The effect of stress on bee foraging behaviour

背景：应激对认知的影响是当前脊椎动物福利的一个关键考虑因素，但很少在无脊椎动物中进行研究。了解蜜蜂等无脊椎动物的类似反应将对它们的传粉者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，因为它们很容易在实验室里饲养，而且它们的认知和行为也很有特点。之前的一些论文已经开始展示压力引起蜜蜂认知变化的证据，并暗示生物胺（如多巴胺）与这些变化有关。本项目将使用动物福利方法来调查蜜蜂对主要自然压力源的反应及其反应机制。目的：1)描述压力源如何影响授粉过程中的即时认知行为；2)确定这些影响与压力持续时间和频率相关的时间过程；3)研究蜜蜂应激反应背后的神经化学物质。方法：我们将使用称为判断偏差测试的行为分析来评估蜜蜂在应激后对模糊奖励来源的反应。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已经使用这种方法来显示蜜蜂的认知偏见。这些测试将描述反应的时间过程，以及应力持续时间和频率的影响（第1年和第2年）。使用deepplabcut（一个基于python的开源软件）的基于机器学习的方法，将被用来描述行为的特征，包括奖励消耗的变化和花的持久性（第2年）。为了了解这些变化背后的机制，第二导师的实验室将研究大脑中多巴胺和血清素受体在RNA（使用qpcr）和蛋白质（使用脑免疫染色和成像）水平上的表达变化（3年级和4年级）。神经药理学干预也将用于确定生物胺如多巴胺和血清素的水平如何随着时间的推移控制对压力源的反应（第2年和第3年）。新颖性：该项目将在决策神经基础、动物福利、传粉生物等领域取得重要进展。它解决了动物福利的基本问题，这些问题在逻辑上和伦理上更难在脊椎动物中提出。因此，这项研究将为一个易于处理的模型系统提供重要信息，对授粉和农业具有重要意义。了解压力如何影响传粉者的行为对NLD-DTP主题粮食安全和可持续农业（也是BBSRC的投资领域）具有重要意义，了解压力对大脑和神经化学的影响符合神经科学主题。了解农药如何影响传粉媒介符合BBSRC对动物毒理学模型、动物健康和管理动物福利的研究职责。发展蜜蜂作为动物福利的典范，而不是脊椎动物，也有助于取代BBSRC的3r优先事项。基于机器学习的行为研究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.