Bugs and bytes: Entomological biomonitoring through the integration of deep learning and molecular analysis for merged community and network analysis

Abstract

Insects play a vital role in ecosystem functioning, but in some parts of the world, their populations have declined significantly in recent decades due to environmental change, agricultural intensification and other anthropogenic drivers. Monitoring insect populations is crucial for understanding and mitigating biodiversity loss, especially in agro‐ecosystems where a focus on pests and beneficial insects is gaining momentum in the context of sustainable food systems. Biomonitoring has long played an important role in providing early warnings of insect pests and their vectored pathogens and for assessing agro‐ecosystem management. However, identification of invertebrates by taxonomists is time‐consuming and fraught with numerous other challenges, particularly when it comes to real‐time monitoring. Recent technological advances in both computational image recognition and molecular ecology have enhanced biomonitoring efficiency and accuracy, reducing labour efforts, but leading to unprecedented volumes of data generated. This perspective article examines the significance and further potential of deep learning with image‐based recognition, aided by complementary technologies, in advancing entomological biomonitoring. Using entomological sticky traps as an example, we discuss each step of the workflow required to create ecological networks using image‐based recognition, multimodal data and deep learning, and we identify the challenges inherent to this method and other insect survey techniques. In order to become mainstream for global biomonitoring, access to long‐term, standardised multimodal data is required for comprehending ecosystem dynamics, structure and function and for mitigating insect population declines.