BEE‐STEWARD: A research and decision‐support software for effective land management to promote bumblebee populations

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.

Toward evidence-based decision support systems to optimize pollination and yields in highbush blueberry

Highbush blueberry (Vaccinium spp.) is a globally important fruit crop that depends on insect-mediated pollination to produce quality fruit and commercially viable yields. Pollination success in blueberry is complex and impacted by multiple interacting factors including flower density, bee diversity and abundance, and weather conditions. Other factors, including floral traits, bee traits, and economics also contribute to pollination success at the farm level but are less well understood. As blueberry production continues to expand globally, decision-aid technologies are needed to optimize and enhance the sustainability of pollination strategies. The objective of this review is to highlight our current knowledge about blueberry pollination, where current research efforts are focused, and where future research should be directed to successfully implement a comprehensive blueberry pollination decision-making framework for modern production systems. Important knowledge gaps remain, including how to integrate wild and managed pollinators to optimize pollination, and how to provide predictable and stable crop pollination across variable environmental conditions. In addition, continued advances in pesticide stewardship are required to optimize pollinator health and crop outcomes. Integration of on- and off-farm data, statistical models, and software tools could distill complex scientific information into decision-aid systems that support sustainable, evidence-based pollination decisions at the farm level. Utility of these tools will require multi-disciplinary research and strategic deployment through effective extension and information-sharing networks of growers, beekeepers, and extension/crop advisors.