Kirse Ameli, Wittenhorst Manus Arian, Scherber Christoph, Posanski Michel, Scherges Alice, Zizka Vera, Ott David, Noll Niklas W, Wägele Wolfgang J
Leibniz Institute for the Analysis of Biodiversity Change (LIB), Museum Koenig, Centre for Biodiversity Monitoring and Conservation Science, Bonn, Germany.
Bonn Institute for Organismic Biology (BIOB), University of Bonn, Bonn, Germany.
J Anim Ecol. 2025 Apr;94(4):597-610. doi: 10.1111/1365-2656.14246. Epub 2025 Jan 24.
Understanding insect behaviour and its underlying drivers is vital for interpreting changes in local biodiversity and predicting future trends. Conventional insect traps are typically limited to assess the composition of local insect communities over longer time periods and provide only limited insights into the effects of abiotic factors, such as light on species activity. Achieving finer temporal resolution is labour-intensive or only possible under laboratory conditions. Here, we demonstrate that time-controlled insect sampling using an automated Malaise trap in combination with metabarcoding allows for the observation and documentation of taxon-specific activity patterns. Furthermore, these recorded activity patterns can provide valuable insights into the underlying ecological processes. Insect activity curves, derived from predicted detection numbers using generalised linear latent variable models, reveal distinct differences in activity patterns at higher and lower taxonomic level. While our findings align with existing literature, they also reveal that the activity patterns of some species are more complex than previously known. Additionally, a comparison of the assessed activity patterns across taxa suggest potential, previously undescribed parasitoid-host relationships. Within taxonomic groups, we observe variations in both the timing and duration of activity patterns, which can be linked to differences in mating strategies among closely related species. By capturing circadian rhythms of insect activity through time-controlled bulk sampling, we can expand our knowledge on species behaviour, ecology and temporal interactions. This contributes significantly to the advancement of chronoecology, allowing for further exploration of the roles of species and benefits in natural and anthropogenic ecosystems, alongside their potentially significant threat.
了解昆虫行为及其潜在驱动因素对于解释当地生物多样性的变化和预测未来趋势至关重要。传统的昆虫诱捕器通常在评估较长时间内当地昆虫群落的组成方面存在局限性,并且只能提供关于非生物因素(如光照对物种活动)影响的有限见解。实现更精细的时间分辨率需要耗费大量人力,或者仅在实验室条件下才有可能。在这里,我们证明使用自动马氏网诱捕器结合代谢条形码进行时间控制的昆虫采样,可以观察和记录特定分类群的活动模式。此外,这些记录的活动模式可以为潜在的生态过程提供有价值的见解。使用广义线性潜变量模型从预测的检测数量得出的昆虫活动曲线,揭示了更高和更低分类水平上活动模式的明显差异。虽然我们的研究结果与现有文献一致,但也表明一些物种的活动模式比以前所知的更为复杂。此外,对不同分类群评估的活动模式进行比较,表明存在潜在的、以前未描述的寄生蜂 - 宿主关系。在分类群中,我们观察到活动模式的时间和持续时间都存在变化,这可能与密切相关物种之间交配策略的差异有关。通过时间控制的大量采样捕捉昆虫活动的昼夜节律,我们可以扩展对物种行为、生态学和时间相互作用的认识。这对chronoecology的发展有重大贡献,有助于进一步探索物种在自然和人为生态系统中的作用和益处,以及它们潜在的重大威胁。