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Carbon storages and sequestration potentials in remnant forests of different patch sizes in northern Ethiopia: an implication for climate change mitigation

Agriculture & Food Security volume 11, Article number: 57 (2022) Cite this article

Abstract

Background

Forests provide various ecosystem services. They are natural capitals that enhance nature to regulate itself via carbon sinks. However, anthropogenic and natural factors have altered their CO2 sequestration and carbon storage potentials. This study is aimed for examining the effect of patch size and biomass extraction on carbon stocks in northern Ethiopia. A total of 61 sample plots measuring 20 m × 20 m size each (0.04 ha) had been systematically assigned on patches classified into three size categories. However, the numbers of plots taken per patch were different with their sizes. Moreover, stump density has been computed at each plot to estimate the difference in the level of disturbance among patches. Carbon stocks had been estimated via models previously developed. One-way ANOVA was used to examine a variation in carbon stocks and sequestration potentials. Besides, a linear regression analysis was discretely done to examine the relationship between patch sizes, disturbance level, and carbon stocks.

Results

The overall aboveground biomass (ton ha−1) for the studied patches was 2059.13. There was a statistically significant variation in carbon stocks (ton ha−1) among patch size categories. The mean levels of disturbance ranges from 10.83% ± 1.30 to 30.8% ± 4.04. However, statistically significant difference in the level of disturbance was observed between large and small patch size categories, respectively (p < 0.05). Besides, a regression analysis confirmed a significant and negative relationship between patch size and patch disturbances (R2 = 0.65, p < 0.05). However, significant positive relation between carbon stocks (ton ha−1) and patch size (R2 = 0.53, p < 0.05) had observed.

Conclusions

In general, patch size and biomass extinction significantly influenced carbon stocks and CO2 sequestration potentials of forests. Consequently, with the pressing need to mitigate the effects of rising atmospheric CO2, maximizing carbon storage in the forest ecosystem is increasingly considered a viable management strategy. Therefore, disturbed land restoration, increasing forest patch size, sustainable management, and conservation of the existing remnant forest patch is needed to enhance carbon stocks and CO2 sequestration potentials.

Background

Forests provide various ecosystem services such as wood products, food, medicine, biodiversity conservation, erosion reduction, water purification, and carbon sinks [1,2,3,4,5,6,7,8,9]. Forests are also natural capitals that enhance nature to regulate itself and support economic growth for humans [10,11,12]. Accordingly, forests regulate nature via global carbon cycle, carbon dioxide sequestration, and climate change mitigations [13]. Besides, forest comprises greater carbon (C) than the entire atmosphere, contributing greatly to the worldwide terrestrial C sink [14] and consequently to the global climate regulation [15]. Therefore, forests have been considered as basins of global atmospheric carbon, storing more than 50% of atmospheric carbon in their aboveground vegetation [16]. Besides, forests store more than 80% of all terrestrial global aboveground C due to photosynthesis [17, 18].

Forests had been sequestered more than 20% of global fossil fuel emissions in the past periods [19] due to their high C sinking power [20]. Moreover, they have enormous possibilities in requisitioning and storing C and have been critical to the overall C sequence [21, 22]. However, recently anthropogenic and natural factors have been altered those ecosystem services and goods provided by forests [1, 23]. Furthermore, forests have been cleared and forced to remain only as small patches by agricultural activities. This leads to the destruction of ecosystem services such as carbon storage, climate regulation, and will give to the loss of global biodiversity [15, 22]. Accordingly, it has been well understood that forest destruction, disturbance, degradation, burning, and deforestation could lead to discharging of CO2 into the global atmosphere [23,24,25]. This in turn changes atmospheric greenhouse gases (GHGs) accumulation, impacted global climate change, ecosystem biomasses, ecosystem productivities, and atmospheric global C cycle [26]. Therefore, global atmospheric C storage has been increasing due to fossil fuel burning and forest conversion recently [27]. For example, in tropics forest degradation and removal have been donated for 15–25% of the yearly global GHGs releases [28]. Besides, lumber production, fuel-wood, and charcoal, uninhibited fire, and grazing annually release 52%, 31%, 9%, & 7% of GHGs, respectively [29, 30]. Therefore, a minor distraction of the forest ecosystem could bring a major alteration in the overall C cycle. In this regard, forest dynamics and the related C storage, play an important role in atmospheric CO2 concentrations [31, 32]. Conversely, little is understood about the effect of biomass removal and patch size on carbon stock of dry Afromontane forests (DAF) in Ethiopia at large and specifically in northern Ethiopia.

To quantify the C stocks, the amount, and changes of total woody biomass need to be measured. C is accumulated both as living tree biomass and as dead organic matter in forest ecosystems [15]. The two largest C pools in the forest ecosystem are aboveground live tree biomass and mineral soil organic matter, with roots, and surface detritus [33]. Therefore, the sum of the changes in the various forest C ponds is a balance between C fixation through photosynthesis and CO2 loss through respiration [18, 22, 34, 35]. This in turn regulates the rate at which ecosystem C accumulates [36]. Recently, accounting for C sequestration and storage in the forest ecosystem has been recognized and extensive attention has been given to its persistent role in regulating global climate change [37]. Besides, the extent of C sequestration and storage of a forest ecosystem is the principal approach to fight and quantify global climate crisis impacts [38, 39]. Therefore, there should be applicable conservation and management practices and plans within forests to have the overall goods and services provided [22, 40, 41].

Although there are scientific studies on C stocks and storages in forest ecosystems of Ethiopia, the effects of patch size and biomass removal (disturbance) on C storage and sequestration potential are poorly understood in the DAF of northern Ethiopia. Besides, it is not clearly understood that whether small forest patches act the same as large patch-sized forests or not in regarding C storage and sequestration [42, 43]. Moreover, the influence of disturbance levels on carbon stocks of remnant forest patches with different patch size has been poorly understood. Therefore, there is a need of estimating and comparing C storage among patches [39, 44]. Besides, little is known about changes in C stocks, their sequestration potential, and climate change regulation roles at forests with different patch sizes in northern Ethiopia [45]. Furthermore, there is a growing global interest to conserve forest ecosystems for C sequestration, storage, and climate change mitigation [22, 46] and reduction of emissions from deforestation and forest degradation [47]. Therefore, scientific information on C sequestration and storage potentials among patches with different patch sizes would be helpful to establish management and conservation plans for those remnant forest patches [15, 22, 48]. Moreover, it could be intended to provide scientific information for administration bodies (governmental and non-governmental organizations), policymakers, researchers, academicians, and other concerned stakeholders. Therefore, the capacities of patches with different size to C storage and climate change regulation need further examination. Accordingly, the present study had been accompanied to examine the linkage between patch sizes, biomass removal (patch disturbance), C storages, and sequestration potentials. Hence, this study is aimed to (1) estimate the aboveground biomass carbon stocks of woody species; (2) examine the effect of patch size and patch biomass extraction on aboveground biomass carbon stocks in northern Ethiopia.

Materials and methods

Study site description

The study was conducted in the DAF [49] remnant state forest patches of northern Ethiopia [50]. Lepidotrichilia volkensii, Albizia gummifera, and Pavetta abyssinica had been reported as dominant species in the area [51]. The area is located between latitudes of 11°1′00′′ N—11°37′00'' N and longitudes 37°3′00'' E—37°13′00'' E (Fig. 1). The altitude of the study site ranges from 1800 to 2350 m above sea level (Table 1). The temperature of the study area ranges from 9.4 °C to 29.4 °C with an average temperature of 18.7 °C. The rainfall distribution is unimodal with substantial rainfall from June to September (2241 mm) [50]. Based on data (from 1984 to 2015) from Ethiopian national meteorological service agency, the mean annual rainfall of the study area was 1300 mm. The major soil types of the study area are Andisols, Vertisols, Nitosols, and Cambisols [52].

Fig. 1
figure 1

Location map of the studied remnant forest patches in northern Ethiopia

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Table 1 Forest patch size category, elevation range, and number of transects laid in northern Ethiopia
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Patch selection and sampling

Based on their sizes, the remnant forest patches were classified into three patch size categories (PSC): small (5–45 ha), medium (120–325 ha), and large (480–747 ha). Such patch size classifications have been used in other Biological study [53]. Forest patches used for this study are owned by government and legal approval had been gained before data gathering. A total of 61 sample plots measuring 20 m ×20 m size each (0.04 ha) had been systematically assigned on each patch following Sutherland [54], Laurance et al.[44], and Magurran and Mcgill [55]. Identification of plants was made in the field. For plants that were difficult to identify in the field, identification was made at the National Herbarium, Addis Ababa University, Ethiopia, and corresponding scientific names were followed [56]. At each plot, DBH (Diameter at Breast Height ≥ 5 cm or (at 1.3 m above ground) and height of woody species had been measured using Caliper and Hypsometer, respectively. Diameter Tape was used to measure the DBH of woody species whose DBH is too big for Calipers. Those woody species with several stalks or trunks at 1.3 m height above ground have been reflected as a single individual. While for those woody plants that bear many trunks below 1.3 m, DBH measurement had been carried out as separate individuals. Moreover, woody plants had been measured when ≥ 50% of their basal area fall within the plot [57]. Woody plants whose stems are located inside the sampling plot and branches outside had also been measured. The sample size was determined based on patch area coverage and time setting effort [58, 59]. Moreover, plot number and transects in forest patch, biological or ecological studies could be determined based on patch area coverage, time setting, available budget, and personal [1, 55, 60]. Conversely, the number of plots taken per patch was varied with their sizes [61]. Therefore, overall transect numbers and plots had been estimated based on the patch size (Table 1). Accordingly, an overall 2.44 ha of forest patches proportional to 0.12% of the study area was sampled. Moreover, 0.20%, 0.10%, and 0.09% of small, medium, and large patch size categories were sampled, respectively. The sampling intensity used in this study is greater than the compulsory standard minimum sampling amount suggested to study forest patches (0.01%) [62]. To remove edge or border effects, plots had been laid at 50 m inwards. Stump number or stump density has been documented at each plot to estimate the difference in the level of biomass removal (disturbance levels) among patches. Besides, the density of woody species (living woody species) had been computed following Mishra et al. [63] and Picard et al.[58].

Data analysis

The aboveground biomass (AGB) and C stock of trees had been estimated following the recently developed allometric model by Chave et al. [64]. The model was chosen among others because it had been developed using tree parameters such as tree Height and wood density with DBH instead of using only DBH or Height. Besides, it is an appropriate model developed to estimate AGB and C stocks of trees in dry tropics and pan-tropical forest types of Africa with extensive climatic varieties [65]. Moreover, Chave allometric model is recommend in delivering midway values for the broadest choice of trees in tropics. Therefore, it shows precise estimates away from sites where it has been developed and advisable to be used in the absence of site-specific allometric equations [66]. So that DBH of trees (DBH, cm), height (m), and wood density (g cm−3) had been used to estimate the aboveground biomass and C stock of woody species using Eq. 1:

$${\mathrm{AGB}}_{\mathrm{est}} \left(\mathrm{kg}\right)=0.0673\mathrm{ x }{\left({\mathrm{\rho D}}^{2}\mathrm{H}\right)}^{0.976},$$
(1)

where AGBest is the aboveground biomass of trees (kg), ρ is the specific wood density (g cm−3), D is the diameter at breast height (cm), and H is the height (m). DBH and height of woody species had been obtained from measurement, while densities of definite woody plants were gained from wood density of tropical tree species database [67]. For woody species whose wood density is unknown, the middling values (0.612 g cm−3) of the known species’ family or genus of tropical tree species were used following Reyes et al. [67]. However, the generic equation developed for shrub species by Mokria et al. [68] was used to estimate the AGB of shrub species (Eq. 2). Moreover, the equation developed by Putz [69] was used to estimate the AGB of Lianas or climbers (Eq. 3):

$$y = 0.{2451}\left( {{\text{DSH}}^{{2}} {\text{x }}H} \right)^{{0.{7}0{38}}} ,$$
(2)

where Y = aboveground biomass (kg), DSH diameter at stump height, and H height.

$${\text{AGB }}\left( {{\text{kg}}} \right) = {\text{exp }}(0.{12} + 0.{\text{91log }}\left( {{\text{BAcm}}^{{2}} } \right),$$
(3)

where AGB aboveground biomass (kg), log logarithm base 10, BA basal area.

The overall AGB at each plot had been estimated by adding AGB of all woody species. Besides, biomass carbon had been estimated following IPCC [70] where 47% of AGB comprise C. Biomass C stocks of woody species had been estimated for each plot per patches and generalized to hectares (ton ha−1). Belowground biomass of woody species (BGB) had been estimated following IPCC [58] where belowground biomass is 27% of aboveground biomass (Eq. 4), and 47% of the BGB is below ground carbon (BGC):

$$\mathrm{BGB}=\mathrm{AGB} \times 0.27,$$
(4)

where BGB is below ground biomass and AGB is aboveground biomass.

Patch disturbance level (patch biomass extraction) had been estimated as a proportion of the addition of the stumps density to overall density (stump density plus live individuals’ density) following Sagar et al. [71]:

$$\mathrm{Patch \,disturbance \,level }\left(\mathrm{PDL}\right)= \frac{\mathrm{ Stump \,density}}{\mathrm{Total \,density}}*100,$$
(5)

where the total density is the summations of the density of stumps and density of live woody plants. Stumps are the remnants of the removed stems of woody plants measuring diameter at stump height (DSH) of ≥ 5 cm. Density represents the number of individuals per hectare. Stump density or the number of stumps per area was determined by counting old and new cut stumps (DSH ≥ 5 cm) in each plot. Therefore, stump density ha−1 of cut stumps were calculated and determined for each patch. while density of live woody species (DBH ≥ 5 cm) was computed by converting the count from each plot to hectare basis. The total density was determined after summation of stump density and density of live or standing woody plants (DBH ≥ 5 cm).

Data of DBH, height, and abundance were tested using Kolmogorov–Smirnova and Shapiro–Wilk test, and normally distributed (P > 0.05) (Appendix Table 10). The C stock data from transects of each patch were combined to estimate the C stock and sequestration potentials of patches. Accordingly, one-way ANOVA was used to examine if there is a variation in aboveground biomass, carbon stocks, and sequestration potentials across patches [72]. Post hoc comparisons were conducted for significant interactions using the Tukey honestly significant difference (HSD) test. A linear regression analysis was discretely carried out to examine the relationship between patch sizes and patch biomass extraction on C stocks and sequestration potentials. Therefore, the aboveground biomass was used as the dependent variable, whereas the patch size and patch disturbance level were used as independent variables [73]. Data were analyzed with SPSS software (IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.) and Microsoft excel 2013.

Results

Aboveground biomass and carbon stocks along with patch size categories

A total of 2841 individuals (DBH ≥ 5 cm), representative of 80 species, 68 genera, and 43 families have been identified, documented, and measured in the studied remnant forest patches (Appendix Table 5). The highest number of species, genera, and families has been recorded at low disturbed large patch size categories (Appendix Table 5). Accordingly, the number of species decreased as patch disturbance increased. Moreover, mean woody species density for small, medium, and large patch size categories ranged from 992 individuals/ha, 1111 individuals/ha, and 1411 individuals/ha, respectively. The overall aboveground biomass for the studied forest patches was 2059 ton ha−1.

The highest and the lowest aboveground biomass and carbon stocks at species level were recorded in large and small patch size categories, respectively (Table 2). Moreover, the highest and the lowest aboveground biomass (ton ha−1) at small patch size categories was retained by Albizia gummifera (J. F. Gmel.) (9.77) and Rytigynia neglecta (Hiern) Robyns (0.093), respectively (Table 3). Similarly, the highest and the lowest aboveground biomass (ton ha−1) at medium patch size categories was reserved by Prunus africana (Hook. f.) Kalkm. (495.6) and Clausena anisata (Willd.) Benth. (0.041), respectively. Besides, Prunus africana (Hook. f.) Kalkm. (551.57) and Entada abyssinica Steud. ex A. Rich. (0.047) has the highest and the lowest aboveground biomass (ton ha−1) at large patch size categories (Appendix Table 6, Table 3). P. africana, and A. gummifera retained the highest aboveground biomass (ton ha−1) and carbon stock values due to their big DBH and height values reaching up to 43 m in height and 170 and 157 cm in DBH, respectively (Fig. 2). Consequently, large stem sizes donated more for aboveground biomass and carbon stocks while small size stems contributed less. Likewise, stem number or high stem density ha−1 contributed more for aboveground biomass and carbon stocks. In this regards, low aboveground biomass for woody species such as Solanum marginatum, Clausena anisata, and Lippia adoensis was because of low stem density, low DBH, and height values (Appendix Table 6).

Table 2 Biomass and C stocks in northern Ethiopia
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Table 3 Woody species with high mean aboveground biomass and carbon stocks of patch size categories in the northern Ethiopia
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Fig. 2
figure 2

Trees with highest DBH and height in remnant forests of northern Ethiopia (a–f). Photo by Melese Genete 2018

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Both aboveground biomass and carbon stocks were higher in large patch size categories than small. Accordingly, there was statistically significant variation in mean aboveground biomass, mean belowground biomass, mean aboveground C, mean below ground C, and mean AGCO2 (ton ha−1) among patch size categories in the studied forest patches (p < 0.05) (Table 2). The result indicated that the removal of large-sized woody species (trees) and biomass removal particularly at small patch size categories had resulted in carbon stocks and biomass reduction in the study area (Fig. 3). Accordingly, the aboveground biomass decreased from the high disturbed (5.45 ± 0.201 ton ha−1) small patches to low disturbed (371.76 ± 59.16 ton ha−1) large patches. Therefore, biomass removal or patch disturbance level links to a 56.7% reduction in aboveground biomass and carbon stocks from high disturbed to low disturbed patches. Similarly, the highest mean CO2 sequestered was high in large-sized patch categories compared to small patches (Table 2). Consequently, the highest amount of stems with greater DBH at medium and large patch size categories have contributed a lot for aboveground mass and carbon stocks (Fig. 2). On the other hand, biomass removal of more woody species (trees) that have big stems particularly in the small patches has resulted in low aboveground biomass, carbon stock values and, carbon dioxide sequestration (Fig. 3).

Fig. 3
figure 3

Biomass removal by anthropogenic means in remnant forests of northern Ethiopia (a–f). Photo by Melese Genete 2018

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As indicated in Table 3, most woody species that have high aboveground biomass values in the small patch size categories have low biomass values in medium and high patch size categories. Similarly, most woody species with high biomass values in medium patches have low biomass in large patch sizes. Furthermore, woody species with greater biomass values at medium and large patch size categories such as Prunus africana, Celtis africana, Ficus vasta, and Allophylus abyssinicus were absent at small patch size categories.

Distributions of aboveground biomass among DBH classes

Though 83.3% of woody species were located in the lower DBH classes (5–25 cm) (Fig. 4a), in each patch size categories, respectively, their contribution to AGB was low (Fig. 4b). Besides, a decreasing trend of individuals was observed with increasing DBH size. Accordingly, the highest AGB (ton ha−1) was observed at a greater DBH classes (DBH ≥ 50 cm) (Fig. 4b). Though the AGB increased with increasing DBH classes, this was not observed in DBH classes ranged from 30 to 35 and 45–50 cm due to intensive removal (selective logging) of trees in these DBH ranges. According to forest guards, trees in these DBH classes are highly removed illegally for the purpose of house construction and timber production.

Fig. 4
figure 4

The mean (mean ± SE) distribution of a number of woody species (individuals ha−1) and b aboveground biomass (ton ha−1) in relation to DBH classes in northern Ethiopia

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Carbon stocks along with patch disturbance levels

The mean level of disturbance ranges from 30.80% ± 4.04 at high disturbed small patches to 10.83% ± 1.30 at low disturbed large patches. Consequently, the highest and the lowest disturbance levels were recorded in small and large patch size categories, respectively. The highest disturbance levels at small patch size categories resulted from removal of the highest amount of woody biomasses or resulted from the presence of highest stump density ha−1 (Table 4). Similarly, low disturbance levels at large patch size categories were resulted from the prevailing low cut stem number ha−1 (Table 4). Accordingly, the level of disturbance in small patch size category was significantly (P < 0.05) highest than the large and small patch size categories. Though there is a difference in stump density ha−1 among large and medium patch categories, there has been no statistically significant difference in the level of disturbance.

Table 4 Patch disturbance levels across patch size categories and carbon stock in northern Ethiopia
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The result has shown that the highest and the lowest carbon stocks were recorded at low disturbed large patch size categories and high disturbed small patch categories, respectively (Table 4). Nevertheless, statistically, the significant difference in C stocks was observed in low disturbed (large patch size categories) and high disturbed (small patch size categories) only (p < 0.05). Therefore, there have been no significant differences in biomass and C stocks at patches with medium disturbance levels (medium patch size categories) and low disturbance levels.

Relationship between patch size, patch disturbance, and carbon stocks

A significant negative relationship between patch size and patch disturbance (R2 = 0.72, p < 0.05) had confirmed in this study (Fig. 5). Accordingly, the disturbance levels of patches decreased as their size increased. Moreover, aboveground biomass, belowground biomass, aboveground C, belowground C, AGCO2, and BGCO2 (ton ha−1) decrease as the level of patch disturbance increase (R2 = 0.68, p < 0.05) (Appendix Table 6). Therefore, disturbance or biomass removal reduced the C sequestration capacity of remnant forest patches.

Fig. 5
figure 5

Relation between patch size patch and disturbance in northern Ethiopia

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On the other hand, there has been significant positive relation between aboveground biomass, belowground biomass, aboveground C, BGC, AGCO2, and belowground CO2 (ton ha−1), and patch size (R2 = 0.92, p < 0.05) (Fig. 6). In this regard, large size forest patches stored more amount of C compared to small-sized patches. Therefore, biomass removal or forest disturbance did not only shrink patch area or sizes, it also declines the aboveground biomass and carbon stocks. Though there has been a significant reduction in aboveground biomass with patch size in the present findings, patch size was not the only cause for the drop of aboveground biomass, disturbance intensity was the other reason too (Fig. 7).

Fig. 6
figure 6

Relation between patch size and above ground biomass in northern Ethiopia

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Fig. 7
figure 7

Relation between patch disturbance and above ground biomass in northern Ethiopia

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Note that the regression graph was presented only for patch size and aboveground biomass. This is because the relation between patch size and belowground biomass, aboveground carbon, belowground carbon, AGCO2, and belowground CO2 shown a similar trend with a trend in aboveground biomass. Note that the regression graph was presented only for patch disturbance and aboveground biomass. This is because the relation between patch disturbance and belowground biomass, aboveground carbon, belowground carbon, AGCO2, and belowground CO2 shown a similar trend with a trend in aboveground biomass.

Discussion

Aboveground biomass and carbon stocks along with patch size categories

Patch size affects aboveground biomass and number of individuals by altering the stand dynamics and level of disturbance across forest patches [74, 75]. Results showing low number of woody species at disturbed small patches were reported by Sagar et al. [71] similar to the results of the present study. Similarly, Shen et al. [75] reported a low number of species at smaller and disturbed patches consistent with this study. According to Kumar et al. [39] fragmented forest patches have lower species and donate to the lowest C stocks. However, [76] reported a different result from this study, reporting an increasing trend of the number of species with increased disturbance. The numbers of individuals reported in this study is greater than 1655 individuals reported in five patches of northwestern Ethiopia by Gebeyehu et al. [77]. More individuals in this study might be due to more number of patches taken and their size differences.

The total carbon stock values in the present study are comparable with the carbon stock value reported by Abere et al. [52] at Banja forests of northwestern Ethiopia. However, there was a variation in contribution of trees for aboveground biomass and carbon stocks across patches due to difference in size and stem density ha−1. For example, tree species such as Albizia gummifera, Croton macrostachyus, Prunus africana, Allophylus abyssinicus, and Apodytes dimidiata had been reported as dominant species which have high mean aboveground biomass and carbon stocks in northwestern Ethiopia [77] in line with the result of this study. However, Abere et al. [52] reported the largest portion of the carbon stocks for Juniperus procera and Ekebergia capensis which this study did not. Moreover, Peng et al. [76] and Shen et al. [75] agreed that trees with the higher DBH contributed a lot for aboveground biomass and carbon stocks in line with the results of these findings. Therefore, there is a differences in carbon socks and sequestration potentials among trees with different size and number [61, 77, 78]. Besides, the variation in the aboveground biomass and the carbon stocks among patches were also due to the difference in the level of biomass removal or patch disturbance level [23, 74, 75, 79]. Accordingly, proper management, conservation, forest restoration, and utilization of woody species might improve biomass, carbon stocks, and CO2 sequestration potentials of those remnant forest patches [22, 35].

Previous studies revealed that forest fragmentation affects carbon stocks and the sequestration potential of forests [39, 66]. Similarly, according to Lamarque et al. [4] carbon storage and sequestration of forests are different based on their patch sizes. Moreover, Paula et al. [80] reported variation of AGB across patches where large-sized patches store more amount of AGB. Therefore, small-sized forest patches store and sequester a small amount of ecosystem carbon compared to the large-sized forest patches [22, 23, 25, 32, 39]. However, [15] confirmed that connected small patches may store and sequester a high amount of carbon and CO2, in reverse to the finding of this study. Therefore, variations among patches in mean biomass, C stocks, and Carbon dioxide sequestration in the present study were because of patch size, the difference in the level of biomass removal (patch disturbance), stem size, and height of woody species. An earlier study revealed that forest disturbances reduced C stocks of forests by 81% [39]. Wekesa et al. [61] and Houghton [81] reported similar results that there is a variation in C stocks between forests of different patch sizes and levels of disturbance. Moreover, C stocks of trees and live biomass in undisturbed forests /closed canopy/ were only 8 and 15% greater, respectively, than those in the moderately closed canopy [39].

This study revealed the variation of C stocks across remnant forest patches and determined which woody species store more carbon. Accordingly, woody species with greatest density and DBH stored more C stocks [39]. However, woody species that contributed more for C stocks at large patch categories were absent at small patch size categories because of high disturbances. Besides, those small patches are close to human settlement areas and are not frequently supervised by forest guards [82]. Therefore, the absence of ecologically important species in the small patch size categories has resulted in low aboveground biomass and carbon stock values. Besides, aboveground biomass is a resolute of species abundance, number, density, and size. Shen et al. [75] suggested that large-sized trees above 40 cm in DBH stowed a greater amount of carbon than smaller trees. Therefore, the removal of a small number of large-sized tree species in the small patch size categories may lead for a low amount of aboveground biomass and C stocks. According to Wekesa et al. [83] forest disturbance affected forest carbon stocks and CO2 sequestrations. Moreover, extraction or removal of large-sized trees affected biomass and carbon stocks too [78]. Therefore, frequent biomass removal highly influences forest biomass, forest structure, and carbon sequestrations [23]. Besides, biomass removal and forest disturbance increase atmospheric carbon emissions [22, 84]. In this regard, frequent forest supervision and preparation of forest management plan greatly enhance aboveground biomass and carbon stocks [22]. Similarly, a previous study by Kumar et al. [39] reported that undisturbed or closed canopy donated 40% of the total forest C stocks.

Distributions of aboveground biomass among DBH classes

The variation of number of individuals and aboveground biomass among DBH classes was reported in northwestern DAF of Ethiopia with similar agro-ecologies to the current study [77]. Moreover, in line to the results of the present study, [85] reported decreasing trends of stem number with DBH size from Afromontane tropical forest of southwestern Rwanda. The highest number of individuals in DBH classes from 5 to 19 cm and the decreasing trends of individuals with increasing DBH were reported by Kumar et al. [39]. Senbeta et al. [56] also reported that selective logging of trees in Afromontane forests of Ethiopia is taking place based on tree size for the purpose of construction and income generation. Furthermore, studies conducted elsewhere indicated that though lower stem classes covered 50% of the stems in a forest [60], only 8% of the carbon increment was contained [86]. Moreover, [77] reported that those lower numbers of individuals at higher DBH classes have higher values of AGB. Preece et al. [66] also reported an increment of AGB with tree age and size.

Relationship between patch size, patch disturbance, and carbon stocks

The present study examined that biomass removal or selective cutting had major consequences on biomass and C stocks of woody species. Scientific studies reported the influence of disturbance or biomass removal on carbon stocks in line with the result of present study [23, 25, 61]. Moreover, [61, 77, 80] reported high disturbed and degraded small patch forest ecosystems store a low amount of carbon compared to low disturbed forests. Similarly, [87] reported a reduction in forest C stocks with different harvesting intensities. Simard et al. [88] agrees that harvesting intensity affects the aboveground and live tree C stocks in a forest. Accordingly, forest disturbances significantly reduce biomasses and C stocks [39]. Besides, [89] argues that clear-cutting, forest harvesting, disturbance, and forest conversion to other land uses such as agricultural crops had been responsible for up to 40% of global anthropogenic CO2 emissions universally.

According to Williams et al. [90] C stocks declined as patch disturbance intensity increased. Besides, [91] find out that decreases in the aboveground biomass of Neo-tropical forests patches as forest loss and disturbance increased. The result of this study is also in line with [92] conveyed that disturbance alters the biomass and C stock patterns of remnant forest patches. Conversely, [93] reported positive roles of forest disturbance for C stocks which this study did not. Likewise, Hume et al. [94] argues that C stocks and aboveground tree biomass decrease linearly with cutting intensity. Moreover, [76] reported an increase in aboveground biomass from moderately disturbed site to the seriously disturbed site in reverse to the results of these findings. On the other hand [94], stated that losses in aboveground C storage declined linearly with reduced tree cutting intensity. Therefore, the level of harvesting reduces C stocks.

Total live biomass C reported in the undisturbed forest stands is higher than disturbed stands [39]. The highest biomass in the large size patches in the present study was due to low disturbance levels and high stem numbers at those patches than smaller patches. Similar results were reported by Ziter et al. [15]. Moreover, [95] confirmed the influence of patch size and disturbance on C stocks in Afromontane forest patches in line with the results of this study. Besides, [13, 66] reported that a C density in the forest fragments was significantly influenced by patch size similar to the results of the present study. According to Shen et al. [75] forest fragmentation or patch size declined aboveground biomass in subtropical forests of China. Therefore, it had frequently stated that biomass removal or forest disturbance did not only shrink patch area or sizes, it also declines the aboveground biomass and carbon stocks [39].

As a result, reduction of forest patch size and forest disturbance or selective removal of biomasses largely affects C stocks and sequestration potentials of forests [22, 23, 25, 35, 39]. Accordingly, variations in the amount of carbon storage and sequestration in the forests might be because of variations in the level of disturbances, patch size, number, and size of species among patches [25, 32]. For example, low C stock values at the small patch size categories compared to large-sized patches in the present study are because of frequent removal of large trees at small patches. Similar results were reported [46, 78, 96, 97]. Shen et al. [75] agrees with quick drops in aboveground biomass with decreasing patch size at subtropical forests.

Implications of carbon sequestration potentials for global climate change mitigation

Carbon storage and sequestration in a forest have been significant concerns to fight global climate change in recent decays. Therefore, there is a straight connection between carbon storage, sequestration, and climate change mitigation. However, forest disturbance, degradation, and patch size affect the carbon storage and sequestration potential of forests. Therefore, if the remnant forest patches are selectively harvested or cut their climate change mitigation potential will significantly decline [88]. For example, the variation in the biomass and carbon stocks in the studied forest patches with their sizes and disturbance implied the linkages of C stocks and storages with patch sizes. Conversely, increasing forest patch size or area might not be the only solution to improve climate change mitigation potentials of remnant forests. In this regard, in the rising interest of mitigating a changing climate due to increased CO2, increasing sustainable forest conservation and management is significant. Furthermore, restoration and rehabilitation of disturbed forest patches will bring positive results to compact future climate change impacts via C storage and sequestration [35]. Sintayehu [98] find out that forests are a means to store atmospheric C and mitigate a changing climate. Therefore, enriched reforestation and rehabilitation had been reflected as actual tactics to mitigate a changing global climate [18, 84, 89, 92].

Forests are wonderful tools to cut off the global warming effects and accumulation of the greenhouse gases such as CO2 through their physiological process (photosynthesis) [61, 83, 99]. However, the carbon sequestration and storage capacity of forests varied with their level of disturbance and patch size. In this regard, scientific studies and information on carbon stocks have been useful to solve global problems such as a global climate crisis, risks, and atmospheric sequence. According to FOA [100], declining a forest cover /area/ or patch size leaded to an annual reduction of global carbon stocks and biomass by 50% Gigaton (Gt) from 2005 to 2010. Besides, biomass removal leads to the reduction of forest patches and enhances CO2 emission. Moreover, scientific findings stated that the conversion of forests to smaller patches donated 6–17% of anthropogenic CO2 emissions [91, 96, 101]. Therefore, forest distractions, degradation, encroachment, fragmentation, and disturbance impacted the global climate change mitigation roles of forests.

The confirmed 2059.13 overall aboveground biomass (ton ha−1) and sequestered overall CO2 (1503.46) (ton ha−1) for the studied forest patches in this study showed the potential of forests to mitigate climate change. Hence, CO2 added to the atmosphere could be shunned by forests. Accordingly, sustainable management and conservation are important to increase C storage and sequestration potentials of forests sustainably. Similarly, [18, 73, 102,103,104,105] reported that appropriate forest management and restoration is a way to display the forest ecosystem C cycle. Therefore, clear consideration of the impact of sustainable forest management and conservation on C storage and sequestration could deliver clear evidence for climate change mitigation via well-managed forests. In contrast, disturbed, fragmented, and degraded forest patches would probably donate more carbon loss to the global atmosphere. In this regard, recently, humans through forest disturbance and fragmentation had positively contributed to forest loss, climate change impact aggravation, and atmospheric CO2 emissions.

Conclusions

In general, the result confirmed the variation in the level of disturbance among patches with different size categories. Besides, the studied remnant forest patches stored and sequestered extensive biomass and CO2. However, both the stored biomass and the sequestered CO2 varied with the size and types of woody species. Accordingly, most of the woody species that have high aboveground biomass values in the small patches have low biomass values in medium and high patch size categories. Besides, patch size and biomass extinction or level of disturbance had a significant influence on carbon stocks and CO2 sequestration potentials of forests. In this regard, low disturbed forest patches with large patch sizes stored and sequestered high amounts of C stocks and CO2. Conversely, small patch size categories with high disturbance levels stored a low amount of carbon. Accordingly, significant variation in mean aboveground biomass, mean belowground biomass, mean aboveground C, mean belowground C and mean AGCO2 (ton ha−1) had been observed among patch size categories. Moreover, a regression analysis confirmed a significant negative relationship between patch size and patch disturbance (R2 = 0.8, p < 0.05). However, there has been significant positive relation between mean aboveground biomass, mean belowground biomass, mean aboveground C, mean belowground C, mean AGCO2, and mean belowground CO2 (ton ha−1) and patch size (R2 = 0.53, p < 0.05).

With the pressing need to mitigate the effects of rising atmospheric CO2, maximizing C storage in the forest ecosystem is increasingly considered a viable management strategy. Moreover, disturbed land restoration, increasing forest patch size, sustainable management, and conservation of the existing remnant forest patch is needed to enhance C stocks and CO2 sequestration potentials. Besides, proper forest management, conservation, and utilization are required to improve biomass, carbon stocks, and carbon dioxide sequestration, and climate change mitigation of studied remnant forest patches.

Data availability

The authors declare that data supporting the findings of this study are available within the article and its supplementary information files.

Abbreviations

GHGs:

Greenhouse gases

DAF:

Dry Afromontane forests

ANOVA:

Analysis of variance

HSD:

Honestly significant difference

ENMA:

Ethiopian National Meteorological Agency

PDL:

Patch disturbance level

PSC:

Patch size category

UTM:

Universal Transverse Mercator

References

  1. Wilson E. Biodiversity. 2. Wilson E, Peter M, editors. Washington: National Academies Press; 1988.

  2. Caliman A, Pires AF, Esteves FA, Bozelli RL, Farjalla VF. The prominence of and biases in biodiversity and ecosystem functioning research. Biodivers Conserv. 2010;19:651–64.

    Article  Google Scholar 

  3. Livesley SJ, McPherson EG, Calfapietra C. The Urban forest and ecosystem services: impacts on urban water, heat, and pollution cycles at the tree, street, and city scale. J Environ Qual. 2016;45:119–24.

    Article  CAS  PubMed  Google Scholar 

  4. Lamarque P, Quétier F, Lavorel S. The diversity of the ecosystem services concept and its implications for their assessment and management. Comptes Rendus Biol. 2011;334:441–9.

    Article  Google Scholar 

  5. Thompson I, Mackey B, McNulty S, Mosseler A. Forest resilience, biodiversity, and climate change: a synthesis of the biodiversity/resilience/stability relationship in forest ecosystems. Secr Conv Biol Divers. 2009;43:1–67.

    Google Scholar 

  6. Wu S, Hou Y, Yuan G. Valuation of forest ecosystem goods and services and forest natural capital of the Beijing municipality. China Unasylva. 2010;61:28–36.

    Google Scholar 

  7. Temperli C, Bugmann H, Elkin C. Adaptive management for competing forest goods and services under climate change. Ecol Appl. 2012;22:2065–77.

    Article  PubMed  Google Scholar 

  8. Foley JA, Asner GP, Costa MH, Coe MT, DeFries R, Gibbs HK, et al. Amazonia revealed: Forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front Ecol Environ. 2007;5:25–32.

    Article  Google Scholar 

  9. Bjärstig T, Sténs A. Social values of forests and production of new goods and services: the views of swedish family forest owners. Small-scale For. 2018;17:125–46.

    Article  Google Scholar 

  10. Brauman KA, Daily GC, Duarteeo TK, Mooney HA. The nature and value of ecosystem services: an overview highlighting hydrologic services. Annu Rev Environ Resour. 2007;32:67–98.

    Article  Google Scholar 

  11. Messier C, Puettmann KJ, Coates KD. Managing Forests as Complex Adaptive Systems: Building Resilience to the Challenge of Global Change. In: Messier C, Puettmann KJ, Coates KD, editors. Managing Forests as Complex Adaptive Systems. Abingdon: Routledge; 2013.

    Chapter  Google Scholar 

  12. Rojas TV, Bartl K, Abad JD. Assessment of the potential responses of ecosystem services to anthropogenic threats in the Eten wetland Peru. Ecosyst Heal Sustain. 2021. https://doi.org/10.1080/20964129.2021.1942224.

    Article  Google Scholar 

  13. Ma L, Shen C, Lou D, Fu S, Guan D. Ecosystem carbon storage in forest fragments of differing patch size. Sci Rep. 2017;13(7):1–8.

    CAS  Google Scholar 

  14. Piao SL, Ito A, Li SG, Huang Y, Ciais P, Wang XH, et al. The carbon budget of terrestrial ecosystems in East Asia over the last two decades. Biogeosciences. 2012;9:3571–86.

    Article  CAS  Google Scholar 

  15. Ziter C, Bennett EM, Gonzalez A. Functional diversity and management mediate aboveground carbon stocks in small forest fragments. Ecosphere. 2013;4:1–21.

    Article  Google Scholar 

  16. Hunter MO, Keller M, Victoria D, Morton DC. Tree height and tropical forest biomass estimation. Biogeosciences. 2013;10:8385–99.

    Article  Google Scholar 

  17. Goodale CL, Apps MJ, Birdsey RA, Field CB, Heath LS, Houghton RA, et al. Forest carbon sinks in the northern hemisphere. Ecol Appl. 2002;12:1–12.

    Article  Google Scholar 

  18. Perschel RT, Evans AM, Summers MJ. Climate change, carbon, and the forests of the Northeast. Santa 2007.

  19. Le Quere C, Andrew R, Canadell J, Sitch S, Korsbakken J, Peters G, et al. Global carbon budget. Earth Syst Sci Data. 2018;10:2141–94.

    Article  Google Scholar 

  20. Pugh TAM, Lindeskog M, Smith B, Poulter B, Arneth A, Haverd V, et al. Role of forest regrowth in global carbon sink dynamics. Proc Natl Acad Sci USA. 2019;116:4382–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Roshetko JM, Delaney M, Hairiah K, Purnomosidhi P. Carbon stocks in Indonesian homegarden systems: can smallholder systems be targeted for increased carbon storage? Am J Altern Agric. 2002;17:138–48.

    Article  Google Scholar 

  22. Gogoi A, Ahirwal J, Sahoo UK. Evaluation of ecosystem carbon storage in major forest types of Eastern Himalaya: Implications for carbon sink management. J Environ Manage. 2022;302: 113972.

    Article  CAS  PubMed  Google Scholar 

  23. Chaudhury G, Bahadur C, Basumatari M, Dutta G, Devi A, Bharadwaj N. Stand structure, biomass and carbon stock along disturbance gradients in differently managed tropical forests of Assam, northeast India. Trees, For People. 2022;9:100296. https://doi.org/10.1016/j.tfp.2022.100296.

    Article  Google Scholar 

  24. Gautam TP, Mandal TN. Effect of disturbance on biomass, production and carbon dynamics in moist tropical forest of eastern Nepal. For Ecosyst. 2016;3:3–11.

    Article  Google Scholar 

  25. Mesa-sierra N, Laborde J, Chaplin-kramer R, Escobar F. Carbon stocks in a highly fragmented landscape with seasonally dry tropical forest in the Neotropics. For Ecosyst. 2022;9:100016. https://doi.org/10.1016/j.fecs.2022.100016.

    Article  Google Scholar 

  26. Harris NL, Brown S, Hagen SC, Saatchi SS, Petrova S, Salas W, et al. Baseline map of carbon emissions from deforestation in tropical regions. Science. 2012;336:1573–6.

    Article  CAS  PubMed  Google Scholar 

  27. Paustian K, Six J, Elliott ET, Hunt HW. Management options for reducing CO2 emissions from agricultural soils: implications for climate change. Biogeochemistry. 2000;48:147–63.

    Article  CAS  Google Scholar 

  28. Houghton RA. Aboveground forest biomass and the global carbon balance. Glob Chang Biol. 2005;11:945–58.

    Article  Google Scholar 

  29. Hosonuma N, Herold M, De Sy V, De Fries RS, Brockhaus M, Verchot L, et al. An assessment of deforestation and forest degradation drivers in developing countries. Environ Res Lett. 2012. https://doi.org/10.1088/1748-9326/7/4/044009.

    Article  Google Scholar 

  30. Silva JA, Sedano F, Flanagan S, Ombe ZA, Machoco R, Meque CH, et al. Charcoal-related forest degradation dynamics in dry African woodlands: Evidence from Mozambique. Appl Geogr Elsevier Ltd. 2019;107:72–81.

    Article  Google Scholar 

  31. Laurance WF, et al. Rapid decay of tree-community composition in Amazonian forest fragments. Proc Nat Acad Sci USA. 2006;103:19010–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Clarisa S, Gatti MG. Carbon stock densities of semi-deciduous Atlantic forest and pine plantations in Argentina. Sci Total Environ. 2020;747:141085. https://doi.org/10.1016/j.scitotenv.2020.141085.

    Article  CAS  Google Scholar 

  33. Fahey TJ, Woodbury PB, Battles JJ, Goodale CL, Hamburg SP, Ollinger SV, et al. Forest carbon storage: Ecology, management, and policy. Front Ecol Environ. 2010;8:245–52.

    Article  Google Scholar 

  34. Tong X, Brandt M, Yue Y, Ciais P, Jepsen MR, Penuelas J, et al. short term extensive carbon sequestration. Nat Commun. 2020;11:1–10. https://doi.org/10.1038/s41467-019-13798-8.

    Article  CAS  Google Scholar 

  35. Matos FAR, Aquila C, Miranda C, De MLFT, Gastauer M, Safar NVH, et al. Secondary forest fragments offer important carbon and biodiversity cobenefits. Glob Chang Biol. 2020;26:509–22.

    Article  PubMed  Google Scholar 

  36. Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA. National-scale biomass estimators for United States tree species. For Sci. 2003;49:12–35.

    Google Scholar 

  37. Pearson RHT, Brown LS Birdsey AR. 2007. Measurement guidelines for the sequestration of forest carbon. Gen Tech Rep. NRS-18. Delaware United States Dep Agric For Serv.

  38. Carlson M, Chen J, Elgie S, Henschel C, Montenegro Á, Roulet N, et al. Maintaining the role of Canada’s forests and peatlands in climate regulation. For Chron. 2010;86:434–43.

    Article  Google Scholar 

  39. Kumar T, Islam A, Uddin J. Forest carbon stocks under three canopy densities in Sitapahar natural forest reserve in Chittagong Hill Tracts of Bangladesh. For Ecol Manage. 2021. https://doi.org/10.1016/j.foreco.2021.119217.

    Article  Google Scholar 

  40. Shen C, Ma L, Hu J, Huang L, Chen Y. Soil carbon storage and its determinants in forest fragments of differentiated patch size. Forests. 2019;10:1–13.

    Article  Google Scholar 

  41. Chen J, John R, Sun G, McNulty S, Noormets A, Xiao J, et al. Forest landscapes and global change: challenges for research and management. In: Azevedo M, editor., et al., For Landscapes glob chang challenges res manag. 1st ed. Berlin: Springer; 2014.

    Google Scholar 

  42. Smithwick EAH, Harmon ME, Domingo JB. Modeling multiscale effects of light limitations and edge-induced mortality on carbon stores in forest landscapes. Landsc Ecol. 2003;18:701–21.

    Article  Google Scholar 

  43. Keane RE, McKenzie D, Falk DA, Smithwick EAH, Miller C, Kellogg LKB. Representing climate, disturbance, and vegetation interactions in landscape models. Ecol Modell. 2015;309–310:33–47.

    Article  Google Scholar 

  44. Laurance WF, Camargo JLC, Luizão RCC, Laurance SG, Pimm SL, Bruna EM, et al. The fate of Amazonian forest fragments: a 32-year investigation. Biol Conserv. 2011;144:56–67.

    Article  Google Scholar 

  45. Solomon N, Pabi O, Annang T, Asante IK, Birhane E. The effects of land cover change on carbon stock dynamics in a dry Afromontane forest in northern Ethiopia. Carbon Balance Manag. 2018. https://doi.org/10.1186/s13021-018-0103-7.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Strassburg BBN, Kelly A, Balmford A, Davies RG, Gibbs HK, Lovett A, et al. Global congruence of carbon storage and biodiversity in terrestrial ecosystems. Conserv Lett. 2010;3:98–105.

    Article  Google Scholar 

  47. Tata HL, Van NM, Dewi S. Will funding to reduce emissions from deforestation and (forest) degradation (REDD+) stop conversion of peat swamps to oil palm in orangutan habitat in Tripa in Aceh, Indonesia ? Mitig Adapt Strateg Glob Chang. 2014;19:693–713.

    Google Scholar 

  48. Billings SA, Gaydess EA. Soil nitrogen and carbon dynamics in a fragmented landscape experiencing forest succession. Landsc Ecol. 2008;23:581–93.

    Article  Google Scholar 

  49. Friis I, Demissew S, Breugel P van. Atlas of the potential vegetation of Ethiopia. 1st ed. Copenhagen: The Royal Danish Academy of Sciences and Letters. 2010.

  50. Gebeyehu G, Soromessa T. Status of soil organic carbon and nitrogen stocks in Koga watershed area, Northwest Ethiopia. Agric Food Secur. 2018;7:1–10.

    Article  Google Scholar 

  51. Muluneh MG, Feyissa MT, Wolde TM. Efect of forest fragmentation and disturbance on diversity and structure of woody species in dry Afromontane forests of northern Ethiopia. Biodivers Conserv. 2021;30:1–27.

    Article  Google Scholar 

  52. Abere F, Belete Y, Kefalew A. Carbon stock of banja forest in banja district, amhara region, Ethiopia: an implication for climate change mitigation. J Sustain For. 2017;9811:1–47.

    Google Scholar 

  53. Laurance WF, Lovejoy TE, Vasconcelos HL, Bruna EM, Didham RK, Stouffer PC, et al. Ecosystem decay of Amazonian forest fragments: a 22-year investigation. Conserv Biol. 2002;16:605–18.

    Article  Google Scholar 

  54. Sutherland WJ. Ecological Census Techniques. 2nd ed. New York: Cambridge University Press; 2006.

  55. Magurran AE, Mcgill BJ. Biological diversity. 2nd ed. Frontiers in Measurement and Assessment. Oxford University Press, NEW YORK; 2011.

  56. Senbeta F, Schmitt C, Woldemariam T, Boehmer HJ, Denich M. Plant diversity, vegetation structure and relationship between plant communities and environmental variables in the Afromontane forests of Ethiopia. SINET Ethiop J Sci. 2014;37:113–30.

    Google Scholar 

  57. Bhishma PS, Shiva SP, Ajay P, Eak BR, Sanjeeb B, Tibendra RB, et al. Forest Carbon Stock Measurement: Guidelines for measuring carbon stocks in community-managed forests. 1st ed. Kathmandu: Asia Network for Sustainable Agriculture and Bioresources (ANSAB) publishing; 2010.

    Google Scholar 

  58. Picard N, Saint-Andre L, Henry M. Manual for building tree volume and biomass allometric equations: from field measurement to prediction. 1st ed Picard N, Saint-André L, Henry M, editors. Rome: FAO food and agricultural organization of the United Nations 2012.

  59. Zanne A, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, et al. Global wood density database. Dryad. 2009;10:1–50.

    Google Scholar 

  60. Hedl R, Svatek M, Dancak M, Rodzay AW, Salleh M, Kamariah AB. A new technique for inventory of permanent plots in tropical forests: a case study from lowland dipterocarp forest in Kuala Belalong Brunei Darussalam. Blumea. 2009;54:124–30.

    Article  Google Scholar 

  61. Wekesa C, Kirui BK, Maranga EK, Muturi GM, Taveta T, Box PO. Variations in forest structure, tree species diversity and above-ground biomass in edges to interior cores of fragmented forest patches of Taita Hills. Kenya For Ecol Manage. 2019;440:48–60.

    Article  Google Scholar 

  62. Sukumar R, Dattaraja HS, Suresh H, Radhakrishnan J, Vasudeva R, Nirmala S, et al. Long-term monitoring of vegetation in a tropical deciduous forest in Mudumalai, Southern India. Curr Sci. 1992;62:608–16.

    Google Scholar 

  63. Mishra BP, Tripathi OP, Tripathi RS, Pandey HN. Effects of anthropogenic disturbance on plant diversity and community structure of a sacred grove in Meghalaya, Northeast India. Biodivers Conserv. 2004;13:421–36.

    Article  Google Scholar 

  64. Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WBC, et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol. 2014;20:3177–90.

    Article  PubMed  Google Scholar 

  65. Henry M, Besnard A, Asante WA, Eshun J, Adu-bredu S, Valentini R, et al. Forest ecology and management wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. For Ecol Manage. 2010;260:1375–88. https://doi.org/10.1016/j.foreco.2010.07.040.

    Article  Google Scholar 

  66. Preece ND, Crowley GM, Lawes MJ, Van Oosterzee P. Comparing above-ground biomass among forest types in the Wet Tropics: small stems and plantation types matter in carbon accounting. For Ecol Manage. 2012;264:228–37.

    Article  Google Scholar 

  67. Reyes G, Brown S, Chapmn G, Lugo AE. Wood densities of tropical tree species: general technical report So-88. New Orleans: Louisiana; 1992.

    Book  Google Scholar 

  68. Mokria M, Mekuria W, Gebrekirstos A, Aynekulu E, Belay B, Gashaw T, et al. Mixed-species allometric equations and estimation of aboveground biomass and carbon stocks in restoring degraded landscape in northern Ethiopia. Environ Res Lett. 2018;13:1–14.

    Article  Google Scholar 

  69. Putz F. Liana biomass and leaf area of a “tierra firme” forest in the Rio Negro Basin. Venezuela Biotropica. 1983;15:185–9.

    Article  Google Scholar 

  70. IPCC. Guidelines for national greenhouse gas inventories. Tokyo 2006.

  71. Sagar R, Raghubanshi A, Singh J. Tree species composition, dispersion and diversity along a disturbance gradient in a dry tropical forest region of India. For Ecol Manag Albert. 2003;186:60–75.

    Google Scholar 

  72. Hammer Ø, Harper DAT, Ryan PD. Past: paleontological statistics software package for education and data analysis. Palaeontol Elec. 2001;4:1–9.

    Google Scholar 

  73. Majumdar K, Choudhary BK, Datta BK. Aboveground woody biomass, carbon stocks potential in selected tropical forest patches of India. Open J Ecol. 2016;6:598–612.

    Article  Google Scholar 

  74. Yohannes H, Soromessa T. Carbon stock analysis along forest disturbance gradient in gedo forest: journal of ecosystem & ecography carbon stock analysis along forest disturbance gradient in gedo forest: implications of managing forest for climate change mitigation. J Ecosyst Ecography. 2015;5:1–6.

    Article  Google Scholar 

  75. Shen C, Shi N, Fu S, Ye W, Ma L, Guan D. Decline in aboveground biomass due to fragmentation in subtropical forests of China. Forests. 2021;12:1–12.

    Article  Google Scholar 

  76. Peng H, Li X, Li G, Zhang Z, Zhang S, Li L, et al. Catena Shrub encroachment with increasing anthropogenic disturbance in the semiarid inner mongolian grasslands of China. CATENA. 2013;109:39–48.

    Article  Google Scholar 

  77. Gebeyehu G, Soromessa T, Bekele T, Teketay D. Carbon stocks and factors affecting their storage in dry Afromontane forests of Awi Zone, northwestern Ethiopia. J Ecol Environ. 2019;8:1–18.

    Google Scholar 

  78. Meyer V, Saatchi S, Clark DB, Keller M, Vincent G, Ferraz A, et al. Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes. Biogeosciences. 2018;15:3377–90.

    Article  Google Scholar 

  79. Mesa-sierra N, Laborde J, Chaplin-kramer R, Escobar F. Forest ecosystems carbon stocks in a highly fragmented landscape with seasonally dry tropical forest in the Neotropics. For Ecosyst. 2022. https://doi.org/10.1016/j.fecs.2022.100016.

    Article  Google Scholar 

  80. De PMD, Patrícia C, Costa A. Carbon storage in a fragmented landscape of Atlantic forest: the role played by edge-affected habitats and emergent trees. Trop Conserv Sci. 2011;4:349–58.

    Article  Google Scholar 

  81. Houghton RA, Hole W, Hole W. Aboveground forest biomass and the global carbon balance. Glob Chang Biol. 2005;11:945–58.

    Article  Google Scholar 

  82. Sagar R, Singh JS. Tree density, basal area and species diversity in a disturbed dry tropical forest of northern India : implications for conservation. Environ Conserv. 2006;33:256–62.

    Article  Google Scholar 

  83. Wekesa C, Leley N, Maranga E, Kirui B, Muturi G, Mbuvi M, et al. Effects of forest disturbance on vegetation structure and above-ground carbon in three isolated forest patches of Taita Hills. Open J For. 2016;6:142–61.

    Google Scholar 

  84. Field CB, Mach KJ. Rightsizing carbon dioxide removal. Science. 2017;356:706–7.

    Article  CAS  PubMed  Google Scholar 

  85. Nyirambangutse B, Zibera E, Uwizeye FK, Nsabimana D, Bizuru E, Pleijel H. Carbon stocks and dynamics at different successional stages in an Afromontane tropical forest. Biogeosciences. 2017;14:1285–303.

    Article  Google Scholar 

  86. Djomo AN, Knohl A, Gravenhorst G. Estimations of total ecosystem carbon pools distribution and carbon biomass current annual increment of a moist tropical forest. For Ecol Manage. 2011;261:1448–59.

    Article  Google Scholar 

  87. Clarke N, Gundersen P, Jönsson-belyazid U, Kjønaas OJ, Persson T, Sigurdsson BD, et al. Influence of different tree-harvesting intensities on forest soil carbon stocks in boreal and northern temperate forest ecosystems. For Ecol Manage. 2015;351:9–19.

    Article  Google Scholar 

  88. Simard SW, Roach WJ, Defrenne CE, Pickles BJ. Harvest intensity effects on carbon stocks and biodiversity are dependent on regional climate in douglas-fir forests of British Columbia. Front For Glob Chang. 2020;3:1–20.

    Article  Google Scholar 

  89. Metsaranta JM, Kurz WA, Neilson ET, Stinson G. Implications of future disturbance regimes on the carbon balance of Canada’s managed forest (2010–2100). Tellus, Ser B Chem Phys Meteorol. 2010;62:719–28.

    Article  Google Scholar 

  90. Williams CA, Collatz GJ, Masek J, Goward SN. Impacts of disturbance history on forest carbon stocks and fluxes : Merging satellite disturbance mapping with forest inventory data in a carbon cycle model framework. Remote Sens Environ J. 2014;151:5771.

    Article  Google Scholar 

  91. Metzger P, Vı MM, Faria D, Oliveira AA. Landscape forest loss decreases aboveground biomass of Neotropical forests patches in moderately disturbed regions. Landsc Ecol. 2021;7:439–53.

    Google Scholar 

  92. Luiz C, Silva F. Effects of anthropogenic disturbances on biodiversity and biomass stock of Cerrado, the Brazilian savanna. Biodivers Conserv. 2020;29:1–13.

    Google Scholar 

  93. Pugh TAM, Arneth A, Kautz M, Poulter B. Important role of forest disturbances in the global biomass turnover and carbon sinks. Nat Geosci. 2020;12:730–5.

    Article  Google Scholar 

  94. Hume AM, Chen HYH, Taylo AR. Intensive forest harvesting increases susceptibility of northern forest soil to carbon, nitrogen and phosphorus loss. J Appl Eco. 2017;55:246–55.

    Article  Google Scholar 

  95. Mohandass D, Campbell MJ, Hughes AC, Mammides C, Davidar P. The effect of altitude, patch size and disturbance on species richness and density of lianas in montane forest patches. Acta Oecologica Elsevier Masson SAS. 2017;83:1–14.

    Article  Google Scholar 

  96. Bustamante MMC, Roitman I, Aide TM. Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity. Glob Chang Biol. 2016;22:92–109.

    Article  PubMed  Google Scholar 

  97. Lindsell JA, Klop E. Spatial and temporal variation of carbon stocks in a lowland tropical forest in West Africa. For Ecol Manage. 2013;289:10–7.

    Article  Google Scholar 

  98. Sintayehu DW. Impact of climate change on biodiversity and associated key ecosystem services in Africa: a systematic review. Ecosyst Heal Sustain. 2018;4:225–39.

    Article  Google Scholar 

  99. Wekesa BM, Ayuya OI, Lagat JK. Effect of climate-smart agricultural practices on household food security in smallholder production systems: micro-level evidence from Kenya. Agric Food Secur BioMed Central. 2018;7:1–14.

    Google Scholar 

  100. FAO. Global forest resources assessment. Main report, FAO Forest paper 163. Rome; 2012.

  101. Strassburg BBN, Rodrigues ASL, Gusti M, Balmford A, Fritz S, Michael Obersteiner R, Turner K, Brooks TM, et al. Impacts of incentives to reduce emissions from deforestation on global species extinctions. Nat Clim Chang. 2012;2:350–5.

    Article  CAS  Google Scholar 

  102. Cowie AL, Penman TD, Gorissen L, Winslow MD, Lehmann J, Tyrrell TD, et al. Towards sustainable land management in the drylands: Scientific connections in monitoring and assessing dryland degradation, climate change and biodiversity. L Degrad Dev. 2011;22:248–60.

    Article  Google Scholar 

  103. Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, et al. Solutions for a cultivated planet. Nature. 2011;478:337–42.

    Article  CAS  PubMed  Google Scholar 

  104. Lindenmayer DB, Laurance WF. The ecology, distribution, conservation and management of large old trees. Biol Rev. 2017;92:1434–58.

    Article  PubMed  Google Scholar 

  105. Meragiaw M. Role of agroforestry and plantation on climate change mitigation and carbon sequestration in Ethiopia. J Tree Sci. 2017;36:1.

    Article  Google Scholar 

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Acknowledgements

We would like to thank Wollo University through the ministry of science and higher education of Ethiopia for funding support to undertake this study.

Funding

Financial support to undertake the study was received from Wollo University through ministry of science and higher education of Ethiopia.

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Authors and Affiliations

  1. College of Agriculture, Department of Forestry, Wollo University, P.O. Box 1145, Dessie, Ethiopia

    Melese Genete Muluneh & Belachew Bogale Worku

Authors
  1. Melese Genete Muluneh
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  2. Belachew Bogale Worku
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by MGM and BBW. The first draft of the manuscript was written by MGM and BBW. Both authors read and approved the final manuscript.

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Correspondence to Melese Genete Muluneh.

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Appendix

Appendix

See Tables 5, 6, 7, 8, 9, 10, 11, 12, 13, 14

Table 5 Woody species found in the study area
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Table 6 Wood density and C stocks of woody species in the study area
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Table 7 Carbon stock of different species in the small patch size category of northern Ethiopia
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Table 8 Carbon stock of different species in the medium patch size category of northern Ethiopia
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Table 9 Carbon stock of different species in the large patch size category of northern Ethiopia
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Table 10 Test of normality on DBH, height and abundance data
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Table 11 Descriptive statistics of biomass, carbon and CO2equivalent of different patch size categories in northern Ethiopia
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Table 12 One-way analysis of variance (ANOVA) result of biomass, carbon and CO2equivalent of different patch size category in northern Ethiopia
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Table 13 Descriptive statistics of stem density/ha, stump density/ha and patch disturbance levels of different patch size categories in northern Ethiopia
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Table 14 One-way analysis of variance (ANOVA) result of stem density/ha, stump density/ha and patch disturbance levels of different patch size category in northern Ethiopia
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Muluneh, M.G., Worku, B.B. Carbon storages and sequestration potentials in remnant forests of different patch sizes in northern Ethiopia: an implication for climate change mitigation. Agric & Food Secur 11, 57 (2022). https://doi.org/10.1186/s40066-022-00395-0

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Keywords

Agriculture & Food Security

ISSN: 2048-7010