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Modelling Tradescantia fluminensis to assess long term survival
We present a simple Poisson process model for the growth of Tradescantia fluminensis, an invasive plant species that inhibits the regeneration of native forest remnants in New Zealand. The model was parameterised with data derived from field experiments in New Zealand and then verified with independ...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4465954/ https://www.ncbi.nlm.nih.gov/pubmed/26082865 http://dx.doi.org/10.7717/peerj.1013 |
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author | James, Alex Molloy, Sue M. Ponder-Sutton, Agate Plank, Michael J. Lamoureaux, Shona L. Bourdôt, Graeme W. Kelly, Dave |
author_facet | James, Alex Molloy, Sue M. Ponder-Sutton, Agate Plank, Michael J. Lamoureaux, Shona L. Bourdôt, Graeme W. Kelly, Dave |
author_sort | James, Alex |
collection | PubMed |
description | We present a simple Poisson process model for the growth of Tradescantia fluminensis, an invasive plant species that inhibits the regeneration of native forest remnants in New Zealand. The model was parameterised with data derived from field experiments in New Zealand and then verified with independent data. The model gave good predictions which showed that its underlying assumptions are sound. However, this simple model had less predictive power for outputs based on variance suggesting that some assumptions were lacking. Therefore, we extended the model to include higher variability between plants thereby improving its predictions. This high variance model suggests that control measures that promote node death at the base of the plant or restrict the main stem growth rate will be more effective than those that reduce the number of branching events. The extended model forms a good basis for assessing the efficacy of various forms of control of this weed, including the recently-released leaf-feeding tradescantia leaf beetle (Neolema ogloblini). |
format | Online Article Text |
id | pubmed-4465954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-44659542015-06-16 Modelling Tradescantia fluminensis to assess long term survival James, Alex Molloy, Sue M. Ponder-Sutton, Agate Plank, Michael J. Lamoureaux, Shona L. Bourdôt, Graeme W. Kelly, Dave PeerJ Computational Biology We present a simple Poisson process model for the growth of Tradescantia fluminensis, an invasive plant species that inhibits the regeneration of native forest remnants in New Zealand. The model was parameterised with data derived from field experiments in New Zealand and then verified with independent data. The model gave good predictions which showed that its underlying assumptions are sound. However, this simple model had less predictive power for outputs based on variance suggesting that some assumptions were lacking. Therefore, we extended the model to include higher variability between plants thereby improving its predictions. This high variance model suggests that control measures that promote node death at the base of the plant or restrict the main stem growth rate will be more effective than those that reduce the number of branching events. The extended model forms a good basis for assessing the efficacy of various forms of control of this weed, including the recently-released leaf-feeding tradescantia leaf beetle (Neolema ogloblini). PeerJ Inc. 2015-06-11 /pmc/articles/PMC4465954/ /pubmed/26082865 http://dx.doi.org/10.7717/peerj.1013 Text en © 2015 James et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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 | Computational Biology James, Alex Molloy, Sue M. Ponder-Sutton, Agate Plank, Michael J. Lamoureaux, Shona L. Bourdôt, Graeme W. Kelly, Dave Modelling Tradescantia fluminensis to assess long term survival |
title | Modelling Tradescantia fluminensis to assess long term survival |
title_full | Modelling Tradescantia fluminensis to assess long term survival |
title_fullStr | Modelling Tradescantia fluminensis to assess long term survival |
title_full_unstemmed | Modelling Tradescantia fluminensis to assess long term survival |
title_short | Modelling Tradescantia fluminensis to assess long term survival |
title_sort | modelling tradescantia fluminensis to assess long term survival |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4465954/ https://www.ncbi.nlm.nih.gov/pubmed/26082865 http://dx.doi.org/10.7717/peerj.1013 |
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