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.

翻译：变点检测越来越多地应用于生态时间序列，但在监测典型短序列长度（10-50个观测值）下的统计功效却鲜有评估。我们针对基于BIC的二分分割法，在108种序列长度、效应量和变点数量的组合下进行了基于仿真的功效分析。结果表明：对于单一变点，仅在样本量n ≥ 30且效应量ES ≥ 2.0时，BIC才能达到≥80%的功效；检测2-3个变点则需要n ≥ 50且ES ≥ 5.0。BIC具有保守性，低估变点数的频率高于高估。AR(1)自相关（φ=0.6）使BIC-Binseg功效降低40%，而采用标准惩罚项的PELT在中等自相关下仍保持85-91%的功效。与早期预警信号（EWS）方差趋势检验的比较揭示了交叉现象：当ES < 1.5时，EWS优于变点检测，但EWS的检出率对效应量不敏感（约73%），表明其检测到的是噪声而非真实信号。跨系统经验验证（珊瑚礁：Moorea，n=18；荒漠啮齿动物：Portal项目，n=49）确认：当效应量落在预测的"可靠"区间时，检测成功。我们提供了实用的功效热图作为查找工具，并建议生态学家：对于自相关数据优先选用PELT而非Binseg-BIC；在应用变点分析前计算预期效应量；将结果与置换检验相结合。