Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy.
Department of Life Sciences and Systems Biology, University of Turin, Torino, Italy.
PeerJ. 2022 Oct 14;10:e14183. doi: 10.7717/peerj.14183. eCollection 2022.
The monitoring of biological indicators is required to assess the impacts of environmental policies, compare ecosystems and guide management and conservation actions. However, the growing availability of ecological data has not been accompanied by concomitant processing tools able to facilitate data handling and analysis. Multiple common challenges limit the usefulness of biomonitoring information across ecosystems and biological groups. Biomonitoring data analysis is currently constrained by time-consuming steps for data preparation and a data processing environment with limited integration in terms of software, biological groups, and protocols. We introduce biomonitoR, a package for the R programming language that addresses technical challenges for the management of ecological data and metrics calculation. biomonitoR implements most of the biological indices currently used or proposed in different fields of ecology and water resource management. Its combination of customizable functions aims to support a transferable and comprehensive biomonitoring workflow in a user-friendly environment. biomonitoR represents a versatile toolbox with five main assets: (i) it checks taxonomic information against reference datasets allowing for customization of trait and sensitivity scores; (ii) it supports heterogeneous taxonomic resolution allowing computations at multiple taxonomic levels; (iii) it calculates multiple biological indices, including metrics for both broad and stressor-specific ecological assessments; (iv) it enables user-friendly data visualization, helping both decision-making processes and data interpretation; and (v) it allows working with an interactive web application straight from R. Overall, biomonitoR can benefit the wide biomonitoring community, including environmental private consultants, ecologists and natural resource managers.
需要监测生物指标，以评估环境政策的影响、比较生态系统并指导管理和保护行动。然而，生态数据的不断增加并没有伴随着相应的处理工具来促进数据处理和分析。多个常见的挑战限制了生物监测信息在不同生态系统和生物群体中的有用性。生物监测数据分析目前受到数据准备耗时步骤以及数据处理环境的限制，在软件、生物群体和协议方面的集成度有限。我们引入了 biomonitoR，这是一个用于 R 编程语言的软件包，用于解决生态数据管理和指标计算的技术挑战。biomonitoR 实现了目前在不同生态学和水资源管理领域中使用或提出的大多数生物指标。它的自定义功能组合旨在在用户友好的环境中支持可转移和全面的生物监测工作流程。biomonitoR 是一个多功能工具包，具有五个主要资产：（i）它会根据参考数据集检查分类信息，从而允许自定义特征和敏感性分数；（ii）它支持异构分类分辨率，允许在多个分类级别上进行计算；（iii）它计算多个生物指标，包括用于广泛和压力特定生态评估的指标；（iv）它能够实现用户友好的数据可视化，有助于决策过程和数据解释；以及（v）它允许直接从 R 使用交互式网络应用程序。总体而言，biomonitoR 可以使广泛的生物监测社区受益，包括环境私人顾问、生态学家和自然资源管理者。
