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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...

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Detalles Bibliográficos
Autores principales: James, Alex, Molloy, Sue M., Ponder-Sutton, Agate, Plank, Michael J., Lamoureaux, Shona L., Bourdôt, Graeme W., Kelly, Dave
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2015
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).
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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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