Creating stewardship zones: managing the intensity of timber harvesting

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Creating stewardship zones managing the
intensity of timber harvesting

• Mark Boyland

UBC
Centre for Applied Conservation Research
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Weyerhaeusers Forest Project

• Zone the intensity of timber harvesting
• Variable Retention logging
• Adaptive Management program

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Stewardship Zones
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• Zoning ! Multiple-use
• Old seral objectives from the OG zone
• Timber objectives from the Timber zone
• Some flexibility in the Habitat zone to capture
  the rest.

Location of the zones VERY important!
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Zone Parameters

• Ecological Representation
• 18 BEC variants
• Size
• 5,000 ha minimum region size
• Shape
• globular, not linear
• Push objectives
• Tenure, Age, Productivity, ENGO proposals

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Zone Allocation Model

• Large, large areas.
• Fixed zone allocations.
• Simulated Annealing algorithm.
• Objectives
• Size and Shape
• Ecological representation Target (/-)
• Push Objectives Minimum Level (-)

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Weyerhaeuser's coastal holdings

• 1.2 Million ha
• 300,000 polygons

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Tiling procedure

• Hexagonal tiles overlaid on the landbase
• Resultant polygons have tile_ID
• Model groups polygons by tile_ID
• No loss of precision
• Seven tile sizes used.

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Tile configurations
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Tile configurations
10K
200K
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Algorithm Performance

• Tested against
• Other algorithms
• Hill Climbing (HC)
• Monte Carlo (MC)
• Theoretical optimum score.
• Problems of increasing difficulty.

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Against other algorithms
100K
Simulated annealing substantially better.
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Against other algorithms
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Against theoretical optimum

• Penalty score (Z) is the sum of the penalties.
• 6 types of penalties all are targets, evaluating
  to 0 when satisfied.
• Z 0 is the theoretical optimum.

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Against theoretical optimum

• Adjust the theoretical optimum

Not all objectives can be satisfied.
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Against theoretical optimum
- Average, best, worst scores u theoretical
optimum score
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Against problems of increasing difficulty
HC
SA
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Zone Allocations
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Zone Allocations
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Conclusions

• Zone model works
• Better than other algorithms
• Within 4 to 11 of theoretical optimum
• Scales against increasingly difficult problems
• No free lunches
• But less expensive than human designers
• Flexibility vs. problem size