Effects of sample size on estimates of population growth rates calculated with matrix models.
Effects of sample size on estimates of population growth rates calculated with matrix models.
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DOI：
10.1371/journal.pone.0003080
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发表时间：
2008-08-28
期刊：
影响因子：
3.7
通讯作者：
Bolker BM
中科院分区：
文献类型：
--
作者：
Fiske IJ;Bruna EM;Bolker BM
矩阵模型广泛用于研究种群动态和人口统计学。一个重要但被忽视的问题是，抽样个体的数量如何影响用矩阵模型计算的种群增长率（λ）的估计。即使对生命率的无偏估计也不能确保λ-詹森不等式的无偏估计，这意味着即使对生命率的估计是准确的，小样本量也会由于增加的抽样方差而导致对λ的有偏估计。我们调查了样本变异性和样本努力在大小类之间的分布是否会导致λ估计值的偏差。使用植物种群的长期实地研究数据，我们通过绘制生命率和计算从3842株植物的总种群中提取的越来越大的种群的λ来模拟抽样方差的影响。然后，我们将这些λ的估计值与基于整个人群的估计值进行比较，并计算得出的偏倚。最后，我们对文献进行了回顾，以确定用于研究植物种群的参数化矩阵模型时通常使用的样本量。当生存率较低（生存率= 0.5）时，我们在小样本量下发现了显著的偏倚，而采用更现实的倒J形人口结构的采样加剧了这种偏倚。 然而，我们的模拟也表明，随着样本量的增加或生存率的增加，这些偏差迅速变得可以忽略不计。对于人口统计学研究中使用的许多样本量，矩阵模型可能对生命率抽样方差所产生的偏差具有鲁棒性。然而，这一结论可能取决于人口的结构或抽样工作的分布方式是未经探索的。我们建议更密集的抽样人群时，个人的生存率低，更大的采样阶段具有高弹性。
Matrix models are widely used to study the dynamics and demography of populations. An important but overlooked issue is how the number of individuals sampled influences estimates of the population growth rate (λ) calculated with matrix models. Even unbiased estimates of vital rates do not ensure unbiased estimates of λ–Jensen's Inequality implies that even when the estimates of the vital rates are accurate, small sample sizes lead to biased estimates of λ due to increased sampling variance. We investigated if sampling variability and the distribution of sampling effort among size classes lead to biases in estimates of λ. Using data from a long-term field study of plant demography, we simulated the effects of sampling variance by drawing vital rates and calculating λ for increasingly larger populations drawn from a total population of 3842 plants. We then compared these estimates of λ with those based on the entire population and calculated the resulting bias. Finally, we conducted a review of the literature to determine the sample sizes typically used when parameterizing matrix models used to study plant demography. We found significant bias at small sample sizes when survival was low (survival = 0.5), and that sampling with a more-realistic inverse J-shaped population structure exacerbated this bias. However our simulations also demonstrate that these biases rapidly become negligible with increasing sample sizes or as survival increases. For many of the sample sizes used in demographic studies, matrix models are probably robust to the biases resulting from sampling variance of vital rates. However, this conclusion may depend on the structure of populations or the distribution of sampling effort in ways that are unexplored. We suggest more intensive sampling of populations when individual survival is low and greater sampling of stages with high elasticities.
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