Beckage Brian, Joseph Lawrence, Belisle Patrick, Wolfson David B, Platt William J
Department of Plant Biology, University of Vermont, Burlington, VT 05452, USA.
Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec, Canada H3A 1A1.
New Phytol. 2007;174(2):456-467. doi: 10.1111/j.1469-8137.2007.01991.x.
Ecological and biological processes can change from one state to another once a threshold has been crossed in space or time. Threshold responses to incremental changes in underlying variables can characterize diverse processes from climate change to the desertification of arid lands from overgrazing. Simultaneously estimating the location of thresholds and associated ecological parameters can be difficult: ecological data are often 'noisy', which can make the identification of the locations of ecological thresholds challenging. We illustrate this problem using two ecological examples and apply a class of statistical models well-suited to addressing this problem. We first consider the case of estimating allometric relationships between tree diameter and height when the trees have distinctly different growth modes across life-history stages. We next estimate the effects of canopy gaps and dense understory vegetation on tree recruitment in transects that transverse both canopy and gap conditions. The Bayesian change-point models that we present estimate both threshold locations and the slope or level of ecological quantities of interest, while incorporating uncertainty in the change-point location into these estimates. This class of models is suitable for problems with multiple thresholds and can account for spatial or temporal autocorrelation.
一旦在空间或时间上越过某个阈值，生态和生物过程就可能从一种状态转变为另一种状态。对基础变量增量变化的阈值响应可以表征从气候变化到干旱地区因过度放牧导致荒漠化等各种过程。同时估计阈值的位置和相关的生态参数可能很困难：生态数据往往存在“噪声”，这会使识别生态阈值的位置具有挑战性。我们用两个生态学例子来说明这个问题，并应用一类非常适合解决这个问题的统计模型。我们首先考虑当树木在整个生命史阶段具有明显不同的生长模式时，估计树木直径与高度之间的异速生长关系的情况。接下来，我们在横穿树冠和林窗条件的样带中，估计树冠间隙和茂密的林下植被对树木更新的影响。我们提出的贝叶斯变化点模型既估计阈值位置，又估计感兴趣的生态量的斜率或水平，同时将变化点位置的不确定性纳入这些估计中。这类模型适用于具有多个阈值的问题，并且可以考虑空间或时间自相关。
