Dynamics of collective flora macrophenology from crowd-sourced data

Monitoring changes in phenology, i.e. changes in flora states, is key to understanding the impact of climate change on ecosystems and biodiversity. Crowd-sourced data from smartphone applications are gaining in popularity in many ecological applications and are especially relevant for automated species recognition. However, the potential of crowd-sourced data for studying phenology at macroecological scales has not been deeply explored. We aim to quantify the collective phenological cycle of plant co-ocurrences based on citizen science data.
We analyse crowd-sourced German plant observation data collected with the smartphone application Flora Incognita, which identifies plant species native to Central Europe from images in real time using deep learning. We propose that the dynamics of collective flora behaviour is embedded in the temporal co-occurrence observations. To extract this collective phenological dynamics we propose the manifold learning method isometric feature mapping. As this approach is data driven no a priori assumptions are made about how to define collective behaviour. We propose a complexity measure to characterise the dynamics across large spatial scales introducing macrophenology.
Our results demonstrate that the macrophenological patterns can be effectively detected from crowd-sourced plant observation data. The strong collective flowering in spring and summer allows us to clearly characterise phenological transitions, specifically the faster changes in spring compared to autumn. The emerging complexity measure of collective behaviour is an indicator for linear and nonlinear temporal changes in  macroecological patterns in the summer and the rest of the year, respectively.
Despite biases and uncertainties associated with opportunistically collected crowd-sourced data it is possible to derive meaningful indicators for monitoring plant phenology. In the near future multi-year records of such data will be available to explore phenological shifts and how they are impacted by climate change in near real time.