用机器学习预测食物网中物种灭绝顺序，关键在捕食效率与系统稳定性关系。
Exploring the Dynamics of Lotka-Volterra Systems: Efficiency, Extinction Order, and Predictive Machine Learning
- 通过简化生态参数和聚类分析，构建可预测模型。
- 随机森林与神经网络准确预测灭绝数量，无需动态模拟。
- 捕食效率是决定灭绝顺序的主导因素，适用于生态建模研究者。
长期以来，生态学研究聚焦于食物网中物种间复杂动力学交互。基于广泛研究的合成食物网——级联模型的连接性特征，本文探讨了洛特卡-沃尔泰拉生态系统的动态行为。研究发现，数学生态学中的经典假设——营养效率，会导致系统无法持续。通过聚类分析，我们证明对出生率、死亡率、自调节及相互作用强度之和的简单不等式，能揭示食物网的持久性或稳定性。基于这些简化的总和指标，我们构建了随机森林模型与神经网络模型，二者均能在不进行动态模拟的情况下，准确预测物种灭绝数量。最后，我们指出决定物种灭绝顺序的关键变量为捕食效率。
原文摘要 · Abstract (English)
For years, a main focus of ecological research has been to better understand the complex dynamical interactions between species which comprise food webs. Using the connectance properties of a widely explored synthetic food web called the cascade model, we explore the behavior of dynamics on Lotka-Volterra ecological systems. We show how trophic efficiency, a staple assumption in mathematical ecology, produces systems which are not persistent. With clustering analysis we show how straightforward inequalities of the summed values of the birth, death, self-regulation and interaction strengths provide insight into which food webs are more enduring or stable. Through these simplified summed values, we develop a random forest model and a neural network model, both of which are able to predict the number of extinctions that would occur without the need to simulate the dynamics. To conclude, we highlight the variable that plays the dominant role in determining the order in which species go extinct.
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