Sample selection disparity: Sampling only cobble overestimates the biomass of stream benthic algae

Author:

Inoue Mitsuya1ORCID,Nozaki Kentaro2,Genkai‐Kato Motomi1

Affiliation:

1. Graduate School of Kuroshio Science Kochi University Kochi Japan

2. School of Education Sugiyama Jogakuen University Nagoya Japan

Abstract

AbstractDespite the fact that scientists are aware that the streambed consists of various substrata in size, the estimation of benthic algal biomass has been conducted based almost exclusively on cobble sampling. This disparity in samples selected for the biomass estimation occurs because frame sampling collects all substrata, encompassed by the frame, including sand and stones, and is a time‐consuming method compared to single‐stone sampling. We conducted frame versus cobble sampling to test for sample selection disparity (SSD) in the estimation of benthic algal biomass. Estimates of algal biomass based on the frame sampling (area: 0.25 m2) were compared with those based on the cobble sampling taken at the same sampling points in a diatom‐dominated stream. Benthic algal biomass estimated based on cobble sampling was larger than the biomass estimated with frame sampling. The contribution of cobbles to the algal biomass encompassed by the frame was considerably higher than smaller substrata. These results suggest that cobble sampling tends to result in an overestimate of the benthic algal biomass in natural streams. Because the frame sampling requires intensive labor and time, we here proposed a general model based on quick visual assessments for percentage cover of cobbles on the streambed to calibrate estimates obtained by cobble sampling.

Publisher

Wiley

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3