Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: The research was conducted in the Kwa-Zulu Natal midlands, South Africa. The vertical distribution of soil organic carbon (SOC) stocks were successfully predicted by stochastic exponential models developed for the three main land uses in the area, which are farmlands, forestry plantations and grasslands. These models, in combination with regular surface sampling, may be used for monitoring SOC dynamics in the area and mapping SOC stocks. Bulk density measurements are needed in combination with SOC content (%wt) to calculate such SOC stocks. Considering the disadvantages of bulk density sampling and measurement, an effort was made to determine if one of the commonly-used existing stochastic models could be used to successfully predict bulk densities for soils with known texture and SOC content to replace direct measurements, taking into account that different managements might affect final results. Statistica software was used to correlate the Saxton & Rawls model predictions and associated regressions with measured values for the study area. A clear distribution trend was achieved using Statistica and the correlations were fair with r2 values close to 0.5 for individual regressions and substantially higher for area averages. However, considering the depth-stratified averages and correcting for the effects of particle density changes for soils with high soil organic matter, high correlations for 2 of the 3 studied land uses were achieved (r2 values of 0.99 and 0.81 in forests and grasslands respectively). Therefore, although Saxton and Rawls (2006) predictions of bulk density may be used, it is preferable to conduct direct bulk density determinations. The proposed models to calculate the vertical distribution of SOC would substantially reduce the cost of soil carbon inventories to 1m soil depth in the study area by limiting observations to the soil surface. Triplicate 5cm-deep soil core samples would be collected at the soil surface per observation point for determination of ρb (bulk density) and Corg (organic carbon). On average, the accuracy of the normalized depth-distribution model is rather high for grasslands and forests/forest plantations (R2 = 0.98), but somewhat lower for cultivated lands (R2 = 0.96) due to mixing of the plough layer to cultivation depth. Carbon stocks to 1m depth were calculated as an integral of the normalized exponential distribution, multiplied by the value of Corg observed at the soil surface and expressed on volume basis as carbon density (Cv, kg∙m-3). The resulting stock assessment was compared to the observed values using piece-integration for sampled depth increments to give SOC stocks on an area basis (kg∙m-2). The estimated prediction error on average was 1.2 (9%) and 3.7 kg∙m-2 (21.6%) in grasslands and forests respectively, while for cultivated lands the error was 1.3 kg.m-2 (9.5%). Further improvement to reduce these errors may be achieved by introducing the soil type as variable and grouping the functions by soil type rather than land uses. The results of this work were presented at the seminar of the department of Soil Science, Stellenbosch University (Ros et al., 2014), the combined congress of the South African Soil Science, Horticulture and Agronomy societies (Rozanov et al., 2015), the First Global Soil Map conference, France (Wiese et al., 2013), the 20th International Congress of Soil Science, Korea (Wiese et al. 2014) and were submitted for publication in Geoderma special issue dedicated to digital soil mapping of soil organic carbon following the presentation at the 20th ICSS, Korea (Wiese et al., 2014). / AFRIKAANSE OPSOMMING: Hierdie navorsing is in die Kwa-Zulu Natalse middellande van Suid-Afrika gedoen. Die vertikale verspreiding van grondorganiese koolstof (GOK) is suksesvol voorspel deur middel van stogastiese eksponensiële modelle wat vir die drie hoof landsgebruike ontwikkel is. In kombinasie met roetine monsterneming by die grondoppervlak kan hierdie modelle suksesvol aangewend word vir die monitering van GOK dinamika in die studiegebied, sowel as kartering van GOK voorraad. Bulkdigtheidsmetings word tesame met GOK inhoud (%massa) benodig om die GOK voorraad te bereken. Weens die nadele van monsterneming vir bulkdigtheidsbepalings is ‘n poging aangewend om te bepaal of een van die mees algemeen gebruikte bestaande stogastiese modelle (Saxton & Rawls 2006) gebruik kan word om die bulkdigtheid van gronde suksesvol vanaf tekstuur en GOK inhoud te voorspel en sodoende direkte metings te vervang. Statistica sagteware is gebruik om die voorspellings met behulp van die Saxton & Rawls modelle en gevolglike regressies met gemete waardes vanuit die studiegebied te korreleer en ‘n duidelike verspreidingstendens is hierdeur opgelewer. Die korrelasies vir individuele regressies was redelik met r2 waardes naby 0.5 en merkwaardig hoër waardes vir area gemiddeldes. Hoë korrelasies is egter behaal vir 2 van die 3 bestudeerde landsgebruike (r2 waardes van 0.99 en 0.81 in bosbou en grasveld onderskeidelik) wanneer die gemiddelde dieptestratifikasies gebruik en gekorrigeer word vir die verandering in deeltjiedigtheid vir gronde met hoë grondorganiese material. Alhoewel die Saxton and Rawls (2006) voorspellings van bulkdigtheid gebruik kan word, behoort bulkdigtheidsbepalings egter verkieslik direk gedoen te word. Die voorgestelde modelle vir die bepaling van vertikale GOK verspreiding tot 1m gronddiepte sou die koste van grondkoolstof opnames in die studiegebied dramaties verlaag deur grondmetings tot die grondoppervlak te beperk. Grondmonsters sal in triplikaat per waarnemingspunt met 5cm diep silinders op die grondoppervlak geneem word vir ρb (bulkdigtheid) and Corg (organiese koolstof) bepalings. Die gemiddelde akkuraatheid van die genormaliseerde diepteverspreidingsmodel is hoog vir grasveld en woude/bosbou plantasies (R2 = 0.98), maar ietwat laer vir bewerkte landerye (R2 = 0.96) as gevolg van die vermenging van die ploeglaag tot op die diepte van bewerking. Koolstof voorraad tot 1m gronddiepte is bepaal deur middel van die integraal van die genormaliseerde eksponensiele verspreiding, vermenigvuldig met die waarde van Corg op die grondoppervlak en op ‘n volume basis uitgedruk as koolstofdigtheid (Cv, kg∙m-3). Die gevolglike voorraadopname is met gemete waardes vergelyk deur middel van ‘n stuksgewyse integrasie van die gemonsterde diepteinkremente om GOK voorraad per area (kg∙m-2) te lewer. Die gemiddelde geskatte fout van voorspelling was 1.2 (9%) en 3.7 kg∙m-2 (21.6%) in grasveld and plantasies onderskeidelik en 1.3 kg.m-2 (9.5%) in bewerkte landerye. Verdere verbetering van die modelle en ‘n verlaging in hierdie foute kan verkry word deur die grondtipe inligting as veranderlike in te bring en die funksies volgens grondtipe eerder as landsgebruik te groepeer. Resultate van hierdie werk is reeds aangebied tydens ‘n seminar by die department Grondkunde, Stellenbosch Universiteit (Ros Mesa et al., 2014), die gesamentlike kongres vir die Suid-Afrikaanse Verenigings vir Grondkunde, Hortologie, Onkruidwetenskap en Gewasproduksie (Rozanov et al. 2015), die Eerste Global Soil Map konferensie, Frankryk (Wiese et al, 2013), die 20ste Internasionale Grondkunde Kongres, Korea (Wiese et al. 2014) en is ingehandig vir publikasie in ‘n spesiale uitgawe van Geoderma wat, na aanleiding van die aanbieding by die 20ste Internasionale Grondkunde Kongres, Korea (Wiese et al., 2014), fokus op digitale grondkartering van grondorganiese koolstof.

Natural tropical deciduous forest (TDF) is considered with a medium to small height (< 15 m). Particularly, in Mexico TDF shows a remnant of 36.2% of primary forest driving changes in the structure and species composition. This vegetation in Mexico is mainly transformed into grassland for cattle raising, and agriculture, primarily for self-consumption. More information about the ecology and the social pressures on this vegetation can be seen in Chapter I. The general methods, including sampling allocation and collection, characteristics of the study site, as well the procedure of the research proposal is presented in Chapter II. The main aim of this thesis is to improve the accuracy of predictions of net carbon emissions and the spatial distribution of AGB in the Tropical Deciduous Forest of Mexico. To address this aim, it is important to take into consideration the forest structure, spatial patterns and processes in a natural forest in a multi-scale analysis; also, it is necessary to characterize the spatial socio-economic drivers that influence current AGB losses. With the understanding of such elements, it is possible to reconstruct the potential carbon stocks and estimate the allocation of net carbon emissions due to deforestation and forest degradation. This study shows that it is possible to count net carbon emissions caused by deforestation and forest degradation at a landscape scale. To come to such estimates, it was necessary to reduce the different sources of uncertainty. Chapter III explores different elements that drive the AGB allocation in a mature forest. The AGB in the mature forest was considered as the potential AGB that the forest could get assuming that it has reached its steady state. Different field sampling strategies and allometric equations were evaluated to account for uncertainty in the AGB estimations. The results showed that small sampling design (300-400 m2) and large-sized plots (4 ha) produce the same tree distribution for trees: ≥30 cm in DBH as well as in AGB. These results contradict what has been reported for others (Chave et al., 2004 and 2005) when they refer to the general definition of tropical forest. However, those other studies referred to forests with a much higher precipitation and which can be classified as tropical rain (perennial) forest (Chave et al., 2004). In the tropical deciduous forest, the kind considered in this study, AGB tends to be allocated in small-sized trees. Diverse biophysical characteristics that may drive AGB allocation were considered over different spatial scales. Water stress was the main driver for AGB density at different spatial scales. Nutrients showed little significance to explain AGB as other studies have suggested in secondary forests and/or chronosequences. With this understanding, Chapter IV shows the use of different multi-variable models. Parsimonious models were the result of the variables selection and sensitivity test. Most of the methodologies showed a better performance to explain AGB allocation than a null-model. However, when they were contrasted with independent observations over different spatial resolutions, it was possible to conclude that only GLM was capable of reproducing the spatial patterns, and its estimations were close to observations. Nevertheless, some observations with very large AGB densities were underestimated by the model. This underestimation was related to the presence of few very large-sized trees. These two chapters depict the possibility of accounting for the potential AGB, and the uncertainty, namely whether the landscape could reach it with the absence of human disturbance. Once the potential AGB map was built and validated, it was transformed to carbon stock, using a local carbon concentration estimate. This potential carbon stock map was contrasted to the different available maps of current carbon stocks. Consequently, it was possible to estimate net carbon emissions due to deforestation and forest degradation (Chapter V), suggesting that the general models tend to agree in the total carbon loss. However, there are some spatial discrepancies in the magnitudes of change. Main differences between maps can be reduced by diverse socio-ecological constraints that dominate the landscape. This is important because it may be possible to make future adjustments that would reduce variability, enabling more accurate AGB estimations. However, to individually account for deforestation and forest degradation, more detailed sources of local information are necessary, such as socio-economic variables. Therefore models with a bottom-up perspective would lead to a better understanding and representation of the landscape. Finally, the growing rural population will have larger demands for wood and food, so while remote or protected areas may have the potential for storing high AGB, forest close to settlements and access routes are likely to continue being disturbed, unless affordable alternatives are available for the sustainable use of the forest. In conclusion, the estimation of spatial heterogeneity of AGB in the landscape is of great importance when measuring carbon stocks and ecological dynamics. Various elements influence the AGB allocation in the mature forest. Among all of them, water availability played the most decisive part of various spatial scales. My models support the hypothesis that water availability plays the major role in explaining AGB in Mexico on a local, sub-regional and landscape scale. Model selection produced contrasting AGB estimates and patterns. Moreover, the results of this study tell us that there is not a clear consensus among various current AGB maps. However, they also show that with a multi-model comparison it is possible to identify carbon emissions drivers and calculate total carbon emissions due to forest disturbances. Socio-economic variables played the major role in explaining AGB losses. Therefore, future studies should look into a bottom-up approach for a better understanding and representation of current AGB.

Seagrass is a valuable and important habitat, providing services such as coastal protection, supporting fisheries, and carbon sequestration. However, it is challenging to map accurately, as remote sensing has limits to how deep in the water column it can penetrate, and uncertainties such as distinguishing between algae and seagrass. Seagrass can exist at depths of theoretically 90 m deep in ultraoligotrophic waters, meaning that there is much of this habitat that cannot be mapped by remote sensing. Green turtles are an ideal candidate to help find seagrass blue carbon resources in the Red Sea. They go through an ontogenetic dietary shift to become almost completely herbivorous, and have a high fidelity to foraging sites. In this study we aim to assess the use of green turtles Chelonia mydas in discovering seagrass blue carbon. We use telemetry from 53 turtles tagged over 2018, 2019, and 2021 to map their foraging areas. 50 out of the 53 (94.34%) foraging sites had not been visited by previous seagrass studies in the Red Sea. We visited 18 locations in 14 of these foraging sites to ground truth them, and all 14 foraging sites (100%) had seagrass present. Comparatively, 18 out of 30 sites where seagrass was indicated by the remote sensing-based Allen Coral Atlas showed no seagrass. The turtles were seen to favour travelling shorter distances, thus it will be necessary to expand the area of tagging in order to achieve complete coverage of the Red Sea. Approximately 1/3 of the visited sites were deeper than 8 m, and so out of range of remote sensing, showing that considerable blue carbon resources may be discovered with the use of turtles. Samples were taken for carbon stock estimation from the ground truthed sites. A mean carbon stock of 4.89 ± 0.83 kg Corg m-2 was estimated for 1 m depth sediment. In the future it is important to develop methods for mapping the surface areas of the deep and inaccessible seagrass habitats that the turtles discover.

Florestas tropicais tem um papel relevante nos ciclos globais do carbono e nitrogênio. A Floresta Atlântica é um bioma tropical ameaçado da América do Sul, de imensa complexidade estrutural, mas ainda pouco investigado quanto ao seu funcionamento. Neste contexto, o objetivo principal desse estudo foi investigar a estrutura da floresta através da quantificação da biomassa viva acima do solo e da queda anual de folhas. Paralelamente, as concentrações de carbono e nitrogênio foram determinadas no solo, e na vegetação, permitindo a determinação de estoques e fluxos desses elementos em áreas de diferentes fitofisionomias localizadas nas escarpas da Serra do Mar, na costa nordeste do Estado de Santa Catarina, sul do Brasil. As áreas de estudo localizam-se no município de Joinville, onde a temperatura média anual é de 20°C e a precipitação média acumulada anualmente é de aproximadamente 2200 mm. Os sítios amostrais foram estabelecidos em elevações de 200 m anm (Submontana) e 800 m anm (Montana) Em cada área, quatro parcelas de aproximadamente 0,25 ha foram delimitadas, e, então, foram determinadas a estrutura florestal, biomassa acima e abaixo do solo (viva e morta) e produção de serrapilheira. Concentração e composição isotópica de carbono e nitrogênio em cada compartimento (folhas, tronco, serrapilheira e solo) foram determinadas e usadas para converter biomassa em estoques e fluxos desses elementos, e compreender as complexas interações dentro desses ecossistemas. A AGLB foi de aproximadamente 300 Mg.ha-1 na floresta Submontana e 380 Mg.ha-1 na floresta Montana, AGDB, por sua vez, foi de 6,8 e 6,6 Mgoha-1 e BGB (raízes) foi de 54 e 66 Mg.ha -1, respectivamente. Não foi encontrada diferença significativa entre a produção de serrapilheira entre as fitofisionomias, sendo encontrada uma produção média de 6,4 Mg.ha-1 ano-1 na floresta Submontana e 6,9 Mg.ha-1 ano-1 na Montana. As folhas foram o componente majoritário da serrapilheira, contribuindo com ~68% em ambas as áreas. Os estoques totais de carbono e nitrogênio foram significativamente maiores na fitofisionomia Montana, aumentando de 208 MgC.ha-1 para 390 MgC.ha-1 e de 7 MgN.ha-1 para 16 MgN.ha-1, respectivamente. Fluxos de carbono e nitrogênio via serrapilheira nas florestas Submontana e Montana foram, respectivamente, igual a 3,0 e 3,2 Mg.ha-1 e de 0,12 e 0,14 Mg.ha-1. Valores de ?13C em cada compartimento foram similares entre as áreas, e típico de plantas que seguem o ciclo fotossintético C3. Conforme esperado, um enriquecimento em 13C e 15N ao longo do perfil do solo foi encontrado em ambas as áreas. Valores de ?15N foram maiores em folhas, serrapilheira e solo na floresta Submontana, possivelmente devido à maior disponibilidade de nitrogênio neste sítio; e/ou ao baixo tempo de residência do solo e serrapilheira empobrecida em 15N no sítio Montana. De maneira geral, posições latitudinal (em termos de disponibilidade de luz) e altitudinal (em termos de precipitação e temperatura) foram determinantes na composição, alocação e dinâmica do carbono e nitrogênio nestes ecossistemas / Tropical evergreen forests have a key role in the global carbon and nitrogen cycles. The Atlantic Forest is a vanishing South American tropical biome of immense structural complexity. The structure and functioning of these forests are relatively unknown. In this context, the main objective of this study was to investigate the forest structure by estimating aboveground live biomass (AGLB), belowground biomass (BGB) and litterfall. The determination of carbon and nitrogen concentrations in soils and vegetation allowed to quantify stocks and fluxes of these two elements. Four 0.25 ha-plots were established in two elevations (200 m asl and 800 m asl) in forests located on the slopes of the Serra do Mar, on the northeast coast of the State of Santa Catarina, southern Brazil. The sampling areas are located in the municipality of Joinville. The historical average annual temperature is 20°C and rainfall is approximately 2200 mm. The AGLB varied along the sites from approximately 300 Mg.ha-1 (submontane) to 380 Mgoha-1 (montane), AGDB varied from 6.8 to 6.6 Mgoha-1 and BGB (roots) varied from 54 to 66 Mg.ha -1, respectively. . The average litterfall production of 6.4 Mg.ha-1 ano-1 and 6.9 Mg.ha-1 ano-1 were found in the submontante and montane study sites, respectively. Leaves were the major component of litterfall contributing ~68% in both sites. Total carbon and nitrogen stocks were higher in the montane site. Total carbon stock (AGLB + BGB) increased from 208 Mg.ha-1 in the submontane site to 390 Mg.ha-1 in the montane. While, total nitrogen stock increased from 7 Mg.ha-1 to 16 Mg.ha-1, respectively. Fluxes of carbon and nitrogen via litterfall in the submontane and montane site varied from 3.0 to 3.2 Mg.ha-1 and from 0.12 to 0.14 Mg.ha-1, respectively. ?13C values in each compartment were similar between the two sites, and representative of C3 plants. As expected, there was an enrichment of ?13C and ?15N values in depth in each studied site. ?15N values where higher in leaves, litter and soil of the submontane site, possibly due higher nitrogen availability in this site; or low residence time of soil and depleted 15N in litterfall of the montane site. Overall, latitutinal (in terms of light availability) and altitudinal (in tems of precipitation and temperature) position were determinant in the nitrogen and carbon composition, allocation and dynamics in these ecosystems

Wetlands provide important ecosystem services (ES) by storing large amounts of organic carbon (OC) and being of high biological, cultural, and economical value. Uganda is covered by vast wetland areas but has with a booming population rapidly been decreasing due to pressure on lands. The aim of this report was to examine important socio-ecological dynamics of rural wetlands in relation to variations of land use in central and eastern Uganda. This by assessing above- (ABG) and belowground (BG) C stocks, soil pH, and capturing provisioning ES and related impacts on soil and vegetation. The methods involved initial spatial analysis followed by two field campaigns with collection of soil samples, biomass measurements and recordings of provisioning ES, following locally developed standardized methods. Laboratory soil analyses included bulk density, loss on ignition and pH. The results shows that the permanent wetland LUC classes store the most total ecosystem C (273.5 to 356.5 t C ha-1), with the BG pool being the largest. It further brings new insights to the much less studied seasonal wetlands that also proves to be an important C stock (331.1 t C ha-1) as well as providing essential ES. In line with previous research, the total ecosystem C and the provisioning ES of wetlands decreases with changing land use management (farmlands 185 to 209; grasslands 125; woodland 120 to 284 t C ha-1). Further knowledge of socio-ecological dynamics of wetlands is needed, especially in seasonal wetlands, to increase sustainable wetland management. This being urgently needed for many communities in Uganda that are dependent on agroecologically-based economies in close relation to wetland ES and vulnerable to climate variations.

Data about the spatial variation of soil attributes is required to address a great number of environmental issues, such as improving water quality, flood mitigation, and determining the effects of the terrestrial carbon cycle. The need for a continuum of soils data is problematic, as it is only possible to observe soil attributes at a limited number of locations, beyond which, prediction is required. There is, however, disparity between the way in which much of the existing information about soil is recorded and the format in which the data is required. There are two primary methods of representing the variation in soil properties, as a set of distinct classes or as a continuum. The former is how the variation in soils has been recorded historically by the soil survey, whereas the latter is how soils data is typically required. One solution to this issue is to use a soil-landscape modelling approach which relates the soil to the wider landscape (including topography, land-use, geology and climatic conditions) using a statistical model. In this study, the soil-landscape modelling approach has been applied to the prediction of soil bulk density (Db). The original contribution to knowledge of the study is demonstrating that producing a continuous surface of Db using a soil-landscape modelling approach is that a viable alternative to the ‘classification’ approach which is most frequently used. The benefit of this method is shown in relation to the prediction of soil carbon stocks, which can be predicted more accurately and with less uncertainty. The second part of this study concerns the inclusion of expert knowledge within the soil-landscape modelling approach. The statistical modelling approaches used to predict Db are data driven, hence it is difficult to interpret the processes which the model represents. In this study, expert knowledge is used to predict Db within a Bayesian network modelling framework, which structures knowledge in terms of probability. This approach creates models which can be more easily interpreted and consequently facilitate knowledge discovery, it also provides a method for expert knowledge to be used as a proxy for empirical data. The contribution to knowledge of this section of the study is twofold, firstly, that Bayesian networks can be used as tools for data-mining to predict a continuous soil attribute such as Db and that in lieu of data, expert knowledge can be used to accurately predict landscape-scale trends in the variation of Db using a Bayesian modelling approach.

Florestas tropicais tem um papel relevante nos ciclos globais do carbono e nitrogênio. A Floresta Atlântica é um bioma tropical ameaçado da América do Sul, de imensa complexidade estrutural, mas ainda pouco investigado quanto ao seu funcionamento. Neste contexto, o objetivo principal desse estudo foi investigar a estrutura da floresta através da quantificação da biomassa viva acima do solo e da queda anual de folhas. Paralelamente, as concentrações de carbono e nitrogênio foram determinadas no solo, e na vegetação, permitindo a determinação de estoques e fluxos desses elementos em áreas de diferentes fitofisionomias localizadas nas escarpas da Serra do Mar, na costa nordeste do Estado de Santa Catarina, sul do Brasil. As áreas de estudo localizam-se no município de Joinville, onde a temperatura média anual é de 20°C e a precipitação média acumulada anualmente é de aproximadamente 2200 mm. Os sítios amostrais foram estabelecidos em elevações de 200 m anm (Submontana) e 800 m anm (Montana) Em cada área, quatro parcelas de aproximadamente 0,25 ha foram delimitadas, e, então, foram determinadas a estrutura florestal, biomassa acima e abaixo do solo (viva e morta) e produção de serrapilheira. Concentração e composição isotópica de carbono e nitrogênio em cada compartimento (folhas, tronco, serrapilheira e solo) foram determinadas e usadas para converter biomassa em estoques e fluxos desses elementos, e compreender as complexas interações dentro desses ecossistemas. A AGLB foi de aproximadamente 300 Mg.ha-1 na floresta Submontana e 380 Mg.ha-1 na floresta Montana, AGDB, por sua vez, foi de 6,8 e 6,6 Mgoha-1 e BGB (raízes) foi de 54 e 66 Mg.ha -1, respectivamente. Não foi encontrada diferença significativa entre a produção de serrapilheira entre as fitofisionomias, sendo encontrada uma produção média de 6,4 Mg.ha-1 ano-1 na floresta Submontana e 6,9 Mg.ha-1 ano-1 na Montana. As folhas foram o componente majoritário da serrapilheira, contribuindo com ~68% em ambas as áreas. Os estoques totais de carbono e nitrogênio foram significativamente maiores na fitofisionomia Montana, aumentando de 208 MgC.ha-1 para 390 MgC.ha-1 e de 7 MgN.ha-1 para 16 MgN.ha-1, respectivamente. Fluxos de carbono e nitrogênio via serrapilheira nas florestas Submontana e Montana foram, respectivamente, igual a 3,0 e 3,2 Mg.ha-1 e de 0,12 e 0,14 Mg.ha-1. Valores de ?13C em cada compartimento foram similares entre as áreas, e típico de plantas que seguem o ciclo fotossintético C3. Conforme esperado, um enriquecimento em 13C e 15N ao longo do perfil do solo foi encontrado em ambas as áreas. Valores de ?15N foram maiores em folhas, serrapilheira e solo na floresta Submontana, possivelmente devido à maior disponibilidade de nitrogênio neste sítio; e/ou ao baixo tempo de residência do solo e serrapilheira empobrecida em 15N no sítio Montana. De maneira geral, posições latitudinal (em termos de disponibilidade de luz) e altitudinal (em termos de precipitação e temperatura) foram determinantes na composição, alocação e dinâmica do carbono e nitrogênio nestes ecossistemas / Tropical evergreen forests have a key role in the global carbon and nitrogen cycles. The Atlantic Forest is a vanishing South American tropical biome of immense structural complexity. The structure and functioning of these forests are relatively unknown. In this context, the main objective of this study was to investigate the forest structure by estimating aboveground live biomass (AGLB), belowground biomass (BGB) and litterfall. The determination of carbon and nitrogen concentrations in soils and vegetation allowed to quantify stocks and fluxes of these two elements. Four 0.25 ha-plots were established in two elevations (200 m asl and 800 m asl) in forests located on the slopes of the Serra do Mar, on the northeast coast of the State of Santa Catarina, southern Brazil. The sampling areas are located in the municipality of Joinville. The historical average annual temperature is 20°C and rainfall is approximately 2200 mm. The AGLB varied along the sites from approximately 300 Mg.ha-1 (submontane) to 380 Mgoha-1 (montane), AGDB varied from 6.8 to 6.6 Mgoha-1 and BGB (roots) varied from 54 to 66 Mg.ha -1, respectively. . The average litterfall production of 6.4 Mg.ha-1 ano-1 and 6.9 Mg.ha-1 ano-1 were found in the submontante and montane study sites, respectively. Leaves were the major component of litterfall contributing ~68% in both sites. Total carbon and nitrogen stocks were higher in the montane site. Total carbon stock (AGLB + BGB) increased from 208 Mg.ha-1 in the submontane site to 390 Mg.ha-1 in the montane. While, total nitrogen stock increased from 7 Mg.ha-1 to 16 Mg.ha-1, respectively. Fluxes of carbon and nitrogen via litterfall in the submontane and montane site varied from 3.0 to 3.2 Mg.ha-1 and from 0.12 to 0.14 Mg.ha-1, respectively. ?13C values in each compartment were similar between the two sites, and representative of C3 plants. As expected, there was an enrichment of ?13C and ?15N values in depth in each studied site. ?15N values where higher in leaves, litter and soil of the submontane site, possibly due higher nitrogen availability in this site; or low residence time of soil and depleted 15N in litterfall of the montane site. Overall, latitutinal (in terms of light availability) and altitudinal (in tems of precipitation and temperature) position were determinant in the nitrogen and carbon composition, allocation and dynamics in these ecosystems

Malgré l'importance de la séquestration du carbone (C) dans les sols forestiers, il existe actuellement peu de projets Mécanisme pour un Développement Propore (MDP) qui tiennent compte de la gestion de ce compartiment dans l'atténuation du réchauffement climatique. La cause principale est que l'accréditation du carbone séquestré dans le sol représente de plus grands défis et des risques par rapport aux autres composantes des écosystèmes forestiers.Connaissant les difficultés économiques et les problèmes environnementaux impliqués dans l'adoption d'un tel projet et l'importance des forêts dans l'atténuation du changement climatique, l'objectif de cette étude était d'évaluer les performances des méthodes dedétermination des stocks de carbone du sol dans deux projets de reboisement, ainsi que pour leurs situations de références (c'est-à-dire utilisation des terres avant la plantation, des pâturages naturels, ainsi que la végétation native) comme base pour diminuer le rapport coût bénéfice des projets MDP dans le secteur forestier. Pour atteindre l'objectif principal, cestravaux de recherches ont consisté à l'étude de: (i) la variabilité spatiale du C du sol dans une zone de reboisement avec des espèces indigènes, établi dans le bassin amazonien dans le MatoGrosso à Cotriguaçu (Zone I) et une chronoséquence de plantations d'Eucalyptus, située dansl'état de São Paulo à Avare (zone II); (ii) la taille des parcelles et la distance idéale de séparation des échantillons à partir de l'étude de la dépendance spatiale du C, (iii)l'estimation de la teneur en C et la densité du sol (Ds) par spectroscopie dans le proche(NIRS) et moyen (MIRS) infrarouge, afin de réduire les coûts sans affecter la qualité des résultats analytiques : et (iv) du calcul des stocks de C du sol pour ces deux projets et l'estimation du bilan C du projet MDP menées dans la zone II, en utilisant l'outil EX-ACT («EX-Ante Carbon-balance Tool»). Les résultats ont confirmé l'existence d'une variabilité spatiale importante du C du sol, ainsi qu'une forte dépendance spatiale pour tous les traitements étudiés. L'analyse du nombre optimal d'échantillons de sol a montré que la collecte de cinq points par parcelle est aussi précise qu'un échantillonnage plus dense. La taille optimale des parcelles a été estimée de 361 à 841 m2 dans les plantations de la zone I et de 900 à 3721 m2 pour la Zone II. La performance de la spectroscopie MIRS et NIRS pourestimer la teneur en C des sols a été jugée très satisfaisante, surtout quand les modèles ont été étalonnés à partir de sous populations constituées de 10 à 50% de l'ensemble des données.Les résultats de l'estimation de Ds ont été légèrement moins satisfaisants que ceux pour le Cdu sol. Les stocks de C dans le sol calculés pour la zone I étaient supérieurs à ceux de la zone II. Si on ne considère que le compartiment du sol, il apparaît que le potentiel de génération de crédits C est plus important dans le reboisement à partir d'espèces indigènes dans un sol argileux que pour un reboisement d'eucalyptus dans un sol sableux. Le bilan C du projet de la zone I a montré un potentiel de séquestration de près de trois millions de tonnes d'équivalentCO2 en 40 ans. Il est espéré que cette étude qui montre des possibilités de réduction des coûts liés aux calculs des stocks de C du sol contribuera à une meilleure prise en compte de ce compartiment dans les projets MDP forestiers. / Considering the great importance of carbon sequestration (C) in forest soils, there are fewCDM projects that include this compartment as an agent of global warming mitigation. Thisoccurs because the quantification of soil C stocks represents a bigger challenge whencompared to other components of forest ecosystems. Considering the economic difficultiesand environmental issues involved in adopting this type of project and the importance offorests in mitigating climate change, the objective of this study was to evaluate theperformance of methods for obtaining soil C stocks in two forestry areas and their respectivebaselines (land use prior to planting, i.e. pastures and native vegetation) as a basis forreducing the cost-benefit ratio of CDM projects. To achieve the main objective, this researchwas composed of the following steps: (i) estimating the spatial variability of soil C in an areareforested with native species, established in Cotriguaçú, MT (Area I) and a Eucalyptuschronosequence, located in Avaré, SP (Area II), (ii) determining the optimal amount of soilsamples and the plot size from the soil C spatial dependence range in the reforestation areas,(iii) estimating soil C content and bulk density (BD) by Near and Mid Infrared ReflectanceSpectroscopy (NIRS and MIRS, respectively) to reduce analytical costs without affecting thequality of the results, and (iv) calculating soil C stocks in both areas and estimating the carbonbalance of a CDM Project conducted in Area II, using EX-ACT ("Ex-Ante Carbon BalanceTool"). The results confirmed the existence of significant soil C spatial variability in bothareas and a strong spatial dependence at all plots. The analysis of the optimal number of soilsamples indicated that the sampling procedure with five points per plot is as accurate asintensive sampling. The optimum size of plots ranged from 361-841 m2 at Area I plantationsand from 900-3721 m2 at Area II. The performance of MIRS and NIRS to estimate the soilcarbon content was very satisfactory, especially when the models were calibrated withamounts between 5-10% of the total data set. The estimations of BD were slightly less precisethan those of soil C content. The soil C stocks obtained at Area I were higher than Area II.Considering only the soil compartment, it is clear that the potential for C credit generation in areforestation with native species on a clayey soil is higher than in a reforestation witheucalyptus on a sandy soil. The C balance of the CDM project conducted in Area I is expectedto sequester almost three million tones of CO2 eq in 40 years. We hope this study contributesto the increased inclusion of soil in CDM projects, by confirming the feasibility of reducingthe costs associated with both sampling and analytical procedures.

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems, which plays an important role in climate change by regulating atmospheric carbon dioxide. Sweden’s forest management has led to a decrease in old growth forests. This change in land use can affect SOC levels in these forests. This report quantified SOC stocks among different wetness classes in 10 old growth forests across multiple regional environmental gradients in Sweden. I tested for the effects of some different environmental factors on SOC: temperature, precipitation, altitude, stem basal area, latitude, and normalized difference vegetation index. Soil measurements were taken from three different horizons: the O-, E-, and B-horizon. The results showed that the mean SOC stock in old growth forests (5.25 ± 0.60 kg m-2)  is a bit higher than the regional average (4.1 ± 0.5 kg m-2) and that local variation within forests, mainly due to hydrology, appears to drive variation that is often as big or larger than regional variations. There were no significant differences between the different forests, but there were significant differences found between the different wetness classes. There were several correlations found between the different regional environmental factors and SOC stocks. The most prominent relationship was a positive link between the O-horizons’ SOC stocks and temperature (R2adj = 0.58). Overall, these results indicate that SOC stocks in old growth forests are affected by both soil wetness at a local scale and air temperature at a regional scale; and that they might contain a slightly larger amount of SOC than managed forests.

Light Detection and Ranging (LiDAR) is increasingly common in forestry applications, yet relatively little research has evaluated its use in quantifying carbon stocks in afforested bottomland hardwood forests. This study relates forest structural field measurements to metrics derived from low pulse density LiDAR data to assess the use of LiDAR in characterization of planted bottomland hardwood oak stands. Univariate and multivariate linear regressions were performed with field and LiDAR variables to determine relationships. The height-related field dependent variables average height, maximum height, and individual tree volume had the highest adjusted R-squared values of 0.5-0.6 (P<0.0001) for the univariate models and adjusted R-squared values of 0.70-0.79 for the multivariate models. These findings suggest that low-density LiDAR is capable of assessing forest structure and suggests that further research evaluating LiDAR quantification of bottomland hardwood carbon stocks is warranted.