How many sites? Methods to assist design decisions when collecting multivariate data in ecology
Sample size estimation through power analysis is a fundamental tool in planning an ecological study, yet there are currently no well-established procedures for when multivariate abundances are to be collected. A power analysis procedure would need to address three challenges: designing a parsimoniou...
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sg-ntu-dr.10356-1705932023-09-21T15:30:27Z How many sites? Methods to assist design decisions when collecting multivariate data in ecology Maslen, Ben Popovic, Gordana Lim, Michelle Marzinelli, Ezequiel Miguel Warton, David Singapore Centre for Environmental Life Sciences and Engineering Science::Biological sciences Copula Multivariate Sample size estimation through power analysis is a fundamental tool in planning an ecological study, yet there are currently no well-established procedures for when multivariate abundances are to be collected. A power analysis procedure would need to address three challenges: designing a parsimonious simulation model that captures key community data properties; measuring effect size in a realistic yet interpretable fashion; and ensuring computational feasibility when simulation is used both for power estimation and significance testing. Here, we propose a power analysis procedure that addresses these three challenges by: using for simulation a Gaussian copula model with factor analytical structure, fitted to pilot data; assuming a common effect size across all taxa, but applied in different directions according to expert opinion (to “increaser”, “decreaser” or “no effect” taxa); using a critical value approach to estimate power, which reduces computation time by a factor of 500 (if we would otherwise use 999 resamples to estimate each p-value) with minor loss of accuracy. The procedure is demonstrated on pilot data from fish assemblages in a restoration study, where it was found that the planned study design would only be capable of detecting relatively large effects (change in abundance by a factor of 1.7 or more). The methods outlined in this paper are available in accompanying R software (the ecopower package), which allows researchers with pilot data to answer a wide range of design questions to assist them in planning their studies. Published version This research was funded by Australian Research Council, Grant/Award Number: DP180104041, DP190102030, DP210101923 and LP160100836; NSW Environmental Trust; NSW Recreational Fishing Trust. 2023-09-20T01:30:03Z 2023-09-20T01:30:03Z 2023 Journal Article Maslen, B., Popovic, G., Lim, M., Marzinelli, E. M. & Warton, D. (2023). How many sites? Methods to assist design decisions when collecting multivariate data in ecology. Methods in Ecology and Evolution, 14(6), 1564-1573. https://dx.doi.org/10.1111/2041-210X.14094 2041-210X https://hdl.handle.net/10356/170593 10.1111/2041-210X.14094 2-s2.0-85153529083 6 14 1564 1573 en Methods in Ecology and Evolution © 2023 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. application/pdf |
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Science::Biological sciences Copula Multivariate Maslen, Ben Popovic, Gordana Lim, Michelle Marzinelli, Ezequiel Miguel Warton, David How many sites? Methods to assist design decisions when collecting multivariate data in ecology |
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Sample size estimation through power analysis is a fundamental tool in planning an ecological study, yet there are currently no well-established procedures for when multivariate abundances are to be collected. A power analysis procedure would need to address three challenges: designing a parsimonious simulation model that captures key community data properties; measuring effect size in a realistic yet interpretable fashion; and ensuring computational feasibility when simulation is used both for power estimation and significance testing. Here, we propose a power analysis procedure that addresses these three challenges by: using for simulation a Gaussian copula model with factor analytical structure, fitted to pilot data; assuming a common effect size across all taxa, but applied in different directions according to expert opinion (to “increaser”, “decreaser” or “no effect” taxa); using a critical value approach to estimate power, which reduces computation time by a factor of 500 (if we would otherwise use 999 resamples to estimate each p-value) with minor loss of accuracy. The procedure is demonstrated on pilot data from fish assemblages in a restoration study, where it was found that the planned study design would only be capable of detecting relatively large effects (change in abundance by a factor of 1.7 or more). The methods outlined in this paper are available in accompanying R software (the ecopower package), which allows researchers with pilot data to answer a wide range of design questions to assist them in planning their studies. |
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Singapore Centre for Environmental Life Sciences and Engineering |
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Singapore Centre for Environmental Life Sciences and Engineering Maslen, Ben Popovic, Gordana Lim, Michelle Marzinelli, Ezequiel Miguel Warton, David |
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Article |
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Maslen, Ben Popovic, Gordana Lim, Michelle Marzinelli, Ezequiel Miguel Warton, David |
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Maslen, Ben |
title |
How many sites? Methods to assist design decisions when collecting multivariate data in ecology |
title_short |
How many sites? Methods to assist design decisions when collecting multivariate data in ecology |
title_full |
How many sites? Methods to assist design decisions when collecting multivariate data in ecology |
title_fullStr |
How many sites? Methods to assist design decisions when collecting multivariate data in ecology |
title_full_unstemmed |
How many sites? Methods to assist design decisions when collecting multivariate data in ecology |
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how many sites? methods to assist design decisions when collecting multivariate data in ecology |
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2023 |
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https://hdl.handle.net/10356/170593 |
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1779156623882715136 |