Measuring probabilistic reaction norms for age and size at maturation
Measuring probabilistic reaction norms for age and size at maturation
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DOI：
10.1111/j.0014-3820.2002.tb01378.x
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发表时间：
2002-04-01
期刊：
影响因子：
3.3
通讯作者：
Godo, OR
中科院分区：
文献类型：
--
作者：
Heino, M;Dieckmann, U;Godo, OR
我们提出了一种新的成熟时年龄和体型反应规范的概率概念，适用于以离散时间间隔进行观察的情况。这种方法还可用于估计变态或其他个体发生转变时年龄和体型的反应规范。这种估计对于理解可变环境中的表型可塑性和生活史变化、评估表型可塑性存在下的遗传变化以及校准规模和年龄结构的种群模型至关重要。我们表明，以前解决这个问题的方法基于将体型与成熟年龄进行回归，与概率反应规范相比，给出的结果存在系统偏差。偏差可能很大，并可能导致定性上不正确的结论；这是由于未能考虑成熟过程的概率性质而造成的。相反，我们解释了为什么成熟反应规范的稳健估计应该基于逻辑回归或其他将成熟概率视为因变量的统计模型。我们通过两个例子展示了我们方法的实用性。首先，对已知反应范数生成的数据的分析凸显了先前方法的一些关键局限性。其次，对东北北极鳕鱼（Gadus morhua）的应用说明了如何使用我们的方法为现有的现实世界数据提供新的线索。
We present a new probabilistic concept of reaction norms for age and size at maturation that is applicable when observations are carried out at discrete time intervals. This approach can also be used to estimate reaction norms for age and size at metamorphosis or at other ontogenetic transitions. Such estimations are critical for understanding phenotypic plasticity and life-history changes in variable environments, assessing genetic changes in the presence of phenotypic plasticity, and calibrating size- and age-structured population models. We show that previous approaches to this problem, based on regressing size against age at maturation, give results that are systematically biased when compared to the probabilistic reaction norms. The bias can be substantial and is likely to lead to qualitatively incorrect conclusions; it is caused by failing to account for the probabilistic nature of the maturation process. We explain why, instead, robust estimations of maturation reaction norms should be based on logistic regression or on other statistical models that treat the probability of maturing as a dependent variable. We demonstrate the utility of our approach with two examples. First, the analysis of data generated for a known reaction norm highlights some crucial limitations of previous approaches. Second, application to the northeast arctic cod (Gadus morhua) illustrates how our approach can be used to shed new light an existing real-world data.