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Title: Multi-analysis of potential and actual above ground biomass in a tropical deciduous forest in Mexico
Author: Corona Núñez, Rogelio Omar
ISNI:       0000 0004 7224 7053
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2017
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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.
Supervisor: Williams, Mathew ; Mitchard, Edward Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: carbon stocks ; patterns ; Mexico ; tropical dry forest ; model ; scale