指南针
阴天
地理
鸟类迁徙
栖息地
昆虫
生态学
气象学
生物
地图学
天空
作者
Jason W. Chapman,V. A. Drake
出处
期刊:Elsevier eBooks
[Elsevier BV]
日期:2016-11-01
卷期号:: 573-580
被引量:5
标识
DOI:10.1016/b978-0-12-809633-8.01248-6
摘要
Entomologists recognize migratory behavior as persistent and straightened-out locomotory activity that enables preemptive escape from ephemeral habitats before they deteriorate and timely colonization of newly favorable ones. It manifests as directed low-altitude upwind movement by day and active ascent to altitudes of hundreds of meters followed by mainly wind-borne movement, both by day and by night, when the wind is favorable. Insect migration patterns often have the general form, at least on average, of a round-trip circuit, but the short life-times and low flying speeds of insects mean that two or more generations are usually required to complete it. Migration depends on an evolved suite of adaptations that is maintained by the action of natural selection on the outcomes of migratory activity, through increased survival and reproductive success in the destination regions. Night-time migrants orient either to the wind or a compass direction; daytime migrants orienting to a compass direction employ a time-compensated sun compass which may be supplemented by a magnetic compass in overcast conditions. Responses to atmospheric conditions lead to concentration in convective updrafts by day and in layers at night. Many migrant insects are important pests of agricultural crops or are vectors of human, animal, or plant diseases; others have beneficial economic and societal impacts. Insect migration is a sometimes spectacular phenomenon that has conservation value in its own right. Knowledge of insect migration has developed especially through experiments using flight simulators, selection experiments, radar observations, pest monitoring and forecasting initiatives, and citizen science. Its study forms an example of integrative biology and constitutes one aspect of the novel discipline of aeroecology.
科研通智能强力驱动
Strongly Powered by AbleSci AI