Unifying ecosystem responses to disturbance into a single statistical framework
Unifying ecosystem responses to disturbance into a single statistical framework
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DOI：
10.1111/oik.07752
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发表时间：
2020-12
期刊：
影响因子：
3.4
通讯作者：
N. Lemoine
中科院分区：
文献类型：
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作者：
N. Lemoine
自然生态系统目前正经历着前所未有的人为干扰。鉴于更频繁的干扰的潜在后果，我们必须准确地量化生态系统对严重干扰的反应。具体而言，生态学家和管理人员需要估计的阻力和恢复的干扰是免费的观测误差，不偏于时间随机性和标准化的干扰幅度之间的许多不同的生态系统相对于正常的年际变化。在这里，我提出了一个统计框架，估计所有四个组成部分的生态系统响应干扰（阻力，恢复，弹性和返回时间），同时解决上述所有问题。将自回归时间序列与具有脉冲响应函数（IRF）的外生预测因子（ARX）模型耦合，使研究人员能够在统计上使所有生态系统受到类似水平的干扰，估计滞后效应，并获得对干扰的抵抗力和恢复的标准化估计，而这些估计不受原始数据中固有的观测误差和随机过程的影响。
Natural ecosystems are currently experiencing unprecedented rates of anthropogenic disturbance. Given the potential ramifications of more frequent disturbances, it is imperative that we accurately quantify ecosystem responses to severe disturbance. Specifically, ecologists and managers need estimates of resistance and recovery from disturbance that are free of observation error, not biased by temporal stochasticity and that standardize disturbance magnitude among many disparate ecosystems relative to normal interannual variability. Here, I propose a statistical framework that estimates all four components of ecosystem responses to disturbance (resistance, recovery, elasticity and return time), while resolving all of the issues described above. Coupling autoregressive time series with exogenous predictors (ARX) models with impulse response functions (IRFs) allows researchers to statistically subject all ecosystems to similar levels of disturbance, estimate lag effects and obtain standardized estimates of resistance to and recovery from disturbance that are free from observation error and stochastic processes inherent in raw data.