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Spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding
Field‐based transplant gardens, including common and reciprocal garden experiments, are a powerful tool for studying genetic variation and gene‐by‐environment interactions. These experiments assume that individuals within the garden represent independent replicates growing in a homogenous environmen...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750843/ https://www.ncbi.nlm.nih.gov/pubmed/36532138 http://dx.doi.org/10.1002/ece3.9630 |
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author | Zaiats, Andrii Requena‐Mullor, Juan M. Germino, Matthew J. Forbey, Jennifer S. Richardson, Bryce A. Caughlin, T. Trevor |
author_facet | Zaiats, Andrii Requena‐Mullor, Juan M. Germino, Matthew J. Forbey, Jennifer S. Richardson, Bryce A. Caughlin, T. Trevor |
author_sort | Zaiats, Andrii |
collection | PubMed |
description | Field‐based transplant gardens, including common and reciprocal garden experiments, are a powerful tool for studying genetic variation and gene‐by‐environment interactions. These experiments assume that individuals within the garden represent independent replicates growing in a homogenous environment. Plant neighborhood interactions are pervasive across plant populations and could violate assumptions of transplant garden experiments. We demonstrate how spatially explicit models for plant–plant interactions can provide novel insights on genotypes' performance in field‐transplant garden designs. We used individual‐based models, based on data from a sagebrush (Artemisia spp.) common garden, to simulate the impact of spatial plant–plant interactions on between‐group differences in plant growth. We found that planting densities within the range of those used in many common gardens can bias experimental outcomes. Our results demonstrate that higher planting densities can lead to inflated group differences and may confound genotypes' competitive ability and genetically underpinned variation. Synthesis. We propose that spatially explicit models can help avoid biased results by informing the design and analysis of field‐based transplant garden experiments. Alternately, including neighborhood effects in post hoc analyses of transplant garden experiments is likely to provide novel insights into the roles of biotic factors and density dependence in genetic differentiation. |
format | Online Article Text |
id | pubmed-9750843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97508432022-12-15 Spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding Zaiats, Andrii Requena‐Mullor, Juan M. Germino, Matthew J. Forbey, Jennifer S. Richardson, Bryce A. Caughlin, T. Trevor Ecol Evol Research Articles Field‐based transplant gardens, including common and reciprocal garden experiments, are a powerful tool for studying genetic variation and gene‐by‐environment interactions. These experiments assume that individuals within the garden represent independent replicates growing in a homogenous environment. Plant neighborhood interactions are pervasive across plant populations and could violate assumptions of transplant garden experiments. We demonstrate how spatially explicit models for plant–plant interactions can provide novel insights on genotypes' performance in field‐transplant garden designs. We used individual‐based models, based on data from a sagebrush (Artemisia spp.) common garden, to simulate the impact of spatial plant–plant interactions on between‐group differences in plant growth. We found that planting densities within the range of those used in many common gardens can bias experimental outcomes. Our results demonstrate that higher planting densities can lead to inflated group differences and may confound genotypes' competitive ability and genetically underpinned variation. Synthesis. We propose that spatially explicit models can help avoid biased results by informing the design and analysis of field‐based transplant garden experiments. Alternately, including neighborhood effects in post hoc analyses of transplant garden experiments is likely to provide novel insights into the roles of biotic factors and density dependence in genetic differentiation. John Wiley and Sons Inc. 2022-12-14 /pmc/articles/PMC9750843/ /pubmed/36532138 http://dx.doi.org/10.1002/ece3.9630 Text en © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Zaiats, Andrii Requena‐Mullor, Juan M. Germino, Matthew J. Forbey, Jennifer S. Richardson, Bryce A. Caughlin, T. Trevor Spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding |
title | Spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding |
title_full | Spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding |
title_fullStr | Spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding |
title_full_unstemmed | Spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding |
title_short | Spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding |
title_sort | spatial models can improve the experimental design of field‐based transplant gardens by preventing bias due to neighborhood crowding |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750843/ https://www.ncbi.nlm.nih.gov/pubmed/36532138 http://dx.doi.org/10.1002/ece3.9630 |
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