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Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models
Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model str...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423976/ https://www.ncbi.nlm.nih.gov/pubmed/37583877 http://dx.doi.org/10.1002/pei3.10118 |
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author | Schädel, Christina Seyednasrollah, Bijan Hanson, Paul J. Hufkens, Koen Pearson, Kyle J. Warren, Jeffrey M. Richardson, Andrew D. |
author_facet | Schädel, Christina Seyednasrollah, Bijan Hanson, Paul J. Hufkens, Koen Pearson, Kyle J. Warren, Jeffrey M. Richardson, Andrew D. |
author_sort | Schädel, Christina |
collection | PubMed |
description | Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea‐Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best‐performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO(2) parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type. |
format | Online Article Text |
id | pubmed-10423976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104239762023-08-15 Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models Schädel, Christina Seyednasrollah, Bijan Hanson, Paul J. Hufkens, Koen Pearson, Kyle J. Warren, Jeffrey M. Richardson, Andrew D. Plant Environ Interact Research Articles Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea‐Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best‐performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO(2) parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type. John Wiley and Sons Inc. 2023-06-29 /pmc/articles/PMC10423976/ /pubmed/37583877 http://dx.doi.org/10.1002/pei3.10118 Text en © 2023 The Authors. Plant‐Environment Interactions published by New Phytologist Foundation and 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 Schädel, Christina Seyednasrollah, Bijan Hanson, Paul J. Hufkens, Koen Pearson, Kyle J. Warren, Jeffrey M. Richardson, Andrew D. Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models |
title | Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models |
title_full | Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models |
title_fullStr | Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models |
title_full_unstemmed | Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models |
title_short | Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models |
title_sort | using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423976/ https://www.ncbi.nlm.nih.gov/pubmed/37583877 http://dx.doi.org/10.1002/pei3.10118 |
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