| 要旨トップ | ESJ64 シンポジウム 一覧 | | 日本生態学会第64回全国大会 (2017年3月、東京) 講演要旨 ESJ64 Abstract |
シンポジウム S13 3月16日 9:00-12:00 E会場
Phenological patterns of biological organisms reflect both ultimate causes (selective advantages and disadvantages of certain phenological patterns) and proximate cues (which trigger physiological controls of phenological events). In temperate regions, wide seasonal changes in temperature and day length can easily serve as proximate cues. It is less obvious how diverse organisms detect seasonal changes in the tropics, where day length and temperature exhibit much lower seasonal variations. Although underlying mechanisms behind phenological events in aseasonal environments remain largely unknown, recent studies based on long-term phenology monitoring at community, population, and molecular levels as well as meteorological observations would provide insights into ultimate causes and proximate cues for phenological events. This symposium will feature recent findings on phenological patterns of flowering, leaf flushing, resource allocation and population dynamics in plants and animals in long-term study sites in the Neotropics and South East Asia, with a goal of developing predictive models that can help forecast community and ecosystem dynamics under global changes. We will discus how to integrate long-term empirical data on phenology from multiple tropical sites to understand the impacts of climate change on phenological events and to evaluate ecosystem vulnerability in a face of rapidly changing environments.
Commentator: Shoko Sakai (Kyoto University)
[S13-1] Tropical Flowering Times
[S13-2] Phenology of vegetative growth and reproduction in lowland and hill dipterocarp forests in Peninsular Malaysia
[S13-3] Weak seasonality in population fluctuations of insects in the Southeast Asian tropical rainforests
[S13-4] On the causes of distinct annual seasonality in leaf and reproductive-organ litterfall in Bornean tropical rain forests
[S13-5] Phenological patterns of vegetative growth, litterfall and reproduction in Lambir Hills National Park
[S13-6] Developing predictive models based on genetic information to forecast future phenological change