4.6 Article

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

期刊

METHODS IN ECOLOGY AND EVOLUTION
卷 12, 期 10, 页码 1809-1815

出版社

WILEY
DOI: 10.1111/2041-210X.13673

关键词

agent-based modelling; BEESCOUT; Bombus terrestris; Bumble-BEEHAVE; individual-based modelling; pollinator; systems model

类别

资金

  1. Natural Environment Research Council [NE/P016731/1, NE/P010725/1, NE/P011217/1, NE/S011870/2]
  2. Fields Institute for Research in Mathematical Sciences
  3. NERC [NE/S011870/2, NE/P010725/1, NE/P011217/1, NE/P016731/1] Funding Source: UKRI

向作者/读者索取更多资源

The BEE-STEWARD software tool serves as a research and decision-support tool for predicting and comparing the effects of bee-friendly management interventions on bumblebee populations. It integrates agent-based models of bumblebee behavior, colony growth, and landscape exploration into a user-friendly interface with automated reporting capabilities.
The demand for agent-based models to explore the effects of environmental change on pollinator population dynamics is growing. However, models need a simple yet flexible interface to enable adoption by a wide range of stakeholders. We introduce BEE-STEWARD: a research and decision-support software tool, enabling researchers, policymakers, land management advisors and practitioners to predict and compare the effects of bee-friendly management interventions on bumblebee populations over several years. BEE-STEWARD integrates the BEESCOUT and Bumble-BEEHAVE agent-based models of bumblebee behaviour, colony growth and landscape exploration into a user-friendly interface, with reconstructed code, and expanded functionality. Bespoke automatic reports can be created to illustrate how different land management interventions can affect the densities of bumblebees and their colonies over time. BEE-STEWARD could be an important virtual test bed for scientists exploring the impacts of different stressors on bumblebees and used by those with little or no modelling experience, enabling a shared methodology between research, policy and practice.

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