A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits
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 sampli...
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ftdoajarticles:oai:doaj.org/article:4838d937adc0452c85c3e477665ddda9 2023-08-27T04:08:46+02:00 A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits Patricia Puerta Lorenzo Ciannelli Bethany Johnson 2019-02-01T00:00:00Z https://doi.org/10.7717/peerj.6471 https://doaj.org/article/4838d937adc0452c85c3e477665ddda9 EN eng PeerJ Inc. https://peerj.com/articles/6471.pdf https://peerj.com/articles/6471/ https://doaj.org/toc/2167-8359 doi:10.7717/peerj.6471 2167-8359 https://doaj.org/article/4838d937adc0452c85c3e477665ddda9 PeerJ, Vol 7, p e6471 (2019) Sampling design Multi-stage sampling Life history traits Spatial ecology Population ecology Medicine R article 2019 ftdoajarticles https://doi.org/10.7717/peerj.6471 2023-08-06T00:44:50Z 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 ... Article in Journal/Newspaper Bering Sea Directory of Open Access Journals: DOAJ Articles Bering Sea Pacific PeerJ 7 e6471 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Sampling design Multi-stage sampling Life history traits Spatial ecology Population ecology Medicine R |
spellingShingle |
Sampling design Multi-stage sampling Life history traits Spatial ecology Population ecology Medicine R Patricia Puerta Lorenzo Ciannelli Bethany Johnson A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits |
topic_facet |
Sampling design Multi-stage sampling Life history traits Spatial ecology Population ecology Medicine R |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
Patricia Puerta Lorenzo Ciannelli Bethany Johnson |
author_facet |
Patricia Puerta Lorenzo Ciannelli Bethany Johnson |
author_sort |
Patricia Puerta |
title |
A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits |
title_short |
A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits |
title_full |
A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits |
title_fullStr |
A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits |
title_full_unstemmed |
A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits |
title_sort |
simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits |
publisher |
PeerJ Inc. |
publishDate |
2019 |
url |
https://doi.org/10.7717/peerj.6471 https://doaj.org/article/4838d937adc0452c85c3e477665ddda9 |
geographic |
Bering Sea Pacific |
geographic_facet |
Bering Sea Pacific |
genre |
Bering Sea |
genre_facet |
Bering Sea |
op_source |
PeerJ, Vol 7, p e6471 (2019) |
op_relation |
https://peerj.com/articles/6471.pdf https://peerj.com/articles/6471/ https://doaj.org/toc/2167-8359 doi:10.7717/peerj.6471 2167-8359 https://doaj.org/article/4838d937adc0452c85c3e477665ddda9 |
op_doi |
https://doi.org/10.7717/peerj.6471 |
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PeerJ |
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7 |
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e6471 |
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