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