Towards the fully automated monitoring of ecological communities.
Towards the fully automated monitoring of ecological communities.
复制标题
DOI：
10.1111/ele.14123
复制
发表时间：
2022-12
期刊：
影响因子：
8.8
通讯作者：
中科院分区：
文献类型：
--
作者：
在全球变化和生物多样性下降的时代，高分辨率监测对于理解生态系统动态至关重要。虽然对非生物成分进行实时和自动化监测已有一段时间，但监测生物成分——例如个体行为和特征以及物种数量和分布——则更具挑战性。近期的技术进步为实现这一目标提供了潜在的解决方案，具体途径包括：（i）日益经济实惠的高通量记录硬件，它能够收集丰富的多维数据；（ii）日益容易获取的人工智能方法，它能够从大型数据集中提取生态知识。然而，通过此类技术对生态群落各方面进行自动化监测主要是在监测工作流程的有限步骤内以较低的时空分辨率实现的。在此，我们综述了能够实现生态群落自动化监测的数据记录和处理现有技术。然后我们提出了将这些技术相结合的新颖框架，形成了全自动的流程，能够以以前无法达到的分辨率对多个物种进行检测、追踪、分类和计数，并记录行为和形态特征。基于这些快速发展的技术，我们阐述了生态学中最大挑战之一的解决方案：在复杂生态环境中快速生成高分辨率、多维且标准化数据的能力。
以高分辨率和多维方式监测生物是一项复杂且劳动密集型的任务，但在全球变化和生物多样性下降的时代，它对于理解和预测生态群落动态至关重要。在此，我们综述了自动化数据记录和处理的现有技术，并提出了将这些技术组合成自动化监测流程的新颖框架，这些流程能够检测、追踪、分类和计数多个物种，甚至能以以前无法达到的分辨率记录行为和形态特征。我们阐述了生态学和保护领域中最大挑战之一的解决方案：在复杂生态环境中快速生成高分辨率、多维且关键的是标准化数据的能力。
High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real‐time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high‐throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high‐resolution, multidimensional and standardised data across complex ecologies. Monitoring living organisms with high‐resolution and multidimensional is a complex and labour‐intensive task, yet it is fundamental to understand and predict the dynamics of ecological communities in an era of global change and biodiversity declines. Here, we review existing technologies for automated data recording and processing, and we present novel frameworks that combine these technologies into automated monitoring pipelines that detect, track, classify and count multiple species, and even record behavioural and morphological traits at resolutions which have previously been impossible to achieve. We illustrate a solution to one of the greatest challenges in ecology and conservation: the ability to rapidly generate high resolution, multidimensional and critically, standardised data across complex ecologies.
登录
查看更多内容
影响因子：
13.6
作者：
Boussarie G;Bakker J;Wangensteen OS;Mariani S;Bonnin L;Juhel JB;Kiszka JJ;Kulbicki M;Manel S;Robbins WD;Vigliola L;Mouillot D
通讯作者：
Mouillot D
影响因子：
3.7
作者：
Bevan, Elizabeth;Whiting, Scott;Douglas, Ryan
通讯作者：
Douglas, Ryan
影响因子：
5.5
作者：
Bjerge, Kim;Mann, Hjalte M. R.;Hoye, Toke Thomas
通讯作者：
Hoye, Toke Thomas
影响因子：
2.7
作者：
Allan, Blake M.;Nimmo, Dale G.;Ritchie, Euan G.
通讯作者：
Ritchie, Euan G.
DOI：
10.1016/j.jag.2021.102414
发表时间：
2021-10-01
影响因子：
7.5
作者：
Cao, Jingjing;Liu, Kai;Peng, Liheng
通讯作者：
Peng, Liheng