基于树木萎缩先验的生态群落数据模型选择
Model selection for ecological community data using tree shrinkage priors
摘要：
研究人员和管理人员对生态群落进行建模，以推断生物和非生物变量，这些变量塑造了物种的范围，栖息地使用和共生，反过来，这些变量又用于支持管理决策和测试生态理论。最近，物种分布模型被开发并应用于生态群落的数据。生态群落数据的模型开发和选择是困难的，因为需要高水平的复杂性，并通过包括许多参数来实现，这可能会降低预测准确性，并具有解释和沟通的挑战性。与其他统计模型一样，多物种分布模型也可能被过度参数化。正则化是一种通过缩小或消除模型参数来优化预测精度的技术。对于贝叶斯模型，先验分布会自动正则化参数。我们提出了一个贝叶斯多物种分布模型的树收缩先验，执行正则化，并减少与预测变量相关的回归系数的数量。利用这一先验知识，通过对较少数量的工会而不是较多数量的物种的唯一回归系数的估计，减少了多物种分布模型中的回归系数的数量.我们证明了我们的树收缩之前使用的例子存在-不存在的数据为6种水生植被和相对丰度数据为15种鱼类。我们的研究结果表明，树木收缩先验可以提高多物种分布模型的预测精度，使研究人员能够推断的数量和物种组成的功能团的生态群落数据。
英文摘要：
Researchers and managers model ecological communities to infer the biotic and abiotic variables that shape species' ranges, habitat use, and co-occurrence which, in turn, are used to support management decisions and test ecological theories. Recently, species distribution models were developed for and applied to data from ecological communities. Model development and selection for ecological community data is difficult because a high level of complexity is desired and achieved by including numerous parameters, which can degrade predictive accuracy and be challenging to interpret and communicate. Like other statistical models, multi-species distribution models can be overparameterized. Regularization is a technique that optimizes predictive accuracy by shrinking or eliminating model parameters. For Bayesian models, the prior distribution automatically regularizes parameters. We propose a tree shrinkage prior for Bayesian multi-species distributions models that performs regularization and reduces the number of regression coefficients associated with predictor variables. Using this prior, the number of regression coefficients in multi-species distributions models is reduced by estimation of unique regression coefficients for a smaller number of guilds rather than a larger number of species. We demonstrated our tree shrinkage prior using examples of presence-absence data for six species of aquatic vegetation and relative abundance data for 15 species of fish. Our results show that the tree shrinkage prior can increase the predictive accuracy of multi-species distribution models and enable researchers to infer the number and species composition of guilds from ecological community data.
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