Machine-learning and image recognition to monitor spatio-temporal changes in the behaviour and dynamics of species interactions

土地利用和气候的变化影响着全球物种的分布、丰度和行为。对这些过程的管理和缓解需要很好地了解它们如何影响野生动物，这需要在大范围和长时间内进行有效监测。哺乳动物通常稀少、害羞、难以捉摸或在夜间活动，因此很难监控。相机陷阱——这种装置可以长时间放置，每天24小时拍摄路过的野生动物——的价格越来越便宜，这使得它们作为一种调查方法越来越受欢迎。摄像机捕捉技术革新了哺乳动物监控的前景，但也产生了大量需要分析的图像。一些项目已经成功地求助于“公民科学家”来帮助进行图像处理，但在不久的将来，需求可能会超过供应。在过去的十年中，分析技术已经发展到能够从相机陷阱图像中估计关键的群落，种群和行为参数，不仅可以获得分布和丰度的数据，还可以获得与活动，运动和种间相互作用相关的参数，适用于广泛的个体可识别和未标记的物种。然而，这些技术会增加图像处理的负担，这就引出了一个问题：自动化图像分析能否使这些有前途的技术更容易获得和广泛应用？-这些技术的广泛、长期使用能否催化我们对哺乳动物种群状况和驱动变化的过程的理解发生重大变化？在这个项目中，学生将与一个由生态学家和计算机科学家组成的跨学科团队合作，利用机器学习技术来推进我们自动处理大量相机陷阱图像的能力，特别关注使用生成的数据来理解物种与其环境之间相互作用的动态。由此产生的生态学见解将旨在为濒危物种的管理提供信息。

Changes in land use and climate affect the distribution, abundance and behaviour of species globally. Management and mitigation of these processes requires a good understanding of how they affect wildlife, which requires effective monitoring over large areas and long time periods. Mammals are often scarce, shy, elusive or nocturnal, so are difficult to monitor. The increasing affordability of camera traps - devices that can be deployed over long periods to take photographs of passing wildlife, 24 hours a day - has contributed to their popularity as a survey method. Camera trapping has revolutionised prospects for monitoring mammals but generates huge numbers of images to be analysed. Some projects have successfully turned to "citizen scientists" to help with image processing but demand is likely to outstrip supply in the near future.Over the past decade, analytical techniques have been developed to enable the estimation of key community, population and behavioural parameters from camera trap imagery, yielding data on not only distribution and abundance, but also parameters related to activity, movement and inter-specific interactions, applicable across a wide range of both individually recognisable and unmarked species. However, these techniques can increase the burden of image processing, begging the questions:- Can automated image analysis make these promising techniques more accessible and widely used? - Can the widespread, long term use of these techniques catalyse a step change in our understanding of the status of mammal populations and the processes driving change?In this project, working with an interdisciplinary team of ecologists and computer scientists, the student will mobilise machine learning techniques to push forward our ability to automatically process large quantities of camera trap imagery, with a particular focus on using the data generated to understand the dynamics of interactions between species and their environment. The ecological insights generated will be aimed at informing the management of threatened species.