Bayesian models for spatially explicit interactions between neighbouring plants
Bayesian models for spatially explicit interactions between neighbouring plants
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DOI：
10.1111/2041-210x.13998
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发表时间：
2022-10
影响因子：
6.6
通讯作者：
Cristina Barber;A. Zaiats;Cara Applestein;Lisa M. Rosenthal;T. T. Caughlin-T.
中科院分区：
文献类型：
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作者：
Cristina Barber;A. Zaiats;Cara Applestein;Lisa M. Rosenthal;T. T. Caughlin-T.
相邻植物之间的相互作用驱动陆地生态系统中的种群和群落动态。理解这些相互作用对于基础生态学和应用生态学都至关重要。空间方法来模拟邻居的相互作用是必要的，因为相互作用的强度取决于相邻植物之间的距离。最近的贝叶斯进步，包括汉密尔顿蒙特卡罗算法，提供了灵活性和速度，以适应模型的空间显式的邻居相互作用。我们提出了一个指导参数化这些模型在斯坦编程语言，并演示了贝叶斯计算如何帮助生态推断植物-植物的相互作用。模拟植物邻居的相互作用提出了生态建模的几个挑战。首先，距离衰减的非线性模型可能容易出现可识别性问题，导致模型收敛性不足。其次，植物-植物相互作用矩阵的成对数据结构通常导致需要高计算能力的大型矩阵。第三，植物-植物相互作用数据中的层次结构是普遍存在的，包括田间小区、物种和个体内的重复测量。分层项（例如“随机效应”）可能导致由系数之间的相关性引起的模型收敛问题。我们探索这些挑战的建模解决方案与代表植物人口统计率的空间数据的例子：生长，存活和招聘。我们表明，粗糙矩阵减少了成对矩阵固有的计算挑战，从而提高了跨数据类型的效率。我们还演示了模型收敛的度量，包括发散转换和有效样本大小，可以帮助诊断复杂的非线性结构导致的问题。最后，我们探讨了何时对层次项使用不同的模型结构，包括中心和非中心参数化。我们提供了可复制的例子斯坦写，使生态学家适应和解决广泛的邻里互动模型。空间上明确的模型越来越成为许多生态问题的核心。我们的工作说明了新的贝叶斯工具如何提供灵活性，速度和诊断能力，用于将植物邻居模型拟合到大型复杂的数据集。我们演示的方法适用于任何数据集，包括一个响应变量和观测位置，从森林资源清查图遥感图像。邻居相互作用统计模型的进一步发展可能会提高我们对植物种群和群落生态学的理解。
Interactions between neighbouring plants drive population and community dynamics in terrestrial ecosystems. Understanding these interactions is critical for both fundamental and applied ecology. Spatial approaches to model neighbour interactions are necessary, as interaction strength depends on the distance between neighbouring plants. Recent Bayesian advancements, including the Hamiltonian Monte Carlo algorithm, offer the flexibility and speed to fit models of spatially explicit neighbour interactions. We present a guide for parameterizing these models in the Stan programming language and demonstrate how Bayesian computation can assist ecological inference on plant–plant interactions. Modelling plant neighbour interactions presents several challenges for ecological modelling. First, nonlinear models for distance decay can be prone to identifiability problems, resulting in lack of model convergence. Second, the pairwise data structure of plant–plant interaction matrices often leads to large matrices that demand high computational power. Third, hierarchical structure in plant–plant interaction data is ubiquitous, including repeated measurements within field plots, species and individuals. Hierarchical terms (e.g. ‘random effects’) can result in model convergence problems caused by correlations between coefficients. We explore modelling solutions for these challenges with examples representing spatial data on plant demographic rates: growth, survival and recruitment. We show that ragged matrices reduce computational challenges inherent to pairwise matrices, resulting in higher efficiency across data types. We also demonstrate how metrics for model convergence, including divergent transitions and effective sample size, can help diagnose problems that result from complex nonlinear structures. Finally, we explore when to use different model structures for hierarchical terms, including centred and non‐centred parameterizations. We provide reproducible examples written in Stan to enable ecologists to fit and troubleshoot a broad range of neighbourhood interaction models. Spatially explicit models are increasingly central to many ecological questions. Our work illustrates how novel Bayesian tools can provide flexibility, speed and diagnostic capacity for fitting plant neighbour models to large, complex datasets. The methods we demonstrate are applicable to any dataset that includes a response variable and locations of observations, from forest inventory plots to remotely sensed imagery. Further developments in statistical models for neighbour interactions are likely to improve our understanding of plant population and community ecology across systems and scales.