Refine
Has Fulltext
- yes (2) (remove)
Is part of the Bibliography
- yes (2)
Year of publication
- 2020 (2) (remove)
Document Type
- Journal article (2)
Language
- English (2)
Keywords
- phenology (2) (remove)
Mycotoxins in agriculturally used plants can cause intoxication in animals and can lead to severe financial losses for farmers. The endophytic fungus Epichloë festucae var. lolii living symbiotically within the cool season grass species Lolium perenne can produce vertebrate and invertebrate toxic alkaloids. Hence, an exact quantitation of alkaloid concentrations is essential to determine intoxication risk for animals. Many studies use different methods to detect alkaloid concentrations, which complicates the comparability. In this study, we showed that alkaloid concentrations of individual plants exceeded toxicity thresholds on real world grasslands in Germany, but not on the population level. Alkaloid concentrations on five German grasslands with high alkaloid levels peaked in summer but were also below toxicity thresholds on population level. Furthermore, we showed that alkaloid concentrations follow the same seasonal trend, regardless of whether plant fresh or dry weight was used, in the field and in a common garden study. However, alkaloid concentrations were around three times higher when detected with dry weight. Finally, we showed that alkaloid concentrations can additionally be biased to different alkaloid detection methods. We highlight that toxicity risks should be analyzed using plant dry weight, but concentration trends of fresh weight are reliable.
Statistical modeling of phenology in Bavaria based on past and future meteorological information
(2020)
Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases arecommonly considered some of the most obvious effects of climate change. However, current climate models lack a representationof vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modelingapproach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data foranalyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of severalmeteorological variables were used as predictors and phenological anomalies of the flowering date of the test plantForsythiasuspensaas predictand. Several cross-validated prediction models using various numbers and differently constructed predictorswere developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observationsper phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust andsuccessful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its averagecoefficient of determination and root mean square error (RMSE) per station are 60% and ± 8.6 days, respectively. However, theprediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparablelevel of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants andregions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century comparedto the phenology of the reference period (1961–1990).