Carbon storage and biodiversity conservation in a changing landscape: the case of Barekese and Owabi watersheds in Ghana

doi:10.21203/rs.3.rs-1612578/v1

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Carbon storage and biodiversity conservation in a changing landscape: the case of Barekese and Owabi watersheds in Ghana

https://doi.org/10.21203/rs.3.rs-1612578/v1

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This study was carried out to explore the effect of land-use intensification by examining variation in species composition, carbon stocks and soil fertility for the Barekese and Owabi watersheds. A nested plot design of five plots of size 50 m x 50 m, 10 m x10 m, 5 m x 5 m and 1 m x 1 m were used to collect data per land-use type. Species richness (F=24.991, df = 4, p = 0.000 and F = 34.184,df = 4 p = 0.000), diversity (df = 4, F = 18.529, P = 0.000 and df = 4, F = 13.964, P =0.000) and basal area (df = 4, F = 7.018, P = 0.001 and df = 4, F = 9.051, P = 0.000) were significantly higher in undisturbed forest compared to other land-use systems for Barekese and Owabi watershed respectively. Carbon stocks followed a similar pattern indicating significant effect of landuse for Barekese (df = 4, F = 24.991, P = 0.000) and Owabi (F = 34.184, df = 4, P = 0.000) with Closed Forest being better. However, open forest (moderately disturbed forest) was not significantly different from Closed Forest (undisturbed forest) in terms of carbon stock, species richness and diversity. The outcome study suggests that natural forest cover favour higher carbon storage and biodiversity conservation. It also illustrates the fact that moderately disturbed primary forests/agroforest systems have high conservation value and may play a significant role in conservation strategies and global carbon cycling.

Land-use

biodiversity

carbon stock

soil fertility

Barekese

Owabi

watershed

Globally, forests are a critical component of the terrestrial ecosystems. This natural resource plays a significant role in regulating the global carbon (C) cycle by serving as C sink, thereby potentially mitigating future impacts of climate change (Liang and Liu 2017; MA, 2010; United Nations 2016). Terrestrial ecosystems of tropical forests are among the most valuable resources on planet earth (Goldstein et al. 2010; Lederer 2011; TEEB, 2010; UNFCCC, 2015). According to the MA report as cited by Carpenter et al. (2009), these habitats conserve biodiversity, reduce soil erosion, protect watersheds and sustain indigenous ways of life. These are natural capital that enable economic growth, and serves as a carbon sink to mitigate greenhouse gas emissions that drive climate change. Tropical forests are among the most complex and species rich ecosystems of the world (Pellikka et al. 2018) and it stores about 40–50% of carbon in terrestrial vegetation (Barker, 2007; Hartmann et. al., 2013; IPCC 2007). Tree diversity is fundamental to tropical forest biodiversity because it provides the resources as well as habitats for almost all other forest species (Foley et al. 2011; Hall and Swaine 1976; Steffen et al. 2015; TEEB, 2010).

Conversion of forested areas into other land use systems are of special concern with respect climate change and its related issues. In Africa, significant amount of carbon is sequestered in woody vegetation (Paquette et al. 2009; Pellikka et al. 2018). Several authors stress that changes in land use systems particularly tropical deforestation are the leading cause of species extinction and contributes about 25% of anthropogenic carbon emissions in the tropics (Barker 2007; Hartmann et. al. 2013; IPCC 2007). In West Africa, significant causes of land use change are linked to agricultural expansion, wood extraction and infrastructural extension (Asase et. al. 2012; Norris 2012; Nsanyi 2017). Tropical forest regions, particularly the Guinean forests of West Africa are currently undergoing rapid changes with direct consequences on biodiversity, carbon stocks and soil productivity (Agboadah 2011; Asase et. al. 2012; Gebresamuel et. al. 2010). Likewise, the Upper Guinean forest of Ghana is being depleted at an alarming rate of 2% annually (FAO 2010; Indufor Oy 2015; FAO, 2015) and this is affecting important ecological systems of the Owabi and Barekese watersheds.

These two watersheds support a diversity of flora and fauna and provides critical ecosystem services such as hydrological adjustment, climate regulation, biodiversity maintenance, water purification and soil erosion control that needs to be sustained (Akoto et al. 2017; FC 2014). However, the forest component of the value chain as in other parts of Ghana, is experiencing degradation as a result of land use change in a way that threatens water quality as well as continued water supply to the inhabitants of Kumasi (the second most densely populated city in Ghana). This has resulted in the degradation of forest ecosystems which is being largely deforested and replaced with extensive agricultural and grasslands. However, integrated studies on research linking the effect and implication of forest loss on biodiversity, carbon sequestration and soil fertility have not received much research attention. Most of these studies focused on spatio-temporal changes in land use (Boakye et al. 2008; Koranteng et. al. 2014) and water quality assessment (Akoto et al. 2017; Akoto and Abankwa 2014; Akoto et. al. 2010). Studies that couple the effect of land use change on biodiversity, carbon sequestration and soil fertility are crucial to understanding the impact of disturbance on watershed ecosystem services. Such information will be important for land use planning and sustainable management of the watershed.

The objective of this research therefore were: to assess the effect of land use change on biodiversity, carbon stocks and soil fertility within the study area; to determine carbon changes at the landscape level for four epochs (1986, 2007, 1997 and 2017); and finally, to investigate the relationship between species richness, diversity and carbon stocks within the Barekese and Owabi watersheds.

2.1 Study site

This study was conducted in the Owabi - Barekese catchment area located between longitude 6°44'50"N and latitude 1^o 42¹ 00 W. It falls within the Moist Semi-deciduous North West forest type. The area experiences an annual temperature of between 24.6°C − 27.8°C with an annual rainfall of about 1402 mm. It is an important watershed in Ghana and serves as a source of water for the Kumasi Metropolis (which is the second largest city in Ghana) and its environs. Owabi (a Ramsar Site) and Barekese catchment were established in 1920 and 1972 respectively through an Executive Instrument, to provide sustainable supply of water to the inhabitants of Kumasi (Akoto et al. 2017; Forestry Commission 2014). A forest cover within the catchment area was established to protect recharge areas, prevent siltation and rapid evaporation of water in the reservoir as well as enhance rainfall.

This catchment area supports an interesting diversity of flora and fauna and provides critical ecosystem services including hydrological adjustment, climate regulation, biodiversity maintenance, water purification and erosion control that needs to be sustained (Forestry Commission 2014). It is characterized by a mixture of land use system such as Forests, Grasslands, Croplands and Settlements. Management objectives of the catchment area include; protection, water supply and ecotourism. The management of the Owabi Wildlife Sanctuary is under Ghana Water Company Limited (GWCL) and the Wildlife Division. However, the Barekese catchment is under the sole management of the GWCL. Majority of the inhabitants (over 100,000 people) depend on water supply from these two catchment areas (Forestry Commission 2014).

2.2 Sampling design

In this study, a stratified random sampling design was used for data collection on biodiversity (species, numbers, etc.) carbon (diameter, height, litter, dead wood, etc.) and soil (chemical and physical properties) attributes in the identified land use and land cover types. In order to increase the accuracy and precision of measuring and estimating carbon, the project site was divided into sub-populations or “strata” that form relatively homogenous units. Stratification, a critical step allowed the association of a given area (deforestation and degradation) with an appropriate vegetation’s carbon stock for the calculation of its emissions (Zerihun, 2013; Pasco 2013). A nested plot design was used to capture data on the different tree diameter classes. The nested plots contain smaller sub-units of various sizes, practically designed for recording discrete stem diameter at breast heights in various size classes. This method is well suited to natural forests and landscapes with wide range of stem diameter sizes and stocking densities (Pearson et. al., 2014; T. Pearson et. al., 2005). Figure 2 illustrates sample plot dimensions of 2500 m² (50 m x 50 m), 100m² (10m x 10m), 25 m² (5 m x 5 m) and 1 m² (1 m x 1 m), respectively (Fig. 2). Six plots were sampled per land cover/ land use type.

2.3 Field Sampling

2.3.1 Tally Trees

This refers to living tree with living branches and leaves, and it must be able to survive until the next growing season. In each 50 m x 50 m (2500m²) plot, all trees > 10 cm at Diameter and at Breast Height (1.3 m above ground level) were identified by species type including lianas which were measured and recorded using diameter tapes. If a trunk is malformed at the buttress or at the breast height, measurements were carried out at 30 cm above the end of the deformation. In such situations, the point of measurement (PoM) were indicated in the field datasheet. All forked trees were measured as one tree, if the tree is forked above the breast height (i.e. 1.3 m). If a tree were forked below breast height, each fork was recorded and measured as an individual tree. Tree heights were measured using Ace hypsometer or Vertex where appropriate. All juvenile trees with diameter between 2 cm − 10 cm were tallied and measured for both diameter and height for subplots (5 m x 5 m) using diameter tapes and calipers. Also, all plant species were identified in the field through visual observation as well as slashing the bark and then looking for diagnostic features like color, smell, and exudates among others. In instances where a species cannot be identified on the field, specimens of the bark, leaves, flowers and fruits were collected and identified at the Herbarium of the Forestry Commission at Kumasi.

2.3.2 Deadwood Measurement

Deadwood in this case include: dead branches, stems and boles of trees and brush that have fallen and lies on or above the ground. Deadwood was differentiated into standing and downed deadwood. Whenever a standing dead tree was found, the state of decomposition, the standing dead wood was classified and recorded into two classes namely: Class 1 - Trees with branches and twigs and resembles a live tree (except for leaves) and Class 2 - Tree with signs of decomposition (in addition to loss of foliage), including the loss twigs and branches. For decomposition Class 1, measurements were taken at the diameter, at breast height (dt) as with living trees. These measurements were indicated on the field recording sheet (SDW1) and for decomposition Class 2, measurements of the diameter at breast height (dt) as with living trees and measurement of top height with a height vertex, as well as the top diameter were recorded with a relascope. All down deadwoods were measured in the 10m x 10m sample plots. For each down deadwood the DBH, length and decomposition were collected. The machete test method was used to determine the level decomposition classes of the deadwood namely; sound, intermediate and/or rotten (Adu-Bredu and Green 2014).

2.3.3 Non-tree vegetation

All herbs and seedlings less than 2.0 cm in diameter at breast height within the 1m x 1m quadrates were uprooted for shoot and root mass measurements in the 1m x 1m quadrate. The shoots were severed from roots and the fresh weight determined in the field with an electronic balance. If the weight of the non-tree vegetation collected were greater than 500g, a subsample was taken and the remaining discarded after weighing. Labelling of samples were done and the labelled samples were taken to the laboratory for oven dry mass and carbon contents determination.

2.3.4 Litter

The litter were collected after the non-tree vegetation had been removed from the quadrats. All litter (leaves, fruits, seeds, wood less than 2.0 cm diameter, etc.) were collected in the quadrats. The samples were weighed with a weighing balance and placed in a plastic bag. If the weight of the litter collected were greater than 500g, then a subsample was taken and the remaining litter discarded after weighing. Samples were then labelled and taken to the laboratory for oven dry mass and carbon content determination.

2.3.5 Soil sampling

Soil samples were taken from the four corners of the quadrate (1m x 1m) and at the centre of the main plot (50m x 50m). Before sampling, all organic layers (litter layer) were removed. Composite soil samples were taken from 0–30 cm depth using an auger. The soil samples were mixed and sub-samples (composite) of 0.5kg were sampled into sampling bags for laboratory analysis. In the lab, the sub-samples were air dried and placed in a dust and wind free, well-ventilated room. The samples were passed through a 2 mm sieve. Sample particles were then grinding with a mortar in order to pass through a 60-mesh screen. For soil bulk density analysis, soil samples (0–30cm) at different locations were taken with a soil core (5 cm diameter). Each sample from the core sampler was transferred separately in a plastic bag, labelled and taken to the laboratory. Samples were oven dried at 105⁰C at a constant mass and the dry weight were measured for the determination of soil organic carbon (Adu-Bredu and Green 2014; Pasco 2013).

2.4 Data analysis

Species richness, diversity, evenness, basal area estimates, tree densities, diameter size classes and carbon stocks were compared using the pooled data at the plot level as replicates for each site. For each variable, normal distribution was tested using the Shapiro-Wilks W-test for homogeneity of variances. Variables that conformed to normal distribution were analyzed using two-way analysis of variance (ANOVA) while those that did not meet the assumptions of ANOVA when transformed were analyzed using Kruskal–Wallis parametric ANOVA on the mean ranks using SPSS statistical software. Differences in means was done at 5% probability level using Turkey’s test. Correlation analyses were carried out to determine the relationship between species richness, diversity, stand structural characteristics (Basal area and tree density) and for carbon stock.

Carbon estimation

The aboveground biomass, Y_a (kg), of the individual trees was generated from diameter at breast height of 1.3 m using the allometric equation (Chave et al. 2014):

$${Y}_{a}=0.0673 x {\left(\rho x {D}^{2}x H\right)}^{0.976}\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots ..Equation 1$$

where: Y_a = Aboveground biomass, ρ = Specific wood density, D = Tree Diameter and

H = Tree height.

Belowground biomass, Y_b (kg), was calculated using data of the aboveground biomass for forest (Adu-Bredu et al. 2010)

$${Y}_{b}=1.582{\left({Y}_{a}\right)}^{0.636}\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots .\dots ..Equation 2$$

Where:

Y_b = Belowground biomass,

Y_a = Aboveground biomass.

Stand biomass per hectare, Y (t/ha), was determined by adding up the individual tree biomass per plot as:

$$W={\sum }_{i=1}^{n}\left({Y}_{a}+ {Y}_{b}\right)\frac{10000}{A}\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots Equation 3$$

A = Size of the sample plot and n is number of trees in the plot. The dry mass of the samples was calculated as:

$${U}_{nt}=\frac{{C}_{sd}}{{C}_{sf}} x {C}_{tf} x \frac{10000}{A}\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots .\dots \dots \dots Equation 4$$

U_nt = either litter or non-tree biomass,

C_sd = sample dry mass,

C_sf = sample fresh mass

C_tf = total fresh mass of either the litter or non-tree in the quadrat,

A = the size of the quadrat.

The carbon content values of 0.4748 (Pasco, 2012; Adu-Bredu et al. 2010) was used to convert the tree, non-tree and litter biomass to the corresponding carbon.

3.1 Species richness and diversity

A total of 82 vascular plant species with abundance of 2,155 and 96 vascular plant species with abundance of 2,211 were recorded for Barekese and Owabi respectively (Tables 1 and 2). Mean species richness was significantly higher in Owabi compared to Barekese watershed (F = 4.557, df = 1, P = 0.039). Closed Forestrecorded the highest number of species with the least species in Food crop and this was significant among land-use types at Barekese (F = 24.991, df = 4, P = 0.000) and Owabi (F = 34.184, df = 4, P = 0.000). Generally, Shannon Wiener diversity index also decreased with increasing disturbance and this was significant among the two sites (F = 5.531, df = 1, P = 0.024). Closed Forestrecorded the highest diversity whilst Food crop recorded the lowest species and this was significant for Barekese watershed (F = 18.529, df = 4, P = 0.000) and Owabi (F = 13.964, df = 4, P = 0.000). Evenness of species distribution was highest in Closed Forestand least in Food crop as has been the pattern for the other parameters.

Table 1

Specie richness, diversity and structural characteristics for different land uses at Barekese watershed. CF (Close Forest), OP (Open Forest), TC (Tree Crop), FC (Food Crop), GR (Grassland) P < 0.05 are significant.
Parameter	Land-use
Parameter	CF	OF	TC	FC	GR	P-value
Basal area/ha	36.81 ± 5.9^a	20.19 ± 2.53^ac	20.45 ± 3.51^ac	16 ± 2.7^bc	4.76 ± 2.02^bc	0.001
Tree density/ha	2764.8 ± 556.8	3225.60 ± 853.7	1332.00 ± 367.6	5020	2120.80 ± 866.5	0.204
Species richness	58 ± 20.4^a	56 ± 25^a	20 ± 7.6^b	4 ± 1.6^b	13 ± 4.4^b	0.000
Species diversity	3.50^a	3.10^a	0.94^bd	0.09^bc	2.05^bd	0.000
Pelou index	0.86	0.76	0.31	0.06	0.79
Mean diameter (cm)	26.39 ± 2.87^a	21.76 ± 1.83^ac	15.74 ± 3.74^bcd	7.2 ± 1.90^b	18.95 ± 2.82^ad	0.00
Mean Height (m)	9.94 ± 2.34^a	5.46 ± 1.32^a	4.91 ± 1.45^b	4.57^b	9.86 ± 2.46^b	0.000

Table 2

Specie richness, diversity and structural characteristics for different land uses at Owabi watershed. CF (Close Forest), OP (Open Forest), TC (Tree Crop), FC (Food Crop), GR (Grassland) P < 0.05 are significant

Variable	CF		OF	TC		FC		GR		P-value
Basal area/ha	47.62 ± 4^a	34.43 ± 1.55^a			24.18 ± 3.2^bc		17 ± 2.5^bc		4.49 ± 1.12^bd		0.000
Tree density/ha	2303.20 ± 410.8^ad	3272.8 ± 585.9^ad			1675.2 ± 508.4^ac		6840 ± 908.4^b		876.8 ± 636.5^ac		0.012
Species richness	70 ± 22.6^a	62 ± 34^a			17 ± 8^b		5 ± 2.2^b		19 ± 6.8^b		0.000
Species diversity	3.43 ± 1.8^a	3.33 ± 2.8^a			1.73 ± 0.6^bd		0.09 ± 0.0^bc		2.23 ± 1^ad		0.000
Pelou index	0.81	0.79			0.61		0.06		0.76
Mean diameter (cm)	27.56 ± 4.13^a	18.95 ± 3.63^ac			13.96 ± 2.14^ac		5.98 ± 1.56^b		20.43 ± 3.1^bc		0.000
Mean height (m)	14.19 ± 3.74^a	4.54 ± 1.01^bdf			5.61 ± 2.46^bc		2.98^bc		10.86 ± 2.78^bde		0.000

3.2 Tree density and basal area

Mean tree densities per hectare did not differ significantly between the two sites (0.587, df = 1, P = 0.448). Tree densities per hectare was highest in Food Crop landuse compared to the other land-use types for both sites (Table 5.1). Differences in tree densities was only significant among land-use types in the case of Owabi (F = 4.232, df = 4, P = 0.012). Stand mean basal area estimate varied significantly between the two study sites (F = 7.763, df = 1, P = 0.008). Mean basal area estimates per hectare also varied among the different land-use types with Closed Forest recording the highest value and the least in Grassland. Differences in mean tree basal area was significant for both Barekese (F = 7.018, df = 4, P = 0.001) and Owabi (F = 9.051, df = 4, P = 0.000). There was no significant difference in mean tree diameter and mean height between the two sites. However, mean tree diameter varied significantly among the land uses within the Barekese watershed (F = 11.856, df = 4, P = 0.000) and Owabi watershed (F = 11.299, df = 4, P = 0.000). Mean tree diameter was largest in Closed forest in both sites. Also, mean tree height varied significantly among landuse located in Barekese (F = 8.619, df = 4, P = 0.001) and Owabi watershed (F = 72.080, df = 4, P = 0.000). Mean tree height was highest in Closed Forest both sites and least in the Food Crop landuse.

3.3 Diameter size class distribution

Stand structure in terms of diameter class distribution varied among the different land-use systems. About 80% of the tree densities per hectare were classified as saplings (2-9.9cm) and poles (10-29.9cm). Large diameter class trees were not encountered in most of the land-uses except for Owabi watershed where trees were recorded in the Open Forest and Closed Forest land-use types (Table 3). In the case of the Barekese watershed, large diameter trees 110 cm dbh and above were not observed in the Closed Forest and Open Forest. However, trees with130 cm bdh were recorded Tree crop landuse system. The large diameter class trees recorded in Tree Crop landuse were shade trees retained during land clearing and crop cultivation. For Food crop systems, diameter class of trees were between 2 cm − 30 cm.

Table 3

Tree density for different tree diameter classes per hectare across land-uses within the Barekese and Owabi Watersheds. Closed forest (CF), Open forest (OF), Tree Cropland (TC), Food Crop (FC) and Grassland (GR)
Site	Land-use	2-9.9cm	10-29.9cm	30-49.9cm	50-69.9cm	70-89.9cm	90-109cm	110-129.9	> 130cm	Total
Barekese	CF	2320	280.8	128	30.4	4	1.6			2764.8
	OF	2960	214.4	32	16	3.2				3225.6
	TC	640.8	671.2	12.8	2.4	3.2			1.6	1332
	FC	4080	940							5020
	GR	2080	25.6	8.8	5.6	0.8				2120.8
Owabi	CF	1840	316	81.6	44	11.2	5.6	1.6	2.4	2302.4
	OF	2720	468	62.4	16	2.4	0.8	1.6	1.6	3272.8
	TC	1360.8	253.6	22.4	29.6	5.6	2.4	0.8		1675.2
	FC	5340	1500							6840
	GR	2800	65.6	10.4	0.8					2876.8

3.4 Carbon stocks

A significant interaction was found between site and land-use (df = 4, 5.350, P = 0.002). Mean carbon stock per hectare was significantly higher for Owabi as compared to Barekese (F = 12.940, df = 1, P = 0.001). The mean carbon stocks per hectare varied among different land uses for both sites. Among the landuse types, total mean carbon stock for all the six carbon pools was highest in Closed Forest; 246.22 ± 26.97 tha^− 1 for Owabi watershed and 140.69 ± 13.67 tha^− 1 for Barekese watershed. The least mean carbon stock was recorded in Grassland landuse for both sites (44.34 ± 2.77 tha^− 1 for Owabi watershed and 27.30 ± 3.33 tha^− 1 for Barekese watershed). The differences aggregate mean carbon stock per hectare all the six carbon pools was significant for both Barekese watershed (F = 10.913, df = 5, P = 0.001) and Owabi watershed (F = 23.861, df = 5, P = 0.000). Significant differences were also observed for mean carbon estimates generated for the different carbon pools per land-use (Table 4). Among the different carbon pools, (aboveground carbon, belowground carbon, deadwood and soil carbon) were consistently highest in Close Forest whiles non-tree vegetation (herbs and seedlings) and litter carbon were highest in Grassland and Tree Crop rerespectively. Aboveground carbon contributed most among the carbon pools per land-use and this was highest in the Closed Forest. The contribution of aboveground compared to the different land-use types was 61% for the Owabi watershed and 52% in the case of Barekese watersheds. Deadwood contributed least among the carbon pools. With the exception of deadwood and litter (Owabi), mean carbon storage in the carbon pools also varied significantly among land-use types within the two study sites. Analysis of carbon dynamics among the landuses, show that a change from Close to Open forest resulted in 36–39% carbon loss whiles conversion of Closed Forest to Tree crop systems led to reduction of between 55–69%. Also, conversion Closed Forest to food crop system resulted in a decrease of carbon stocks by 65–79% but Closed Forest to Grassland contributed to carbon loss of 73–84% for Barekese and Owabi watersheds respectively.

Table 4

Estimates of the mean carbon stock () per hectare for six carbon pools in different land-use types within the Barekese and Owabi Watersheds. CF (Close Forest), OF (Open Forest), TC (Tree Crop), FC (Food Crop), and GL (Grassland). Statistically significant (P < 0.05).
		tCha^− 1
Site	Carbon pool	CF	OF	TC	FC	GL	P-value
Barekese	Aboveground	87.32 ± 22.19^a	42.10 ± 4.96^b	23.04 ± 6.04^b	8.18 ± 1.28^b	5.08 ± 2.39^b	0.000
	Below ground	22.70 ± 5.77^a	10.95 ± 1.29^bc	5.99 ± 1.57^bc	1.09 ± 0.33^bc	1.32 ± 0.62^bc	0.000
	Deadwood	0.53 ± 0.32^a	0.14 ± 0.05^a	0.01 ± 0.01^a	0.05 ± 001^a	0.04 ± 003^a	0.106
	Litter	1.68 ± 0.29^a	0.91 ± 0.19^ab	1.83 ± 0.43^a	0.57 ± 0.07^b	0.54 ± 0.24^b	0.003
	Non-tree	0.79 ± 0.14^a	0.40 ± 0.10^a	0.46 ± 0.21^a	0.44 ± 0.07^a	1.94 ± 0.51^b	0.002
	Soil	55.34 ± 1.46^a	47.87 ± 1.41^a	42.69 ± 0.16^a	29.18 ± 2.72^b	33.43 ± 0.22^b	0.040
Total %		168.36 ± 13.67 100	102.35 ± 6.93 60.7	74.02 ± 4.33 44.1	39.50 ± 0.74 20.8	42.35 ± 3.33 16.8	0.001
Owabi	Aboveground	171.35 ± 40.61^a	99.17 ± 15.8^ac	26.40 ± 3.22^bc	5.60 ± 1.90^b	5.25 ± 1.46^b	0.000
	Below ground	44.55 ± 10.56^a	25.78 ± 4.11^ac	6.87 ± 0.84^bc	1.46 ± 0.49^b	1.37 ± 0.38^b	0.000
	Deadwood	4.00 ± 1.67	2.71 ± 1.49	2.12 ± 2.09		0.01 ± 0.001	0.735
	Litter	1.60 ± 0.41	1.55 ± 0.30	2.98 ± 1.25	0.75 ± 0.13	0.76 ± 0.05	0.054
	Non-tree	0.75 ± 0.10^a	0.57 ± 0.07^ac	0.58 ± 0.21^a	0.76 ± 0.09^a	1.24 ± 0.24^ab	0.045
	Soil	53.82 ± 4.81^a	48.94 ± 3.14^a	46.48 ± 11.21^a	28.79 ± 1.29^b	35.06 ± 15.48^b	0.043
Total		276.07 ± 26.97	177.72 ± 15.43	85.44 ± 4.67	30.97 ± 1.16	44.06 ± 2.77	0.000
%		100	64	30.8	10.9	15.9

4.1 Land-use and biodiversity

In this study we observed that land use changes in the highlands of La Antigua watershed have resulted in loss of species richness and biodiversity (Martınez et al 2009). Landscape condition has usually been used as a proxy for habitat and species diversity, with disturbed land cover signifying a deterioration in habitat condition and diversity (Boakye et al. 2015; DeWalt et. al. 2003; Garcia-Montiel et al. 2007; Liang and Liu 2017). Though habitat loss is due to vegetation clearing, the habitat modification and fragmentation of residual habitats also exert serious secondary deleterious effect (Feurer 2013; Oy 2014; Pambudi and Rahayu 2017). The results of this study indicate that land-use changes in the two watersheds have resulted in the decline of species richness and diversity. Biodiversity loss increased with the land-use transformation, from no effect in Open forest to more than 95 per cent loss in Food Crop landuse. This finding from the study is similar to other studies that also found a reduction in species richness also found reduction in species richness of woody species from 40 in Forest to 19 in Farmland in the northern region of Ghana (Boakye et al, 2015) and reduction from 137 and 121 species in Forest to 14 and 2 species in Cocoa agroforest and rubber plantation in the Tano Offin Forest Reserve and Cape Three Points Forest Reseve respectively in Ghana (Ayesu, 2018). Species diversity in most cases are higher in less disturbed forest than in more intensively managed lands like Cropland (Tree crop and Food crop) where land preparation including burning leads to depletion of trees and other wood plants (Boakye et al. 2015; Frank, 2012; Mensah et al. 2016; Rudmann-Maurer et al. 2008). In contrast, several studies also found that species diversity in agricultural systems in the tropics was improved by the retention of trees on croplands by farmers (Boakye et al. 2015, 2016; Käyhkö et al. 2011; Kotoky et. al., 2012; Tendaupenyu et. al., 2017).

The decrease in species richness and diversity with increasing forest degradation and deforestation may reduce resilience of such socio-ecological systems. This in the long term would result in climate change induced impacts such as spread of invasive species (Ruf et. al., 2015; Sandifer et. al., 2015; Tilman 1999). Also, the loss of plant diversity may result in poor habitat conditions for species that facilitate provision of important ecosystem services, including crop pollination, nutrient cycling and seed dispersal (Addai and Baidoo 2013). Species diversity has been reported for making ecosystems more resistant and resilient to disturbances. Diverse plant communities are more likely to adapt to environmental change. This would facilitate the provision of essential goods and services within an ecosystem in order to continuously promote and maintain a diverse composition of species (Asante-Yeboah 2010). Landscape connectivity have been found to relate strongly with a biodiversity (Asase et. al. 2012; Bowen et al. 2007; Attua et. al. 2018; Castro et al. 2016; Martínez et al. 2009; Taylor et. al. 2014). Though the findings relate to other recent research works which regulated the effects of ecological variables (Boakye et al. 2015, 2016; Polasky et al. 2011), it is consistent with the generally accepted pattern in very diverse natural forests (Asase et. al. 2012; Pappoe et al. 2010).

4.2 Stand structure

Stand structural characteristics helps estimate forest biomass and could be used to generate geographical information on factors that influence species distributions (Couteron et al., 2005). In this work, stand structure refers to tree basal area, tree density and tree diameter-size class distribution. The results indicate that large diameter classes were generally absent in intensively managed landuses except in the case of agroforest tree crop systems that was characterized by retention of trees. In the Barekese watershed, large tree > 110 cm bdh were even absent in Closed Forestand open forest but were present in tree crop systems. The situation was different for the Owabi watershed where Open forest and Closed Foresthad representation in all the diameter class both small and large diameter classes whiles diameter classes in tree crops systems were below 130 cm dbh. This variation in diameter representation could be due to the level of protection accorded the two sites (Forestry Commission, 2014). Most of the diameter class recorded in the Owabi sites were found within the Wildlife Sanctuary which is continuously patrolled by the staff of the Wildlife Division.

The Barekese watershed on the other hand is not adequately protected and therefore has over the years been encroached by illegal chainsaw operators who harvest the large sized trees (Forestry Commission, 2014; Atwima Nwabiagya District Assembly, 2017). These activities have over the years contributed to the depletion of large sized trees within Barekese watershed. Basal area estimates were relatively higher for both close and open forests as compared to other landuses. The presence of large trees in close and open forests contributed to the higher basal area estimates for the Closed Forestand open forest at both sites. The mean tree basal area of 47.62m²/ha and 36.81m²/ha for Closed Forestat Owabi and Barekese respectively were higher than that estimated for a similar forest type in the Kakum National Park of 33.76m²/ha (Pappoe et. al. 2010). The higher basal area estimates for close and open forest compared with the tree crop, food crop and grassland landuse suggests that the conversion of natural forest to other landuse use affect the stand structure in respect of the total covered by trees per unit area. High basal area estimates are typical of established forest stands and reflect growth performance of trees.

4.3 Land use change effects on carbon stocks

The overall mean density for all carbon pools recorded for Owabi watershed (276.1 tC ha − 1) and Barekese watershed (168 tC ha − 1) were higher than the global default value of 141 MgC ha − 1 as stated by Aalde et al. (2006) in his studies in tropical forests. Baccini et al. (2012) indicated that smaller carbon densities were reported for tropical Africa (82 MgC ha − 1) compared with other tropical areas with 116 Mg C ha − 1 for America and 119 Mg C ha − 1 for Asia. The conversion of natural forests to other land-use systems has resulted in a significant reduction in mean carbon stocks by 65–79% (food crop system) and 73–84% (Grassland) for Barekese and Owabi watersheds respectively. Also, conversion Closed Forestto open forest and Tree crop system resulted 36% − 39% and 55–69% carbon loss respectively. The outcome of this study also clearly indicates that land-use change has significant influence on mean carbon stocks per hectare. Carbon stock was found to decrease with increasing intensity in the use of land for cultivation and this findings coroborate other recent studies that reported similar results (Pasco, 2013; Ensslin et al. 2015; Feurer 2013; Grieco et. al., 2012; Makafui and Agboadoh 2011; Pambudi and Rahayu 2017). Adu-Bredu et al. 2008 observed carbon loss with increased with conversion of forest to other land uses; Natural forest (326.75 MgCha-1), Teak plantation (138.33 MgCha-1), Fallow (95.46 MgCha-1) and Cultivated areas (75.12 MgCha-1). Also, a decrease in mean carbon stocks in the dense forests (181.78 ± 27.06 MgC ha − 1, open forests (104.83 ± 12.35 MgC ha − 1), Grasslands (108.77 ± 6.77 MgC ha − 1 and cultivated lands (83.11 ± 8.53 MgC ha − 1) for Afromontane forest in Ethiopia (Solomon et al. 2018). Other studies examining the effects of land-use change on carbon storage found a decrease in carbon stocks with increasing land cover transformation (Pellikka et al. 2018; Kuusipalo et al. 1996; Arets, 2005; Swaine and Agyeman 2008). In most cases, forest conversion lead to the removal of a significant quantity of large trees which are considered to store the greatest amount of carbon (Kirby and Potvin 2007). Mean carbon stocks were significantly higher for Owabi compared to the Barekese watershed. This was attributed to the high incidence of illegal logging, wildfire incidences and intensive farming activities that have denuded the landscape of large diameter size class trees (Atwima Nwabiagya and District Assembly 2017; Ghana Statistical Service 2014). For instance, trees greater than 90 cm DBH are virtually missing in the case of Barekese watershed. These results are also consistent with the Forest Transition Model (Reed et al 2017; Pellikka et al., 2018).

However, in this investigation, higher carbon densities were recorded for tree croplands than the food crops systems and grassland. This observation is due to the agroforestry practices in tree crop systems through the deliberate retention of indigenous trees on farms. The retained trees are characteristically of large diameter size which contribuite substantially to carbon storage. This indicates greater tree volume in cropland system, in cases when agroforestry is practiced. This emphasizes the importance of retained trees on farms in increasing carbon storage (Dawoe et. al., 2016; Mohammed et. al., 2016; Pellikka et al. 2018). Although land-use change resulted in forest degradation, deforestation had a negative effect on carbon storage. However, the findings of this study suggest that Open forest and tree crop land-use systems were less detrimental as compared to Food crop systems and Grasslands. The findings of this study suggest that Food crops and Grasslands are less favourable ecological systems for carbon storage. This supports the research findings of Luedeling et al. (2011) and (Ensslin et al. 2015) who suggested that carbon stocks in agricultural landscapes depend mostly on density and diameter size of trees.There absence of significant difference in mean carbon stocks between Closed Forestand open forest for both watersheds indicates that selective removal of large individual trees did not affect carbon stocks. This observation supports other findings in West, East and Central Africa that selective removal of trees did not affect carbon stocks (Asase et. al., 2012; Ensslin et al. 2015). Though biomass regenerate rapidly, degraded forests may vary in species richness, composition and structure. The results stress on the negative effect of land-use change on carbon stock accumulation and the need to minimize natural forest disturbances to less complex systems.

Implications for watershed management

Generally, there is a positive relationship between biodiversity and carbon stocks globally (Midgley et al., 2010). For instance, natural tropical moist forests unaffected by anthropogenic disturbances are rich in both carbon stocks and biodiversity. Within tropical forests, there were less correlation between spatial patterns of carbon stocks and biodiversity in undisturbed areas and these patterns are considered complex (Talbot, 2010). In this study, a positive and significant effect was found between species richness, diversity and amount of carbon stocks for the two sites. Whilst the findings of this study are consistent with other studies (Asante-yeboah 2010; Asase et. al., 2012; Dawoe et al. 2016), it also supports the commonly described pattern in highly diverse natural forests; thus, biomass and carbon stocks increase with increasing diversity. Several studies on forest ecosystems have shown a positive relationship between species richness and forest biomass and/or carbon at the local, regional and global level (Asase et. al., 2012; Dawoe et al. 2016). In particular, studies across various ecosystems such as boreal (Paquette & Messier, 2011), temperate (Paquette & Messier, 2011; Vilà et al., 2007), and tropical forests (Barrufol et al., 2013) have reported an increase in productivity with increasing diversity. Increasing species richness and diversity usually facilitate increased carbon storage since higher taxonomic diversity lead to higher stem density and forest productivity (Ruiz- Benito et al., 2014). The positive effects of species diversity can be explained through the benefits of plant–plant interactions through facilitation, a process where by some species could enhance soil fertility (by fixing nitrogen) for the productivity of other species. This fact is often used to support the reason that mixed species communities of plantations are far more productive than mono-specific stands. However, it is possible that increasing species richness increases the chances of inclusion of highly productive and naturally favoured dominant species (Ruiz- Benito et al., 2014) as shown by Mensah et. al., (2016) on the influence of most dominant species on carbon stocks in Mistbelt tropical forests in Cameroun.

Whilst the findings support the positive species richness–diversity–carbon relationship, however other studies provide evidence of the inverse effect also exists. For instance, studies by Ruiz- Jaen and Potvin (2011) in the natural forest of Barro, in Central Panama and Szwagrzyk and Gazda (2007) in natural forests of central Europe, reported a negative relationship of species diversity and plant biomass. Furthermore, other studies concluded that such relationships were significant (Gairola et al., 2011). These opposing findings suggest that the effects of diversity on forest carbon may be explained by other environmental factors (Lasky et al., 2014; Wu et al., 2015) and also may dependent on the type of diversity measure used (Con et al., 2013; Lasky et al., 2014; Ouyang et al., 2016). The outcome also supports the idea that complementarity and selection effects do not exclusively affect carbon storage (Ruiz- Benito et al., 2014; Wu et al., 2015). That diversity promotes carbon stock through effects of functional diversity and dominance, and this is because these components of diversity are based on specific functional traits, which reflect functional differences among species (Song et al., 2014). This could also be due to the fact that increased species richness indirectly accounted for differences among species, in terms of ecological niche and resource use.

The findings of the study indicate that conversion from natural forest into other land uses has a negative effect on plant richness, diversity and stand structure. It is also evident that the transformation of forest into other land uses such as agricultural landscapes is a major cause of carbon loss within the Barekese and Owabi watershed. However, multi-layered Open forest and agroforestry systems, can still store up to a quarter of the carbon of the natural forests, underlining the importance of agroforestry for maintaining carbon stocks compared to grassland and Food crop systems. This may be important for other ecosystem services, such as plant productivity and biodiversity conservation. This decline in riparian forest cover puts the landscape at greater risk of decline in water quality and quantity through increased sedimentation and evaporation. Based on the above findings, the study recommends the enforcement of the Riparian buffer zone policy of Ghana to ensure that farmers are excluded from the buffer zone. Farmers should also be discouraged from further conversion of natural forest for agricultural purpose. Restoration of degraded areas may enhance biodiversity, carbon stocks and soil fertility. Further chrono sequence studies on tree crop and food crop systems may be required to further understand how time lag influences carbon dynamics and soil productivity.

Authors’ contributions

The lead author, Samuel Ayesu designed, collected and analyzed the data and prepared the paper. The other Authors supported designed, data collection and critical review of the paper and suggested useful comments on the structure and contents of the paper. All authors have read and approved the final manuscript.

Author details

Author 1: Resource Management Support Centre, Forestry Commission, Ghana. P.O. Box 1457, Kumasi. Authors 2-3: Department of Agroforestry, Faculty of Renewable Natural Resources, Kwame Nkrumah University of Science and Technology, Ghana. Private Mail Box, Kumasi, Ghana.

Acknowledgements

We are most grateful to International Tropical Timber Organization (ITTO) for providing funding for the fieldwork and all the people who supported the fieldwork.

Competing interest

The authors declare that there are no competing interests.

Availability of data and materials

The data is provided as an additional file.

Consent for publication

All co-authors have agreed to be part of the publication.

Ethics approval and consent to participate

Not applicable.

Funding

This study was financed by the International Tropical Timber Organization (ITTO) under the ITTO Fellowship Programme.

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Carbon storage and biodiversity conservation in a changing landscape: the case of Barekese and Owabi watersheds in Ghana

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Abstract

Figures

1.0 Introduction

2.0 Methodology

2.1 Study site

2.2 Sampling design

2.3 Field Sampling

2.3.1 Tally Trees

2.3.2 Deadwood Measurement

2.3.3 Non-tree vegetation

2.3.4 Litter

2.3.5 Soil sampling

2.4 Data analysis

3.0 Results

3.1 Species richness and diversity

3.2 Tree density and basal area

3.3 Diameter size class distribution

3.4 Carbon stocks

4.0 Discussion

4.1 Land-use and biodiversity

4.2 Stand structure

4.3 Land use change effects on carbon stocks

5.0 Conclusion And Recommendations

Declarations

References

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