The interplay between scale, pollination niche and floral attractiveness on density-dependent plant-pollinator interactions

Pollinators mediate interspecific and intraspecific plant-plant indirect interactions (competition vs. facilitation) via density-dependent processes, potentially shaping the dynamics of plant communities. However, it is still unclear which ecological drivers regulate density-dependent patterns, including scale, pollination niches (i.e., the main pollinator functional group) and floral attractiveness to pollinators. In this study, we conducted three-year field observations in Hengduan Mountains of southwest China. By gathering data for more than 100 animal-pollinated plant species, we quantified the effect (positive vs. negative) of conspecific and heterospecific flower density on pollination at two scales: plot-level (4 m2) and site-level (100-5000 m2). Then, we investigated how pollination niches and floral attractiveness to pollinators (estimated here as average per-flower visitation rates) modulated density-dependent pollination interactions. Pollinator visitation depended on conspecific and heterospecific flower density, with rare plants subjected to interspecific competition at the plot-level and interspecific facilitation at the site-level. Such interspecific competition at the plot-level was stronger for plants pollinated by diverse insects, while interspecific facilitation at the site-level was stronger for bee-pollinated plants. Moreover, we also found stronger positive conspecific density-dependence for plants with lower floral attractiveness at the site-level, meaning that they become more frequently visited when abundant. Our study indicates that the role of pollination in maintaining rare plants and plant diversity depends on the balance of density-dependent processes in species-rich communities. We show here that such balance is modulated by scale, pollination niches and floral attractiveness to pollinators, indicating the context-dependency of diversity maintenance mechanisms.

Flower Mapping in Grasslands With Drones and Deep Learning

Manual assessment of flower abundance of different flowering plant species in grasslands is a time-consuming process. We present an automated approach to determine the flower abundance in grasslands from drone-based aerial images by using deep learning (Faster R-CNN) object detection approach, which was trained and evaluated on data from five flights at two sites. Our deep learning network was able to identify and classify individual flowers. The novel method allowed generating spatially explicit maps of flower abundance that met or exceeded the accuracy of the manual-count-data extrapolation method while being less labor intensive. The results were very good for some types of flowers, with precision and recall being close to or higher than 90%. Other flowers were detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology, or flowers being too small to be reliably distinguishable on the aerial images. The method was able to give precise estimates of the abundance of many flowering plant species. In the future, the collection of more training data will allow better predictions for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problem.