Agent-based Ecosystem Simulation Research under Forest Fire

doi:10.16182/j.issn1004731x.joss.22-0155

Abstract

Abstract:

An Agent-based multi-species simulation model under forest fire is proposed to study the effect of forest fire on the balance of animal species population. By abstracting elements of each type of species and fire in the forest fire process as agents, the attributes and behavior rules of each type of agents according to the real characteristics of each type of species and forest fire are refined. ABM model is used to show the characteristics of multi-agent interaction in complex systems, and construct a multi-species forest ecological model and a forest fire model. On the basis of validating the rationality of the models, two models are integrated, and the changes of species and the effects before and after the occurrence of forest fires are analyzed. The results show that the species with high reproductive capacity and high mortality rates are still able to reproduce offspring rapidly under the harsh environmental conditions after the fire suppression. The higher the intensity of fire disturbance, the faster the species category grow. In conversely, the species with high competitive ability but low reproductive capacity are difficult to recover from the disaster.

Key words: Agent-based model, ecosystem, forest fire, multi-species, species changes

CLC Number:

TP391.9

Ying Li, Nisuo Du, Zhi Ouyang. Agent-based Ecosystem Simulation Research under Forest Fire[J]. Journal of System Simulation, 2023, 35(6): 1362-1371.

Figures/Tables 13

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 1

Classification of various animals and range of related parameters

动物等级

参数范围

解释

低

$R b ∈ [0.16,1]$

$R m ∈ [0.000 318,0.000 513]$

$R c ∈ [0.027,0.14]$

初始数量大，繁殖能力强，死亡率高，竞争能力低代表个体弱小

中

$R b ∈ [0.000 26,0.001 6]$ $R m ∈ [0.000 258,0.000 281]$

$R c ∈ [0.19,0.36]$

初始数量中等，繁殖能力和死亡率中等，竞争能力中等

高

$R b ∈ [0.000 26,0.00 16]$ $R m ∈ [0.000 256,0.000 257]$

$R c ∈ [0.41,0.52]$

初始种群少，繁殖能力弱，死亡率低，竞争能力强

Table 1

Fig. 10

Fig. 11

Fig. 12

References 21

1	Cattau M E, Wessman C, Mahood A, et al. Anthropogenic and Lightning-started Fires Are Becoming Larger and More Frequent Over a Longer Season Length in the U.S.A.[J]. Global Ecology and Biogeography, 2020, 29(4): 668-681.
2	王正非. 山火初始蔓延速度测算法[J]. 山地研究, 1983, 1(2): 42-51.
	Wang Zhengfei. The Measurement Method of the Wildfire Initial Spread Rate[J]. Mountain Research, 1983, 1(2): 42-51.
3	李兴东, 李鑫, 李晓锟, 等. 一种基于元胞自动机的林火蔓延模型[J]. 林业机械与木工设备, 2019, 47(6): 48-55.
	Li Xingdong, Li Xin, Li Xiaokun, et al. A Forest Fire Spread Model Based on Cellular Automata[J]. Forestry Machinery & Woodworking Equipment, 2019, 47(6): 48-55.
4	Mutthulakshmi K, Wee M R E, Wong Y C K, et al. Simulating Forest Fire Spread and Fire-fighting Using Cellular Automata[J]. Chinese Journal of Physics, 2020, 65: 642-650.
5	Rui Xiaoping, Hui Shan, Yu Xuetao, et al. Forest Fire Spread Simulation Algorithm Based on Cellular Automata[J]. Natural Hazards, 2018, 91(1): 309-319.
6	Prieto Herráez D, Asensio Sevilla M I, Ferragut Canals L, et al. A GIS-based Fire Spread Simulator Integrating a Simplified Physical Wildland Fire Model and a Wind Field Model[J]. International Journal of Geographical Information Science, 2017, 31(11): 2142-2163.
7	Carolina Veronese Corrêa da Silva, Camila Goldas da Silva, Dáttilo Wesley, et al. Effects of Time-since-fire on Ant-plant Interactions in Southern Brazilian Grasslands[J]. Ecological Indicators, 2020, 112: 106094.
8	Anjos D, Campos R, Campos R, et al. Monitoring Effect of Fire on Ant Assemblages in Brazilian Rupestrian Grasslands: Contrasting Effects on Ground and Arboreal Fauna[J]. Insects, 2017, 8(3): 64.
9	Steel Z L, Campos B, Frick W F, et al. The Effects of Wildfire Severity and Pyrodiversity on Bat Occupancy and Diversity in Fire-Suppressed Forests[J]. Scientific Reports, 2019, 9(1): 16300.
10	Chudzinska M, Dupont Y L, Nabe-Nielsen J, et al. Combining the Strengths of Agent-based Modelling and Network Statistics to Understand Animal Movement and Interactions with Resources: Example from Within-patch Foraging Decisions of Bumblebees[J]. Ecological Modelling, 2020, 430: 109119.
11	Newton A C, Boscolo D, Ferreira P A, et al. Impacts of Deforestation on Plant-pollinator Networks Assessed Using an Agent Based Model[J]. PloS One, 2018, 13(12): e0209406.
12	Diaz S G, DeAngelis D L, Gaines M S, et al. Development and Validation of a Spatially-explicit Agent-based Model for Space Utilization by African Savanna Elephants (Loxodonta Africana) Based on Determinants of Movement[J]. Ecological Modelling, 2021, 447: 109499.
13	Neil E, Madsen J K, Carrella E, et al. Agent-based Modelling as a Tool for Elephant Poaching Mitigation[J]. Ecological Modelling, 2020, 427: 109054.
14	Potterf M, Bone C. Simulating Bark Beetle Population Dynamics in Response to Windthrow Events[J]. Ecological Complexity, 2017, 32, Part A: 21-30.
15	Chazdon R L, Brancalion P H S, Laestadius L, et al. When is a Forest a Forest? Forest Concepts and Definitions in the Era of Forest and Landscape Restoration[J]. Ambio, 2016, 45(5): 538-550.
16	Machida W S, Gomes L, Moser P, et al. Long Term Post-fire Recovery of Woody Plants in Savannas of Central Brazil[J]. Forest Ecology and Management, 2021, 493: 119255.
17	Tidière M, Gaillard J M, Berger V, et al. Comparative Analyses of Longevity and Senescence Reveal Variable Survival Benefits of Living in Zoos Across Mammals[J]. Scientific Reports, 2016, 6(1): 36361.
18	Lan B L, Chandran P. Distribution of Animal Population Fluctuations[J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(7): 1289-1294.
19	Looman J. Biological Equilibrium in Ecosystems 1:A Theory of Biological Equilibrium[J]. Folia Geobotanica & Phytotaxonomica, 1976, 11(1): 1-21.
20	Green D G, Gill A M, Noble I R. Fire Shapes and the Adequacy of Fire-spread Models[J]. Ecological Modelling, 1983, 20(1): 33-45.
21	Grover J P. Nonequilibrium Resource Competition[M]//Grover J P. Resource Competition. Boston, MA: Springer US, 1997: 100-131.

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