Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate
Shells of bivalve mollusks serve as archives for past climates and ecosystems, and human-environmental interactions as well as life history traits and physiology of the animals. Amongst other proxies, data can be recorded in the shells in the form of element chemical properties. As demonstrated here...
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ftdoajarticles:oai:doaj.org/article:8a0f234048e44a84a740e5fd037721a1 2023-05-15T15:22:35+02:00 Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate Bernd R. Schöne Soraya Marali Regina Mertz-Kraus Paul G. Butler Alan D. Wanamaker Lukas Fröhlich 2022-05-01T00:00:00Z https://doi.org/10.3389/feart.2022.889115 https://doaj.org/article/8a0f234048e44a84a740e5fd037721a1 EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/feart.2022.889115/full https://doaj.org/toc/2296-6463 2296-6463 doi:10.3389/feart.2022.889115 https://doaj.org/article/8a0f234048e44a84a740e5fd037721a1 Frontiers in Earth Science, Vol 10 (2022) bivalve sclerochronology shell element chemistry seasonal growth rate weighted average arithmetic average Science Q article 2022 ftdoajarticles https://doi.org/10.3389/feart.2022.889115 2022-12-31T02:29:57Z Shells of bivalve mollusks serve as archives for past climates and ecosystems, and human-environmental interactions as well as life history traits and physiology of the animals. Amongst other proxies, data can be recorded in the shells in the form of element chemical properties. As demonstrated here with measured chemical data (10 elements) from 12 Arctica islandica specimens complemented by numerical simulations, mistakes during sclerochronological data processing can introduce significant bias, adding a further source of error to paleoenvironmental or biological reconstructions. Specifically, signal extraction from noisy LA-ICP-MS (Laser Ablation—Inductively Coupled Plasma—Mass Spectrometry) data generated in line scan mode with circular LA spots requires a weighted rather than an arithmetic moving average. Otherwise, results can be in error by more than 41%. Furthermore, if variations of seasonal shell growth rate remain unconsidered, arithmetic annual averages of intra-annual data will be biased toward the fast-growing season of the year. Actual chemical data differed by between 3.7 and 33.7% from weighted averages. Numerical simulations not only corroborated these findings, but indicated that arithmetic annual means can overestimate or underestimate the actual environmental variable by nearly 40% relative to its seasonal range. The magnitude and direction of the error depends on the timing and rate of both seasonal shell growth and environmental change. With appropriate spatial sampling resolution, weighting can reduce this bias to almost zero. On average, the error reduction attains 80% at a sample depth of 10, 92% when 20 samples were analyzed and nearly 100% when 100 samples were taken from an annual increment. Under some exceptional, though unrealistic circumstances, arithmetic means can be superior to weighted means. To identify the presence of such cases, a numerical simulation is advised based on the shape, amplitude and phase relationships of both curves, i.e., seasonal shell growth and the ... Article in Journal/Newspaper Arctica islandica Directory of Open Access Journals: DOAJ Articles Frontiers in Earth Science 10 |
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bivalve sclerochronology shell element chemistry seasonal growth rate weighted average arithmetic average Science Q |
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bivalve sclerochronology shell element chemistry seasonal growth rate weighted average arithmetic average Science Q Bernd R. Schöne Soraya Marali Regina Mertz-Kraus Paul G. Butler Alan D. Wanamaker Lukas Fröhlich Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate |
topic_facet |
bivalve sclerochronology shell element chemistry seasonal growth rate weighted average arithmetic average Science Q |
description |
Shells of bivalve mollusks serve as archives for past climates and ecosystems, and human-environmental interactions as well as life history traits and physiology of the animals. Amongst other proxies, data can be recorded in the shells in the form of element chemical properties. As demonstrated here with measured chemical data (10 elements) from 12 Arctica islandica specimens complemented by numerical simulations, mistakes during sclerochronological data processing can introduce significant bias, adding a further source of error to paleoenvironmental or biological reconstructions. Specifically, signal extraction from noisy LA-ICP-MS (Laser Ablation—Inductively Coupled Plasma—Mass Spectrometry) data generated in line scan mode with circular LA spots requires a weighted rather than an arithmetic moving average. Otherwise, results can be in error by more than 41%. Furthermore, if variations of seasonal shell growth rate remain unconsidered, arithmetic annual averages of intra-annual data will be biased toward the fast-growing season of the year. Actual chemical data differed by between 3.7 and 33.7% from weighted averages. Numerical simulations not only corroborated these findings, but indicated that arithmetic annual means can overestimate or underestimate the actual environmental variable by nearly 40% relative to its seasonal range. The magnitude and direction of the error depends on the timing and rate of both seasonal shell growth and environmental change. With appropriate spatial sampling resolution, weighting can reduce this bias to almost zero. On average, the error reduction attains 80% at a sample depth of 10, 92% when 20 samples were analyzed and nearly 100% when 100 samples were taken from an annual increment. Under some exceptional, though unrealistic circumstances, arithmetic means can be superior to weighted means. To identify the presence of such cases, a numerical simulation is advised based on the shape, amplitude and phase relationships of both curves, i.e., seasonal shell growth and the ... |
format |
Article in Journal/Newspaper |
author |
Bernd R. Schöne Soraya Marali Regina Mertz-Kraus Paul G. Butler Alan D. Wanamaker Lukas Fröhlich |
author_facet |
Bernd R. Schöne Soraya Marali Regina Mertz-Kraus Paul G. Butler Alan D. Wanamaker Lukas Fröhlich |
author_sort |
Bernd R. Schöne |
title |
Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate |
title_short |
Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate |
title_full |
Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate |
title_fullStr |
Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate |
title_full_unstemmed |
Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate |
title_sort |
importance of weighting high-resolution proxy data from bivalve shells to avoid bias caused by sample spot geometry and variability in seasonal growth rate |
publisher |
Frontiers Media S.A. |
publishDate |
2022 |
url |
https://doi.org/10.3389/feart.2022.889115 https://doaj.org/article/8a0f234048e44a84a740e5fd037721a1 |
genre |
Arctica islandica |
genre_facet |
Arctica islandica |
op_source |
Frontiers in Earth Science, Vol 10 (2022) |
op_relation |
https://www.frontiersin.org/articles/10.3389/feart.2022.889115/full https://doaj.org/toc/2296-6463 2296-6463 doi:10.3389/feart.2022.889115 https://doaj.org/article/8a0f234048e44a84a740e5fd037721a1 |
op_doi |
https://doi.org/10.3389/feart.2022.889115 |
container_title |
Frontiers in Earth Science |
container_volume |
10 |
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1766353230345797632 |