Scope for quantitative bioeconomics Bas Kooijman Dept theoretical biology VU University Amsterdam Bas.Kooijman@vu.nl http://www.bio.vu.nl/thb A manuscript with this title has been submitted to J BioEcon at 2016/06/10. It discusses more issues than I can present is this 20 min lecture Copenhagen, 2016/06/22

Contents Introduction Allocation to soma Waste – to – hurry Supply-demand spectra Overview over this 20 min lecture

Biology ↔ Economy similarities at various levels internal organisation: individual ↔ firm resource acquisition: specialist ↔ generalist interaction: syntrophy, competition, predation, parasitism dynamics: merging ↔ splitting broad picture: micro ↔ macro level individual: central point of focus of DEB theory unit of evolutionary selection input – output fluxes most clear at this level can be a single bacterial cell, a tree, an animal

Space-time scales Each process has its characteristic domain of space-time scales molecule cell individual population ecosystem system earth time space When changing the space-time scale, new processes will become important other will become less important This can be used to simplify models, by coupling space-time scales Complex models are required for small time and big space scales and vv Models with many variables & parameters hardly contribute to insight Basic to DEB theory is the coherence between levels of organisation, using the life cycle of an individual as primary focus, from which sub- and supra-organismic levels are considered. Space and time scales are tightly coupled methodologically. Since many species are unicellular, the step to biochemical systems is not always big. Populations are considered as sets of interacting individuals, ecosystems as sets of interacting populations. While walking up- and down the time-space-scale, some processes loose their importance, others gain. The primary motivation in my research on the theory is to answer the question: how can we deal with the local coherence of levels of metabolic organisation, while avoiding the massive complexity of models with many variables and parameters; I have never seen useful conclusions coming out of complex models.

Standard DEB scheme offspring food faeces reserve structure  1 food type, 1 reserve, 1 structure, isomorph 1- maturity maintenance offspring maturation reproduction food faeces assimilation reserve feeding defecation structure somatic growth  Assumptions of standard DEB model as member of DEB models One food type of constant composition and particle size that can be used to complete life cycle One reserve type One structure type Isomorphy only food is limiting, not e.g. dioxygen Extensions that are sometimes included in the standard DEB model foetal development (as alternative to egg development) ageing metabolic acceleration between birth and metamorphosis Applies to most animals, which live off other organisms, so uptake of all required chemical compounds are coupled. The number of required reserves equals the number of required resources that are taken up independently.

Homeostasis strong homeostasis weak homeostasis structural homeostasis constant composition of pools (reserves/structures) generalized compounds, stoichiometric constraints on synthesis weak homeostasis constant composition of biomass during growth in constant environments determines reserve dynamics (in combination with strong homeostasis) structural homeostasis constant relative proportions during growth in constant environments isomorphy .work load allocation thermal homeostasis ectothermy  homeothermy  endothermy acquisition homeostasis supply  demand systems development of sensors, behavioural adaptations Homeostasis is the ability to run metabolism independent from environmental conditions. This can obviously not be perfect, all organisms require food and/or nutrients. We need 5 difference homeostasis concepts to capture the extent organisms sport homeostasis.

Allocation to soma sR = actual max reprod rate 413 animal species at 2016/06/11 Population growth (where ageing is the only cause of death) and maximum reproduction are optimum functions of the allocation fraction to soma. The optimum fraction is about kappa = 0.45, the median observed value in the add_my_pet collection is 0.85. For each of the 413 species the value of kappa is computed that maximizes max reproduction, given the other parameters. 50% of the species reproduce at 0.1 times their maximum possible rate, 20% reproduce at 0.01 times their maximum possible rate.. sR = actual max reprod rate as fraction of that with optimized κ Lika et al 2011 , Kooijman & Lika 2014 J. Sea Res, 22: 278-288, Biol Rev, 89: 849-859

Selection for reproduction Red Jungle fowl IR RJ IR WL Indian River broiler WL RJ White Leghorn Kooijman & Lika 2014 Biol Rev, 89: 849-859 If selection is for maximum reproduction, like here in the White Leghorn, kappa has the value that maximizes reproduction rate. Notice that selection hardly affected specific assimilation in Indian River females, and reduced it in Indian River makes; it is the other way around in the White Leghorn. Selection hardly affected energy conductance, but reduced somatic maintenance a bit. The latter might be related to lack of natural behaviour. Selection reduced life span is substantially.

Type R acceleration 12 °C Crinia georgiana Pseudophryne bibronii hatch Mueller et al 2012, Comp. Physiol. Biochem. A, 163:103-110 Crinia georgiana max dry weight 500 mg hatch birth hatch birth metam metam 12 °C Pseudophryne bibronii age, d These two myobatrachid frogs are very similar in many respects, but the tadpoles of P. bibronii live in permament pools, while that of C. georgiana in temporary ones that dry up, soon after their metamorphosis. The latter accelerate development by lowering  temporarily, which also reduces growth. In this way it can leave the pond at the age of 110 days, while P. bibronii needs 200 d. Hatch coincides with birth for P. bibronii. C. georgiana is 4 mg at metamorphosis, P. bibrionii 35 mg dry, while the maximum weights are 500 and 200 mg, respectively. Both frogs have a (constant) somatic maintenance rate of some 400 J/d.cm3. The graphs in the middle just enlarge the graph on the right till birth. The step-up in respiration at birth is due to the onset of assimilation. Mueller C., Augustine, S., Kearney, M., Seymour, R. and Kooijman, S.A.L.M. 2011 Tradeoffs between maturation and growth during accelerated development in frogs Comp. Physiol. Biochem. A, 163:103-110 Data: Casey Mueller Model fit: Starrlight Augustine max dry weight 200 mg hatch birth hatch birth metam metam

Waste to hurry Exploiting blooming resources Kooijman 2013 Oikos 122: 348-357 Waste to hurry Exploiting blooming resources requires blooming yourself high numerical response short life cycle small body size fast reproduction fast growth high feeding rate resting stages between blooms -rule explains why [pM] needs to be high Ecosystem significance: flux through basis food pyramid Waste-to-hurry is the hypothesis that species increase somatic maintenance for the purpose of boosting growth and reproduction, such that max body size remains small. This strategy is possible because of the kappa-rule. Fast growth and reproduction is only possible if specific assimilation is large. If that would be the only difference a very large body size results, which comes with a long life cycle. Not fit for profiting from temporary resources.

Supply-demand spectrum Species can be ranked in the gradient from supply to demand systems as stages in the evolution toward a high level of homeostasis. Extreme supply or demand systems don’t exist, all species represent a mixture of these extremes. Plants come close to the supply-end of the spectrum and can adapt their metabolism to the local environment relatively well. Demand systems adapt their metabolism much less and compensate that by a high level of behavioural flexibility. The characterizing property of demand systems is that the use of resources (growth, reproduction) is `pre-programmed’, which causes a particular need for food and growth curves that are given functions of age.

Supply-demand spectrum Lika et al 2014 J. Theor. Biol., 354:35-47 Supply-demand spectrum The constraint R_m > 0 can be translated into the constraint kap^2 (1 – kap) < s_s, from which follows that s_s < 4/27. Moreover kap must be between the two positive roots of kap^2 (1 – kap) – s_s = 0. The metric s_s has the interpretation of the distance to the supply-end of the supply-demand spectrum The metric s_d = 4/27 – s_s has the interpretation of the distance to the demand-end of the supply-demand spectrum. Most species are supply species, only vertebrates classify as demand species. The 2 plots are identical, but the abscissa is logarithmically the left plot to expose extreme supply species. The colour coding is also different. identical points & plots colour coding different 413 species 2016/06/11

DEB tele course 2017 Audience: thank you for your attention http://www.bio.vu.nl/thb/deb/ Free of financial costs; Some 108 or 216 h effort investment Program for 2017: Feb/Mar general theory (5w) May symposium in Tromso (N) (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/ More info can be found on the web The organisation of courses for specialised audience is negotiable Cambridge Univ Press 2009 Audience: thank you for your attention