New Publication: Adopting Flora Incognita Plant Observations for Phenology Monitoring

Members of the “Data-intensive Systems and Visualization Group” at the Institute of Applied Computer Science of the TU Ilmenau under the leadership of Prof. Mäder develop and run the “Flora Incognita” application, a mobile app that can identify more than 16.000 plant species based on AI algorithms, also created and trained in-house. Plant identifications by citizen scientists create a new source of species occurrence data that enables biodiversity research.

A new paper, published by the Flora Incognita Research Group at the MPI for Biogeochemistry in Jena and the TU Ilmenau, shows that opportunistic plant observations, collected by thousands of plant enthusiasts via the plant identification app Flora Incognita (Mäder et al. 2021) can support phenological monitoring initiatives. Instead of being recorded on purpose, opportunistic plant observations are created when a plant catches a person’s eye and is captured in this instant.
Why is that important? Understanding plant phenology is helping scientists assess (for example) how nature reacts to climate change. Traditional species-specific monitoring of pheno phases (such as bud break, leaf-out, beginning of flowering, and leaf senescence) is done by trained volunteers – and those numbers are steadily declining.
 

Can Flora Incognita be used for phenology monitoring?

In Germany, the “official” plant phenology monitoring is mainly conducted by the German Weather Service (Deutscher Wetterdienst, DWD). Here, every observer is given a specific "station," which corresponds to a dedicated spot for the observation of one tree, shrub or herbaceous plant. The number of these stations varies depending on the observed species. This means that some species have more stations than others. During the growing season, observers need to check on the plants they are studying at least two times each week and record the day when specific pheno phases begin. But now there’s a new way to generate onset-of-flowering data of individual plants: The new study’s lead authors Negin Katal and Michael Rzanny found a way to process Flora Incognita data in a way that they resemble DWD stations:

They identified the locations of the DWD stations, and for each species, created a 5 km circle around them. Within this circle, they collected all the Flora Incognita observations for that species within a certain altitude range. At least 35 of those opportunistic records were needed to create a “Flora Incognita station”. If those could not be reached, even within an additional buffer (1 km at a time, up to 55 km), no Flora Incognita station was established at that location.
 

As a next step, for each of those Flora Incognita stations, the onset of flowering in 2020 and 2021 was calculated by fitting the number of observations against their Day-of-Year by using a special statistical method ( a parametric bootstrapping approach based on the Weibull distribution). The percentile closest to the median of the DWD interpolation in 2020 was then chosen as the species-specific onset of flowering for both years. The resulting interpolation maps show similar results to those provided by manual documentation done by the DWD for most species.