The patterns and consequences of post-fire successional trajectories in Alaska’s boreal

forest

The “Generators”

- Fire severity

- Abiotic and biotic site characteristics

The consequences for

-Nitrogen cycling, carbon storage and other key ecosystem processes

-Future landscape and vegetation patterns (including biome shifts)

• 90 sites established in 2004 burns along Dalton, Taylor, and Steese Highways

• 32 intensive study sites– arranged across combinations of high-low site moisture &

high-low burn severity• 7 treeline sites• Detailed pre-fire stand data available for 14 sites• Reconstruction of pre-fire conditions at remaining sites

JFSP Study design

Dry

Wet

Low High

JFSP Study design

N

5 15 2510 20 300

V V V V V V V

Belt transect for stand structur e and tr ee cookies

V V V V V V V

S S S S S S S

S S S S S S S

O O O O O O O O OO

O O O O O O O O OO

Set aside for potential destructive sampling

N N N N N N

N N N N N N

N = Mineralization

S = SeedlingsV =Vegetation plots

O = Organic depthsSAMPLING SCHEMEFOR EACH PLOT

X X

X

XX

X =Stakes= Areas sensitive to disturbance

Belt transect for stand structur e and tr ee cookies

What drives natural post-fire seedling recruiment?

Differential sensitivity of functional groups to site characteristics and fire regime

-Spruce recruitment (as measured by post-fire seedling density) is most influenced by elevation, pre-fire spruce density, and site moisture. Fire severity (CBI) and stand age had weaker effects

-50 % of Deciduous recruitment can be explained by fire severity; also important were elevation, latitude, moisture, and distance to nearest unburned deciduous stand.

- The ratio of spruce/deciduous recruitment is driven by the relationship between deciduous and fire severity.

Important role of fire severity in “tipping” the balance between coniferous and deciduous dominance

Johnstone, Hollingsworth, Chapin, and Mack (in prep) Global Change Biology

What drives post-fire vegetation composition?

Bernhardt, Hollingsworth, Chapin, and Viereck (in prep) Journal of Vegetation Science

- Energy balance

NECB?

↓ N avail.

↓ productivity

↑[CO2]

↑ T

↑ Fire

Frequency, intensity, and area

↑[CO2]

↑ T

↑ Fire

NECB+

↓ N avail.

↓ productivity

Frequency, intensity, and area

FireN availability

• N pool size• N turnover time• Environment for decomposition• Plant species composition

– Pool size– TT– Environment– Uptake and use

Net loss of N from forest floor/organic soil =

Pre-fire forest floor N pool - Remaining N pool

[+ Ash from plants and upper layers]

[- Leaching, erosion, gaseous loss]

Calculating soil N loss

Mean offset between adv.

roots and moss across 30 unburned sites: 3.2 ±

0.3 cm

Adventitious root

Adventitious root collar to burned soil = depth of organic matter combusted

Residual organic soil depth, bulk density and [N]

N pool in missing layers = root collar depth x empirical relationships derived from unburned stands

Dry WetMoisture class

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.11

0.12

N loss (

kg

N m

-2)

• Mean =80 ± 4 g·N m-2

• 1-94 % of pre-fire organic layer N pool

Soil organic layer N loss across sites

HighLow

Burn severity

Moisture: F=0.69, P=0.41Severity:F=4.62. P=0.04M x S: F=0.19, P=0.67

• N inputs are low•Alder fixation max.•Lichen/moss norm. (<0.1 g·N m-2 yr-1)

• Mean stand age: 94 ± 5.4 yrs

• Mining N? Alder inputs? Occult N?

*

www.gina.alaska.edu

Comparing the boreal forest to the Arctic

Anaktuvuk River Fire 2007

An opportunity for synthesisbetween a fire-naïve versus a fire-experienced biome

Research Questions

• How much carbon and nitrogen was lost during the Anaktuvuk River Fire?

• Do our known relationships between seedling regeneration, vegetation composition, and site characteristics hold true for tundra fires?

Estimating pre-fire soil organic matter pools

Meristem to Mineral Soil Depth (cm)

10 15 20 25 30 35

Org

anic

Lay

er D

epth

(cm

)

5

10

15

20

25

30

Meristem to mineral soil depth (cm)

Org

anic

layer

depth

(cm

) R2=0.94, P=<0.01

Pre-fire pools and fire-driven losses of nitrogen from tundra and

taigaPools MATBlack spruce

Pre-fire O layer Mean: 280 200(g N m-2) Range: 206 to 578 50 to 429

N: 20 90

O layer N loss 33 103(g N m-2) 7to 209 30 to 180

% O layer N loss 10.4 502 to 36 1 to 94

0.0 100 200 300 400 500

Dry, highDry, lowWet, highWet, low

Pre-fire N pool (g N m-2)

N loss versus pre-fire N pool

High-Dry R2=0.61, P<0.02No relationship for other categories

All sites R2=0.86, P<0.001

Black spruce taigaMoist Acidic Tundra

0 100 200 300 400 500 600X

0

50

100

150

200

250

NLOST

N loss

(kg

N m

-2)

• 9 transects across the Anaktuvuk River fire sampled• 10 points along each transect sampled and combined

for a) mineral soil and b) organic soils• Germinated alder and spruce seedlings• Autoclaved Tanana River silt as growth substrate• Innoculated with organic or mineral soil from 9 sites (18

treatments)• Minimal fertilizer (want them to survive but not thrive)

Plant methods

Preliminary results

00.51

1.52

2.53

3.54

4.5

1 2

gro

wth

(m

m)

control

low

high

Spruce

Alder

0

1

2

3

4

5

6

1 2

grow

th (m

m) control

organic

mineral

Spruce Alder

aa

b

Conclusions• In taiga, sites with more accumulated N lost a

smaller % of total N, while sites with more N lost a larger %– Fire driven N loss reinforces landscape patterns

of N accumulation• In arctic tundra, sites with more accumulated N lost

more N – Fire smoothes landscape heterogeneity in N

accumulation• In arctic tundra, effects of mycorrhizal innoculum

on plant height growth is related to soil horizon, not fire severity