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A statistically significant difference (a=0.05) was observed only for the diffusive fluxes, with an average of 19.3 ± 31.5mgCH4 m-2 d-1 in vegetated waters («=261) and 11.1 ± 15.4 mg CH4 m-2 d-1 in open waters (n=81). The correlation among the fluxes and the measured variables (depth, water and air temperature), although positive, but were weak, (r<0.4 for all cases). Table 7.1 presents the fluxes divided in diffusive and ebullitive, for lakes and floodplains, with vegetated or open water. It shows that the vegetation influence was higher in the floodplains than in the lakes. In the floodplains, the methane diffusive flux reached an average of 22.6 ± 35.3 mg CH4 m-2 d-1 in vegetated waters (n=55), while the average in open waters (n=86) was 8.7±13.9mg CH4 m-2 d-1, thus revealing the importance of the vegetation in the methane transport in this environment.

As reported by Devol et al. (1988) and Christensen et al. (2003), the transport by the vegetation is effective when the roots are linked to the sediments, and it is also dependent on the density of the plants. In the Pantanal, the vegetation covers large extensions of the flooded areas, but in general, the vegetation is linked to the sediments mainly in the margins, where the depth is lower. The majority of the measurements in the lakes were performed in the central part, where the depth is sufficient to avoid the fixation of the roots to the sediments; so, the methane transport by vegetation was minimized. As the floodplains are shallower, the linking of the roots to the sediments is increased which facilitates the transport of methane by the stems. The comparison of the ebullitive fluxes of the open and vegetated areas did not show statistical significant differences, mainly due to the large variability observed in these fluxes.

The correlation analysis among the fluxes and the ambient variables (depth, water temperature and pH) showed that the correlations were weak (r<0.4) in all cases, although some of then had significant differences as seen above. The same correlations resulted from a Principal Component Analyses (PCA) of the data.

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