Applying conservation technology to wildlife research and monitoring

保护自然具有全球性的重要性，科学家们负责监测自然的变化，从野生动物种群到景观。面对这一巨大的挑战，生物学家正在转向新技术，以提供帮助收集和分析数据所需的工具，以更好地了解野生动物和监测趋势。保护技术领域的最新发展侧重于解决这些需求。生物学家现在正与计算机科学家和工程师一起开发和实施以生态为重点的技术。该项目旨在利用保护技术产生可用于物种和景观管理和保护的知识。我们以前使用机器学习开发软件，通过面部识别识别图像中的单个灰熊（Ursus arctos）。我们现在的目标是应用这个软件，通过推进它的发展，包括远程摄像机镜头。为了实现这一目标，我们将与土著守护者和生态旅游运营商合作，并参与合作科学，以进一步开发和应用我们的技术。结合多种以知识为基础的系统的野生动物监测项目可以改善野生物种和野生场所的管理和保护。这项研究很重要，因为决策者需要野生动物的数据来为管理提供信息。然而，监测野生动物既耗时又昂贵。随着世界生物多样性受到威胁，需要快速而准确地分析数据，以便能够做出基于证据的决策。我们的研究和软件工具将提供一种可复制的技术和一般方法，可应用于加拿大和其他地区的其他物种。这项研究的结果将为南部大熊雨林的物种和景观规划提供信息，这是地球上现存的最大的温带雨林，因此具有国内和国际意义。这个地区是灰熊的大本营，灰熊是加拿大特别关注的物种。我们开发的软件，以及我们打算应用和改进的软件是开放获取和开源的，允许所有加拿大人使用，从研究科学家到各级政府再到自然爱好者。

The conservation of nature is of global importance and scientists are responsible for monitoring changes in nature, from wildlife populations to landscapes. Tasked with this huge challenge, biologists are turning to new technologies to provide tools that aid in the collection and analysis of data required to better understand wildlife and monitor trends. Recent developments in the field of conservation technology focus on addressing these needs. Biologists are now working alongside computer scientists and engineers to develop and implement ecologically-focused technology. This project aims to use conservation technology to generate knowledge that can be used in species and landscape management and conservation. We previously used machine learning to develop software that identifies individual grizzly bears (Ursus arctos) in images using facial recognition. We now aim to apply this software by advancing its development to include remote camera footage. To achieve this, we will partner with Indigenous Guardians and an ecotourism operator and participate in collaborative science to further develop and apply our technology. Wildlife monitoring programs that combine multiple knowledge-based systems could improve the management and conservation of wild species and places. This research is important as decision-makers need data on wildlife to inform management. However, monitoring wildlife is time-consuming and expensive. With the world's biodiversity under threat, data need to be analysed quickly yet accurately, so that evidence-based decisions can be made. Our research and software tool will provide a replicable technique and general approach that can be applied to other species in Canada and beyond. Results of this research will inform species and landscape planning in the southern Great Bear Rainforest -- the largest tract of connected temperate rainforest left on the planet, and therefore of national and international significance. This area is a stronghold for grizzly bears, a species of special concern in Canada. The software that we have developed and are proposing to apply and enhance is open access and open source, allowing use by all Canadians, from research scientists to all levels of government to nature enthusiasts.