Migration After the Flood: Kangaroo, Armadillo and Koala
Description
How did kangaroos, koalas, and giant armadillos get from the Mountains of Ararat to Australia and the Americas in only a few centuries? This paper builds a post-Flood migration model using Ice Age land bridges, rafting, and rapid range expansion. From Indian kangaroo rock art to South American glyptodont kill sites, the evidence points to swift, testable dispersal within a biblical timeframe. If you enjoy biogeography, population dynamics, or testing models against real-world data, you’ll want to read this.
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Study Type & Aim Conceptual, data-informed *dispersal simulation* under a young-earth (YEC) timeline to test whether koalas, kangaroos, and the armadillo/glyptodont “kind” could reach present ranges **within \~1,000–1,500 years** after disembarkation near the Mountains of Ararat. Inputs (Data & Assumptions) * **Geography/Sea level:** Post-Flood Ice Age lowers sea level, exposing **Sunda–Sahul** connections and Beringia. * Dispersal mechanics:** Generational diffusion (range expansion per generation), rafting by storm-driven **vegetation mats**, island stepping-stones, limited swimming, and possible **human-mediated transport**. * Rates:** Conservative front speeds **5–15 km/year** (benchmarked to modern invasive spread; e.g., red fox in Australia), with sensitivity down to **5 km/year**. * Time anchors:** Arrival windows **600–1,000 AF** (years after the Flood) for Australia/New World. * Constraints:** Predation pressure off-route; habitat suitability; marsupial dietary flexibility (koala ancestor not obligately eucalyptus-specialized). Sources & Evidence *Literature:** Creationist Ice Age/rafting papers; conventional work on natural rafting and late Pleistocene rock art; biogeography of Sahul/Sunda; glyptodont archaeology; paleo-ocean currents. *Artifact cues:** Indian rock art with kangaroo-like figures; Amazonian megafauna rock art; butchery evidence on glyptodont remains. * Comparanda:** Documented long-distance rafting events in conventional biogeography. Model Structure 1. Network graph of corridors and gaps** (Ararat → S Asia → Sunda → Sahul; Ararat → N/W Africa/Europe → Atlantic → S America). 2. Annual timestep diffusion:** Population front advances by chosen km/year; reproduction enables persistence and further spread. 3. Gap handling:** If sea gap ≤ stepping-stone threshold, proceed; else apply **raft event** with probability p over a window (e.g., 200 years) using prevailing currents. 4. Speciation/trait sorting:* Post-arrival diversification modeled qualitatively (founder effects, selection, isolation). Parameters & Sensitivity *Base spread:** 10 km/year (tested 5–15). *Raft success per attempt:* low (e.g., 5–10%), repeated stochastically → cumulative success ≥ one crossing in window. *Sea-level window: centuries; close when post-Ice-Age seas rise. *Predation filter:* reduces persistence off-route (e.g., India for kangaroos). Outputs tracked: **time-to-arrival, distance covered, probability of ≥1 successful ocean crossing. Software & Workflow *Data sheets:* route segments (km), habitat flags, currents, gap widths, parameter ranges. Python/R. ## Reproducibility Steps 1. Encode routes and gaps; load base parameters. 2. Run diffusion per year; log cumulative km and arrival times. 3. For ocean gaps, simulate raft attempts each storm season with current-aligned drift time; record first success. 4. Sweep sensitivity over spread (5–15 km/yr), raft p (1–10%), and window lengths; export summaries and plots.
Institutions
- University of Southern California