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罕见事件引发的模式形成:狂犬病传播的教训

Pattern formation triggered by rare events: lessons from the spread of rabies.

作者信息

Jeltsch F, Müller M S, Grimm V, Wissel C, Brandl R

机构信息

Department of Ecological Modelling, UFZ Leipzig-Halle, Germany.

出版信息

Proc Biol Sci. 1997 Apr 22;264(1381):495-503. doi: 10.1098/rspb.1997.0071.

Abstract

Understanding of large-scale spatial pattern formation is a key to successful management in ecology and epidemiology. Neighbourhood interactions between local units are known to contribute to large-scale patterns, but how much do they contribute and what is the role of regional interactions caused by long-distance processes? How much long-distance dispersal do we need to explain the patterns that we observe in nature? There seems to be no way to answer these questions empirically. Therefore, we present a modelling approach that is a combination of a grid-based model describing local interactions and an individual-based model describing dispersal. Applying our approach to the spread of rabies, we show that in addition to local rabies dynamics, one long-distance infection per 14000 km2 per year is sufficient to reproduce the wave-like spread of this disease. We conclude that even rare ecological events that couple local dynamics on a regional scale may have profound impacts on large-scale patterns and, in turn, dynamics. Furthermore, the following results emerge: (i) Both neighbourhood infection and long-distance infection are needed to generate the wave-like dispersal pattern of rabies; (ii) randomly walking rabid foxes are not sufficient to generate the wave pattern; and (iii) on a scale of less than 100 km x 100 km, temporal oscillations emerge that are independent from long-distance dispersal.

摘要

理解大规模空间格局的形成是生态与流行病学成功管理的关键。已知局部单元之间的邻域相互作用会促成大规模格局,但它们的贡献有多大,以及由长距离过程引起的区域相互作用的作用是什么?为了解释我们在自然界中观察到的格局,我们需要多少长距离扩散?似乎没有办法通过实证来回答这些问题。因此,我们提出了一种建模方法,它是描述局部相互作用的基于网格的模型与描述扩散的基于个体的模型的结合。将我们的方法应用于狂犬病的传播,我们发现,除了局部狂犬病动态外,每年每14000平方公里发生一次长距离感染就足以重现这种疾病的波状传播。我们得出结论,即使是在区域尺度上连接局部动态的罕见生态事件,也可能对大规模格局进而对动态产生深远影响。此外,还得出以下结果:(i)需要邻域感染和长距离感染才能产生狂犬病的波状扩散模式;(ii)随机游走的狂犬病狐狸不足以产生波状模式;(iii)在小于100公里×100公里的尺度上,会出现与长距离扩散无关的时间振荡。

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