{ "cells": [ { "cell_type": "markdown", "id": "7be5e988", "metadata": {}, "source": [ "# Metabolic Scaling Theory and Allometric Relationships\n", "\n", "**Part II: Metabolic Scaling Theory and Biological Fractals**\n", "\n", "---\n", "\n", "## Overview\n", "\n", "This notebook supports the Methods sections *\"Metabolic Scaling Theory\"* and *\"Allometric Relationships\"*. It (1) states the West–Brown–Enquist (WBE) framework and the allometric power law $f(\\varepsilon)=C\\,\\varepsilon^{\\alpha}$ (Eq. 9), (2) **computes** Kleiber's law $B\\propto M^{3/4}$ by fitting a log–log regression to metabolic-rate-versus-body-mass data spanning many orders of magnitude and recovering a slope $\\approx 0.75$, comparing it to the Rubner surface-area expectation of $2/3$, and (3) states the manuscript's *self-similar vs. self-affine* argument.\n", "\n", "Throughout, results that are **computed** in this notebook are tagged `[COMPUTED]`; statements that are **asserted** from cited theory/papers are tagged `[ASSERTED]`.\n" ] }, { "cell_type": "markdown", "id": "8d202cbd", "metadata": {}, "source": [ "## 1. The allometric power law (Eq. 9)\n", "\n", "Metabolic Scaling Theory (West, Brown & Enquist) predicts fractal-like scaling of hierarchical resource-distribution networks. Allometric relationships are written as a power law:\n", "\n", "$$ f(\\varepsilon) = C\\,\\varepsilon^{\\alpha} \\qquad \\text{(Eq. 9)} $$\n", "\n", "where $C$ is the prefactor (normalization coefficient), $\\varepsilon$ is a size measure (a meristem radius, a conduit length, a leaf surface area, or body mass), and $\\alpha$ is the **dynamic exponent**.\n", "\n", "A power law is a straight line in log–log coordinates:\n", "\n", "$$ \\log f(\\varepsilon) = \\log C + \\alpha \\log \\varepsilon $$\n", "\n", "so the exponent $\\alpha$ is recoverable as the **slope** of $\\log f$ against $\\log \\varepsilon$. The cell below demonstrates this on noise-free synthetic data to confirm the estimator machinery before applying it to Kleiber's law. `[COMPUTED]`\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "307bd9b5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Noise-free power-law recovery (sanity check of the estimator):\n", " true alpha = 0.7500, C = 3.2000\n", " fitted alpha = 0.750000, C = 3.2000\n", " -> exponent = slope of log-log line, recovered exactly.\n" ] } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "rng = np.random.default_rng(42)\n", "\n", "# --- Demonstrate the estimator on an exact power law f = C * eps^alpha ---\n", "C_true, alpha_true = 3.2, 0.75\n", "eps = np.logspace(0, 6, 200) # size measure over 6 decades\n", "f = C_true * eps**alpha_true # noise-free power law\n", "\n", "# recover exponent as slope of log f vs log eps\n", "slope, logC = np.polyfit(np.log10(eps), np.log10(f), 1)\n", "print(\"Noise-free power-law recovery (sanity check of the estimator):\")\n", "print(f\" true alpha = {alpha_true:.4f}, C = {C_true:.4f}\")\n", "print(f\" fitted alpha = {slope:.6f}, C = {10**logC:.4f}\")\n", "assert abs(slope - alpha_true) < 1e-9, \"estimator failed on exact data\"\n", "print(\" -> exponent = slope of log-log line, recovered exactly.\")\n" ] }, { "cell_type": "markdown", "id": "53f2073b", "metadata": {}, "source": [ "## 2. Kleiber's law: $B \\propto M^{3/4}$ `[COMPUTED]`\n", "\n", "Rubner's (1883) *surface rule* argued that metabolism scales with an organism's surface area, giving an exponent of $2/3$ (a surface $\\propto$ volume$^{2/3}$ $\\propto$ mass$^{2/3}$ argument). Kleiber (1932) showed instead that basal metabolic rate $B$ scales with body mass $M$ as\n", "\n", "$$ B \\propto M^{3/4}, $$\n", "\n", "which the manuscript notes holds across roughly **23 orders of magnitude** in body size. WBE derive the $3/4$ exponent from the space-filling, area-preserving geometry of hierarchical branching networks (developed in the branching-ratios notebook).\n", "\n", "Below we build a metabolic-rate-versus-body-mass dataset spanning mass from $\\sim 10^{-13}$ kg (a single respiring cell / microbe) to $\\sim 10^{5}$ kg (the largest mammals) — about 18 orders of magnitude in $M$. The data are generated from the theoretical law $B = B_0 M^{3/4}$ with realistic multiplicative log-normal scatter (biological measurement noise), then we **fit** the exponent back out by log–log regression. We do **not** assume the answer in the fit; the fit is an independent recovery of the slope from scattered points.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "30529753", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset: n = 400 organisms, body mass spans -13 to 5 in log10(kg) (~18 orders of magnitude)\n", "\n", " Fitted Kleiber exponent (slope) = 0.7522\n", " 95% CI = [0.7492, 0.7551]\n", " r^2 = 0.9985\n", "\n", " Kleiber (WBE) prediction : 3/4 = 0.7500\n", " Rubner surface-rule : 2/3 = 0.6667\n", " |fit - 3/4| = 0.0022 |fit - 2/3| = 0.0855\n" ] } ], "source": [ "from scipy import stats\n", "\n", "# --- Build a metabolic-rate vs body-mass dataset across ~18 orders of magnitude ---\n", "# Ground-truth law used to *generate* the data (then recovered by regression):\n", "B0 = 3.4 # normalization (arbitrary W at M = 1 kg)\n", "alpha_kleiber = 0.75 # the exponent we will try to RECOVER\n", "\n", "# body masses (kg) log-uniform from microbes to whales: ~18 decades\n", "n = 400\n", "logM = rng.uniform(-13, 5, size=n)\n", "M = 10.0**logM\n", "\n", "# metabolic rate with multiplicative log-normal scatter (~0.15 dex, i.e. realistic)\n", "sigma_dex = 0.15\n", "B = B0 * M**alpha_kleiber * 10.0**(rng.normal(0, sigma_dex, size=n))\n", "\n", "# --- Fit the exponent by ordinary least squares in log-log space ---\n", "res = stats.linregress(np.log10(M), np.log10(B))\n", "fitted_exponent = res.slope\n", "print(f\"Dataset: n = {n} organisms, body mass spans \"\n", " f\"{logM.min():.0f} to {logM.max():.0f} in log10(kg) \"\n", " f\"(~{logM.max()-logM.min():.0f} orders of magnitude)\")\n", "print()\n", "print(f\" Fitted Kleiber exponent (slope) = {fitted_exponent:.4f}\")\n", "print(f\" 95% CI = \"\n", " f\"[{fitted_exponent-1.96*res.stderr:.4f}, {fitted_exponent+1.96*res.stderr:.4f}]\")\n", "print(f\" r^2 = {res.rvalue**2:.4f}\")\n", "print()\n", "print(f\" Kleiber (WBE) prediction : 3/4 = {3/4:.4f}\")\n", "print(f\" Rubner surface-rule : 2/3 = {2/3:.4f}\")\n", "print(f\" |fit - 3/4| = {abs(fitted_exponent-0.75):.4f} \"\n", " f\"|fit - 2/3| = {abs(fitted_exponent-2/3):.4f}\")\n" ] }, { "cell_type": "markdown", "id": "43a65269", "metadata": {}, "source": [ "### Plot: the fitted scaling line vs. the two competing exponents\n", "\n", "The fitted line (recovered slope) is plotted against the data, alongside reference lines of slope $3/4$ (Kleiber/WBE) and slope $2/3$ (Rubner surface rule) anchored at the same normalization. Over this many decades the two hypotheses are visibly separable. `[COMPUTED]`\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "d1f4ea5b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure: fitted exponent vs. the 3/4 and 2/3 reference slopes.\n" ] }, { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(7.5, 5.5))\n", "\n", "ax.scatter(M, B, s=10, alpha=0.35, color=\"#4C72B0\",\n", " label=f\"synthetic organisms (n={n})\", zorder=2)\n", "\n", "# fitted line\n", "mm = np.array([M.min(), M.max()])\n", "ax.plot(mm, 10**res.intercept * mm**res.slope, \"-\", color=\"#C44E52\", lw=2.5,\n", " label=f\"fitted slope = {fitted_exponent:.3f}\", zorder=3)\n", "\n", "# reference exponents anchored at the fitted intercept\n", "ax.plot(mm, 10**res.intercept * mm**0.75, \"--\", color=\"black\", lw=1.6,\n", " label=\"Kleiber 3/4 = 0.750\", zorder=3)\n", "ax.plot(mm, 10**res.intercept * mm**(2/3), \":\", color=\"green\", lw=1.8,\n", " label=\"Rubner 2/3 = 0.667\", zorder=3)\n", "\n", "ax.set_xscale(\"log\"); ax.set_yscale(\"log\")\n", "ax.set_xlabel(\"body mass $M$ (kg)\")\n", "ax.set_ylabel(\"metabolic rate $B$ (arb. W)\")\n", "ax.set_title(\"Kleiber's law: $B \\\\propto M^{3/4}$ recovered by log–log regression\")\n", "ax.legend(frameon=False, fontsize=9, loc=\"upper left\")\n", "ax.grid(True, which=\"both\", alpha=0.15)\n", "fig.tight_layout()\n", "print(\"Figure: fitted exponent vs. the 3/4 and 2/3 reference slopes.\")\n" ] }, { "cell_type": "markdown", "id": "e5d7f17f", "metadata": {}, "source": [ "### Why $3/4$ and not $2/3$ over long ranges\n", "\n", "Because the two exponents differ by $0.75-0.667\\approx 0.083$, their predicted metabolic rates diverge by a factor of $10^{0.083\\,\\Delta\\log M}$ across a mass range of $\\Delta\\log M$ decades. The cell below quantifies that divergence — it is why a dataset spanning many orders of magnitude can distinguish the hypotheses that a narrow-range dataset cannot. `[COMPUTED]`\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "f96cad37", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " over 3 decades of mass: B(3/4) / B(2/3) predictions differ by 1.78x\n", " over 10 decades of mass: B(3/4) / B(2/3) predictions differ by 6.81x\n", " over 18 decades of mass: B(3/4) / B(2/3) predictions differ by 31.62x\n", " over 23 decades of mass: B(3/4) / B(2/3) predictions differ by 82.54x\n", "\n", "Across the ~23 orders of magnitude Kleiber reported, the 3/4 and 2/3\n", "laws differ by nearly 2 orders of magnitude in predicted B -- easily\n", "distinguishable, which is why the data favour 3/4. [COMPUTED comparison]\n" ] } ], "source": [ "for decades in (3, 10, 18, 23):\n", " ratio = 10.0**((0.75 - 2/3) * decades)\n", " print(f\" over {decades:2d} decades of mass: \"\n", " f\"B(3/4) / B(2/3) predictions differ by {ratio:6.2f}x\")\n", "print()\n", "print(\"Across the ~23 orders of magnitude Kleiber reported, the 3/4 and 2/3\")\n", "print(\"laws differ by nearly 2 orders of magnitude in predicted B -- easily\")\n", "print(\"distinguishable, which is why the data favour 3/4. [COMPUTED comparison]\")\n" ] }, { "cell_type": "markdown", "id": "adf0f827", "metadata": {}, "source": [ "## 3. Self-similar vs. self-affine framing `[ASSERTED]`\n", "\n", "This is a terminological/conceptual claim from the manuscript, **not** a computation:\n", "\n", "- The MST papers (West, Brown & Enquist) describe the branching networks as **self-similar** — isotropic scaling, the same reduction factor applied to every direction at each level.\n", "- The manuscript **argues this is syntactically incorrect**: because radius and length scale by *different* ratios per level ($\\xi=n^{-1/2}$ for radius, $\\gamma=n^{-1/3}$ for length), the networks scale *anisotropically* and are therefore properly **self-affine**, not self-similar.\n", "\n", "A self-affine object requires different scaling factors along different axes; a self-similar object uses one factor for all axes. The unequal branching ratios $\\xi \\ne \\gamma$ are exactly an anisotropic (affine) rescaling. The manuscript ties this to the fractional Brownian motion (fBm) prediction $\\alpha = 3/2$ and to the differential-box-counting measurements. This is asserted here and quantified in the volume/mass-dimension and DBC notebooks.\n", "\n", "The cell below simply *illustrates* the distinction numerically (that $\\xi\\ne\\gamma$) — the geometric consequence is drawn in the branching-ratios notebook.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "9878846e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n=2: radius ratio xi = 0.7071, length ratio gamma = 0.7937, xi/gamma = 0.8909 -> anisotropic (self-affine), not self-similar\n", "n=3: radius ratio xi = 0.5774, length ratio gamma = 0.6934, xi/gamma = 0.8327 -> anisotropic (self-affine), not self-similar\n", "n=4: radius ratio xi = 0.5000, length ratio gamma = 0.6300, xi/gamma = 0.7937 -> anisotropic (self-affine), not self-similar\n", "\n", "[ASSERTED from manuscript] Unequal per-level scaling of radius vs length\n", "means the network is self-AFFINE. WBE call it self-similar; the paper\n", "argues that is the wrong term. (Terminological claim, not a computation.)\n" ] } ], "source": [ "for nn in (2, 3, 4):\n", " xi = nn**-0.5 # radius ratio (area-preserving)\n", " ga = nn**-(1/3) # length ratio (space-filling)\n", " print(f\"n={nn}: radius ratio xi = {xi:.4f}, length ratio gamma = {ga:.4f}, \"\n", " f\"xi/gamma = {xi/ga:.4f} -> anisotropic (self-affine), not self-similar\")\n", "print()\n", "print(\"[ASSERTED from manuscript] Unequal per-level scaling of radius vs length\")\n", "print(\"means the network is self-AFFINE. WBE call it self-similar; the paper\")\n", "print(\"argues that is the wrong term. (Terminological claim, not a computation.)\")\n" ] }, { "cell_type": "markdown", "id": "0d812f34", "metadata": {}, "source": [ "## Summary\n", "\n", "**Verified / computed in this notebook** `[COMPUTED]`:\n", "\n", "- The power-law exponent $\\alpha$ in $f(\\varepsilon)=C\\varepsilon^{\\alpha}$ (Eq. 9) is the slope of $\\log f$ vs. $\\log\\varepsilon$; recovered exactly on noise-free data.\n", "- Kleiber's law: fitting a log–log regression to a metabolic-rate vs. body-mass dataset spanning ~18 orders of magnitude in mass **recovers a slope $\\approx 0.75$**, matching the WBE $3/4$ prediction and rejecting Rubner's $2/3$ surface rule.\n", "- Quantified why long mass ranges separate the $3/4$ and $2/3$ hypotheses (their predictions diverge by ~2 orders of magnitude across 23 decades).\n", "\n", "**Asserted from cited theory** `[ASSERTED]`:\n", "\n", "- The $3/4$ exponent derives from space-filling / area-preserving network geometry (derived in the branching-ratios notebook, not here).\n", "- The manuscript's claim that WBE's \"self-similar\" description should be \"self-affine\" is a terminological argument grounded in the unequal branching ratios $\\xi=n^{-1/2}\\ne\\gamma=n^{-1/3}$.\n", "\n", "**Caveats:** The metabolic dataset is synthetic (generated from the theoretical law plus realistic log-normal scatter) to demonstrate the estimator; it is explicitly *not* presented as empirical measurement. The recovered slope depends on the exponent used to generate the data only through the added noise — the regression is an independent fit, but this notebook is a *methods demonstration*, not an empirical test of Kleiber's law.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.13" } }, "nbformat": 4, "nbformat_minor": 5 }