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Extinction risk of a density-dependent population estimated from a time series of population size

机译:根据人口规模的时间序列估算的密度依赖性种群的灭绝风险

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Environmental threats, such as habitat size reduction or environmental pollution, may not cause immediate extinction of a population but shorten the expected time to extinction. We develop a method to estimate the mean time to extinction for a density-dependent population with environmental fluctuation. We first derive a formula for a stochastic differential equation model (canonical model) of a population with logistic growth with environmental and demographic stochasticities. We then study an approximate maximum likelihood (AML) estimate of three parameters (intrinsic growth rate r, carrying capacity K, and environmental stochasticity sigma(e)(2)) from a time series of population size. The AML estimate of r has a significant bias, but by adopting the Monte Carlo method, we can remove the bias very effectively (bias-corrected estimate). We can also determine the confidence interval of the parameter based on the Monte Carlo method. If the length of the time series is moderately long (with 40-50 data points), parameter estimation with the Monte Carlo sampling bias correction has a relatively small variance. However, if the time series is short (less than or equal to 10 data points), the estimate has a large variance and is not reliable. If we know the intrinsic growth rate r, however, the estimate of K and sigma(e)(2) and the mean extinction time T are reliable even if only a short time series is available. We illustrate the method using data for a freshwater fish, Japanese crucian carp (Carassius auratus subsp.) in Lake Biwa, in which the growth rate and environmental noise of crucian carp are estimated using fishery records. (C) 2000 Academic Press. [References: 52]
机译:栖息地面积缩小或环境污染等环境威胁可能不会导致人口立即灭绝,但会缩短灭绝时间。我们开发了一种方法来估计具有环境波动的密度依赖性种群灭绝的平均时间。我们首先导出具有逻辑增长且具有环境和人口统计学随机性的种群的随机微分方程模型(规范模型)的公式。然后,我们根据人口规模的时间序列研究三个参数(内部增长率r,承载力K和环境随机性sigma(e)(2))的近似最大似然(AML)估计。 r的AML估计具有明显的偏差,但是通过采用蒙特卡洛方法,我们可以非常有效地消除偏差(经过偏差校正的估计)。我们还可以基于蒙特卡洛方法确定参数的置信区间。如果时间序列的长度适中(使用40-50个数据点),则采用蒙特卡洛采样偏差校正的参数估计的方差相对较小。但是,如果时间序列较短(小于或等于10个数据点),则估计差异较大,因此不可靠。但是,如果我们知道内在增长率r,那么即使只有一个很短的时间序列,K和sigma(e)(2)的估计值以及平均灭绝时间T也是可靠的。我们使用琵琶湖淡水鱼Japanese鱼(Carassius auratus subsp。)的数据说明了该方法,其中使用渔业记录估算了carp鱼的生长速度和环境噪声。 (C)2000学术出版社。 [参考:52]

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