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From population-level effects to individual response: modelling temperature dependence in Gammarus pulex
- Population-level effects of global warming result from concurrent direct and indirect processes. They are typically described by physiologically structured population models (PSPMs). Therefore, inverse modelling offers a tool to identify parameters of individual physiological processes through population-level data analysis, e. g. the temperature dependence of growth from size-frequency data of a field population. Here, we make use of experiments under laboratory conditions, in mesocosms and field monitoring to determine the temperature dependence of growth and mortality of Gammarus pulex. We found an optimum temperature for growth of approximately 17 degrees C and a related temperature coefficient, Q(10), of 1.5 degrees C(-1), irrespective of whether we classically fitted individual growth curves or applied inverse modelling based on PSPMs to laboratory data. From a comparison of underlying data sets we conclude that applying inverse modelling techniques to population-level data results in meaningful response parameters forPopulation-level effects of global warming result from concurrent direct and indirect processes. They are typically described by physiologically structured population models (PSPMs). Therefore, inverse modelling offers a tool to identify parameters of individual physiological processes through population-level data analysis, e. g. the temperature dependence of growth from size-frequency data of a field population. Here, we make use of experiments under laboratory conditions, in mesocosms and field monitoring to determine the temperature dependence of growth and mortality of Gammarus pulex. We found an optimum temperature for growth of approximately 17 degrees C and a related temperature coefficient, Q(10), of 1.5 degrees C(-1), irrespective of whether we classically fitted individual growth curves or applied inverse modelling based on PSPMs to laboratory data. From a comparison of underlying data sets we conclude that applying inverse modelling techniques to population-level data results in meaningful response parameters for physiological processes if additional temperature-driven effects, including within-population interaction, can be excluded or determined independently. If this is not the case, parameter estimates describe a cumulative response, e. g. comprising temperature-dependent resource dynamics. Finally, fluctuating temperatures in natural habitats increased the uncertainty in parameter values. Here, PSPM should be applied for virtual monitoring in order to determine a sampling scheme that comprises important dates to reduce parameter uncertainty.…
Author details: | Sylvia Moenickes, Anne-Kathrin Schneider, Lesley Muehle, Lena Rohe, Otto Richter, Frank Suhling |
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DOI: | https://doi.org/10.1242/jeb.061945 |
ISSN: | 0022-0949 |
Title of parent work (English): | The journal of experimental biology |
Publisher: | Company of Biologists Limited |
Place of publishing: | Cambridge |
Publication type: | Article |
Language: | English |
Year of first publication: | 2011 |
Publication year: | 2011 |
Release date: | 2017/03/26 |
Tag: | Q(10); inverse modelling; optimum temperature; parameter estimation; temperature coefficient; temperature response |
Volume: | 214 |
Issue: | 21 |
Number of pages: | 10 |
First page: | 3678 |
Last Page: | 3687 |
Funding institution: | German Science Foundation [1162 AQUASHIFT [Ri 534/11-1,2]] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
Peer review: | Referiert |
Institution name at the time of the publication: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Erd- und Umweltwissenschaften |