Advertisement

BioControl

pp 1–12 | Cite as

The efficacy of Chondrostereum purpureum in sprout control of birch during mechanized pre-commercial thinning

  • Tiina LaineEmail author
  • Leena Hamberg
  • Veli-Matti Saarinen
  • Timo Saksa
Open Access
Article
  • 29 Downloads

Abstract

The efficacy of mechanized pre-commercial thinning (PCT) done by a lightweight mini-harvester Tehojätkä together with the Chondrostereum purpureum (Pers. ex Fr.) Pouzar fungal treatment (dilutions 1:100, 1:200, 1:400) and control (cutting only) was studied for three years. The efficacy of the fungal treatment was defined as capability to prevent sprouting of birch (Betula pendula Roth. and B. pubescens Ehrh.). The fungal treatment resulted in higher stump mortality and lower number of sprouts but it did not have a clear effect on the maximum height of stump sprouts. However, mortalities obtained in this study (34.1%, 26.8%, and 25.6% for dilutions 1:100, 1:200, and 1:400, respectively) were notably lower compared to previous studies which indicate that the accuracy of the spreading mechanism was not satisfactory. We conclude that it is possible to decrease stump sprouting with the fully mechanized fungal treatment but putting this implementation into practice needs more testing to increase efficacy.

Keywords

Biological control Vegetation management Silviculture Mechanization Stump sprouts Betula spp. 

Introduction

After artificial forest regeneration the natural regeneration of pioneering deciduous species creates a need for young stand management (Huuskonen and Hynynen 2006; Uotila et al. 2012). Removing unwanted deciduous tree species, mostly silver birch (Betula pendula Roth.) and downy birch (Betula pubescens Ehrh.), assures growth of commercially valuable conifers and decreases competition between conifers and faster growing deciduous species (Lindholm and Vasander 1987; Vanha-Majamaa et al. 1996; Gobakken and Næsset 2002; Huuskonen and Hynynen 2006; Uotila et al. 2012; Äijälä et al. 2014).

Conventionally, young stand management is done motor-manually by cutting competing (deciduous) trees to assure the development of target (conifer) trees. However, cutting young deciduous trees is a quite ineffective method since it stimulates vigorous regrowth whereby most of the stumps produce several new sprouts (Kauppi et al. 1987; Johansson 1992, 2008). Thus, young stand management is usually done in two phases. First, early cleaning (EC) is performed about five years after regeneration, when the height of conifer saplings is ca. 1 m. Second, when pines are from five to seven meters and spruces from 3 to 4 m in height, it is time for the pre-commercial thinning (PCT): spruce-dominated stands are thinned to the density of 1800–2000 and pine-dominated to 2000–2200 saplings per hectare (Huuskonen et al. 2008; Pettersson et al. 2012; Fahlvik 2005; Äijälä et al. 2014; Uotila 2017). Pine-dominated stands are grown in denser spacing to ensure high-quality timber by branch mortality and natural pruning (Mäkinen 1999; Varmola and Salminen 2004). Cleaning of deciduous saplings also decreases the risk of moose browsing in pine stands (Härkönen 1998). Although repeated cuttings increase the total cost of young stand management, it considerably enhances the diameter development of target trees and is therefore desirable (Huuskonen and Hynynen 2006; Uotila 2017).

The cost-efficiency of young stand management could be improved if repeated cuttings were not needed. Here, the use of chemical herbicides would be efficient, but this is restricted by forest certification (PEFC 2017). Therefore, mechanical cutting combined with the use of a white-rot fungus, Chondrostereum purpureum (Pers. Ex Fr.) Pouzar, as a biocontrol agent has been studied (e.g. Wall 1986; Gosselin et al. 1999; Setliff 2002; Vartiamäki 2009; Hamberg et al. 2015). This is an environmentally friendly alternative to chemical herbicides. The idea is to apply C. purpureum inoculum on freshly cut stumps to suppress re-sprouting (Wall 1990). C. purpureum is a lignicolous, basidiomycete fungus occurring naturally worldwide in temperate and boreal vegetation zones (Wall 1990; Setliff 2002; Vartiamäki et al. 2008). It invades woody tissue through fresh wounds, e.g. via cut surfaces, causing discoloration, decay, and even death of the host (Wall 1990; Hintz 2007; Vartiamäki 2009). However, it has been shown not to cause disease in non-targeted healthy trees, so its use as a biocontrol agent does not pose the risk of disease epidemics (Gosselin et al. 1999; De Jong 2000).

The use of C. purpureum has been promising in field tests even though there is variation in results due to timing of the treatment, the accuracy of the spreading mechanism, the basal area of the stump, and tree species (Wall 1986; Setliff 2002; Vartiamäki et al. 2009; Roy et al. 2010; Hamberg et al. 2015, 2017; Hamberg and Hantula 2016, 2018). As a method, applying C. purpureum inoculum manually on cut surfaces is challenging due to the high number of stumps within regeneration areas. Also, motor-manually using a clearing saw, takes 2.6 times longer to perform the fungal treatment compared to cutting only (without fungus), mainly due to the additional operations and the weight of the equipment needed for the application (Roy et al. 2010).

There are mechanical solutions available for young stand management, but currently less than 1% is done fully mechanically in Finland (Strandström 2016; Luke 2018). Even though the main target of mechanization is to improve the cost-efficiency of young stand management through higher productivity, clear cost-savings have not been achieved compared to the use of a clearing saw (Mattson and Westerberg 1992; Strandström 2016). The use of C. purpureum in sprout control may provide an opportunity to improve the cost-efficiency of mechanical solutions since machines could easily carry the additional weight required by C. purpureum inoculum. However, there is no previous research combining the fungal treatment with mechanized young stand management (cutting excess deciduous saplings), although this method likely decreases the need for the subsequent PCT.

In this study, the aim was to measure the efficacy of a lightweight mini-harvester Tehojätkä (Usewood Ltd., Finland) application of C. purpureum inoculum to freshly cut stumps in order to control competition from deciduous trees in juvenile conifer stands. The hypothesis was that mechanized pre-commercial thinning together with the fungal treatment is more effective compared to the control without a fungal treatment (cutting only). The efficacy was defined as (1) increased stump mortality, (2) decreased number of sprouts on a stump, and (3) shorter length of sprouts. In this study, the young stand management method is called “early PCT”, with the idea that EC and PCT are combined into one young stand management method without repetitive operations.

Materials and methods

Stands, machines and treatments

Early PCT was done between June and September 2014 on eleven juvenile forest stands, 7 ± 0.6 years after regeneration (mean ± SE) (Table 1). Stands were regenerated either to spruce (n = 7) or pine (n = 4) and they were located in eight geographically separate areas in central Finland. Every stand included 1–3 different fungal treatments: dilution 1:100 (n = 8), 1:200 (n = 5), 1:400 (n = 7), and one control treatment (cutting only) (n = 11) (Table 1). Due to practical arrangements at stands, all treatments were not repeated on each stand but the control was included in each stand.
Table 1

Description of the study design of eleven stands located in eight geographically separate areas in central Finland: number of sample plots (n = 480), regenerated tree species and year, the fungal treatments (Chondrostereum purpureum dilutions 1:100, 1:200 or 1:400), dosing method to apply C. purpureum inoculum (standard/nozzle) and the number of machines used within a stand as a superscript (1–3), the method used for the control (cutting only: CS clearing saw, UW Usewood Tehojätkä), timing (the number of calendar week when the treatment was done), and mean dampness of the stand six-level classification based on the evaluation of cover of bryophytes

Stand

Regene-ration year

Tree species

Number of plots

Fungal treatment

Dosing method

Control method

Timing (work-week)

Damp-ness

1:100

1:200

1:400

1.1

2005

Pine

30

 

×

 

Nozzle1

UW

24

3.4

1.2

2005

Pine

30

  

×

Nozzle1

CS

24

2.9

1.3

2005

Pine

30

×

  

Nozzle1

CS

24

2.8

2.1

2007

Spruce

30

  

×

Nozzle1

CS

28

2.9

2.2

2003

Spruce

30

×

  

Nozzle1

CS

29

2.6

3.1

2009

Spruce

60

×

×

×

Standard2

UW

24–26

4.2

4.1

2008

Pine

60

×

×

 

Standard2

UW

27–28

2.5

5.1

2008

Spruce

60

×

×

×

Standard2

UW

29–30

3.3

6.1

2009

Spruce

45

×

 

×

Standard2

UW

30–32

3.4

7.1

2008

Spruce

60

×

×

×

Standard3

CS

37–39

2.9

8.1

2008

Spruce

45

×

 

×

Standard2

UW

40

2.9

The work was done by a lightweight mini-harvester Tehojätkä (Usewood Ltd., Finland) equipped with a boom-mounted UW40-cleaning head (Supplementary Figure S1). The Tehojätkä weighs 1800 kg, and is 3.8 m in length and 1.5 m in width (Usewood Ltd., Finland). Horizontal boom reach is 3.8 m. The UW40-cleaning head (no longer in production) is especially designed for small-diameter stems. It has either a cutting (for stems < 40 mm in diameter) or a sawing function (for stems 20–90 mm in diameter), depending on the rotation direction of the blade. The cleaning head weighs 90 kg and the speed of rotation is one cycle per second. The UW40-cleaning head has active tilt, i.e., the cutter may be tilted and locked during the cutting.

The basal suspension of C. purpureum (provided by Verdera Ltd.) included the fungal strain R5 (Hamberg et al. 2015) containing a minimum of 106 colony forming units (Gonzáles 1996). It was diluted with tap water just before the treatments in the field (dilutions 1:100, 1:200 or 1:400). Tehojätkä machines were equipped with tanks (capacity of 200 l) for applying liquid suspension of C. purpureum mycelium onto freshly cut stump surfaces. Dilutions were conducted through a hose along the boom to the cleaning head. There were three different Tehojätkä machines with two different dosing methods (Table 1). In the standard dosing method, used by two machines, C. purpureum inoculum was dosed to the upper side of the cutting blade from which it flowed through holes, made in the blade, onto the surface of cut stumps. In the third machine, C. purpureum suspension was dosed straight to underside of the blade through a nozzle. In our final models (see below), we did not find any differences between the dosing methods (p > 0.05), and therefore all Tehojätkä machines were treated as one group.

The control treatments (cutting only, without the fungus) were performed using either the Tehojätkä machines or motor-manually with a clearing saw (Table 1). There was no statistically significant difference in stump mortality between the controls done either by the Tehojätkä machines or a clearing saw (p > 0.05), and therefore the control treatments were not treated separately in the statistical analysis.

Fieldwork

In years 2015, 2016 and 2017, i.e., 1–3 years after the treatments, inventories in the field were done between September and November. Inventories were carried out by measuring a systematic, regularly shaped grid of 15 circular sample plots (r = 1.0 m) per treatment (control and fungal treatments), except that, in stand 4.1, altogether 30 plots were measured for the control (cutting only). The distance between the plots was determined according to the area (0.3–0.7 ha) excluding a buffer zone of 10 m between the treatments and from trees of neighboring stands to avoid their immediate effects on sprouting (Hamberg et al. 2015). Altogether, the data consisted of 480 sample plots, but some plots were not found during the study, and, therefore, the final data consisted of 470 sample plots: 116 for the C. purpureum dilution 1:100, 73 for 1:200, 102 for 1:400, and 179 for the control (Table 1).

In every sample plot, all stumps (diameter > 0.5 cm at ground level) and saplings (height > 0.5 m) were measured in order to evaluate the efficacy of the fungal treatments. However, we present here results only for birch stumps (Betula pendula and B. pubescens) since they were the most common tree species in investigated stands (71% of all stumps), and since C. purpureum is especially efficient in birch (Hamberg et al. 2017). This provided us with a good basis for our efficacy investigations in mechanized fungal treatments. When stumps were investigated, tree species, the diameter of the cut surface (mm), the height of the stump (cm), the number and maximum height of stump sprouts (cm from the ground), and the presence of fruiting bodies of C. purpureum on a stump (0 = fruiting bodies not found, and 1 = fruiting bodies found) were recorded. Furthermore, on every plot, the dampness of the ground was evaluated based on the cover of bryophytes commonly found in mires (Sphagnum and Polytrichum commune) using a six-level classification (1 = 0%, 2 = 0.1–1%, 3 = 1–10%; 4 = 11–25%, 5 = 26–50%, 6 = > 50%, Tamminen and Mälkönen 1999).

Statistical analysis

In data analysis, we concentrated on the efficacy of different treatments (control vs. fungal treatments), i.e., the mortality of stumps and the ability to alleviate sprouting (number and maximum height of sprouts) after the treatments. Mortality means that a stump had no sprouts and was therefore considered dead. Silver (Betula pendula) and downy birches (B. pubescens) were not separated in the analyses since they have been shown to respond similarly in these treatments (Hamberg et al. 2015). The data used in the analysis consisted of 1436 birch stumps in 2015 and 2017, but, in 2016, only 1371 stumps were found and measured.

The statistical program R was used in the analyses (R Core Team 2018). The mortality of stumps (0 = stump is alive, 1 = stump is dead), the presence of fruiting bodies on a stump, and the number of sprouts on a living stump were investigated with generalized linear mixed models (GLMMs) using the function glmer in the library lme4 (Bates et al. 2015). In the mortality and the fruiting body models, a binomial distribution with logit link function was used, whereas in the number of sprouts model, a Poisson distribution with log link function was used. The maximum height of a sprout in different treatments was investigated with linear mixed models (LMMs), using the lme function in the nlme library (Pinheiro et al. 2018). All birch stumps were included in the mortality models, but only living stumps were included in the stump sprout models (number and height). Models were estimated separately for each year.

Models for mortality, sprout number, and the maximum height of the sprout included the following explanatory variables: (1) treatment (a factor with four levels: control, and three different C. purpureum dilutions 1:100, 1:200, and 1:400), (2) timing (the number of calendar week when the treatment was done), (3) dampness of the ground (scale 1–6), (4) stand density (the number of saplings on the plot before the early PCT), and (5) the diameter of a stump (mm). The fruiting body models included the same variables as above, except that the control treatment was excluded since no fruiting bodies were found from the control sample plots. Correlations between the explanatory variables were < 0.35. As we wanted to separate the treatment effects from the other effects (affecting mortality, and the number and height of stump sprouts), explanatory variables 2–5 were included in the final models regardless of whether they were statistically significant or not (see Hamberg et al. 2015). Stand and sample plot were included as nested random factors as conditions within the same stand and sample plot may be more similar than on a randomly selected stand or a sample plot. Residual plots were inspected after each model to identify outliers and to check that the variances of residuals are homogenous (O’Hara and Kotze 2010; Warton and Hui 2011). Overdispersion was also inspected by including observation-level random effect (OLRE) (Harrison 2014).

Results

Before the early PCT, the densities of coniferous and deciduous saplings were 5067 ± 582 and 15,497 ± 1527, respectively (mean ± SE). The early PCT lowered the density of deciduous trees by ca. 87% to the densities 4324 ± 520 and 2029 ± 227 ha−1, respectively. The mean height of conifers after the early PCT in 2014 (measured from remaining saplings in 2015) was 130.4 ± 3.7 cm. The mean diameter and height of cut birch stumps was 12.9 ± 0.2 mm and 33.0 ± 0.4 cm, respectively.

Mortality

Mortality was higher on the fungal treated stumps than on the control stumps (cutting only) one, two and three years after the early PCT (p < 0.001) (Table 2; Fig. 1a). Mortality increased with increasing C. purpureum concentration (1:400 < 1:200 < 1:100) and time lag after the treatments (year 2015 < 2016 < 2017). After three growing seasons, the mortality of birch stumps was 34.1, 26.8, and 25.6% for the C. purpureum dilutions 1:100, 1:200, and 1:400, respectively (predicted mean value based on GLMM). Stump mortality on the control stands (cutting only) was 11.5%.
Table 2

The effects of (1) the treatment (the control vs. the fungal treatments with the Chondrostereum purpureum dilutions 1:100, 1:200 or 1:400), (2) timing of the treatment (the number of calendar week when the treatment was done), (3) dampness of the ground, (4) the number of saplings on the plot before the early pre-commercial thinning, and (5) the diameter of an investigated stump (mm) on the mortality of birch stumps (0 = alive, 1 = dead) one (2015), two (2016), and three years (2017) after the treatments (generalized linear mixed models)

Explanatory variables

Mortality in 2015

n = 1436

Mortality in 2016

n = 1371

Mortality in 2017

n = 1436

Coeff. ± SE

DF

z

p

Coeff. ± SE

DF

z

p

Coeff. ± SE

DF

z

p

Intercept

3.137 ± 1.175

1280

2.669

0.008

3.419 ± 0.909

1215

3.763

< 0.001

2.472 ± 0.827

1280

2.987

0.003

C. purpureum treatment

 1:100

2.021 ± 0.271

1280

7.461

< 0.001

1.672 ± 0.233

1215

7.157

< 0.001

1.380 ± 0.200

1280

6.890

< 0.001

 1:200

1.282 ± 0.290

1280

4.414

< 0.001

1.352 ± 0.258

1215

5.246

< 0.001

1.031 ± 0.227

1280

4.531

< 0.001

 1:400

1.244 ± 0.276

1280

4.511

< 0.001

1.313 ± 0.228

1215

5.752

< 0.001

0.970 ± 0.191

1280

5.061

< 0.001

Timing

− 0.252 ± 0.036

1280

− 6.906

< 0.001

− 0.215 ± 0.027

1215

− 8.013

< 0.001

− 0.169 ± 0.023

1280

− 7.393

< 0.001

Dampness

0.061 ± 0.119

1280

0.509

0.610

− 0.094 ± 0.104

1215

− 0.908

0.363

− 0.022 ± 0.102

1280

− 0.213

0.831

Density (saplings per plot)

0.009 ± 0.012

1280

0.734

0.463

0.009 ± 0.011

1215

0.790

0.430

0.019 ± 0.011

1280

1.832

0.067

Stump diameter (mm)

0.050 ± 0.010

1280

4.836

< 0.001

0.047 ± 0.009

1215

0.009

< 0.001

0.035 ± 0.008

1280

4.109

< 0.001

Coefficients and their standard errors (SE), degrees of freedom (DF), associated Wald’s z-score (= coeff./SE) and significance level p when all coefficients in the models are presented. Statistically significant p values (p < 0.05) are in bold. See also Fig. 1a

Fig. 1

The effects of the control (cutting only) and the fungal treatments (Chondrostereum purpureum dilutions 1:100, 1:200, and 1:400) on a the mortality of birch stumps, b the number of sprouts per stump, c the maximum height of sprouts per stump, and d the presence of fruiting bodies (only for the fungal treatments) one, two and three growing seasons after the early pre-commercial thinning. Means with SE are presented. Figures have been drawn based on the predicted values of the generalized linear or linear mixed models. Statistically significant difference (p < 0.05) between the control and at least one of the fungal treatments is indicated with an asterisk. The asterisk relates to the specific year in the x-axis. See Tables 2, 3, 4, and 5

Timing of the treatment (i.e., the number of calendar week when the treatment was performed) affected the mortality of birch stumps (p < 0.001): when the early PCT was performed later in the growing season, the mortality decreased (Table 2; Fig. 2). Mortality was ca. 60% in the fungal treatment (dilution 1:100) when performed in the middle of June (calendar week 25), ca. 35% in the end of July (calendar week 30), and no more than ca. 10% in the beginning of October (calendar week 40). In the other dilutions (1:200 and 1:400) and the control, the mortality was lower, but the trend was similar: the mortality of birch stumps was lower later in the growing season the treatment was done. Furthermore, mortality was higher for larger diameter stumps than for those with smaller diameters (p <0.001).
Fig. 2

Mortality of birch stumps in the fungal (Chondrostereum purpureum dilutions 1:100, 1:200, and 1:400) and the control treatments (cutting only) three years after the treatments by the timing of the treatment done in 2014 (week number). Values in the curves are based on the generalized linear mixed model (see Table 2). Values in points are average weekly mortality figures based on raw data (mean ± SE)

Number of sprouts

The number of sprouts in living birch stumps was lower in the fungal treatments compared to the control (cutting only) (Table 3; Fig. 1b). The C. purpureum dilutions 1:100 (p = 0.047) and 1:400 (p = 0.019) differed from the control (cutting only). In stumps, the number of sprouts decreased every year (2015 > 2016 > 2017). Three years after the early PCT, the predicted mean number of sprouts per stump was 2.8 for the control, and 2.5, 2.6, and 2.4 for the C. purpureum dilutions 1:100, 1:200, and 1:400, respectively. The density of saplings (per plot) (p < 0.001) and the diameter of a stump (p <0.001) affected the number of sprouts per birch stump. When the number of saplings increased on a plot before the early PCT, the number of sprouts decreased. The number of stump sprouts increased with increasing diameter of stumps. One and two years after the treatment, timing of the treatment (i.e., the number of calendar week when the treatment was done) affected the number of sprouts (p < 0.001): when the early PCT was performed later in the growing season, the number of sprouts increased. Dampness increased the number of sprouts one year after the treatments (p = 0.004).
Table 3

The effects of (1) the treatments (the control vs. the fungal treatments with the Chondrostereum purpureum dilutions 1:100, 1:200 or 1:400), (2) timing of the treatment (the number of calendar week when the treatment was done), (3) dampness of the ground, (4) the number of saplings on the plot before the early pre-commercial thinning, and (5) the diameter of an investigated stump (mm) on the number of sprouts in a birch stump one (2015), two (2016), and three years (2017) after the treatments (generalized linear mixed models)

Explanatory variables

Number of sprouts in 2015

n = 1244

Number of sprouts in 2016

n = 1093

Number of sprouts in 2017

n = 1064

Coeff. ± SE

DF

z

p

Coeff. ± SE

DF

z

p

Coeff. ± SE

DF

z

p

Intercept

1.067 ± 0.186

1091

5.743

< 0.001

0.917 ± 0.195

940

4.699

< 0.001

1.020 ± 0.300

914

3.400

< 0.001

C. purpureum treatment

 1:100

− 0.151 ± 0.038

1091

− 4.031

< 0.001

− 0.065 ± 0.048

940

− 1.373

0.170

− 0.108 ± 0.054

914

− 1.991

0.047

 1:200

− 0.081 ± 0.053

1091

− 1.523

0.128

− 0.082 ± 0.054

940

− 1.271

0.204

− 0.055 ± 0.072

914

− 0.763

0.445

 1:400

− 0.137 ± 0.036

1091

− 3.792

< 0.001

− 0.081 ± 0.045

940

− 1.803

0.071

− 0.121 ± 0.051

914

− 2.342

0.019

Timing

0.026 ± 0.005

1091

5.343

< 0.001

0.014 ± 0.005

940

3.009

0.003

0.005 ± 0.009

914

0.585

0.559

Dampness

− 0.071 ± 0.025

1091

− 2.865

0.004

− 0.019 ± 0.028

940

− 0.691

0.490

− 0.059 ± 0.031

914

− 1.873

0.061

Density (saplings per plot)

− 0.015 ± 0.003

1091

− 5.539

< 0.001

− 0.018 ± 0.003

940

− 5.725

< 0.001

− 0.013 ± 0.003

914

− 4.075

< 0.001

Stump diameter (mm)

0.019 ± 0.001

1091

14.722

< 0.001

0.018 ± 0.002

940

10.336

< 0.001

0.017 ± 0.002

914

8.275

< 0.001

Coefficients and their standard errors (SE), degrees of freedom (DF), associated Wald’s z-score (= coeff./SE) and significance level p when all coefficients in the models are presented. Statistically significant p values (p < 0.05) are in bold. See also Fig. 1b

Maximum height of sprouts

The fungal treatments did not have a clear effect on the maximum height of sprouts as it had on the mortality of stumps and the number of stump sprouts per living stump (Table 4; Fig. 1c). The C. purpureum dilution 1:200 decreased (p = 0.030) the maximum height of sprouts whereas the dilution 1:100 increased (p = 0.008) it. Three years after the early PCT, the maximum height of sprouts was 129.3 cm in the control treatment whereas in the C. purpureum dilutions 1:100, 1:200, and 1:400, the maximum heights were 138.8, 118.8, and 122.8 cm, respectively (predicted mean values based on LMM). The maximum height of sprouts increased with the increasing diameter of a stump (p < 0.001).
Table 4

The effects of (1) the treatments (the control vs. the fungal treatments with the Chondrostereum purpureum dilutions 1:100, 1:200 or 1:400), (2) timing of the treatment (the number of calendar week when the treatment was done), (3) dampness of the ground, (4) the number of saplings on the plot before the early pre-commercial thinning, and (5) the diameter of an investigated stump (mm) on the maximum height of the sprout in birch stumps one (2015), two (2016), and three years (2017) after the treatments (linear mixed models)

Explanatory variables

Max. stump sprout height in 2015

n = 1244

Max. stump sprout

height in 2016

n = 1093

Max. stump sprout

height in 2017

n = 1064

Coeff. ± SE

DF

t

p

Coeff. ± SE

DF

t

p

Coeff. ± SE

DF

t

p

Intercept

56.751 ± 19.326

1091

2.936

0.003

76.489 ± 29.867

940

2.561

0.011

62.714 ± 36.513

914

1.718

0.086

C. purpureum treatment

 1:100

3.924 ± 1.936

1091

2.026

0.043

5.287 ± 2.849

940

1.856

0.064

9.495 ± 3.566

914

2.663

0.008

 1:200

− 7.168 ± 2.579

1091

− 2.780

0.006

− 12.348 ± 4.018

940

− 3.073

0.002

− 10.494 ± 4.818

914

− 2.178

0.030

 1:400

− 3.546 ± 1.912

1091

− 1.854

0.064

− 9.081 ± 2.823

940

− 3.217

0.001

− 6.475 ± 3.504

914

− 1.848

0.065

Timing

0.297 ± 0.623

1091

0.477

0.634

1.218 ± 0.966

940

1.261

0.208

2.302 ± 1.178

914

1.955

0.051

Dampness

− 0.631 ± 1.164

1091

− 0.543

0.588

− 2.813 ± 1.722

940

− 1.633

0.103

− 2.857 ± 2.170

914

− 1.317

0.188

Density (saplings per plot)

− 0.205 ± 0.115

1091

− 1.786

0.074

− 0.302 ± 0.187

940

− 1.615

0.107

− 0.332 ± 0.217

914

− 1.525

0.128

Stump diameter (mm)

0.691 ± 0.085

1091

8.177

< 0.001

0.894 ± 0.121

940

7.401

< 0.001

1.218 ± 0.153

914

7.986

< 0.001

Coefficients and their standard errors (SE), degrees of freedom (DF), t value and significance level p when all coefficients in the models are presented. Statistically significant p values (p < 0.05) are in bold. See also Fig. 1c

Fruiting bodies

The presence of fruiting bodies on birch stumps treated with C. purpureum inoculum decreased with time (year 2015 > 2016 > 2017) (except that dilutions 1:100 and 1:400 had a minor increment from 2015 to 2016) and increased with increasing C. purpureum concentration (1:400 < 1:200 < 1:100) (Table 5; Fig. 1d). One year after the treatments, fruiting bodies were present on 17.9, 15.3, and 10.4%, for dilutions 1:100, 1:200, and 1:400, respectively. Three years after the treatments the corresponding figures were only 0.4, 0.2, and 0.9%, respectively. Stump size affected the presence of fruiting bodies so that the presence was higher on larger stumps (p < 0.001)
Table 5

The effects of (1) the fungal treatments (Chondrostereum purpureum dilutions 1:100, 1:200 or 1:400 as a reference), (2) timing of the treatment (the number of calendar week when the treatment was done), (3) dampness of the ground, (4) the number of saplings on the plot before the early pre-commercial thinning, and (5) the diameter of an investigated stump (mm) on the presence of fruiting bodies in birch stumps (0 = no fruiting bodies, 1 = fruiting bodies found) one (2015), two (2016), and three years (2017) after the treatments (generalized linear mixed models)

Explanatory variables

Presence of fruiting bodies in 2015

n = 907

Presence of fruiting bodies in 2016

n = 898

Presence of fruiting bodies in 2017

n = 907

Coeff. ± SE

DF

z

p

Coeff. ± SE

DF

z

p

Coeff. ± SE

DF

z

p

Intercept

2.655 ± 1.592

779

1.668

0.095

− 0.234 ± 1.459

770

− 0.160

0.873

− 1.019 ± 2.508

779

− 0.406

0.685

C. purpureum treatment

 1:100

0.598 ± 0.242

779

2.476

0.013

0.552 ± 0.260

770

2.123

0.034

− 0.943 ± 0.590

779

− 1.597

0.110

 1:200

0.411 ± 0.280

779

1.470

0.142

− 0.152 ± 0.343

770

− 0.443

0.658

− 1.423 ± 0.881

779

− 1.616

0.106

Timing

− 0.198 ± 0.050

779

− 3.949

< 0.001

− 0.103 ± 0.041

770

− 2.516

0.012

− 0.020 ± 0.058

779

− 0.336

0.737

Dampness

− 0.103 ± 0.143

779

− 0.720

0.472

− 0.075 ± 0.174

770

− 0.428

0.668

− 0.777 ± 0.442

779

− 1.758

0.079

Density (saplings per plot)

0.012 ± 0.014

779

0.905

0.366

0.015 ± 0.018

770

0.801

0.423

− 0.091 ± 0.058

779

− 1.575

0.115

Stump diameter (mm)

0.117 ± 0.014

779

8.281

< 0.001

0.122 ± 0.163

770

7.463

< 0.001

0.064 ± 0.023

779

2.813

0.005

The control treatment (cutting only) was not included in the models. Coefficients and their standard errors (SE), degrees of freedom (DF), associated Wald’s z-score (= coeff./SE) and significance level p when all coefficients in the models are presented. Statistically significant p values (p < 0.05) are in bold. See also Fig. 1d

Discussion

Based on our results, mechanized pre-commercial thinning together with the Chondrostereum purpureum treatment was more effective compared to the control without fungal treatment (cutting only), resulting in increased stump mortality and decreased number of sprouts. However, the fungal treatment did not have a clear effect on the maximum height of stump sprouts as on stump mortality and the number of stump sprouts. The mortality of birch stumps increased with the increasing concentration of C. purpureum inoculum.

This was one of the first studies presenting the results of mechanized biocontrol treatment using C. purpureum inoculum. However, mortalities obtained in this study were lower compared to previous studies. In our study, the same C. purpureum strain R5 was used as in Hamberg et al. (2015) where the mortality of birch stumps was 78% after three growing seasons. Also, in other studies, the mortality of stumps has been more than 75% for different birch species and the difference between the control and the fungal treatments has been notable (Wall 1990; Roy et al. 2010; Vartiamäki 2009; Lygis et al. 2012).

In earlier studies, the suspension of C. purpureum has been inoculated manually, for example via a plastic squirt bottle, to ensure high accuracy (Hamberg et al. 2015; Vartiamäki et al. 2009). Even though during the early PCT operation, an application of C. purpureum inoculum on the surface of fresh stump surfaces is technically feasible with Tehojätkä, the accuracy of the spreading mechanism seemed not to be satisfactory. There may have been application malfunctions resulting in the low mortality figures obtained in this study (we do not have any exact data of the accuracy of the Tehojätkä spreading mechanism revealing whether the C. purpureum inoculum reached the surface of the stump or not).

Timing of the early PCT application affected stump mortality. The fungal treatment performed in June resulted in promising sprout control efficacy since mortality was relatively high (60%) three years after the treatment. Similar findings of the fungal treatment being more effective in the beginning and the middle of the growing season have previously been reported (Vartiamäki et al. 2009; Lygis et al. 2012).

C. purpureum treatment affects sprouting for a relatively long time as mortality usually increases during the first three years after the treatment (Hamberg et al. 2015; Vartiamäki et al. 2009). This was also shown in our study, as the mortality of stumps increased annually from 2015 to 2017. However, mortality increased for the control as well, but clearly less than in the fungal treatments. Also, the diameter of stumps affected mortality, being lower for smaller stumps as was also shown by Hamberg et al. (2015). Most likely, the increasing stump diameter increased the possibility of C. purpureum inoculum to hit the target and improved application accuracy. However, in larger stumps, it has also been observed that after one growing season, mortality does not necessarily increase with increasing stump diameter (the most resistant stumps were 13 cm in diameter), and in some cases, almost all stumps may die regardless of the stump diameter (Hamberg and Hantula 2018).

Even though the fungal treatment did increase the mortality of stumps and decreased the number of sprouts, the maximum height of sprouts was not as clearly affected. Similar findings have been observed in earlier studies (Vartiamäki et al. 2009; Roy et al. 2010; Hamberg et al. 2015). Yet, the diameter of the stump was related both to the number and the maximum height of the sprouts, so that larger stumps had more and taller sprouts (see also Hamberg et al. 2015). Also, density (the number of saplings per plot) was related to the decreasing number of sprouts on a stump, similar to earlier studies (Hamberg et al. 2015). This is mostly due to shading and competition of neighboring trees causing shoot bud death (Jones and Harper 1987). The early PCT, especially together with the fungal treatment, provides more space and light for stumps still living in a stand (Roy et al. 2010). This may have caused even an increase in the number and maximum height of sprouts, as was seen in our study for the maximum height. In our study, the differences in the number of sprouts between treatments were small.

Solutions, such as the fungal treatment, are needed in order to improve the cost-efficiency of young stand management by removing the need for repeated operations. In another method, unwanted deciduous saplings are uprooted mechanically to prevent sprouting (Strandström 2016; Hallongren and Rantala 2013; Saksa et al. 2018). If the later PCT is not needed after uprooting, the costs are at the same level or 6–28% lower than summed costs of two motor-manual operations done by clearing saw, depending on stump diameter of the removed saplings (Strandström 2016). Although no cost-efficiency estimations were included in our study, at least the same cost savings can be expected when the fungal treatment is used to suppress re-sprouting.

In conclusion, it is evident that the fungal treatment decreases stump sprouting. However, potential future implementations of the mechanized fungal treatments require more testing relating to the accuracy and the reliability of applying this method. If repeated cuttings are not needed, cost savings may be achieved.

Notes

Acknowledgements

Open access funding provided by Natural Resources Institute Finland (LUKE). Authors thank UPM Forest, Metsä Group and Tornator for arranging the machines and the research stands needed in the field experiments. We also thank Mr. Raimo Jaatinen and Mr. Aulis Leppänen for their efforts in data collection. Preliminary results of this article were presented in EFFORTE deliverable 2.3 (Saksa et al. 2018). Mr. Andreas Salmi made his Master degree thesis using the first-year biocontrol inventory data (Salmi 2017). Heikki Kiheri is thanked for revising the English language and Juha Heikkinen for statistical advice.

Funding

This work was supported by the EU, through the EFFORTE (Efficient forestry by precision planning and management for sustainable environment and cost competitive bio-based industry) project (Grant Agreement Number: 720712 — EFFORTE — H2020-BBI-PPP-2015-02/H2020-BBI-PPP-2015-2-1).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10526_2019_9971_MOESM1_ESM.doc (5.5 mb)
Supplementary material 1 (DOC 5648 kb)

References

  1. Äijälä O, Koistinen A, Sved J, Vanhatalo K, Väisänen P (2014) Metsänhoidon suositukset. [Finnish forest management practice recommendations]. Forestry Development Centre TapioGoogle Scholar
  2. Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48CrossRefGoogle Scholar
  3. De Jong MD (2000) The BioChon story: deployment of Chondrostereum purpureum to suppress stump sprouting in hardwoods. Mycologist 14(2):58–62CrossRefGoogle Scholar
  4. Fahlvik N (2005) Aspects of precommercial thinning in heterogeneous forests in southern Sweden. Acta Universitatis Agriculturae Sueciae 2005:68. ISSN 1652-6880, ISBN 91-576-6967-8.Google Scholar
  5. Gobakken T, Næsset E (2002) Spruce diameter growth in young mixed stands of Norway spruce (Picea abies (L.) Karst) and birch (Betula pedula Roth. B. pubescens Ehrh.). For Ecol Manag 171:297–308CrossRefGoogle Scholar
  6. Gonzáles JM (1996) A general purpose program for obtaining most probable number tables. J Microbiol Methods 26:215–218CrossRefGoogle Scholar
  7. Gosselin L, Jobidon R, Bernier L (1999) Biological control of stump sprouting of broadleaf species in rights-of-way with Chondrostereum purpureum: incidence of the disease on nontarget hosts. Biol Control 16:60–67CrossRefGoogle Scholar
  8. Hallongren H, Rantala J (2013) A search for better competitiveness in mechanized early cleaning through product development: evaluation of two Naarva uprooters. Int J Forest Eng 24(2):91–100CrossRefGoogle Scholar
  9. Hamberg L, Hantula J (2016) The efficacy of six elite isolates of the fungus Chondrostereum purpureum against the sprouting of European aspen. J Environ Manag 171:217–224CrossRefGoogle Scholar
  10. Hamberg L, Hantula J (2018) Tree size as a determinant of recovery of birch (Betula pendula and B. pubescens) and grey alder (Alnus incana) trees after cutting and inoculation with Chondrostereum purpureum. Biol Control 126:83–89CrossRefGoogle Scholar
  11. Hamberg L, Vartiamäki H, Hantula J (2015) Breeding increases the efficacy of Chondrostereum purpureum in the sprout control of birch. PLoS ONE 10(2):e0117381CrossRefGoogle Scholar
  12. Hamberg L, Lemola J, Hantula J (2017) The potential of the decay fungus Chondrostereum purpureum in the biocontrol of broadleaved tree species. Fungal Ecol 30:67–75CrossRefGoogle Scholar
  13. Härkönen S (1998) Effects of silvicultural cleaning in mixed pine-deciduous stands on moose damage to Scots pine (Pinus sylvestris). Scand J For Res 13(1–4):429–436CrossRefGoogle Scholar
  14. Harrison XA (2014) Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2:e616CrossRefGoogle Scholar
  15. Hintz W (2007) Development of Chondrostereum purpureum as a mycoherbicide for deciduous brush control. In: Vincent C, Goettel MS, Lazarovits G (eds) Biological control: a global perspective. CAB International, Wallingford, pp 284–290CrossRefGoogle Scholar
  16. Huuskonen S, Hynynen J (2006) Timing and intensity of precommercial thinning and their effects on the first commercial thinning in Scots pine stands. Silva Fenn 40(4):645–662CrossRefGoogle Scholar
  17. Huuskonen S, Hynynen J, Ojansuu R (2008) Stand characteristics and external quality of young Scots pine stands in Finland. Silva Fenn 42(3):397–412CrossRefGoogle Scholar
  18. Johansson T (1992) Sprouting of 2- to 5-year-old birches (Betula pubescens Ehrh. and Betula pendula Roth) in relation to stump height and felling time. For Ecol Manag 53:263–281CrossRefGoogle Scholar
  19. Johansson T (2008) Sprouting ability and biomass production of downy and silver birch stumps of different diameters. Biomass Bioenerg 32:944–951CrossRefGoogle Scholar
  20. Jones M, Harper JL (1987) The influence of neighbours on the growth of trees II. The fate of buds on long and short shoots in Betula pendula. Proc R Soc Lond Biol Sci 232:1–18CrossRefGoogle Scholar
  21. Kauppi A, Rinne P, Ferm A (1987) Initiation, structure and sprouting of dormant basal buds in Betula pubescens. Flora. 179(1):55–83CrossRefGoogle Scholar
  22. Lindholm T, Vasander H (1987) Vegetation and stand development of mesic forest after prescribed burning. Silva Fenn 21(3):259–278CrossRefGoogle Scholar
  23. Lygis V, Bakys R, Burokienė D, Vasiliauskaitė I (2012) Chondrostereum purpureum-based control of stump sprouting of seven hardwood species in Lithuania. Balt For 18(1):41–55Google Scholar
  24. Mäkinen H (1999) Growth, suppression, death, and self-pruning of branches of Scots pine in southern and central Finland. Can J For Res 29(5):585–594CrossRefGoogle Scholar
  25. Mattson S, Westerberg D (1992) Röjmaskiner i praktiskdrift. [Clearing machines in practical use] Skogforsk, Resultat Nr 5; 4 p. SwedishGoogle Scholar
  26. Natural resources institute Finland (Luke) (2018) Statistical services. Silvicultural and forest improvement work 2017. http://stat.luke.fi/en/node/6967. Cited 11 September 2018
  27. O’Hara RB, Kotze DJ (2010) Do not log-transform count data. Methods Ecol Evol 1:118–122CrossRefGoogle Scholar
  28. Pettersson N, Fahlvik N, Karlsson A (2012) Röjning. 2nd edn. Skogsstyrelsen. Skogsskötselserien nr 6. SwedishGoogle Scholar
  29. Pinheiro, J, Bates, D, DebRoy, S, Sarkar, D, R Core Team (2018) nlme: linear and nonlinear mixed effects models. R package version 3.1–137. https://cran.r-project.org/package=nlme
  30. R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna. https://www.r-project.org/
  31. Roy V, Dubeau D, Auger I (2010) Biological control of intolerant hardwood competition: silvicultural efficacy of Chondrostereum purpureum and worker productivity in conifer plantations. For Ecol Manag 259:1571–1579CrossRefGoogle Scholar
  32. Saksa T, Laine T, Saarinen V-M, Salmi A, Luukkonen O (2018) Silvicultural feasibility of new early PCT methods. EFFORTE devilarible 2.3. https://www.luke.fi/efforte/wp-content/uploads/sites/14/2018/09/EFFORTE-D2.3-Silvicultural-feasibility-of-new-early-PCT-methods_29082018.pdf
  33. Salmi A (2017) Koneellisen purppuranahakkakäsittelyn vaikutus lehtipuiden vesomiseen [Sprouting of hardwoods after biological sprout control with Chondrostereum purpureum]. Master thesis. University of Helsinki, Faculty of Agriculture and Forestry, Department of Forest SciencesGoogle Scholar
  34. Setliff EC (2002) The wound pathogen Chondrostereum purpureum, its history and incidence on trees in North America. Aust J Bot 50(5):645–651CrossRefGoogle Scholar
  35. Strandström M (2016) Mechanized young stand management in Finland. In: Saksa T (ed) Proceedings of the OSCAR Workshop: mechanized and efficient silviculture. Nat Res Bioecon Studies 8/2016, 7–9Google Scholar
  36. Tamminen P, Mälkönen E (1999) Näytteenotto metsämaan ominaisuuksien määrittämistä varten [Sampling of forest land’s properties]. Metsäntutkimuslaitoksen tiedonantoja 729. FinnishGoogle Scholar
  37. The Programme for the endorsement of forest certification (PEFC) (2017) Criteria for PEFC forest certification. PEFC FI 1002:2014. http://pefc.fi/wp-content/uploads/2016/09/PEFC_FI_1002_2014_Criteria_for_Forest_Certification_20141027.pdf. Cited 14 August 2018
  38. Uotila K (2017) Optimization of early cleaning and precommercial thinning methods in juvenile stand management of Norway spruce stands. Dissertationes Forestales 231Google Scholar
  39. Uotila K, Rantala J, Saksa T (2012) Estimating the need for early cleaning in Norway spruce plantations in Finland. Silva Fenn 46(5):683–693CrossRefGoogle Scholar
  40. Vanha-Majamaa I, Tuittila E-S, Tonteri T, Suominen R (1996) Seedling establishment after prescribed burning of a clear-cut and a partially cut mesic boreal forest in southern Finland. Silva Fenn 30(1):31–45CrossRefGoogle Scholar
  41. Varmola M, Salminen H (2004) Timing and intensity of precommercial thinning in Pinus sylvestris stands. Scand J For Res 19(2):142–151CrossRefGoogle Scholar
  42. Vartiamäki H (2009) The efficacy and potential risks of controlling sprouting in Finnish birches (Betula spp.) with the fungal decomposer Chondrostereum purpureum. Dissertationes Forestales 93Google Scholar
  43. Vartiamäki H, Uotila A, Vasaitis R, Hantula J (2008) Genetic diversity in Nordic and Baltic populations of Chondrostereum purpureum: a potential herbicide biocontrol agent. Forest Pathol 38:381–393CrossRefGoogle Scholar
  44. Vartiamäki H, Hantula J, Uotila A (2009) Effect of application time on the efficacy of Chondrostereum purpureum treatment against the sprouting of birch in Finland. Can J For Res 39(4):731–739CrossRefGoogle Scholar
  45. Wall RE (1986) Pathogenicity of Chondrostereum purpureum to yellow birch. Plant Dis 70:158–160CrossRefGoogle Scholar
  46. Wall RE (1990) The fungus Chondrostereum purpureum as a silvicide to control stump sprouting in hardwoods. North J Appl For 7(1):17–19CrossRefGoogle Scholar
  47. Warton DI, Hui FKC (2011) The arcsine is asinine: the analysis of proportions in ecology. Ecology 92(1):3–10CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Natural Resources Institute Finland (Luke)SuonenjokiFinland
  2. 2.Natural Resources Institute Finland (Luke)HelsinkiFinland

Personalised recommendations