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

Twiston-Davies, Grace; Becher, Melissa; Osborne, Juliet L.

doi:10.1111/2041-210x.13673

Cited by 7 publications

(7 citation statements)

References 27 publications

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“…All simulations were conducted using BEE‐STEWARD (Twiston‐Davies et al, 2021), a software tool that integrates the fully documented and validated Bumble‐BEEHAVE and BEESCOUT models of bee colony growth, survival and landscape exploration (Becher et al, 2018). This group of models has been widely applied in research investigating the impact of disease and resource availability on bee colony success and in the development of land management recommendations (see https://beehave-model.net/publications/).…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we use the BEE-STEWARD agent-based model (Twiston-Davies et al, 2021), coded in Netlogo 5.3.1 (Wilensky, 1999), to investigate two questions for two bumblebee We hypothesise that the ability of a bumblebee colony to cope with a forage gap depends on (H1) the timing of the gap, and (H2) the colony composition (numbers of eggs, larvae, pupae and adults) (e.g. Horn et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we use the BEE‐STEWARD agent‐based model (Twiston‐Davies et al, 2021), coded in Netlogo 5.3.1 (Wilensky, 1999), to investigate two questions for two bumblebee species, Bombus terrestris (L. 1758) and B. pascuorum (Scopoli, 1763): (Q1) ‘When during the season, and to what extent, does spatio‐temporally transient resource limitation affect bumblebee fitness?’. We hypothesise that the ability of a bumblebee colony to cope with a forage gap depends on (H1) the timing of the gap, and (H2) the colony composition (numbers of eggs, larvae, pupae and adults) (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Resource gaps pose the greatest threat for bumblebees during the colony establishment phase

Becher,

Twiston‐Davies,

Osborne

et al. 2024

Insect Conserv Diversity

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A common management intervention to support declining wild pollinators is ‘pollinator planting’. However, despite years of inclusion in conservation initiatives, global pollinator declines continue. Using the agent‐based model BEE‐STEWARD with two example species, Bombus terrestris (L. 1758) and B. pascuorum (Scopoli, 1763) (Hymenoptera: Apidae), we explore when during the year bumblebee resource demand is the highest and how that relates to seasonal changes in colony composition (numbers of eggs, larvae, pupae and adults). We then examine the impact of two‐week periods of resource scarcity across the year. Finally, we explore how enhancement with early spring‐blooming herbaceous species or trees changes colony survival and queen production. In the United Kingdom, there is a previously under‐appreciated ‘hungry gap’ for bumblebees in March–April, before the peak flight period, driven by the demands of larvae for protein and thermoregulation in the colony, rather than the number of adult bees. A 2‐week gap in forage availability during this period drives a 50%–87% drop in the production of daughter queens. Adding early‐blooming species in the model had significant, positive, long‐term effects on colony survival probability and daughter queen production. Pollinator‐planting initiatives in both national and international conservation schemes need to include plants that flower up to 1 month before the adults of target social pollinator species are apparent in the field, during the period that larvae dominate the colony. This approach is likely to increase colony survival and queen production, contributing towards halting and reversing global pollinator decline.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resource gaps pose the greatest threat for bumblebees during the colony establishment phase

Becher,

Twiston‐Davies,

Osborne

et al. 2024

Insect Conserv Diversity

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“…DAS is starting to be used to inform pollination-related decisions and could be a valuable tool for ICP. BEE-STEWARD is one research and decision-support software tool that can show how different environmental variables impact wild bumble bees in the landscape (Twiston-Davies et al, 2021). Climate-induced declines in fruit set in bog blueberry (Vaccinium uliginosum L.) has also been predicted to be mitigated with increases in honey bee and bumble bee densities using predictive modeling approaches based on the transfer learning concept to solve for data deficiency problems (Qu et al, 2021).…”

Section: Decision Aid Systemsmentioning

confidence: 99%

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

DeVetter

Chabert

Milbrath

et al. 2022

Front. Sustain. Food Syst.

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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.

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“…Despite the empirical evidence in favor of using wildflower enhancements to increase crop pollination, the costs of these management strategies together with the lack of clarity on the optimal location, quantity, flower type, or flowering time of the wildflower enhancements diminishes the practical effectiveness of these strategies (Albrecht et al, 2020). Recently, useful insights have been provided by several modelling studies (Capera-Aragones, Foxall, et al, 2021, 2022; Carturan et al, 2023; Häussler et al, 2017; Lonsdorf et al, 2009; MacQueen et al, 2022; Twiston-Davies et al, 2021). However, none of these models are able to fully describe the population dynamics, the spatial dynamics, the balance between nutrition and energy needs of bees, and their interplay, all of which are important in determining the effect of wildflower patches on pollination services.…”

Section: Introductionmentioning

confidence: 99%

Dynamic energy budget model for a bumble bee colony: Predicting the spatial distribution and dynamics of colonies across multiple seasons

Capera-Aragones,

Mariño,

Hurford

et al. 2024

Preprint

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Bumble bees are important pollinators of many crops around the world. In recent decades, agricultural intensification has resulted in significant declines in bumble bee populations and the pollination services they provide. Empirical studies have shown that this trend can be reversed, however, by enhancing the agricultural landscape with natural habitat, such as adding wildflower patches adjacent to crops. Despite the empirical evidence, the mechanisms behind these positive effects are not fully understood, and the specific characteristics of the enhanced natural habitat that would maximize benefits are unclear at this time. Theoretical studies, in the form of mathematical models, have proven useful in elucidating the underlying mechanisms and determining the optimal natural habitat configurations. Existing models, however, generally focus only on particular aspects of bumble bee behaviour; some models are accurate at describing population dynamics, while others are accurate at describing their spatial distribution. In this work, we build a unique model coupling population dynamics, using a whole-colony Dynamic Energy Budget (DEB) approach, to a spatial distribution model based on the maximum energy principle. This coupling gives valuable new insights into the effects of spatial arrangements on population dynamics, and vice-versa. With our model, we answer questions such as when, how much, or what type of wildflower patches should be planted to maximize crop pollination services and minimize bee decline. We find that planting wildflowers that bloom before and after crop bloom is crucial to achieve high pollination services and preserving wild pollinator populations. We also find that small quantities of natural habitat are needed when the crop is nutritionally rich, but higher quantities are most beneficial when the crop is nutritionally deficient.

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

Cited by 7 publications

References 27 publications

Resource gaps pose the greatest threat for bumblebees during the colony establishment phase

Resource gaps pose the greatest threat for bumblebees during the colony establishment phase

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

Dynamic energy budget model for a bumble bee colony: Predicting the spatial distribution and dynamics of colonies across multiple seasons

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