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The Necessity, Promise and Challenge of Automated Biodiversity Surveys
Environmental Conservation ( IF 2.7 ) Pub Date : 2019-07-18 , DOI: 10.1017/s0376892919000146
Justin Kitzes , Lauren Schricker

SummaryWe are in the midst of a transformation in the way that biodiversity is observed on the planet. The approach of direct human observation, combining efforts of both professional and citizen scientists, has recently generated unprecedented amounts of data on species distributions and populations. Within just a few years, however, we believe that these data will be swamped by indirect biodiversity observations that are generated by autonomous sensors and machine learning classification models. In this commentary, we discuss three important elements of this shift towards indirect, technology driven observations. First, we note that the biodiversity data sets available today cover a very small fraction of all places and times that could potentially be observed, which suggests the necessity of developing new approaches that can gather such data at even larger scales, with lower costs. Second, we highlight existing tools and efforts that are already available today to demonstrate the promise of automated methods to radically increase biodiversity data collection. Finally, we discuss one specific outstanding challenge in automated biodiversity survey methods, which is how to extract useful knowledge from observations that are uncertain in nature. Throughout, we focus on one particular type of biodiversity data - point occurrence records - that are frequently produced by citizen science projects, museum records and systematic biodiversity surveys. As indirect observation methods increase the spatiotemporal scope of these point occurrence records, ecologists and conservation biologists will be better able to predict shifting species distributions, track changes to populations over time and understand the drivers of biodiversity occurrence.

中文翻译:

自动化生物多样性调查的必要性、前景和挑战

摘要我们正处于地球上观察生物多样性方式的转变之中。直接人类观察的方法,结合专业和公民科学家的努力,最近产生了前所未有的物种分布和种群数据。然而,在短短几年内,我们相信这些数据将被自主传感器和机器学习分类模型生成的间接生物多样性观测所淹没。在本评论中,我们讨论了这种向间接、技术驱动的观察转变的三个重要因素。首先,我们注意到今天可用的生物多样性数据集涵盖了可能被观察到的所有地点和时间的一小部分,这表明有必要开发能够以更低的成本以更大的规模收集此类数据的新方法。其次,我们强调了当今已经可用的现有工具和努力,以展示自动化方法从根本上增加生物多样性数据收集的前景。最后,我们讨论了自动化生物多样性调查方法中的一个具体突出挑战,即如何从本质上不确定的观察中提取有用的知识。在整个过程中,我们专注于一种特定类型的生物多样性数据——点发生记录——这些数据经常由公民科学项目、博物馆记录和系统的生物多样性调查产生。由于间接观测方法增加了这些点出现记录的时空范围,
更新日期:2019-07-18
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