Biological control is a promising approach to reduce plant diseases caused by nematodes. We tested the effect of the fungus Clonostachys rosea strain IK726 inoculation on nematode community composition in a naturally nematode infested soil in a pot experiment, and the effect of C. rosea on plant health. The numbers of plant-parasitic nematode genera extracted from soil and plant roots decreased by 40 to 73% when C. rosea was applied, while genera of nonparasitic nematodes were not affected. Soil inoculation of C. rosea increased fresh shoot weight and shoot length of wheat plants by 20 and 24%, respectively, while only shoot dry weight increased by 48% in carrots. Light microscopy of in vitro C. rosea-nematode interactions did not reveal evidence of direct parasitism. However, culture filtrates of C. rosea growing in potato dextrose broth, malt extract broth and synthetic nutrient broth exhibited toxicity toward nematodes and immobilized 57, 62, and 100% of the nematodes, respectively, within 48 h. This study demonstrates that C. rosea can control plant-parasitic nematodes and thereby improve plant growth. The most likely mechanism responsible for the antagonism is antibiosis through production of nematicidal compounds, rather than direct parasitism.

Long-term water quality monitoring is of high value for environmental management as well as for research. Artificial level shifts in time series due to method improvements, flaws in laboratory practices or changes in laboratory are a common limitation for analysis, which, however, are often ignored. Statistical estimation of such artefacts is complicated by the simultaneous existence of trends, seasonal variation and effects of other influencing factors, such as weather conditions. Here, we investigate the performance of generalised additive mixed models (GAMM) to simultaneously identify one or more artefacts associated with artificial level shifts, longitudinal effects related to temporal trends and seasonal variation, as well as to model the serial correlation structure of the data. In the same model, it is possible to estimate separate residual variances for different periods so as to identify if artefacts not only influence the mean level but also the dispersion of a series. Even with an appropriate statistical methodology, it is difficult to quantify artificial level shifts and make appropriate adjustments to the time series. The underlying temporal structure of the series is especially important. As long as there is no prominent underlying trend in the series, the shift estimates are rather stable and show less variation. If an artificial shift occurs during a slower downward or upward tendency, it is difficult to separate these two effects and shift estimates can be both biased and have large variation. In the case of a change in method or laboratory, we show that conducting the analyses with both methods in parallel strongly improves estimates of artefact effects on the time series, even if certain problems remain. Due to the difficulties of estimating artificial level shifts, posterior adjustment is problematic and can lead to time series that no longer can be used for trend analysis or other analysis based on the longitudinal structure of the series. Before carrying out a change in analytic method or laboratory, it should be considered if this is absolutely necessary. If changes cannot be avoided, the analysis of the two methods considered, or the two laboratories contracted, should be run in parallel for a considerable period of time so as to enable a good assessment of changes introduced to the data series.