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Integrating plant and animalprocesses in grazing system models
Peter J. WeisbergUniversity of Nevada, Reno
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How many ungulates should there be?
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Is this a difficult question?
• Variable forage base
• Variable intake rate
Stocking Rate = (Production * Allowable Use) / Animal Intake
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Sources of Variation
• Weather
• Soils
• Topography
• Hydrology
• Grazing Behavior
• Population Management
• Grazing Management
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Savanna Model, M. Coughenour, NREL
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1000
1200
1400
1600
1800
2000
2000
4000
6000
8000
10000
12000
Elk Population Level
Elk
Inta
ke
(kg/
elk/
year
)
Dry Year
Wet Year
DeterministicModel
1000
1200
1400
1600
1800
2000
2000
4000
6000
8000
10000
12000
Elk Population Level
Elk
Inta
ke
(kg/
elk/
year
)
StochasticWeather
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Why Model Grazing Systems?
• Aid understanding – complex problems
fuzzy notions -> Data + Algorithms
• Evaluate logical outcomes of our beliefs
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Mule Deer Decline
Coyote Predation Winter Habitat Loss
Shrub SenescenceCompetition from Elk
? ?
? ?
1935 1945 1955 1965 1975 1985 1995 2005
Coyote H0
Habitat Loss H0
Coyote * Habitat Loss
OBSERVED
1935 1945 1955 1965 1975 1985 1995 2005
1935 1945 1955 1965 1975 1985 1995 2005
1935 1945 1955 1965 1975 1985 1995 2005
HYPOTHETICAL SIMULATIONS
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Why Model Grazing Systems?
• Aid understanding – complex problemsfuzzy notions -> Data + Algorithms
• Evaluate logical outcomes of our beliefs
• Scenario modeling for assessingmanagement alternatives (forecasting)
• Knowledge transfer– SCIENTIST MANAGER
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• Why model grazing systems?
• Types of grazing system models
• Spatial heterogeneity and scale
• Management models
• Where do we go from here?
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Energetics
Population ForageIntake
Distribution
AvailableForage
AvailableEnergy
HabitatMosaic
Energetics
Population ForageIntake
Distribution
AvailableForage
AvailableEnergy
Energetics
Population ForageIntake
Distribution
AvailableForage
AvailableEnergy
HabitatMosaicHabitatMosaic
Animal Model
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Plant Model
Production
PopulationAllocation
PropaguleDispersal
Solar Energy
BiomassRemoval Production
PopulationAllocation
PropaguleDispersal
Solar Energy
BiomassRemoval Production
PopulationAllocation
PropaguleDispersal
Solar Energy
BiomassRemoval
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Integrated Model
Energetics
Population
Distribution
Productionand
Allocation
Population
Dispersal
HERBIVORY
Solar Energy
Habitat
Energetics
Population
Distribution
Productionand
Allocation
Population
Dispersal
HERBIVORY
Solar EnergySolar Energy
Habitat
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Integrated Grazing Models andSpatial Heterogeneity
• Animal distribution, seeddispersal
• Dynamic climate variablesas drivers
• Climate interpolated overa spatially heterogeneouslandscape
• Linkages to underlyingGIS (soils, vegetation,topography)
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Mean Elk Density,Feb. 1994 - 99
Aspen RegenerationProbability,
Landscape ScaleAll Winter Jan.-Feb.
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Modeling HerbivoreDistribution Patterns-Difficult to validate-Time-dependent-Dependent on factors otherthan forage
(Weisberg and Coughenour, 2003
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Larsen, G. 1988.The Far Side
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When grazing system models areintegrative and spatially-explicit:
• We improve our ability to make predictionsabout the real world, IF– Good data– Understanding of how processes and data
translate across spatial and temporal scales
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Herbivore Vegetation:-one bite at a time-effects may amplify overlarge areas and long timeperiods
Vegetation Herbivore:-influences over multiple scales-coarse scale effects constrainfiner scale herbivore processes
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ShRSRI
MAX
MAX
+=
I=f(B,D,CI, Snow, …)
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SaplingModel
GapModel
New Tree Establishment
Light Availability,Overstory Composition
HerbivoreModel
Browsing
Forage Availability
Habitat Selection:Intermediate time stepsCoarse spatial resolution
Forage Intake andPlant ResponseShort time stepsFine spatial resolution
Forest Gap DynamicsLong time stepsIntermediate resolution
Linking models across scales
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SaplingPhysiology and
Growth
SaplingMortality
ForestForestRegenerationRegenerationBrowsing
Temperature
Light
Small-SeedlingEstablishment
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Winter Browsing of Current Annual Growth (distributed over 6-months)
Light
Browsing
Browsing HH HH
HH
HH
Years to Exceed 150 cm
Picea abies
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SaplingModel
GapModel
New Tree Establishment
Light Availability,Overstory Composition
HerbivoreModel
Browsing
Forage Availability
Linking models across scales
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• We are working in the realm of limited theory,limited observations (often at the wrong scales)
When grazing system models areintegrative and spatially-explicit:
DATA
UNDERSTANDINGAfter Holling 1978, fromStarfield and Bleloch 1986
1
4
3
2
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• Why model grazing systems?
• Types of grazing system models
• Spatial heterogeneity and scale
• Management models
• Where do we go from here?
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• Management models are especiallydifficult…– Focus is integration not abstraction
– Management decisions often made overheterogeneous landscape or regional units
– Objective is accurate forecasts
Modeller’schallenge
COMPLEX
SPATIALLYEXPLICIT PREDICTIVE
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Management Model Requirements
• Accessibility and transparency
• Strong connections to real-world data
010203040506070
0 10 20 30 40 50
Predicted Live Herbaceous Biomass (g/sq m)
Obs
erve
d
Highly Productive
Moderately Productive
Poorly Productive
Pasture Quality Obs NDVI Sim NDVI
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Model-Data Linkages
• Data collected overextensive areas
• Historical data tovalidate models forcurrent conditions
• Experimental,long-term controlledgrazing studies
We need more:
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Management Model Requirements
• Accessibility and transparency
• Strong connections to real-world data
• Stakeholder and End-User interactionduring model development
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Manager-Scientist Interaction in theModeling Process
Que
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Mat
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Dec
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Institutionalized Model-Making• Emphasizes process over
product
• Facilitates and structuresmanager-scientist-stakeholder interactions
• Integrates and organizesknowledge
• Guides research direction
• Placeholder for continuingdevelopment
Sage, Patten & Salmon. 2003. J. Nat. Cons. 10:280-294.
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• “Process over Product”– Decision-maker involvement a must
– Requires transparency, “gaming”
OR• Accurate Predictions
– Elusive target
– Only useful product of “black box” models
How can Models SupportDecision Making?
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ABSTRACTION– Isolate system
components ofparticular interest
– Assume homogeneity,equilibrium
– Incremental approach,limited scope
– Easier to do goodscience
INTEGRATION– Inclusive of relevant
system components;Focus on interactionsand linkages
– Incorporate & explaincontingency
– “reverse reductionism”– Fuzzy “answers” to
big-picture questions
For Management Models:Need cross-fertilization of the two approaches
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Happy Holidays!!!