How Short Is Too Short? Power Analysis for BIC-Based Changepoint Detection in Ecological Monitorin
Ang A. Li

TL;DR
This study evaluates the statistical power of BIC-based changepoint detection methods in ecological time series, highlighting limitations at short series lengths and recommending more robust alternatives like PELT for autocorrelated data.
Contribution
The paper provides a comprehensive simulation-based power analysis for BIC-based changepoint detection, offering practical guidelines and validation for ecological monitoring applications.
Findings
BIC detects a single changepoint reliably only at n ≥ 30 with effect size ≥ 2.0.
Detecting 2-3 changepoints requires n ≥ 50 and ES ≥ 5.0.
Autocorrelation reduces BIC-Binseg power, while PELT maintains high power under autocorrelation.
Abstract
Changepoint detection is increasingly applied to ecological time series, yet statistical power at the short series lengths typical of monitoring (10-50 observations) is rarely assessed. We present a simulation-based power analysis for BIC-based Binary Segmentation across 108 combinations of series length, effect size, and number of changepoints. BIC achieves 80% power for a single changepoint only at with effect size ; detecting 2-3 changepoints requires and ES . BIC is conservative, underestimating changepoints more often than overestimating. AR(1) autocorrelation () reduces BIC-Binseg power by 40%, but PELT with a standard penalty maintains 85-91% power even under moderate autocorrelation. Comparison with early warning signal (EWS) variance-trend tests reveals a crossover: at ES , EWS outperforms changepoint…
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Taxonomy
TopicsEcosystem dynamics and resilience · Data Analysis with R · Climate variability and models
