Measuring the impact of rewilding on pollinator biodiversity: what can machine learning tell us?

重新放野是一种保护方法，旨在以自我维持的方式使退化的生态系统再生，目前的管理相对较少。这种方法通常包括有计划地退出集约化农业，重新引入大型食草动物和其他关键物种。在英国，尤其是在西萨塞克斯郡的Knepp Wildland项目取得了引人注目的成功之后，重新放归野生动物的项目正在激增，并且在保护和增强全国生物多样性的尝试中具有潜在的变革性。迫切需要监测和评估放归项目的成功，以促进基于证据的场地设计和管理，以实现最大的保护价值。评估保护项目影响的核心是对生物多样性进行准确和具有成本效益的调查。然而，物种鉴定的挑战往往导致除了最容易识别的分类群外，对所有分类群的监测都不完整或无效。最近的技术发展，特别是在物种识别中使用机器学习（ML），有可能大大降低生物多样性监测的成本。到目前为止，机器学习图像（和声音）识别在公民科学项目中显示出巨大的前景，在这些项目中，对时空生物多样性模式感兴趣的生态学家可以挖掘公开贡献的记录。但这些工具也有巨大的潜力，可以提高专业生态学家直接实地调查的效率。该项目将评估机器学习物种识别工具，以监测生物多样性，该工具将在一个重大的新野生化项目（Boothby Wildland）的背景下实施，该项目由Knepp Wildland的创建者创建的Nattergal Ltd实施。我们将在Boothby项目的前三年对主要传粉昆虫分类群进行调查，评估耕地减少对生物多样性时空格局的影响。同时，我们将验证一系列物种识别应用程序与传统的专家主导的昆虫识别，使我们能够评估快速、低成本监测生物多样性的长期可行性。

Rewilding is an approach to conservation which seeks to regenerate degraded ecosystems in a self-sustaining way, with relatively little ongoing management. The approach commonly involves the planned retreat from intensive agriculture, and the reintroduction of large herbivores and other keystone species. Rewilding projects are proliferating in the UK, especially following the high-profile success of the Knepp Wildland project in West Sussex, and are potentially transformative in attempts to protect and enhance biodiversity nationally. There is an urgent need to monitor and evaluate the success of rewilding projects, to facilitate evidence-based design and management of sites for maximal conservation value. Central to evaluating the impact of conservation projects is accurate and cost-effective surveying of biodiversity. However, the challenges of species identification often lead to patchy or ineffective monitoring of all but the most easily identifiable taxa. Recent technological developments, especially using machine learning (ML) in species recognition, have the potential to reduce dramatically the costs of biodiversity monitoring. Thus far, ML image (and sound) recognition has shown great promise in citizen science projects, where publicly contributed records can be mined by ecologists interested in spatial and temporal biodiversity patterns. But these tools also have huge potential to increase the efficiency of directed field surveys by professional ecologists. This project will evaluate ML species recognition tools for monitoring biodiversity in the context of a major new rewilding project (Boothby Wildland) being implemented by Nattergal Ltd, founded by the creators of the Knepp Wildland. We will survey key pollinator taxa over the first three years of the Boothby project, assessing the impact of the retreat from arable farming on spatial and temporal patterns in biodiversity. Simultaneously, we will validate an ensemble of species recognition apps with conventional expert-led insect identification, enabling us to assess the long-term feasibility of rapid, low-cost monitoring of biodiversity.