Habitat loss can have a negative effect on the number, abundance, and composition of species in plant-pollinator communities. Although we have a general understanding of the negative consequences of habitat loss for biodiversity, much less is known about the resulting effects on the pattern of interactions in mutualistic networks. Ecological networks formed by mutualistic interactions often exhibit a highly nested architecture with low modularity, especially in comparison with antagonistic networks. These patterns of interaction are thought to confer stability on mutualistic communities. With the growing threat of environmental change, it is important to expand our understanding of the factors that affect biodiversity and the stability of the communities that provide critical ecosystem functions and services. We studied the effects of habitat loss on plant--pollinator network architecture and found that regional habitat loss contributes directly to species loss and indirectly to the reorganization of interspecific interactions in a local community. Networks became more highly connected and more modular with habitat loss. Species richness and abundance were the primary drivers of variation in network architecture, though species compositi n affected modularity. Theory suggests that an increase in modularity with habitat loss will threaten community stability, which may contribute to an extinction debt in communities already affected by habitat loss.

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Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.

Conservation measures for bees often focus on increasing the diversity and abundance of floral resources. But it has not been clear if observed benefits of floral enhancements result from greater population growth, which is critical for the long-term success of conservation, or from mobile foragers aggregating in high-resource locations. Experimental evidence is only beginning to emerge in favor of the former mechanism and it is not well-established how different aspects of floral resources affect population growth. For example, bumble bee colonies may benefit from greater overall floral abundance, richness, or relative dominance of resource species. Because bumble bees are highly mobile, resource variability in the surrounding landscape is also important for colonies and may mediate local-scale effects. We experimentally assessed the growth and reproduction of bumble bee colonies (Bombus impatiens) deployed in grasslands in different local-and landscape-scale resource environments. We found that floral dominance, rather than the overall abundance or richness of floral resources, was the most important local factor for colony growth and reproduction. This may reflect more efficient foraging on a few numerically dominant and abundant resource species. Local-and landscape-scale predictor variables had interacting effects on colony growth and reproduction, suggesting that foraging distance depends on where in the landscape efficiently used resources are located. Our results provide further evidence that conservation strategies aimed at enhancing floral resources can increase bumble bee population growth. However, the most effective form of floral enhancement may vary among bee species.