二十年来英国河流中幼鱼和幼鱼种群的体长测量。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Rachel F Ainsworth, Lauren H Vickers, Jonathan D Bolland, Marie J Taylor, Jon P Harvey, Richard A A Noble, Ian G Cowx, Andy D Nunn
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引用次数: 0

摘要

长期数据集提供了复杂生态过程的背景和理解,包括物种多样性和生态系统动态的时间变化。该数据集包括二十年来几乎每月收集的英国五条河流流域 30 个物种 38 万多条幼鱼或幼鱼的体长测量值(毫米)。这些信息可用于确定生长率、未来的繁殖成功率、种群结构以及是否符合监测协议和保护目标。该数据集为分析气候变化、污染和栖息地退化等人为干扰的影响提供了基线,而且,鉴于许多鱼类种群的大小结构与鱼体长度和游泳能力、存活率和繁殖率等各种生物属性之间存在正相关关系,该数据集对于调查自然和人为干扰将非常有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two decades of body length measurements of larval and juvenile fish populations in English rivers.

Long-term datasets provide context and understanding of complex ecological processes, including temporal variations in species diversity and ecosystem dynamics. This dataset is comprised of body length measurements (mm) of more than 380,000 larval or juvenile fish of 30 species from five English river catchments collected almost monthly over two decades. Such information can be used to determine growth rates, future recruitment success, population structure and compliance with monitoring protocols and conservation objectives. The dataset provides a baseline for analysing the impacts of anthropogenic disturbances such as climate change, pollution and habitat degradation, and, given that many fish populations are size structured with a positive relationship between fish body length and various biological attributes such as swimming ability, survival and fecundity, it will be invaluable for investigating natural and human- induced disturbances.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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