DECIDE - Delivering Enhanced Biodiversity Information with Adaptive Citizen Science and Intelligent Digital Engagements

DECIDE - 通过适应性公民科学和智能数字参与提供增强的生物多样性信息

• 批准号: NE/V003194/1
• 负责人: Advaith Siddharthan
• 金额: $ 12.01万
• 依托单位: The Open University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV003194%2F1
• 关键词: DECIDE; Delivering; Enhanced; Biodiversity; Information

Biodiversity is under increasing pressure, with consequent impacts on the benefits people gain from nature. This means that it is vital to include biodiversity in our decision-making and for this we need high quality, fine-resolution, spatial biodiversity information. With this information we can better value nature, and this can be done formally through a process called 'natural capital' assessment, such as by government agencies or local economic partnerships. We also need this information to develop better plans for protecting nature, undertaking ecological restoration to develop resilient ecological networks, and make good decisions about infrastructure development (to achieve net biodiversity gain, as is the ambition in Defra's 25 Year Environment Plan). Much of our existing biodiversity information comes from volunteer-collected species records (a process often called 'citizen science'). However, in many cases, people record where and when they want - leading to large spatial unevenness in recording, both at a national scale and at a local scale. The people and organisations who need to use biodiversity information don't simply require more records: they require better information. This requires us to construct good biodiversity models generated from the available data, communicate these models well, and preferentially target effort to add records from times and places that optimally improve the model outputs. This project seeks to achieve all of this by addressing three important questions. Firstly, can we enhance existing biodiversity information through near real-time, fine resolution, species distribution models? Secondly, can we make biodiversity information more accessible and useful to end users through data flows and automated data communication? Thirdly, can we encourage adaptive sampling behaviour in recorders, by using intelligent digital engagements, so that they re-deploy a portion of their effort to optimally improve biodiversity models? Our team is expertly placed to address these questions because we are a multidisciplinary team (environmental, computer, social and data scientists), and we will use a service design approach that actively engages data users (from national to local levels) and biodiversity recorders alongside the research team. In this project we will produce fine-resolution distribution models for about 1000 insect species across the UK (in this study focusing on butterflies, moths and grasshoppers) using earth observation sensor data, and a data lab (an online analysis platform) to automatically update outputs as new data are available. It is important to communicate these results and their uncertainty so, in collaboration, with data end users we will develop interactive and automatically-generated visualisations and text to do this effectively. We will also develop ways of assessing when and where new data will be most valuable in improving the model outputs. This, when combined with constraints (such as land access or people's recording preferences) will be communicated to recorders as bespoke recommendations via a web app. This will be developed for recording butterflies and grasshoppers (a sunny day activity), and recording moths (supported by our provision of portable, low cost light traps). We will engage recorders through established recording projects across the UK, including with partners in London (many people, but relatively few biodiversity data) and North and East Yorkshire (fewer people, and a wide variety of land uses). Throughout this project our work flows will be implemented in an data lab, so they will be flexible for use with any species and indeed could be adapted for any environmental data. The outcome of this project will be a process for enhancing biodiversity information that can be incorporated into existing recording projects and data streams, so that the outputs will be accessible and useful, for the benefit of nature and people.

生物多样性面临着越来越大的压力，随之而来的是对人们从自然中获得的好处的影响。这意味着将生物多样性纳入我们的决策是至关重要的，为此，我们需要高质量、高分辨率的空间生物多样性信息。有了这些信息，我们可以更好地评估自然的价值，这可以通过一种称为‘自然资本’评估的过程正式完成，例如由政府机构或地方经济伙伴关系进行评估。我们还需要这些信息来制定更好的计划来保护自然，进行生态恢复以发展有弹性的生态网络，并就基础设施发展做出良好的决策(实现生物多样性的净收益，这是Defra的25年环境计划中的雄心)。我们现有的大部分生物多样性信息来自志愿者收集的物种记录(这一过程通常被称为“公民科学”)。然而，在许多情况下，人们在他们想要的时间和地点进行记录-导致在国家和地方尺度上记录的巨大的空间不均匀。需要使用生物多样性信息的个人和组织不仅仅需要更多的记录：他们需要更好的信息。这要求我们根据现有数据构建良好的生物多样性模型，很好地传达这些模型，并优先瞄准添加来自时间和地点的记录，以最佳地改善模型输出。这个项目试图通过解决三个重要问题来实现所有这些。首先，我们能否通过近乎实时、高分辨率的物种分布模型来增强现有的生物多样性信息？其次，我们能否通过数据流和自动化数据通信使终端用户更容易获得和使用生物多样性信息？第三，我们能否通过使用智能数字活动来鼓励记录器的自适应采样行为，以便它们重新部署一部分努力，以最佳地改善生物多样性模型？我们的团队能够很好地解决这些问题，因为我们是一个多学科团队(环境、计算机、社会和数据科学家)，我们将使用一种服务设计方法，与研究团队一起积极接触数据用户(从国家到地方级别)和生物多样性记录器。在这个项目中，我们将使用地球观测传感器数据和数据实验室(一个在线分析平台)，为英国约1000种昆虫(在本研究中，重点是蝴蝶、蛾子和蝗虫)制作高分辨率的分布模型，以便在新数据可用时自动更新输出。重要的是要传达这些结果及其不确定性，因此，在与数据终端用户的合作中，我们将开发交互式和自动生成的可视化和文本，以有效地实现这一点。我们还将制定评估新数据何时何地对改进模型产出最有价值的方法。当这一点与限制(如土地使用权或人们的录音偏好)结合在一起时，将通过网络应用程序作为定制建议传达给记录器。它将被开发用于记录蝴蝶和蚱蜢(阳光明媚的一天活动)，以及记录飞蛾(由我们提供的便携式、低成本的捕光器支持)。我们将通过在英国各地建立的录音项目来让记录器参与进来，包括与伦敦(人口众多，但生物多样性数据相对较少)以及北约克郡和东约克郡(人口较少，土地用途广泛)的合作伙伴。在整个项目中，我们的工作流程将在数据实验室中实施，因此它们将灵活地用于任何物种，并且确实可以适应任何环境数据。该项目的成果将是一个加强生物多样性信息的进程，这些信息可纳入现有的记录项目和数据流，从而使产出可供查阅和使用，造福自然和人类。

项目成果

STEM Education Hub - Science Education in Schools: connections between Brazil and the United Kingdom

STEM 教育中心 - 学校科学教育：巴西和英国之间的联系

• 发表时间: 2022
• 作者: Ansine, J.,