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/V002856/1
• 负责人: Sarah West
• 金额: $ 17.48万
• 依托单位: University of York
• 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%2FV002856%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，从而影响了人们从大自然中获得的利益。这意味着将生物多样性纳入我们的决策至关重要，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，这可以通过称为“自然资本”评估的过程正式完成，例如通过政府机构或当地经济合作伙伴。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正确的决策（以实现净生物多样性增益，正如英国食品和农村事务部的 25 年环境计划中的目标）。我们现有的生物多样性信息大部分来自志愿者收集的物种记录（这一过程通常称为“公民科学”）。然而，在许多情况下，人们记录的地点和时间是他们想要的，这导致记录时在国家范围内和地方范围内都存在很大的空间不均匀性。需要使用生物多样性信息的个人和组织不仅仅需要更多记录：他们需要更好的信息。这要求我们根据可用数据构建良好的生物多样性模型，很好地传达这些模型，并优先致力于添加来自时间和地点的记录，以最佳地改进模型输出。该项目旨在通过解决三个重要问题来实现所有这些目标。首先，我们能否通过近实时、精细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，让最终用户更容易获取和利用生物多样性信息？第三，我们是否可以通过使用智能数字参与来鼓励记录器中的自适应采样行为，以便他们重新部署部分努力以最佳地改进生物多样性模型？我们的团队能够专业地解决这些问题，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，积极吸引数据用户（从国家到地方层面）和生物多样性记录者与研究团队一起参与。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（在线分析平台）为英国约 1000 种昆虫物种（在本研究中重点关注蝴蝶、飞蛾和蚱蜢）制作高分辨率分布模型，并在新数据可用时自动更新输出。传达这些结果及其不确定性非常重要，因此，我们将与数据最终用户合作开发交互式和自动生成的可视化和文本，以有效地做到这一点。我们还将开发评估新数据何时何地对改进模型输出最有价值的方法。当与限制条件（例如土地访问或人们的录音偏好）相结合时，这将通过网络应用程序作为定制建议传达给记录者。这将用于记录蝴蝶和蚱蜢（阳光明媚的日子活动）和记录飞蛾（由我们提供的便携式低成本光陷阱支持）。我们将通过英国各地已建立的记录项目来吸引记录员，包括伦敦（人数很多，但生物多样性数据相对较少）和北约克郡和东约克郡（人口较少，土地用途多种多样）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，因此它们可以灵活地用于任何物种，并且实际上可以适应任何环境数据。该项目的成果将是一个增强生物多样性信息的过程，可以将其纳入现有的记录项目和数据流中，以便使产出易于获取和有用，造福于自然和人类。