Table6_Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate.XLSX
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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Online Access: | https://doi.org/10.3389/feart.2022.889115.s006 https://figshare.com/articles/dataset/Table6_Importance_of_Weighting_High-Resolution_Proxy_Data_From_Bivalve_Shells_to_Avoid_Bias_Caused_by_Sample_Spot_Geometry_and_Variability_in_Seasonal_Growth_Rate_XLSX/19738405 |
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ftfrontimediafig:oai:figshare.com:article/19738405 2023-05-15T15:22:36+02:00 Table6_Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate.XLSX Bernd R. Schöne Soraya Marali Regina Mertz-Kraus Paul G. Butler Alan D. Wanamaker Lukas Fröhlich 2022-05-10T08:31:16Z https://doi.org/10.3389/feart.2022.889115.s006 https://figshare.com/articles/dataset/Table6_Importance_of_Weighting_High-Resolution_Proxy_Data_From_Bivalve_Shells_to_Avoid_Bias_Caused_by_Sample_Spot_Geometry_and_Variability_in_Seasonal_Growth_Rate_XLSX/19738405 unknown doi:10.3389/feart.2022.889115.s006 https://figshare.com/articles/dataset/Table6_Importance_of_Weighting_High-Resolution_Proxy_Data_From_Bivalve_Shells_to_Avoid_Bias_Caused_by_Sample_Spot_Geometry_and_Variability_in_Seasonal_Growth_Rate_XLSX/19738405 CC BY 4.0 CC-BY Solid Earth Sciences Climate Science Atmospheric Sciences not elsewhere classified Exploration Geochemistry Inorganic Geochemistry Isotope Geochemistry Organic Geochemistry Geochemistry not elsewhere classified Igneous and Metamorphic Petrology Ore Deposit Petrology Palaeontology (incl. Palynology) Structural Geology Tectonics Volcanology Geology not elsewhere classified Seismology and Seismic Exploration Glaciology Hydrogeology Natural Hazards Quaternary Environments Earth Sciences not elsewhere classified Evolutionary Impacts of Climate Change bivalve sclerochronology shell element chemistry seasonal growth rate weighted average arithmetic average denoising proxy data Dataset 2022 ftfrontimediafig https://doi.org/10.3389/feart.2022.889115.s006 2022-05-11T23:04:37Z 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 ... Dataset Arctica islandica Frontiers: Figshare |
institution |
Open Polar |
collection |
Frontiers: Figshare |
op_collection_id |
ftfrontimediafig |
language |
unknown |
topic |
Solid Earth Sciences Climate Science Atmospheric Sciences not elsewhere classified Exploration Geochemistry Inorganic Geochemistry Isotope Geochemistry Organic Geochemistry Geochemistry not elsewhere classified Igneous and Metamorphic Petrology Ore Deposit Petrology Palaeontology (incl. Palynology) Structural Geology Tectonics Volcanology Geology not elsewhere classified Seismology and Seismic Exploration Glaciology Hydrogeology Natural Hazards Quaternary Environments Earth Sciences not elsewhere classified Evolutionary Impacts of Climate Change bivalve sclerochronology shell element chemistry seasonal growth rate weighted average arithmetic average denoising proxy data |
spellingShingle |
Solid Earth Sciences Climate Science Atmospheric Sciences not elsewhere classified Exploration Geochemistry Inorganic Geochemistry Isotope Geochemistry Organic Geochemistry Geochemistry not elsewhere classified Igneous and Metamorphic Petrology Ore Deposit Petrology Palaeontology (incl. Palynology) Structural Geology Tectonics Volcanology Geology not elsewhere classified Seismology and Seismic Exploration Glaciology Hydrogeology Natural Hazards Quaternary Environments Earth Sciences not elsewhere classified Evolutionary Impacts of Climate Change bivalve sclerochronology shell element chemistry seasonal growth rate weighted average arithmetic average denoising proxy data Bernd R. Schöne Soraya Marali Regina Mertz-Kraus Paul G. Butler Alan D. Wanamaker Lukas Fröhlich Table6_Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate.XLSX |
topic_facet |
Solid Earth Sciences Climate Science Atmospheric Sciences not elsewhere classified Exploration Geochemistry Inorganic Geochemistry Isotope Geochemistry Organic Geochemistry Geochemistry not elsewhere classified Igneous and Metamorphic Petrology Ore Deposit Petrology Palaeontology (incl. Palynology) Structural Geology Tectonics Volcanology Geology not elsewhere classified Seismology and Seismic Exploration Glaciology Hydrogeology Natural Hazards Quaternary Environments Earth Sciences not elsewhere classified Evolutionary Impacts of Climate Change bivalve sclerochronology shell element chemistry seasonal growth rate weighted average arithmetic average denoising proxy data |
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 |
Dataset |
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 |
Table6_Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate.XLSX |
title_short |
Table6_Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate.XLSX |
title_full |
Table6_Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate.XLSX |
title_fullStr |
Table6_Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate.XLSX |
title_full_unstemmed |
Table6_Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate.XLSX |
title_sort |
table6_importance of weighting high-resolution proxy data from bivalve shells to avoid bias caused by sample spot geometry and variability in seasonal growth rate.xlsx |
publishDate |
2022 |
url |
https://doi.org/10.3389/feart.2022.889115.s006 https://figshare.com/articles/dataset/Table6_Importance_of_Weighting_High-Resolution_Proxy_Data_From_Bivalve_Shells_to_Avoid_Bias_Caused_by_Sample_Spot_Geometry_and_Variability_in_Seasonal_Growth_Rate_XLSX/19738405 |
genre |
Arctica islandica |
genre_facet |
Arctica islandica |
op_relation |
doi:10.3389/feart.2022.889115.s006 https://figshare.com/articles/dataset/Table6_Importance_of_Weighting_High-Resolution_Proxy_Data_From_Bivalve_Shells_to_Avoid_Bias_Caused_by_Sample_Spot_Geometry_and_Variability_in_Seasonal_Growth_Rate_XLSX/19738405 |
op_rights |
CC BY 4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3389/feart.2022.889115.s006 |
_version_ |
1766353237791735808 |