Trends in Early-Seral Forest Trends in Early-Seral Forest at the Landscape Scaleat the Landscape Scale

Janet L. Ohmann, PNW Research Station, Janet L. Ohmann, PNW Research Station, USDA Forest Service, Corvallis, OregonUSDA Forest Service, Corvallis, Oregon

Landscape trends in early-seral forest

• Regional context for landowners and land managers

• Information for regional planning and policy

• Emphasis on western Oregon

– Coastal Oregon (CLAMS)

– Central Oregon Cascades (COLA)

< 10

11 - 4041 - 8081 - 200> 200

Stand Age

300 yearsof simulated

fire history in

Oregon Coast

Range

(LADS model: Wimberly, Nonaka, Spies, et al.)

Stand-Replacing Disturbance and Forest Land Ownership in Coastal Oregon, 1972-2004

(Cohen, Healey, et al.)

1972-1977

Stand-Replacing Disturbance and Forest Land Ownership in Coastal Oregon, 1972-2004

(Cohen, Healey, et al.)

1978-1984

Stand-Replacing Disturbance and Forest Land Ownership in Coastal Oregon, 1972-2004

(Cohen, Healey, et al.)

1985-1988

Stand-Replacing Disturbance and Forest Land Ownership in Coastal Oregon, 1972-2004

(Cohen, Healey, et al.)

1989-1991

Stand-Replacing Disturbance and Forest Land Ownership in Coastal Oregon, 1972-2004

(Cohen, Healey, et al.)

1992-1995

Stand-Replacing Disturbance and Forest Land Ownership in Coastal Oregon, 1972-2004

(Cohen, Healey, et al.)

1996-2000

Stand-Replacing Disturbance and Forest Land Ownership in Coastal Oregon, 1972-2004

(Cohen, Healey, et al.)

2000-2002

Stand-Replacing Disturbance and Forest Land Ownership in Coastal Oregon, 1972-2004

(Cohen, Healey, et al.)

2002-2004

Age Class Distributions in Coastal Oregon

(various CLAMS analyses, Spies et al. 2007)Early seral

Historical Range of Variability (HRV) of Dead Wood in Early-Seral Forest in Coastal Oregon

Dead wood biomass level

% o

f are

a

(Nonaka et al., in press)

Change in Forest Cover Type, 1936-1996

+ 21% open/young+ 101% hardwood+ 87% small conifer - 58% large conifer

(Wimberly and Ohmann 2004)

1936 1996

Current Landscape Distributions of Early-Seral Forest

A Continuum of Ownership and Forest Biodiversity Policies

Owner/allocation

Policies GoalsManagement

strategies

Federal wilderness

LegislativeNatural

conditions and other values

Consistent with wilderness character

Federal reserves

Policy, administrative

(NWFP, Roadless)

Late-successional forest, T&E

species, aquatic, timber

Reserves, Adaptive Management Areas

Federal general forest

Management Plans

MultipleMany, green-tree

retention

State of Oregon

State Forest Plans

Healthy forests, native species,

timber, T&E species

Structure-based management,

Habitat Conservation Plans

Nonindustrial private Forest

PracticesAct (FPA)

Multiple FPA: tree retention in clearcuts, streamside

protection rulesForest

industryTimber

production

Economic

goals

Ecologicalgoals

Pri

vate

Pu

blic

37%

21%

10%

14%

18%

7%

17%

1%

12%

23%

40%

Forest Land Ownership

Coast (5.7 mill. acres)

Cascades (3.4 mill. acres)Oregon

(GIS data from Oreg. Dept. of Forestry)

Forest Structural Conditions

Young

Old

• Coast: linked to disturbance history and ownership

• Cascades: confounding of environment, disturbance, ownership

(Ohmann and Gregory, unpublished)

0

500

1000

1500

2000

Sparse<10%

Open10-40%

Sap/pole(1-10")

Sm/md(10-20")

Large(20-30")

Giant>30")

Forest industry

Nonindustrial private

State

Federal general forest

Federal reserved

Federal wilderness

Coast

0

200

400

600

800

1000

1200

Sparse<10%

Open10-40%

Sap/pole(1-10")

Sm/md(10-20")

Large(20-30")

Giant>30")

Cascades

Forest Distribution by Structural Condition and Ownership, 1996

(10%)

(26%)

(6%)(11%)

(38%)

(10%)

(6%)

(15%)

(6%)

(16%)

(32%)

(25%)

10

00

acr

es

10

00

acr

es

(Ohmann and Gregory, unpublished)

Coastal Oregon: Dominant Ecological Gradients

Sitka spruce (1.0 mill.ac.)

Westside lowland conifer-hardwood

(3.1 mill.ac.)

Foothill hardwoods (42,000 ac)

Montane conifer (0.1 mill.ac.)

Westside lowland dry/mixed evergreen (1.0 mill.ac.)

Forest Vegetation Types

(Ohmann et al. 2007)

Central Oregon Cascades: Dominant Ecological Gradients

Ponderosa pine (0.7 mill.ac.)

Montane conifer (0.7

mill.ac.)

Juniper (0.2 mill.ac.)

Foothill hardwoods(11,000 ac)

W. lowland con-hdw (1.3

mill.ac.)

Lodgepole pine(0.4 mill.ac.)

Forest Vegetatio

n Types

White fir (0.1 mill.ac.)

(Ohmann and Gregory, unpublished)

Forest Types and Structural Conditions

(Ohmann and Gregory, unpublished)

0

500

1,000

1,500

2,000

2,500

3,000

3,500

Sitka spruce

W.lowland conifer-

hardwood

Montaneconifer

W.lowland dry /mixed-evergreen

Foothillhardwoods

Giant>30")

Large(20-30")

Sm/md(10-20")

Sap/pole(1-10")

Open10-40%

Sparse<10%

10

00

acr

es

(22%)

(16%)

(7%)

(17%)

(43%)

Coast

0

200

400

600

800

1,000

1,200

1,400

Foothill hardwoods

W.lowlandconifer-

hardwood

Montane conifer

White/grand fir

Ponderosa pine

Lodgepole pine

Juniper

10

00

acr

es

(9%)

(10%)

(7%)

(22%) (93%)

(60%)

(61%)

% early seral (sparse+open)

Cascades

• % in early seral varies widely

• Most early seral is westside lowland

• eastside types predominantly open-canopy

• little high-elevation forest is early seral

Natural legaciesafter wildfire

Lack oflegacies under intensive management

Forest management w/ legacy retention

Remnant (Legacy) Trees

Totaldeadwood

(m3/ha)

Coastal Oregon: Legacy Trees in Young Forest,* 1996

0

20

40

60

80

100

Forest Service

BLM State Nonindustrial private

Forest industry

m3/

ha

Downwood

* Stand <40% cover and tree >50 cm, or stand >40% cover and <50 cm QMD

and tree > (QMD+50 cm).

0

0.5

1

1.5

2

Forest Service

BLM State Nonindustrial private

Forest industry

Tree

s/ha

live trees

snags

(Ohmann et al. 2007)

% hardwoodbasal area

0

40

80

120

160

Federal wilderness

Federal reserved

Federal general forest

State Nonindustrialprivate

Forest industry

1000 a

cres

Coast

Cascades

Area of hardwood forest(>65% of basal area)

Hardwoods• Associated with:

– Particular habitats (maritime climate, riparian, foothills)

– Disturbance (legacy, early seral)

• Reduced by intensive forest management favoring conifers

• More abundant in coastal Oregon, on private lands

(Ohmann and Gregory, unpublished)

Future (Projected) Landscape Conditions

CLAMS Projected 100-Year Change Under Current Policies

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

1996 2046 2096

Mil

lio

n a

cre

s

Open Forest Remnants

Broadleaf Small Mixed/ Conifer

Medium Mixed/ Conifer Large Mixed/ Conifer

Very Large Mixed/ Conifer

1996(GNN)

2096 projected(base policy)

Northern Spotted Owl (H9) HCI > 36--All Owners

200,000

250,000

300,000

350,000

400,000

450,000

500,000

550,000

600,000

650,000

Initial Year 25 Year 50 Year 75 Year 100

Time

Hec

tare

s

Western Bluebird (H6) HCI > 29

0

50,000

100,000

150,000

200,000

250,000

300,000

Initial Year 25 Year 50 Year 75 Year 100

Time

Hec

tare

s

Current policy

Green tree retention

No Fed thinning

Potential Effects of Alternative Scenarios on Bird Habitat in coastal Oregon

Northern Spotted Owl

Western Bluebird

Pileated Woodpecker (H7) HCI > 32

0

200

400

600

800

1,000

1,200

1,400

Initial Year 25 Year 50 Year 75 Year 100

Time

Tho

usan

d he

ctar

es

Pileated Woodpecker

(CLAMS, Spies et al. 2007)

Summary: landscape trends in early-seral forest

• No shortage of early seral:

– more than HRV, more than 1936

• But character has changed:

– Linked to ownership and management practices rather than natural disturbance

– Fewer legacy trees of lesser quality, shorter residence time, changes in patch size and shape

• Diverse young forest will continue to decline under current policies

Implications for Landscape and Conservation Planning

• Requires perspective that is multi-ownership, beyond reserves:

– Much of early seral is on nonfederal lands

– Reserves not designed for active management for early seral

– Reserves not distributed across regional gradients

• Forest policies:

– Small shifts can make a difference (e.g., leave trees)

– Much can be done (beyond regulation) to influence private landowner behavior

• New tools and data available for landscape analysis can inform management and policy decisions as never before