Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany.
Department of Environmental Science, Institute for Water and Wetland Research, Radboud University, Nijmegen, The Netherlands.
Sci Rep. 2021 Aug 12;11(1):16395. doi: 10.1038/s41598-021-95616-0.
Plant functional traits ('traits') are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth's plant functional diversity.
植物功能性状（“性状”）是评估生物多样性和生态系统过程的基础，但测量起来很繁琐。为了便于性状测量，我们通过卷积神经网络（CNN）将来自公民科学（iNaturalist）的异质照片与性状观测（TRY 数据库）结合起来，检验性状是否可以通过可见的形态特征来预测。结果表明，图像特征足以预测几个代表植物主要功能轴的性状。当使用 CNN 集成并纳入性状可塑性和气候的先验知识时，准确性会提高。我们的结果表明，这些模型在全球范围内的生长形式、分类群和生物群中具有通用性。我们通过生成反映已知宏观生态模式的全球性状图来突出这种方法的适用性。这些发现证明了来自专业和公民科学的大数据与 CNN 相结合作为一种高效和自动化评估地球植物功能多样性的强大工具的潜力。
