Forests and climate change have an intricate connection. Whilst forests lessen climate change by absorbing atmospheric carbon, they can contribute to climate change if they are degraded or destroyed (Mansourian et al., 2009) . On the other hand, climate change worsens when the changes in climatic variables lead to a further degradation or loss of forests. Tropical forests sequester approximately 37% of the atmospheric carbon in the aboveground tree biomass and soil (Martin et al., 2015) . The Amazon forest, for example, stores an estimated 150 to 200 Gt of carbon in biomass and soil (Feldspausch et al., 2012) . Likewise, the forests in Ghana assist in the regulation of climate change by storing considerable volumes of atmospheric carbon in the form of carbon dioxide (CO2). Although detailed information about carbon stocks of forests for the entire country is scanty (Donkor et al., 2016) , their protection is of importance for the conservation of global species diversity and atmospheric carbon sequestration.
Protected areas (PAs) are elements of complex landscapes (Crooks & Sanjayan, 2006) . According to Dudley (2008) , a PA is “a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values”. They are managed mainly for biodiversity conservation, yet an important sink for atmospheric CO2 (Crooks & Sanjayan, 2006) . Since their degradation affects global carbon cycle and biodiversity conservation, reliable and timely information on the abundance of species, their distribution and information about their habitats as well as threats are requisites for proper management after establishing protected areas (De Leeum et al., 2002) . Such information will serve as reference for identification and monitoring of areas within the landscape that will need specific attention. It will also enable ecosystem managers to be able to identify location specific factors that affect the distribution of biodiversity and landscape. However, even when this information is established, changes resulting from processes of natural selection, resource use, and changes in environmental conditions make further studies necessary (De Leeum et al., 2002) .
The study focused on the University of Energy and Natural Resources Bat Sanctuary (UENRBS). There has been an increase in land use conversions in recent times in the study site due to increasing infrastructural development thereby raising concerns as regards the sustainability of the remnant forests at the bat sanctuary. Information on species distribution and carbon stocks in biomass is an important consideration in evaluating not only the responsiveness of the PA to current developments but also its ecological values. Thus, an assessment and mapping of the ecosystem’s values and health will require reliable information on floral species distribution and stocks of carbon stored in biomass.
In Ghana, various studies have documented the kinds, distribution and abundance of plant and animal species for conservation purposes (Burgess et al., 2007; Hall and Swaine, 1981) . Yet, the stocks of carbon, diversity, abundance, richness and spatial distribution of species within the sanctuary have been given limited attention. Again, limited effort has been made towards assessment of edge effects at the sanctuary. This is important because subtle changes in the microhabitat along the edges of the sanctuary, could affect the distribution, abundance and diversity of plant species (Boakye et al., 2017) . Thus, effects of edges on the sanctuary need to be assessed in order to make informed decisions on the conservation status of the protected forest ecosystem.
As a result, this study aims at mapping carbon stocks and spatial distribution in the sanctuary by mapping the distribution of the floral biodiversity and assessing the effects of edges on the sanctuary. Specifically, the study 1) undertakes a total enumeration of floral species of the PA; 2) to examine the diversity of functional groups of species in the sanctuary; and 3) explores the relationship between species composition and carbon stocks at both the edge (buffer) and core areas of the sanctuary.
2.1. Description of the Study Area
The study was undertaken at the bat sanctuary of the University of Energy and Natural Resources (UENR), Sunyani. The sanctuary is located at latitude 7˚20' 50''N and longitude 2˚20'30''W. It has a total area of 3.6 hectares and occupies about 7.3% of the UENR campus area. It was established to provide a natural habitat for animals (mainly wildlife) which would be used for research purposes. The dominant tree species is Newbouldia laevis and the least abundant is Triplochiton scleroxylon. The dominant undergrowth is the Psychotria spp., which can support duikers. According to MoFA (2010) the mean monthly temperature of Sunyani is between 23˚C and 33˚C whilst the relative humidity is generally high; averaging between 75 and 80 percent during the rainy seasons and 70 and 80 percent during the dry seasons of the year. The average annual rainfall for Sunyani is about 1100 mm. The area experiences a double maxima rainfall pattern which offers farmers the opportunity to farm twice in a year; the main season and the minor season (MoFA, 2010) although the rainfall pattern appears to be changing in recent times due in part to deforestation and forest degradation. The main rocks underlying the area are the Precambrian Birimian formations which have high mineral deposits and widespread masses of granite. The soils, mainly Ochrosols, are generally fertile with high water holding capacity and support a wide range of agricultural crops (MoFA, 2010) .
2.2. Floral Survey Procedures
The study commenced by mapping the boundaries of the protected area (PA) using the Garmin Global Positioning System (GPS) 64 sc. The points were plotted to ascertain the actual boundaries of the PA. To find out the effects of edges on carbon stocks and floral species diversity, a 10 m width of the edges from the boundaries of the protected area was created using the Buffer Wizard of the ArcGIS (v10.4) and designated as a buffer zone whilst the interior beyond the 10 m buffer was designated as core zone. In all, twenty-nine (29) sampling points (plots) were randomly generated for species enumeration with 10 plots located within the buffer and 19 plots located within the core zone (Figure 1). The coordinates were transferred to the GPS to help in plots location on the field for survey. At the field, a three-nest square sampling plots comprising a 10 m × 10 m quadrat (main plot), 5 m × 5 m and 1 m × 1 m subplots were constructed for vegetation assessment. Whilst enumeration of tree species was undertaken in the 100 m2 plots, shrubs and seedlings enumeration was undertaken in the 25 m2 and 1 m2 subplots, respectively. The trees had diameter at breast height (DBH) greater than 10 cm, saplings had DBH greater than 2 cm but less than 10 cm and seedlings had girth less than 2 cm.
2.3. Statistical Analysis
We calculated plant species richness and diversity using the Shannon-Wiener indices. The Shannon-Weiner index (H’) and Family Importance Value Index (FIVI) were calculated by using equations simplified in Table 1. The carbon stocks of the floral species of the PA were computed as summation of the aboveground carbon (AGC), the belowground carbon (BGC) and dead wood carbon (DWC) using the allometric equations described by Brown (1997) and Cairns et al. (1997) (Table 1). The data obtained from the field survey were analysed to extract salient information to assess the impacts of the explanatory variables on the dependent variables. A two-sample T-test was conducted to test the differences in the vegetation and carbon parameters. Beforehand, the data were cleaned and arranged under themes.
Figure 1. Location of plots within the protected area (PA) studied.
Table 1. Description of indices.
Such thematic representation of data allowed easier usage of the MS Excel T-test algorithm. The T-test was computed to test whether significant differences exist between the mean carbon of the edges and inner core of the PA at 5% level of significance. Again, the diversities of species of the respective zones of the PA were compared. All statistical analyses were done using Excel application of the Microsoft office suite, 2016 (Microsoft Corp.).
3. Results and Discussion
3.1. Species Diversity and Composition
Overall, 485 individuals belonging to 58 species and 25 families were enumerated at the study area. The most represented species are Newbouldia laevis (19.4%), Griffonia simplicifolia (9.3%), Salacia elegans (7.8%), Senna siamea (5.4%), Holarrhena floribunda (5.4%), Psychotria ankasensis (4.9%), and Millettia thonningii (4.5%). The most dominant families included Bignoniaceae (19.4%), Caesalpiniaceae (15.3%) Rubiaceae (9.1%), Clastraceae (7.8%), Apocynaceae (6.4%) Mimosaceae (6.4%) Sapindaceae (6.2%) and Papilionaceae (6.2%).
3.1.1. Classiﬁcation by Growth Form
Table 2 shows the diversity of floral species in core area compared to the buffer zone. The tree species were less diverse (H’ = 4.32 ± 0.09) in the core compared to the buffer zone (H’ = 4.55 ± 0.09), though difference in mean diversities between the two zones where statistically insignificant at 95% confidence level. A similar situation was observed for the seedling life form, where diversity was higher in the buffer (H’ = 5.11 ± 0.11) than the core zones (H’ = 4.93 ± 0.09), yet statistically insignificant (p-value > 0.05). However, the sapling life forms were more diverse within the core zone of the protected area than the buffer zone. The higher diversity of seedlings at the buffer zone may be a result of the exposure to sunlight, which helps to promote germination. Kettenring et al. (2006) reports that light requirements for germination contribute to the formation of persistent seed banks in Carex species. Other factors accounting for the difference in diversity could be the uneven spatial distribution of soil nutrients and other growth requirements in the soil (Boakye et al., 2017) .
3.1.2. The Family Importance Value Index (FIVI) of the Species
Importance Value is a measure of how dominant a species is in a given forest area. A high importance value indicates that members of a family are well represented in the stand. The family important value (FIV) summed the relative percentage values of density, frequency and dominance of all the families of the first five most documented floral species within the PA. Bignoniaceae (16.38%), Apocynaceae (10.02%), Caesalpiniaceae (9.21%), Rubiaceae (8.84%), Mimosaceae (7.06%), and Moraceae (6.71%) were the common families within the PA, whilst Boraginaceae (0.60%), Fabaceae (0.60%), Connaraceae (0.56%), Dilleniaceae (0.27%), and Araliaceae (0.27%) were the rarest families of the PA (Table 3). The Shannon Wiener diversity indices of the first five most common families were 4.39, 4.79, 4.33, 4.70, 4.77 and 4.99 respectively.
3.2. Carbon Stocks Estimation
Table 4 shows the total carbon storage of floral species per plot and their respective carbon dioxide (CO2) equivalences. The total amount of CO2 sequestered per hectare of the bat sanctuary ranges from 1992.5 to 31,781.2 t/ha, with a mean of 10,227.4 t/ha. Generally, the total terrestrial ecosystem carbon is estimated based on six carbon pools (Eggleston et al., 2006) . However, for the purpose of this study, the total carbon of the PA was estimated by the summation of three major carbon pools of the AGC, BGC and DWC of the different life forms of
Table 2. Variation in floral species diversity in the bat sanctuary.
(Mean ±std. error).
Table 3. Family importance value Index (FIVI) for the documented families.
Table 4. Estimates of carbon pools of the sanctuary.
AGC = Aboveground carbon, BGC = Belowground carbon, TC = Total carbon, CO2e = Carbon dioxide equivalent.
species enumerated. The AGC ranges from 478.9 tons/ha to 7799.9 tons/ha with an average of 2491.9 tons/ha. With an average carbon content of 297.4 tons/ha, the BGC range from 64.5 tons/ha to 867.7 tons/ha. This carbon stock per hectare obtained was relatively higher compared to that of Donkor et al. (2016) who performed a similar study in the Bosomkese forest reserve in the dry semi-deciduous forest zone (Table 5). The total carbon stocks per hectare within the floral species biomass and their respective carbon dioxide equivalent values have been appendiced (Appendix I).
We tested the management option of having a buffer zone around the PA. This may be required in addition to the laid down rules to help in the conservation of the protected area. Thus, we created a 10 m buffer zone as a management option from the edges of the forested ecosystem with the help of the buffer wizard of the ArcGIS application suite. The carbon stocks obtained from the sample plots found within the core of the PA were compared to those estimated for the buffer zone by means of the T-test. The mean carbon sequestration of the floral species in the core area was higher than that of the buffer area. Typically, most PAs are perceived to sequester varied amounts of carbon with respect to the buffer and core zones respectively due to the unequal distribution of soil nutrients and other growth requirements (Boakye et al., 2017) . However, from Figure 2, our analysis indicated that the buffer zone of the protected area has no influence on the mean carbon stocks. Thus, the mean difference between the carbon stocks observed for the buffer and the core zones was statistically insignificant at 95% confidence level.
3.3. Geostatistical-Based Inference of Carbon Stocks
Figure 3 shows the spatial representation of total carbon drawn by using the Inverse Distance Weighing algorithm of the ArcGIS software. The carbon stocks ranged between 551.8 t/ha and about 8655.3 t/ha. The majority of the PA had estimated carbon stock values closer to the slightly overestimated mean of 2804.7 (±975.9) t/ha determined by the geostatistical modelling tool. This can be attributed to the fact that the species of the PA are of even age and the sampling technique helped to capture average values that are good representation of the protected area. The study area is averagely storing about 2804.7 t/ha of carbon. We found a relatively higher carbon per hectare of forest compared to Donkor et al. (2016) who found an average of 1748.4 tons/ha of carbon (Table 5). The higher carbon per hectare from our study is an indication that natural forest sequesters the greatest amount
Table 5. Comparison of estimates.
FS is Field Survey; GIS is Geographic Information Science.
Figure 2. Carbon stocks dynamics of the PA.
Figure 3. Carbon storage intensity (CSI) map of the PA produced by using the inverse distance weighting (IDW) interpolation approach.
of CO2 and management regimes has an impact on the overall health of a protected forest area.
Additionally, the spatial extent and high carbon sequestration of the PA was computed. To assess fluctuation in relation to carbon dynamics, three categories of total carbon, with natural breaks, were identified. These were: the highest (TC ≥ 4111.0), middle (2681.0 ≤ S ≥ 4111.0), and lower (0.0 ≤ S ≥ 2681.0). About 130,434.2 sq. meters (53.7%) of the PA had a relatively low carbon stock, compared to medium carbon (9036.5 sq. meters; 41.9%) and high carbon storage areas (3587.4 sq. meters; 14.8%) (Table 6).
Figure 4 shows the carbon-diversity map obtained by superimposing the Shannon Wiener diversity image on the carbon image. Mostly, a larger proportion (8783.3 m2; 36.2%) of the PA that stores low carbon has high diversity, which is represented in Table 7. The larger spatial coverage may be an evidence of the
Table 6. Estimate of the area occupied by intensity major carbon of the study area.
Table 7. Statistics obtained by superimposing floral diversity and carbon images.
Figure 4. Spatial representation of the overlap of carbon and diversity.
poor correlation between carbon stock and floral diversity. Although negatively, Lange et al. (2015) found that legumes affect soil carbon concentration. Similarly, Mandal et al. (2013) also found a very weak relationship between carbon stock and species richness of collaborative forests.
The present study assessed the functional status of Protected Area (PA) by analysing the variations in carbon stocks and floral species diversity of both the buffer and core zones of the UENR’s Bat sanctuary. Our results revealed that a total of 485 floral individuals belonging to 58 species and 25 families were enumerated at the sanctuary. Bignoniaceae (16.38%), Apocynaceae (10.02%), Caesalpiniaceae (9.21%), Rubiaceae (8.84%), Mimosaceae (7.06%), and Moraceae (6.71%) were the dominant families based on their Importance Value (IV). These functional groups were dominated by individual species such as Newbouldia laevis, Griffonia simplicifolia, Salacia elegans, and Senna siamea. The average carbon stock per hectare sequestered by the PA was 2789.30 tons. The buffer zone (edges) had no significant impact on the floral species diversity and carbon stocks of the sanctuary. The study has provided valuable information on the functional status of the bat sanctuary, a protected forest ecosystem, which will help promote its conservation for sustained provision of ecosystem services for posterity.
We are grateful to Mr. Ernest Frimpong Asamoah, Mr. Jonathan Dabo and Mr. Emmanuel Owusu for their support during the field survey and species identification.
Appendix I. Total biomass and carbon per hectare per plot.
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