Deep learning for accurate classification of conifer pollen grains: enhancing species identification in palynology.

Accurate identification of pollen grains from (fir), (spruce), and (pine) is an important method for reconstructing historical environments, past landscapes and understanding human-environment interactions. However, distinguishing between pollen grains of conifer genera poses challenges in palynology due to their morphological similarities. To address this identification challenge, this study leverages advanced deep learning techniques, specifically transfer learning models, which are effective in identifying similarities among detailed features.

Effects of climate change on Lepidoptera pollen loads and their pollination services in space and time.

Shifts in flowering time among plant communities as a result of climate change, including extreme weather events, are a growing concern. These plant phenological changes may affect the quantity and quality of food sources for specialized insect pollinators. Plant-pollinator interactions are threatened by habitat alterations and biodiversity loss, and changes in these interactions may lead to declines in flower visitors and pollination services.