The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow

Expanding the Scope of the Bumblebee Model BEE-STEWARD: A Simple Foraging Module Facilitates the Parameterization

The BEE-STEWARD model simulates the population dynamics and behavior of bumblebees, including foraging, in remarkable detail, allowing the impact of various stressors on their populations to be assessed. To support the underlying detailed mechanistic descriptions, BEE-STEWARD requires extensive parameterization, including corolla depth, which affects the handling time of foraging bees, for each flower species in the simulated landscape. However, this detailed approach limits the applicability of BEE-STEWARD due to the lack of data for corolla depths, while also resulting in unrealistic foraging trip durations. Here we present a simplified foraging module that uses a constant handling time for foraging, thus eliminating the need to parameterize corolla depth. This simplification allows us both to apply the model to large scales and to assume handling times that reproduce observed foraging trip durations. Our new foraging module allows large-scale population projections with BEE-STEWARD. This increases its value in policy contexts and contributes to understanding and mitigating bumblebee declines.

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.

Using individual-based trait frequency distributions to forecast plant-pollinator network responses to environmental change

Determining how and why organisms interact is fundamental to understanding ecosystem responses to future environmental change. To assess the impact on plant-pollinator interactions, recent studies have examined how the effects of environmental change on individual interactions accumulate to generate species-level responses. Here, we review recent developments in using plant-pollinator networks of interacting individuals along with their functional traits, where individuals are nested within species nodes. We highlight how these individual-level, trait-based networks connect intraspecific trait variation (as frequency distributions of multiple traits) with dynamic responses within plant-pollinator communities. This approach can better explain interaction plasticity, and changes to interaction probabilities and network structure over spatiotemporal or other environmental gradients. We argue that only through appreciating such trait-based interaction plasticity can we accurately forecast the potential vulnerability of interactions to future environmental change. We follow this with general guidance on how future studies can collect and analyse high-resolution interaction and trait data, with the hope of improving predictions of future plant-pollinator network responses for targeted and effective conservation.