NESP MaC Project 1.29 b – NESP Marine and Coastal Hub scoping study: New approaches to monitoring (2022) (CSIRO)

本记录概述了NESP海洋和沿海枢纽小规模研究- NESP海洋和沿海枢纽范围界定研究：新的监测方法。<br /> <br /> 在过去十年中，对海洋物种的观测迅速多样化，通过公民科学计划或公民机会主义收集的观测结果越来越多，然后提交到公共数据库。贡献这些数据的公民期望这些数据被纳入正式分析，并用于科学或管理目的，其结果是改善目前对海洋物种的了解和保护。此外，鉴于开展正式调查的成本，特别是在访问偏远地区或需要重复收集（跨季节或跨年）的情况下，越来越多地依赖更非正式和公开驱动的方法来收集观察结果。<br /> <br /> 然而，将机会性观测与更正式收集的观测（例如通过健全的调查设计收集的观测）结合起来并不简单。估计种群丰度、物种组合变化、丰度和分布的传统方法需要对与这些观测有关的努力以及这种努力如何随时间分布有一定的了解;鉴于这些观测的性质，机会性观测缺少这些信息。此外，许多机会主义收集的数据集不符合可能适用于科学数据集的许多分析方法的统计假设。因此，机会主义观测目前未得到充分利用，并且在很大程度上仅在研究应用中定性使用。<br /> <br /> 为了充分利用这一迅速扩大的资源，并确保与收集这些观测数据有关的努力不被浪费，需要将机会性收集的数据与科学数据集相结合的方法。<br /> <br /> 该项目是发展综合方法的第一阶段。它将汇集数据持有者、定量生态学家和统计学家，探讨可能用于促进机会数据集与科学数据集相结合的潜在方法。在此过程中，它将确定哪些数据集可能最适合制定综合方法（哪些可能不适合），并概述制定这些方法的途径，这些方法可通过未来的项目实施<br /> <br /> 计划产出<br /> ·工作坊<br /> ·最后技术报告，包括分析数据和为决策者提供的关键调查结果的建议的简短摘要[书面]

This record provides an overview of the NESP Marine and Coastal Hub small-scale study - NESP Marine and Coastal Hub scoping study: New approaches to monitoring. <br /> <br /> Observations of marine species have rapidly diversified over the last decade with increasing numbers of observations collected either through citizen science programs or opportunistically by citizens and then submitted into public databases. The expectation of citizens contributing those data is that the data is incorporated into formal analyses and utilised either for scientific or management purposes, with outcomes that improve current understanding and conservation of marine species. Further, there is increasing reliance on more informal and publicly driven approaches to the collection of observations given the costs of conducting formal surveys particularly if accessing remote regions, or if repetition of collection (across seasons or years) is needed.<br /> <br /> Integrating opportunistic observations with more formally collected observations (e.g. those collected through robust survey designs), however, is not straightforward. Traditional approaches to estimating population abundance, changes in species assemblages, abundances and distributions through time require some understanding of the effort associated with those observations and how that effort is also distributed through time; information that is missing from opportunistic observations, given the nature of those observations. Further, many opportunistically collected datasets do not meet the statistical assumptions of many analytical approaches that might be applied to scientific datasets. As a result, opportunistic observations are underutilised at present and are largely only used qualitatively within research applications. <br /> <br /> To take full advantage of this rapidly expanding resource and ensure that efforts associated with the collection of these observations are not wasted, methods that can integrate opportunistically collected data with scientific datasets are needed.<br /> <br /> This project represents the first stage in developing integrative methods. It will bring together data holders, quantitative ecologists and statisticians to explore potential approaches that might be applied to facilitate the integration of opportunistic datasets with scientific datasets. In doing so, it will identify what datasets might be best suited to the development of integrative approaches (and what might not) and outline a pathway for developing those methods that could be implemented through a future project<br /> <br /> Planned Outputs<br /> • Workshop<br /> • Final technical report with analysed data and a short summary of recommendations for policy makers of key findings [written]