Belowground biomass ................................................................................................... 39 
4.2.3 Soil organic carbon (SOC) ............................................................................................ 40 
4.2.4: Total carbon stocks ...................................................................................................... 42 
4.3: Nutrients status ................................................................................................................... 42 
4.3.1: Phosphates .................................................................................................................... 42 
4.3.2: Ammonium .................................................................................................................. 44 
4.3.3 Nitrates .......................................................................................................................... 45 
4.3.4 Belowground biomass and Nutrient status .................................................................... 46 
5 DISCUSSION CONCLUSIONS AND RECOMMENDATIONS .......................................... 47 
5.1 DISCUSSION ...................................................................................................................... 47 
5.1.1 Forest structure ................................................................................................................. 47 
5.1.2 Carbon pools ..................................................................................................................... 49 
5.1.2.1 Aboveground biomass (AGB) carbon ........................................................................ 49 
5.1.2.2 Belowground biomass ................................................................................................ 52 
5.1.2.3 Soil organic carbon (SOC) ......................................................................................... 55 
5.1.2.3 Total carbon stocks ........................................................................................................ 58 
5.1.2.4 Nutrients ......................................................................................................................... 61 
5.2 CONCLUSIONS ................................................................................................................. 63 
5.3 RECOMENDATIONS ........................................................................................................ 64 
APPENDICES ............................................................................................................................... 79 
APPENDIX 1: Mangrove species in Kenya in their local names and uses. .............................. 79 
APPENDIX 2: Photographs of mangroves encountered in the study site ................................. 80 


 

 

 

 

 

 

 


 LIST OF TABLES 

Table 3. 1: Specific densitiy for different mangrove species (Bosire et al., 2012) ....................... 23 
Table 4. 1: Structural characteristics of mangroves species for the dry season ............................ 29 
Table 4. 2: Structural characteristics of mangroves species for the wet season . .......................... 30 
Table 4. 3: Aboveground biomass contributions by different species encountered. ..................... 36 
Table 4. 4: Sequestration potential of different mangrove species encountered ........................... 38 
Table 4. 5: Carbon stocks in Mg C ha-1 for various carbon pools of Mida Creek ........................ 42 


 

 

 

 

 

 

 

 

 

 


 LIST OF FIGURES 

 
Figure 3. 1: Map of Mida Creek Mangrove Reserve .................................................................... 16 
Figure 4. 1: Changes in basal area for different mangrove species in Mida Creek. ...................... 31 
Figure 4. 2: Size class distribution for tree species in Green Island Plantation. ........................... 32 
Figure 4. 3: Size class distribution for tree species in 15 year-old Kibusa Plantation .................. 33 
Figure 4. 4: Size class distribution for tree species in the Natural Stand. ..................................... 33 
Figure 4. 5: Scatter plot for height against DBH for Kibusa Plantation. ....................................... 34 
Figure 4. 6: Scatter plot for height against DBH for Green Island Plantation. ............................. 34 
Figure 4. 7: Scatter plot for height against DBH for the Natural Stand. ....................................... 35 
Figure 4. 8: Total root carbon among different depth profiles across study sites. ........................ 40 
Figure 4. 9: Mean soil carbon stocks for the study sites in Mida Creek ....................................... 41 
Figure 4. 10: Amount of phosphate in the sediment of plantations and natural stand .................. 43 
Figure 4. 11: Amount of Ammonium in the sediment for the study sites ..................................... 45 
Figure 4. 12: Amount of Nitrate in the sediment of Plantations and Natural Stands .................... 46 


 

 

 

 


 LIST OF PLATES 
Plate 1. 1: Photograph of Rhizophora mucronata ......................................................................... 80 
Plate 1. 2: Ceriops tagal tree with stilt roots ................................................................................. 80 
Plate 1. 3: Avicenia marina tree with aerial roots ......................................................................... 81 
Plate 1. 4: Photograph of Xylocarpus granatum tree with Horizontal roots ................................. 81 
Plate 1. 5: Photograph of Sonneratia alba ..................................................................................... 82 
Plate 1. 6: Photograph of Bruguiera gymnorrhiza ........................................................................ 82 


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


 LIST OF ABBREVIATIONS 

 

AGB Above Ground Biomass 

ANOVA Analysis of Variance 

BGB Below Ground Biomass 

C Carbon 

CO2 Carbon dioxide 

Cr3+ Chromium ion 

DBH Diameter at Breast Height 

FAO Food Agricultural Organization 

FAO Food and Agricultural Organization 

GOK Government of Kenya 

GPS Global Positioning System 

Ho Null Hypothesis 

IVI Importance value index 

KFS Kenya Forest Service 

KMFRI Kenya Marine and Fisheries Research Institute 

KWS Kenya Wildlife Service 

Mg C ha-1 Milligrams Carbon per hectare. 

NaHCO3 Sodium Hydrogen Carbonate 

NaOH Sodium hydroxide. 

NH4+ Ammonium ion 


NO3- Nitrate ion 

PO43- Phosphate ion 

REDD+ Reduced emissions from avoided deforestation and forest 
degradation 

SOC Soil Organic Carbon 

SPSS Statistical Packages of Social Sciences 

T C yr-1 Tone Carbon per year 

t C/ha Tones Carbon per hectare. 

USD United State Dollar 

UV Ultra Violet light 

UV/VS Visible Ultra Violet Spectrometer 

 

 

 

 

 

 

 

 

 

 

 

 


 DEFINITION OF TERMS 

 

Biomass: Total mass of a living thing or part of a living thing. 

Carbon Sequestration: To incorporate or convert carbon (IV) oxide from the 
atmosphere into the tree biomass 

Colorimeter: An instrument designed to determine the color of something by 
comparison with standard colors 

Cuvette: A small vessel with at least two flat and transparent sides, used to hold a 
liquid sample to be analyzed in the light path of a spectrometer. 

Hypothetical: A state of being theoretical 

Inundation: Refers to flooding of an area. 

Neap tide: A tide in which the difference between high and low tide is the least. 

Sea ward position: Refers to =towards the sea‘ 

Silviculture: An agricultural practice that involves care and development of forests in 
order to obtain a product or provide a benefit. 

Spectrophotometer: An instrument used to measure the intensity of electromagnetic 
radiation at different wavelengths. 

Spring tide: A tide in which the difference between high and low tide is the greatest. 

Stand: A homogenous plant community with distinctive plant association that may be 
recognized elsewhere. 

Supernatant: A situation where a less dense liquid floats on the surface of another 
more-dense liquid. 

Tidal limits: The furthest point the water level reaches at a given tide. 

Tide: The periodic rise and fall of the waters in the ocean produced by the attraction of 
the moon and sun. 

Turbid: Water mass which has sediment; that which is not clear. 

Viviparous: A mode of reproduction where an embryo arises and develops from the 
outset. 

 

 

 


 ABSTRACT 

 

Mangrove forests play a significant role along the coastal environment throughout the 
tropical coast. They provide ecosystem services that are able to sustain both flora and 
faunal organisms found in such ecosystems. They are stores of large quantities of 
carbon in their biomass hence referred to as carbon sinks. This carbon can be emitted 
into the atmosphere when mangrove forests are degraded through unsustainable 
utilization. With accurate quantification of carbon stocks in these forests, it will be easy 
to define their potential role in global climate regulation through their carbon 
sequestration ability. Since the sequestration potential of the replanted managed 
mangrove species of Mida Creek is unknown, this indicates an information gap which 
calls for bridging to help develop a baseline inventory data for effective forest 
conservation and management. The main objective of this study is to quantify the 
amount of carbon sequestered by the managed mangrove forest of the Mida Creek, 
Kenya. Three sites were selected for this study; 15 year-old Kibusa Plantation, 20 year-
old Green Island Plantation, and a Natural Stand. Fifty Plots in the plantations and sixty 
plots in the natural stand of (10 x10) m2 were selected in each study site. Three carbon 
pools were investigated; aboveground carbon, belowground carbon, and soil organic 
carbon. Biomass for carbon determination in Kibusa and Natural Stands was estimated 
using a general allometric equation. Aboveground carbon was determined by measuring 
the diameter at breast height using a DBH meter. Belowground biomass and soil 
organic carbon was determined through root coring method using a soil corer at 
different depth profiles. Mean total carbon stocks in Kibusa and Green Island 
Plantations was 424.52±11.68 Mg C/ha and 958.57±50.01 Mg C/ha while the natural 
stand contained significantly higher total Carbon stocks of 2159.77±31.09Mg C/ha. 
There was no significant difference in the amount of soil organic carbon among the 
three different sites (F0.05(1)2,15=0.35, p>0.05). This study indicates that reforestation 
enhances structural development of replanted mangroves and that replanted mangroves 
are significant carbon stores. Rhizophora mucronata species was found to be the most 
abundant species compared to all the other species in all the three study sites, with an 
Importance Value Index of 117.1. In addition, Ammonium, Phosphates and Nitrates 
from the soil were also determined. Ammonium was the most abundant nutrient in all 
the three study sites. High Ammonium concentration in the mangrove sediment led to 
high amount of carbon sequestered in the root biomass. Therefore high ammonium 
concentration in the soil leads to increase in amount of carbon sequestered in the root 
biomass. From these results, we can deduce that awareness should be raised among the 
community members on the need for conservation and reforestation that will increase 
the amount of carbon sequestered since more mangroves increase the amount of carbon 
(IV) oxide capture. This will help in mitigating the issue of global warming at local 
levels. 


 CHAPTER ONE 

1 INTRODUCTION 

1.1 Background of the study 

Mangrove ecosystem is a wetland found along the coastal regions of both tropical and 
subtropical areas worldwide (Ellison et al., 2015). Globally, mangrove coastal forest 
cover approximately 137,760 km2-152,360 km2 of the world‘s surface (Kainuma et al., 
2013) with 50-75 species in 20-26 genera found in about 16-20 families (Hamilton and 
Friess, 2018). A total of 73 mangrove species and hybrids are dispersed across 123 
countries and territories around the globe (Giri et al., 2011). 

Global estimation of mangrove forest cover shows that mangroves have declined to a 
range not less than 50% of the original total cover (Hamilton et al., 2016). This has 
consequently led to a loss of the services provided by the mangroves due to biomass 
losses hence high accumulation of carbon dioxide in the atmosphere leading to global 
warming (Abino et al., 2014). Clearance of mangrove forests leads to permanent loss of 
sequestration ability hence making future mitigation targets difficult to achieve 
(Lagomasino et al., 2019). The need to reduce the continuous climate change trends has 
been of great interest to many scientific researchers. Most studies have therefore been 
done to better understand the functions of the mangrove ecosystem in carbon 
sequestration potential worldwide (zhang et al., 2018). 

Since mangrove trees are very dynamic (Donnelly et al., 2017), they are susceptible to 
changes in structure and composition hence may result in alteration of the ecosystem 
function and structure of the mangroves. Such changes may be as a result of response to 


direct human effect like over harvesting and over-exploitation due to illegal and poorly 
managed logging activities and settlement (Hayashi et al., 2019). Another threat that 
has been linked to changes in the structure of mangrove is climate change (Gilman et 
al., 2016). According to Ellison (2015), climate change is likely to have a tremendous 
effect on the mangrove ecosystems since it results in changes in ocean currents, 
frequent storms, elevated temperatures, changes in the patterns of precipitation, sea 
level rise, and increased levels of carbon dioxide (Srivastava et al., 2015). This may 
lead to a shift in species composition and distribution (Srivastava et al., 2015). Since 
Mangrove, plantations are normally carbon sinks, their degradation leads to emission of 
the stored carbon into the atmosphere. The program by the United Nations that was 
aimed at Reducing Emissions from Deforestation and Forest Degradation (REDD+) 
states that it will be .practically impossible. to achieve an atmosphere with stable 
global temperatures without reducing emissions from the forests and other mitigation 
actions (Chen et al., 2017). 

The decline of mangroves in Mida Creek and in Kenya at large accounts for an overall 
loss of 18% carbon as from 1985 and 2000 (Kirui et al., 2013). This therefore calls for 
understanding of the mangrove vegetation structure so as to predict future changes to 
aid in forest management through provision of site-specific quantitative vegetation data 
(Otero et al., 2018). The plan of mangrove restoration in Kenya began in 1918 when 
Smith and McKenzie Company did mangrove planting at Mobore in Lamu, following 
the forest clearance in the first world war (Kairo et al., 2001). Most recently, efforts on 
mangrove restoration has been done in Gazi bay, Mombasa estuary, Rasimi estuary and 
Mida Creek in activities coordinated by the Kenya Marine Forest Research Institute 


(KMFRI) and other organizations (Bosire et al., 2008; Kairo et al., 2008). In East 
Africa, there are few studies that have been conducted on carbon storage in the 
mangrove ecosystems. However, studies on mangrove carbon stocks are still limited in 
Kenya and are mostly focused on natural mangroves with few studies on managed 
mangroves. This study therefore seeks to establish the sequestration potential of the re-
planted mangrove forest in order to come up with appropriate management strategies 
that will ensure effective global warming mitigation process locally. 

1.2 Statement of the problem 

Carbon quantities stored in both above and below ground components of the mangrove 
trees are very high (Njana et al., 2016). For example, a study conducted by 
Kridiborworn et al. (2012) in Thailand indicated that mangrove stores a carbon stock of 
1400.9 Mg C ha-1. Degradation of mangroves may lead to emission of this carbon into 
the atmosphere; thereby accelerating the global warming. The rate of decline of 
mangroves in Mida Creek and in Kenya at large accounts for an overall loss of 18 % 
carbon as per 1985 and 2000 (Kirui et al., 2013). This has been brought about by the 
unsustainable utilization, coupled with human population increase that exerts pressure 
on the mangrove as the demand for the forest products increase. Mangrove management 
plan focuses on restoration of degraded mangroves forests through reforestation. 
Restored mangroves have high ability to sequester carbon into their biomass due to their 
high turnover ratio. However, their ability to play a role in climate regulation is 
compromised due to unsustainable use (Taillardat et al., 2018). Soil nutrient status also 
affects the accumulation of carbon stored in the root biomass. For instance, a study by 


Wang et al., (2016) indicated that soil organic carbon, total nitrogen and total 
phosphorus were the main factors that influenced belowground biomass. 

In order to understand the role of mangroves in climate change regulation, it is prudent 
to understand their carbon sequestration rates in different mangrove ecosystems 
(Kauffman et al., 2011). A few studies have been done on the true status of the restored 
mangroves and their carbon sequestration rates in Kenya. For instance, a study by Kairo 
et al. (2004) and Richard et al., (2014) on carbon stocks of mangroves majorly focused 
on carbon stocks of the natural and degraded mangrove forest stands (Sharma et al., 
2020). However, the sequestration potential of the replanted species remains unknown 
in the Mida Creek. This indicates an information gap which calls for bridging to help 
develop a baseline inventory data for effective forest conservation and management and 
to define the potential roles of the mangroves in regulation of climate. The present study 
has examined sequestration potential and carbon stocks of mangrove plantations in 
Mida Creek, Kenya. 

1.3 Justification of the study 

Carbon quantities stored in both above and below ground components of the mangrove 
trees are very high (Njana et al., 2016). For example, a study conducted by 
Kridiborworn et al. (2012) in Thailand indicated that mangrove stores a carbon stock of 
1400.9 Mg C ha-1. Kenyan mangroves are classified as threatened by both 
anthropogenic activities and climate change events (Kabede et al.,2012). Some of the 
anthropogenic activities that have led to degradation of the mangrove species include 
illegal lodging for timber, fuel wood and for medicinal use (Rasquinha, and Mishra, 


2020). Due to lack of data on sequestration rates of managed mangroves in Mida Creek, 
this study focuses on determination of carbon sequestration rate and biomass 
accumulation of the managed mangrove plantations of Mida Creek. This will help in 
coming up with management policies that will ensure effective global warming 
mitigation locally. In addition, the sequestered carbon can be traded on in the carbon 
market hence supporting sustainable harvesting and conservation, which can improve 
the community livelihood (Sapkota and White, 2020). With accurate quantification of 
carbon stocks in these forests, it will be easy to define their potential role in global 
climate regulation through their carbon sequestration ability. 

1.4 Hypotheses 

i. The forest structure and composition of managed mangroves in Mida Creek 
does not differ significantly from that of the natural stand. 
ii. The quantity of carbon stock in the managed Rhizophora mucronata does not 
significantly differ from that of Avicenia marina. 
iii. The sequestration potential of managed mangrove trees does not significantly 
differ from the mangrove species in the natural stand. 
iv. Sediment nutrient status does not significantly affect belowground biomass 
distribution. 


1.5 Objectives 

1.5.1 General objectives 

To assess the sequestration potential and the biomass accumulation of the managed 
mangrove forest of the Mida Creek, Kilifi County, Kenya. 


1.5.2 Specific objectives 

(i) Assess forest structure and composition of managed mangroves in Mida Creek. 
(ii) Determine total biomass accumulation of managed mangroves, Rhizophora 
mucronata and Avicenia marina species in the Mida Creek 
(iii) Determine the carbon sequestration potential of managed mangroves in the Mida 
Creek. 
(iv) Determine the effect of nutrient status on belowground biomass accumulation on 
mangrove sediment. 


1.6 Significance of the study 

Mangroves are the main carbon sinks in the intertidal zone but are threatened by 
degradation (Taillardat et al., 2018). This has led to release of the stored carbon in their 
biomass in form of carbon (IV) oxide, thereby leading to heightened threat of global 
warming. Conservation of the mangroves will maximize on their ability to sequester 
carbon since this will minimize emission of large quantities of carbon through 
degradation (Kelleway et al., 2017). Reforestation, as a mitigation measure to the effect 
of mangrove degradation has led to increased population of the mangrove tree species 
(Basyuni et al., 2018). The higher the number of the tree species the higher the 
efficiency of carbon sequestered, thereby offsetting global warming effects. The study 
on the assessment of sequestration potential of managed mangrove plantations is 
therefore important since this will help to quantify their ability to mitigate global 
warming at local level. Again, the management of the mangrove trees will ensure 
sustainable use of the mangrove products for both current and future generation. The 


accumulated carbon by the mangroves also benefits the local individuals through 
provision of fuel wood, timber, and herbal medicines (Huxham et al., 2017). 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


 CHAPTER TWO 

2 LITERATURE REVIEW 

2.1 Mangrove environment and dynamics 

Mangroves are vegetation types found in the transitional zone between sea and land 
along the coastal regions (Nopiana et al., 2020). They occur in various habitat settings 
including river dominated, draw downed areas, deltas, creeks, bays, islands and 
estuaries (Winata et al., 2017). The mangroves found along the river deltas are very 
much productive because of the high input of alluvial soils along the riverbanks 
(Truong, and Do, 2018). They possess certain unique features that enable them to adapt 
in such harsh environmental conditions along the coast such as soft substrate, high 
levels of salinity, submergence under turbid conditions, growing under alterations of 
inundation conditions (Boateng, 2018). Some of these adaptive mechanisms include 
viviparous embryos, reliance on wave motion and tides as means of seed dispersal, 
possession of aerial roots for respiration, knee roots for support, buttress roots for 
support and ability to tolerate high salt levels in their tissues and active secretion of 
excess salt from the plant body (Xu et al., 2017). 

2.2 Importance of mangroves 

Mangroves provide diverse ecosystem goods and services for the betterment of both 
fauna and flora. For instance, mangrove trees have been harvested by the local people 
and used as sources of fuel wood through charcoal burning as well as firewood (Himes-
Cornell et al., 2018). They are also good sources of durable timber since their wood is 
made up of hard tannins (Rog et al., 2017). They also inhabit a variety of faunal 


organisms such as fish, shrimp and crabs and so they provide a good avenue for 
aquaculture and shrimp farming (Owuor et al., 2019; Seary et al., 2020). 

Xylocarpus granatum mangrove species produce extracts that is used as drugs for 
treating human, animal and plant pathogens. Other extracts is also used for the 
management of incurable diseases like HIV/AIDS (Abdel-Aziz et al., 2016). Mangrove 
forests also act as spawning grounds for fish and feeding places for most birds 
migrating from place to place (Ram et al., 2020). Mangrove trees are also buffer zones 
against natural calamities such as cyclones, storms and tsunami (Oyana et al., 2009). 

2.3 Mangrove structure 

Environmental changes such as altered tide levels, nutrient levels, drought, and salt 
accumulation, hydro-period, and rainfall amounts may contribute to influence mangrove 
community structure (Sippo et al., 2018). Due to dynamic nature of mangroves, it is 
necessary to do an assessment on the mangrove structure with the aim of predicting 
changes in the future for effective forest management and planning to ensure that the 
mangrove ability to sequester carbon is not compromised (Kamruzzaman et al., 2018). 
According to Kairo et al. (2020), the parameters estimated in the study of the mangrove 
structure include tree height, stem diameter, basal area, stand density, forest canopy, 
understory and diameter at breast height. These are then used to derive attributes like 
stand tables, distribution patterns, aboveground and complexity indices. 

2.4 Natural regeneration 

Some of the adaptive mechanism possessed by the mangroves that makes them survive 
in the tidal flats is as a result of their regeneration potential (Sillanpää, et al., 2017). 


Linear regeneration sampling is a technique that describes the site regeneration potential 
in terms of distribution, size and seedling abundance (Otero et al., 2020). Seedlings that 
are above 40cm are classified as established regeneration class while those that are 
below 40cm are classified as potential regeneration class (Otero et al., 2020). The site 
conditions that may affect natural recruitment of the mangroves include levels of 
salinity, substrate nature, inundation, erosion due to sediment deposition and accretion 
(Das et al., 2019). Other factors that inhibit mangrove regeneration include propagule 
predation, persistent of the seed coat among the seed-bearing species, and scouring by 
driftwood ((Langston et al., 2017). Anthropogenic activities such as logging coupled 
with over-accumulation of debris from logging further threaten regeneration (Kodikara 
et al., 2017). 

The practices that have been put in place in order to enhance species diversity and for 
maintenance of forest integrity is silviculture (Jenke et al., 2020). This practice ensures 
that there is natural regeneration as well as increase in girth through pruning and 
thinning to create more openings for light penetration (Hassan et al., 2018). For a 
successful natural regeneration of mangroves, a minimum of 2500 seedlings ha-1as 
suggested by Alemayehu, (2017). A study conducted by Kirui et al. (2020) in Kenyan 
mangrove forests confirms these figures to be true for a successful natural regeneration. 

2.5 Biomass accumulation 

The mean annual primary production of the mangrove forest is approximately 218 Tg C 
yr-1 hence are considered to be one of the most productive systems (Ribeiro et al., 
2019). Their mean sequestration potential is about 20 billion t C and this value exceeds 


the amount of carbon sequestered in the tropical, temperate, and Taiga forests 
(Sanderman et al.,2018). The mangrove productivity is mainly dependent on the factors 
as latitude, climate, hydrodynamics, type of dominant species, and topography in 
relation to geomorphology (Komiyama et al., 2008). Most of the studies focusing on the 
carbon stocks of mangrove have mainly focused on the above ground biomass with 
some few studies on belowground biomass (Donato et al., 2011). According to Duke et 
al. 2013, a comparison done in the amount of above ground biomass for mangroves in 
tropical areas is higher than in temperate regions. According to global estimates, Japan 
is found to have AGB of 80.6 t ha-1 (Adame et al., 2017) while in Malaysia it is 116.8 
ha-1 (Akhand et al., 2016). According to local estimates of carbon stocks of mangroves, 
Mida Creek is found to have total biomass of 296.14 t ha-1 (Kinyanjui et al., 2014) 
while mangroves of Mtwapa creek have carbon stocks of 245.54±20.95 Mg C ha-1. 

2.6 Threats to mangroves 

The threats to the mangrove ecosystem are both natural and anthropogenic factors 
which alter forest structure and characteristics (Owuor et al., 2019). Natural threats to 
the mangrove ecosystem may originate from such calamities as cyclones, wind-storms, 
damage by frost, and floods (Alban et al., 2020). Sea level rise, ocean acidification, 
altered rainfall patterns, and increased temperatures are effects of climate change that 
pose challenges to the mangrove ecosystem (Owuor et al., 2019). On the other hand, the 
human induced threats include, mining operations, clearing, urban development, 
overharvesting and over-exploitation for timber, fish and crustaceans (Owuor et al., 
2019). 


Despite all the threats faced by the mangroves, they have developed some of the 
adaptive mechanisms to survive in the harsh, muddy and soft substrate environment. 
Some of these adaptive features include; the Red mangroves have prop roots descending 
from the trunk and branches, providing a stable support system. Shallow wide spreading 
roots, surrounds the trunks of black mangroves, adding to the structural stability of the 
tree. While these roots come in many different shapes and sizes, they all perform an 
important function of structural support in the soft soils. The roots of some mangrove 
species (e.g. Bruguiera gymnorrhiza) form into "knees" that project above the mud 
surface to facilitate gaseous exchange. These complex root system found in mangroves 
also contributes to trapping of debris, which therefore leads to much carbon 
accumulation in the mangrove sediment. 

2.7 Mangrove management in Kenya 

In Kenya, the following institutions are responsible for the development of legal 
framework for forest resources management; statement by the Forest act 2016. In 1932, 
the mangroves were originally gazette as forests. However, currently, the Forest 
Department is the one taking care of mangroves in Kenya (GOK, 2016). Other 
legislative bodies responsible for mangrove management include the Wildlife 
Conservation and Management Act of 2013. This institution provides for the sustainable 
harvest and management of wildlife and their habitats. In Marine Protected Areas such 
as the Watamu Marine national park and the Kiunga Marine National Reserve where 
mangroves occur, a legal jurisdiction has been bestowed purposely for conservation of 
mangroves (Kavoi et al., 2019). The Kenyan Constitution of 2010 also provides guiding 


tenets on the governance of the environment. In Article 60 (1) (e), a framework for 
sound conservation and protection of ecologically sensitive areas like the mangrove 
ecosystems has been established. 

 2.8 Mangroves in Kenya. 

Mangroves in Kenya cover approximately 45590 hectares (Kenduiywo, et al., 2020). In 
the coastal region, their distribution is widespread in areas like the creeks, bays, deltas 
and estuaries (Kairo et al., 2020). The greatest percentage cover of mangroves is mainly 
in the Tana River and Lamu, accounting for about 70% of the total mangrove area in 
Kenya (Kirui et al., 2012; Owuor et al., 2019). The total number of mangrove species 
found in Kenya is nine and these include; Rhizophora mucronata, Ceriops tagal, 
Sonneratia alba, Bruguiera gymnorrhiza, Avicenia marina, Lumnitzera racemose, 
Xylocarpus granatum, Xylocarpus moluccensis, and Heritiera littoralis (Kairo, 2020; 
Dharani, 2019). The most dominant species are the Rhizophora mucronata and Ceriops 
tagal that constitute 70% of the total mangrove formation along the Coast (Owuor et al., 
2019). Plates 2.1- 2.5 below show the photographs of the various mangrove species in 
Kenya. 

2.9 Mangrove Restoration and conservation measures in Kenya. 

The plan of mangrove restoration in Kenya began in 1918 when Smith and McKenzie 
Company did mangrove planting at Mobore in Lamu, following the forest clearance in 
the first world war (Kairo et al., 2001). Most recently, efforts on mangrove restoration 
has been done in Gazi bay, Mombasa estuary, Rasimi estuary and Mida Creek in 
activities coordinated by the Kemya Marine Forest Research Institute (KMFRI) and 


other organizations (Bosire et al., 2008; Kairo et al., 2008). Community based 
organizations in the Mida Creek are also actively involved in mangrove forest 
conservation and restoration. The community derive benefits from mangrove 
conservation through selective harvesting of timber, honey harvesting, protection 
against tides and sources of herbal medicines. Although a lot of studies in the naturally 
growing mangroves of Mida Creek, the carbon sequestration and biomass accumulation 
for the replanted mangroves remains unknown. Quantification of the ecosystems total 
carbon stocks in this study will enable the communities to understand the role of 
mangroves in local climate change mitigation. 

 

 

 

 

 

 

 

 

 

 

 

 


 CHAPTER THREE 

3 RESEARCH METHODOLOGY 

3.1 Study site 

The study was conducted in Mida Creek located in Kilifi County (03o21‘S, 39o59‘E). 
Mida Creek is located 88km north of Mombasa and 25 km south of Malindi (Kairo et 
al.,, 2020). The Arabuko Sokoke Forest borders it, which is the largest indigenous forest 
relief in the East African Coast. Mida Creek is a tidal inlet that expands across an area 
of 32km2 (Cowburn et al., 2018) and it is bordered by several villages like Majaoni, 
Uyombo and Dabaso. The dominant tree species in the whole area include Rhizophora. 
mucronata, Avicenia marina, and Ceriops tagal (Kairo et al., 2020). The Creek consist 
of a channel whose length is 11km long that is narrower (width 500 m) at the mouth, 
while its depth becomes wider in the middle part (1500-2000 m). The depth varies 
between four and a maximum of seven meters (Kairo et al.,, 2020). The annual amount 
of rainfall is about 600-1000 mm (Owuor et al., 2019). The highest precipitation occurs 
in the month of May and the rainy season extends from May to September. The average 
temperatures between the months of July- October is about 24oC, which is the lowest 
temperature recorded throughout the year while the highest temperatures are recorded in 
the months of November- March whose average is 32oC (Boitt et al., 2019). This study 
was conducted within a period of 11 months, that is, data was collected in the month of 
January and towards the end of October 2019. 


 

Kibusa 
plantation 

Green Island 

Natura stand 

Sudi Island 

Malindi 

Mombasa 

Figure 3. 1: Map of Mida Creek Mangrove Reserve (adapted from Owuor et al., 
2017) 

3.1.1 Managed mangrove forests of Mida Creek. 

The mangrove forests of Mida Creek have been exploited for many years, especially for 
wood fuel and building poles (Kairo et al,. 2011; Owuor et al., 2017) which has left 
some areas along the coastline completely bare. Mangrove reforestation program to 
rehabilitate degraded mangrove areas and to transform disturbed forests into uniform 
stands of higher productivity was initiated at Mida Creek in 1991 (Kairo, 1995). In 
1994, almost 70, 000 R. mucronata seedlings were replanted in 6.74 ha in the site 
located on the western side of the Creek, bordering Kinondo village. The sites, which 


were replanted, had been cleared early in the 1970s hence, did not show any element of 
natural regeneration for almost 27 years (Kairo, 1995). Subsequent development of the 
reforested areas has been monitored by comparing diameter and height increment of the 
replanted trees (Bosire et al., 2003; 2004) and by estimating aboveground tree biomass 
for mangrove plantations (Kairo, 2001). The plantation of Kibusa was mainly 
dominated by Rhizophora mucronata which was planted in 2004 while Green Island 
plantation which was also dominated by Rhizophora mucronata were planted in the 
year 2001 (Kairo, 2001). The mangroves in the 15 year-old Kibusa Plantation were 
younger than 20 year-old Green Island Plantation. The natural stand had increased 
spacing between trees with comparatively bigger stem diameters. The mangroves in the 
Natural forest had been exposed to disturbance such as logging for fuel wood and 
timber (Owuor et al., 2016). 

3.2 Study design 

Systematic sampling design was used in the plantations while in the natural stands, 
stratified random sampling design was used to identify study sites and to classify 
corresponding zones that are representative of the area across the topographic gradient 
based on vegetation type and stand conditions (Lance and Hattori, 2016). Sampling 
design involved random selection of the vegetation types where samples were collected. 
In the plantations, where systematic sampling design was adopted, five (5) transects 
were laid perpendicularly to the shoreline across the study sites. A total of fifty (50) 
plots of 10m x 10m were marked along the transects. The distance from one transect to 
another was about 30m depending on vegetation structure and landscape. Sampling was 


done systematically from sea to land at interval of 10m based on the thickness of 
mangrove forest stand condition (Sayed and Ibrahim, 2018). In the natural stand, where 
stratified random sampling was done, ten line transects were laid across the selected 
forest fragments. This was followed by divisions of the transects into ten equal 
segments. Within the smaller subdivisions, sixty (10m x 10m) quadrats were placed at 
random, within which samples were collected. 

3.3 Sampling procedures 

3.3.1 Forest structure sampling 

Forest structure is the horizontal and vertical distribution of layers in a forest including 
the trees, shrubs, and ground cover. Structure looks at the proportion of small, medium, 
and large trees and is usually reported as trees per acre by diameter class. 

In the natural forest stand, ten line transects were laid perpendicularly to the shoreline. 
Forty 10m x 10 m quadrats for high density areas and twenty 20m x 20m quadrats for 
low density areas were randomly established basing on vegetation zone stratification. In 
each line transect, five plots were placed randomly for sample collection. In each of the 
plantations, five transects were laid where ten plots were placed systematically per 
transect. Fifty plots in each plantation were sampled. The various vegetation parameters 
that were measured included plant population, tree height using a pre-marked long pole 
and diameter at breast height (DBH) which was measured for each tree with a DBH 
meter at 1.3m above the ground (Thompson, 2015) and by use of Vanier calipers. These 
measurements were then used to determine the tree basal area (m2/ha), stand density 
(stems/ha), importance value and frequency (Cintron and Schaeffer-Novelli, 1984). In 


addition to this, diameter size class distribution obtained from the DBH was used to 
describe the forest structure of the mangroves. 

3.3.2 Biomass and Carbon Stock accumulation estimates 

3.3.2.1 Aboveground Carbon 

In the natural forest stand, ten line transects were laid perpendicularly to the shoreline. 
Forty 10m x 10 m quadrats for high density areas and twenty 20m x 20m quadrats for 
low density areas were randomly established basing on vegetation zone stratification. In 
each line transect, five plots were placed randomly for sample collection. In each of the 
plantations, five transects were laid where ten plots were placed systematically per 
transect. Fifty plots in each plantation were sampled. This was then followed by 
identification of individual trees whose diameter is greater than 2.5 cm. The GPS 
coordinates for each plot was taken and trees marked using a colored ribbon. Vegetation 
parameters that were sampled both on the plantations and the natural stand were 
diameter at breast height (DBH); which was measured for each tree using a DBH meter 
at 1.3 m above the ground (Thompson, 2015) and tree height in meters using a pre-
marked long pole. 

3.3.2.2 Belowground biomass 

Below ground biomass was sampled using the improvised soil coring method according 
to Tamooh et al (2008). This method involves random selection of trees within the 10m 
x 10 m plots for root coring. Cores (60cm length and 7 cm diameter) were made in three 
20cm vertical root profile; 0 - 20cm, 20 - 40cm, and 40 - 60cm at each of the parent root 
base chosen for coring. These vertical root profiles were obtained by hammering the 


corer into the root base of the selected tree. Seven trees in each plot were randomly 
selected for root coring. Each sample was then carried to the seashore and washed using 
a 1mm mesh sieve. Fresh live roots (brown in color) were then put in a labeled carrier 
bag and kept in a refrigerator until processed. Roots were then separated into different 
diameter classes; i.e., 5mm, between 5-10mm, 10-20mm, 20-30mm, 30-40mm, 40-
50mm, and larger than 50mm using a Vanier calipers. The screened roots were then 
stored and dried at 70oC in an oven; to obtain the dry weight (Ha et al., 2018). 

The following procedure was used to collect soil sample for nutrient analysis (Nitrogen 
in form of NO3-, NH4+and phosphorus in form of PO43-). Soil samples were collected at 
varying depth profiles of 0-20 cm, 20-40 cm and 40-60 cm using a soil corer. The soil 
samples were then put in a carrier bag and stored in cool boxes for transfer in the 
laboratory for analysis (Tamooh et al ., 2008). 

3.4 Data analysis 

3.4.1 Analysis of species structure and stand composition 

One-way ANOVA was performed on total stocking densities of different size classes, to 
be assumed as a measure of age, plant population, tree height and diameter at breast 
height (DBH). The relative density, dominance, relative frequency was estimated and 
the importance value index for each species was established according to Dahdouh-
Guebas and Koedam (2006). These measurements were then used to determine the tree 
basal area (m2/ha), stand density (stems/ha), importance value and frequency (Cintron 
& Schaeffer-Novelli, 1984). In addition to this, diameter size class distribution obtained 


from the DBH was used to describe the forest structure of the mangroves. The 
following equations were used for the calculations of the various community indices: 

Basal area for each tree species was determined as the sum of the area of cross section 
for all the tree species at DBH. 

Basal area (m2ha-1) = pie/4(dbh)2 =0.00007854 D1302 
× 10000...........3.1 

 Stand density (Density per hectare) = (Number of stems in plots X 10000)/ plot area. 

Relative density 
× 100…........3.2 

Relative dominance 
X 100……….........3.3 

Relative frequency 
X 100………..3.4 

Importance Value Index (IVI) = Relative Density + Relative Dominance + Relative 
frequency......................................................................................................................3.5 

 

3.4.2 Analysis on estimation of biomass and carbon stock accumulation 

Analysis was carried out using Microsoft Excel spreadsheet 2010 and Statistica 8. All 
data were tested for normality and homogeneity of variance and normalized where 
necessary for parametric tests. Mean values of biomass and carbon data sets that were 
collected from various representative sites of the Creek were subjected to significance 
tests using one-way ANOVA to compare the total variation in the above and below 


ground mean biomass and carbon accumulation. Descriptive and simple statistical 
calculations were used to determine root densities and vertical distribution of each 
species encountered. These were then described using a summary of graphical 
presentations. 

The following equation was used for determination of biomass content; 

AGB =0.251.D2.46;(Komiyama et al. 2005), ................................................................3.6 

Where; 

AGB is the aboveground biomass in kg, 

.= is the wood density in gcm-3, 

D= is the tree diameter at breast height in cm. 

Carbon content was calculated through multiplication of biomass content of the 
mangrove tree species by its specific carbon concentration using a default value of 0.5 (; 
Mcleod and Salm, 2006; Kauffman and Donato, 2012). Specific wood density values as 
developed by Bosire et al. (2012) were used for computing tree biomass. These values 
are as shown in the table 3.1 below: 

 

 

 

 


 

Table 3. 1: Specific densitiy for different mangrove species (Bosire et al., 2012) 

Mangrove species 

 Wood density (gcm-3) 

Rhizophora mucronata 

Avicenia marina 

Bruguiera gymnorrhiza 

Ceriops tagal 

Xylocarpus granatum 

 1.1 

 0.9 

 1.3 

 1.1 

 0.8 



The equation by Komiyama et al. (2005) was used in calculation of aboveground 
biomass because it clearly outlines how carbon content can be easily obtained from the 
specific wood densities of different species. However, its limitation is that it fails to 
provide the specific wood densities for different mangrove species and so, his equation 
must be linked to the specific wood densities that were developed by other researches 
like Bosire et al. (2012) 

3.4.3 Analysis for carbon sequestration for the managed mangroves 

This was done using the SPSS software where the mean values for total carbon 
sequestered in the dry season was calculated independently and compared with that 
which was sequestered during the rainy season for a period of 11 months. The 
percentage carbon sequestration was determined using the formula below: 

 
x100..........3.7 


3.4.4 Soil analysis 

Data collected was analyzed using SPSS version 20 and Microsoft spreadsheet 
statistical packages. Logarithmic transformation was done on soil and nutrient data. 
One-way ANOVA was used to determine whether there is any difference between 
carbon stocks, nutrient concentration between sites and within blocks in the plantation. 
Correlation analysis was carried out to determine if belowground biomass is dependent 
on nutrient availability. Soil nutrient analysis was done using the procedure outlined by 
Okalebo et al. (2002) as discussed below: 

3.4.4.1 Ammonium determination 

An amount of 10 grams of oven dried, ground and sieved soil was accurately weighed 
and placed into a plastic shaking bottle. 100 ml of 0.5M potassium sulphate was added 
and the contents shaken for 1 hour in a mechanical rotary shaker. The contents were 
then filtered through No. 42 Whatman filter paper. 5.0 ml of reagent N1 (prepared by 
dissolving 34g of sodium salicylate, 25g sodium citrate and 25g sodium tartrate together 
in 750ml distilled H2O) was added to 0.2ml of the extract in a test tube and allowed to 
stand for fifteen minutes. 5.0 ml of reagent N2 (prepared by dissolving 30g NaOH in 
750ml distilled water and adding 10 ml sodium hypochlorite, then making up to 1 liter) 
was then added to the above mixture and allowed to settle for 1 hour. The blue colored 
solution was then read through UV/VS spectrophotometer at a wavelength of 655 nm 
and compared to the standard curve of ammonium solution. 

The amount of ammonium concentration in the soil sample was calculated as: 

 


NH4-N(ug kg-1) = {(a-b)*V*MCF*f*1000}/w..............................................................3.8 

Where a= concentration of N in the solution, b= concentration of N the blank, v= 
volume of the extract; w= weight of the fresh soil; MCF= moisture correction factor; f= 
multiplication factor. 

3.4.4.2 Nitrate determination 

An amount of 10 grams of oven dried, ground and sieved soil was accurately weighed 
and placed into a plastic shaking bottle. 100 ml of 0.5M potassium sulphate was added 
and the contents shaken for 1 hour in a mechanical rotary shaker at room temperature. 
The contents were then filtered through No. 42 Whattman filter paper. The extract was 
then transferred (0.5ml) to a test tube followed by 1.0ml salicylic acid and allowed to 
settle for 30 minutes. 10ml of 4M NaOH was added to the mixture and allowed to settle 
for one hour for the yellow color to develop. The yellow colored solution was then read 
through UV/VS spectrophotometer at a wavelength of 419 nm and compared to the 
standard curve of nitrate solutions. For determination of nitrate, the formula below was 
used; 

NO3-1(ug Kg-1) = {(a-b)*V*MCF*1000}/w...................................................................3.9 

Where a= concentration of NO3-1 in the solution, b= concentration of NO3-1 the blank, 
v= volume of the extract; w= weight of the fresh soil; MCF= moisture correction factor. 

3.4.4.3 Phosphate determination 

An amount of 2.5 grams of oven dried, ground and sieved soil was accurately weighed 
and place into a 150cm3 shaking bottle. 50 ml of the Olsen‘s extracting solution (0.5 M 


NaHCO3 PH 8.5) was added. The mixture was then shaken for 30 minutes, and then 
filtered using a Whattman filter paper. An amount of 10ml of the sample filtrate was 
then transferred into 50ml volumetric flask and 5ml of 0.8M boric acid was added to the 
filtrate. This was then followed by 10ml of the ascorbic acid and topped up to the mark 
with distilled water. The mixture was left to stand for 1 hour for a clear blue color to 
form. The absorbance of the solution was read at a wavelength of 880 nm using a 
UV/VS spectrophotometer and compared to the standard curve for phosphate 
concentration. The concentration of phosphorus in each sample was calculated using the 
formulae below; 

P(mg kg-1) 
……………………………………………………4.0 

Where a =the concentration of P in the sample; b =the concentration of P in the blank; v 
=volume of the extracting solution; f = dilution factor; w =weight of the sample. 

3.4.4.4 Soil organic carbon 

An amount of 0.30g of ground soil was put into a clean, labelled 100ml-digestion tube 
and the weight recorded. 2ml of distilled water was added, followed by 10ml 5% 
potassium dichromate solution. The above mixture was carefully titrated with 5ml 
sulphuric acid and digested at 150oC for 30 minutes. After cooling, 50cm3 of 0.4% 
BaCl2 was added to the mixture; swirled and the volume topped up to 100 ml with 
distilled water. This was then left to stand overnight for a visible supernatant solution to 
form. A small amount of the supernatant was then transferred into a colorimeter cuvette 
and the absorbance measured at 600nm. 

The percentage of total organic carbon in the dry soil was calculated as follows; 


% organic carbon ={(a-b) *0.10}/w...............................................................................4.1 

Where, a=concentration of Cr3+ in the sample; b =concentration of chromic (III) ion in 
the blank; w= weight of soil taken for analysis. (Okalebo et al., 2002). Soil organic 
carbon (Mg ha-1) was then calculated as the product of percentage of organic carbon and 
a default value of 56% according to Brady (1990) and Pearson et al. (2005). Therefore; 

Soil organic carbon (SOC) (Mg ha-1 ) = % organic carbon*0.56...................................4.2 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


 CHAPTER FOUR 

 4 RESULTS 

4.1 Structural characteristics 

In the dry season, average height and stem diameter for the Rhizophora mucronata in 
15 year-old Kibusa plantation was 4.8±1.7 m and 3.0±2.5cm respectively. During the 
wet season, the average height and stem diameter for the Rhizophora mucronata was 
6.8±1.7m and 4.0±2.5cm respectively. This means that there was a significant increase 
in both height and stem diameter during the wet season (P<0.05). The mean height for 
Rhizophora mucronata in 20 year-old Green Island Plantation was found to be 
2.8±1.7m while the mean stem diameter was found to be 2.8±1.2cm in the dry season. 
In the wet season, the mean height for Rhizophora mucronata in the Green Island 
Plantation was found to be 3.9±2.7m while the mean stem diameter was found to be 
4.8±1.2cm. This shows a significant increase in both height and stem diameter after the 
dry spell. In the Natural Stand, the mean height for Rhizophora mucronata was 
obtained to be 4.9±0.1m while the mean diameter was found to be 5.9±0.2cm for the 
dry season. In the wet season, the average height for Rhizophora mucronata was 
estimated to be 5.8±0.1m while the mean diameter was found to be 6.7±0.2cm. 
Generally, both height and stem diameter for all the species increased during the wet 
season as a result of nutrients brought into the mangrove ecosystem by the surface run 
off. Table (4.1 and 4.2) below shows the structural characteristics for the Plantations 
and the Natural Stand for both dry and rainy season. 

 


 

 

Table 4. 1: Structural characteristics of plantations and the Natural Stand for dry 
season in Mida Creek, Kenya (N= Number of individuals per species, IVI = 
Importance Value Index) 

 

Study 
Site 

 

 

 

 

 

Species 

 

 

Relative values in % 

 

N 

Mean 
Height(m) 

Basal 
Area 
(m2/ha) 

Density 

Dominance 

Frequency 

IVI 

Kibusa 
plantation 

A. marina 

4 

7.8±3.1 

0.04 

1.21 

14.34 

9.07 

24.63 

B. gymnorrhiza 

41 

2.5 ±1.3 

0.001 

12.42 

0.36 

18.15 

30.93 

C. tagal 

108 

4.2±1.5 

0.079 

32.73 

28.33 

27.22 

88.28 

R. mucronata 

161 

4.8±1.7 

0.151 

48.79 

54.14 

31.76 

134.69 

X. granatum 

16 

4.4±1.2 

0.0079 

4.85 

2.83 

13.61 

21.29 

Green 
Island 

A. marina 

 13 

1.5±2.5 

0.185 

4.78 

48.43 

22.22 

75.43 

B. gymnorrhiza 

 63 

2.8±1.7 

0.087 

23.16 

22.77 

16.67 

62.60 

C. tagal 

 63 

5.1±1.9 

0.044 

24.32 

11.52 

27.78 

62.45 

R. mucronata 

133 

2.8±1.7 

0.066 

48.90 

17.27 

 33.33 

99.51 

Natural 

stand 

A. marina 

96 

5.2±1.5 

1.38 

34.29 

45.25 

35.71 

115.25 

B. gymnorrhiza 

20 

4.9±2.3 

0.39 

7.14 

12.79 

14.28 

34.22 

C. tagal 

45 

3.5±1.2 

0.10 

16.07 

3.28 

17.86 

37.20 

R. mucronata 

 71 

4.9±0.1 

0.39 

25.36 

12.79 

14.29 

52.43 

 X. granatum 

48 

5.8±1.2 

0.79 

17.14 

25.90 

17.86 

60.90 




Table 4. 2: Structural characteristics of plantations and the Natural stand for the 
wet season in Mida Creek, Kenya (N= Number of individuals per species, IVI = 
Importance Value Index) 

 

Study 
Site 

 

 

 

Species 

 

 

Relative values in % 

 

N 

Mean 
Height(m) 

Basal Area 
(m2 ha-1) 

Density 

Dominance 

Frequency 

IVI 

Kibusa 

plantation 

A. marina 

4 

8.4±1.3 

0.069 

39.33 

17.94 

9.07 

19.22 

B. gymnorrhiza 

41 

5.6±5.8 

0.072 

18.22 

13.69 

18.15 

44.26 

C. tagal 

108 

5.2±3.4 

0.095 

37.23 

37.06 

27.22 

78.01 

R. mucronata 

161 

6.8±1.7 

0.30 

56.4.3 

57.03 

31.76 

137.58 

X. granatum 

16 

5.3±6.2 

0.012 

17.14 

9.28 

13.61 

20.74 

Green 
Island 

A. marina 

13 

7.7±3.5 

0.72 

24.35 

58.52 

22.22 

 27.70 

B. gymnorrhiza 

63 

4.2±2.7 

0.50 

30.26 

29.74 

16.67 

 41.57 

C. tagal 

63 

6.2.±1.9 

0.34 

36.29 

34.21 

27.78 

51.29 

R. mucronata 

133 

3.9±2.7 

0.42 

58.60 

31.21 

 33.33 

179.44 

Natural 

stand 

A. marina 

96 

6.6±1.5 

2.82 

42.27 

53.35 

35.71 

96.35 

B. gymnorrhiza 

20 

6.2±2.3 

0.56 

15.40 

17.73 

14.28 

32.15 

C. tagal 

45 

4.8±1.2 

0.38 

23.64 

9.82 

17.86 

 58.75 

R. mucronata 

71 

5.5±0.1 

0.56 

45.25 

23.09 

14.29 

 72.74 

X. granatum 

48 

6.7±1.2 

1.32 

29.32 

35.01 

17.86 

40.00 



 

The total basal area during dry season for Rhizophora mucronata in 15 year-old Kibusa 
and the 20 year-old Green Island Plantations were 0.151m2/ha and 0.066m2/ha 
respectively while that for the Natural Stands was 0.39m2/ha. When this was compared 
to the rainy season after the ten months, it was found that the total basal area for 
Rhizophora mucronata in Kibusa Plantation, Green Island Plantations and Natural 
Stands were 0.30, 0.42, 0.56 m2/ha respectively. This showed that there was increase in 


-5

0

5

10

15

20

25

30

35

40

45

kibusa plantation

natural stand

Green Island plantation

Basal area m2/ha for R. 
mucronata 

Study site 

dry season

wet season

the total basal area after the ten-month period as shown in figure 4.1. However, there 
was a significant difference between the Plantations and the Natural Stands in terms of 
tree height, basal area, and stem diameter. Kibusa Plantation showed a significantly 
larger value for both tree height, stem diameter, and basal area than in the Natural 
Stands (Tukey test, P<0.005). 

 

Figure 4. 1: Changes in basal area for different mangrove species for the 
plantations and the natural stand in Mida Creek, Kenya 

R. mucronata was the dominating species in the 15 year-old Kibusa plantation hence 
mono-stand. However, there was recolonization by species like B. gymnorrhiza, 
Ceriops tagal and X. granatum. After the ten-month period, the total stem density was 
estimated to be 3666.67stems/ha and 4533.33 stems/ha for Kibusa and Green Island 
Plantations respectively, while that for the natural stand was found to be 1120 stems/ha. 

 

 


0

500

1000

1500

2000

2500

3000

3500

<3


3.1-5


5.1-7


7.1-9


9.1-11


11.1-13


13.1-15


15.1-17


>17


Density (stems/ha) 

Size class(cm) 

4.1.1 Size class distribution for the tree species 

The size class distribution based on DBH in the 20 year-old Green Island Plantation had 
a hypothetical negative exponential curve. This means that there was a higher stem 
density between the size class < 3cm (figure 4.2). Size classes <2 cm, and <5cm in 
Kibusa Plantation and Natural Stands respectively had the highest stem densities (figure 
4.3 and 4.4). 

 

Figure 4.2: Size class distribution for tree species in 20 year-old Green Island 
Plantation 

 


0

100

200

300

400

500

600

700

800

900

1000

<2


2-2.5


2.6-3


3.1-3.5


3.6-4


4.1-5


5.1-5.5


5.6-6.0


6.1-7


>7


Density(stems/ha) 

Size class(cm) 

0

100

200

300

400

500

600

<5


5.1-7


7.1-9


9.1-11


11.1-13


13.1-15


15.1-17


17.1-19


19.1-21


21.1-23


23.1-25


>25


Density (Stems/ha) 

Size class (cm) 

 

Figure 4.3: Size class distribution for tree species in 15 year-old Kibusa Plantation 

 

Figure 4.4: Size class distribution for tree species in the Natural Stand 

4.1.2: Distribution of tree height against diameter 

Figures 4.5, 4.6, and 4.7 show the scatter plots for the tree height against diameter at 
breast height (DBH) at each study site. Thirty percent of the stem diameter in Kibusa 
Plantation indicates that most of the trees had a diameter class between 3.2-4.3 cm and a 
height between 3.5-4.5 m (Figure 4.5). In Green Island Plantation, the DBH ranged 


y = 0.4307x + 3.061 
R² = 0.4177 

0

2

4

6

8

10

12

14

0

5

10

15

20

Height (m) 

DBH130(cm) 

height

Linear (height )

y = 0.3114x + 2.7946 
R² = 0.2185 

0

2

4

6

8

10

12

0

5

10

15

20

25

Height(m) 

DBH130(cm) 

Height

Linear (Height)

between 2.5-4.5 cm and a height of between 2.0-4.3m while in the Natural Stand, DBH 
ranged from 4-11cm and the height ranged from 3.7-7m (figure 4.6 and 4.7). 

 

Figure 4.5: Scatter plot diagram for height against diameter at breast height for 
mangrove trees in 15 year-old Kibusa Plantation 

 

Figure 4.6: Scatter plot diagram for height against diameter at breast height for 
mangrove trees in the 20 year-old Green Island Plantation 


y = 0.2184x + 3.3488 
R² = 0.4642 

0

2

4

6

8

10

12

14

0

10

20

30

40

50

Height(m) 

DBH130(cm) 

Height

Linear (Height)

 

Figure 4.7: Scatter plot diagram for height against diameter at breast height for 
mangrove trees in the Natural Stand 

4.2 Carbon pools 

4.2.1 Aboveground biomass 

The data obtained during dry seasons showed that the total aboveground biomass in 15 
year-old Kibusa Plantation, 20 year-old Green Island Plantation and the Natural stand 
was 269.44±5.0 t/ha, 325.40±3.0 t/ha, and 3020.28±5.2 t/ha respectively. On the other 
hand, the data obtained during the wet season had a total aboveground biomass of 
452.47±2.5 t/ha, 450.42±5.2 t/ha, and 3666.63±8.5 t/ha in Kibusa Plantation, Green 
Island Plantation, and Natural Stands respectively. The wet season had a higher 
aboveground biomass compared to that of the dry season in all the study sites. 

Avicenia marina had the highest biomass contribution in the natural stand followed by 
Bruguiera gymnorrhiza in both dry and wet season. However, in comparison to the dry 
season, biomass accumulation by the A. marina and B. gymnorrhiza was higher in the 
wet season. In in the 15 year-old Kibusa Plantation, biomass contribution by R. 


mucronata was highest in both dry and wet season. B. gymnorrhiza and C. tagal had 
almost similar biomass contribution in both dry and wet season. In the 20 year-old 
Green Island, A. marina had the highest aboveground biomass followed by B. 
gymnorrhiza in both dry and wet season. However aboveground biomass contribution 
for both A. marina and B. gymnorrhiza was higher in the wet season than in the dry 
season. There was variation in the percentage biomass contribution among the different 
mangrove species in all the three study sites for both dry and rainy season. 

Table 4. 3: Aboveground biomass contributions by different species encountered in 
the three different sites for both dry and wet season in Mida Creek, Kenya 

 

Site 

Species 

encountered 

ABG t C/ha 

Dry season 

(January) 

%contribution 

ABG t C/ha 

Wet season 

(October) 

 

%contribution 

Kibusa 
plantation 

 

 A. marina 

37.04±2.2 

5.30 

45.62±6.7 

6.52 

B. gymnorrhiza 

36.93±4.6 

5.28 

70.83±7.2 

10.13 

C. tagal 

54.47±1.8 

7.79 

70.89±3.4 

10.14 

X. granatum 

4.39±9.7 

0.70 

5.23±4.6 

0.77 

R. mucronata 

114.10±4.2 

16.31 

259.9±1.2 

37.16 

Total ABG t C/ha 

 

269.44±5.0 

35.28 

452.47±2.5 

64.72 

Green Island 
plantation 

A. marina 

192.44±3.4 

24.80 

220.36±7.8 

28.40 

B. gymnorrhiza 

58.02±4.5 

7.48 

132.04±3.6 

17.02 

C. tagal 

37.17±2.4 

4.79 

38.59± 4.8 

4.97 

R. mucronata 

37.78±4.9 

4.87 

59.43±8.6 

7.66 

Total ABG t C/ha 

 

325.40±3.0 

41.95 

450.42±5.2 

58.05 

Natural stand 

A. marina 

1737.96±1.6 

25.99 

2175.79±4.6 

32.54 

B. 
gymnorrhiza 

679.16±2.4 

10.16 

819.71±2.5 

12.26 

C. tagal 

100.65±3.6 

1.51 

118.27±4.3 

1.77 

X. granatum 

46.57±2.7 

0.70 

52.78±2.3 

0.79 

R. mucronata 

455.95±3.8 

6.82 

500.08±2.2 

7.49 

Total ABG t 
C/ha 

 

3020.28±5.2 

45.15 

3666.63±8.5 

54.85 



 


When 50% of the aboveground biomass is assumed to be carbon, then average 
aboveground carbon for Kibusa Plantation was computed and found to be 0.68±4.9 t 
C/ha in the dry season and had a range between 0.01381±8.5 and 9.2731±3.1 t C/ha. In 
the wet season the average aboveground carbon was 0.748±5.5t C/ha and had a range of 
0.003929±2.7 – 10.9778±3.5 t C/ha. Green Island Plantation had an average 
aboveground biomass of 1.162±7.0 t C/ha and had a range of 0.00797±2.3-21.413±2.8 t 
C/ha in the dry season. In the wet season, Green Island Plantation had an average 
carbon of 1.65±4.7 t C/ha and had a range of 0.0047±2.1- 26.8851±3.8 t C/ha. The 
Natural Stand had the highest aboveground biomass with an average carbon of 
1.162±7.0 t C/ha and 13.048±4.6 in dry and wet season respectively. 

Within the period of ten months, the amount of carbon sequestered by A. marina in the 
Kibusa Plantation, Green Island Plantation, and the Natural Stand was estimated to be 
4.29±5.1 t C/ha, 13.96±4.5 t C/ha, and 218.92±1.3 t C/ha respectively. In contrast, the 
amount of carbon sequestered by R. mucronata within the ten-month period was 
estimated to be 72.90±2.6, 10.826±2.0, and 22.067±5.8 t C/ha in Kibusa Plantation, 
Green Island Plantation and Natural Stand respectively. The total amount of carbon 
sequestered by A. marina in all the three sites within the ten-month period was 
237.16±5.3 t C/ha while that sequestered by R. mucronata was 105.80±4.7 t C/ha. 

Based on the ANOVA test done for the wet season, there was a significant difference in 
the aboveground biomass among the three different plantations since the calculated F 
(12.76373) is greater than the critical F (F;0.05 (1) 2,831=3.00065; P<0.05). This was also 
true for the dry season, where the calculated F (35.57665) was greater than the critical 


(F;0.05 (1) 2,882=3.00595). There was no significant difference in the aboveground biomass 
among the four different mangrove species (F;0.05, (1), 3,195=2.65; P>0.05) in the Natural 
stand. On the other hand, in Kibusa plantation, there was a significant difference in the 
aboveground biomass among the four different mangrove species (F;0.05 (1), 3,103=2.69; 
P<0.05). Table 4.4 below shows the amount of carbon sequestered between the two 
seasons from January to October. 

Table 4. 4: Sequestration potential of different mangrove species encountered in 
the three study sites 

 

Site 

Species 
encountered 

ABG g C/ha 

Dry season 
(January) 

ABG g C/ha 

Wet season 
(October) 

 

 Amount of 
carbon 

 

sequestered 

% 
sequestration 
potential 

Kibusa 
Plantation 

 

R. mucronata 

57.052±4.9 

129.954±3.4 

72.902±2.6 

32.940 

B. gymnorrhiza 

18.465±5.7 

35.415±3.7 

16.95±4.3 

16.494 

C. tagal 

27.236±6.6 

35.444±4.9 

8.207±4.5 

7.986 

X. granatum 

2.195±3.8 

0.419±2.5 

0.419±3.4 

0.408 

A. marina 

18.521±6.5 

22.808±2.9 

4.287±5.1 

4.172 

Total g C/ha 

 

123.469±5.0 

226.236±4.8 

102.76±6.2 

 

Average 

 

0.6856±4.9 

0.7483±5.5 

 

 

Green Island 

Plantation 

B. gymnorrhiza 

29.007±6.8 

66.020±2.5 

37.012±7.8 

26.423 

A. marina 

96.219±2.1 

110.178±1.6 

13.958±4.5 

21.279 

R. mucronata 

18.888±1.7 

29.715±2.3 

10.826±2.0 

16.504 

C. tagal 

15.110±2.9 

19.296±2.6 

4.186±3.4 

6.382 

Total g C/ha 

 

159.225±4.0 

225.208±3.5 

65.983±5.7 

 

Average 

 

1.162±7.0 

1.650±4.7 

 

 

Natural stand 

A. marina 

868.979±2.4 

1087.89±2.7 

218.917±1.3 

27.740 

B. 
gymnorrhiza 

339.579±4.6 

409.853±1.6 

70.275±4.7 

21.745 

R. mucronata 

50.324±9.7 

250.040±4.2 

22.067±5.8 

6.828 

C. tagal 

50.324±3.4 

51.135±8.4 

8.810±6.9 

2.726 

X. granatum 

 23.283±2.0 

26.388±9.2 

3.105±3.4 

0.961 

Total ABG g 
C/ha 

 

1510.139±4.0 

1833.313±7.9 

323.174±5.0 

 

Average 

 

10.710±3.5 

13.048±4.6 

 

 

 

 

 

 

 

 




4.2.2 Belowground biomass 

Kibusa Plantation had the lowest root carbon concentration of 47.34±5.6 Mg C/ha 
(Figure 4.8). The natural stand had the highest root carbon concentration of 78.36±3.8 
Mg C/ha, followed by the 20 year-old Green Island Plantation of 53.43±4.1 Mg C/ha. 
There was a significant difference in root carbon concentration among the three study 
sites (F;0.05, (1),2,67=3.05, P<0.05). There was a significant difference in belowground 
biomass between the natural stand and the two plantations (P<0.05, Tukey test). There 
was a significant difference in belowground biomass within the different depth profiles 
among the sites (F; 0.05, (1), 2, 67=5.14 P<0.05). However, there was no significant 
difference in root carbon concentration between Green Island Plantation and the Natural 
Stand (P>0.05, Turkey test). There was a significant difference in root carbon 
concentration between the depth profiles of 20-40cm and 40-60cm of the Kibusa 
Plantation and the other study sites (P<0.05, Tukey test). From the three study sites, the 
Kibusa Plantation had the lowest amount of root carbon concentration in all the three-
depth profiles sampled i.e. 15±8.9 Mg C/ha, 30±7.4 Mg C/ha, and 17±6.6 Mg C/ha in 0-
20 cm, 20-40cm, and 40-60cm respectively. In all the three study sites, root carbon 
concentration was highest at the depth of 20-40 cm, where Kibusa Plantation, Natural 
Stand, and the Green Island Plantation recorded 30±4.2 Mg C/ha, 33±1.6 Mg C/ha, and 
29.5±2.6 Mg C/ha respectively. 

 


0

5

10

15

20

25

30

35

40

0-20

20-40

40-60

Root carbon(Mg C/ha) 

Depth profile 

Kibusa

Natural

Green Island

 

Figure 4.8: Total root carbon distribution among different depth profiles across 
different study sites in Mida Creek, Kenya 

 

4.2.3 Soil organic carbon (SOC) 

The 15 year-old Kibusa Plantation had the least SOC with a mean of 2156.27±736.50 
Mg ha-1 .The 20 year-old Green Island Plantation had the highest amount of SOC in the 
rainy season with a mean of 9714.79±4732.56 Mg ha-1. This was followed by the 
Natural Stand, which had an average of 3544.25±1149.37 Mg ha-1. There is no 
significant difference between the means of the samples from the two seasons since the 
calculated t (t=1.527) was less than the critical t (t=4.30) (t 0.05(2),3=1.527). Figure 4.9 
below summarizes the variation in SOC between the dry and rainy season. 


0

2000

4000

6000

8000

10000

Natural stand

Kibusa plantation

Green Island plantation

Average Soil Organic Carbon (Mg ha-1) 

Study sites 

Dry season

Rainy season

 

Figure 4.9: Mean soil carbon stocks for the plantations and natural stands in Mida 
Creek, Kenya 

In the dry season, there was no significance difference in the amount of SOC between 
the three different sites (F0.05(1)2,15=0.35, p>0.05). This was also true for the rainy 
season where there was no significance difference in the amount of SOC among the 
different sites (F 0.05(1)2,15=3.55, p>0.05). Based on soil profile, SOC increased with 
increase in depth in both the Plantations and the Natural Stand. At a depth of 50-100cm, 
there was the highest amount of SOC while the depth of 0-15 had lowest amount of 
SOC in both dry and rainy season. 

In 15 year-old Kibusa Plantation, 56.3% of the total SOC in the upper 100 cm was 
contributed by that obtained at 50-100 cm depth while that of the 20-year-old Green 
Island Plantation and the natural stand were 83.5% and 44.3% respectively. There was a 
more significant difference in soil organic carbon concentration between 50-100 cm 
depth profile and other depth profiles (P < 0.05, Tukey test). 


4.2.4: Total carbon stocks 

Total carbon stock was obtained by summing up all the carbon components accounted 
for during the study. Average total carbon stocks were estimated for the whole period of 
study. Kibusa and Green Island Plantation had an average of 199.00±11.68 Mg C/ha 
and 734.88±50.01 Mg C/ha respectively, while the Natural Stand had 338.34±31.09 Mg 
C/ha. The total carbon stocks differed significantly between the sites (F;0.05(1),2,6 = 
262.91, P < 0.05). There was a significant difference between the two Plantations and 
Natural Stand (P < 0.05, Tukey test). Plantations had a significant amount of soil 
organic carbon compared to the Natural Stand. Of all the carbon pools sampled, soil 
organic carbon was highest in all the study sites (Table 4.5) 

Table 4. 5: Carbon stocks in Mg C ha-1 for various carbon pools of Natural Stand 
and Plantations of Mida Creek, Kenya 

Carbon stock Mg 
C/ha 

Aboveground 
carbon 

Belowground 
carbon 

Soil organic 
carbon 

Total Carbon 
Stock 

Kibusa Plantation 

0.717±1.62 

47.34±2.7 

150.94±7.36 

199.00±11.68 

Green Island 
Plantation 

1.406±0.179 

53.43±25 

680.04±47.33 

734.88±50.01 

Natural Stand 

11.879±1.04 

78.36±18.56 

248.1±11.49 

338.34±31.09 



 

4.3: Nutrients status 

4.3.1: Phosphates 

During the dry season, Green Island Plantation had the highest phosphate concentration 
with a mean of 43.09±10.35 mg kg-1. This was followed by the Natural Stands, which 
had an average phosphate concentration of 31.44±3.44 mg kg-1. Kibusa Plantation had 


0

10

20

30

40

50

60

70

80

90

Natural stand

Green Island plantation

Kibusa plantation

Mean phosphate (mg/kg) 

Study sites 

wet season

dry season

the least phosphate concentration with a mean of 29.72±3.36 mg kg-1. There was a 
significant difference in phosphate concentration among the three study sites 
(F0.05;(1)2,64 =1.22, P< 0.05). In Kibusa Plantation, the phosphate concentration 
decreased with increase in depth profile while the Green Island Plantation and Natural 
Stands displayed a fluctuating trend with 0-20 cm depth having the highest 
concentrations. During the rainy season, the Green Island Plantation had an average 
phosphate concentration of 66.92±24.77 mg kg-1, while the Kibusa Plantation had an 
average of 34.48±4.67 mg kg-1. The Natural Stands had an average phosphate 
concentration of 41.21±2.31 mg kg-1. In comparison to the dry season, the rainy season 
had a higher phosphate concentration in all the study sites (Figure 4.10). 

 

Figure 4.10: Amount of phosphate in the sediment of plantations and natural stand 

A significant difference in phosphorus concentration was obtained between different 
depth profiles within the study sites (F;0.05 (1), 63, =5.631, P< 0.05). In depth profiles like 


50-100 cm, there was a significant difference in the quantity of phosphorus compared to 
other depth profiles (P<0.05, Tukey test). 

4.3.2: Ammonium 

During the dry season, the Kibusa Plantation had an average ammonium concetration of 
191.56 µg/kg while the Green Island Plantation and the Natural Stand had an average 
concentartion of 141.02 µg/kg and 141.28 µg/kg respectively. According to the Anova 
test done to compare the variation in ammonium concentration among the three 
different sites, it was found that there is a significant difference in ammonium 
concentration among the three different sites (F;0.05(1) 2,63, =3.63, P<0.05). There was a 
significant difference in the ammonium concentration between Kibusa Plantation and 
Natural Stands (P< 0.05, Turkey test). There was a significant difference in ammonium 
concentration between the Green Island Plantation and the other two sites (P.0.05, 
Turkey test). According to depth profile, there was a rise in ammonium concentration 
with rise in depth and the maximum amount of ammonium was between the depth of 
50-100 cm in both wet and dry season. However, when the amount of ammonium in the 
dry season was compared to that of the rainy season, it was found that the rainy season 
had a significantly higher ammonium concentration (Figure 4.11). 

 


0

50

100

150

200

250

300

Natural stand

Kibusa plantation

Green Island plantation

Average Ammonium (ug/kg) 

Study sites 

Wet season

Dry season

 

Figure 4.11: Amount of Ammonium in the sediment of Plantations and Natural 
Stands in Mida Creek, Kenya 

 

4.3.3 Nitrates 

On average, the Natural Stand had the highest concentration of nitrates (27.466 µg/kg), 
followed by the Green Island Plantation (21.15 µg/kg). Kibusa Plantation had the least 
nitrate concentration of 20.60 µg/kg (Figure 4.12). There was a significant difference in 
nitrate concentration among the three different sites (F;2, =1.14, P<0.05). Nitrate 
concentration decreased with increase in depth. 


0

2

4

6

8

10

12

Natural stand

Kibusa plantation

Green Island plantation

Mean Nitrate (ug/kg) 

Study sites 

Wet season

Dry season

 

Figure 4.12: Amount of Nitrate in the sediment of Plantations and Natural Stands 
in Mida Creek, Kenya 

 

4.3.4 Belowground biomass and Nutrient status 

In the Natural Stand, in all the depths sampled, belowground biomass was found not to 
be negatively correlated with the amount of phosphorus (n=54, r =0.0, p > 0.05). This 
was also true for both Kibusa and Green Island Plantations. However, there was a 
strong positive correlation between nitrogen and belowground biomass in all the 
sampled areas (n= 54, r=0.72, P<0.05). Ammonium was also positively correlated to the 
BGB but it had a weak correlation in both the Natural Stand and the plantations (n=54, r 
= 0.56, P>0.05). In general, nitrogen significantly contributed to the accumulation of the 
belowground biomass of the mangrove species in the Mida Creek. 

 

 

 


 CHAPTER FIVE 

5 DISCUSSION CONCLUSIONS AND RECOMMENDATIONS 

5.1 DISCUSSION 

5.1.1 Forest structure 

In all the Plantations, the species Importance Value Index (IVI) for R. mucronata was 
the highest (95.54), making it the most dominant species. This is because R. mucronata 
was established as a mono-stand in the Plantations with high density. In the Natural 
Stand mangroves, A. marina had the highest Importance Value Index (115.25) hence 
the most dominant species followed by X. granatum (Table 4.5). In the Natural Stand, 
Kibusa Plantation and Green Island Plantation, R. mucronata had an overall basal area 
of 0.151, 0.39, 0.066 m2/ha respectively. 

Based on stem density, the 20 year-old Green Island Plantation had the highest stem 
density of 4533.33 stems/ha followed by the 15 year-old Kibusa Plantation which had 
3666.67stems/ha. Management practices like thinning had been done for the mangrove 
trees in the Green Island Plantation and this had an effect of reducing the stand density 
but allowing biomass increase. Since no thinning had been done in the 15 year-old 
Kibusa Plantation, the mangroves there in recorded a high stem density compared to the 
20-year-old Green Island Plantation. This is because the mangroves in the 15 year-old 
Kibusa Plantation were younger than the Natural Stand and the 20 year-old Green 
Island Plantation were. Unlike the Plantations, the Natural Stands had the lowest stem 
density. This was due to increased spacing between trees with comparatively bigger 


stem diameters. Furthermore, the mangroves in the Natural forest had been exposed to 
disturbance such as logging for fuel wood and timber, which consequently led to 
decline in tree stem density. Similar study was done in Florida by Lugo, (2009) and 
reported a high abundance of regenerated mangroves in a young eleven-year old 
plantation of R. mangle. 

Factors that might have contributed to discrepancy between the results on stem density 
for Plantations and Natural Stands include, planting density, sapling survival rate, and 
recruitment of seedlings at early stages of forest development (Sreelekshmi et al., 
2020). In addition to this, space exploitation, resource exploitation and increased light 
competition between adult trees also contribute to the differences in plant stem density 
in the three different sites (Sreelekshmi et al., 2020). Regardless of Kibusa Plantation 
having, the highest stem density compared to the Green Island Plantation; its basal area 
is proportionately smaller. According to a study by Zadworny et al. (2020), they 
reported that a negative correlation between stand density and basal area is a 
characteristic of a developing forest. The stem diameters in 15 year-old Kibusa 
Plantation are relatively larger than those reported by Kairo et al. (2020) for the 
Kinondo Plantation in the Gazi bay Kenya. 

The pattern of tree distribution for the mangroves in the Natural Stands based on DBH 
size class follows an inverse J-shaped curve, where the number of trees decrease with 
increase in the stem diameter (Figure 4.2). This is so because seedlings and saplings 
have a comparatively higher growth rate than matured trees, and hypothetically because 
of degradation by age in an unequal-aged forest (Hayes et al., 2017). However, the 


curve can be modified by biotic and abiotic factors. The biotic factors that can modify 
the curve include overharvesting tree for charcoal, fuel, medicines and fencing poles, 
interspecific competition for the limited resources, and regeneration patterns. The 
abiotic factors that can modify the curve include; sediment characteristics, and 
unpredictable seasonal climatic events, sea level rise, and soil nutrient status 
(Sreelekshmi et al., 2020). 

Variation in diameter size class has been used in assessment of level of disturbance 
within forests (Sreelekshmi et al., 2020), and to discover patterns in regeneration 
(Zadworny et al., 2020). The distribution of tree density across different DBH classes 
also elucidates the extent of resource utility by the growing forest (Hudak et al., 2020; 
Sreelekshmi et al., 2020). Similarities in most structural aspects between the mangroves 
in the Plantations and those in the Natural Stand stipulate that reforestation can be used 
to change the human induced degradation through illegal logging, thereby restoring the 
ecosystem functions (Kairo et al., 2020). 

5.1.2 Carbon pools 

5.1.2.1 Aboveground biomass (AGB) carbon 

In this study, there was a significant difference in aboveground carbon among the three 
sites. The mangrove species in the Natural Stand had a high aboveground carbon 
compared to the Plantations. This can be due to the difference in the age, since they 
were older (Jagodzinski et al., 2019), difference in soil nutrient regime (Weaver et al., 
2020), differences in topography, light conditions, natural disturbances and their 
interactions (Gillis et al., 2019). The 15 year-old Kibusa and the 20 year-old Green 


Island Plantations had younger mangrove trees compared to the Natural Stand, which 
had trees for more than 51 years old (Owuor et al., 2019). This explains why the 
Natural Stand had a higher foliage and consequently higher primary productivity 
compared to the Plantations. In addition to this, the high species richness in the Natural 
Stand can also explain why there was higher aboveground carbon since it was a mixed 
stand compared to the Plantations (Cohen et al., 2016). These results conforms to many 
previous studies which showed that biodiversity impacted on biomass production (Chen 
and Zhang, 2017). Maintenance practices done on the Plantations such as pruning could 
also be a plausible reason to the low amount of aboveground carbon in Plantations since 
this leads to loss of biomass (Hassan et al.,2018). Other factors that might have 
contributed to the variation in the aboveground biomass include global positioning of 
the mangrove forest. In this case, mangroves located near the seashore may differ from 
those in the offshore in terms of height and stem diameter. This is due to the 
differences in the nutrient statuses, soil type, and wave effects in these zones. 
Ecological differences between different mangrove positions may also contribute to the 
variations in the aboveground biomass in the mangrove species (Trujillo et al., 2020). 
This is due to the differences in the level of microbial activities that cause 
decomposition that leads to nutrient availability within a given plantation. Plant age 
may also lead to differences in the AGB that is noticed within the three sites since in 
each year, in dicotyledonous stems, there is annual increase in girth of a tree 
(Jagodzinski et al., 2019). 


The results obtained from this study on the aboveground biomass for the Plantations 
agree well with other research studies in other countries. For instance, a study carried 
out by Abib and Appaddo, (2016) in Mauritius on mangrove plantations where they 
obtained the aboveground biomass to be 16.63 t/ha. This is almost equal to that obtained 
in this research study, which was found to be 13.67 t/ha for the 20 year-old Green Island 
Plantation. In another research study conducted by Kridiborworn et al., (2012) in 
Central Thailand, the aboveground carbon was estimated to be 140.49 Mg C/ha in 
twelve-year old Rhizophora apiculate plantation. This was lower than that reported for 
the R. mucronata in the 15 year-old Kibusa Plantation which was estimated to be 
226.24 Mg C/ha. Similarly, the aboveground carbon reported in Japan for a mangrove 
plantation made of R. mucronata was estimated to be 108 Mg/ha (Komiyama et al., 
2008; Abib and Appaddo., 2016). In Malaysia, the research done in Matang Forest 
Plantation, the AGB carbon for both 18 and 23-year old R. apiculata was estimated to 
be 60 and 77.5 Mg C/ha. These values are lower than that obtained from the 15 year-old 
Kibusa Plantation of Mida Creek. 

In the Natural Stand, the aboveground carbon values obtained in this study were 
corresponding to those obtained from similar research studies in other countries. For 
instance, in the West African mangroves, Cameroon, Gabon, DRC and the Republic of 
Congo reported an estimate of the aboveground carbon to be 22.5 Mg C/ha, 170.5 Mg 
C/ha, 125.5 Mg C/ha, and 204.5 Mg C/ha respectively (Ajonina et al., 2016). This was 
similar to the results obtained from the Mexican mangroves which was estimated to be 
176 Mg C/ha (Adame et al., 2016). This was also true for the mangroves in Yap of 


Micronesia which aboveground carbon estimate of 249 Mg C/ha (Donato et al., 2017). 
Based on the results from these studies, it is true to say that, there exist great variations 
in aboveground biomass and carbon stocks for R. mucronata and for other mangrove 
species across the world. 

5.1.2.2 Belowground biomass 

The results obtained from the above study indicates that the Natural Stand had the 
highest belowground root carbon, followed by the Green Island Plantation while Kibusa 
Plantation had the least concentration of the belowground carbon. The high root carbon 
concentration in the Natural Stand compared to the two Plantations could be due to low 
amount of phosphate (Mommer et al., 2018). This can also be explained by the proper 
root development of the mature trees in the Natural Stand (Paul et al., 2019). The low 
root carbon concentration in the 15 year-old Kibusa Plantation could be attributed to 
their age since a young forest has incomplete root development (Johnson and 
Freudenberger 2015). The trees were not yet mature hence their root systems were not 
well developed. This low root carbon content in the Kibusa Plantation could also be due 
to reduced species diversity there in (Mommer et al., 2018). 

Root carbon concentration was abundant at the depth of 20-40 cm in all the three study 
sites. A plausible reason to this could be due to the presence of low nutrient 
concentration at this depth profile, requiring plants to invest more carbon in roots to 
maximally absorb available nutrients (Zhang et al., 2015; Chen et al., 2018). The higher 
belowground biomass at this depth (20-40 cm) may also be associated with relatively 
slow carbohydrate metabolization from roots, resulting from low respiration rates 


underneath (Ren et al., 2017). More so, since the mangroves grow in a soft substrate, 
their roots grow to a deep profile to provide anchorage to make them withstand storm 
and tide inundation effect (Srikanth et al., 2016). This depth is also appropriate for the 
root development since it allows for aeration in the soil (Cheng et al., 2015). This result 
agrees with that found in a study conducted by Castaneda-Moya et al. (2011) who 
reported a high root carbon concentration at a depth of 0-40 cm compared to deeper root 
zones where there were no free air circulations. The deeper horizons had low root 
carbon concentration due to lack oxygen coupled with low microbial activities in these 
zones (Rozainah et al., 2018). 

Tamooh et al. (2008), did a similar research in Gazi Bay, Kenya and they found a lower 
root carbon concentration in a twelve-year-old plantation dominated by R. mucronata. 
In their study, they estimated belowground biomass for the Natural Stand to be 18.1 Mg 
C/ha and that for the twelve-year old R. mucronata to be 17.9 Mg Cha-1. In Gabon and 
DRC Congo, belowground root carbon was estimated to be75.5 Mg Cha-1 and 61 Mg 
Cha-1 respectively (Ajonina et al., 2014). These results were equivalent to that obtained 
in this study where in the Natural Stand, belowground biomass content was 78.36 Mg 
Cha-1. However, in a similar research done in Cameroon by Ajonina et al. (2014), the 
root carbon content for Rhizophora. racemosa was higher (153 Mg Cha-1) compared to 
the root carbon content for R. mucronata in the 20 year-old Green Island Plantation 
(53.4 Mg C/ha). In a research study done by Komiyama et al. (2008) in Halmahera 
Island, Eastern Indonesia, the belowground carbon content for Rhizophora. apiculata 
was reported to be 98.05 Mg Cha-1 which was higher compared to that obtained in the 


20 year-old Green Island Plantation of the Mida Creek for R. mucronata. In Thailand, 
the belowground root carbon for mangroves of Sawi Bay in the natural stand dominated 
by R. mucronata was reported to range between 70.3-176.3 Mg C/ha (Matsui et al., 
2012). Studies by Lovelock, 2008 in the Cuban mangroves, the total belowground 
biomass for the R. mucronata forest at a sampling depth of 40 cm was reported to be 
16.3 Mg C/ha. This is lower compared to that obtained in the 15 year-old Kibusa 
Plantation of the Mida Creek for the R. mucronata (47.34 Mg C/ha). Studies by 
Fujimoto et al. (1999) and Kridiborworn et al. 2012 reported that belowground root 
carbon for Rhizophora species ranges between 19.5-142 Mg C/ha. 

As an adaptive feature for living in soft and wet sediment in the mangrove ecosystem, 
mangrove trees have a higher amount of belowground biomass. This is due to their 
inability to support too much weight on the aboveground without a heavy root system 
(Peng et al., 2018). 

The root carbon content estimates obtained from this study differs significantly from 
those given in the reviewed literature. These estimates differ due to adoption of 
different methods for the study such as different sampling techniques. Other factors that 
might have contributed to this disparity include species, soil type and hydrological 
properties, climatic conditions of the study area and the age of the mangroves (Hogarth, 
2015). 


5.1.2.3 Soil organic carbon (SOC) 

In this study, soil organic carbon content was low in the Natural Stand compared to that 
in both Kibusa and Green Island Plantations. The high soil organic carbon in the 
plantations is due to previous deposition by the pre-existing stands. Another reason 
could be due to deposition of sediment from dryland. The higher soil organic carbon 
stocks in the Green Island Plantation shows that there was incomplete oxidation of 
carbon from the dead debris after the destruction of the pre-existing stand. The soil 
organic content also increased during the rainy season due to increased deposition by 
soil erosion from the hinterland. The higher SOC in the young plantations can also be 
due to high root turnover rate. The sustained anoxic conditions in the Plantations than in 
the Natural stand could also provide a plausible reason for the high soil organic content 
therein (Xiong et al., 2019). This is because, unlike the Plantations, the Natural Stand 
had been degraded and exposed to the scorching sun effects, which provided better 
conditions for microbial decomposition, resulting to low organic matter in the soil 
(Duan et al., 2018). 

During the study, an observation was made for the periodic inundation of the 
Plantations, especially during the neap tide. This led to anoxic conditions in the 
Plantations and some parts of the Natural Stand at such times. Consequently, there is a 
slower rate of decomposition of organic matter in the sediment in the Plantations and 
the affected parts of the Natural Stand (Xiong et al., 2019). This therefore led to high 
accumulation of the organic debris in the Plantations compared to the Natural Stands 
where microbial decomposition was not severely affected by inundation. This result is 


analogous to the findings by Pandey et al. (2016) who found that mangrove ecosystems 
exposed to adequate inundation had higher soil organic content due to a low rate of 
microbial decomposition. 

Accumulation of SOC in the mangrove sediment can be due to inputs of organic carbon 
in form of litter, tide and root turnover while depletion of carbon from the mangrove 
sediment may be due to mineralization, dilution by inorganic material and export by 
tide (Chen et al., 2020). The high SOC content in the 20 year-old Green Island 
Plantation can also be explained by the fact that it was dominated by the R. mucronata. 
This species has unique features responsible for sediment trapping. Some of these 
features include stilt roots, which is a complex root system (Chaudhuri et al., 2019). 
The complex root system is responsible for trapping and providing a suitable 
environment for accumulation of detritus materials such as litter and fallen dead wood, 
hence sequestered in the soil sediment (Chaudhuri et al., 2019). The stilt root system in 
the Plantations dominated by R. mucronata also facilitated high sedimentation due to 
reduced speed of water flow, hence deposition of organic matter in the sediment 
(Chaudhuri et al., 2019). This also explains why there was a high amount of SOC in the 
plantations than in the Natural stand, which was a mixed forest. According to the study 
by Kamal et al. (2017), she found out that 80% of the soil particles carried by coastal 
waters were trapped and stagnated under the mangrove root areas. Machado et al. 
(2017), reported that leaf litter production combines with low rate of microbial 
decomposition of organic matter in the plantations, leading to high Soil Organic Carbon 


in the mangrove sediment. The result from this study therefore indicates that restoration 
of mangroves forest leads to increased SOC accumulation in the sediments. 

In other parts of the world, similar researches were conducted and the result agrees with 
the ones obtained from this study. For instance, a research done in China by Chen et al. 
(2018) found that there was a high accumulation of soil organic matter in mangrove 
plantations compared to the Naturals Stand. Similarly, in the Indo-pacific region, 
Sasmito et al. (2020) did a research and their findings was an average SOC of 864 Mg 
C/ha. This was lower compared to that obtained in the mangrove sediments of Mexican 
Caribbean which had an average SOC of 1166 Mg C/ha (Adame et al., 2013). In 
another research done by Ajonina et al. ( 2014) in the West and Central Africa, soil 
organic carbon in the mangrove sediment was 827 ± 170 Mg Cha- but the undisturbed 
mangrove stands had a higher quantity of SOC content of 967±58 Mg C/ha. These 
results were comparable to those obtained by Adam et al. (2013) in the Mexican 
Caribbean. Soil organic carbon varied at different depth profiles, whereby SOC content 
was highest at a depth of 50-100 cm and lowest at a depth of 0-15 cm. This was true for 
both Plantations and the Natural Stand. These results are in agreement with those for 
similar studies done in other parts of the world. For example, in India, while working in 
the mangroves of Gujarat, Pandey et al. (2016) found a higher carbon concentration at 
the deeper horizons (16-30 cm) compared to the shallow layers (0-15cm). The efficacy 
of carbon conversion in soil sediments increases with age of mangroves forests. For 
instance, carbon sequestration efficiency improves from 16% for a five-year-old forest 
to 27% for and eighty-five-year-old forest stand (Chen et al., 2020). 


The results obtained in this study, together with that reviewed form the existing 
literature shows that the amount of carbon sequestered in the sediment increases when 
mangrove forests are restored. This is because mangrove restoration locks the previous 
carbon left in the soil after destruction of the pre-existing mangrove stand (Thompson, 
2018). More so, forest plantation with dissimilar rates of carbon sequestration results to 
differences in carbon conversion rates in the sediment (Ren et al., 2018). The high 
carbon content within the mangrove sediment could also be due to faster growth rates in 
these ecosystems as they try to keep pace with sea-level rise and trap debris and 
residues from tidal movements and alluvial deposits (Kraus et al., 2018). Research by 
Demopoulos et al. (2018) shows that mangrove ecosystem can keep on accumulating 
sediment over millennia (a period of a thousand years), thus making them more critical 
carbon sinks compared to terrestrial ecosystems that reach maximum soil carbon 
content over decades (a period of ten years). 

5.1.2.3 Total carbon stocks 

The total carbon stocks among the three sites studied in mangroves of Mida Creek 
differed significantly. The variation in the carbon stock among the sites differed due to 
the difference in structure of the aboveground vegetation and the species composition, 
tree density, age, management regime, and soil depth sampled in each study site. 
Homogeneity in appearance was evident in the managed mangroves with uniform 
diameter stems while in the Natural Stands, there was heterogeneity in both structure 
and diameter. The high amount of carbon stocks in the Natural forest could be due to 
their old age compared to the Plantations. The increase in the carbon stocks during the 


rainy season could be attributed to increase in secondary growth since xylem vessels 
and tracheids were formed in large numbers. These cells are large, have thin walls and 
the wood has a light texture. High carbon stocks in 20 year-old Green Island Plantation 
could be due to restoration and good management practices carried out there. Therefore, 
to maintain a maximum carbon stocks in both aboveground and in sediments, it is 
important to manage the mangrove plantations without any form of disturbance. 

The high SOC content in the Plantations than in the Natural stand, which was a mixed 
forest can be explained by the fact that the plantations were dominated by the R. 
mucronata. This species has unique features responsible for sediment trapping. Some of 
these features include stilt roots, which is a complex root system (Chaudhuri et al., 
2019). The complex root system is responsible for trapping and providing a suitable 
environment for accumulation of detritus materials such as litter and fallen dead wood, 
hence sequestered in the soil sediment (Chaudhuri et al., 2019). The stilt root system in 
the Plantations dominated by R. mucronata also facilitated high sedimentation due to 
reduced speed of water flow, hence deposition of organic matter in the sediment 
(Chaudhuri et al., 2019). According to the study by Kamal et al. (2017), she found out 
that 80% of the soil particles carried by coastal waters were trapped and stagnated under 
the mangrove root areas. Machado et al. (2017), reported that leaf litter production 
combines with low rate of microbial decomposition of organic matter in the plantations, 
leading to high Soil Organic Carbon in the mangrove sediment. 

The result obtained for total carbon stocks in the three sites in Mida Creek (1180.95±67 
Mg C/ha) can be compared to similar studies done in other places around the world. For 


example, in the Indo pacific region, a study by Donate et al. (2017) for mangroves gave 
a report of total carbon stock to be 1023 ±88 Mg C/ha. The total carbon stock of 
mangroves in the Mexican Caribbean was 987±338 Mg C/ha (Adame et al., 2016) 
while that for Indonesian mangrove was estimated to be 986 Mg C/ha (Mudivarso et al., 
2009). In the West and Central Africa had higher values of carbon stocks of 1520±164 
Mg C/ha (Ajonina et al., 2014). In the Gazi Bay, Kenya, the total carbon stock for the 
twelve- year old Kinondo was 65.8 Mg C/ha. This was lower compared to the one 
obtained in the 15 year-old Kibusa Plantation (424.52±11.68) of Mida Creek, Kenya. 
As a tree matures, its biomass production also increases (Joshi and Ghose, 2014). 

Other factors such as availability of nutrients, climatic conditions and edaphic factors 
also affect biomass accumulation. However, the level of interactions between these 
factors and the mangrove accretion makes it difficult to identify the main factors 
contributing to biomass production in any given site (Scales et al., 2019). Unlike other 
major forests, the total carbon storage reported in mangroves is exceptionally high 
(Kauffman et al., 2020). For instance, in Kenya, the total carbon stock for Arabuko 
Sokoke forest which is an indigenous coastal forest is reported to range between 53-80 
Mg C/ha (Glenday, 2006). In Riverine forests of Tana River County, the aboveground 
carbon pool was 257±43 Mg C/ha in levee forests, while that in evergreen forests was 
170±13 Mg C/ha and that for woodland areas was 163±15 (Kipkorir, 2017). According 
to a research by Lung (2018) in Kakamega forest, the total carbon stock was estimated 
at 218±17.7 Mg C/ha. This is lower compared to those reported for the mangrove 
forests; thus, mangroves have a greater potential for sequestering carbon thereby good 


for global climate change regulation. Differences in carbon stocks between the Natural 
mangrove stand and the Plantations could be due differences in species composition, 
elemental carbon concentration in trees, forest structure, tree density, age, and level of 
management and the depth of soil sampled for soil carbon analysis. 

5.1.2.4 Nutrients 

Availability of nutrients is a major factor affecting mangrove productivity (Malik et al., 
2020). The key elements that are a limiting factor to the production of mangroves are 
Nitrogen and Phosphorus (Hena et al., 2020), which occur in extremely low amounts in 
mangrove soils (Malik et al., 2020). Following the large disturbance that caused 
degradation and sedimentation prior to setting up the re-plantations, it was theoretically 
said that there was a significant difference in nutrient status between Natural Stand and 
the Plantations (Kairo et al., 2020). However, this claim was rejected after the analysis 
of the result obtained since even after disturbance, the pattern of nutrient distribution in 
the Plantations was still able to re-establish and be similar to that of Natural Stand. 

In the mangrove sediments, the most abundant nutrient is ammonium for all the three 
sites (Malik et al., 2020). This is because of the higher quantities of organic matter, 
coupled with abundance of denitrifying bacteria (e.g., Paracoccus denitrificans and 
Thiobacillus denitrificans) which reduces the nitrates in the organic matter into nitrites, 
ammonia and even nitrogen gas (Mozumder et al., 2020). This therefore gives an 
account for the high amount of ammonium and for low amounts of nitrates in the three 
study sites. Similar study on the amount of nutrients in the mangrove sediment in 


Dominican Republic reported a little concentration of nitrates, with a greater amount of 
ammonium (Barcellos et al., 2019). 

In a mangrove ecosystem, the available phosphorus is immobile and unreachable by the 
mangrove plants. This makes organisms that metabolize phosphorus important for plant 
growth, especially in environments with less nutrients as the mangrove habitat (Prakash 
et al., 2019). In most cases, the microorganisms that metabolize phosphorus are aerobes 
hence occur near the mangrove roots (Chambers et al., 2016). This accounts for 
availability of phosphorus in the upper depth profile compared to deeper layers for the 
three study sites. More so, in the top layers, there are crab holes, which increase soil 
aeration; creating a favorable condition for the bacteria to solubilize Phosphorus 
(Prakash et al., 2019). 

Based on review of related literature, it is reported that high nutrient availability makes 
mangroves to invest more in aboveground biomass that maximize on carbon capture 
while when nutrients are in low supply, they divert resources to increase root biomass 
development (Cheng et al., 2020). This is also apparent in this study, in that, 
phosphorus had a negative correlation to the BGB, especially in the middle depth 
horizon where belowground biomass was relatively higher than the upper layers, with 
plentiful nutrients. In a similar study done by Gao et al. (2019), they reported a negative 
correlation between nutrients and soil organic carbon. Similarly, a study by Castaneda-
Moya et al. (2020) reported that root variation depended on availability of phosphorus. 
They also discovered that, decrease in amount of phosphorus led to increase in 
allocation of root biomass. This indicates a strong association between availability of 


phosphorus and carbon allocation to fine root production, which later results to 
increased belowground biomass. This also makes it easier for nutrient acquisition (Ola 
et al., 2020). 

Absence of correlation between availability off phosphorus and BGB might reflect a 
weak relationship between total phosphate and the periodic dynamics of available 
Phosphorus. The positive association between Nitrates availability and belowground 
biomass indicates that Nitrogen is the most limiting element in mangroves of Mida 
Creek (Anton et al., 2020). One of the elements that have been reported to be the 
limiting factor for both aboveground and belowground biomass production in the 
mangrove ecosystem is nitrogen (Dangremond et al., 2020). Since there was no 
comprehensive information on the availability of the above nutrients, it was difficult to 
make a conclusion on the role of each element in determining root distribution and 
accumulation. This therefore calls for a further study to investigate the association 
between nutrient dynamics and belowground biomass accumulation. 

5.2 CONCLUSIONS 

i. The homogeneity in distribution of stem diameters in the plantations shows that 
management and restoration practices significantly contributed to structural 
development of the replanted mangroves. This is evident through homogeneity 
in distribution of stem diameter in the plantations unlike in the natural stand 
where there was heterogeneity in stem distribution. 
ii. Based on the data obtained for carbon stocks in the mangroves, we can deduce 
that despite their occupation of small area, mangrove plantations of Mida Creek 



are a significant carbon stores. They are therefore extremely valuable for their 
long-term carbon sequestration potential. 
iii. Mangrove forests of Avicenia marina are greater carbon stores compared to the 
Rhizophora mucronata species and so more of the A. marina should be 
replanted to offset global warming effects. 
iv. The results also concluded that there is negative correlation between root 
biomass allocation and phosphate availability. This illustrates that mangrove 
invest more in root biomass when phosphate is limiting in the environment. On 
the other hand, available nitrogen in both nitrate and ammonia is positively 
correlated to the belowground biomass accumulation. Therefore, the mangrove 
carbon sequestration ability can be increased by adding nitrogenous fertilizers 
within the plantations 


5.3 RECOMENDATIONS 

The following recommendations are made from the study; 

i. Mangrove rehabilitation of Avicenia marina should be established due to their 
ability to store large amounts of carbon in their biomass to offset global 
warming effects. 
ii. Long-term management practices such as addition of organic fertilizers should 
be added to ensure that the replanted mangroves do not lose their value of 
carbon sequestration as a means of regulating local climate change. 
iii. Outreach and awareness programs should be developed to solicit community 
support for sustainable conservation and management of mangroves. 



iv. The current management regime also needs to develop alternative livelihoods to 
relieve pressure on mangroves by diversifying mangrove benefits such as 
aquaculture and ecotourism. 


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


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 APPENDICES 

 

APPENDIX 1: Mangrove species in Kenya in their local names and uses (Dharani, 
2011). 

Family 

Species name 

Local name 

Uses 

Avicenniaceae 

Avicenia marina 

Mchu 

Firewood, poles, timber, 
making canoes, and fencing 

Combretaceae 

Lumnitzera racemosa 

Kikandaa 

Firewood and boat ribs 

Sonneratiaceae 

Sonneratia alba 

Mlilana 

Timber, firewood, and 
fencing 

Meliaceae 

Xylocarpus granatum 

Mkomafi 

Timber, firewood and 
curving 

Xylocarpus moluccensis 

Mkomafi 
dume 

Firewood and fencing 

Sterculiaceae 

Heritiera littoralis 

Msikundazi 

Pole, timber and boat mast. 

Rhizophoraceae 

Rhizophora mucronata 

Mkoko 

Timber, firewood and 
charcoal 

Bruguiera gymnorrhiza 

Muia 

Timber and firewood 

Ceriops tagal 

Mkandaa 

Timber and firewood 



 

 

 


C:\Users\Janet\AppData\Local\Microsoft\Windows\INetCache\Content.Word\rhizophora mucronata.jpeg
C:\Users\Janet\AppData\Local\Microsoft\Windows\INetCache\Content.Word\ceriops tagal.jpeg
APPENDIX 2: Photographs of mangroves encountered in the study site 


Plate 1. 1: Photograph of Rhizophora mucronata 

 

Plate 1. 2: Ceriops tagal 


F:\2016 & 2017 PICTURES\Pwani Uni Student Trip - Mida Creek -8th-9th & 12th May 2017\Avicennia marina-tree with aerial roots.JPG 


Plate 1. 3: Avicenia marina tree 

 

 Plate 1. 4: Photograph of Xylocarpus granatum 


F:\2016 & 2017 PICTURES\Meda Creek pics-6th Nov 2016\P1030747.JPG 


Plate 1. 5: Photograph of Sonneratia alba 

 

Plate 1. 6: Photograph of Bruguiera gymnorrhiza