Institute of Evolutionary Biology, University of Edinburgh, Kings Buildings, West Mains Road, Edinburgh EH9 3JT United Kingdom.
Ecology. 2011 Mar;92(3):687-98. doi: 10.1890/10-1110.1.
Ecological interaction networks are a valuable approach to understanding plant-pollinator interactions at the community level. Highly structured daily activity patterns are a feature of the biology of many flower visitors, particularly provisioning female bees, which often visit different floral sources at different times. Such temporal structure implies that presence/absence and relative abundance of specific flower-visitor interactions (links) in interaction networks may be highly sensitive to the daily timing of data collection. Further, relative timing of interactions is central to their possible role in competition or facilitation of seed set among coflowering plants sharing pollinators. To date, however, no study has examined the network impacts of daily temporal variation in visitor activity at a community scale. Here we use temporally structured sampling to examine the consequences of daily activity patterns upon network properties using fully quantified flower-visitor interaction data for a Kenyan savanna habitat. Interactions were sampled at four sequential three-hour time intervals between 06:00 and 18:00, across multiple seasonal time points for two sampling sites. In all data sets the richness and relative abundance of links depended critically on when during the day visitation was observed. Permutation-based null modeling revealed significant temporal structure across daily time intervals at three of the four seasonal time points, driven primarily by patterns in bee activity. This sensitivity of network structure shows the need to consider daily time in network sampling design, both to maximize the probability of sampling links relevant to plant reproductive success and to facilitate appropriate interpretation of interspecific relationships. Our data also suggest that daily structuring at a community level could reduce indirect competitive interactions when coflowering plants share pollinators, as is commonly observed during flowering in highly seasonal habitats.
生态相互作用网络是理解群落水平植物-传粉者相互作用的一种有价值的方法。许多花访客的生物学特征具有高度结构化的日常活动模式,特别是提供雌性蜜蜂,它们经常在不同的时间访问不同的花卉源。这种时间结构意味着在相互作用网络中,特定花访客相互作用(链接)的存在/缺失和相对丰度可能对数据收集的每日时间高度敏感。此外,相互作用的相对时间对于它们在共同授粉植物中种子设置的竞争或促进中可能发挥的作用至关重要。然而,迄今为止,没有研究在群落尺度上检查访客活动的日常时间变化对网络的影响。在这里,我们使用时间结构采样来检查使用肯尼亚热带稀树草原栖息地的完全量化的花访客相互作用数据在网络属性上的日常活动模式的后果。在两个采样点的多个季节性时间点上,在 06:00 至 18:00 之间的四个连续三小时时间间隔内,按顺序对相互作用进行采样。在所有数据集,链接的丰富度和相对丰度都严重依赖于一天中观察到的访问时间。基于排列的零模型揭示了在四个季节性时间点中的三个中,每日时间间隔内存在显著的时间结构,主要由蜜蜂活动模式驱动。网络结构的这种敏感性表明需要在网络采样设计中考虑每日时间,既可以最大程度地提高与植物生殖成功相关的链接的采样概率,又可以促进对种间关系的适当解释。我们的数据还表明,在群落水平上的每日结构可以减少共同授粉植物之间的间接竞争相互作用,这在高度季节性栖息地的开花期间经常观察到。