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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.show moreshow less

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Metadaten
Author:Sylvia Moenickes, Anne-Kathrin Schneider, Lesley Muehle, Lena Rohe, Otto Richter, Frank Suhling
DOI:https://doi.org/10.1242/jeb.061945
ISSN:0022-0949
Parent Title (English):The journal of experimental biology
Publisher:Company of Biologists Limited
Place of publication:Cambridge
Document Type:Article
Language:English
Year of first Publication:2011
Year of Completion:2011
Release Date:2017/03/26
Tag:Q(10); inverse modelling; optimum temperature; parameter estimation; temperature coefficient; temperature response
Volume:214
Issue:21
Page Number:10
First Page:3678
Last Page:3687
Funder:German Science Foundation [1162 AQUASHIFT [Ri 534/11-1,2]]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Erd- und Umweltwissenschaften
Peer Review:Referiert