Bas Kooijman Dept theoretical biology Vrije Universiteit Amsterdam Estimating DEB parameters Melbourne, 2012/08/08

Estimating DEB parameters Bas Kooijman Dept theoretical biology Vrije Universiteit Amsterdam 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

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

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:

Zero-variate data Life history events

Uni-variate data

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

COMPLETE mark Each level includes all lower levels

Primary parameters standard DEB model

Compound parameters

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

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 Lika et al 2011 J. Sea Res, 22:

Data parameters

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

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

Growth efficiency

Cost for structure

Energy conductance before acceleration after acceleration

Energy conductance 20°C

FIT mark

FIT & COMPLETE marks

DEB tele course 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 Cambridge Univ Press 2009 Audience : thank you for your attention