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Variance in offspring sex ratio and maternal allocation in a highly invasive mammal

Cite this dataset

Chinn, Sarah; Smyser, Timothy; Beasley, James (2024). Variance in offspring sex ratio and maternal allocation in a highly invasive mammal [Dataset]. Dryad.


Skewed sex ratios at birth are widely reported in wild populations, however the extent to which parents are able to modulate the sex ratio of offspring to maximize their own fitness remains unclear. This is particularly true for highly polytocous species as maximizing fitness may include trade-offs between sex ratio and the size and number of offspring in litters. In such cases, it may be adaptive for mothers to adjust both the number of offspring per litter and offspring sex to maximize individual fitness. Investigating maternal sex allocation in wild pigs (Sus scrofa) under stochastic environmental conditions, we predicted that, under favorable conditions, high quality mothers (larger, older) would produce male-biased litters and invest more in producing larger litters with more males. We also predicted sex ratio would vary relative to litter size, with a male-bias among smaller litters. We found evidence that increasing wild boar ancestry, maternal age and condition, and resource availability may weakly contribute to male-biased sex ratio, however, unknown factors not measured in this study are assumed to be more influential. High quality mothers allocated more resources in litter production, but this relationship was driven by adjustment of litter size, not sex ratio. There was no relationship between sex ratio and litter size. Collectively, our results emphasized that adjustment of litter size appeared to be the primary reproductive characteristic manipulated in wild pigs to increase fitness rather than adjustment of offspring sex ratio.

README: Chinn et al. 2023 Variance in offspring sex ratio and maternal allocation in a highly invasive mammal

Manuscript citation: Chinn, S. M., Smyser, T., & Beasley, J. C. (2023). Variance in offspring sex ratio and maternal allocation in a highly invasive mammal. Ecology and Evolution, 13(5), e10136.

## Corresponding author: Dr. Sarah M. Chinn,

These are original data for the manuscript published in Ecology and Evolution. This file explains all variables in the dataset that accompany Chinn et al. 2023 (Ecology and Evolution).

For data not collected for a specific variable are represented by 'NA' in the datasets.

Description of the data and file structure

# 1. Dataset:

Analyses: Sex Ratio Triver-Willard (GLM), Allocation Williams (LM), Fig5a Allocation vs boar ancestry, Fig5b Allocation vs maternal condition, Summary stats for litter size, Fig6 Average sex ratio by litter size (figure)

sow: individual female sampled (n=89)

age: age class of sow

yr: age in years (aged by Matson lab, tooth cementum)

year: year sow sampled (2017-2019)

males: # of male fetuses/offspring in litter

females: # of female fetuses/offspring in litter

litterno: litter size

sexratio: litter sex ratio - # males/(# males + # females)

allocation: maternal allocation in litter = # males * cost of producing a male + # females * cost of producing a female

Ex: males are 2% greater in mass compared to female fetuses

Cost of male = 1.02*cost of female, when cost of female=1

litter has 2 males + 3 females. Allocation = 2*1.02 + 3*1 = 5.04

size: maternal condition index - mass/length

fat: scaled rump fat values

littersize: scaled litter size

fetusmass: scaled average fetus mass

rumpfat: raw rump fat values

boar: scaled % wild boar ancestry

size_raw: raw maternal condition index values

boar_raw: raw % wild boar ancestry

# 2. Dataset:,,

Analyses: Rump fat summary (mean + sd for each year)

sow: ID of individual female sampled

year: year sow was sampled

rumpfat: raw measure of rump fat (cm)

# 3. Dataset:

Analysis: ANOVA to determine if there were differences in rump fat by year for all sows in study, n=514 (some missing data)

sow: ID of individual female sampled

year: year sow was sampled

rumpfat: raw measure of rump fat (cm)

# 4.Dataset:

Analysis: ANOVA to determine if there were difference sin rump fat by year for only pregnant sows, n=155 (some missing data)

sow: ID of individual female sampled

repro: reproductive status (all females in this data set are pregnant)

year: year sow was sampled

rumpfat: raw measure of rump fat (cm)

# 5. Dataset:

Analyses: Determine if there were differences in fetal mass by age- Fig2; Difference in sex ratio of fetal litters by fetal age- Fig4; Determine if fetal mass changed over time by sex- Fig3

sow: individual female sampled (n=89)

sexratio: litter sex ratio - # males/(# males + # females)

CRL: mean crown-rump length for the litter

henrydays: age (days) of litter based on CRL

predictedageR: predicted age (days) of litter using regression to fill in gaps that Henry could not estimate (smaller CRL values)

LitterMass: mean litter mass (all males and females in a litter)

littersize: litter size (number of offspring)

maleMass: mean mass of all male offspring

males: # of males in litter

femaleMass: mean mass of all female offspring

females: # of females in litter

male-female: value of the mean mass of males-mean mass of females


This study was conducted at the Savannah River Site (SRS), a 78,000 ha U.S. Department of Energy facility located in Aiken, Barnwell, and Allendale counties, South Carolina, USA. We sampled from live-trapped (and consequently released for other studies, e.g., neonate survival [Chinn et al. 2021]) and humanely-euthanized according to established protocols (A2015 12-017) wild pigs throughout the year between March 2017 and July 2019. We collected fetal data, tissue for genetic analysis, and morphological measurements from females >27 kg to quantify litter and maternal attributes (n = 160). We weighed and measured each female dorsally from snout to base of tail. We calculated a standardized body condition index (subsequently referred to as maternal condition) for each individual as mass/length (LaBocha et al., 2014). We measured extraneous fat reserves, a quantitative measurement of maternal nutritional condition, from culled females and used it as a proxy for local resource availability. We assessed age by tooth eruption and replacement patterns (Matschke, 1967; Mayer, 2002), and individuals were classified into 3 age classes: juvenile (between 6 months and 1 year), yearling (1-1.5 years), and adults (≥ 1.5 years old). If present, fetuses were removed, weighed and measured in a straight-line from crown to rump (CRL). Fetuses ≥36 days old were sufficiently developed to visually determine sex. We quantified the ancestry of wild pigs following methods described in Smyser et al. (2020). 

We centered and z-transformed continuous variables to a mean of 0 and a standard deviation of 1 to allow for standardized comparison for all analyses. We censored individuals from analyses if they did not have all variables measured (i.e., removed a female from analysis if she was missing rump fat data). 

Usage notes

All analyses were performed in R 4.0.5 (R Core Team, 2021). We used package lme4 (Bates et al. 2015) for some analyses.


United States Department of Energy, Award: DE-EM0005228