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Land-use change in peatlands affects important drivers of CH4 emission such as groundwater level and nutrient availability. Due to the high spatial and temporal variability of such environmental drivers, it is hard to make good predictions of CH4 emissions in the context of land-use changes. Here, we used plant species composition as a stable integrator of environmental drivers of CH4 emissions. We used weighted averaging regression and calibration to make a direct link between plant species composition and CH4 flux in an effort to predict values of CH4 emission for a land-use gradient in two extensive peatland sites. Our predicted CH4 emissions showed good fit with observed values. Additionally, we showed that a quick characterization of vegetation composition, by the dominant species only, provides equally good predictions of CH4 emissions. The use of mean groundwater level alone for predicting emissions showed the same predictive power as our models. However, water level showed strong variability in time. Furthermore, the inverse relationship between water level and CH4 emission can lead to higher errors in predictions at sites with higher CH4 emission. The performance of our model was comparable with those of mechanistic models developed for natural wetland ecosystems. However, such mechanistic models require complex input parameters that are rarely available. We propose the use of plant species composition as a simple and effective alternative for deriving predictions of CH4 emissions in peatlands in the context of land-use change.

Plant Species Composition Can Be Used as a Proxy to Predict Methane Emissions in Peatland Ecosystems After Land-Use Changes