Giorgio Gilestro
db9452c9d4
New analytic result for the evolution-of-sex paper: how far can two lineages diverge before recombination (model merging) stops working? Frames merge failure as biological reproductive isolation via Bateson-Dobzhansky-Muller incompatibilities. src/knowledge/speciation.py, kind: speciation, on the E7-E11 genotype machinery (pure seeded NumPy, bitwise-reproducible; no external simulator whose separate RNG would break that). - BDM construction (E12.yaml): ancestor + two lineages substituting disjoint loci (each parent adaptive, incompatibility-free), a fraction rho of cross-lineage pairs are BDMIs. Sweeping divergence d reproduces the predicted compatible -> outbreeding depression -> hybrid inviability curve; the isolation cliff moves to lower d as epistasis density rises (iso at d=20: 0.00/0.03/0.50 for rho 0.1/0.25/0.5); incompatibilities snowball ~ (d/2)^2 (Orr-Turelli). - NK variant (E12_nk.yaml): parents = hill-climbed local optima; the epistasis wedge — recombination gain flips 0 -> -0.13 and OD rate 0 -> 0.90 as ruggedness K rises. At matched divergence, mergeability is governed by epistasis, the axis no divergence-only ML merge predictor captures. plot_E12.py (3-panel), +7 tests (138 green), README with honest positioning (concedes the empirical phenomenon to Pari 2024 / Zhou 2026 + permutation artefacts to Git Re-Basin; claims the predictive theory + the epistasis wedge). Wired into make layer1. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
79 lines
3.6 KiB
Python
79 lines
3.6 KiB
Python
"""E12 figure — model speciation: the merge-compatibility limit of the sexual society.
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Three panels, reading only the committed bundles. (A) BDM: mean recombinant (hybrid) fitness vs
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parental divergence, one line per epistasis density rho, against the rising parent fitness — the
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compatible -> outbreeding-depression -> hybrid-inviability trajectory, peaking then crashing sooner the
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denser the epistasis. (B) BDM: the reproductive-isolation rate (fraction of hybrids below the ancestor)
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vs divergence — the isolation cliff, moving to lower divergence as epistasis density rises. (C) NK: the
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epistasis wedge — as landscape ruggedness K grows, recombining two adapted local-optimum parents flips
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from a gain to outbreeding depression.
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Usage: python figures/plot_E12.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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sys.path.insert(0, str(Path(__file__).parent))
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from _figlib import load_bundle, savefig # noqa: E402
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def _agg(df, keys, value):
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g = df.groupby(keys)[value].agg(["mean", "std", "count"]).reset_index()
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g["se"] = g["std"] / np.sqrt(g["count"].clip(lower=1))
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return g
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def main() -> None:
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bdm, _ = load_bundle("results/E12")
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nk, _ = load_bundle("results/E12_nk")
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rhos = sorted(bdm["rho"].unique())
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colors = plt.cm.viridis(np.linspace(0.15, 0.85, len(rhos)))
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fig, axes = plt.subplots(1, 3, figsize=(16, 5))
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# Panel A: hybrid fitness vs divergence, per epistasis density, + parent fitness.
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ax = axes[0]
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par = _agg(bdm, "divergence", "parent_fitness")
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ax.plot(par["divergence"], par["mean"], "k--", lw=1.6, label="parent fitness")
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for rho, c in zip(rhos, colors):
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g = _agg(bdm[bdm["rho"] == rho], "divergence", "offspring_fitness")
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ax.plot(g["divergence"], g["mean"], "-o", color=c, lw=2, label=f"hybrid, ρ={rho}")
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ax.fill_between(g["divergence"], g["mean"] - g["se"], g["mean"] + g["se"], color=c, alpha=0.15)
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ax.axhline(0, color="#999", lw=0.8, ls=":")
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ax.set(xlabel="parental divergence (substitutions $d$)", ylabel="fitness",
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title="Hybrid fitness collapses as lineages diverge\n(compatible → outbreeding depression → inviability)")
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ax.legend(frameon=False, fontsize=8)
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# Panel B: reproductive-isolation rate vs divergence, per epistasis density.
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ax = axes[1]
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for rho, c in zip(rhos, colors):
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g = _agg(bdm[bdm["rho"] == rho], "divergence", "isolation")
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ax.plot(g["divergence"], g["mean"], "-o", color=c, lw=2, label=f"ρ={rho}")
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ax.set(xlabel="parental divergence (substitutions $d$)", ylabel="reproductive isolation\n(P hybrid inviable)",
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ylim=(-0.02, 1.02),
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title="The isolation cliff moves to lower divergence\nas epistasis density rises")
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ax.legend(frameon=False, fontsize=9, title="epistasis density")
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# Panel C: NK epistasis wedge — recombination gain vs ruggedness K.
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ax = axes[2]
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g = _agg(nk, "K", "offspring_minus_parent")
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ax.axhline(0, color="#999", lw=0.8, ls=":")
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ax.plot(g["K"], g["mean"], "-o", color="#d62728", lw=2)
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ax.fill_between(g["K"], g["mean"] - g["se"], g["mean"] + g["se"], color="#d62728", alpha=0.15)
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ax.set(xlabel="landscape ruggedness $K$ (epistasis)", ylabel="recombination gain\n(hybrid − worse parent)",
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title="Epistasis wedge: recombining adapted parents\nflips from gain to loss as ruggedness grows")
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fig.suptitle("E12 — model speciation: when two diverged models are too incompatible to merge",
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y=1.02, fontsize=13)
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fig.tight_layout()
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savefig(fig, "results/E12", "E12")
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if __name__ == "__main__":
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main()
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