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Ecobat: An online resource to facilitate transparent, evidence‐based interpretation of bat activity data

机译:Ecobat:在线资源,有助于透明,基于证据的蝙蝠活动数据解释

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Acoustic surveys of bats are one of the techniques most commonly used by ecological practitioners. The results are used in Ecological Impact Assessments to assess the likely impacts of future developments on species that are widely protected in law, and to monitor developments’ postconstruction. However, there is no standardized methodology for analyzing or interpreting these data, which can make the assessment of the ecological value of a site very subjective. Comparisons of sites and projects are therefore difficult for ecologists and decision‐makers, for example, when trying to identify the best location for a new road based on relative bat activity levels along alternative routes. Here, we present a new web‐based, data‐driven tool, Ecobat, which addresses the need for a more robust way of interpreting ecological data. Ecobat offers users an easy, standardized, and objective method for analyzing bat activity data. It allows ecological practitioners to compare bat activity data at regional and national scales and to generate a numerical indicator of the relative importance of a night's worth of bat activity. The tool is free and open‐source; because the underlying algorithms are already developed, it could easily be expanded to new geographical regions and species. Data donation is required to ensure the robustness of the analyses; we use a positive feedback mechanism to encourage ecological practitioners to share data by providing in return high quality, contextualized data analysis, and graphical visualizations for direct use in ecological reports.
机译:蝙蝠的声学调查是生态工作者最常使用的技术之一。研究结果用于生态影响评估,以评估未来开发对受法律广泛保护的物种的可能影响,并监测开发后的构造。但是,没有用于分析或解释这些数据的标准化方法,可以使对站点生态价值的评估非常主观。因此,生态学家和决策者很难进行站点和项目的比较,例如,当尝试根据蝙蝠沿替代路线的相对活动水平来确定一条新道路的最佳位置时。在这里,我们介绍了一种新的基于Web的,数据驱动的工具Ecobat,它满足了对更强大的生态数据解释方式的需求。 Ecobat为用户提供了一种简单,标准化和客观的方法来分析蝙蝠活动数据。它使生态从业人员可以比较区域和国家范围内的蝙蝠活动数据,并生成一个数值来表明一夜的蝙蝠活动价值的相对重要性。该工具是免费和开源的;由于基础算法已经开发,因此可以轻松地扩展到新的地理区域和物种。需要数据捐赠以确保分析的鲁棒性;我们使用正反馈机制来鼓励生态从业者共享数据,方法是提供高质量,上下文相关的数据分析和图形化可视化结果,以直接用于生态报告中。

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