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回到未来:重新整合生物学以了解过去的生态进化变化如何预测未来的结果。

Back to the Future: Reintegrating Biology to Understand How Past Eco-evolutionary Change Can Predict Future Outcomes.

机构信息

Department of Biomedical Sciences, Grand Valley State University, 1 Campus Dr, Allendale, MI 49401, USA.

Department of Urban Design and Planning, University of Washington, 3950 University Way NE, Seattle, WA 98105, USA.

出版信息

Integr Comp Biol. 2022 Feb 5;61(6):2218-2232. doi: 10.1093/icb/icab068.

Abstract

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: (1) utilizing knowledge of biological systems to better inform eco-evolutionary models, (2) generating models with more accurate predictions, and (3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere.

摘要

在过去的几十年里,生物学家在理解塑造生命的基本过程方面取得了显著的进展。但是,尽管现在已经拥有了前所未有的知识水平,但我们对生态进化机制在生命各个尺度上的复杂相互作用的理解仍然存在很大的差距。地球环境的快速变化迫切需要我们了解生态进化动态的潜在影响,而这可以通过改进现有的生态进化模型和促进生物学各子领域之间的融合来实现。我们提出了一种新的、数据驱动的方法,利用我们对生物系统功能的了解来扩展当前的概念框架,并开发相应的模型,这些模型可以更准确地表示和预测未来的生态进化结果。我们提出了实现这一目标的路线图。这一长期愿景将使生物学朝着能够为造福人类和提高自然生物系统恢复力的科学应用而运用这些预测模型的方向发展。我们确定了短期、中期和长期的关键目标,将我们目前的知识状态与这一长期愿景联系起来,通过三个阶段迭代推进:(1)利用生物系统的知识更好地为生态进化模型提供信息,(2)生成更准确预测的模型,以及(3)应用预测模型造福生物圈。在每个阶段,我们都概述了与进化发生的时间尺度、生态进化过程的参数空间以及这些机制之间的动态相互作用有关的研究途径和科学应用。准确地对生态进化变化进行建模、监测和预测的能力将对人类与全球环境的相互作用产生变革性的影响,为人类健康、保护自然世界和管理地球生物圈提供新的工具。

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