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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2603.21154 |
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| _version_ | 1866911535222226944 |
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| author | Li, Ang A. |
| author_facet | Li, Ang A. |
| contents | 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 $\geq$80% power for a single changepoint only at $n \geq 30$ with effect size $\geq 2.0$; detecting 2-3 changepoints requires $n \geq 50$ and ES $\geq 5.0$. BIC is conservative, underestimating changepoints more often than overestimating. AR(1) autocorrelation ($ϕ= 0.6$) 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 $< 1.5$, EWS outperforms changepoint detection, but EWS rates are invariant to effect size ($\sim$73%), suggesting noise detection rather than genuine signals. Cross-system empirical validation on coral reef (Moorea, $n = 18$) and desert rodent (Portal Project, $n = 49$) time series confirms that detection succeeds when effect sizes fall in the predicted "reliable" zone. We provide power heatmaps as practical lookup tools and recommend that ecologists prefer PELT over Binseg-BIC for autocorrelated data, compute expected effect sizes before applying changepoint analysis, and pair results with permutation tests. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_21154 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How Short Is Too Short? Power Analysis for BIC-Based Changepoint Detection in Ecological Monitorin Li, Ang A. Computational Engineering, Finance, and Science 62M10, 62P10, 92D40 G.3; J.3 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 $\geq$80% power for a single changepoint only at $n \geq 30$ with effect size $\geq 2.0$; detecting 2-3 changepoints requires $n \geq 50$ and ES $\geq 5.0$. BIC is conservative, underestimating changepoints more often than overestimating. AR(1) autocorrelation ($ϕ= 0.6$) 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 $< 1.5$, EWS outperforms changepoint detection, but EWS rates are invariant to effect size ($\sim$73%), suggesting noise detection rather than genuine signals. Cross-system empirical validation on coral reef (Moorea, $n = 18$) and desert rodent (Portal Project, $n = 49$) time series confirms that detection succeeds when effect sizes fall in the predicted "reliable" zone. We provide power heatmaps as practical lookup tools and recommend that ecologists prefer PELT over Binseg-BIC for autocorrelated data, compute expected effect sizes before applying changepoint analysis, and pair results with permutation tests. |
| title | How Short Is Too Short? Power Analysis for BIC-Based Changepoint Detection in Ecological Monitorin |
| topic | Computational Engineering, Finance, and Science 62M10, 62P10, 92D40 G.3; J.3 |
| url | https://arxiv.org/abs/2603.21154 |