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STAR+METHODS
KEY RESOURCES TABLE
REAGENT or RESOURCE
SOURCE
IDENTIFIER
Deposited data
Raw and analyzed data
This paper
Mendeley Data: https://doi.org/
10.17632/mm824csy77.1
Experimental models: Organisms/strains
Reticulitermes speratus
Wild-caught
N/A
R: A language and environment for statistical computing.
https://www.r-project.org
Software and algorithms
R ver. 3.3.3
R Foundation for Statistical Computing.
RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Mamoru Takata (takata.mamoru.7z@kyoto-u.ac.jp).
Materials availability
Not applicable.
Data and code availability
The dataset reported in this paper have been deposited at Mendeley and are publicly available as of the
date of publication. The DOI is listed in the key resources table.
This paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the
lead contact upon request.
METHOD DETAILS
Egg-fostering experiment
To identify heritable and environmental effects on larval caste fate under field conditions, we conducted an
egg-fostering experiment and compared caste fate between larvae developed in foster and natal colonies
in the subterranean termite Reticulitermes speratus (Figure 2A). Thirteen colonies that contained one PK,
multiple SQs, W1s, and N1s were collected in oak/pine mixed forests in Kyoto, Shiga, and Fukui, Japan,
from July to September 2020. Two additional colonies were collected to supply foster workers in Kyoto
in July 2020. Within a week of collection, all termites were extracted from each piece of wood, and 100
of each sex of N1s and W1s (without distinguishing between the nymph and worker castes, since the castes
at the developmental stage are only distinguishable under a microscope) were randomly selected from
each colony (hereafter, larvae developed in the natal colony), and the number of individuals of each caste
was recorded. In total, 2,600 larvae were investigated. The caste and sex were distinguished by the presence or absence of wing buds and sex-specific morphology of seventh and eighth sternites,
respectively.39–41
To collect eggs, 10–80 SQs from each of the 13 colonies were separated into groups of ten, then were transferred into individual dishes (ca. 60 mm) lined with a moist unwoven cloth and 50 foster workers (including
both sexes). Each of the 13 colonies was randomly assigned to one of two foster worker colonies, to have
enough eggs for each natal and foster colony replication. After 48 h, 100 eggs laid by the secondary queens
were transferred into dishes (ca. 30 mm) with mixed sawdust bait42 and 50 male workers from previously
assigned foster colonies. Four dishes were made for each of the 13 colonies; thus, 400 eggs were set in
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each colony. Then, the dishes were maintained at 25 C under dark conditions. The dishes were checked
weekly, and if N1s and/or W1s (hereafter, larvae developed in the foster colony) were found, the numbers
of individuals of each caste and sex were recorded by observers who were naı¨ve to the identity of the natal
colonies. In total, 1,368 male and 1,362 female individuals were collected and used for analysis.
To confirm that nymphs and workers were produced by normal sexual reproduction between a PK and SQ
pair, we performed genotyping of a PK, SQs, N1s, and W1s to identify their parents in representative colonies. Two field-collected representative colonies were randomly selected (the nymph sex ratios in colonies GA and GB were 0.51 and 0.72, respectively). One PK, four randomly selected SQs, and 10 of each
sex of N1s and W1s individuals were analyzed. Total DNA was extracted using a modified Chelex extraction
protocol.43 The heads or antennae were digested in 20 mL of Chelex solution (10% w/v; TE pH 8.0) and 0.2 mL
of proteinase K at 55 C for 3 h. After incubation, the samples were heated at 95 C for 15 min. Polymerase
chain reaction (PCR) amplifications were performed in the multiplex to analyze four microsatellite loci
(Rf21-1, Rf24-2, Rf6-1,44 and Rs1545). Primers for Rf6-1, Rf21-1, Rf24-2, and Rs15 were labeled with
6-FAM, VIC, NED, and PET fluorescent tags, respectively. The 10-mL PCR cocktail contained 1 mL of template DNA, 0.20 mL of 10 mM dNTP, 0.99 mL of 103 PCR buffer, 0.07 mL of 5 U/mL Taq DNA polymerase
(New England Biolabs, Ipswich, MA, USA), 1.15 mL of 5 mM multiplex primers, and 6.59 mL of distilled water.
Amplification consisted of initial denaturation at 95 C for 3 min, followed by 35 cycles of denaturation at
95 C for 30 s, annealing at 60 C for 75 s, and extension at 72 C for 2 min. The PCR products were mixed
with 10 mL of Hi-Di formamide and 0.3 mL of GeneScan 600 LIZ size standard. An Applied BioSystems
3500 Genetic Analyzer was used to perform sample detection. GeneMapper 5.0 software (Applied Biosystems, Foster City, CA, USA) was used to analyze raw data. We defined offspring carrying both paternal
and maternal alleles as sexually produced, and ones carrying only maternal alleles as parthenogenetically
produced.
Comparison of larval and alate numerical sex ratios
To investigate whether the variations in larval caste fate result in variation in colony-level sex allocation, we
evaluated the influence of three potential factors affecting the numerical sex ratio of alates (Figure 3) by
comparing the sex ratio of L2s and N1s to that of alates. To compare the sex ratio of L2s and N1s with
that in alates in field colonies, 10 nests with L2s, W1s, N1s, and sixth-instar nymphs (N6: pre-alates),
were collected in Kyoto, Shiga, and Fukui, Japan, from May to June of 2019–2020, just before the swarming
season. Each nest was kept at 20 C under dark conditions until the N6s molted into alates. Then, all the
termites in each colony were extracted from the wood. From each colony, 200 individuals of L2s, N1s
and W1s mix (without distinguishing between their castes), and alates were randomly selected, and the
number of each sex was recorded. For the N1s and W1s, the number in each caste (nymph/worker) was
also recorded. Additional N1 individuals were collected until their total number reached 100, then the number of each sex was recorded. In total, 2,000 individuals for each developmental stage (L2s, N1s and W1s
mix, and alates) and an additional 513 individuals of N1 were investigated. Observers were naı¨ve to the colony information during the investigation.
Relationship between alate sex ratio and their sexual difference in biomass
To investigate whether the difference in the numerical sex ratio of alates is reflected in colony-level sex allocation, the sexual difference in body weight in alates in field colonies were investigated among colonies
with different alate sex ratio. One hundred colonies with alates were collected in Kyoto, Shiga, and Fukui,
Japan, from April to June of 2018–2019. All alates were extracted from the wood, then 100 individuals were
randomly selected and the number of each sex was recorded. From the selected 100, 10 alates of each sex
were randomly selected and their fresh body weight was recorded to the nearest 0.1 mg.
QUANTIFICATION AND STATISTICAL ANALYSIS
The binomial linear model
The binomial linear model for examining the linearity of the relationship between two variables is defined
below. Suppose we have an objective variable y given T trials and an explanatory variable X. The binary
linear model supposes that for i = 1, 2, . N,
yi Binomial (Ti, pi)
(Equation 1)
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pi = beta * Xi + intercept, s.t. 0 < p < 1
(Equation 2)
where Binomial (a, b) is the binomial distribution with trial number a and probability parameter b, and beta
is the regression parameter. Note that the possible range of the parameters (beta, intercept) depends on
datum X and the remaining parameter to satisfy 0 < p < 1. We defined the log likelihood function of this
model and obtained the maximum likelihood estimates (MLEs) of the parameters via numerical optimization (‘‘optim’’ function in R). We used the Broyden–Fletcher–Goldfarb–Shanno algorithm whenever
possible. When convergence was not attained during optimization, a simulated annealing algorithm was
used instead. Before the actual data were analyzed, we confirmed that the usual asymptotic properties
of the MLEs held for this model (e.g., normality and unbiasedness) using numerical simulations. Although
numerical optimization is more difficult than the usual logit/probit model owing to complex restrictions on
the parameter space, our approach is useful for analyzing the linear relationship between two ratios and
still yields statistically reliable results.
Statistical analysis
The binomial linear models were applied for the comparison of caste fate between the larvae developed in
the foster and natal colony in the egg-fostering experiment. The objective variable was the nymph ratio of
male or female larvae developed in the foster colony, and the respective explanatory variable was the
nymph ratio of male or female larvae developed in the natal colony or the social origin of the foster workers.
To evaluate the impact of each factor on the objective variable, we compared the Akaike information criterion (AIC) and Bayesian information criterion (BIC) values of these models and the null model. We did not
report p-values because theoretical rationale is lost when the post-model selection estimator is applied.
Note that AIC and BIC values are identical to log-likelihoods up to a constant if the compared models
have the same number of free parameters. Generalized linear mixed models (GLMMs) were run to investigate the effect of the social environment—natal vs. foster colony—on the offspring nymph ratio. Data for
males and females were analyzed separately. The binomial objective variable was the nymph ratio; the
post-hatching environment (natal vs. foster colony) was treated as an explanatory variable and the genetic
origin of the larvae (colony identity) was treated as a random factor. An exact binomial test was applied to
compare the observed numerical sex ratio for larvae developed in the foster colony against the null hypothesis assuming that the numbers of males and females were equal.
For the comparison of alate and larval numerical sex ratios in the different developmental stages in field
colonies, we applied a binomial linear model. The binomial objective variable was the sex ratio for alates
(the number of male subjects given the total number of subjects as the trial number) and the explanatory
variable was the sex ratio (continuous) for second-instar larva (L2s), N1s, or all third-instar individuals (sum of
N1s and W1s). To evaluate the impact of each factor on the objective variable, we compared the AIC and
BIC values of these models and the null model. A GLMM was used to investigate the effect of caste—in N1s
or alates—on the sex ratio. The binomial objective variable was the sex ratio; caste was treated as an
explanatory variable and colony identity was treated as a random factor.
Generalized linear models (GLMs) were used to investigate the influence of the alate sex ratio in the colony
on sexual differences in alate body weight. The body weight ratio (male/female) for individual alates was
treated as a response variable assuming a Gaussian distribution; the numerical sex ratio for alates was
treated as an explanatory variable. An exact binomial test was applied to compare the observed numerical
sex ratio for field-collected alates against the null hypothesis assuming that the numbers of males and females were equal. A two-tailed paired t-test was used to compare the biomass of male and female alates. A
GLMM was run to investigate the sexual difference in alate body weight. The fresh body weight of each
alate was treated as a response variable assuming a Gaussian distribution; their sex was treated as an
explanatory variable and the colony identity was treated as a random factor.
All statistical analyses were performed and graphs were generated using R v.3.3.3 software46; all data are
available in the Supplementary Materials. For GLMMs and GLMs, the likelihood ratio test was used to
determine the statistical significance of each explanatory variable. A significance value of p < 0.05 was
considered to indicate statistical significance.
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