R code for Snyder, Ellner, and Hooker, "Time and chance: using age partitioning to understand how luck drives variation in reproductive success"
Data files
Sep 10, 2020 version files 150.69 KB
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age2SizePars.csv
234 B
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DacrydiumPartitioning.R
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domEig.R
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FalconMatrix.Rdata
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FalconPartitioning.R
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growPars.csv
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IdahoIPMFunctions.R
11.08 KB
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IdahoPartitioning-Plots.R
3.01 KB
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IdahoPartitioning.R
5.94 KB
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KittiwakeLifeTrajecPlot.R
6.46 KB
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KittiwakeMatrices.R
3.27 KB
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KittiwakePartitioningNewLuckPluck13.R
20.61 KB
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MegamatrixFunctions.R
2.89 KB
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roeMakeP.R
5.90 KB
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roePartitioningNewLuckPluck4.R
14.67 KB
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SeedlingSurvPars.csv
164 B
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SneetchCalculations3.R
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SneetchMatrices.R
7.41 KB
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Standard_Graphical_Pars.R
1.36 KB
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SurvPars.csv
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TsugaMatrix.Rdata
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TsugaPartitioning.R
10.28 KB
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Utilities.R
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wPars.csv
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ZuidemaParams.R
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ZuidemaTreeFunctions.R
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Abstract
Over the course of individual lifetimes, luck usually explains a large fraction of the between- individual variation in lifespan or lifetime reproductive output (LRO) within a population, while variation in individual traits or “quality” explains much less. To understand how, where in the life cycle, and through which demographic processes luck trumps trait variation, we show how to partition by age the contributions of luck and trait variation to LRO variance, and how to quantify three distinct components of luck. We apply these tools to several empirical case studies.
We find that luck swamps effects of trait variation at all ages, primarily due to randomness in individual state dynamics (“state trajectory luck”). Luck early in life is most important. Very early state trajectory luck generally determines whether or not an individual ever breeds, likely by ensuring that they are not dead or doomed quickly. Less early luck drives variation in success among those breeding at least once. Consequently, the importance of luck often has a sharp peak early in life, or two peaks. We suggest that ages/stages where the importance luck peaks are potential targets for interventions to benefit a population of concern, different from those identified by eigenvalue elasticity analysis.
R code
The code should be self-contained.