Nguyen Phuong L, Pomati Francesco, Rohr Rudolf P
Department of Biology, University of Fribourg, Fribourg CH-1700, Switzerland.
Department of Aquatic Ecology, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf CH-8600, Switzerland.
Proc Natl Acad Sci U S A. 2025 Jul 15;122(28):e2417905122. doi: 10.1073/pnas.2417905122. Epub 2025 Jul 10.
Inferring coexistence metrics, such as niche and fitness differences, in changing environments is key for understanding the mechanism behind species coexistence and predicting its likelihood. However, it first requires estimating the per capita interactions between organisms and their intrinsic growth rates-parameters that are typically measured by isolating organisms from their natural context. Here, we first use weighted multivariate regression on the per capita growth rates of populations to estimate these key ecological parameters directly from time-series data of species-rich communities. Second, we infer niche differences and species resistance, which are two important metrics for understanding species coexistence. Our approach allows these metrics to vary over time and under different environmental conditions. We validate our approach using synthetic data and apply it to both experimental and observational data as a proof of concept. Experimental results show an expected allocative trade-off between grazing resistance and rapid growth in algae. Moreover, coexistence likelihood decreases, and coexistence balance is disturbed under stressful environmental conditions. Observational data suggests variations in intrinsic growth rates and per capita interactions among autotrophic guilds with respect to seasonal patterns. In addition, interactions between cyanobacteria with green algae and chrysophytes might indicate a potential cause for bloom development. Our approach offers a powerful toolbox to gain insight into the mechanisms underlying ecological dynamics, species coexistence, and community structures under varying environments. Such an understanding will help us address important ecological and evolutionary questions, such as explaining biodiversity patterns and solving the problem of cyanobacteria bloom.
推断不断变化的环境中的共存指标，如生态位和适合度差异，对于理解物种共存背后的机制以及预测其可能性至关重要。然而，这首先需要估计生物体之间的人均相互作用及其内在增长率——这些参数通常是通过将生物体与其自然环境隔离开来进行测量的。在这里，我们首先对种群的人均增长率进行加权多元回归，以直接从物种丰富的群落的时间序列数据中估计这些关键的生态参数。其次，我们推断生态位差异和物种抗性，这是理解物种共存的两个重要指标。我们的方法允许这些指标随时间和不同环境条件而变化。我们使用合成数据验证了我们的方法，并将其应用于实验数据和观测数据作为概念验证。实验结果表明，藻类在抗牧食性和快速生长之间存在预期的分配权衡。此外，在压力环境条件下，共存可能性降低，共存平衡受到干扰。观测数据表明，自养类群的内在增长率和人均相互作用随季节模式而变化。此外，蓝藻与绿藻和金藻之间的相互作用可能表明了水华形成的潜在原因。我们的方法提供了一个强大的工具箱，以深入了解不同环境下生态动态、物种共存和群落结构背后的机制。这样的理解将帮助我们解决重要的生态和进化问题，如解释生物多样性模式和解决蓝藻水华问题。
