Effects of stand type on ground
lichen height and species richness
in boreal forests
P. contorta as an alternative to P. sylvestris in terms of
providing a suitable habitat
Maria Johansson
Student
Degree Thesis in Botany, 15 ECTS
Bachelor´s Level
Report passed: 30 October 2015
Supervisors: Professor Jon Moen and Dr. Alessia Uboni
Effects of stand type on ground lichen height and
species richness in boreal forests.
P. contorta as an alternative to P. sylvestris in terms of providing a suitable habitat
Maria Johansson
Abstract
Lichen-rich forests are essential to reindeer but up to 50 % of the lichen-rich areas in Sweden
have been lost since the 1950s. Ground lichens thrive in pine-heaths and with an increasing
area of plantations of the non-native tree species Pinus contorta, as an alternative to the
native P. sylvestris, it is important to investigate if P. contorta can provide such an optimal
habitat to ground lichens. The purpose of this study was to assess if forest characteristics,
such as tree density, canopy cover and production capacity, affect the abundance and growth
of five lichen species (Cladonia rangiferina, C. arbuscula/mitis, C. stygia, C. stellaris and
Cetraria islandica) and whether these characteristics differ between forests dominated by P.
contorta and forests dominated by P. sylvestris. Fieldwork was conducted in Norrbotten and
Västerbotten on sample plots previously used by the Swedish National Forest Inventory
(SNFI). The statistical analysis was based on data collected from 22 sample plots, 11 of each
forest type, visited during July and September 2015. No statistical significant differences
were found between forest characteristics of the two forest types, and none of the forest
characteristics were found to relate to lichen height. Forest age did not seem to have an effect
on ground lichens, while both canopy cover and production capacity were found to negatively
relate to the proxy for lichen biomass as well as the abundance of the most common lichen
species, C. rangiferina and C. arbuscula/mitis. The result suggests that a shadier canopy as
well as a higher production capacity contributes to a reduced distribution of ground lichens.
Keywords: Ground lichens, Pinus contorta, Pinus sylvestris, forest characteristics, forestry,
reindeer husbandry
Table of contents
1 Introduction…………………………………………………………………………………………….1
2 Methods………………………………………………………………………………………………….3
2.1 Sample plots…………………………………………………………………………………………………………….3
2.2 Lichen species and abundance…………………………………………………………………………………..4
2.3 Forest characteristics………………………………………………………………………………………………..5
2.4 Data analysis……………………………………………………………………………………………………………6
3 Results……………………………………………………………………………………………………6
3.1 Specie abundance……………………………………………………………………………………………………..7
3.2 LH and LHWC…………………………………………………………………………………………………………8
3.3 Forest characteristics……………………………………………………………………………………………….8
3.4 Effects of forest characteristics on LH, LHWC and abundance……………………………………..8
4 Discussion…………………………………………………………………………………………..…11
4.1 Specie abundance……………………………………………………………………………………………………..8
4.2 LH and LHWC………………………………………………………………………………………………………...8
4.3 Forest characteristics……………………………………………………………………………………………....8
5 References…………………………………………………………………………………………….13
Appendix A………………………………………………………………………………………………………………….15
Appendix B………………………………………………………………………………………………………………….16
Appendix C………………………………………………………………………………………………………………….17
Appendix D…………………………………………………………………………………………………………………18
1 Introduction and background
Boreal forest floors are commonly covered by reindeer lichens, which in nutrient-poor pineheaths often form dense ground covers. Understory vegetation such as lichens play an
important role by impeding soil moisture loss and influencing nutrient availability (Nilsson
and Wardle 2005). Ground lichens are essential to reindeer (Rangifer tarandus tarandus)
because they constitute the main food source during winter and early spring (Kivinien et al.
2010). During this time, up to 80 % of the reindeer diet consists of ground lichens, in
particular species in the genus Cladonia spp. and Cetraria spp. (Crittenden 1999). Estimates
indicate that as much as 50 % of the lichen-rich forests that could be found in Sweden during
the 1950s have been lost (Berg et al. 2008). Reindeer husbandry and forestry are the two
main land uses in forests of northern Sweden and the last century of changes within these
industries has caused subsequent conflicts (Kivinien et al. 2010). On one hand, reindeer can
cause substantial damage to small tree seedlings during their search for lichens hidden under
snow during winter. On the other hand, forestry activities such as soil scarification lead to a
decrease in the amount of reindeer lichens (Roturier and Bergsten 2006).
Reindeer husbandry is a meat-producing industry and all reindeer in Sweden are semidomesticated. The herding area covers about 40% of Sweden and the majority of the 51
herding districts are characterized by migrations between summer and winter grazing areas.
In the Swedish reindeer husbandry area, half of the forest lands are owned by forest
companies. Since the mechanization during the 1950s the forestry industry has transformed
from being small-scale to a modern large-scale operation. Forestry has a significant impact
on the physical structure of the forest landscape and consequently it also affects the quality of
reindeer pastures, such as the distribution and abundance of lichen species (Kivinen et al.
2010). Forest management that alters the composition of understory vegetation may result in
long-term consequences and affects the access to ground lichens for reindeer (Nilsson and
Wardle 2005).
The growth of ground lichens is highly light-dependent and the regeneration after
disturbance event takes time. An ageing forest leads to a denser canopy cover which leads to
mosses and vascular plants easily out-competing lichens (Berg et al. 2008). Lichens are
poikilohydric, i.e. their water status is influenced by current humidity and during dry periods
evaporation causes the lichen to lose water and become largely metabolically and
photosynthetic inactive (Crittenden 1999). Growth only occurs when they receive enough
light during wet active periods yet lichens can survive extended periods of desiccation. The
microclimate of different forest stands is connected to the distribution and abundance of
lichens. Characteristics such as light, moisture, temperature and available nutrients also have
influence on competition with mosses and vascular plants (Kivinen et al. 2010).
Ground lichens are particularly sensitive to both forestry and reindeer grazing because of
their poor dispersal ability and relatively low growth rates. Forestry activities can affect
ground lichens both directly and indirectly, by physically removing them or by changing the
abiotic and biotic conditions. For example, burning of clear-cut areas usually removes all
lichens and soil scarification may result in a decrease of the lichen cover by between 35 and
90 % (Berg et al. 2008). Some lichen species are particularly sensitive to reindeer trampling
during summer and grazing during winter, which can reduce their height, cover and
abundance (Den Herder et al. 2003). If an area occupied by ground lichens experience
recurrent, intense trampling by reindeer, the stress can lead to lichen loss, especially in
summer when there is no snow to protect the lichens. During winter, the damage caused by
reindeer, when digging for lichens in the snow, has been estimated to exceed the
consumption by between 2-10 times the mass (Crittenden 1999).
1
Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and deciduous trees (mainly
Betula spp.) are the main native tree species in boreal forests of Sweden (Elfving, Ericsson
and Rosvall 2001). Since the introduction of Pinus contorta in 1928, the plantations now
cover about 600 000 ha (Knight et al. 2001). Starting in the 1970s (Ågren and Knecht 2001),
P. contorta was introduced due to its high growth rate. Early experiments suggested that it
was harmless to native biota. However, present predictions suggest that it could potentially
invade all habitats now occupied by P. sylvestris, and modify forest ecosystems (Nilsson et al.
2008). Some studies indicate that both lichen diversity and biomass may be higher in P.
sylvestris than P. contorta stands, mainly caused by the greater shading in P. contorta
(Engelmark et al. 2001).
The original objective for introducing the new non-native tree species, P. contorta, was to
prevent an anticipated timber shortage caused by an over-exploitation of old-growth forests.
The initial efforts were achieved, however the faster growth in P. contorta compared to P.
sylvestris, in terms of increased timber stock, led to further decisions to extend the
plantations of P. contorta. P. contorta has been proven to produce about 36 % more volume
than P. sylvestris, irrespective of stand properties. P. contorta is also less exposed to
pathogenic fungi, insects and mammals, can survive in more harsh environments, and thrives
in a wider range of habitats. The highest proportion of P. contorta is the northern parts of the
country because of these qualities, in combination with a deficit of hardy P. sylvestris seeds
suitable for the northern climate (Elfving, Ericsson and Rosvall 2001).
The forestry practice for P. contorta is very similar to P. sylvestris. The same regeneration
methods, such as soil preparation, planting and sowing are applied to the two pine species. P.
contorta is mainly planted on previously average to more productive pine stands and less
productive spruce stands, while damp and fertile soils are avoided, as well as wind-exposed
places. The first thinning mostly occurs in earlier stages and is less intense than the one
practiced in P. sylvestris stands, in order to reduce the risk of instability injuries (Andersson
et al. 1999). Comparing stands with similar tree densities, stands dominated by P. contorta
are shadier than P. sylvestris stands (Engelmark et al. 2001).
The aim of this study was to examine if P. contorta stands are as suitable as P. sylvestris
stands in providing ground lichen habitat. The objectives were: (1) to investigate if there are
any differences in lichen biomass, average lichen height, and abundance of the different
lichen species between the two forest types, (2) to consider possible differences between the
characteristics of the two forest types and (3) to analyze if lichen biomass, height or species
abundance relate to any of the forest characteristics. My null hypotheses was that biomass,
height and abundance of ground lichens differ between the two forest types and that ground
lichens thrive better in forests dominated by P. sylvestris because of differences in forest
characteristics between the two pine species.
2
2 Methods
2.1 Sample plots
Fieldwork was conducted in the north of Sweden in the Västerbotten and Norrbotten County
during July and the beginning of September 2015 (Figure 1). The sample plots corresponded
to temporary Swedish National Forest Inventory (SNFI) plots visited during the years of
2012-2014. They were randomly selected using ArcGIS® software by Esri (2013). After the
first random selection, sample plots were chosen based on forest age (i.e. young forests < 10
years old were discarded), and reachability (< 1 km hike from the nearest road). The plots
were located by using geographic coordinates provided by the SNFI database. The GPS
devise, a GARMIN Oregon 600 t, had an accuracy of 5 to 10 meters. Most plots had still the
center marked with a wooden stick left from the SNFI field crew and measurements could be
performed at the exact location. In cases where a wooden stick could not be found, a
temporary plastic stick was placed at the location given by the GPS devise. The sample
surface was circular with a 20 m diameter (area=314.16 m2) as the area used by the SNFI.
Figure 1. Map of Sweden where fieldwork was conducted.
I visited 14 plots dominated by P. contorta and 11 plots dominated by P. sylvestris. A plot was
defined as dominated by a certain tree species if > 70% of the trees in the plot belonged to
that species. The number of P. sylvestris plots to visit was chosen in order to have an equal
sample size for the two forest types. I had to remove two P. contorta plots from the analysis
because on the contrary to what was stated in the SNFI dataset they were mixed forests with
< 70 % P. contorta. Another plot was removed because of a recent clear-cut. This left me with
11 plots from each forest types. The fieldwork was part of a larger project in which we visited
a total of 70 SNFI plots.
3
2.2 Lichen species and abundance
The lichen species sampled were Cetraria islandica and the so-called “reindeer lichens”,
which include five species occurring in northern Sweden: Cladonia arbuscula (and C. mitis),
C. rangiferina, C. stellaris and C. stygia. Reindeer lichens have a shrub-like shape with plate
and channel like branches. The trunks are often covered by bright spots underneath
(pseudocyphelles). Reindeer lichens are richly branched with small tines on the edges
(pyknides). The uppermost parts of the branches are always bent down or to the side, also
lacking for example goblets, phylloclades (scales) and soral. The upper living part is
supported by a necrotic part (podetium) that unlike other lichen species lacks bark making it
hairy like. C. rangiferina is grey with clearly one-sided bent branches and can be mixed up
with C. stygia which unlike C. rangiferina has a pitch-black podetium. C. arbuscula and C.
mitis have a very similar appearance, yellowish white to greenish and are not distinguishable
without the use of chemicals, which were not used in this study. C. stellaris is grayish white
with all-round bent branches, densely branched giving it a cushion-like appearance. When
wet the reindeer lichens turn greenish brown. Cetraria islandica has wide green lobes, often
with apothecia and a sometimes reddish base. These lichen species are common in all of
Sweden but mostly in dry areas and coniferous forests. They can often be found growing on
heaths, boulders and tufts of marshes (Hylander and Esseen 2004).
Lichen height was measured, using a graded rod with a plate that rested on the lichen thalli
during measurements (Figure 2). The measurements started from the center of the sample
plot moving in the direction of the cardinal (north, south, west, and east) and half-cardinal
(northwest, southeast, northeast, southwest) directions, and were recorded every meter,
summing up to 81 sample points for each plot, following Uotila, Hotanen and Kouki (2005)
(Figure 3). Lichen height was determined with a precision of 0.5 cm. The lichen species
present in a radius of 25 cm from each sample point were also identified and noted.
Figure 2. Graded rod for measuring lichen height.
4
Figure 3. Sampling design for measuring lichen height and recording lichen species, as well as determining the
tree density.
2.3 Forest characteristics
In terms of stand properties or forest characteristics that may have an effect on lichen height,
biomass and abundance, tree density, canopy cover, forest age and production capacity were
taken into account. The tree density was determined using a relascope, which is a tool
designed to assess the stem basal area (m 2/ha), an angle gauge commonly used for forest
inventory (Olofsson et al. 2011). The tree density was determined by calculating the average
of the measurements taken at each plot center and at the northern and southern edge of the
sample area. The set spacing marker at the end of the relascope was used to determine what
trees to count. All trees wider than the marking were counted as one, those with the same
width were counted as halves, and narrower trees were not counted at all. Both tree density
and canopy cover have been considered proxies for the amount of light that reaches the forest
floor. Data on production capacity, forest age and canopy cover were provided by the Swedish
National Forest Inventory database (SNFI). The calculations of the production capacity, i.e.
the productivity of each stand, made by SNFI have only been adapted from annual growth of
spruce and pine and vary between 1.1 to 13.9 m3 forest/ha and year. The production capacity
was determined using the average height of the trees and a site index. In this study, the
production capacity was considered as a proxy for soil nutrients. The forest age was
determined by calculating the average age at each sample plot. Undergrowth, seed trees, dead
trees and individual trees with a much higher age than the population in general were
disregarded in the estimation. The forest age was either defined by counting shoots or by
drilling and counting the rings at breast height. Then a supplement was added for the growth
upon reaching breast height. The canopy cover was determined subjectively by estimating the
percentage (%) of cover made by the tree crowns.
5
2.4 Data analysis
The data were stored and managed during fieldwork in Microsoft Excel (2013) and all
statistical analyses were performed in Minitab 16 (2010) with a significance level set at 0.05.
Mean height was calculated using the measurements of lichen height for each sample plot
excluding sample points where no lichens were found, as a proxy for vertical lichen growth
(hereafter abbreviated LH). Mean lichen height weighted by cover (hereafter abbreviated as
LHWC) was calculated for each sample plot using all 81 measurements of lichen height,
including the sample points with no lichens (i.e. lichen height=0) as a proxy of lichen
biomass. Normality tests and histograms with fit were performed to evaluate the distribution
of all variables. Most data did not follow a normal distribution, not even when transformed.
Therefore, all tests performed are the non-parametric equivalent of parametric tests as
follows: the “Mann-Whitney test” as an equivalent of the “two sample t-test” and the
“Kruskal-Wallis test” as an equivalent of the “one-way ANOVA”. A Kruskal-Wallis test was
performed to detect differences between the abundance of the five lichen species and MannWhitney tests were performed to identify how the species differed from each other in terms of
abundance. Mann-Whitney tests were also performed to detect differences between the two
forest types regarding all forest characteristics and lichen variables. Where no differences
were found between forests dominated by P. contorta and forests dominated by P. sylvestris,
the following analyses were performed on all plots, leading to a sample size of 22 plots. Boxplots were created to report all results visually. Regression analyses were performed to
investigate whether LH, LHWC and lichen abundance are affected by forest characteristics.
Because of the violation of independence between predictors, regression analyses were
performed for each forest characteristics individually. All regression residuals were checked
for normality and heteroscedasticity using diagnosis plots, and the relationships between
response and predictor variables were also interpreted with scatter plots. Violations against
the assumption of a constant variance in the residuals (heteroscedasticity) were solved using
weighted least squares regression. The weights were calculated by transforming the
predictors (x2). Lower fitted values were given a higher weight in order to increase their
squared residuals to create a constant spread over all fitted values.
3. Results
The forest age varied between 10-37 years in the forests dominated by P. contorta and
between 12-161 years in the forests dominated by P. sylvestris (with two plots exceeding a
hundred years). The tree density ranged from 2.8 to 21.8 in forests dominated by P. contorta
and from 1.5 to 27.5 in forests dominated by P. sylvestris. The production capacity varied
between 1.3-5.5 m3forest/ha and year. The canopy cover varied between 23-87 % in P.
contorta forests and between 13-73 % in P. sylvestris forests. Maximum lichen height was
11.5 cm in P. contorta forests and 12.5 cm in P. sylvestris forests. There was one plot from
each forest type where no lichens were found i.e. no data on LH, LHWC or lichen abundance
was collected.
6
3.1 Species abundance
No statistically significant differences were found on the abundance of each lichen species
between the two different forest types (p>0.05, see appendix A1) (Figure 4).
90
80
Abundance (nr of obs)
70
60
50
40
30
20
10
0
Forest type
P. c P. s
C. rangiferina
P. c P. s
C. arbuscula/mitis
P. c P. s
C. stygia
P. c
P. s
C. stellaris
P. c P. s
C. islandica
Figure 4. Lichen species abundance (number of observations per plot) related to forest types. The bars in the
centre of each box represent the median, the boxe´s upper and lower limits represent the interquartile range, and
the lines outside the boxes extend to the range of observed data. Circles represent outliers. P. c=Pinus contorta; P.
s=Pinus sylvestris.
After combining data for the two forest types, statistically significant differences between the
abundances of the different lichen species were found (Kruskal-Wallis test: H(4) =44.36,
n1=n2=n3=n4=n5=22, p<0.001). There were no significant differences between C. rangiferina
and C. arbuscula/mitis (p=0.4887), C. stygia and C. stellaris (p=0.4668) or C. stellaris and
C. islandica (p=0.1213). However, C. rangiferina and C. arbuscula/mitis were significantly
different from all the other species (p<0.05, see appendix A2) (Figure 5).
90
80
Abundance (nr of obs.)
70
60
50
40
30
20
10
0
C. r a n g ifer in a C. a r bu scu la /m it is
C. st y g ia
C. st ella r is
C. isla n dica
Figure 5. Lichen species abundance in all sample plots, independently of forest type. For an explanation of the plot
design, see Fig. 4.
7
3.2 LH and LHWC
No statically significant differences were found between the forest types regarding either LH
or LHWC (Mann-Whitney test: W=122.5, p=0.8182, W=109.5, p=0.2786 respectively)
(Figure 6).
7
6
Height (cm)
5
4
3
2
1
0
Forest type
P. c
P. s
P. c
LH
P. s
LHWC
Figure 6. LH and LHWC related to forest types. For an explanation of the plot design, see Fig. 4.
3.3 Forest characteristics
No statically significant differences were found in tree density, canopy cover, forest age and
production capacity between the two forest types (p>0.05, see appendix A3)
3.4 Effects of forest characteristics on LH, LHWC and abundance
Since there were no statistically significant differences between the two forest types in forest
characteristics and lichen species composition, LH and LHWC, the sample size could be
doubled for further regression analyses by pooling the data for the two forest types.
Regression models with LHWC as the response variable and forest characteristics as
explanatory variables violated the assumption of constant variance of the residuals
(heteroscedasticity). This was also the case for the abundance of C. rangiferina and C.
arbuscula/mitis as response variables and forest age as well as production capacity as
explanatory variables (Appendix B1, B2). None of the forest characteristics showed a
statistically significant effect on LH (p>0.05) (Appendix C1). Only canopy cover and
production capacity had a statistically significant effect on LHWC (coefficient estimate (β) =0.04, p=0.005 and (β) =-0.09, p=0.001) (Figure 7) (Appendix C2). All forest characteristics
showed a statistically significant negative effect on the abundance of C. rangiferina (Figure
8) (Appendix D1). However, only tree density, canopy cover and the production capacity had
a statistically significant negative effect on the abundance of C. arbuscula/mitis, while forest
age had no effect (Figure 9) (Appendix D2).
8
B
5
4
4
3
3
LHWC
LHWC
A
5
2
2
1
1
0
0
10
20
30
40
50
60
70
80
10
90
20
30
40
50
60
Production capacity
Canopy cover
Figure 7. Scatter plots representing the relationship between forest characteristics (canopy cover and production
capacity) and LHWC with p<0.05. The regression lines were obtained through weighted least squares to deal with
heteroscedasticity of residuals. Panel A=relationship between canopy cover and LHWC, Panel B=relationship
between production capacity and LHWC.
B
90
80
80
70
70
60
60
C. rangiferina
C. rangiferina
A
90
50
40
30
50
40
30
20
20
10
10
0
0
0
5
10
15
20
25
30
10
20
30
Tree density
40
C
60
70
80
90
D
90
90
80
80
70
70
60
60
C. rangiferina
C. rangiferina
50
Canopy cover
50
40
30
50
40
30
20
20
10
10
0
0
0
20
40
60
80
100
120
140
160
180
10
Forest age
20
30
40
50
60
Production capacity
Figure 8. Scatter plots representing the relationship between all forest characteristics and the abundance of C.
rangiferina (p<0.05). The regression lines of Panel A and B were obtained through weighted least squares to deal
with heteroscedasticity of residuals. Panel A=relationship between tree density and the abundance of C.
rangiferina, Panel B=relationship between canopy cover and the abundance of C. rangiferina, Panel
C=relationship between forest age and the abundance of C. rangiferina, Panel D=relationship between
production capacity and the abundance of C. rangiferina.
9
A
B
90
80
C. arbuscula/mitis
C. arbuscula/mitis
80
60
40
20
70
60
50
40
30
20
10
0
0
0
5
10
15
20
25
30
10
20
30
Tree density
40
50
60
70
80
90
Canopy cover
C
90
C. arbuscula/mitis
80
70
60
50
40
30
20
10
0
10
20
30
40
50
60
Production capacity
Figure 9. Scatter plots representing the relationship between forest characteristics (tree density, canopy cover and
production capacity) and the abundance of C. arbuscula/mitis (p<0.05). The regression lines of Panel A and B
were obtained through weighted least squares to deal with heteroscedasticity of residuals. Panel A=relationship
between tree density and the abundance of C. arbuscula/mitis, Panel B=relationship between canopy cover and
the abundance of C. arbuscula/mitis, Panel C=relationship between production capacity and the abundance of C.
arbuscula/mitis.
10
4 Discussion
Although no differences were found between forest types, the regression analyses resulted in
several indications pointing towards the assumption that ground lichens are affected by the
amount of light that reaches the forest floor. All statistically significant effects found were
negative e.g. greater shading results in decreased LHWC and less abundance among species.
The productivity was also found to have a negative effect on both LHWC and abundance,
which might be due to greater competition by mosses and vascular plants in more nutritious
sites. The results suggest that a shadier canopy as well as a higher productivity is negative for
ground lichens.
4.1 Species abundance
C. rangiferina and C. arbuscula/mitis were found to be the most common lichen species in
forests dominated by P. sylvestris and P. contorta. Since C. arbuscula and C. mitis were not
distinguished from one another, we cannot tell which one of these was more abundant.
Discrimination between C. stygia and C. rangiferina was only attempted on some individuals
at each sample point, so we might have misclassified C. stygia for C. rangiferina, leading to
an underestimation of the abundance. No differences were found between the forest types on
the abundance of the different species and they seemed to thrive as well in forests dominated
by P. contorta as in forests dominated by P. sylvestris. However, considering figure 4, a
boxplot illustrating the differences in abundance of the five lichen species, we can see a larger
variance and higher mean values occurring in forests dominated by P. sylvestris. A larger
sample size might be needed to assess if C. rangiferina, C. arbuscula and C. stygia are more
abundant in forests dominated by P. sylvestris. A similar study on species richness
performed by Nilsson et al. (2008) led to the conclusion that there are no differences in
species richness between P. contorta and P. sylvestris sites, yet the species pool of understory
flora is larger in P. sylvestris stands. All forest characteristics had a statistically significant
effect on the abundance of C. rangiferina (Table 2). The same forest characteristics had an
effect on the abundance of C. arbuscula/mitis except forest age which nevertheless was close
to being significant (p=0.053, Table 3). The abundance of C. rangiferina and C.
arbuscula/mitis decreased with an increasing tree density, canopy cover, and productivity.
4.2 LH and LHWC
No differences were found between the forest types concerning LH and LHWC and none of
the forest characteristics were found to relate to LH. This indicates that LH is not particularly
influenced by such parameters as tree density, canopy cover, forest age or production
capacity. LH is probably more influenced by other variables. For an example, plots
dominated by mosses tended to have a lower LHWC and a higher LH (pers. observation),
because ground lichens strived to reach the light outside of the thick moss layer. Humidity
conditions may also be related to the stand location, rather than stand type, likely influencing
lichen growth. However, higher LHWC did seem much scarcer in P. contorta than P.
sylvestris stands. LHWC was negatively influenced by canopy cover and production capacity.
This result suggests that lichen cover, rather than lichen height, is reduced in more dense
forests. Trampling and grazing by reindeer must as well be taken into consideration when
interpreting LH and LHWC. A low LH and LHWC can as well be a result of reindeer use of
the area and not of forest characteristics.
11
4.3 Forest characteristics
There were no differences found in the forest characteristics of the forest types. Since the
forestry practice for P. contorta is similar to P. sylvestris and the same methods are applied
to the two pine species, no differences on productivity between sites and forest types are
expected. However, a high productivity would imply a nutrient rich stand, making the site
less suitable for lichen growth. However, regarding canopy cover several studies state that P.
contorta stands are shadier than P. sylvestris stands when tree densities are comparable, by
producing more needles (Engelmark et al. 2001; Elfving, Ericsson and Rosvall 2001; Ågren
and Knecht 2001; Nilsson et al. 2008). This would also result in a greater canopy closure at
an earlier age in P. contorta stands (Elfving, Ericsson and Rosvall 2001). Since my analysis
did not consider forest age when comparing canopy cover between forest types, no conclusion
on whether canopy closure happens faster in P. contorta stands compared to P. sylvestris can
be made. Further studies are needed to address this topic.
Forest age does not seem to have much effect on ground lichens. My results indicate that
there is not any clear relationship between forest age and LH, LHWC or abundance of lichen
species. Kivinen et al. (2010) suggests that forest age per se might not have any effect on
lichens. Lichens might rather be related to other forest characteristics that influence for
example light penetration. Some studies have found that lichen cover increases up to the first
30 years since plantation (Brakenhielm and Persson 1980; Nieppola 1992), and as the canopy
closes, less light reaches the forest floor and the lichens are outcompeted by mosses (Kivinen
2010). My study suggests that both canopy cover and production capacity negatively
influence LHWC.
Another forest characteristic, tree density, was also found to affect the abundance of the
lichen species, which underlines that ground lichens are affected by shading. The method for
deciding the tree density does not take into account all small trees and the effect of those on
lichens and the effect of tree density especially in young forests might be underestimated.
However, this should be the case in both forest types, and therefore the impact would not
result in any differences between forest types. In this study I was able to sample only 11 plots
of each forest types which might have affected the results, showing no differences between
forest dominated by P. contorta and forests dominated by P. sylvestris. However, this lack of
differences made it possible to add the data giving me 22 sample plots to base the analysis
regarding plausible affects of forest characteristics on ground lichens. Those results are
however consistent with several other recent studies.
As mentioned above, according to Engelmark et al. 2001; Elfving, Ericsson and Rosvall 2001;
Ågren and Knecht 2001; Nilsson et al. 2008, P. contorta produces more needles than P.
sylvestris and as a result the greater shading decreases the amount of light that reaches the
forest floor. Nilsson et al. (2008) found that P. contorta produces about three times as much
needle litter as P. sylvestris and concluded that this shadier conditions can cause less habitat
variation and suppress understory flora. Greater shading might change the conditions for
plants and animals influencing even the soil processes. According to Engelmark et al. (2001)
this seems to be the main difference between these two forest types. Therefore, I suggest
future studies to consider needle litter and assess its role on lichen growth and competition
with mosses and vascular plants.
12
5 References
Andersson, B., Rosvall, O., Engelmark, O. and Sjoeberg, K. 1999. Environmental impact
analysis (EIA) concerning Lodgepole-pine forestry in Sweden. The Forestry Research
Institute of Sweden. Uppsala, SE. Report No. 3.
Berg, A., Östlund, L., Moen, J. and Olofsson, J. 2008. A century of logging and forestry in a
reindeer herding area in northern Sweden. Forest Ecology and Management. 256:10091020.
Bråkenhielm, S., Persson, H. 1980. Vegetation dynamics in developing Scots pine stands in
central Sweden. Ecological Bulletins. 32:139-152.
Bäcklund, S., Jonsson, M. T., Strengbom, J. and Thor, G. 2015. Composition of functional
groups of ground vegetation differ between planted stands of non-native Pinus contorta and
native Pinus sylvestris and Picea abies in northern Sweden. Silva Fennica. vol. 49 no. 2
article id 1321
Crittenden, P.D. 1999. Aspects of the ecology of mat-forming lichens. Rangifer 20(2/3):127139.
Den Herder, M., Kytoviita, MM., Niemela, P. 2003. Growth of reindeer lichens and effects of
reindeer grazing on ground cover vegetation in a Scots pine forest and a subartic heathland in
Finnish Lapland. Ecography. 26:3-12.
Elfving, B., Ericsson, T. and Rosvall O. 2001. The introduction of lodgepole pine for wood
production In Sweden – A rewiev. Forest Ecology and Mangement 141:15-29.
Engelmark, O., Sjöberg, K., Andersson, B., Rosvall, O., Ågren, G.I., Baker, W.L., Barklund, P.,
Björkman, C., Despain, D.G., Elfving, B., Ennos, R.A., Karlman, M., Knecht, M.F., Knight,
D.H., Ledgard, N.J., Lindelöw, Å., Nilsson, C., Peterken, G.F., Sörlin, S. and Sykes, M.T.
2001. Ecological effects and management aspects of an exotic tree species: the case of
lodgepole pine in Sweden. Forest Ecology Management. 141:3-13.
ESRI. 2014. ArcGIS Desktop: Release 10.2.1. Redlands, CA: Environmental Systems
Research Institute.
Hylander, K. and Esseen, P.-A. 2004. Lavkompendium för Nationell Inventering av
Landskapet i Sverige (NILS). Sveriges Lantbruksuniversitet, Arbetsrapport 135.
Kivinen, S., Moen, J., Berg, A. and Eriksson, Å. 2010. Effects of modern forest management
on winter grazing resources for reindeer in Sweden. Ambio 39:269-278.
Knight, D.H., Baker, W.L., Engelmark, O. and Nilsson, C. 2001. A landscape perspective on
the establishment of exotic tree plantations: Lodgepole pine (Pinus contorta) in Sweden.
Forest Ecology Management. 141:131-142.
Microsoft. 2013. Microsoft Excel [computer software]. Microsoft: Redmond, USA.
Minitab 16. 2010. [computer software]. Minitab Inc: Pennsylvania, USA.
Nieppola, J. 1992. Long-term vegetation changes in stands of Pinus sylvestris in southern
Finland. Journal of Vegetation Science. 3:475-484.
13
Nilsson, C., Engelmark, O., Cory, J., Forslund, A. and Carlborg, E. 2008. Differences in litter
cover and understorey flora between stands of introduced lodgepole pine and native Scots
pine in Sweden. Forest Ecology and Management. 255:1900-1905.
Nilsson, M.C. and Wardle, D.A. 2005. Understory vegetation as a forest ecosystem driver:
evidence from the northern Swedish boreal forest. Frontiers in Ecology and the Enviroment.
3:421-428.
Olofsson, A., Danell, Ö., Forslund, P. and Åhman, B. 2011. Monitoring changes in lichen
resources for range management purposes in reindeer husbandry. Ecological Indicators.
11:1149-1159.
Roturier, S. and Bergsten, U. 2006. Influence of soil scarification on reindeer foraging and
damage to planted Pinus sylvestris seedlings. Scandinavian Journal of Forest Research.
21:209-220.
Uotila, A., Hotanen, J-P., Kouki, J. 2005. Succession of understory vegetation in managed
and seminatural Scots pine forests in eastern Finland and Russian Karelia. Canadian
Journal of Forest Research. 35:1422-1441.
Ågren, G.I. and Knecht, M.F. 2001. Simulation of soil carbon and nutrient development
under Pinus sylvestris and Pinus contorta. Forest Ecology Management. 141:117-129.
14
Appendix A
Table A1. Results of Mann-Whitney tests, comparing the abundance of each lichen species between the two forest
types.
C. rangiferina
C. arbuscula/mitis
C. stygia
C. stellaris
C. islandica
W
102.5
101.5
107.0
121.0
-
p
0.1228
0.1077
0.2122
0.7427
-
Table A2. Results of Mann-Whitney tests, comparing the abundance of the five lichen species, between the two
forest types.
rangiferina; arbuscula/mitis
rangiferina; stygia
rangiferina; stellaris
rangiferina; islandica
arbuscula/mitis; stygia
arbuscula/mitis; stellaris
arbuscula/mitis; islandica
stygia; stellaris
stygia; islandica
stellaris; islandica
W
525.0
648.0
672.0
699.5
633.0
665.0
698.0
526.5
583.5
561.5
p
0.4887
0.0003
0.0000
0.0000
0.0012
0.0001
0.0000
0.4668
0.0389
0.1213
Table A3. Results of Mann-Whitney tests, comparing the forest characteristics between the two forest types.
Tree density:
Canopy cover:
Production capacity:
Forest age:
W
136.0
154.0
128.0
112.5
p
0.5545
0.0762
0.9476
0.3754
15
Appendix B
Normal Probability Plot
Versus Fits
Standardized Residual
99
Percent
90
50
10
1
-2
-1
0
1
Standardized Residual
2
2
1
0
-1
-2
0,0
0,5
Histogram
Standardized Residual
Frequency
6
4
2
-1,5 -1,0 -0,5 0,0 0,5 1,0 1,5
Standardized Residual
2,0
Versus Order
8
0
1,0
1,5
Fitted Value
2,0
2
1
0
-1
-2
2
4
6
8 10 12 14 16
Observation Order
18
20
22
Figure B1. Diagnosis plot representing the relationship between LHWC and tree density. The residuals do not
show a constant spread across the fitted values and the residuals are not normally distributed.
Normal Probability Plot
Versus Fits
Standardized Residual
99
Percent
90
50
10
1
-2
-1
0
1
Standardized Residual
2
2
1
0
-1
0,50
0,75
Frequency
4,8
3,6
2,4
1,2
0,0
-1,5 -1,0 -0,5 0,0 0,5 1,0 1,5
Standardized Residual
1,50
Versus Order
Standardized Residual
Histogram
1,00
1,25
Fitted Value
2,0
2
1
0
-1
2
4
6
8 10 12 14 16
Observation Order
18
20
22
Figure B2. Diagnosis plot representing the relationship between LHWC and tree density after correcting the
residuals with weighted regression. The fitted values show a more constant spread and the probability plot results
in a more linear distribution.
16
Appendix C
Table C1. Results of regression analyses with LH as the response variable and forest characteristics as explanatory
variables.
(Intercept)
Tree density
β
2.85
0.06
Std. Error t value
0.82
3.49
0.05
1.07
p
0.002
0.297
(Intercept)
Canopy cover
β
3.91
-0.01
Std. Error t value
1.16
3.36
0.02
-0.27
p
0.003
0.792
(Intercept)
Forest age
β
2.85
0.02
Std. Error t value
0.59
4.80
0.01
1.68
p
0.000
0.108
(Intercept)
Production capacity
β
5.22
-0.04
Std. Error t value
1.58
3.30
0.04
-1.04
p
0.004
0.310
Table C2. Results of univariate weighted least squares regression models with LHWC as the response variable and
forest characteristics as explanatory variables. (Intercept)= the value at which the fitted line crosses the y-axis. β =
mean change in the response variable for one unit of change in the predictor variable. Std.Error= standard error,
average distance between residuals and the regression line. t value=the regression coefficient divided by its
standard error, used to determine p. p= tests the null hypothesis that the coefficient is equal to zero.
(Intercept)
Tree density
β
1.67
-0.04
Std. Error t value
0.64
2.61
0.03
-1.27
p
0.017
0.218
(Intercept)
Canopy cover
β
3.31
-0.04
Std. Error t value
0.81
4.10
0.01
-3.13
p
0.001
0.005
(Intercept)
Forest age
β
1.62
0.00
Std. Error t value
0.48
3.37
0.00
-1.10
p
0.003
0.284
(Intercept)
Production capacity
β
4.72
-0.09
Std. Error t value
1.03
4.57
0.03
-3.69
p
0.000
0.001
17
Appendix D
Table D1. Results of univariate weighted least squares regression models with the abundance of C. rangiferina as
the response variable and forest characteristics as explanatory variables. For an explanation of the table design see
Table C2.
(Intercept)
Tree density
β
51.14
-1.77
Std. Error t value
11.57
4.42
0.77
-2.31
p
0.000
0.032
(Intercept)
Canopy cover
β
68.42
-0.73
Std. Error t value
15.24
4.49
0.26
-2.85
p
0.000
0.010
(Intercept)
Forest age
β
39.58
-0.20
Std. Error t value
9.05
4.38
0.07
-2.61
p
0.000
0.017
(Intercept)
Production capacity
β
102.52
-2.05
Std. Error t value
21.63
4.74
0.53
-3.87
p
0.000
0.001
Table D2. Results of univariate weighted least squares regression models with the abundance of C.
arbuscula/mitis as the response variable and forest characteristics as explanatory variables. For an explanation of
the table design see Table C2.
(Intercept)
Tree density
β
47.62
-1.96
Std. Error t value
10.62
4.49
0.70
-2.78
p
0.000
0.011
(Intercept)
Canopy cover
β
61.67
-0.71
Std. Error t value
14.51
4.25
0.24
-2.94
p
0.000
0.008
(Intercept)
Forest age
β
29.13
-0.14
Std. Error t value
8.41
3.46
0.07
-2.06
p
0.002
0.053
(Intercept)
Production capacity
β
84.60
-1.73
Std. Error t value
20.63
4.10
0.51
-3.42
p
0.001
0.003
18