The primary data underlying worldwide conservation efforts come from observational field studies (Butchart et al. 2010, Geijzendorffer et al. 2016, Proença et al. 2017). Large‐scale networks for biodiversity monitoring, especially based on citizen science, have been important sources of standardized time‐series datasets that feed biodiversity indicators (Bunce et al. 2008, Proença et al. 2017, Guralnick et al. 2018). Human observers are usually the core of a biological record and our inability to foresee the consequences for biodiversity conservation in the midst of pandemics (e.g., 2019 novel coronavirus [COVID‐19]) is opening a gap in primary data underlying long‐term biodiversity monitoring programs worldwide. Considering the high stakes of disrupting time‐series data collections and monitoring programs (Wintle et al. 2010) and the urge to prepare for economic and social effects (Corlett et al. 2020), biodiversity monitoring programs should consider broadening the use of automated methods of in situ data collection.

Following advices from the World Health Organization for social distancing, many countries and provinces adopted sanctions and mandatory lockdown. Because ecological fieldwork is seldom considered an essential service, many researchers were prevented from carrying out field collection. Even where lockdown has not been decreed, setting up logistics for a field season may be challenging amidst an ongoing pandemic. For instance, a number of protected areas worldwide have been temporarily closed to safeguard the staff and deter overcrowding (Parks Canada 2020, Repanshek 2020). For the first time in almost 5 decades, the North American Breeding Bird Survey suspended volunteer surveys and field work for the 2020 breeding season (Paul 2020). Other field studies underlying the census of bird populations have also been affected (Renault 2020) and this situation is also being experienced by other researchers around the world (Kimbrough 2020). Although some activities are resuming in countries employing proper population testing and assisted by a good healthcare system, the uncertainties arising from an underestimated spread of COVID‐19 elsewhere in the world hinder estimating when normality will resume. Further, ecologists are far from understanding whether COVID‐19 can be transmitted to wildlife and generate severe outcomes on wild populations (e.g., great apes; Gillespie and Leendertz 2020). With the recommendation of suspending and reducing fieldwork during the COVID‐19 outbreak, ecologists could more widely adopt the use of regular and remote observational systems as standard practice to avoid data gaps.

With the emergence of new technologies for data collection, there was a broad uptake of sensor technologies into ecology and conservation research (Pimm et al. 2015). Sensors installed in satellites and aircrafts have expanded our capabilities to collect high‐resolution environmental data over large spatial extents and in the long term (Turner 2014) and they have become key to tracking environmental changes and ecosystem functioning (Pettorelli et al. 2014, 2016). Nevertheless, many biological indicators require data on the occurrence and abundance of organisms and obtaining standardized baselines for biodiversity monitoring is fundamental for conservation (Beaudrot et al. 2016, Jetz et al. 2019). To improve the capacities of direct observations in fieldwork, automated methods using image, video, and sound sampling emerged as complementary tools for biodiversity monitoring (Hamel et al. 2013, Dell et al. 2014, Weinstein 2018, Sugai et al. 2020). These in situ remote sensing methods provide standardized techniques for wildlife research, enabling the monitoring of animal behavior and population dynamics for a variety taxa and ecosystems (Linke et al. 2018, Gibb et al. 2019). For instance, digital cameras can be employed to monitor plant phenology through the time‐series analysis of red, green, and blue channels of digital images (Alberton et al. 2017). Motion‐sensitive camera techniques enable estimating the composition and abundance of animal communities, especially for medium and large‐sized terrestrial vertebrates (Tobler et al. 2008, Burton et al. 2015, Steenweg et al. 2017). Automated acoustic recorders are employed in passive acoustic monitoring of birds, anurans, invertebrates, mammals (terrestrial and aquatic), and freshwater fauna (André et al. 2011, Sugai et al. 2019, Desjonquères et al. 2020).

A network for standardized biodiversity data acquisition is required to track global changes in biodiversity (Steenweg et al. 2017). Large‐scale biodiversity monitoring programs can take advantage of standardized spatial designs and include networks of in situ sensors (Muelbert et al. 2019). Examples include the standardized motion‐sensitive camera arrays across continental tropical forests of Africa, Asia, and Latin America provided by the Tropical Ecology Assessment and Monitoring Network (Ahumada et al. 2011) and Wildlife Insights (https://www.wildlifeinsights.org, accessed 21 May 2020); the continental‐scale network of acoustic sensors of the Australian Acoustic Observatory (https://acousticobservatory.org/, accessed 21 May 2020); the Long Term Ecological Research (LTER) Grid Pilot Study using acoustic sensors (Butler et al. 2007); and the multi‐sensor network from the Okinawa Environmental Observation Network (Ross et al. 2018).

The implementation and maintenance of a network of sensors in biodiversity monitoring programs can provide better cost‐benefit ratios compared to traditional field observation (Marvin et al. 2016, Sugai et al. 2020). The gap between state‐of‐the‐art sensors and budget alternatives has been narrowed with the launch of affordable sensors, reduced size, and optimized microprocessors (Whytock and Christie 2017, Hill et al. 2018, Glover‐Kapfer et al. 2019). A remaining challenge is the reduction of manual efforts to maintain such passive biodiversity monitoring systems. Specifically, most motion‐sensitive cameras and automated acoustic devices require periodic maintenance for retrieving memory units (Harris et al. 2010, Browning et al. 2017). Thereby, wireless data transfer is becoming a pressing demand by the research community employing passive monitoring systems (Collins et al. 2006, Meek and Pittet 2012) and would likely have broad application with the release of fit‐for‐purpose and user‐friendly solutions. Custom devices that allow researchers to add wireless network units already exist for audio and image trapping (Nazir et al. 2017, Hill et al. 2018) and data transfer can be provided with satellite internet service and radio ethernet (Porter et al. 2005, Aide et al. 2013, Saito et al. 2015). Creative possibilities of data transfer can also be achieved through mobile data networks (Sethi et al. 2018), including smart recycling of cell phones (e.g., Rainforest connection, https://rfcx.org/, accessed 21 May 2020), or standard telephony platforms (Garrido Sanchis et al. 2020). Additionally, real‐time monitoring could be achieved by merging network sensors with edge computing to enable in situ analysis and less bandwidth than raw data for data transfer (Sheng et al. 2019, Sturley and Matalonga 2020).

Long‐term and large‐scale biodiversity monitoring programs should consider including automated passive monitoring systems to guarantee the continuity of data collection, especially under unusual situations (e.g., COVID‐19). In addition to guaranteeing an ecological register for a specific goal, image and sound recordings can also be analyzed in the future (in parallel to satellite‐image archives) and provide new opportunities for ecological research (Sugai and Llusia 2019, Jarić et al. 2020).

全球范围内保护工作的主要数据来自实地观察研究（Butchart等，  2010； Geijzendorffer等，  2016；Proença等，  2017）。大规模的生物多样性监测网络，特别是基于公民科学的生物多样性监测网络，已成为提供生物多样性指标的标准化时间序列数据集的重要来源（Bunce等人 2008，Proença等人 2017，Guralnick等人 2018））。人类观察者通常是生物记录的核心，我们无法预见大流行期间对生物多样性保护的后果（例如，2019年新型冠状病毒[COVID-19]）正在为长期生物多样性监测提供基础数据方面的空白全球范围内的程序。考虑到破坏时间序列数据收集和监测计划的重大风险（Wintle等人 2010）和为经济和社会影响做准备的冲动（Corlett等人 2020），生物多样性监测计划应考虑扩大自动化方法的使用的原位数据收集。

遵循世界卫生组织关于社会隔离的建议，许多国家和省采取了制裁和强制封锁措施。由于很少将生态野外工作视为必不可少的服务，因此许多研究人员无法进行野外采集。即使没有降低锁定水平，在流行病持续发展的情况下，为野外季节设置物流也可能具有挑战性。例如，为了保护工作人员和防止人满为患，全球范围内的一些保护区已经暂时关闭（Parks Canada  2020，Repanshek  2020）。北美种禽调查是近5年来的第一次，暂停了2020年繁殖季节的志愿者调查和野外工作（Paul  2020）。鸟类种群普查的其他实地研究也受到影响（雷诺 2020年），世界其他研究人员也正在经历这种情况（金伯劳 2020年）。尽管在使用适当的人口测试并得到良好医疗保健系统辅助的国家中，一些活动正在恢复，但是，COVID-19的低估在世界其他地方的传播所带来的不确定性阻碍了估计何时恢复正常。此外，生态学家还远未了解是否可以将COVID-19传播给野生生物并在野生种群上产生严重后果（例如，大猿； Gillespie和Leendertz 2020年））。通过建议在COVID-19爆发期间暂停和减少野外工作，生态学家可以更广泛地采用常规和远程观测系统作为标准做法，以避免数据缺口。

随着数据收集新技术的出现，出现了传感器技术的广泛摄取到生态和保护研究（皮姆等人 2015年）。安装在卫星和飞机的传感器扩大了我们的能力，以收集高分辨率的环境数据，在大的空间范围，并在长期内（特纳 2014年），他们已经成为关键，跟踪环境变化和生态系统功能（Pettorelli等人。  2014，  2016）。然而，许多生物学指标需要有关生物的发生和丰富度的数据，为生物多样性监测获得标准化基准是保护的基础（Beaudrot等，2016 ; Jetz等，  2019）。）。为了提高在野外工作中直接观察的能力，使用图像，视频和声音采样的自动化方法逐渐成为生物多样性监测的补充工具（Hamel等，  2013 ; Dell等，2014 ; Weinstein，  2018 ; Sugai等，  2020）。这些原位遥感方法为野生动植物研究提供了标准化技术，从而能够监测各种分类单元和生态系统的动物行为和种群动态（Linke等人 2018，Gibb等人 2019）。例如，通过对数字图像的红色，绿色和蓝色通道进行时间序列分析，可以使用数码相机来监视植物物候（Alberton等。 2017）。运动敏感的摄像头技术可以估计动物群落的组成和数量，特别是对于中型和大型陆地脊椎动物而言（Tobler等人 2008 ; Burton等人 2015 ; Steenweg等人2017）。自动声记录器用于鸟类，无脊椎动物，无脊椎动物，哺乳动物（陆地和水生）和淡水动物的被动声监测（André等人，  2011； Sugai等人，  2019；Desjonquères等人，  2020）。

一种用于标准化的生物多样性的数据采集网络是必需的，以跟踪生物多样性全球变化（Steenweg等人 2017）。大规模的生物多样性监测程序可以采取标准化空间设计的优点和包括的网络原位传感器（Muelbert等人 2019）。例如，热带生态评估与监测网络（Ahumada等人，2011年）提供的横跨非洲，亚洲和拉丁美洲大陆热带森林的标准化运动感应相机阵列）和Wildlife Insights（https://www.wildlifeinsights.org，于2020年5月21日访问）；澳大利亚声学天文台的大陆级声学传感器网络（https://acousticobservatory.org/，2020年5月21日访问）; 长期生态研究（LTER）电网试点研究使用声学传感器（巴特勒等人。  2007）; 以及来自冲绳环境观测网的多传感器网络（Ross等人 2018）。

与传统的实地观察相比，在生物多样性监测计划中实施和维护传感器网络可以提供更好的成本效益比（Marvin等人，  2016； Sugai等人，  2020）。随着价格适中的传感器的推出，缩小的尺寸和优化的微处理器，最先进的传感器与替代预算之间的差距已经缩小（Whytock和Christie  2017，Hill等人 2018，Glover-Kapfer等人 2019）。仍然存在的挑战是减少维护此类被动生物多样性监测系统的人工工作。具体来说，大多数运动敏感型相机和自动声学设备都需要定期维护才能检索存储单元（Harris等，  2010； Browning等，  2017）。因此，无线数据传输已成为采用无源监控系统的研究社区的紧迫需求（Collins等，2006； Meek和Pittet，  2012），并且随着适用目的和用户友好解决方案的发布，无线数据传输 可能会得到广泛应用。 。允许研究人员添加无线网络单元的自定义设备已经存在，用于音频和图像捕获（Nazir等人 2017，Hill等人。 2018）和数据传输可以提供卫星因特网服务和无线以太网（Porter等 2005，备忘录等人 2013，Saito等 2015）。数据传输的创新可能性也可以通过移动数据网络（Sethi等人，  2018年）实现，包括智能回收手机（例如Rainforest连接，https：//rfcx.org/，2020年5月21日访问）或标准电话平台（Garrido Sanchis等人，  2020年）。此外，可以通过将网络传感器与边缘计算相结合来实现实时监控，以进行原位分析，并且带宽小于原始数据以进行数据传输（Sheng等人，  2019年），Sturley和Matalonga  2020年）。

长期的大规模生物多样性监测计划应考虑包括自动被动监测系统，以确保数据收集的连续性，尤其是在异常情况下（例如，COVID-19）。除了为特定目标保证生态注册外，将来还可以分析图像和录音（与卫星图像档案同时进行），并为生态研究提供新的机遇（Sugai和Llusia  2019年，Jarić等人 2020年））。