Disturbance, Spatial Heterogeneity and Ecosystem Function:

Effects of Fire in Yellowstone National Park

Monica G. TurnerUniversity of Wisconsin-Madison

Funded byNSF, USDA,

National Geographic The Andrew W. Mellon Foundation

• Established in 1872 as the world’s first national park

• Considered the “crown jewel” of the US park system

• Encompasses ~900,000 ha

Yellowstone National Park

Yellowstone is well known for its many unique natural features…

… and diversity and abundance of native wildlife populations.

The 1988 Yellowstone Fires

Burned under conditions of severe drought and high winds

Affected ~40% of the park

Burned in all ages of forest

Stopped by snow in mid September

Fire History in Yellowstone(Romme 1982; Romme and Knight 1982; Romme and

Despain 1989)

• Extensive subalpine forests dominated by lodgepole pine (Pinus contorta)

• Stand-replacing fires have occurred at 100 to 500 yr intervals throughout the Holocene

ContextHeterogeneity and Disturbance

• Disturbance–key source of spatial and temporal heterogeneity in many ecosystems

• Large, severe, infrequent disturbances not well understood

• Many temperate and boreal coniferous forests characterized by infrequent, severe, stand-replacing fire

Outline

What are the causes and consequences

of postfire heterogeneity?

1. Burn severity2. Postfire succession3. Ecosystem function

1. Burn Severity

Postfire Mosaic of Burn Severity

• Fire spread largely determined by weather

• Burned through stands of all age

• Prefire heterogeneity had some but little influence

• Historic fire suppression (1945-1972) had little effect

Postfire Mosaic of Burn Severity–Summary

• The 1988 fires produced a spatially complex landscape with patches of varying size, shape and severity.– Of the area affected by crown fire

• > 50% was within 50 m of a green edge• > 75% was within 200 m of a green edge

• ~16,000 ha > 200 m of green edge

• How does this landscape mosaic influence postfire vegetation?

2. Postfire Succession

Oct. 1988

Same area,July 1989

Postfire Succession

• Tested hypotheses about effects of the burn mosaic (variation in patch size and fire severity) and environmental variation

• Nine crown-fire patches of varying size studied since 1990, >700 permanent plots– Small (1 ha)– Moderate (75-200 ha)– Large (500-2700 ha)

Postfire Succession

Burn severity, patch size, geographic location affected early succession(Turner et al. 1997, Ecol. Monogr., Turner et al. 2003, Frontiers)

Species Richness, 1991-2000

Herbs, graminoids and shrubs resprouted in 1989 and flowered profusely in 1990. Seedling recruitment peaked in 1991.

Less flowering and few seedlings observed since 1992.

Surprise: Survivors dominated.

Surprise: Wide variation in postfire stand structure and serotiny

Pine sapling density,1990-2000

Burn severityHigher postfire pine densities in severe surface burn than in similar areas of crown fire.

Why?

SerotinyHigher postfire pine densities in areas of high prefire serotiny.

Lodgepole Pine Density and Serotiny

Site Prefire serotiny (% of stand)

1993 Pine Density

Cougar Creek 65% 21.1/m2

(211,000/ha)

Fern Cascades 10% 0.23/m2

(2,300/ha)

Yellowstone Lake

<1% 0.06/m2

(600/ha)

What explains variation in serotiny?(Schoennagel et al. 2003, Ecology)

• Serotiny is generally low at high elevations– Fire interval ~300 yrs

• Serotiny varies with stand age at low elevations– Fire interval ~180 yrs– Young trees (< 70 yr) have low probability of being serotinous

Spatial Variation in Serotiny

How spatially variable is postfire lodgepole pine density across the entire burned landscape, and what explains that variation?

90 plots (0.25 ha) sampled in 1999 for stand structure and function

Variation in Pine Sapling Density

>50,000 stems/ha

1,000 stems/ha

0 stems/ha

•1999 densities spanned 6 orders of magnitude!

•Range: 0 – 535,000/ha•Mean: 29,380/ha•Median; 3,100/ha

(n = 90 0.25-ha plots)

(Turner et al. 2004, Ecosystems)

Explaining the Variation

• ANOVA: 36% of variability in PICO sapling density explained by elevation (r = -0.61) and distance to unburned forest– Understanding controls on serotiny (strongly

correlated with elevation) is critical link for predicting postfire pine density

PICO Density vs. Elevation

y = -0.0043x + 13.663

R2 = 0.332

0.00

1.00

2.00

3.00

4.00

5.00

6.00

2000 2100 2200 2300 2400 2500 2600 2700

Elevation (m)

• 1:30,000 color infrared aerial photographs obtained in August 1998

• Photos scanned, georectified to produce orthophotos, and classified

• Best map– Supervised classification using the 90 pts,

similar to procedure used with satellite imagery (Kashian et al. 2004, CJFR)

– Pine sapling density mapped with 76% accuracy using 5 density classes.

Mapping Pine Sapling Density

Densities are < 5,000/ha over ~55% of the burned landscape, but 20% of the landscape has densities > 20,000/ha.

Density of mature stands (60 - 90 years old)

11,000 stems/ha

3,000 stems/ha

1,100 stems/ha

What happens over time?

Stand age class

100

0

20

40

60

80

50-100 125-175 200-250 300-350

Co

eff

icie

nt

of

Va

riat

ion

(%

) aa

bb

0

De

nsi

ty (

tre

es/h

a)

5000

2000

3000

4000

1000

(Kashian et al. In press, Ecosystems)

0

5

10

15

20

25

30

35

40

45

50

0 2 4 6 8 10

Self-thinning stand

Age: 126 yearsDensity: 5,400 stems/haSerotiny: 53.3%

(Kashian et al. In press Ecology)

Filling-in stand

Age: 125 yearsDensity: 1,020 stems/haSerotiny: 0%

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

0.0 2.0 4.0 6.0 8.0 10.0(Kashian et al. In press Ecology)

Long-term Spatial Heterogeneity

• Spatial heterogeneity diminishes through successional time– Highest variance in stand structure (and

growth rates) in younger age classes– Stand density (and growth rates) converged

by 200 years

• Self-thinning and infilling both contributed to convergence

(Kashian et al. In press Ecology and In ptess Ecosystems)

Postfire Succession–Summary

• Fire severity and patch size influenced plant cover and species richness

• Postfire landscape is a very heterogeneous mosaic of widely variable stand densities – Serotiny, fire severity especially important

• The legacy of the spatial variation in stand density created by the 1988 fires may persist for 200 years.

3. Ecosystem Processes

• How does the spatial variation in postfire vegetation influence ecosystem function?– Aboveground net primary production (ANPP)

and leaf area index (LAI)• Important indicators of carbon dynamics

– Nitrogen (N) cycling• System reported to be N limited

A Broader Context

• Understanding patterns, causes and consequences of spatial heterogeneity in ecosystem processes–a frontier in both ecosystem and landscape ecology– No spatially explicit theory of ecosystem

function & few empirical studies– Ecosystem ecology usually focuses on mean

rates and change through time– Landscape ecology focuses on spatial

variability but not of process rates

ANPP and LAI

• Developed our own allometric relationships for lodgepole pine and dominant herbaceous species and applied them to vegetation measurements from the 90 0.25-ha plots

• 1999 ANPP (11-yr old stands)• Lodgepole pine: 0 to 14.5 Mg/ha/yr (mean = 1.8)fr

– ANPP increased with PICO density and decreased with elevation (ANOVA, r2 = 0.86, P = 0.0001)

• Total: 0.04 to 15.0 Mg/ha/yr (mean = 2.9)– Total ANPP increased with PICO density, also influenced by

elevation and soil classes (ANOVA, r2 = 0.80)

ANPP and LAI

• Pine, herbaceous and total ANPP and LAI influenced by lodgepole pine sapling density– Tree and total positively related to PICO

density– Herbaceous negatively related to PICO

density

• Explanatory power – High for tree and total ANPP and LAI– Low for herbaceous ANPP and LAI

Extrapolating to the Landscape

• If lodgepole pine tree density is known, then ANPP and LAI can be predicted reasonably well across the burned landscape– Pine density and elevation are key

predictors

ANPP is already > 2 Mg/ha/yr across 45% of the overall burned landscape, with about 15% of the landscape > 4 Mg/ha/yr.

Nitrogen (N) Cycling

• Most knowledge of postfire N cycling derived from low-severity fire or other ecosystems

(Wan et al. 2001; Smithwick et al. In press, Ecosystems)

• Spatial/temporal variability in net N mineralization rates following natural, stand-replacing wildfire not well understood

• No evidence of N loss after the 1988 fires• Fires during 2000 burned ~3,000 ha

Fire alters vegetation, productivity and nutrient status…

Questions

• Among stands (broad scale):– How does net N mineralization vary

following stand-replacing fire, and what explains this variation?

• Expected results: initial NH4 pulse followed by NO3 pulse; positive correlation between net N mineralization and herbaceous cover

Questions, cont’d

• Within stands (fine scale):– How variable is net N mineralization

within a stand, and what explains this variation?

– Is variation in net N mineralization spatially structured?

– Is the spatial structure of variability in net N mineralization coincident with that of aboveground cover?

• Expected results: Little initial spatial structure, then similar scales of spatial autocorrelation in net N mineralization rates and herbaceous cover

Methods

• Field studies initiated in 2001 in areas burned during summer 2000– Glade Fire (just S of Yellowstone)

• 1,280 ha fire in 150-yr old lodgepole pine

– Moran Fire (in Grand Tetons)• 840 ha fire in >200-yr old Engelmann spruce,

subalpine fir, and lodgepole pine

• Established ten 0.25-ha plots – Five in each fire– All in stand-replacing burns, both crown and

severe-surface fire severity

Plot Layout and Sampling Design

Within-stand Variation(4 plots)

Among-stand Variation(6 plots)

n = 20 cores/plot n = 81 cores/plotminimum separation of 2 m

2002 2003

Glade

Moran

Field Measurements

• Percent cover recorded annually 2001-04 in 0.25-m2 circular frames

(n = 20 or 81 per plot)

• Net N mineralization measured in situ using resin cores– One-yr incubations– 15-cm depth– 5-cm diameter PVC cores– Extracted within 24 hr

– Analyzed for NO3 and NH4

Data Analyses for NO3, NH4 and net N

mineralization • Broad-scale variation among stands

– Repeated measures ANOVA with mean percent cover variables for each stand (n = 10 stands)

• Fine-scale variation within stands– Repeated measures ANOVA (n = 444 cores)

• Aboveground percent cover around each core

– Semivariogram analysis• (n = 4 stands, 81 cores/stand)

• Exponential and spherical models estimated for NO3, NH4, net N mineralization, and all percent cover

• Nugget, sill and range estimated

ResultsBroad-scale Variation among

Stands

(N = 10 stands, 95% CI)

NO3 about 10x greater than in similar but older forests

NO3 explained bycharred litter (+)coarse wood (-)graminoids (-)(r2=0.82)

ResultsFine-scale Variation Within

Stands

Variation Within Stands:Repeated Measures ANOVA

(n = 444 individual cores over 2 years)

• NO3 (r2 = 0.17, P < 0.0001)– Significant variation by year and site– Current year % litter (-) and graminoids

• NH4 (r2 = 0.05, P = 0.0033)– No effect of year and site – % litter (+)

Is variation in net nitrogen mineralization spatially structured?

Semivariograms, Glade A, 2001-02

2.1 3.8 3.5

Fine-scale Variation Within Stands:

Spatial Structure (Range) for N

Stand 2001-02 2002-03

Glade A NO3 2.1 mNH4 3.8 mNet 3.5 m

None

Glade B None None

Moran A None NO3 2.1 mNH4 1.6 mNet 1.8 m

Moran B None NH4 12 m

Spatial Structure in Aboveground Cover?

Semivariograms, Glade A, Graminoids

(Note similar range but increasing sill)

4.8 4.9 5.4

Within-stand variation:Aboveground Cover (Range in

m)Glade Site

Stand 2001 2002 2003Glade A Charred litter 1.5

Mineral soil 2.6Rock 2.5

Graminoids 4.8

Charred litter 3.0

Mineral soil 1.6 Litter 1.4

Graminoids 4.9

Charred litter 1.8Mineral soil 1.7

CWD 2.1Graminoids 5.4

Lupine 1.4

Glade B

Graminoids 1.5 Lupine 1.8

Charred litter 1.9Rock 2.4

Litter 11.0Graminoids 2.8

Charred litter 3.8

Mineral soil 3.4

Graminoids 1.3Lupine 4.4Forbs 2.1

Are there scale-dependent relationships between cover and

net N mineralization?

Graminoid patches in Glade A, 2002

Exploratory Multi-scale Analysis Glade A Data Averaged by 4, 6 or 9

Cells

Multi-scale Correlations (r), Glade A2003 Cover and 2002-03 Net NO3 Availability

Analysis scale

% Graminoids(range 5.4

m)

% Charred litter

(range 1.8 m)

Individual core (0.25 m2, n=81)

-0.12 0.07

4-core mean(2m x 2m, n=9)

-0.17 0.29

6-core mean(2m x 4m, n=9)

-0.22 0.01

9-core mean(4m x 6m, n=9)

-0.43 0.11

Fine-scale Variation Within Stands

• N mineralization rates among individual cores not strongly coupled with aboveground cover (low r2 values)

• When spatial structure was present, autocorrelation was generally at short distances (< 6 m)– Scales of variation (ranges) in N and cover

were similar in magnitude and through time

Fine-scale Variation Within Stands

• Exploratory analyses suggest scale-dependent relationships between aboveground cover variables and net NO3 availability– Strongest correlations detected at the scale of

spatial structure in the predictor

• These relationships may become stronger in time

Caveats

• Mechanisms need further study– microbial communities and activity? carbon?

belowground resources?

• Organic N may also be important • Stand-replacing fires create significant

spatial heterogeneity at multiple scales– It may be necessary to account for scale-

dependent relationships to explain fine-scale variation in ecosystem process rates

Ecosystem Function-Summary

• Striking spatial variation in postfire landscape structure, ANPP and LAI– ANPP (and LAI) controlled primarily by postfire pine

sapling density, with secondary effects of elevation and soils

– RECALL! Pine sapling density is a contingent response determined by prefire levels of serotiny and by fire severity and size

• System appears to conserve nitrogen…stay tuned!

• Spatial variability in ecosystem function across landscapes is not well understood; our studies contribute to this growing knowledge.

Causes and Consequences of Postfire Heterogeneity

• Burn severity: fire spread and pattern controlled primarily by weather and produced complex mosaic of burn severities

• Succession: Fires produce complex mosaic of burn severity and patterns of succession, even in these “relatively simple” systems.– Rapid recovery of community composition and structure – Detectable effects of the postfire mosaic on succession– Legacy in stand structure may persist for 175-200 years

Causes and Consequences of Postfire Heterogeneity

• Ecosystem process: The spatial variability in structure in turn influences– perhaps even dominates–ecosystem function across the landscape.– Spatial variability after fire is of similar magnitude to

variability through successional time– Evidence for functional legacy up to 200 yrs after fire– Intriguing scale dependence bears further investigation