Statistical analysis and dataset for: Acute exposure to caffeine improves foraging in an invasive ant

Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2023.10.10.561519). Abstract Invasive alien species are a major and growing problem, devastating ecosystems and costing billions of euros in damage and control efforts. Argentine ants, Linepithema humile, are particularly concerning, with control efforts often falling short likely due to a lack of sufficient bait consumption. Using neuroactives to manipulate ant navigation and learning could increase recruitment and consumption, ultimately leading to more efficient control strategies. Caffeine is naturally occurring, cheap, and has been found to cause motivational and cognitive improvements in bees. Here, we subject L. humile to a wide range of caffeine concentrations and a complex but ecologically relevant task: an open landscape foraging experiment. Without caffeine, we find no effect of consecutive foraging visits on the time the ants take to reach a reward, suggesting a failure to learn the reward's location. However, low (25ppm) to intermediate (250ppm) concentrations of caffeine lead to a decrease of up to 38% in the time taken to find the reward during each consecutive visit, implying that caffeine boosts learning. Interestingly, such improvements are lost at high (2000ppm) doses. In contrast, caffeine appears to have no impact on the ants' homing behaviour, as the time required to reach the nest was similar across treatments. The effect of caffeine is thus not only dose-dependent, but also differentially targets neurologically distinct navigational mechanisms. Adding moderate levels of caffeine to baits could be a simple way to improve ant's ability to learn its location, potentially leading to increased recruitment to, and consumption of, the toxicant. sample_videos.zip: A subset of the videos used for data extraction. The complete collection of videos is not publicly accessible primarily due to their considerable size (105.35GB). Requests for access to the entire video set are encouraged. OpLan_D1_metadata.csv: Manually collected metadata pertaining to experimental conditions, subjects, and treatments. OpLan_D2_DLC_coordinates.zip: Cartesian coordinates obtained from DeepLabCut for each of the videos analysed. OpLan_C1_reproject_coordinates.py: Python code used to standardise the ants' coordinates by ensuring the same corner of the A4 platform was used as the origin of the cartesian referential of all videos. The known dimensions of the A4 were further used to convert coordinates from pixels to millimetres. OpLan_C2_remove_impossibilities.py: Python code used to account for DeepLabCut tracking errors, with any ant movement exceeding two millimetres per frame being considered implausible and subsequently removed. OpLan_C3_find_changepoints.py: Python code used to automatically derive the times at which an ant reached and left the reward from the tracking data. OpLan_C4_inward_outward_data.py: Python code used to calculate relevant measures for the foodward (inward) and nestward (outward) journey such as journey duration, mean instantaneous speed and path tortuosity. OpLan_C5_Figure_2.R: R code used to produce the raw elements of Figure 2. OpLan_C6_Figure_4.R: R code used to produce the raw elements of Figure 4. OpLan_C7_Statistical_Analysis.html: Complete statistical analysis and code for the manuscript.

本数据集关联于发表在bioRxiv的期刊论文（https://doi.org/10.1101/2023.10.10.561519）。

摘要 外来入侵物种是一项日益严峻的重大问题，其会破坏生态系统并造成数十亿欧元的损失与防控成本。阿根廷蚁（Linepithema humile）尤其值得关注，当前的防控措施往往效果不佳，这大概率是因为蚁群对诱饵的取食量不足。利用神经活性物质调控蚂蚁的导航与学习能力，有望提升其招募行为与取食量，最终形成更高效的防控策略。

咖啡因是一种天然存在的廉价物质，已被证实可提升蜜蜂的动机与认知能力。本研究对阿根廷蚁设置了一系列梯度浓度的咖啡因处理，并开展了一项复杂但符合生态场景的任务：开放景观觅食实验。在未添加咖啡因的对照组中，连续觅食访次并未对蚂蚁抵达奖励物的耗时产生显著影响，表明蚂蚁未能学会奖励物的位置。然而，低浓度（25ppm）至中浓度（250ppm）的咖啡因可使单次连续访次中找到奖励物的耗时最多降低38%，提示咖啡因可增强蚂蚁的学习能力。有趣的是，高浓度（2000ppm）处理下这种提升效果消失。与之相对，咖啡因对蚂蚁的归巢行为无显著影响，不同处理组中蚂蚁返回蚁巢的耗时并无明显差异。由此可见，咖啡因的作用不仅具有剂量依赖性，还会差异化靶向神经层面不同的导航机制。在诱饵中添加适量浓度的咖啡因，可通过提升蚂蚁对诱饵位置的学习能力，潜在增加蚁群对有毒诱饵的招募与取食量。

sample_videos.zip：用于数据提取的部分视频素材。完整视频集因体积较大（105.35GB）暂未公开，欢迎申请获取完整视频数据集。
OpLan_D1_metadata.csv：手动收集的实验条件、受试个体与处理方案相关元数据。
OpLan_D2_DLC_coordinates.zip：经DeepLabCut分析得到的所有待测视频的笛卡尔坐标数据。
OpLan_C1_reproject_coordinates.py：用于标准化蚂蚁坐标的Python代码，以A4平板的同一角作为所有视频笛卡尔参考系的原点，并利用A4平板的已知尺寸将像素坐标转换为毫米单位。
OpLan_C2_remove_impossibilities.py：用于修正DeepLabCut追踪误差的Python代码，将每帧移动超过2毫米的蚂蚁轨迹判定为不合理并予以剔除。
OpLan_C3_find_changepoints.py：用于从追踪数据中自动推导蚂蚁抵达与离开奖励物的时间节点的Python代码。
OpLan_C4_inward_outward_data.py：用于计算觅食正向（朝向食物，即向内）与归巢（朝向蚁巢，即向外）旅程的相关指标的Python代码，包括旅程时长、平均瞬时速度与路径曲折度。
OpLan_C5_Figure_2.R：用于生成图2原始元素的R代码。
OpLan_C6_Figure_4.R：用于生成图4原始元素的R代码。
OpLan_C7_Statistical_Analysis.html：包含本论文完整统计分析流程与代码的网页文档。