Forest Stocks Research Articles

Spodosol development and soil organic carbon distribution along a lithosequence in perhumid coastal temperate rainforest

AbstractA dense concentration of old‐growth forest and a wet, cold climate promote mineral weathering and leaching in coastal temperate rainforest soils. Our objective was to assess soil development and soil organic carbon (SOC) distribution across 18 soil profiles in remote, upland terrain of southeast Alaska where pedon data are sparse. We made soil morphological observations, collected samples, and completed laboratory analyses to measure SOC content, pH, and particle size distribution. The survey of upland backslope soils included north‐ and south‐facing hillslopes derived from three lithologies (slate, metavolcanic, and phyllite). The soils across all sites were very gravelly (51.8 ± 20.4% coarse fragments), acidic (mineral soil pH 4.85 ± 0.45), and moderately deep (96.56 ± 37.80 cm); thin, broken E horizons were underlain by thick, carbon‐rich spodic horizons. Soil development was relatively consistent as demonstrated by the Profile Development Index with values from 15 to 26 and Podzolization Index values spanning 8 to 14. A mean pedon SOC stock of 198.02 ± 81.42 Mg C ha−1 (n = 18) was calculated using data collected for all upland organic and mineral soils from our work. The accumulation of SOC was similar among soils formed from contrasting lithologies with averages of 182 ± 15.70 Mg C ha−1 for slate, 188 ± 53.80 Mg C ha−1 for metavolcanic, and 218 ± 124 Mg C ha−1 for phyllite. Our work contributes to soil morphological observations, laboratory data, and SOC stock estimates required to better constrain and model pedogenic processes and SOC stock in remote forests where data sets are limited.

The Carbon Stocks Estimation on Tangga Community Forests of Lombok Utara

Forests are one of the largest carbon dioxide absorbers, the forest's function as a carbon dioxide absorber causes an increase in greenhouse gases in the atmosphere, and carbon dioxide is stored in forest biomass. Forest development to increase carbon dioxide absorption can be carried out in state forest areas or private forests, which also include community forests. This research aims to estimate carbon stocks in the Tangga community forest (HKm) , in the Tangga HKm, Lombok utara, NTB. This research uses quantitative methods. The data collected is standing vegetation data. Standing vegetation data includes saplings, poles and trees by measuring diameter and height using purposive sampling collection techniques. Carbon stocks are estimated using the allometric equation formula. The total carbon reserves in the HKm Tangga obtained from each stand were 9.972,11 ton/ha. The highest carbon stock value is at the tree level with a value of 8.785,08 tons/ha, followed by the sapling level at 731,34 ton/ha, and the pole level at 455,69 tons/ha with the highest amount at the candlenut tree level (Aleurites moluccana) at 2.200,63 ton/ha and the lowest was the breadfruit (Artocarpus altilis) with a value of 1,96 ton/ha.

Carbon Stocks of Forests and Tree Plantations Along an Elevational Gradient in the Western Ghats: Does Plant Diversity Impact Forest Carbon Stocks?

Carbon Stocks of Forests and Tree Plantations Along an Elevational Gradient in the Western Ghats: Does Plant Diversity Impact Forest Carbon Stocks?

Changes in soil organic carbon in native forests of Argentina related to land use change and environmental factors

AbstractNative forests host important pools of soil organic carbon (SOC). This is a key element not only for ecosystem functioning but also for the global carbon cycle. Globally, and particularly in Argentina, native forests are being rapidly replaced by other land uses, raising questions about the impact of these transformations on SOC and its environmental controls. Based on the construction of the largest SOC database in Argentina to date, we investigated the patterns and controls of changes in SOC stocks associated with the replacement of native forests by other land uses. We constructed the database with a total of 818 sites with SOC data (0–30 cm depth), covering the main ecoregions, to which we added environmental information (e.g. satellite data, soil database and climate database), to study the environmental controls on SOC change after deforestation and on the original SOC content of native forests. Considering all ecoregions and all land use alternatives together, we found an average decrease in SOC stock of 18.2 Mg C ha−1, which represents a loss of more than a quarter of the original SOC stock of the native forest sites. A boosted regression tree explained 89% of the variation in SOC stock change and indicated that the initial forest SOC stock and the post‐deforestation land use were the most important variables explaining this variation (relative influence of 30.9% and 18.2%, respectively). The replacement of native forests by rainfed annual crops resulted in the largest decrease in SOC (−28 Mg C ha−1), which was twice as large as the decrease observed in rangelands (−14 Mg C ha−1). On the contrary, neither irrigated croplands nor tree plantations of fast‐growing species caused a decrease in SOC stocks (p > .10). Climate and soil texture had an indirect effect on SOC changes through a strong influence on the initial SOC stocks in native forests (p < .01). Our study highlighted the significant impact of land use change on SOC stocks, overshadowing other relevant environmental controls. Understanding how the SOC pool responds to land use change, environmental conditions and management practices is essential to increase the effectiveness of practices implemented to improve soil properties and mitigate climate change.

Mixed effects of climate and species richness on aboveground carbon stock in subtropical Atlantic forests

Tropical forests are global biodiversity hotspots and are crucial in the global carbon (C) cycle. Understanding the drivers of aboveground carbon stock (AGC) in a heterogeneous and biodiverse system can shed light on the processes underlying the relationship between biodiversity and carbon accumulation. Here, we investigate how biodiversity, environment, and landscape structure affect AGC. We examined such associations in 349 plots comprising over 95,346 km2 the Atlantic Forest of southern Brazil, encompassing three forest types: Dense Ombrophylous Forest (DF), Mixed Ombrophylous Forest (MF), and Seasonal Deciduous Forest (SF). Each plot was described by environmental variables, landscape metrics, and biodiversity (species richness and functional diversity). We used diversity, environmental, and landscape variables to build generalized linear mixed models and understand which can affect the forest AGC. We found that species richness is associated positively with AGC in all forest types, combined and separately. Seasonal temperature and isothermality affect AGC in all forest types; additionally, stocks are positively influenced by annual precipitation in SF and isothermality in MF. Among landscape metrics, total fragment edge negatively affects carbon stocks in MF. Our results show the importance of species diversity for carbon stocks in subtropical forests. The climate effect was also relevant, showing the importance of these factors, especially in a world where climate change tends to affect forest stock capacity negatively.

Mapping Forest Stock Volume Using Phenological Features Derived from Time-Serial Sentinel-2 Imagery in Planted Larch

Mapping Forest Stock Volume Using Phenological Features Derived from Time-Serial Sentinel-2 Imagery in Planted Larch

Maize straw increases while its biochar decreases native organic carbon mineralization in a subtropical forest soil

Organic soil amendments have been widely adopted to enhance soil organic carbon (SOC) stocks in agroforestry ecosystems. However, the contrasting impacts of pyrogenic and fresh organic matter on native SOC mineralization and the underlying mechanisms mediating those processes remain poorly understood. Here, an 80-day experiment was conducted to compare the effects of maize straw and its derived biochar on native SOC mineralization within a Moso bamboo (Phyllostachys edulis) forest soil. The quantity and quality of SOC, the expression of microbial functional genes concerning soil C cycling, and the activity of associated enzymes were determined. Maize straw enhanced while its biochar decreased the emissions of native SOC-derived CO2. The addition of maize straw (cf. control) enhanced the O-alkyl C proportion, activities of β-glucosidase (BG), cellobiohydrolase (CBH) and dehydrogenase (DH), and abundances of GH48 and cbhI genes, while lowered aromatic C proportion, RubisCO enzyme activity, and cbbL abundance; the application of biochar induced the opposite effects. In all treatments, the cumulative native SOC-derived CO2 efflux increased with enhanced O-alkyl C proportion, activities of BG, CBH, and DH, and abundances of GH48 and cbhI genes, and with decreases in aromatic C, RubisCO enzyme activity and cbbL gene abundance. The enhanced emissions of native SOC-derived CO2 by the maize straw were associated with a higher O-alkyl C proportion, activities of BG and CBH, and abundance of GH48 and cbhI genes, as well as a lower aromatic C proportion and cbbL gene abundance, while biochar induced the opposite effects. We concluded that maize straw induced positive priming, while its biochar induced negative priming within a subtropical forest soil, due to the contrasting microbial responses resulted from changes in SOC speciation and compositions. Our findings highlight that biochar application is an effective approach for enhancing soil C stocks in subtropical forests.

Carbon carrying capacity in primary forests shows potential for mitigation achieving the European Green Deal 2030 target

Carbon accounting in the land sector requires a reference level from which to calculate past losses of carbon and potential for gains using a stock-based target. Carbon carrying capacity represented by the carbon stock in primary forests is an ecologically-based reference level that allows estimation of the mitigation potential derived from protecting and restoring forests to increase their carbon stocks. Here we measured and collated tree inventory data at primary forest sites including from research studies, literature and forest inventories (7982 sites, 288,262 trees, 27 countries) across boreal, temperate, and subtropical Global Ecological Zones within Europe. We calculated total biomass carbon stock per hectare (above- and below-ground, dead biomass) and found it was 1.6 times larger on average than modelled global maps for primary forests and 2.3 times for all forests. Large trees (diameter greater than 60 cm) accounted for 50% of biomass and are important carbon reservoirs. Carbon stock foregone by harvesting of 12–52% demonstrated the mitigation potential. Estimated carbon gain by protecting, restoring and ongoing growth of existing forests equated to 309 megatons carbon dioxide equivalents per year, additional to, and higher than, the current forest sink, and comparable to the Green Deal 2030 target for carbon dioxide removals.

The Dynamics of Vegetation Structure, Composition and Carbon Stock in Peatland Ecosystem of Old Secondary Forest in Riau and South Sumatra Provinces

Lowland tropical rainforests provide an abundance of biodiversity as well as dynamic and stable ecosystems. These forests include tropical peat forests in various locations and forest types that have vegetation structure and composition characteristics, and carbon stocks that still need to be explored more deeply. Research on the structure and the composition of the vegetation and carbon stock in the old secondary peat forest was carried out in the protected areas of Bukit Batu, Riau Province, and Muara Merang, South Sumatra Province. Based on a 1-hectare permanent plot established in Bukit Batu and Muara Merang, 25 subplots of 20 m by 20 m were established in each location for measurement purposes. The results showed that Bukit Batu and Muara Merang had 42 and 36 species belonging to 26 and 20 families, respectively. Bukit Batu had a species diversity index (H’) of 2.93, and the dominant tree species were Palaquium xanthochymum with an importance value index (IVI) = 66.27%, Eugenia sp. (IVI = 32.76%), and Litsea sp. (IVI = 18.39%). The Muara Merang location had a species diversity index (H’) of 2.82, and the dominant tree species were Eugenia sp. (IVI = 60.88%), Alseodaphne insignis (IVI = 26.34%), and Adenanthera pavonina (IVI = 22.11%). In Bukit Batu, forest stands with a diameter of ≥10 cm contained a biomass of 178.10 tonnes/ha and carbon stock of 83.70 tonnes C/ha, which is equal to 307.20 tonnes CO2/ha. Meanwhile, in Muara Merang, it was 190.41 tonnes/ha and 89.49 tonnes C/ha, which is equal to 328.44 tonnes CO2/ha. This research, especially that in Bukit Batu, Riau Province, enriches the data and information available to date and is very useful in supporting restoration practices in Riau Province’s Giam Siak Kecil Biosphere Reserve, which was designated by UNESCO as part of the Man and Biosphere Program.

Estimating Forest Stock Volume Based on Airborne Lidar Data

Abstract. Forest stock volume (FSV) stands as an important indicator in evaluating the potential for carbon sequestration. It is crucial for forest resource management at local, regional, and national scales. In order to achieve an accurate estimation of FSV, this article takes Mengyin County, Shandong Province, China as the research area, builds a random forest (RF) model for four tree species based on airborne Lidar data, and forms a monitoring system of "individual tree - grid - county" granularities. The results demonstrated that all four models exhibited excellent generalization capabilities, with no signs of overfitting. In the test phase, the R2 of the poplar and pine models exceeded 0.9, while the R2 of the cypress model was 0.81, and the rRMSE was controlled within 20%, indicating that the fitting effect of the three tree species models was better; the accuracy of the robinia pseudoacacia model was relatively poor, with R2 of 0.60 and rRMSE of 20.60%. This study provides a feasible method for estimating forest stock volume within the county, which provides strong technical support for forest resource management and planning, and helps promote sustainable forestry development.

Structural diversity and carbon stock of forest stands: tradeoff as modified by silvicultural thinning

AbstractIn forest management, merging stand structural diversity with carbon storage is essential for resilience and climate mitigation. This study assesses (1) how structural diversity in stands of spruce (Picea abies (L.) H. Karst.), pine (Pinus sylvestris L.), beech (Fagus sylvatica L.), and oak (Quercus robur L. and Quercus petraea (Matt.) Liebl.) in Central Europe varies with age, site quality, and applied thinning grade; (2) these factors' impact on carbon stock; and (3) the link between structural diversity and carbon stock. Analyzing 26 long-term thinning experiments, we used the Gini coefficient of tree heights to measure structural diversity and species-specific biomass functions for carbon stock assessments. Our results show that structural diversity, highest in beech and spruce, decreases with stand age and on richer sites. Thinning enhances structural diversity in spruce and beech but reduces it in pine and oak. Unthinned or only moderately thinned mature spruce and beech stands outperform pine and oak in carbon stock (200–300 vs. 100–150 Mg C ha−1). C- and D&E-grade thinning halves carbon stock. A decrease in vertical layering with increased carbon storage varies across species. Given the same carbon stock, thinning from above maintains structural diversity in spruce and beech, while non-thinning or thinning from below promotes structural diversity in pine and oak. Based on the current silvicultural practice reflected by the NFI data of Germany, we argue that reduced thinning in previously D&E-grade thinned stands may slightly reduce their structural diversity. However, a suspension or reduction of thinning would strongly improve carbon storage (+ 100–200 Mg C ha−1) in the next 3–5 decades. We discussed options for reconciling structural diversity and carbon storage by silvicultural management of the four considered species in Central Europe.

Aboveground biomass stocks of species-rich natural forests in southern China are influenced by stand structural attributes, species richness and precipitation

Forests, the largest terrestrial carbon sinks, play an important role in carbon sequestration and climate change mitigation. Although forest attributes and environmental factors have been shown to impact aboveground biomass, their influence on biomass stocks in species-rich forests in southern China, a biodiversity hotspot, has rarely been investigated. In this study, we characterized the effects of environmental factors, forest structure, and species diversity on aboveground biomass stocks of 30 plots (1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi, China. Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions. Furthermore, we found that aboveground biomass was positively correlated with stand age, mean annual precipitation, elevation, structural attributes and species richness, although not with species evenness. When we compared stands with the same basal area, we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height. These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China. Notably, many natural forests in southern China are not fully stocked. Therefore, their continued growth will increase their carbon storage over time.

Comparative assessment of woody species diversity, structure and carbon stock of PFM and Non-PFM forests and its implication for REDD+ in Ethiopia

Maintaining forest biodiversity and carbon sequestration potential of forest resources enhances the provision capacity of forest ecosystem services. Experience from around the world and in Ethiopia has shown that shifting forest management from state-centred to community-centred arrangement can result in a better forest stock. Therefore, this study examined the status of woody species diversity, regeneration and total living biomass carbon stock of forests under participatory forest management (PFM) and the adjacent state managed non-participatory forest management (Non-PFM) in South eastern Ethiopia and implications to REDD+. Data were collected from 89 (44 PFM and 45 Non-PFM) nested circular plots from four PFM and three Non-PFM selected forest sites with transects laid systematically. Tree DBH and height were measured, the number of saplings, seedlings, mature trees were counted and species names were recorded. Woody species diversity was estimated using shannon, simpson, and evenness diversity indices. A total of 29 and 23 woody species were recorded in PFM and Non-PFM forests, respectively. Woody species diversity did not show significant difference between PFM and Non-PFM forests but it was relatively higher in PFM forest. The density per hectare of seedlings, sapling and mature trees were significantly greater in PFM forest than in Non-PFM forest. The mean aboveground biomass carbon stock of PFM forest (225.50±26.54) was significantly greater than that of the Non-PFM (156.24±15.72) forest. Hence, managing forests through participatory approaches contributes to the enhancement of sustainable management and climate change mitigation potentials through reducing emission from deforestation and forest degradation.

Large variation in carbon dioxide emissions from tropical peat swamp forests due to disturbances

The huge carbon stock of tropical peat swamp forest (PSF) in Southeast Asia has been threatened by environmental disturbances due to quasi-periodic El Niño-Southern Oscillation (ENSO) droughts, biomass and peat burning, smoke haze, drainage, and deforestation. Carbon dioxide (CO2) emissions from such disturbances have not been well quantified because of insufficient field data. Therefore, we quantified the ecosystem-scale CO2 balance and examine the disturbance effects from a long-term field experiment for 12–15 years at three PSF sites with different degrees of degradation in Indonesia. Here, we show a drastic change of an undrained PSF from a CO2 sink to a source owing to the transient groundwater lowering by the droughts, a significant decrease in ecosystem photosynthesis due to the radiation attenuation by smoke haze in drought years, and long-lasting CO2 emissions through enhanced peat decomposition by drainage. The impact on CO2 emissions was greater from drainage than drought-induced disturbances.

Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms

Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and Landsat 8 multispectral remote sensing imagery, this research applies four machine learning algorithms available on the GEE platform: Random Forest (RF), Classification and Regression Trees (CART), Gradient Boosting Trees (GBT), and Support Vector Machine (SVM). Using these algorithms, the entire Yunnan Province is classified into seven categories, including broadleaf forest, coniferous forest, mixed broadleaf-coniferous forest, water bodies, built-up areas, cultivated land, and other types. After a thorough comparison, the research reveals that the RF algorithm surpasses others in terms of accuracy and reliability, making it the most suitable choice for estimating aboveground carbon storage in forests using remote sensing data. Therefore, the study used the RF algorithm for both forest classification and the estimation of carbon storage. By extracting remote sensing factors; by using the Pearson correlation coefficient to select the most relevant factors; and by utilizing multiple linear regression, RF regression, and decision tree regression, a model for estimating aboveground carbon stocks in forests was developed. The results indicate that among the four classification algorithms, the RF classifier demonstrates superior performance, with an overall accuracy of 84.96% and a Kappa coefficient of 76.46%. In the RF regression models, the R2 values for the coniferous forest, broadleaf forest, and mixed needle-broadleaf forest models are 0.636, 0.663, and 0.638, respectively. In both RF and CART, the R2 values for the three forest-type models are greater than 0.6, indicating satisfactory model fitting performance. This study aims to explore the possibility of improving the estimation of forest carbon stocks in large-scale areas through fine land use classification. Additionally, the data sources used are completely free, and medium to low resolution can provide a better reference value for practical applications, thereby reducing the cost of utilization.

Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning

Estimating the carbon sequestration potential of Moso bamboo (Phyllostachys pubescens) forests and optimizing management strategies play pivotal roles in enhancing quality and promoting sustainable development. However, there is a lack of methods to simulate changes in carbon sequestration capacity in Moso bamboo forests and to screen and optimize the best management measures based on long-term time series data from fixed-sample fine surveys. Therefore, this study utilized continuous survey data and climate data from fixed sample plots in Zhejiang Province spanning from 2004 to 2019. By comparing four different algorithms, namely random forest, support vector machine, XGBoost, and BP neural network, to construct aboveground carbon stock models for Moso bamboo forests. The ultimate goal was to identify the optimal algorithmic model. Additionally, the key driving parameters for future carbon stocks were considered and future aboveground carbon stocks were predicted in Moso bamboo forests. Then formulated an optimal management strategy based on these predictions. The results indicated that the carbon stock model constructed using the XGBoost algorithm, with an R2 of 0.9895 and root mean square error of 0.1059, achieved the best performance and was considered the optimal algorithmic model. The most influential driving parameters for vegetation carbon stocks in Moso bamboo forests were found to be mean age, mean diameter at breast height, and mean culm density. Under optimal management measures, which involve no harvesting of 1–3 du bamboo, 30% harvesting of 4 du bamboo, and 80% harvesting of bamboo aged 5 du and above. Our predictions show that aboveground carbon stocks in Moso bamboo forests in Zhejiang Province will peak at 36.25 ± 8.47 Tg C in 2046 and remain stable from 2046 to 2060. Conversely, degradation is detrimental to the long-term maintenance of carbon sequestration capacity in Moso bamboo forests, resulting in a peak aboveground carbon stock of 29.50 ± 7.49 Tg C in 2033, followed by a continuous decline. This study underscores the significant influence of estimating carbon sequestration potential and optimizing management decisions on enhancing and sustaining the carbon sequestration capacity of Moso bamboo forests.

Carbon stock of shatto forest along altitudinal gradients in masha district, southwest ethiopia: a contribution to climate change mitigation

Forests play a significant role in climate change mitigation through carbon sequestration from the atmosphere and retaining carbon in above and belowground biomass of trees, litter biomass and soil. However, there is little empirical data on the potential carbon stock of Ethiopia’s various forests. Therefore, this study was carried out to estimate the carbon stock potential of Shatto Forest along altitudinal gradients and its contribution to climate change mitigation in Masha district, southwest Ethiopia. Using a systematic sampling design, a total of 30 plots measuring 20 x 20 m were established at 200 m intervals along seven transect lines in order to inventory the woody species. Within the main plot, a 1 m x 1 m sub-plot was used to collect soil samples and litter biomass. The biomass carbon stock was estimated using allometric equations. Results revealed that the mean total carbon stock of the study forest was 714.4 ton/ha, of which 337.38ton/ha, 67.48 ton/ha, 0.79 ton/ha and 308.75 ton/ha were stored in the aboveground carbon, belowground carbon, leaf litter carbon and soil organic carbon, respectively. The mean total CO2 equivalent of the study forest was also 2621.85 ton/ha. Along the altitudinal gradients of the study forest, the total carbon stocks contributed by the forest varied significantly at (p < 0.05), with the largest stock found at lower elevation and the lowest stock found at higher elevation. Based on the study findings, it is concluded that the forest under investigation possessed a substantial carbon stock capacity, allowing it to sequester a significant amount of CO2 and mitigate climate change while offering other ecosystem services. Thus, in order to optimize the forest’s capacity to store carbon and obtain the financial benefits of CO2 mitigation, reduced emission from deforestation and degradation (REDD+) program in the area should be integrated with carbon sequestration.

Forest cover change and its carbon dynamic of the karst area in Bulusaraung, South Sulawesi, Indonesia

Information on land forest cover changes and carbon stocks in karst areas is vital for management planning. Therefore, an analysis of the dynamics of land forest cover, carbon stocks of secondary karst forests, and their future projections is necessary. This study aimed to analyze the carbon stocks of secondary forests, forest cover change, and their projections in karst areas. We used 43 sample plots of 20 m x 20 m to measure Above Ground Biomass (AGB) and soil organic carbon stocks. Forest cover data were collected using remote sensing and GIS tools. Data analysis of the ABG of the secondary forest for the karst plain and karst tower was conducted using the Chave equation. Land-cover change was analyzed by delineating the 2011, 2016, and 2021 land-cover data from satellite imagery and testing the accuracy using survey data fields and high-resolution images. Land-cover projections for 2026 and 2031 were obtained using Cellular Automata Markov (CA-Markov) analysis. The results revealed ten land cover types in the karst area of the Bulusaraung Forest Management Unit (FMU). Secondary dryland karst forests (tower and plain karst) were the dominant forest types (60%) in the Maros-Pangkep essential karst ecosystem area. Both karst types of secondary forests are relatively high carbon stores. Data projection shows that the mining area will grow extensively by 2031. Secondary dryland karst forests will be degraded by 15.5% in 20 years due to increased mining activities, conversion to paddy fields, dryland agriculture, and mixed dryland agriculture. Karst ecosystems are vital for carbon and water storage. Therefore, some strategies are required for the sustainable management of Maros-Pangkep Karst are as follows: (i) controlling the opening of mining areas according to sustainable environmental principles, (ii) encouraging the implementation of post-mining land rehabilitation, (iii) increasing public awareness of the importance of preserving karst areas, and (iv) implementing soil conservation rules by prioritizing agroforestry systems in dryland farming.

Characterization of the Vegetation Community and its Contribution to a Carbon Stock in a Dry Forest

The dry forest ecosystem is characterized by their rich biodiversity and adaptations to arid conditions. This study focused on determining the composition and structure of the vegetation, examining species interactions, and estimating carbon stored in its aboveground biomass (AGB) using an allometric equation proposed for mixed dry forests. We used 10 plots of 10 x 20 m to record data on trees with a diameter at breast height (DBH) ≥ 5 cm. Taxonomic classification was initially obtained using experts and specialized databases. Ecological importance was assessed through the Importance Value Index (IVI), and species association was determined using the Indicator Value Index (IndVal%). We identified 148 individuals of 21 species, 19 genera, and 12 families in four groups with strong associations, with C. Trischistandra standing out for its high IVI. The Kruskal-Wallis test did not show significant differences in carbon stored between plots, and was estimated a storage potential of 70.47 Mg C ha-1. This research highlights the importance of key species in carbon capture, which is crucial for mitigating climate change. Effective management of these species could have a positive impact on the conservation of the dry forest ecosystem and the fight against global warming. This analysis provides a deep understanding of the structure of this ecosystem.

The effect of repeated hurricanes on the age of organic carbon in humid tropical forest soil.

Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30-year period that spanned repeated hurricanes. We used a nonlinear matrix model of soil C pools and fluxes ("soilR") and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times of soil C in 0-10 cm and 35-60 cm depths relative to a modeled control soil with constant inputs over the 30-year period. Between 1988 and 2018, the occluded C stock increased and δ13C in all pools decreased, while changes in particulate and mineral-associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris-deposited C, and possible microbial scavenging of old and young C in the particulate and mineral-associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that the increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, making soil C more sensitive to future tropical forest stressors.