orth

MOFEP Data to Adds to Other Studies -- Coarse Woody Debris Estimation -- Landscape-scale Forest Planning -- Cavity Tree Estimation

orthentral Research Station

• Stephen R. Shifley• Zaofei Fan• Frank R. Thompson III• William Dijak• David R. Larsen • Josh Millspaugh

• Michael Larson • Martin Spetich• John Kabrick • Randy Jensen • Brian Brookshire,• Laura Brookshire

Harvest Patterns Year 10Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 20Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 30Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 40Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 50Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 60Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 70Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 80Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 90Even-aged harvestingMOFEP sites 7 and 8

Harvest Patterns Year 100Even-aged harvestingMOFEP sites 7 and 8

Wind and Weather DisturbanceWind/weather disturbances Wind/weather disturbances creating crown openings affecting creating crown openings affecting 0.1 to 2.5 ha per event have a 0.1 to 2.5 ha per event have a return interval of approximately return interval of approximately 670 years 670 years

Tornados are a factor, but one we Tornados are a factor, but one we could not simulate spatially with could not simulate spatially with LANDISLANDIS

Fires once were common Fires once were common -- -- Every 5-10 years in 1800’sEvery 5-10 years in 1800’s

With active suppression the mean fire With active suppression the mean fire return interval return interval is now about 300 years.is now about 300 years.

-- Crown fires are rare-- Crown fires are rare-- Prescribed fires can be simulated-- Prescribed fires can be simulated

Initial Age ClassesMOFEP sites 7 and 8 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 10-year simulationMOFEP sites 7 and 8

Even-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 20-year simulationMOFEP sites 7 and 8

Even-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 40-year simulationMOFEP sites 7 and 8

Even-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 60-year simulationMOFEP sites 7 and 8

Even-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 80-year simulationMOFEP sites 7 and 8

Even-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 100-year simulationMOFEP sites 7 and 8

Even-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Initial Age ClassesMOFEP sites 7 and 8 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Initial Age ClassesMOFEP sites 7 and 8

0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 10-year simulationMOFEP sites 7 and 8

Uneven-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 20-year simulationMOFEP sites 7 and 8

Uneven-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 40-year simulationMOFEP sites 7 and 8

Uneven-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 60-year simulationMOFEP sites 7 and 8

Uneven-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 80-year simulationMOFEP sites 7 and 8

Uneven-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 100-year simulationMOFEP sites 7 and 8

Uneven-aged harvesting 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 20-year simulationMOFEP sites 7 and 8100-year simulation

No harvest 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 40-year simulationMOFEP sites 7 and 8100-year simulation

No harvest 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 60-year simulationMOFEP sites 7 and 8100-year simulation

No harvest 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 80-year simulationMOFEP sites 7 and 8100-year simulation

No harvest 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 100-year simulationMOFEP sites 7 and 8100-year simulation

No harvest 0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

Age Classes after 100-year simulationMOFEP sites 7 and 8

0- 29 yrs

30- 59 yrs

60- 89 yrs

90-119 yrs

120-149 yrs

150-179 yrs

> 180 yrs

EAM

UAMNo Harvest

Tree size classes - year 100

No Harv.

Even 10%

Uneven 5%

5 km

Ovenbird

Late successional

Edge sensitive

Tree age & Landtype

Pine

Edge

OvenbirdHabitat Model

0.25 km

Ovenbird Habitat Suitability

No harvest Even-age 10%Y

ear

50Y

ear

200

Black bear habitat

• Fall food– Hard mast

• Summer food– Soft mast (tree age & land type)

• Interspersion of food types– Circular moving window

• Road density– Auxiliary map

photo courtesy of Elaine Bindler

Black Bear Habitat Suitability

4 km wide

Habitat model links

Ovenbird

Prairie warbler

Hooded warbler

Pine warbler

Wild turkey

Ruffed grouse

Gray squirrel

Black bear

Bobcat

Red bat

Northern bat

Redback salamander

Could we create a working hypothesis of MOFEP change over the life of the experiment?

• Vegetation pattern• Woody species composition• Volume • CWD• Cavities• Wildlife habitat• …

Cavity tree estimation at multiple spatial scales

Tree level

Stand level

Landscape level

Probability of cavity trees

• Tree level– Live, dead– Dbh– Species group– Decay class if dead

• Stand level

• Landscape level

Probability of cavity trees

• Tree level

• Stand level– Stand age class– Dbh class probability distribution

• Landscape level

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140 160

Cavity tree density (trees/ha)

Cu

mu

lati

ve

pro

ba

bil

ity

Old growth

Sawtimber

Seedling/sapling

Pole

Fitted Weibull curves of cavity tree density by stand size class

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140 160

Cavity tree density (trees/ha)

Cu

mu

lati

ve

pro

ba

bil

ity

Old growth

Sawtimber

Seedling/sapling

Pole

Fitted Weibull curves of cavity tree density by stand size class

Probability of cavity trees

• Tree level

• Stand level

• Landscape level– Acres by age class

• Seed/sap, pole, sawtimber, old-growth

Initial efforts were in the SE Missouri Ozarks

DevelopedAgriculturalDeciduousConiferousMixedForested WetlandWaterBarren

N̂

Land use classification, southeast Missouri

Ellington

Bunker

Eminence Clearwater Lake

5,040 sq.. km

80 km

63 k

m

Goal: Develop A Landscape Model• Simulates the impact of various disturbances on forests.

• Predicts the composite impacts (in aggregate) on a landscape composed of numerous forest stands.

• Predicts/contrasts changes in ecosystem attributes that result from alternative disturbance regimes.

1995

20252055

Our Basic Modeling Assumptions

• Vegetation change is relentless.• Vegetation is constantly responding to (recovering from)

disturbance.• To some degree (and to a greater degree than most other

ecosystem components), patterns of vegetation change are predictable.

• The landscape can be divided into ecologically similar units (ECS).

• If we know (or can predict) the vegetation conditions across a landscape at some future point in time, we can say significant things about other ecosystem components.

• Requires a team effort.

This work utilizes the LANDIS model

• Generic framework for simulating landscape change in response to disturbance

• Handles all the basic bookkeeping and mapping• Scaleable pixel size (0.1 ha)• Tracks presence/absence of tree species by age and location• High degree of stochastic variation• Simulates stochastic fire events• Simulates stochastic wind events • Newly completed harvest simulator• Can be calibrated for different forest conditions

Calibration Process for LANDIS

• Identify Land Units• Calibrate species reproduction and survival dynamics based

on life history characteristics– Longevity, shade tolerance, fire tolerance, dominance– Sprouting, age to sexual maturity, seed dispersal

• Calibrate wind and fire disturbance– Simulates stochastic fire events that differ by ELT– Simulates stochastic wind events that differ by ELT

Required Input Maps (raster)

• Land units• Initial vegetation cover and age class

• Additional maps required to simulate harvest– Management areas– Stand boundaries

Harvest Scenarios

• Even-aged management– Clearcut 10% of stands each decade– Oldest first– No adjacency constraints– Fire and wind disturbance turned on

• Uneven-aged management– Group openings averaging 0.2 ha (2 pixels)– Harvest 8% of area in each stand each decade– No adjacency constraints– Fire and wind disturbance turned on

• No Harvest– Fire and wind disturbance turned on

Wind and Fire Disturbance

Wind Fire

Mean return interval 800 yrs 300 yrs

Mean size 1 ha 8 ha

Minimum size 0.1 ha 0.1 ha

Maximum size 20 ha 600 ha

Severity N/ A Low-med

Wind and Fire Disturbance

Wind Fire Mean return interval 800 yrs 300 yrs

Mean Size 1 ha 8 ha

Minimum size 0.1 ha 0.1 ha

Maximum Size 20 ha 600 ha

Severity N/A Low-Med

Output Maps for Each Decade of Simulation

• Vegetation cover

• Vegetation age class

• Fire damage

• Wind damage

• Type and location of harvest

Strengths of This Approach

• Provides the big picture. Great tool to view

large scale forest change

• Compare management alternatives visually

• Analyze projected landscape characteristics

• Compare landscape statistics among

alternatives

• Assess change over time

• Make linkages to other resources

Limitations

• Not suitable for site-specific planning• Probabilistic model (+/-)• Requires GIS capability• Big effort to learn to use it • Requires maps of land units and stands for

most harvest simulations• Needs lots of computing horsepower for big

landscapes

LANDIS Representation of a Site (pixel)

Species 10 year age classes 1 = present, 0 = absent

maple 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

shortleaf 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

black oak 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

white oak 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Number of Snags by Dbh Class

0

5

10

15

20

Dbh (cm)

Tre

es

/ha

Dead O-GDead SEFDead MOFEP

Down Wood Volume

0

10

20

30

40

50

Dark HollowEngelmannSchnabelBig SpringRoaring SinkinMOFEP

Vo

lum

e (

cu.m

/ha)

Down Wood Size Distribution

05

1015

2025

3035

4045

50

10 20 30 40 50 60 70

Dbh class (cm)

Pie

ce

s/h

aSecond-GrowthOld-Growth

Dbh Distribution by Species

2 186 10 14 22 26 2 186 10 14 22 26

60

40

20

Dbh (inches) Dbh (inches)

Shortleaf PineShortleaf Pine

Red OakRed Oak

White OakWhite Oak

Big Spring MOFEP

%

Harvest Patterns Year 10Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 20Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 30Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 40Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 50Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 60Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 70Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 80Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 90Uneven-aged harvesting

MOFEP sites 7 and 8

Harvest Patterns Year 100Uneven-aged harvesting

MOFEP sites 7 and 8