Not All Bees Pollinate: Using Machine Learning to Predict Ecological Roles

Summary

Image credit: José Neiva Mesquita-Neto Understanding Pollination Through Ecological Function Pollination is one of the most important ecological processes on Earth, but understanding how different insects contribute to it remains surprisingly difficult. While many bee species visit flowers, they do not all transfer pollen. Distinguishing between different ecological functions has traditionally required labor-intensive field experiments that are difficult to scale. At our co-organized 2025 NeurIPS Workshop on AI for Non-Human Animal Communication , Alef Iury Siqueira Ferreira shared how machine learning can help shift the focus from identifying which species visited a flower to understanding the ecological role it played. You can see the paper here . Drawing on expertise in ecology and machine learning, the team developed acoustic models that classify bees according to their ecological function, offering a scalable way to study pollination. In this Community Spotlight, Alef discusses the challenges of measuring pollination in the field, why function-based monitoring may provide deeper ecological insights than species inventories alone, and how machine learning could help researchers better understand the complex relationships between pollinators, plants, and the ecosystems they support. What challenges do researchers typically face when trying to measure pollination quality in the field? While many insects visit flowers, they play different ecological roles. Some transfer pollen between flowers, contributing to plant reproduction, while others primarily collect nectar or pollen without transferring pollen in the same way. Simply observing that an insect visited a flower, or even identifying it taxonomically, does not directly tell us how much it contributed to pollination. In practice, measuring pollination quality often requires biologically grounded validation, such as single-visit pollen deposition experiments, which are labor-intensive, time-consuming, and difficult to scale under real field conditions. This is why assessing pollination in the field remains challenging: the real question is not only who visited the flower, but what ecological function that visitor actually performed. How did collaboration between ecologists and machine learning researchers shape this project? The collaboration between ecologists and machine learning (ML) researchers was…