Why Do Hives Die? Using Mathematics to Solve the Problem of Honey Bee Colony Collapse

Myerscough, Mary R.; Khoury, David S.; Ronzani, Sean; Barron, Andrew B.

doi:10.1007/978-981-10-0962-4_4

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…If an individual bee dies, another rapidly takes her place; indeed, in several species there are exquisite physiological and/or behavioural mechanisms that allow individuals to alter their developmental trajectory to replace lost nest mates (Robinson 1992). However, it is becoming increasingly clear that social bees may, under certain conditions, suffer from colony collapses due to positive feedback mechanisms within the colony (Bryden et al 2013, Perry et al 2015, Myerscough et al 2017.…”

Section: Empirical Evidence For Tipping Points and Thresholds In Pollinations Systemsmentioning

confidence: 99%

“…Younger workers typically perform in-nest tasks, while the oldest workers function as foragers (Huang and Robinson 1996). Up to a critical mortality rate, colonies can maintain a stable population size by replacing lost workers with suboptimally performing precocious foragers (bees that accelerate their development to become foragers earlier than usual) (Khoury et al 2011, Barron 2015, Perry et al 2015, Myerscough et al 2017). However, precocious foragers are not as proficient as normal-aged foragers and thus suffer from a higher mortality rate.…”

Section: Empirical Evidence For Tipping Points and Thresholds In Pollinations Systemsmentioning

confidence: 99%

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The risk of threshold responses, tipping points, and cascading failures in pollination systems

Latty

Dakos

2019

Biodivers Conserv

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Growing evidence of global declines in pollinator abundance and diversity has raised concerns about the resilience of pollination systems. When subjected to stressors, each nested component of the pollination system (communities, populations, and colonies) can respond in either a smooth linear fashion, or in an abrupt nonlinear manner. Threshold and tipping point responses to stress are of particular concern because they result in sudden changes with little warning; such changes may lead to persistent non-functional states that are difficult to reverse. Here, we review evidence for threshold and tipping point responses at the colony, population and community levels of the pollination system. We find that while there are strong theoretical reasons to expect tipping point and threshold responses at all three levels of the pollination system, evidence in the field is lacking for all levels except the colony level.While this is encouraging, caution is still warranted as tipping point and threshold responses -by their very nature-may not be apparent until they are underway. Moreover, we propose that the interaction of nonlinear stress responses across different levels of the pollination system can increase the risk of cascading failures. We therefore suggest a cautious approach toward the management of pollination systems. Since environmental change will almost certainly continue to accelerate, understanding the potential for thresholds, tipping points and cascading failures is key to safeguarding global pollination systems.

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Section: Empirical Evidence For Tipping Points and Thresholds In Pollinations Systemsmentioning

confidence: 99%

Section: Empirical Evidence For Tipping Points and Thresholds In Pollinations Systemsmentioning

confidence: 99%

The risk of threshold responses, tipping points, and cascading failures in pollination systems

Latty

Dakos

2019

Biodivers Conserv

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“…Food collection by foragers is in some models constant in time (e.g. Khoury et al, 2013;Perry et al, 2015;Myerscough et al, 2017;Schmickl and Karsai, 2017). Other models simulate changes during the year, either just by accounting for a stop of foraging during winter (Betti et al, 2014(Betti et al, , 2016, or by accounting for seasonal fluctuations of food availability in the environment (e.g.…”

Section: The Use Of the Beehave Modelmentioning

confidence: 99%

Analysis of background variability of honey bee colony size

Ippolito¹,

Focks²,

Rundlöf³

et al. 2021

EFS3

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In the context of the definition of specific protection goals for bees, risk managers asked EFSA to provide scientific background to support them in their decision‐making process about what needs to be protected and to what extent. The risk managers indicated that the derivation of a threshold of acceptable effects on colony size based on their variability was the preferred option for honey bees. This approach assumes that when evaluating a pesticide, the magnitude of acceptable effects should be set within the range of the background variability of colonies not exposed to pesticides. In this report EFSA used the BEEHAVE model to assess background variability of colony size in 19 EU environmental scenarios covering a range of geographical, climatic and beekeeping conditions. A comparison was made between the model outcome and the measurements performed on control groups of experimental field studies. The analysis of the background variability presented in this document should support risk managers in defining a threshold for colony size reduction that is considered acceptable.

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Adjoint State Optimization Algorithm for Prediction of Honeybee Population Losses

Atanasov

Georgiev

2023

Advanced Computing in Industrial Mathematics

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Why Do Hives Die? Using Mathematics to Solve the Problem of Honey Bee Colony Collapse

Cited by 9 publications

References 19 publications

The risk of threshold responses, tipping points, and cascading failures in pollination systems

The risk of threshold responses, tipping points, and cascading failures in pollination systems

Analysis of background variability of honey bee colony size

Adjoint State Optimization Algorithm for Prediction of Honeybee Population Losses

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