The effect of stress on bee foraging behaviour
Lead Research Organisation:
Newcastle University
Department Name: Biosciences Institute
Abstract
Background: The influence of stress on cognition is a key current consideration for animal welfare in vertebrates, but this has rarely been studied in invertebrates. Understanding similar responses in invertebrates such as bees would have crucial implications for their pollinator services and agriculture. Bumblebees are ideal systems to study these stress responses as they are easily kept in the lab and their cognition and behaviour is well characterized. A handful of previous papers have begun to show evidence of stress-induced cognitive changes in bees and have implicated bioamines such as dopamine in these changes. This project will use an animal welfare approach to investigate bee responses to major natural stressors and the mechanisms underlying these responses.
Aims: 1) To characterize how stressors affect the immediate cognitive behaviours during pollination 2) To determine the time course of these effects in relation to the duration and frequency of stress 3) To investigate the neurochemicals underlying the stress response in bees
Methodology: We will use behavioural assays called judgement bias tests that evaluate bee responses post-stress to ambiguous sources of reward. These classic tests for welfare have rarely been used with bees, but ongoing work in the PI's lab has used the approach to show cognitive biases in bees. These tests will characterize the time course of the response as well as the effect of stress duration and frequency (Years 1 and 2). Machine-learning based approaches using DeepLabCut, a Python-based open source software, will be used to characterize behaviour including changes in reward consumption and persistence on flowers (Year 2). To understand the mechanisms underlying these changes, work in the second supervisor's lab will investigate changes to the expression of dopamine and serotonin receptors in the brain at the level of both RNA (with qPCRs) and protein (with brain immunostaining and imaging) (Years 3 and 4). Neuropharmacological interventions will also be used to determine how the levels of bioamines such as dopamine and serotonin control the response to stressors over time (Years 2 and 3).
Novelty: This project will make important progress for several fields including the neural basis of decision-making, animal welfare and pollinator biology. It addresses fundamental questions in animal welfare that are logistically and ethically more difficult to ask in vertebrates. This study will thus provide vital information in a tractable model system with important implications for pollination and agriculture.
Understanding how stress affects pollinator behaviour has important implications for the NLD-DTP themes food security and sustainable agriculture (also BBSRC areas of investment) and understanding the effect of stress on brains and neurochemistry fits the neuroscience theme. Understanding how pesticides impact pollinators fits the BBSRC remit of research into animal models of toxicology, animal health and the welfare of managed animals. Developing bees as a model for animal welfare instead of vertebrates also works towards the replacement aspect of the 3Rs priorities of the BBSRC. Machine-learning based approaches to looking at behaviour also fulfil the remit of data-driven biology.
Aims: 1) To characterize how stressors affect the immediate cognitive behaviours during pollination 2) To determine the time course of these effects in relation to the duration and frequency of stress 3) To investigate the neurochemicals underlying the stress response in bees
Methodology: We will use behavioural assays called judgement bias tests that evaluate bee responses post-stress to ambiguous sources of reward. These classic tests for welfare have rarely been used with bees, but ongoing work in the PI's lab has used the approach to show cognitive biases in bees. These tests will characterize the time course of the response as well as the effect of stress duration and frequency (Years 1 and 2). Machine-learning based approaches using DeepLabCut, a Python-based open source software, will be used to characterize behaviour including changes in reward consumption and persistence on flowers (Year 2). To understand the mechanisms underlying these changes, work in the second supervisor's lab will investigate changes to the expression of dopamine and serotonin receptors in the brain at the level of both RNA (with qPCRs) and protein (with brain immunostaining and imaging) (Years 3 and 4). Neuropharmacological interventions will also be used to determine how the levels of bioamines such as dopamine and serotonin control the response to stressors over time (Years 2 and 3).
Novelty: This project will make important progress for several fields including the neural basis of decision-making, animal welfare and pollinator biology. It addresses fundamental questions in animal welfare that are logistically and ethically more difficult to ask in vertebrates. This study will thus provide vital information in a tractable model system with important implications for pollination and agriculture.
Understanding how stress affects pollinator behaviour has important implications for the NLD-DTP themes food security and sustainable agriculture (also BBSRC areas of investment) and understanding the effect of stress on brains and neurochemistry fits the neuroscience theme. Understanding how pesticides impact pollinators fits the BBSRC remit of research into animal models of toxicology, animal health and the welfare of managed animals. Developing bees as a model for animal welfare instead of vertebrates also works towards the replacement aspect of the 3Rs priorities of the BBSRC. Machine-learning based approaches to looking at behaviour also fulfil the remit of data-driven biology.
Organisations
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
BB/T008695/1 | 01/10/2020 | 30/09/2028 | |||
2884607 | Studentship | BB/T008695/1 | 01/10/2023 | 30/09/2027 |