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Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model—Part I: Model Validation

In pesticide risk assessments, semifield studies, such as large‐scale colony feeding studies (LSCFSs), are conducted to assess potential risks at the honey bee colony level. However, such studies are very cost and time intensive, and high overwintering losses of untreated control hives have been obs...

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Autores principales: Schmolke, Amelie, Abi‐Akar, Farah, Roy, Colleen, Galic, Nika, Hinarejos, Silvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702171/
https://www.ncbi.nlm.nih.gov/pubmed/32761964
http://dx.doi.org/10.1002/etc.4839
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author Schmolke, Amelie
Abi‐Akar, Farah
Roy, Colleen
Galic, Nika
Hinarejos, Silvia
author_facet Schmolke, Amelie
Abi‐Akar, Farah
Roy, Colleen
Galic, Nika
Hinarejos, Silvia
author_sort Schmolke, Amelie
collection PubMed
description In pesticide risk assessments, semifield studies, such as large‐scale colony feeding studies (LSCFSs), are conducted to assess potential risks at the honey bee colony level. However, such studies are very cost and time intensive, and high overwintering losses of untreated control hives have been observed in some studies. Honey bee colony models such as BEEHAVE may provide tools to systematically assess multiple factors influencing colony outcomes, to inform study design, and to estimate pesticide impacts under varying environmental conditions. Before they can be used reliably, models should be validated to demonstrate they can appropriately reproduce patterns observed in the field. Despite the recognized need for validation, methodologies to be used in the context of applied ecological models are not agreed on. For the parameterization, calibration, and validation of BEEHAVE, we used control data from multiple LSCFSs. We conducted detailed visual and quantitative performance analyses as a demonstration of validation methodologies. The BEEHAVE outputs showed good agreement with apiary‐specific validation data sets representing the first year of the studies. However, the simulations of colony dynamics in the spring periods following overwintering were identified as less reliable. The comprehensive validation effort applied provides important insights that can inform the usability of BEEHAVE in applications related to higher tier risk assessments. In addition, the validation methodology applied could be used in a wider context of ecological models. Environ Toxicol Chem 2020;39:2269–2285. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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spelling pubmed-77021712020-12-14 Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model—Part I: Model Validation Schmolke, Amelie Abi‐Akar, Farah Roy, Colleen Galic, Nika Hinarejos, Silvia Environ Toxicol Chem Hazard/Risk Assessment In pesticide risk assessments, semifield studies, such as large‐scale colony feeding studies (LSCFSs), are conducted to assess potential risks at the honey bee colony level. However, such studies are very cost and time intensive, and high overwintering losses of untreated control hives have been observed in some studies. Honey bee colony models such as BEEHAVE may provide tools to systematically assess multiple factors influencing colony outcomes, to inform study design, and to estimate pesticide impacts under varying environmental conditions. Before they can be used reliably, models should be validated to demonstrate they can appropriately reproduce patterns observed in the field. Despite the recognized need for validation, methodologies to be used in the context of applied ecological models are not agreed on. For the parameterization, calibration, and validation of BEEHAVE, we used control data from multiple LSCFSs. We conducted detailed visual and quantitative performance analyses as a demonstration of validation methodologies. The BEEHAVE outputs showed good agreement with apiary‐specific validation data sets representing the first year of the studies. However, the simulations of colony dynamics in the spring periods following overwintering were identified as less reliable. The comprehensive validation effort applied provides important insights that can inform the usability of BEEHAVE in applications related to higher tier risk assessments. In addition, the validation methodology applied could be used in a wider context of ecological models. Environ Toxicol Chem 2020;39:2269–2285. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. John Wiley and Sons Inc. 2020-09-22 2020-11 /pmc/articles/PMC7702171/ /pubmed/32761964 http://dx.doi.org/10.1002/etc.4839 Text en © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Hazard/Risk Assessment
Schmolke, Amelie
Abi‐Akar, Farah
Roy, Colleen
Galic, Nika
Hinarejos, Silvia
Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model—Part I: Model Validation
title Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model—Part I: Model Validation
title_full Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model—Part I: Model Validation
title_fullStr Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model—Part I: Model Validation
title_full_unstemmed Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model—Part I: Model Validation
title_short Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model—Part I: Model Validation
title_sort simulating honey bee large‐scale colony feeding studies using the beehave model—part i: model validation
topic Hazard/Risk Assessment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702171/
https://www.ncbi.nlm.nih.gov/pubmed/32761964
http://dx.doi.org/10.1002/etc.4839
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