Understanding the mechanisms by which invasive plants maintain dominance is essential to achieving long-term restoration goals. While many reports have suggested invasive plants alter resource availability, experimental tests of feedbacks between invasive plants and soil resources are lacking. We used field observations and experimental manipulations to test if the invasive grass Microstegium vimineum both causes and benefits from altered soil nitrogen (N) cycling. To quantify M. vimineum effects on N dynamics, we compared inorganic N pools and nitrification rates in 20 naturally invaded and uninvaded plots across a range of mixed hardwood forests, and in experimentally invaded and uninvaded common garden plots. Potential nitrification rates were 142 and 63 % greater in invaded than uninvaded plots in forest and common garden soils, respectively. As a result, soil nitrate was the dominant form of inorganic N during peak M. vimineum productivity in both studies. To determine the response of M. vimineum to altered nitrogen availability, we manipulated the dominant N form (nitrate or ammonium) in greenhouse pots containing M. vimineum alone, M. vimineum with native species, and native species alone. M. vimineum productivity was highest in monocultures receiving nitrate; in contrast, uninvaded native communities showed no response to N form. Notably, the positive response of M. vimineum to nitrate was not apparent when grown in competition with natives, suggesting an invader density threshold is required before positive feedbacks occur. Collectively, our results demonstrate that persistence of invasive plants can be promoted by positive feedbacks with soil resources but that the magnitude of feedbacks may depend on interspecific interactions.

SummaryMany exotic species have little apparent impact on ecosystem processes, whereas others have dramatic consequences for human and ecosystem health. There is growing evidence that invasions foster eutrophication. We need to identify species that are harmful and systems that are vulnerable to anticipate these consequences. Species' traits may provide the necessary insights.We conducted a global meta-analysis to determine whether plant leaf and litter functional traits, and particularly leaf and litter nitrogen (N) content and carbon: nitrogen (C : N) ratio, explain variation in invasive species' impacts on soil N cycling.Dissimilarity in leaf and litter traits among invaded and noninvaded plant communities control the magnitude and direction of invasion impacts on N cycling. Invasions that caused the greatest increases in soil inorganic N and mineralization rates had a much greater litter N content and lower litter C : N in the invaded than the reference community. Trait dissimilarities were better predictors than the trait values of invasive species alone.Quantifying baseline community tissue traits, in addition to those of the invasive species, is critical to understanding the impacts of invasion on soil N cycling.

Whether global change will drive changing forests from net carbon (C) sinks to sources relates to how quickly deadwood decomposes. Because complete wood mineralization takes years, most experiments focus on how traits, environments and decomposer communities interact as wood decay begins. Few experiments last long enough to test whether drivers change with decay rates through time, with unknown consequences for scaling short‐term results up to long‐term forest ecosystem projections. Using a 7 year experiment that captured complete mineralization among 21 temperate tree species, we demonstrate that trait effects fade with advancing decay. However, wood density and vessel diameter, which may influence permeability, control how decay rates change through time. Denser wood loses mass more slowly at first but more quickly with advancing decay, which resolves ambiguity about the after‐life consequences of this key plant functional trait by demonstrating that its effect on decay depends on experiment duration and sampling frequency. Only long‐term data and a time‐varying model yielded accurate predictions of both mass loss in a concurrent experiment and naturally recruited deadwood structure in a 32‐year‐old forest plot. Given the importance of forests in the carbon cycle, and the pivotal role for wood decay, accurate ecosystem projections are critical and they require experiments that go beyond enumerating potential mechanisms by identifying the temporal scale for their effects.

Background Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. Methods Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. Results The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. Conclusions Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.