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From population-level effects to individual response: modelling temperature dependence in Gammarus pulex
Sylvia Moenickes1, Anne-Kathrin Schneider, Lesley Mühle
1Institut für Geoökologie, Langer Kamp 19c, TU Braunschweig 38106, Germany. s.moenickes@tu-bs.de
The Journal of Experimental Biology
|October 14, 2011
Summary
Global warming affects populations through direct and indirect processes. This study uses inverse modeling to determine temperature-dependent growth and mortality in Gammarus pulex, finding an optimal growth temperature of 17°C.
Area of Science:
- Ecology
- Population Dynamics
- Environmental Science
Background:
- Global warming impacts populations via direct and indirect processes.
- Physiologically Structured Population Models (PSPMs) are key to understanding these effects.
- Inverse modeling offers a method to analyze population-level data for physiological parameters.
Purpose of the Study:
- To determine the temperature dependence of growth and mortality for Gammarus pulex.
- To evaluate the efficacy of inverse modeling using PSPMs with population data.
- To identify conditions under which inverse modeling yields reliable physiological parameters.
Main Methods:
- Laboratory experiments, mesocosm studies, and field monitoring were conducted.
- Data on growth and mortality of Gammarus pulex were collected.
- Inverse modeling techniques based on PSPMs were applied to population-level data.
Main Results:
- An optimal growth temperature of approximately 17°C was identified for Gammarus pulex.
- A temperature coefficient, Q(10), of 1.5°C(-1) for growth was found, consistent across methods.
- Inverse modeling provided meaningful parameters when temperature-driven interactions were excluded.
Conclusions:
- Inverse modeling is effective for estimating physiological parameters from population data when confounding factors are controlled.
- Parameter estimates can reflect cumulative responses (e.g., resource dynamics) if interactions are not independently determined.
- Fluctuating temperatures increase parameter uncertainty; PSPMs aid in optimizing sampling strategies for reduced uncertainty.
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