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

## TL;DR

A simplified foraging module improves the BEE-STEWARD model for simulating bumblebee populations at large scales.

## Contribution

A simplified foraging module eliminates the need for detailed flower parameters like corolla depth.

## Key findings

- The simplified foraging module allows large-scale population projections with BEE-STEWARD.
- Handling times in the new module align with observed foraging trip durations.
- The model becomes more applicable for policy and conservation efforts.

## Abstract

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.

We present a simplified foraging model for the BEE‐STEWARD model that has been developed to simulate the population dynamics of bumblebees at a landscape scale. The new foraging model will facilitate the application and parameterisation of the model. In addition, the new foraging model results in handling times that are in line with empirical observations.