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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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 |
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Portland State University: PDXScholar |
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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 |
op_container_end_page |
43 |
_version_ |
1768373471249694720 |