International Journal of Trend in Scientific Research and Development (IJTSRD)

Volume 4 Issue 2, February 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470

Structure, Biomass Carbon Stock and Sequestration

Rate of Mangroves in the Bakassi Peninsula, S-W Cameroon
Kamah Pascal Bumtu, Nkwatoh Athanasius Fuashi, Longonje Simon Ngomba
University of Buea, Faculty of Science, Department of Environmental Science,
Republic of Cameroon, Central Africa

ABSTRACT How to cite this paper: Kamah Pascal

The forest plays a major role in stabilizing increasing temperatures due to Bumtu | Nkwatoh Athanasius Fuashi |
its climate mitigation capacity. This is not unconnected to the carbon Longonje Simon Ngomba "Structure,
storing and sequestration potentials of forests. The mangrove as one of the Biomass Carbon Stock and
global forest types is said to be a major carbon store. This conclusion is Sequestration Rate of Mangroves in the
characterized by some knowledge gaps on the actual carbon stock and Bakassi Peninsula,
sequestration potentials of some mangroves forest on the Central African S-W Cameroon"
Sub-regional landscape. Some of these areas are the Bakassi mangroves in Published in
the South West Cameroon. Cross-border conflicts, piracy and over International
exploitation have rendered the sourcing of appropriate data on its carbon Journal of Trend in
stock and sequestration potentials difficult. In strive to bridge this Scientific Research
knowledge gap, this work carried out a baseline assessment of the carbon and Development IJTSRD30171
stock and sequestration rate of the area. To achieve the study objectives, (ijtsrd), ISSN: 2456-
stratified random opportunistic sampling inventory design based on five 6470, Volume-4 | Issue-2, February
forest canopy height classes, tree Diameter at Breast Height (DBH) and 2020, pp.843-854, URL:
canopy nature using digital elevation model (DEM) of the shuttle Radar www.ijtsrd.com/papers/ijtsrd30171.pdf
Topographic Mission (SRTM). This combination evaluated the species type
and forest structure around the areas. Carbon stocks were estimated with Copyright © 2019 by author(s) and
the use of allometric equations using biomass data collected within main International Journal of Trend in
plots, sub plots, micro-plots and transects. Results showed that; mean Scientific Research and Development
biomass carbon stock density for the height classes for Bakassi ranged from Journal. This is an Open Access article
33.5 Mg/ha to 598.9Mg/ha. Thus on average, for a hectare in Bakassi, the distributed under
carbon stock is 880.437 (Mg/ha) and a sequestration rate of 3231.204 the terms of the
(tCO2e/ha). Creative Commons
Attribution License (CC BY 4.0)
KEYWORDS: Biomass, structure, Carbon stock, sequestration rate, mangroves (http://creativecommons.org/licenses/
and Bakassi Peninsula by/4.0)
INRODUCTION
The forest plays an important role of stocking and generate revenue to support and incentivize locally-led
sequestrating biomass carbon (Stringer et al., 2016).This sustainable mangrove management, improve livelihoods
role is possible due to their ability to carry out carbon and alleviate anthropogenic pressures on the ecosystem
fixation. Carbon fixation occurs in the chloroplasts of .Developing policy tools to protect and restore mangroves
green plants or any photosynthetic or chemoautotrophic through payment for ecosystem services (Friess et al.,
organism (Rittnera and McCabe, 2004) resulting to large 2016, Howard et al., 2017) are important in their role in
pools of carbon from sinking or cleansed carbon dioxide the terrestrial and oceanic carbon cycling (Alongi, 2012;
from the atmosphere (Mbobda et al., 2016).Tropical Donato et al., 2012; Liu et al., 2014). In these regard, they
forests cover a surface area of more than 13 million km2; contribute about 10 % of the total net primary production
corresponding to 33 % of total forest area on earth (FAO, and 25 % of the carbon burial in the global coastal zone,
2011). Mangrove forests are amongst and occupies less though they colonize only 0.7 % of the global coastal zone
than 14 million ha of global forest cover (Giri et al., 2011), (Alongi, 2007; Kathiresan and Bingham, 2001).Besides
just 0.1% of the Earth's continental surface, i.e. 81,485 km2 climate change mitigation, mangroves also render other
(Hamilton and Casey, 2016). Mangroves of West and services like; reducing hazards from winds, serving as
Central Africa extend over 20,144 km2, representing 59 % breeding and spawning grounds for fishes, fuel wood for
of the African mangroves and 11 % of the total mangroves the community, construction materials and collection of
area in the World (UNEP-WCMC, 2007) and provide a other non-timber forest products amongst others.
broad array of ecosystem services (Barbier et al., 2011),
valued at an average of 4200 US/ ha/yr in Southeast Asia Also, they are estimated to sequester carbon about 10–50
(Brander et al., 2007). Amongst these values are those of times faster than terrestrial systems (Chmura et al., 2003;
carbon stock and sequestration potentials. They are Bouillon et al., 2008; Copertino, 2011; McLeod et al., 2011;
amongst the most carbon (C) rich forests on Earth (Donato Siikamäki et al., 2012; Alongi, 2014 and Howard et al.,
et al.,2012;Jones et al., 2014) and have highest value per 2017). With the increasing concern over climate change,
hectare of any blue carbon ecosystem ( Nellemann et al., efforts to evaluate the rate and value of carbon
2016). On the voluntary carbon market, this values could sequestration in forests systems has been increasing

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(Bretón et al., 2010; McLeod et al., 2011; Nelleman et al., 2012). Therefore, the role of mangrove forests in the
2016). At a time when, Africa’s mangroves are the most global carbon budget is significant (Bouillon et al., 2008).
understudied in the world (stringer et al., 2015) especially
in the subject of its carbon stock and sequestration According to Ajonina et al., 2014, ‘Reducing Emissions
services. This knowledge gap in carbon stock and from Deforestation and forest Degradation’ (REDD+) is an
sequestration potentials of most mangroves in Africa and emerging international financial mechanism enabling
particularly in Cameroon have prompted the assessment tropical countries to get rewarded for their efforts in
of the carbon stock and sequestration potentials of the reducing CO2 emissions from deforestation and forest
mangroves of Bakassi Peninsula with hopes to bridge this degradation, and a number of Central African countries
gap and inform researchers, practitioners and policy including Cameroon have embarked on ambitious national
makers on the nature of stock and sequestration reforms and investments to improve forest landscapes
potentials of the mangroves. This entailed assessing the management in order to benefit from REDD+, this is good
biomass carbon (standing dead wood, standing live wood, in mangroves since they can store several times more
litter herbs and grass, lianas, stumps down dead wood) carbon per unit area than productive terrestrial forests
(Kaufman and Donato, 2012) through a non-destructive (Donato et al., 2011) and in Bakassi area that makes up
method. Their effective assessment will give the about 10% of the mangroves of West Africa and half of the
government a high bargaining power in the carbon market mangroves of Cameroon (CECO, Socio-economic studies,
since policy makers across the tropics propose that carbon 2014)
finance could provide incentives for forest frontier The main problem that this research seeked to address is
communities to transition away from swidden agriculture the lack or in appropriate nature of mangrove data, both in
(slash – and – burn or shifting cultivation) to other system quality and quantity, especially on carbon stock and
that potentially reduce emissions and or increase carbon sequestration in this area where research is hampered by
sequestration (Ziegler et al., 2012).The biomass insecurity, mangroves destroyed by encroaches and the
assessments will also give a reflection of the capacity of subject neglected by scientists, since this trans-boundary
that ecosystem to sequester carbon. Many studies have site between Cameroon and Nigeria is a remote and an
been published on aboveground carbon stocks in tropical undeveloped coastline
forests around the world (Komiyama et al., 2005), but
limited studies exist in Bakassi (Ajonina et al., 2014). A gab MATERIALS AND METHODS
which this study stand to partly bridge for the Bakassi Study site
mangrove areas and to establish baseline data on biomass This study was carried around the Bakassi Peninsula
pools for future studies in these areas since this forests particularly in Ndian Divisions South-West Region of
role relies on reliable quantification of current carbon Cameroon, a biodiversity hotspot that supports high
storage in the ecosystem as the baseline Also, concerns diversity of animal and plant species (MINEPDED,
over increasing atmospheric carbon emissions are driving 2009um). The work touched 7 mangroves subdivisions
the need to improve understanding of carbon (Bamuso, Ekondo Titi, Mundemba, Isangele, Kombo
sequestration within global ecosystems and investigating Abedimo, Kombo Itindi and Idabato), between latitudes
solutions to mitigate the effects of resulting climate change 4°25′E and 5°10′N and longitudes 8°20′E and 9°08′N (GEF,
(McLeod et al., 2011; Siikamäki et al., 2012; Alongi, 2014 2016). Here, strong ocean waves work against the
and Howard et al., 2017). Thus, protecting, enhancing and incoming river current to precipitate deposits in the form
restoring natural carbon sinks have become political of large inter-tidal mud or sand flats which favours the
priorities (Sanderman et al., 2018). Mangrove forests can growths of mangrove tree species. The climate is the
play an important role in carbon removals; in addition to equatorial and littoral types with two distinct seasons: a
being some of the most carbon dense ecosystems in the short dry season of 4 months (November to February) and
world (Donato et al 2011; Wang et al., 2013), though the a long rainy season almost 8 months (from March to
role of mangroves in global carbon cycles has been October). The average rainfall ranges from 5000 mm to
somewhat ignored, due to their relatively small total area 10000 mm with July, August and September been the
and often lower physical build (Spalding et al., wettest months. Relative humidity is very high, above
2010).Globally, the total net primary production of the 85%. The main annual temperature is from 25, 5 ⁰C to 27⁰
ecosystems has been estimated at 218 × 109 kg C year−1 C (GEF, 2016). The average tides waltz between 0.1 m to
(Bouillon et al., 2008; Twilley et al., 1992), ranking as one 2.9 m accompanied very often by scorching heat waves
of the most productive biomes on the earth (Tue et al., sometimes going up to 45 ° in the shade (Ocholi, 1986).

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Figure: 1. map of Bakassi Peninsula

With a low elevation of 0 – 2m above sea level (Smoak et al., 1999) the area is predominantly mangroves both indigenous
and foreign species (Fig 1) with Rhizophora racemosa, dorminating (WWF, 2019). The soils rang from; sandy, ferralitic, to
claylike or peat that are generally formed by the deposition of plant particles on watery soils (Smoak et al., 1999).with
very old and deeply weathered bedrock, the soils are depleted of nutrients (Bond, 2010) following leaching after heavy
rains (Wong & Rowell, 1994).

This area is sparsely populated (about 150,000 and 300,000 ) by ethnic groups from Nigeria and Cameroon (Ejaghams and
the Efiks) where about 70% of the population comes from Nigeria. Their primary economic activity is fishing, farming for
subsistence needs as well as timber harvesting which is limited to artisanal tree cutting. Also, the area has rich oil reserves
in neighboring areas of Nigeria (GEF, 2016) where off-shore oil exploitation has been going on since 1960, accounting for
over 70% of Cameroon’s oil production

Methodology
Stratification of the area was done following a Digital Elevation Model of the Shuttle Radar Topographic Mission (SRTM) to
differentiate height ranges, since height was the basics of this stratification. Distinguished in to five classes (0-8, 8.1-14,
14.1-21, 21.1-28 and ≥ 28.1 with identities 1, 2, 3, 4 and 5 respectively. This was with the use of geometric intervals which
is a compromise method between equal intervals, natural break (Jenks), and quantile (Carl et al., 2015) thus, highlighting
changes in the middle values and extreme values, giving a visually appealing and cartographically comprehensive result
using ESRI (WWF, 2019). Preliminary stock assessments and test questionnaires for validity was carried out where
random and opportunistic sampling was used to identify and establish plots. This took in to account the species, cost,
security conditions, accessibility (nature of soil, tides) as recommended in stringer et al., (2014) for better accuracy,
precision ,efficiency and ascertain that the study is representative. This was following the heterogeneous nature of the
forest and its functional reliability with the necessity to capture relevant variables in the equations coupled to the fact that
the area was finite or known as recommended in Kauffman and Donato (2012), Zerim and Yerimu, (2013).

Plot design and establishment

With 50 m transect tape (Tibre)and compass (silver polasis), square plots of 20m x 20m were established, a 10 x 10m
subplot was further design and lastly, four 1m x 1m nested plots establish at the extremes of the main quadrant. Four lines
transects of 12m each were established at the corners of the plots and the plots centers were marked, i.e. inside the 10m x
10m as recommended in Jones (2014) and Kaufman and Donato (2012). Plots centers were collected with a Garmin GPS
(Map 62).This design was preferred due to the diversity of the ecosystem, the need to measure trees of variant sizes,
capture all variations within the area and to give better quantification of carbon stock estimates (ICIMOD, 2016)

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Data collection for carbon stock assessment
To get species specific gravity, a botanical survey to identify all plant species within the sampled plots was done. Standard
plant identification procedures as recorded in Letouzey (1986) from morphological features (leaves, aerial roots, flower,
fruits, trunk) was used. When the species were identified, their species specific densities were referred from the World
Agroforestry Data base for identified species and used in the allometric equation as recommended in Kaufman and Donato
(2012).

Standing live tree DBH was gotten at 1.3m from the ground using the diameter tape and to the nearest 0.1cm. Climbing
was done and measurements at 0.3m-0.5m for hight stilt roots. Trees diameters were measured as; > 5cm within the 20 m
x 20 m, between 2.5cm - 5cm within the 10m x10m subplots and regenerating trees (< 2.5cm) within the 1m x1m plots.
With a bold maker trees with at least 50% of their trunks inside the plots were marked and counted while those with 50 %
outside the plot were not counted as recommended in Kaufman and Dnato (2012). Large lianas were considered as trees,
and their DBH were measured DBH as recommended in Donato and Kauffman (2012). Standing dead trees were measured
same but their status noted where, they were categorized in to 3 statuses; status I (having branches and twigs still present
or recent dead trees), status 2 (having secondary branches only or no twigs) and status 3 (only the main branches or
standing stems). For status 3 both the DBH and basal diameter were measured and this was used to calculate the top
diameter of the trees as recommended in Kaufman and Donato (2012). Stumps were measured like standing dead trees
(where DBH was > 1.3m), but where it was < 1.3m the diameter was measured as close as possible to the top. The height
and dead state of the stumps were noted and classified as classes; 1 (a machete/knife did not sink into stump at a single
strike), 2 (intermediate, a machete sank partly into at a single strike), and 3 (Rotten /crumble wood, machete cut through
at a single strike) as recommended in Donato and Kauffman (2012). For biomass of the palms, all palm leaves (fronts) that
occurred within each sample plot were counted for all the palms with height greater than 1.3m and at least 15-25 palm
fronts from different individuals collected from ground level outside these sample plots. Their initial weights were taken
on the field and samples taken to the laboratory to determine the dry weight. Trees heights were measured using a Suunto
clinometer for a number of trees and the process continued through estimation using expert judgment. For Herbs and
Grasses, all vegetation <1.3m in height were clipped off in the two 50cm by 50 cm micro quadrats at the 10m point on the
12m transect using a scissors down to the mineral soil surface as described in Striger et al., (2015) while all Litter in the
other two micro plots of 50cm x 50 cm were established at both the 6m and 12m points along the same 12m transect line
on which herbs and grass were collected. The total weights of the samples in each plot were measured using a portable
electronic (Wriheng) scale balance and readings documented to the nearest gram. Well mixed samples were composed
from each subplot, weighed on the field and 50g at most collected for drying in the laboratory as recommended in
Kaufman and Donato (2012). For Downed Dead Wood Debris, their diameter (in cm) were measured along the 12m line
transects. Those that intersected these transects were counted and measured using aluminum caliper (go-no-go gauge)
based on the classification: Fine (0-0.6), small (0.6-2.5), medium (2.5-7.6) and large(> 7.6) with a measurement approach
(tallied from); 10m -12m, 7- 10m, 2-7m, and entire length respectively( Brown, 1971). Where each of the 3 smaller size
classes where encountered, the number of debris intersections were tallied along the corresponding designated length of
these transects. The numbers of intersections were counted, not the number of debris pieces. Individual diameters of large
wood (> 7.6 cm) were measured and recorded along the entire transect length and its decay status recorded as solid or
rotten as recorded in Kaufman and Donato (2012)

To get the specific gravity of wood debris, at least 20-25 pieces were randomly collected throughout the entire area from
each class size where a representative range of size and species present in the sample plots was insured. Each piece
collected had a mass of about 0.5 –50g, and were collected outside the sample plots to avoid disturbance and taken to the
laboratory as recommended in Donato and Kauffman (2012)

Laboratory and Data analysis

Mean Specific Gravity for wood debris was determined dividing each wood mass (g) by its volume (cm -3). To get the
masses, wood debris pieces were oven dried using a memmert Oven at 105oC for 24 hours and their masses gotten
through measurements on an electronic balance. Debris volumes were obtained by submerging wood in to a container
placed on a digital balance and displaced water measured in a burrete at 0.2mL with error margine of 0.1mL. Since specific
gravity of the water was known (1gcm-3), the resultant displaced water in the burette was the volume of the particle. Their
mean specific gravity was gotten by summing their individual specific gravities and dividing by the number of the wood
counts.

To get the Dry mass for Litter Herbs and Grass (LHG), their samples taken to the laboratory where dried at 105oC for 24
hours using a memmert Oven and the dry masses measured on an electronic balance. These masses were inputted in to the
formula to get the biomass of leaf, litter herbs and grass using; weight of fresh samples of leaf, litter, herbs and grass
in metre square, weight of oven dry sample of leaf, litter and grass in grams, weight of fresh subsample of leaf, litter and
grass in grams. These masses were converted to carbon concentration by multiplying it with the recommended
representative conversion factor of 0.45 as mean carbon concentration for tropical forest litter, herbs and grass(Kauffman
and Donato, 2012). Dry mass measurement for Nypa as gotten just as those for LHG

Data and statistical Analysis

For live trees, general allometric equations by Komiyama et al., (2005) were used for estimating the biomass of live
standing trees for with DBH > 5cm (Above ground biomass) as recommended in Kauffman and Donato (2012). Also,

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Species specific densities of the identified species used were gotten from World Agroforestry Centre data base (G.W.W
MEY) .To get biomass for Nypa, the number of leaves (leave density) was multiplied by average of the different leave
masses.

Biomass were then converted to carbon using the carbon concentration of 0.5 above-ground carbpn (Kauffman and
Donato, 2012).The sequestration rate of carbondioxied was gotten by multiplying the C by 3.67 for all the biomass
(Pearson et al., 2007).

With Lianas, the equation in Schnitzer et al., (2006) inputing diameter as a variable was used to get the biomass of
lianas.The biomass was converted to carbon mass by multiplying with a default value for carbon concentration of 0.46
(Jaramillo et al., 2003b).

For Standing Dead Trees, corresponding to the decay status of each wood category, statuses 1 and 2 used the same
allometric equation as in live trees and the densities used were those of the identified species.Where more than one
species was identified, the density was their average. Thus, status 1 (those with almost all branches) were estimated using
the live tree equation minus a constant of 2.5% from the tree biomass, while status 2(those that had lost its leaves and a
portion of its main branches) was estimated by subtracting a constant of 20 % biomass accounting for both leaves and
some branches. Status 3, were those that lost a significant part of their branches, and were difficult to subtract. In this case,
the tree volume was estimated using an equation for a truncated cone where the top diameter was estimated with a taper
equation, using the tree basal diameter and height as recommended in Kaufman and Donato (2012). The volume was then
determined by assuming that the tree is a truncated cone. Once the volume was gotten, biomass of the dead trees in grams
was then determined by multiplying the volume with the wood density. Biomass density estimated was converted to
carbon mass by using the carbon conversion factor of 50% or 0.5.

With the Dead Downed Wood; small, fine and medium wood classes of the debris, the diameter of each wood particle was
derived from the measurement of about 50-100 randomly selected particles of each class on the field.bTo get the quadratic
mean diameter of the different classes, the diameter of each sampled piece of wood in the size class and the total number
of pieces sampled were inputed in to the equation for this variable as mentioned in Kaufman Donato and (2012). For large
down wood, their diameters were measured at the points of intersection of the transect using a diameter tape or caliper
(go-no-go) and these diameters were used to get the volumes of the different downed woods.

Volumes calculation for fine, small and medium size woods, was through the equation developed for volume in Kaufman
and Donato (2012) using the number of count of intersecting woody debris pieces in a size class, the quadratic mean
diameter of each size class (c and the transect length (m) while for the large wood, the equation for volume using
diameters of each intersecting pieces of the large wood (cm), and Length of transect was used as recommended in
Kaufman and Donato (2012)

The biomasses of the downed wood debris (fine, small and medium) were then gotten by multiplying their volumes by the
calculated mean specific gravity of their respective classes while those of large downed dead wood where equally
calculated by multiplying its volume by the specific gravity of the plot’s live wood species specific gravity or average of the
different species. Thus, wood biomass was gotten by, multiplying the volume by the specific gravit. Finally the downed
wood biomass was converted to carbon mass by multiplying the biomass by the carbon concentration of the wood using
0.5 or 50% which is the acceptable default value of carbon concentration for dead wood in the tropics (Kauffman and
Donato, 2012).

Total carbon stock or the total ecosystem carbon pool

Total carbon stock in Mg per hectar for each height class were estimated by adding C of; standing live tree, standing dead
tree, stump, lianas, palm, LHG and downed dead wood. These different height class carbon stock values were summed
across the different height classes and divided by the total sampled area to get the average or baseline carbon stock in the
entire project zone.

The data was inputted in to the excell spread following the stratification then charts and tables where produced in a
simple and understandable way for all potential redears as commended in Djomo (2015).

Variables like the standard error of the mean in the carbon stock gotten was the standard deviation to the true mean of all
the different means from the population (Tesfaye & Astrat (2013) while the standard deviations was taken as the square
root of the variance, variations were the average of the squared deviations between each data thus, the mean was sum of
all the values of the variable divided by the total as recommended in Yeomans (1968), Ullman (1978).

RESULTS
Plot flora diversity
The species variations amongst the different height classes were heterogeneous(Table 1). Plots had homogenous species
in most cases and heterogenous in some. Within the sampled plots twelve plant (both true and associate mangrove)
species were identifies belonging to 11 families.

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Table1: True and associate mangrove species in sampled plots
SN Species in Bakassi Family Habit
1 *Rhizophora racemosa Rhizophoraceae Tree
2 *Laguncularia racemosa Combretaceae Tree
3 *Nypa fructican (Thumb) Wurmb Arecaceae Palm
4 *Acrostichum aureum (Linner) Pteridaceae Herb
5 #Ceasalpinia bonduc Ceasalpinoideae Shrub
6 #Cynometra ramiflora L. Leguminosea Shrub
7 #Dalbergia menoeides (Prain) Leguminosea Liana
8 #Dolichanrone spathacea Bignoniaceae
9 #Excoecaria agallocha Euphorbiaceae Shrub
10 #Hibiscus tiliaceus Malvaceae Tree
11 #Pandanus odoratissimus (Boa Ikasbikeyo) Pandanaceae Shrub
12 #Tristellateria australasiae (A. Rich) Malpighiaceae
* = true mangroves species; # = associate mangrove species

The dominant family was Rhizophoraceae followed by Combretaceae with the two dominant species being Rhizophora
racemose and Laguncularia racemosa for Rhizophoraceae and Combretaceae families respectively. The true mangrove
species identified were; Rhizophora racemosa, Laguncularia racemose, Acrostichum aureum,and Nypa fructican (Thumb)
Wurmb. The associate species recorded were; Ceasalpinia bonduc, Cynometra ramiflora L., Dalbergia menoeides,
Dolichanrone spathacea, Excoecaria agallocha, Hibiscus tiliaceus, Pandanus odoratissimus (Boa Ikasbikeyo), Tristellateria
australasiae (A. Rich). Nypa fructican (nipa palm) identified is an invasive species to this area. The 8 associate mangrove
species habits are normally; herbs, shrubs, lianas and epiphytes but those noticed were less than one meter thus
considered as herbs or grass except for the Lianas and epiphytes. Also, Acrostichum aureum or mangrove fern occurred as
underground vegetation in most degraded plots as well as plots with high elevation and those inland towards terrestrial
forest.

Forest Structure
The mean heights ranged from 4.40 m to 25.81 m from height classes 1 to 5, respectively. This variation showed a steady
increase from one class to the other (Table 2)

Table2: Structure for different height classes of over story

Height classes 1 2 3 4 5
Elements mean SE Mean SE Mean SE Mean SE Mean SE
Height(M) 4.36 1.18 9.92 0.26 12.89 0.33 17.77 0.16 25.81 5.39
Diameter(CM) 8.17 1.49 12.46 5.48 24.93 1.52 34.24 1.68 47.43 13.77
Basal Area(M2/ha) 2.31 0.63 8.06 1.66 20.13 3.71 30.20 3.06 59.64 28.87
Density((Stem/ha) 525.00 212.38 1225.00 1024.83 390.63 53.55 279.17 15.97 300.00 86.60

Figure2: Structure of mangrove stands in Bakassi

The mean tree diameter equally increased as their height classes changed. This ranged from 8.17 cm to 47.43 cm from
height classes 1 to 5, respectively (Fig.2). The increasing diameter with height class was steady from the lower to the
higher height class. The mean Basal areas ranged from 2.31 m2 ha-1 to 59.64 m2 ha-1, exhibiting an increasing steady trend
with height from height class 1 to height class 5(Fig.2). The mean stem density ranged from the minimum value of
279.17(height class 4) stem/ha to a maximum value of 1225 (height class 2) stem/ha. These values were however
irregular amongst the classes. Averagely, it showed a decreasing trend with height (Fig.2).

Biomass Carbon stocks

The Biomass carbon stock involved all the vegetative components of these ecosystems or study areas; standing live wood,
standing dead wood, stumps, down woody debris, LHG (Tab. 3). Standing live wood’s carbon density ranged from
8.83(Mg/ha) for height class 1, to 594.16 (Mg/ha) for height class 5, increasing with class height and in a steady manner
and had highest % of biomass C

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Table3: Biomass mangrove density per height class
Biomass Carbon density per height class in Mg/ha
Element 1 2 3 4 5
Over story mean SE Mean SE Mean SE Mean SE Mean SE
Standing live trees 8.83 2.48 37.32 23.08 166.44 25.17 238.51 25.02 594.16 416.57
Standing dead trees 0.25 0.15 0.19 0.22 5.87 2.86 0.30 0.15 0.00 0.00
Stumps 0.30 0.30 4.31 4.97 26.10 18.46 16.70 7.46 0.00 0.00
Lianas 0.13 0.13 0.27 0.31 0.00 0.00 0.00 0.00 0.00 0.00
Down wood debris 0.00 0.00 69.10 75.65 10.71 6.10 28.15 6.70 0.00 0.00
Litter and Understory 24.01 14.42 2.12 1.42 6.98 2.81 2.50 0.54 4.79 2.26
Total 33.53 17.48 113.32 105.64 216.10 55.40 286.16 39.87 598.95 418.83

Figure3: Varaiations of components of AGC in Bakassi

Standing dead wood mean carbon density ranged from 0.00 (Mg/ha) in height class 5 to 5.87(Mg/ha) in height class 3.The
variations where irregular and an irregular trend was witnessed as well (Fig.3). Stumps mean carbon density values per
height class ranged from 0.00(Mg/ha) in height class 5 to 26.10 (Mg/ha) in class 3. Its variation across the different height
classes were not steady and no steady trend could be witnessed. There was however an increasing trend with height.
Lianas mean carbon densities ranging from 0.00 (Mg/ha) in height classes 3, 4 and 5 to 0.27 (Mg/ha) in height class 2.This
variation across the sample plots were irregular and up to 3 height classes had no lianas. Where they were recorded, there
was an increase in C stock values with height class (Fig.3). LHG mean carbon densities per height class ranged from 2.12
(Mg/ha) in height class 2 to 24.01 (Mg/ha) in height class 1. About 67.96% of the plots had litter and underground
vegetation though the presence was not significant while 32.04% did not have any LHG. Even with this, their contribution
to the total ecosystem carbon was low since the litter is constantly washed away by the backwash of the tidal waters. The
trends of variations in this area were irregular from one height class to the other but generally decreasing (Fig.3). Down
wood debris had mean carbon density values that ranged from 0.00 (Mg/ha) for height class 1 and 5, to 69.10 (Mg/ha) for
height class 2. The wood debris was observed in about 42.86% of the sampled plots with very little samples within these
plots.

However, during this research , trees considered as understory with DBH < 5cm where found only in height class 1 and
made up about 2.5 % of the total trees sampled in that class. They had an average diameter of 8.17cm for all the trees
recorded in that height class 1 so, they could not be sampled as understory since their average diameters were greater
than 5 cm, since in this study understory was classified as trees with diameter < 5 cm.

Ecosystem Biomass Carbon Stocks

A sum of the different mean biomass carbon stock for the different pools ranged from 33.53(Mg/ha) in height class 1 to
598.95 (Mg/ha) in height class 5 and the results showed an increasing trend with increasing height class. Values were
highest for height class 5 and lowest for height class 1(Fig.4)

Figure4: Total Biomass Carbon density in Bakassi

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 International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Average carbon stock per height class
Average carbon stock per height class ranged from 739.298 (Mg/ha) in height class 1 to 1145.201(Mg/ha) in height class
5.The values increased in a regular manner from height class 1 to 5.

Per hectare average carbon for area

Table4: Average carbon per hectare
Height class Total C stock(Mg C/ha) Area(ha) Total carbon/class height(Mg/ha)
1 739.30 0.16 118.29
2 740.29 0.12 88.83
3 989.88 0.32 316.76
4 823.37 0.48 395.22
5 1145.20 0.12 137.42
Total 1.20 1056.52
Av. Mg/ha 880.44

Thus on average, for a hectare in Bakassi, the carbon stock is 880.437 in Mg/ha (Tab. 4) and a sequestration rate of
3231.204 (tCO2e/ha).

DISCUSION
Inventory Design Africa, 8 species are unique with Rhizophora species and
For a better quantification, accuracy and precision of the Avicennia germinan as the dominant species (Ajonina,
desired results a rectangular sampling design adapted 2008; Ajonina et al., 2016 and Orock et al., 2019). Amongst
from kaufman and Donato (2012), Jones (2014), these species was the Nypa palm which is identified as an
WWF(2019), was used. Thus; trampling was reduced, invasive species, out competing the indigenous mangroves
accessibility was enhanced and sampling was consistent in in this area.
all plots irrespective of the species composition. This
approach was different from the circular plots Forest Structure
recommended by Murdiyarso et al., (2009), Kauffman and The stand, structure and canopy height are influenced by;
Donato (2012) in the Indo-pacific mangroves. The climate, topography and human disturbance. Mature
application of canopy height classes as the basis for undisturbed stands may have high dense canopy with little
stratification proved effective, since canopy height classes stratification. Competition for light promots linear growth
reflected variations in stand density and height that and fewer stems grow per hectare than those on the edge
reflected the corresponding difference in biomass and of the forest (Tomlinson, 1986). This could be attributed
carbon estimates. Thus the design gave the study an to the nature of the terrain, rates of sedimentation in and
inclusion of the range in composition and structure of the varying soil type. The diameter range (8.17 to 47.43cm) in
entire forest area since; adults, mature, juvenile, seedlings, the area were less than those in the Douala Edea national
standing death, lianas, stumps, litter, herbs and grass as park where plant species could reach a diameter of up to
well as down dead trees were all studied. 131.7cm in well stocked stands (Ajonina, 2008). For the
stem density (Fig. 2), where all necessary conditions are
Flora diversity equal, the more dense a forest is, the taller the trees. This
Mangrove type and distribution pattern follow is because they do compete for sunlight in order to
topographical dynamics to tidal movements and tolerance produce their required food in the process of
to salinity. Climbers are often absent and few epiphytes photosynthesis. The variation in the number of stems per
are associated with mangroves due to their wide vessels plot (279.17 to 1225 stem/ha).The decrease of stem
and subjection to extreme water tension as they grow very density with height class is due to the intact nature of most
high up the canopy or on inland mangrove fringes plots showing less regeneration in the area.Where
Tomlinson (1986). Four true and eight associate regeneration is ongoing stem density might be high than in
mangrove species were record making a total of 12 than in established stands that tend to take up more space
species identified (Tab.1).This is not up to the six true due to wide nature of their canopy.
mangrove species in Cameroon as reported by UNEP
(2007). Worthy of note are the facts that the species Biomass carbon
identifies were just those found within the sampled plots, The forest type, age and size class of trees influence the
and the sampling was random opportunistic thus may not potential of forest to sequester carbon (Terakunpisut et
cover species for the entire mangrove block (Djomo, al., 2007) while basal area and height of the dominant
2015). Adekanmbi and Ogundipe (2009) reported more mangrove species in each vegetation types are the key
than 11 plant species in the mangroves of the indicator determining the nature of biomass ecosystem
neighbouring west African city of Lagos in Nigeria carbon stock (Mizanur et al., 2014)
amongst were, Laguncularia racemosa, Acrosticum
aureum, , Hibiscus tiliaceus, supporting the conclusion of Standing Live wood range (8.83 to 594.16 Mg/ha) may
their presence in the area. Like the rest of the mangroves have been influenced by human activities like; harvesting,
in Cameroon, the area is dominated by Rhizophora slash and burnt agriculture, human induced fire and war
sprecies. This is in line with other studies which have that lead to their reduction in the stands (Tab. 3). The
revealed that along the coast line of West and Central Carbon value was similar to (505 Mg/ha) those reported

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 International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
by Ajonina et al., (2014) in Cameroon, around intact plots and carbon stock than those with lower value of the
in Bamusso. This range differed slightly from the live tree factors. The values increased with class height (33.53 to
values ranging from 75.4 Mg/ ha to 268.5 Mg /ha recorded 599.0 Mg/ha) close to the values (32.47 Mg/ ha to 261.64
by Stringer et al., 2016 in Eastern Zambezi. Ocuurence of Mg/ha) reported by Hall and Uhling (1991), Ravindranath
dead standing trees might be due to the age of the forest, et al. (1997) and Haripriya (2000) and the (40 – 400
pollution, deliberate killing or common mangrove Mg/ha) range reported by Ziegler et al., (2012) in S-E Asia.
diseases, which might not occure and the forest will be The values are partly within the ranges reported by
void of dead stands. Standing dead wood were realised Ajonina et al., (2014) in the degraded plots (394Mg/ha)) in
with an average carbon stock density (5.87 Mg/ha) the Republic of Congo and the intact plots (825 Mg/ha) in
recorded (Fig. 3).This value falls within the range (5.37- Bamusso, Cameroon and larger than that reported (75.4-
10.97 Mg/ha) recorded by Stringer et al., (2016) in the 268.5 Mg/ha) by stringer et al., (2014) in the Zambezi
mangroves of the Zambezi. Stump stands could be mangroves.
justification of tree harvest.Though stumps stands are left
in the soil after a greater portion of the wood have been Total Ecosystem Carbon density (TEC)
harvested, they also make up a relevant proportion of The total ecosystem carbon is often the summation of the
carbon. Stumps will point if the forest is intact or degraded different biomass components of carbon stocks. During
and rate of harvesting in the case of an agroforestry or this study, the TEC for the Bakassi mangroves ranged from
silvicultural system. The range (0.00Mg/ha to 739.30Mg/ha to 1145.2Mg/ha (Fig. 38). The ranges of
26.10(Mg/ha) of their average carbon density for the these values were slightly lower than that reported for
different class heights varied across the different plots undisturbed plots (1520 Mg/ha) in Cameroon by Ajonina
following different species of interest to the community. et al., (2014) and higher than the range (119-737 Mg/ha)
Most plots with stumps were dominated by Rhizophora reported by Ziegler et al., (2012) while calculating the
racemosa giving their need for drying of fish, construction REDD+ uncertainties in S-E Asia. The values recorded in
of furniture and buildings. A few laguncularia racemosa this study are far above the ecosystem carbon density
species stump stands did exist but the community rarely among the five height classes (373.78 Mg/ ha to
used this species so they were least harvested. In the case 620.98Mg/ ha) recorded by Stringer et al., (2014) in the
of Lianas, though they do not regularly occur in Zambezi mangroves.Thus, Looking at the per hectare
mangroves, their value range (0.00Mg/ha to o.27Mg/ha) carbon and sequestration rate; Per hecta, the Bakassi
were low but recorded for the purpose of accuracy and mangroves carbon stock stands at 880.437(Mg/ha) and
efficiency of the study. They were recorded in plots with has a sequestration rate of 3231.204 (tCO2e /ha). This
higher elevation and closer to the terrestrial habitat or values are below the 1520Mg/ha reported for the
associated forests. LHG are always few in the mangrove, undisturbed plots in Cameroon but larger than the values
since tidal waters often carry them away. They occur in (454.92Mg C/ha and 340.87MgC/ha) reported by Benson
areas of high natural or artificial degradation. In this area, et al., (2017) for the assessment of open and closed canopy
litter occurred in areas of; higher elevation where they mangrove respectively in S-W Madagascar. Also, the
could not be washed off or decomposed easily from tidal values are higher than the mean value (799MgC/ha)
or logging took place. Herbs and grass were witnessed in reported by kaufman and Bhomia (2017) for the
areas of open canopy, degraded plots or those closer to mangroves of West-Central Africa and closer to the global
terrestrial environment. In this area, the mangrove fern values (885MgC/ha) for mangroves.
was amongst the highest recorded. The assessment of LHG
had values (2.12 to 24.01 Mg/ha) varied from one height Conclusion
class to the other. Most of the higher values occurred in Increase with diameter, stem density and height, the mean
the lower height class where litter fall or productivity was biomass carbon density for the Bakassi peninsular ranged
higher. This values were higher than those (0.17-0.66 from 33.53 Mg C/ha to 598.95 Mg C/ha for the five
Mg/ha) recorded by Stringer et al., (2014) in the different height classes witnessed and the sequestration
Mangroves of the Zambezi River Delta. Down wooden rate of mangroves in this Peninsular ranged from
debris occurs in the mangroves in instances of 123.06tCO2e/ha to 2198.15tCO2e/ha for this same height
degradation like; harvesting agriculture, or wind disaster, classes.
pollution, disease infestation or old age. Also, in plots that
are constantly inundated, the down wood debris easily Recommendation
decomposes due to the presence of water and the elevated Sustainable management of the mangrove should be
temperature in mangrove areas. They are equally reduced promoted through afforestation and setting up of Forest
at times when humans pick them up to use as fuel wood or Village Management Units to ensure good management
for other related uses. In Bakassi, DDW C values practices, laws and community participate the process.
(0.00Mg/ha to 69.10 Mg/ha).This was higher than the
values (6.72 -12.51 Mg/ha) reported by Stringer et al., Perspectives
(2014) in the Zambezi River Delta. From this baseline assessment, a block to block
assessment should bedone and the results used to set up a
Total Biomass Carbon density REDD+ pilot project for the area. Also, site specific
The variation in biomass carbon is often influenced by; allometric equations should be developed to realize
canopy cover and basal area, the biotic, edaphic, accurate results.
topographic and disturbance factors and age (Forrester et
al., 2013; Gebeyehu and Soromessa, 2019; Bekele. et al. Acknowledgement
2019). Plots with factors like larger tree; heights, We are grateful for the entire WWF-Cameroon and the
diameter, stem densities and diameter had more biomass MINEPDED disaster consultants for involving us in the

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 International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
assessments in the Rio Del Rey. These facilitated data [13] Alongi, D.M. Carbon Cycling and Storage in Mangrove
collection for this assessment. WWF’s mentorship Forests. Ann. Rev. Mar. Sci. 2014, 6, 195–219.
throughout the field activities are highly appreciated. I [CrossRef] [PubMed]
equally thank carl Tridden of the Silva carbon USA for
[14] Assefa, G., Mengistu, T., Getu, Z., & Zewdie, S. (2013).
recommending mr. Buh Gaston to mentor me throughout
Training manual on: Forest carbon pools and carbon
this exercise. The different consultants and laboratories
stock assessment in the context of SFM and REDD+.
and the entire carbon assessment crew are not left out
Wondo Genet, Ethiopia: Wondo Genet College of
Forestry and Natural
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