Uplift associated demographic history
We used multidimensional folded site frequency spectrum (mSFS) to infer
post-uplift recolonization dynamics using the continuous-time coalescent
simulator fastsimcoal v.2.6 (Excoffier et al. 2013) on those
species that showed a distinct cluster in the uplifted region. Based on
the clustering results we defined three-population models for D.
poha and L. segnis (i.e. North, Uplift, and South lineages) and
four-population models for D. antarctica and O. neglectus(i.e. Far North, Nearby North, Uplift, and South lineages; see
Supplementary Methods and Fig. S7-S8 for details).
Given the absence of pre-quake genetic data for the studied species, we
considered a constant population size for all non-uplift lineages and
characterized all models by a bottleneck event at the time of the
earthquake. For each demographic model we performed 50-100 independent
runs of 500,000 genealogical simulations and 40 cycles of Brent
algorithm to estimate the expected SFS and composite likelihood for a
given set of demographic parameters. We chose the best fit
recolonization scenario by calculating the Akaike Information Criterion
(AIC) and Akaike’s weights.
To infer the degree of synchrony in size change among the uplift
lineages of the host kelps and their epifauna we performed a
hierarchical co-demographic modeling using the R packageMulti-DICE (Xue & Hickerson 2017). We first projected down the
SFS of each ‘uplift’ lineage to 38 haploid individuals. We simulated
100,000 scenarios with species expanding from a relative low population
size by a factor 100 - 10,000. Effective population size before
expansion was constrained between 100 and 1000. Species were considered
co-expanding in scenarios where their expansions started within the same
50 years (τ buffer prior = 50 years). Additionally, a more relaxed 200
years co-expansion threshold (τ buffer prior = 200 years) was applied in
a different set of simulations. In order to infer the most likely
scenario, the proportion of co-expanding species, ζ, and the timing of
the expansion events, τ, we generated the aggregate site frequency
spectrum (aSFS; Xue and Hickerson 2015) for each simulation. We used R
to sample the best 5% of the simulations of the aSFS (5000 simulations)
using the rejection method implemented in the ABC package
(Csilléry et al. 2012). We visualised the fit of the observed
aSFS within the retained simulations using a PCA produced with the R
package ade4 (Dray & Dufour 2007).