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Friday, 23 September 2016 17:36

New model quantifies uncertainty in species response curves

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Many ecological questions require information on species' optima in relation to environmental gradients. These attributes can be important in determining which species will coexistence and how this may vary with climate changes. However, existing methods do not quantify the uncertainty in the attributes or they rely on assumptions about the shape of species' responses to the environmental gradient. To remedy this, Mick Ashcroft and the team recently developed a model to quantify the uncertainty in the attributes of species response curves and allow them to be tested for differences without making assumptions about the shape of the responses.

 Mick developed a model that used Bayesian penalised splines to produce and compare response curves for any two given species. This method allows the data to determine the shape of the response curves rather than making a priori assumptions. The models repetitively fit alternative response curves to the data, with each iteration producing 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, typically found in ecological research. This noise resulted in considerable uncertainty in the optima and limits of species response curves, but substantive differences were found. At sites near Casey Station in Antarctica the endemic moss Schistidium antarctici was found to inhabit wetter habitats than Ceratodon purpureus. On the other side of the continent on King George Island, the moss 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 the optima and other attributes of species response curves. We found that apparent differences in optima of 7.5 °C were not necessarily substantive when dealing with noisy ecological data, and it is necessary to consider the uncertainty in attributes when comparing the curves for different species. The model we have produced could increase the robustness of research on niche partitioning, species coexistence and niche conservatism.

The paper is published here in Ecological informatics. if you would like a pdf email This email address is being protected from spambots. You need JavaScript enabled to view it..

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Sharon Robinson

Senior Professor at University of Wollongong