Allometric Model Development, Biomass Allocation Patterns, and Nitrogen Use Efficiency of Lodgepole Pine in the Greater Yellowstone Ecosystem

Rick Arcano Forest and Fire Ecology Lab Department of Botany University of Wyoming

Introduction

CLIMATE CHANGE Warmer and Drier FIRE REGIME Increased Fire Frequency and Severity FOREST STRUCTURE NPP and NEP Younger Forests, Increased Serotiny, Denser Forests Carbon Sink or Source?

• To determine if the GYE is a sink or source – Quantify forest biomass!

• Do we cut down, dig up, and weigh a subsample of forests?

500 400 300 200 100 0 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 Volume (cm 3 ) = (basal area (cm 2 ) * total height (cm)) "Volume" as a predictor of total aboveground biomass for all sites combined in the Greater Yellowstone Ecosystem

y = .0048x

.7935

• NO!!!

– Minimize Destructive Sampling • Develop Allometric Equations – Estimate biomass from easily obtained parameters

• Allometrics have already been developed for lodgepole pine – Other locations – And in the GYE • Why do we need more?

• Allometric models allow for biomass estimation in the GYE

Biomass Allocation Patterns

CLIMATE CHANGE Warmer and Drier • Biomass is estimated FIRE REGIME FOREST STRUCTURE Increased Fire Frequency and Severity Younger Forests, Increased Serotiny, Denser Forests • What Now?

– Determine Biomass Allocation Patterns • Impacted by: – Forest Structure • And Impacts: – NPP and NEP Biomass Allocation Aboveground?

Belowground?

NPP and NEP Carbon Sink or Source?

• Don’t we already know how lodgepole pine allocate their biomass in the GYE?

– Sort of… • Only for < 15 year old forests • But we know biomass allocation for mature lodgepole pine elsewhere… – True, but… • Substrate, topography, and growing season differ • Biomass allocation controlled by: – At least in part, by NUE

Nitrogen Use Efficiency (NUE)

• NPP = GPP - R A • GPP is the product of: – Resource Supply – Proportion Uptaken – efficiency of resource use • Nitrogen for this study!

• NUE = – g biomass or carbon fixed per g nitrogen uptaken – or C:N ratio in litterfall

• What do we know about NUE?

– NUE inversely related to nitrogen supply (Vitousek 1982) – Olsson found the opposite • Southeast Wyoming • Patterns of NUE are unclear!

• Little information for lodgepole pine – Studies in the intermountain west limited – Studies of forest structure effects rare

OBJECTIVES

1.) Develop accurate and applicable allometric models ?

?

?

2.) Will allometric models differ?

– By Forest Density and Age – Between Geographic Locations

3. Determine biomass allocation patterns – How does biomass allocation of lodgepole differ between forest densities and ages?

– How does biomass allocation of lodgepole compare among geographic locations?

4. What factors effect NUE of lodgepole pine?

– – Site productivity?

Forest Density and Age?

5. Interaction between NUE and biomass allocation?

Methods Outline

• Study Area • Description of Sites • Tree biomass determination – Subsampling – Aboveground Biomass – Belowground Biomass • Allometric Model Development – Statistical Analyses • Biomass Allocation – Tree Level – Stand Level – Statistical Analyses • Nitrogen Use Efficiency – Measuring NUE – Statistical Analyses

• The Caribou-Targhee NF in the GYE – Why the Targhee NF?

• Logistically prohibited in YNP • Winters are long and cold • Summers are cool and dry • Soils

Study Area

Distribution of Lodgepole Pine

Sites

Site Name Old, Sparse Young, Sparse Young, Dense Elevation (m) 2249 Stand Age (years) 165 Stand Density (trees > 5cm DBH per hectare) 674 1951 1951 64 64 725 2452 Stand basal area (m2 per hectare) 16.84 19.71 28.32 # of Trees Harvested 17 15 14 # of Trees excavated 5 5 14

• There is only one old stand

– Convergence in forest structure

• Soils (Inceptisols, Koffgo)

SITES

Tree biomass determination

• Trees harvested along three 25m transects – From a range of diameter classes • Trees had to meet criteria: – No poor tree form – No significant defect – Tree had to be w/in 15 year age range

Subsampling

• We weighed the whole tree in field – We couldn’t take it all back • Subsamples taken for dry weight determination • 1 st weighed when still wet • Dried to a constant weight at 70  C • Dry: wet ratio applied to respective component

Aboveground Biomass

• Bole – discs taken for subsamples in 3-4 bole sections • Branches and Foliage – Three 4.0 L subsamples – ¼” cut off between branch and foliage components • Smaller materials will be consumed by fire – Needle biomass and LAI determined from 10 fascicle subsample

Belowground

• All roots >10mm diameter excavated • 4 classes – Root crown – >50mm – 25-50mm – 10-25mm Lateral Roots

Lodgepole Pine Components

1.

2.

3.

4.

5.

6.

7.

8.

Total Aboveground Biomass Tree Bole Biomass Branch Biomass Foliage Biomass Needle Biomass Total Coarse Root (>10mm) Biomass Root Crown Biomass Lateral Root Biomass

Allometric Model Development

• Model form must be biologically reasonable • – Model form must fit the data All potential predictors plotted against biomass component on a simple scatter plot – Residuals plotted against the predictor • If two models fit the previous criteria, statistical parameters used to determine the best model

Linear?

(cm 3 ) All Sites: Total Aboveground Biomass (cm 3 ) All Sites: Bole Biomass

Statistical Analyses (Allometrics)

1. Regression Models Compared Statistically Among Sites (Are there differences in the equations between stand densities and ages?) • Extra Sum of Squares Analysis 2. Geographic Location Comparisons – • Paired t-tests Compare Actual Biomass from this study vs… – Estimated values » This study’s equations » Equations from seWY and BC

ALLOCATION

• Tree Level – Determined from actual measurements of biomass • From allometric model development portion of the study.

Stand Level Biomass

• Variable Radius Point Sampling – We calculated tree density and biomass • Stand Level biomass was then determined (Mg/ha)

Statistical Analyses (Allocation) • Stand Density and Age Comparison

– Individual Tree Level • One-Way ANOVA w/ Tukey’s post-hoc – Stand Level • One-Way ANOVA w/ Tukey’s post-hoc

• Geographic Location Comparison

– Direct statistical comparisons not possible • due to absence of raw data from other studies • but graphical comparisons were possible • Caveat w/ root biomass

Measuring NUE

• NUE = g C fixed / g N uptaken in litterfall – Or…C:N ratio • 8 litterfall traps at fixed intervals (50X50m plot) • Litter traps out on 1 st – Collected August 25 th week of July ’04 • Lodgepole Needles Isolated • C:N determined w/ C:H:N analyzer

Statistical Analyses (NUE) • Stand Age and Density comparisons

– One-way ANOVA w/ Tukey’s post-hoc.

• Relation to site productivity and allocation

– Correlation Analyses • Site index • below: aboveground biomass / TCR: TAB ratio • root crown: lateral root biomass ratio • Proportion of Total Tree Biomass – Bole, branches, foliage, and needles

Results and Discussion

• Trust me… I have lots of allometric models – For each of 8 lodgepole components • one model for individual stands (3 models) • 1 pooled young model • 1 pooled sparse model • 1 pooled model for all stands – 8 components * 6 models per component • = 48 allometric models

• Most equations non-linear, except the tree bole • R 2 range from 0.54 to 0.99

– But – 37/48 are above 0.80

• The Most Robust Regressions – Total aboveground, bole, TCR, lateral root • R 2  0.79 for all but one regression • Less Robust Regressions – Root crown, branches, foliage, and needles • R2 < 0.80 for 9 out of 24 models

• Interesting Patterns – More variability in biomass with larger trees • Variability during tree development may accumulate – Different trees growing under different conditions » The effects of different environmental conditions accumulate over time – Pooled models: • Better by age than by density – Less variability when pooling by age

– Best Predictors: • “Volume” for bole and aboveground • Basal Area for root biomass • Basal Area and crown length for branch biomass • Basal Area and Sapwood Area for foliage

Do models differ between density and age?

• Yes… – But with exceptions • We need different models for different forest structures • Models more different by age than by density Allometric model comparison between densities and ages of mature

P.contorta

stands in the Greater Yellowstone Ecosystem Dependent Variable Model Comparison p-value Total Aboveground Biomass Branches Total Coarse Roots (>10mm) Total Coarse Roots (>10mm) Old vs. Young Old vs. Young Old vs. Young Sparse vs. Dense Total Aboveground Biomass Root Crown Lateral Root Biomass (>10mm) Sparse vs. Dense Old vs. Young Old vs. Young Bole Root Crown Sparse vs. Dense Sparse vs. Dense Lateral Root Sparse vs. Dense Biomass * p-values are significantly different (  = 0.05) <0.0001* 0.020 – 0.037* 0.396- 0.622

ALLOMETRIC COMPARISON

• Do allometric equations differ among geographic locations?

– This Study versus Comeau and Kimmins (1989)

180 160 140 120 100 80 60 40 20 0 0 Tree Bole This Study (Actual) This Study (estimated) Comeau and Kimmins (estimated) P = 0.804

P < 0.001

5 10 15 20 diameter at breast height (cm) 20 10 0 Needles 70 60 This Study (Actual) This Study (estimated) Comeau and Kimmins (estimated) 50 40 P = 0.739

P < 0.001

30 0 5 10 15 20 diameter at breast height (cm) 25 25 30 30 60 50 40 Branches This Study (Actual) This Study (estimated) Comeau and Kimmins (estimated) P = 0.401

P = 0.037

30 20 10 0 0 5 10 15 20 diameter at breast height (cm) Total Coarse Roots 40 30 This Study (Actual) This Study (estimated) Comeau and Kimmins (estimated) P = 0.400

P < 0.001

20 25 30 10 0 0 5 10 15 20 diameter at breast height (cm) 25 30

ALLOMETRIC EQUATION COMPARISON

• Do allometric equations differ among geographic locations?

– This Study versus Pearson et al. (1984)

400 300 200 100 Tree Bole This Study (Actual) This Study (estimated) Pearson et al. (estimated) P = 0.514

P = 0.063

0 0 5 10 15 20 25 diameter at breast height (cm) 50 This study (Actual) This study (estimated) Pearson et al. (estimated) Needles 40 30 P = 0.928

P < 0.001

20 10 0 5 10 15 20 25 diameter at breast height (cm) 30 30 35 35 60 50 40 Branches This Study (Actual) This Study (estimated) Pearson et al. (estimated) P < 0.001

P = 0.005

30 20 10 0 0 5 10 15 20 25 diameter at breast height (cm) 30 35 20 10 0 0 60 50 40 30 Root Crown This study (Actual) This study (estimated) Pearson et al. (estimated) P = 0.763

P < 0.001

5 10 15 20 25 diameter at breast height (cm) 30 35

Tree Level Biomass Allocation

• Proportion of total biomass allocated to TAB – Lowest in young, dense stand – Highest in old, sparse stand • Proportion allocated to bole biomass highest of all components – No surprise • Old, sparse and young, dense stands – Bole>foliage>TCR>branches • Young, sparse stand – Bole>foliage>branches>TCR

Tree Level

• The Most structurally dissimilar stands had the most similar biomass allocation patterns – Are the stands really that different?

1.0

0.8

a a • Proportion allocated to the Tree bole – Lowest for young, sparse • Function of allocation to other components • Proportion allocated to Branches, foliage, and needles – Highest for young, sparse 0.6

0.4

0.2

0.0

b Tree Level Old, Sparse Young, Sparse Young, Dense a b a b a a b a a

Tree Bol e Br anches Fol iag e Component Ne edl es

Stand Level Biomass Allocation

• Proportion allocated to TAB – comparable among sites • Balance between bole and branches and foliage • Proportion allocated to bole biomass highest • Old, Sparse and Young, Sparse – Bole > foliage > TCR > branches • Young, Dense Stand – bole > foliage > branches > TCR

Stand Level

• Every stand different for all aboveground components 1.0

• Proportion allocated to the tree bole – Lowest for young, sparse • Function of allocation to other components 0.8

0.6

0.4

0.2

a c b • Proportion allocated to branches, foliage, and needles – Highest for young, sparse 0.0

bo le Stand Level

old, sparse young, sparse young, dense a b c a b

branc hes fo liag e Component

c a

need les

b c

Tree vs. Stand Level

Tree Level • Proportion allocated to the bole – Tree Level • Young, Dense Highest – Stand Level • Old, Sparse Highest 1.0

0.8

0.6

0.4

0.2

0.0

a b a

Tree Bol e

a b a a

Br anches Fol iag e Component

b a • Proportion allocated to branches, foliage and needles – Tree Level • Old, Sparse Highest – Stand Level • Young, Dense Highest 1.0

0.8

0.6

0.4

0.2

0.0

a

bo le

b c

Stand Level

a b c a b c

es fo liage branch Component

Old, Sparse Young, Sparse Young, Dense a b a

Ne edl es

old, sparse young, sparse young, dense a b c

needl es

Ratio of Total Coarse Root: Total Aboveground Biomass Ratio

• At the Stand Level, the ratio of TCR: TAB is 0.10 to 0.12

– Constant!

Different patterns seen at the stand level than the tree level !

Site Comparisons (1) Old, Sparse (2) Young, Sparse Old, Sparse Young, Sparse Young, Dense Young, Dense Site Comparisons (1) Old, Sparse Old, Sparse Young, Sparse (2) Young, Sparse Young, Dense Young, Dense - Tree Level - p-values total coarse root: total aboveground Root crown: lateral root 0.234 NSD

0.004

NSD 0.282 NSD - Stand Level - p-values total coarse root: total aboveground Root crown: lateral root NSD

0.010

NSD

NSD

0.001

0.719

100

Allocation Among Geographic Locations

Young, Dense Stand Old, Sparse Stand Young, Sparse Stand This Study Pearson et al. This Study Pearson et al. This Study Comeau and Kimmins 80 60 40 20 0 Tot al Abov egro und Bol e bran ches needles Tot al Abov egro und Bol e bran ches needles Component Tot al Abov egro und

Component Component

• Aboveground allocation higher Bol e bran ches needles • % Bole lower except for old, sparse stand – Lower productivity in the GYE • % Branch and needle allocation higher for young stands – Compensation for lower productivity

Nitrogen Use Efficiency

• Stand age vs. Stand Density – Older Stand had the highest NUE – Age clearly impacts NUE more 160 140 120 100 80 60 40 20 0 a Nitrogen Use Efficiency b b Old, Sparse Young, Sparse

STAND

Young, Dense

Correlation analyses between NUE, site index, and biomass allocation.

SI Total Coarse Root: Total aboveground Root crown: lateral root Bole Branches Foliage Needles

r

-0.94 -0.99 0.97 0.71 -0.66 -0.75 -0.80 p 0.230 0.082 0.155 0.497 0.593 0.463 0.409 • NUE – Appears to be strong relationships, despite high p values (Only 3 data points per correlation) – (-) correlated w/ • SI, TCR: TAB, branches, foliage, and needles – (+) correlated w/ • RC: Lateral Root and tree bole

Summary and Conclusions (Allometrics)

• Allometric models are of high quality – R 2 were often above 0.80

• Allometric models are different – Between Stand Densities and Ages – Estimates of biomass from equations in the GYE were better than estimates from other locales.

What does all this stuff mean?

Summary and Conclusions (Allocation)

• Coarse Root Allocation relatively constant across forest structures – Coarse Root: total aboveground biomass is 0.10 to 0.12. • Is this true for all forests and tree species?

• Allocation within above and belowground biomass is much more variable – The ratio of root crown: lateral roots was affected by stand density and age – Allocation to the bole, branches, foliage, and needles was affected by stand density and age.

• Different allocation patterns at different scales – The individual tree vs. stand levels • Differences for geographic location comparisons – Bole biomass was lower for this study – Branch, Needle, and Foliage allocation was higher for this study

Summary and Conclusions (NUE) • NUE was highest in the oldest stand • NUE affected by stand age, but not density • NUE affected by site productivity, and is related to biomass allocation patterns

Summary and Conclusions (Allometrics)

• Allometric models are useful!

• Be careful where and when you use these equations!

• Biomass allocation patterns of the GYE vary with forest structure and geographic location, but some similarities can be found • Future studies of biomass allocation should be at multiple scales • NUE is affected by forest structure and site productivity • There is an interaction between NUE and biomass allocation patterns Ok, but what is the big picture?

Acknowledgments

• I would like to thank: – My committee members (Greg Brown, Dan Tinker, Elise Pendall, and Pete Stahl) – Jan and B.J.

– The Entire Botany Department and fellow graduate students – Awesome Field Assistants (Lance Farman, Lance East, Kellen Nelson, Terez Tepe, and Heather Lyons) – Our colleagues at Colorado State University (Dan Kashian, Bill Romme, and Mike Ryan) – Statistical Advice from Rudy King and David Legg – USDA Joint Fire Sciences – Plummer Scholarships for the Environment and Natural Resources Sciences – UW-NPS Research Center