Leveraging machine learning and citizen science data to describe flowering phenology across South Africa

Summary:• Phenology — the timing of recurring life history events—is strongly linked to climate. Shifts in phenology have important implications for trophic interactions, ecosystem functioning and community ecology. However, data on plant phenology can be time consuming to collect and current records are biased across space and taxonomy. • Here, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images. We analyse >1.8 million iNaturalist records for plants listed in the National Botanical Gardens within South Africa, a country famed for its floristic diversity (~21,000 species) but poorly represented in phenological databases. • We were able to correctly classify images with >90% accuracy. Using metadata associated with each image, we then reconstructed the timing of peak flower production and length of the flowering season for the 6,986 species with >5 iNaturalist records. • Our analysis illustrates how machine learning tools can leverage the vast wealth of citizen science biodiversity data to describe large-scale phenological dynamics. We suggest such approaches may be particularly valuable where data on plant phenology is currently lacking.

• 物候学（Phenology）——即周期性生活史事件的发生时间——与气候密切相关。物候变化对营养相互作用（trophic interactions）、生态系统功能（ecosystem functioning）及群落生态学（community ecology）具有重要意义。然而，植物物候数据的采集耗时费力，且现有记录在空间分布与分类学（taxonomy）上存在偏差。
• 在此，我们探究了卷积神经网络（Convolutional Neural Networks, CNN）在公民科学（citizen science）图像的超大、分类学多样化数据集上对开花物候进行分类的性能。我们分析了南非国家植物园（National Botanical Gardens）所列植物的180多万条iNaturalist记录——南非以其植物区系多样性（floristic diversity）（约21000种）闻名，但在物候数据库（phenological databases）中的代表性不足。
• 我们实现了>90%的图像正确分类准确率。利用每张图像的关联元数据（metadata），我们进而重构了拥有>5条iNaturalist记录的6986个物种的盛花期时间及开花季节长度。
• 本研究表明，机器学习工具（machine learning tools）可利用海量公民科学生物多样性数据（biodiversity data）来描述大规模物候动态（phenological dynamics）。我们认为，此类方法在植物物候数据匮乏的地区可能具有尤为重要的价值。