Bayesian Methods for Comparing Species Physiological and Ecological Response Curves

Many ecological questions require information on species' optimal conditions or critical limits along environmental gradients. These attributes can be compared to answer questions on niche partitioning, species coexistence and niche conservatism. However, these comparisons are unconvincing when...

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Published in:Ecological Informatics
Main Authors: Ashcroft, Michael B., Casanova-Katny, Angélica, Mengersen, Kerrie, Rosenstiel, Todd N., Turnbull, Johanna D., Wasley, Jane, Waterman, Melinda J., Zúñiga, Gustavo E., Robinson, Sharon A.
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Published: PDXScholar 2016
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Online Access:https://pdxscholar.library.pdx.edu/bio_fac/119
https://doi.org/10.1016/j.ecoinf.2016.03.001
https://pdxscholar.library.pdx.edu/context/bio_fac/article/1119/viewcontent/AshcroftEtAlManuscriptFinal.pdf
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spelling ftportlandstate:oai:pdxscholar.library.pdx.edu:bio_fac-1119 2023-06-11T04:05:15+02:00 Bayesian Methods for Comparing Species Physiological and Ecological Response Curves Ashcroft, Michael B. Casanova-Katny, Angélica Mengersen, Kerrie Rosenstiel, Todd N. Turnbull, Johanna D. Wasley, Jane Waterman, Melinda J. Zúñiga, Gustavo E. Robinson, Sharon A. 2016-07-01T07:00:00Z application/pdf https://pdxscholar.library.pdx.edu/bio_fac/119 https://doi.org/10.1016/j.ecoinf.2016.03.001 https://pdxscholar.library.pdx.edu/context/bio_fac/article/1119/viewcontent/AshcroftEtAlManuscriptFinal.pdf unknown PDXScholar https://pdxscholar.library.pdx.edu/bio_fac/119 doi:10.1016/j.ecoinf.2016.03.001 https://pdxscholar.library.pdx.edu/context/bio_fac/article/1119/viewcontent/AshcroftEtAlManuscriptFinal.pdf Biology Faculty Publications and Presentations Ecological research -- Antarctica Simulation methods Bayesian analysis Biology text 2016 ftportlandstate https://doi.org/10.1016/j.ecoinf.2016.03.001 2023-05-04T18:03:46Z Many ecological questions require information on species' optimal conditions or critical limits along environmental gradients. These attributes can be compared to answer questions on niche partitioning, species coexistence and niche conservatism. However, these comparisons are unconvincing when existing methods do not quantify the uncertainty in the attributes or rely on assumptions about the shape of species' responses to the environmental gradient. The aim of this study was to develop a model to quantify the uncertainty in the attributes of species response curves and allow them to be tested for substantive differences without making assumptions about the shape of the responses. We developed a model that used Bayesian penalised splines to produce and compare response curves for any two given species. These splines allow the data to determine the shape of the response curves rather than making a priori assumptions. The models were implemented using the R2OpenBUGS package for R, which uses Markov Chain Monte Carlo simulation to repetitively fit alternative response curves to the data. As each iteration produces a different curve that varies in optima, niche breadth and limits, the model estimates the uncertainty in each of these attributes and the probability that the two curves are different. The models were tested using two datasets of mosses from Antarctica. Both datasets had a high degree of scatter, which is typical of ecological research. This noise resulted in considerable uncertainty in the optima and limits of species response curves, but substantive differences were found. Schistidium antarcticiwas found to inhabit wetter habitats than Ceratodon purpureus, and Polytrichastrum alpinum had a lower optimal temperature for photosynthesis than Chorisodontium aciphyllum under high light conditions. Our study highlights the importance of considering uncertainty in physiological optima and other attributes of species response curves. We found that apparent differences in optima of 7.5 °C were not necessarily ... Text Antarc* Antarctica Portland State University: PDXScholar Ecological Informatics 34 35 43
institution Open Polar
collection Portland State University: PDXScholar
op_collection_id ftportlandstate
language unknown
topic Ecological research -- Antarctica
Simulation methods
Bayesian analysis
Biology
spellingShingle Ecological research -- Antarctica
Simulation methods
Bayesian analysis
Biology
Ashcroft, Michael B.
Casanova-Katny, Angélica
Mengersen, Kerrie
Rosenstiel, Todd N.
Turnbull, Johanna D.
Wasley, Jane
Waterman, Melinda J.
Zúñiga, Gustavo E.
Robinson, Sharon A.
Bayesian Methods for Comparing Species Physiological and Ecological Response Curves
topic_facet Ecological research -- Antarctica
Simulation methods
Bayesian analysis
Biology
description Many ecological questions require information on species' optimal conditions or critical limits along environmental gradients. These attributes can be compared to answer questions on niche partitioning, species coexistence and niche conservatism. However, these comparisons are unconvincing when existing methods do not quantify the uncertainty in the attributes or rely on assumptions about the shape of species' responses to the environmental gradient. The aim of this study was to develop a model to quantify the uncertainty in the attributes of species response curves and allow them to be tested for substantive differences without making assumptions about the shape of the responses. We developed a model that used Bayesian penalised splines to produce and compare response curves for any two given species. These splines allow the data to determine the shape of the response curves rather than making a priori assumptions. The models were implemented using the R2OpenBUGS package for R, which uses Markov Chain Monte Carlo simulation to repetitively fit alternative response curves to the data. As each iteration produces a different curve that varies in optima, niche breadth and limits, the model estimates the uncertainty in each of these attributes and the probability that the two curves are different. The models were tested using two datasets of mosses from Antarctica. Both datasets had a high degree of scatter, which is typical of ecological research. This noise resulted in considerable uncertainty in the optima and limits of species response curves, but substantive differences were found. Schistidium antarcticiwas found to inhabit wetter habitats than Ceratodon purpureus, and Polytrichastrum alpinum had a lower optimal temperature for photosynthesis than Chorisodontium aciphyllum under high light conditions. Our study highlights the importance of considering uncertainty in physiological optima and other attributes of species response curves. We found that apparent differences in optima of 7.5 °C were not necessarily ...
format Text
author Ashcroft, Michael B.
Casanova-Katny, Angélica
Mengersen, Kerrie
Rosenstiel, Todd N.
Turnbull, Johanna D.
Wasley, Jane
Waterman, Melinda J.
Zúñiga, Gustavo E.
Robinson, Sharon A.
author_facet Ashcroft, Michael B.
Casanova-Katny, Angélica
Mengersen, Kerrie
Rosenstiel, Todd N.
Turnbull, Johanna D.
Wasley, Jane
Waterman, Melinda J.
Zúñiga, Gustavo E.
Robinson, Sharon A.
author_sort Ashcroft, Michael B.
title Bayesian Methods for Comparing Species Physiological and Ecological Response Curves
title_short Bayesian Methods for Comparing Species Physiological and Ecological Response Curves
title_full Bayesian Methods for Comparing Species Physiological and Ecological Response Curves
title_fullStr Bayesian Methods for Comparing Species Physiological and Ecological Response Curves
title_full_unstemmed Bayesian Methods for Comparing Species Physiological and Ecological Response Curves
title_sort bayesian methods for comparing species physiological and ecological response curves
publisher PDXScholar
publishDate 2016
url https://pdxscholar.library.pdx.edu/bio_fac/119
https://doi.org/10.1016/j.ecoinf.2016.03.001
https://pdxscholar.library.pdx.edu/context/bio_fac/article/1119/viewcontent/AshcroftEtAlManuscriptFinal.pdf
genre Antarc*
Antarctica
genre_facet Antarc*
Antarctica
op_source Biology Faculty Publications and Presentations
op_relation https://pdxscholar.library.pdx.edu/bio_fac/119
doi:10.1016/j.ecoinf.2016.03.001
https://pdxscholar.library.pdx.edu/context/bio_fac/article/1119/viewcontent/AshcroftEtAlManuscriptFinal.pdf
op_doi https://doi.org/10.1016/j.ecoinf.2016.03.001
container_title Ecological Informatics
container_volume 34
container_start_page 35
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