Four Reasons to Question the Accuracy of a Biotic Index; the Risk of Metric Bias and the Scope to Improve Accuracy
Natural ecological variability and analytical design can bias the derived value of a biotic index through the variable influence of indicator body-size, abundance, richness, and ascribed tolerance scores. Descriptive statistics highlight this risk for 26 aquatic indicator systems; detailed analysis...
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Published in | PloS one Vol. 11; no. 7; p. e0158383 |
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Format | Journal Article |
Language | English |
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08.07.2016
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Abstract | Natural ecological variability and analytical design can bias the derived value of a biotic index through the variable influence of indicator body-size, abundance, richness, and ascribed tolerance scores. Descriptive statistics highlight this risk for 26 aquatic indicator systems; detailed analysis is provided for contrasting weighted-average indices applying the example of the BMWP, which has the best supporting data. Differences in body size between taxa from respective tolerance classes is a common feature of indicator systems; in some it represents a trend ranging from comparatively small pollution tolerant to larger intolerant organisms. Under this scenario, the propensity to collect a greater proportion of smaller organisms is associated with negative bias however, positive bias may occur when equipment (e.g. mesh-size) selectively samples larger organisms. Biotic indices are often derived from systems where indicator taxa are unevenly distributed along the gradient of tolerance classes. Such skews in indicator richness can distort index values in the direction of taxonomically rich indicator classes with the subsequent degree of bias related to the treatment of abundance data. The misclassification of indicator taxa causes bias that varies with the magnitude of the misclassification, the relative abundance of misclassified taxa and the treatment of abundance data. These artifacts of assessment design can compromise the ability to monitor biological quality. The statistical treatment of abundance data and the manipulation of indicator assignment and class richness can be used to improve index accuracy. While advances in methods of data collection (i.e. DNA barcoding) may facilitate improvement, the scope to reduce systematic bias is ultimately limited to a strategy of optimal compromise. The shortfall in accuracy must be addressed by statistical pragmatism. At any particular site, the net bias is a probabilistic function of the sample data, resulting in an error variance around an average deviation. Following standardized protocols and assigning precise reference conditions, the error variance of their comparative ratio (test-site:reference) can be measured and used to estimate the accuracy of the resultant assessment. |
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AbstractList | Natural ecological variability and analytical design can bias the derived value of a biotic index through the variable influence of indicator body-size, abundance, richness, and ascribed tolerance scores. Descriptive statistics highlight this risk for 26 aquatic indicator systems; detailed analysis is provided for contrasting weighted-average indices applying the example of the BMWP, which has the best supporting data. Differences in body size between taxa from respective tolerance classes is a common feature of indicator systems; in some it represents a trend ranging from comparatively small pollution tolerant to larger intolerant organisms. Under this scenario, the propensity to collect a greater proportion of smaller organisms is associated with negative bias however, positive bias may occur when equipment (e.g. mesh-size) selectively samples larger organisms. Biotic indices are often derived from systems where indicator taxa are unevenly distributed along the gradient of tolerance classes. Such skews in indicator richness can distort index values in the direction of taxonomically rich indicator classes with the subsequent degree of bias related to the treatment of abundance data. The misclassification of indicator taxa causes bias that varies with the magnitude of the misclassification, the relative abundance of misclassified taxa and the treatment of abundance data. These artifacts of assessment design can compromise the ability to monitor biological quality. The statistical treatment of abundance data and the manipulation of indicator assignment and class richness can be used to improve index accuracy. While advances in methods of data collection (i.e. DNA barcoding) may facilitate improvement, the scope to reduce systematic bias is ultimately limited to a strategy of optimal compromise. The shortfall in accuracy must be addressed by statistical pragmatism. At any particular site, the net bias is a probabilistic function of the sample data, resulting in an error variance around an average deviation. Following standardized protocols and assigning precise reference conditions, the error variance of their comparative ratio (test-site:reference) can be measured and used to estimate the accuracy of the resultant assessment. |
Audience | Academic |
Author | Monaghan, Kieran A |
AuthorAffiliation | University of Brighton, UNITED KINGDOM CESAM – Centre for Environmental and Marine Studies & Department of Biology, Universidade de Aveiro, Campus Universitario de Santiago, 3810-193, Aveiro, Portugal |
AuthorAffiliation_xml | – name: University of Brighton, UNITED KINGDOM – name: CESAM – Centre for Environmental and Marine Studies & Department of Biology, Universidade de Aveiro, Campus Universitario de Santiago, 3810-193, Aveiro, Portugal |
Author_xml | – sequence: 1 givenname: Kieran A surname: Monaghan fullname: Monaghan, Kieran A organization: CESAM - Centre for Environmental and Marine Studies & Department of Biology, Universidade de Aveiro, Campus Universitario de Santiago, 3810-193, Aveiro, Portugal |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27392036$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1016_j_ecolind_2019_105665 crossref_primary_10_1071_AJ21182 crossref_primary_10_3390_d13110606 crossref_primary_10_1007_s11356_023_27559_0 crossref_primary_10_1016_j_jenvman_2021_113124 crossref_primary_10_1016_j_ecolind_2020_106374 crossref_primary_10_1002_aqc_3289 crossref_primary_10_15446_ga_v23n1_83792 crossref_primary_10_1007_s11356_024_33115_1 crossref_primary_10_1002_aqc_4141 crossref_primary_10_1016_j_scitotenv_2020_142997 |
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Copyright | COPYRIGHT 2016 Public Library of Science 2016 Kieran A. Monaghan. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2016 Kieran A. Monaghan 2016 Kieran A. Monaghan |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The author has declared that no competing interests exist. Conceived and designed the experiments: KAM. Performed the experiments: KAM. Analyzed the data: KAM. Contributed reagents/materials/analysis tools: KAM. Wrote the paper: KAM. |
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SubjectTerms | Abundance Accuracy Animals Bias Biodiversity Biology Biology and Life Sciences Biomonitoring Biota Biotic integrity indexes Body Size Community ecology Computer and Information Sciences Data Accuracy Data collection Deoxyribonucleic acid DNA DNA barcoding Earth Sciences Ecological risk assessment Ecology and Environmental Sciences Ecosystem biology Ecosystems Engineering and Technology Gene sequencing Indicators Models, Theoretical Organisms Physical Sciences Pollution tolerance Relative abundance Statistical analysis Statistical methods Statistics Taxa Variance Water quality |
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Title | Four Reasons to Question the Accuracy of a Biotic Index; the Risk of Metric Bias and the Scope to Improve Accuracy |
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