ALONG THE KENYAN COAST
SEIF LANDI MOHAMED
A thesis submitted in partial fulfilment of the requirements for the Degree of Master of
Environmental Science of Pwani University
MAY, 2017
ii
Declaration
iii
Dedication
I dedicate my work to Allah who guided me this far. I also dedicate my work to my beloved
siblings for their support.
iv
Acknowledgement
I would like to acknowledge my supervisors: Dr. Maarifa Ali Mwakumanya and Dr.
Rose Kigathi for their continuous guidance and assistance towards writing this thesis. I
also acknowledge Dr. James G. Kairo of Kenya Marine and Fisheries Research Institute
(KMFRI), Mombasa for his generosity towards sharing information and knowledge
about mangroves. I would also like to acknowledge Dr. Benard Okeyo for teaching me
the skills of thesis writing. Lastly, I would like to acknowledge Kenya Coast
Development Project (KCDP) for their financial support towards my entire Masters
Degree programme.
v
Abstract
Mangrove forests play important roles benefit both the coastal communities and nation
at large. However, no comparisons on structural variations of these ecosystems have ever
been done and made available to forest managers and policy makers. This study aimed
at characterizing structure of mangrove of the forests with the purpose of obtaining
detailed data and information necessary for national management plan. A total of
627plots were established along entire belt transects identified and oriented
perpendicular to the shoreline. The data was analyzed using Analysis-of-Variance
(ANOVA) to assess possible spatial structural variations between mangrove species and
among the mangrove ecosystems. The results show that the structural attributes: stem
density, tree height, Diameter-at-Breast Height (DBH) and pole quality differed
significantly between different mangrove species. Only tree height was significantly
different between mangrove forests. Rhizophora mucronata recorded 292±131stems/ha,
and had the highest stem densities while Lumnitzera racemosa had the lowest density
with 2±1stems/ha. Basal area was highest in R. mucronata with 6.6±1.6m2 and lowest in
L. racemosa with 0.3±0.1m2.The tallest mangrove species was Sonneratia alba with
6.6±2.4 m while the shortest mangrove species wasH. littoralis with 0.5±0.2m.
Comparing the mangrove forests, tree height was highest in Kiunga Marine National
Reserve (KMNR) with 7.5±3.8m and lowest in Mtwapa with 3.6±1m. Sonneratia alba
and H. littoralis also had the highest and lowest DBH with 13.1±7.7cm and 0.5±0.2cm,
respectively. Species diversity varied significantly with A. marina, C. tagal, and R.
mucronata being the most dominant compared with the other species. The species R.
mucronata and L. racemosa had the highest and lowest percentages of their poles of
merchantable as well as non-merchantable qualities respectively.
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1.3.1 Main objective ......................................................................................................... 4
1.3.2 Specific objectives ................................................................................................... 4
CHAPTER TWO: LITERATURE REVIEW .................................................................. 7
2.1 Mangrove structure and functions .............................................................................. 7
2.2 Factors affecting structural characteristics of mangroves .......................................... 8
2.3 Threats to mangrove ecosystems .............................................................................. 10
CHAPTER THREE: MATERIALS AND METHODS ................................................. 13
3.1 Description of the study area .................................................................................... 13
3.2 Research Design ....................................................................................................... 20
3.3 Data Collection ......................................................................................................... 21
3.4 Data Analysis ........................................................................................................... 21
CHAPTER FOUR: RESULTS ....................................................................................... 24
4.1 Variability in stem density of the mangrove species and forests ............................. 24
4.2: Variability in tree height of the mangrove species and forests ............................... 24
4.3 Variability in DBH of the mangrove species and forests ......................................... 27
4.4 Variability in pole quality of the mangrove species and forests .............................. 27
CHAPTER FIVE: DISCUSSION .................................................................................. 32
5.1 Variability in stem density of the mangrove species and forests ............................. 32
5.2 Variability in tree height of the mangrove species and forests ................................ 32
5.3 Variability in DBH of the mangrove species and forests ......................................... 33
5.4 Variability in pole quality of the mangrove species and forests .............................. 34
CHAPTER SIX: CONCLUSION AND RECOMMENDATIONS ............................... 35
REFERENCES ............................................................................................................... 37
APPENDICES ................................................................................................................ 45
Table 1: Mangrove species found along the Kenyan Coast ................................................3
Table 2: Stem density (Mean ± SD; stems/ha) of mangrove species in various forests along
the Kenyan Coast ..............................................................................................................25
Table 3: Tree height (Mean ± SD; m) of mangrove species in various forests along the
Kenyan Coast ....................................................................................................................26
Table 4: Diameter-at-Breast Height (Mean ± SD; cm) of mangrove species in various
forests along the Kenyan Coast .........................................................................................28
Table 5: Percentages of merchantable poles of mangrove species in various forests along
the Kenyan Coast ..............................................................................................................29
Table 6: Percentages of non-merchantable poles of mangrove species in various forests
along the Kenyan Coast .................................................................................................…31
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List of Figures
Figure 1: Map of Kenya (Inset) showing the coastline and locations of Vanga-Funzi
mangrove ecosystems (Source: GoK, 2017) ...................................................................... 16
Figure 2: A map of Kenya (Inset) showing the coastline and locations of Tudor and
Portreitz/ Mwache mangrove ecosystems (Source: GoK, 2017) ....................................... 17
Figure 3: A map of Kenya (Inset) showing the coastline and locations of Kilifi-Takaungu,
Mtwapa, Mida and Ngomeni mangrove ecosystems (Source: GoK, 2017) ....................... 18
Figure 4: A map of Kenya (Inset) showing the coastline and locations of Mto-Tana and
Tanakipi mangrove ecosystems (Source: GoK, 2017) ....................................................... 19
Figure 5: A map of Kenya (Inset) showing the coastline and locations of KMNR and
Outside KMNR mangrove ecosystems (Source: GoK, 2017). ........................................... 20
x
GoK Government of Kenya
GPS Geographic Positioning System
KMFRI Kenya Marine and Fisheries Research Institute
KMNR Kiunga Marine National Reserve
UNEP United Nations Environmental Program
1
CHAPTER ONE: INTRODUCTION
1.1 Background Information
Mangroves are shrubs and small trees that grow in the tropical and sub-tropical coastal
saline and brackish water, mainly between latitudes 25ºN and 25ºS (Giri et al., 2011).
Mangrove forests perform critical roles in maintaining a high diversity and abundance of
other flora and fauna in near-by reefs and sea-grass meadows. They sequester more
carbon than most of other forest types world-wide and provide coastal protection from
erosion caused by storm surge and tsunamis (Mumby, 2004; Danielsen et al., 2005;
Donato et al., 2011). The mangrove ecosystem constitutes an important source of organic
material for adjoining waters (Spalding et al., 2010) as well as providing goods such as
firewood, food and construction materials which are of immense value to the local
communities (Barbier et al., 2008). The ecological complexity of these ecosystems may
be observed in structural changes occurring on the local, spatial and temporal scales
(Alongi, 2009).
Mangrove forests are however, threatened globally due to human influence such as
overexploitation, conversion and pollution effects have reduced the total original cover
to less than 50% (Giri et al., 2011). Future sea levels are forecast to increase from 0.18
to 1.8 m by 2100 (Alongi, 2009; Rahmstorf et al., 2007) with the potential to strongly
affect mangrove forests. Epidemic diseases such as gall disease have been reported to
cause death of more than 30, 000 ha of Rhizophora forest in Gambia River (Teas &
McEwan, 1982). Currently the mangrove forests cover is less than 1% of the earth’s
surface, estimated at 137,760Km2. This represents a 12% decline from the earlier
estimate of 1.5 million Km2 (Giri et al., 2011).
Mangrove forests grow under the influence of environmental factors varying in intensity
and frequency (Alongi, 2009) according to latitudinal distribution and local history.
2
Physiognomies inside the mangrove forest area are shaped by changes in fresh water and
tide levels, wave energy, depositional and erosional processes (Schaeffer-Novelli et al.,
2000). Among the local environmental factors influencing mangrove forests, soil
composition and salinity (Estrada et al., 2013) are important because species are
distributed according to their salt tolerance and resource competitiveness (Liang, 2008).
In addition to soil, tidal flooding frequency can influence the distribution of species by
causing changes in soil characteristics (Cunha et al., 2006). Natural events such as the
rise in sea level, disasters such as the tsunamis and El Nino in East Africa (Alongi, 2007)
cause physical damage to the structure of the mangrove forests. The change in sea level
over the years, for example, has caused shift of mangrove zonation and change in species
composition as species tend to colonize other habitats towards the land keeping pace with
the rate of coastal erosion (Alongi, 2007). Lastly, the type of mangrove species that
survive and dominate a certain zone depends on its biophysical characteristics (Alongi,
2007).
The aim of this study was to assess spatial structural variations of the mangrove forests
along the Kenyan Coast using variables such as mean stem density, mean tree height,
mean Diameter-at Breast-Height (DBH) and pole quality.
From the available literature, there are nine species of mangrove trees and shrubs found
along the Kenya coast (Table 1). None of the mangrove species is endemic to Kenya.
The most common mangrove species in Kenya are Avicennia marina and Rhizophora
mucronata and are found all along the entire Kenyan coast. On the other hand, Heritiera
littoralis is found only in a small pure stand at the Tana River Delta near Tanakipi
(UNEP, 1998).
Species Name Local Name
Bruguieragymnorrhiza(L.) Lamk Muia
Ceriops tagal(Perr) Mkandaa
Heritiera littoralis Msindukazi
Lumnitzera racemosa Kikandaa
Rhizophora mucronata Mkoko
Sonneratia alba Mlilana
1.2 Statement of the problem
Mangrove forests in Kenya cover about 61,271 ha, 59% of which occur in Lamu County
(GoK, 2017). Mangrove forests are of great economic, social and environmental
importance to the local communities who derive various goods and services from the
mangrove ecosystems (Kairo & Lang‘at, 2008). While the vitality of mangrove
ecosystems has been recognized, the major problem is lack of data and information on
their spatial structural variability that will help in development of a detailed management
plan (Kairo & Lang‘at, 2008). It was therefore, important to carry out this study in order
to provide data and information on spatial variations of their structural attributes.
4
1.3.1 Main objective
The main objective of the study was to conduct an assessment of the structural variability
of the mangrove forests along the Kenya Coast and provide data and information to aid
the development of a detailed mangrove ecosystem management plan.
1.3.2 Specific objectives
The specific objectives of the study included:
i) To assess variability in stem density of the mangrove species and forests along the
Coast of Kenya
ii) To assess variability in tree height of the mangrove species and forests along the Coast
of Kenya
iii) To assess variability in DBH of the mangrove species and forests along the Coast of
Kenya
iv) To assess variability in pole quality of the mangrove species and forests along the
Coast of Kenya
The study evaluated the following null hypotheses:
i) There are no significant differences in stem density of the mangrove species
and forests along the Coast of Kenya
ii) There are no significant differences in tree height of the mangrove species
and forests along the Coast of Kenya
iii) There are no significant differences in DBH of the mangrove species and
forests along the Coast of Kenya
5
iv) There are no significant differences in pole quality of the mangrove species
and forests along the Coast of Kenya
1.5 Justification of the study
Mangroves perform vital functions both ecologically and economically. They protect
coastal areas from erosion, storm surge (especially during hurricanes), and tsunamis
(Mazda et al., 2005) while the mangroves’ massive root system is efficient at dissipating
wave energy (Massel et al., 1999). Furthermore, they slow down tidal water so that its
sediment is deposited as the tide comes in, leaving all except fine particles when the tide
ebbs (Mazda et al., 1997). In this way, mangroves build their own environments (Mazda
et al., 2005).
Globally, mangroves have been approximated at a value of US $ 181 billion (Mazda et
al., 1997). Due to their great importance mangrove forests ought to be sustainably used
to enable both the present and future generations benefit from the ecosystems. To achieve
this, management plan must be drawn.
In its preliminary meetings the Mangrove Technical Committee opted to adopt
Ecosystem-Based Management (EBM) approach to guide them develop the management
plan. This approach considers vegetation structure, community composition, and
utilization and zoningas important tools that can be used for effective forest management
(GoK, 2017). A lot of data on vegetation structure is available from previous surveys.
However, this data does not assess variations in the vegetation structure. Information on
spatial structural variation of the mangrove forests will act as a comparison tool and a
base for the mangrove managers to refer to as they seek for the best mangrove
management programmes. The mangrove ecosystem with most superior structure could
be acted as a benchmark for the rest.
6
1.6 Relevance of the Study
This study provides detailed data and information that can help forest managers and
policy makers to make informed decisions on the management of the mangrove forests
in the respective Counties.
2.1 Mangrove structure and functions
Mangroves are evergreen trees that grow in the tropical and sub-tropical coastal saline
and brackish water environments, mainly between latitudes 25ºN and 25ºS (Giri et al.,
2011). Mangrove forests cover a total area of 137,760Km2 worldwide (Giri et al., 2011)
and they are composed of about 110 species different species (Mathias, 2012).
Approximately 75% of the world’s mangroves are found in 15 countries with Asia having
the highest coverage of about 42% of the world’s mangrove forests, followed by Africa
with 21%, North/Central America (15%), Oceania (12%) and South America (11%) (Giri
et al., 2011)
Mangrove forest structure is characterized by attributes such as zonation, species
richness, canopy height, stem diameter, basal area, tree density and age/size-class
distribution. Mangrove forests exhibit varied zonation patterns in a number of different
geographic regions (Mendelssohn & McKee, 2000). The typical zonation described by
McNae (1968) for the Indo-West Pacific is generally from landward edge toward the sea:
L. racemosa, C. tagal, B. gynmorrhiza, R. mucronata, A. marina, S. alba. For mangrove
ecosystems along the tropical bays the seaward zone is characteristically inhabited by S.
alba, A. marina and R. mucronata. The latter is also found dominating the middle zone.
The landward zone is where mangrove associates are found.
In Kenya, zonation of mangroves from one ecosystem to the other depending on the
frequency of flooding by tidal water, salinity of the coastal waters, soil type, drainage
and plant interaction. Generally, R. mucronata is found dominating seaward zone of
muddy soils with inundation by high tides. Avicennia marina is widely distributed on the
landward margin, middle and seaward zones of the ecosystems since it can tolerate high
ranges of salinities, varied flooding regimes, compacted substrate, sand flats or newly
8
deposited sediments. It can also be found dominating seaward zone with S. alba where
muddy sandy soils and frequent inundation by high tides are experienced. Lumnitzera
racemosa is most found in landward zone of high clay content. A co-dominance of B.
gymnorrhiza and C. tagal is normally found in drier soils that are subject to tidal
inundation. Heritiera littoralis is mainly found widely distributed in very low salinity.
Xylocarpus granatum grows mixed with A. marina where flooding takes place.
Xylocarpus molucensis is a rare species only found in Diani, Ngomeni and KMNR.
Large variation in floristic composition of mangrove communities means that patterns of
species distribution across the intertidal zone also vary substantially among geographic
regions (Mendelssohn & McKee, 2000). Zonation patterns in mangrove forests may also
vary on a local scale.
2.2 Factors affecting structural characteristics of mangroves
The structural characteristics of mangrove ecosystems are functions of a number of
environmental factors or forcing functions: First, mangrove forests, like other
ecosystems, are subjected to the effects of natural events such as the rise in sea level,
disasters such as the tsunamis and El Nino in East Africa (Alongi, 2007). Such events
cause physical damage to the structure of the mangrove forests. The change in sea level
over the years, for example, has caused shift of mangrove zonation and change in species
composition as species tend to colonize other habitats towards the land keeping pace with
the rate of coastal erosion (Alongi, 2007).
Secondly, soil salinity plays a vital role in the distribution of species, their productivity
and growth of mangrove forests (Twilley & Chen, 1998). Mangroves generally tolerate
higher salinity than non-mangrove plants, but their tolerance also varies among the
mangrove species. Generally, mangroves thrive well in low salinity levels (Kathiresan et
9
al., 1996). At high salinity levels, experiments have shown that mangroves spend more
energy to maintain water balance and ion concentration than for primary production and
growth (Clough, 1984). It has also been shown that under high salinity levels, mangrove
biomass production and retention are greatly affected (Shan & He, 2008).
Thirdly, human disturbances have resulted in more than 50% of the world’s mangrove
forests being destroyed (Spalding et al., 2010). This huge loss of mangrove forests
globally has been attributed to urban development, aquaculture, mining along coastal
zones and over-exploitation of flora and fauna of mangrove forests (Walter et al., 2008;
Kairo et al., 2008; Alongi, 2009). The connection between coastal developments and
mangrove loss or transformation has been recorded in many parts of the world (Walter
et al, 2008; Kairo et al., 2008; Alongi, 2009). The gaps created during harvesting of either
individual or group of trees provide opportunities for seedling recruitment and growth
(Sherman et al., 2000). The size-class structure of mangrove forests in localities that
experience harvesting show under-representation in large-size classes which is the result
of selective harvesting (Walters, 2005).
The mangrove species is another factor which determines the structure of the forests. The
mechanisms by which mangrove species establish themselves determine their level of
success on particular zones. The different zones in mangrove ecosystems have conditions
which mangrove species have to be adapted to in order to establish or re-establish
themselves (Duke et al., 1998).
Lastly, management regimes or the collective anthropogenic activities that serve one or
multiple land-use purpose(s) (Van et al., 2014). These management regimes result in a
distinguishable land-use, land cover, ecological and other characteristics of a given area
(Van et al., 2014). In general, there may be multiple management regimes at the same
time in a given mangrove forest. For example; a mangrove forest can have aquaculture
10
ponds bordering highly protected area. In such a case, management activities obviously
differ whereby for the former will include additional feeding, artificial stocking, pesticide
application and using water pumps to simulate tidal movement; whereas activities in a
protected area include fishing, harvesting fuel wood and active protection. As a result,
aquaculture ponds are likely to have fewer mangrove trees and species while protected
areas are likely to have higher species richness and considerably more mature mangrove
trees (Van et al., 2014).Furthermore, mangrove management programs such as
restoration and rehabilitation are increasingly undertaken to re-establish ecosystem
services in the context of community-based biodiversity conservation. Restoration
involves returning a habitat to the most natural condition, whereas rehabilitation often
focuses on optimizing ecosystem services along biodiversity.
2.3 Threats to mangrove ecosystems
Mangroves are among the most productive terrestrial ecosystems and are a natural,
renewable resource (FAO, 2010). However, the world’s mangrove habitats are destroyed
as rivers are dammed, waters diverted and the intertidal zone extensively developed for
agriculture or aquaculture and generally dried up (FAO, 2010). Large tracts of intertidal
land have been converted to rice fields, fish and shrimp ponds, industrial and land
development and other non-forest uses. Nowadays, in addition to intense local and
regional impacts on mangroves, global climate change is putting pressure on the
dynamics of the ecosystems and their communities, with scale and intensity still
uncertain (FAO, 2007). Changes in temperature, rainfall and elevations in sea level have
the potential to change existing hydrological and biogeochemical characteristics,
threatening the biodiversity and ecological balance of the mangrove (Gilman et al., 2008,
Soares, 2009).
11
Mangroves in Kenya are not spared to the threats facing most parts of the world.
Overexploitation for wood products is the main agent of degradation. Lack of selective
cutting plans escalates problems of mangrove management in Kenya. The government
agencies vested with the responsibilities of managing mangroves and other forests in
Kenya lack adequate resources for implementing management guidelines. In most cases,
therefore, selective removal of quality poles of suitable species has tended to leave out
inferior species unsuitable for the market. Quality poles have been wiped out in most
mangrove areas of Mombasa, Kwale and Kilifi counties where population density is
highest along the coast. Salt extraction has also led to loss of mangroves. Currently there
are more than six (6) salt works in Ngomeni where most of extraction is carried in Kenya;
landing 71,400 tonnes of salt per year. Environmental impacts associated with salt
extraction include hypersalinity in areas close to mangroves leading to their deaths. Poor
land use practices in the hinterland has increased sediment loads into mangrove leading
to siltation of breathing roots of the trees and eventual death of the system. The situation
was worsened during the 1997/98 El Nino rains that hit the country causing massive
death of mangroves in many areas along the coast, most of which have experienced no
recovery up to date. Another threat facing Kenyan mangroves is oil pollution. For
instance, between 1983 and 1993 Mombasa port and surrounding waters experienced
391,680 tonnes of oil spills that affected mangroves of Port Reitz and Makupa creeks. A
new threat to mangroves in Eastern Africa is the projected sea-level rise due to climate
change. Climate change impacts are also associated with increased flooding,
sedimentation and aridity. Since coastal area where mangroves occur is low lying land a
small increase in sea level will mean that mangrove will be submerged unless they can
migrate to new areas mainland. Looking at the Kenyan coast, most areas where
mangroves could migrate to have already been occupied by human settlement and/or
12
infrastructure. Evidence of death of mangroves due to climate change impacts has been
observed in several areas along the coast such as Gazi bay, Mwache creek, Ngomeni,
Tana River and Dodori creek (Lang'at & Kairo, 2013).
13
3.1 Description of the study area
This study was carried out within the main mangrove forest blocks on each County,
which include Vanga-Funzi and Gazi (Kwale County), Tudor and Mwache/Port Reitz
(Mombasa County), Mtwapa, Kilifi-Takaungu, Mida and Ngomeni (Kilifi County),
Tanakipi and Mtotana (Tana River County) and KMNR and outside KMNR (Lamu
County), from April to November, 2015. The selection of the study areas was based on
representativeness (based on area coverage), importance and accessibility.
The Vanga-Funzi mangrove ecosystem (4°34′S, 39°26′E) is the largest tract of mangrove
forest in the south coast of Kenya, with an approximated total coverage area of 6980
hectares (Dute et al., 1981). The seaward zone is dominated by A. marina. Rhizophora
mucronata form one solid row with dominant S. alba at the middle zone on the seaward
margins. There is no distinctive zone of C. tagal instead it is interspersed with R.
mucronata and A. marina where the two species meet (Dute et al., 1981). The transition
between middle and landward zones is dominated by Bruguiera gymnorrhiza and
Xylocarpus granatum
Gazi mangrove forest (4°25´S and 39°32´E) is an open mangrove ecosystem situated in
the south Coast of Kenya, approximately 47km south of Mombasa city (Kitheka, 1997).
The mangroves at Gazi bay, which consists of all the nine mangrove species, cover a
total area of about 615 ha. This ecosystem includes the Makongeni, Kinondo, Chale and
Mwandamo forests (Kairo, 2001).
Tudor mangrove forest (4°2´S and 39°40´E) is located at the northwest of Mombasa
Island, and extends some 10-15km inland with two main seasonal rivers: Kombeni and
Tsalu, draining over 45,000 ha and 10,000 ha, respectively. The creek is characterized
14
by a 20m mean depth single narrow sinuous inlet that widens to a central 5m depth basin,
covering an area of 637ha and 2,235ha at low and high water spring tides respectively
(Omar et al., 2008). Within the creek is a mangrove forest which covers approximate
area of 1,465ha (Dute et al., 1981). The structure of the mangroves follows that A. marina
and L. racemosa occupy the landward zone; C. tagal and R. mucronata mosaic cover the
middle zone and where present S. alba occupies the seaward margin, but is replaced by
A. marina and R. mucronata along small creeks. The mangroves of Tudor creek are
separated naturally by two main tidal creeks-Kombeni and Tsalu, measuring 4.5km and
3km long, respectively cutting through the mangroves. Tsalu is the eastern tidal creek
and includes mangroves near the rural villages of Mirarani, Voroni, Junda and Kijiwe.
Kombeni, the western tidal creek, stretches between the cosmopolitan townships of
Mikindani, Jomvu and Miritini (Abuodha, Kairo, 2001)
Mwache/Port Reitz mangrove ecosystem (4° 3´S and 39° 38´E) is located 20km
northwest of Mombasa Island. The total area of the wetland is approximately 1,575 ha
(Dute et al., 1981) with about 70% of the surface area being covered with mangroves
(Lugo & Snedaker, 1974). The main mangrove species found in Mwache creek include
A. marina, R. mucronata, C. tagal and S. alba (Kitheka et al., 2002). The creek receives
freshwater from seasonal Mwache River.
Mida mangrove ecosystem (3°20´S, 40°00´E) is situated 100km north of Mombasa in
Kilifi County. The creek contains natural elements such as mangroves, coral reefs, and
mud flats. Seven (7) of the nine mangrove species described in Kenya are found in Mida,
and cover a total area of 1,600 ha (Dute et al., 1981). Two species; A. marina and L.
racemosa, occupy the landward zone, whereas C. tagal and R. mucronata mosaic covers
the middle zone. Where present, S. alba occupies the seaward margin while A. marina
15
and R. mucronata are found in small patches along small creeks. The mangroves at Mida
are separated naturally by the main creek and the two areas are named according to the
nearby local villages or islands: Kirepwe and Uyombo. Kirepwe occupies the eastern side
of the creek and includes mangroves near the villages of Sita, Dabaso and Dongokundu.
Mtwapa is a peri-urban trans-county town found in Kilifiat the border of Kilifi and
Mombasa Counties, approximately 16km north-east of Mombasa. Mtwapa mangrove
forests (3º57S, 3º44E) cover an approximate area of 525 ha (Dute et al, 1981).
Takaungu is a small rural village situated 10km south of the Kilifi town. Kilifi-Takaungu
mangrove ecosystem (3º41S, 39º51E) covers an area of 590 ha (Dute et al., 1981).
Ngomeni (0º39S, 38º24E) has the largest mangrove area in Kilifi County, covering a
total area of 4,240 hectares.
The Tana River delta (2°30´S and 40°20´E) is roughly a triangular wetland, with its apex
at Lake Bilisa and its base along Ungwana Bay, stretching from Kipini in the north-east
to MtoKilifi in the south-west (Robertson and Luke, 1993). The delta has mangroves
along the main river course between Ozi and Kipini and in the tidal delta south of the
main river. It consists of two major forest zones: Tanakipi in the nort-east and Mto-Tana
in the south-west. Both Tanakipi and Mto-Tana cover an estimate area of 2,085 ha (Dute
et al., 1981). The major mangrove species found in the delta are H. littoralis, A. marina,
R. mucronata, C. tagal, B. gymnorrhiza, X. granatum and S. alba (Ferguson, 1995).
Lamu mangrove forests (2°26′S, 40°90′E) cover the largest area, estimated at 37,350 ha,
representing about 59% of national coverage (Koedam et al., 2002).The Lamu County
mangroves are grouped into five (5) management blocks namely; Northern Swamps
(covering the entire KMNR), Pate Island Swamps, North Central Swamps, Southern
Swamps and Mongoni and Dodori Creek Swamps (these later referred to as Outside
16
KMNR). The KMNR covers about 3,025 ha while Outside KMNR covers approximately
30,475 ha (Dute et al., 1981). The structure of the mangrove forests follows that a narrow
belt of S. alba species is found in the seaward margins, followed by a belt of R. mucronata
species and finally a belt of A. marina species at the highest tide.
Figure 1: Map of Kenya (Inset) showing the coastline and locations of
Vanga-Funzi mangrove ecosystems (Source: GoK, 2017)
Munje
17
Figure 2: A map of Kenya (Inset) showing the coastline and locations of Tudor and
Port Reitz/ Mwache mangrove ecosystems (Source: GoK, 2017)
18
Figure 3: A map of Kenya (Inset) showing the coastline and locations of Kilifi-Takaungu,
Mtwapa, Mida and Ngomeni mangrove ecosystems (Source: GoK, 2017)
19
Figure 4: A map of Kenya (Inset) showing the coastline and locations of Mto-Tana
and Tanakipi mangrove ecosystems (Source: GoK, 2017)
20
Figure 5: A map of Kenya (Inset) showing the coastline and locations of KMNR and
Outside KMNR mangrove ecosystems (Source: GoK, 2017).
3.2 Research Design
An initial view of medium-scale (1:25000) colored panchromatic aerial photographs
were used to identify and stratify the study areas. A total of 627 study plots were
established all along the study areas, ranging from 6-139 plots. Thereafter, belt transects
were identified and oriented perpendicular to the waterline across the study area and 30m
away from each other. Along the belt transects, plots measuring 10m ×10m or 20m×20m
each, depending on the stem density of the forest, were laid located 100 m away from
each other. GPS coordinates for each plot were taken and recorded for future reference.
Data collection was done on all the mangrove trees with ≥2.5cm diameter at breast-
height, DBH (130cm). The following parameters were measured and recorded: tree
species name, tree height (m), pole quality and stem diameter, DBH (cm) from which
21
mean stem density (stems/ha) was calculated. Data analyses of the various structural
attributes were carried out using Microsoft Excel® 2007. Spatial structural differences
were analyzed using a One-Way ANOVA. Where structural differences were confirmed
present, Tukeys test was carried out to test for pair-wise differences between the
mangrove tree species and forest areas. Note: Some places like KMNR and some parts
of Outside KMNR were No-go zones due to insecurity caused by Al-Shabab. Because of
this, secondary data of 2015 was obtained from Kairo’s mangrove team and used.
3.3 Data Collection
Data collection was done on all sites for the mangrove trees with ≥2.5cm diameter-at-
breast height, DBH (D130cm, sensu Brokaw and Thompson 2000, exceptions to this rule
are described below). Tree height (m) was measured using wooden rule while tape
measure was used to measure tree perimeter (cm) from which stem diameter (DBH, cm)
was calculated using the formula: DBH=c/π; where: “c” is tree perimeter and “π” is pi.
Pole quality was obtained through observation by naked eye. From these parameters,
other structural attributes such as stem density (stems/ha) was computed using
Microsoft® Excel. Note that for C. tagal the stem diameter was taken at 100cm above
ground due to the low height of the trees. In the case of R. mucronata, the stem diameter
was measured 30cm above the highest prop root. For A. marina, when the stem is forked
below 130cm, individual “branches” in a clump were treated as separate stems.
3.4 Data Analysis
Data analyses were carried out using Microsoft Excel® 2007 and Minitab 14. Mean stem
density (De, stems/ha) (Mueller-Dombois & Ellenberg, 1974; Cintrón & Schaeffer-
22
Novelli, 1984) was derived using equation (i) below. Note that for equation (i) we
multiplied by 10,000 to convert m2 into hectares [since 1ha=10,000m2]
Mean stem density (stems/ha)
= (No. of stems of a species/Area of a plot) x 10,000……………………...... ()
Mean tree height (m) was computed as the total height (m) of all the stems in a plot
divided by the number of tree stems in the plot (Woods et al., 2008; equation (ii) below)
Mean tree height (m)
=Total height of all stems in a plot/ No. of stems in the plot……….…… ()
Mean DBH (cm) was computed as the total DBH (cm) divided by the number of tree
stems in a plot (equation (iii) below)
Mean DBH (cm)
=Total DBH (cm) of all tree stems in a plot/ No. of tree stems…..….. ()
Pole quality was designated as *merchantable or *non-merchantable poles (Kairo, 2001;
Kairo et al., 2008).
*Merchantable poles require no or minimum modification for commercial purpose
*Non-merchantable refers to mangrove wood (mostly branches and, in case of
Rhizophora, aerial roots) utilized by local community as firewood (Kairo et al., 2008).
Descriptive analysis was used to describe the structural attributes of the mangroves. The
data was tested for normality and homogeneity using Kolmogorov-Smirnov. Therefore,
a One-Way Analysis of Variance (ANOVA) was carried out to assess the structural
variations of the various attributes between tree species and among the mangroves sites
studied. ANOVA is a parametric test which assumes that data is normally distributed.
23
Thereafter, where structural variations were confirmed present, Tukeys test was used to
test for pair-wise structural variation between mangrove sites
24
CHAPTER FOUR: RESULTS
4.1 Variability in stem density of the mangrove species and forests
Tests of structural attributes between mangrove species revealed that mean stem density
differed significantly (F (7, 15) =10.16; α<0.05). It was highest in R. mucronata
(292±131stems/ha) and lowest in L. racemosa (2±1 stems/ha). Among the forests, tests
of structural revealed that mean stem density did not differ significantly (F (11, 23) = 0.64,
α >0.05) though it was highest in Gazi bay (264±97stems/ha) and lowest in Mtwapa
(66±11stems/ha) as shown in Table 2 below.
4.2: Variability in tree height of the mangrove species and forests
Mean tree height differed significantly (F (7, 15) =3.14, α <0.05) and the highest was in S.
alba (6.6±2.4 m) and lowest in H. littoralis (0.5±0.2 m).Mean tree height differed
significantly (F (11, 23) =2.14, α 0.05) among the forests whereby the highest mean tree
height was in KMNR (7.5±3.8 m) and lowest in Mtwapa (3.4±1.1 m) as shown in Table
3 below.
25
Table 2: Stem density (Mean ± SD; stems/ha) of mangrove species in various forests along the Kenyan Coast
A.
marina
B.
gymnorrhiza
C.
tagal
H.
littoralis
L.
racemosa
R.
mucronata
S.
alba
X.
granatum
Overall
mean
Vanga-Funzi 236±164 148±114 488±97 - 1±1 351±191 164±130 152±121 138±12a
Gazi 24±5 146±48 254±138 - - 661±78 28±2 32±17 264±97a
Tudor 91±9 11±4 11±1 - - 333±165 104±18 2±1 159±16a
Port Reitz 56±4 6±2 44±20 - - 166±91 81±54 - 71±34a
KilifiTakaungu 41±30 10±5 86±41 - - 224±82 38±12 - 80±34a
Mtwapa 191±1 - 174±26 - - 179±27 - 15±14 66±11a
Mida 36±8 24±21 299±28 - - 300±44 - - 165±25a
Ngomeni 241±99 33±30 168±18 - - 131±25 19±2 16±2 77±29a
Tanakipi 242±59 1±1 - 83±4 - 4±1 - 1±1 83±13a
Mto-Tana 198±30 5±1 178±138 - - 2±1 - - 96±18a
KMNR 278±2 18±1 272±30 - 5±2 733±44 32±17 - 225±17a
OutsideKMNR 131±83 23±1 135±61 - 1±1 187±62 89±21 6±4 82±33a
Overall Mean 202±33b 38±20c 250±155ab 7±1d 2±1d 292±131a 69±32c 32±22cd
Means followed by the same letter were not significantly different at α=0.05. The empty entries indicated by hyphens are sampling
areas where the species were absent
26
Table 3: Tree height (Mean ± SD; m) of mangrove species in various forests along the Kenyan Coast
A.
Marina
B.
gymnorrhiza
C.
Tagal
H.
littoralis
L.
racemosa
R.
Mucronata
S.
alba
X.
granatum
Overall
Mean
VangaFunzi 3.6±2.9 5.0±2.1 3.0±1.4 - 1.9±1.0 5.3±2.7 8.2±4.7 4.9±2 4.6±2.4b
Gazi 6.7±3.1 5.2±2.4 3.7±1.5 - - 5.4±3.0 7.7±2.7 5.8±3.3 5.8±2.7ab
Tudor 4.6±3.1 2.8±0.5 1.7±0.6 - - 2.6±0.8 5.6±1.9 3.1±0.2 3.6±1.2c
Port Reitz 5.8±2.6 4.0±1.4 3.1±1.0 - - 5.1±2.3 6.6±2.1 - 4.9±1.9b
KilifiTakaungu 5.5±4.4 3.1±0.9 2.0±0.7 - - 4.4±2.5 5.8±1.8 - 4.2±2.1bc
Mtwapa 5.0±1.4 - 2.0±0.6 - - 3.5±1.1 - 3.9±0.9 3.4±1.1c
Mida 5.6±1.9 7.0±4.0 8.8±1.6 - - 6.2±2.8 - - 6.9±2.6a
Ngomeni 5.4±2.9 4.4±3.5 4.8±2.3 - - 4.5±2.8 4.8±1.6 4.6±1.9 3.3±2.5c
Tanakipi 5.7±2.0 12.0±2.8±5.8 4.4±10 - 3.3±11.4 - 2.3±5.6 - ab
Mto-Tana 4.2±3.1 8.3±3.9 3.8±2.3 - - 8±2.5 - - 6.1±3.1ab
KMNR 3.5±2.1 6.3±2.5 4.1±2.3 - 6.7±1.5 7.1±4.1 7.3±2.3 - 7.5±3.8a
OutsideKMNR 4.9±3.0 6.6±2.4 3.4±1.9 - 2.2±0.5 7.1±3.5 7.2±2.6 3.3±1.1 5.0±2.1b
Overall Mean 5.1±2.7ab 6.4±2.3a 4.1±1.4b 0.5±0.2c 2.2±1.0bc 6.1±2.6a 6.6±2.4a 5.5±1.9ab
Means followed by the same letter were not significantly different at α=0.05. The empty entries indicated by hyphens are sampling
areas where the species were absent
27
4.3 Variability in DBH of the mangrove species and forests
Tests revealed that mean DBH was significantly different (F (7, 15) =5.64, α <0.05). It was
highest in S. alba (13.1±7.7 cm) and lowest in H. littoralis (0.7±0.3cm). Among the forests,
mean DBH was not significantly different (F (11, 23) =1.23, α>0.05). However, it was highest
in KMNR (10.3±2.1cm) and lowest in Mtwapa (3.2±1.1cm) as shown in Table 4 below.
4.4 Variability in pole quality of the mangrove species and forests
Pole quality differed significantly among the species (F(7, 15) =5.96, α <0.05) but failed to
differ significantly between forests (F(11,23)=0.77,α>0.05).
Among the species, R. mucronata and H. littoralis had the highest (64.1%) and lowest
(4.1%) percentages of their poles of merchantable quality, respectively. On the other hand,
KMNR had the highest (53.4%) percentage while Mtwapa had the lowest (20.5%)
percentage of their poles of merchantable quality, respectively among the forests as shown
in Table 5 below.
28
Table 4: Diameter-at-Breast Height (Mean±SD; cm) of mangrove species in various forests along the Kenyan Coast
A.
marina
B.
gymnorrhiza
C.
tagal
H.
littoralis
L.
racemosa
R.
mucronata
S.
alba
X.
granatum
Overall
mean
Vanga-Funzi 6.6±6.5 6.9±5.3 4.4±2.2 - 3.5±0.5 6.9±5.3 15.6±10 7.±4.8 6.3±2.9a
Gazi 10.8±8.2 8.8±6.0 4.4±2.4 - - 7.9±6.4 11.4±8.5 9.6±8.2 6.6±1.6a
Tudor 10.4±10 8.6±8.4 3.6±0.9 - - 3.8±1.6 13.1±7.3 6.7±2.3 5.7±1.1a
Port Reitz 11.9±8.8 6.5±4.3 4.6±2.3 - - 8.8±6.8 12.0±6.9 - 5.4±0.8a
Kilifi-Takaungu 7.8±7.8 4.0±1.4 3.7±1. - - 6.4±6.0 8.6±5.8 - 3.8±1.4a
Mtwapa 11.6±5.4 - 3.7±1.2 - - 4.5±2.2 - 6.0±3.4 3.2±1.1a
Mida 11.3±8.3 11.9±8.3 4.3±2.5 - - 7.2±5.8 - - 4.9±1.2a
Ngomeni 11.3±10.9 10.0±9.2 6.0±5.1 - - 6.9±6.3 11.9±5.9 11.5±8 7.2±2.6a
Tanakipi 6.1±3.4 14.0±1.9±8.0 14±11.7 - 6.4±12.4 - 3.8±8.2 a
Mto-Tana 7.1±4.7 9.6±5.6 4.9±4.0 - - 7.0±2.8 - - 3.5±1.3a
KMNR 11.1±7.5 10.5±7.9 7.1±4.2 - 10.1±8.8 10.8±7.1 15.0±7.3 - 10.3±2.1a
Outside KMNR 12.6±6.1 13.1±11.3 5.3±2.9 - 8.3±3.2 9.6±6.7 13.3±10.2 16.2±28.5) 9.8±1.2a
Overall Mean 10.3±7.3a 8.6±6.7ab 4.7±2.6b 0.7±0.3c 9.3±4.1ab 8.1±5.2b 13.1±7.7a 11.6±6.2a
Means followed by the same letter were not significantly different at α=0.05. The empty entries indicated by hyphens are sampling
areas where the species were absent
29
Table 5: Percentages of merchantable poles of mangrove species in various forests along the Kenyan Coast
A.
marina
B.
gymnorrhiza
C.
tagal
H.
littoralis
L.
racemosa
R.
mucronata
S.
alba
X.
granatum
Overall
percentage(%)
Gazi 80.8 61.7 83.0 - - 71.1 76.7 54.1 49.5a
Tudor 39.5 66.7 71.4 - - 56.2 40.4 0 34.3a
Port Reitz 26.8 42.9 62.1 - - 55.3 63.0 0 31.3a
KilifiTakaungu 33.9 30.4 40.8 - - 39.4 80.6 0 28.1a
Mtwapa 65.2 0 0 - - 44.7 0 23.9 20.5a
Mida 46.9 68.8 65.6 - - 68.8 0 0 26.3a
Ngomeni 26.8 51.4 67.0 - - 81.0 23.8 25 34.4a
Tanakipi 54.8 100.0 0 49.4 - 75.8 0 0 35.0a
Mto-Tana 6.1 61.1 84.9 - - 73.3 0 0 28.2a
KMNR 44.1 73.1 81.5 - 100 77.3 20 0 53.4a
OutsideKMNR 24.6 76.2 63.6 - 80 65.5 17.8 66.7 49.3a
Overall
Percentage (%) 37.7ab 57.7a 56.3a 4.1c 23.3b 64.1a 28.7b 18.1c
Percentages followed by the same letter were not significantly different at α=0.05
30
In non-merchantable poles category, the species A. marina and L. Racemosa had the highest
(62.3%) and lowest (1.7%) percentages while Mtwapa and KMNR had the highest (49.5%)
and lowest (21.5%) percentages, respectively, among the forests as shown in Table 6 below.
31
Table 6: Percentages of non-merchantable poles of mangrove species in various forests along the Kenyan Coast
A.
marina
B.
gymnorrhiza
C.
tagal
H.
littoralis
L.
racemosa
R.
mucronata
S.
alba
X.
granatum
Overall
Percentages(%)
Gazi 19.2 38.3 17.0 - - 28.9 23.3 45.9 25.6a
Tudor 60.5 33.3 28.6 - - 43.8 59.6 100.0 40.7a
Port Reitz 73.2 57.1 37.9 - - 44.7 37.0 0.0 31.2a
KilifiTakaungu 66.1 69.6 59.2 - - 60.6 19.4 0.0 34.4a
Mtwapa 34.8 100 0.0 - - 55.3 0.0 76.1 49.5a
Mida 53.1 31.3 34.4 - - 31.2 0.0 0.0 23.8a
Ngomeni 73.2 48.6 33.0 - - 19.0 76.2 75.0 40.6a
Tanakipi 45.2 0 0.0 50.6 - 24.2 0.0 100.0 27.5a
Mto-Tana 93.9 38.9 15.1 - - 26.7 0.0 0.0 21.8a
KMNR 55.9 26.9 18.5 - 0 22.7 40.0 0.0 21.5a
OutsideKMNR 75.4 23.8 36.4 - 20 34.5 82.2 33.3 38.2a
Overall
Percentages(%) 62.3a 42.3ab 27.0b 4.2c 1.7c 36.0ab 38.0ab 40.3ab
Percentages followed by the same letter were not significantly different at α=0.05
32
CHAPTER FIVE: DISCUSSION
5.1 Variability in stem density of the mangrove species and forests
In this survey R. mucronata had the highest mean density. This could be due to the
restoration measures initiated by KMFRI whereby large R. mucronata plantations were
established in Gazi bay (Kairo, 1995). Restoration is defined as the act of returning an
ecosystem as close as possible to its original condition or functional state (Field, 1999).
With subsequent development, the R. mucronata plantations have promoted re-
colonization of the stands by non-planted mangrove species as confirmed by Bosire et al
(2003; 2006). The low mean stem density (2±1 stems/ha) for L. racemosa can be attributed
to its rarity. The high mean density recorded in Gazi bay can be attributed to the large
plantation of R. mucronata that was established in 1995 by KMFRI. On the other hand,
low stem density in Mtwapa could be attributed to heavy influence of human activities
(Mohamed et al., 2008) where either encroachment or selective harvesting of mature
stems has greatly affected the tree growth (Kirui et al., 2012).
5.2 Variability in tree height of the mangrove species and forests
In this study S. alba had the highest mean tree height. According to Felipe et al. (2013)
and Harwood et al. (2007) it can be argued that the genetic make-up (since ‘genes’ are the
building units that determine the traits of any organism) of S. alba coupled with favorable
climate in coast of Kenya (Super, 2014) support its height. On the other hand, KMNR
recorded the highest mean tree height. The mean tree height values obtained in this study,
however, vary with those obtained by Amol and Thivakaran (2013) in Gujarat (India).
33
5.3 Variability in DBH of the mangrove species and forests
In this study S. alba recorded the highest mean DBH. This has again demonstrated that
the mean tree height and mean DBH are positively correlated (Estrada et al., 2013).
Therefore, the high mean DBH can similarly be attributed to genetic make-up (since
‘genes’ are the building units that determine the traits of any organism) of S. alba coupled
with favorable climate in coast of Kenya (Super, 2014) as reported by Felipe et a., (2013)
and Harwood et al. (2007).KMNR also had the highest mean DBH. The mean DBH for
KMNR and Mtwapa recorded in the present survey confirm opinions by several studies
(Jimenez et al., 1985; Schaeffer-Novelli & Cintrón, 1986; Alongi, 2002; Fromard et al.,
2004; Estrada et al., 2013) that maturity and mean DBH are positively correlated.
Generally, the mean tree height and mean DBH values obtained in this study vary with
those obtained by Amol and Thivakaran (2013) in Gujarat (India). This can be argued that
the temperature gradient (Norman et al., 2009) between the two places (India is on higher
latitude than Kenya). Mangroves are tropical and occur on either side of the equator (Duke
et al., 1998; Ellison, 2002; Spalding et al., 2010) and it has been reported that tree height
declines with increasing latitude (Ellison, 2002). Additionally, the sheltered coast of
Kenya supports luxuriant growth of mangroves because of the existing favorable
conditions such as muddy sediment, frequent water exchange, high rainfall and high
humidity. In contrast, the arid region of Gujarat where the sediment is sandy, highly saline
and poor in nutrients have only dwarf mangrove stands (Kathiresan & Bingham, 2001).
The mean tree height and mean DBH in KMNR can be attributed to maturity of most
stems as the forests are well protected and conserved (Samoilys & Kanyange, 2008).
Contrary, the same structural attributes were lowest in Mtwapa mangrove forests due to
heavy influence of human activities (Mohamed et al., 2008) where they are either
34
encroached or the mature stems are selected for harvesting (Kiruiet al., 2012). This could
have left the forests with fewer and less mature stems compared with the rest of the forests.
5.4 Variability in pole quality of the mangrove species and forests
The highest merchantable poles were also found in R. mucronata. According to Harwood
et al., (2007) and Felipe et al., (2013) pole quality is related to genetic control of height
and diameter of a tree which greatly influence its stem straightness. Similar studies,
however, found that stem straightness is related to the type of species (Mahmood et al.,
2003; Henson et al., 2008).In addition, the pole quality is influenced by stem density
whereby according to Hai (2009), high stem densities favor high rates of straight stems.
That is why it was also observed that places like KMNR (which recorded high stem
density) and Mtwapa (which recorded low density) have high and low poles of
merchantable qualities, respectively.
CHAPTER SIX: CONCLUSION AND RECOMMENDATIONS
Lack of clear management plans and inadequate resources have contributed to the
continual degradation of Kenyan mangroves (Abuodha & Kairo, 2001) leading to about
50 % of mangrove cover lost in the last 50 years (FAO, 2003). Findings from this study
provide the detailed data and information for development of a comprehensive
management plan for sustainable utilization of mangroves along the Kenyan coast. The
mangroves of KMNR and Gazi showed superior structural features than those of most
sites investigated. This is probably attributed to the influence of restoration, conservation
and protection measures undertaken in those areas. Mangroves of Tanakipi and Mto-
Tana were typical of forests with large trees since they had large mean DBH (implying
that they have large mean basal areas since the latter is directly attached to DBH size)
and low mean stem densities. This is likely attributed to more access to freshwater and
nutrients (Lugo, 1997) than maturity of forests because of presence of large river system
for each case. On the contrary, the mangroves of Tudor, Kilifi-Takaungu and Mtwapa
were observed to have the poorest structural attributes. This can be attributed to the
influence of degradation associated with the urban settings (Omar Said et al., 2008). This
study has shown evidence of spatial variations in some structural attributes of Kenyan
mangrove forests. Despite a number of sites having low mean stem densities (e.g.
Tanakipi and Mto-Tana) as it was observed in this study, their mean DBH are among the
largest indicating that most of the existing stems are either mature and old(Kairo, 2001)
with low regeneration rate (Okello et al., 2013) or have more access to freshwater and
nutrients (Lugo, 1997). Other forests such as Port Reitz, Kilifi-Takaungu, Mtwapa and
Ngomeni have low mean stem densities, low mean DBH and low mean tree height
indicating the effect of human pressure(Bundotich, 2007). The results have also shown
that the stems are of poor quality probably due to selective harvesting of good quality
36
poles (Kirui et al., 2012).Nevertheless, the presence of dead stumps as it was observed
in areas such as Kidongo indicate high consumptive extraction of mangrove wood
products in close proximity to human settlements (Kairo et al., 2002) as well as ease of
access to the forest area. This study, therefore recommends the following:
1) Adopt restoration measures in Mtwapa, Tudor and Kilifi-Takaungu that take into
account suitable mangrove species such as A. marina and B. gymnorrhiza, since
they can tolerate stress best and harvesting systems
2) Mtwapa, Tudor and Kilifi-Takaungu need to benchmark and apply restoration
and protective programs carried out in Gazi bay and KMNR respectively, to
counteract the rate of degradation in mangroves by human activities
37
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Appendix iv: Photo of Heritiera littoralis
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Appendix viii: Photo of Xylocarpus granatum (source:
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