Estimating competition coefficients in tree communities: a hierarchical Bayesian approach to neighborhood analysis
Estimating competition coefficients in tree communities: a hierarchical Bayesian approach to neighborhood analysis
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DOI：
10.1002/ecs2.1273
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发表时间：
2016-03-01
期刊：
影响因子：
2.7
通讯作者：
Mori, Akira S.
中科院分区：
文献类型：
--
作者：
Tatsumi, Shinichi;Owari, Toshiaki;Mori, Akira S.
量化竞争的强度并了解其如何转化为社区层面的后果是植物生态学的主要目标之一。基于邻域竞争指数的邻域分析已被广泛用于估计树木群落中物种特定的竞争系数。然而，对于样本量较小的稀有物种，无法使用传统的逐个物种方法来估计这些估计值。在这里，我们开发了一种用于邻域分析的新建模框架，其中假设竞争系数具有分层参数结构。使用由 38 个物种组成的实际树木普查数据，我们证明层次模型使我们能够估计群落内所有物种（包括稀有物种）的竞争系数。作为模型选择的结果，在我们假设竞争强度由生态位差异或竞争能力差异决定的两种情况下，都选择了基于逐个物种方法的模型。我们的结果表明，分层方法可以作为逐种方法的有用替代方法来估计树木群落的竞争系数。
Quantifying the strength of competition and understanding how it translates into consequences at the community level are among the key aims of plant ecology. Neighborhood analysis based on the neighborhood competition index has been widely used to estimate species-specific competition coefficients in tree communities. These estimates, however, could not be estimated for rare species with small sample sizes using the conventional species-by-species approach. Here, we develop a new modeling framework for neighborhood analysis in which the competition coefficient is assumed to have a hierarchical parameter structure. Using actual tree census data consisting of 38 species, we demonstrate that the hierarchical model enables us to estimate competition coefficients for all species, including rare ones, within a community. The hierarchical models were selected over the models based on the species-by-species approach as a result of model selection, in either cases where we assumed the competitive strength is determined by niche difference or competitive ability difference. Our results suggest that the hierarchical approaches can serve as a useful alternative to species-by-species approach for estimating competition coefficients in tree communities.