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首页> 外文期刊>Review in aquaculture >Dynamic self-thinning model for sessile animal populations with multilayered distribution
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Dynamic self-thinning model for sessile animal populations with multilayered distribution

机译：具有多层分布的固着性动植物种群的动态自稀疏模型

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摘要

The main drawback of the traditional self-thinning model is how time is handled. Self-thinning (ST) has been formally recognized as a dynamic process, while the current ST models have not included the temporal effect. This restricts the analysis to the average competitive behaviour of the population and produces a biased estimation of the self-thinning parameters. In this study, we extend the dynamic ST model introduced by Roderick and Barnes (2004) to the analysis of multilayered sessile animal populations. For this purpose, we incorporate the number of layers and the density per layer into the dynamical approach. The performance of the dynamic model was checked and compared with the classical ST model through the analysis of mussel populations grown at different density treatments. Unlike the traditional model, the dynamical approach detected the effect of culture density on the competitive behaviour of individuals and allowed to analyse the temporal evolution of intraspecific competition by estimating the ST exponent trajectory. Moreover, this approach provided an ecological interpretation of any possible value of the ST exponent. Thus, our results support the use of the dynamic model in the analysis of self-thinning in sessile animal multilayered populations. The estimation of the ST exponent trajectory reflects the dynamic nature of the ST process, providing a more realistic description of population dynamics than the traditional model.
机译：传统的自我瘦身模型的主要缺点是如何处理时间。自瘦（ST）已被正式认为是一个动态过程，而当前的ST模型尚未包括时间效应。这将分析限制在人群的平均竞争行为上，并对自我瘦化参数产生了偏差估计。在这项研究中，我们将Roderick和Barnes（2004）引入的动态ST模型扩展到多层无柄动物种群的分析中。为此，我们将层数和每层密度纳入动态方法。通过分析在不同密度处理下生长的贻贝种群，检查了动态模型的性能并与经典的ST模型进行了比较。与传统模型不同，动力学方法检测文化密度对个体竞争行为的影响，并通过估计ST指数轨迹来分析​​种内竞争的时间演变。而且，这种方法对ST指数的任何可能值提供了生态学解释。因此，我们的结果支持动态模型在无柄动物多层种群自我稀疏分析中的应用。 ST指数轨迹的估计反映了ST过程的动态性质，与传统模型相比，它提供了对人口动态的更现实的描述。

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