Data and code from: Lucky to be alive, luckier to breed: Lifetime reproduction in Weddell seals
Data files
Jun 07, 2026 version files 65.65 KB
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1-LRS_Distributions.R
7.99 KB
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2-Lifespan_Distributions.R
3.02 KB
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3-Gini_index.R
2.17 KB
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4-Figures.R
8.23 KB
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5-Cohort_LRS.R
10.48 KB
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cohorts.LRS.emp.RData
400 B
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Dist_Lifespan_ENV.RData
1.10 KB
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Dist_Lifespan_IH.RData
1.03 KB
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empiricalLRS.RData
413 B
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gini_index_ENV.RData
1.36 KB
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IPM_0.RData
4.81 KB
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IPM_1.RData
4.81 KB
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IPM_2.RData
4.81 KB
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LRS_ENV_all.RData
1.11 KB
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LRS_IH_all.RData
2.60 KB
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matrix_historical.RData
1.79 KB
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matrix_iceberg.RData
1.79 KB
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matrix_noniceberg.RData
1.79 KB
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MPM_vitalrates.RData
773 B
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README.md
5.17 KB
Abstract
We utilized a long-term study of Weddell seals to compute lifetime reproductive success (LRS) distributions using a theoretical approach and an empirical approach. These comparisons are often difficult to achieve among natural populations, but are important for disentangling sources of variation in lifetime measures. We performed three independent analyses: the first compared LRS and age-at-death distributions among populations living under different environmental conditions, the second focused on populations differing in individual heterogeneity (i.e., high or low reproductive strategy), and the third compared a theoretical LRS distribution with an empirical LRS distribution. Iceberg conditions increased the probability of LRS = 0 while individual heterogeneity had little influence on LRS distributions. Age-at-death distributions were also highly skewed, and only early life mortality was affected by environmental conditions. Our theoretical LRS distribution was strikingly similar to the empirical distribution estimated from females experiencing natural intrinsic trait variability and extrinsic environmental variability. We also examine inequality measures, which show that females must be ``lucky" to survive past maturity, and of those who do, only 74% breed. These findings contribute to ongoing research that reveals how diversity in lifetime outcomes is largely governed by chance alone.
Data and codes provide examples for estimating the main results presented in tables and figures. This study uses demographic data based on capture-mark-recapture studies provided by Jay Rotella.
Description of the data and file structure
Data files:
- matrix_historical.RData - Historical matrix that uses mean vital rates for the years 1979-2008. Columns represent 30 age classes with 4 stages in order of pre-breeder, first-time breeder, experienced breeder, and skipped breeder.
- matrix_noniceberg.RData - Noniceberg matrix that uses mean vital rates for the years 1982-2000 and 2006-2010. Columns represent 30 age classes with 4 stages in order of pre-breeder, first-time breeder, experienced breeder, and skipped breeder.
- matrix_iceberg.RData- Iceberg matrix that uses mean vital rates for the years 2001-2006. Columns represent 30 age classes with 4 stages in order of pre-breeder, first-time breeder, experienced breeder, and skipped breeder.
- IPM_0.RData - IPM of no IH that uses mean vital rates for the years 1979-2018. Columns represent 32 age classes with 5 stages (pre-breeder "PB", first-time breeder "FB", experienced breeder "EB", skipped breeder present "SBP", and skipped breeder absent "SBA")
- IPM_1.RData - IPM of high IH that uses mean vital rates for the years 1979-2018. Columns represent 32 age classes with 5 stages (pre-breeder "PB", first-time breeder "FB", experienced breeder "EB", skipped breeder present "SBP", and skipped breeder absent "SBA")
- IPM_2.RData - IPM of low IH that uses mean vital rates for the years 1979-2018. Columns represent 32 age classes with 5 stages (pre-breeder "PB", first-time breeder "FB", experienced breeder "EB", skipped breeder present "SBP", and skipped breeder absent "SBA")
- Dist_Lifespan_ENV.RData - List contains results to construct the lifespan distribution and estimate moments for historical, noniceberg, and iceberg periods. Columns: "Dist" are probabilities, "Age" are ages in years, and "Environment" can be historical, noniceberg, or iceberg.
- Dist_Lifespan_IH.RData - List contains results to construct the lifespan distribution and estimate moments for high IH, no IH, and low IH populations. Columns: "Dist" are probabilities, "Age" are ages in years, and "IH" can be High, None (no IH), or Low
- LRS_ENV_all.RData - List contains results to construct the lifetime reproductive success distribution and estimate moments for historical, non-iceberg, and iceberg periods. Columns: "LRS" are probabilities, "mids" are ages in years, and "Environment" can be historical, non-iceberg, or iceberg
- LRS_IH_all.RData - List contains results to construct the lifetime reproductive success distribution and estimate moments for high IH, no IH, and low IH populations. Columns: "LRS" are probabilities, "mids" are ages in years, and "RE" can be High, None (no IH), or Low
- empiricalLRS.RData - List contains results to construct the empirical lifetime reproductive success distribution in Figure 4. - List contains results to construct the empirical lifetime reproductive success distribution in Figure 4.
- MPM_vitalrates.RData - List contains estimates of previously published vital rates for multistate models pertaining to historical, non-iceberg, and ice-berg environments. Used in analysis and to produce Figure S2.
- gini_index_ENV.RData- List contains results from the Gini index to construct the Lorenz curve in supplemental Figure S3. Columns: "x" is the cumulative share of individuals, "y" is the cumulative share of offspring reproduced, and "Environment" is historical, iceberg, or non-iceberg.
- cohorts.LRS.emp.RData - Lifetime number of offspring estimates for the empirical cohorts used to estimate cohort effects in the supplement. Columns: "x" are the possible lifetime number of offspring, "y" are LRS, and "Birth_year" is the year females were born (cohort).
Code files:
- 1-LRS_Distributions.R - These codes provide the workflow for estimating the distribution for lifetime reproductive success and to estimate the moments in Table 2 (lines 153 & 193) and Table S3-4 (lines 166 & 206).
- 2-Lifespan_Distributions.R - These codes provide the workflow for estimating the distributions for lifespan.
- 3-Gini_index.R - These codes provide the workflow for estimating the Gini index and plotting the Lorenz curve. Codes to figure S3 provided.
- 4-Figures.R. - These codes provide the workflow to plot figures 2, 3, 4,4 and figure S2.
- 5-Cohort_LRS.R- These codes provide the workflow to the "Cohort effect" found in the supplemental. Includes estimates of the theoretical LRS distributions and plotting Figure S4.
Sharing/Access information
Data was shared by Jay Rotella and Kaitlin Macdonald, and has no DOI. This includes data used in matrix models (Table S1-S3) and integral projection models.
For access to codes generating the vital rates found in Table S1-S3 and that generate the integral projection models, contact Jay Rotella.
Code/Software
All data was run using R Version 4.5.2.
Scripts can be run in order or separately.
