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Estimating flowering transition dates from status-based phenological observations: a test of methods
The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back t...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764363/ https://www.ncbi.nlm.nih.gov/pubmed/31579602 http://dx.doi.org/10.7717/peerj.7720 |
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author | Taylor, Shawn D. |
author_facet | Taylor, Shawn D. |
author_sort | Taylor, Shawn D. |
collection | PubMed |
description | The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb Echinacea angustifolia. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall, a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise, the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available. |
format | Online Article Text |
id | pubmed-6764363 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67643632019-10-02 Estimating flowering transition dates from status-based phenological observations: a test of methods Taylor, Shawn D. PeerJ Agricultural Science The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb Echinacea angustifolia. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall, a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise, the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available. PeerJ Inc. 2019-09-24 /pmc/articles/PMC6764363/ /pubmed/31579602 http://dx.doi.org/10.7717/peerj.7720 Text en ©2019 Taylor https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Agricultural Science Taylor, Shawn D. Estimating flowering transition dates from status-based phenological observations: a test of methods |
title | Estimating flowering transition dates from status-based phenological observations: a test of methods |
title_full | Estimating flowering transition dates from status-based phenological observations: a test of methods |
title_fullStr | Estimating flowering transition dates from status-based phenological observations: a test of methods |
title_full_unstemmed | Estimating flowering transition dates from status-based phenological observations: a test of methods |
title_short | Estimating flowering transition dates from status-based phenological observations: a test of methods |
title_sort | estimating flowering transition dates from status-based phenological observations: a test of methods |
topic | Agricultural Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764363/ https://www.ncbi.nlm.nih.gov/pubmed/31579602 http://dx.doi.org/10.7717/peerj.7720 |
work_keys_str_mv | AT taylorshawnd estimatingfloweringtransitiondatesfromstatusbasedphenologicalobservationsatestofmethods |