Detection of a recent bottleneck
We used a model-selection approach to detect whether the black-faced
spoonbill had experienced a recent population bottleneck. Two
demographic models were constructed for the recent demography of the
black-faced spoonbill. The null model assumed that the effective
population size (N e) of the black-faced spoonbill
had been constant (constant size model). The alternative model assumed
that the black-faced spoonbill had experienced a recent bottleneck event
(recent bottleneck model) and therefore contained the following five
parameters: N e before the bottleneck,N e for the bottleneck, N eafter bottleneck, the date of the initiation of the bottleneck and the
date of the end of the bottleneck. We used a method implemented inFastsimcoal2 (Excoffier et al., 2013) to conduct model selection
and parameter inference for the selected model. First, we generated the
observed folded (minor allele) site frequency spectrum from the 215,722
unlinked autosomal SNPs of the black-faced spoonbill with the scripteasySFS (https://github.com/isaacovercast/easySFS#easysfs). For
the constant population size model, the prior for theN e was set to 100-1,000 haploid individuals.
Assuming a generation time of ten years and that the bottleneck event
occurred between the 1950 and the 1980s(La Touche,
1931),(Austin, 1948), the priors for the dates of
bottleneck initiation (T bot) and termination
(T endbot) were set to 2-5 and 2-10 generations
ago, respectively. Bracketing the long-term N e(1/2 to 2×) since the last glacial maximum estimated from theSMC ++, we set the prior of N e for the
pre-bottleneck (N anc) to 7,500-30,000 haploid
individuals. Considering black-faced spoonbill’s population size of 288-
4000 since 1988, the priors for bottleneck (N bot)
and post-bottleneck N e(N cur) were set to 2-100 and 100-1,000 haploid
individuals respectively. Using uniform random samples from the priors
of the two demographic models, we generated 100,000 folded SFSs for each
model from the coalescent simulations and ran 40 optimization cycles to
estimate each parameter and its composite likelihood in each simulation.
The set of parameters with the highest likelihood was used for model
selection. The simulation procedure described above was repeated 1,000
times, and a total of 5,000 sets of parameters were generated for each
model. The parameters of the model from the set of simulations with the
highest estimated likelihood was chosen as the best estimate of
parameters for a given model. The Akaike information criterion (AIC) was
used to compare the best simulation of the two demographic models to the
observed folded site frequency spectrum (SFS). We used Δ AIC and
AIC weight (w ) to evaluate which model better fitted the observed
folded SFS. Then we ran the parameter estimation procedure under the
best model to obtain 100 bootstrapped folded SFSs. Based on these
bootstrapped folded SFS, we ran the parameter estimate procedure for
each bootstrapped folded SFS again to compute the confidence interval of
each parameter of the selected model.