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.