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Can we detect a nonlinear response to temperature in European plant phenology?

Overview of attention for article published in International Journal of Biometeorology, March 2016
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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54 Dimensions

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53 Mendeley
Title
Can we detect a nonlinear response to temperature in European plant phenology?
Published in
International Journal of Biometeorology, March 2016
DOI 10.1007/s00484-016-1146-7
Pubmed ID
Authors

Susanne Jochner, Tim H. Sparks, Julia Laube, Annette Menzel

Abstract

Over a large temperature range, the statistical association between spring phenology and temperature is often regarded and treated as a linear function. There are suggestions that a sigmoidal relationship with definite upper and lower limits to leaf unfolding and flowering onset dates might be more realistic. We utilised European plant phenological records provided by the European phenology database PEP725 and gridded monthly mean temperature data for 1951-2012 calculated from the ENSEMBLES data set E-OBS (version 7.0). We analysed 568,456 observations of ten spring flowering or leafing phenophases derived from 3657 stations in 22 European countries in order to detect possible nonlinear responses to temperature. Linear response rates averaged for all stations ranged between -7.7 (flowering of hazel) and -2.7 days °C(-1) (leaf unfolding of beech and oak). A lower sensitivity at the cooler end of the temperature range was detected for most phenophases. However, a similar lower sensitivity at the warmer end was not that evident. For only ∼14 % of the station time series (where a comparison between linear and nonlinear model was possible), nonlinear models described the relationship significantly better than linear models. Although in most cases simple linear models might be still sufficient to predict future changes, this linear relationship between phenology and temperature might not be appropriate when incorporating phenological data of very cold (and possibly very warm) environments. For these cases, extrapolations on the basis of linear models would introduce uncertainty in expected ecosystem changes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
France 1 2%
Unknown 51 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 12 23%
Student > Doctoral Student 5 9%
Professor > Associate Professor 4 8%
Other 3 6%
Other 4 8%
Unknown 12 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 34%
Environmental Science 13 25%
Earth and Planetary Sciences 4 8%
Nursing and Health Professions 1 2%
Unspecified 1 2%
Other 1 2%
Unknown 15 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 March 2016.
All research outputs
#5,904,928
of 22,877,793 outputs
Outputs from International Journal of Biometeorology
#593
of 1,297 outputs
Outputs of similar age
#82,038
of 298,972 outputs
Outputs of similar age from International Journal of Biometeorology
#7
of 20 outputs
Altmetric has tracked 22,877,793 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,297 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 298,972 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.