审美
冈比亚按蚊
生物
旱季
载体(分子生物学)
生态学
滞育
人口
幼虫
持久性(不连续性)
动物
疟疾
人口学
基因
工程类
社会学
岩土工程
免疫学
生物化学
重组DNA
作者
Adama Dao,Alpha S. Yaro,Mamadou Alpha Diallo,Seydou Timbiné,Diana L. Huestis,Yaya Kassogue,Adama Traoré,Zimogo Zié Sanogo,Djibril Samaké,Thomas Lehmann
出处
期刊:Nature
[Nature Portfolio]
日期:2014-12-18
卷期号:516 (7531): 387-390
被引量:134
摘要
During the long Sahelian dry season, mosquito vectors of malaria are expected to perish when no larval sites are available; yet, days after the first rains, mosquitoes reappear in large numbers. How these vectors persist over the 3-6-month long dry season has not been resolved, despite extensive research for over a century. Hypotheses for vector persistence include dry-season diapause (aestivation) and long-distance migration (LDM); both are facets of vector biology that have been highly controversial owing to lack of concrete evidence. Here we show that certain species persist by a form of aestivation, while others engage in LDM. Using time-series analyses, the seasonal cycles of Anopheles coluzzii, Anopheles gambiae sensu stricto (s.s.), and Anopheles arabiensis were estimated, and their effects were found to be significant, stable and highly species-specific. Contrary to all expectations, the most complex dynamics occurred during the dry season, when the density of A. coluzzii fluctuated markedly, peaking when migration would seem highly unlikely, whereas A. gambiae s.s. was undetected. The population growth of A. coluzzii followed the first rains closely, consistent with aestivation, whereas the growth phase of both A. gambiae s.s. and A. arabiensis lagged by two months. Such a delay is incompatible with local persistence, but fits LDM. Surviving the long dry season in situ allows A. coluzzii to predominate and form the primary force of malaria transmission. Our results reveal profound ecological divergence between A. coluzzii and A. gambiae s.s., whose standing as distinct species has been challenged, and suggest that climate is one of the selective pressures that led to their speciation. Incorporating vector dormancy and LDM is key to predicting shifts in the range of malaria due to global climate change, and to the elimination of malaria from Africa.
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