Macrophenological dynamics from citizen science plant occurrence data

IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY
Karin Mora, Michael Rzanny, Jana Wäldchen, Hannes Feilhauer, Teja Kattenborn, Guido Kraemer, Patrick Mäder, Daria Svidzinska, Sophie Wolf, Miguel D. Mahecha
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引用次数: 0

Abstract

Abstract Image

从公民科学的植物发生数据看宏观生态动态
植物物种的物候变化是量化气候变化影响的有力指标。如今,可自动识别物种的移动应用程序为跨时空物候监测提供了新的可能性。在此,我们介绍了一种创新的时空机器学习方法,该方法可利用此类众包数据来量化不同类群、不同空间和不同时间的物候动态。我们的算法采用相似性测量方法,将数千个物种和地理位置的个体物候反应联系起来。该分析借鉴了 2018 年至 2021 年通过基于人工智能的植物识别应用程序 Flora Incognita 在德国收集的近千万植物观测数据。我们的方法量化了整个年周期中同步性的变化。在生长季节,同步行为可由一些特征性的宏观表观模式编码。这些模式的非线性时空变化可通过数据压缩度量进行有效量化。在生长季节之外,物候同步性会减弱,从而在模式中引入噪声。尽管众包数据存在偏差和不确定性,例如人为数据收集行为造成的偏差和不确定性,但我们的研究证明了从单个植物观测数据中得出有意义的植物宏观表观监测指标的可行性。随着众包数据库的不断扩大,我们的方法有望用于研究气候引起的物候变化和反馈回路。
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来源期刊
CiteScore
11.60
自引率
3.00%
发文量
236
审稿时长
4-8 weeks
期刊介绍: A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.
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