Dunnett post hoc test results of robustness.

As species extinction accelerates globally and biodiversity declines dramatically, identifying keystone species becomes an effective way to conserve biodiversity. In traditional approaches, it is considered that the extinction of species with high centrality poses the greatest threat to secondary extinction. However, the indirect effect, which is equally important as the local and direct effects, is not included. Here, we propose an optimized disintegration strategy model for quantitative food webs and introduced tabu search, a metaheuristic optimization algorithm, to identify keystone species. Topological simulations are used to record secondary extinctions during species removal and secondary extinction areas, as well as to evaluate food web robustness. The effectiveness of the proposed strategy is also validated by comparing it with traditional methods. Results of our experiments demonstrate that our strategy can optimize the effect of food web disintegration and identify the species whose extinction is most destructive to the food web through global search. The algorithm provides an innovative and efficient way for further development of keystone species identification in the ecosystem.

随着全球物种灭绝速度加快、生物多样性急剧衰退，识别关键种（keystone species）已成为保护生物多样性的有效手段。传统研究认为，节点中心性较高的物种灭绝会对次生灭绝（secondary extinction）造成最严重的威胁，但却忽略了与局域直接效应同等重要的间接效应。本研究针对定量食物网（food web）提出了一种优化的解体策略模型，并引入元启发式优化算法——禁忌搜索（tabu search）以识别关键种。研究采用拓扑模拟（topological simulations）记录物种移除过程中的次生灭绝事件与次生灭绝区域，并评估食物网的鲁棒性。通过与传统方法对比，验证了所提策略的有效性。实验结果表明，本策略可优化食物网解体效果，并通过全局搜索定位那些灭绝后对食物网破坏性最强的物种。该算法为生态系统关键种识别研究的进一步发展提供了创新性且高效的路径。