Lytle David A, Tonkin Jonathan D
Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA.
School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand.
NPJ Biodivers. 2023 Dec 4;2(1):26. doi: 10.1038/s44185-023-00031-5.
Ecological communities are shaped by biotic interactions as well as environmental forces, and both must be incorporated to obtain models capable of forecasting realistic community dynamics. Many community models first specify pairwise biotic interactions and then secondarily examine how extrinsic factors such as abiotic conditions affect species abundances. A disadvantage of this approach is that the species interactions themselves are often environment and context specific, making parameterization difficult. We propose an alternative approach, matrix community models (MCMs), which are sets of matrix population models linked by an assumption of aggregate density dependence. MCMs incorporate detailed species autecology but are neutral with respect to pairwise species interactions, instead allowing interactions to be revealed within the model structure. These model-revealed species interactions, including competitive exclusion, facilitation, and interference competition, shape the distribution and abundance of species within communities and generate empirically testable predictions about species interactions. We develop a framework for building MCMs using vital rates in a stochastic, multispecies framework. Single-species matrix population models are connected via an assumption of aggregate density dependence, pairwise species interactions are estimated with sensitivity analysis, and community trajectories are analyzed under different environmental regimes using standard statistical tools and network analysis. MCMs have the advantage that pairwise species interactions need not be specified a priori, and that mechanistic demographic-environment linkages permit forecasting of community dynamics under novel, non-stationary environmental regimes. A challenge is that species' autecological vital rates, such as fecundity, growth and survivorship, must be measured under a diverse range of environmental conditions to parameterize the models. We illustrate the approach with examples and discuss prospects for future theoretical and empirical developments.
生态群落由生物相互作用以及环境力量塑造而成，要获得能够预测现实群落动态的模型，就必须将这两者都纳入考虑。许多群落模型首先指定成对的生物相互作用，然后再研究诸如非生物条件等外在因素如何影响物种丰度。这种方法的一个缺点是，物种相互作用本身往往取决于环境和背景，这使得参数化变得困难。我们提出了一种替代方法，即矩阵群落模型（MCMs），它是通过总体密度依赖假设联系起来的一组矩阵种群模型。MCMs纳入了详细的物种个体生态学，但对于成对的物种相互作用是中性的，而是允许在模型结构中揭示相互作用。这些模型揭示的物种相互作用，包括竞争排斥、促进作用和干扰竞争，塑造了群落内物种的分布和丰度，并产生了关于物种相互作用的可实证检验的预测。我们在一个随机的多物种框架中，利用生命率开发了一个构建MCMs的框架。单物种矩阵种群模型通过总体密度依赖假设连接起来，通过敏感性分析估计成对的物种相互作用，并使用标准统计工具和网络分析在不同环境条件下分析群落轨迹。MCMs的优点是不需要事先指定成对的物种相互作用，而且机械的种群统计学 - 环境联系允许预测新的、非平稳环境条件下的群落动态。一个挑战是，必须在各种环境条件下测量物种的个体生态生命率，如繁殖力、生长和存活率，以便对模型进行参数化。我们用例子说明了这种方法，并讨论了未来理论和实证发展的前景。
