Vucetich John A, Waite Thomas A, Qvarnemark Linda, Ibargüen Siri
School of Forestry, Michigan Technological University, Houghton, MI 49931, U.S.A.
Department of Evolution, Ecology, and Organismal Biology, and Department of Anthropology, Ohio State University, Columbus, OH 43210, U.S.A.
Conserv Biol. 2000 Dec 18;14(6):1704-1714. doi: 10.1111/j.1523-1739.2000.99359.x.
Population models generally predict increased extinction risk (ER) with increased population variability ( PV ), yet some empirical tests have provided contradictory findings. We resolve this conflict by attributing negative measured relationships to a statistical artifact that arises because PV tends to be underestimated for populations with short persistence. Such populations do not go extinct quickly as a consequence of low intrinsic variability; instead, the measured variability is low because they go extinct so quickly. Consequently, any underlying positive relationship between PV and ER tends to be obscured. We conducted a series of analyses to evaluate this claim. Simulations showed that negative measured relationships are to be expected, despite an underlying positive relationship. Simulations also identified properties of data, minimizing this bias and thereby permitting meaningful analysis. Experimental data on laboratory populations of a bruchid beetle (Callosobruchus maculatus) supported the simulation results. Likewise, with an appropriate statistical approach (Cox regression on untransformed data), reanalysis of a controversial data set on British island bird populations revealed a significant positive association between PV and ER (p = 0.03). Finally, a similar analysis of time series for naturally regulated animal populations revealed a positive association between PV and quasiextinction risk (p < 0.01). Without exception, our simulation results, experimental findings, reanalysis of published data, and analysis of quasiextinction risk all contradict previous reports of negative or equivocal relationships. Valid analysis of meaningful data provides strong evidence that increased population variability leads to increased extinction risk.
种群模型通常预测，随着种群变异性（PV）增加，灭绝风险（ER）也会上升，但一些实证检验却给出了相互矛盾的结果。我们通过将实测的负相关关系归因于一种统计假象来解决这一冲突，这种假象的出现是因为对于存续期短的种群，PV往往被低估。这类种群并非因内在变异性低而迅速灭绝；相反，实测的变异性低是因为它们灭绝得太快。因此，PV与ER之间任何潜在的正相关关系往往都会被掩盖。我们进行了一系列分析来评估这一说法。模拟结果表明，尽管存在潜在的正相关关系，但实测的负相关关系是可以预期的。模拟还确定了数据的属性，将这种偏差最小化，从而能够进行有意义的分析。对一种豆象甲虫（四纹豆象）实验室种群的实验数据支持了模拟结果。同样，采用适当的统计方法（对未转换数据进行Cox回归），对一组关于英国岛屿鸟类种群的有争议数据集重新分析后发现，PV与ER之间存在显著的正相关关系（p = 0.03）。最后，对自然调节动物种群时间序列的类似分析表明，PV与准灭绝风险之间存在正相关关系（p < 0.01）。毫无例外。我们的模拟结果、实验发现、对已发表数据的重新分析以及对准灭绝风险的分析，均与之前关于负相关或不明确关系的报告相矛盾。对有意义数据的有效分析提供了强有力的证据，表明种群变异性增加会导致灭绝风险上升。
