The effects of gap size on some microclimate variables during late summer and autumn in a temperate broadleaved deciduous forest.

The creation of gaps can strongly influence forest regeneration and habitat diversity within forest ecosystems. However, the precise characteristics of such effects depend, to a large extent, upon the way in which gaps modify microclimate and soil water content. Hence, the aim of this study was to understand the effects of gap creation and variations in gap size on forest microclimate and soil water content. The study site, in North West England, was a mixed temperate broadleaved deciduous forest dominated by mature sessile oak (Quercus petraea), beech (Fagus sylvatica) and ash (Fraxinus excelsior) with some representatives of sycamore (Acer pseudoplatanus). Solar radiation (I), air temperature (T (A)), soil temperature (T (S)), relative humidity (h), wind speed (v) and soil water content (I) were measured at four natural treefall gaps created after a severe storm in 2006 and adjacent sub-canopy sites. I, T (A), T (S), and I increased significantly with gap size; h was consistently lower in gaps than the sub-canopy but did not vary with gap size, while the variability of v could not be explained by the presence or size of gaps. There were systematic diurnal patterns in all microclimate variables in response to gaps, but no such patterns existed for I. These results further our understanding of the abiotic and consequent biotic responses to gaps in broadleaved deciduous forests created by natural treefalls, and provide a useful basis for evaluating the implications of forest management practices

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)