integrating plant and animal processes in grazing system ... · • dynamic climate variables as...
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Integrating plant and animalprocesses in grazing system models
Peter J. WeisbergUniversity of Nevada, Reno
How many ungulates should there be?
Is this a difficult question?
• Variable forage base
• Variable intake rate
Stocking Rate = (Production * Allowable Use) / Animal Intake
Sources of Variation
• Weather
• Soils
• Topography
• Hydrology
• Grazing Behavior
• Population Management
• Grazing Management
Savanna Model, M. Coughenour, NREL
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Elk Population Level
Elk
Inta
ke
(kg/
elk/
year
)
Dry Year
Wet Year
DeterministicModel
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10000
12000
Elk Population Level
Elk
Inta
ke
(kg/
elk/
year
)
StochasticWeather
Why Model Grazing Systems?
• Aid understanding – complex problems
fuzzy notions -> Data + Algorithms
• Evaluate logical outcomes of our beliefs
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
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
• Why model grazing systems?
• Types of grazing system models
• Spatial heterogeneity and scale
• Management models
• Where do we go from here?
Energetics
Population ForageIntake
Distribution
AvailableForage
AvailableEnergy
HabitatMosaic
Energetics
Population ForageIntake
Distribution
AvailableForage
AvailableEnergy
Energetics
Population ForageIntake
Distribution
AvailableForage
AvailableEnergy
HabitatMosaicHabitatMosaic
Animal Model
Plant Model
Production
PopulationAllocation
PropaguleDispersal
Solar Energy
BiomassRemoval Production
PopulationAllocation
PropaguleDispersal
Solar Energy
BiomassRemoval Production
PopulationAllocation
PropaguleDispersal
Solar Energy
BiomassRemoval
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
Integrated Grazing Models andSpatial Heterogeneity
• Animal distribution, seeddispersal
• Dynamic climate variablesas drivers
• Climate interpolated overa spatially heterogeneouslandscape
• Linkages to underlyingGIS (soils, vegetation,topography)
Mean Elk Density,Feb. 1994 - 99
Aspen RegenerationProbability,
Landscape ScaleAll Winter Jan.-Feb.
Modeling HerbivoreDistribution Patterns-Difficult to validate-Time-dependent-Dependent on factors otherthan forage
(Weisberg and Coughenour, 2003
Larsen, G. 1988.The Far Side
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
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
ShRSRI
MAX
MAX
+=
I=f(B,D,CI, Snow, …)
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
SaplingPhysiology and
Growth
SaplingMortality
ForestForestRegenerationRegenerationBrowsing
Temperature
Light
Small-SeedlingEstablishment
Winter Browsing of Current Annual Growth (distributed over 6-months)
Light
Browsing
Browsing HH HH
HH
HH
Years to Exceed 150 cm
Picea abies
SaplingModel
GapModel
New Tree Establishment
Light Availability,Overstory Composition
HerbivoreModel
Browsing
Forage Availability
Linking models across scales
• 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
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2
• Why model grazing systems?
• Types of grazing system models
• Spatial heterogeneity and scale
• Management models
• Where do we go from here?
• 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
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
Model-Data Linkages
• Data collected overextensive areas
• Historical data tovalidate models forcurrent conditions
• Experimental,long-term controlledgrazing studies
We need more:
Management Model Requirements
• Accessibility and transparency
• Strong connections to real-world data
• Stakeholder and End-User interactionduring model development
Manager-Scientist Interaction in theModeling Process
Que
stio
nsD
ecid
e on
Out
puts
Mat
hem
atic
al M
odel
Dec
ide
onD
rivin
g Va
riabl
esC
once
ptua
lizat
ion
Link
ages
toEm
piric
al D
ata
Impl
emen
tatio
nM
odel
Tes
ting
Inte
rpre
t Res
ults
New
Site
s an
d Sp
ecie
sN
ew Q
uest
ions
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.
• “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?
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
Happy Holidays!!!