Designs for Evaluating Local and Regional Scale Trends

Larsen, David P.; Kincaid, Thomas M.; Jacobs, Steven E.; Urquhart, N. Scott

doi:10.1641/0006-3568(2001)051[1069:dfelar]2.0.co;2

Cited by 114 publications

(97 citation statements)

References 16 publications

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“…We briefly investigated the sensitivity of the power estimates for CAP, PerMANOVA, and the Mantel test to the assumed values. We noted a slight decrease in power for an increase in variance parameter values as expected from the knowledge of univariate power analyses (Larsen et al 2001). Also, increasing the site-level variance components and species pool may decrease the amount of convergence among sites within time periods.…”

Section: Discussionsupporting

confidence: 68%

“…We assume that species within the same group have a common average deterministic trend over time. Also, we assume a degree of temporal coherence among sites; the ecological processes effecting species composition within a site are exerting an influence across all sites (Larsen et al 2001); i.e., all sites display increasing or decreasing directional change. Our methods are appropriate for programs that use a fixed species list and monitor permanent sites over time (always revisit design).…”

Section: Statistical Analysesmentioning

confidence: 99%

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A power analysis for multivariate tests of temporal trend in species composition

Irvine

Dinger

Sarr

2011

Ecology

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Abstract. Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

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Section: Discussionsupporting

confidence: 68%

Section: Statistical Analysesmentioning

confidence: 99%

A power analysis for multivariate tests of temporal trend in species composition

Irvine

Dinger

Sarr

2011

Ecology

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“…The data used in this design-based simulation comes from the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) Northeast lakes survey (Larsen et al, 2001). Between 1991 and 1995, researchers from the U.S. Environmental Protection Agency (EPA)…”

Section: A Design-based Simulationmentioning

confidence: 99%

Small area estimation via M-quantile geographically weighted regression

Salvati

Tzavidis

Pratesi

et al. 2010

TEST

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Abstract.One popular approach to small area estimation when data are spatially correlated is to employ Simultaneous Autoregressive (SAR) random effects models to define the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP). See Singh et al. (2005) and . SAR models allow for spatial correlation in the error structure. An alternative approach that incorporates the spatial information in the regression model is to use Geographically Weighted Regression (GWR). See Brunsdon et al. (1996) and Fotheringham et al. (1997). GWR extends the traditional regression model by characterising the relationship between the outcome variable and the covariates via local rather than global parameters. In this paper we investigate GWR-based small area estimation under the M-quantile modelling approach (Chambers and Tzavidis, 2006). In particular, we integrate the concepts of outlier-robust small area estimation and borrowing strength over space within a unified modelling framework by specifying an M-quantile GWR model that is a local model for the M-quantiles of the conditional distribution of the outcome variable given the covariates. This model is then used to define an outlier-robust predictor of the small area characteristic of interest that also accounts for spatial association in the data. An additional important spin-off from applying the M-quantile GWR small area model is more efficient synthetic estimation for out of sample areas. We demonstrate the usefulness of this framework through both model-based as well as design-based simulation, with the latter based on a realistic survey data set. The paper concludes with an application to environmental data for predicting average levels of the Acid Neutralizing Capacity at 8-digit Hydrologic Unit Code level in the Northeast states of the U.S.A.

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“…While estimating statistical power is fairly straightforward for simple survey designs, estimating power in complex designs requires more sophisticated techniques (Urquhart and Kincaid 1999;Larsen et al 2001). Surveys for larval DHP are hierarchically nested and include samples, subsamples, and sub-subsamples.…”

Section: Introductionmentioning

confidence: 99%

“…Decomposing these sources of survey variation into variance components can help determine the relative contribution of factors to overall variation in the survey (Lewis 1978;Matthews 1990;Morrissey et al 1992;Kincaid et al 2003). Increasing the sample size at levels of sampling with high variance will result in a survey design focused on minimizing variation, thereby maximizing the survey's power to detect changes in the population (Morrissey et al 1992;Larsen et al 2001). As a general rule, increasing the number of samples will result in a greater increase in power than increasing the number of subsamples or sub-subsamples (Urquhart and Kincaid 1999).…”

Section: Introductionmentioning

confidence: 99%

Using variance components to estimate power in a hierarchically nested sampling design

Dzul

Dixon

Quist

et al. 2012

Environ Monit Assess

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We used variance components to assess allocation of sampling effort in a hierarchically nested sampling design for ongoing monitoring of early life history stages of the federally endangered Devils Hole pupfish (DHP) (Cyprinodon diabolis). Sampling design for larval DHP included surveys (5 days each spring 2007-2009), events, and plots. Each survey was comprised of three counting events, where DHP larvae on nine plots were counted plot by plot. Statistical analysis of larval abundance included three components: (1) evaluation of power from various sample size combinations, (2) comparison of power in fixed and random plot designs, and (3) assessment of yearly differences in the power of the survey. Results indicated that increasing the sample size at the lowest level of sampling represented the most realistic option to increase the survey's power, fixed plot designs had greater power than random plot designs, and the power of the larval survey varied by year. This study provides an example of how monitoring efforts may benefit from coupling variance components estimation with power analysis to assess sampling design. Abstract We used variance components to assess allocation of sampling effort in a hierarchically nested sampling design for ongoing monitoring of early life history stages of the federally endangered Devils Hole pupfish (DHP) (Cyprinodon diabolis). Sampling design for larval DHP included surveys (5 days each spring 2007-2009), events, and plots. Each survey was comprised of three counting events, where DHP larvae on nine plots were counted plot by plot. Statistical analysis of larval abundance included three components:(1) evaluation of power from various sample size combinations, (2) comparison of power in fixed and random plot designs, and (3) assessment of yearly differences in the power of the survey. Results indicated that increasing the sample size at the lowest level of sampling represented the most realistic option to increase the survey's power, fixed plot designs had greater power than random plot designs, and the power of the larval survey varied by year. This study provides an example of how monitoring efforts may benefit from coupling variance components estimation with power analysis to assess sampling design.

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Designs for Evaluating Local and Regional Scale Trends

Cited by 114 publications

References 16 publications

A power analysis for multivariate tests of temporal trend in species composition

A power analysis for multivariate tests of temporal trend in species composition

Small area estimation via M-quantile geographically weighted regression

Using variance components to estimate power in a hierarchically nested sampling design

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