Introduction
Deforestation and ecosystem degradation is, after fossil fuel combustion,
the largest cause of carbon dioxide (CO2) emissions to the atmosphere
(Van der Werf et al., 2009). Wetlands have one of the highest deforestation
rates; one-third of the world's mangrove forests have been lost in the past
50 years, while one-third of salt marshes has disappeared since the 1800s
(Alongi, 2002; McLeod et al., 2011 and references therein). Because wetlands
are rich in carbon (C), deforestation or disturbance of these ecosystems
results in large emissions of CO2 to the atmosphere (Lovelock et al.,
2011). To prevent the large emissions that result from wetland loss,
programmes such as REDD+ (Reducing Emissions from Deforestation and forest
Degradation) have been proposed. In order to target coastal wetlands within
REDD+ and other financing programmes, accurate estimates of C stocks and
sequestration rates are needed (Alongi, 2011).
C stocks within wetlands can be associated with forest structure, with
largest stocks in tall and vigorous forests (Adame et al., 2013). However,
this is not always the case, as in some locations mangrove C stocks do not
reflect the aboveground structure (Kauffman et al., 2014b). This could be
partly because wetlands are highly dynamic and the existing vegetation might
not reflect the wetland long-term condition (Thom, 1967; Thom et al., 1975). For example,
sparse mangroves growing in an accreting riverbank could become tall,
productive forests in 50 years, but the soil C will take longer to
accumulate. Thus, tall mangrove forests might not necessarily have larger C
stocks than shorter ones. Geomorphic processes will constantly create,
transform and destroy landforms, resulting in changes in vegetation,
production, sedimentation, and thus in C stocks (Adame et al., 2010, Alongi
2011).
Many forces contribute to the formation of riverine wetlands in
deltaic-estuarine landscapes, including: river run-off, wave action, tidal
inundation and the incidence of cyclones (Thom, 1967; Woodroffe, 1992). In the Mexican southeast coast, wetlands
are formed by a mosaic of marshes and peat swamps where freshwater input is
constant, tidal inundation is negligible and wave and storm damage is
relatively low. Lowland, mangroves dominate the vegetation from the upper to
the lower estuary. Upper estuarine mangroves have periodic input of
freshwater and lower tidal and wave influence compared to mangroves in the
lower estuarine region (Thom et al., 1975). In this study, we compared C stocks
(trees, soil and downed wood) of riverine wetlands of La Encrucijada
Biosphere Reserve (LEBR) in the Pacific south coast of Mexico. We compared C
stocks of different vegetation types (mangroves, peat swamps and marsh) and
throughout a geomorphological gradient (upper to lower estuary).
Mangrove, peat swamp and marsh sampling sites within La Encrucijada
Biosphere Reserve, Mexico. Mangroves were classified according to the NDVI (see
Sect. 2.2) into three classes, which broadly corresponded to a range of
mangroves from the upper to the lower estuary.
Wetlands are not only efficient at accumulating C, but also nitrogen (N)
when production exceeds N demand (Rivera Monroy et al., 1995). C and N
cycles interact closely; thus N stocks can increase with increments in C
(Yimer et al., 2006). N accumulation increases with rainfall, runoff and
production by organisms such as cyanobacteria and algae (Alongi, 2009; Reef
et al., 2010). Soil N accumulation is also associated with large foliage
cover and wood biomass (e.g. Hooker and Compton, 2003; Liao et al., 2007).
In this study we compare the capacity of different types of wetlands
(mangroves, marshes and peat swamps) and geomorphological settings (upper
and lower estuary) to store N in the soil.
The high capacity of wetlands to store C and N is partly due to their high
productivity and low soil decomposition rates. Mangroves and marshes can
store up to 3 times more C than terrestrial ecosystems (Chmura et al.,
2003; Donato et al., 2011; McLeod et al., 2011). For example, mangroves in
the Caribbean can store up to 987 MgC ha-1, while in the Indo-Pacific,
mangroves store 1,023 MgC ha-1 (Donato et al., 2011; Adame et al.,
2013). These values typically exceed those of tropical and temperate forests
(< 400 Mg ha-1, IPCC, 2003). Similarly, soil C sequestration
rates of coastal wetlands (210 g C m-2 yr-1) and freshwater
wetlands (20–30 g C m-2 yr-1) are higher than those of
terrestrial forests (∼ 10 g C m-2 yr-1; Chmura et al.,
2003; McLeod et al., 2011). Long-term carbon sequestration rates of
mangroves are very difficult to obtain, but are required to participate in
carbon payments (Alongi, 2011). In this study, we use a unique natural marker
(ash horizon from a volcanic eruption in 1902, Fig. S1 in the Supplement) to
calculate soil carbon sequestration rates during the last century from a
large number of locations (n= 36). We compared C sequestration rates of
mangroves across a geomorphological gradient, from mangroves in the upper
estuary to those in the lower estuary.
Riverine wetlands, particularly mangroves, are one of the most extensive
types of wetlands and are predicted to have one of the largest C stocks on
Earth (Ewel et al., 1998). We expect that C stocks within the riverine
wetlands of the south Mexican Pacific coast have large C stocks compared to
any other terrestrial forest. We also predict that mangroves and peat swamps
have higher C stocks compared to marshes. Finally, we expect that
geomorphological setting will affect C and N stocks and C sequestration
rates with higher values for mangroves in the upper estuary compared to
those in the lower estuary.
Methodology
Study site
The Encrucijada Biosphere Reserve (LEBR) is located in Chiapas, in the south
Pacific coast of Mexico (14∘43′ N, 92∘26′ W; Fig. 1).
The LEBR comprises an area of 144 868 ha. The LEBR has five coastal
lagoons connected to seven river systems. The LEBR is characterized by large
areas of wetlands including mangroves, marsh and peat swamp forests. The
LEBR has one of the most extensive mangrove areas of the region, with
forests dominated by trees of Rhizophora mangle that range between 20–40 m in height, and
are believed to be the tallest in the country (Tovilla et al., 2007). The
mangroves of LEBR support a high biodiversity, as well as fisheries and
tourist activities (UNESCO, 2013).
The climate of the LEBR is warm, sub-humid with most precipitation occurring
in the summer months (June–October). The mean annual temperature of the
region is 28.2 ∘C, with a mean annual minimum of
19.2 ∘C and a mean annual maximum of 36.5 ∘C; mean annual precipitation is 1567 mm (Sistema Meteorológico Nacional
– Comisión Nacional del Agua, station no. 7320, 1951–2010).
Site stratification
In this study, we sampled three types of wetlands: peat swamp forest, marsh
and mangroves. To determine a criterion for stratification of mangroves, we
used two SPOT 5 satellite images with geographical, geometric and
radiometric correction, and the Universal Transverse Mercator projection
system. From each image, the Normalized Difference Vegetation Index (NDVI)
was obtained with ERDAS Imagine. The NDVI values ranged from -1 to 1, where
negative values indicates areas without vegetation, values close to zero
indicate senescent or stressed vegetation, and values close to 1 indicate
green or healthy vegetation (Chuvieco, 2006). NDVI values were extracted
from the mangrove coverage map (CONABIO, 2013) and classified according to
Ruiz-Luna et al. (2010). The mangrove vegetation was divided into three
classes: the most vigorous vegetation was Class I (9253 ha), the least
vigorous vegetation was Class III (11 467 ha), and Class II (6757 ha) had
intermediate values of vegetation vigour. The mangrove classes along with
the distance to the mouth of the estuary were used to classify our sites
into three categories: upper estuary mangroves with the most vigorous
vegetation, lower estuary mangroves with the least vigorous vegetation and
intermediate mangroves in terms of vigour and distance to the mouth of the
estuary (Fig. 1). Hereafter, we refer to our mangrove locations as
“upper estuary”, “intermediate” and “lower estuary”.
Field and laboratory analyses
Sampling was conducted during December 2012, where ecosystem C stocks, soil
N stocks and soil C sequestration rates were measured. We sampled nine sites: a
peat swamp forest dominated by Pachira aquatica, a marsh dominated by the grass Typha domingensis and seven
mangrove forests (three sites in the upper estuary, two in the intermediate
estuary and two in the lower estuary; Fig. 1; Table 1). We measured
whole-ecosystem C stocks in six plots (radius of 7 m; 154 m2) per site
using methodologies described in Kauffman et al. (2014a). The plots were
established 25 m apart along a 125 m transect set in a perpendicular
direction from the water edge. At each plot, we sampled C stocks within
trees and shrubs, downed wood and the soil profile. We also sampled soil N
stocks and interstitial salinity. To estimate C sequestration rates in
mangroves, we used a natural ash horizon marker to calculate soil C
accumulation. The detailed methodology is explained below.
Characteristics of sampling sites within La Encrucijada Biosphere
Reserve. Values are shown as mean (standard error). Max – maximum;
DBH – diameter at breast height; n.a. – not assessed; Rm – Rhizophora mangle; Ag – Avicennia germinans; Lr – Laguncularia racemosa.
Max height
DBH
Tree density
Salinity
Dominant species
(m)
(cm)
(trees ha-1)
(ppt)
Mangroves
Upper estuary
Panzacola
40
10.5 (1.1)
1213 (278)
n.a.
Rm (97.5 %)
Teculapa
30
7.5 (1.0)
2761 (398)
19.3 (5.3)
Rm (94.5 %)
Paixtalon
25
9.9 (0.9)
2035 (134)
n.a.
Rm (100 %)
Intermediate
Esterillo
n.a.
8.8 (1.0)
3346 (148)
37.6 (5.3)
Rm (87.7 %), Ag (12.3 %)
Santa Chila
22
9.9 (0.6)
2371 (157)
37.5 (0.6)
Rm (68.9 %), Ag (25.1 %)
Lower estuary
Zacapulco
n.a.
8.8 (0.8)
1765 (274)
7.6 (0.4)
Rm (87.6 %), Lr (10.6 %)
Las Palmas
28
7.9 (0.4)
5370 (388)
28.9 (0.6)
Ag (83.2 %), Lr (13.9 %)
Peat swamp
22
14.5 (0.9)
2469 (301)
0.0 (0.0)
P. aquatica (96.9 %)
Marsh
3
–
–
n.a.
T. domingensis (100 %)
Biomass and C stock within trees and shrubs
Forest structure was measured at each plot through measurements of the
species and the diameter at 1.3 m height (DBH) of all trees. The diameter of
trees of R. mangle and R. harrisonii was measured at the main branch, above the highest prop root
(DR). Aboveground biomass in the marsh communities was determined
through plant harvest within two 20 × 20 cm quadrants within each of the six
plots. The wet mass was determined in the field and then a subsample was
collected from each quadrant and oven-dried to determine its dry weight.
Tree biomass was calculated using allometric equations (Table 2). We used
the formula by Fromard et al. (1998), which was obtained for mangroves of
French Guiana, which is a location with similar characteristics as those
found in LEBR (riverine mangroves with a tropical hot humid climate). We
compared the formulas of Fromard et al. (1998) and Day et al. (1987), the
latter obtained from mangroves in Campeche, Mexico. The results using both
formulas were not significantly different (t= 1.027; df = 2284; p= 0.30). We chose the formula by Fromard et al. (1998) because it included
trees with a DBH range similar to those found in LEBR (DBHMax= 32 cm
for R. mangle, 9.6 cm for Laguncularia racemosa and 42 cm for Avicennia germinans). Aboveground biomass of trees from the
peat swamp (P. aquatica) was calculated with the formula of van Breugel et al. (2011),
while belowground biomass of P. aquatica was determined with the equation of Cairns et al. (1997) for trees of tropical forests. Belowground root biomass for
mangroves was calculated using the formula by Komiyama et al. (2005) and
wood density values (Chave et al., 2009, Zanne et al., 2009) of comparable
climatic regions as the LEBR (Table 2). Tree C was calculated from biomass
by multiplying by a factor of 0.48 for aboveground and 0.39 for belowground
biomass; C content of marshes was calculated using a factor of 0.45 of the
total biomass (Kauffman et al., 2014a).
Allometric equations used to calculate aboveground and belowground
biomass (kg) of mangrove and peat swamp trees. AGB – aboveground biomass;
BGB – belowground biomass; DR – diameter above highest prop root
(cm); DBH – diameter at breast height. Wood density (g cm-3) values
used for calculating belowground biomass were obtained from Chave et al. (2009) and Zanne et
al. (2009).
Aboveground biomass
R. mangle
AGB = 0.1282 × DR2.6
Fromard et al. (1998)
A. germinans
AGB = 0.140 × DBH2.4
L. racemosa
AGB = 0.1023 × DBH2.5
Pachira sp.
lnAGB = -2.514 + 2.295 × lnDBH
Van Breugel et al. (2011)
Belowground biomass
R. mangle
BGB = 0.199 × (0.840.899) × (DR2.22)
Komiyama et al. (2005)
A. germinans
BGB = 0.199 × (0.670.899) × (DBH2.22)
L. racemosa
BGB = 0.199 × (0.600.899) × (DBH2.22)
P. aquatica
BGB = Exp (-1.0587 + 0.8836 × lnAGB)
Cairns et al. (1997)
Standing dead trees were also included in the tree C stocks estimations.
Each dead tree was assigned to one of three decay status (Kauffman et al., 2014a): Status 1, dead trees without leaves; Status 2, dead trees without
secondary branches; and Status 3, dead trees without primary or secondary
branches. The biomass for each tree status was calculated as a percentage of
the total biomass using the values provided by Fromard et al. (1998). For
dead trees of Status 1, biomass was calculated as the total dry biomass
minus the biomass of leaves, equivalent to 2.8 % of the total biomass.
The biomass of trees of Status 2 was calculated as the total biomass minus
the biomass of leaves (2.8 % of the total) and minus secondary branches
(equivalent to 18.7 % of the total biomass). Finally, the biomass of
trees of Status 3 was calculated as the biomass of the main stem, which is
equivalent to 76.6 % of the total biomass (Table 2).
Downed wood
The mass of dead and downed wood was calculated with the planar intersect
technique (Van Wagner, 1968) adapted for mangroves (Kauffman et al., 2014a).
Four 14 m transects were established at the centre of each plot: the first
one was established at 45∘ off the direction of the main transect, the
other three were established 90∘ off from the previous transect. The
diameter of any downed, dead woody material (fallen/detached twigs,
branches, prop roots or stems of trees and shrubs) intersecting each
transect was measured. Along the last 5 m of the transect, wood debris
> 2.5 cm but < 7.5 cm in diameter (hereafter “small”
debris) was counted. From the second metre to the end of the transect (12 m
in total), wood debris > 7.5 cm in diameter (hereafter “large”
debris) was measured. Large downed wood was separated into two categories:
sound and rotten. Wood debris was considered rotten if it visually appeared
decomposed and broke apart when kicked. To determine specific gravity of
downed wood we collected ∼ 60 pieces of downed wood of different
sizes (small, large-sound, and large-rotten) and calculated their specific
gravity as the oven-dried weight divided by its volume. Using the specific
gravity for each group of wood debris, biomass was calculated and converted
to C using a conversion factor of 0.50 (Kauffman et al., 1995)
Soil C and N
Soil samples for bulk density and nutrient concentration were collected at
each plot using a peat auger consisting of a semi-cylindrical chamber of 6.4
cm radius attached to a cross-handle (Kauffman et al., 1995). The core was
systematically divided into depth intervals of 0–15, 15–30, 30–50, 50–100 and > 100 cm. Soil depth was measured using a
steel 2 m rod that was inserted in the ground at each plot. Samples of a
known volume were collected in the field and then dried to constant mass to
determine bulk density. Samples were sieved and homogenized and treated with
hydrochloric acid to eliminate the inorganic carbon portion before analyses.
Concentration of organic C and N were determined using a Costech Elemental
Combustion System 4010 (CA, USA, Michigan Technological University, Forest
Ecology Stable Isotope Laboratory).
Soil C sequestration rates
We estimated C sequestration rates in mangroves as the amount of C
accumulated in the soil profile. To date the soil cores, we used a natural
marker that consisted of a volcanic ash horizon that was clearly identified
in all the cores (Fig. S1). This ash horizon marks the volcano Santa Maria's eruption in 1902 that represented one of the
four largest volcano eruptions of the 20th century (Volcanic Explosivity
Index of 6 out of 7, Williams and Self, 1983). As a result of the eruption,
a recognizable Plinian deposit of known date ashes can be established in the
Mexican Pacific coast, northwest of the volcano. We estimated soil C
sequestration within each plot of six of our mangrove sites by dividing the
depth of the ash horizon by years since the volcano eruption occurred and
multiplying it by bulk density and C content. Soil C sequestration rates are
expressed in g C m-2 yr-1. We were unable to measure soil C
sequestration rates of marsh and peats swamp forest, as these vegetation
types frequently suffer from fires and thus have confounding ash horizons.
Interstitial salinity
Salinity was measured with a YSI-30 multiprobe sensor (YSI, Xylem Inc.
Ohio, USA) from water extracted from 30 cm deep. The water was obtained with
a syringe and an acrylic tube (McKee et al., 1988).
Scaling up
To scale up our field measurements to the LEBR, we conducted different
approaches for each vegetation type. We relied on modelling approaches to
predict values of variables of interest in places where no information was
available.
For mangroves, we first estimated aboveground C (trees) throughout the LEBR.
Data were spatially harmonized with vegetation-related remote sensing
products and the first three principal components of the SAGA GIS standard
terrain parameters derived from a digital elevation model (Table S1 in the Supplement). A pixel size of 25 m was selected to resample remote sensing and
topographic layers given the coarser spatial resolution of ALOS Palsar
products. Upscaling of aboveground C was performed in R (Core Team, 2015) by
the means of a machine learning random forest tree ensemble (Breiman, 2001).
The number of covariates to fit each tree (mtyr parameter) was tuned by
10-fold cross-validation. The number of trees to grow was 1000, which was
enough to stabilize the error. For external validation purposes, 20 % of
available data was randomly left out of the model. Selection of external
validation and modelling was repeated 400 times to analyse the effects of
the random split on error metrics by the correlation between observed and
modelled and the root mean squared error (RMSE). Additionally, we
implemented the quantile regression forest method proposed by Meinhausen (2006), which allows the inference of the full conditional distribution of
the response variable as a function of its covariates. Having this
information, prediction intervals (at 95 %) were identified and their
range was used to provide a spatially explicit measure of uncertainty,
considering the number of data, the correlation among predictive variables,
as well as the complexity and geographical dimensions of the study area. The
aboveground C was extrapolated to total C stocks based on the
field-collected data. As a comparison exercise, we also estimated mean
ecosystem C stocks times the estimated area for each vegetation type on the
basis of the NDVI classification, which broadly represented mangroves from
the upper, intermediate and lower estuary (Fig. 1).
For the extrapolation of marsh dominated by T. dominguensis to the whole LEBR, we included
a number of sites where aboveground and belowground biomass and organic
matter content have been measured (C. Tovilla, unpublished data, Fig. 1),
which together with our field measurements, were used to roughly estimate C
stocks within the LEBR. The total area of marsh was calculated on the basis
of the “other wetlands” category obtained from the coastal vegetation map
of the Pacific south region (CONABIO, 2013), as well as from auxiliary
cartographic (SERIE IV; INEGI, 2012) and our field experience. It is likely
that the area of the marsh – and thus its C stock – was over- or
underestimated, as the marsh area included waterholes and inundated
vegetation (popales) with unknown C stocks.
For peat swamps, we extrapolated our six sampling plots to the forest
surrounding our sampling area, which had an area of 844 ha (Fig. 1). The
rest of the area of peat swamp forest is not available for the LEBR.
Therefore, the C stock estimated for peat swamp forests was underestimated.
Aboveground (a) (trees and shrubs and down wood) and belowground
(b) (soil at different depths and roots) carbon stocks (MgC ha-1) of
mangroves, peat swamp forests and marsh wetlands within La Encrucijada
Biosphere Reserve.
Statistical analyses
One-way analysis of variance (ANOVA) was performed to test differences of
above- and belowground biomass and C stocks among wetland types (mangroves,
marsh and peat swamp forest), sites and geomorphological setting (upper
estuary, intermediate and lower estuary mangroves). To avoid uncertainties
associated with imbalance designs, when comparing vegetation types (mangroves
vs. peat swamps vs. marsh), we used the mean for all mangrove sites for each
of the five plots which represented a range a vegetation from the water edge
to the landward side of the forest. The mean of the plots was compared
against the plots laid in a similar way for peat swamp forest and marsh
(n= 5 plots per site). Differences in soil C and N concentrations by depth
were tested with a two-way ANOVA, with site as the fixed effect and depth as
the random effect of the model. Normality was assessed using Shapiro–Wilk
tests. When significant differences were found, pair-wise comparisons were
explored using Scheffé post hoc tests. Analyses were performed using
Prism v6.0 (GraphPad Software, La Jolla, CA, USA) and SPSS Statistics v20
(IBM, New York, USA). Throughout the paper, data are reported as mean
± standard error.
Results
Forest structure
Mangroves of the LEBR were dominated by trees of R. mangle with lesser contributions
of A. germinans, L. racemosa and a few trees of R. harrisonii (in sites Panzacola and Teculapa). Only one of our
study sites – Las Palmas – was dominated by a different species, A. germinans. All the
sampling sites were characterized by relatively low tree density forests
(1213–5370 trees ha-1) with tall trees (∼ 20–40 m
in height) of mean DBH of 8–11 cm (Table 1). The peat swamp forest was
dominated by P. aquatica and had a similar structure to that of mangroves with a tree
density of 2469 trees ha-1, trees of up to 22 m in height and mean DBH
of 14.5 cm. Finally, the marsh was dominated by tall grasses (2–3 m in
height) of T. dominguensis (Table 1).
Aboveground biomass, belowground biomass (Mg ha-1) and total
carbon (C) in vegetation (MgC ha-1) within wetlands of La Encrucijada
Biosphere Reserve. Values are shown as mean (standard error). Different
letters indicate significant differences among sites (p < 0.05). The
marsh was not included in analysis due to missing belowground biomass.
Biomass (Mg ha-1)
C (MgC ha-1)
Site
Aboveground
Belowground
Mangroves
Panzacola
383.6 (153.6)ab
127.9 (47.6)ab
234.0 (92.3)ab
Teculapa
342.4 (87.0)ab
118.3 (20.4)ab
210.5 (49.4)ab
Paixtalon
391.6 (87.0)ab
140.0 (25.4)ab
242.6 (51.6)ab
Esterillo
621.3 (310.9)b
203.1 (85.1)b
377.4 (182.4)bc
Santa Chila
198.8 (13.4)a
93.9 (3.8)ab
132.1 (7.8)a
Zacapulco
303.5 (76.5)a
127.8 (29.9)a
195.5 (48.3)ab
Las Palmas
706.6 (172.6)b
268.7 (52.5)c
440.0 (103.1)c
Peat swamp
162.2 (27.3)a
43.5 (6.8)a
95.1 (15.7)a
Marsh
76.5 (11.6)a
n.a.
38.2 (5.8)
Tree biomass and C
Mean tree aboveground biomass of mangroves was 421.1 ± 67.8 Mg ha-1 and was higher than the biomass for the peat swamp and marsh,
which was 162.2 ± 27.3 and 76.5 ± 11.6 Mg ha-1, respectively. Thus, mean C stock in mangrove trees was
significantly higher in mangroves (215.0 ± 44.4 MgC ha-1)
compared to swamp forests and marsh (95.1 ± 15.7 and 38.2 ± 5.8 MgC ha-1, respectively; F2, 12= 167.4;
p < 0.0001; Table 3, Fig. 2).
Aboveground (a) (trees and shrubs and down wood) and belowground
(b) (soil at different depths and roots) carbon stocks (MgC ha-1) of
mangroves along a gradient from the upper to the lower estuary within La
Encrucijada Biosphere Reserve.
Tree biomass and vegetation C stocks were not significantly different among
upper, intermediate and lower estuary mangroves (F7, 40= 1.826; p= 0.109). However, there were significant differences among sites with lowest
C stocks measured in the vegetation of Santa Chila (132.1 MgC ha-1; t= 2.54; p= 0.015) and highest at Las Palmas (440.0 MgC ha-1; t= 2.03; p= 0.049), the only site dominated by A. germinans and not R. mangle. The vegetation
biomass and C stocks were quite similar among sites within the upper estuary
(range 211–243 MgC ha-1), but highly variable among sites within the
intermediate and lower estuary (132–440 C Mg ha-1; Table 3, Fig. 3).
Downed wood C
Downed wood C was low in peat swamp wetlands (12.5 ± 2.8 MgC ha-1),
but considerable in some mangrove sites (mean of 29.4 ± 3.7 MgC ha-1). The amount of downed wood in mangroves had a wide range
within sites, from 11 Mg ha-1 to 205 Mg ha-1, with a mean biomass
of 59.4 ± 26.0 Mg ha-1 (Table 4, Fig. 3). Mangroves in the lower
estuary had the highest biomass and C stocks of downed wood
(F2, 39= 6.86; p= 0.0028), mainly due to large amounts of
downed wood at Zacapulco (102.4 ± 27.0 MgC ha-1; F7, 47= 8.147; p < 0.0001). Small downed wood comprised
10.2 % of the total biomass (6.0 ± 0.8 Mg ha-1); large sound
wood the 55.4 % (33.0 ± 13.9 Mg ha-1) and large rotten wood
comprised 34.4 % of the total (20.4 ± 15.2 Mg ha-1).
Biomass (Mg ha-1) and C stocks (MgC ha-1) of downed wood
in La Encrucijada Biosphere Reserve. Wood debris was calculated separately
for small wood (diameter > 2.5 and < 7.5 cm), and large
sound and large rotten wood (diameter > 7.5 cm). Values are shown
as mean (standard error).
Site
Small wood
Large wood
C stock
(< 7.5 cm; Mg ha-1)
(> 7.5 cm; Mg ha-1)
(MgC ha-1)
Sound
Rotten
Mangroves
Panzacola
5.8 (1.0)
79.8 (24.0)
1.4 (0.6)
43.5 (15.5)
Teculapa
10.3 (2.8)
14.0 (4.4)
3.4 (1.3)
11.9 (3.0)
Paixtalon
5.3 (1.1)
7.7 (2.7)
5.8 (3.0)
9.4 (2.2)
Esterillo
5.5 (0.9)
0.5 (0.4)
4.5 (1.4)
5.3 (0.4)
Santa Chila
6.6 (1.9)
5.7 (1.7)
10.8 (3.5)
11.5 (1.9)
Zacapulco
4.4 (1.3)
88.9 (26.7)
111.5 (45.2)
102.4 (27.0)
Las Palmas
4.4 (0.9)
34.1 (11.1)
5.7 (2.1)
22.1 (6.6)
Peat swamp
9.2 (1.5)
3.0 (1.6)
20.4 (6.2)
12.5 (2.8)
Soil C and N
Soil C content ( %) was higher in peat swamps (19.9 ± 3.4 %)
compared to marsh (10.1 ± 2.5 %); mangroves had intermediate values
(14.6 ± 2.5 %; F2, 12= 3.616; p= 0.04). Soil N (%)
was higher in peat swamps (1.2 ± 0.2 %) compared to mangroves and
marsh (0.6 ± 0.1 and 0.6 ± 0.2 %, respectively; F2, 12= 5.558; p= 0.019). Soil C stock (MgC ha-1) was
significantly higher in mangroves (505.9 ± 72.6 MgC ha-1) and the
peat swamp forest (614.6 ± 85.7 MgC ha-1) compared to the marsh
(298.3 ± 39.0 MgC ha-1; Fig. 2; F2, 12= 5.42; p= 0.02). Finally, soil N stocks were higher for peat swamps
(40.4 ± 5.5 Mg ha-1) compared to mangroves (19.2 ± 2.7 Mg ha-1) and
marshes (18.5 ± 1.7 Mg ha-1; F2, 12= 11.51; p= 0.0016; Table 5).
Soil carbon (C) and nitrogen (N) concentrations (%), and soil C
and N stock (Mg ha-1) at different depths (0–150 cm) of wetlands from
La Encrucijada Biosphere Reserve. Values are shown as mean (standard error).
Different letters indicate significant differences between sites (p < 0.05).
Site
Depth
C
N
C stock
N stock
(cm)
(%)
(%)
(Mg ha-1)
(Mg ha1)
Panzacola
0–15
16.6 (1.5)
0.88 (0.08)
71.0 (4.2)
3.6 (0.2)
15–30
14.6 (3.7)
0.76 (0.19)
37.9 (7.1)
1.9 (0.3)
30–50
21.0 (2.8)
0.92 (0.13)
73.6 (7.8)
3.5 (0.5)
> 50
26.8 (1.4)
1.04 (0.07)
451.6 (30.0)
17.5 (1.3)
Total
634.0 (25.7)a
26.5 (1.1)ac
Teculapa
0–15
14.8 (4.0)
0.78 (0.20)
64.3 (9.1)
3.7 (0.4)
15–30
20.1 (3.9)
0.76 (0.21)
68.6 (6.7)
2.6 (0.5)
30–50
8.8 (3.7)
0.37 (0.16)
59.4 (11.8)
2.4 (0.5)
> 50
15.9 (3.1)
0.67 (0.12)
421.2 (29.5)
18.4 (1.4)
Total
613.6 (32.2)a
27.2 (2.0)ac
Paixtalon
0–15
22.3 (4.4)
0.82 (0.15)
91.6 (7.5)
3.6 (0.4)
15–30
19.4 (4.0)
0.82 (0.16)
63.0 (6.5)
2.6 (0.2)
30–50
13.0 (4.0)
0.50 (0.13)
69.6 (9.0)
2.9 (0.1)
> 50
17.1 (3.9)
0.71 (0.17)
389.4 (21.1)
16.4 (1.3)
Total
613.6 (23.6)a
25.4 (1.5)ac
Esterillo
0–15
20.4 (3.7)
0.95 (0.18)
98.1 (6.6)
4.8 (0.4)
15–30
21.7 (4.2)
0.91 (0.17)
66.7 (8.2)
3.1 (0.4)
30–50
16.5 (4.1)
0.65 (0.15)
88.1 (14.3)
3.1 (0.6)
> 50
16.1 (3.4)
0.56 (0.11)
479.3 (44.6)
2.6 (0.2)
Total
732.2 (53.8)b
13.6 (1.1)ab
Santa Chila
0–15
29.1 (1.3)
1.30 (0.06)
66.1 (6.2)
3.1 (0.4)
15–30
23.2 (2.4)
1.08 (0.12)
47.2 (5.6)
2.8 (0.3)
30–50
12.0 (2.6)
0.45 (0.09)
71.9 (8.8)
3.4 (0.4)
> 50
14.8 (1.7)
0.49 (0.07)
317.7 (83.8)
11.7 (3.3)
Total
393.0 (128.8)ac
16.9 (5.7)ab
Zacapulco
0–15
12.4 (2.9)
0.58 (0.15)
49.6 (8.1)
2.9 (0.6)
15–30
11.8 (3.7)
0.58 (0.20)
37.9 (5.6)
3.8 (1.5)
30–50
3.9 (1.6)
0.18 (0.06)
45.5 (10.9)
1.3 (0.5)
> 50
8.5 (1.5)
0.34 (0.06)
247.2 (61.2)
12.7 (2.1)
Total
380.1 (68.6)ac
15.5 ( 4.3)ab
Las Palmas
0–15
6.2 (1.2)
0.32 (0.07)
43.1 (5.5)
2.8 (0.3)
15–30
1.7 (0.4)
0.09 (0.03)
20.3 (3.1)
1.3 (0.2)
30–50
1.2 (0.2)
0.07 (0.01)
28.0 (5.5)
1.5 (0.2)
> 50
0.8 (0.3)
0.04 (0.01)
83.4 (34.7)
3.5 (1.3)
Total
174.8 (41.9)c
9.1 (1.7)b
Peat swamp
0–15
16.3 (5.5)
1.05 (0.32)
59.5 (15.13)
3.6 (0.1)
15–30
19.2 (5.9)
1.18 (0.41)
70.3 (26.1)
3.6 (1.2)
30–50
30.0 (7.2)
1.69 (0.35)
105.0 (21.8)
6.8 (1.4)
> 50
16.7 (5.2)
1.02 (0.39)
379.8 (68.8)
26.4 (5.6)
Total
614.6 (85.7)a
40.4 (5.5)c
Marsh
0–15
15.6 (4.0)
1.10 (0.28)
38.3 (7.9)
3.0 (0.5)
15–30
6.9 (1.8)
0.42 (0.08)
32.0 (6.0)
2.8 (0.3)
30–50
13.0 (3.0)
0.65 (0.17)
113.8 (19.2)
5.8 (0.8)
> 50
4.7 (0.9)
0.24 (0.03)
114.1 (21.1)
6.8 (0.7)
Total
298.3 (39.0)c
18.5 (1.7)ab
When comparing mangroves from the upper to the lower estuary we found that
the soil C stocks from the upper and intermediate estuary were significantly
higher than those from the lower estuary (F2, 12= 25.43; p < 0.0001). Soil C stocks were also significantly different among
sites and depths (Site F7, 64= 16.03, p < 0.0001; Depth
F3, 64= 8.83; p < 0.001; Table 5), with highest C
density in the soil horizon > 50 cm. Soil N stocks were higher in
mangroves of the upper estuary (26.4 ± 0.5 Mg ha-1) compared to
mangroves in the intermediate and lower estuary (15.3 ± 1.6 and 12.3 ± 3.2 Mg ha-1, respectively; F2, 4 = 20.35; p= 0.008; Table 5). We also found a trend of the distribution
of soil C with depth among mangroves from the upper to the lower estuary.
Soil C values increased with depth at Panzacola (upper estuary), remained
similar in depth in Teculapa and Paixtalon (upper estuary) and decreased in
depth at the rest of the mangroves within the intermediate and lower estuary
(Table 5).
Overall, C stocks were highest in mangroves and peat swamp forests, while N
stocks were highest in peat swamp forests. Soil C and N stocks were highest
in the upper estuary and decreased towards the lower estuary. Finally, the
variation of site replicates was different within the upper and lower
estuary: inter-site variability was much lower in mangroves from the upper
estuary compared to the mangroves from the intermediate and lower estuary
(Fig. 3).
Ecosystem C stocks
Mean C stocks of wetlands in the LEBR were significantly different, with
highest stocks for mangroves (784.5 ± 73.5 MgC ha-1) and peat
swamps (722.2 ± 63.6 MgC ha-1) and lowest for marsh (336.5 ± 38.3 MgC ha-1; F2, 12= 16.9; p= 0.0004; Fig. 2,
Table 6).
Ecosystem C stocks (MgC ha-1) for wetlands of La Encrucijada
Biosphere Reserve. Values are shown as mean (standard error).
Vegetation
Site
C (MgC ha-1)
Mangrove
Upper estuary
Panzacola
911.6 (74.5)
Teculapa
835.8 (42.2)
Paixtalon
865.6 (55.1)
mean
871.0 (22.0)
Intermediate
Esterillo
1114.9 (150.3)
Santa Chila
536.6 (88.8)
mean
825.8 (289.2)
Lower estuary
Zacapulco
678.1 (115.7)
Las Palmas
640.9 (114.8)
mean
659.5 (18.6)
Mangrove mean
784.5 (73.5)
Peat swamp
722.2 (63.6)
Marsh
336.5 (38.3)
There was a significant difference among mangroves along the estuary, with
mangroves from the upper (871.0 ± 22.0 MgC ha-1) and intermediate
estuary (825.8 ± 289.2 MgC ha-1) having higher C stocks compared
to those in the lower estuary (659.5 ± 18.6 MgC ha-1)
(F2, 12= 25.43; p < 0.0001). Largest C stocks were
measured at Esterillo (1114.9 ± 150.3 MgC ha-1) and lowest at
Santa Chila (536.6 ± 88.8 MgC ha-1). The C stocks of mangroves
within the upper estuary were quite similar among sites (CV = 4.4 %),
while the stocks from mangroves within the intermediate and lower estuary
had large variability (CV = 34.4 %).
C stocks of LEBR
With the use of the cross-validated correlation from 400 realizations, we
selected a model that was able to explain 34 % of aboveground C variance,
with a RMSE of 111.29 MgC ha-1. External validation had a higher
correlation value (R2= 0.73, RMSE = 60.28 MgC ha-1), but was
less reliable since there were only 12 points (20 % of available data).
Predicted aboveground C for the LEBR ranged between 18 and 567 MgC ha-1,
with a mean of 118 ± 54 MgC ha-1, with an estimated total of 3.5
million MgC for aboveground mangrove C for the LEBR (Fig. 4). However, the
results had a large degree of uncertainty, mostly in mangroves at the water
edge, at the landward side, and mangroves close to the estuary mouth (Fig. 4b), some of these sites identified as monospecific forests of A. germinans.
Aboveground C stocks (trees; Mg ha-1) (a) estimated for the
sampling region within La Encrucijada Biosphere Reserve; (b) uncertainty
associated with estimations; and (c) frequency of occurrence of estimated C
stock values of mangroves.
Although the prediction of the aboveground C was low, we were able to
identify that most forests within the LEBR have less than 300 MgC ha-1
(Fig. 4c). Based on our field data, we identified that fringe forest
dominated by R. mangle had between 300 and 400 MgC ha-1, while forest of A. germinans had
aboveground biomass > 400 MgC ha-1, and most forests with
aboveground values below 300 MgC ha-1 were basin forests dominated by
R. mangle. According to the model, and agreeing with our field experience, this kind
of forest comprises more than 90 % of the mangroves of the LEBR. On the
basis of this result, we calculated the mean C stock for plots of mangroves
with these characteristics and obtained a value of 848.0 ± 31.6 MgC ha-1, which extrapolated to the whole LEBR provides a rough estimate of
23.3 million MgC. The uncertainty of this estimation is highest in
mangroves from the lower estuary and mangroves close to water or the
landward edge. As a comparison, if we extrapolated the C stocks of the
mangroves using the classes obtained from the NDVI classification (upper,
intermediate and lower estuary) the estimation is similar with 20.9 million MgC for the LEBR.
The C stock of marshes was estimated to vary between 37.1 and 720.4 MgC ha-1 across the LEBR. Using the mean value of 432.2 MgC ha-1
obtained from data from this study and from Tovilla et al. (unpublished
data, Fig. 1), we estimated that the C stock of marshes within the LEBRE is
close to 14.0 million MgC. Finally, peat swamps only cover a very small
area of the LEBR and their C stocks were estimated to be of at least 0.6
million MgC. In summary, the approximate C stock value for the LEBR is 38
million MgC.
Soil carbon (C) sequestration rates (MgC ha-1 yr-1) of
mangroves within La Encrucijada Biosphere Reserve, Mexico.
Site
Soil C sequestration
rate (Mg ha-1 yr-1)
Upper estuary
Panzacola
1.0 (0.1)
Teculapa
1.4 (0.1)
Paixtalon
1.7 (0.1)
Intermediate
Esterillo
1.8 (0.1)
Santa Chila
1.3 (0.1)
Lower estuary
Zacapulco
1.5 (0.0)
Las Palmas
0.4 (0.0)
Mean
1.3 (0.2)
Soil C sequestration rates
Mean soil C sequestration rate in mangroves was 1.3 ± 0.2 Mg ha-1 yr-1; soil C sequestration was similar among all sites
(upper, intermediate and lower estuary; F2, 4= 0.78; p= 0.516). Lowest values (0.4 ± 0.0 MgC ha-1 yr-1) were
measured in the site Las Palmas, which was dominated by A. germinans (Table 7).
Considering that less than 10 % of the mangroves in LEBR are dominated by
A. germinans, we can estimate that the C sequestration of mangroves in LEBR through soil
accretion is close to 39 842 MgC every year.
Discussion
The riverine wetlands measured in this study had large C stocks, with values
for mangroves and peat swamps almost twice as high as those measured in
terrestrial forests (typically < 400 MgC ha-1, IPCC, 2003). C
stocks of mangroves within LEBR (mean of 784.5 ± 73.5 MgC ha-1;
maximum of 1,115 MgC ha-1) were similar to other mangroves around the
world, such as in Vietnam (762.2 ± 57.2 MgC ha-1, Nguyen et al.,
2014), the Dominican Republic (853 MgC ha-1, Kauffman et al., 2014b),
Yucatan, Mexico (663 ± 176 MgC ha-1; Adame et al., 2013) and
Northwest Madagascar (367–593 MgC ha-1; Jones et al., 2014). As
hypothesized, C stocks of mangroves and peat swamps were higher than those
of marshes (336.5 ± 38.3 MgC ha-1).
In general, mangroves within the upper estuary had higher C stocks compared
to mangroves in the lower estuary. However, the most striking difference was
not related to C content, but to site variability. Mangroves from the upper
estuary were quite similar in structure and C stocks within sites. In
contrast, mangroves from the intermediate and lower estuary were much more
variable. We also found differences in soil C with depth: soil C increased
or was similar with depth at mangroves in the upper estuary, while soil C
decreased with depth in mangroves from the lower estuary (similar to Donato
et al., 2011). We suggest that differences in geomorphological forces explain
the variation in C stocks and soil C distribution within the sediment
column. Mangroves in the upper estuary have grown in a relatively stable
environment that allowed C to be buried and forests to develop into a mature
state. Comparatively, mangroves in the lower estuary are exposed to frequent
changes in hydrology, sedimentology and are directly struck by tropical
storms (Woodroffe, 1992). As a result, mangroves in the lower estuary are a
mosaic of old and young forests, some of them with productivities and soil C
similar to those in the upper estuary, but others with low productivity,
statures and soil C, and thus C stocks.
The N stocks within mangroves also differed among sites, with highest
stocks in mangroves from the upper estuary. Upland mangroves receive high N
inputs due to agricultural activity in the catchment (UNESCO, 2014); lowland
mangroves probably receive lower N loads as oceanic water has usually lower
nutrients than riverine water. Differences in N content have also been
associated with microbial activity such as bacteria and protozoans, which are
in turn linked to tidal flushing in the mangrove soil (Alongi, 1988). Higher
nitrification and denitrification and lower N fixation rates could further
explain low N stocks in lowland mangroves; however, this remains to be
tested. The higher N inputs in mangroves in the upper estuary, coupled with
lower salinity values throughout the year, probably contribute to the higher
productivity of mangroves in the upper estuary compared to those in the
lower estuary (Tovilla et al., unpublished data).
Besides the differences in C and N stocks between upland and downland
mangroves, it stands out that the mangrove forest dominated by A. germinans (Las Palmas)
was notably different. This forest had the highest tree biomass, lowest soil
C and lowest C sequestration rates measured in this study. Lowest C stocks
in soils of A. germinans can be due to the lower C wood content that is buried in the soil.
Wood density of A. germinans is lower (0.67–0.90 g cm-3) than wood
density of R. mangle (0.810–1.05 g cm-3; Chave et al., 2009, Zanne
et al., 2009), which dominated all other sites. Wood density is a major
predictor of stored C in wood biomass and could explain the low values of C
buried in the soil (Flores and Coomes, 2011), and thus, the low C stocks in
the mangrove forest dominated by A. germinans.
Most of the C stock in mangroves is stored in the soil (Donato et al.,
2011; Adame et al., 2013); thus the potential of mangroves to sequester C is
closely related to their soil C sequestration rates. The soil C
sequestration rates measured in mangroves of LEBR (0.4–1.8 MgC ha-1 yr-1) were similar throughout upper and lower estuary mangroves, which
suggests that over the long term, variability among sites in C sequestration
was not high enough to be detected with our method. However, the C
sequestration rate of the site dominated by A. germinans was two to three times lower
compared to forests dominated by R. mangle. The soil C sequestration estimates in
this study are within the range of those reported in the review by Chmura et al. (2003), with lowest values in Rookery Bay, Florida (0.2 MgC ha-1)
and highest in Términos Lagoon, Campeche, Mexico (6.5 MgC ha-1 yr-1), and are similar to those measured in Moreton Bay, Australia
(0.8 MgC ha-1 yr-1; Lovelock et al., 2014). Long-term soil C
sequestration rates are difficult to obtain; thus the values obtained in
this study are valuable estimations of C sequestration rates of mangrove
forests. For example, we can roughly estimate that the sequestration rate of
the mangrove soil of LEBR is 39 842 MgC yr-1, which is equivalent to
the annual emissions of approximately 10 348 Mexicans (using emissions by
country from IEA, 2014).
To include wetlands in REED+ and other financial incentives for climate
change mitigation, it is usually necessary to estimate C stocks and
sequestration data for large areas of wetlands. Extrapolation of field data
was challenging, with models showing poor agreement between external and
cross-validation, and high uncertainty in some areas of mangroves. Other
studies have faced similar problems, with previous reports at a national
level only being able to explain 2 % of spatial variability (Cartus et al.,
2014). Water level dynamics and the complexity of structural diversity of
mangroves are important sources of uncertainty when using remote sensing
sources. It is important to distribute sampling efforts wisely so as to include
as much spatial variability as possible. Additionally, sampling variables
such as pH and salinity, which could further explain vegetation variability,
could be helpful (Vaiphasa, et al, 2006). In this study, we identified that
species composition is an important variable as well as geomorphic location
(upper and lower estuary) to explain spatial variability within C stocks.
Our results also show that the most variable, and thus where field sampling
should be concentrated, are mangroves close to the mouth of the estuary and
in the landward and water edges.
Mangroves in riverine deltas are the most extensive and developed forests
(Woodroffe, 1992). Thus, the results in this study contribute to the C
budgets of riverine wetlands, which are likely to be one of the most C rich
ecosystems in the world. The wetlands of LEBR store about 38.0 Mton C,
which is equivalent to 139.5 Mton CO2. Degradation of wetlands in the
region due to increased sediment loads derived from upriver dredging, fires,
hydrological modifications and illegal harvesting threaten the potential C
storage of these wetlands.
The C stocks and sequestration values shown in this study can help provide
incentives into the reforestation and conservation projects of this reserve
and throughout similar wetland ecosystems. For example, marsh and swamp
forests are very susceptible to fire damage during the dry season (L.
Castro, personal communication, 2013). With the C stocks calculated in this study, we
estimated that if fire consumes all the vegetation and the top 15 cm of soil
(Schmalzer and Hinkle, 1992), every hectare of burned marsh or peat swamp
could emit 287 ton CO2 and 567.4 ton CO2, respectively. Every year,
between 500 and 4500 ha of marshes are burned within the LEBR (L. Castro,
personal communication, 2013), which results in an annual mean emission of ∼ 0.6 million tons of C or 4.6 % of the emissions of the state of Chiapas
(based on emissions reported by IEA, 2014). This information can be used to
emphasize the importance of managing fires in the LEBR in order to maintain
its large C stocks and avoid CO2 emissions to the atmosphere. Another
example is to use the C stocks provided in this study to negotiate for
offsetting emissions within the country or abroad. For instance, California
USA has signed an agreement to import C credits from forests in Chiapas,
the state where this study takes place (Morris et al., 2011). To include
mangroves and other wetlands in similar agreements could be a cost-effective
way to reduce C emissions (Siikamäki et al., 2012), while at the same
time protecting the biodiversity and the ecosystem services they provide
(Adame et al., 2014). Finally, our results have also shown that
extrapolation of C stocks to larger areas requires the inclusion of not only
aboveground biomass, but also field measurements of soil C stocks and to
consider differences among vegetation types, species composition and
geomorphological setting.