Southern Forests: a Journal of Forest Science
ISSN: 2070-2620 (Print) 2070-2639 (Online) Journal homepage: http://www.tandfonline.com/loi/tsfs20
Inferring population trends of Araucaria
angustifolia (Araucariaceae) using a transition
matrix model in an old-growth forest
Giovani F Paludo, Miguel B Lauterjung, Maurício S dos Reis & Adelar
Mantovani
To cite this article: Giovani F Paludo, Miguel B Lauterjung, Maurício S dos Reis & Adelar
Mantovani (2016): Inferring population trends of Araucaria angustifolia (Araucariaceae) using a
transition matrix model in an old-growth forest, Southern Forests: a Journal of Forest Science,
DOI: 10.2989/20702620.2015.1136506
To link to this article: http://dx.doi.org/10.2989/20702620.2015.1136506
Published online: 01 Mar 2016.
Submit your article to this journal
Article views: 1
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=tsfs20
Download by: [University of California, San Diego]
Date: 04 March 2016, At: 19:15
Copyright © NISC (Pty) Ltd
Southern Forests 2016: 1–7
Printed in South Africa — All rights reserved
SOUTHERN FORESTS
ISSN 2070-2620 EISSN 2070-2639
http://dx.doi.org/10.2989/20702620.2015.1136506
This is the final version of the article that is
published ahead of the print and online issue
Inferring population trends of Araucaria angustifolia (Araucariaceae) using
a transition matrix model in an old-growth forest
Giovani F Paludo1*, Miguel B Lauterjung1, Maurício S dos Reis2 and Adelar Mantovani1
Grupo Uso e Conservação dos Recursos Florestais, Universidade do Estado de Santa Catarina, Lages, Brazil
Núcleo de Pesquisas em Florestas Tropicais, Universidade Federal de Santa Catarina, Florianópolis, Brazil
* Corresponding author, email: [email protected]
1
Downloaded by [University of California, San Diego] at 19:15 04 March 2016
2
Matrix population models may generate important information to prevent undesirable outcomes for endangered
species. This is the case for Araucaria angustifolia, a Critically Endangered conifer, with little knowledge regarding
its life history and trends in development over time. This study sought to investigate life-history trends of an
A. angustifolia population in a subtropical forest in Santa Catarina, Brazil. Predictions based on the Lozenge
regeneration model were established in order to determine if this model could predict changes in the species’
population dynamics: (1) the established individuals exhibit long persistence and (2) seedling and sapling
abundance, as well as population descriptors, should exhibit behaviour that indicates one of the stages prescribed
by the model. All A. angustifolia individuals were evaluated within a 5.1 ha plot at the study site over a six-year
period. Lefkovitch’s transition model was used and population descriptors were calculated. Both predictions were
fulfilled. The population had λ = 0.9977 (0.9864 < λ < 1.0020; CI 95%), indicating a declining stability. The basal area
remained stable, whereas tree density tended to decrease, and seedlings and saplings did not promote an increase
in λ. These results indicate that the population was in a phase called thinning, defined by the Lozenge model. The
results led to three conclusions: recruitment seems insufficient; survival of reproductive individuals is responsible
for the longevity of the population; and the predictions did not refute the Lozenge model. According to this model,
the population is expected to regenerate in the future. However, the species exhibits declining stability, which
aggravates the endangerment situation.
Keywords: Araucaria forest, Brazilian pine, mixed ombrophilous forest, natural regeneration, population dynamics
Introduction
Population matrix models (PMMs) have been used to
develop conservation strategies (Enneson and Litzguz
2008; Portela et al. 2010; López-Mata 2013), sustainable exploitation of non-timber forest products (Schmidt et
al. 2011; Venter and Witkowski 2013) and predictions of a
population’s future (Crone et al. 2013). PMMs have shown
both successes and failures in predicting tree population behaviour (Bierzychudek 1999; Schodelbauerová
et al. 2010; Crone et al. 2013). Despite occasional model
failure, PMMs are still a useful tool in elaborating conservation strategies and may be used to reduce chances of
undesirable outcomes for endangered species (Crone et
al. 2013). PMMs are especially important for long-lived
species, because population trends are difficult to define for
these species without the use of these models.
Long-lived species are either climax or pioneer species of
large size (Turner 2004). In a climax species, seedlings and
saplings are shade-tolerant and grow beneath the canopy
layer, promoting a continuous replacement of older trees
without severe disturbance regimes (Gutiérrez et al. 2008),
also referred to as ‘continuous regeneration’ (Ogden and
Stewart 1995). However, in long-lived pioneers, it is usual
to find stands with a high density of mature trees while
exhibiting complete absence of seedlings and saplings
(Ogden 1985; Enright et al. 1999; Turner 2004), because
saplings of long-lived pioneers are frequently shadeintolerant and are not able to grow under a dense tree
canopy. In some long-lived species and canopy-dominant
species, the appearance of seedlings and saplings will
depend upon the occurrence of an episodic landscapelevel disturbance, such as the dieback of a large number
of mature trees (Boehmer et al. 2013) and landslides or
wind-throw from storms (Pollmann and Veblen 2004). In
other long-lived species, the appearance of seedlings and
saplings will depend upon the occurrence of large-scale
disturbances, as was found for Agathis australis in New
Zealand, whose behaviour can be described by the stand
replacement model called the Lozenge model (Ogden 1985;
Ogden and Stewart 1995).
The Lozenge model states that population regeneration occurs periodically through a gap-filling event that
is structured into cohorts after disturbances (Ogden and
Stewart 1995). After a cohort dies, which can occur up to
1 000 years after its establishment, it is replaced by a new
generation. This model has three main phases: establishment, thinning and senescence. The establishment phase is
marked by a rise in the population after a major disturbance,
where the first seedling cohort is recruited aided by pioneer
nurse plants, and the individuals’ density and biomass
increases. The second stage is the thinning phase, where
Southern Forests is co-published by NISC (Pty) Ltd and Taylor & Francis
Downloaded by [University of California, San Diego] at 19:15 04 March 2016
2
the individuals’ density decreases through a self-thinning
process and biomass remains constant or increases. In this
phase, Agathis australis had a basal area up to 56 m² ha−1
(Enright et al. 1999). The forest also exhibits a pattern of
high-density mature trees and it is often characterised by
seedling and sapling absence (regeneration gap; Enright et
al. 1999). The third stage is the senescence phase, where
the density and biomass of the first cohort decreases,
and now a second seedling cohort begins to replace the
old mature trees through a natural regeneration process.
There may be small gaps and seedlings present in other
phases, but no recruitment will occur until the first cohort
enters the senescence phase (Ogden 1985; Ogden
and Stewart 1995). The Lozenge model has also been
utilised to examine the life history of Araucaria husteinii
in New Guinea and A. laubenfelsi in New Caledonia
(Enright et al. 1999).
The regeneration of A. angustifolia populations, considered
a long-lived pioneer species, can also be described by the
Lozenge model (Souza 2007; Souza et al. 2008). However,
previously predictions for A. angustifolia using the Lozenge
model were tested using stem size distributions (Souza
2007), and size distributions are not always the best
descriptor for population trends (Feeley et al. 2007; Virillo
et al. 2011). Therefore, one might question whether this
model could predict the temporal changes in descriptors of a
conserved A. angustifolia population.
If the Lozenge model is suitable in describing the
A. angustifolia regeneration strategy, the population
dynamics predictions will suggest that a group of established
individuals will exhibit longevity at a site, possibly attaining
hundreds of years until senescence and death (first
prediction) (Ogden and Stewart 1995). Furthermore, it is
expected that the population’s growth rate, changes in
biomass, and sapling abundance follow a pattern indicating
its status in one of the proposed model’s three phases
(second prediction).
Moreover, Araucaria angustifolia is one of the most
endangered Araucaria species, as well as one of the most
poorly understood regarding stand dynamics (IUCN 2013).
Its distribution spans the southern part of South America
(originally it covered nearly 98.96% of southern Brazil and
1.03% of Argentina; IUCN 2013), under the Araucaria
Forest domain, which originally spanned an area of
25.4 Mha. Araucaria angustifolia has a great socio-cultural
and economic importance throughout its natural distribution
because it produces large edible seeds and timber (Reis
and Ladio 2012). Of the original occurrence area of the
Araucaria forests in Brazil, only 0.65% are legally protected
(Ribeiro et al. 2009). In addition, the seeds are exploited
by local seed collectors with no control throughout its area
of distribution, including parks designated for its protection.
Because of the man-made threats to the tree species,
research on population trends are biologically important.
This study aimed to test the hypothesis of whether
Araucaria angustifolia follows the Lozenge regeneration model. In order to test this hypothesis, two predictions
were established for the model, which are stated above.
Furthermore, this study also attempted to analyse population descriptors for an A. angustifolia population over time
through a matrix model.
Paludo, Lauterjung, dos Reis and Mantovani
The study seeks to answer the following questions: What
are the population trends in a developed Araucaria forest?
Will the population remain stable, increase or decrease over
time? Are seedling and sapling abundance important for the
population’s growth rate?
Material and methods
Study area and data collection
The old growth forest used in this study is located in the
Caçador Genetic Forest Reserve (CGFR), in the municipality of Caçador, state of Santa Catarina, southern Brazil.
The reserve is situated between the coordinates 26°49′
and 26°53′ S and 50°59′ and 50°53′ W, with an altitude
between 920 m and 1 075 m (Kurasz et al. 2008). It is a
forest fragment with an area of 1 157.48 ha. The vegetation type is Mixed Ombrophilous Forest, also known as
Araucaria Forest, and is found in the Atlantic Forest biome
hotspot (Myers et al. 2000). The old growth forest in this
study refers to a forest with numerous large long-standing
trees. A plot was installed inside the old growth forest in
an area with no evidence of selective logging during the
past 80 years.
The target population consisted of all A. angustifolia
individuals, from seedlings to adults within a 5.1 ha (300 m
× 170 m) plot. Each individual was identified, mapped
with X and Y coordinates using a topographic survey, and
followed annually from 2007 to 2013 in January, totalling
seven assessments in six years.
Data were collected on the life status of the individual
(alive or dead), diameter at breast height (DBH) for trees
with height ≥ 1.5 m, and the presence of lateral branches
along the stem and total height for trees < 1.5 m. During
the subsequent years from 2008 to 2013, recruits (new
individuals and recently germinated individuals) were also
mapped and evaluated for the same descriptors as the
others. These data were used to determine to which class
the individual belonged during each year.
Data analysis
A Lefkovitch matrix model (LMM) (Lefkovitch 1965) was
used to estimate the population’s longevity and growth
rate, as well as the population trends and the role of
seedlings and saplings in population growth. This method
was adapted from age-based matrix models, developed
mainly for animals (Leslie 1945). The LMM does not
require knowledge of the individual’s age, but requires
the population’s division into different stages or classes
(Lefkovitch 1965).
Therefore, individuals were classified into five categories:
(1) seedlings (SE) were post-germinated individuals without
any lateral ramifications; (2) juvenile I (J1) were individuals with height < 1.5 m, possessing lateral ramifications;
(3) juvenile II (J2) were individuals with height ≥ 1.5 m
and DBH < 10 cm; (4) immature (IM) were individuals
with DBH ≥ 10 cm and < 29.2 cm; and (5) potentially
reproductive (PR) were individuals with a probability
>50% of being in the reproductive phase, taking into
consideration DBH ≥ 29.2 cm. This DBH was estimated
according to the diametric relationship with the reproductive phase established in Paludo et al. (unpublished data)
Southern Forests 2016: 1–7
3
method, each category had its individual frequency fixed,
and the transitions (S, G or D) were randomly selected over
100 000 times with replacement (Maloney 2011). When
no death appeared in a class within the randomly selected
matrices, a mortality rate of 0.001 was assigned. In each
iteration a randomly selected fecundity value was assumed
from a set containing five field-observed values (n = 5; from
2008 to 2013). The elasticity analysis was used to access
the relative participation of each transition element in λ.
Indicators of the species longevity were also calculated
using each class size divided by the class’s average
growth rate, suggesting the possible amount of time that an
individual tree could remain in the class (Rigg et al. 2010).
Basal area was used as an approximation to biomass
measurement in order to verify increases or decreases in
biomass for the second prediction. The basal area was
calculated for 2007 and 2013, and a confidence interval was
constructed using 100 000 randomly selected iterations for
the same amount of individuals in a population corresponding
to the year in question. According to the asymmetric distribution of this descriptor, a non-parametric comparison test was
used (Zar 2010). A two-sample Wilcoxon test (Zar 2010) was
used for non-paired samples.
Analyses were performed using the R software (R Core
Team 2012), in association with the popbio package
(Stubben and Milligan 2007).
for the CGFR. The relationship was expressed by the
following equation:
Downloaded by [University of California, San Diego] at 19:15 04 March 2016
DBH = (ln((1 − P)−1 − 1) + 7.61299) 0.26070−1
where ln is the natural logarithm, P is the probability of an
individual being in the reproductive phase, and DBH the
diameter at breast height.
In old and large trees DBH measurements are susceptible
to errors such as expansion or contraction of bark thickness
induced by atmospheric conditions or simply due to random
errors. Initially, in order to avoid such problems, a regression
between size and year was calculated, which generated
an equation with the coefficients (b – DBH growth rate) for
each individual. Secondly, during each year, the size of each
individual was estimated through the calculated regression.
This procedure allowed for the estimation of the size of each
individual every year, and reduces variations as well as
random errors between size measurements.
It is important to note that for A. angustifolia, seed
quantity was slightly correlated to the size of the reproductive tree measured (r = 48% for basal area and r = 58% for
height; Mantovani et al. 2004).
Transition probabilities for each class were stasis
(remaining within the same class; S), advancing classes (G)
or death (D). Those probabilities were calculated using the
observed data and class criteria for each year. Afterwards,
five matrices were assembled, one for each annual transition
between 2008 and 2013. Fecundity was estimated as
the number of seedlings found in 5.1 ha, divided by the
number of PR and multiplied by the category’s stasis for the
specified year (Caswell 1989). The transition matrix used
for the analysis was generated with the transition elements
average from the five matrices. The mortality rates (D) for
each class were calculated as follow:
Results
During the study the population showed an average of 113
(22 individuals ha−1) SE, 136 (27 individuals ha−1) J1, 107
(21 individuals ha−1) J2, 99 (19 individuals ha−1) IM and 144
(28 individuals ha−1) PR within 5.1 ha, totaling 117 individuals ha−1. The generated transition matrix was summarised
in the population’s life cycle (Figure 1). In the study area,
only the seeding process produced new individuals. In
this unlogged site, no resprouting individuals or vegetative
propagation were recorded. Female PR produced seeds
that generated SE and sometimes J1. A seed could only
generate a J1 when the individuals grew fast and grew
‘branches’ prior to the first census after the seeds fell.
The mortality rates (D) for the categories PL, J1, J2, IM
and PR were, respectively, 0.795, 0.196, 0.064, 0.022 and
0.003. The transition between categories followed a similar
D=1–S−G
The long-term population growth rate was estimated by
calculating the population’s finite growth rate (λ), using
the dominant eigenvalue of the matrix (Caswell 1989).
Confidence intervals with 95% probability were constructed
for this value using bootstrap resampling and the percentile method (Caswell 1989). To compute the percentile
0.688
0.071
SE
0.008
0.197
J1
0.792
0.013
J2
0.927
0.009
IM
0.970
0.008
PR
0.997
Figure 1: Population life cycle of Araucaria angustifolia at the Caçador Genetic Forest Reserve, Santa Catarina State, Brazil. The
circles represent the different stages (SE = seedling, J1 = juvenile 1, J2 = juvenile 2, IM = immature, PR = potentially reproductive). The
horizontal lines represent stage advancement, the lower lines represent permanence within a class and the upper lines represent fecundity
accompanied by their respective probabilities of occurrence between each census, determined by the six annual censuses in a 5.1 ha plot
Paludo, Lauterjung, dos Reis and Mantovani
pattern of reduction, where the larger the size the lower the
transition rates (Figure 1).
The population’s growth rate (λ) was 0.9977, close
to 1 (95% CI: 0.9864 < λ < 1.002; CI based on 100 000
bootstrap resampling). The distribution of λ values obtained
through bootstrap resampling is shown in Figure 2.
Elasticity was mainly concentrated in the stasis of PR
individuals, and secondly in the stasis of IM individuals, with
a low participation from other transitions (Table 1).
The growth in height for J1 was 0.0436 m y−1. Whereas
the growth in DBH for J2 was 0.0952 cm y−1, for IM it was
0.110 cm y−1 and for PR it was 0.419 cm y−1. The time
given by the ratio between growth rate and size for each
category was 29.5 years for J1 (considering that individuals
of age 1 year have a 0.20 m height), 105.0 years for J2,
174.5 years for IM, and 286.9 years for PR. In total, the
time between germination until the current moment for the
largest individual (149.4 cm) in the plot was estimated to be
595.9 years.
The population’s basal area in 2007 was 12.1 m² ha−1
(CI 95% using the percentile method: 9.9–14.4 m² ha−1), and
for 2013 it was 12.8 m² ha−1 (CI 95% using the percentile
method: 10.6–15.2 m² ha−1). The difference between the
years was not significant (W = 52 108; P = 0.67).
Discussion
The results presented in this study were constructed based
on a six-year period of observation, which is a small interval
when considering the lifespan of a long-lived conifer. Thus,
caution is required when interpreting the presented data,
because it may not be an exact and precise forecast of the
studied population’s future.
(a)
(a) No deaths in PR
50 000
FREQUENCY
Downloaded by [University of California, San Diego] at 19:15 04 March 2016
4
(b) One death in PR
(c) Two deaths in PR
30 000
(b)
Finite population growth rate (λ) and longevity
For the A. angustifolia population in Santa Catarina, this
study found a λ close to 1 (λ = 0.9977, CI95% = 0.9864–
1.002), but with a large concentration of values <1
(Figure 2). In two populations of A. laubenfelsii in New
Caledonia, which is also a long-lived pioneer, a λ consistently >1 (from 1.013 to 1.184) was found (Rigg et al. 2010).
Furthermore, in New Caledonia in four A. muelleri populations, the average λ was 1.0020, varying from 0.9955 to
1.0039, with all CI values including the value 1 (Enright et
al. 2013). For the species A. husteinii and A. cunninghamii
in New Guinea, λ values ranged from 1.055 to 1.010,
respectively (Enright et al. 1995). The first prediction
(a group of established individuals will exhibit longevity at a
site, possibly reaching hundreds of years until senescence
and death) is confirmed by the λ found for A. angustifolia,
plus the time given by the ratio between growth rate and
class size of 595.9 years, where the species exhibits high
longevity. When only considering the demographic rates
(λ = 0.9977, CI95% = 0.9864–1.002), the present population will persist for a long time at the study site, unless
affected by a catastrophic event. As an example of the
species longevity, a deterministic projection of λ for the
studied population indicates that 50% (59 individuals ha−1)
of individuals (117 individuals ha −1) would remain for
301 years, and 10% (12 individuals ha−1) for 999 years.
Re-establishment of adults
Araucaria angustifolia at this site exhibited an average
recruitment rate of 0.75 individuals y−1 adult−1. This value
can be contrasted to A. laubenfelsii whose average rate
was 104 individuals y−1 adult−1 and λ consistently >1 (Rigg
et al. 2010). Araucaria muelleri had an average rate of
0.585 individuals y−1 adult−1, with λ close to 1 (Enright et al.
2013). In contrast, for Carya glabra in the USA, even with
recruitment of 18.5 individuals y −1 adult−1, the projection
indicated a population decline (Evans and Keen 2013).
With the exception of A. muelleri, which shows one of the
highest known survival rates (Enright et al. 2013), it can
be concluded that the number of recruits for A. angustifolia
is low relative to other studies, indicating that these rates
may be insufficient to guarantee the maintenance of adult
individuals in the population. Some of the causes for this
may be the high demand for A. angustifolia seeds by
humans (IUCN 2013) and small rodents (Iob and Vieira
10 000
(c)
0
0.97
0.98
0.99
LAMBDA
1.00
1.01
Figure 2: Distribution of λ values resampled 100 000 times from
a set containing all observations for t = 1 year, with replacement.
The average abundance between years in each category remained
the same (SE = 22, J1 = 113, J2 = 107, IM = 99 and PR = 144)
and the status of the individual (alive, dead or advanced) was
randomly selected for each category separately (SE, J1, J2, IM and
PR) from the observed data. Fecundity was randomly selected from
observed values. The intervals constructed based on the percentile
method indicated a λ value close to 1 (λ = 0.9864–1.0020). These
are the possible λ values for a population of A. angustifolia within a
conserved forest and were taken from six annual censuses
Table 1: Elasticity matrix calculated from an average transition
matrix of an A. angustifolia population in a forest area in Santa
Catarina State, southern Brazil. The data were obtained from seven
annual censuses in a 5.1 ha plot (170 m × 300 m). SE = seedling,
J1 = juvenile 1, J2 = juvenile 2, IM = immature, PR = potentially
reproductive
Category at time t
Category at
time t + 1
SE
J1
J2
IM
PR
SE
0.000003
0
0
0
0.000320
J1
0.000320 0.001866
0
0
0.000165
J2
0
0.000485 0.006348
0
0
IM
0
0
0.000485 0.016860
0
PR
0
0
0
0.000485 0.972661
Downloaded by [University of California, San Diego] at 19:15 04 March 2016
Southern Forests 2016: 1–7
2008), unsuitable microsites or some other process that
has not been studied or was not considered, because
this was not the objective of the present study. It is
possible that the low recruitment is the first ‘bottleneck’ of
A. angustifolia regeneration; this aspect should receive
more attention and additional research is needed to explain
the main causes of this pattern.
Moreover, in the present study, the recruitment elasticity
and transition elements of the classes PL, J1 and J2
were low. Longevity in the studied population is due to
the already initially established trees in the PR category
(Table 1), which possess a low mortality rate and may
remain for a long time within the population. The concentration of elasticity in the last class’s stasis is an expected
pattern for forest tree species (Watkinson 1997) and
for long-lived conifers (e.g. Kwitt et al. 2004; Enright et
al. 2013), which may indicate a possible characteristic
of long-lived species for model matrices. Meanwhile,
according to the λ distribution values obtained through
iterations (Figure 2), λ is basically determined by the
annual mortality rate of the PR individuals. A value of λ > 1
(for t = 1 year) was observed only in iterations where no
PR died (Figure 2a). In iterations where at least one PR
individual died, λ was always less than 1 (Figure 2b and
c), and the peaks in the simulation distributions coincide
with the number of dead PR individuals (Figure 2a–c).
This result indicates that the random selection of individual
transitions in all of the other categories did not cause
variations in λ greater than those caused by the deaths
of the PR. Araucaria angustifolia seedlings and saplings
were present at the study site (about 70 individuals ha−1),
but the results indicate that the seedlings and saplings
were insufficient to completely replace the deaths of the PR
individuals in the observed conditions. Random selection
generated different transition rates for each class and, even
then, this did not promote significant increases in λ. Given
that the number of recruits per year was low compared with
other species, such results suggest a regeneration failure
in the current conditions. According to Turner (2004) for a
long-lived pioneer species, the regeneration categories
might be absent when referring to a mature stand.
The reasons for such a failure were not evaluated in the
present study and are not sufficiently known for A. angustifolia. Although A. angustifolia seedlings and saplings are
able to live under low light availability (Duarte et al. 2002),
the results of the present study showed negligible transitions
between classes. Several hypotheses can be formulated
to help explain the regeneration failure, for example (1) the
slow growth of individuals with low light availability and
(2) damage to and death of trees caused by a wide range
of herbivores (Crawley 1997). For Carya glabra, herbivory
was indicated to be responsible for regeneration failure
(Evans and Keen 2013). Drake et al. (2012) suggest that
regeneration failure for A. araucana in Chile, which has
characteristics similar to those of A. angustifolia, was
possibly due to seed predation, low production of reproductive individuals and/or competition with fast-growing species.
Lozenge regeneration model
The basal area for the A. angustifolia population between
2007 and 2013 did not demonstrate significant differences.
5
Schaaf et al. (2006) found an increase from 11.3 to
16.47 m² ha−1 during a 21-year period for A. angustifolia, in
a forest without clear-cutting, but with previous exploitation
before the first survey. Considering the predominance of λ
values < 1, the maintenance of basal area overtime, and the
low participation of regeneration in λ, the second prediction
(it is expected that the population’s growth rate, changes in
biomass, and sapling abundance follow a pattern indicating
its status in one of the proposed model’s three phases) is
not rejected, because it is possible to identify the population
in the thinning phase of the Lozenge model.
It is expected that the population’s future behaviour also
will follow the Lozenge model, because the predictions
constructed using the Lozenge model (Ogden and Stewart
1995) were able to predict the established behaviour. If this
were to happen in practice, the population would gradually
pass into the next phase, which will occur when the current
adult population enters into senescence. Concurrently
with senescence, the appearance of a large number of
seedlings and saplings sufficient to form a new group of
reproductive individuals is expected. However, to expect a
new group to re-establish from senescent individuals may
seem counter-intuitive. Boehmer et al. (2013) observed a
similar situation for Metrosideros polymorpha in Hawaii,
where episodic regeneration, denominated ‘waves of
regeneration’, were found, even though the adult population
was already in senescence.
The Lozenge model can be accepted for A. angustifolia,
with hopes that in the future regeneration will appear in
the population. Nonetheless, the present data suggests
that the current regeneration is insufficient in this study
area in Santa Catarina, and strong pressure exists on this
species overall. In the twentieth century, at least 30 Mm3
of A. angustifolia lumber was produced from native forests
(Carvalho 2010), and currently the pressure on the species’
future is caused via by excessive seed collection (IUCN
2013). Collection occurs across the entire Araucaria
forest area, with no locations being free of collection.
Moreover, only 0.65% of Araucaria forests in Brazil are
legally protected (Ribeiro et al. 2009). All these factors
lead to greater risk of species extinction, especially since
the species distribution is in a country that has recently
approved changes in legislation that increase the reduction
of native forest areas (Nazareno et al. 2011). Further
studies should focus on the causes of the low number of
individuals in the first developmental classes and the
reasons for a limited entry of individuals.
Conclusion
The native population of A. angustifolia studied tends
towards a very slow decline in the number of individuals,
defined here as declining stability. The decline occurs
because of the low recruitment of new individuals, and
seedlings and saplings do not grow to the reproductive stage
at rates that are equal or higher than the rate of deaths of
the reproductive individuals. Nonetheless, the decline is very
slow, and is closer to stability because the individuals in the
reproductive stage exhibit expressive longevity.
However, the predictions regarding the Lozenge model
were confirmed, and the results did not refute the hypothesis
6
Downloaded by [University of California, San Diego] at 19:15 04 March 2016
that regeneration of A. angustifolia follows this model.
Thus, the model predicts that regeneration should occur in
the future, when the population enters into the next phase,
i.e. senescence.
Acknowledgements — We are thankful to the Fundação de Amparo
a Pesquisa e Inovação do Estado de Santa Catarina (FAPESC)
for financial support in this study, to the Conselho Nacional de
Desenvolvimento Científico e Tecnológico (CNPq) for providing
a productivity scholarship to MSR, and to the Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior for providing
a scholarship to GFP. We also would like to thank the Empresa
Brasileira de Pesquisa Agropecuária for availability of the study
area. Lastly, we would like to thank Alison Paulo Bernardi and
Newton Clóvis Freitas da Costa for helping in the development of
this research, the Núcleo de Pesquisas em Florestas Tropicais
for their assistance, and Anna J Mello for the translating process.
The constructive input from two anonymous reviewers is gratefully
acknowledged.
References
Bierzychudek P. 1999. Looking backwards: assessing the
projections of a transition matrix model. Ecological Applications
9: 1278–1287.
Boehmer HJ, Wagner HH, Jacobi JD, Gerrish GC, Mueller-Dombois
D. 2013. Rebuilding after collapse: evidence for long-term cohort
dynamics in the native Hawaiian rain forest. Journal of Vegetation
Science 24: 639–650.
Carvalho MMX. 2010. Uma grande empresa em meio à floresta: a
história da devastação da floresta com Araucária e a Southern
Brazil lumber and colonization (1870-1970). PhD thesis,
Universidade Federal de Santa Catarina, Brazil.
Caswell H. 1989. Matrix population models: construction, analysis,
and interpretation. Sunderland, MA: Sinauer Associates.
Crawley MJ. 1997. Plant–herbivore dynamics. In: Crawley MJ
(ed.), Plant ecology (2nd edn). Oxford: Blackwell Publishing.
pp 401–474.
Crone EE, Ellis MM, Morris WF, Stanley A, Bell T, Bierzychudek
P, Ehrlén J, Kaye TN, Knight TM, Lesica P, Oostermeijer G,
Quintania-Ascencio PF, Ticktin T, Valverde T, Williams JL, Doak
DF, Ganesan R, Mceachern K, Thorpe AS, Menges ES. 2013.
Ability of matrix models to explain the past and predict the future
of plant populations. Conservation Biology 27: 968–978.
Drake F, Molina JR, Herrera MA. 2012. An ecophysiographic
approach for Araucaria araucana regeneration management.
Ciencia e Investigación Agraria 39: 159–176.
Duarte LS, Dillenburg LR, Rosa LMG. 2002. Assessing the role
of light availability in the regeneration of Araucaria angustifolia
(Araucariaceae). Australian Journal of Botany 50: 741–751.
Enneson JJ, Litzgus JD. 2008. Using long-term data and a stageclassified matrix to assess conservation strategies for an
endangered turtle (Clemmys guttata). Biological Conservation
141: 1560–1568.
Enright NJ, Franco M, Silvertown J. 1995. Comparing plant life
histories using elasticity analysis: the importance of life span and
the number of life-cycle stages. Oecologia 104: 79–84.
Enright NJ, Miller BP, Perry GLW, Goldblum D, Jafré T. 2013.
Stress-tolerator leaf traits determine population dynamics in the
endangered New Caledonian conifer Araucaria muelleri. Austral
Ecology 39: 60–71.
Enright NJ, Ogden J, Rigg LS. 1999. Dynamics of forests with
Araucariaceae in the western Pacific. Journal of Vegetation
Science 10: 793–804.
Evans JP, Keen EM. 2013. Regeneration failure in a remnant stand
of pignut hickory (Carya glabra) on a protected barrier island in
Paludo, Lauterjung, dos Reis and Mantovani
Georgia, USA. Natural Areas Journal 33: 171–176.
Feeley KJ, Davies SJ, Noor MNS, Kassim AR, Tan S. 2007. Do
current stem size distributions predict future population changes?
An empirical test of intraspecific patterns in tropical trees at two
spatial scales. Journal of Tropical Ecology 23: 191–198.
Gutiérrez AG, Avarena JC, Carrasco-Farías NV, Christie DA,
Fuentes M, Armesto JJ. 2008. Gap-phase dynamics and coexistence of a long-lived pioneer and shade-tolerant tree species
in the canopy of an old-growth coastal temperate rain forest of
Chiloe Island, Chile. Journal of Biogeography 35: 1674–1687.
Iob G, Vieira EM. 2008. Seed predation of Araucaria angustifolia
(Araucariaceae) in the Brazilian Araucaria forest: influence
of deposition site and comparative role of small and ‘large’
mammals. Plant Ecology 198: 185–196.
IUCN (International Union for the Conservation of Nature and
Natural Resources). 2013. IUCN Red List of Threatened Species.
Version 2013.1. Available at http://www.iucnredlist.org [accessed
8 July 2013].
Kurasz G, Rosot NC, Oliveira YMM, Rosot MAD. 2008. Caracterização do entorno da Reserva Florestal Embrapa/Epagri de
Caçador (SC) usando imagem Ikonos. Floresta 38: 641–649.
Kwitt C, Horvitz CC, Platt WJ. 2004. Conserving slow-growing,
long-lived tree species: input from the demography of a rare
understory conifer, Taxus floridana. Conservation Biology 18:
432–443.
Lefkovitch LP. 1965. The study of population growth in organisms
grouped by stages. Biometrics 21: 1–18.
Leslie PH. 1945. On the use of matrices in certain population
mathematics. Biometrika 33: 183–212.
López-Mata L. 2013. The impact of seed extraction on the population dynamics of Pinus maximartinezii. Acta Oecologia 49: 39–44.
Maloney PE. 2011. Population ecology and demography of an
endemic subalpine conifer (Pinus balfouriana) with a disjunct
distribution in California. Madroño 58: 234–248.
Mantovani A, Morellato LPC, Reis MS. 2004. Fenologia reprodutiva
e produção de sementes em Araucaria angustifolia (Bert.)
O. Ktze. Revista Brasileira de Botânica 27: 787–796.
Myers N, Mittermeier RA, Mittermeier CG, Fonseca GAB, Kent J.
2000. Biodiversity hotspots for conservation priorities. Nature
403: 853–858.
Nazareno AG, Feres JM, Carvalho D, Sebbenn AM, Lovejoy TE,
Laurance WF. 2011. Serious new threat to Brazilian forests.
Conservation Biology 26: 5–6.
Ogden J. 1985. An introduction to plant demography with special
reference to New Zealand trees. New Zealand Journal of Botany
23: 751–772.
Ogden J, Stewart GH. 1995. Community dynamics of the New
Zealand conifers. In: Enright N, Hill RS (eds), Ecology of the
southern conifers. Washington, DC: Smithsonian Institution
Press. pp 120–155.
Pollmann W, Veblen TT. 2004. Nothofagus regeneration dynamics
in South-Central Chile: a test of a general model. Ecological
Monographs 74: 615–634.
Portela RCQ, Bruna EM, Santos FAM. 2010. Demography of palm
species in Brazil’s Atlantic forest: a comparison of harvested
and unharvested species using matrix models. Biodiversity and
Conservation 19: 2389–2403.
R Core Team. 2012. R: a language and environment for statistical
computing. Vienna: R Foundation for Statistical Computing.
Reis MS, Ladio A. 2012. Paisajes con Araucarias en Sudamérica:
construcciones culturales pre-colombinas y del presente para
producción de alimento In: Navarro V, Espinosa S (eds), Paisajes
Culturales: memorias de las Jornadas de reflexión acerca de los
paisajes culturales de Argentina y Chile, en especial los situados
en la región Patagónica. Rio Gallegos: ICOMOS/UNPA/UMAG.
pp 224–244.
Ribeiro MC, Metzger JP, Martensen AC, Ponzoni FJ, Hirota MM.
Downloaded by [University of California, San Diego] at 19:15 04 March 2016
Southern Forests 2016: 1–7
7
2009. The Brazilian Atlantic Forest: how much is left, and how is
the remaining forest distributed? Implications for conservation.
Biological Conservation 142: 1141–1153.
Rigg LS, Enright NJ, Jaffré T, Perry GLW. 2010. Contrasting population dynamics of the endemic new caledonian conifer Araucaria
laubenfelsii in maquis and rain forest. Biotropica 42: 479–487.
Schaaf LB, Figueiredo-Filho A, Galvão F, Sanquetta CR, Longhi SJ.
2006. Modificações florístico-estruturais de um remanescente de
floresta ombrófila mista montana no período entre 1979 e 2000.
Ciência Florestal 16: 271–291.
Schmidt IB, Mandle L, Ticktin T, Gaoue OG. 2011. What do matrix
population models reveal about the sustainability of non-timber
forest product harvest? Journal of Applied Ecology 48: 815–826.
Schodelbauerová I, Tremblay RL, Kindlmann P. 2010. Prediction
vs. reality: can PVA model predict population persistence 13
years later? Biodiversity and Conservation 19: 637–650.
Souza AF. 2007. Ecological interpretation of multiple population
size structure in trees: the case of Araucaria angustifolia in South
America. Austral Ecology 32: 524–533.
Souza AF, Forgiarini C, Longhi SJ, Brena DA. 2008. Regeneration
patterns of a long-lived dominant conifer and the effects of
logging in southern South America. Acta Oecologica 34: 221–232.
Stubben CJ, Milligan BG. 2007. Estimating and analyzing
demographic models using the popbio package in R. Journal of
Statistical Software 22(11): 1–23.
Turner IM. 2004. The ecology of trees in the tropical rain forest.
Cambridge: Cambridge University Press.
Venter SM, Witkowski ETF. 2013. Using a deterministic population model to evaluate population stability and the effects of
fruit harvesting and livestock on baobab (Adansonia digitata L.)
populations in five land-use types. Forest Ecology and Management 303: 113–120.
Virillo CB, Martins FR, Tamashiro JY, Santos FAM. 2011. Is size
structure a good measure of future trends of plant populations?
An empirical approach using five woody species from the
Cerrado (Brazilian savanna). Acta Botanica Brasilica 25:
593–600.
Watkinson AR. 1997. Plant population dynamics. In: Crawley MJ
(ed.), Plant ecology (2nd edn). Oxford: Blackwell Publishing.
pp 359–400.
Zar JH. 2010. Biostatistical analysis (5th edn). Upper Saddle River:
Prentice Hall.
Received 1 February 2015, revised 8 October 2015, accepted 15 November 2015