bioRxivJClub
Proportional variability relative to lower abundances: An interpretable and efficient index of deep lows in population time series
bioRxiv · · Preprint · Open access
Schlemmer, M.
Abstract ↗AI summary
The abstract is read at the publisher; the summary is JClub's.
PV_lo, a bounded 0–1 index of proportional variability relative to lower abundances, stays sensitive to deep crashes and level contrasts in simulated population time series.
- Why it matters: Existing variability metrics like CV and SDL can be biased under non-Gaussian dynamics and zero counts, and PV_hi responds weakly to isolated crashes and decline depth.
- What they did: The authors define PV_lo using all pairwise comparisons, handle zero counts without transformation, and use a sorting-based method to cut computation from O(n^2) to O(n log n).
- The result: Across example series and simulations (Gaussian, Cauchy, rare-crash, bimodal, gradual-decline), PV_lo separates crashes better than PV_hi and CV when ordinary variability is unknown, offering an efficient screening alternative to SDL.
The findingWhy it mattersWhat they didThe result
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