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

PANOPS – 通过公民科学揭示地球植物的功能多样性

• 批准号: 504978936
• 负责人: Dr. Teja Kattenborn
• 金额: --
• 依托单位: Professur für Fernerkundung und Landschaftsinformationssysteme (FeLis)
• 依托单位国家: 德国
• 项目类别: Independent Junior Research Groups
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/504978936?language=en
• 关键词: PANOPS; Revealing; Earth; plant; functional

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.

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.