Department of Biology and Ecology, Faculty of Science, University of Kragujevac, Serbia.
Department of Biology and Ecology, Faculty of Science, University of Kragujevac, Serbia.
Sci Total Environ. 2024 Nov 15;951:175723. doi: 10.1016/j.scitotenv.2024.175723. Epub 2024 Aug 22.
Combining single-species ecological modeling with advanced machine learning to investigate the long-term population dynamics of the rheophilic fish spirlin offers a powerful approach to understanding environmental changes and climate shifts in aquatic ecosystems. A new ESHIPPOClim model was developed by integrating climate change assessment into the ESHIPPO model. The model identifies spirlin as a potential early indicator of environmental changes, highlighting the interactive effects of climate change and anthropogenic stressors on fish populations and freshwater ecosystems. The ESHIPPOClim model reveals that 28.57 % of the spirlin's data indicates high resilience and ecological responsiveness, with 34.92 % showing medium-high adaptability, suggesting its substantial ability to withstand environmental stressors. With 36.51 % of the data in medium level and no data in the low category, spirlin may serve as a sentinel species, providing early warnings of environmental stressors before they severely impact other species or ecosystems. The results of uniform manifold approximation and projection (UMAP) and a decision tree show that pollution has the highest impact on the population dynamics of spirlin, followed by annual water temperature, overexploitation, and invasive species. Despite the obtained key drivers, higher abundance, dominance, and frequency values were detected in habitats with higher HIPPO stressors and climate change effects. Integrating state-of-the-art machine learning models has enhanced the predictive power of the ESHIPPOClim model, achieving approximately 90 % accuracy in identifying spirlin as an early indicator of climate change and anthropogenic stressors. The ESHIPPOClim model offers a holistic approach with broad practical applications using a simplified three-point scale, adaptable to various fish species, communities, and regions. The ecological modeling supported with advanced machine learning could serve as a foundation for rapid and cost-effective management of aquatic ecosystems, revealing the adaptability potential of fish species, which is crucial in rapidly changing environments.
将单一物种生态模型与先进的机器学习相结合，研究洄游性鱼类旋口鱼的长期种群动态，为了解水生生态系统中的环境变化和气候转变提供了一种强有力的方法。通过将气候变化评估纳入 ESHIPPO 模型，开发了一个新的 ESHIPPOClim 模型。该模型将旋口鱼确定为环境变化的潜在早期指标，突出了气候变化和人为压力因素对鱼类种群和淡水生态系统的相互影响。ESHIPPOClim 模型表明，旋口鱼数据的 28.57%显示出高弹性和生态响应能力，34.92%显示出中高度适应性，这表明其有很大的能力来承受环境压力。数据中有 36.51%处于中等水平，没有低水平的数据，因此旋口鱼可以作为一种指示物种，在其他物种或生态系统受到严重影响之前，对环境压力源发出早期预警。统一流形逼近和投影（UMAP）和决策树的结果表明，污染对旋口鱼种群动态的影响最大，其次是年水温、过度捕捞和入侵物种。尽管获得了关键驱动因素，但在 HIPPO 压力和气候变化影响较高的栖息地中，发现了更高的丰度、优势度和频率值。结合最先进的机器学习模型，提高了 ESHIPPOClim 模型的预测能力，实现了大约 90%的准确性，能够识别旋口鱼作为气候变化和人为压力源的早期指标。ESHIPPOClim 模型采用简化的三点式尺度，具有广泛的实际应用，结合了最先进的机器学习，提供了一种整体方法，适用于各种鱼类物种、群落和地区。先进机器学习支持的生态建模可以作为快速、经济高效地管理水生生态系统的基础，揭示鱼类物种的适应潜力，这在快速变化的环境中至关重要。
