Long-term citizen science data reveals climate influences of tropical tree flowering at the regional scale

Tropical tree reproductive phenology is sensitive to changing climate, but inter-individual and interannual variability at the regional scale is poorly understood. While large-scale and long-term datasets of environmental variables are available, reproductive phenology needs to be measured in-site, limiting the spatiotemporal scales of the data. We leveraged a unique dataset assembled by SeasonWatch, a citizen-science phenology monitoring programme in India to assess the environmental correlates of flowering in three ubiquitous and economically important tree species - jackfruit, mango and tamarind - in the south-western Indian state of Kerala. We explored (i) seasonal patterns in the flowering of trees (ii) environmental correlates of flowering onset considering only trees with consecutive observations. We used 165,006 phenology observations spread over 19,591 individual trees over 9 years. We first used bootstrapped circular statistics that accounts for observation biases in time to examine consistency in seasonality of flowering status over the whole season, and flowering onset across years and trees. Similar to results from cohort-based tree monitoring, we demonstrate seasonality in flowering status and onset across species, but also report large interannual and inter-individual variability. We then used used generalized linear mixed models with remotely sensed observations (ERA5-LAND) to show that some of the interannual variation in flowering onset across individuals was associated with environmental variables. Soil moisture, minimum temperature and solar radiation had significant associations with the onset of flowering but these effects were heterogeneous across species and habitats across Kerala. Our results become increasingly important in the face of large spatiotemporal change in the climate of this landscape and other tropical regions. We demonstrate the potential and limitations of citizen-science observations in making and testing predictions at scale for predictive climate science in tropical landscapes.