Artificial Intelligence for an automated Insect Biodiversity Monitoring (AIforIBM)

昆虫和草地因其高度多样性、对环境强迫的敏感性以及对多种生态系统功能和服务的重要性而被广泛研究。生物多样性探索和其他地方已经记录了昆虫丰度和多样性的大幅下降，土地利用集约化已被确定为全球物种丧失的最重要驱动因素之一。加强草地管理可减少节肢动物的丰度和多样性，但其影响因土地利用成分和节肢动物研究类群而异。由于现有监测方法的劳动强度造成资源限制，昆虫监测的空间和时间分辨率较低，这阻碍了对这种变异性的机制理解。因此，需要新的昆虫监测方法来获得所需的高分辨率数据，并进一步了解土地利用对昆虫群落的影响。基于照片和人工智能的图像识别的数字监控系统是实现这些目标的有前途的技术。目前的项目旨在以非常高的时间分辨率监测生物多样性探索的草地地块上的飞虫。我们将使用现有的rgb相机陷阱系统，并开发深度学习算法来量化昆虫的丰度、形态群多样性和体型分布。我们将利用生物多样性探索实验室现有的丰富昆虫数据来训练数据，并对人工智能图像识别的性能进行基准测试。利用具有移动连接功能的嵌入式传感器系统对昆虫进行监测，在不同的土地管理活动之前、期间和之后，对三个地区的草地进行连续的原位测量。由于我们的数据具有较高的时间分辨率，我们可以通过量化参考状态、干扰效应和恢复阶段来直接研究土地利用活动如何影响昆虫群落的动态和稳定性。我们将区分人口和（再）定殖过程对土地利用干扰的响应，利用人口和行为过程在不同的时间尺度上运作，例如，再定殖比繁殖快得多，因此，该项目将推进昆虫监测技术和对土地利用对昆虫群落影响的机制理解。

Insects and grasslands are intensively studied ecological models because of their high diversity, sensitivity to environmental forcing, and importance for multiple ecosystem functions and services. Substantial decreases in insect abundances and diversity have been documented in the Biodiversity Exploratories and elsewhere, and land-use intensification has been identified as one of the most important drivers of global species loss. Intensification of grassland management has been documented to reduce the abundance and diversity of arthropods, but effects vary depending on the land-use component and group of arthropods studies. A mechanistic understanding of this variability is hindered by the coarse spatial and temporal resolution of insect monitoring imposed by resource constraints because of the labor intensity of established monitoring methods. Consequently, new methods for insect monitoring are needed to enable the required high-resolution data and to further our understanding of land-use effects on insect communities. Digital monitoring systems based on photographs and AI-enabled image recognition are promising techniques to enable these. The current project aims to monitor flying insects in the grassland plots of the Biodiversity Exploratories at very high temporal resolution. We will use available RGB-camera trap systems and develop deep learning algorithms to quantify insect abundance, morpho-group diversity, and body size distributions. We will be building on the wealth of existing data on insects from the Biodiversity Exploratories for training data and to benchmark the performance of the AI image recognition. Deploying an embedded sensor system with mobile connectivity to monitor insects, we aim to conduct continuous in-situ measurements in the grassland plots of the three regions of the Biodiversity Exploratories before, during, and after different land-management activities. Because of the high temporal resolution of our data, we can investigate how land-use activities affect the dynamics and stability of the insect communities by quantifying the reference state, disturbance effects, and recovery phases directly. We will differentiate between demographic and (re)colonization processes in response to disturbances by land-use, exploiting that demographic and behavioral processes operate at different time scales, for example recolonization being much faster than reproduction, Thereby, this project will advance the technology available for insect monitoring and the mechanistic understanding of the effects of land use on insect communities.