Biodiv Sci ›› 2022, Vol. 30 ›› Issue (10): 22456.  DOI: 10.17520/biods.2022456

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Reconstructing community assembly using a numerical simulation model

Huijie Qiao1,*(), Junhua Hu2,*()   

  1. 1. Institute of Zoology, Chinese Academy of Sciences, Beijing 100101
    2. Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041
  • Received:2022-08-10 Accepted:2022-09-30 Online:2022-10-20 Published:2022-10-13
  • Contact: Huijie Qiao,Junhua Hu


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.

Key words: species diversity, simulation, climate change, dynamic environment