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with acknowledgements to Titles Self-assembling plants and integration across ecological scales Philip Grime ( Sheffield, UK ) Andrew Askew ( Sheffield, UK ) Presentation ready Roderick Hunt ( Exeter, UK ) Ric Colasanti ( Corvallis, OR )
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Community image patches of resource depletion showing above- and below-ground A community of self-assembling virtual plants
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CSR type, frame 1 A single propagule … … about to grow
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ditto f. 20 Abundant growth above- and below-ground … … with zones of resource depletion
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Binary tree diagram Above-ground binary tree base module Below-ground binary tree base module Above-ground array Below-ground array Above-ground binary tree ( = shoot system) Below-ground binary tree ( = root system) A branching module An end module Each plant is built like this … … only a diagram, not a painting !
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Water and nutrients from below-ground Parent or offspring modules can pass resources to any adjoining modules Explanation End-modules capture resources: Light and carbon dioxide from above-ground … so whole plants can grow
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The virtual plants interact with their environment and neighbours They possess most properties of real individuals and populations Explanation For example …
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S-shaped growth curves Partitioning between root and shoot Validation Functional equilibria Foraging towards resources Self-thinning in crowded populations Size Time Allometric coefficient Below-ground resource Individual size Self-thinning line Population density
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The foregoing plants all have the same functional specification ( modular rulebase ) But specifications can be changed if we want some plants to behave differently … Explanation … and we can simulate plant functional types … not yet comparative plant ecology !
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Some working definitions … Species within one functional group share a single important trait Species within one functional type share a similar set of traits. Definitions
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Functional types are multi-species levels of organization, lying above the population but below the community … and some implications A single species can simultaneously be a member of several functional groups, e.g. both ‘legume’ and ’woody’ … Implications … but a member of only one functional type, e.g. ‘ K -strategist’
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Why use functional types? They reduce the high dimensionality of real plant life “ There are many more actors on the stage than roles that can be played ” Why use?
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PFTs give a continuous view of vegetation even when relative abundances and identities of species are in flux Tools exist to allocate types to species (and type- mixes to whole communities) Large-scale (or cross-scale) studies of effects of environment or management on (e.g.) biodiversity, vulnerability and stability become possible... continued
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How do we recreate basic PFTs within the self- assembling model ? … we change the modular rulebases controlling morphology, physiology and reproduction … PFTs in CA … but we must begin to model at a high enough level to get “ airborne ”... we need access to the emergent properties
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Building blocks So we don’t build with these… we build with these !
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Specifications TypeMorphologyPhysiologyReproduction module size,tissue longevity,flowering speed, 1LargeFastSlow 2SmallSlowSlow 3SmallFastFast 4MediumMediumSlow 5SmallMediumMedium 6MediumFastMedium 7MediumMediumMedium resource demandRGR, SLA, allocation, decomposabilitypropagule size Building a set of PFTs …
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Three levels in each of our three ‘ super-traits ’ = 27 possible PFTs … … but we model only 7 types the other 20 would include Darwinian Demons that do not respect evolutionary tradeoffs Explanation
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Competition between two different types of plant … Explanation
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R-CSR-R, frame 1 Small size, rapid growth and fast reproduction Medium size, moderately fast in growth and reproduction
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ditto f. 9 ( Red enters its 2 nd generation)
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ditto f. 20 White has won !
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This one-on-one competition is very realistic … … but most communities involve more than two types of plant Explanation
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Seven PFTs can cover the entire range of variation shown by herbaceous plant life … … and to a first approximation, these seven can simulate complex community processes very realistically Explanation
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For example, we grow an equal mixture of all seven types together … … in an environment with high levels of resource above- and below-ground Explanation
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7 types, high nutrient, f.1
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Blue has eliminated almost everything except White and Green … … and the simulation has almost run out of space Explanation
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ditto f.20 again
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Environmental gradients = stepwise increases in resource level Explanation Whittaker-type niches emerge for each PFT
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Whittaker-type gradient (types) Three PFTs in an initially equal mixture
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The equal mixture of all seven types again … … but under environmental gradients of increasing mineral nutrient resource or increasing trampling disturbance Explanation
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Stress-driven hump Greatest biodiversity at intermediate stress
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Disturbance-driven hump Greatest biodiversity at intermediate disturbance
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Stresses and disturbances can be applied together … Explanation … in many forms and combinations, generating a big range of productivity classes
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Productivity-driven hump Greatest biodiversity at intermediate productivity
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The biomass-driven ‘humpbacked’ relationship … one of the highest-level properties of real plant communities … … emerges solely from the resource-capturing activity of modules in the self-assembling plants Explanation
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In our closed system, the biodiversity-productivity hump eventually collapses with time … Explanation
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Surface 1
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But additional treatments can prevent this collapse … Explanation Environmental heterogeneity (spatial and / or temporal) ‘ Seed rain ’ (propagules introduced from external sources)
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Surface 2 No external seed rain Probability of one external seeding event per cell per iteration (random plant types)
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Real experiments with virtual plants … individual, population and community processes emerge from one modular rulebase We can ‘plant’ communities in any PFT mix and grow them under any environmental or management regime … … to look at topics like biodiversity, vulnerability, resistance, resilience, stability, habitat / community heterogeneity, etc, etc. Summary
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The modular rulebase is simply a string of numbers 2 3 1 4 2 3 2 1 2 2 1 3 3 1 2 3 controlling how big, how much, how long, how often … 2 3 1 4 2 3 2 1 2 2 1 3 3 1 2 3 2 3 1 4 2 3 2 1 2 2 1 2 3 1 2 3 2 3 1 4 2 3 2 1 2 2 3 2 1 1 2 3 … so we can modify this virtual genome when / wherever we like either accurately or inaccurately and follow the downstream community consequences of GM
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In real experiments with virtual plants … One overnight run on one PC Approx. 100 person-years of growth experiments Want to get airborne with us ? http://www.ex.ac.uk/~rh203/ Signoff
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