1. Bas Kooijman Dept theoretical biology Vrije Universiteit Amsterdam Bas@bio.vu.nl http://www.bio.vu.nl/thbhttp://www.bio.vu.nl/thb/ Estimating DEB parameters Melbourne, 2012/08/08

2. Estimating DEB parameters Bas Kooijman Dept theoretical biology Vrije Universiteit Amsterdam Bas@bio.vu.nl http://www.bio.vu.nl/thbhttp://www.bio.vu.nl/thb/ Melbourne, 2012/08/08 Contents auxiliary theory compound parameters add_my_pet covariation method primary parameters zero-,uni-variate data examples library FIT, COMPLETE marks

3. Assumptions of auxiliary theory A well-chosen physical length  (volumetric) structural length for isomorphs Volume, wet/dry weight have contributions from structure, reserve, reproduction buffer Constant specific mass & volume of structure, reserve, reproduction buffer Constant chemical composition of juvenile growing at constant food

4. Zero-variate data Life history events: hatching, birth, metamorphosis, puberty, death Real data: age, length, dry-, wet-weight at life history events max rates: reproduction, respiration, feeding, growth Modified by food, temperature Pseudo data:

5. Zero-variate data Life history events

6. Uni-variate data

7. length, weight, reproduction, respiration, feeding as functions of time, temperature, food Incubation time, juvenile period, life span as functions of time, temperature, food weight as function of length egg number as function of weight/length

8. COMPLETE mark Each level includes all lower levels

9. Primary parameters standard DEB model

10. Compound parameters

11. Aims of add_my_pet Estimation of all parameters of standard DEB model for a large collection of animal species on the basis of traceable data and software (open source) fits can be re-done, data can be added Computation of implied properties, given parameters template: pars_my_pet Tool in mastering DEB theory (DEB tele course) Stimulation of application DEB theory Compare species on the basis of parameter values (adaptation) Use patterns in parameter values to improve estimation

12. Co variation method estimate all parameters simultaneously using all data: single-step-procedure zero & univariate data: > 100 different types use pseudo-data: size-corrected parameter values of `typical’ animal size-correction: covariation method motivation: all parameters must be determined & physical realism of values estimation criterion: weighted least squares, weight coefficients  value -2 max likelihood with constant variation coefficient numerical method: simplex local minimum problem mark goodness of fit & completeness of data template: mydata_my_pet & predict_my_pet: par estimation & testing Templet pars_my_pet: implied properties http://www.bio.vu/nl/thb/deb/deblab Lika et al 2011 J. Sea Res, 22: 270-277

13. Data parameters

14. Add_my_pet: Python_regius weight, g time since birth, d Data by Bart Laarhoven

15. Zoom factor 0.01: Brachionus plicatilis 138: Balaenoptera musculus 50: Loxodonta africana 10: Sparus aurata 1: Pimephalus promelas 0.1: Sagitta hispida

16. Growth efficiency

17. Cost for structure

18. Energy conductance before acceleration after acceleration

19. Energy conductance 20°C

20. FIT mark

21. FIT & COMPLETE marks

22. DEB tele course 2013 http://www.bio.vu.nl/thb/deb/ Free of financial costs; Some 108 or 216 h effort investment Program for 2013: Feb/Mar general theory (5w) April symposium at NIOZ-Texel (NL) (8d +3 d) Target audience: PhD students We encourage participation in groups who organize local meetings weekly Software package DEBtool for Octave/ Matlab freely downloadable Slides of this presentation are downloadable from http://www.bio.vu.nl/thb/users/bas/lectures/ Cambridge Univ Press 2009 Audience : thank you for your attention