A simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for 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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ftieo:oai:repositorio.ieo.es:10508/15403 2023-06-11T04:10:39+02:00 A simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for spatially structured traits Puerta, P. (Patricia) Johnson, Bethany Ciannelli, L. (Lorenzo) 2019 http://hdl.handle.net/10508/15403 https://peerj.com/articles/6471.pdf https://doi.org/10.7717/peerj.6471 eng eng PeerJ Centro Oceanográfico de Baleares 2167-8359 https://peerj.com/articles/6471.pdf http://hdl.handle.net/10508/15403 doi:10.7717/peerj.6471 Atribución 3.0 España http://creativecommons.org/licenses/by/3.0/es/ open access research article 2019 ftieo https://doi.org/10.7717/peerj.6471 2023-05-02T23:49:34Z 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 Instituto Español de Oceanografía: e-IEO Bering Sea Pacific PeerJ 7 e6471 |
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Open Polar |
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Instituto Español de Oceanografía: e-IEO |
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ftieo |
language |
English |
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 |
Puerta, P. (Patricia) Johnson, Bethany Ciannelli, L. (Lorenzo) |
spellingShingle |
Puerta, P. (Patricia) Johnson, Bethany Ciannelli, L. (Lorenzo) A simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for spatially structured traits |
author_facet |
Puerta, P. (Patricia) Johnson, Bethany Ciannelli, L. (Lorenzo) |
author_sort |
Puerta, P. (Patricia) |
title |
A simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for spatially structured traits |
title_short |
A simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for spatially structured traits |
title_full |
A simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for spatially structured traits |
title_fullStr |
A simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for spatially structured traits |
title_full_unstemmed |
A simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for spatially structured traits |
title_sort |
simulation framework for sub-sampling strategy evaluation in multi-stage sampling designs that accounts for spatially structured traits |
publisher |
PeerJ |
publishDate |
2019 |
url |
http://hdl.handle.net/10508/15403 https://peerj.com/articles/6471.pdf https://doi.org/10.7717/peerj.6471 |
geographic |
Bering Sea Pacific |
geographic_facet |
Bering Sea Pacific |
genre |
Bering Sea |
genre_facet |
Bering Sea |
op_relation |
2167-8359 https://peerj.com/articles/6471.pdf http://hdl.handle.net/10508/15403 doi:10.7717/peerj.6471 |
op_rights |
Atribución 3.0 España http://creativecommons.org/licenses/by/3.0/es/ open access |
op_doi |
https://doi.org/10.7717/peerj.6471 |
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PeerJ |
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7 |
container_start_page |
e6471 |
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1768385222082035712 |