了解什么使種群發(fā)生同步波動很重要,，因為同步對滅絕風(fēng)險、食物鏈穩(wěn)定性和影響一個生態(tài)系統(tǒng)的其他因素都有明顯效應(yīng),。相似的捕食者-獵物循環(huán)中所涉及的相鄰種群經(jīng)常發(fā)生同步振蕩,，David Vasseur 和Jeremy Fox利用理論及實驗室縮微環(huán)境發(fā)現(xiàn)，當(dāng)捕食者存在時,，獵物種群之間的擴散是造成這種相位鎖定（相位同步）的原因,。擴散是不同生物（Vasseur 和 Fox所研究的動物是雪兔和加拿大猞猁）從一個分離的種群向另一個轉(zhuǎn)移的能力。由這項工作所獲得的模型對于代表捕食者-獵物和宿主-病原體系統(tǒng)的參數(shù)的大范圍變化都是可靠的,，說明它可能具有普遍適用性,。（生物谷Bioon.com）
生物谷推薦原始出處：
Nature 460, 1007-1010 (20 August 2009) | doi:10.1038/nature08208
Phase-locking and environmental fluctuations generate synchrony in a predator–prey community
David A. Vasseur1 & Jeremy W. Fox2
1 Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA
2 Department of Biological Sciences, University of Calgary, Calgary, Alberta T2N 1N4, Canada
Spatially synchronized fluctuations in system state are common in physical and biological systems ranging from individual atoms1 to species as diverse as viruses, insects and mammals2, 3, 4, 5, 6, 7, 8, 9, 10. Although the causal factors are well known for many synchronized phenomena, several processes concurrently have an impact on spatial synchrony of species, making their separate effects and interactions difficult to quantify. Here we develop a general stochastic model of predator–prey spatial dynamics to predict the outcome of a laboratory microcosm experiment testing for interactions among all known synchronizing factors: (1) dispersal of individuals between populations; (2) spatially synchronous fluctuations in exogenous environmental factors (the Moran effect); and (3) interactions with other species (for example, predators) that are themselves spatially synchronized. The Moran effect synchronized populations of the ciliate protist Tetrahymena pyriformis; however, dispersal only synchronized prey populations in the presence of the predator Euplotes patella. Both model and data indicate that synchrony depends on cyclic dynamics generated by the predator. Dispersal, but not the Moran effect, 'phase-locks' cycles, which otherwise become 'decoherent' and drift out of phase. In the absence of cycles, phase-locking is not possible and the synchronizing effect of dispersal is negligible. Interspecific interactions determine population synchrony, not by providing an additional source of synchronized fluctuations, but by altering population dynamics and thereby enhancing the action of dispersal. Our results are robust to wide variation in model parameters representative of many natural predator–prey or host–pathogen systems. This explains why cyclic systems provide many of the most dramatic examples of spatial synchrony in nature.
