Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
DOI 10.1186/s40490-017-0103-5
RESEARCH ARTICLE
New Zealand Journal of
Forestry Science
Open Access
Short-term effects of single-tree selection
cutting on stand structure and tree species
composition in Valdivian rainforests of
Chile
Florian Schnabel1* , Pablo J. Donoso2 and Carolin Winter3
Abstract
Background: The Valdivian temperate rainforest, one of the world’s 25 biodiversity hotspots, is under a continued
process of degradation through mismanagement. An approach to reverse this situation might be the development
of uneven-aged silviculture, combining biodiversity conservation and timber production.
Methods: We examined the short-term effects of single-tree selection cutting on stand structure and tree species
(richness, diversity and composition) in the Llancahue Experimental Forest in south-central Chile to quantify
changes in comparison with old-growth rainforests of the evergreen forest type. We compared plots with high and
low residual basal areas (60 and 40 m2 ha−1) and a control old-growth forest.
Results: Both cutting regimes achieved a balanced structure with reverse-J diameter distribution, continuous forest
cover and sufficient small-sized trees. Compared to the old-growth forest, there were no significant changes in tree
species richness and diversity. The only shortcomings detected were significant reductions in diameter and height
complexity as assessed by the Gini coefficient, Shannon H′ and standard deviation, with a significantly lower
number of large-sized trees (dbh 50 cm+, height 23 m+), especially in the low residual basal area regime.
Conclusions: We suggest the intentional retention of a certain number of large-sized and emergent trees as
strategy for biodiversity conservation. If adjusted accordingly, single-tree selection is a promising approach to retain
many old-growth attributes of the Valdivian rainforest in managed stands while providing timber for landowners.
Keywords: Uneven-aged silviculture, Old-growth forest attributes, Biodiversity, Evergreen forest type, Sustainable
forest management, Temperate rainforests
Background
The Chilean evergreen rainforest in the Valdivian Rainforest Ecoregion (35–48° S) is a unique, but endangered,
ecosystem. It is one of the world’s 25 biodiversity hotspots due to its abundance of vascular plant and vertebrate species and high degree of endemism, as well as a
conservation priority due to it undergoing exceptional
loss of habitat (Myers et al. 2000; Olson and Dinerstein
1998). This loss is caused basically due to illegal logging
and inappropriately conducted legal selective cutting
* Correspondence: florianschnabel@posteo.org
1
Chair of Silviculture, Faculty of Environment and Natural Resources,
University of Freiburg, Tennenbacherstr. 4, 79106 Freiburg, Germany
Full list of author information is available at the end of the article
(cut the best and leave the worst; sensu Nyland (2002)),
which destroy the multi-aged stand structure of these
old-growth forests, leading to thousands of hectares of
degraded forests (Moorman et al. 2013; Donoso 2013;
Schütz et al. 2012; Myers et al. 2000; Olson and Dinerstein 1998). Old-growth forests of the evergreen forest
type harbour the highest tree species richness in Chile
and consist of a mixture of mostly shade-tolerant and
moderately shade-tolerant (hereafter referred to as “midtolerant”) broadleaved evergreen hardwood species and
some conifers of the Podocarpaceae family (Donoso and
Donoso 2007). The biodiversity associated with the
structural and compositional attributes of these oldgrowth forests must not only be maintained in reserves
© The Author(s). 2017 Open Access This 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.
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
(Moorman et al. 2013; Bauhus et al. 2009) but also in
managed forests, combining the needs of the local population for forest products with biodiversity conservation
(Moorman et al. 2013). A promising way to address this
is the development of a silviculture regime that: (a)
maintains forest attributes that are close to the natural
state of old-growth forests; and (b) allows stakeholders
to benefit from timber harvesting.
In this study, we use the term “old-growthness” to
refer to the degree of the retention of old-growth structural and compositional attributes in managed stands
following Bauhus et al. (2009). Old-growth forests are
defined here through the presence of key structural and
compositional attributes including a high number of
large trees, a wide range of tree sizes, complex vertical
layering, the presence of late successional tree species
and large amounts of standing and lying dead wood
among others (Bauhus et al. 2009; Mosseler et al. 2003).
The maintenance of these attributes in managed stands
is essential for sustaining forest biodiversity as has been
illustrated, for example, in boreal ecosystems (Bauhus et
al. 2009 and citations within). The rationale is that since
natural forest ecosystems and their dynamics are able to
sustain the whole range of forest-dwelling species and
forest functions, silviculture that mimics natural dynamics should be a good approach for sustaining forest biodiversity (Schütz et al. 2012).
Currently in Chile, the application of single-tree selection cutting is believed to be the most promising and adequate approach for uneven-aged forests (Donoso 2013;
Schütz et al. 2012; Donoso et al. 2009; Siebert 1998).
Chilean native evergreen forests in south-central Chile
are dominated by several commercially valuable hardwood shade-tolerant or mid-tolerant species, a major
requisite to work with selection silviculture. Moreover, it
has been shown that some of these species have much
faster diameter growth rates under lower levels of basal
area than those found in dense unmanaged old-growth
forests (Donoso 2002; Donoso et al. 2009). Due to rare
implementation, however, it remains unknown how the
forest ecosystem is influenced through this type of silviculture and which would be the economic benefits in
Chile, although preliminary estimates of timber revenues
are positive (Nahuelhual et al. 2007).
Nonetheless, there is abundant evidence for other forests that selection systems can maintain a high forest
cover, complex vertical layering and balanced/regulated
structures while providing income through timber sales
at regular intervals on a sustainable basis (e.g. O’Hara
2014; Schütz et al. 2012; Pukkala and Gadow 2012;
Gronewold et al. 2010; O’Hara et al. 2007; Keeton 2006;
Bagnaresi 2002). For example, forests in the European
Alps can harbour high structural and vegetation diversity
even after several centuries of uneven-aged management
Page 2 of 14
(Bagnaresi 2002). The possible lack of old-growth attributes like large-sized trees can, however, be a concern
(Bauhus et al. 2009 and citations within). Another general concern regarding selection silviculture is that
through their evenly distributed small-scale disturbances, single-tree selection cutting might favour the development of shade-tolerant species at the expense of
mid-tolerant ones, creating an abundant but
homogenous regeneration and relatively low horizontal
heterogeneity (Angers et al. 2005; Doyon et al. 2005).
These considerations should be addressed before a
new silvicultural scheme is applied at a large scale to
avoid unwanted side effects. In the present work, our
aim was, therefore, to evaluate the impacts of single-tree
selection cutting with two different residual basal areas,
upon structural and compositional attributes of oldgrowth temperate rainforests of the evergreen forest
type. We were interested in finding management approaches that could avoid negative impacts on oldgrowth attributes and associated biodiversity at the stand
scale. The objectives were to: (a) quantify the type and
magnitude of structural and compositional changes induced through single-tree selection cutting with high residual basal areas (HRBA; 60 m2 ha−1) and low residual
basal areas (LRBA; 40 m2 ha−1); and (b) identify key
structural and compositional attributes of oldgrowthness that were affected through single-tree selection cutting with HRBA and LRBA. Unmanaged and
well-conserved forests of the evergreen forest type in
Chile reach 80–100 m2 ha−1 in basal area and support
regeneration of almost exclusively shade-tolerant species
(Donoso and Nyland 2005). The rationale for these two
levels of residual basal areas was, therefore, that singletree selection with LRBA would create relatively more
light availability and was expected to favour the development of both ecologically and economically important
mid-tolerant tree species (Donoso 2013). However, there
might be trade-offs in terms of greater structural and
compositional changes at LRBA compared with HRBA.
Methods
Study area and experimental design
The study was conducted in the Llancahue watershed
(39° 50′ 20″ south and 73° 07′ 18″ west) in the intermediate depression of south-central Chile, a 1270-ha
state-owned reserve that is administered by the University Austral de Chile (UACh) (Fig. 1).
The low-elevation forest of the study area corresponds
to the evergreen forest type, more specifically to the subtype dominated by shade tolerant species with few emergent Nothofagus trees, according to the official
classification in Chile, and is part of the Valdivian Rainforest Ecoregion (Donoso and Donoso 2007). Llancahue
lies between 50 and 410 m a.s.l., receives 2100 mm
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
Page 3 of 14
Fig. 1 Study area showing the location of old-growth control (n = 4), high residual basal area (HRBA, n = 4) and low residual basal area
(LRBA, n = 4) plots
average annual rainfall and has an average annual
temperature of 12.2 °C (Oyarzún et al. 2005; Fuenzalida 1971). Stands dominated by the shade-tolerant
species Aextoxicon punctatum R. et Pav. and Laureliopsis philippiana (Looser) Schodde and the midtolerant species Eucryphia cordifolia Cav. and Drimys
winteri J.R. et G. Forster were chosen. All stands had
an uneven-aged structure and basal areas characteristic for this forest type. In the intermediate depression
of south-central Chile, nearly all remnant old-growth
forests show signs of illegal selective cuttings, especially since the twentieth century (Donoso and Lara
1995) and at low elevations. Signs include large
stumps of few valuable species and increased cover of
Chusquea spp., especially at low residual densities.
This is also the case for stands selected in this study,
which show signs of past harvests of a few large trees
over the last three decades.
The experimental design consisted of eight plots
2000 m2 (50 × 40 m) each, which were subjected to
single-tree selection cutting in 2012 and were reevaluated two growing seasons afterwards in 2014. Four
plots were cut to achieve a residual basal area of
60 m2 ha−1 and four plots to 40 m2 ha−1, called high and
low residual basal area (HRBA and LRBA), respectively
(Table 1). The BDq method proposed by Guldin (1991)
with a maximum diameter of 80 cm and a q factor (the
difference between successive diameter classes) of 1.3 in
average for a balanced diameter distribution was used
based on recommendations in Schütz et al. (2012). Since
this was the first time the stands were cut following a selection system, only half of the trees above the maximum diameter were cut to avoid a severe change and
potential damage to residual trees. The main target
species of selection silviculture are A. punctatum, L. philippiana, D. winteri, E. cordifolia and Podocarpaceae
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
Page 4 of 14
Table 1 Basal area (m2 ha−1) per treatment and plot before (2012) and after the harvesting (2014)
Control
Plot
High residual basal area (HRBA)
Low residual basal area (LRBA)
Plot
Before
After
Harvested
Plot
Before
After
harvested
S1
50.0
41.3
8.7
C1
83.8
S2
73.3
59.4
13.9
C2
99.2
S4
93.1
59.9
33.2
S3
65.2
41.4
23.8
C3
77.6
S6
59.2
53.0
6.2
S5
61.1
38.7
22.4
C4
85.8
S8
80.2
60.6
19.6
S7
Average
86.6
76.5
58.2
18.2
conifers if the expected product is timber and E. cordifolia if the objective of the harvest is firewood. For this
first harvest, the rule “cut the worst, leave the best” was
applied to enhance the quality and growth of the residual stock by preferentially harvesting defective and
unhealthy trees. This approach contrasts with current
selective cuttings that are used under the Chilean law,
which do not control for residual stand structure, allow
the harvest of 35% of the basal area per hectare in 5year cutting cycles and preferably cut the most valuable
trees instead of the worst (Donoso 2013; Schütz et al.
2012).
Four permanent plots 900 m2 (30 × 30 m) each that
showed only minimal signs of past illegal cuttings were
used as control. Although smaller than the cut plots,
plot sampling sizes for temperate old-growth forests
have been traditionally considered adequate with at least
500 m2 in Chile (Prodan et al. 1997) and elsewhere
(Lombardi et al. 2015), so the plots used in this study
provide a reliable sampling of the variables tested. Moreover, different plot sizes were addressed through choosing analysis methods that allow for unbiased testing of
different sample sizes (see below).
In Chile, the cutting intensity for the evergreen forest
type is restricted to an average maximum of 35% of the
original basal area (Donoso 2013). To achieve two levels
of residual basal areas (HRBA and LRBA), while at the
same time complying with the legal restrictions, we had
to choose plots with the lowest initial basal areas for
LRBA (average 34% of the basal area cut) and those with
the largest basal areas for HRBA (average 24% of the
basal area cut) (Table 1). Final average residual basal
areas were 58.2 m2 ha−1 for HRBA plots and 41.2 m2 ha
−1
for LRBA plots (Table 1). The plot where the least
trees were cut was number S6 (10.5%), and the one with
the most trees cut was S7 (41.6%). Apart from these extremes, plots had a cutting intensity that ranged between
17 and 37% of the original basal area.
We acknowledge differences in the original basal areas
of the three groups of plots selected for this study (oldgrowth, HRBA and LRBA). However, to reach the expected residual basal areas proposed by Donoso (2002)
for uneven-aged silviculture in Chilean forests, within
legal restrictions, we had to choose these partially cut
74.2
43.4
30.9
62.7
41.2
21.4
stands that are common in the landscape. From there,
rather than from pristine old-growth forests, we sought
to find out how selection stands do, or do not, maintain
old-growth attributes.
Sampling design and data collection
Three parameters for quantifying structural and compositional attributes were used in this study that have
been largely and successfully applied in other ecosystems
(e.g. Gadow et al. 2012; Lexerød and Eid 2006; McElhinny et al. 2005): (a) diameter at breast height (dbh)
measured at 1.3 m; (b) tree height; and (c) tree species.
All trees with a dbh ≥ 5 cm were recorded by species
and diameter for the eight plots before cutting (2012)
and were re-evaluated two growing seasons after harvesting (2014). The four control plots were measured
once in 2014. Tree height was included as an additional
and more direct measurement of vertical complexity
only in 2014. Tree height was measured for all trees with
dbh ≥ 10 cm with a Vertex III hypsometer.
To quantify tree size complexity, three diversity indices were used to analyse the diameter and height data of
the trees: (a) standard deviation; (b) Gini coefficient
(Lexerød and Eid 2006; Gini 1912); and (c) ln-based
Shannon index (H′) (Lexerød and Eid 2006; Shannon
1948). Standard deviation has been widely used as a way
to calculate diameter and height complexity and can be
compared with more complex indices for stand structural comparisons (McElhinny et al. 2005 and citations
within). The Gini coefficient has also been used successfully to describe structural changes. For example,
Lexerød and Eid (2006) found that the Gini coefficient
was superior in discriminating between stands and was
considered to have a very low sensitivity to sample size
in a comparison of eight diameter diversity indices. It is
calculated with the following equation:
Pn
j¼i 2j −n−1 baj
GC ¼ Pn
ð1Þ
j¼i baj ðn−1Þ−1Þ
where ba stands for the basal area of tree j (m2 ha−1).
Finally, the Shannon index is a widely used measure of
tree size complexity for diameter distributions, which allows a direct comparison of different distributions through
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
one single value (e.g. Lexerød and Eid 2006; McElhinny et
al. 2005; Wikström and Eriksson 2000). It is calculated
after the following equation:
0
H ¼−
S
X
P i lnðP i Þ
ð2Þ
i¼1
where P stands for the proportion of number of trees in
size class i or per species i and S, for the number of dbh
classes or species.
An important quality of the Shannon index and the
Gini coefficient is their independence of stand density as
proven for example by Lexerød and Eid (2006). These
indices were used: (a) due to their abilities documented
in the literature (especially independence of sample size);
and (b) to have a more robust result than using only one
index. The standard deviation and the Gini coefficient
were calculated from original individual tree data while
the Shannon index was calculated on the basis of 5-cm
diameter classes as suggested by Lexerød and Eid (2006).
All three indices have been used similarly to describe
diameter complexity as well as height complexity
(Lexerød and Eid 2006). The values of the Gini coefficient range from (0, 1), with 1 standing for total inequality, while the Shannon index values range from (0, ln(S))
(Lexerød and Eid 2006). The standard deviation ranges
from [0, ∞]. For all three indices, a higher index value
reflects a wider range of tree diameters and heights and
consequently greater complexity (Lexerød and Eid
2006). Index values were calculated for each plot and
then compared between treatments to quantify the
changes in structural complexity after management. To
quantify a potential loss of large and/or emergent trees
in more detail than with the complexity indices, trees
were grouped in five diameter classes and five height
strata based on diameter and height ranges known for
these forests (Table 2).
Tree species richness was assessed using rarefaction, a
statistical method to repeatedly re-sample richness out
of a random pool of samples constructed out of the field
data (e.g. different plots). This allowed an unbiased comparison of richness among different plot sizes (Kindt and
Coe 2005). The rarefaction curve represents the average
Table 2 Diameter (dbh) classes and height strata used to
compare plots in this study
Diameter class
Range (cm)
Height stratum
Range (m)
Very small
5–9.9
Low understorey
0–9.9
Small
10–24.9
Upper understorey
10–14.9
Intermediate
25–49.9
Low canopy
15–22.9
Large
50–99.9
Upper canopy
23–29.9
Very large
100+
Emergent
30+
Page 5 of 14
richness of a treatment at a given number of sampled
area.
Species diversity and evenness per treatment were calculated for each plot using the ln-based Shannon index
(H′) (Eq. 2) and evenness using Pielou’s evenness index
(J′) as proposed by several authors (e.g. Alberdi et al.
2010; Kern et al. 2006). The value of J′ was calculated as
H′/ln(S) where H is the Shannon diversity index and S,
the species richness. Species diversity indices are
dependent on sample size (Kindt and Coe 2005) so they
were calculated only for the plots with selection cutting.
This was done to avoid a biased comparison of species
diversity due to the different sample sizes between
treated and untreated plots.
To assess changes in species composition in more detail, the number of trees per species was calculated for
each plot and then compared between treatments. Furthermore, the importance value (IV; Eq. 3) of each species was calculated as the sum of its relative density
(RD), relative dominance (Rd) and relative frequency
(RF) where density (D) is the number of individuals per
hectare, dominance (d) is the basal area (BA) of each
species per hectare and frequency (F) is the number of
plots where a species is present divided by the total
number of plots (de Iongh Arbainsyah et al. 2014;
Mueller-Dombois and Ellenberg 1974).
IV ðImportance ValueÞ ¼ ðRD þ Rd þ RFÞ=3
ð3Þ
Statistical analysis
Index data as well as the number of trees in the diameter
and height classes were compared between the different
years of observation (pre/post harvesting) and among
treatments (control/HRBA/LRBA). Index data was analysed using linear mixed models (LMM) and generalised
least square models (GLS). For the number of trees, generalised linear mixed models (GLMM) and generalised
linear models (GLM) with Poisson distribution were
used, since the data correspond to counts of individuals.
Overdispersion was tested and, if found, a quasi-GLM
model was used with a variance of ø × μ with ø as dispersion parameter and μ as mean, as suggested by Zuur
et al. (2009).
The effect of harvesting was determined using year
and treatment as fixed effects. The difference between
years was analysed through comparing the treatments
with selection cutting, without incorporating the control.
For this analysis, plots were incorporated as random
effect since repeated measurements were used in this
study, thus using LMM or GLMM for this analysis. The
differences among the three treatments were analysed
before and after the harvesting by GLS and GLM using
management as fixed effect. The assumptions of
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
Page 6 of 14
normality and heterogeneity of variance were tested
through examining the model residuals and the ShapiroWilk test for normality. If heterogeneity of variance was
found, the variance function (varIdent) was used to
model heteroscedasticity to avoid transformations.
Models with and without variance functions were compared through the information criteria AIC and the
model presenting the lowest AIC was chosen. All statistical analysis was conducted using R 3.1 (R Core Team
2014), the R packages nlme (Pinheiro et al. 2014), vegan
(Oksanen et al. 2014), BiodiversityR (Kindt and Coe
2005) and AER (Kleiber and Zeileis 2008) as well as the
software InfoStat (DiRienzo et al. 2011).
Results
Diameter distribution
The control plots showed a reverse-J diameter distribution with a slight trend to a rotated-sigmoid distribution,
due to a relatively high number of large trees between
50 and 100 cm dbh (Fig. 2). The HRBA and LRBA plots
showed a reverse-J diameter distribution before and after
the application of single-tree selection cutting (Fig. 2).
In comparison to the control plots, treated plots had
around twice as many young trees between 5 and 15 cm
dbh (Fig. 2) before and after harvesting. For emergent
700
trees (100 + cm dbh), a clear trend existed, with most
trees in this diameter class in the control plots (max.
diameter 160 cm) and few in the LRBA plots (max.
diameter 105 cm). The LRBA plots had already fewer
emergent trees before harvesting (plots had been slightly
subjected to “selective” cuts in the past as mentioned
above) but the difference became more pronounced after
single-tree selection cutting. Additionally, selection cutting reduced the number of large trees (50–100 cm
dbh), especially in the LRBA plots (Fig. 2).
Structural complexity indices
Harvesting significantly reduced diameter complexity as
assessed by the Gini coefficient (p = 0.0012), the Shannon index (p = 0.0039) and the standard deviation
(p = 0.0002) (Table 3). Moreover, all three indices provided a logical and consistent ranking of diameter complexity, with the highest index values in the control,
followed by the HRBA and then by the LRBA plots after
harvesting (Table 3).
The Gini coefficient for diameter complexity was not
significantly different between control and treated plots,
while the Shannon index had already significantly higher
values in the control plots before harvesting (see asterisks, Table 3). The significance of this difference became
Control
Density (trees ha-1)
600
500
400
N initial/final
300
200
100
0
5
20
35
50
65
80
95 110 125 140 155
(a)
700
700
HRBA
LRBA
600
500
400
N initial
300
N final
200
Density (trees ha-1)
Density (trees ha-1)
600
dbh class (cm)
500
400
N initial
300
N final
200
100
100
0
0
5
20 35 50 65 80 95 110 125 140 155
dbh class (cm)
(b)
5
20 35 50 65 80 95 110 125 140 155
dbh class (cm)
(c)
Fig. 2 Observed diameter distributions of a the old-growth control, b high residual basal area (HRBA) and c low residual basal area (LRBA). The
histograms represent the average number of trees per hectare per treatment (four plots each) before and after harvesting
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
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Table 3 Mean values for the Gini coefficient, the Shannon index and the standard deviation for diameter at breast height (dbh) and
height data and for the Shannon index and the evenness index for species diversity before and after harvesting. Values are
expressed as mean ± 1 standard deviation. Significant treatment differences with the control are shown with asterisks after the
index value, with *p = 0.05–0.01, **p = 0.01–0.001 and ***p = <0.001, respectively. Significant harvesting impacts are mentioned in
the text
Control
High residual basal area (HRBA)
Low residual basal area (LRBA)
Before
After
Before
After
Diameter complexity
Gini coefficient
0.814 ± 0.020
0.811 ± 0.040
0.801 ± 0.041
0.804 ± 0.021
0.789 ± 0.020
Shannon index
2.051 ± 0.147
1.833 ± 0.069*
1.773 ± 0.068**
1.793 ± 0.038**
1.655 ± 0.102***
Standard deviation
25.085 ± 3.867
19.001 ± 4.354*
16.762 ± 2.834**
17.438 ± 1.696*
14.499 ± 1.036***
Height complexity
Gini coefficient
0.214 ± 0.026
–
0.198 ± 0.028
–
0.207 ± 0.017
Shannon index
1.747 ± 0.172
–
1.458 ± 0.185*
–
1.457 ± 0.093*
Standard deviation
7.973 ± 1.233
–
5.739 ± 1.378*
–
5.846 ± 0.549*
Shannon index
–
1.888 ± 0.177
1.869 ± 0.206
1.979 ± 0.342
1.975 ± 0.316
Evenness index
–
0.725 ± 0.043
0.717 ± 0.046
0.767 ± 0.070
0.765 ± 0.066
Tree species diversity
more pronounced for both HRBA and LRBA plots
through harvesting (Table 3). The standard deviation
showed the same pattern as the Shannon index
(Table 3). Before harvesting, control plots had
already a significantly higher complexity than the
treated plots, but these differences were only slightly
significant (Table 3). After harvesting, these differences became highly significant for both HRBA and
LRBA plots.
The Gini coefficient of height complexity showed no
significant difference between the untreated and treated
plots (Table 3). The Shannon index showed a significantly higher height complexity after harvesting in the
unmanaged plots than in the managed ones. The same
was observed for the standard deviation, which was significantly higher in the control plots. No clear differences in height complexity existed between the HRBA
and LRBA plots.
Diameter classes and height strata
Small-sized trees, small diameter classes (very small and
small) and low height strata (low understorey and upper
understorey) were more abundant in the treated plots
than in the control plots before harvesting (Table 4). In
regard to diameter classes, harvesting reduced the number of small-sized trees only marginally, leaving an abundant residual growing stock. On the contrary,
intermediate- to very large-sized trees (dbh 25 cm+,
height 23 m+) were strongly influenced by harvesting.
The density of trees with an intermediate diameter was
already significantly higher in the control compared to
the LRBA plots before harvesting (see asterisks, Table 4).
Harvesting significantly (p = 0.0011) reduced their
number, leading to a significantly higher number of trees
in the intermediate diameter class in the control compared to selection plots, with a more pronounced difference for the LRBA plots after harvesting (Table 4).
The number of large diameter trees was nearly the
same for control, HRBA and LRBA plots before harvesting (Table 4). Harvesting significantly reduced their
density (p < 0.0001), resulting in a significantly higher
number of large diameter trees in the control compared
to the LRBA but not to HRBA plots (Table 4). The number of very large diameter trees was highest in the control already before harvesting, but without significant
differences due to the high variability between plots
(Table 4). Harvesting significantly reduced their number
(p < 0.0001), resulting in a significantly higher tree density of these trees in the control compared to LRBA plots
(Table 4). No significant differences could be found between control and HRBA plots (Table 4). The HRBA
plots had a higher number of intermediate to very large
diameter trees than their LRBA counterparts (Table 4).
In regard to height classes, no significant difference
was found for the number of low canopy trees, while
upper canopy trees were significantly more abundant in
the control than in the treated plots (Table 4). The difference between untreated and treated plots was even
more pronounced for emergent trees with significantly
more emergent trees in the control (72 trees ha−1) compared to HRBA (19 trees ha−1) and LRBA (10 trees ha−1)
plots after harvesting (Table 4).
Tree species richness and diversity
Tree species richness was not changed as a result of harvesting. Thus, only the results after harvesting are
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
Page 8 of 14
Table 4 Mean number of trees per hectare per diameter class (trees dbh ≥ 5 cm) and height strata (trees dbh ≥ 10 cm) before and
after harvesting. Diameter classes and height strata are explained in Table 3. Values are expressed as mean ± 1 standard deviation.
Significant treatment differences with the control are shown through an asterisk after the mean number of trees, with *p = 0.05–
0.01, **p = 0.01–0.001 and ***p ≤ 0.001, respectively. Significant harvesting impacts are mentioned in the text
Control
High residual basal area (HRBA)
Low residual basal area (LRBA)
Before
After
Before
After
Diameter class
Total
1039 ± 252
1579 ± 301*
1491 ± 295*
1455 ± 202*
1331 ± 190
Very small
494 ± 166
895 ± 167**
861 ± 164*
849 ± 140*
818 ± 156*
Small
258 ± 115
446 ± 141*
429 ± 130
384 ± 50
363 ± 53
Intermediate
175 ± 29
134 ± 31
126 ± 27*
130 ± 11*
101 ± 18**
Large
89 ± 33
90 ± 27
68 ± 14
88 ± 41
48 ± 12*
Very large
22 ± 13
14 ± 15
8±6
5±4
3 ± 3*
533 ± 137
–
624 ± 141
–
510 ± 58
Low understorey
42 ± 32
–
84 ± 43
–
75 ± 35
Upper understorey
114 ± 62
–
271 ± 90*
–
211 ± 35
Low canopy
172 ± 72
–
195 ± 75
–
159 ± 52
Upper canopy
136 ± 36
–
55 ± 21**
–
55 ± 12**
Emergent
72 ± 39
–
19 ± 22*
–
10 ± 8*
Height stratum
Total
presented here. The confidence intervals of the three
rarefaction curves overlap at the total sampled area of
the control plots (Fig. 3), reflecting that tree species
richness was not significantly different between unmanaged and managed plots.
Tree species diversity, as evaluated through the Shannon index, did not significantly change after harvesting
(comp. Table 3). Also, no significant differences in tree
species diversity existed between treated and untreated
plots (comp. Table 3). The same was observed for tree
species evenness which was not significantly changed
through harvesting and was not different between
treated and untreated plots (comp. Table 3).
Tree species composition
In general, treated and untreated plots showed a similar
species composition regarding dominant tree species, as
evaluated through the average number of trees per species and the importance value (IV) of each species before
harvesting (Table 5). The shade-tolerant species A. punctatum and Myrceugenia planipes (H et A.) Berg, however, had far higher IVs in the control plots compared
Fig. 3 Species rarefaction curves showing the mean tree species richness per sampled area for the control, high and low residual basal area
(HRBA and LRBA) plots after harvesting. The rarefaction curves were calculated through repeatedly re-sampling richness out of a random pool of
samples constructed out of the four sampled plots
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
Page 9 of 14
Table 5 Average number of trees per hectare and importance value (IV) in % per treatment and species in 2012 and 2014
Control
Tree species
N°
trees
IV
Laureliopsis
philippiana
486
Aextoxicon
punctatum
Tree species
High residual basal area (HRBA)
Low Residual Basal Area (LRBA)
Before
before
After
No. of
trees
IV
27.7 Laureliopsis
philippiana
591
25.6 569
200
18.5 Eucryphia cordifolia
189
Eucryphia cordifolia
53
16.1 Aextoxicon
punctatum
Myrceugenia planipes
147
8.1
Laurelia sempervirens
6
Amomyrtus meli
19
Drimys winteri
Gevuina avellana
IV
Tree species
after
No. of
trees
IV
27.6 Laureliopsis
philippiana
525
27.1 496
29.4
20.9 153
16.6 Eucryphia cordifolia
154
19.3 105
14.0
126
7.3
123
8.0
Amomyrtus luma
228
8.3
215
8.7
Amomyrtus luma
194
6.9
186
7.1
Aextoxicon
punctatum
91
7.3
86
8.0
4.6
Gevuina avellana
139
5.9
136
6.1
Drimys winteri
123
6.5
120
7.4
4.0
Drimys winteri
51
3.9
51
4.2
Amomyrtus meli
86
4.9
83
5.2
33
4.0
Amomyrtus meli
74
3.8
74
4.0
Gevuina avellana
78
4.7
69
4.8
25
3.2
Lomatia ferruginea
31
3.1
24
3.0
Rhaphithamnus
spinosus
49
3.8
48
4.0
Amomyrtus luma
22
2.9
Myrceugenia planipes 54
3.1
53
3.1
Lomatia ferruginea
48
3.7
44
3.8
Nothofagus dombeyi
8
2.5
Dasyphyllum
diacanthoides
38
3.0
34
2.9
Weinmannia
trichosperma
5
3.1
5
3.6
Weinmannia
trichosperma
3
1.8
Weinmannia
trichosperma
5
2.9
5
3.2
Lomatia dentata
19
1.7
16
1.7
Lomatia dentata
19
1.7
Rhaphithamnus
spinosus
19
2.8
18
2.9
Dasyphyllum
diacanthoides
11
1.7
9
1.6
Dasyphyllum
diacanthoides
8
1.4
Podocarpus salignus
21
2.4
21
2.5
Myrceugenia planipes
16
1.7
14
1.7
Podocarpus salignus
3
1.3
Lomatia dentata
25
2.4
24
2.4
Saxegothaea
conspicua
6
1.7
5
1.6
Persea lingue
3
1.2
Nothofagus dombeyi
11
2.0
11
2.4
Podocarpus salignus
6
1.4
6
1.4
Lomatia ferruginea
3
1.1
Podocarpus nubigena 6
1.4
6
1.4
Caldcluvia paniculata 4
1.4
4
1.4
Raukaua laetevirens
3
1.3
3
1.3
Podocarpus nubigena 3
0.8
3
0.9
Luma apiculata
1
0.7
1
0.7
Luma apiculata
5
0.8
5
0.8
Persea lingue
1
0.6
1
0.6
with the treated ones (Table 5). The application of the
single-tree selection cutting regime applied in this study
had the strongest effect on E. cordifolia. The IV of this
species decreased by 21%, and 20% of individuals were
removed as a result of the HRBA treatment (Table 5).
The LRBA treatment had a more severe effect with the
IV decreasing by 27%, and 32% of individuals being removed (Table 5). The decrease in IV of E. cordifolia
through management led to an increase of the IV of
most other species (Table 5). The only other species that
experienced a clear decline in the number of trees
through harvesting was L. philippiana, but the strong
loss of E. cordifolia still led to a rise in its IV. Except for
E. cordifolia, LRBA management did not induce stronger
changes in the tree species community as compared with
HRBA management (Table 5). The selection cutting regime led only to marginal changes in the number of
trees of all less frequent species, and no species were lost
No. of
trees
No. of
trees
IV
through harvesting (Table 5). Moreover, the extraction
of dominant species led to a rise of IV of several less frequent species in the forest community (Table 5).
Discussion
Key structural attributes and biodiversity conservation
We examined changes in key structural attributes such
as reverse-J diameter distributions, complex vertical
layering, variability of tree sizes, presence of advance regeneration and large/emergent trees as measures of oldgrowthness (Bauhus et al. 2009). Both residual basal area
regimes evaluated in this study on single-tree selection
cutting were found to maintain a balanced uneven-aged
structure, forest cover continuity and a sufficient growing stock of small-sized trees. All plots, managed and
unmanaged, were characterised by reverse-J shaped
diameter distributions before and after harvesting.
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
These findings are in accordance with studies in other
forest types, where selection cutting maintained these
structural forest attributes over decades while providing
timber yields at regular intervals (e.g. Pukkala and
Gadow 2012; Gronewold et al. 2010; O’Hara et al. 2007;
Keeton 2006; Bagnaresi 2002). The observed reverse-J
shaped distributions are typical for old-growth stands in
the Valdivian Costal Range and the Valdivian Andes
(Donoso 2013; Donoso 2005), showing that single-tree
selection cutting is able to maintain this old-growth attribute. Still, results of this study cover only the shortterm impacts of selection cutting (2 years), and there
may be lag effects with sensitive species. However, the
growth model predictions of Rüger et al. (2007), which
were parameterised for the evergreen forest type on Chiloé Island, Chile, suggest that single-tree selection cutting can maintain the above-mentioned forest attributes
also on the long term. Moreover, balanced structures
with similar crown covers for small-, intermediate- and
large-sized trees allow more abundant regeneration and
tree growth in the evergreen forest type in Chile than
unbalanced ones (Schütz et al. 2012; Donoso and Nyland
2005; Donoso 2005).
Further considerations should be given to the fact that
plots with a high residual basal area (HRBA) tend to approximate old-growth conditions more closely through
maintaining higher numbers of large-sized trees and
higher diameter complexity than plots with a low residual
basal area (LRBA) (Tables 3 and 4). Similarly, Gronewold
et al. (2010) concluded that (after a survey of 57 years in
northern hardwood stands of North America managed
through single-tree selection cutting) stands with high residual basal areas better approximated the natural disturbance history and diameter distributions of unmanaged
uneven-aged stands, while low residual basal areas resulted in simpler and more regulated distributions. The
higher number of small trees (advanced regeneration)
already present before cutting compared to the control
plots in our study most likely results from higher light
availability, especially in the LRBA plots caused by previous illegal cuttings (as mentioned before).
The only shortcoming detected was the significant reduction and lower numbers of large-sized (dbh 50 cm+,
height 23 m+) and emergent trees (height 30 m+) in the
treated plots (especially in LRBA ones), compared to the
control plots after harvesting. This finding was supported
by the significant reduction of diameter and height complexity (shown by all three indices) and significantly higher
diameter and height complexity in the untreated plots
(shown by the Shannon index and standard deviation) as a
result of a reduction in the range of tree diameters and
heights. All three structural indices have been widely used
to quantify diameter and height complexity in managed
and unmanaged forest stands and to a lesser extent to
Page 10 of 14
evaluate the impacts of single-tree selection cutting
(Torras et al. 2012; O’Hara et al. 2007; Lexerød and Eid
2006; McElhinny et al. 2005; Acker et al. 1998). Similar to
results of this study, Acker et al. (1998) reported higher
values of the standard deviation of tree diameter in oldgrowth northern hardwood stands of North America
compared to managed ones, but there are also studies that
report a rise of diameter and height complexity under selection silviculture over time using the same three indices
(Torras et al. 2012; O’Hara et al. 2007).
In particular, very large and emergent trees were far
less numerous in the managed plots. Similarly, numerous other studies have found that stands managed
through selection cutting have less large trees than comparable old-growth stands (e.g. Torras and Saura 2008;
Rüger et al. 2007; Keeton 2006; Angers et al. 2005; Crow
et al. 2002). Furthermore, stands with lower residual
basal areas were found to present less large trees than
stands with higher residual basal areas (Gronewold et al.
2010; Rüger et al. 2007). This is partly consistent with
the findings of this study where only LRBA plots presented significantly lower numbers of large and very
large diameter trees than the control plots.
Large-sized and emergent trees are, however, an important habitat for many forest dwelling species and
communities that depend on this specific structural attribute of old-growth forests (Bauhus et al. 2009), such
as cavity-dependent animal species like birds and mammals as well as bryophytes, lichens, fungi, and saproxylic
beetles (Paillet et al. 2010; Bauhus et al. 2009). One specific example in the Chilean evergreen rainforest is the
abundant flora of endemic epiphytic plants that depend
on emergent trees (Díaz et al. 2010) and their greater
frequency on trees with large diameters (Muñoz et al.
2003). Furthermore, bird species diversity in the evergreen forest type can be predicted by the presence of
emergent trees, and their diversity is consequently
higher in old-growth than in early or mid-successional
forest stands (Díaz et al. 2005). It follows that a certain
number of large-sized, especially emergent trees, in
managed stands is crucial for biodiversity conservation.
Although we do not deal with dead wood (i.e. snags and
coarse woody debris) in this paper, preliminary results
suggest that plots subjected to single-tree selection cutting have similar or even higher amounts of this key
structural attribute (sensu Bauhus et al. 2009) compared
to unmanaged old-growth forests (Schnabel et al.,
unpublished).
Impacts on tree species richness, diversity and
composition
An important attribute for old-growthness is the high
number of late successional tree species (Bauhus et al.
2009), i.e. shade-tolerant and emergent mid-tolerant
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
ones in the evergreen forest type. Tree species richness,
diversity and evenness were not changed through selection cutting in the short-term, which is in accordance
with findings in northern hardwood forest in North
America (Angers et al. 2005; Crow et al. 2002). It is
therefore reasonable to conclude that the direct harvesting effects of single-tree selection (e.g. tree felling), as
conducted in the present study, are compatible with preserving tree species richness and diversity within the
evergreen forest type in the short term. If management
guidelines such as those applied in this study are used,
the same should also apply for future applications.
The numbers of less frequently occurring tree species
were not reduced through selection cutting; a fact that
further supports the conclusion that single-tree selection
cutting is compatible with preserving tree species diversity. While most dominant tree species experienced no
severe losses through selection cutting, a clear impact
was noted for the mid-tolerant species E. cordifolia. It
clearly declined in abundance and IV, although it
remained the second species in IV. The reason for this
was the preferential harvest of old/large, poor-quality E.
cordifolia trees, to improve the quality of the residual
stock in the first harvest. Moreover, E. cordifolia trees
were mostly large individuals, since regeneration for this
mid-tolerant species is generally scarce under closed forests (Escobar et al. 2006; Donoso and Nyland 2005) and
was thus strongly impacted by the harvesting criteria of
a maximum residual diameter of 80 cm. In future harvests, impacts are likely to be more equally distributed,
as most of the defective E. cordifolia trees would have
already been harvested after this first selection cut. Also,
E. cordifolia is one of the target tree species of selection
silviculture due to its high economic value and expected
fast growth and abundant regeneration at low residual
basal areas (especially at 40 m2 ha−1). In addition,
retaining some emergent E. cordifolia trees is a conservation priority as this species harbours an exceptional high diversity and abundance of epiphytes,
acting as key structure for biodiversity conservation
and ecosystem processes like water and nutrient cycling (Díaz et al. 2010).
Finally, single-tree selection might favour both the
recruitment of mid-tolerant species like E. cordifolia
(Torras and Saura 2008; Angers et al. 2005) and/or
shade-tolerant species (Keyser and Loftis 2013; Gronewold et al. 2010; Rüger et al. 2007) depending on the
size of crown opening and consequent light availability
(i.e. LRBA should induce more regeneration of midtolerant tree species like E. cordifolia). As the effects on
tree regeneration in the evergreen forest type remain unknown in the field, different harvesting intensities might
be currently the best option to promote the regeneration
of both mid- and shade-tolerant tree species.
Page 11 of 14
Implications for management
Overall, our results support the claim that single-tree selection cutting is a promising silvicultural approach for
the evergreen forest type. This approach is certainly
more promising than the currently supported selective
harvesting guidelines of the Chilean law which do not
control for a balanced residual stock and allow the harvest of 35% of the basal area in 5-year cutting cycles,
which is unsustainable (Donoso 2013; Schütz et al.
2012). In contrast, the only negative affect detected for
the single-tree selection cutting regime applied in the
present study was the clearly lower number of largesized and emergent trees in managed plots, a key
structural attribute of old-growth forests and crucial
for biodiversity conservation.
The management strategy of single-tree selection cutting would need to be adjusted by forest managers who
wish to preserve some emergent and large-sized trees in
stands managed through selection silviculture. The use
of a maximum residual diameter, such as 80 cm in this
study, actually impairs the preservation of large-sized
trees (Keeton 2006). One possibility in this context
would be the intentional retention of a still to be a specified number of large (>80 cm diameter) trees, especially
emergent ones (e.g. Bauhus et al. 2009; Angers et al.
2005). We did maintain some large trees in the managed
stands in this study because otherwise, basal area harvesting would have been too destructive, but our results
suggest that leaving trees above a given maximum diameter must be an ongoing requirement. In particular,
retaining emergent trees with crowns over the main canopy of the residual stock is beneficial as: (a) they may
not impede the growth of either young or mature trees
nor tree regeneration in the evergreen forest type
(Donoso 2005); and (b) they are key structures for biodiversity conservation (Díaz et al. 2010). An additional
possibility might be the use of diameter-guiding curves
other than the reverse-J distribution curve used in the
current study. For example, a rotated sigmoid distribution curve may satisfy ecological needs more closely
through allocating more basal area and growing space to
larger diameter classes (Keeton 2006). The HRBA plots
tended to better approximate old-growth conditions
than LRBA plots, in terms of higher numbers of largesized trees and higher structural complexity. However, it
remains untested in the field as to which residual basal
area selection cutting generates sufficient light availability to allow the regeneration of both shade-tolerant and
mid-tolerant species in the evergreen forest type. Thus,
using a combination of the two residual basal area regimes examined here (HRBA with 60 m2 ha−1 and LRBA
with 40 m2 ha−1) might be an advisable option, which
would contribute to more diverse and species-rich
stands and additionally to the generation of a more
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
heterogeneous forest structure on a broader scale, e.g.
Angers et al. (2005).
Little is known about the required quantity and spatial
distribution of retained emergent trees, which would be
necessary to develop sound ecological management guidelines, like retention targets for biodiversity conservation
(Bauhus et al. 2009; McElhinny et al. 2005). Due to a lack
of information on this topic in Chile and in other ecosystems (e.g. Bauhus et al. 2009; McElhinny et al. 2005), this
is a major challenge for uneven-aged silviculture, especially in forests of high diversity and endemism like the
evergreen temperate forests of south-central Chile. A final
concern in Chile (and elsewhere) is that Chusquea bamboos in the understorey may be a threat for regeneration
(Donoso and Nyland 2005). These are usually lightdemanding species, so the creation of canopy openings
above a certain size following selection cuts, especially if
using group selection, could promote Chusquea spp. regeneration. This poses an important research challenge
for selection silviculture in Chilean forests, i.e. determining adequate densities (for example expressed in basal
area) that would maintain low levels of Chusquea spp.
cover while allowing the forest stand to sustain good
growth rates. Donoso (2002) studied uneven-aged forest
with basal areas from 38 to 140 m2 ha−1 in the lowlands of
south-central Chile, and Chusquea spp. had levels of cover
that ranged from 3 to 12%. This result suggests that managed forest stands with residual basal areas as low as
40 m2 ha−1 should not have major competition from
Chusquea spp. upon tree regeneration.
From a management perspective, a great advantage of
selection silviculture is the production of large logs for
saw timber or veneer, products of high commercial value,
while in the same time, logs of smaller dimensions are
harvested that can be used for firewood or charcoal production (Moorman et al. 2013; Puettmann et al. 2015).
Siebert (1998), Donoso (2002) and Donoso et al. (2009)
have proposed target maximum diameters of 60–90 cm
(80 cm in this study), which should generate high-value
products. Operationally, harvesting requires skilled
workers and marked stands after determining adequate
marking guides according to the BDq or a similar technique. In addition, Donoso (2002) proposed 10-year cutting cycles for evergreen forests on productive lowelevation sites. Single-tree selection would thus especially
offer landholders with small properties a variety of wood
products at regular intervals (Puettmann et al. 2015).
Overall, major considerations to better conserve structural features and biodiversity of old-growth forests in
managed stands, while also achieving good rates of timber productivity, could include: (a) retaining a certain
number of large-sized, especially emergent trees; (b)
using a diverse but relatively narrow range of residual
basal areas that may support good development of
Page 12 of 14
relatively fast-growing and valuable mid-tolerant tree
species associated to shade-tolerant ones; and (c) applying diameter distributions that allow for a greater allocation of basal area in relatively large trees. These
considerations for stand variability in managed forests
should be included in forest regulations, which should
adapt to new knowledge generated through research.
Considering that mostly, we did not cut beyond 35%
harvested basal area, the maximum established in Chilean regulations, research in selection silviculture should
also evaluate an ample range of harvesting intensities
using a relatively ample range of initial and residual
basal areas. This would allow a more robust information
on thresholds to conserve in the best possible manner
“old-growthness” (sensu Bauhus et al. (2009)) in managed forest ecosystems.
Conclusions
We examined changes in forest structure and tree species composition as well as possible detrimental effects
on key attributes of old-growthness in stands managed
through single-tree selection cutting. Through both harvest variants, high and low residual basal areas (HRBA
and LRBA), a balanced, uneven-aged structure with
reverse-J diameter distribution and forest cover were
maintained. Also, a sufficient growing stock of smallsized trees was kept. Moreover, neither tree species richness, diversity and evenness, nor the presence of less frequent species were negatively affected on the short term.
As the effects on tree regeneration remain unknown,
using a combination of HRBA and LRBA may be advisable to support good development of relatively fastgrowing and valuable mid-tolerant tree species associated with shade-tolerant ones. The only negative effect
detected was the clearly lower number of large-sized and
emergent trees in managed plots (especially for LRBA),
which are a key structural attribute of old-growth forests
and crucial for biodiversity conservation. These results
suggest that single-tree selection cutting, if adjusted to
retain a certain number of large-sized and emergent
trees, can serve as a possible means to preserve many
old-growth structural and compositional attributes of
the evergreen forest type in managed stands while harvesting timber for the landowners. Future experiments
should test the effects of alternative selection cutting
upon structural heterogeneity, diversity and productivity
to balance the varied societal demands of ecosystem
services expected from forest management.
Additional file
Additional file 1: Data of the Llancahue Experimental Forest in
south-central Chile. (XLSX 427 kb)
Schnabel et al. New Zealand Journal of Forestry Science (2017) 47:21
Abbreviations
Dbh: Diameter at breast height; GLMM: Generalised linear mixed models;
GLS: Generalised least square models; HRBA: High residual basal area;
IV: Importance value; LMM: Linear mixed models; LRBA: Low residual basal area
Acknowledgements
We sincerely thank Jürgen Huss for commenting and revising this
manuscript during its elaboration and Simone Ciuti for the statistical support.
Moreover, we acknowledge the dedication of our field workers Ronald
Rocco, Nicole Raimilla Fonseca and Pol Bacardit from the University Austral
de Chile. Finally, we greatly appreciated the accommodations provided by
the Lomas de Sol community during our stay in Llancahue. PJ Donoso
acknowledges the support of FIBN-CONAF project no. 034/2011 and FONDECYT project no. 1150496.
Funding
P.J. Donoso thanks FONDECYT Grant No. 1150496 and Project 034/2011 of
the “Fondo de Investigación en Bosque Nativo” administered by the forest
service CONAF.
Availability of data and materials
The dataset(s) supporting the conclusions of this article are included within
the article (and its Additional file 1). The use of the Llancahue database (in
Additional file 1) requires the authorization of the authors.
Authors’ contributions
FS, the lead author, conceived and designed this experiment. He performed
the experiment through planning and leading the field data collection,
analysed the data and wrote most parts of the paper. PD conceived and
coordinated the general single-tree selection cutting experiment, designed
and supervised the management-interventions and established the plots. He
advised in conceiving this experiment and in the data analysis, supervised
the process of writing the paper and wrote parts himself. CW helped in
taking the field data and analysed minor parts. She contributed during the
whole study through revisions and wrote parts of the paper. FS and CW
created figures and tables and formatted the paper. Author contribution
rephrased without making a change to the content to provide better clarity
of the indiviudal contributions. This change has been approved by CW and
FS. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
Chair of Silviculture, Faculty of Environment and Natural Resources,
University of Freiburg, Tennenbacherstr. 4, 79106 Freiburg, Germany. 2Insituto
de Bosques y Sociedad, Facultad de Ciencias Forestales y Recursos Naturales,
Universidad Austral de Chile, Casilla 567, Valdivia, Chile. 3Faculty of
Environment and Natural Resources, University of Freiburg, Tennenbacherstr.
4, 79085 Freiburg, Germany.
1
Received: 5 March 2017 Accepted: 18 September 2017
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