climate and subsistence hunting the ‘sustainability of arctic communities’ project

Climate and Subsistence Hunting

The ‘Sustainability of Arctic Communities’ Project

Arctic Forum, May 2002

ARCUS Annual Meeting

An interdisciplinary & collaborative effort

Principal Investigator (Phases 1 & 2)

– Jack Kruse1

Community Involvement / Local Knowledge

– Gary Kofinas1,3, Steve BraundSynthesis modelers

– Craig Nicolson14, Tony Starfield3 Vegetation ecologists

– Marilyn Walker5, Terry Chapin2

Howie Epstein5

Caribou biologists

– Don Russell9, Brad Griffith10 , Bob White2

Whale biologists

– Craig George13, Robert Suydam13, Harry Brower Jr13 , Todd O’Hara13

Oil field – caribou interactions

– Steve Murphy, Brad Griffith10

Economists

– Matt Berman1, Lee Huskey1, Sharman Haley1 , Stephanie Martin1

21 Participating scientists 6 Arctic communities- Aklavik, NWT

- Arctic Village, AK

- Barrow, AK

- Fort MacPherson, NWT

- Kaktovik, AK

- Old Crow, YT

13 Universities/Agencies1. U of Alaska Anchorage (ISER)2. U of Alaska Fairbanks (IAB)3. U of Minnesota (Phase 1 only)4. Dartmouth College5. U of Colorado, Boulder6. National Science Foundation7. US Man and the Biosphere8. Environment Canada9. Canadian Wildlife Service10. US Geological Survey11. Alaska Dept of Fish and Game12. Yukon Renewable Resources Council13. North Slope Borough14. University of Massachusetts

Two streams of science…

The science of parts

focused, reductionist reduce uncertainty consensus among peers

The science of the integration of parts

broad understand interactions complex! Experts may not all agree...

The Sustainability of Arctic Communities

Began with a core group of researchers who had overlapping interests…

– Tundra vegetation ecology (ITEX)

– Caribou physiology and ecology (IAB, CWS)

– Bowhead whale ecology (NSB)

– Economics of Arctic communities (ISER)

There were ‘bricks’ but no ‘wall’

two TERRESTRIAL components...

Caribou

Vegetation

...a MARINE component...

Caribou

Vegetation

Beluga & Bowhead

whales

…and three HUMAN components.

Hunting

Caribou

Vegetation

Demogr-aphics

Employ-ment

Beluga & Bowhead

whales

Tourism + Govt Funding pathways

Hunting

Demogr-aphics

Employ-ment

Tourism and Gov’t

$$

Oil development pathways

Hunting

Caribou

Onshore oil

development

Employ-ment

Beluga & Bowhead

whales

Offshore oil development

Climate pathways

Hunting

Caribou

Vegetation

Climate

Beluga & Bowhead

whales

Summary of the system and pathways

Hunting

Caribou

Vegetation

Climate

Onshore oil

development

Demogr-aphics

Employ-ment

Tourism and Gov’t $

$Beluga & Bowhead

whales

Offshore oil development

Linking climate and subsistence hunting

2 case studies

a) Spring bowhead hunting (Barrow) b) Annual Caribou hunting round (Old Crow)

Whales are hunted at Barrow in both spring and fall Spring (April 20 - May31)

– hunt from umiaks in open leads; camps on shorefast ice

The number of whales landed varies each spring

Hunting Bowhead Whales

The influence of climate:

02

46

81012

1416

18

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Whales Landed at Barrow in Spring

Spring hunt Bowhead harvest

(Barrow )

Environmental conditions

Number seen

Food quality

Regulation & management

What factors affect hunting success?

– Craig George & Harry Brower Jr spoke to captains

– Four main themes emerged

Lead condition

Ice condition (camp, transport)Environmental

conditionsSpring hunt

Bowhead harvest (Barrow )

Lead condition

Ice condition (camp, transport)Environmental

conditions

Ice conc. in lead

Rough water

Lead widthFog

Fall freeze-up

TemperatureBlowing

snowSea smoke

Wind (speed, dir)

Multi-year ice

Ocean currents (speed, dir)

Abrupt sea level change

Spring hunt Bowhead harvest

(Barrow )

Lead condition

Ice condition (camp, transport)Environmental

conditions

Ice conc. in lead

Rough water

Lead widthFog

Multi-year ice

Ocean currents (speed, dir)

Fall freeze-up

Abrupt sea level change

TemperatureBlowing

snowSea smoke

Wind (speed, dir)

Number seen

Migration distrib. and timing

Bering Sea ice

Hunting activities

NoiseStrikes

Quality of muscle, organ tissue Quality of

muktuk

Perceived food safety, taste

Contaminants

Time taken for butchering

Density dep factors

Regulation & management

Technology

MSYR threshold

Measured rate of increase

Spring Census

Need and use

Incidental take by industry

Measured abundance

Real rate of increase

Calf production

Mortality factors

Primary productivity

Food quality

Spring hunt Bowhead harvest

(Barrow )

Other factors, and the Primary Climate-related Pathway

What factors affect harvest success?

Wind speed

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Daily average wind speed (km/h)

Frequency

All days

Harvest days

Spring daily wind speed in Barrow

Wind direction

Daily wind speed and direction(Vector plot)

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Wind speed from east (km/hr)

Wind speed from north (km/hr)

We have daily wind data for Barrow airport…

Plot each day’s average wind conditions on a vector plot to show both speed and direction…

N

W

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Apr 20

Apr 23, 1997ESE, 23km/h

Apr 22

Apr 21

Spring hunting season in Barrow April 20 – May 31 From 1990 to 1997

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N

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Looking at wind data (cont’d)

Daily wind speed and direction(Vector plot)

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Wind speed from east (km/hr)

Wind speed from north (km/hr)

On days when one or more whales were harvested, we can show the dots in a different color.

N

W

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Apr 20

Apr 22

Apr 21

Apr 23

2 whales harvested

Look at the wind direction on successful harvest days

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All days

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Red dots: days with whale harvest

Note how winds are almost always from the east when whales are taken in the spring.

Scientific and quantified representation of what the captains had told Harry B and Craig G.

Climate factors drive spring hunting!

Policy Implications – International Whaling Commission– Subsistence quota may be reduced if it is not used– This shows that there may be good reasons why whale

harvest is low in some years: not a function of need

y = 0.0794e0.049x

R2 = 0.9602

0%

20%

40%

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100%

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'Good wind' day weighted index (Apr-20 to May31)

% of quota filled

Arctic climate (AO) and Barrow wind

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-2 -1 0 1 2 3

April

R 2 = 0.951

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June

1999 outlier ignored in regression

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1992

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Wind direction: # days with easterly winds vs monthly AO, 1990-99

Wind speed: days with winds >15km/h

R2 = 0.010

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Monthly AO value

April

R2 = 0.012

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Monthly AO value

May

R 2 = 0.179

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Monthly AO value

June

Arctic climate (PNA) and local conditions

• Next, we tried the PNA index instead of AO. And there’s a strong relationship between the May PNA value and the number of good wind days in May.

• So there seems to be a link between PNA and Barrow spring wind conditions. But the two years with the worst wind conditions (and thus the 2 worst whale harvests in 1990-7) don’t fit the general pattern at all.

R2 = 0.9062

05

1015202530354045

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PNA value

Good wind day index

May

1996 1992

Hunting Caribou

An example of modeling the interaction between human and natural systems:

1. Understanding system components

2. Mapping a conceptual model

3. Developing quantitative relationships

4. Synthesis: simulation model

5. Exploring scenarios

1. Understanding the systems

Focus groups with elders and hunters Generative theory building

Sets of propositions

1)   Caribou availability to communities2)  Distribution and movements of caribou3)   Environmental factors affecting hunters’ access to caribou4)  Participation in the wage economy and caribou hunting5)   Cash – how does it affect caribou hunting6)   Exchanging caribou between households and communities7)   Moving away from or back to communities

example… “If a local hunter has a full time job,

he has little time for hunting”

became modified and nuanced… Those with full time jobs have equipment that allows for fast

access to hunting grounds distant from communities.

Those with full-time jobs hunt on weekends in crowded and unsafe conditions.

Those without full time jobs avoiding hunting on weekends

2. Caribou Hunting: Conceptual model

3Gen

Eld

Mom

Bach C+K

Households• Caribou need

• Resources

• Sharing (gear/meat)

Geographic hunting zones• Effort for a trip in each season

• Caribou availability

i.e. complex adaptive system (agent-based approach)

2. Conceptual model of hunting (cont’d)

Climate

HH meat needs

HH hunting ‘resources’

HH Time & $$

Gear sharing

Wage economy

P(hunting)

2. Conceptual model of hunting (cont’d)

ClimateWage economy

P(hunting)

Caribou distribution

Caribou availability

Access to hunting areas

2. Conceptual model of hunting (cont’d)

Climate

Caribou distribution

Caribou availability

HH meat needs

HH hunting ‘resources’

Access to hunting areas

HH Time & $$

Gear sharing

Wage economy

P(hunting)

Time on the land

Actual HH harvest

2. Conceptual model of hunting (cont’d)

Climate

HH meat needs

HH hunting ‘resources’

HH Time & $$

Gear sharing

Wage economy

P(hunting)

Time on the land

Actual HH harvest

Meatsharing

Caribou distribution

Caribou availability

Access to hunting areas

2. Conceptual model of hunting (cont’d)

Climate

HH meat needs

HH hunting ‘resources’

HH Time & $$

Gear sharing

Wage economy

P(hunting)

Time on the land

Actual HH harvest

Meatsharing

Collectivehunt

Caribou distribution

Caribou availability

Access to hunting areas

3. Quantifying the relationships

Example:

P(hunting) = f [ N, Reshh , Cavail , Caccess ]

HQI = g [ Cavail , Caccess ]

Logistic analysis (logit equation) of ~150 hh’s data

4. Synthesis: simulation model

5. Exploring scenarios with the modelTotal Annual Caribou Harvest for Old Crow, YT

Three scenarios, 3 replicates each

0100200

300400500600700800900

1.1 1.2 1.3 2.1 2.2 2.3 3.1 3.2 3.3Number of caribou harvested

High availability Few caribou in winterCollective hunt

Few caribou in winterNo collective hunt

Households meeting various need levels

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1.1 1.2 1.3 2.1 2.2 2.3 3.1 3.2 3.3

Number of HHs

>85%

50% - 85%

< 50%

Summary:

In the Arctic, human socio-cultural systems are closely coupled to biophysical systems and to the climate system

These systems interact in complex ways and have emergent properties that would be difficult to predict without an integrated perspective

There are adaptive strategies that help to make communities less vulnerable to climate effects

Some lessons we learned…

Interdisciplinary communication is harder than you think

Gaps between system components aren’t always easy to fill in

Disciplinary expertise doesn’t guarantee integrative expertise

It’s a challenge to communicate the results of integrated models

But… This kind of complex systems approach is also a

lot of fun!