TY - JOUR
T1 - Development and assessment of a new method for combining catch per unit effort data from different fish sampling gears
T2 - Multigear mean standardization (MGMS)
AU - Gibson-Reinemer, Daniel K.
AU - Ickes, Brian S.
AU - Chick, John H.
N1 - Funding Information:
Funding for this project was provided by the US Army Corps of Engineers Upper Mississippi River Restoration Program. We thank Jennifer Sauer from the USGS Upper Midwest Environmental Sciences Center for editorial comments. Peter Minchin and Valerie Barko provided valuable input during the early development of the MGMS standardization method.
Publisher Copyright:
© 2017, Canadian Science Publishing. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Fish community assessments are often based on sampling with multiple gear types. However, multivariate methods used to assess fish community structure and composition are sensitive to differences in the relative scale of indices or measures of abundance produced by different sampling methods. This makes combining data from different sampling gears and methods a serious challenge. We developed a method of combining catch per unit effort data that standardizes catch per unit effort data across gear types, which we call multigear mean standardization (MGMS). We evaluated how well MGMS and other types of standardization reflect underlying community structure through a computer simulation that generated model riverine-fish communities and simulated sampling data for two gears. In these simulations, combining sampling observations from two gears with MGMS produced community structure estimates that were highly correlated with true community structure under a variety of conditions that are common in large rivers. Our simulation results indicate that the use of MGMS to combine data from different sampling gears is an effective data manipulation method for the analysis of fish community structure.
AB - Fish community assessments are often based on sampling with multiple gear types. However, multivariate methods used to assess fish community structure and composition are sensitive to differences in the relative scale of indices or measures of abundance produced by different sampling methods. This makes combining data from different sampling gears and methods a serious challenge. We developed a method of combining catch per unit effort data that standardizes catch per unit effort data across gear types, which we call multigear mean standardization (MGMS). We evaluated how well MGMS and other types of standardization reflect underlying community structure through a computer simulation that generated model riverine-fish communities and simulated sampling data for two gears. In these simulations, combining sampling observations from two gears with MGMS produced community structure estimates that were highly correlated with true community structure under a variety of conditions that are common in large rivers. Our simulation results indicate that the use of MGMS to combine data from different sampling gears is an effective data manipulation method for the analysis of fish community structure.
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U2 - 10.1139/cjfas-2016-0003
DO - 10.1139/cjfas-2016-0003
M3 - Article
AN - SCOPUS:85038219699
SN - 0706-652X
VL - 74
SP - 8
EP - 14
JO - Canadian Journal of Fisheries and Aquatic Sciences
JF - Canadian Journal of Fisheries and Aquatic Sciences
IS - 1
ER -