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

野化是一种保护方法，旨在以自我维持的方式再生退化的生态系统，相对较少进行持续管理。这种方法通常包括有计划地退出集约化农业，并重新引入大型食草动物和其他关键物种。在英国，特别是在西萨塞克斯郡的Knepp Wildland项目取得引人注目的成功之后，重新野化项目正在激增，并且在保护和增强国家生物多样性方面具有潜在的变革性。迫切需要监测和评估野化项目的成功，以促进以证据为基础的设计和管理的网站，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对所有物种的监测不完整或无效，但最容易识别的分类群除外。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，ML图像（和声音）识别在公民科学项目中表现出了巨大的前景，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进行实地调查的效率。该项目将评估用于监测生物多样性的ML物种识别工具，该工具是由Knepp Wildland的创建者Nattergal Ltd实施的一个主要的新野生化项目（Boothby Wildland）。我们将在Boothby项目的前三年调查关键的传粉者类群，评估从可耕地农业的撤退对生物多样性的时空模式的影响。同时，我们将验证物种识别应用程序与传统的专家主导的昆虫识别的集合，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.