Code for manuscript: “Scarce Data, Noisy Inferences, and Overfitting: The Hidden Flaws in Ecological Dynamics Modeling”

Python codes

Codes that implement model reduction for the generalized lotka volterra model

Installation

• Create and environment

cd python
python -m venv mbam_venv

Activate it

source mbam_venv/bin/activate

Upgrade pip, just in case

pip install --upgrade pip

Install required packages

pip install -r requirements.txt

Run one example

python fourpop_mbam_reduction.py

R codes

We simulate the deterministic generalized Lotka-Volterra model using the R library deSolve. For inference, the state-of-art-bayesian engine, stan.

All the auxiliary functions are packed in file R/lotka_volterra_stan_functions.R. The file R/batch_434.R illustrates how to choose a seed (434 in this case), noise levels and population sizes to reproduce the figures in the article. This script calls R/lotka_volterra_rk4_stan.R that makes the bayesian inference.

Required libraries: deSolve, rstan, ggplot2, psych, deSolve, rstan, ggplot2, psych, bayesplot.