System-level Hypothesis Testing: A Novel Approach to Developing Parsimonious Models of Complex Ecosystem Dynamics

Geoffrey PooleClem IzurietaRobert PaynAshley HeltonMeredith WrightEmily BernhardtJack Stanford

AN IMPERATIVESE techniques yield: Model parsimony Code reliability,

extensibility, reuse, longevity

Grid-based high-performance computing

Virtual laboratories

Scheller et al. 2009

THE NEED Virtual laboratory; model parsimony

Requires: rapid creation, maintenance, management, and execution of multiple competing model variants

Code reliability, extensibility, and reuse Requires: encapsulation and

modularization of code High performance computing

Requires: A simple means of developing multi-threaded code

WHAT IS A MODEL?

[Solute]

Upta

ke

Umax

k

Time

[Solute]

Biomass

RETHINKING MODELS...

Input/Output/

Initialization

Execution control

CalculationsR = 6.5

X = 13.4

r = 0.034

Ks = 1.73u = 6.2

U = 17.3W = 600

S =1800.0[S] = 3.00

Values Code

NEO

Model

Simulation run

CREATING A NEO SIMULATIONSolution Biomass

Uptake Respiration

C1 C2E1 E2

EdgeID FromCell ToCellE1 C1 C2E2 C2

NEO INPUT FILE: Matrix Table

C1 C2E1 E2

HolonID Variable ValueC1 W 600E1 Ks 1.73E1 u 6.2E2 R 0.034

NEO INPUT FILE: Parameter file

Wµ

Ks

r

Holon ID Holon TypeC1 SolutionC2 BiomassE1 UptakeE2 Respiration

NEO INPUT FILE: Holon Type Table

Solution BiomassUptake Respiration

C1 C2E1 E2

WS

[S] µ

Ks

r

eq4

eq5 Ueq1 R

eq2

Xeq3

45

3

1

2

Model code hierarchy

Cell type CarbonSolution aqueousBiomass microbial

NEO INPUT FILE: Cell Type Behavior Table

Edge type CarbonUptake michaelis_mentenRespiration microbial

NEO INPUT FILE: Edge Type Behavior Table

Solution BiomassUptake Respiration

C1 C2E1 E2

WS

[S] µ

Ks

r

eq4

eq5 Ueq1 R

eq2

Xeq3

Time

[Solute]

Biomass

public class Carbon extends Dynam { private StateVal Ks, u, conc, X; public double initialize() { Ks = myEdge.getStateVal(“Ks”); u = myEdge.getStateVal(“u”); conc = fromCell.getStateVal(“[S]”); X = toCell.getStateVal(“X”); return 0.0; } public double calculate() { return u.v * (Ks.v / (Ks.v + conc.v)) * X.v; } }

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3

1

2

Model code hierarchy

Pseudo code

Solution BiomassUptake Respiration

C1 C2E1 E2

WS

[S] µ

Ks

r

eq4

eq5 Ueq1 R

eq2

Xeq3

“ToCell”

“MyEdge”“FromCe

ll”

“HorzGroundwaterFlux”

Wat

er fl

ux a

nd s

olut

e tr

ansp

ort

Microbial uptake and respiration

UptakeBiomassResp

S[S]

µKs

rURX

S

S

S[S]

µKs

rURX

S[S]

µKs

rURX

S

S

W

Q

Q

W

W

Q

Q

MULTIPLE INTERACTIVE CURRENCIES

HorizontalGWFluxH2ODOC

GWSWExchange

SWFlux

OC

H20DOC

SurfaceWater

Areal

UptakeOC

M-M

UptakeCO2

SWFlux

Biomass

CO2

Respiration

RespirationHorizont

alGWFlux

AdvectiveSoluteFlux

Biomass

Biomass

SurfaceWater

Surface Water Flux

GroundWater

Horizontal GW Flux

Areal

Uptake

CO2

Respiration

GWSW Exchange

CODE REUSE AND RECOMBINATION

M-M

Uptake

NEO model

spawner

Model outputs

Step 1: Perform multiple independent model executions (with different parameters) simultaneouslyacross multiple processing threads. Speedsapplications such as sensitivityanalyses.

Model execution

Model output

Processing threads

Step 2: Use parallel processing to

expedite run-time of individual model

executions.Speeds

applications ofhighly detailed or

complexmodels.

Model execution

s

Processing threads

PARALLEL PROCESSING

THE BOTTOM LINE… As a modeling framework, NEO allows:

Specification of model parameters, structure, and algorithms at run time

NEO facilitates: Rapid model development Simple management of code complexity Addition and removal of model dynamics Applications on HPC platforms Maintenance, management and execution of

multiple model variants

SYSTEM LEVEL HYPOTHESIS TESTING Hypothesis testing requires making predictions At a whole-system level, a model is required Models variants are competing hypotheses

Complexity can be added/removed Alternative behaviors can be substituted

Empirical data are used to reject models Last model standing (if any) is supported

SYNERGY WITH BAYESIAN APPROACHES? Sources of error in model assessment

Empirical data Model parameters Model structure

Bayesian approaches presume that model structure is fixed.

NEO opens the possibility of Bayesian assessment to determine the probability of alternative model structures.

http://www.montana.edu/FLL

public class Carbon extends Dynam { private StateVal k, u_max, conc, biomass; public double initialize() { k = myholon.getStateVal(“K”); u_max = myEdge.getStateVal(“u_max”); conc = fromCell.getStateVal(“CarbonConc”); biomass = toCell.getStateVal(“Carbon”); return 0.0; } public double calculate() { return u_max * (k / (k + conc)) * biomass; } }public class Carbon extends Dynam { private StateVal r, biomass; public double initialize() { r = myEdge.getStateVal(“r”); biomass = fromCell.getStateVal(“Carbon”); return 0.0; } public double calculate() { return -r * biomass; } }

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3

1

2

Packages

Holons

Behaviors

Deviant

1

2

A) Model B) Illustrative codepublic class Carbon extends Dynam { private StateVal conc, water; public double initialize() { conc = fromCell.getStateVal(“CarbonConc”); water = myEdge.getStateVal(“Water”); return 0.0; } public double calculate() { return conc * water; } }

Model

ECOSYSTEMS AS MULTI-CURRENCY FLUX NETS

Model of solute transport/processing (currencies are water, heat, carbon, nitrogen, oxygen) within a linked hydro-biological system (network)

Helton et al. Accepted

PHYSICAL SYSTEMS AS FLUX NETWORKS

Surface and ground water (currency) flux across and through the Nyack Floodplain, Middle Fork Flathead River (network)

Poole et al. 2006

BIOTIC COMMUNITIES AS FLUX NETWORKS

Food web (network) representing carbon (currency) fluxBaxter et al. 2005