Department of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada.
Flatiron Institute, New York, NY, USA.
PeerJ. 2023 Feb 2;11:e14701. doi: 10.7717/peerj.14701. eCollection 2023.
Density-dependent regulation is ubiquitous in population dynamics, and its potential interaction with environmental stochasticity complicates the characterization of the random component of population dynamics. Yet, this issue has not received attention commensurate with its relevance for descriptive and predictive modeling of population dynamics. Here we use a Bayesian modeling approach to investigate the contribution of density regulation to population variability in stochastic environments.
We analytically derive a formula linking the stationary variance of population abundance/density under Gompertz regulation in a stochastic environment with constant variance to the environmental variance and the strength of density feedback, to investigate whether and how density regulation affects the stationary variance. We examine through simulations whether the relationship between stationary variance and density regulation inferred analytically under the Gompertz model carries over to the Ricker model, widely used in population dynamics modeling.
The analytical decomposition of the stationary variance under stochastic Gompertz dynamics implies higher variability for strongly regulated populations. Simulation results demonstrate that the pattern of increasing population variability with increasing density feedback found under the Gompertz model holds for the Ricker model as well, and is expected to be a general phenomenon with stochastic population models. We also analytically established and empirically validated that the square of the autoregressive parameter of the Gompertz model in AR(1) form represents the proportion of stationary variance due to density dependence.
Our results suggest that neither environmental stochasticity nor density regulation can alone explain the patterns of population variability in stochastic environments, as these two components of temporal variation interact, with a tendency for density regulation to amplify the magnitude of environmentally induced population fluctuations. This finding has far-reaching implications for population viability. It implies that intense intra-specific resource competition increases the risk of environment-driven population collapse at high density, making opportune harvesting a sensible practice for improving the resistance of managed populations such as fish stocks to environmental perturbations. The separation of density-dependent and density-independent processes will help improve population dynamics modeling, while providing a basis for evaluating the relative importance of these two categories of processes that remains a topic of long-standing controversy among ecologists.
密度依赖调节在种群动态中普遍存在,其与环境随机性的潜在相互作用使得种群动态的随机成分的特征变得复杂。然而,这个问题并没有得到与其对种群动态描述和预测建模的相关性相称的关注。在这里,我们使用贝叶斯建模方法来研究密度调节对随机环境中种群变异性的贡献。
我们通过分析推导出一个公式,将随机环境中 Gompertz 调节下的种群丰度/密度的稳定方差与环境方差和密度反馈强度联系起来,以研究密度调节是否以及如何影响稳定方差。我们通过模拟检验了在 Gompertz 模型下分析得出的稳定方差与密度调节之间的关系是否适用于广泛应用于种群动态建模的 Ricker 模型。
随机 Gompertz 动力学下稳定方差的分析分解意味着受强调节的种群具有更高的变异性。模拟结果表明,在 Gompertz 模型下发现的随着密度反馈增加而增加的种群变异性模式也适用于 Ricker 模型,并且预计这将是具有随机种群模型的一般现象。我们还通过分析建立并通过实证验证了 Gompertz 模型在 AR(1)形式中的自回归参数的平方代表了由于密度依赖而导致的稳定方差的比例。
我们的结果表明,在随机环境中,环境随机性和密度调节都不能单独解释种群变异性的模式,因为这两个时间变化的组成部分相互作用,密度调节有放大环境引起的种群波动幅度的趋势。这一发现对种群生存力具有深远的影响。它意味着强烈的种内资源竞争增加了高密度下环境驱动的种群崩溃的风险,使得适时收获成为改善鱼类等管理种群对环境扰动的抵抗力的明智做法。密度依赖和密度独立过程的分离将有助于改进种群动态建模,同时为评估这两个类别的过程的相对重要性提供基础,这仍然是生态学家长期争论的话题。