Botany Department, University of Wyoming, Laramie, Wyoming, USA.
School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia.
Ecol Lett. 2022 May;25(5):1263-1276. doi: 10.1111/ele.13977. Epub 2022 Feb 2.
Modelling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species' intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighbouring species from the diverse community that had non-generic interactions with our focal species. This sparse modelling approach facilitates exploration of species interactions in diverse communities while maintaining a manageable number of parameters.
在多样化的群落中进行物种相互作用建模传统上需要大量的物种相互作用系数，特别是当考虑参数的环境依赖性时。我们通过在非线性物种丰度模型上施加稀疏先验来实现贝叶斯变量选择，以确定应该保留哪些物种相互作用，以及哪些可以表示为平均异源相互作用项，从而减少模型参数的数量。我们使用模拟群落来评估模型性能，计算不同输入样本大小下的样本外预测准确性和参数恢复。我们将我们的方法应用于一个多样化的经验群落，使我们能够从竞争相互作用的间接影响中分离出环境梯度对物种固有增长率的直接作用。我们还从多样化的群落中确定了几个邻近物种，它们与我们的焦点物种有非通用的相互作用。这种稀疏建模方法有助于探索多样化群落中的物种相互作用，同时保持可管理的参数数量。
