PhD Thesis, 2007 - University College Cork

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Chapter 3 Materials and Methods predictors for the values of all the species and community data. The axes of direct gradient analyses are instead computed as linear combinations of the environmental variables (Ter Braak, 1986). The goal of direct gradient analysis is to find the variability in species composition that can be explained by the measured environmental variables (Lepš & Šmilauer, 2003). By using the constrained approach the main part of the biological variability explained by the measured environmental variables is considered, but the main part of the variability not related to the measured environmental variability can be missed (Lepš & Šmilauer, 2003). Since we believe to have measured the main environmental parameters influencing the vegetation pattern, a direct constrained technique, CCA, was used in the data analysis. We applied CCA to relate vegetation patterns and plant species to environmental parameters and to explore the variation of both vegetation and environmental variables along the artificial and natural bog borders, to investigate the influence of the peatland margins on the vegetation composition (Chapter 4). 3.2.3 Vegetation survey data analyses The CCA was computed through the weighted averaging method and with a biplot scaling procedure. The weighted averaging method was chosen rather than a linear method, because it well represents unimodal curves, which characterise the species distribution responses to environmental variables when a wide range of the species distribution is covered by the sample dataset (Lepš & Šmilauer, 2003). This was the case in Glencar. We applied the biplot scaling procedure because it provides diagrams that can be interpreted in a more quantitative way, in comparison to the diagram resulting from a Hill’s scaling approach (Lepš & Šmilauer, 2003). The significance of the CCA axes was tested with Monte Carlo permutation tests. Together with the axes significance, the eigenvalue of the axes, the percentage of the plant distribution explained by the axes, and the inter set correlation of environmental variables with the axes was also reported. The eigenvalue represents a measure of the explanatory power of the single axis while the inter set correlation 19
Chapter 3 Materials and Methods represents the correspondence between environmental parameters and the CCA axes (Lepš & Šmilauer, 2003). To perform the analyses we used the software CANOCO version 4.5 for Windows (Ter Braak & Šmilauer, 2002) (Chapter 4). Canonical correspondence analysis was applied to species and sample datasets separately. In the analysis focusing on the species, only percentage cover of plants occurring in at least five of the 336 surveyed plots were included, to reduce the influence of low occurring species. The distances from the peatland borders were used as covariables to exclude the location of the plant species in the analysis and to explore the influence of chemical and physical parameters on the plant distribution in the blanket bog. The environmental measurements considered in the CCA therefore were the high and low water tables, the peat depth and the chemical parameters: pH, corrected conductivity, NH + 4 , TON, Na + , Ca 2+ , Cl - 2- , SO 4 and colour. We performed separate analyses for bryophytes and vascular plants, with the purpose of discovering the vegetation-environmental gradients for the two different plant groups (Chapter 4). The significant responses of the most common species to the main environmental gradients were further investigated using a generalized additive model (GAM) procedure. The GAM is a type of regression model, an extension of Generalized Linear Models (GLMs), where the effect of a particular predictor on the response variable is not expressed using a linear combination of the predictor values, as in the GLMs, but by a smooth semi-parametric term, based on a smoothing model (Ter Braak & Šmilauer, 2002). Therefore, the GAMs have a less rigid form than GLMs and determine the unknown shape of the response curve (Ter Braak & Šmilauer, 2002). The performance of the individual model is verified using an Akaike Information Criterion (AIC), a procedure that measures the parsimony of the model (Ter Braak & Šmilauer, 2002) (Chapter 4). Canonical correspondence analysis was also applied to the sample plot dataset. Since we were interested in investigating the role of the artificial and natural 20
Page 1 and 2: Department of Civil and Environment
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Chapter 6 water and energy budgets
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Chapter 8 Recommendations for futur
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References Aurela, M., Laurila, T.
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References Campbell, D. R. & Rochef
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References Friborg, T., Soegaard, H
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References Horst, T. W. & Weil, J.
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References Lafleur, P. M., Hember,
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References Malmer, N. 1986. Vegetat
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References Økland, R. H., Økland,
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References Shimoyama, K., Hiyama, T
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References Tahvanainen, T. & Tuomal
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References Vourlitis, G. L. & Oeche
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Appendix 1 Four-year CO 2 fluxes Da
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Appendix 1 Four-year CO 2 fluxes 5
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PhD Thesis, 2007 - University College Cork

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