合并搁浅数据库的指南:巴西最南部的franciscana (Pontoporia blainvillei)案例

Ingrid Schommer Penha, Danielle S. Monteiro, Eduardo R. Secchi, Sergio C. Estima, Paul G. Kinas
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摘要

多年来,人们一直在巴西南部的南海岸监测到几种海洋哺乳动物搁浅的情况。长时间的记录序列意味着大量的搁浅数据库。在该区域,这些数据库由两个机构维持:(i) Núcleo de educa o e Monitoring amento Ambiental[环境教育和监测中心]和(ii) Laboratório de Ecologia e conserva o o da Megafauna (ECOMEGA)[海洋Megafauna生态学和养护实验室],位于巴西联邦大学- FURG。为了使时间序列尽可能完整,并使其成为与特定物种和区域有关的研究的可靠来源,本工作提出了一种统一2000 - 2020年期间franciscana (Pontoporia blainvillei)数据库的方法(NEMA, n = 3,029;FURG, n = 4,629)。为了建立统一的数据库,为物种和地区制定了具体的指标,以确认记录的“匹配”。“匹配”变量是一个主观值,它将搁浅动物的重新安置分为优秀(1),良好(2)或常规(3)。随着合并搁浅数据库指南的实施,记录了1,812个“优秀”和97个“良好”组合。60条记录被分类为30个“常规”比赛保存在数据库中,因为它们可能无法重新计算,因为我们的主要和次要指标存在很大差异。为了描述不同数据库之间的可靠匹配,需要根据感兴趣的物种和每个监视程序的特殊性对这里概述的一般指导原则进行调整。为统一数据库而开发的方法的主要目标是确定匹配项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Guidelines to merge stranding databases: the case of franciscana (Pontoporia blainvillei) in the extreme South of Brazil
Strandings of several species of marine mammals have been monitored over the years off the south coast of Rio Grande do Sul State, southern Brazil. A long time series of records implies extensive databases on strandings. In the region, these databases are maintained by two institutions: (i) Núcleo de Educação e Monitoramento Ambiental (NEMA) [Environmental Education and Monitoring Nucleus] and (ii) Laboratório de Ecologia e Conservação da Megafauna Marinha (ECOMEGA) [Ecology and Conservation of Marine Mefagauna Laboratory] at the Federal University of Rio Grande – FURG. In order to make the time series as complete as possible and to make it a reliable source for research related to a particular species and region, the present work proposes a methodology for unifying both databases for franciscana (Pontoporia blainvillei) for the period 2000 - 2020 (NEMA, n = 3,029; FURG, n = 4,629). To build the unified database, specific metrics were outlined for the species and region, in order to confirm a ‘match’ of a record. The ‘match’ variable is a subjective value that classifies the resighting of a stranded animal as excellent (1), good (2), or regular (3). With the implementation of guidelines to merge stranding databases 1,812 ‘excellent’ and 97 ‘good’ combinations were recorded. Sixty records classified as 30 ‘regular’ matches were kept in the database, as they are possibly not recounts due to large differences in our primary and secondary metrics. To characterize a reliable match between distinct databases, the general guidelines outlined here need to be adapted according to the species of interest and the specificities of each monitoring program. The methodology developed to unify the databases had as its main objective the identification of matches.
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