The effect of stress on bee foraging behaviour

背景：应激对认知的影响是目前脊椎动物动物福利的一个关键考虑因素，但在无脊椎动物中很少有人研究这一点。了解蜜蜂等无脊椎动物的类似反应，将对它们的授粉服务和农业产生至关重要的影响。大黄蜂是研究这些应激反应的理想系统，因为它们很容易保存在实验室，而且它们的认知和行为特征很好。之前的几篇论文已经开始显示压力导致蜜蜂认知变化的证据，并将生物胺(如多巴胺)与这些变化联系在一起。本项目将使用动物福利方法来研究蜜蜂对主要自然应激源的反应以及这些反应的潜在机制。目的：1)表征应激源如何影响授粉期间的即时认知行为2)确定这些影响的时间进程与应激持续时间和频率3)调查蜜蜂应激反应背后的神经化学物质方法：我们将使用被称为判断偏差测试的行为分析来评估蜜蜂在应激后对模棱两可的奖赏来源的反应。这些经典的福利测试很少用于蜜蜂，但PI实验室正在进行的工作使用了这种方法来显示蜜蜂的认知偏见。这些测试将表征反应的时间进程以及压力持续时间和频率的影响(第一年和第二年)。基于机器学习的方法，使用基于Python的开源软件DeepLabCut，将被用来表征行为，包括花卉奖励消费和持久性的变化(第二年)。为了了解这些变化背后的机制，第二主管实验室的工作将调查大脑中多巴胺和5-羟色胺受体在RNA(带有qPCRs)和蛋白质(带有大脑免疫染色和成像)水平上的表达变化(第3年和第4年)。神经药理学干预也将被用来确定生物胺水平如何随着时间的推移(第二年和第三年)控制对应激源的反应。新奇：该项目将在决策的神经基础、动物福利和传粉生物学等几个领域取得重要进展。它解决了动物福利中的基本问题，这些问题在逻辑和伦理上更难在脊椎动物身上提出。因此，这项研究将在一个易于处理的模型系统中提供重要信息，对授粉和农业具有重要影响。了解压力如何影响传粉者行为对NLD-DTP主题、粮食安全和可持续农业(也是BBSRC投资领域)以及了解压力对大脑和神经化学的影响符合神经科学主题具有重要意义。了解杀虫剂如何影响传粉者符合BBSRC对毒理学、动物健康和受管动物福利的动物模型的研究范围。发展蜜蜂作为动物福利的模型，而不是脊椎动物，也有助于替代BBSRC的3R优先事项。基于机器学习的观察行为的方法也满足了数据驱动生物学的要求。

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 beesMethodology: 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.