Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web

Despite intensive research, predicting pairwise species associations in pollination networks remains a challenge. The morphological fit between flowers and pollinators acts as a filter that allows only some species within the network to interact. Previous studies emphasized the depth of floral tubes as a key shape trait that explains the composition of their animal visitors. Yet, additional shape-related parameters, related to the handling difficulty of flowers, may be important as well. We analyzed a dataset of 2288 visits by six bee genera to 53 flowering species in a Mediterranean plant community. We characterized the plant species by five discrete shape parameters, which potentially affect their accessibility to insects: floral shape class, tube depth, symmetry, corolla segmentation and type of reproductive unit. We then trained a random forest machine-learning model to predict visitor identities, based on the shape traits. The model’s predictor variables also included the Julian date on which each bee visit was observed and the year of observation, as proxies for within- and between-season variation in flower and bee abundance. The model attained a classification accuracy of 0.86 (AUC = 0.96). Using only shape parameters as predictors reduced its classification accuracy to 0.76 (AUC = 0.86), while using only the date and year variables resulted in a prediction accuracy of 0.69 (AUC = 0.80). Among the shape-related variables considered, flower shape class was the most important predictor of visitor identity in a logistic regression model. Our study demonstrates the power of machine-learning algorithms for understanding pollination interactions in a species-rich plant community, based on multiple features of flower morphology

The Effect of Movement on Cognitive Performance

The study examines the relationship between walking, cognitive, and academic skills. Students from elementary, middle, high school, and college were required to walk for 10 min prior to completing feature detection, Simon-type memory, and mathematical problem-solving tasks. Participants were counterbalanced to remove a time bias. Ten minutes of walking had a significant positive effect on Simon-type memory and critical feature-detection tasks among all age groups. Separately, with mathematical problem-solving ability, higher performing high-school students demonstrated significant positive effects on mathematical reasoning tasks based on the Bloom Taxonomy. However, poorly achieving high-school students performed significantly better than those with higher grades in mathematics on tests of mathematical problem-solving ability based on the Bloom’s Taxonomy. The study indicates that there is justification to employ relatively simple means to effect lifestyle, academic, and cognitive performance