Abstract
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.
Highlights
IntroductionComparisons 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)
Multi-stage design is very common in the monitoring of fish populations, whereby fish are caught at different sampling sites, all or a high proportion of the fish in that catch are measured for length, but only a sub-sample of the measured fish are aged
To develop a simulation framework for a sub-sampling strategy evaluation in spatially structured populations the following steps are required: (1) create a virtual population where all the parameters and features of interest are known; (2) simulate a multi-stage field sampling for each sampling design targeted; (3) account for further sample and data processing for the trait estimates by addressing and adding potential bias and errors resulting from those proceedings; (4) compare and evaluate the estimates obtained from each sampling strategy and ; (5) perform a sensitivity test to quantify errors in the population estimates and disentangle error sources from the sampling design or other processes such as those included in step 3
Summary
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. This is problematic when the individual traits within a population are structured across space and time, calling into question the statistical representativeness of the subsample
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