Data supporting the paper

Data1 is a dataframe with 7 variables: 1) id = female identity, 2) RS = annual reproductive success of females aged 7 years and over (0 = failed, 1 = success), 3) year, 4) ROSutero = rain on-snow events experienced in utero (0 = high, 1 = low), 5) alo = age at last observation, 6) ROScurrent = rain-...

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Bibliographic Details
Main Authors: Douhard, Mathieu, Loe, Leif Egil, Stien, Audun, Bonenfant, Christophe, Irvine, R. Justin, Veiberg, Vebjørn, Ropstad, Erik, Albon, Steve
Format: Report
Language:unknown
Published: 2016
Subjects:
climate change
cohort
development
phenotypic plasticity
Svalbard reindeer
predictive adaptive response
Svalbard
svalbard reindeer
Online Access:http://hdl.handle.net/10255/dryad.127041
https://doi.org/10.5061/dryad.fp505/1
Description
Summary:Data1 is a dataframe with 7 variables: 1) id = female identity, 2) RS = annual reproductive success of females aged 7 years and over (0 = failed, 1 = success), 3) year, 4) ROSutero = rain on-snow events experienced in utero (0 = high, 1 = low), 5) alo = age at last observation, 6) ROScurrent = rain-on-snow experienced in current year, 7) age. Data2 is a dataframe with 7 variables: 1) id = female identity, 2) RS = annual reproductive success of females aged 2-6 years (0 = failed, 1 = success), 3) year, 4) ROSutero = rain on-snow events experienced in utero (0 = high, 1 = low), 5) alo = age at last observation between 2 and 6 years, 6) ROScurrent = rain-on-snow experienced in current year, 7) age . Data3 is a dataframe with 5 variables: 1) id = female identity, 2) year, 3) preg = annual pregnancy probability of females aged 2-6 years, 4) ROSutero = rain on-snow events experienced in utero (0 = high, 1 = low), 5) BM = body mass. All continuous variables have been centered and divided by two standard deviations.

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