Reliably predicting pollinator abundance: Challenges of calibrating process‐based ecological models

Models of bee responses to land use and land cover changes in agricultural landscapes - a review and research agenda

Predictive modelling tools can be used to support the design of agricultural landscapes to promote pollinator biodiversity and pollination services. Despite the proliferation of such modelling tools in recent decades, there remains a gap in synthesising their main characteristics and representation capacities. Here, we reviewed 42 studies that developed non-correlative models to explore the impact of land use and land cover changes on bee populations, and synthesised information about the modelled systems, modelling approaches, and key model characteristics like spatiotemporal extent and resolution. Various modelling approaches are employed to predict the biodiversity of bees and the pollination services they provide, with a prevalence of models focusing on wild populations compared to managed ones. Of these models, landscape indicators and distance decay models are relatively simple, with few parameters. They allow mapping bee visitation probabilities using basic land cover data and considering bee foraging ranges. Conversely, mechanistic or agent-based models delineate, with varying degrees of complexity, a multitude of processes that characterise, among others, the foraging behaviour and population dynamics of bees. The reviewed models collectively encompass 38 ecological, agronomic, and economic processes, producing various outputs including bee abundance, habitat visitation rate, and crop yield. To advance the development of predictive modelling tools aimed at fostering pollinator biodiversity and pollination services in agricultural landscapes, we highlight future avenues for increasing biophysical realism in models predicting the impact of land use and land cover changes on bees. Additionally, we address the challenges associated with balancing model complexity and practical usability.

Pollinator-mediated effects of landscape-scale land use on grassland plant community composition and ecosystem functioning - seven hypotheses

Environmental change is disrupting mutualisms between organisms worldwide. Reported declines in insect populations and changes in pollinator community compositions in response to land use and other environmental drivers have put the spotlight on the need to conserve pollinators. While this is often motivated by their role in supporting crop yields, the role of pollinators for reproduction and resulting taxonomic and functional assembly in wild plant communities has received less attention. Recent findings suggest that observed and experimental gradients in pollinator availability can affect plant community composition, but we know little about when such shifts are to be expected, or the impact they have on ecosystem functioning. Correlations between plant traits related to pollination and plant traits related to other important ecosystem functions, such as productivity, nitrogen uptake or palatability to herbivores, lead us to expect non-random shifts in ecosystem functioning in response to changes in pollinator communities. At the same time, ecological and evolutionary processes may counteract these effects of pollinator declines, limiting changes in plant community composition, and in ecosystem functioning. Despite calls to investigate community- and ecosystem-level impacts of reduced pollination, the study of pollinator effects on plants has largely been confined to impacts on plant individuals or single-species populations. With this review we aim to break new ground by bringing together aspects of landscape ecology, ecological and evolutionary plant-insect interactions, and biodiversity-ecosystem functioning research, to generate new ideas and hypotheses about the ecosystem-level consequences of pollinator declines in response to land-use change, using grasslands as a focal system. Based on an integrated set of seven hypotheses, we call for more research investigating the putative pollinator-mediated links between landscape-scale land use and ecosystem functioning. In particular, future research should use combinations of experimental and observational approaches to assess the effects of changes in pollinator communities over multiple years and across species on plant communities and on trait distributions both within and among species.

Pesticide Exposure and Effects on Non-Apis Bees

Bees are essential pollinators of many crops and wild plants, and pesticide exposure is one of the key environmental stressors affecting their health in anthropogenically modified landscapes. Until recently, almost all information on routes and impacts of pesticide exposure came from honey bees, at least partially because they were the only model species required for environmental risk assessments (ERAs) for insect pollinators. Recently, there has been a surge in research activity focusing on pesticide exposure and effects for non-Apis bees, including other social bees (bumble bees and stingless bees) and solitary bees. These taxa vary substantially from honey bees and one another in several important ecological traits, including spatial and temporal activity patterns, foraging and nesting requirements, and degree of sociality. In this article, we review the current evidence base about pesticide exposure pathways and the consequences of exposure for non-Apis bees. We find that the insights into non-Apis bee pesticide exposure and resulting impacts across biological organizations, landscapes, mixtures, and multiple stressors are still in their infancy. The good news is that there are many promising approaches that could be used to advance our understanding, with priority given to informing exposure pathways, extrapolating effects, and determining how well our current insights (limited to very few species and mostly neonicotinoid insecticides under unrealistic conditions) can be generalized to the diversity of species and lifestyles in the global bee community. We conclude that future research to expand our knowledge would also be beneficial for ERAs and wider policy decisions concerning pollinator conservation and pesticide regulation.

Mapping nectar-rich pollinator floral resources using airborne multispectral imagery

Wild pollinator numbers are known to be positively associated with amounts of flower-rich habitat at landscape level. Increasing floral resources can be particularly beneficial in relatively nectar-poor agricultural systems and having a baseline understanding of the temporal and spatial availability of resources can allow targeted habitat management. Very high-resolution remote sensing has potential to facilitate accurate mapping of fine-scale, within-habitat pollinator foraging resources, thereby allowing spatial and temporal gaps to be identified and addressed, improving predictions of pollinator numbers, and enabling remote monitoring of pollinator conservation measures. Concentrating on hedgerow and flower-rich field margins in a UK agricultural landscape, we showed that multispectral airborne imagery with 3 cm and 7 cm spatial resolutions can be used to classify five nectar-rich flowering plant species (Prunus spinosa, Crataegus monogyna, Rubus fruticosus, Silene dioica and Centaurea nigra) using a maximum likelihood classification algorithm. In 2019, we separately acquired 3 cm and 7 cm imagery for the months of March, May and July, respectively. Overall accuracies were above 90% for each month at both 3 cm and 7 cm resolutions (range 92.32%-98.72%), supporting previous research that suggests higher spatial resolutions do not necessarily lead to higher accuracies, as pixel variability is increased. Remaining challenges include determining which co-flowering species of similar colours in the visible range can be distinguished from one another within classifications and quantifying floral unit density from classifications so that the nectar sugar supply can be calculated. Nonetheless, we provided a prototype approach for mapping pollinator foraging resources in an agricultural context, which can be extended to other nectar-rich species. The foundation is set for developing a remote sensing pipeline that can provide valuable data on the availability of nectar-rich flowering plant species at different time-points throughout the year.