Open Access

Abstract Root phenology influences the timing of plant resource acquisition and carbon fluxes into the soil. This is particularly important in fen peatlands, in which peat is primarily formed by roots and rhizomes of vascular plants. However, most fens in Central Europe are drained for agriculture, leading to large carbon losses, and further threatened by increasing frequency and intensity of droughts. Rewetting fens aims to restore the original carbon sink, but how root phenology is affected by drainage and rewetting is largely unknown. We monitored root phenology with minirhizotrons in drained and rewetted fens (alder forest, percolation fen and coastal fen) as well as its soil temperature and water table depth during the 2018 drought. For each fen type, we studied a drained site and a site that was rewetted ~25 years ago, while all the sites studied had been drained for almost a century. Overall, the growing season was longer with rewetting, allowing roots to grow over a longer period in the year and have a higher root production than under drainage. With increasing depth, the growing season shifted to later in time but remained a similar length, and the relative importance of soil temperature for root length changes increased with soil depth. Synthesis and applications. Rewetting extended the growing season of roots, highlighting the importance of phenology in explaining root productivity in peatlands. A longer growing season allows a longer period of carbon sequestration in form of root biomass and promotes the peatlands' carbon sink function, especially through longer growth in deep soil layers. Thus, management practices that focus on rewetting peatland ecosystems are necessary to maintain their function as carbon sinks, particularly under drought conditions, and are a top priority to reduce carbon emissions and address climate change.

Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root growth dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras or circular scanners are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. Our minirhizotron data come from various wetlands on organic soils, i.e. highly heterogeneous substrates consisting of dead plant material, often times mainly roots, in various degrees of decomposition. This may be seen as one of the most challenging soil types for root segmentation in minirhizotron images. RootDetector showed a high capability to correctly segment root pixels in minirhizotron images from field observations (F1 = 0.6044; r2 compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average more than 13-times higher root length/cm2 per image compared to expert analysts. CNNs such as RootDetector provide a reliable and efficient method for the detection of roots and root length in minirhizotron images even from challenging field conditions. Analyses with RootDetector thus save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts.