E12: model speciation — the merge-compatibility limit of the sexual society

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>

tests/test_speciation.py

@@ -0,0 +1,62 @@
+"""Tests for E12 model speciation — the BDM construction, the snowball, and the isolation falsifiers."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from knowledge.speciation import _bdm_point, _nk_point, run_speciation
+
+
+def test_bdm_parents_carry_no_incompatibility():
+    # The BDM construction's defining property: each derived allele is benign on its OWN parent's
+    # background (disjoint substitutions), so a parent's fitness is purely additive even at rho=1.
+    rng = np.random.default_rng(0)
+    r = _bdm_point(L=20, d=10, rho=1.0, s=5.0, beta=1.0, rate=0.5, n_off=200, rng=rng)
+    assert r["parent_fitness"] == pytest.approx(1.0 * (10 // 2))    # beta * (d/2), no penalty
+
+
+def test_bdm_snowball_is_superlinear_in_divergence():
+    # Orr-Turelli: # incompatibilities ~ (d/2)^2, so doubling divergence ~quadruples them.
+    def mean_ndmi(d, reps=40):
+        return np.mean([_bdm_point(24, d, 0.5, 1.0, 1.0, 0.5, 50,
+                                   np.random.default_rng(i))["n_dmi"] for i in range(reps)])
+    ratio = mean_ndmi(12) / max(mean_ndmi(6), 1e-9)
+    assert ratio > 3.0                                             # ~4x (quadratic), well above linear (2x)
+
+
+def test_bdm_no_epistasis_means_no_isolation():
+    rng = np.random.default_rng(1)
+    r = _bdm_point(L=20, d=20, rho=0.0, s=1.0, beta=1.0, rate=0.5, n_off=300, rng=rng)
+    assert r["n_dmi"] == 0 and r["isolation"] == 0.0               # no BDMIs -> hybrids always viable
+
+
+def test_bdm_isolation_rises_with_epistasis_density():
+    # At fixed high divergence, denser epistasis (rho) -> more reproductive isolation.
+    def iso(rho):
+        return np.mean([_bdm_point(20, 20, rho, 1.0, 1.0, 0.5, 300,
+                                   np.random.default_rng(i))["isolation"] for i in range(8)])
+    assert iso(0.5) > iso(0.1)
+
+
+def test_nk_additive_landscape_has_no_isolation():
+    # K=0 is a single-peak additive landscape: parents hill-climb to the same optimum (divergence 0),
+    # and recombination cannot produce outbreeding depression.
+    r = _nk_point(L=12, K=0, landscape_seed=3, rate=0.5, n_pairs=20, n_off=50,
+                  rng=np.random.default_rng(0))
+    assert r["divergence"] == pytest.approx(0.0) and r["outbreeding_depression"] == pytest.approx(0.0)
+
+
+def test_nk_ruggedness_increases_outbreeding_depression():
+    def od(K):
+        return _nk_point(12, K, 3, 0.5, 30, 60, np.random.default_rng(0))["outbreeding_depression"]
+    assert od(8) > od(0)                                           # rugged landscapes punish recombination
+
+
+def test_run_speciation_is_deterministic():
+    cfg = {"seed": 7, "n_replicates": 3,
+           "speciation": {"landscape": "bdm", "L": 12, "rho": [0.3], "divergences": [0, 4, 8],
+                          "s": 1.0, "beta": 1.0, "recomb_rate": 0.5, "n_offspring": 80}}
+    a = run_speciation(cfg, 7)
+    b = run_speciation(cfg, 7)
+    assert a.equals(b)                                             # pure function of the resolved config + seed