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\begin{document}

\title{The cost of restoring carbon stocks in Brazil's Atlantic Forest}



\author[1]{Pedro H. S. Brancalion}%
\author[2]{Joannès Guillemot}%
\author[3]{Ricardo César}%
\author[3]{Henrique Andrade}%
\author[3]{Alex Mendes}%
\author[3]{Taísi Sorrini}%
\author[4]{Marisa Piccolo}%
\author[5]{Marina Peluci}%
\author[3,6]{Vanessa Moreno}%
\author[3]{Gabriel Colletta}%
\author[7]{RL Chazdon}%
\affil[1]{University of São Paulo Luiz de Queiroz College of Agriculture}%
\affil[2]{CIRAD}%
\affil[3]{USP ESALQ}%
\affil[4]{Universidade de São Paulo}%
\affil[5]{Universidade de São Paulo Centro de Energia Nuclear na Agricultura}%
\affil[6]{University of São Paulo}%
\affil[7]{University of Connecticut}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Knowing which restoration approach provides the best returns on
investment for accumulating carbon is essential to foster restoration
planning, financing, and implementation. We assessed the recovery of
carbon stocks, implementation and land opportunity costs of forests
established by natural regeneration and high-diversity native tree
plantations. Our study was based on chronosequences (10-60 yr) of 12
naturally regenerating forests, 13 restoration plantations, and 5
reference forests located in Brazil's Atlantic Forest. Restoration
plantations accumulated approximately 50\% more above-ground carbon than
regenerating forests throughout the chronosequence. When controlling for
soil clay content, soil carbon stocks were higher in reference than in
restored forests, but they were comparable between plantations and
regenerating forests. After 60 years of stand development, recovery of
total carbon stocks in both restoration management types reached only
half of the average stocks of reference forests. Total
cost-effectiveness for carbon accumulation, including both
implementation and land opportunity costs, was on average 60\% higher
for regenerating forests than for plantations (15.1 kgC.US\$-1 and 9.4
kgC.US\$-1, respectively). Both restoration management types had
cost-effectiveness for carbon accumulation markedly lower than the price
of carbon credits considered, so some voluntary forest carbon markets
are not adequately priced to support restoration derived offsets.
Although tree plantations initially had higher rates of carbon storage
than regenerating forests, their higher implementation and land
opportunity costs make them less cost-effective for carbon farming. Our
results further suggest that carbon markets alone have a limited
potential to up-scale restoration efforts in Brazil's Atlantic Forest.%
\end{abstract}%



\sloppy


\textbf{The cost of restoring carbon stocks in Brazil's Atlantic Forest}

Pedro H. S. Brancalion\textsuperscript{1}*, Joann\selectlanguage{ngerman}ès
Guillemot\textsuperscript{1,2,3}, Ricardo Gomes
César\textsuperscript{1}, Henrique Sverzut Andrade\textsuperscript{1},
Alex Mendes¹, Taísi Bech Sorrini\textsuperscript{1}, Marisa de Cássia
Piccolo\textsuperscript{4}, Marina Conte Peluci\textsuperscript{4},
Vanessa de Souza Moreno\textsuperscript{1}, Gabriel
Colletta\textsuperscript{5}, Robin L. Chazdon\textsuperscript{1,6,7,8}

\textsuperscript{1}Department of Forest Sciences, ``Luiz de Queiroz''
College of Agriculture, University of São Paulo, Piracicaba, SP,
13418-900, Brazil.

\textsuperscript{2}CIRAD, UMR Eco\&Sols, 34060 Montpellier, France

\textsuperscript{3}Eco\&Sols, University of Montpellier, CIRAD, INRA,
IRD, Montpellier SupAgro, 34060 Montpellier, France

\textsuperscript{4}Nuclear Energy Center for Agriculture, University of
São Paulo, Piracicaba, SP, 13400-970, Brazil.

\textsuperscript{5}Postgraduate Program in Plant Biology, Institute of
Biology, State University of Campinas, Campinas, SP, 13083-862, Brazil.

\textsuperscript{6}Department of Ecology \& Evolutionary Biology,
University of Connecticut, Storrs, CT, 06269-3043, USA

\textsuperscript{7}Tropical Forests and People Research Centre,
University of the Sunshine Coast, Maroochydore, Queensland 4558,
Australia

\textsuperscript{8}Global Restoration Initiative, World Resources
Institute, Washington, DC, 20002, USA

*Correspondence to: \emph{Pedro H. S. Brancalion} Email: pedrob@usp.br

\textbf{Short running title:} The cost of restoring forest carbon stocks

\textbf{Abstract:} Knowing which restoration approach provides the best
returns on investment for accumulating carbon is essential to foster
restoration planning, financing, and implementation. We assessed the
recovery of carbon stocks, implementation and land opportunity costs of
forests established by natural regeneration and high-diversity native
tree plantations. Our study was based on chronosequences (10-60 yr) of
12 naturally regenerating forests, 13 restoration plantations, and 5
reference forests located in Brazil's Atlantic Forest. Restoration
plantations accumulated approximately 50\% more above-ground carbon than
regenerating forests throughout the chronosequence. When controlling for
soil clay content, soil carbon stocks were higher in reference than in
restored forests, but they were comparable between plantations and
regenerating forests. After 60 years of stand development, recovery of
total carbon stocks in both restoration management types reached only
half of the average stocks of reference forests. Total
cost-effectiveness for carbon accumulation, including both
implementation and land opportunity costs, was on average 60\% higher
for regenerating forests than for plantations (15.1
kgC.US\$\textsuperscript{-1} and 9.4 kgC.US\$\textsuperscript{-1},
respectively). Both restoration management types had cost-effectiveness
for carbon accumulation markedly lower than the price of carbon credits
considered, so some voluntary forest carbon markets are not adequately
priced to support restoration derived offsets. Although tree plantations
initially had higher rates of carbon storage than regenerating forests,
their higher implementation and land opportunity costs make them less
cost-effective for carbon farming. Our results further suggest that
carbon markets alone have a limited potential to up-scale restoration
efforts in Brazil's Atlantic Forest.

\textbf{Keywords:} carbon sequestration; passive restoration; natural
regeneration; restoration plantations; soil carbon; tree planting

\textbf{Introduction}

Tropical forest restoration is a central strategy in the global efforts
to mitigate climate change (Bernal, Murray, \& Pearson, 2018; Griscom et
al., 2017; Houghton, Unruh, \& Lefebvre, 1993; Pugh et al., 2019;
Silver, Ostertag, \& Lugo, 2000). Regenerating forests in the lowland
tropics have high rates of carbon accumulation in aboveground biomass.
On average, regenerating forests recover over 100 Mg of biomass per
hectare within 20 years (L. Poorter et al., 2008) and reach biomass
stocks comparable to old-growth forests within 66 to 80 years (Martin,
Newton, \& Bullock, 2013). Protecting young regenerating forests in
Neotropics over the next 40 years could accumulate an amount of carbon
sufficient to offset emissions from fossil fuel burning and industrial
processes across all Latin American and Caribbean countries over the
1993-2014 period (Robin L. Chazdon et al., 2016).

The Bonn challenge, launched in 2011, is a global commitment of
countries, NGOs, and private companies that aims to restore 350 M ha of
degraded forests by 2030 (R. L. Chazdon et al., 2017; Grassi et al.,
2017; K. D. Holl, 2017). The success of large-scale restoration programs
will rely crucially on their economic viability over both short- and
long- time scales, as well as the benefits they may provide to human
wellbeing in relation to alternative land uses (Pedro H. S. Brancalion
et al., 2019). Restoring large areas of tropical forests requires
knowledge about which restoration approach provides the best returns on
investment for accumulating carbon and other expected benefits, such as
reducing extinction risk, improving water supplies, and increasing food
security (Pedro H.S. Brancalion et al., 2018; Crouzeilles et al., 2017;
Luke P. Shoo et al., 2017).

Natural regeneration is widely recognized as a core restoration strategy
to mitigate climate change due to its potential to accumulate biomass at
large spatial scales and at lower costs compared to tree plantations (R.
L. Chazdon \& Guariguata, 2016; Evans et al., 2015; Gilroy et al., 2014;
Lewis, Wheeler, Mitchard, \& Koch, 2019). This potential is already well
demonstrated worldwide through several examples of forest transitions,
where the abandonment of marginal agricultural lands led to the
widespread expansion of forest cover through natural regeneration (Aide
et al., 2013; Nanni et al., 2019; Rudel et al., 2005). As a spontaneous,
uncontrolled reassembling process, natural regeneration trajectories can
vary remarkably even among stands with similar local biophysical
conditions and disturbance regime (Arroyo-Rodríguez et al., 2016;
Mesquita, Massoca, Jakovac, Bentos, \& Williamson, 2015; Norden et al.,
2015). This variation has to be accounted for in restoration planning
(Pedro H. S. Brancalion, Schweizer, et al., 2016; Uriarte \& Chazdon,
2016).

In contrast with natural regeneration, tree plantings permit tighter
control on the characteristics of the restored forest stand. Tree
planting has often been established by restoration practitioners when
ecosystem resilience is expected to be insufficient to support effective
natural regeneration (K. D. Holl \& Aide, 2011), or where rapid recovery
of forest structure is required for legal compliance (Pedro H. S.
Brancalion, Schweizer, et al., 2016; Chaves, Durigan, Brancalion, \&
Aronson, 2015; Rodrigues et al., 2011). Intensive management practices,
such as planting trees at regular spacing, fertilizing soil, and
controlling weeds and leaf-cutter ants, can promote rapid accumulation
of above-ground biomass in tree stands (Rubilar et al., 2018; Wheeler et
al., 2016). However, plantation management is costly, and restoration
projects do not provide sufficient revenues to offset high
implementation and maintenance costs (P.H.S.; Brancalion et al., 2019).
In addition, most low-resilience sites where tree planting is required
to support restoration have been historically used for intensive
agricultural production and therefore have high land opportunity costs
(Laurance, Sayer, \& Cassman, 2014) and altered biophysical conditions
that reduce the potential for natural regeneration (R.L. Chazdon, 2014).

Comparing restored forests established by tree planting and natural
regeneration (i.e. second-growth forests) within the same geographic
region is key to guide the selection of appropriate restoration
approaches for different socioecological contexts and expected benefits
(Luke P. Shoo et al., 2017). As stated above, tree planting and natural
regeneration are usually found in contrasting biophysical conditions
within landscapes. Consequently,\emph{in situ} comparisons cannot be
used to develop general guidelines about the relative costs and benefits
of restoration practice (Reid, Fagan, \& Zahawi, 2018), to provide a
basis for guiding decisions regarding \emph{how} to restore a given
piece of land. Rather, such comparisons have the potential to highlight
the actual efficiencies of ``real-world'' restoration practices, as they
are determined by environmental, social and economic constraints. Such
comparisons can help to guide decisions regarding how to invest limited
resources for implementing forest restoration approaches across a range
of socio-ecological conditions.

At the global scale, natural regeneration was shown to be more effective
(Crouzeilles et al., 2017) or present similar outcomes (Meli et al.,
2017) than tree plantations for recovering forest biodiversity and
structure. At the local scale, however, L. P. Shoo, Freebody, Kanowski,
and Catterall (2016) found that restoration plantations 1-25 yr old in
the Australian wet tropics had a faster recovery of wood volume than
natural regeneration sites, and César et al. (2017) found similar
results in the Atlantic Forest of Brazil for 7-15 yr old forests
undergoing restoration. These previous works focused mainly on
above-ground carbon, with no assessment of the cost-effectiveness of
restoration approaches. Cost-effectiveness assessments, rather than the
evaluation of costs and effectiveness independently, are essential for
guiding decision-making in restoration (Birch et al., 2010; Molin,
Chazdon, Ferraz, \& Brancalion, 2018; Strassburg et al., 2019).
Moreover, the relative effect of tree planting and natural regeneration
for soil carbon sequestration remain unknown.

Here, we assessed the carbon accumulation (above-ground and soil),
implementation and land opportunity costs of forests established by
natural regeneration (i.e, passive restoration) and high-diversity
native tree plantations (i.e, active restoration). Our study was based
on chronosequences (10-60 yr) in human-modified landscapes of Brazil's
Atlantic Forest. Brazil's Atlantic Forest is a highly degraded
biodiversity hotspot with low remnant native forest cover (only 12-28\%
of the original Atlantic Forest remains today, depending on remote
sensing resolution; Rezende et al., 2018; Ribeiro, Metzger, Martensen,
Ponzoni, \& Hirota, 2009). Deforestation and fragmentation resulted
mainly from the expansion of profitable agricultural and forestry
commodities such as sugarcane, cattle ranching, and eucalypt plantations
(Joly, Metzger, \& Tabarelli, 2014), making cost-effectiveness a
fundamental topic for the upscaling of restoration in this biome (Pedro
Henrique Santin Brancalion, Viani, Strassburg, \& Rodrigues, 2012). This
work aimed to compare current management practices in order to guide the
ambitious restoration programs recently established for the Atlantic
Forest (Molin et al., 2018; Soares et al., 2014; Strassburg et al.,
2019). Restoration practitioners and organizations worldwide have to
make hard decisions on where and how to invest in restoration to enhance
carbon accumulation while promoting biodiversity and other ecosystem
services (Luke P. Shoo et al., 2017). The solutions identified for
carbon farming through restoration with native trees in the Atlantic
Forest of Brazil may be readily transferrable to other tropical forest
regions with comparable economic and environmental contexts, and
hopefully will contribute to more effective use of the limited resources
currently available for forest restoration.

\textbf{Methods}

Study sites

We studied 12 second-growth forests established for 11-47 years on
former pasturelands, 13 restoration plantations of 10-61 years of age
established on pasturelands and croplands, and five old-growth,
reference forests (Fig. S1; Table S1). Second-growth forests were
sampled in the Corumbataí watershed, southeastern Brazil (Fig. S1).
Pastureland is the principal land use in this watershed (43.7\% of
area), followed by sugarcane fields (29.4\%), and remaining native
forest cover (12.4\%; Silvio F. B. Ferraz et al., 2014). Previous land
use and the age of regenerating forests were determined based on
panchromatic aerial photographs and annual LANDSAT 5 and 8 images.
Further information about this watershed and forest patch sampling can
be found in César et al. (2017). Second-growth forests of the Corumbataí
watershed are found regenerating almost exclusively in marginal lands,
such as steep slopes and/or sandy or rocky soil, where mechanized
agriculture is not possible. Extensive cattle ranching or eucalypt
woodlots have been established in these marginal agricultural lands, and
their less intensive historical soil management, at least compared to
sugarcane or other crop production, have led to the regeneration of
secondary forests when land is abandoned (César et al., 2017). All
second-growth forests were found away from riparian areas and were
expansions of existing forest remnants.

Restoration plantations were mostly identified in the same region
(\textless{} 100 km) of second-growth forests, but some additional
plantations (three out of the four plantations older than 25 years old)
were sampled (\textless{} 300 km) to include older sites in our dataset
(Fig. S1). These plantations were established with a high diversity of
native tree species (20-100 species) and had comparable levels of tree
richness than the studied second-growth and reference forests (Fig. S2).
Plantation management consisted of planting nursery-grown seedlings in
regular spacing (usually 3 x 2 m), fertilizing the soil before (base
fertilization) and after planting (broadcast fertilization), and weeding
ruderal plants with glyphosate spraying or mowing (see details in
P.H.S.; Brancalion et al., 2019). Land use prior to plantation
establishment was determined by interviewing restoration project
managers. Only three sites were established on pasturelands, and the
rest were established on former sugarcane fields. Twelve sites were
found in riparian buffers and were established to comply with the Forest
Code (Pedro H. S. Brancalion, Garcia, et al., 2016). Nine of the 12
sites were isolated from native forest remnants, and three sites were
established within the neighborhood of existing degraded remnants.
Consequently, our analysis did not allow for a controlled comparison of
properties between tree plantations and natural regeneration because
these restoration approaches were established in quite different
biophysical conditions in the studied landscapes. Although not
appropriate for objectively comparing these restoration
approaches\emph{per se} and isolating uncontrolled factors (Reid et al.,
2018), our study design depicts the cost-effectiveness of restoration
approaches currently being implemented in agricultural landscapes of
Brazil's Atlantic Forest (see Pedro H. S. Brancalion, Schweizer, et al.,
2016).

We complemented our sampling with five reference forests, represented by
old-growth remnants distributed around the Corumbataí watershed. These
remnants were selected for being the best conserved forests of the study
region, with a well-developed forest structure and some large
(\textgreater{} 50 cm diameter at breast height) late-successional
remnant trees (Fig. S1). These forests do not represent the
pre-disturbance carbon stocks of the original forests of this region,
since these remnants are small (\textless{}200 hectares) and are
embedded within a patchy agricultural matrix. However, they represent an
appropriate benchmark for restoration in this region, given that
restored forests will likely persist as small patches surrounded by
agriculture.

The forests sampled were selected according to different criteria. The
second-growth forests were selected systematically based on available
information on second-growth forest age and previous land use, aiming at
a representation of approximately five forests per age class x previous
land use (S. F. B. Ferraz et al., 2014). Restoration plantations and
old-growth forests were selected based on the availability of these
forest typologies with different ages within and closer to the
Corumbataí watershed, the focus area of the study. We sampled all
restoration plantation \textgreater{}15 years old and old-growth forests
we knew in the region due to the scarcity of these kinds of forest, and
seven restoration plantations \textless{}15 years old based on their
location (forests found within the watershed or closer to it was
prioritized) and access.

Forest inventories

We established one 20 × 45 m plot in each of the aforementioned forest
stands, totaling 30 plots. For second-growth forests and restoration
plantations, which usually had a small and elongated area, we allocated
the inventory plots as centralized as possible in the forest stand in
order to avoid edge effects. In old-growth forest stands, which were
larger than the aforementioned forest typologies, we took the main trail
that crossed these forests an established the inventory plot when
distant at least 50 m from the forest edge. For all plots, we avoided
forest gaps or patches affected by human disturbances (e.g. fire or
logging).

We measured the diameter at breast height (DBH) of all trees and shrubs
(DBH \selectlanguage{english}[?] 5 cm) within each plot and identified them to species level,
without distinguishing between planted or spontaneously regenerated
trees, and used these data to calculate above-ground biomass based on
the equations developed by Chave et al. (2014), with wood density values
mostly compiled from Chave et al. (2009) and Cesar et al. (2017).
Above-ground carbon stocks were calculated by multiplying above-ground
biomass by 0.46, the average carbon content of dry woody biomass of
Atlantic Forest trees (Ferez, Campoe, Mendes, \& Stape, 2015).

Soil characteristics

One composite soil sample (18 sub-samples systematically distributed per
plot) was made within each plot at both 0-10 cm and 10-20 cm depth and
analyzed for sum of bases (cations were determined by spectrophotometry
and P by colorimetry), cation exchange capacity, and texture, which was
analyzed using the hydrometer method. Soil samples were additionally
collected with a cylindrical auger of known volume at 0-10 cm and 10-20
cm depth, in three sampling points distributed systematically within
each plot (one at the center of the plot and one 10 m apart from each of
its lateral edges), to measure soil texture and total soil carbon. The
soil bulk density of each plot (n = 3) and depth was determined by the
core method on a core of 0.5 cm diameter and 5.0 cm deep for the 0-10
and 10-20 cm depth (samples oven-dried at 105degC until constant weight),
using the methodology described by Donagema, Campos, Calderano,
Teixeira, and Viana (2011). Soil samples were air-dried and sieved in 2
mm open mesh to obtain air-dried fine soil, and then a portion of the
sample was ground to pass through 100 mesh sieve to measure the C
content. Soil texture was measured according to (EMBRAPA, 1997). Total C
content was quantified by dry combustion method (1000degC) using a LECO
elemental analyzer (TruSpec CHNS, LECO). Due to logistical problems,
texture data were missing for one of the reference forest. Soil carbon
stock (SCS, Mg.ha\textsuperscript{-1}) of a soil layer \emph{i} was then
calculated as follows:

\begin{equation}
\text{SCS}_{i}=t_{i}{\times TCC}_{i}\times\text{BD}_{i}\ \ \ \ \ \ \ \ (Eq.\ 1)\nonumber \\
 \end{equation}

where \emph{t} is layer thickness (cm), TCC is total carbon content
(gC.g\textsuperscript{-1}), and BD is soil bulk density
(g.cm\textsuperscript{-3}). We considered a soil layer thickness of 10
cm, allowing in this case the estimate of SCS in the 0-10 and 10-20 cm
horizons, which was subsequently summed to obtain SCS in the 0-20 cm
horizon.

Restoration costs

Restoration implementation costs were obtained from Molin et al. (2018),
which indicated that restoration plantations established on croplands
and pasturelands in the region cost US\$2,500.ha\textsuperscript{-1} and
US\$3,750.ha\textsuperscript{-1}, respectively (more expensive in
pasturelands due to fencing costs); these estimates include all costs
associated with tree planting and maintenance for up to three years
(Molin et al., 2018). Unassisted natural regeneration in croplands had
no implementation cost and in pasturelands it was
US\$1,250.ha\textsuperscript{-1} due to fencing costs (Molin et al.,
2018). Annual land opportunity cost was estimated based on land rental
prices for sugarcane and cattle ranching in the Piracicaba region (where
the Corumbata\selectlanguage{ngerman}í watershed is located), based on official survey of the
Institute of Agriculture Economics of São Paulo (SãoPaulo, 2017), and
was estimated as
US\$106.66.ha\textsuperscript{-1}.year\textsuperscript{-1} for cattle
ranching and
US\$389.23.ha\textsuperscript{-1}.year\textsuperscript{-1}for sugarcane
(Molin et al., 2018). We calculated the accumulated land opportunity
cost by multiplying these aforementioned annual costs by the age of the
restored forests. Total restoration cost was calculated by summing
implementation and accumulated land opportunity costs (e.g., total cost
for 10-year old restoration plantation on cropland =
US\$2,500.ha\textsuperscript{-1} +
US\$389.23.ha\textsuperscript{-1}.year\textsuperscript{-1} x 10 years =
US\$6392.3). No discount rate was applied because we aimed to compare
tree planting and natural regeneration on the present value.

\textbf{Data Analysis}

The cost-effectiveness of restoration was calculated by dividing
above-ground carbon by the cost of restoration. Cost-effectiveness was
calculated based on above-ground carbon only (e.g. Birch et al., 2010;
Strassburg et al., 2019), because no information on the net soil carbon
changes attributable to forest restoration was available in this study,
and soil carbon dynamics following restoration in the Atlantic Forest
remains poorly known (Mendes et al., 2019). The explored model was as
follows:

\(Y\ \sim\ MT\ \times stem_{\text{density}}+MT\times clay+MT\ \times age\)

where Y is above-ground carbon accumulation, soil carbon accumulation or
cost-effectiveness for above-ground carbon accumulation - models were
adjusted separately for each dependent variables. MT is management type
(categorical variable). stem\textsubscript{density}, clay and age are
density of stems per hectare (\#stem/ha), clay content (\%) and stand
age (years), respectively (covariates).

For each dependent variable, two models were adjusted, where MT factor
was used to test for differences between reference and restored forests,
and between plantations and naturally regenerated forests. When
comparing reference and restored forests, stand age was not included in
the models. The models were simplified using F-tests. Data from
different sites were considered as independent observations. Log-log
transformation was used when necessary to linearize relationships among
variables and/or correct for residual heteroscedasticity. When log-log
transformation was applied, model fit was presented with its natural
power form in the figures. We did not report critical autocorrelation of
the explanatory variables in our dataset (VIF\textless{}4). The
compliance to linear model assumptions was checked using standard
procedures (Zuur, Ieno, Walker, Saveliev, \& Smith, 2009).

We further explored the drivers of above-ground biomass and soil carbon
accumulation by conducting a model selection procedure (Burnham \&
Anderson 2002). For each variable, competing models including all
possible combinations of explanatory variables were adjusted and ranked
using \selectlanguage{english}[?]AICci. \selectlanguage{english}[?]AICci is the difference between the Akaike Information
Criteria corrected for small samples (AICc) of a given model and the
AICc of the best-fitting model (minimum AICc). To assess the
contribution of explanatory variables, we retained for each forest
carbon stock components a subset of best-fitting models
(\selectlanguage{english}[?]AICc\textless{}2). The averaged coefficient, standard error and
relative importance of explanatory variables (the weighted proportion of
the models \selectlanguage{english}[?]AICc \selectlanguage{english}[?] 2 that contain the driver) were calculated from the
final model subsets. The soil characteristics considered in this
analysis included sum of bases, cation exchange capacity, and texture.
Soil carbon content was analyzed separately at depths 0-10 cm and 10-20
cm. All analyses were carried out in the R 3.0 environment (R
Development Core Team, 2013), using the package ``MuMIn'' (Barton,
2016).

\textbf{Results}

\emph{Carbon accumulation outcomes in restored forests}

Restoration management significantly affected biomass accumulation
(F\textsubscript{1,21} = 7.9, P \textless{} 0.001): tree plantations
accumulated approximately 50\% more above-ground carbon than
second-growth forests throughout the chronosequence (Fig. 1A). Soil
carbon stocks at 0-20 cm depth were not affected by forest age for any
of the restoration management type (F\textsubscript{1,11} = 0.25, P =
0.62 and F\textsubscript{1,10} = 1.6, P = 0.22 for plantations and
second-growth forests, respectively; Fig. 1B). Similar results were
observed when soil layers 0-10 and 10-20 cm were analyzed separately
(Fig. S2). Soil carbon stocks were strongly correlated with clay content
(Fig. 2), which positively influenced soil carbon stocks in restored
forests (F\textsubscript{1,11} = 26.2, P \textless{} 0.001 and
F\textsubscript{1,10} = 22.6, P \textless{} 0.001 for plantations and
second-growth forests, respectively; Fig. 2). When controlling for the
effect of clay content, soil carbon stocks were higher for reference
than for restored forests (F\textsubscript{1,27} = 23.9, P =
\textless{}0.001), but were similar between plantations and
second-growth forests (F\textsubscript{1,22} = 2.18, P = 0.15). Stem
density had no significant effect on any of the explored dependent
variables.

\emph{Drivers of above-ground and soil carbon accumulation in restored
forests}

The best-fitting models (\selectlanguage{greek}Δ\selectlanguage{english}AIC [?] 2) explaining above-ground carbon
accumulation in restored forests included restoration type and/or forest
age, whereas the best-fitting models explaining soil carbon stocks
included exclusively soil attributes, except for the inclusion of
above-ground carbon stocks as explanatory variable of 10-20 cm soil
carbon (Table 1). Clay content was included in all soil carbon models,
whereas soil sum of bases and soil density were included in two models
(one for 0-10 cm and one for 10-20 cm; Table 1). Restoration through
natural regeneration accumulated less above-ground carbon than native
tree plantations (Fig. 3; Fig. S3). Soil carbon stocks were positively
correlated with higher sum of bases, clay content (Fig. S3), and soil
density at both 0-10 and 10-20 cm, with the relative importance of each
variable changing for the different soil depths (Fig. 3).

\emph{Cost-effectiveness of restoration approaches for above-ground and
soil carbon accumulation}

The amount of carbon accumulated per invested US\$ in restored forests
increased with time when only implementation costs were considered
(F\textsubscript{1,22} = 12.8, P = 0.001), because of the gradual
increase of carbon stocks and the absence of additional costs after the
first years of implementation (Fig. 4A). However, the increase of land
opportunity costs was higher than the increase of carbon accumulation,
which resulted in a decrease of the opportunity cost-effectiveness for
carbon accumulation along the chronosequence (F\textsubscript{1,22} =
10.5, P = 0.003; Fig. 4B). These opposing trajectories of implementation
and accumulated land opportunity cost-effectiveness along the
chronosequence nullified changes in total cost-effectiveness with age in
restored forests (F\textsubscript{1,22} = 2.8, P = 0.1; Fig. 4C).
Implementation cost-effectiveness was significantly higher for
second-growth forests than for plantations throughout the chronosequence
(F\textsubscript{1,22} = 9.4, P = 0.005; Fig. 4A). Opportunity
cost-effectiveness showed a similar trend but the average difference
between plantations and second-growth forests was not significant
(F\textsubscript{1,22} = 2.7, P = 0.1; Fig. 4B). Total
cost-effectiveness, i.e. the amount of accumulated carbon standardized
by the total cost of restoration (implementation + opportunity costs),
was significantly higher for second-growth forests
(F\textsubscript{1,22} = 9.2, P = 0.006; Fig. 4C). On average,
second-growth forests displayed a total cost-effectiveness that was 60\%
higher than plantations (15.1 kgC.US\$\textsuperscript{-1} and 9.4
kgC.US\$\textsuperscript{-1}, respectively). Average differences between
second-growth forests and plantations were higher for total
cost-effectiveness than for its components (i.e. implementation and
accumulated land opportunity cost-effectiveness). This indicates that
differences in both implementation and opportunity costs contributed to
the resulting differences in total cost-effectiveness between
restoration practices. We note that one tree planting site was
significantly older (61 years old) than the rest of the restoration
dataset. Removing this site from the analyses did not change any results
or conclusions of this work.

\textbf{Discussion}

Native tree plantations were shown to be more effective in accumulating
above-ground and soil carbon stocks than natural regeneration during the
first 50 years, but their higher implementation (tree plantations:
US\$2,788 \emph{versus} natural regeneration: US\$1,250) and land
opportunity (tree plantations: US\$324 per year \emph{versus} natural
regeneration: US\$106 per year; Molin et al. 2018) costs make them less
cost-effective for carbon farming. It is important to note that our work
assessed high-diversity plantations of native tree species rather than
monoculture tree plantations, to which natural regeneration has often
been compared (Lewis et al., 2019). Several independent factors have
contributed to this outcome. Exploring them is key to understanding how
the cost-effectiveness of different restoration approaches may vary
within human-modified tropical landscapes.

Most of the plantations included in the present study were found in
riparian buffers - a privileged environmental condition for biomass
accumulation due to the reduced water deficit and higher soil fertility
- and in landholdings dominated by intensive agriculture, where soils
usually display high nutrient and clay content. The studied tree
plantations may therefore have benefited from higher nutrient and water
availability than second-growth forests, which usually regenerate on
slopes and sandy soils distant from watercourses. Water deficit and low
soil fertility are known to limit tropical forest successional
development (Jakovac, Pena-Claros, Kuyper, \& Bongers, 2015; Martins,
Marques, dos Santos, \& Marques, 2015; Lourens Poorter et al., 2016;
Zermeno-Hernandez, Mendez-Toribio, Siebe, Benitez-Malvido, \&
Martinez-Ramos, 2015), and may have reduced the biomass accumulation
rates of second-growth forests in our study. Water limitation, in
particular, may have played a critical role for the differential
performance of restoration approaches, since the study region has a
seasonal climate with annual water deficits of 20 mm or more depending
on geographical features (Alvares, Stape, Sentelhas, Gon\selectlanguage{ngerman}çalves, \&
Sparovek, 2013). In addition, tree plantations were fertilized, weeded,
and planted at a regular spacing, which allow an efficient occupation of
the deforested area by trees and enhance their growth, resulting in a
higher accumulation of biomass per area (P.H.S.; Brancalion et al.,
2019; Ferez et al., 2015). Our observation that tree plantations
initially accumulate more carbon than second-growth forest corroborates
previous results obtained in southern Costa Rica (Karen D. Holl \&
Zahawi, 2014) and Queensland, Australia (L. P. Shoo et al., 2016), but
contradict the findings of Lewis et al. (2019) based on commercial
forestry plantations.

The predominance of soil properties over land use effects on soil carbon
stocks in the tropics has been reported by Powers, Corre, Twine, and
Veldkamp (2011) and is confirmed in our study landscape. Greater biomass
productivity and carbon inputs are expected to increase soil carbon
stocks (Jandl et al., 2007; Karlen \& Cambardella, 1996), even though
changes in soil microbial communities may affect this causal dependency
(Fontaine, Bardoux, Abbadie, \& Mariotti, 2004). As a result, intensive
forest management, afforestation, and reforestation are commonly
associated with increased soil carbon stocks (Don, Schumacher, \&
Freibauer, 2011; Guo \& Gifford, 2002). We do not report such an
association between above-ground biomass and soil carbon stocks, as
plantations displayed soil C stocks comparable to naturally regenerated
forests, and the temporal accumulation of above-ground biomass in
restored forests was not accompanied by a similar increase in soil
carbon stocks (Fig. 1 and 2). Variations in soil properties among study
plots may have obscured the relationship between soil carbon stocks and
restored forest age at landscape level, as also reported by Mora et al.
(2018) and Martin et al. (2013). Strikingly, none of the restoration
management types recovered soil carbon stocks comparable to reference
forests. Our results thus corroborate a global meta-analysis in tropical
regions that found that second-growth forests stored 9\% less soil
carbon than primary forests (Don et al., 2011). The differences between
forest types was in our case remarkably higher, with reference forests
having approximately 50\% higher soil carbon stocks at similar soil clay
content. Taken as a whole, our results show that both above-ground
biomass and soil carbon were enhanced in plantations compared to second
growth forests, but that 60 years of stand development was not
sufficient for the restored areas to recover stocks comparable to
reference forests.

The inclusion of restoration costs in the analysis reversed the priority
order of restoration approaches for carbon farming, and natural
regeneration emerged as the most cost-effective solution. In other
words, the cost reduction allowed by natural regeneration compared to
planting more than compensated for the slower rate of accumulation of
biomass, even in the unfavorable environmental conditions for tree
growth in which second-growth forests regenerated in the study region.
It is also important to note that we included in our analysis the direct
financial costs of passive restoration (i.e., natural regeneration;
Zahawi, Reid, \& Holl, 2014) and considered both implementation and land
opportunity costs. Natural regeneration may show even higher
cost-effectiveness in regions where these costs are reduced, such as in
landscapes not dominated by agriculture, where land rental prices are
usually low. In addition, natural regeneration can be assisted (Shono,
Cadaweng, \& Durst, 2007), potentially at much lower costs than tree
planting over the whole area. Assisted regeneration has the potential to
enhance the growth performance of spontaneously regenerating trees, thus
enhancing the biomass accumulation potential with lower implementation
costs. We note that our results may be affected by a site selection
bias, possibly resulting in an over-estimation of natural regeneration
success. As was highlighted recently (Reid et al., 2018), natural
regeneration is commonly conducted at sites closed to secondary forests
remnants, which could bias conclusions of restoration practice
comparisons. Additional studies would be needed to adequately represent
the Atlantic Forest, a 1.3 million km² ecosystem with several
biogeographical zones and socioeconomic conditions that would affect the
comparisons made in this study.

Carbon market currently values a carbon credit approximately 5 US\$
(Hamrick \& Goldstein, 2016) for 1 ton of CO\textsubscript{2} (273 kgC),
i.e. 54.6 kgC.US\$\textsuperscript{-1}. Here, we report average total
cost-effectiveness values for above-ground carbon of 9.1
kgC.US\$\textsuperscript{-1} and 15.1 kgC.US\$\textsuperscript{-1}, for
plantations and naturally regenerated forests, respectively. The market
price of 5 US\$ thus underestimates by more than a factor of 3 the
actual price of carbon accumulation in restored forests. This clearly
demonstrates that the revenues potentially obtained by trading carbon
credits do not adequately cover the basic costs of both active and
passive restoration. Overall, our results suggest that carbon markets as
they are today offer a very low potential to up-scale restoration
efforts in the Atlantic Forest. Other complementary revenue sources like
those resulting from timber and non-timber forest products' exploitation
and payments for watershed services have been proposed to make tropical
forest restoration financially viable (P.H.S.; Brancalion et al., 2017),
which could be bundled with carbon farming for more favorable financial
results. Notwithstanding, carbon farming will continue to be one of the
major demands of environmental organizations, private companies, and
governments supporting forest restoration in tropical regions, and
finding the most cost-effective restoration approaches for this
objective remains as a critical research challenge.

\textbf{Acknowledgements}

We thank the National Council for Scientific and Technological
Development of Brazil (CNPq; grant \#304817/2015-5), the São Paulo
Research Foundation (FAPESP; grants \#2013/50718-5, \#2014/14503-7,
2017/05662-2, and 2018/18416-2), the Coordination for the Improvement of
Higher Education Personnel of Brazil (CAPES; grant
\#88881.064976/2014-01), U.S. NSF Coupled Natural and Human Systems
Program (PARTNERS Research Coordination Network grant \#DEB1313788), and
IN-SYLVA French network for financial support. The authors also would
like to thank the several volunteers during field work and the
landowners that allowed forest and soil sampling in their properties.

\textbf{Authors' contributions}

PHSB, RGC and RLC conceived the ideas and designed methodology; AM, RGC,
HSA, TBS, MCP, and VSM collected the data; MCP led soil carbon analysis;
JG analyzed the data; PHSB and JG led the writing of the manuscript. All
authors contributed critically to the drafts and gave final approval for
publication.

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\textbf{Tables}

Table 1. Best models (\selectlanguage{greek}Δ\selectlanguage{english}AIC [?] 2) describing above-ground carbon and soil
carbon accumulation in cronossequences of tree plantations and
second-growth forests established in agricultural landscapes of the
Atlantic Forest of Brazil.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllll@{}}
\toprule
\textbf{Response variable} & \textbf{Soil data depth} & \textbf{Model} &
\textbf{Intercept} & \textbf{Forest age} & \textbf{Clay content} &
\textbf{Sum of bases} & \textbf{Species richness} & \textbf{Restoration
(regeneration)} & \textbf{Soil density} & \textbf{above-ground biomass}
& \selectlanguage{ngerman}\textbf{Adj.R²} & \selectlanguage{greek}\textbf{Δ AIC}\selectlanguage{english} & \textbf{weight}\tabularnewline
\midrule
\endhead
Above-ground C & 0-20 cm & 1 & 54.98 & 1.30 & NS & NS & NS & -28.36 & NA
& NA & 0.30 & 0,00 & 0.27\tabularnewline
& & 2 & 47.06 & 1.06 & NS & NS & NS & NS & NA & NA & 0.16 & 1.65 &
0.12\tabularnewline
Soil C & 0-10 cm & 1 & -24.45 & NS & 0.33 & 0.05 & NA & NS & 23.89 & NS
& 0.69 & 0.00 & 0.26\tabularnewline
& & 2 & -15.63 & NS & 0.34 & NS & NA & NS & 19.10 & NS & 0.65 & 0.15 &
0.24\tabularnewline
& 10-20 cm & 1 & 7.92 & NS & 0.24 & NS & NA & NS & NS & NS & 0.63 & 0.00
& 0.22\tabularnewline
& & 2 & 7.11 & NS & 0.24 & 0.02 & NA & NS & NS & NS & 0.65 & 1.66 &
0.10\tabularnewline
& & 3 & 6.67 & NS & 0.24 & NS & NA & NS & NS & 0.02 & 0.65 & 1.73 &
0.09\tabularnewline
& & 4 & 0.85 & NS & 0.26 & NS & NA & NS & 4.68 & NS & 0.65 & 1.87 &
0.09\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

Figure 1. Above-ground carbon (A) and soil carbon accumulation (B) in
chronosequences of tree plantations and second-growth forests, and
reference forest values, in agricultural landscapes of Brazil's Atlantic
Forest. Solid and dashed lines represent, respectively, significant and
non-significant power regressions. ANCOVA on log-log transformed data
indicated a significant difference (P \textless{} 0.05) of estimated
intercepts between plantations and second-growth forests for
above-ground carbon biomass (A) and soil carbon stocks (B).

Figure 2. Soil carbon association with clay content in tree plantations,
second-growth forests, and reference forests within agricultural
landscapes of Brazil's Atlantic Forest. Solid lines represent
significant power regressions. Soil carbon stocks \emph{versus} clay
content relationship: ANCOVA on log-log transformed data indicated a
significant difference (P\textless{}0.05) of estimated intercepts
between restored and reference forests, and between plantations and
naturally regenerated forests.

Figure 3. Graphical representation of the influence of different drivers
of above-ground carbon and soil carbon stocks in chronosequences of tree
plantations and second-growth forests established in agricultural
landscapes of Brazil's Atlantic Forest. Results reflect the average
model developed by merging all models [?]AICc [?] 2. Arrow color represent
the sign of the average coefficient, and relative importance express the
weighted proportion of the models [?]AICc [?] 2 that contain the driver.

Figure 4. Temporal variation of restoration implementation costs (A),
accumulated land opportunity costs (B), and total (C) costs for
accumulating above-ground carbon stocks (i.e., cost-effectiveness) in
chronosequences of tree plantations and second-growth forests
established in agricultural landscapes of Brazil's Atlantic Forest.
Solid and dashed lines represent, respectively, significant and
non-significant power regressions. ANCOVA on log-log transformed data
indicated a significant difference (P \textless{} 0.05) of estimated
intercepts between plantations and second-growth forests for
implementation cost-effectiveness (A) and total cost-effectiveness (C).\selectlanguage{english}
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