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Human-mediated dispersal of non-native earthworms can cause substantial changes to the functioning and composition of ecosystems previously earthworm-free. Some of these earthworm species have the potential to “geoengineer” soils and increase plant nitrogen (N) uptake. Yet the possible consequences of increased plant N concentrations on rodent grazing remains poorly understood. In this study, we present findings from a common garden experiment with two tundra communities, meadow (forb dominated) and heath (shrub dominated), half of them subjected to 4 years of earthworm presence (Lumbricus spp. and Aporrectodea spp.). Within four summers, our earthworm treatment changed plant community composition by increasing graminoid density by, on average, 94% in the heath vegetation and by 49% in the meadow. Rodent winter grazing was more intense on plants growing in soils with earthworms, an effect that coincided with higher N concentrations in plants, indicating a higher palatability. Even though earthworms reduced soil moisture, plant community productivity, as indicated by vegetation greenness (normalized difference vegetation index), was not negatively impacted. We conclude that earthworm-induced changes in plant composition and trophic interactions may fundamentally alter the functioning of tundra ecosystems.

Targeted removal experiments are a powerful tool to assess the effects of plant species or (functional) groups on ecosystem functions. However, removing plant biomass in itself can bias the observed responses. This bias is commonly addressed by waiting until ecosystem recovery, but this is inherently based on unverified proxies or anecdotal evidence. Statistical control methods are efficient, but restricted in scope by underlying assumptions.

We propose accounting for such biases within the experimental design, using a gradient of biomass removal controls. We demonstrate the relevance of this design by presenting (1) conceptual examples of suspected biases and (2) how to observe and control for these biases.

Using data from a mycorrhizal association-based removal experiment, we show that ignoring biomass removal biases (including by assuming ecosystem recovery) can lead to incorrect, or even contrary conclusions (e.g. false positive and false negative). Our gradient design can prevent such incorrect interpretations, regardless of whether aboveground biomass has fully recovered.

Our approach provides more objective and quantitative insights, independently assessed for each variable, than using a proxy to assume ecosystem recovery. Our approach circumvents the strict statistical assumptions of, for example, ANCOVA and thus offers greater flexibility in data analysis.

• Seasonal dynamics of root growth play an important role in large-scale ecosystem processes; they are largely governed by growth regulatory compounds and influenced by environmental conditions. Yet, our knowledge about physiological drivers of root growth is mostly limited to laboratory-based studies on model plant species.
• We sampled root tips of Eriophorum vaginatum and analyzed their auxin concentrations and meristem lengths biweekly over a growing season in situ in a subarctic peatland, both in surface soil and at the permafrost thawfront.
• Auxin concentrations were almost five times higher in surface than in thawfront soils and increased over the season, especially at the thawfront. Surprisingly, meristem length showed an opposite pattern and was almost double in thawfront compared with surface soils. Meristem length increased from peak to late season in the surface soils but decreased at the thawfront.
• Our study of in situ seasonal dynamics in root physiological parameters illustrates the potential for physiological methods to be applied in ecological studies and emphasizes the importance of in situ measurements. The strong effect of root location and the unexpected opposite patterns of meristem length and auxin concentrations likely show that auxin actively governs root growth to ensure a high potential for nutrient uptake at the thawfront.

Vegetation changes in a warming Arctic may affect plant-associated soil microbial communities with possible consequences for the biogeochemical cycling of carbon (C) and nitrogen (N). In a sub-arctic tundra heath, we factorially removed plant species with ecto- and ericoid mycorrhizal associations. After two years, we explored how mycorrhizal type-specific plant removal influences microbial communities, soil and microbial C and N pools, and extracellular enzymatic activities. Removal of ecto- and ericoid mycorrhizal plants did not change the soil fungal or bacterial community composition or their extracellular enzyme activities. However, ericoid plant removal decreased microbial C:N ratio, suggesting a stoichiometric effect decoupled from microbial community composition. In other words, microbial communities appear to show initial plasticity in response to major changes in tundra vegetation. This highlights the importance of longer-term perspectives when investigating the effects of vegetation changes on biogeochemical processes in Arctic ecosystems.

The rewetting of formerly drained peatlands can help to counteract climate change through the reduction of CO2 emissions. However, this can lead to resuming CH4 emissions due to changes in the microbiome, favoring CH4-producing archaea. How plants, hydrology and microbiomes interact as ultimate determinants of CH4 dynamics is still poorly understood. Using a mesocosm approach, we studied peat microbiomes, below-ground root biomass and CH4 fluxes with three different water level regimes (stable high, stable low and fluctuating) and four different plant communities (bare peat, Carex rostrata, Juncus inflexus and their mixture) over the course of one growing season. A significant difference in microbiome composition was found between mesocosms with and without plants, while the difference between plant species identity or water regimes was rather weak. A significant difference was also found between the upper and lower peat, with the difference increasing as plants grew. By the end of the growing season, the methanogen relative abundance was higher in the sub-soil layer, as well as in the bare peat and C. rostrata pots, as compared to J. inflexus or mixture pots. This was inversely linked to the larger root area of J. inflexus. The root area also negatively correlated with CH4 fluxes which positively correlated with the relative abundance of methanogens. Despite the absence or low abundance of methanotrophs in many samples, the integration of methanotroph abundance improved the quality of the correlation with CH4 fluxes, and methanogens and methanotrophs together determined CH4 fluxes in a structural equation model. However, water regime showed no significant impact on plant roots and methanogens, and consequently, on CH4 fluxes. This study showed that plant roots determined the microbiome composition and, in particular, the relative abundance of methanogens and methanotrophs, which, in interaction, drove the CH4 fluxes.

The distribution of roots throughout the soil drives depth-dependent plant–soil interactions and ecosystem processes, particularly in arctic tundra where plant biomass, is predominantly belowground. Vegetation is usually classified from aboveground, but it is unclear whether such classifications are suitable to estimate belowground attributes and their consequences, such as rooting depth distribution and its influence on carbon cycling. We performed a meta-analysis of 55 published arctic rooting depth profiles, testing for differences both between distributions based on aboveground vegetation types (Graminoid, Wetland, Erect-shrub, and Prostrate-shrub tundra) and between ‘Root Profile Types’ for which we defined three representative and contrasting clusters. We further analyzed potential impacts of these different rooting depth distributions on rhizosphere priming-induced carbon losses from tundra soils. Rooting depth distribution hardly differed between aboveground vegetation types but varied between Root Profile Types. Accordingly, modelled priming-induced carbon emissions were similar between aboveground vegetation types when they were applied to the entire tundra, but ranged from 7.2 to 17.6 Pg C cumulative emissions until 2100 between individual Root Profile Types. Variations in rooting depth distribution are important for the circumpolar tundra carbon-climate feedback but can currently not be inferred adequately from aboveground vegetation type classifications.

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.

Maximum rooting depth is a key functional trait to increase the fitness of trees and also influences terrestrial ecosystem processes. Despite its importance, the drivers of the interspecific variation of maximum rooting depth or its relation to other plant traits and plant age are not well understood. In this study, we aimed to clarify the drivers of the interspecific variation of maximum rooting depth with special reference to its relation to plant leaf traits and age. We analyzed how maximum rooting depth of single individuals of 227 tree species planted in the same common garden in the temperate region of central Japan is correlated to commonly measured leaf functional traits (specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen (N) concentration) extracted from the TRY database. We did this by employing the phylogenetic comparable method and included the age of all target trees. When excluding the effect of phylogenetic signals from the relationships between rooting depth and leaf traits, SLA was negatively correlated with maximum rooting depth in deciduous, but not evergreen species. Further, rooting depth and leaf N concentration were negatively correlated in evergreen trees, a pattern driven by young trees. These results implicate that the relationship between maximum rooting depth and leaf traits differed depending on the leaf habits and age of the tree species.

Arctic soils play an important role in Earth's climate system, as they store large amounts of carbon that, if released, could strongly increase greenhouse gas levels in our atmosphere. Most research to date has focused on how the turnover of organic matter in these soils is regulated by abiotic factors, and few studies have considered the potential role of biotic regulation. However, arctic soils are currently missing important groups of soil organisms, and here, we highlight recent empirical evidence that soil organisms' presence or absence is key to understanding and predicting future climate feedbacks from arctic soils. We propose that the arrival of soil organisms into arctic soils may introduce "novel functions", resulting in increased rates of, for example, nitrification, methanogenesis, litter fragmentation, or bioturbation, and thereby alleviate functional limitations of the current community. This alleviation can greatly enhance decomposition rates, in parity with effects predicted due to increasing temperatures. We base this argument on a series of emerging experimental evidence suggesting that the dispersal of until-then absent micro-, meso-, and macroorganisms (i.e. from bacteria to earthworms) into new regions and newly thawed soil layers can drastically affect soil functioning. These new observations make us question the current view that neglects organism-driven "alleviation effects"when predicting future feedbacks between arctic ecosystems and our planet's climate. We therefore advocate for an updated framework in which soil biota and the functions by which they influence ecosystem processes become essential when predicting the fate of soil functions in warming arctic ecosystems.