Carbon Stock Recovery after Selective Logging in the East Region of Cameroon ()
1. Introduction
The current anthropogenic climate change and its global impact have increased the urgent need for reliable estimates of biomass and carbon pools for terrestrial ecosystems (Brown, 2002). Tropical forests are the most important forest vegetation type with large carbon stocks that have the potential to offset global warming through the removal of atmospheric Carbon dioxide (CO2) (Löf et al., 2019). Sub-Saharan Africa has 25% (2.1 Gt per year) of the global carbon stock of the forests (Pearson et al., 2017) with the largest from above-ground tree biomass (≥80%), mostly held in stems, wood and branches (Henry et al., 2010). However, this carbon pool is the most vulnerable to anthropogenic activities causing deforestation for agriculture and forest degradation through selective logging (Hairiah & Rahayu, 2007) to directly affect the aboveground biomass of trees (Gibbs et al., 2007). In contrast to deforested areas that are used for agriculture and grazing, most buffering capacity and resilience are high in selectively logged forests that are well managed (Asner et al., 2006) and may recover carbon stocks (West et al., 2014). However, carbon stock from tropical forest regrowth is subject to high uncertainties (Sierra et al., 2012).
Selective logging can diminish timber resources, carbon reserves and habitat quality for local fauna (Poudyal et al., 2018) slowing its recovery (Hu et al., 2020); but these disturbances also create environmental conditions that are favorable for the regeneration and recovery of other tree species and forest biomass (Goulamoussene et al., 2017; Mokake et al., 2024) and thus the capture and recovery of carbon (Herault & Piponiot, 2018). This recovery forms part of the reincorporation of carbon into both the soil and the forest biomass (Houghton, 2005). However, the recovery of forest areas as a result of disturbances generated from selective logging has received little attention as little is known about recovery and the dynamics of their carbon stock. Though some studies have been carried out in tropical Africa especially Ghana, (Duah-Gyamfi et al., 2014), most focused on the regeneration of commercial timber species as was the case with Mokake et al. (2022) in Eastern Cameroon, without taking into consideration the recovery of the whole forest biomass and carbon stock especially in the East Region of Cameroon. More importantly, uncertainty remains about the rate of biomass recovery in secondary and primary forests in the landscape (Sierra et al. 2012). Therefore, it is necessary to accurately estimate the carbon stocks of primary and secondary forests in order to determine biomass accumulation at the different stages of succession after disturbances in order to rebuild the ecological functions provided by the forests.
Before using allometric equations, which is a non destructive method of determining the above ground biomass, their validity within a particular area needs to be tested (Chave et al., 2005) in order to be sure of the carbon stored by the forest taking into consideration that the biomass in the tropics is affected by topography, climatic condition and woody species composition along with other factors like natural and anthropogenic disturbances like selective logging. The main source of error in the estimation of biomass and carbon stock is thus the choice of the allometric equation (Chave et al., 2005, 2014) which depends greatly on the number of trials carried out and the site in question (Chave et al., 2014). Thus a considerable uncertainty exists in the spatial distribution of biomass. Pantropical models based on large compiled datasets contain more community-level variation in specific forests than local models (Brown et al., 1989; Chave et al., 2005; Djomo et al., 2010), as site specific equations are usually fitted using a limited number of trees and species (Kenzo et al., 2009), which may not be representative of species pool in other regions (Fayolle et al., 2013). However significant bias in the estimates of Carbon has been reported by Henry et al. (2010), Alvarez et al. (2012) and Lima et al. (2012) in Ghana, Columbia and Brazil, respectively using pantropical equations. Therefore, there’s a need to investigate if a substantial difference exist between the pantropical, regional and site-specific allometric equations in the estimation of tree/wood plant biomass and carbon of African tropical forests in order to avoid misinformation in the development of specific allometric models for Africa improving on the quality of biomass estimates under the UN-REDD + programme as locally developed allometric equations are thought to provide more accurate estimates of the aboveground biomass (AGB).
For countries without specific allometric equations two options exist based on the IPCC (2006). The first option is tier 1 which uses existing generalized equations for their national forests; attained in Africa by Brown et al. (1989) and Chave et al. (2005) and Cameroon by Mokake et al. (2022, 2023). The second option is tier 2 which explores the need and subsequent process of developing country or region-specific allometric equations with few specific allometric equations in tropical African forests in general (in Cameroon by Fayolle et al., 2013, 2018) with consequences in the variation in the attainment of Tier-2 and Tier-3 (tier 3 is the site specific allometric equation attained in Cameroon by Djomo et al. (2010) approaches of the IPCC. Therefore pantropical equations may not be appropriate in reflecting specific growth patterns or conditions. It was against this backdrop that this study sought to determine the carbon stock recovery after selectively logged while assessing the validity of using pantropical multi-species, regional and site specific allometric equations to estimate the biomass of trees in the East Region of Cameroon thereby contributing to the information gap that exists in Africa in general and Cameroon in particular. It was therefore hypothesized that:
The specific objectives were to:
2. Methodology
2.1. Study Site
The East Region lies between latitude 3˚08 to 3˚18 North and longitude 14˚31 to14˚52 East, occupying an area of 109,011 km2 with a ferralitic soil. It has a wet equatorial climate, an average temperature of 24˚C and lacks traditional seasons. It has an average annual total precipitation between 1500 - 2000 mm except in the extreme Eastern and Northern portions of the region, where it is slightly less. Relative humidity is highest in the month of June-December. The stratified forest is composed of hardwood evergreens of species, some of which grow to heights of 70 m or more (Fitzpatrick, 2002).
This study was carried out in six 1.0ha permanent forest monitoring plots established by the Centre for Tropical Forest Society (CTFS) and World Wide Fund for Nature (WWF)-Cameroon in 2005/2006 at the Société Forestiére Industrielle de Lokoundje (SFIL) (FMU10-052) and Green Valley Incorporated (GVI) (FMU10-021) belonging to la Groupe Decolvenaere (GDC) timber company located at the Boumba and Ngoko Division of the East Region to establish baseline conditions of the forest. Permanent monitoring plots were established in Forest Management Unit (FMU) 10-052 and 10-021 (Figure 1 and Table 1). A recensus was done between June 2011 and December 2012 where Plots 1 and 2 were the unlogged forests, Plots 3 and 4 were the forests of 11 Years After Logging (YAL) and plots 5 and 6 were the forests of 21 Years After Logging (YAL) (Table 1).
Figure 1. Localization of the six 1.0 ha permanent forest monitoring plots established by CTFS and WWF-Cameroon in 2005 and 2006.
Table 1. Location of some permanent forest monitoring plots in the East Region of Cameroon.
PLOTS |
YAL (2005) |
YAL (2011) |
Location |
Datum |
Utm 33n Easting |
Utm 33n Northing |
1 |
0 |
0 |
Green Valley, south end 1 |
WGS84 |
442,288 |
360,111 |
2 |
Green Valley, south end 2 |
WGS84 |
444,740 |
359,972 |
3 |
5 |
11 |
Green Valley near mill 3 |
WGS84 |
450,047 |
372,871 |
4 |
Green Valley near mill 4 |
WGS84 |
449,274 |
373,999 |
5 |
15 |
21 |
Green Valley SOTREF 5 |
WGS84 |
469,601 |
422,247 |
6 |
Green Valley SOTREF 6 |
WGS84 |
457,847 |
422,263 |
WGS = World Geodetic System, YAL = Years After Logging.
2.2. Field Design and Sampling
Plot demarcation was carried out in 2005/2006 using the standard protocols by Condit (1998) for long-term permanent monitoring plots. Each 1.0 ha plot was divided into 25 nested 20 × 20m subplots or quadrats and each 20 × 20 m quadrat was subdivided into 16 nested 5 × 5m sub-quadrats, using temporary demarcated posts at 5 m intervals, and nylon rope around each 5 × 5 m subquadrat.
In 2005/2006, all woody plants with DBH ≥ 2 cm were measured at 1.3 m from the ground. They were assigned a unique tag number and painted at the point of measurement to guide future measurements. The location of each censured stem within the plot was determined by the X and Y coordinates within each 5 m × 5 m subquadrat and pooled for the whole plot. In the first re-census in 2011/2012, we received the entire data sets for the six sites, and we were able to trace all the tagged plants. We then remeasured the old and new woody stems in 2011/2012 corresponding to 6 years interval after the initial measurement. During the re-census the recruits (new individuals that enter the diameter size of ≥ 2 cm) were treated in a similar manner with new number tags and their positions mapped to the nearest 5 cm. Every individual was identified by a team of botanists and voucher specimens collected were matched with herbarium specimens at the National herbarium in Yaounde according to the Angiosperm Phylogeny Group (APG) III system classification (APG, 2009).
A) Assessment of the stand compositional change after selective logging in the East Region of Cameroon.
The tree species density and richness determined the stand composition. Species density was determined as the number of individuals of the different species/ha (Mueller-Dombois & Ellenberg, 1974) in each site. Species richness (S) was the number of species and was determined by tallying living stems (Yap et al., 2016). These were used to estimate the % change in species richness and plant density, Importance Value Index (IVI) and the Family Importance Value (FIV).
The Species Importance Value Index (IVI) was calculated as described by Curtis (1959);
=Species Relative Density + Species Relative Dominance
+Species Relative Frequency Equation 1
The Family Important Value Index (FIV) was calculated as described by Mori et al. (1983);
=Family Relative Density + Family Relative Dominance
+Family Relative Frequency Equation 2
B) Evaluation of the Aboveground Carbon Stock (ACS) recovered after selective logging in the East Region of Cameroon.
The Above Ground Biomass (AGB) recovered was determined using the pantropical regression model of Chave et al. (2005), regional and site specific allometric equations.
AGB = ρs × exp[−1.499 + 2.148 × In(DBH) + 0.207
× In(DBH)2 – 0.0281 × In(DBH)3] Equation 3
AGB = ρ × exp(−1.183 + 1.940 × In(D) + 0.239 × (In(D))2
− 0.0285 × (In(D))3 Equation 4
AGB = exp (−1.8623 + 2.4023 ln(D) − 0.3414 ln(ρ))Equation 5
Where DBH or D = diameter at breast height in centimeter, ρs or ρ = specific wood density in all equations. For species without wood densities, an average for the genera or family was used.
The data was sub-sample into two categories: individuals with diameter < 10 cm and individuals with diameter ≥ 10 cm) in order to assess tree stands of the different diameter class that occupy different strata. The AGB values of individual stems were summed to get the total AGB of each forest type. Hence, for each forest type, the following were calculated:
i) Changes in AGB over time;
AGB2011 − AGB2005 Equation 6
ii) Annualized net change in AGB for each forest types as;
AGB2011 – AGB2005/T Equation 7
iii) Recovery rates of AGB as;
((X2011 – X2005)/X2005) × 100 Equation 8
(According to Martin et al. (2013) with slight modification)
The Aboveground Carbon Stock (ACS) was determined according to Zapfack et al. (2013) which basically estimates the amount of Carbon stock by multiplying the biomass by 0.47.
C) Evaluation of the Belowground biomass Carbon Stock (BCS) after selective logging in the East Region of Cameroon.
The Belowground biomass (BGB) was determined using recommendations from:
The difference in their estimations of BGB was compared and BCS calculated according to Zapfack et al. (2013).
D) Soil Organic Carbon after selective logging in the East Region of Cameroon.
Soil samples of the different forest types were collected in triplicates across soil depths of 0 - 5 cm, 5 - 15 cm and 15 - 30 cm using a soil corer and auger. This was air dried, sieved using a 2 mm sieve, put in zip lock bags with codes and analyzed at the University of Dschang soil laboratory. Soil Organic Carbon (%SOC) was evaluated by chromic acid digestion and spectrophotometric analyses (Haynes, 1984).
2.3. Statistical Analyses
We calculated the aboveground biomass of each plot by summing the estimated biomass of each stem for species. We separated the size classes of all the stems into two groups (<10 cm ≥10 cm) and compared the forest type means of the aboveground biomass estimated by the allometry equations. To test for differences in the means, One-way analysis of variance (ANOVA) was used where P-values less than 0.05 were considered significant. We used MINITAB version 17 for all summary statistics (measures of central tendencies) and inferential statistics (ANOVA and t-test). The Turkey’s Honesty Test was used to separate means which differed from one another. Multivariate statistics such as the Principal Component Analysis (PCA) was used to describe the variation of selected timber species by forest types. Diversity indices (Shannon-Weiner) and Jaccard similarity were all computed in PAST statistical package.
3. Results
3.1. Stand Composition after Selective Logging in the East Region of Cameroon
The six, 1 ha permanent monitoring plots sampled for trees ≥ 2 cm dbh contained on average 3744 stems/ha in 2005, with the highest obtained from the unlogged forest (4191 ± 46.9 stems/ha) and the least obtained from forests of 11YAL (3808 ± 51.2 stems /ha). In 2011, the average number of individuals decreased to 2993 stems/ha with the unlogged forest having the highest plant density (3405 ± 39.3 stems/ha) and the least in the forests of 11YAL (2440 ± 27.0 stems/ha). This led to 79.9% of the initial plants being censured and a reduction of 20% (Table 2).
The percentage change in plant density and species richness was highest in the forests of 11YAL. The species richness (294 - 281 in 2011), number of genera (162 - 157 in 2011) and number of identified families (51 - 50 in 2011) decreased with the highest in the forests of 21 YAL in both inventories. Proportions of fully identified and unidentified families did not differ between logged and unlogged forest (Table 2).
Table 2. Plant density and species richness after selective logging in the East Region of Cameroon.
Forest type |
Plant density (stems/ha) |
Species richness (S/ha) |
Number of genera |
N/S ratio |
Number of identified
families |
|
2005 |
2011 |
%C |
2005 |
2011 |
%C |
2005 |
2011 |
%C |
2005 |
2011 |
2005 |
2011 |
Unlogged |
4191 ± 46.9 |
3405 ± 39.3 |
18.8 |
279 ± 8.0 |
266 ± 7.6 |
4.7 |
171(2) |
163(2) |
4.7 |
15.0 |
12.8 |
50 (2)* |
49(3)* |
11 YAL |
3235 ± 36.6 |
2440 ± 27.0 |
24.6 |
297 ± 7.8 |
280 ± 7.3 |
5.7 |
154(5) |
147(5) |
4.5 |
10.9 |
8.7 |
50 (1)* |
50(3)* |
21 YAL |
3808 ± 51.2 |
3134 ± 40.1 |
21.8 |
306 ± 8.2 |
298 ± 7.5 |
2.6 |
161 (3) |
163(6) |
−1.2 |
12.5 |
10.5 |
51 (3)* |
51(3)* |
TOTAL (3 ha) |
11234 ± 134.7 |
8979 ± 106.4 |
20.1 |
882 |
844 |
4.3 |
486 |
473 |
2.6 |
38.4 |
32 |
151 (6) |
150 (9) |
Total/ha |
3744 |
2993 |
20.1 |
294 |
281 |
4.4 |
162 |
157 |
3.1 |
12.8 |
10.7 |
51 |
50 |
% Recensured |
|
(79. 9%) |
|
|
|
|
|
|
|
|
|
|
|
Values for plant density and species richness were reported in mean ± standard error while for number of genera and families the values in ()*represent unidentified individuals. Figures in bold indicate the highest or the lowest.
3.2. Temporal Change of the Most Important Species (IVI) and Family (FIV) after Selective Logging in the East Region of Cameroon
After selective logging there was no change in the most important species in the forests of 21 YAL and the unlogged forests as Sloetiopsis usambarensis was the most important species in 2005 and 2011. However, there was a change in the forests of 11YAL from Meiocarpidium lepidotium to Keayodendron bridelioides. Contrarily, there was a constant change in the most important family for all the forest types with the Euphorbiaceae being the most important in the unlogged forest (31.6) and forests of 11YAL (31.6) in 2011. The Violaceae (43.38) family was the most important family in the forests 21 YAL and it had the highest FIV in the whole study in 2011. The least FIV was found in Euphorbiaceae in the forests of 11YAL and the unlogged forest (31.64). The number of families increased from 50 to 51 families from 2005 to 2011 as the logged forests maintained their number of families, while the unlogged forest had a decrease (Table 3).
Table 3. Change in the most important species (IVI) and family (FIV) after selective logging in the East Region of Cameroon.
Forest types |
Census Year |
Most important species |
IVI |
Most important family |
FIV |
Unlogged |
2005 |
Sloetiopsis usambarensis |
17.51 |
Putrangivaceae |
36.63 |
2011 |
Sloetiopsis usambarensis |
19.37 |
Euphorbiaceae |
31.64 |
11YAL |
2005 |
Meiocarpidium lepidotium |
15.69 |
Annonaceae |
31.30 |
2011 |
Clerodendron bridelioides |
28.21 |
Euphorbiaceae |
31.64 |
21 YAL |
2005 |
Sloetiopsis usambarensis |
26.70 |
Sterculiaceae |
35.58 |
2011 |
Sloetiopsis usambarensis |
27.20 |
Violaceae |
43.38 |
Figures in bold indicate the highest or the lowest.
3.3. The Most Common Species after Selective Logging in the East Region of Cameroon
Sloetiopsis usambarensis was the most common species in the unlogged forests
Table 4. Most common tree species after selective logging in the East Region of Cameroon.
Forest type |
Census Year |
Most common species |
Family |
Abundance |
% Abundance |
Unlogged |
2005 |
Sloetiopsis usambarensis |
Moraceae |
616 |
15.8 |
|
2011 |
Sloetiopsis usambarensis |
Moraceae |
538 |
14.7 |
11YAL |
2005 |
Rinorea sciaphila |
Violaceae |
303 |
9.4 |
|
2011 |
Rinorea sciaphila |
Violaceae |
214 |
8.8 |
21 YAL |
2005 |
Sloetiopsis usambarensis |
Moraceae |
711 |
18.6 |
|
2011 |
Sloetiopsis usambarensis |
Moraceae |
553 |
17.7 |
Figures in bold indicate the highest or the lowest.
Table 5. 10 most common tree species after selective logging in the East Region of Cameroon.
|
|
2005 CENSUS |
2011 CENSUS |
|
SPECIES |
FAMILY |
Unlogged |
11 YAL |
21 YAL |
Unlogged |
11 YAL |
T3 |
Overall |
|
|
A |
R |
A |
R |
A |
R |
A |
R |
A |
R |
A |
R |
A |
R |
Sloetiopsis
usambarensis |
Moraceae |
616 |
1 |
0 |
0 |
711 |
1 |
538 |
1 |
0 |
0 |
553 |
1 |
1091 |
1 |
Meiocarpidium
Lepidotum |
Annonaceae |
0 |
0 |
196 |
4 |
0 |
0 |
0 |
0 |
164 |
3 |
0 |
0 |
164 |
10 |
Rinorea sciaphila |
Violaceae |
98 |
10 |
302 |
1 |
128 |
7 |
86 |
8 |
214 |
1 |
115 |
6 |
415 |
5 |
Millettia barteri |
Fabaceae |
267 |
3 |
284 |
2 |
253 |
2 |
207 |
3 |
192 |
2 |
202 |
2 |
601 |
2 |
Rinorea dentata |
Violaceae |
312 |
2 |
0 |
0 |
93 |
10 |
252 |
2 |
0 |
0 |
79 |
10 |
331 |
7 |
Musanga
cecropioides |
Urticaceae |
0 |
0 |
135 |
7 |
0 |
0 |
0 |
0 |
60 |
7 |
0 |
0 |
60 |
14 |
Drypetes preussii |
Putrangivaceae |
100 |
9 |
0 |
0 |
0 |
0 |
82 |
9 |
0 |
0 |
0 |
0 |
82 |
13 |
Diospyros sp |
Ebenaceae |
148 |
7 |
166 |
5 |
183 |
3 |
121 |
7 |
126 |
5 |
152 |
3 |
399 |
4 |
Rinorea
brachypetala |
Violaceae |
216 |
4 |
210 |
3 |
181 |
4 |
170 |
4 |
160 |
4 |
150 |
4 |
480 |
3 |
Drypetes molunduana |
Putrangivaceae |
199 |
5 |
0 |
0 |
0 |
0 |
163 |
5 |
0 |
0 |
0 |
0 |
163 |
11 |
Tabernaemontana brachyantha |
Apocynaceae |
155 |
6 |
159 |
6 |
145 |
6 |
124 |
6 |
116 |
6 |
112 |
7 |
352 |
6 |
Drypetes ivorensis |
Putrangivaceae |
0 |
0 |
85 |
8 |
155 |
5 |
0 |
0 |
57 |
8 |
120 |
5 |
177 |
9 |
Trichilia rubescens |
Meliaceae |
103 |
8 |
78 |
9 |
117 |
8 |
77 |
10 |
54 |
9 |
90 |
8 |
221 |
8 |
Pennianthus sp |
Menispermaceae |
0 |
0 |
59 |
10 |
0 |
0 |
0 |
0 |
48 |
10 |
0 |
0 |
48 |
15 |
Rinorea oblongifolia |
Violaceae |
0 |
0 |
0 |
0 |
107 |
9 |
0 |
0 |
0 |
0 |
84 |
9 |
84 |
12 |
A = Abundance, R=Ranking. Figures in bold represent species present in all forest types while figures in red represent species that maintained a particular rank in both inventories. Figures in bold indicate the highest or the lowest.
and forests of 21 YAL; while in the forests of 11 YAL Rinorea sciaphila was the most common. The forests of 21 YAL had the highest abundance of common species; accounting for 17.7% of the total abundance (Table 4). However, the top 10 most abundant species in every forest type accounted for almost half of the overall abundance of the most common species. The 50 abundant common species in the various forest types are noted in Appendix. Some of the 10 most exploitable species in Cameroon like Entandrophragma cylindricum (Sapeli) Triplochiton scleroxylon (Ayous) Pterocarpus soyauxii (Red padouk), Erythrophleum sauveolens (Tali), Milicia excelsa (Iroko) and Terminalia superba (Frake) were recorded in the forests of 11YAL and forests of 21 YAL respectively in 2011 (Table 5).
Rinorea sciaphila, Millettia barteri, Diospyros sp, Rinorea brachypetala, Tabernaemontana brachyantha, and Trichillia rubescens were present in all the forest. Rinorea brachypetala and Sloetiopsis usambarensis held the same rank in all the forest types (Table 5).
3.4. Tree Species Rarity and Singletons after Selective Logging in the East Region of Cameroon
Tree species became rare in 2011 for all forest types. The forests of 11YAL had the highest number of singletons (142/ha in 2005 and 143/ha in 2011) while the least was found in the unlogged forests (97/ha in 2005 and 100/ha in 2011), making the forests of 11YAL having the rarest species (Table 6).
Table 6. Tree species rarity after selective logging in the East Region of Cameroon.
Forest types |
Census year |
Number of singletons |
Rarity |
% rarity |
Unlogged |
2005 |
97 |
0.3 |
34.8 |
2011 |
100 |
0.4 |
37.6 |
11YAL |
2005 |
142 |
0.5 |
47.8 |
2011 |
143 |
0.5 |
51.1 |
21 YAL |
2005 |
138 |
0.5 |
45.1 |
2011 |
142 |
0.5 |
47.7 |
Figures in bold indicate the highest or the lowest.
3.5. Tree Species Ordination
In order to determine the variation of identified timber species among plots in each forest type, we carried out a Principal Component Analysis (PCA) which is direct ordination analysis. The result indicated that there was an increase in the timber species over time among all forest types (Figure 2). In 2005, the timber species present were Guarea thompsonii, Mansonia altissima, Entandrophragma cylindricum, and Terminalia superba in the unlogged forests and forests logged 5 years ago (T1 and T2 respectively) indicating their similarity. While the timber species present in T3 were Pterocarpus soyauxii and Triplochiton scleroxylon. In 2011, there was an increase in the timber species in T1 with the species Mansonia altissima, Entandrophragma cylindricum, Pericopsis elata and Diospyros crassifolia. In T2, the timber species were Guarea thompsonii, Afzelia sp., and Entandrophragma utile. In T3, the timber species were Pterocarpus soyauxii,Erythrophleum suaveolens, Milicia excelsa, Staudtii gabonensis, Ficus sp. and Afzelia sp. Thus there was generally an increase in all forest types with the forest logged 21 years ago having the highest amount of timber (Figure 2).
![]()
(a)
(b)
Figure 2. The variation of identified exploitable timber species in 2005 (a) and 2011 (b). T1 is an unlogged plot, T2 is logged 11 years and T3 is logged 21 years.
SAPELI = Entandrophragma cylindricum, SAPO = Gambeya boukokoensis, STERT = Sterculia rhinopetala, AFRO = Pericopsis elata, AFZE = Afzelia sp, ALBO = Alstonia boonei, MELI = Entandrophragma utile, EROB = Eribroma oblongum, DESBO = Desbordesia glaucescens, NAUCL = Nauclea diderrichii, BETE = Mansonia altissima CANAR = Canarium schweinfurthii, FRIV = Ficus sp. COMAF = Combretodendron africanum, CYGA = Cylicodiscus gabonensis, PYCN = Pycnanthus angolensis, PIAF = Piptadeniastrum africanum, NEPA = Nesogordonia papaverifera, ERIV = Erythrophleum suaveolens, TESU = Terminalia superba, IROKO = Milicia excelsa, CELT2 = Celstis tessmannii, PTER = Pterocarpus soyauxii, DIOSCR = Diospyros crassifolia, GUAI = Guarea thompsonii, AYOUS = Triplochiton scleroxylon., AFZ2 = Afzelia sp, STAU = Staudtii gabonensis, AFZE2 = Afzelia sp.
3.6. Forest Diversity after Selective Logging in the East Region of Cameroon
Diversity decreased in the unlogged forests, maintained in forests of 11YAL and increased in forests of 21 YAL. However, the unlogged forests were more even (0.24) than the logged forests while the unlogged forest and the forest 21 YAL were most similar (0.97) than the forests of 11YAL (Table 7).
Table 7. Diversity and similarity after selective logging in the East Region of Cameroon.
|
Shannon |
Evenness |
Similarity index |
Forest type |
2005 |
2011 |
2005 |
2011 |
Unlogged |
11 YAL |
21YAL |
Unlogged |
3.9 |
3.8 |
0.24 |
0.23 |
|
|
|
11 YAL |
3.7 |
3.7 |
0.21 |
0.22 |
0.63 |
|
|
21 YAL |
3.6 |
3.7 |
0.18 |
0.19 |
0.97 |
0.95 |
|
Figures in bold indicate the highest or the lowest.
We observed species found in Cameroon’s commercial list of exploitable species at different diameter size classes. However, the exploitable species in the < 10 cm dbh had the highest number of individuals especially in the forests of 11YAL (Figure 3).
Figure 3. Diameter class distribution of exploitable tree species after selective logging in the East Region of Cameroon.
3.7. Carbon Stock Recovery after Selective Logging in the East Region of Cameroon
a) Aboveground Carbon Stock (ACS)
There was no significant difference in the determination of the AGB using the Pantropical equation of Chave et al. (2005) and the regional and site specific allometric equations of Fayolle et al. (2013) and Djomo et al. (2010) respectively (Table 8). Triplochiton scleroxylon had the highest AGB and consequently the ACS in the unlogged and the forests of 21 YAL in both inventories. In the forests of 11YAL, there was a change in the highest AGB and ACS from 2005 (Triplochiton scleroxylon) to 2011 from (Polyalthia suaveolens) (Table 9).
Table 8. Mean AGB using different allometric equations in the East Region of Cameroon.
Forest |
YAL |
DBH size class (cm) |
Chave et al. (2005) |
Fayolle et al. (2013) |
Djomo et al. (2010) |
F-value |
P-value |
1 |
UNLOGGED |
<10 |
115.5 ± 26.7 |
112.0 ± 25.8 |
154.5 ± 43.8 |
0.5 |
0.604 |
|
|
≥10 |
4418 ± 1274 |
5006 ± 1433 |
6111 ± 1995 |
0.29 |
0.749 |
2 |
UNLOGGED |
<10 |
119.5 ± 25.6 |
116.0 ± 24.7 |
136 ± 27.3 |
0.19 |
0.831 |
|
|
≥10 |
4108 ± 1088 |
4690 ± 1252 |
5078 ± 1528 |
0.14 |
0.869 |
3 |
11 YAL |
<10 |
92.3 ± 23.0 |
89.6 ± 22.3 |
122.2 ± 29.7 |
0.52 |
0.597 |
|
|
≥10 |
2793 ± 692 |
3191 ± 797 |
3450 ± 909 |
0.17 |
0.844 |
4 |
11 YAL |
<10 |
95.7 ± 23.7 |
92.9 ± 22.9 |
111.8 ± 24.6 |
0.19 |
0.831 |
|
|
≥10 |
3775 ± 808 |
4258 ± 907 |
5267 ± 1299 |
0.55 |
0.578 |
5 |
21 YAL |
<10 |
115.9 ± 34.3 |
112.5 ± 33.2 |
157.4 ± 58.3 |
0.33 |
0.719 |
|
|
≥10 |
4439 ± 1292 |
4982 ± 1441 |
5241 ± 1553 |
0.08 |
0.922 |
6 |
21 YAL |
<10 |
116.3 ± 27.9 |
112.8 ± 27.0 |
133.7 ± 27.8 |
0.17 |
0.848 |
|
|
≥10 |
3695 ± 1302 |
4192 ± 1456 |
4911 ± 2001 |
0.14 |
0.866 |
Table 9. Tree species with the highest Above ground biomass Carbon Stock (ACS) after selective logging in the East Region of Cameroon.
Forest type |
|
Species |
AGB (Mg/ha) |
ACS (Mg/ha) |
Unlogged |
2005 |
Triplochiton scleroxylon |
81.8 |
38.5 |
|
2011 |
Triplochiton scleroxylon |
97.7 |
45.9 |
11YAL |
2005 |
Triplochiton scleroxylon |
37.6 |
17.7 |
|
2011 |
Polyalthia suaveolens |
59.6 |
28.0 |
21 YAL |
2005 |
Triplochiton scleroxylon |
101.3 |
47.6 |
|
2011 |
Triplochiton scleroxylon |
99.9 |
47.0 |
Figures in bold indicate the highest or the lowest.
While mean AGB values varied numerically, the differences were not significant (P ≤ 0.83) with an increase over time from 541.4 - 961.8 Mg/ha in 2011. The unlogged forests had the highest AGB and ACS (961.8 Mg/ha and 452.1 Mg/ha) while the forests of 11YAL had the least (650.8 Mg/ha and 305.9 Mg/ha) respectively (Table 10). The mean AGB increased in all forest types for stems > 10 cm diameter size class with the highest in the unlogged forest and least in the forests of 11YAL. The mean AGB decreased in all forest types in stems < 10 cm except in the unlogged forest. A change in the AGB over time indicated that individuals in the < 10 cm had the least change in AGB in logged forests while individuals in the > 10 cm had the highest change in all forest types. However all forest types recovered more than 50% of its AGB with the forests of 11YAL (80.7%) having the highest and the forests of 21YAL (59.4%) having the least.
Table 10. Mean AGB and ACS after selective logging in the East Region of Cameroon.
|
Unlogged (Mg/ha) |
11YAL (Mg/ha) |
21 YAL (Mg/ha) |
Exploitation year |
2005 |
2011 |
2005 |
2011 |
2005 |
2011 |
DBH |
<10 cm |
>10 cm |
<10 cm |
>10 cm |
<10 cm |
>10 cm |
<10 cm |
>10 cm |
<10 cm |
>10 cm |
<10 cm |
>10 cm |
Mean AGB |
0.08 ± 0.01 |
3.5 ± 0.8 |
0.10 ± 0.02 |
5.3 ± 0.9 |
0.06 ± 0.02 |
2.8 ± 0.6 |
4.6 ± 0.8 |
0.05 ± 0.01 |
0.08 ± 0.02 |
3.2 ± 0.9 |
0.07 ± 0.02 |
4.3 ± 0.1 |
Total AGB |
541.4 |
961.8 |
359 |
650.8 |
446.7 |
713.1 |
ACS |
254.5 |
452.1 |
168.7 |
305.9 |
210 |
335.2 |
Change in AGB |
- |
- |
4.1 |
416.5 |
- |
- |
−2.1 |
291.9 |
- |
- |
−0.91 |
266.7 |
% Recovered |
77.7 |
80.7 |
59.4 |
Figures in bold indicate the highest or the lowest.
b) Belowground biomass Carbon Stock (BCS) after selective logging in the East Region of Cameroon.
There was no significant difference in the BGB using the formulae of Cairns et al. (1997) and Mokany et al. (2006) in the calculations of the BGB of the different forest types (Table 11). The BGB was highest in unlogged forests and least in the forests of 11YAL in both inventories (Table 11). However the BCS was highest in the forests 21YAL and least in the unlogged forests after logging (Table 11).
Table 11. Determination of the BGB and BCS after selective logging in the East Region of Cameroon.
DBH size class |
Cairns et al. (1997) |
Mokany et al. (2006) |
BCS |
|
2005 |
2011 |
2005 |
2011 |
2005 |
2011 |
Unlogged |
1.1 ± 0.2 |
1.0 ± 0.2 |
0.7 ± 0.2 |
1.5 ± 0.3 |
1594 ± 374 |
3353 ± 701 |
11 YAL |
0.8 ± 0.1 |
1.4 ± 0.5 |
0.5 ± 0.1 |
2.6 ± 1.1 |
1212 ± 249 |
5796 ± 2503 |
21YAL |
1.0 ± 0.3 |
1.4 ± 0.4 |
0.4 ± 0.2 |
2.6 ± 0.9 |
1429 ± 398 |
5854 ± 1936 |
P-value |
0.3 |
0.4 |
0.3 |
0.4 |
|
|
F-value |
1.1 |
1.1 |
1.1 |
1.1 |
|
|
Figures in bold indicate the highest or the lowest.
c) Soil Organic Carbon (SOC) after selective logging in the East Region of Cameroon.
The SOC was not significantly (p ≥ 0.65) different across the different forest types and soil depth (p ≥ 0.1). The mean % SOC was highest in forests of 11YAL (4.1 ± 1.2) and least in the unlogged forests (3.5 ± 0.1) with a decrease of the % SOC with an increase in soil depth in all forest types (Table 12).
Table 12. Percentage Soil Organic Carbon (SOC) after selective logging at different soil depths in the East Region of Cameroon.
Soil depth |
0 - 5 cm |
5 - 15 cm |
15 - 30 cm |
Mean |
Unlogged |
3.6% |
3.5% |
3.4% |
3.5 ± 0.1 |
11 YAL |
5.4% |
3.9% |
3.1% |
4.1 ± 1.2 |
21 YAL |
4.9% |
4.0% |
3.3% |
4.1 ± 0.8 |
Figures in bold indicate the highest or the lowest.
4. Discussion
The objective of this study was to determine the carbon stock recovery of a tropical forest after selective logging using different allometric equations in the East Region of Cameroon. A 20% reduction in the number of recensured plants may be due to natural causes (tree fall, mortality) and anthropogenic activities (illegal logging and farming activities) in the logged and unlogged forests decreasing the number of individuals. However, the logged forests had the highest % change in the stand composition due to collateral damages and death of individuals, species and genera after logging. Our findings were consistent with previous studies that have investigated how the removal of large-size trees altered the composition and structure of the forest (Diway et al., 2023; Hayward et al., 2021).
There were commercial species of exploitable diameter that were still present in all the forest types. It is unsurprising that there were more trees above harvestable size in the unlogged forests, but the relatively high abundance of exploitable tree species of harvestable sizes in the logged forest especially in the ≥ 50 cm suggests that trees of commercial species in the remnant cohort survived within or were recruited into the logged areas after logging. Commercial trees of ≥ 50 cm DBH size classes in logged forests could also be potential seed/pollen sources left during logging as their absence would generally reduce recruitment and regeneration of species and individuals. Also there was a high density of individuals in the < 10 cm DBH size-class in the forests of 11 YAL. With canopy openness after selective logging, seeds in the soil seed bank and nearby secondary forests became activated due to the presence of light and then eventually germinated to increase the number of species in the < 10 cm DBH size-class. This highlights the potential for regeneration in logged forest and therefore indicates the necessity of an assessment of natural regeneration in selectively logged forests as a useful tool for sustainable forest management. This also calls for silvicultural management of selectively logged sites. This result is consistent with other studies in Sabah, Malaysia (Berry et al., 2008). However, a reduction in the density of individuals in unlogged forests for DBH size classes 10 - 40 cm may be due to illegal logging being carried out or the growth of remnant cohorts to higher DBH size classes. Evidence from the comparisons of PCA axis scores between logged and unlogged forest, indicated that natural regeneration will not be enough even 21YAL to control the effects of selectively logging; thus the need for silvicultural management. This is in line with the studies of Hayward et al. (2021) who indicated that the effect of selective logging can still be observed 23 - 35 YAL.
Though the diversity decreased in the unlogged forest, species evenness was highest which may be due to canopy formation that prevents seed establishment from nearby vegetation. The forests of 21 YAL, though having a canopy, had already been opened to received seeds from the nearby forest vegetation thereby increasing its diversity. This high diversity in the logged forests is different from the results obtained from other selectively logged forests that had lower diversity in Peninsular Malaysia (Okuda et al., 2003) and Central Kalimantan, Indonesia (Brearley et al., 2004). This difference may be due to additional disturbance in the logged forests leading to further degradation and thus decreasing the diversity. Maximum diversity is predicted with repeated local disturbances that are frequent enough to prevent competitive exclusion over an entire area, but not so frequent as to eliminate most species (Whittaker et al., 2001). The unlogged forests and forests of 21 YAL were greatly similar in diversity, indicating their change towards a stable primary forest; consistent with studies carried out in the forest of 13 YAL (Bischoff et al., 2005) and 5 - 20 YAL (Verburg & van Eijk-Bos, 2003). The lack of difference in alpha diversity seen here suggests that the disturbance from logging was not severe enough to prevent the maintenance of a species-rich tree community.
The insignificant difference in the AGB estimate using Pan tropical, regional and site specific allometric equations is an indication firstly that mixed-species regression equations provide good estimates of the total aboveground biomass when the diameter and wood density are input variables. Djomo et al. (2010) in the Campo-Ma’an forest in Cameroon using only diameter as input variable, indicated an average error of only 7.4%. This was confirmed by Fayolle et al. (2018) in Central African forests who indicated that the diameter and wood density had a significant influence on the calculations of AGB. Secondly, this is an indication that the equation of Chave et al. (2005) is a good reference for the calculations of AGB in moist tropical forests as it takes into consideration both the wood density and the DBH (Gourlet-Fleury et al., 2013) which are the variables which have a significant difference in the calculations of the AGB using allometric equations (Brown, 2002). Also the Chave et al. (2005) pantropical allometric equation is attributed to a large input of dataset of 2410 trees ≥ 5 cm diameter from 27 study sites across the tropics used for the development of this equation and one of such sites is in the South eastern part of Cameroon (Djomo et al., 2010; Chave et al., 2005; Fayolle et al., 2013). Site-specific allometric equations are required only in localized sites when the generalized equations may not adequately represent all forest types (Gibbs et al., 2007). This may be true where forest types have specific growth patterns, and unique species compositions such as young regenerating forests, areas of shifting cultivation mosaic, areas undergoing heavy selective cutting resulting in stunted trees, and abandoned plantations.
The more than 50% of recovery rates of the ACS, may be due to the fact that during regeneration, the recuperation of forest composition, biomass and carbon is initiated, promoting the recovery of the forest areas to a state of maturity or at least to a state prior to that of the disturbance, in a few decades. This is similar to the studies of Rutishauser et al. (2015) who indicated that 7 - 21 years is required to recover their initial ACS, in the Amazon and Butarbutar et al. (2019) who indicated that the recovery time of the aboveground carbon is between 26 - 46 years even after silvicultural treatment in the tropical forest of Indonesia. Thus ours study indicates that at 21 years, the forest had already recovered more than 50% of the initial ACS.
The observed pattern showed an increase in the growth of biomass in logged compared to unlogged forests as reported in GourletFleury et al. 2013. The highest AGB in the forests of 11 YAL may be due to the fast growth of the remnant cohort of species after logging (Fayolle et al., 2013). As observed by some authors (Gourlet-Fleury et al., 2004; Suarez et al., 2019), openings increased the growth of remaining trees and the recruitment of new trees, with an increase in the proportion of fast-growing pioneers. Specifically, Suarez et al. (2019) indicated that the biomass accumulation rate of secondary forests (≤20 years) in Africa is 5.8 times that of primary forests. Gourlet-Fleury et al. (2013) in a similar study found that after selective logging, the gain in biomass proved to be high leading to fast recovery rates of the AGB. The gain however, decreased with time since the forests of 21 YAL had a lower AGB in our study; due to competition which increased as stands accumulated biomass.
The observed decreased in AGB and ACS 21YAL may be due to the forests losing large timber species after disturbance mostly which are light demanding timber species (non-pioneer timber species) which have high wood density which takes time to recover (as in the forests of 21 YAL) as opposed to the pioneer species which immediately sprout up after logging (11YAL) with low wood density. With time these pioneer species fade out giving way to non-pioneer species. In literature, it has been well established that it is impossible for a first-time-logged tropical forest to recover its timber stock within the usual duration of a felling cycle (synthesis in Putz et al., 2012). Rutishauser et al. (2015) found that the above-ground carbon pool recovered within 75 - 85 years after disturbance as Bourgoin et al. (2024) indicated that tropical moist forests don’t significantly recover from canopy loss, after 30 years following disturbance such as fire and selective logging. Additionally, Poorter et al. (2016) reported the annual ACS recovery for tropical forest is 1 - 3 Mg C/ha/yr in a decade after logging and 3 – 5 Mg C/ha/yr in two decades. These findings are supported by our study with a net annual recovery of 4.3 and 2.1 Mg C/ha/yr after selective logging in the forests of 11 and 21 YAL respectively. Thus, further research is required to determine the required time to get to the initial climax carbon pool in Cameroon.
A higher ACS in the unlogged forests than in the similar forests of 21 YAL may be due to the fact that in the process of forest succession, plant community composition has an important impact on biomass stocks. At the early stages of succession, fast-growing species have a higher contribution, while the contribution of slow-growing is significant at later stages of succession (Shimamoto et al., 2014), due to the change in biomass accumulation patterns over time; plant composition along successional trajectories and the different succession stages that have different biomass allocation patterns under light-limited conditions. This is in line with Vieira et al. (2004) who observed that the 10 - 20 cm DBH size class which is more prominent in the logged forests contains as much as 14% of total ACS and may be highly dynamic in some Amazonian forests.
The high BCS in the forests logged 21YAL may be due to the increased number of individuals that arise after logging. The removal of timber tree species decreases the ACS and subsequently the BCS. This however increases as the forest gets back to a stable primary state. Optimal partitioning theory suggests that plants allocate more resources to the organ that acquires the most limiting resource (Johnson & Thornley, 1987). Accordingly, plants would allocate more carbon to roots if the limiting resources are belowground, i.e. water and nutrients, and would allocate more carbon aboveground when the limiting resource is light or CO2. In the forests logged 21YAL the limiting factor is water and nutrients due to a rapid increase in the number of individuals just after logging, thus an increase in the BCS. Also the destruction of the canopy encourages the growth of individuals in the <10 cm DBH size class which presumably invest more biomass below ground to take up nutrients and water for fast growth and survival.
The non-significant alteration in soil organic carbon encountered in this study revealed that selective logging doesn’t extensively change the soil entirely but acts locally in the area of logging. However, the amount of most nutrients increased in the logged sites. This could be related to high release of nutrients via the decomposition processes thereby reflecting the greater input from organic matter from the logging process. Additionally, the decrease in soil carbon stock with soil depth might be due to the decomposition of litter at the top soil, which contains more nutrients at the top soil than at the lower soil depths. The effect of selective logging 21 YAL indicates that the impact is felt after a very long time. The duration of nutrients released from decomposed stems, twigs and leaves of trees damaged by logging may thus explain the substantial but long-duration release of nutrients on these nutrient-poor soils.
Implications for Forest Management and Climate Change
The study goal was to determine the change of the forest and subsequently the carbon stock recovery in a tropical forest using different allometric equations after selective logging. This was to fill the information gaps for forest managers and decision makers and practical guidance to define sustainable harvest intensities or cutting rotations that at the same time ensure long-term timber harvest, maintenance of biodiversity and carbon stocks. This provides forest managers and policy makers with preliminary information before any effective conclusive decision can be made, taking into consideration that alternative management methods like the Reduced Impact Logging (RIL) exist to maximize a compromise between timber production and preservation of the environment.
One of the targets of sustainable forestry would be continuous timber production in every term of logging practice. Our results indicate that though there was a high forest stem density of timber species after logging, the composition and timber stock was still different from the initial forests. It is therefore recommended that the high timber density in the smaller DBH size class after logging should be maintained through silvicultural activities which will ensure future timber exploitation. This is also a call for timber exploiters to think of the exploitation of lesser known timber species with similar commercial timber qualities such as Polyalthia suaveolens which is a lesser known species was abundant in this study. With respect to climate change the real problem lies in the recovery rate of the carbon stock after selective logging as this study concluded that a 30-years cycle is not sufficient for carbon stock recovery in the East Region of Cameroon. Thus further research is required to confirm the rotation cycle of 30 years stipulated by the 1994 Forestry law of Cameroon in order to know the exact time for timber stock recovery that is very necessary to combat climate change.
5. Conclusion
In a nutshell, this study showed that carbon and biomass recovery after selective logging in a tropical forest like the Eastern forest in Cameroon do not accumulate at the same rate as the unlogged forests due to the change in the species composition after the removal of key commercial species. Thus, further research over a larger surface area and on a longer time frame is required for more conclusive information on the Carbon stock recovery after selective logging; as on a regional scale, alternative management methods of exploitation may coexist to maximize a compromise between timber production and preservation of the environment.
Acknowledgement
2005 inventory results were provided by CTFS through their Regional Coordinator Professor Chuyong George. We would like to thank Mr Bekolo Bekolo and Mr Njombe Ewusi of the National Forestry Agency (ANAFOR) for introducing us to the timber company through cosupervision, Mr Decolvenaere of SFIL-GVI for allowing us use his forest concessions. We also thank Mr Bounoungou Zibi for field supervision; Mr Blaise Jumbam, Njoh Jean and Mr Nakoe Roger for field assistance and the staff of the forest company SFIL-GVI Ndeng.
Author’s Contribution
Seraphine E. Mokake: In charge of the research design, supervision of field work and data collection, guided analysis and interpretation, and wrote manuscript. Chuyong George was the CTFS representative and project coordinator who suggested the topic and the research design, contributed in perfecting the methodology and analyses and reviewed the manuscript. Egbe E. Andrew contributed in research concept and design and reviewed the manuscript. Lyonga M. Ngoh: analysed data collected, contributed in the research concept and design and methodology. Also contributed in data interpretation and review of manuscript.
Funding Statement
Funding for the fieldwork was supported by the International Tropical Timber Organisation (ITTO) grant, the equipment grant was provided by IDEA WILD, the Carnegie Corporation of New York through the Future Africa Research Leadership (FAR-LeaF) grant of the University of Pretoria also supported the fieldwork and data analyses, the mobility grant and research modernization allowance of the Ministry of Higher Education (MINESUP) Cameroon and finally, the Fako America Scholarship.
Data Availability Statement
Data are available within the article and/or its supplementary materials.
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
The authors all accept the publication of this work.
Abbreviations
AGB |
Above Ground Biomass |
ACS |
Above ground Carbon Stock |
BCS |
Below ground Carbon Stock |
SOC |
Soil Organic Carbon |
YAL |
Years After Logging |
Appendix: 50 Most Common Species
2005 |
2011 |
T1 |
T2 |
T3 |
|
T1 |
T2 |
T3 |
|
Species code |
Frequency |
Species code |
Frequency |
SpCode |
Frequency |
SpCode |
Frequency |
SpCode |
Frequency |
Ranking |
SLOET |
616 |
RINI |
302 |
SLOET |
538 |
RINI |
214 |
SLOET |
553 |
1 |
RIND |
312 |
MILL |
284 |
RIND |
252 |
MILL |
192 |
MILL |
202 |
2 |
MILL |
267 |
RIN2 |
210 |
MILL |
207 |
MELE |
164 |
DIOSI |
152 |
3 |
RIN2 |
216 |
MELE |
196 |
RIN2 |
170 |
RIN2 |
160 |
RIN2 |
150 |
4 |
DRYMO |
199 |
DIOSI |
166 |
DRYMO |
163 |
DIOSI |
126 |
DRYW3 |
120 |
5 |
TABR |
155 |
TABR |
159 |
TABR |
124 |
TABR |
116 |
RINI |
115 |
6 |
DIOSI |
148 |
MUCE |
135 |
DIOSI |
121 |
MUCE |
60 |
TABR |
112 |
7 |
TRICI |
103 |
DRYW3 |
85 |
RINI |
86 |
DRYW3 |
57 |
TRICI |
90 |
8 |
DRY6 |
100 |
TRICI |
78 |
DRY6 |
82 |
TRICI |
54 |
RINOB |
84 |
9 |
RINI |
98 |
PENN |
59 |
TRICI |
77 |
PENN |
48 |
RIND |
79 |
10 |
DIOS2 |
84 |
DRYI |
48 |
MIPU |
70 |
DRYI |
40 |
MIPU |
71 |
11 |
MIPU |
82 |
POSU |
46 |
DRYC |
65 |
MIPU |
37 |
CELTZ |
65 |
12 |
DRYC |
76 |
MIPU |
44 |
DIOS2 |
64 |
POSU |
33 |
DIOS2 |
59 |
13 |
DRYW3 |
76 |
ANGY |
39 |
DRYW3 |
61 |
ANGY |
31 |
RIN4 |
54 |
14 |
LASIO |
73 |
DIOS2 |
38 |
LASIO |
59 |
DIOS2 |
31 |
DESBO |
53 |
15 |
PENN |
69 |
DRY3 |
38 |
POSU |
54 |
DRY3 |
26 |
DRYTL |
38 |
16 |
CELTZ |
64 |
DRYMO |
37 |
CELTZ |
53 |
DRYMO |
26 |
DRY6 |
36 |
17 |
POSU |
64 |
CELTP |
30 |
PENN |
51 |
CELTP |
24 |
PENN |
32 |
18 |
DRYTL |
61 |
PAMA |
29 |
ANGY |
45 |
PAMA |
22 |
DRYMO |
31 |
19 |
ANGY |
53 |
DRY6 |
27 |
DRYTL |
45 |
STRO |
22 |
ANON |
29 |
20 |
DESBO |
48 |
KEAY |
27 |
MUCE |
40 |
CELTZ |
22 |
MASP |
25 |
21 |
MUCE |
46 |
RAMA |
27 |
DESBO |
40 |
KEAY |
22 |
ANGY |
23 |
22 |
PAMA |
41 |
STRO |
27 |
DRIRV |
33 |
MAAC |
21 |
PAMA |
22 |
23 |
DRIRV |
41 |
CELTZ |
24 |
PAMA |
32 |
PINI |
20 |
MABA |
20 |
24 |
BAPI |
35 |
MAAC |
23 |
BAPI |
30 |
DIMA |
19 |
BAPI |
19 |
25 |
RIN4 |
32 |
DIMA |
21 |
RIN4 |
29 |
DRY6 |
19 |
DRYC |
19 |
26 |
STRO |
29 |
PINI |
21 |
STRO |
22 |
TRIC2 |
16 |
POSU |
19 |
27 |
TETDI |
28 |
DRYTL |
21 |
CONI |
18 |
RAMA |
15 |
MUCE |
18 |
28 |
MABA |
27 |
TRIC2 |
20 |
RINOB |
18 |
TRHE |
15 |
CELTI |
16 |
29 |
RINOB |
23 |
SAPW |
19 |
DRYWI |
17 |
DRYTL |
14 |
PTER |
15 |
30 |
CONI |
23 |
COAF |
18 |
ANON |
16 |
SAPW |
13 |
TRIC2 |
15 |
31 |
MASP |
23 |
TRHE |
18 |
MABA |
15 |
WTET |
13 |
TRHE |
15 |
32 |
DRYWI |
21 |
MARK |
16 |
TESU |
15 |
MYAR |
12 |
MADI |
14 |
33 |
MAMO |
21 |
UVAR2 |
16 |
TRHE |
15 |
BETE |
11 |
AYOUS |
14 |
34 |
PAUR2 |
19 |
BETE |
15 |
CELTI |
14 |
MARK |
11 |
STRO |
11 |
35 |
TRHE |
18 |
MYAR |
15 |
TETDI |
14 |
GUA3 |
11 |
DRYI |
10 |
36 |
CELTI |
18 |
WTET |
15 |
DRYI |
13 |
PYCN |
10 |
KEAY |
10 |
37 |
ANON |
17 |
EUPHI |
13 |
UVARI |
13 |
UVAR2 |
10 |
TETDI |
9 |
38 |
DRYI |
17 |
PYCN |
13 |
MARK |
13 |
SPATH |
9 |
FUEL |
9 |
39 |
TESU |
17 |
GUA3 |
12 |
STST |
13 |
UVARI |
9 |
COMAF |
8 |
40 |
UVARI |
17 |
UVARI |
11 |
TRIC2 |
13 |
EUPHI |
8 |
EUPHI |
7 |
41 |
ALCONI |
15 |
PAOL |
11 |
DRYSAW |
11 |
FUEL |
8 |
MAMO |
7 |
42 |
TRIC2 |
15 |
TROR |
11 |
ALCONI |
11 |
SAPELI |
8 |
VOAC |
7 |
43 |
MADI |
14 |
EUPH2 |
10 |
ANMA |
11 |
FICI |
7 |
DUMA |
6 |
44 |
STST |
14 |
SAPELI |
10 |
DIMA |
11 |
NEPA |
7 |
EROB |
6 |
45 |
DIMA |
14 |
SPATH |
9 |
MASP |
11 |
ANMA |
7 |
MONO |
6 |
46 |
ANMA |
13 |
BLIG |
8 |
KEAY |
10 |
DIAL |
7 |
BLIG |
5 |
47 |
DRYLA |
12 |
DIAL |
8 |
MADI |
10 |
BLIG |
6 |
CONI |
5 |
48 |
DRYSAW |
12 |
DIOSN |
8 |
BETE |
10 |
COAF |
6 |
DRYWI |
5 |
49 |
DRY3 |
12 |
NEPA |
8 |
DRY3 |
10 |
DIOSN |
6 |
CELTP |
5 |
50 |