High resolution prediction maps of solitary bee diversity can guide conservation measures

Wild bees are key ecosystem components making their decline a cause for concern. An effective measure to increase wild bee diversity is to enhance plant diversity. However, the effect on bee diversity of augmenting plant diversity depends on site-specific environmental conditions. We aimed to make spatial predictions of where: (a) environmental conditions maximize bee diversity, so that such areas can be prioritized for augmenting plant diversity; and (b) populations of threatened wild bee species are most likely to occur. We surveyed bee communities in traditionally managed hay meadows in SE Norway and modelled bee diversity as a function of climate, habitat area, and distance to nesting substrates. We used independent data to validate our predictions and found that plant and predicted bee species richness together explained 76% and 69% of the variation in observed solitary bee species richness in forested and agricultural ecosystems, respectively. In urban areas, the predicted bee species richness alone explained 31% of the variation in observed solitary bee species richness. Using data from online species occurrence records, we found that – compared to species of lower conservation concern – threatened solitary bee species were more typically recorded in areas with a high predicted solitary bee species richness. We show that spatial predictions of bee diversity can identify sites where augmenting plant diversity is likely to be most effective. Maps of predicted bee diversity can guide species surveys and monitoring projects and increase the chances of locating populations of threatened bees.publishedVersio

Climatic conditions and landscape diversity predict plant–beeinteractions and pollen deposition in bee-pollinated plants

Climate change, landscape homogenization, and the decline of beneficial insectsthreaten pollination services to wild plants and crops. Understanding how pollinationpotential (i.e. the capacity of ecosystems to support pollination of plants) is affectedby climate change and landscape homogenization is fundamental for our ability topredict how such anthropogenic stressors affect plant biodiversity. Models of pollina-tor potential are improved when based on pairwise plant–pollinator interactions andpollinator’s plant preferences. However, whether the sum of predicted pairwise interac-tions with a plant within a habitat (a proxy for pollination potential) relates to pollendeposition on flowering plants has not yet been investigated. We sampled plant–beeinteractions in 68 Scandinavian plant communities in landscapes of varying land-coverheterogeneity along a latitudinal temperature gradient of 4–8°C, and estimated pollendeposition as the number of pollen grains on flowers of the bee-pollinated plants Lotuscorniculatus and Vicia cracca. We show that plant–bee interactions, and the pollinationpotential for these bee-pollinated plants increase with landscape diversity, annual meantemperature, and plant abundance, and decrease with distances to sand-dominatedsoils. Furthermore, the pollen deposition in flowers increased with the predicted pol-lination potential, which was driven by landscape diversity and plant abundance. Ourstudy illustrates that the pollination potential, and thus pollen deposition, for wildplants can be mapped based on spatial models of plant–bee interactions that incorpo-rate pollinator-specific plant preferences. Maps of pollination potential can be used toguide conservation and restoration planning. ecological networks, ecosystem service mapping, landscape diversity, plant–pollinator interactions, pollinationpublishedVersio

The influence of nest-site limitation on the species richness and abundance of bees : linking biodiversity and geology

There is mounting evidence of declines in global biodiversity. Declining wild bee populations are subject to concern because bees are considered the most important pollinators worldwide, contributing to the production of agricultural crops and reproduction of wild plants. A suitable habitat for bees must include both foraging recourses and nesting recourses. While the link between flowers and bees has been shown innumerable times, the importance of nesting recourses is poorly documented. A large portion of the Norwegian bee fauna are solitary mining bees, because of their habits of not nesting on colonies and excavating subterranean nests in, most often, sandy soils. Due to enhanced nesting recourses for such mining bees, I hypothesised that sediment type would influence on the species richness and abundance of bees. To test this, I sampled bees in road verges on sediments with differing textures, comparing the bee fauna on glaciofluvial (sandy) versus marine (clayey/silty) sediments. The influence of sediment type was larger for the solitary compared to the social bees, and both the species richness of bees, as well as the species richness and abundance of solitary bees were higher on glaciofluvial compared to marine sediments. These results indicate nest site limitation as an important ecological factor influencing bee communities, implying that geological processes control the distribution of nesting recourses for mining bees in areas affected by glaciation processes.The Directorate of Public RoadssubmittedVersionM-N

On the distribution of the rare solitary bee Coelioxys lanceolata Nylander, 1852 (Hymenoptera, Megachilidae) in Norway

publishedVersio

High resolution prediction maps of solitary bee diversity can guide conservation measures

Wild bees are key ecosystem components making their decline a cause for concern. An effective measure to increase wild bee diversity is to enhance plant diversity. However, the effect on bee diversity of augmenting plant diversity depends on site-specific environmental conditions. We aimed to make spatial predictions of where: (a) environmental conditions maximize bee diversity, so that such areas can be prioritized for augmenting plant diversity; and (b) populations of threatened wild bee species are most likely to occur. We surveyed bee communities in traditionally managed hay meadows in SE Norway and modelled bee diversity as a function of climate, habitat area, and distance to nesting substrates. We used independent data to validate our predictions and found that plant and predicted bee species richness together explained 76% and 69% of the variation in observed solitary bee species richness in forested and agricultural ecosystems, respectively. In urban areas, the predicted bee species richness alone explained 31% of the variation in observed solitary bee species richness. Using data from online species occurrence records, we found that – compared to species of lower conservation concern – threatened solitary bee species were more typically recorded in areas with a high predicted solitary bee species richness. We show that spatial predictions of bee diversity can identify sites where augmenting plant diversity is likely to be most effective. Maps of predicted bee diversity can guide species surveys and monitoring projects and increase the chances of locating populations of threatened bees

MetaComNet: A random forest-based framework for making spatial predictions of plant-pollinator interactions

Predicting plant–pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant–pollinator interactions and of the species richness, diversity and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. To illustrate the MetaComNet functionality, we used a dataset on bee–flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee–flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250 m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant–bee interactions across the landscape. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee–flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48) and abundance (r = 0.42) of wild bees interacting with plant species within sites. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant–pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function

MetaComNet: A random forest- based framework for making spatial predictions of plant– pollinator interactions

1. Predicting plant–pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant–pollinator interactions and of the species richness, diversity and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. 2. To illustrate the MetaComNet functionality, we used a dataset on bee–flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee–flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250 m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant–bee interactions across the landscape. 3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee–flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48) and abundance (r = 0.42) of wild bees interacting with plant species within sites. 4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant–pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function. interactions, network, plants, pollinators, predict, random fores

MetaComNet: A random forest-based framework for making spatial predictions of plant-pollinator interactions

Predicting plant–pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant–pollinator interactions and of the species richness, diversity and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. To illustrate the MetaComNet functionality, we used a dataset on bee–flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee–flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250 m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant–bee interactions across the landscape. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee–flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48) and abundance (r = 0.42) of wild bees interacting with plant species within sites. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant–pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function

MetaComNet: A random forest- based framework for making spatial predictions of plant– pollinator interactions

1. Predicting plant–pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant–pollinator interactions and of the species richness, diversity and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. 2. To illustrate the MetaComNet functionality, we used a dataset on bee–flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee–flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250 m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant–bee interactions across the landscape. 3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee–flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48) and abundance (r = 0.42) of wild bees interacting with plant species within sites. 4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant–pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function. interactions, network, plants, pollinators, predict, random fores

MetaComNet: A random forest-based framework for making spatial predictions of plant–pollinator interactions

1. Predicting plant–pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant–pollinator interactions and of the species richness, diversity and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. 2. To illustrate the MetaComNet functionality, we used a dataset on bee–flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee–flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250 m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant–bee interactions across the landscape. 3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee–flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48) and abundance (r = 0.42) of wild bees interacting with plant species within sites. 4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant–pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function. interactions, network, plants, pollinators, predict, random fores