A global dataset of salmonid biomass in streams.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-10-29 DOI:10.1038/s41597-024-04026-0

Kyleisha J Foote, James W A Grant, Pascale M Biron

引用次数: 0

Abstract

Salmonid fishes are arguably one of the most studied fish taxa on Earth, but little is known about their biomass range in many parts of the world. We created a dataset of estimated salmonid biomass using published material of over 1000 rivers, covering 27 countries and 11 species. The dataset, spanning 84 years of data, is the largest known compilation of published studies on salmonid biomass in streams, allowing detailed analyses of differences in biomass by species, region, period, and sampling techniques. Production is also recorded for 194 rivers, allowing further analyses and relationships between biomass and production to be explored. There is scope to expand the list of variables in the dataset, which would be useful to the scientific community as it would enable models to be developed to predict salmonid biomass and production, among many other analyses.

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溪流中鲑鱼生物量的全球数据集。

鲑科鱼类可以说是地球上研究最多的鱼类类群之一，但人们对它们在世界许多地方的生物量范围却知之甚少。我们利用已发表的 1000 多条河流的资料创建了一个估计鲑鱼生物量的数据集，涵盖 27 个国家和 11 个物种。该数据集的数据时间跨度长达 84 年，是目前已知的关于溪流中鲑鱼生物量的最大规模的已发表研究汇编，可对不同物种、地区、时期和采样技术的生物量差异进行详细分析。该数据还记录了 194 条河流的产量，以便进一步分析和探讨生物量与产量之间的关系。数据集中的变量清单还有扩大的余地，这将对科学界很有帮助，因为这样就可以建立模型来预测鲑鱼的生物量和产量，以及其他许多分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.