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Protein/Energy Model: Applied to cumulative effects assessment
FOOD INTAKE
RUMEN
ENERGY (MEI)
NITROGEN (MNI)
ALLOCATION
Model structure Energy – Protein model
disturbance displacement
disturbance displacement
Why Protein? • By ignoring protein, we assume the growth of the
fetus, the production of milk and the replenishment of muscle tissue is entirely dictated by available energy. NOT TRUE
• An integral part of the weaning strategy in caribou – critical to buffer environmental change
• While energy may be the key nutrient in winter, protein is the key nutrient in summer
• “The resilience of Rangifer populations to respond to variable patterns of food supply and metabolic demand may be related to their ability to alter the timing and allocation of body protein to reproduction.”
– Barboza 2008
DECISION TREE FOR CARIBOU WEANING STRATEGIES
Date Above
Threshold
Below Threshold
Weaning Class
Reproductive Impact
Late June
Mid July
Early October
Early September
BIRTH
Calf Weight gain
Cow Protein gain
Cow fat weight
Calf fat weight
Cow Protein gain
Cow fat gain
Calf fat weight
Calf Weight gain
Extended lactator
Early weaner
Summer weaner
Post-natal weaner
Lower overwinter calf survival
Later age of 1st reproduction
Higher overwinter cow survival
Increased probability of pregnancy
Calf dies High probability of pregnancy
Calf dies High probability of pregnancy
Lower probability of pregnancy
Higher overwinter calf survival
Normal weaner
“Normal” probability of pregnancy
“Normal” overwinter calf survival
Climate database
Energy Protein Model
Physiological Predictions
Birth rate Impacts on
Cow survival Impacts on Calf survival
Age first reproduction
POPULATION MODEL
annually
Scenarios (Daily Resolution)
Climate
Snow Depth
Insects
Veg
Activity Development
Linking energy-protein model with a population model
Model applications
• Porcupine – 1002 development – Climate change
• George River – Vehicle for data integration
• Bathurst – Cumulative effects pilot project
• Central Arctic – Prudhoe Bay oil development
• North Baffin – Baffinland’s Mary River project
• Qamanirjuaq – AREVA’s Kiggavik project
Kiggavik assessment approach
0
1
2
3
4
5
Re
sid
en
ce t
ime
(d
ays)
Climate database (average)
Energy Protein Model
• 100 cows/scenario • Weaning status • Fall body weights
pregnancy
calf mortality Population model projected to 2050
Harvest adjustment needed to
offset development
effects
EERX EENRX
ENERGY COST = EERX - EENRX
Jay assessment components
EIRX EERX EINRX
EENRX
EIC EEC
PIRX PERX PINRX
PENRX
PIC PEC
ENERGY BALANCE (EB) = EI-EE
ENERGY “COST” for RX caribou = EBC - EBRX
ENERGY “COST” for NRX caribou = EBC - EBNRX
PROTEIN BALANCE (PB) = PI-PE
PROTEIN “COST” for RX caribou = PBC - PBRX
PROTEIN “COST” for NRX caribou = PBC - PBNRX
Kiggavik assessment components
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.4 0.5 0.6 0.7 0.8 0.9 1
Pro
bab
ility
of
Pre
gnan
cy
RELATIVE Body Weight
Probability of Pregnancy
RESILIENCE the steeper the line; the less the resilience
PRODUCTIVITY
The lower the RBW associated with 50% p of pregnancy, the more productive
0
0.2
0.4
0.6
0.8
1
1.2
0.4 0.5 0.6 0.7 0.8 0.9 1
Pro
bab
ility
of
Pre
gnan
cy
Relative Body Weight
Probability of pregnancy among arctic caribou herds in relation to Relative Body Weight
GEORGE
QAMANIRJUAQ
TAIMYR
0
0.2
0.4
0.6
0.8
1
1.2
0.4 0.5 0.6 0.7 0.8 0.9 1
Pro
bab
ility
of
Pre
gnan
cy
Relative Body Weight
Probability of pregnancy among arctic caribou herds in relation to Relative Body Weight
GEORGE
QAMINURJUAQ
TAIMYR
BEV/BATHURST
PEARY
0
0.2
0.4
0.6
0.8
1
1.2
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Pro
bab
ility
of
Pre
gnan
cy
Relative Body Weight
Probability of pregnancy among arctic caribou herds in relation to Relative Body Weight
GEORGE
QAMINURJUAQ
TAIMYR
BEV/BATHURST
PEARY
CENTRAL ARCTIC
PORCUPINE
Major advantages of E-P approach linked to a population model
• Accounts for protein dynamics • Flexible in designing scenarios – ask the “what-if”
questions • Multi-scale: integrates from climate to population
– thus can develop scenarios that effect any scale (climate, habitat, behaviour, demography)
• Incorporates age structure – important when populations cycle
• Can model up to 1000 animals at once through a scenario – i.e. can capture the population variability and identify vulnerable cohorts