TY  - JOUR
A1  - Vogel, Johannes
T1  - Drivers of phenological changes in southern Europe
JF  - International Journal of Biometeorology
N2  - The life cycle of plants is largely determined by climate, which renders phenological responses to climate change a highly suitable bioindicator of climate change. Yet, it remains unclear, which are the key drivers of phenological patterns at certain life stages. Furthermore, the varying responses of species belonging to different plant functional types are not fully understood. In this study, the role of temperature and precipitation as environmental drivers of phenological changes in southern Europe is assessed. The trends of the phenophases leaf unfolding, flowering, fruiting, and senescence are quantified, and the corresponding main environmental drivers are identified. A clear trend towards an earlier onset of leaf unfolding, flowering, and fruiting is detected, while there is no clear pattern for senescence. In general, the advancement of leaf unfolding, flowering and fruiting is smaller for deciduous broadleaf trees in comparison to deciduous shrubs and crops. Many broadleaf trees are photoperiod-sensitive; therefore, their comparatively small phenological advancements are likely the effect of photoperiod counterbalancing the impact of increasing temperatures. While temperature is identified as the main driver of phenological changes, precipitation also plays a crucial role in determining the onset of leaf unfolding and flowering. Phenological phases advance under dry conditions, which can be linked to the lack of transpirational cooling leading to rising temperatures, which subsequently accelerate plant growth.
KW  - Phenology
KW  - Southern Europe
KW  - Plant functional types
KW  - Linear mixed effect model
KW  - Climate change
Y1  - 2022
U6  - https://doi.org/10.1007/s00484-022-02331-0
SN  - 0020-7128
SN  - 1432-1254
VL  - 66
IS  - 9
SP  - 1903
EP  - 1914
PB  - Springer
CY  - New York
ER  - 
TY  - JOUR
A1  - Matuschek, Hannes
A1  - Kliegl, Reinhold
A1  - Vasishth, Shravan
A1  - Baayen, Harald R.
A1  - Bates, Douglas
T1  - Balancing Type I error and power in linear mixed models
JF  - Journal of memory and language
N2  - Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for data analysis also requires some care. One simple option, when numerically possible, is to fit the full variance covariance structure of random effects (the maximal model; Barr, Levy, Scheepers & Tily, 2013), presumably to keep Type I error down to the nominal a in the presence of random effects. Although it is true that fitting a model with only random intercepts may lead to higher Type I error, fitting a maximal model also has a cost: it can lead to a significant loss of power. We demonstrate this with simulations and suggest that for typical psychological and psycholinguistic data, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data. (C) 2017 The Authors. Published by Elsevier Inc.
KW  - Power
KW  - Linear mixed effect model
KW  - Hypothesis testing
Y1  - 2017
U6  - https://doi.org/10.1016/j.jml.2017.01.001
SN  - 0749-596X
SN  - 1096-0821
VL  - 94
SP  - 305
EP  - 315
PB  - Elsevier
CY  - San Diego
ER  -