Loading…
Towards a robust baseline for long-term monitoring of Antarctic coastal benthos
The Southern Ocean represents one of the world regions most sensitive to warming and there is an urgent need for quantitative data to understand changes in coastal communities. This goal can be achieved through the establishment of permanent monitoring sites and robust sampling designs. In this stud...
Saved in:
Published in: | Hydrobiologia 2020-04, Vol.847 (7), p.1753-1771 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c392t-e7847299460336dcf797bbe0e0c11b0a877e055033fe804b2002cb3938d55ca23 |
---|---|
cites | cdi_FETCH-LOGICAL-c392t-e7847299460336dcf797bbe0e0c11b0a877e055033fe804b2002cb3938d55ca23 |
container_end_page | 1771 |
container_issue | 7 |
container_start_page | 1753 |
container_title | Hydrobiologia |
container_volume | 847 |
creator | Piazza, Paola Gattone, Stefano Antonio Guzzi, Alice Schiaparelli, Stefano |
description | The Southern Ocean represents one of the world regions most sensitive to warming and there is an urgent need for quantitative data to understand changes in coastal communities. This goal can be achieved through the establishment of permanent monitoring sites and robust sampling designs. In this study, we used an emerging, photogrammetry-based technique to simulate a pilot study and test the efficiency of different sampling schemes (Simple Random—SRS-, Systematic—SyS- and Strip—SS-) for estimating the abundances of megabenthic taxa. For taxa showing an aggregated distribution, we also applied an adaptive cluster sampling (ACS) design. In almost the totality of cases, the best accuracy of estimates was achieved with SyS combined with plots of 0.0625 m
2
. ACS design gave better performances but required a calibration of both the initial sample size and the threshold value to increase efficiency. The ‘one-size-fits-all’ 1 m
2
plot size never emerged as the best in any sampling schemes, hence the previously published literature data can be biased. This study represents a fine-scale reference baseline for the study area and the simulations performed will be pivotal in establishing sound-monitoring programmes with sufficient statistical power to detect significative changes in the Antarctic benthos. |
doi_str_mv | 10.1007/s10750-020-04177-2 |
format | article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2377614376</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A617744686</galeid><sourcerecordid>A617744686</sourcerecordid><originalsourceid>FETCH-LOGICAL-c392t-e7847299460336dcf797bbe0e0c11b0a877e055033fe804b2002cb3938d55ca23</originalsourceid><addsrcrecordid>eNp9kU1LAzEQhoMoWKt_wFPAk4etk2R3s3ssxY9CQdB6Dtl0tm7ZJjVJUf-9qSuIFwkhkDzPZJiXkEsGEwYgbwIDWUAGPO2cSZnxIzJihRRZwZg8JiMAVmUVK6pTchbCBpJUcxiRx6V7134VqKbeNfsQaaMD9p1F2jpPe2fXWUS_pVtnu-h8Z9fUtXRqo_YmdoYap0PUPW3QxlcXzslJq_uAFz_nmLzc3S5nD9ni8X4-my4yI2oeM5RVLnld5yUIUa5MK2vZNAgIhrEGdCUlQlGkxxYryBsOwE0jalGtisJoLsbkaqi78-5tjyGqjdt7m75UXEhZslzIMlGTgVrrHlVnWxe9NmmtcNsZZ7Ht0v20TBPL87I6CNd_hMRE_IhrvQ9BzZ-f_rJ8YI13IXhs1c53W-0_FQN1SEUNqaiUivpORR36FoMUdodZov_t-x_rC0TIjXo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2377614376</pqid></control><display><type>article</type><title>Towards a robust baseline for long-term monitoring of Antarctic coastal benthos</title><source>Springer Link</source><creator>Piazza, Paola ; Gattone, Stefano Antonio ; Guzzi, Alice ; Schiaparelli, Stefano</creator><creatorcontrib>Piazza, Paola ; Gattone, Stefano Antonio ; Guzzi, Alice ; Schiaparelli, Stefano</creatorcontrib><description>The Southern Ocean represents one of the world regions most sensitive to warming and there is an urgent need for quantitative data to understand changes in coastal communities. This goal can be achieved through the establishment of permanent monitoring sites and robust sampling designs. In this study, we used an emerging, photogrammetry-based technique to simulate a pilot study and test the efficiency of different sampling schemes (Simple Random—SRS-, Systematic—SyS- and Strip—SS-) for estimating the abundances of megabenthic taxa. For taxa showing an aggregated distribution, we also applied an adaptive cluster sampling (ACS) design. In almost the totality of cases, the best accuracy of estimates was achieved with SyS combined with plots of 0.0625 m
2
. ACS design gave better performances but required a calibration of both the initial sample size and the threshold value to increase efficiency. The ‘one-size-fits-all’ 1 m
2
plot size never emerged as the best in any sampling schemes, hence the previously published literature data can be biased. This study represents a fine-scale reference baseline for the study area and the simulations performed will be pivotal in establishing sound-monitoring programmes with sufficient statistical power to detect significative changes in the Antarctic benthos.</description><identifier>ISSN: 0018-8158</identifier><identifier>EISSN: 1573-5117</identifier><identifier>DOI: 10.1007/s10750-020-04177-2</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Adaptive sampling ; Aquatic ecology ; Benthos ; Biomedical and Life Sciences ; Change detection ; Design ; Ecology ; Efficiency ; Environmental monitoring ; Freshwater & Marine Ecology ; Life Sciences ; Monitoring ; Photogrammetry ; Robustness ; Sampling ; Sampling designs ; Trends ; Trends in Aquatic Ecology III ; Zoology</subject><ispartof>Hydrobiologia, 2020-04, Vol.847 (7), p.1753-1771</ispartof><rights>Springer Nature Switzerland AG 2020</rights><rights>COPYRIGHT 2020 Springer</rights><rights>Hydrobiologia is a copyright of Springer, (2020). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c392t-e7847299460336dcf797bbe0e0c11b0a877e055033fe804b2002cb3938d55ca23</citedby><cites>FETCH-LOGICAL-c392t-e7847299460336dcf797bbe0e0c11b0a877e055033fe804b2002cb3938d55ca23</cites><orcidid>0000-0002-0137-3605</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Piazza, Paola</creatorcontrib><creatorcontrib>Gattone, Stefano Antonio</creatorcontrib><creatorcontrib>Guzzi, Alice</creatorcontrib><creatorcontrib>Schiaparelli, Stefano</creatorcontrib><title>Towards a robust baseline for long-term monitoring of Antarctic coastal benthos</title><title>Hydrobiologia</title><addtitle>Hydrobiologia</addtitle><description>The Southern Ocean represents one of the world regions most sensitive to warming and there is an urgent need for quantitative data to understand changes in coastal communities. This goal can be achieved through the establishment of permanent monitoring sites and robust sampling designs. In this study, we used an emerging, photogrammetry-based technique to simulate a pilot study and test the efficiency of different sampling schemes (Simple Random—SRS-, Systematic—SyS- and Strip—SS-) for estimating the abundances of megabenthic taxa. For taxa showing an aggregated distribution, we also applied an adaptive cluster sampling (ACS) design. In almost the totality of cases, the best accuracy of estimates was achieved with SyS combined with plots of 0.0625 m
2
. ACS design gave better performances but required a calibration of both the initial sample size and the threshold value to increase efficiency. The ‘one-size-fits-all’ 1 m
2
plot size never emerged as the best in any sampling schemes, hence the previously published literature data can be biased. This study represents a fine-scale reference baseline for the study area and the simulations performed will be pivotal in establishing sound-monitoring programmes with sufficient statistical power to detect significative changes in the Antarctic benthos.</description><subject>Adaptive sampling</subject><subject>Aquatic ecology</subject><subject>Benthos</subject><subject>Biomedical and Life Sciences</subject><subject>Change detection</subject><subject>Design</subject><subject>Ecology</subject><subject>Efficiency</subject><subject>Environmental monitoring</subject><subject>Freshwater & Marine Ecology</subject><subject>Life Sciences</subject><subject>Monitoring</subject><subject>Photogrammetry</subject><subject>Robustness</subject><subject>Sampling</subject><subject>Sampling designs</subject><subject>Trends</subject><subject>Trends in Aquatic Ecology III</subject><subject>Zoology</subject><issn>0018-8158</issn><issn>1573-5117</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kU1LAzEQhoMoWKt_wFPAk4etk2R3s3ssxY9CQdB6Dtl0tm7ZJjVJUf-9qSuIFwkhkDzPZJiXkEsGEwYgbwIDWUAGPO2cSZnxIzJihRRZwZg8JiMAVmUVK6pTchbCBpJUcxiRx6V7134VqKbeNfsQaaMD9p1F2jpPe2fXWUS_pVtnu-h8Z9fUtXRqo_YmdoYap0PUPW3QxlcXzslJq_uAFz_nmLzc3S5nD9ni8X4-my4yI2oeM5RVLnld5yUIUa5MK2vZNAgIhrEGdCUlQlGkxxYryBsOwE0jalGtisJoLsbkaqi78-5tjyGqjdt7m75UXEhZslzIMlGTgVrrHlVnWxe9NmmtcNsZZ7Ht0v20TBPL87I6CNd_hMRE_IhrvQ9BzZ-f_rJ8YI13IXhs1c53W-0_FQN1SEUNqaiUivpORR36FoMUdodZov_t-x_rC0TIjXo</recordid><startdate>20200401</startdate><enddate>20200401</enddate><creator>Piazza, Paola</creator><creator>Gattone, Stefano Antonio</creator><creator>Guzzi, Alice</creator><creator>Schiaparelli, Stefano</creator><general>Springer International Publishing</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QH</scope><scope>7SN</scope><scope>7SS</scope><scope>7U7</scope><scope>7UA</scope><scope>88A</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H95</scope><scope>HCIFZ</scope><scope>L.G</scope><scope>LK8</scope><scope>M7N</scope><scope>M7P</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>RC3</scope><orcidid>https://orcid.org/0000-0002-0137-3605</orcidid></search><sort><creationdate>20200401</creationdate><title>Towards a robust baseline for long-term monitoring of Antarctic coastal benthos</title><author>Piazza, Paola ; Gattone, Stefano Antonio ; Guzzi, Alice ; Schiaparelli, Stefano</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c392t-e7847299460336dcf797bbe0e0c11b0a877e055033fe804b2002cb3938d55ca23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptive sampling</topic><topic>Aquatic ecology</topic><topic>Benthos</topic><topic>Biomedical and Life Sciences</topic><topic>Change detection</topic><topic>Design</topic><topic>Ecology</topic><topic>Efficiency</topic><topic>Environmental monitoring</topic><topic>Freshwater & Marine Ecology</topic><topic>Life Sciences</topic><topic>Monitoring</topic><topic>Photogrammetry</topic><topic>Robustness</topic><topic>Sampling</topic><topic>Sampling designs</topic><topic>Trends</topic><topic>Trends in Aquatic Ecology III</topic><topic>Zoology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Piazza, Paola</creatorcontrib><creatorcontrib>Gattone, Stefano Antonio</creatorcontrib><creatorcontrib>Guzzi, Alice</creatorcontrib><creatorcontrib>Schiaparelli, Stefano</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Aqualine</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Toxicology Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Biology Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Biological Science Collection</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>ProQuest Biological Science Journals</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><jtitle>Hydrobiologia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Piazza, Paola</au><au>Gattone, Stefano Antonio</au><au>Guzzi, Alice</au><au>Schiaparelli, Stefano</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards a robust baseline for long-term monitoring of Antarctic coastal benthos</atitle><jtitle>Hydrobiologia</jtitle><stitle>Hydrobiologia</stitle><date>2020-04-01</date><risdate>2020</risdate><volume>847</volume><issue>7</issue><spage>1753</spage><epage>1771</epage><pages>1753-1771</pages><issn>0018-8158</issn><eissn>1573-5117</eissn><abstract>The Southern Ocean represents one of the world regions most sensitive to warming and there is an urgent need for quantitative data to understand changes in coastal communities. This goal can be achieved through the establishment of permanent monitoring sites and robust sampling designs. In this study, we used an emerging, photogrammetry-based technique to simulate a pilot study and test the efficiency of different sampling schemes (Simple Random—SRS-, Systematic—SyS- and Strip—SS-) for estimating the abundances of megabenthic taxa. For taxa showing an aggregated distribution, we also applied an adaptive cluster sampling (ACS) design. In almost the totality of cases, the best accuracy of estimates was achieved with SyS combined with plots of 0.0625 m
2
. ACS design gave better performances but required a calibration of both the initial sample size and the threshold value to increase efficiency. The ‘one-size-fits-all’ 1 m
2
plot size never emerged as the best in any sampling schemes, hence the previously published literature data can be biased. This study represents a fine-scale reference baseline for the study area and the simulations performed will be pivotal in establishing sound-monitoring programmes with sufficient statistical power to detect significative changes in the Antarctic benthos.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10750-020-04177-2</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-0137-3605</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0018-8158 |
ispartof | Hydrobiologia, 2020-04, Vol.847 (7), p.1753-1771 |
issn | 0018-8158 1573-5117 |
language | eng |
recordid | cdi_proquest_journals_2377614376 |
source | Springer Link |
subjects | Adaptive sampling Aquatic ecology Benthos Biomedical and Life Sciences Change detection Design Ecology Efficiency Environmental monitoring Freshwater & Marine Ecology Life Sciences Monitoring Photogrammetry Robustness Sampling Sampling designs Trends Trends in Aquatic Ecology III Zoology |
title | Towards a robust baseline for long-term monitoring of Antarctic coastal benthos |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T22%3A16%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Towards%20a%20robust%20baseline%20for%20long-term%20monitoring%20of%20Antarctic%20coastal%20benthos&rft.jtitle=Hydrobiologia&rft.au=Piazza,%20Paola&rft.date=2020-04-01&rft.volume=847&rft.issue=7&rft.spage=1753&rft.epage=1771&rft.pages=1753-1771&rft.issn=0018-8158&rft.eissn=1573-5117&rft_id=info:doi/10.1007/s10750-020-04177-2&rft_dat=%3Cgale_proqu%3EA617744686%3C/gale_proqu%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c392t-e7847299460336dcf797bbe0e0c11b0a877e055033fe804b2002cb3938d55ca23%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2377614376&rft_id=info:pmid/&rft_galeid=A617744686&rfr_iscdi=true |