Katal Negin, Rzanny Michael, Mäder Patrick, Römermann Christine, Wittich Hans Christian, Boho David, Musavi Talie, Wäldchen Jana
Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
Data Intensive Systems and Visualisation, Technische Universitat Ilmenau, Ilmenau, Germany.
Front Plant Sci. 2023 Oct 4;14:1150956. doi: 10.3389/fpls.2023.1150956. eCollection 2023.
Plant phenology plays a vital role in assessing climate change. To monitor this, individual plants are traditionally visited and observed by trained volunteers organized in national or international networks - in Germany, for example, by the German Weather Service, DWD. However, their number of observers is continuously decreasing. In this study, we explore the feasibility of using opportunistically captured plant observations, collected via the plant identification app Flora Incognita to determine the onset of flowering and, based on that, create interpolation maps comparable to those of the DWD. Therefore, the opportunistic observations of 17 species collected in 2020 and 2021 were assigned to "Flora Incognita stations" based on location and altitude in order to mimic the network of stations forming the data basis for the interpolation conducted by the DWD. From the distribution of observations, the percentile representing onset of flowering date was calculated using a parametric bootstrapping approach and then interpolated following the same process as applied by the DWD. Our results show that for frequently observed, herbaceous and conspicuous species, the patterns of onset of flowering were similar and comparable between both data sources. We argue that a prominent flowering stage is crucial for accurately determining the onset of flowering from opportunistic plant observations, and we discuss additional factors, such as species distribution, location bias and societal events contributing to the differences among species and phenology data. In conclusion, our study demonstrates that the phenological monitoring of certain species can benefit from incorporating opportunistic plant observations. Furthermore, we highlight the potential to expand the taxonomic range of monitored species for phenological stage assessment through opportunistic plant observation data.
植物物候学在评估气候变化中起着至关重要的作用。为了监测这一情况，传统上由在国家或国际网络中组织起来的训练有素的志愿者对单株植物进行实地考察和观测——例如在德国，由德国气象局（DWD）负责此项工作。然而，他们的观测人员数量在持续减少。在本研究中，我们探讨了利用通过植物识别应用程序“无名植物志”（Flora Incognita）机会性获取的植物观测数据来确定开花始期的可行性，并在此基础上创建与德国气象局类似的插值地图。因此，根据地点和海拔高度，将2020年和2021年收集的17个物种的机会性观测数据分配到“无名植物志站点”，以模拟构成德国气象局插值数据基础的站点网络。根据观测数据的分布，采用参数自举法计算代表开花日期始期的百分位数，然后按照德国气象局应用的相同流程进行插值。我们的结果表明，对于经常被观测、草本且显眼的物种，两个数据源的开花始期模式相似且具有可比性。我们认为，一个突出的开花阶段对于从机会性植物观测中准确确定开花始期至关重要，并且我们讨论了其他因素，如物种分布、位置偏差以及导致物种和物候数据差异的社会事件。总之，我们的研究表明，某些物种的物候监测可以受益于纳入机会性植物观测数据。此外，我们强调了通过机会性植物观测数据扩大用于物候阶段评估的监测物种分类范围的潜力。
