PANOPS – Revealing Earth´s plant functional diversity with citizen science

关于功能性植物属性(‘植物性状’)和功能多样性的全球模式的知识在地理覆盖面、分类群、特征及其生态功能方面是有限的。这限制了我们对生物多样性-环境关系和地球系统动力学的理解，以及对全球变化影响的预测。一系列研究试图使用具有全球和空间连续覆盖范围的环境预测因子，在空间上外推植物特性数据库(TRY数据库)中精选的特性观测。然而，这种外推的全球性状分布仍然具有很大的不确定性和很少的一致性。此外，卫星对地观测通常正在成为植被评估的一项关键技术，但在这方面的潜力有限，因为“鸟类视角”只能了解树冠顶层的植物和少数几个功能特征。为了填补这些关于功能多样性的数据空白，并揭示整个陆地植物群的生物多样性与环境的关系，PanOps将使用一个完全不同的“视角”：数百万张分布在全球各地的众包植物照片。由于形式遵循功能，在照片中可见的植物形态特征可以直接或间接地影响植物的几个功能特征。申请者的初步研究将数千张众包植物照片(INaturist)和植物特征(Try数据库)与深度学习和计算机视觉技术(卷积神经网络，CNN)联系起来。这不仅使得能够从一张照片中预测被认为对功能多样性至关重要的几个植物特征(例如，单位面积的叶质量、叶面积、植株高度)，甚至能够通过使用照片的地理位置在空间上聚集数千个这样的预测来预测它们的全球分布。因此，深度学习与来自公民(照片)和专业科学(特征)的数据相结合，为在宏观生态研究的聚光灯下研究问题提供了一个高潜力和协同的工具包。这一愿景在Panopps中得到了四个中心目标的证实：1)鉴定可从植物照片中推断的植物性状，结合辅助环境数据和有关种内性状变异的先验信息。2)评估模型在图像环境、植物生长形式和生物群落中的概括性，并评估潜在的机制，即，当从照片中预测特性时，模型真正‘看到’了什么？3)从空间聚集的植物特性预测生成和评估植物功能特性和多样性度量的全球地图。4)在植物特性的地理趋同、功能特性多样性与非生物环境和功能生态系统特性的关系以及功能多样性对功能生态系统特性的弹性和抵抗力的印记方面，扩大关于生物多样性-环境关系的知识。

Knowledge on global patterns of functional plant properties ('plant traits') and functional diversity are limited in terms of geographic coverage, taxa, traits, and their ecological functions. This restricts our understanding of biodiversity-environment relationships and Earth system dynamics, as well as to project impacts of global change. A series of studies attempted to spatially extrapolate trait observations curated in plant trait databases (TRY database) using environmental predictors with global and spatially continuous coverage. However, such extrapolated global trait distributions still feature large uncertainties and little agreement. Also, Earth observation with satellites, which is generally becoming a key technology for vegetation assessments, has limited potential in this regard as the ‘bird perspective’ can only inform on plants in top canopy layers and a few functional traits. Aiming to fill these data gaps on functional diversity and reveal biodiversity-environment relationships for the entire terrestrial flora, PANOPS will use an entirely different ‘perspective’: Millions of globally distributed, crowdsourced plant photographs. As form follows function, plant morphological properties visible in photographs can, directly and indirectly, inform on several functional characteristics of plants. Preliminary studies by the applicant linked thousands of crowdsourced plant photographs (iNaturalist) and plant traits (TRY database) with deep learning and computer vision techniques (Convolutional Neural Networks, CNN). This not only enabled to predict several plant traits considered key for functional diversity from single photographs (e.g. leaf mass per area, leaf area, plant height), but even their global distribution by spatially aggregating thousands of such predictions using the photographs’ geolocations. Accordingly, deep learning in concert with data from citizen (photographs) and professional science (traits) provides a high potential and synergetic toolset to study questions in the spotlight of macroecological research. This vision is substantiated in PANOPS by four central objectives: 1) Identification of plant traits inferable from plant photographs, incorporating ancillary environmental data and priors on intraspecific trait variation. 2) Assess the generalization of models across image settings, plant growth forms, and biomes and assess underlying mechanisms, i.e. what do models really ‘see’ when predicting traits from photographs? 3) Generate and evaluate global maps of plant functional traits and diversity metrics from spatially aggregated plant trait predictions. 4) Expand knowledge on biodiversity-environment relationships, in terms of the geographic convergence of plant traits, relationships of functional trait diversity with abiotic environmental and functional ecosystem properties, as well as imprints of functional diversity on the resilience and resistance of functional ecosystem properties.