Microsoft Word - CUSJ 04.docx
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Abstract — Amazonian tropical forests are critical to
global carbon cycling and sequestration, and in direct
danger from deforestation. In order to contribute to
limited existing literature on the carbon sequestration
potential of secondary forest ecosystems and their
aboveground biomass (AGB), we established permanent
0.2-ha plots in a primary and a secondary forest near
Iquitos, Loreto, Peru. We measured diameter at breast
height (DBH), tree height, and wood density for trees ≥10
cm DBH and took the diameter of lianas at 30 cm shoot
extension, then used published allometric equations to
estimate AGB and compare it between forests. Trees
within the primary forest plot had a significantly greater
mean DBH and higher mean wood density, as well as a
greater overall AGB than trees within the secondary
forest. AGB was calculated to be 322.05 Mg/ha for the
primary forest and 51.17 Mg/ha for the secondary forest.
Sequestered carbon quantities were 151.36 Mg/ha and
24.05 Mg/ha, respectively. Higher estimates of stored
carbon within the primary forest are attributed to old-
growth trees with large DBH values and increased wood
density, and discrepancies between our carbon estimates
for the secondary forest and past estimates for the same
site suggest the need to focus more research and
attention on allometric equation use. The results of this
study provide a potential incentive for carbon
sequestration funding to be awarded to the primary
forest property studied and establish a foundation for
future estimations of the carbon storage capacities of
tropical secondary forests.
I. INTRODUCTION
Tropical forests play a fundamental role in the global
carbon cycle by sequestering up to 40% of the world’s
terrestrial biomass-bound carbon (Pan et al. 2011). More
than half of the carbon in tropical forests is found in the
neotropics (Ngo et al. 2013). However, the carbon storage
potential of these forests can be truncated by deforestation
and ecological degradation. An estimated 1.6 to 2.4 Pg of
carbon is released into the atmosphere from tropical forest
clearing each year, representing 20-29% of global
anthropogenic carbon emissions (Naughton-Treves 2004).
In Peru, forests are being cleared at a rate of 0.4% per year,
releasing enormous amounts of stored carbon (Naughton-
Treves 2004).
After a forest has been disturbed, it will regenerate as
a secondary forest and accumulate carbon in its
aboveground and belowground tissues as it grows. Light-
demanding ruderal species grow quickly and form wood
with lower specific gravity than later successive, shade-
tolerant species that require time to establish and are not a
significant fraction of secondary forest composition
(Ramanantoandro et al. 2016, Gao et al. 2015, King et al.
2006). Wood density is directly related to above ground
biomass (AGB), and therefore secondary forests tend to
have less accumulated biomass than primary forests.
Carbon storage is often estimated as half of total AGB
(Day et al. 2013, Chave et al. 2005), so it can be assumed
secondary forests store less overall carbon than primary
forests.
The proportion of tropical forests that are secondary is
projected to increase continually due movement of
populations toward urban centers (Thomlinson et al 1996).
Abandonment of cleared sites may also contribute to
increased secondary forest cover. This may be the case in
regions of the Amazon whereland-use practices continue
to be characterized by reliance upon fallow periods (e.g.
Marquart et al. 2013, Naughton-Treves 2004). Though
secondary forests contain an estimated 60% less biomass
and 19% less total stored carbon than primary forests (Ngo
et al. 2013), they exhibit slightly higher regeneration rates.
Naughton-Treves (2004) found that the growth rate of
secondary forests in the Peruvian Amazon was as high as
11.47 Mg/ha per year, compared to Chave et al. (2001)’s
estimate of primary forest growth of 2-4 Mg/ha per year,
indicating that tropical secondary forests should not be
overlooked for their carbon storage potential.
Significant attention in the literature has been given to
the C storage abilities of tropical primary growth forests
(Chave et al. 2001, Baker et al. 2004, Ramanantoandro et al.
2016). To date, however, there has not been a comparable
number of studies on the C storage potential of
regenerating secondary forests in tropical areas. This is
also the case in Amazonia specifically, partially because
much of the existing literature on Amazonian AGB has
been compiled based only on trees ≥ 10cm DBH, and
secondary forests are often excluded from these
calculations entirely, due to their low abundance of large
trees (e.g., Baker et al. 2004, Chave et al. 2001). Further
study of secondary forest carbon storage will help ensure
they are properly valued for their ability to offset the
effects of climate change and protected from further
deforestation.
Proper evaluation of carbon storage in both secondary
and primary forests could also improve the ability to use
REDD+ incentives to protect tropical forests. REDD+
stands for “reducing emissions from deforestation and
degradation” and aims to develop policies that add high
financial value for carbon stored in intact forests (Kricher
2011). It serves as an international incentive to reduce
deforestation. To help offset global carbon emissions,
REDD+ is focused on maintaining carbon storage within
Aboveground Biomass and Carbon Storage Capacities of a Western
Amazonian Primary and Secondary Growth Forest
Lindsay Finnell1,3 and Bailey J. Nordin2,3*
1 Drake University, Des Moines, IA, USA lindsay.finnell@drake.edu�
2 Columbia University in the City of New York, NY, USA bjn2122@columbia.edu
3 Center for Amazon Studies School for Field Studies, Loreto, Peru
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the tropical forests of developing countries by reducing
land use change of natural ecosystems (Pan et al. 2011).
Forest concessions can apply for REDD+ funding based on
calculations of their total carbon storage capacity. To
ensure the equitable distribution of REDD+ funds, it is
imperative that the carbon storage of different ecosystems
is accurately estimated.
In this study, we estimated and compared AGB and
carbon storage capacities for a section of primary terra
firme forest and a section of secondary terra firme forest in
the Northeastern Peruvian Amazon (Loreto, Peru). With
the objective of refining carbon estimates and improving
the local understanding of land-use history, we established
a permanent plot system to measure biomass at two
locations: the Universidad Cientifica del Peru
Conservation Concession (UCP), a primary forest sites,
and SFS Center for Amazon Studies (CAS), a secondary
forest site. We also hope to contribute to the information
bank needed to garner REDD+ funding at these two sites.
Given that the trees at the two sites are found within a
primary and secondary forest of different ages with
significant differences in species composition, we expected
that the AGB of each site would reflect these differences.
We predicted that the trees at UCP would have greater
DBH and greater average height than those at CAS. Since
faster growing, pioneer tree species found more
commonly in secondary forests disturbed recently like
those at CAS may have less dense wood than older,
slower-growing trees in the primary forest at UCP, we also
predicted greater wood densities and more overall AGB to
be found at UCP.
II. METHODS
Study Site
We established permanent forest plots at two locations
in the Amazon Basin: secondary broadleaf terra firme forest
at CAS that was cleared for mixed agricultural purposes
16-20 years ago, and primary terra firme forest at UCP that
has never been cleared for agriculture. CAS is located at
km 54 of the Iquitos-Nauta road within the Loreto region
of Peru (18 M 668840 9535047), and UCP is located two
hours up the Itaya River from the town of Cahuide
(Iquitos-Nauta Road km 57) (18 M 651648 9528215) (Figure
1). CAS has 69.5 forested hectares; 22.0 are 15-20 year-old
secondary regrowth and the remaining 47.5 are older
forest (>20 years-old) (L. Marshall, unpublished data).
UCP has an estimated 10,500 hectares of forested area (H.
Portocarrero, personal communication). Data collection at
both sites took place between the 5th and 15th of
November 2018, which is roughly the end of the dry
season and the beginning of the rainy season in Loreto.
Field Data Collection
At each location, we established a 20 x 100-m plot
composed of five contiguous 20 x 20-m subplots,
according to RAINFOR field manual standards (Phillips et
al. 2016). These 20 x 20-m subplots are the replicate unit for
plot-level variables (tree density and AGB) in this study.
Within each of these subplots, measurements were
taken and recorded for three variables: diameter at breast
Figure 1. Study Area Overview: Field data collection took
place at School for Field Studies Center for Amazon Studies
(SFS CAS) and Universidad Cientifica del Peru’s conservation
concession (UCP). The route from CAS to UCP includes a short
drive, a boat ride up the Itaya River, and a hike of nearly 2 km,
shown in purple. Aerial imagery sourced from Google Earth
(2018).
height (DBH) in cm, height in m, and wood density in
g/cm3. DBH was taken for each tree ≥ 10 cm, and for each
liana ≥ 10 cm diameter at any point below 2.5 m vertical
height using a standard DBH tape. A clinometer was used
to estimate height for each tree. We measured the angle in
degrees from observer’s eye level to the top of the tree (a)
and the angle from eye level to the ground (b), and a Bosch
GLM80 laser rangefinder to measure the horizontal
distance from tree to person measuring height (x) in
meters. Wood density was measured by drilling into each
tree and collecting the wood shavings in a marked bag. At
each tree drilled, the hole was measured for depth and
width in order to calculate the green volume of wood
extracted. The dry weight of the wood was determined by
weighing the wood shavings after drying them at 104°C
for a minimum of 24 hours. All trees were measured for
DBH and height, but due to time constraints, only 48 out
of 83 trees at CAS and 67 out of 150 trees at UCP were
measured for wood density. In final AGB calculations, a
site average density was used for the trees that were not
directly measured for wood density, as recommended by
Chave et al. (2005).
AGB Calculation
Tree height was calculated in meters using the
following trigonometric equation:
tree height = x(tan(a) + tan(b))
Wood density was estimated by calculating the green
volume of the drilled hole as a cylinder, then dividing the
resulting number by dry weight. AGB for each non-palm
tree was calculated in kg using the following equation
from Chave et al (2005):
ABGest = exp( -2.557 + 0.940*ln(ρ(D^2)H))
where ρ = wood specific gravity, D = DBH, and H = total
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tree height. This equation was chosen based on Chave et
al. (2005)’s analysis of allometric equations to measure
AGB, since in our study region, evapotranspiration
exceeds rainfall for less than a month per year (Rivas-
Martinez 1994), thereby classifying our study sites within
what Chave et al. defines as “wet” forest. AGB for lianas
was calculated in kg using the following allometric
equation:
ln(total biomass) = -7.114 + 2.276*ln(D)
where D = diameter at 30cm shoot extension. This
equation is based on Gehring et al. 2004’s evaluation of
liana AGB for primary and secondary forests of the
Amazon. AGB for all palms (Family: Arecaceae) was
estimated in kg using the following equation:
AGB^0.25 = 0.55512(dmf D^2 Hstem)^0.25
where dmf = dry mass fraction, D = DBH, and Hstem= stem
height. This allometric equation is based on Goodman et
al. (2013)’s assessment of the allometry of Amazonian
palms. We used Goodman et al.’s mean dry mass fraction,
0.370, for all palm stems. Tree, liana, and palm AGB were
summed for both study sites to get an estimate of total
AGB per plot.
Mean AGB values per hectare were then estimated by
calculating the mean AGB for each 20 x 20-m subplot, and
multiplying the high, low, and mean subplot values by 25.
Our subplot estimates of AGB and hectare estimate of
AGB were converted from kg/ha into Mg/ha and
multiplied by the carbon conversion rate of 0.47 to obtain
an estimate of carbon storage per hectare and the total
carbon storage at each site (Day et al. 2013). A carbon mass
fraction ~50% of total AGB has been used in other studies
of Amazonian terra firme forests (Chave et al. 2005) Our
Mg/ha estimates of AGB and carbon were converted into
site-wide equivalencies of total carbon for both CAS and
UCP by multiplying by the total forested area at each site.
When calculating total location AGB for CAS, we used the
UCP estimate of AGB for the areas of older forest at CAS
and the CAS estimate for younger secondary regrowth.
Statistical Analysis
PAst3 (Hammer et al. 2001) analysis software was
used to compare the mean values of DBH, height, and
wood density using standard t-tests. Differences in DBH,
height, and wood density between sites all had unequal
variance. Trees surveyed were grouped into four DBH size
classes: DBH ≥10 – 20 cm, DBH >20 – 30 cm, DBH >30 – 50
cm, and DBH >50 cm. Wood density was then compared
between these size classes using an ANOVA test. Densities
of trees and AGB per subplot were also compared between
sites using t-tests. A p-value ≤0.05 was considered
statistically significant.
III. RESULTS
In total, 228 trees (CAS = 83 and UCP = 145) were
surveyed and tagged within our plots. Overall, trees of
smaller size classes (DBH of 10-30cm) were more common
at both sites, and more trees of larger size classes (DBH ≥
30cm) were encountered at UCP (Figure 2). No lianas were
surveyed at CAS, and 5 lianas fitting our DBH criteria
were surveyed at UCP. Four palms were measured at CAS
and 2 at UCP, and a total of 3 trees surveyed were not
included in AGB calculations because they were either
dead or leaning too severely to estimate height.
Figure 2. Size class distributions: Number of individual trees
surveyed in each DBH size class at both CAS and UCP. 188 out
of 228 trees had a DBHof 10-30cm, and 29 out of the 40 trees
surveyed with a DBH≥ 30cm were found at UCP.
The trees measured at UCP were found to have, on
average, a greater DBH (CAS = 18.9cm and UCP = 22.3cm;
t-value = 2.231, df = 236.04, p-value = 0.027) and a higher
wood density (CAS = 0.328g/cm^3 and UCP =
0.732g/cm^3; t-value = 9.953, df = 112.98, p-value =
3.89E^- 17) (Figure 3). No significant difference was found
in wood density between size classes, however (F3,110=
1.05, p- value = 0.373). Mean tree height also did not
exhibit significant differences between CAS and UCP (t-
value = 1.571, df =149.13, p-value = 0.118) (Figure 3).
Figure 3. Mean DBH, height, and wood density:
Comparisons of mean values (± S.E.) calculated for tree DBH
(cm), tree height (m), and wood density (g/cm^3) between CAS
secondary forest and UCP primary forest plots. The asterisk
indicates that significant differences were found between the two
sites at the critical p-value of 0.05.
The total AGB estimated within our 20 x 100-m plot at
0
20
40
60
80
100
DBH ≥ 10cm DBH 20-
30cm
DBH 30-
50cm
DBH >50cm
Nu
m
be
r o
f i
nd
iv
id
ua
ls
CAS
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UCP was greater than the total estimated at CAS (Table 1).
Per hectare, the UCP primary forest plot was also
estimated to store more AGB than the CAS secondary
forest plot, with a mean AGB estimate of 322.05 Mg/ha
(95% conf. interval: 236.04-408.06) (Range: 254.67-
433.19Mg/ha) at UCP and 51.17 Mg/ha (95% conf.
interval: 31.44-70.90) (Range: 37.71-61.75Mg/ha) at CAS.
Carbon estimates for each forest in Mg/ha are also
reported in the table, as well as estimates of the total AGB
and total carbon stored in the forested area of each
location. Density of trees at UCP was also higher per
subplot, with a per hectare mean of 725 (95% conf.
interval: 613.08-836.92), whereas the mean number of trees
per hectare at CAS was 415 (95% conf. interval: 350.63-
479.37).
Table 1. Estimated AGB and carbon: Quantities of AGB and
carbon estimated within the 20x100m plots, calculated
quantities of AGB and carbon in Mg/ha, and the quantity of
AGB and stored carbon calculated for the total area of CAS (69.5
forested ha) and UCP (10,500 ha).
Site Plot
AGB
(Mg)
Mean
AGB
(Mg/ha)
Total site
AGB
(Mg)
Plot
carbon
(Mg)
Mean
carbon
(Mg/
ha)
Total site
carbon
(Mg)
CAS 10.55 51.17 16,423 4.86 24.05 7,718
UCP 64.41 322.0 3,381,525 30.27 151.3 1,589,317
IV. DISCUSSION
The objective of our study was to contribute to existing
literature on the AGB, carbon sequestration capacities, and
regenerative potential of secondary terra firme forests in
the Amazon basin. With these goals in mind, we tested the
hypotheses that we would observe a greater mean tree
DBH, wood density, and tree height within the primary
forest at UCP, contributing to higher overall AGB
estimates and carbon storage than at the secondary forest
at CAS. With the exception of height, which did not vary
significantly between sites, all the predicted differences
were observed. We will first address the contributions of
increased DBH and wood density to the biomass at UCP,
followed by sources of variation concerning tree height
and tree density, and finally, we will address the
implications of the disparity between our AGB estimations
for CAS and those of a previous study of the area as well
as the implications of the greater carbon sequestration
observed at UCP.
DBH
According to Chave et al. (2005), the variables most
important to predicting the AGB of an individual tree are
trunk diameter, wood density, and tree height, in that
order. Therefore, it is reasonable to assume that the
significantly higher mean DBH measured at UCP directly
influenced the site’s greater AGB estimate and carbon
storage capabilities. Primary forests tend to have greater
DBH measurements than secondary forests of the same
forest type, simply because they have had more time to
grow and accumulate biomass, although secondary forests
are able to increase average DBH more rapidly (Ngo et al.
2013).
Though we did not measure every stem located within
our plots, we have reason to believe that the trees
surveyed did give us a close estimation of actual AGB.
Based on studies conducted in Amazon forests, more than
80% of total AGB is found in trees greater than 10cm in
diameter (Baker et al. 2014). In addition, trees less than 10
cm DBH only contribute an estimated 2% of total AGB; the
remaining quantities can be found in lianas, dead wood,
and leaf litter (Chave et al 2001). Thus, our decision to
omit smaller stems from our field measurements is
supported by existing literature as a time-efficient way to
accurately sample a large area of land.
It is important to note the significance of especially
large trees to AGB. Chave et al. (2001) found that trees
greater than 70 cm DBH disproportionately contribute to
AGB estimates, with roughly 2.5% of trees surveyed
contributing 36% - 39% of biomass. Our results were not
nearly as dramatic, perhaps because we only measured
two trees with DBH greater than 70 cm, but those two
trees (1.3% of trees surveyed) still accounted for nearly 6%
of the total AGB at UCP. Though forests quickly
regenerate and accumulate biomass after clearing, the
carbon contained within old-growth trees of such huge
stature cannot simply be replaced by the rapid carbon
sequestration of saplings. The disproportionate biomass of
ancient and enormous trees in old-growth forests
emphasizes the importance of incentivizing the
preservation of these forests through REDD+ funding.
Wood Density
Studies of forests in temperate regions report that
DBH alone is sufficient to estimate biomass (Chave et al.
2005). Indeed, the only other existing biomass estimate for
CAS was calculated using an allometric equation based
solely on DBH (Marshall 2018, unpublished data).
However, it should be emphasized that many temperate
models are based on simpler forests with dominant tree
species, whereas tropical forests are more complex and
require models that utilize more variables to estimate AGB
(Chave et al. 2005). In order to refine our AGB and carbon
estimates, we chose a model that accounts for wood
density and tree height in addition to DBH.
Wood density is a measurement that reflects the
amount of biomass per unit volume of tree trunk and is
significant in calculating AGB (Ramanantoandro et al.
2016). It has been concluded that fast-growing, light-
demanding species have lower wood densities than
slower-growing, shade-tolerant species; there is a trade-off
between the volume of wood produced, facilitating rapid
growth toward the canopy, and its resulting density,
which provides structural support and protects against
breakage (Baker et al. 2004, Ramanantoandro et al. 2016).
Therefore, the greater wood densities observed at UCP
suggests a species compositional difference within the
mature forest, which probably contains more shade-
tolerant, slow growth trees as opposed to the higher
number of light-demanding pioneer species in a secondary
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forest such as CAS. The importance of individual tree
wood densities in estimating AGB may not seem as
significant in old-growth forests that are mainly composed
of hardwood species with a narrow range in wood
densities, but Baker et al. (2004) has shown that ignoring
variations in wood density results in poor prediction of
overall AGB. The landscape of this study is a mosaic of
species and differing wood densities, and Baker’s assertion
here implies that, on the landscape level, it is important to
account for the spatial variation of species composition
and wood density.
For tropical trees, species compositions appears to be
more important than size class in influencing wood
density. Indeed, we found no correlation between DBH
size class and wood density at either of our study sites.
Thus, our study supports previous findings in Madagascar
that tree diameter had no relationship to wood density
within or between tree species, despite the variation of
wood densities between species (Ramanantoandro et al.
2016).
Height
Since we did not observe a significant difference in
heights between our two sites, our data tends to agree
with the assertion that tree height on its own is not a good
estimator of AGB (Chave et al. 2001). Though height on its
own is not significantly important to AGB, when used in
conjunction with DBH it becomes D2H, which Chave et al.
(2001) asserts is the best estimator of AGB. Indeed, the
standard error in estimation of biomass decreases from
19.5% to 12% when height is included in the equation
(Chave et al. 2005); we therefore chose to measure and
include tree heights in our study.
Our results support previous findings that after trees
have grown to a certain height, they focus most of their
energy on growing outward rather than growing upward
(Da Silva Scaranello et al. 2012). This helps explain our
lack of significant difference between tree height at CAS
and UCP despite the greater DBH of UCP trees. It can be
assumed that there is a threshold past which vertical
growth does not confer greater benefits to trees, and that
trees at CAS and UCP have both reached that point and
have begun to invest more energy into increasing girth.
Again, the old-growth trees at UCP have merely had a
longer lifespan to accrue biomass and increase in diameter,
resulting in greater total AGB.
Tree Density
We were not able to find much mention in literature of
tree density differences between primary and secondary
forests. This is indicative of a lack of available information
concerning lower secondary forest tree density of trees ≥
10 cm DBH. Perhaps the comparison of tree density
between forest types is not worth scientific attention
because it can be assumed that primary forests will contain
more trees of larger size classes than secondary forests,
based merely on time required for recruitment and
growth.
Carbon Storage and AGB
Our carbon estimates of 151.36 Mg C/ha for UCP and
24.05 Mg C/ha for CAS are both slightly lower than
average carbon storage values for primary forests found in
the Peruvian Amazon by Nebel et al. (2001) (220.2 Mg
C/ha) and previous estimates for the CAS property by
Marshall (2018) (138.8 Mg C/ha). However, AGB and
carbon storage estimates vary throughout the primary,
wet tropical forests of Central and Western Amazonia,
ranging from 74.5 Mg C/ha to 203.9 Mg C/ha in certain
areas (Kauffman et al. 2009). Even within a given tropical
evergreen forest, the total sequestered carbon values
derived from AGB estimates can vary by as much as 117.5
Mg C/ha across a local landscape (Kauffman et al. 2009).
UCP’s carbon estimate, therefore, falls within a normal
range for primary terra firme forests of the Amazon, and
certainly warrants the area’s continued conservation and
consideration for REDD+ funding.
Just as the diversity of structure and composition
among trees in tropical ecosystems represents a potential
source of variation in calculating carbon quantities across
the landscapes, the choice of equation used to estimate
AGB can also affect the predicted quantities (Chave et al.
2005). This is in part because the broad applicability of
each allometric equation is limited by the diversity and
composition of the original data set used to derive it. For
example, the Chambers et al. (2001) equation previously
used to estimate biomass at the CAS site is known to
consistently give the highest predictions of AGB of any
Amazonian allometric equation, and was developed using
only data from forest plots in the Central Amazon, where
tree wood density is reportedly higher than in the Western
Amazon (Baker et al. 2004). Local topographical variation
within the CAS site might also account for some of the
variation between our estimates and Marshall’s, and
sampling a larger area could produce more representative
AGB values. Tree densities, for example, were different
between the region we sampled and the area sampled in
the previous study (our range was 325-450 as compared to
500-575 trees per hectare) (L. Marshall, unpublished data).
Regardless, the AGB and carbon estimates derived using
the Chambers et al. (2001) equation are likely an
overestimate of actual values for the CAS property, as they
were calculated without taking into account the unique
wood density values and height/diameter relationships of
this region that lead to variations in allometry across
Amazonia.
It is important to note that even the Chave et al. (2005)
equation we used tends to overestimate AGB to some
degree (0-5%) when averaged across an entire site.
Nonetheless, equations such as the one chosen for our
study, which estimate AGB using forest type (dry, moist,
or wet) as a predictive measure, represent a significant
improvement over others because models without this
measure reliably overestimate AGB by as much as 50%
(Chave et al. 2005). We also believe that, had we used a
different equation to estimate AGB that did not include
wood density in the calculations, our AGB and carbon
storage values would have been even greater
overestimates. When we input our DBH and height
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measurements into the Chambers et al. (2001) used by
Marshall (2018), our AGB and carbon estimates in Mg/ha
nearly tripled (Our CAS range changed from 17.72-29.02
Mg C/ha to 56.74-121.20 Mg C/ha). While these increased
values are still smaller than Marshall’s estimate of 138.8
Mg C/ha for the CAS property, the parameters and
assumptions of the equations being used explain some of
this inconsistency. Differences in equation used also help
contextualize the disparity between Marshall’s (2018) total
carbon estimate for the CAS property (9,650.2 Mg C) and
ours (7,718.84Mg C). This indicates that in many cases
AGB estimates are highly dependent on the allometric
equation applied, and emphasizes the importance of
applying an equation which allows for the use of more
site-specific variables.
V. CONCLUSION
In order to improve AGB estimates for our study area,
we recommend either calculating the individual wood
density of every tree sampled or generating information
on species composition in the area to improve the accuracy
of wood density averages and thereby AGB estimations
(Day et al. 2013). We also recommend the sampling of a
larger area to account for local topographical variation,
since our range of potential AGB values calculated from
inter-subplot variation was relatively large (UCP range:
254.67-433.19Mg/ha, and CAS range: 37.71-61.75Mg/ha).
Any additional carbon estimates generated for secondary
forests of the region would contribute significantly to
existing research.
The AGB and carbon storage estimations for the UCP
primary forest concession place it within the range of other
highly productive primary tropical forests of the Amazon
basin. However, although our data supports the consensus
that primary forests store more AGB and sequester more
carbon than secondary growth forests through increased
parameters such as tree DBH, height, and wood density,
this does not indicate that mature forests should be the
only conservation priority. While old growth forests like
UCP tend to store more of their carbon above ground,
secondary growth forests like CAS store only about 38.1%
of forest carbon in AGB, with the rest being stored
primarily soil (Ngo et al. 2013). Our carbon storage
estimates for CAS, therefore, may be significant
underestimates of total ecosystem carbon storage. More
research on below ground biomass in Western Amazonia
would contribute substantially to estimations of carbon
storage in the secondary forests of the region. Moreover,
existing research on regenerating tropical ecosystems
states that secondary forests, which already act as
substantial carbon sinks, increase their capacity to store
carbon with increasing time since disturbance (Mukul et
al. 2016). The potential carbon sequestration abilities of
both primary and secondary terra firme forests of the
Western Amazon, therefore, should not be undervalued.
AUTHOR INFORMATION
Lindsay Finnell, Drake University, Des Moines, IA, USA:
lindsay.finnell@drake.edu
*Bailey Nordin, Columbia University in the City of New
York, NY, USA: bjn2122@columbia.edu
(717) 830-4730�
604 W 114 St, New York, NY 10027
ACKNOWLEDGMENTS
We would like to express the sincerest gratitude for the
academic support, hard work in the field, and friendship
of Dr. Laura Morales, Eliza Amber, Henry Hurt, Molly
Sayles, Christian Perez, and Jose David Urquiza, as well as
all of the staff and students at Universidad Cientifica de
Peru and the Center for Amazon Studies. We thank the
staff and students of The School for Field Studies, Peru
Center for Amazon Studies for making this research
possible.
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