draft - university of toronto t-space · 145 (93%; table 1). the goodness-of-fit test indicated...

Draft

Comparing lodgepole pine growth and disease occurrence at

six Long-Term Soil Productivity (LTSP) sites in British

Columbia, Canada

Journal: Canadian Journal of Forest Research

Manuscript ID cjfr-2015-0441.R2

Manuscript Type: Note

Date Submitted by the Author: 27-Dec-2015

Complete List of Authors: Reid, Anya; University of British Columbia, Forest and Conservation

Sciences Chapman, William; BC Ministry of Forests Prescott, Cindy; The University of British Columbia

Keyword: tree disease, lodgepole pine, forest productivity, tree growth, timber supply

https://mc06.manuscriptcentral.com/cjfr-pubs

Canadian Journal of Forest Research

Draft

1

Comparing lodgepole pine growth and disease occurrence at six Long-Term Soil 1

Productivity (LTSP) sites in British Columbia, Canada 2

3

Anya M. Reid1*, William K. Chapman2 and Cindy E. Prescott1 4

5

1University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada. 6

2 BC Ministry of Forests, Lands and Natural Resource Operations, 200-640 Borland 7

Street Williams Lake, BC, V2G 4T1, Canada. 8

*Corresponding author: [email protected] 9

Abstract 10

• Recently, the assumption that stands with fast growth will have minor losses to 11

insect and disease attack has been challenged. 12

• Although tree growth and health are both critical for long-term forest productivity, 13

standardized forest-health data is rarely collected in conjunction with tree-growth 14

data. 15

• Using six Long-Term Soil Productivity (LTSP) installations in British Columbia, 16

Canada, we explore the relationships between lodgepole pine (Pinus contorta Dougl. 17

ex Loud.) growth and disease occurrence. 18

• Treatment plots and random groups of 100 trees that had larger trees generally had 19

more disease. 20

• These findings suggest we can no longer assume that fast growing plantations will 21

be free of disease, which has implications for predicting future timber supply. 22

Page 1 of 20



Draft

2

23

Keywords: tree disease, lodgepole pine, forest productivity, tree growth, timber supply 24

25

Introduction 26

Tree growth is the main metric used to monitor forest productivity and predict future 27

timber supply. This approach, and the absence of disease occurrence data collected in 28

conjunction with tree-growth data, is based on the assumption that fast-growing stands 29

rarely have significant insect or disease damage (Skovsgaard and Vanclay 2008; 30

Puettmann et al. 2009). This assumption has been challenged by recent findings 31

suggesting current predictive models overestimate wood production because of higher 32

than expected insect and disease damage (Woods and Coates 2013). Contrary to 33

predictions, damage was sustained in dominant and co-dominant trees rather than 34

supressed trees (Woods and Coates 2013). Although both stand growth and disease 35

occurrence profoundly influence long-term forest productivity, surprisingly few studies 36

simultaneously monitor these metrics or compare them on the same plots. 37

38

There are conflicting theories regarding the relationship between plant growth and 39

health. On one hand it has been suggested that vigorously growing trees are able to ‘out-40

grow’ mild insect or disease attack, resulting in fast-growing trees also being healthier 41

(Andersson et al. 2000). Conversely, the ‘dilemma of plants’ theory (Herms and Mattson 42

1992) predicts that, under similar environmental conditions, faster-growing trees will 43

have less resources invested in chemical and anatomical defence mechanisms and 44

Page 2 of 20



Draft

3

therefore will be more susceptible to insect or disease attack. Related to this theory is 45

the phenomenon that nutrient limitations that reduce growth also increase plant’s 46

defense mechanisms, both chemical (Donaldson et al. 2006, Osier and Lindroth 2006, 47

Sampedro et al. 2011) and anatomical (Moreira et al. 2008). Simultaneous monitoring 48

of tree growth and health in well-designed field trials would better enable us to 49

disentangle the relationships between these critical variables. 50

51

Existing long-term experiments monitoring forest productivity are a significant 52

resource for testing the relationship between tree growth and disease occurrence. The 53

Long-Term Soil Productivity (LTSP) project monitors the effect of soil compaction and 54

organic-matter removal on stand productivity across North America (Powers 2006). 55

Stand productivity is monitored using measures of tree growth and nutrient status 56

(Powers 2006). Treatment effects on stand health (vigour, mortality and disease 57

occurrence) were recently tested at six LTSP sites in British Columbia, Canada (Reid et 58

al. 2015). In this paper, we examine the relationships between tree growth and disease 59

occurrence in these 15- to 20-year-old lodgepole pine plantations. 60

Methods 61

Study sites 62

This study was conducted on six LTSP sites in the interior of British Columbia. Three 63

sites were in the Interior Douglas Fir (IDF) biogeoclimatic zone (Black Pines, Dairy 64

Creek and O’Connor Lake) and three in the Sub Boreal Spruce (SBS) zone (Log Lake, 65

Skulow Lake and Topley). Each site contains nine treatment plots that are 40 m by 70 m 66

Page 3 of 20



Draft

4

(0.28 ha) in size, which constitute a full factorial combination of three levels of organic-67

matter removal and three levels of soil compaction (Berch et al. 2010). These plots are 68

the experimental units. Organic-matter removal treatments consist of bole-only 69

harvesting (OM1), whole-tree harvesting (OM2), and whole-tree harvesting plus forest-70

floor removal (OM3). Soil compaction treatments consist of no soil compaction (C0), 71

light soil compaction of 2 cm surface elevation depression (C1), and heavy soil 72

compaction of 4 cm depression (C2). For more detailed treatment descriptions see 73

Holcomb (1996) and Berch et al. (2010). One hundred containerized lodgepole pine 74

(Pinus contorta Dougl. ex Loud.) seedlings were planted with 2.5-metre spacing in each 75

treatment plot around 1995 in the SBS and 2000 in the IDF. These one hundred trees 76

were the focus of this study. Age differences among sites were accounted for in the 77

analysis by all tests being conducted within each site separately. 78

79

Tree growth 80

Lodgpole pine growth was measured by height (m), height increment (m/5yrs), average 81

tree volume (cm3), total tree volume (cm3) and average tree volume increment 82

(cm3/5yrs). Tree height was measured with a pole every five years and height 83

increment (m/5yrs) calculated by subtracting the two most recent height 84

measurements. Individual tree volume was calculated as, 85

86

volume = EXP(-2.15873+1.86671*LN(Dia)+1.18486*LN(Ht)+1.49899*(h/Ht)), 87

88

Page 4 of 20



Draft

5

where Dia is tree diameter (cm), Ht is tree height (cm) and h is the height (cm) 89

above ground level at which diameter was measured (Kovats 1977). Here we used 90

diameter at breast height measurements so h = 130 cm above ground level. Average 91

tree volume (cm3) is the plot average of each trees volume. Total tree volume (cm3) is 92

the sum of all the tree volumes per plot. Volume increment (cm3/5yrs) was calculated 93

by subtracting two most recent average volume measurements. Trees that died during 94

the 5-year period (resulting in negative increment values) were deleted from the 95

analysis. 96

97

Disease occurrence and tree mortality 98

Forest health was surveyed in the summer of 2013 when trees at the IDF and SBS sites 99

were 15 and 20 years old, respectively. From these data, five stand-health metrics were 100

calculated: the number of dead or dying trees (mortality) out of the 100 original planted 101

trees, the percent of living trees with high or medium foliar disease, the percent of living 102

trees with galls from western gall rust (Endocronartium harknessii), the percent of living 103

trees with root disease symptoms, and the sum of trees with foliar disease, western gall 104

rust and root disease symptoms occurrence per plot (hereafter total disease) that could 105

be greater than 100 (Reid et al. 2015). Foliar disease included Lophodermella concolor 106

and Elytroderma deformans with severity being measured as the percent of foliar 107

affected (Reid et al. 2015). The most common root disease in this region is caused by 108

Armillaria solidipes (previously ostoyae) (Morrison et al. 1991), and its presence has 109

been confirmed at Skulow Lake (unpublished data). 110

111

Page 5 of 20



Draft

6

Data analysis 112

Relationships between measures of tree growth and disease occurrence were tested 113

using simple linear least-squares regressions. As the sites differed in environmental 114

conditions and tree age, tree growth and disease occurrence were compared within 115

each site separately. Relationships were tested using treatment-plot averages as well as 116

random groupings of trees within each site (n = 9). Trees were assigned to random 117

groups using a random-number generator. Tree growth and disease occurrence were 118

then averaged within these random groups. For both the treatment plot averages and 119

random group averages, a total of 150 relationships were tested (5 tree growth 120

measures * 5 disease measures * 6 sites). Two-sided goodness-of-fit tests were used to 121

test whether the number of significant positive relationships significantly differed from 122

the number of significant negative relationships (H0: n[+] = n[-] and HA: n[+] ≠ n[-]). 123

124

To avoid over-estimating significant relationships by using multiple co-linear tree 125

growth measures, we replicated the above-mentioned analysis using only height 126

increment (m/5yrs) and average volume (cm3). These two tree-growth measures were 127

the least related and capture both incremental and cumulative growth. For this analysis, 128

a total of 60 relationships were tested (2 tree growth measures * 5 disease measures * 6 129

sites). 130

131

Positive relationships between measures of tree growth and disease occurrence would 132

indicate that groups of trees with higher growth rates or larger volumes also had higher 133

disease occurrence. Comparing results from treatment plot averages to random group 134

averages enables us to explore the possible influence of the treatments themselves on 135

Page 6 of 20



Draft

7

this relationship. For example, significant relationships when using treatment plot 136

averages but no significant relationships when using random plot averages would 137

suggest that the treatments themselves influence both tree growth and disease 138

occurrence. Significance was considered when p < 0.05. All analyses were conducted in 139

JMP 11 (SAS 2012). 140

Results 141

Using treatment plot averages, there were 28 significant relationships between tree 142

growth and tree disease occurrence (Table 1). Therefore, 19% of the relationships were 143

significant (28 out of 150 cases). Of the 28 significant relationships, 26 were positive 144

(93%; Table 1). The goodness-of-fit test indicated significantly more positive 145

relationships than negative relationships (p < 0.0001). 146

147

The relationship between tree growth and disease occurrence independent of 148

treatments was compared using random group averages. There were 12 significant 149

relationships out of the 150 possible (8%), 11 of which were positive (92%; Table 2). 150

The goodness-of-fit test indicated significantly more positive relationships than 151

negative relationships (p = 0.003). 152

153

Using only the tree-growth measures of height increment (m/5yrs) and average volume 154

(cm3) to avoid co-linearity, the overall results were the same. Using treatment plots, 10 155

cases out of 60 were significant (17%). Significantly more of these had positive 156

relationships than negative relationships (9 out of 10, p = 0.0107, Figure 1). Using 157

Page 7 of 20



Draft

8

random groups, 6 cases out of 60 were significant (10%). Significantly more of these 158

had positive relationships than negative relationships (6 out of 6, p = 0.0156, Figure 2). 159

Discussion 160

Our finding that treatment plots and random groups with larger lodgepole pine trees 161

generally also had more disease concurs with Woods and Coates (2013) finding of 162

substantial insect and disease occurrence in mid-aged plantations in BC, particularly 163

targeting larger trees. Some of these relationships appear to be driven by a few high 164

values for disease occurrence (Fig. 1A, C, D, E, and F). The results would be more robust 165

if the distribution of disease occurrence had been more even among plots. Nevertheless, 166

it is striking to observe that the few plots with high disease occurrence also had the 167

highest growth rates and cumulative growth. This situation may apply to large areas of 168

forestland in British Columbia, as lodgepole-pine seedlings have been planted on 3.8 169

million hectares (53% of all second-growth forests; BC Ministry of Forests, Mines and 170

Lands 2010; Weaver 2013), and the levels of disease occurrence at these sites is 171

comparable to previous studies (Reid et al. 2015). Long-term monitoring of both health 172

and growth is warranted, with no omission of trees, plots or sites where damaging 173

agents are present. 174

175

It is unlikely that disease occurrence increased tree growth; therefore, faster-growing 176

trees must have become more diseased. It could be that faster-growing trees have 177

greater likelihood of encountering disease. This could be the situation for root disease, 178

where a larger root system would come into contact with more disease inoculum, but is 179

Page 8 of 20



Draft

9

a less satisfying explanation for positive relationships with foliar disease severity, 180

which was based on the percentage of foliage affected. 181

182

The reduced number of positive relationships when using random groups compared to 183

treatment plots suggests that the treatments themselves may influence the relationship 184

between tree growth and disease occurrence. The organic-matter removal and soil 185

compaction treatments could directly influence disease populations, habitat and 186

substrate and indirectly influence disease through effects on disease antagonists and 187

host tree susceptibility. For example, disease inoculum could be reduced by whole-tree 188

harvest but increased by forest-floor removal (Oblinger et al. 2011). Another example 189

involving organic-matter removal is that changes to the amount of woody debris could 190

alter the abundance of saprotrophic fungi, such as Hypholoma, which are antagonistic to 191

Armillaria root disease (Chapman et al. 2004). Soil compaction has been linked to 192

disease occurrence with the damaging fungal pathogen Phytophthora being highest on 193

seedlings in wet compacted soils (Rhoades et al. 2003). 194

195

Another possible mechanism for the observed positive relationship between tree 196

growth and disease occurrence could be that higher mortality caused by disease would 197

thin the stands, allowing them to grow faster. This, however, was not the case at our 198

sites because plots with higher disease have not yet experienced higher rates of overall 199

mortality. The only relationship that was significant was at O’Connor Lake where plots 200

with more root disease had less overall mortality (R2 = 0.67, p = 0.0071). 201

202

Page 9 of 20



Draft

10

It could also be that in faster-growing trees, more resources are allocated to growth 203

rather than defence against disease. Within all plants there is a continually changing 204

allocation of resources to vital processes of growth, defence, reproduction, storage and 205

maintenance (Herms and Mattson 1992). Resource allocation varies according to plant 206

species, developmental stage, environmental conditions, competition, occurrence of 207

damaging agents and resource limitation (Herms and Mattson 1992; Boege and Marquis 208

2006; Boege et al. 2007). Nutritional differences, in particular, may be the most likely 209

variable that differed between groups of trees with higher growth, which could 210

influence allocation of resources to growth and defence (Figure 3). In all plants, 211

photosynthesis can continue even when growth is constrained by nutrient availability 212

(Herms and Mattson 1992); the ‘extra’ photosynthates produced under conditions of 213

nutrient-limited growth are used in the production of secondary metabolites such as 214

monoterpenes and phenols (Chishaki and Horiguchi 1997; Wallis et al. 2008). These 215

secondary metabolites inhibit growth and spore-germination of common fungal 216

pathogens associated with pine (Wallis et al. 2008; Eckhardt et al. 2009) and can also 217

reduce damage from herbivorous insects (Bryant et al. 1987). Nutrient limitations have 218

also been found to increase the amount of chemical (Sampedro et al. 2011) and 219

anatomical defence strategies in pine (Moreira et al. 2008). Therefore, any intervention 220

to increase tree growth may simultaneously cause trees to be less defended against 221

damaging insects and disease. 222

223

Our results thus support the contention of Andersson et al. (2000) that the central aim 224

of forest ecosystem research and forest management should be the optimization of 225

both tree growth and pest resistance, because pest resistance, in part, depends on 226

Page 10 of 20



Draft

11

processes involved in tree growth. Interactions among nutrient availability, tree growth 227

and tree health warrant further examination to determine the conditions that promote 228

both growth and health, and optimize long-term forest productivity. Disease occurrence 229

was measured only once in this trial, so it is not possible to determine when the disease 230

arrived, how it progressed, or how growth changed after disease onset. This knowledge 231

gap is widespread, given the lack of standardized forest-health data collected in 232

conjunction with tree-growth data. Tree health, in addition to tree growth, should be 233

frequently monitored in silvicultural trials to examine the effects of growth-enhancing 234

silvicultural interventions on subsequent tree health and long-term stand productivity. 235

Acknowledgements 236

Funding from this project came from and NSERC CGS D3 grant to AMR and from the BC 237

Ministry of Forests, Lands, and Natural Resource Operations. This project would not 238

have been possible without the hard work and foresight of the BC Ministry of Forests, 239

Lands, and Natural Resource Operations to install and monitor these long-term 240

research sites. We thank Peter Ott for statistical advice and two anonymous reviewers 241

for greatly improving this manuscript. 242

243

Page 11 of 20



Draft

12

References 244

Andersson, F.O., Ågren, G.I., and Führer, E. 2000. Sustainable tree biomass production. 245

Forest Ecology and Management 132:51-62. 246

BC Ministry of Forests, M.a.L. 2010. The State of British Columbia’s Forests, 3rd ed. 247

Type, Institution, Victoria, BC. 248

Berch, S., Curran, M., Chapman, W.K., Dube, S., Hope, G., Kabzems, R., Kranabetter, J.M., 249

and Hannam, K.D. 2010. Long-term soil productivity study (LTSP): The effects of 250

soil compaction and organic matter retention on long-term soil productivity in 251

British Columbia. Type, Institution, Victoria. 252

Boege, K., Dirzo, R., Siemens, D., and Brown, P. 2007. Ontogenetic switches from plant 253

resistance to tolerance: minimizing costs with age? Ecology Letters 10:177-187. 254

Boege, K. and Marquis, R.J. 2006. Plant quality and predation risk mediated by plant 255

ontogeny: consequences for herbivores and plants. Oikos 115:559-572. 256

Bryant, J.P., Clausen, T.P., Reichardt, P.B., McCarthy, M.C., and Werner, R.A. 1987. Effect 257

of nitrogen fertilization upon the secondary chemistry and nutritional value of 258

quaking aspen (Populus tremuloides Michx.) leaves for the large aspen tortrix 259

(Choristoneura conflictana (Walker)). Oecologia 73:513-517. 260

Chapman, B.K., Xiao, G., and Myer, S. 2004. Early results from field trials using 261

Hypholoma fasciculare to reduce Armillaria ostoyae root disease. Canadian 262

Journal of Botany 82:962-969. 263

Chishaki, N. and Horiguchi, T. 1997. Responses of secondary metabolism in plants to 264

nutrient deficiency. Plant nutrition - for sustainable food production and 265

environment:341-345. 266

Page 12 of 20



Draft

13

Eckhardt, L.G., Menard, R.D., and Gray, E.D. 2009. Effects of oleoresins and 267

monoterpenes on in vitro growth of fungi associated with pine decline in the 268

Southern United States. Forest Pathology 39:157-167. 269

Herms, D.A. and Mattson, M.J. 1992. The dilemma of plants: to grow or defend. Quarterly 270

Review of Biology 67:283-335. 271

Holcomb, R.W. 1996. The Long-Term Soil Productivity Study in British Columbia. 272

Canadian Forest Service and B.C. Ministry of Forests. Victoria, B.C. FRDA Rep. 273

256. 274

Kovats, M. 1977. Estimating juvenille tree volumes for provenance and progeny testing. 275

Canadian Journal of Forest Research 7:335-342. 276

Moreira, X., Zas, R., Solla, A., and Sampedro, L. 2015. Differentiation of persistent 277

anatomical defensive structures is costly and determined by nutrient availability 278

and genetic growth-defence constraints. Tree Physiology 35:112-123. 279

Morrison, D.J., Merler, H., and Norris, D. 1991. Detection, recognition and management 280

of Armillaria and Phellinus root diseases in the southern interior of British 281

Columbia. Type, Institution, Victoria. 282

Oblinger, B.W., Smith, D.R., and Stanosz, G.R. 2011. Red pine harvest debris as a 283

potential source of inoculum of Diplodia shoot blight pathogens. Forest Ecology 284

and Management 262:663-670. 285

Ponder, F.J., Fleming, R.L., Berch, S., Busse, M.D., Elioff, J.D., Hazlett, P.W., Kabzems, R.D., 286

Kranabetter, J.M., Morris, D.M., Page-Dumroese, D., Palik, B.J., Powers, R.F., 287

Sanchez, F.G., Scott, D.A., Stagg, R.H., Stone, D.M., Young, D.H., Zhang, J., Ludovici, 288

K.H., McKenney, D.W., Mossa, D.S., Sanborn, P.T., and Voldseth, R.A. 2012. Effects 289

of organic matter removal, soil compaction and vegetation control on 10th year 290

Page 13 of 20



Draft

14

biomass and foliar nutrition: LTSP continent-wide comparisons. Forest Ecology 291

and Management 278:35-54. 292

Powers, R.F. 2006. Long-Term Soil Productivity: genesis of the concept and principles 293

behind the program. Canadian Journal of Forest Research 36:519-528. 294

Puettmann, K.J., Coates, K.D., and Messier, C. 2009. A Critique of Silviculture: Managing 295

for Complexity. Island Press, Washington, DC. 296

Reid, A.M., Chapman, B.K., Kranabetter, J.M., and Prescott, C.E. 2015. Response of 297

lodgepole pine health to soil disturbance treatments in British Columbia, Canada. 298

Canadian Journal of Forest Research 45:1045-1055. 10.1139/cjfr-2015-0029. 299

Rhoades, C.C., Brosi, S.L., Dattilo, A.J., and Vincelli, P. 2003. Effects of soil compaction and 300

moisture on incidence of phytophthora root rot on American chestnut (Castanea 301

dentata) seedlings. Forest Ecology and Management 184:47-54. 302

SAS. Institute Inc. 2012. JMP, Version 10. Cary, N.C. 303

Skovsgaard, J.P., and Vanclay, J.K. 2008. Forest site productivity: a review of the 304

evolution of dendrometric concepts for even-aged stands. Forestry 81:13-31. 305

Wallis, C., Eyles, A., Chorbadjian, R., Gardener, B.M., Hansen, R., Cipollini, D., Herms, D.A., 306

and Bonello, P. 2008. Systemic induction of phloem secondary metabolism and 307

its relationship to resistance to a canker pathogen in Austrian pine. New 308

Phytologist 177:767-778. 309

Weaver, D. 2013. Table 2.4 Seedlings planted on crown land in 2012/2013 by forest 310

region. Type, Institution, 311

http://www.for.gov.bc.ca/hfp/silviculture/statistics/2012-13.htm. 312

Page 14 of 20



Draft

15

Woods, A. and Coates, K.D. 2013. Are biotic disturbance agents challenging basic tenets 313

of growth and yield and sustainable forest management? Forestry 86:543-554. 314

doi:10.1093/forestry/cpt026. 315

316

317

Page 15 of 20



Draft

16

Tables 318

319

Table 1. Relationships among tree growth and health metrics based on treatment-plot 320

averages. 321

Site Growth metric Health metric Relationship R2 p-value

Dairy Creek Average volume Gall rust Positive 0.46 0.0452

Height Total disease Positive 0.50 0.0335

Total volume Dead or dying Negative 0.58 0.0172

O’Connor Average volume Foliar disease Positive 0.62 0.0113

Average volume Gall rust Positive 0.79 0.0013

Average volume Total disease Positive 0.79 0.0013

Height Foliar disease Positive 0.49 0.0364

Height Gall rust Positive 0.90 0.0001

Height Root disease Positive 0.53 0.0256

Height Total disease Positive 0.70 0.0052

Height increment Foliar disease Positive 0.47 0.0408

Height increment Gall rust Positive 0.91 <0.0001

Height increment Root disease Positive 0.55 0.0223

Height increment Total disease Positive 0.68 0.0060

Total volume Foliar disease Positive 0.52 0.0294

Total volume Gall rust Positive 0.82 0.0008

Total volume Root disease Positive 0.47 0.0421

Total volume Total disease Positive 0.71 0.0046

Volume increment Foliar disease Positive 0.62 0.0113

Volume increment Gall rust Positive 0.79 0.0014

Volume increment Total disease Positive 0.79 0.0013

Skulow Total volume Total disease Positive 0.47 0.0430 Topley Average volume Total disease Positive 0.45 0.0467

Height Gall rust Positive 0.47 0.0409

Height increment Root disease Negative 0.45 0.0491

Total volume Gall rust Positive 0.50 0.0318

Total volume Total disease Positive 0.58 0.0173

Volume increment Total disease Positive 0.47 0.0400 322

Page 16 of 20



Draft

17

Table 2. Relationships among tree growth and health metrics based on random-group 323

averages. 324

Site Growth metric Health metric Relationship R2 p-value

Black Pines Average volume Root disease Positive 0.56 0.0199


Volume increment Root disease Positive 0.58 0.0171

Dairy Creek Height Dead or dying Positive 0.47 0.0427 Log Lake Height increment Total disease Positive 0.46 0.0443

Total volume Dead or dying Negative 0.59 0.0158

O'Connor Lake Average volume Root disease Positive 0.86 0.0003


Height increment Root disease Positive 0.77 0.0019

Height increment Total disease Positive 0.47 0.0413

Total volume Root disease Positive 0.76 0.0023

Volume increment Root disease Positive 0.85 0.0004

325

Page 17 of 20



Draft

18

Figures 326

327

Figure 1. Treatment plot averages of disease occurrence and tree growth were positively related. A) Average volume (cm3) 328

and the percent of living trees with western gall rust occurrence were positively related at Dairy Creek (cross) and O’Connor 329

Lake (blue). B) Average volume (cm3) and total number of disease occurrences were positively related at Topley (triangles) 330

and O’Connor Lake (circles). C) Average volume (cm3) and the percent of living trees with foliar disease were positively 331

related at O’Connor Lake (circles). D) Height increment (m/5yrs) and the percent of living trees with western gall rust 332

Page 18 of 20



Draft

19

occurrence were positively related at O’Connor Lake (circles). E) Height increment (m/5yrs) and the percent of living trees 333

with foliar disease were positively related at O’Connor Lake (circles). F) Height increment (m/5yrs) and the percent of living 334

trees with root disease symptoms were positively related at O’Connor Lake (circles) and slightly negatively related at Topley 335

(triangles). 336

337

338

Figure 2. Random group averages of disease occurrence and tree growth were positively related. A) Average tree volume 339

(cm3) and the percent of living trees with root disease symptoms were positively related at Black Pines (squares) and 340

O’Connor Lake (circles). B) Height increment (m/5yrs) and total number of disease occurrences were positively related at Log 341

Lake (diamonds) and O’Connor Lake (circles). C) Height increment (m/5yrs) and the percent of living trees with root disease 342

symptoms were positively related at O’Connor Lake (circles). 343

344

Page 19 of 20



Draft

20

345

Figure 3. The balance of resource allocation to growth and secondary metabolites differs under optimum nutrition (left) and 346

nutrient limitation (right). Arrow size represents relative amounts and is not based on real data. Secondary metabolites are 347

related to constitutive and induced tree defence against disease and insect attack. Stand productivity is a product of both 348

health (disease and insect occurrence) and tree growth. 349

350

Page 20 of 20



draft - university of toronto t-space · 145 (93%; table 1). the goodness-of-fit test indicated...

Documents