Linkages between tree diversity and carbon stocks in unlogged and logged West African tropical forests

Alex Asase; Bismark K. Asitoakor; Patrick K. Ekpe

doi:10.1080/21513732.2012.707152

Linkages between tree diversity and carbon stocks in unlogged and logged West African tropical forests

Asase, Alex; Asitoakor, Bismark K.; Ekpe, Patrick K. 2012-09-01 00:00:00 International Journal of Biodiversity Science, Ecosystem Services & Management Vol. 8, No. 3, September 2012, 217–230 Linkages between tree diversity and carbon stocks in unlogged and logged West African tropical forests a b a Alex Asase *, Bismark K. Asitoakor and Patrick K. Ekpe a b Department of Botany, University of Ghana, PO Box LG 55, Legon, Ghana; Environmental Science Programme, University of Ghana, PO Box LG 71, Legon, Ghana Understanding the long-term effects of logging disturbances on the linkages between tree diversity and carbon stocks is important for conservation efforts and mitigation of global climate change. This study was carried out in unlogged and 14–29 years post-logged forests in the Bia Conservation Area in southwest Ghana. The study results showed that both large (diameter at breast height or dbh ≥ 10 cm) and smaller (dbh ≤ 10 cm but ≥5 cm) tree diversity increased signiﬁcantly in logged forest compared with unlogged forest while tree dominance was similar between the two land-use types. Tree species guild composition was signiﬁcantly different due to the higher proportion of pioneer species of large trees in logged forest and shade bearer species of smaller trees in the unlogged forest. Total mean carbon stock was 322.8 Mg C per ha [95% Conﬁdence Interval (CI): 191.6–443.8] for logged forest and 211.2 Mg C per ha (95% CI: 196.9–228.0) for unlogged forest, although no signiﬁcant difference was detected (p > 0.05). There was a signiﬁcant interaction (p < 0.01) between ecological guilds and land use types in total tree-stored carbon stocks. The results of the study showed that logging has comparatively long-term effects on tree diversity while its effect on carbon stocks might only be short term. The ﬁndings from this study underscore the need for more comparative data from other areas in West Africa. Keywords: land use change; logging; biodiversity; ecosystem services; carbon storage Introduction harvesting of timber have increased in frequency and extent (Asner et al. 2005; Norris et al. 2010). The effects of log- Tropical forests are among the most complex and species- ging on ﬂora (Hawthorne 1993; Pinard et al. 2000; Hall rich ecosystems of the world (Martin 1991; Primark and et al. 2003; Asner et al. 2005; Swaine and Agyeman 2008) Corlett 2005) and store 40–50% of carbon in terrestrial and wider biodiversity (Norris et al. 2010) have been docu- vegetation (Lewis et al. 2009). Tree diversity is fundamen- mented. However, the effects of logging are highly variable tal to tropical forest biodiversity because it provides the due to differences in harvest intensity and logging practice resources and habitats for almost all other forest species as well as site differences due to factors such as topography, (Hall and Swaine 1976; Mabberly 1983; Huston 1994). soil and biota (Kuusipalo et al. 1996; Pinard et al. 1996; In tropical forests, tree diversity varies from place to place Magnusson et al. 1999; Dickinson et al. 2000; Arets 2005; mainly due to differences in biogeography, habitat and Park et al. 2005). disturbances (Whitmore 1989). In tropical forest ecosystems, carbon is stored in dif- In large parts of the African tropical forest biome, dis- ferent pools including plant biomass and soil. There is turbances due to hurricanes, river dynamics and volcanic evidence that plant assemblages with high species diver- activities are rare (Jans et al. 1993). However, the conver- sity may also promote a more efﬁcient use of resources sion of tropical primary forests into various land-use types and greater net primary production (Vandermer 1989), and has had serious impacts on distribution, community struc- consequently, high rate of carbon sequestration (Catovsky ture and population characteristics of ﬂora (Van Gemerden, et al. 2002; Kirby and Potvin 2007) compared with areas Olff, et al. 2003). Other disturbances such as windfall, with lower plant species diversity. The magnitude of carbon insect breaks and wild ﬁres have also been noted. Land use stocks in tropical forests depends on species composition changes, particularly tropical deforestation, are the leading (Bunker et al. 2005). Forest management provides oppor- cause of species extinction (Sala et al. 2000) and contribute tunities to conserve plant diversity as well as retain carbon about 25% of anthropogenic carbon emissions (IPPC 2001; stocks in vegetation and soils (Feldpausch et al. 2005). Thomas et al. 2004). The most signiﬁcant causes of land However, there is paucity of data on studies that have com- use change in West Africa are agricultural expansion, pared tree diversity and carbon stocks in unlogged and wood extraction and infrastructural extension (Norris et al. logged forests. The majority of studies have been carried 2010). Although deforestation, largely the conversion of out in the Amazon region (Feldpausch et al. 2005; Sist land to food crops or pasture, is the major destructive and Ferreira 2007) and hitherto none in the West African force in tropical forests; other forest disturbances such as *Corresponding author. Email: aasase@ug.edu.gh ISSN 2151-3732 print/ISSN 2151-3740 online © 2012 Taylor & Francis http://dx.doi.org/10.1080/21513732.2012.707152 http://www.tandfonline.com 218 A. Asase et al. region. Understanding the relationships between tree diver- distance between forest stands was 1 km and topography sity and carbon stocks in logged forests compared to was generally ﬂat among stands. unlogged forests is very important for conservation efforts and mitigation of global climate change in the West African Enumeration of tree diversity region. Large trees were enumerated within each 25 m × 25 m In this study, we investigated the linkages between forest stand selected in the two land-use types. Within each tree diversity and carbon stocks in relation to logging 25 m × 25 m forest stand, all trees with diameter at breast disturbances in West African tropical forests. The study height (dbh) ≥ 10 cm were individually identiﬁed and their was carried out in unlogged and 14–29 years post-logged dbh measured (1.3 m above ground). The 625 m plot size forests in the Bia Conservation Area in southwest Ghana. has been successfully used to sample forest trees in West Speciﬁcally, the study aims to address the question: what Africa (Hawthorne and Abu-Juam 1995; Van Gemerden, are the long-term effects of selective logging on tree Shu, Olff 2003). Smaller trees with dbh ≥ 5cmbut diversity (tree species diversity, dominance and species ≤ 10 cm were studied using 5 m × 5 m subplots nested ecological guild composition) and carbon stocks in tropical within the 625 m forest stands. One 5 m × 5 m subplot forests? was demarcated at the centre, that is, point of intersection of two perpendicular lines from the corners of each 625 m plot. All individual smaller trees within the 5 m × 5 m sub- Materials and methods plots were identiﬁed and their dbh were measured. Species Study area identiﬁcation and nomenclature of both large and smaller This study was carried out in the Bia Conservation Area trees follows Hawthorne and Jongkind (2009). located in the Juabeso-Bia District in southwest Ghana Tree species were classiﬁed according to their shade close to the Ghana–Côte d’Ivoire border. The area falls tolerance/ecological guilds (Hawthorne and Abu-Juam ◦ ◦ ◦ within latitude 6 32 Nto6 37 N and longitude 3 02 W 1995; Hall et al. 2003). Following Hawthorne and Abu- ◦ 2 to 3 08 W and covers a total area of 306 km .The Bia Juam (1995), pioneers are species for which seedlings need Conservation Area comprises the Bia National Park and sun to establish. Non-pioneer light demanders (NPLD) are Resource Reserve (Figure 1). The study area lies within the species that need gaps to develop beyond the sapling stage transition zone between Moist Semi-deciduous and Moist while shade bearer guild consist of species that can persist Evergreen Forest Zones of Ghana (Hall and Swaine 1981). and grow in understory shade at seedling and sapling stage The climate is that of a typical moist evergreen forest with (Hawthorne and Abu-Juam 1995). bimodal rainfall between May and June and also between September and October. Annual rainfall is 1500–1800 mm Estimation of tree biomass and carbon stocks and mean monthly temperatures are between 24 C and 28 C. Relative humidity is between 75% in the afternoon The above-ground biomass of trees was estimated using the and 90% in the night. The topography of the study area allometric model of Henry et al. (2010) which estimates 2.31 is generally ﬂat with elevations ranging between 168 m tree biomass, Y,as Y = 0.30 × dbh . This model was and 238 m (Symonds 2001). Logging activities in the study speciﬁcally developed for tropical African forest unlike the area started from 1981 and continued until 1998 under the equations of Chave et al. (2005) and Brown et al. (1989). Ghana selection system using salvage practices (Symonds The allometric model of Henry et al. (2010) included trees 2001). From 1982 to 1998, a total of 3169 trees belonging of different ecological guilds and was not biased towards to 27 tree species were harvested (Symonds 2001). reporting relatively high biomass for rapidly growing, low- density wood species such as pioneers. The dbh measures of trees during the enumeration of tree diversity were used. Sampling methods Mean biomass of palm from a previous study (Thenkabail Sampling took place in two broad land-use types, namely et al. 2004) was used as the biomass value for all individ- unlogged and logged forests within the study area. The ual palm species encountered as a result of the lower dbh logged forests were harvested for timber 14–29 years to biomass ratio of palm compared with other tree species before this study was performed. The logged and unlogged (Wade et al. 2010). Root biomass was estimated indi- forest stands were geographically interspersed within the rectly from above-ground biomass following the method study area. Satellite imagery and reconnaissance survey of Cairns et al. (1997) which estimates root as 24% of methods were used to identify logged forest stands that above-ground tree biomass. Tree-stored carbon stocks were were believed to be similar to unlogged forest stands before calculated by multiplying the sum of the above-ground and harvesting. The logged forest stands were in tree felling root biomass by 0.5 (Albrecht and Kandji 2003; Glenday gaps stands directly affected by felled trees. We avoided 2006). sampling skid trails, hauling tracks and loading bays. Five To determine soil organic carbon, soil samples were forest stands in unlogged forest and ﬁve in logged forest collected from two randomly selected spots in each of the of size 25 m × 25 m were randomly sampled for data 25 m × 25 m forest stands enumerated for plant diversity collection taken into consideration the range of variation following the method of previous works (Ofori-Frimpong within the land use types in the study area. The minimum et al. 2010; Wade et al. 2010). Soil samples were collected International Journal of Biodiversity Science, Ecosystem Services & Management 219 3° 11′ 40″ W3° 9′ 35″ W3° 7′ 30″ W3° 5′ 25″ W3° 3′ 20″ W3° 1′ 15″ W2° 59′ 10″ W 3° 11′ 40″ W 3° 9′ 35″ W3° 7′ 30″ W3° 5′ 25″ W3° 3′ 20″ W3° 1′ 15″ W2° 59′ 10″ W km Figure 1. Map of the study area. at two different depths, namely 0–15 cm and 15–30 cm, Bulk density was determined for every soil sample col- because soil carbon stocks vary with depth. Previous lected following the core method (Blake 1965). The soil works have sampled soil carbon stocks in the tropics at organic carbon content of soil per hectare was ﬁnally deter- similar depths (Kirby and Potvin 2007; Williams et al. mined using the formula: [%C] × [bulk density] × [soil –3 2008). Percentage soil organic carbon was determined depth], with bulk density measured in g cm and soil depth using Walkley–Black method (Walkley and Black 1934); in cm (Kirby and Potvin 2007). as modiﬁed by Anderson and Ingram (1989) and cor- rected for the un-oxidized organic carbon as described by Data analysis Nelson and Sommers (1996). Percentage soil moisture was Species diversity was evaluated using Shannon–Wiener calculated using the following formula: index (Magguran 1988): % soil moisture H = p ln p (2) i i i=1 Fresh weight of soil − Constant dried weight of soil = × 100% where s is the total number of species and p is the Constant dried weight of soil (1) relative abundance of the species i. Basal area of trees 6° 19′ 10″ N6° 21′ 15″ N6° 23′ 20″ N6° 25′ 25″ N6° 27′ 30″ N6° 29′ 35″ N6° 31′ 40″ N6° 33′ 45″ N6° 35′ 50″ N6° 37′ 55″ N 6° 19′ 10″ N6° 21′ 15″ N6° 23′ 20″ N6° 25′ 25″ N6° 27′ 30″ N6° 29′ 35″ N6° 31′ 40″ N6° 33′ 45″ N6° 35′ 50″ N6° 37′ 55″ N 220 A. Asase et al. per hectare (dominance) was estimated from the for- with information on their ecological guilds, density, fre- mula 0.0000785d , where d is dbh per tree (Asase quency, dominance and IVI are presented in Appendix 1. and Tetteh 2010). Calculation of density and frequency A total of 49 large tree species were found in the unlogged of trees follows standard methods (Curtis and McIntosh forest, while 63 species were encountered in the logged 1950). Importance Value Index (IVI) of trees was calcu- forest. Mean Shannon–Wiener index was signiﬁcantly lated as the sum of their relative density, relative frequency (p < 0.01) higher in the logged forest compared with and relative dominance. Homoscedasticity of variance was the unlogged forest. However, no signiﬁcant difference analysed using Fisher’s F-test. Student’s t-test was used (p > 0.05) was found in large tree dominance between to compare means with normal errors, whereas Wilcoxon logged and unlogged forests (Table 2). The ecological signed-rank test was used for means with non-normal guild composition of species in the two types of land errors. The shapiro.test function of R statistical software use was signiﬁcantly different (ANOVA, p < 0.05) due was used to test normality of data. The proportions of to the predominance of pioneer species in the logged species in different ecological guilds in the unlogged and forest. logged forests were analysed using analysis of variance With regard to smaller trees, 112 individual trees (ANOVA). belonging to 43 species were encountered (Appendix 2). Carbon stocks were estimated for each plot, extrapo- The logged forest contained 25 species, while 26 species lated to Mg per ha and means determined for each land were identiﬁed in the unlogged forest. Similar to use type and carbon pool. The mean carbon content of the large trees, Shannon–Wiener index was signiﬁcantly two soil cores sampled per plot was used in calculating soil (p < 0.05) higher in the logged forest compared carbon stocks per hectare. Coefﬁcient of variation (CV) of with the unlogged forest and no signiﬁcant difference soil organic carbon stocks measurements was 10.4% for (p > 0.05) was detected in tree dominance. There was unlogged forest and 5.6% for logged forest. The magni- a signiﬁcantly high proportion (ANOVA, p < 0.01) of tudes of soil carbon stocks in this study are comparable smaller-tree shade bearer species in the unlogged forest to those published for West Africa (Batjes 2001; Saiz compared with the logged forest. et al. 2012). Bootstrapping methodology with 10,000 ran- Total mean carbon stocks were not signiﬁcantly differ- dom sampling with replacement was used to estimate 95% ent between the logged and the unlogged forests (p > 0.05) Conﬁdence Intervals (CIs) of the standard error of means. (Table 3). Tree-stored carbon contributed the largest pro- Bias-corrected accelerated percentiles conﬁdence intervals portion of carbon stocks in both logged forest (91.3%) and are reported (Crawley 2007). unlogged forest (89.1%), although the difference between The relationships between tree species diversity and the two types of land use was insigniﬁcant at 95% CI. Total soil carbon stocks were non-linear and models were con- soil organic carbon was signiﬁcantly higher (p < 0.05) in structed using the ‘nls’ function of R statistical software the logged forest compared with unlogged forest. (www.r-project.org). The same model explained the rela- tionships between total soil organic carbon stocks, and both Relationships between tree diversity and carbon stocks large and smaller tree species diversity: There were no signiﬁcant correlations between tree species B − A diversity with total carbon stocks and total tree-stored car- y = A + (3) −x C 1 + e bon stocks (Table 4). However, both large and smaller tree species diversity were signiﬁcantly related to total soil where y is soil organic carbon, x is tree species diversity organic carbon stocks (p < 0.001 for both large and smaller (Shannon–Wiener index), A is maximum soil carbon which trees). Dominance of both large and smaller trees was the soil holds while B is minimum soil carbon stock, and C not related to soil carbon stocks. The mechanistic forms is the slope of the non-linear function. of the relationships between tree diversity and soil car- Factorial ANOVA was used to investigate the interac- bon stocks were non-linear showing asymptotes at both tions between ecological guilds and land use types in terms left- and right-hand sides (Figure 2). The estimated val- of tree-stored carbon stocks. Statistical analyses were exe- ues for the parameters of the model were: A = 22.1 ± 1.0 cuted with R version 2.7.2 (R Core Development Team (t = 21.8, p < 0.01), B = 29.7 ± 2.5 (t = 12.1, p < 0.001), 2009). C = 3.7 ± 0.1 (t = 42.9, p < 0.001) for large tree species diversity and A = 22.1 ± 1.1 (t = 20.9, p < 0.01), B = 29.6 ± 2.2 (t = 13.4, p < 0.001), C = 2.9 ± 0.1 Results (t = 27.7, p < 0.001) for smaller tree species diver- Tree diversity and carbon stocks in unlogged and logged sity. A signiﬁcant interaction between ecological guilds forests and land use types was detected in total tree-stored car- In total, 310 individual large trees belonging to 87 taxa of bon stocks (ANOVA; p < 0.01). There were signiﬁcant which 80 were identiﬁed to species level were encountered differences in carbon stocks contributed by trees belong- during the study. General site characteristics of the forest ing to NPLD (p < 0.01), pioneer (p < 0.05) and other stands with information on number of trees encountered (p < 0.05) species ecological guilds to total tree-stored are presented in Table 1. The species of trees identiﬁed carbon stocks (Figure 3). International Journal of Biodiversity Science, Ecosystem Services & Management 221 Table 1. General characteristics of forest stands sampled. Estimated height of Soil moisture Soil bulk density Shannon–Wiener Shannon– Altitude Number of trees tallest (% weight) (g/cm ) (0–30 cm index for large Wiener index Total carbon stock Forest stand Land use type (±m) (dbh ≥ 5cm) tree (m) (0–30 cm depth) depth) trees for smaller trees (Mg/ha) IF 01 Unlogged 9 48 39 78.48 ± 1.70 4.19 ± 0.31 2.55 1.64 239.73 forest IF 02 Unlogged 4 41 40 72.97 ± 5.50 3.91 ± 0.81 3.12 2.17 200.10 forest IF 03 Unlogged 4 35 38 76.62 ± 4.82 4.40 ± 0.38 3.39 2.50 220.58 forest IF 04 Unlogged 6 34 42 72.11 ± 4.37 4.34 ± 0.28 3.57 2.71 206.98 forest IF 05 Unlogged 17 36 50 76.03 ± 2.29 4.43 ± 0.13 3.69 2.85 188.77 forest LF 01 Logged forest 11 52 49 71.37 ± 9.25 3.33 ± 0.72 3.77 2.96 521.43 LF 02 Logged forest 14 47 30 72.90 ± 4.64 4.28 ± 0.28 3.84 3.03 122.26 LF 03 Logged forest 17 45 45 70.70 ± 1.67 4.27 ± 0.09 3.89 3.11 378.85 LF 04 Logged forest 5 39 30 70.43 ± 14.08 3.87 ± 0.49 3.94 3.17 192.71 LF 05 Logged forest 13 45 35 68.38 ± 3.93 3.81 ± 0.15 3.97 3.23 398.72 222 A. Asase et al. Table 2. Tree species diversity and dominance in logged and unlogged West African forests. Statistical method of comparison of means Parameters Logged forest Unlogged forest (p-value) Shannon–Wiener index Large tree 3.9 [3.8–3.9] 3.3 [2.7–3.5] Wilcoxon test (0.012) Smaller tree 3.1 [3.0–3.2] 2.4 [1.9–2.7] Wilcoxon test (0.0079) Tree dominance (m/ha) Large tree 34.6 [20.9–46.5] 25.5 [23.6–28.1] Wilcoxon test (0.69) Smaller tree 2.1 [1.7–2.8] 2.7 [1.8–3.4] Student’s t-test (0.32) Note: 95% bootstrapped Conﬁdence Interval of standard error provided in squared brackets. Table 3. Mean carbon stocks (Mg C per ha) arranged according to carbon pools. Statistical method of comparison of means Carbon pool Logged forest Unlogged forest (p-value) Tree-stored carbon Large trees 287.7 [155.9–410.2] 179.1 [165.3–194.0] Wilcoxon test (0.69) Smaller trees 7.0 [5.3–9.2] 9.0 [6.3–11.8] Student’s t-test (0.21) Total tree-stored carbon 294.6 [173.2–416.1] 188.2 [173.2–203.1] Wilcoxon test (0.55) Soil organic carbon 0–15 cm depth 13.7 [12.5–14.6] 11.8 [10.9–12.3] Student’s t-test (0.04) 15–30 cm depth 14.6 [13.4–15.6] 11.3 [10.6–12.3] Student’s t-test (0.0044) Total soil organic carbon 28.1 [26.4–29.5] 23.1 [21.4–24.8] Student’s t-test (0.0049) Total carbon stocks 322.8 [191.6–443.8] 211.2 [196.9–228.0] Wilcoxon test (0.55) Note: 95% bootstrapped Conﬁdence Interval of standard error provided in squared brackets. Table 4. Results of correlation analyses between carbon stocks and tree diversity (species diversity and dominance). Carbon pool Variable Correlation coefﬁcient, p-value Total carbon stocks Shannon–Wiener index of large trees 0.26, 0.47 Shannon–Wiener index of smaller trees 0.28, 0.43 Total tree-stored carbon Shannon–Wiener index of large trees 0.24, 0.50 Shannon–Wiener index of smaller trees 0.26, 0.46 Total soil organic carbon Dominance of large trees 0.18, 0.61 Dominance of smaller trees −0.58, 0.12 Discussion shade at both seedling and sapling stages unlike pioneer species (Hawthorne and Abu-Juam 1995). Our study was Long-term effects of selective logging on tree diversity performed after 14–29 years of logging operations and and carbon stocks therefore means that the effects of logging on tree diversity The results of this study showed that logging has signiﬁcant are long term. According to Brown and Gurevitch (2004), effect on tree diversity, although carbon stocks are compa- invasive plants are not transient members of logged tropical rable between unlogged and logged forests. With regard to forest but maintain long-term viable populations after their tree diversity, signiﬁcantly increased tree species diversity initial colonization and can dramatically alter the trajectory and abundance of large-tree pioneer species were recorded of forest succession. in the logged forest while that of smaller-tree shade bearer For carbon stocks, a number of previous studies have species decreased in logged forests. Other studies exam- shown that selective logging can impact negatively on ining the effects of logging on tree diversity have shown forest carbon stocks (Putz and Pinard 1993; Pinard and that the most common trend is an increase in pioneer Cropper 2000; Asner et al. 2005). For example, Asner et al. species in those areas disturbed by logging activities sim- (2005) reported that a gross ﬂux of approximately 0.1 bil- ilar to the ﬁndings of this study (Kuusipalo et al. 1996; lion metric tons of carbon was destined for release to the Arets 2005; Swaine and Agyeman 2008). The abundance atmosphere each year in ﬁve timber-producing states of the of shade bearer trees signiﬁcantly decreased in logged for- Brazilian Amazon. Putz and Pinard (1993) estimated that est due to decreased shading following tree harvesting. conventional selective logging in Sabah, Malaysia reduced Shade bearer species can persist and grow in understory carbon stocks of unlogged forest by one-third. There are International Journal of Biodiversity Science, Ecosystem Services & Management 223 after logging activities. Therefore, the effects of logging on carbon stocks unlike that of tree diversity are short term as carbon stocks can recover from previously logged forests in the longer term. Relationships between tree diversity and carbon stocks in logged and unlogged forests The relationships between biodiversity and ecosystem function have emerged as a central issue in ecological and environmental sciences during the last decade (Loreau et al. 2001). While the majority of studies have focused on tree diversity and production, our study explored the link- ages between tree diversity and carbon storage in logged and unlogged forests. Previous studies on the relationships 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 between plant diversity and carbon storage have shown that Shannon–Wiener index both tree diversity and composition have important impacts on carbon dynamics (Fornara and Tilman 2008; Steinbeiss Figure 2. Relationships between total soil organic carbon stocks et al. 2008; Ruiz-Jaen and Potvin 2010). A number of and tree species diversity. For large trees, overall p-value < 0.001, Intercept = 16.29 ± 6.3 SE, t =−257, −31.2 to 31.3 CI; mechanisms, including the effects of tree species on water Slope = 11.42 ± 1.71 SE, t = 6.68, 7.37–15.46 CI. For smaller availability, litter quantity and quality, the amount and trees, overall p-value < 0.001, Intercept = 0.80 ± 4.01 SE, composition of root exudates and the distribution of carbon t = 0.19, −10.28 to 8.68 CI; Slope = 9.30 ± 1.40 SE, t = 6.67, in the soil proﬁle have been proposed to explain how plant 12.60–6.01 CI. Unlogged forest is indicated by + and logged for- diversity could inﬂuence carbon storage in soils (Gleixner est by . The regressions illustrating the results for large and smaller trees were exactly the same because the Shannon–Wiener et al. 2005). Previous studies have also indicated that func- index for large and smaller trees depends on the percentage tional traits or the presence of speciﬁc species were better dominance of tree species that is large or smaller. predictors of ecosystems functioning than species diversity and richness (Kahmen et al. 2005; Kirby and Potvin 2007). The largest proportion of carbon pool in both logged and NPLD unlogged forests was contributed by trees similar to the studies of Kirby and Potvin (2007). Other According to Bunker et al. (2005), tropical forest car- Pioneer Pioneer 120 120 bon storage depends on species composition and on the 100 Shade bearer mode and manner in which species are lost. One of the key ﬁndings of this study was that the proportions of tree-stored carbon stocks contributed by species of different ecological 60 60 guilds were signiﬁcantly different due to the abundance of 40 pioneer species in the logged forests. This ﬁnding there- fore suggests that any future harvesting that affects the ecological guild composition and abundance of large trees could lead to impoverishment of carbon stocks in the Logged forest Unlogged forest forests. Figure 3. Contributions of tree species’ ecological guilds to total tree-stored carbon stocks. Error bars are ±95% CI. Conclusion a number of ways through which carbon can be lost in This study has shown that logging has long-term effects logged forests. However, carbon exports as logs is the most on tree diversity, because of the signiﬁcantly increased tree important and signiﬁcant loss of carbon stocks in logged species diversity and changes in species composition due forest and this is because the greatest amount of carbon in to the recruitment of pioneer species and a decrease in forests is stored in tree biomass (Kirby and Potvin 2007). the abundance of shade bearer species in logged forest Plant functional types signiﬁcantly affect vertical distri- compared to unlogged forest. In contrast, the effects of bution of soil carbon stocks (Jobbagy and Jackson 2000) logging on carbon stocks might be short term as carbon and this might explain the recorded differences in deep stocks are recovered in logged forest in the longer term. (15–30 cm) soil carbon stocks between the unlogged and The study also indicated tree species diversity was signif- logged forests. In this study, the difference in the mag- icantly positively associated with total soil carbon stocks. nitude of total carbon stocks in the logged and unlogged The ﬁndings from this study inform the need for more com- forests were found to be insigniﬁcant and this could be due parative data from other areas of West Africa on this very largely to the fact that our study was carried out decades important topic. –1 –1 Tree-stored carbon (Mg C per ha ) Soil organic carbon (Mg C per ha ) 20 22 24 26 28 30 224 A. Asase et al. Acknowledgements impact logging in southern Amazonia. Forest Ecol Manag. 219(2–3):199–215. The authors are very grateful to the Wildlife Services Division Fornara DA, Tilman D. 2008. Plant functional composition inﬂu- of the Ghana Forestry Commission (FC) and the managers of the ences rates of soil carbon and nitrogen accumulation. J Ecol. Bia Conservation Area, especially Messers Alex N. Akwoviah, 96(2):314–322. Richard Gyamﬁ Boakye, Fredrick Amankwa and Richard Ofori- Gleixner G, Kramer C, Hahn V, Sachse D. 2005. The effect of Amafo for their permission and support during the ﬁeld work. Mr. biodiversity on carbon storage in soils. In: Scherer-Lorenzen John Yaw Amponsah of the Ghana Herbarium at the Department M, Korner C, Schulze ED, editors. Forest diversity and of Botany, University of Ghana helped with identiﬁcation of function: temperate and boreal systems. Berlin (Germany): plants. Thanks to the Ecological Laboratory at the University of Springer. p.165–183. Ghana for laboratory analysis of soil samples. This work was car- Glenday J. 2006. Carbon storage and emissions offset potential in ried out with funding from UNESCO MAB Young Scientist’s an East African tropical rainforest. Forest Ecol Manag. 235 Award to the lead author. (1–3): 72–83. Hall JB, Swaine MD. 1976. Classiﬁcation and ecology of closed- canopy forest in Ghana. J Ecol. 64(5):913–951. References Hall JB, Swaine MD. 1981. Distribution and ecology of vascular Albrecht A, Kandji ST. 2003. Carbon sequestration in tropical plants in tropical rainforest: forest vegetation in Ghana. The agroforestry systems. Agr Ecosyst Environ. 99:15–27. Hague (the Netherlands): W. Junk. Anderson JM, Ingram JS. 1989. Tropical soil biology and fertility, Hall JS, Harris DJ, Medjibe V, Ashton PMS. 2003. The effects of selective logging on forest structure and tree species a handbook of methods. Wallingford (CT): Commonwealth composition in a central African forest: implications for man- Agricultural Bureau. 221 p. agement of conservation areas. Forest Ecol Manag. 183(1): Arets EJM. 2005. Long term responses of populations and 249–264. communities of trees to selective logging in tropical rain Hawthorne WD. 1993. Forest regeneration after logging. ODA forests in Guyana. Tropenbos-Guyana Series 13. George- Forestry Series No. 3 London (UK): ODA. town (Guyana): Tropenbos-Guyana Programme. Hawthorne WD, Abu-Juam M. 1995. Forest protection in Ghana: Asase A, Tetteh DK. 2010. The role of complex agroforestry sys- with particular reference to vegetation and plant species. tems in the conservation of forest tree diversity and structure Gland (Switzerland): IUCN. 203 p. in southeastern Ghana. Agroforest syst. 79(3):355–368. Hawthorne W, Jongkind C 2009. Woody plants of Western Asner GP, Knapp DE, Broadbent EN, Oliveira PJC, Keller African forests. A guide to the forest trees, shrubs and lianes M, Silva JN. 2005. Selective logging in Brazilian Amazon. from Senegal to Ghana. Royal Botanic Gardens, Kew, Surrey Science. 310(5747):480–482. (UK): Kew Publishing. 1023 p. Batjes NH. 2001. Options for increasing carbon sequestration in West African soils: an exploratory study with special focus Henry M, Besnard A, Asante WA, Eshun J, Adu-Bredu S, on Senegal. Land Degrad Dev. 12(2):131–142. Valentini R, Bernoux M, Saint-Andre L. 2010. Wood density, Blake GR. 1965. Bulk density in methods of soil analysis, Part 1. phytomass variations within and among trees, and allomet- Physical and mineralogical properties including statistics of ric equations in a tropical rainforest of Africa. Forest Ecol measurement and sampling. No. 9. Madison (WI): American Manag. 260(8):1375–1388. Society of Agronomy Inc. p. 374–377. Huston MA. 1994. Biological diversity: the coexistence of species Brown S, Gillespie AJ, Lugo AE. 1989. Biomass estimation on changing landscapes. Cambridge (UK): Cambridge methods for tropical forests with application to forest inven- University Press. 708 p. tory data. Forest Sci. 35(4):881–902. IPPC. 2001. Integrated Pollution Prevention and Control (IPPC): Brown KA, Gurevitch J 2004. Long-term impacts of logging reference document on best available techniques in the on forest biodiversity in Madagascar. Proc Natl Acad Sci. pulp and paper industry. Brussels (Belgium): European 101(16):6045–6049. Commission. Bunker DE, Declerk F, Bradford JC, Colwell RK, Perfecto Y, Jans L, Poorter L, Van Rompaey SAR, Bongers F. 1993. Gaps Phillips OL, Sankaran M, Naeem S. 2005. Species loss and and forest zones in tropical moist forest in Ivory Coast. Biotropica. 25(3):258–269. above-ground carbon storage in a tropical forest. Science. Jobbagy EG, Jackson RD. 2000. The vertical distribution of soil 310(5750):1029–1031. organic carbon and its relation to climate and vegetation. Ecol Cairns MA, Brown S, Helmer EH, Baumgardner GA. 1997. Roots Appl. 10(2):423–436. biomass allocation in the world’s upland forests. Oecologia. Kahmen A, Perner J, Audorff V, Weisser W, Buchmann N. 2005. 111(1):1–11. Effects of plant diversity, community composition and envi- Catovsky S, Kobe RK, Bazzaz FA. 2002. Nitrogen-induced ronmental parameters on productivity in montane European changes in seedling regeneration and dynamics of mixed grasslands. Oecologia. 142(4):606–616. conifer-broad-leaved forests. Ecol Appl. 12:1611–1625. Kirby KR, Potvin C. 2007. Variation in carbon storage among Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus tree species: implications for the management of a small D, Folster H, Fromard F, Higuchi N, Kira T, et al. 2005. scale carbon sink project. Forest Ecol Manag. 246(2–3): Tree allometry and improved estimation of carbon stocks and 208–221. balance in tropical forests. Oecologia. 145(1):87–99. Kuusipalo J, Jafarsidik Y, Ådjers G, Tuomela K. 1996. Population Crawley MC. 2007. The R book. West Sussex (UK): John Wiley & Sons Ltd. 942 p. dynamics of tree seedlings in a mixed dipterocarp rainforest Curtis JT, McIntosh RP. 1950. The interrelation of certain ana- before and after selective logging and crown liberation. Forest lytic and synthetic phytosociological characters. Ecology. Ecol Manag. 81(1–3):85–94. 31(3):434–455. Lewis SL, Lopez-Gonzalez G, Sonke B, Affum-Baffoe K, Baker Dickinson MB, Wigham DF, Hermann SM. 2000. Tree regenera- TR, Ojo L, Phillips OL, Reitsma J, White L, Comiskey J, tion in felling and natural treefall disturbance in a semide- et al. 2009. Increasing carbon storage in intact African cidous tropical forest in Mexico. Forest Ecol Manag. 134 tropical forests. Nature. 457(7232):1003–1006. (1–3):137–151. Loreau M, Naeem S, Inchaustil P, Bengtsson J, Grime JP, Feldpausch TR, Jirka S, Passos CAM, Jasper F, Riha SJ. 2005. Hector A, Hooper DU, Huston MA, Raffaellis D, Schmid B, When big trees fall: damage and carbon export by reduced et al. 2001. Biodiversity and ecosystem functioning: current International Journal of Biodiversity Science, Ecosystem Services & Management 225 knowledge and future challenges. Science. 294(5543): Kinzig A, et al. 2000. Global biodiversity scenarios for the 804–808. year 2100. Science. 287(5459):1770–1774. Mabberley DJ. 1983. Tropical rain forest ecology. Glasgow (UK): Sist P, Ferreira FN. 2007. Sustainability of reduced-impact Blackie. 156 p. logging in the Eastern Amazon. Forest Ecol Manag. 243 Magnusson WE, De Lima OP, Reis FQ, Higuchi N, Ramos (2–3):199–209. JF. 1999. Logging activity and tree regeneration in an Steinbeiss S, Bebler H, Engels C, Temperton VM, Buchmann N, Amazonian forest. Forest Ecol Manag. 113(1):67–74. Roscher CH, Kreutziger Y, Baade J, Habekost M, Gleixner Magurran AE. 1988. Ecological diversity and its measurement. G. 2008. Plant diversity positively affects short-term soil car- Cambridge (UK): Great Britain University Press. bon storage in experimental grasslands. Global Change Biol. Martin C. 1991. The rainforests of West Africa: ecology, threats, 14(12):2937–2949. conservation. Basel (Switzerland): Birkhauser Verlag. 235 p. Swaine MD, Agyeman VK. 2008. Enhanced tree recruitment fol- Nelson DW, Sommers LE. 1996. Total carbon, organic carbon, lowing logging in two forest reserves in Ghana. Biotropica. and organic matter. In: Page AL, Miller RH, Keeney DR, 40(3):370–374. editors. Methods of soil analysis, Part 2. 2nd ed. Madison Symonds P. 2001. Bio conservation area management plan. (WI): American Society of Agronomy Inc. p. 961–1010. Protected areas development programme. Ghana (West Norris K, Asase A, Collen B, Gockowksi J, Mason J, Phalan B, Africa): Wildlife Division. Wade A. 2010. Biodiversity in a forest-agriculture mosaic – Thenkabail PS, Stucky N, Griscom BW, Ashton MS, Diels J, Van the changing face of West African Rainforests. Biol Conserv. der Meer B, Enclona E. 2004. Biomass estimations and car- 143(10):2341–2350. bon stock calculations in the oil palm plantations of Africa Ofori-Frimpong K, Afrifa AA, Acquaye S. 2010. Impact of shade derived savannas using IKONOS data. Int Remote Sens. and cocoa plant densities on soil organic carbon sequestration 25(23):5447–5472. rates in a cocoa growing soil of Ghana. Afr J Environ Sci Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Technol. 4(9):621–624. Collingham YC, Barend F, Erasmus N, Ferreira De Siqueira Park A, Justiniano J, Fredericksen TS. 2005. Natural regen- M, Grainger A, et al. 2004. Extinction risk from climate eration and environmental relationships of tree species in change. Nature. 427(6970):145–148. logging gaps in a Bolivian tropical forest. Forest Ecol Manag. Van Gemerden BS, Olff H, Parren MPE, Bongres F. 2003. The 217(2–3):147–157. pristine rain forest? Remnants of historical human impacts Pinard MA, Cropper WP. 2000. Simulated effects of log- on current tree species composition and diversity. J Biogeogr. ging on carbon storage in dipterocarp forest. J Appl Ecol. 30(9):1381–1390. 37(2):267–283. Van Gemerden BS, Shu GN, Olff H. 2003. Recovery of conser- Pinard M, Howlett B, Davidson D. 1996. Site conditions limit vation values in central African rainforest after logging and pioneer tree recruitment after logging of dipterocarp forests shifting cultivation. Biodivers Conserv. 12(8):1553–1570. in Sabah, Malaysia. Biotropica. 28(1):2–12. Vandermeer J. 1989. The ecology of intercropping. Cambridge Primark R, Corlett R. 2005. Tropical rainforest: an ecological (UK): Cambridge University Press. p. 249. and biogeographically comparison. Oxford (UK): Blackwell Wade ASI, Asase A, Hadley P, Mason J, Ofori-Frimpong K, Publishing. p. 319. Preece D, Spring N, Norris K. 2010. Management strate- Putz FE, Pinard M. 1993. Reduced impact logging as a carbon gies for maximizing carbon storage and tree species diversity offset method. Conserv Biol. 7(4):755–757. in cocoa-growing landscapes. Agric Ecosyst Environ. 138 Ruiz-Jaen MC, Potvin C. 2010. Tree diversity explained patterns (3–4):342–334. of variation in ecosystem function in a Neotropical forest. Walkley A, Black IA. 1934. An examination of Degtjareff method Biotropica. 42(6):638–646. for determining soil organic matter and a proposed method of Saiz G, Bird MI, Domingues T, Schrodt F, Schwarz M, the chronic acid titration method. Soil Sci. 37(1):29–54. Feldpausch TR, Veenendaal E, Djagbletey G, Hien F, Whitmore TC. 1989. Canopy gaps and the two major groups of Compaore H, et al. 2012. Variation in soil carbon stocks forest trees. Ecology. 70(3):536–538. and their determinants across a precipitation gradient in West Williams M, Ryan CM, Rees RM, Sambane E, Fernando J, Grace Africa. Global Change Biol. 18(5):1670–1683. J. 2008. Carbon sequestration and biodiversity of re-growing Sala OE, Chapin III FS, Armesto JJ, Berlow E, Bloomﬁeld miombo woodlands in Mozambique. Forest Ecol Manag. J, Dirzo R, Huber-Sanwald E, Huenneke LF, Jackson RB, 254:145–155. 226 A. Asase et al. Appendix 1. Relative density, density, frequency, dominance and Importance Value Index (IVI) of species of large trees sampled in 25 m × 25 m plots with information on their family and ecological guilds. Rel. Rel. Relative Species Family Guild Density density Frequency freq. Dominance dominance I V I Albizia Mimosaceae NPLD 2.46 0.51 0.15 0.83 0.24 0.8 2.13 adianthifolia Alstonia boonei Apocynaceae Pioneer 4.92 1.02 0.23 1.24 0.19 0.61 2.87 Amphimas Caesalpiniaceae NPLD 2.46 0.51 0.08 0.41 0.03 0.09 1.01 pterocarpoides Aningeria Sapotaceae NPLD 1.23 0.26 0.08 0.41 0.01 0.03 0.7 altissima Anonidium mannii Annonaceae Shade bearer 1.23 0.26 0.08 0.41 0.14 0.46 1.12 Anthonotha Caesalpiniaceae Shade bearer 2.46 0.51 0.15 0.83 0.34 1.1 2.44 macrophylla Antiaris toxicaria Moraceae NPLD 2.46 0.51 0.15 0.83 0.09 0.28 1.62 Antrocaryon Anacardiaceae NPLD 1.23 0.26 0.08 0.41 0.03 0.11 0.78 micraster Baphia nitida Papilionaceae Shade bearer 32 6.65 0.69 3.72 0.4 1.31 11.68 Baphia pubescens Papilionaceae Pioneer 6.15 1.28 0.15 0.83 0.11 0.37 2.48 Blighia sapida Sapindaceae NPLD 1.23 0.26 0.08 0.41 0.04 0.14 0.8 Bombax Bombaceae Pioneer 2.46 0.51 0.15 0.83 0.53 1.72 3.06 buonopozense Buchholzia Capparaceae Shade bearer 3.69 0.77 0.15 0.83 0.06 0.18 1.78 coriacea Bussea sp Caesalpiniaceae Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.04 0.71 Bussea Caesalpiniaceae NPLD 9.85 2.05 0.31 1.65 1.11 3.63 7.33 occidentalis Calpocalyx Mimosaceae Shade bearer 11.08 2.3 0.23 1.24 0.29 0.93 4.47 brevibracteatus Canarium Burseraceae NPLD 1.23 0.26 0.08 0.41 0.01 0.03 0.7 schweinfurthii Ceiba pentandra Bombaceae Pioneer 1.23 0.26 0.08 0.41 0.08 0.27 0.93 Celtis Ulmaceae Pioneer 3.69 0.77 0.15 0.83 0.08 0.25 1.84 adolﬁ-friderici Celtis mildbraedii Ulmaceae Shade bearer 24.62 5.12 0.46 2.48 1.96 6.4 13.99 Chrysophyllum Sapotaceae Shade bearer 6.15 1.28 0.23 1.24 0.29 0.96 3.48 subnudum Cleistopholis Annonaceae Pioneer 1.23 0.26 0.08 0.41 0.04 0.14 0.81 pattens Cola gigantean Sterculiaceae NPLD 1.23 0.26 0.08 0.41 0.03 0.08 0.75 Cola lateritia Sterculiaceae Shade bearer 7.38 1.53 0.23 1.24 0.27 0.89 3.67 Cola millenii Sterculiaceae NPLD 1.23 0.26 0.08 0.41 0.01 0.04 0.7 Cordia millenii Boraginaceae Pioneer 1.23 0.26 0.08 0.41 1.32 4.32 4.99 Corynanthe Rubiaceae NPLD 41.85 8.7 0.69 3.72 3.06 10.01 22.42 pachyceras Dacryodes Burseraceae Shade bearer 1.23 0.26 0.08 0.41 0.07 0.23 0.9 klaineana Daniellia thurifera Caesalpiniaceae Pioneer 1.23 0.26 0.08 0.41 0.01 0.03 0.7 Dialium Caesalpiniaceae Shade bearer 30.77 6.39 0.62 3.31 0.98 3.2 12.9 aubrevillei Dialium dinklagei Caesalpiniaceae NPLD 2.46 0.51 0.08 0.41 0.09 0.31 1.23 Diospyros Ebenaceae Shade bearer 1.23 0.26 0.08 0.41 0.01 0.03 0.7 canaliculata Diospyros Ebenaceae Shade bearer 4.92 1.02 0.15 0.83 0.1 0.34 2.19 kamerunensis Diospyros Ebenaceae Shade bearer 1.23 0.26 0.08 0.41 0.01 0.03 0.7 monbuttensis Discoglypremna Euphorbiaceae Pioneer 12.31 2.56 0.08 0.41 0.15 0.49 3.46 caloneura Duboscia Tiliaceae NPLD 2.46 0.51 0.08 0.41 0.19 0.63 1.55 viridiﬂora Elaeis guineensis Palmaceae Pioneer 1.23 0.26 0.08 0.41 0.12 0.39 1.06 Enantia polycarpa Annonaceae Shade bearer 1.23 0.26 0.08 0.41 0.02 0.07 0.74 Entandrophragma Meliaceae NPLD 8.62 1.79 0.38 2.07 0.61 1.99 5.85 angolense (Continued) International Journal of Biodiversity Science, Ecosystem Services & Management 227 Appendix 1. (Continued). Rel. Rel. Relative Species Family Guild Density density Frequency freq. Dominance dominance I V I Entandrophragma Meliaceae NPLD 1.23 0.26 0.08 0.41 0.12 0.4 1.07 cylindricum Erythrophleum Caesalpiniaceae NPLD 1.23 0.26 0.08 0.41 0.02 0.05 0.72 ivorense Funtumia elastica Apocynaceae NPLD 1.23 0.26 0.08 0.41 0.08 0.26 0.93 Gilbertiodendron Caesalpiniaceae Swamp 1.23 0.26 0.08 0.41 0.01 0.03 0.7 limba Greenwayodendron Annonaceae Shade bearer 2.46 0.51 0.08 0.41 0.02 0.08 1 oliveri Griffonia Caesalpiniaceae Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.06 0.73 simplicifolia Guarea cedrata Meliaceae Shade bearer 4.92 1.02 0.31 1.65 0.22 0.7 3.38 Hannoa klaineana Simaroubaceae Pioneer 2.46 0.51 0.15 0.83 0.18 0.59 1.93 Hexalobus Annonaceae Shade bearer 2.46 0.51 0.15 0.83 0.13 0.43 1.77 crispiﬂorus Khaya Meliaceae NPLD 1.23 0.26 0.08 0.41 0.01 0.03 0.7 anthothetica Khaya Meliaceae NPLD 2.46 0.51 0.15 0.83 0.05 0.15 1.49 grandifoliola Khaya ivorensis Meliaceae NPLD 1.23 0.26 0.08 0.41 0.04 0.13 0.8 Lannea welwitschii Anacardiaceae Pioneer 2.46 0.51 0.08 0.41 0.03 0.1 1.03 Macaranga barteri Euphorbiaceae Pioneer 3.69 0.77 0.15 0.83 0.28 0.91 2.51 Macaranga Euphorbiaceae Pioneer 1.23 0.26 0.08 0.41 0.04 0.13 0.8 hurifolia Macaranga Euphorbiaceae Pioneer 1.23 0.26 0.08 0.41 0.03 0.09 0.76 heterophylla Mansonia Sterculiaceae NPLD 1.23 0.26 0.08 0.41 0.01 0.04 0.71 alttissima Mareya micrantha Euphorbiaceae Shade bearer 2.46 0.51 0.15 0.83 0.04 0.15 1.48 Millettia Papilionaceae Shade bearer 2.46 0.51 0.08 0.41 0.12 0.38 1.3 rhodantha Millettia Papilionaceae Savanna/ 1.23 0.26 0.08 0.41 0.01 0.04 0.71 thonningii non-forest Monodora Annonaceae Shade bearer 1.23 0.26 0.08 0.41 2.03 6.64 7.31 myristica Monodora Annonaceae Shade bearer 3.69 0.77 0.15 0.83 0.05 0.16 1.75 tenuifolia Morus mesozygia Moraceae Pioneer 1.23 0.26 0.08 0.41 0.02 0.06 0.73 Myria sp. Combretaceae Other/Unknown 1.23 0.26 0.08 0.41 0.03 0.09 0.75 Myrianthus Moraceae Shade bearer 7.38 1.53 0.31 1.65 0.26 0.86 4.05 arboreus Myrianthus Moraceae Shade bearer 4.92 1.02 0.15 0.83 0.14 0.44 2.29 libericus Nauclea Rubiaceae Pioneer 1.23 0.26 0.08 0.41 0.27 0.89 1.56 diderrichii Nesogordonia Sterculiaceae Shade bearer 11.08 2.3 0.62 3.31 0.85 2.79 8.4 papaverifera Ongokea gore Olacaceae NPLD 1.23 0.26 0.08 0.41 0.08 0.25 0.91 Panda oleosa Pandaceae Shade bearer 1.23 0.26 0.08 0.41 0.17 0.56 1.23 Pentaclethra Mimosaceae NPLD 1.23 0.26 0.08 0.41 0.02 0.06 0.73 macrophylla Phyllocosmus Ixonanthaceae Shade bearer 8.62 1.79 0.31 1.65 0.76 2.48 5.92 africanus Piptadeniastrum Mimosaceae NPLD 2.46 0.51 0.15 0.83 0.24 0.8 2.13 africanum Placodiscus boya Sapindaceae Shade bearer 2.46 0.51 0.08 0.41 0.02 0.08 1 Psydrax sp. Rubiaceae Pioneer 1.23 0.26 0.08 0.41 0.02 0.07 0.74 Pterygota Sterculiaceae NPLD 2.46 0.51 0.15 0.83 0.34 1.1 2.44 macrocarpa Pycnanthus Myristicaceae NPLD 7.38 1.53 0.38 2.07 1.65 5.38 8.98 angolensis Raphia hookeri Palmaceae Swamp 3.69 0.77 0.15 0.83 0.18 0.58 2.18 Ricinodendron Euphorbiaceae Pioneer 2.46 0.51 0.15 0.83 0.05 0.16 1.5 heudelotii (Continued) 228 A. Asase et al. Appendix 1. (Continued). Rel. Rel. Relative Species Family Guild Density density Frequency freq. Dominance dominance I V I Rinorea Violaceae Shade bearer 2.46 0.51 0.08 0.41 0.02 0.07 0.99 oblongifolia Sterculia oblonga Sterculiaceae NPLD 8.62 1.79 0.54 2.89 0.27 0.89 5.58 Sterculia Sterculiaceae NPLD 9.85 2.05 0.38 2.07 1.48 4.84 8.95 rhinopetala Strombosia Olacaceae Shade bearer 23.38 4.86 0.77 4.13 0.47 1.54 10.53 glaucescens Tabernaemontana Apocynaceae Shade bearer 1.23 0.26 0.08 0.41 0.01 0.03 0.7 africana Terminalia superba Combretaceae Pioneer 2.46 0.51 0.15 0.83 0.06 0.2 1.54 Tetrapleura Mimosaceae Pioneer 1.23 0.26 0.08 0.41 0.01 0.05 0.71 tetraptera Tetrorchidium Euphorbiaceae Pioneer 2.46 0.51 0.15 0.83 0.05 0.15 1.49 didymostemon Treculia africana Moraceae NPLD 1.23 0.26 0.08 0.41 0.03 0.1 0.77 Tricalysia discolor Rubiaceae Shade bearer 3.69 0.77 0.23 1.24 0.08 0.27 2.28 Trichilia Meliaceae NPLD 6.15 1.28 0.31 1.65 0.2 0.67 3.6 monadelpha Trichilia prieuriana Meliaceae NPLD 8.62 1.79 0.23 1.24 0.39 1.28 4.31 Trichilia tessmannii Meliaceae NPLD 1.23 0.26 0.08 0.41 0.02 0.08 0.75 Triplochiton Sterculiaceae Pioneer 6.15 1.28 0.23 1.24 3.98 13 15.52 scleroxylon Uvariodendron Annonaceae Shade bearer 4.92 1.02 0.23 1.24 0.05 0.17 2.43 angustifolium Vitex ferruginea Verbenaceae NPLD 2.46 0.51 0.15 0.83 0.14 0.47 1.8 Xylia evansii Mimosaceae NPLD 4.92 1.02 0.23 1.24 0.09 0.31 2.57 Xylopia aethiopica Annonaceae Pioneer 1.23 0.26 0.08 0.41 0.07 0.23 0.9 Xylopia sp. Annonaceae Shade bearer/ 2.46 0.51 0.08 0.41 0.06 0.2 1.12 Pioneer Xylopia villosa Annonaceae Shade bearer 1.23 0.26 0.08 0.41 0.03 0.1 0.77 Zanthoxylum gilletii Rutaceae Pioneer 11.08 2.3 0.69 3.72 0.35 1.14 7.16 Unknown IF 01 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.06 0.73 Unknown IF 01 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.04 0.71 Unknown IF 01 A3 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.08 0.26 0.93 Unknown IF 02 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.11 0.36 1.03 Unknown IF 02 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.03 0.7 Unknown IF 02 A3 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.4 1.29 1.96 Unknown IF 04 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.07 0.74 Unknown IF 04 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.06 0.73 Unknown LF 01 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.05 0.72 Unknown LF 01 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.03 0.7 Unknown LF 02 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.03 0.1 0.77 Unknown LF 03 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.07 0.74 Unknown LF 04 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.05 0.71 Unknown LF 04 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.06 0.73 Unknown LF 05 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.03 0.69 Unknown SF 01 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.05 0.15 0.82 Unknown SF 01 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.11 0.35 1.02 Unknown SF 03 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.03 0.08 0.75 International Journal of Biodiversity Science, Ecosystem Services & Management 229 Appendix 2. Relative density, density, frequency, dominance and Importance Value Index (IVI) of species of smaller trees sampled in 5 m × 5 m plots with information on their family and ecological guilds. Rel. Rel. Rel. Species Family Guild Density density Freq. freq Dominance dominance IVI Acacia Mimosaceae Other/Unknown 30.77 0.68 0.08 1.1 0.04 1.74 3.52 kamerunensis Agelaea nitida Connaraceae Other/Unknown 30.77 0.68 0.08 1.1 0 0.18 1.96 Alaﬁa sp. Apocynaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.35 2.13 Aningeria sp. Sapotaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.44 2.21 Anthonotha Caesalpiniaceae NPLD 30.77 0.68 0.08 1.1 0.04 1.74 3.52 fragrans Baphia nitida Papilionaceae Shade bearer 646.15 14.29 0.69 9.89 0.27 12.08 36.25 Blighia welwitschii Sapindaceae NPLD 30.77 0.68 0.08 1.1 0.03 1.33 3.11 Calpocalyx Mimosaceae Shade bearer 92.31 2.04 0.08 1.1 0.05 2.1 5.24 brevibracteatus Calycobolus Convolvulaceae Other/Unknown 30.77 0.68 0.08 1.1 0.04 1.74 3.52 heudelotii Celtis Ulmaceae Pioneer 30.77 0.68 0.08 1.1 0.01 0.24 2.02 adolﬁ-friderici Celtis mildbraedii Ulmaceae Shade bearer 92.31 2.04 0.08 1.1 0.09 4.06 7.2 Celtis zenkeri Ulmaceae NPLD 30.77 0.68 0.08 1.1 0.04 1.74 3.52 Cola caricifolia Sterculiaceae Pioneer 30.77 0.68 0.08 1.1 0.03 1.33 3.11 Cola millenii Sterculiaceae NPLD 30.77 0.68 0.08 1.1 0.02 0.68 2.46 Cola nitida Sterculiaceae Shade bearer 30.77 0.68 0.08 1.1 0 0.11 1.89 Dacryodes Burseraceae Shade bearer 30.77 0.68 0.08 1.1 0.03 1.33 3.11 klaineana Dialium aubrevillei Caesalpiniaceae Shade bearer 92.31 2.04 0.23 3.3 0.03 1.15 6.49 Diospyros Ebenaceae Pioneer 61.54 1.36 0.08 1.1 0.01 0.6 3.06 abyssinica Diospyros Ebenaceae Shade bearer 30.77 0.68 0.08 1.1 0.02 0.68 2.46 heudelotii Diospyros Ebenaceae Shade bearer 61.54 1.36 0.08 1.1 0.04 1.86 4.32 kamerunensis Diospyros Ebenaceae Shade bearer 30.77 0.68 0.08 1.1 0 0.11 1.89 soubreana Drypetes afzelii Euphorbiaceae Shade bearer 30.77 0.68 0.08 1.1 0.01 0.44 2.21 Drypetes chevalieri Euphorbiaceae Shade bearer 338.46 7.48 0.31 4.4 0.08 3.73 15.61 Drypetes gilgiana Euphorbiaceae Shade bearer 123.08 2.72 0.31 4.4 0.04 2 9.11 Enantia polycarpa Annonaceae Shade bearer 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Entandrophragma Meliaceae NPLD 30.77 0.68 0.08 1.1 0.02 0.98 2.76 angolense Eremospatha Palmaceae Other/Unknown 30.77 0.68 0.08 1.1 0 0.11 1.89 hookeri Euclinia longiﬂora Rubiaceae Shade bearer 92.31 2.04 0.15 2.2 0.03 1.55 5.79 Eugenla sp. Myrtaceae Other/Unknown 30.77 0.68 0.08 1.1 0 0.11 1.89 Greenwayodendron Annonaceae Shade bearer 30.77 0.68 0.08 1.1 0.02 0.85 2.63 oliveri Griffonia Caesalpiniaceae Other/Unknown 123.08 2.72 0.15 2.2 0.08 3.39 8.31 simplicifolia Guarea cedrata Meliaceae Shade bearer 30.77 0.68 0.08 1.1 0 0.11 1.89 Hexalobus Annonaceae Shade bearer 30.77 0.68 0.08 1.1 0 0.16 1.94 crispiﬂorus Hunteria eburnea Apocynaceae Shade bearer 30.77 0.68 0.08 1.1 0.05 2.3 4.08 Hypselodelphys Marantaceae Other/Unknown 61.54 1.36 0.08 1.1 0.02 0.87 3.33 poggeana Khaya grandifoliola Meliaceae NPLD 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Microdesmis Pandaceae Shade bearer 215.38 4.76 0.38 5.49 0.1 4.54 14.8 puberula Millettia Papilionaceae Other/Unknown 307.69 6.8 0.38 5.49 0.12 5.28 17.57 chrysophylla Myrtaceae Myrtaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Napoleonaea Lecythidaceae Shade bearer 92.31 2.04 0.15 2.2 0.03 1.36 5.6 vogelii Nesogordonia Sterculiaceae Shade bearer 61.54 1.36 0.08 1.1 0.07 2.97 5.43 papaverifera (Continued) 230 A. Asase et al. Appendix 2. (Continued). Rel. Rel. Rel. Species Family Guild Density density Freq. freq Dominance dominance IVI Newbouldonia Bignoniaceae Pioneer 30.77 0.68 0.08 1.1 0.01 0.44 2.21 laevis Pauridiantha Rubiaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 hirtella Piptostigma Annonaceae Shade bearer 184.62 4.08 0.08 1.1 0.07 3.19 8.37 fasciculatum Pleiocarpa mutica Apocynaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Rutidea sp. Rubiaceae Other/Unknown 61.54 1.36 0.08 1.1 0.02 1.12 3.58 Sterculia oblonga Sterculiaceae NPLD 30.77 0.68 0.08 1.1 0.06 2.72 4.5 Strombosia Olacaceae Shade bearer 584.62 12.93 0.54 7.69 0.36 16.2 36.81 glaucescens Strophanthus Apocynaceae Other/Unknown 30.77 0.68 0.08 1.1 0.02 0.74 2.52 hispidus Strychnos aculeate Loganiaceae Other/Unknown 61.54 1.36 0.15 2.2 0.08 3.82 7.38 Tetrorchidium Loganiaceae Pioneer 30.77 0.68 0.08 1.1 0.02 0.68 2.46 didymostemon Tiliacora dinklagei Menispermaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.28 2.06 Unknown SF 02 A1 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Unknown IF 01 A1 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.03 1.33 3.11 Unknown IF 01 A2 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.58 2.36 Unknown IF 01 A3 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.02 0.85 2.63 Unknown IF 01 A4 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biodiversity Science, Ecosystem Services & Management Taylor & Francis http://www.deepdyve.com/lp/taylor-francis/linkages-between-tree-diversity-and-carbon-stocks-in-unlogged-and-Z0r3Kw2pEW

Loading next page...

References (69)

P. Sist, Fabrício Ferreira (2007)
Sustainability of reduced-impact logging in the Eastern Amazon
Forest Ecology and Management, 243
G. Asner, D. Knapp, E. Broadbent, P. Oliveira, M. Keller, J. Silva (2005)
Selective Logging in the Brazilian Amazon
Science, 310
Claude Martin (1991)
The Rainforests of West Africa
E. Arets (2005)
Long-term responses of populations and communities of trees to selective logging in tropical rain forests in Guyana
T. Whitmore, J. Hall, M. Swaine (1981)
Distribution and Ecology of Vascular Plants in a Tropical Rain Forest. Forest Vegetation in Ghana.
Journal of Ecology, 70
W. Hawthorne, C. Jongkind (2008)
Woody Plants of Western African Forests: A Guide to the Forest Trees, Shrubs and Lianes from Senegal to Ghana
Barend Gemerden, H. Olff, M. Parren, F. Bongers (2003)
The pristine rain forest? Remnants of historical human impacts on current tree species composition and diversity
Journal of Biogeography, 30
D. Bunker, F. DeClerck, J. Bradford, Robert Colwell, I. Perfecto, O. Phillips, M. Sankaran, S. Naeem (2005)
Species Loss and Aboveground Carbon Storage in a Tropical Forest
Science, 310
T. Feldpausch, S. Jirka, C. Passos, F. Jasper, S. Riha (2005)
When big trees fall: Damage and carbon export by reduced impact logging in southern Amazonia
Forest Ecology and Management, 219
M. Pinard, W. Cropper (2000)
Simulated effects of logging on carbon storage in dipterocarp forest.
Journal of Applied Ecology, 37
Wm. Thomas, J. Denslow (1984)
Book review
Brittonia, 36
(2005)
Tropical rainforest: an ecological and biogeographically comparison
C. Thomas, A. Cameron, R. Green, R. Green, M. Bakkenes, L. Beaumont, Yvonne Collingham, B. Erasmus, Marinez Siqueira, A. Grainger, L. Hannah, L. Hughes, B. Huntley, A. Jaarsveld, G. Midgley, L. Miles, L. Miles, M. Ortega-Huerta, A. Peterson, O. Phillips, S. Williams (2004)
Extinction risk from climate change
Nature, 427
K. Brown, J. Gurevitch (2004)
Long-term impacts of logging on forest diversity in Madagascar.
Proceedings of the National Academy of Sciences of the United States of America, 101 16
A. Magurran (1990)
Ecological Diversity and Its Measurement
Biometrics, 46
Richard Nisbertt (1993)
The rainforests of West Africa: Ecology-threats-conservation
International Journal of Primatology, 14
O. Sala, F. Chapin, J. Armesto, Eric Berlow, J. Bloomfield, R. Dirzo, Elisabeth Huber-Sanwald, L. Huenneke, R. Jackson, A. Kinzig, R. Leemans, D. Lodge, H. Mooney, M. Oesterheld, N. Poff, M. Sykes, B. Walker, M. Walker, D. Wall (2000)
Global biodiversity scenarios for the year 2100.
Science, 287 5459
A. Park, M. Justiniano, T. Fredericksen (2005)
Natural regeneration and environmental relationships of tree species in logging gaps in a Bolivian tropical forest
Forest Ecology and Management, 217
Sandra Brown, A. Gillespie, A. Lugo (1989)
Biomass Estimation Methods for Tropical Forests with Applications to Forest Inventory Data
Forest Science, 35
M. Ramos-Peralonso (2014)
Integrated Pollution Prevention and Control (IPPC)
K. Norris, A. Asase, B. Collen, Jim Gockowksi, J. Mason, B. Phalan, A. Wade (2010)
Biodiversity in a forest-agriculture mosaic – The changing face of West African rainforests
Biological Conservation, 143
J. Hall, M. Swaine (1976)
Classification and ecology of closed-canopy forest in Ghana.
Journal of Ecology, 64
Michael Crawley (2022)
The R book
I. Baillie, J. Anderson, J. Ingram (1990)
Tropical Soil Biology and Fertility: A Handbook of Methods.
Journal of Ecology, 78
J. Curtis, R. McIntosh (1950)
The interrelations of certain analytic and synthetic phytosociological characters
Ecology, 31
N. Batjes (2001)
Options for increasing carbon sequestration in West African soils: an exploratory study with special focus on Senegal
Land Degradation & Development, 12
M. Pinard, Bruce Howlett, D. Davidson (1996)
SITE CONDITIONS LIMIT PIONEER TREE RECRUITMENT AFTER LOGGING OF DIPTEROCARP FORESTS IN SABAH, MALAYSIA
Biotropica, 28
G. Gleixner, C. Kramer, V. Hahn, D. Sachse (2005)
The effect of biodiversity on carbon storage in soils
D. Fornara, D. Tilman (2008)
Plant functional composition influences rates of soil carbon and nitrogen accumulation
Journal of Ecology, 96
T. Whitmore (1989)
Canopy Gaps and the Two Major Groups of Forest Trees
Ecology, 70
K. Ofori-Frimpong, A. Afrifa, S. Acquaye (2010)
Impact of shade and cocoa plant densities on soil organic carbon sequestration rates in a cocoa growing soil of Ghana
African Journal of Environmental Science and Technology, 4
M. Dickinson, D. Whigham, S. Hermann (2000)
Tree regeneration in felling and natural treefall disturbances in a semideciduous tropical forest in Mexico
Forest Ecology and Management, 134
T. Brncic (2007)
Woody Plants of Western African Forests
, 61
M. Scherer‐Lorenzen, C. Körner, E. Schulze (2005)
Forest diversity and function: temperate and boreal systems
G. Saiz, M. Bird, T. Domingues, F. Schrodt, M. Schwarz, T. Feldpausch, E. Veenendaal, G. Djagbletey, F. Hien, H. Compaore, A. Diallo, J. Lloyd (2012)
Variation in soil carbon stocks and their determinants across a precipitation gradient in West Africa
Global Change Biology, 18
S. Catovsky, R. Kobe, F. Bazzaz (2002)
NITROGEN-INDUCED CHANGES IN SEEDLING REGENERATION AND DYNAMICS OF MIXED CONIFER–BROAD-LEAVED FORESTS
Ecological Applications, 12
L. Jans, L. Poorter, R. Rompaey, F. Bongers (1993)
Gaps and forest zones in tropical moist forest in Ivory Coast.
Biotropica, 25
C. Black (1965)
Physical and mineralogical properties, including statistics of measurement and sampling
W. Hawthorne, M. Abu-Juam (1995)
Forest Protection in Ghana: With Particular Reference to Vegetation and Plant Species
F. Putz, M. Pinard (1993)
Reduced-Impact Logging as a Carbon-Offset Method
Conservation Biology, 7
F. Hepper, J. Hall, M. Swaine (1982)
Distribution and Ecology of Vascular Plants in a Tropical Rainforest. Forest Vegetation in Ghana
Kew Bulletin, 37
M. Swaine, V. Agyeman (2008)
Enhanced Tree Recruitment Following Logging in Two Forest Reserves in Ghana
Biotropica, 40
E. Jobbágy, R. Jackson (2000)
THE VERTICAL DISTRIBUTION OF SOIL ORGANIC CARBON AND ITS RELATION TO CLIMATE AND VEGETATION
Ecological Applications, 10
S. Lewis, G. Lopez-Gonzalez, B. Sonké, K. Affum‐Baffoe, T. Baker, L. Ojo, O. Phillips, J. Reitsma, L. White, J. Comiskey, Marie-Noël K, C. Ewango, T. Feldpausch, A. Hamilton, M. Gloor, T. Hart, A. Hladik, J. Lloyd, J. Lovett, J. Makana, Y. Malhi, Frank Mbago, H. Ndangalasi, J. Peacock, K. Peh, D. Sheil, T. Sunderland, M. Swaine, J. Taplin, David Taylor, S. Thomas, Raymond Votere, H. Wöll (2009)
Increasing carbon storage in intact African tropical forests
Nature, 457
J. Glenday (2006)
Carbon storage and emissions offset potential in an East African tropical rainforest
Forest Ecology and Management, 235
M. Loreau, S. Naeem, P. Inchausti, J. Bengtsson, J. Grime, A. Hector, D. Hooper, M. Huston, D. Raffaelli, B. Schmid, D. Tilman, D. Wardle (2001)
Biodiversity and Ecosystem Functioning: Current Knowledge and Future Challenges
Science, 294
María Ruiz-Jaen, C. Potvin (2010)
Tree Diversity Explains Variation in Ecosystem Function in a Neotropical Forest in Panama
Biotropica, 42
A. Kahmen, J. Perner, Volker Audorff, W. Weisser, N. Buchmann (2005)
Effects of plant diversity, community composition and environmental parameters on productivity in montane European grasslands
Oecologia, 142
Barend Gemerden, G. Shu, H. Olff (2003)
Recovery of conservation values in Central African rain forest after logging and shifting cultivation
Biodiversity & Conservation, 12
A. Wade, A. Asase, P. Hadley, J. Mason, K. Ofori-Frimpong, D. Preece, Nat Spring, K. Norris (2010)
Management strategies for maximizing carbon storage and tree species diversity in cocoa-growing landscapes
Agriculture, Ecosystems & Environment, 138
M. Henry, A. Besnard, W. Asante, J. Eshun, S. Adu‐Bredu, R. Valentini, M. Bernoux, L. Saint-André (2010)
Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa
Forest Ecology and Management, 260
A. Albrecht, S. Kandji (2003)
Carbon sequestration in tropical agroforestry systems
Agriculture, Ecosystems & Environment, 99
Jefferson Hall, David Harris, V. Medjibe, P. Ashton (2003)
The effects of selective logging on forest structure and tree species composition in a Central African forest: implications for management of conservation areas
Forest Ecology and Management, 183
M. Cairns, Sandra Brown, E. Helmer, Greg Baumgardner (1997)
Root biomass allocation in the world's upland forests
Oecologia, 111
A. Asase, Daniel Tetteh (2010)
The role of complex agroforestry systems in the conservation of forest tree diversity and structure in southeastern Ghana
Agroforestry Systems, 79
A. Walkley, I. Black (1934)
AN EXAMINATION OF THE DEGTJAREFF METHOD FOR DETERMINING SOIL ORGANIC MATTER, AND A PROPOSED MODIFICATION OF THE CHROMIC ACID TITRATION METHOD
Soil Science, 37
W. Magnusson, Odilon Lima, F. Reis, N. Higuchi, J. Ramos (1999)
Logging activity and tree regeneration in an Amazonian forest
Forest Ecology and Management, 113
M. Huston (1994)
Biological diversity : the coexistence of species on changing landscapes
(1993)
Forest regeneration after logging
S. Steinbeiss, H. Bessler, C. Engels, V. Temperton, N. Buchmann, C. Roscher, Yvonne Kreutziger, J. Baade, Maike Habekost, G. Gleixner (2008)
Plant diversity positively affects short‐term soil carbon storage in experimental grasslands
Global Change Biology, 14
M. Williams, C. Ryan, R. Rees, E. Sambane, J. Fernando, J. Grace (2008)
Carbon sequestration and biodiversity of re-growing miombo woodlands in Mozambique
Forest Ecology and Management, 254
J. Chave, Ae Andalo, Ae Brown, Ae Cairns, J. Chambers, A. Eamus, Ae Foïster, Ae Fromard, N. Higuchi, Ae Kira, J. Lescure, Ae Nelson, H. Ogawa, Ae Puig, Ae Rie´ra, Ae Yamakura, S. Brown, M. Cairns, D. Eamus, H. Foïster, F. Fromard, T. Kira, B. Rie´ra (2005)
Tree allometry and improved estimation of carbon stocks and balance in tropical forests
Oecologia, 145
P. Jacquard (1990)
The ecology of intercropping
Agriculture, Ecosystems & Environment, 32
J. Kuusipalo, Y. Jafarsidik, G. Ådjers, K. Tuomela (1996)
Population dynamics of tree seedlings in a mixed dipterocarp rainforest before and after logging and crown liberation
Forest Ecology and Management, 81
P. Thenkabail, N. Stucky, B. Griscom, M. Ashton, J. Diels, B. Meer, E. Enclona (2004)
Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data
International Journal of Remote Sensing, 25
(2001)
Bio conservation area management plan
J. Vandermeer (1989)
The Ecology of Intercropping by John H. Vandermeer
K. Kirby, C. Potvin (2007)
Variation in carbon storage among tree species: Implications for the management of a small-scale carbon sink project
Forest Ecology and Management, 246
(1965)
Bulk density in methods of soil analysis, Part 1

Publisher: Taylor & Francis
Copyright: Copyright Taylor & Francis Group, LLC
ISSN: 2151-3732
eISSN: 2151-3740
DOI: 10.1080/21513732.2012.707152
Publisher site: See Article on Publisher Site

Abstract

International Journal of Biodiversity Science, Ecosystem Services & Management Vol. 8, No. 3, September 2012, 217–230 Linkages between tree diversity and carbon stocks in unlogged and logged West African tropical forests a b a Alex Asase *, Bismark K. Asitoakor and Patrick K. Ekpe a b Department of Botany, University of Ghana, PO Box LG 55, Legon, Ghana; Environmental Science Programme, University of Ghana, PO Box LG 71, Legon, Ghana Understanding the long-term effects of logging disturbances on the linkages between tree diversity and carbon stocks is important for conservation efforts and mitigation of global climate change. This study was carried out in unlogged and 14–29 years post-logged forests in the Bia Conservation Area in southwest Ghana. The study results showed that both large (diameter at breast height or dbh ≥ 10 cm) and smaller (dbh ≤ 10 cm but ≥5 cm) tree diversity increased signiﬁcantly in logged forest compared with unlogged forest while tree dominance was similar between the two land-use types. Tree species guild composition was signiﬁcantly different due to the higher proportion of pioneer species of large trees in logged forest and shade bearer species of smaller trees in the unlogged forest. Total mean carbon stock was 322.8 Mg C per ha [95% Conﬁdence Interval (CI): 191.6–443.8] for logged forest and 211.2 Mg C per ha (95% CI: 196.9–228.0) for unlogged forest, although no signiﬁcant difference was detected (p > 0.05). There was a signiﬁcant interaction (p < 0.01) between ecological guilds and land use types in total tree-stored carbon stocks. The results of the study showed that logging has comparatively long-term effects on tree diversity while its effect on carbon stocks might only be short term. The ﬁndings from this study underscore the need for more comparative data from other areas in West Africa. Keywords: land use change; logging; biodiversity; ecosystem services; carbon storage Introduction harvesting of timber have increased in frequency and extent (Asner et al. 2005; Norris et al. 2010). The effects of log- Tropical forests are among the most complex and species- ging on ﬂora (Hawthorne 1993; Pinard et al. 2000; Hall rich ecosystems of the world (Martin 1991; Primark and et al. 2003; Asner et al. 2005; Swaine and Agyeman 2008) Corlett 2005) and store 40–50% of carbon in terrestrial and wider biodiversity (Norris et al. 2010) have been docu- vegetation (Lewis et al. 2009). Tree diversity is fundamen- mented. However, the effects of logging are highly variable tal to tropical forest biodiversity because it provides the due to differences in harvest intensity and logging practice resources and habitats for almost all other forest species as well as site differences due to factors such as topography, (Hall and Swaine 1976; Mabberly 1983; Huston 1994). soil and biota (Kuusipalo et al. 1996; Pinard et al. 1996; In tropical forests, tree diversity varies from place to place Magnusson et al. 1999; Dickinson et al. 2000; Arets 2005; mainly due to differences in biogeography, habitat and Park et al. 2005). disturbances (Whitmore 1989). In tropical forest ecosystems, carbon is stored in dif- In large parts of the African tropical forest biome, dis- ferent pools including plant biomass and soil. There is turbances due to hurricanes, river dynamics and volcanic evidence that plant assemblages with high species diver- activities are rare (Jans et al. 1993). However, the conver- sity may also promote a more efﬁcient use of resources sion of tropical primary forests into various land-use types and greater net primary production (Vandermer 1989), and has had serious impacts on distribution, community struc- consequently, high rate of carbon sequestration (Catovsky ture and population characteristics of ﬂora (Van Gemerden, et al. 2002; Kirby and Potvin 2007) compared with areas Olff, et al. 2003). Other disturbances such as windfall, with lower plant species diversity. The magnitude of carbon insect breaks and wild ﬁres have also been noted. Land use stocks in tropical forests depends on species composition changes, particularly tropical deforestation, are the leading (Bunker et al. 2005). Forest management provides oppor- cause of species extinction (Sala et al. 2000) and contribute tunities to conserve plant diversity as well as retain carbon about 25% of anthropogenic carbon emissions (IPPC 2001; stocks in vegetation and soils (Feldpausch et al. 2005). Thomas et al. 2004). The most signiﬁcant causes of land However, there is paucity of data on studies that have com- use change in West Africa are agricultural expansion, pared tree diversity and carbon stocks in unlogged and wood extraction and infrastructural extension (Norris et al. logged forests. The majority of studies have been carried 2010). Although deforestation, largely the conversion of out in the Amazon region (Feldpausch et al. 2005; Sist land to food crops or pasture, is the major destructive and Ferreira 2007) and hitherto none in the West African force in tropical forests; other forest disturbances such as *Corresponding author. Email: aasase@ug.edu.gh ISSN 2151-3732 print/ISSN 2151-3740 online © 2012 Taylor & Francis http://dx.doi.org/10.1080/21513732.2012.707152 http://www.tandfonline.com 218 A. Asase et al. region. Understanding the relationships between tree diver- distance between forest stands was 1 km and topography sity and carbon stocks in logged forests compared to was generally ﬂat among stands. unlogged forests is very important for conservation efforts and mitigation of global climate change in the West African Enumeration of tree diversity region. Large trees were enumerated within each 25 m × 25 m In this study, we investigated the linkages between forest stand selected in the two land-use types. Within each tree diversity and carbon stocks in relation to logging 25 m × 25 m forest stand, all trees with diameter at breast disturbances in West African tropical forests. The study height (dbh) ≥ 10 cm were individually identiﬁed and their was carried out in unlogged and 14–29 years post-logged dbh measured (1.3 m above ground). The 625 m plot size forests in the Bia Conservation Area in southwest Ghana. has been successfully used to sample forest trees in West Speciﬁcally, the study aims to address the question: what Africa (Hawthorne and Abu-Juam 1995; Van Gemerden, are the long-term effects of selective logging on tree Shu, Olff 2003). Smaller trees with dbh ≥ 5cmbut diversity (tree species diversity, dominance and species ≤ 10 cm were studied using 5 m × 5 m subplots nested ecological guild composition) and carbon stocks in tropical within the 625 m forest stands. One 5 m × 5 m subplot forests? was demarcated at the centre, that is, point of intersection of two perpendicular lines from the corners of each 625 m plot. All individual smaller trees within the 5 m × 5 m sub- Materials and methods plots were identiﬁed and their dbh were measured. Species Study area identiﬁcation and nomenclature of both large and smaller This study was carried out in the Bia Conservation Area trees follows Hawthorne and Jongkind (2009). located in the Juabeso-Bia District in southwest Ghana Tree species were classiﬁed according to their shade close to the Ghana–Côte d’Ivoire border. The area falls tolerance/ecological guilds (Hawthorne and Abu-Juam ◦ ◦ ◦ within latitude 6 32 Nto6 37 N and longitude 3 02 W 1995; Hall et al. 2003). Following Hawthorne and Abu- ◦ 2 to 3 08 W and covers a total area of 306 km .The Bia Juam (1995), pioneers are species for which seedlings need Conservation Area comprises the Bia National Park and sun to establish. Non-pioneer light demanders (NPLD) are Resource Reserve (Figure 1). The study area lies within the species that need gaps to develop beyond the sapling stage transition zone between Moist Semi-deciduous and Moist while shade bearer guild consist of species that can persist Evergreen Forest Zones of Ghana (Hall and Swaine 1981). and grow in understory shade at seedling and sapling stage The climate is that of a typical moist evergreen forest with (Hawthorne and Abu-Juam 1995). bimodal rainfall between May and June and also between September and October. Annual rainfall is 1500–1800 mm Estimation of tree biomass and carbon stocks and mean monthly temperatures are between 24 C and 28 C. Relative humidity is between 75% in the afternoon The above-ground biomass of trees was estimated using the and 90% in the night. The topography of the study area allometric model of Henry et al. (2010) which estimates 2.31 is generally ﬂat with elevations ranging between 168 m tree biomass, Y,as Y = 0.30 × dbh . This model was and 238 m (Symonds 2001). Logging activities in the study speciﬁcally developed for tropical African forest unlike the area started from 1981 and continued until 1998 under the equations of Chave et al. (2005) and Brown et al. (1989). Ghana selection system using salvage practices (Symonds The allometric model of Henry et al. (2010) included trees 2001). From 1982 to 1998, a total of 3169 trees belonging of different ecological guilds and was not biased towards to 27 tree species were harvested (Symonds 2001). reporting relatively high biomass for rapidly growing, low- density wood species such as pioneers. The dbh measures of trees during the enumeration of tree diversity were used. Sampling methods Mean biomass of palm from a previous study (Thenkabail Sampling took place in two broad land-use types, namely et al. 2004) was used as the biomass value for all individ- unlogged and logged forests within the study area. The ual palm species encountered as a result of the lower dbh logged forests were harvested for timber 14–29 years to biomass ratio of palm compared with other tree species before this study was performed. The logged and unlogged (Wade et al. 2010). Root biomass was estimated indi- forest stands were geographically interspersed within the rectly from above-ground biomass following the method study area. Satellite imagery and reconnaissance survey of Cairns et al. (1997) which estimates root as 24% of methods were used to identify logged forest stands that above-ground tree biomass. Tree-stored carbon stocks were were believed to be similar to unlogged forest stands before calculated by multiplying the sum of the above-ground and harvesting. The logged forest stands were in tree felling root biomass by 0.5 (Albrecht and Kandji 2003; Glenday gaps stands directly affected by felled trees. We avoided 2006). sampling skid trails, hauling tracks and loading bays. Five To determine soil organic carbon, soil samples were forest stands in unlogged forest and ﬁve in logged forest collected from two randomly selected spots in each of the of size 25 m × 25 m were randomly sampled for data 25 m × 25 m forest stands enumerated for plant diversity collection taken into consideration the range of variation following the method of previous works (Ofori-Frimpong within the land use types in the study area. The minimum et al. 2010; Wade et al. 2010). Soil samples were collected International Journal of Biodiversity Science, Ecosystem Services & Management 219 3° 11′ 40″ W3° 9′ 35″ W3° 7′ 30″ W3° 5′ 25″ W3° 3′ 20″ W3° 1′ 15″ W2° 59′ 10″ W 3° 11′ 40″ W 3° 9′ 35″ W3° 7′ 30″ W3° 5′ 25″ W3° 3′ 20″ W3° 1′ 15″ W2° 59′ 10″ W km Figure 1. Map of the study area. at two different depths, namely 0–15 cm and 15–30 cm, Bulk density was determined for every soil sample col- because soil carbon stocks vary with depth. Previous lected following the core method (Blake 1965). The soil works have sampled soil carbon stocks in the tropics at organic carbon content of soil per hectare was ﬁnally deter- similar depths (Kirby and Potvin 2007; Williams et al. mined using the formula: [%C] × [bulk density] × [soil –3 2008). Percentage soil organic carbon was determined depth], with bulk density measured in g cm and soil depth using Walkley–Black method (Walkley and Black 1934); in cm (Kirby and Potvin 2007). as modiﬁed by Anderson and Ingram (1989) and cor- rected for the un-oxidized organic carbon as described by Data analysis Nelson and Sommers (1996). Percentage soil moisture was Species diversity was evaluated using Shannon–Wiener calculated using the following formula: index (Magguran 1988): % soil moisture H = p ln p (2) i i i=1 Fresh weight of soil − Constant dried weight of soil = × 100% where s is the total number of species and p is the Constant dried weight of soil (1) relative abundance of the species i. Basal area of trees 6° 19′ 10″ N6° 21′ 15″ N6° 23′ 20″ N6° 25′ 25″ N6° 27′ 30″ N6° 29′ 35″ N6° 31′ 40″ N6° 33′ 45″ N6° 35′ 50″ N6° 37′ 55″ N 6° 19′ 10″ N6° 21′ 15″ N6° 23′ 20″ N6° 25′ 25″ N6° 27′ 30″ N6° 29′ 35″ N6° 31′ 40″ N6° 33′ 45″ N6° 35′ 50″ N6° 37′ 55″ N 220 A. Asase et al. per hectare (dominance) was estimated from the for- with information on their ecological guilds, density, fre- mula 0.0000785d , where d is dbh per tree (Asase quency, dominance and IVI are presented in Appendix 1. and Tetteh 2010). Calculation of density and frequency A total of 49 large tree species were found in the unlogged of trees follows standard methods (Curtis and McIntosh forest, while 63 species were encountered in the logged 1950). Importance Value Index (IVI) of trees was calcu- forest. Mean Shannon–Wiener index was signiﬁcantly lated as the sum of their relative density, relative frequency (p < 0.01) higher in the logged forest compared with and relative dominance. Homoscedasticity of variance was the unlogged forest. However, no signiﬁcant difference analysed using Fisher’s F-test. Student’s t-test was used (p > 0.05) was found in large tree dominance between to compare means with normal errors, whereas Wilcoxon logged and unlogged forests (Table 2). The ecological signed-rank test was used for means with non-normal guild composition of species in the two types of land errors. The shapiro.test function of R statistical software use was signiﬁcantly different (ANOVA, p < 0.05) due was used to test normality of data. The proportions of to the predominance of pioneer species in the logged species in different ecological guilds in the unlogged and forest. logged forests were analysed using analysis of variance With regard to smaller trees, 112 individual trees (ANOVA). belonging to 43 species were encountered (Appendix 2). Carbon stocks were estimated for each plot, extrapo- The logged forest contained 25 species, while 26 species lated to Mg per ha and means determined for each land were identiﬁed in the unlogged forest. Similar to use type and carbon pool. The mean carbon content of the large trees, Shannon–Wiener index was signiﬁcantly two soil cores sampled per plot was used in calculating soil (p < 0.05) higher in the logged forest compared carbon stocks per hectare. Coefﬁcient of variation (CV) of with the unlogged forest and no signiﬁcant difference soil organic carbon stocks measurements was 10.4% for (p > 0.05) was detected in tree dominance. There was unlogged forest and 5.6% for logged forest. The magni- a signiﬁcantly high proportion (ANOVA, p < 0.01) of tudes of soil carbon stocks in this study are comparable smaller-tree shade bearer species in the unlogged forest to those published for West Africa (Batjes 2001; Saiz compared with the logged forest. et al. 2012). Bootstrapping methodology with 10,000 ran- Total mean carbon stocks were not signiﬁcantly differ- dom sampling with replacement was used to estimate 95% ent between the logged and the unlogged forests (p > 0.05) Conﬁdence Intervals (CIs) of the standard error of means. (Table 3). Tree-stored carbon contributed the largest pro- Bias-corrected accelerated percentiles conﬁdence intervals portion of carbon stocks in both logged forest (91.3%) and are reported (Crawley 2007). unlogged forest (89.1%), although the difference between The relationships between tree species diversity and the two types of land use was insigniﬁcant at 95% CI. Total soil carbon stocks were non-linear and models were con- soil organic carbon was signiﬁcantly higher (p < 0.05) in structed using the ‘nls’ function of R statistical software the logged forest compared with unlogged forest. (www.r-project.org). The same model explained the rela- tionships between total soil organic carbon stocks, and both Relationships between tree diversity and carbon stocks large and smaller tree species diversity: There were no signiﬁcant correlations between tree species B − A diversity with total carbon stocks and total tree-stored car- y = A + (3) −x C 1 + e bon stocks (Table 4). However, both large and smaller tree species diversity were signiﬁcantly related to total soil where y is soil organic carbon, x is tree species diversity organic carbon stocks (p < 0.001 for both large and smaller (Shannon–Wiener index), A is maximum soil carbon which trees). Dominance of both large and smaller trees was the soil holds while B is minimum soil carbon stock, and C not related to soil carbon stocks. The mechanistic forms is the slope of the non-linear function. of the relationships between tree diversity and soil car- Factorial ANOVA was used to investigate the interac- bon stocks were non-linear showing asymptotes at both tions between ecological guilds and land use types in terms left- and right-hand sides (Figure 2). The estimated val- of tree-stored carbon stocks. Statistical analyses were exe- ues for the parameters of the model were: A = 22.1 ± 1.0 cuted with R version 2.7.2 (R Core Development Team (t = 21.8, p < 0.01), B = 29.7 ± 2.5 (t = 12.1, p < 0.001), 2009). C = 3.7 ± 0.1 (t = 42.9, p < 0.001) for large tree species diversity and A = 22.1 ± 1.1 (t = 20.9, p < 0.01), B = 29.6 ± 2.2 (t = 13.4, p < 0.001), C = 2.9 ± 0.1 Results (t = 27.7, p < 0.001) for smaller tree species diver- Tree diversity and carbon stocks in unlogged and logged sity. A signiﬁcant interaction between ecological guilds forests and land use types was detected in total tree-stored car- In total, 310 individual large trees belonging to 87 taxa of bon stocks (ANOVA; p < 0.01). There were signiﬁcant which 80 were identiﬁed to species level were encountered differences in carbon stocks contributed by trees belong- during the study. General site characteristics of the forest ing to NPLD (p < 0.01), pioneer (p < 0.05) and other stands with information on number of trees encountered (p < 0.05) species ecological guilds to total tree-stored are presented in Table 1. The species of trees identiﬁed carbon stocks (Figure 3). International Journal of Biodiversity Science, Ecosystem Services & Management 221 Table 1. General characteristics of forest stands sampled. Estimated height of Soil moisture Soil bulk density Shannon–Wiener Shannon– Altitude Number of trees tallest (% weight) (g/cm ) (0–30 cm index for large Wiener index Total carbon stock Forest stand Land use type (±m) (dbh ≥ 5cm) tree (m) (0–30 cm depth) depth) trees for smaller trees (Mg/ha) IF 01 Unlogged 9 48 39 78.48 ± 1.70 4.19 ± 0.31 2.55 1.64 239.73 forest IF 02 Unlogged 4 41 40 72.97 ± 5.50 3.91 ± 0.81 3.12 2.17 200.10 forest IF 03 Unlogged 4 35 38 76.62 ± 4.82 4.40 ± 0.38 3.39 2.50 220.58 forest IF 04 Unlogged 6 34 42 72.11 ± 4.37 4.34 ± 0.28 3.57 2.71 206.98 forest IF 05 Unlogged 17 36 50 76.03 ± 2.29 4.43 ± 0.13 3.69 2.85 188.77 forest LF 01 Logged forest 11 52 49 71.37 ± 9.25 3.33 ± 0.72 3.77 2.96 521.43 LF 02 Logged forest 14 47 30 72.90 ± 4.64 4.28 ± 0.28 3.84 3.03 122.26 LF 03 Logged forest 17 45 45 70.70 ± 1.67 4.27 ± 0.09 3.89 3.11 378.85 LF 04 Logged forest 5 39 30 70.43 ± 14.08 3.87 ± 0.49 3.94 3.17 192.71 LF 05 Logged forest 13 45 35 68.38 ± 3.93 3.81 ± 0.15 3.97 3.23 398.72 222 A. Asase et al. Table 2. Tree species diversity and dominance in logged and unlogged West African forests. Statistical method of comparison of means Parameters Logged forest Unlogged forest (p-value) Shannon–Wiener index Large tree 3.9 [3.8–3.9] 3.3 [2.7–3.5] Wilcoxon test (0.012) Smaller tree 3.1 [3.0–3.2] 2.4 [1.9–2.7] Wilcoxon test (0.0079) Tree dominance (m/ha) Large tree 34.6 [20.9–46.5] 25.5 [23.6–28.1] Wilcoxon test (0.69) Smaller tree 2.1 [1.7–2.8] 2.7 [1.8–3.4] Student’s t-test (0.32) Note: 95% bootstrapped Conﬁdence Interval of standard error provided in squared brackets. Table 3. Mean carbon stocks (Mg C per ha) arranged according to carbon pools. Statistical method of comparison of means Carbon pool Logged forest Unlogged forest (p-value) Tree-stored carbon Large trees 287.7 [155.9–410.2] 179.1 [165.3–194.0] Wilcoxon test (0.69) Smaller trees 7.0 [5.3–9.2] 9.0 [6.3–11.8] Student’s t-test (0.21) Total tree-stored carbon 294.6 [173.2–416.1] 188.2 [173.2–203.1] Wilcoxon test (0.55) Soil organic carbon 0–15 cm depth 13.7 [12.5–14.6] 11.8 [10.9–12.3] Student’s t-test (0.04) 15–30 cm depth 14.6 [13.4–15.6] 11.3 [10.6–12.3] Student’s t-test (0.0044) Total soil organic carbon 28.1 [26.4–29.5] 23.1 [21.4–24.8] Student’s t-test (0.0049) Total carbon stocks 322.8 [191.6–443.8] 211.2 [196.9–228.0] Wilcoxon test (0.55) Note: 95% bootstrapped Conﬁdence Interval of standard error provided in squared brackets. Table 4. Results of correlation analyses between carbon stocks and tree diversity (species diversity and dominance). Carbon pool Variable Correlation coefﬁcient, p-value Total carbon stocks Shannon–Wiener index of large trees 0.26, 0.47 Shannon–Wiener index of smaller trees 0.28, 0.43 Total tree-stored carbon Shannon–Wiener index of large trees 0.24, 0.50 Shannon–Wiener index of smaller trees 0.26, 0.46 Total soil organic carbon Dominance of large trees 0.18, 0.61 Dominance of smaller trees −0.58, 0.12 Discussion shade at both seedling and sapling stages unlike pioneer species (Hawthorne and Abu-Juam 1995). Our study was Long-term effects of selective logging on tree diversity performed after 14–29 years of logging operations and and carbon stocks therefore means that the effects of logging on tree diversity The results of this study showed that logging has signiﬁcant are long term. According to Brown and Gurevitch (2004), effect on tree diversity, although carbon stocks are compa- invasive plants are not transient members of logged tropical rable between unlogged and logged forests. With regard to forest but maintain long-term viable populations after their tree diversity, signiﬁcantly increased tree species diversity initial colonization and can dramatically alter the trajectory and abundance of large-tree pioneer species were recorded of forest succession. in the logged forest while that of smaller-tree shade bearer For carbon stocks, a number of previous studies have species decreased in logged forests. Other studies exam- shown that selective logging can impact negatively on ining the effects of logging on tree diversity have shown forest carbon stocks (Putz and Pinard 1993; Pinard and that the most common trend is an increase in pioneer Cropper 2000; Asner et al. 2005). For example, Asner et al. species in those areas disturbed by logging activities sim- (2005) reported that a gross ﬂux of approximately 0.1 bil- ilar to the ﬁndings of this study (Kuusipalo et al. 1996; lion metric tons of carbon was destined for release to the Arets 2005; Swaine and Agyeman 2008). The abundance atmosphere each year in ﬁve timber-producing states of the of shade bearer trees signiﬁcantly decreased in logged for- Brazilian Amazon. Putz and Pinard (1993) estimated that est due to decreased shading following tree harvesting. conventional selective logging in Sabah, Malaysia reduced Shade bearer species can persist and grow in understory carbon stocks of unlogged forest by one-third. There are International Journal of Biodiversity Science, Ecosystem Services & Management 223 after logging activities. Therefore, the effects of logging on carbon stocks unlike that of tree diversity are short term as carbon stocks can recover from previously logged forests in the longer term. Relationships between tree diversity and carbon stocks in logged and unlogged forests The relationships between biodiversity and ecosystem function have emerged as a central issue in ecological and environmental sciences during the last decade (Loreau et al. 2001). While the majority of studies have focused on tree diversity and production, our study explored the link- ages between tree diversity and carbon storage in logged and unlogged forests. Previous studies on the relationships 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 between plant diversity and carbon storage have shown that Shannon–Wiener index both tree diversity and composition have important impacts on carbon dynamics (Fornara and Tilman 2008; Steinbeiss Figure 2. Relationships between total soil organic carbon stocks et al. 2008; Ruiz-Jaen and Potvin 2010). A number of and tree species diversity. For large trees, overall p-value < 0.001, Intercept = 16.29 ± 6.3 SE, t =−257, −31.2 to 31.3 CI; mechanisms, including the effects of tree species on water Slope = 11.42 ± 1.71 SE, t = 6.68, 7.37–15.46 CI. For smaller availability, litter quantity and quality, the amount and trees, overall p-value < 0.001, Intercept = 0.80 ± 4.01 SE, composition of root exudates and the distribution of carbon t = 0.19, −10.28 to 8.68 CI; Slope = 9.30 ± 1.40 SE, t = 6.67, in the soil proﬁle have been proposed to explain how plant 12.60–6.01 CI. Unlogged forest is indicated by + and logged for- diversity could inﬂuence carbon storage in soils (Gleixner est by . The regressions illustrating the results for large and smaller trees were exactly the same because the Shannon–Wiener et al. 2005). Previous studies have also indicated that func- index for large and smaller trees depends on the percentage tional traits or the presence of speciﬁc species were better dominance of tree species that is large or smaller. predictors of ecosystems functioning than species diversity and richness (Kahmen et al. 2005; Kirby and Potvin 2007). The largest proportion of carbon pool in both logged and NPLD unlogged forests was contributed by trees similar to the studies of Kirby and Potvin (2007). Other According to Bunker et al. (2005), tropical forest car- Pioneer Pioneer 120 120 bon storage depends on species composition and on the 100 Shade bearer mode and manner in which species are lost. One of the key ﬁndings of this study was that the proportions of tree-stored carbon stocks contributed by species of different ecological 60 60 guilds were signiﬁcantly different due to the abundance of 40 pioneer species in the logged forests. This ﬁnding there- fore suggests that any future harvesting that affects the ecological guild composition and abundance of large trees could lead to impoverishment of carbon stocks in the Logged forest Unlogged forest forests. Figure 3. Contributions of tree species’ ecological guilds to total tree-stored carbon stocks. Error bars are ±95% CI. Conclusion a number of ways through which carbon can be lost in This study has shown that logging has long-term effects logged forests. However, carbon exports as logs is the most on tree diversity, because of the signiﬁcantly increased tree important and signiﬁcant loss of carbon stocks in logged species diversity and changes in species composition due forest and this is because the greatest amount of carbon in to the recruitment of pioneer species and a decrease in forests is stored in tree biomass (Kirby and Potvin 2007). the abundance of shade bearer species in logged forest Plant functional types signiﬁcantly affect vertical distri- compared to unlogged forest. In contrast, the effects of bution of soil carbon stocks (Jobbagy and Jackson 2000) logging on carbon stocks might be short term as carbon and this might explain the recorded differences in deep stocks are recovered in logged forest in the longer term. (15–30 cm) soil carbon stocks between the unlogged and The study also indicated tree species diversity was signif- logged forests. In this study, the difference in the mag- icantly positively associated with total soil carbon stocks. nitude of total carbon stocks in the logged and unlogged The ﬁndings from this study inform the need for more com- forests were found to be insigniﬁcant and this could be due parative data from other areas of West Africa on this very largely to the fact that our study was carried out decades important topic. –1 –1 Tree-stored carbon (Mg C per ha ) Soil organic carbon (Mg C per ha ) 20 22 24 26 28 30 224 A. Asase et al. Acknowledgements impact logging in southern Amazonia. Forest Ecol Manag. 219(2–3):199–215. The authors are very grateful to the Wildlife Services Division Fornara DA, Tilman D. 2008. Plant functional composition inﬂu- of the Ghana Forestry Commission (FC) and the managers of the ences rates of soil carbon and nitrogen accumulation. J Ecol. Bia Conservation Area, especially Messers Alex N. Akwoviah, 96(2):314–322. Richard Gyamﬁ Boakye, Fredrick Amankwa and Richard Ofori- Gleixner G, Kramer C, Hahn V, Sachse D. 2005. The effect of Amafo for their permission and support during the ﬁeld work. Mr. biodiversity on carbon storage in soils. In: Scherer-Lorenzen John Yaw Amponsah of the Ghana Herbarium at the Department M, Korner C, Schulze ED, editors. Forest diversity and of Botany, University of Ghana helped with identiﬁcation of function: temperate and boreal systems. Berlin (Germany): plants. Thanks to the Ecological Laboratory at the University of Springer. p.165–183. Ghana for laboratory analysis of soil samples. This work was car- Glenday J. 2006. Carbon storage and emissions offset potential in ried out with funding from UNESCO MAB Young Scientist’s an East African tropical rainforest. Forest Ecol Manag. 235 Award to the lead author. (1–3): 72–83. Hall JB, Swaine MD. 1976. Classiﬁcation and ecology of closed- canopy forest in Ghana. J Ecol. 64(5):913–951. References Hall JB, Swaine MD. 1981. Distribution and ecology of vascular Albrecht A, Kandji ST. 2003. Carbon sequestration in tropical plants in tropical rainforest: forest vegetation in Ghana. The agroforestry systems. Agr Ecosyst Environ. 99:15–27. Hague (the Netherlands): W. Junk. Anderson JM, Ingram JS. 1989. Tropical soil biology and fertility, Hall JS, Harris DJ, Medjibe V, Ashton PMS. 2003. The effects of selective logging on forest structure and tree species a handbook of methods. Wallingford (CT): Commonwealth composition in a central African forest: implications for man- Agricultural Bureau. 221 p. agement of conservation areas. Forest Ecol Manag. 183(1): Arets EJM. 2005. Long term responses of populations and 249–264. communities of trees to selective logging in tropical rain Hawthorne WD. 1993. Forest regeneration after logging. ODA forests in Guyana. Tropenbos-Guyana Series 13. George- Forestry Series No. 3 London (UK): ODA. town (Guyana): Tropenbos-Guyana Programme. Hawthorne WD, Abu-Juam M. 1995. Forest protection in Ghana: Asase A, Tetteh DK. 2010. The role of complex agroforestry sys- with particular reference to vegetation and plant species. tems in the conservation of forest tree diversity and structure Gland (Switzerland): IUCN. 203 p. in southeastern Ghana. Agroforest syst. 79(3):355–368. Hawthorne W, Jongkind C 2009. Woody plants of Western Asner GP, Knapp DE, Broadbent EN, Oliveira PJC, Keller African forests. A guide to the forest trees, shrubs and lianes M, Silva JN. 2005. Selective logging in Brazilian Amazon. from Senegal to Ghana. Royal Botanic Gardens, Kew, Surrey Science. 310(5747):480–482. (UK): Kew Publishing. 1023 p. Batjes NH. 2001. Options for increasing carbon sequestration in West African soils: an exploratory study with special focus Henry M, Besnard A, Asante WA, Eshun J, Adu-Bredu S, on Senegal. Land Degrad Dev. 12(2):131–142. Valentini R, Bernoux M, Saint-Andre L. 2010. Wood density, Blake GR. 1965. Bulk density in methods of soil analysis, Part 1. phytomass variations within and among trees, and allomet- Physical and mineralogical properties including statistics of ric equations in a tropical rainforest of Africa. Forest Ecol measurement and sampling. No. 9. Madison (WI): American Manag. 260(8):1375–1388. Society of Agronomy Inc. p. 374–377. Huston MA. 1994. Biological diversity: the coexistence of species Brown S, Gillespie AJ, Lugo AE. 1989. Biomass estimation on changing landscapes. Cambridge (UK): Cambridge methods for tropical forests with application to forest inven- University Press. 708 p. tory data. Forest Sci. 35(4):881–902. IPPC. 2001. Integrated Pollution Prevention and Control (IPPC): Brown KA, Gurevitch J 2004. Long-term impacts of logging reference document on best available techniques in the on forest biodiversity in Madagascar. Proc Natl Acad Sci. pulp and paper industry. Brussels (Belgium): European 101(16):6045–6049. Commission. Bunker DE, Declerk F, Bradford JC, Colwell RK, Perfecto Y, Jans L, Poorter L, Van Rompaey SAR, Bongers F. 1993. Gaps Phillips OL, Sankaran M, Naeem S. 2005. Species loss and and forest zones in tropical moist forest in Ivory Coast. Biotropica. 25(3):258–269. above-ground carbon storage in a tropical forest. Science. Jobbagy EG, Jackson RD. 2000. The vertical distribution of soil 310(5750):1029–1031. organic carbon and its relation to climate and vegetation. Ecol Cairns MA, Brown S, Helmer EH, Baumgardner GA. 1997. Roots Appl. 10(2):423–436. biomass allocation in the world’s upland forests. Oecologia. Kahmen A, Perner J, Audorff V, Weisser W, Buchmann N. 2005. 111(1):1–11. Effects of plant diversity, community composition and envi- Catovsky S, Kobe RK, Bazzaz FA. 2002. Nitrogen-induced ronmental parameters on productivity in montane European changes in seedling regeneration and dynamics of mixed grasslands. Oecologia. 142(4):606–616. conifer-broad-leaved forests. Ecol Appl. 12:1611–1625. Kirby KR, Potvin C. 2007. Variation in carbon storage among Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus tree species: implications for the management of a small D, Folster H, Fromard F, Higuchi N, Kira T, et al. 2005. scale carbon sink project. Forest Ecol Manag. 246(2–3): Tree allometry and improved estimation of carbon stocks and 208–221. balance in tropical forests. Oecologia. 145(1):87–99. Kuusipalo J, Jafarsidik Y, Ådjers G, Tuomela K. 1996. Population Crawley MC. 2007. The R book. West Sussex (UK): John Wiley & Sons Ltd. 942 p. dynamics of tree seedlings in a mixed dipterocarp rainforest Curtis JT, McIntosh RP. 1950. The interrelation of certain ana- before and after selective logging and crown liberation. Forest lytic and synthetic phytosociological characters. Ecology. Ecol Manag. 81(1–3):85–94. 31(3):434–455. Lewis SL, Lopez-Gonzalez G, Sonke B, Affum-Baffoe K, Baker Dickinson MB, Wigham DF, Hermann SM. 2000. Tree regenera- TR, Ojo L, Phillips OL, Reitsma J, White L, Comiskey J, tion in felling and natural treefall disturbance in a semide- et al. 2009. Increasing carbon storage in intact African cidous tropical forest in Mexico. Forest Ecol Manag. 134 tropical forests. Nature. 457(7232):1003–1006. (1–3):137–151. Loreau M, Naeem S, Inchaustil P, Bengtsson J, Grime JP, Feldpausch TR, Jirka S, Passos CAM, Jasper F, Riha SJ. 2005. Hector A, Hooper DU, Huston MA, Raffaellis D, Schmid B, When big trees fall: damage and carbon export by reduced et al. 2001. Biodiversity and ecosystem functioning: current International Journal of Biodiversity Science, Ecosystem Services & Management 225 knowledge and future challenges. Science. 294(5543): Kinzig A, et al. 2000. Global biodiversity scenarios for the 804–808. year 2100. Science. 287(5459):1770–1774. Mabberley DJ. 1983. Tropical rain forest ecology. Glasgow (UK): Sist P, Ferreira FN. 2007. Sustainability of reduced-impact Blackie. 156 p. logging in the Eastern Amazon. Forest Ecol Manag. 243 Magnusson WE, De Lima OP, Reis FQ, Higuchi N, Ramos (2–3):199–209. JF. 1999. Logging activity and tree regeneration in an Steinbeiss S, Bebler H, Engels C, Temperton VM, Buchmann N, Amazonian forest. Forest Ecol Manag. 113(1):67–74. Roscher CH, Kreutziger Y, Baade J, Habekost M, Gleixner Magurran AE. 1988. Ecological diversity and its measurement. G. 2008. Plant diversity positively affects short-term soil car- Cambridge (UK): Great Britain University Press. bon storage in experimental grasslands. Global Change Biol. Martin C. 1991. The rainforests of West Africa: ecology, threats, 14(12):2937–2949. conservation. Basel (Switzerland): Birkhauser Verlag. 235 p. Swaine MD, Agyeman VK. 2008. Enhanced tree recruitment fol- Nelson DW, Sommers LE. 1996. Total carbon, organic carbon, lowing logging in two forest reserves in Ghana. Biotropica. and organic matter. In: Page AL, Miller RH, Keeney DR, 40(3):370–374. editors. Methods of soil analysis, Part 2. 2nd ed. Madison Symonds P. 2001. Bio conservation area management plan. (WI): American Society of Agronomy Inc. p. 961–1010. Protected areas development programme. Ghana (West Norris K, Asase A, Collen B, Gockowksi J, Mason J, Phalan B, Africa): Wildlife Division. Wade A. 2010. Biodiversity in a forest-agriculture mosaic – Thenkabail PS, Stucky N, Griscom BW, Ashton MS, Diels J, Van the changing face of West African Rainforests. Biol Conserv. der Meer B, Enclona E. 2004. Biomass estimations and car- 143(10):2341–2350. bon stock calculations in the oil palm plantations of Africa Ofori-Frimpong K, Afrifa AA, Acquaye S. 2010. Impact of shade derived savannas using IKONOS data. Int Remote Sens. and cocoa plant densities on soil organic carbon sequestration 25(23):5447–5472. rates in a cocoa growing soil of Ghana. Afr J Environ Sci Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Technol. 4(9):621–624. Collingham YC, Barend F, Erasmus N, Ferreira De Siqueira Park A, Justiniano J, Fredericksen TS. 2005. Natural regen- M, Grainger A, et al. 2004. Extinction risk from climate eration and environmental relationships of tree species in change. Nature. 427(6970):145–148. logging gaps in a Bolivian tropical forest. Forest Ecol Manag. Van Gemerden BS, Olff H, Parren MPE, Bongres F. 2003. The 217(2–3):147–157. pristine rain forest? Remnants of historical human impacts Pinard MA, Cropper WP. 2000. Simulated effects of log- on current tree species composition and diversity. J Biogeogr. ging on carbon storage in dipterocarp forest. J Appl Ecol. 30(9):1381–1390. 37(2):267–283. Van Gemerden BS, Shu GN, Olff H. 2003. Recovery of conser- Pinard M, Howlett B, Davidson D. 1996. Site conditions limit vation values in central African rainforest after logging and pioneer tree recruitment after logging of dipterocarp forests shifting cultivation. Biodivers Conserv. 12(8):1553–1570. in Sabah, Malaysia. Biotropica. 28(1):2–12. Vandermeer J. 1989. The ecology of intercropping. Cambridge Primark R, Corlett R. 2005. Tropical rainforest: an ecological (UK): Cambridge University Press. p. 249. and biogeographically comparison. Oxford (UK): Blackwell Wade ASI, Asase A, Hadley P, Mason J, Ofori-Frimpong K, Publishing. p. 319. Preece D, Spring N, Norris K. 2010. Management strate- Putz FE, Pinard M. 1993. Reduced impact logging as a carbon gies for maximizing carbon storage and tree species diversity offset method. Conserv Biol. 7(4):755–757. in cocoa-growing landscapes. Agric Ecosyst Environ. 138 Ruiz-Jaen MC, Potvin C. 2010. Tree diversity explained patterns (3–4):342–334. of variation in ecosystem function in a Neotropical forest. Walkley A, Black IA. 1934. An examination of Degtjareff method Biotropica. 42(6):638–646. for determining soil organic matter and a proposed method of Saiz G, Bird MI, Domingues T, Schrodt F, Schwarz M, the chronic acid titration method. Soil Sci. 37(1):29–54. Feldpausch TR, Veenendaal E, Djagbletey G, Hien F, Whitmore TC. 1989. Canopy gaps and the two major groups of Compaore H, et al. 2012. Variation in soil carbon stocks forest trees. Ecology. 70(3):536–538. and their determinants across a precipitation gradient in West Williams M, Ryan CM, Rees RM, Sambane E, Fernando J, Grace Africa. Global Change Biol. 18(5):1670–1683. J. 2008. Carbon sequestration and biodiversity of re-growing Sala OE, Chapin III FS, Armesto JJ, Berlow E, Bloomﬁeld miombo woodlands in Mozambique. Forest Ecol Manag. J, Dirzo R, Huber-Sanwald E, Huenneke LF, Jackson RB, 254:145–155. 226 A. Asase et al. Appendix 1. Relative density, density, frequency, dominance and Importance Value Index (IVI) of species of large trees sampled in 25 m × 25 m plots with information on their family and ecological guilds. Rel. Rel. Relative Species Family Guild Density density Frequency freq. Dominance dominance I V I Albizia Mimosaceae NPLD 2.46 0.51 0.15 0.83 0.24 0.8 2.13 adianthifolia Alstonia boonei Apocynaceae Pioneer 4.92 1.02 0.23 1.24 0.19 0.61 2.87 Amphimas Caesalpiniaceae NPLD 2.46 0.51 0.08 0.41 0.03 0.09 1.01 pterocarpoides Aningeria Sapotaceae NPLD 1.23 0.26 0.08 0.41 0.01 0.03 0.7 altissima Anonidium mannii Annonaceae Shade bearer 1.23 0.26 0.08 0.41 0.14 0.46 1.12 Anthonotha Caesalpiniaceae Shade bearer 2.46 0.51 0.15 0.83 0.34 1.1 2.44 macrophylla Antiaris toxicaria Moraceae NPLD 2.46 0.51 0.15 0.83 0.09 0.28 1.62 Antrocaryon Anacardiaceae NPLD 1.23 0.26 0.08 0.41 0.03 0.11 0.78 micraster Baphia nitida Papilionaceae Shade bearer 32 6.65 0.69 3.72 0.4 1.31 11.68 Baphia pubescens Papilionaceae Pioneer 6.15 1.28 0.15 0.83 0.11 0.37 2.48 Blighia sapida Sapindaceae NPLD 1.23 0.26 0.08 0.41 0.04 0.14 0.8 Bombax Bombaceae Pioneer 2.46 0.51 0.15 0.83 0.53 1.72 3.06 buonopozense Buchholzia Capparaceae Shade bearer 3.69 0.77 0.15 0.83 0.06 0.18 1.78 coriacea Bussea sp Caesalpiniaceae Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.04 0.71 Bussea Caesalpiniaceae NPLD 9.85 2.05 0.31 1.65 1.11 3.63 7.33 occidentalis Calpocalyx Mimosaceae Shade bearer 11.08 2.3 0.23 1.24 0.29 0.93 4.47 brevibracteatus Canarium Burseraceae NPLD 1.23 0.26 0.08 0.41 0.01 0.03 0.7 schweinfurthii Ceiba pentandra Bombaceae Pioneer 1.23 0.26 0.08 0.41 0.08 0.27 0.93 Celtis Ulmaceae Pioneer 3.69 0.77 0.15 0.83 0.08 0.25 1.84 adolﬁ-friderici Celtis mildbraedii Ulmaceae Shade bearer 24.62 5.12 0.46 2.48 1.96 6.4 13.99 Chrysophyllum Sapotaceae Shade bearer 6.15 1.28 0.23 1.24 0.29 0.96 3.48 subnudum Cleistopholis Annonaceae Pioneer 1.23 0.26 0.08 0.41 0.04 0.14 0.81 pattens Cola gigantean Sterculiaceae NPLD 1.23 0.26 0.08 0.41 0.03 0.08 0.75 Cola lateritia Sterculiaceae Shade bearer 7.38 1.53 0.23 1.24 0.27 0.89 3.67 Cola millenii Sterculiaceae NPLD 1.23 0.26 0.08 0.41 0.01 0.04 0.7 Cordia millenii Boraginaceae Pioneer 1.23 0.26 0.08 0.41 1.32 4.32 4.99 Corynanthe Rubiaceae NPLD 41.85 8.7 0.69 3.72 3.06 10.01 22.42 pachyceras Dacryodes Burseraceae Shade bearer 1.23 0.26 0.08 0.41 0.07 0.23 0.9 klaineana Daniellia thurifera Caesalpiniaceae Pioneer 1.23 0.26 0.08 0.41 0.01 0.03 0.7 Dialium Caesalpiniaceae Shade bearer 30.77 6.39 0.62 3.31 0.98 3.2 12.9 aubrevillei Dialium dinklagei Caesalpiniaceae NPLD 2.46 0.51 0.08 0.41 0.09 0.31 1.23 Diospyros Ebenaceae Shade bearer 1.23 0.26 0.08 0.41 0.01 0.03 0.7 canaliculata Diospyros Ebenaceae Shade bearer 4.92 1.02 0.15 0.83 0.1 0.34 2.19 kamerunensis Diospyros Ebenaceae Shade bearer 1.23 0.26 0.08 0.41 0.01 0.03 0.7 monbuttensis Discoglypremna Euphorbiaceae Pioneer 12.31 2.56 0.08 0.41 0.15 0.49 3.46 caloneura Duboscia Tiliaceae NPLD 2.46 0.51 0.08 0.41 0.19 0.63 1.55 viridiﬂora Elaeis guineensis Palmaceae Pioneer 1.23 0.26 0.08 0.41 0.12 0.39 1.06 Enantia polycarpa Annonaceae Shade bearer 1.23 0.26 0.08 0.41 0.02 0.07 0.74 Entandrophragma Meliaceae NPLD 8.62 1.79 0.38 2.07 0.61 1.99 5.85 angolense (Continued) International Journal of Biodiversity Science, Ecosystem Services & Management 227 Appendix 1. (Continued). Rel. Rel. Relative Species Family Guild Density density Frequency freq. Dominance dominance I V I Entandrophragma Meliaceae NPLD 1.23 0.26 0.08 0.41 0.12 0.4 1.07 cylindricum Erythrophleum Caesalpiniaceae NPLD 1.23 0.26 0.08 0.41 0.02 0.05 0.72 ivorense Funtumia elastica Apocynaceae NPLD 1.23 0.26 0.08 0.41 0.08 0.26 0.93 Gilbertiodendron Caesalpiniaceae Swamp 1.23 0.26 0.08 0.41 0.01 0.03 0.7 limba Greenwayodendron Annonaceae Shade bearer 2.46 0.51 0.08 0.41 0.02 0.08 1 oliveri Griffonia Caesalpiniaceae Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.06 0.73 simplicifolia Guarea cedrata Meliaceae Shade bearer 4.92 1.02 0.31 1.65 0.22 0.7 3.38 Hannoa klaineana Simaroubaceae Pioneer 2.46 0.51 0.15 0.83 0.18 0.59 1.93 Hexalobus Annonaceae Shade bearer 2.46 0.51 0.15 0.83 0.13 0.43 1.77 crispiﬂorus Khaya Meliaceae NPLD 1.23 0.26 0.08 0.41 0.01 0.03 0.7 anthothetica Khaya Meliaceae NPLD 2.46 0.51 0.15 0.83 0.05 0.15 1.49 grandifoliola Khaya ivorensis Meliaceae NPLD 1.23 0.26 0.08 0.41 0.04 0.13 0.8 Lannea welwitschii Anacardiaceae Pioneer 2.46 0.51 0.08 0.41 0.03 0.1 1.03 Macaranga barteri Euphorbiaceae Pioneer 3.69 0.77 0.15 0.83 0.28 0.91 2.51 Macaranga Euphorbiaceae Pioneer 1.23 0.26 0.08 0.41 0.04 0.13 0.8 hurifolia Macaranga Euphorbiaceae Pioneer 1.23 0.26 0.08 0.41 0.03 0.09 0.76 heterophylla Mansonia Sterculiaceae NPLD 1.23 0.26 0.08 0.41 0.01 0.04 0.71 alttissima Mareya micrantha Euphorbiaceae Shade bearer 2.46 0.51 0.15 0.83 0.04 0.15 1.48 Millettia Papilionaceae Shade bearer 2.46 0.51 0.08 0.41 0.12 0.38 1.3 rhodantha Millettia Papilionaceae Savanna/ 1.23 0.26 0.08 0.41 0.01 0.04 0.71 thonningii non-forest Monodora Annonaceae Shade bearer 1.23 0.26 0.08 0.41 2.03 6.64 7.31 myristica Monodora Annonaceae Shade bearer 3.69 0.77 0.15 0.83 0.05 0.16 1.75 tenuifolia Morus mesozygia Moraceae Pioneer 1.23 0.26 0.08 0.41 0.02 0.06 0.73 Myria sp. Combretaceae Other/Unknown 1.23 0.26 0.08 0.41 0.03 0.09 0.75 Myrianthus Moraceae Shade bearer 7.38 1.53 0.31 1.65 0.26 0.86 4.05 arboreus Myrianthus Moraceae Shade bearer 4.92 1.02 0.15 0.83 0.14 0.44 2.29 libericus Nauclea Rubiaceae Pioneer 1.23 0.26 0.08 0.41 0.27 0.89 1.56 diderrichii Nesogordonia Sterculiaceae Shade bearer 11.08 2.3 0.62 3.31 0.85 2.79 8.4 papaverifera Ongokea gore Olacaceae NPLD 1.23 0.26 0.08 0.41 0.08 0.25 0.91 Panda oleosa Pandaceae Shade bearer 1.23 0.26 0.08 0.41 0.17 0.56 1.23 Pentaclethra Mimosaceae NPLD 1.23 0.26 0.08 0.41 0.02 0.06 0.73 macrophylla Phyllocosmus Ixonanthaceae Shade bearer 8.62 1.79 0.31 1.65 0.76 2.48 5.92 africanus Piptadeniastrum Mimosaceae NPLD 2.46 0.51 0.15 0.83 0.24 0.8 2.13 africanum Placodiscus boya Sapindaceae Shade bearer 2.46 0.51 0.08 0.41 0.02 0.08 1 Psydrax sp. Rubiaceae Pioneer 1.23 0.26 0.08 0.41 0.02 0.07 0.74 Pterygota Sterculiaceae NPLD 2.46 0.51 0.15 0.83 0.34 1.1 2.44 macrocarpa Pycnanthus Myristicaceae NPLD 7.38 1.53 0.38 2.07 1.65 5.38 8.98 angolensis Raphia hookeri Palmaceae Swamp 3.69 0.77 0.15 0.83 0.18 0.58 2.18 Ricinodendron Euphorbiaceae Pioneer 2.46 0.51 0.15 0.83 0.05 0.16 1.5 heudelotii (Continued) 228 A. Asase et al. Appendix 1. (Continued). Rel. Rel. Relative Species Family Guild Density density Frequency freq. Dominance dominance I V I Rinorea Violaceae Shade bearer 2.46 0.51 0.08 0.41 0.02 0.07 0.99 oblongifolia Sterculia oblonga Sterculiaceae NPLD 8.62 1.79 0.54 2.89 0.27 0.89 5.58 Sterculia Sterculiaceae NPLD 9.85 2.05 0.38 2.07 1.48 4.84 8.95 rhinopetala Strombosia Olacaceae Shade bearer 23.38 4.86 0.77 4.13 0.47 1.54 10.53 glaucescens Tabernaemontana Apocynaceae Shade bearer 1.23 0.26 0.08 0.41 0.01 0.03 0.7 africana Terminalia superba Combretaceae Pioneer 2.46 0.51 0.15 0.83 0.06 0.2 1.54 Tetrapleura Mimosaceae Pioneer 1.23 0.26 0.08 0.41 0.01 0.05 0.71 tetraptera Tetrorchidium Euphorbiaceae Pioneer 2.46 0.51 0.15 0.83 0.05 0.15 1.49 didymostemon Treculia africana Moraceae NPLD 1.23 0.26 0.08 0.41 0.03 0.1 0.77 Tricalysia discolor Rubiaceae Shade bearer 3.69 0.77 0.23 1.24 0.08 0.27 2.28 Trichilia Meliaceae NPLD 6.15 1.28 0.31 1.65 0.2 0.67 3.6 monadelpha Trichilia prieuriana Meliaceae NPLD 8.62 1.79 0.23 1.24 0.39 1.28 4.31 Trichilia tessmannii Meliaceae NPLD 1.23 0.26 0.08 0.41 0.02 0.08 0.75 Triplochiton Sterculiaceae Pioneer 6.15 1.28 0.23 1.24 3.98 13 15.52 scleroxylon Uvariodendron Annonaceae Shade bearer 4.92 1.02 0.23 1.24 0.05 0.17 2.43 angustifolium Vitex ferruginea Verbenaceae NPLD 2.46 0.51 0.15 0.83 0.14 0.47 1.8 Xylia evansii Mimosaceae NPLD 4.92 1.02 0.23 1.24 0.09 0.31 2.57 Xylopia aethiopica Annonaceae Pioneer 1.23 0.26 0.08 0.41 0.07 0.23 0.9 Xylopia sp. Annonaceae Shade bearer/ 2.46 0.51 0.08 0.41 0.06 0.2 1.12 Pioneer Xylopia villosa Annonaceae Shade bearer 1.23 0.26 0.08 0.41 0.03 0.1 0.77 Zanthoxylum gilletii Rutaceae Pioneer 11.08 2.3 0.69 3.72 0.35 1.14 7.16 Unknown IF 01 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.06 0.73 Unknown IF 01 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.04 0.71 Unknown IF 01 A3 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.08 0.26 0.93 Unknown IF 02 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.11 0.36 1.03 Unknown IF 02 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.03 0.7 Unknown IF 02 A3 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.4 1.29 1.96 Unknown IF 04 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.07 0.74 Unknown IF 04 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.06 0.73 Unknown LF 01 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.05 0.72 Unknown LF 01 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.03 0.7 Unknown LF 02 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.03 0.1 0.77 Unknown LF 03 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.07 0.74 Unknown LF 04 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.05 0.71 Unknown LF 04 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.02 0.06 0.73 Unknown LF 05 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.01 0.03 0.69 Unknown SF 01 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.05 0.15 0.82 Unknown SF 01 A2 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.11 0.35 1.02 Unknown SF 03 A1 Unknown Other/Unknown 1.23 0.26 0.08 0.41 0.03 0.08 0.75 International Journal of Biodiversity Science, Ecosystem Services & Management 229 Appendix 2. Relative density, density, frequency, dominance and Importance Value Index (IVI) of species of smaller trees sampled in 5 m × 5 m plots with information on their family and ecological guilds. Rel. Rel. Rel. Species Family Guild Density density Freq. freq Dominance dominance IVI Acacia Mimosaceae Other/Unknown 30.77 0.68 0.08 1.1 0.04 1.74 3.52 kamerunensis Agelaea nitida Connaraceae Other/Unknown 30.77 0.68 0.08 1.1 0 0.18 1.96 Alaﬁa sp. Apocynaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.35 2.13 Aningeria sp. Sapotaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.44 2.21 Anthonotha Caesalpiniaceae NPLD 30.77 0.68 0.08 1.1 0.04 1.74 3.52 fragrans Baphia nitida Papilionaceae Shade bearer 646.15 14.29 0.69 9.89 0.27 12.08 36.25 Blighia welwitschii Sapindaceae NPLD 30.77 0.68 0.08 1.1 0.03 1.33 3.11 Calpocalyx Mimosaceae Shade bearer 92.31 2.04 0.08 1.1 0.05 2.1 5.24 brevibracteatus Calycobolus Convolvulaceae Other/Unknown 30.77 0.68 0.08 1.1 0.04 1.74 3.52 heudelotii Celtis Ulmaceae Pioneer 30.77 0.68 0.08 1.1 0.01 0.24 2.02 adolﬁ-friderici Celtis mildbraedii Ulmaceae Shade bearer 92.31 2.04 0.08 1.1 0.09 4.06 7.2 Celtis zenkeri Ulmaceae NPLD 30.77 0.68 0.08 1.1 0.04 1.74 3.52 Cola caricifolia Sterculiaceae Pioneer 30.77 0.68 0.08 1.1 0.03 1.33 3.11 Cola millenii Sterculiaceae NPLD 30.77 0.68 0.08 1.1 0.02 0.68 2.46 Cola nitida Sterculiaceae Shade bearer 30.77 0.68 0.08 1.1 0 0.11 1.89 Dacryodes Burseraceae Shade bearer 30.77 0.68 0.08 1.1 0.03 1.33 3.11 klaineana Dialium aubrevillei Caesalpiniaceae Shade bearer 92.31 2.04 0.23 3.3 0.03 1.15 6.49 Diospyros Ebenaceae Pioneer 61.54 1.36 0.08 1.1 0.01 0.6 3.06 abyssinica Diospyros Ebenaceae Shade bearer 30.77 0.68 0.08 1.1 0.02 0.68 2.46 heudelotii Diospyros Ebenaceae Shade bearer 61.54 1.36 0.08 1.1 0.04 1.86 4.32 kamerunensis Diospyros Ebenaceae Shade bearer 30.77 0.68 0.08 1.1 0 0.11 1.89 soubreana Drypetes afzelii Euphorbiaceae Shade bearer 30.77 0.68 0.08 1.1 0.01 0.44 2.21 Drypetes chevalieri Euphorbiaceae Shade bearer 338.46 7.48 0.31 4.4 0.08 3.73 15.61 Drypetes gilgiana Euphorbiaceae Shade bearer 123.08 2.72 0.31 4.4 0.04 2 9.11 Enantia polycarpa Annonaceae Shade bearer 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Entandrophragma Meliaceae NPLD 30.77 0.68 0.08 1.1 0.02 0.98 2.76 angolense Eremospatha Palmaceae Other/Unknown 30.77 0.68 0.08 1.1 0 0.11 1.89 hookeri Euclinia longiﬂora Rubiaceae Shade bearer 92.31 2.04 0.15 2.2 0.03 1.55 5.79 Eugenla sp. Myrtaceae Other/Unknown 30.77 0.68 0.08 1.1 0 0.11 1.89 Greenwayodendron Annonaceae Shade bearer 30.77 0.68 0.08 1.1 0.02 0.85 2.63 oliveri Griffonia Caesalpiniaceae Other/Unknown 123.08 2.72 0.15 2.2 0.08 3.39 8.31 simplicifolia Guarea cedrata Meliaceae Shade bearer 30.77 0.68 0.08 1.1 0 0.11 1.89 Hexalobus Annonaceae Shade bearer 30.77 0.68 0.08 1.1 0 0.16 1.94 crispiﬂorus Hunteria eburnea Apocynaceae Shade bearer 30.77 0.68 0.08 1.1 0.05 2.3 4.08 Hypselodelphys Marantaceae Other/Unknown 61.54 1.36 0.08 1.1 0.02 0.87 3.33 poggeana Khaya grandifoliola Meliaceae NPLD 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Microdesmis Pandaceae Shade bearer 215.38 4.76 0.38 5.49 0.1 4.54 14.8 puberula Millettia Papilionaceae Other/Unknown 307.69 6.8 0.38 5.49 0.12 5.28 17.57 chrysophylla Myrtaceae Myrtaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Napoleonaea Lecythidaceae Shade bearer 92.31 2.04 0.15 2.2 0.03 1.36 5.6 vogelii Nesogordonia Sterculiaceae Shade bearer 61.54 1.36 0.08 1.1 0.07 2.97 5.43 papaverifera (Continued) 230 A. Asase et al. Appendix 2. (Continued). Rel. Rel. Rel. Species Family Guild Density density Freq. freq Dominance dominance IVI Newbouldonia Bignoniaceae Pioneer 30.77 0.68 0.08 1.1 0.01 0.44 2.21 laevis Pauridiantha Rubiaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 hirtella Piptostigma Annonaceae Shade bearer 184.62 4.08 0.08 1.1 0.07 3.19 8.37 fasciculatum Pleiocarpa mutica Apocynaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Rutidea sp. Rubiaceae Other/Unknown 61.54 1.36 0.08 1.1 0.02 1.12 3.58 Sterculia oblonga Sterculiaceae NPLD 30.77 0.68 0.08 1.1 0.06 2.72 4.5 Strombosia Olacaceae Shade bearer 584.62 12.93 0.54 7.69 0.36 16.2 36.81 glaucescens Strophanthus Apocynaceae Other/Unknown 30.77 0.68 0.08 1.1 0.02 0.74 2.52 hispidus Strychnos aculeate Loganiaceae Other/Unknown 61.54 1.36 0.15 2.2 0.08 3.82 7.38 Tetrorchidium Loganiaceae Pioneer 30.77 0.68 0.08 1.1 0.02 0.68 2.46 didymostemon Tiliacora dinklagei Menispermaceae Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.28 2.06 Unknown SF 02 A1 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02 Unknown IF 01 A1 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.03 1.33 3.11 Unknown IF 01 A2 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.58 2.36 Unknown IF 01 A3 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.02 0.85 2.63 Unknown IF 01 A4 Unknown Other/Unknown 30.77 0.68 0.08 1.1 0.01 0.24 2.02

Journal

International Journal of Biodiversity Science, Ecosystem Services & Management – Taylor & Francis

Published: Sep 1, 2012

Keywords: land use change; logging; biodiversity; ecosystem services; carbon storage

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Linkages between tree diversity and carbon stocks in unlogged and logged West African tropical forests

Linkages between tree diversity and carbon stocks in unlogged and logged West African tropical forests

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Linkages between tree diversity and carbon stocks in unlogged and logged West African tropical forests

Linkages between tree diversity and carbon stocks in unlogged and logged West African tropical forests

References (69)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies