十三年来对当地高加索鸢尾种群的监测:繁殖不确定性下的随机增长率

D. O. Logofet, L. L. Golubyatnikov, E. S. Kazantseva, N. G. Ulanova, M. I. Khomutovsky, D. K. Tekeev
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摘要

摘要-Eritrichium caucasicum 是高加索地区特有的高山短寿命多年生物种。13 年来(2009-2021 年),每年都在高加索西北部高寒地带的永久性地块上观察当地种群的阶段结构,积累了 "来自未知亲本的已识别个体 "类型的数据。后一种情况预先决定了离散结构种群动态矩阵模型术语中所谓的繁殖不确定性,并意味着无法以独特的方式校准生成植株群和最终开花生成植株群固有的年招募率。因此,该模型给出的不是渐近增长率的年度值,而是与数据相对应的每年变化的某些数值范围。这给基于渐近增长率的可行性预测带来了技术上的困难和不确定性。一个众所周知的替代方法是估算随机增长率 λS,但文献中只提出了计算 λS 所涉及的人为随机性模式。我们现实中的随机性模式与栖息地的天气和微气候条件变化有关。它是根据相当长(60 年)的天气指标时间序列重建的。利用这一现实模型对 λS 进行蒙特卡罗计算,我们获得了更可靠、更准确的随机增长率估计值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Thirteen Years of Monitoring a Local Population of Eritrichium caucasicum: Stochastic Growth Rate under Reproductive Uncertainty

Abstract

Eritrichium caucasicum is an alpine short-lived perennial species endemic to the Caucasus. The stage structure of a local population has been observed on permanent plots in the alpine belt of the Northwestern Caucasus annually for 13 years (2009–2021), accumulating data of the “identified individuals from unknown parents” type. The latter circumstance has predetermined what is called reproductive uncertainty in the terminology of matrix models for discrete-structured population dynamics and means that the annual recruitment rates inherent in the groups of generative plants and final-flowering generative plants cannot be calibrated in a unique way. As a result, instead of the annual values of the asymptotic growth rate, the model gives only certain ranges of their values that vary from year to year, corresponding to the data. This introduces both technical difficulties and uncertainty in the viability forecast based on the asymptotic growth rates. A well-known alternative approach is to estimate the stochastic growth rate λS, but only artificial modes of randomness involved in the calculation of λS have been proposed in the literature. Our realistic model of randomness is related to variations in weather and microclimatic conditions of the habitat. It is reconstructed from a fairly long (60 years) time series of the weather indicator. Using this realistic model in Monte Carlo calculations of λS, we have obtained a more reliable and accurate estimate of the stochastic growth rate.

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