利用数值模拟重构物种多样性格局的形成过程

生命形成的过程极其漫长, 经历了地球系统复杂的沧海桑田变化。当前人类所观察到的物种分布格局的形成除了由物种本身特征决定外, 还受到环境变化、人类活动以及各种随机事件的影响。受限于实验条件、时间、经费、人力等诸多因素, 我们尚无法完整地观察并记录到物种多样性形成的全过程, 只能通过片段化数据来推测该过程。信息科学中包括数值模拟在内的仿真技术以其高效、可控及全过程记录等优势, 能从某种程度上解决物种多样性格局形成过程中的部分数据黑箱问题。本文介绍了数值模拟的概念和工作原理及在物种多样性研究中应用的特点, 列举了物种生态位、扩散模式、种间互作及物种分布应对气候变化等方面的数值模拟研究, 基于已有研究系统地介绍了如何综合上述数值模拟研究构建虚拟物种、气候和场景来解释物种多样性的形成与维持机制, 并阐述了数值模拟在物种多样性研究中的优缺点及应用前景。

Reconstructing community assembly using a numerical simulation model

Background: The formation of ecological communities has occurred through a long process of evolution. The current community composition we have observed is not only determined by the ecological traits of the species itself but also affected by environmental changes, human activities, and various random events. Time scales and experimental constraints mean we cannot fully observe the process of community assembly, and can only speculate on this process through fragmented data. Simulations can be used to test aspects of community assembly thanks to their relative efficiency, controllability, and traceability. Aim: We review efforts to simulate of community assembly and the approaches taken to combine different explanations for assembly. We note advantages, disadvantages, and prospects of simulation for study of community assembly. To introduce numerical simulation into the study of community assembly, it is necessary to extract the factors and rules that affect the assembly pathways and that can be modeled within the requirements of a chosen simulation model. Process: Robert Paine used virtual species to build community food web structure, and discussed the relationship between food web complexity and species diversity from a purely mathematical perspective approximately 50 years ago. Many subsequent studies, such as exploring the impact of isolation and sub-networks in complex food webs, and evaluating the impact of network isolation on ecological stability through food web complexity and other related theories, are typical cases of using numerical simulation at to consider the impact of interspecific interactions and the complexity-stability relationship. At the cross-community scale, May et al. modelled the abundance and distribution of individuals of different species in a spatially defined landscape, defining key attributes of multiple communities (total individuals, population density, and intraspecific degree of spatial aggregation, etc.), deducing the relevant indicators of biodiversity and comparing the performance of the biodiversity-related indicators of multiple community structures under different sampling modes and intensities. For protected area planning, numerical simulations can use artificial intelligence to prioritize protected areas, and quantify the trade-offs between the costs and benefits of regional and biodiversity conservation. On regional or global scales, the relationship between species niche breadth, dispersal capacity, environmental change rate and each of species extinction and new species formation was analyzed. We confirmed that topography and climate drive the evolution of species and the formation of species diversity along the latitudinal gradient of niche breadth and species diversity for bird communities in South America over the past 800,000 years. We also modelled the formation of species in the Ordovician, late Pliocene and Pleistocene, and the discussion of the impact of topographic factors on species extinction. Prospect: The change of biodiversity can be a long-term and complex process. Understanding how these processes change over time requires the integration of multidisciplinary theories and research methods such as macroevolution, paleontology, biogeography, and community ecology. The study of large-scale biodiversity patterns has reached a global scale, and it is becoming harder and harder to find the drivers of biodiversity patterns via simple correlation analysis. In fact, macroecology is now shifting its focus from finding correlations between ecological phenomena and environmental factors to understanding, explaining, and predicting observed patterns of biodiversity from a causal perspective. Simulation provides an opportunity to observe community assembly.