10.5061/DRYAD.57QR203
Endriss, Stacy B.
Colorado State University
Cornell University
Vahsen, Megan L.
Colorado State University
University of Notre Dame
Bitume, Ellyn V.
Colorado State University
Agricultural Research Service
United States Department of Agriculture
Monroe, J. Grey
Colorado State University
Turner, Kathryn G.
Colorado State University
Pennsylvania State University
Norton, Andrew P.
Colorado State University
Hufbauer, Ruth A.
Colorado State University
Data from: The importance of growing up: juvenile environment influences
dispersal of individuals and their neighbours
Dryad
dataset
2019
phenotype dependence
informed dispersal
neighbors
juvenile environment
Tribolium castaneum
Condition dependence
red flour beetles
National Science Foundation
http://dx.doi.org/10.13039/100000001
DEB-0949619, PRFB-1523842
2019-09-04T00:00:00Z
en
https://doi.org/10.1111/ele.13166
1803456 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Dispersal is a key ecological process that is strongly influenced by both
phenotype and environment. Here, we show that juvenile environment
influences dispersal not only by shaping individual phenotypes, but also
by changing the phenotypes of neighbouring conspecifics, which influence
how individuals disperse. We used a model system (Tribolium castaneum, red
flour beetles) to test how the past environment of dispersing individuals
and their neighbours influences how they disperse in their current
environment. We found that individuals dispersed especially far when
exposed to a poor environment as adults if their phenotype, or even
one‐third of their neighbours’ phenotypes, were shaped by a poor
environment as juveniles. Juvenile environment therefore shapes dispersal
both directly, by influencing phenotype, as well as indirectly, by
influencing the external social environment. Thus, the juvenile
environment of even a minority of individuals in a group can influence the
dispersal of the entire group.
Raw Data from ExperimentData of dispersal of individual red flour beetles
within experimental arrays. As described in the manuscript: "We
allowed populations of Tribolium castaneum to disperse across replicate
linear arrays, manipulating current density (low = 18 adults, high = 90
adults), current habitat quality (low = 99.5% corn flour, 0.475% wheat
flour, 0.025% brewer’s yeast; high = 98.2% corn flour, 1.71% wheat flour,
0.09% brewer’s yeast), and juvenile density (low and high) in a
fully-factorial design (2 current densities x 2 current habitat qualities
x 2 juvenile densities x 15–20 replicate dispersal arrays = 143 arrays).
One-third of beetles within each array were experimental beetles that
experienced either a low or a high juvenile density (n=18 or 90), while
the remaining two-thirds of beetles were standardized beetles that
experienced an intermediate juvenile density
(n=40)."Endriss&Vahsen_Dispersal_EcologyLetters_Data.csvStewart et al 2017 supporting dataThis data highlights that the experimental treatments used in this experiment were biologically meaningful in terms of carrying capacity. More details can be found in the README file.Stewart_etal_2017_All_Data_EcoEvo_Final_Data.csvOrdinal RegressionFits ordinal regression to dispersal distances for experimental and background beetles. Extracts predicted mean decumulative probabilities for some treatment combinations.Endriss&Vahsen_OrdinalRegression.RDispersal kernel parameters modelCalculates mean, standard deviation, skew, kurtosis, and maximum for random samples of 6 individuals for each status in each array. Fits linear mixed models to these data and extracts test-statistics and p-values for models.Endriss&Vahsen_DispersalKernelParams.RDispersal kernel parameters predicted means (mean)Calculates mean for random samples of 6 individuals per status in each array. Fits linear mixed model to these data and extracts predicted means for some treatment combinations.Endriss&Vahsen_DispersalKernelMeans_Mean.RDispersal kernel parameters predicted means (standard deviation and maximum)Calculates standard deviation and maximum for random samples of 6 individuals per status in each array. Fits linear mixed model to these data and extracts predicted means for some treatment combinations.Endriss&Vahsen_DispersalKernelMeans_SD&Max.RFigure 3Code to produce figure 3.Endriss&Vahsen_Fig3.RPredicted means from ordinal regression (DM)Predicted means from ordinal regression model for current density by juvenile density treatment combinations.OrdinalRegression_DM_Means.csvPredicted means from ordinal regression (DH)Predicted means from ordinal regression for current density by habitat quality treatment combinations.OrdinalRegression_DH_Means.csvFigure 4Code to produce figure 4.Endriss&Vahsen_Fig4.RPredicted means from LMM mean dispersalPredicted means for mean dispersal distance for current density by habitat quality and current density by juvenile density treatment combinations for experimental and background beetles.LMM_DH_DM_Means.csvFigure 5Code to produce figure 5.Endriss&Vahsen_Fig5.RFigure S1Code to calculate estimated carrying capacities using data from Stewart et al. 2017.Endriss&Vahsen_FigS1.RFigure S2Plots correlation between mean distance dispersed of experimental and standardized beetles within an array for each treatment combination (Fig S2).Endriss&Vahsen_CorrelationTest_FigS2.RPredicted means from LMM (standard deviation)Predicted means and CIs for linear mixed model of standard deviation of dispersal kernels of experimental and standardized beetles.LMM_DM_SD.csvPredicted means from LMM (maximum)Predicted means of maximum distance linear mixed model dispersed for current density by juvenile density treatment combinations.LMM_DM_Max.csvFigure S3Code for producing figure S3.Endriss&Vahsen_FigS3.RPredicted means from ordinal regression (JH)Predicted means from ordinal regression for juvenile density by habitat quality treatment combinations.OrdinalRegression_JH_Means.csvFigure S4Code to produce figure S4.Endriss&Vahsen_FigS4.RGrowth RateCalculates average growth rate for different juvenile density treatments.GrowthRate.R