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Cole, Russell; Healy, Terry R.; Wood, M.; Foster, Dean

doi:10.1023/a:1010756729485

Cited by 24 publications

(3 citation statements)

References 2 publications

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“…These two data sets also had the least regular transition from within to between province sample pair comparisons with distance ( Fig 3 ). Both the precision and shape of an empirical variogram is known to vary with the configuration of sampling locations [ 68 , 69 , 75 ], and these factors could certainly have played a role in the nugget effect estimates [ 76 ]. In any event, estimates of explained variance at all sites are substantially above an amount that would be considered acceptable in a multivariate study of biotic-environmental relationships.…”

Section: Discussionmentioning

confidence: 99%

The relationship between observational scale and explained variance in benthic communities

et al. 2018

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This study addresses the impact of spatial scale on explaining variance in benthic communities. In particular, the analysis estimated the fraction of community variation that occurred at a spatial scale smaller than the sampling interval (i.e., the geographic distance between samples). This estimate is important because it sets a limit on the amount of community variation that can be explained based on the spatial configuration of a study area and sampling design. Six benthic data sets were examined that consisted of faunal abundances, common environmental variables (water depth, grain size, and surficial percent cover), and sonar backscatter treated as a habitat proxy (categorical acoustic provinces). Redundancy analysis was coupled with spatial variograms generated by multiscale ordination to quantify the explained and residual variance at different spatial scales and within and between acoustic provinces. The amount of community variation below the sampling interval of the surveys (< 100 m) was estimated to be 36–59% of the total. Once adjusted for this small-scale variation, > 71% of the remaining variance was explained by the environmental and province variables. Furthermore, these variables effectively explained the spatial structure present in the infaunal community. Overall, no scale problems remained to compromise inferences, and unexplained infaunal community variation had no apparent spatial structure within the observational scale of the surveys (> 100 m), although small-scale gradients (< 100 m) below the observational scale may be present.

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Section: Discussionmentioning

confidence: 99%

The relationship between observational scale and explained variance in benthic communities

et al. 2018

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“…Comparisons of different sampling strategies used to collect biological data have been performed in multiple fields such as forestry (Kulow, 1966; Gove, Ducey & Valentine, 2002; Broich et al, 2009; Bhatta, Chaudhary & Vetaas, 2012); grasslands and crops (Colbach, Dessaint & Forcella, 2000; Stafford et al, 2006); land-use (Nusser et al, 2013); terrestrial mammals (Parmenter et al, 2003; Harris et al, 2013; Wright, Newson & Noble, 2014; Calmanti et al, 2015); birds (Johnson et al, 2009; Pavlacky et al, 2017); marine invertebrates (Miller & Ambrose, 2000; Cole et al, 2001; Li et al, 2015); and fish (Kimura, 1977; Lai, 1993; Goodyear, 1995; Liu, Chen & Cheng, 2009). These comparisons identify an optimal design that balances sampling effort and data quality to produce accurate estimates of the studied population parameters.…”

Section: Introductionmentioning

confidence: 99%

A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits

Puerta

Ciannelli

Johnson

2019

PeerJ

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Selecting an appropriate and efficient sampling strategy in biological surveys is a major concern in ecological research, particularly when the population abundance and individual traits of the sampled population are highly structured over space. Multi-stage sampling designs typically present sampling sites as primary units. However, to collect trait data, such as age or maturity, only a sub-sample of individuals collected in the sampling site is retained. Therefore, not only the sampling design, but also the sub-sampling strategy can have a major impact on important population estimates, commonly used as reference points for management and conservation. We developed a simulation framework to evaluate sub-sampling strategies from multi-stage biological surveys. Specifically, we compare quantitatively precision and bias of the population estimates obtained using two common but contrasting sub-sampling strategies: the random and the stratified designs. The sub-sampling strategy evaluation was applied to age data collection of a virtual fish population that has the same statistical and biological characteristics of the Eastern Bering Sea population of Pacific cod. The simulation scheme allowed us to incorporate contributions of several sources of error and to analyze the sensitivity of the different strategies in the population estimates. We found that, on average across all scenarios tested, the main differences between sub-sampling designs arise from the inability of the stratified design to reproduce spatial patterns of the individual traits. However, differences between the sub-sampling strategies in other population estimates may be small, particularly when large sub-sample sizes are used. On isolated scenarios (representative of specific environmental or demographic conditions), the random sub-sampling provided better precision in all population estimates analyzed. The sensitivity analysis revealed the important contribution of spatial autocorrelation in the error of population trait estimates, regardless of the sub-sampling design. This framework will be a useful tool for monitoring and assessment of natural populations with spatially structured traits in multi-stage sampling designs.

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“…An important aspect of environmental quality studies is to understand the impacts of lipophillic organic pollutants so as to minimize the risks of adverse effects. 1,2 As the impacts and geochemical fates of these pollutants are closely interrelated, it is essential to understand the underlying geochemical controls on the fates of these compounds. This requires intensive sampling and high quality analyses of a broad spectrum of contaminants, which enter the marine environment from different sources including river runoff, atmospheric precipitation, industrial and sewage outfalls, and maritime transport.…”

Section: Introductionmentioning

confidence: 99%

Description and evaluation of a sampling system for monitoring hydrocarbons in sediments

et al. 2007

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A composite random sampling design was used to estimate the concentrations of hydrocarbons in sediments from two near-shore areas of Scotland (Firth of Clyde and Firth of Forth). The aim of this work was to estimate a mean value for each parameter in these areas, and to determine whether this can be done with more thorough coverage (better representation), better precision and less variance at lower analytical cost through a composite random sampling scheme rather than a simple random sampling scheme, and thereby contribute to the re-design of the UK National Marine Monitoring Programme (NMMP), re-named the UK Clean Seas Environmental Monitoring Programme (CSEMP) in 2006. Samples were collected using a simple random sampling design during 2005. All sediment samples were analysed for their particle size distribution and total organic carbon (TOC). All sediments were analysed for polycyclic aromatic hydrocarbons (PAHs) and n-alkanes. The concentrations of PAHs and n-alkanes in the study areas are described, and sources of PAHs were investigated through the PAH distributions and n-alkane profiles. Individual sediment samples from each area were combined to give a series of composite sub-samples, each comprised of 5 individual sediment samples. These composite samples were re-analysed for the same parameters as the individual samples. Mean total PAH (2- to 6-ring parent and branched) concentrations, based on the individual original sediment samples collected through simple random sampling, were 1858 microg kg(-1) dry weight (SE = 196 microg kg(-1) dry weight, n = 25) and 532.4 microg kg(-1) dry weight (SE = 59 microg kg(-1) dry weight, n = 25) in the Clyde and Forth, respectively. Mean total PAH concentrations of the composite samples were 1745 microg kg(-1) dry weight (SE = 121.0 microg kg(-1) dry weight, n = 5) in the Clyde and 511.6 microg kg(-1) dry weight (SE = 37.4 microg kg(-1) dry weight, n = 5) in the Forth. No significant differences were found between the mean PAH concentrations from the two sampling designs. This study demonstrated that the composite random sampling design gave a mean value with less variance than the simple random sampling design, at significantly reduced analytical effort (and cost).

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Untitled

Cited by 24 publications

References 2 publications

The relationship between observational scale and explained variance in benthic communities

The relationship between observational scale and explained variance in benthic communities

A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits

Description and evaluation of a sampling system for monitoring hydrocarbons in sediments

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