Introduction

Tropical deforestation is the second largest cause of global greenhouse gas emissions behind burning of fossil fuels and is responsible for releasing on average 1.4 Pg C yr^-1 between 1980 and 2005 [1–4]. Tropical forests contain the highest carbon reservoirs of all global forests with between 228.7 Pg C [1] and 247 Pg C [5] stored within them. This equates to 55 percent of global forest carbon [6]. It has been suggested that these global estimates of tropical forest carbon stocks, and similarly those of emissions, are likely underestimations due to the fact that the current levels of carbon stored in tropical mangroves and other organic-rich peatlands, particularly belowground, remain relatively unknown and unaccounted for in many global analyses [6–9].

It has been estimated that global mangrove forests contain between 937 t C ha-¹ and 1023 t C ha-¹ [7, 10] with higher biomass, and hence higher carbon densities closer to the equator [11, 12]. This calculation of mangrove forest carbon storage per unit area is approximately three to four times higher than that of other tropical forests types that only average between 223 t C ha-¹ and 316 t C ha-¹ [13]. For this reason, mangrove deforestation has the potential to release more CO₂ per unit area that almost any other global forest type. Recent work on carbon within mangrove forests, both aboveground and belowground, is expanding and is even placing economic values on these potential carbon reservoirs. For example, in addition to the recent creation of one time snapshots of whole-system carbon levels in mangrove forests [7] others have attempted to apply an economic value to mangrove carbon sinks [14]. Although such snapshot mangrove carbon storage studies are spatial in nature, few spatiotemporal carbon-based analyses of mangroves appear to exist and even fewer focus on specific land use / land cover transitions, such as mangrove to aquaculture conversion, that are likely responsible for the majority of the carbon losses.

We use a unique high-resolution 10 m by 10 m LUCC grid spread across the majority of Ecuador’s estuaries to determine mangrove carbon holdings and account for factors driving mangrove biomass such as mangrove latitude [11, 12], mangrove intra-estuarine location [15, 16], and mangrove species type [16, 17]. In doing so we not only present estimates of current and historic mangrove carbon levels, but more importantly we document the actual land use / land cover transitions that are responsible for the majority of carbon losses over the analysis period.

The 1980s and 1990s growth of aquaculture is well documented [18–20] and shows no sign of abating (Fig. 1). As of 2012 seafood production via aquaculture almost outstripped that of wild catch, with production levels of 90.43 and 91.3 million t respectively [21, 22]. With fisheries capture production declining and aquaculture production expanding it is likely that aquaculture has already passed capture as the primary source of global seafood production. Within Ecuador the expansion of aquaculture exceeds the global trend (Fig. 1). From essentially nothing in the early 1980s, shrimp aquaculture has grown to a $1.39 billion industry by 2012 and is now the second largest component of the Ecuadorian economy after fossil fuels. This expansion is almost entirely attributable to shrimp aquaculture (Fig. 2) and has led to land use / land cover transitions in Ecuadorian estuaries with both historic mangrove and other estuarine land cover now converted to shrimp ponds [23].

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. Global and Ecuadorian aquaculture and shrimp aquaculture growth [22, 24, 25].

Fig. 1 (top panel) depicts the global growth in aquaculture from a nominal amount in 1970 to greater than 90 million t in 2012. Figure 1 (lower panel) depicts the growth rate of shrimp aquaculture in Ecuador from approximately 200 million USD in 1984 to approximately 1.4 billion USD in 2012.

https://doi.org/10.1371/journal.pone.0118880.g001

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. Study Sites.

Study sites in Ecuador utilized for carbon estimations.

https://doi.org/10.1371/journal.pone.0118880.g002

It is well established that non-mangrove tropical forests are often converted to agriculture, resulting in increased levels of atmospheric carbon [6, 26]. On the other hand, mangrove forests globally are most at risk from conversion to aquaculture, as opposed to agriculture, with estimates as high as 28–40 percent of the total global mangrove area being already converted to aquaculture [27, 28]. Of all regions with mangrove to aquaculture conversion, coastal Ecuador has likely undergone the highest levels of transformation with approximately 40 percent of mangrove converted to aquaculture with certain regions experiencing almost total mangrove to aquaculture conversion [27]. Despite mangrove to aquaculture conversion rates that far outstrip other tropical forest to agriculture conversion rates, and the fact that mangrove forests having a far higher carbon level per unit area than other tropical forests, a paucity of research exists examining the mangrove conversion question in terms of changes in carbon stocks over space and time.

Study Area

The study area consists of all the major coastal estuaries of mainland Ecuador, with the exception of the mouth of the Guayas River near the city of Guayaquil and the Galapagos Islands. For security reasons, the Instituto Geográfico Militar does not release historical aerial photographs of this portion of Guayas province and thus it has been excluded from this study. The Galapagos is excluded due to its remote location away from the Ecuadorian mainland. Ecuador was selected for analysis due to its long history of estuarine shrimp aquaculture, availability of high-resolution spatiotemporal data for each estuary, pre-established mangrove and aquaculture surveys, participation in payment for performance carbon programs, and tropical location on the equator. The combined area of our study area is 201,151 1 ha grid cells across all estuaries resulting in 201,151 x 10² 10 m by 10 m LUCC analysis cells.

From north to south, the Ecuadorian estuaries analyzed (Fig. 2 & S1 GIS) are: (i) Cayapas-Mataje, located wholly within Esmeraldas province along the Colombian border in and around the town of San Lorenzo; (ii) Muisné, located wholly within Esmeraldas province near the town of the same name; (iii) Cojimíes, located on the border between Esmeraldas and Manabí provinces in and around the city of Pedernales; (iv) Chone estuary, located wholly within Manabí province in and around the city of Bahia de Caráquez; (v) Isla Puná; an island in the Gulf of Guayaquil, (vi) the entire coastal region of El Oro province in and around the city of Machala from the southern edge of Guayas province in the north to the major estuary known as Grande Estuary on the Peruvian border in the south. We estimate these regions to comprise greater than 95 percent of the historic mangrove habitat in Esmeraldas, Manabí, and El Oro provinces and approximately 26 percent of the historic pre-aquaculture forest in Guayas province.

As of 2013, all remaining mangrove stands in Ecuador are protected at the federal level. Prior to the national protection decree issued in 2013, the mangroves in each study area had varying levels of protected status beginning at different times. The mangroves of Cayapas-Mataje (i) are almost wholly contained within an original RAMSAR site with the federal government as the long-term legal owner of the estuary and the Ministry of the Environment overseeing the mangrove resource within the estuary since at least 1995. Since 2003 approximately 1 percent of the mangroves in Muisné (ii) estuary are privately protected as the Muisné River Estuary Wildlife Reserve while the majority of the estuary appears without protection. According to the Ministry of the Environment SNAP (National System of Protected Areas) database, conversations with local fisherman, and a literature search; Cojimíes (iii) appears to have no government support or protected status at any level. Since 2002, Chone estuary (iv) has a small portion of the estuary protected as the Corazón and Frigatas Islands Wildlife Reserve [29]. Chone estuary in its entirety has been covered under a voluntary special area management since 1988 with the goal of improving the health of the estuary and surrounding area [30, 31]. The mangroves of the Gulf of Guayaquil and the Guayas River Estuary within Guayas Province (v) have federal protection along the eastern portion of the estuary but this is outside of our analysis area and none of the other areas analyzed have protection beyond the recent national decree. This lack of historic protected status extends to Isla Puná within the Gulf of Guayaquil. El Oro (vi) has no protection beyond the recent national decree, although federal protection within the estuary exists once you cross the international border into northern Peru.

Materials and Methods

Field research permission for ground truthing in Manabí province was obtained from MAE (Ministry of the Environment) Manabí office in Portoviejo and field permission for ground truthing in Esmeraldas province was obtained from the MAE offices in San Lorenzo and Muisné Ecuador. Ground truthing was conducted by the authors of this paper in combination with local staff from the Ministry of the Environment.

Estimating Mangrove Carbon

Across all forest types, including mangrove, biomass is utilized as a proxy for living carbon storage. Mangrove biomass across all species is proportional to the ambient isolation or solar energy at each mangrove location, therefore latitude can be used to account for most of the variability of biomass within a mangrove forest at differing locations [11, 12]. This fact likely explains why the tallest Rhizophora mangroves in the world are found straddling the equator within northern Ecuador. Upon completion of the remote sensing analysis mangrove living carbon estimates were generated via the four methods listed below.

Synthesizing the peer-reviewed above-ground mangrove biomass (AGMB) estimates across 11 nations and 5 dominant species, a 1993 study utilized linear regression to calculate biomass as a function of latitude and reported that 69 percent of the variance in aboveground mangrove biomass (AGMB) can be explained solely by latitude [11]. Using this linear model, AGMB was calculated as a function of latitude across all estuaries, in all grid cells, and at all time periods. Once AGMB grids were created, belowground mangrove biomass (BGMB) was calculated as an allometrically derived function of AGMB across all estuaries, in all grid cells, and during all time periods.

Synthesizing data from global mangrove studies across the peer-reviewed literature, the AGMB: BGMB ratio is shown to average 1: 0.52 [2]. Combined mangrove biomass (CMB) can thus be expressed simply as CMB = BGMB + AGMB, but using the AGMB: BGMB conversion factors, CMB can also be expressed as CMB = (1 + AGMB: BGMB) * AGMB. Using the methods defined by [2, 11], CMB was calculated across all estuaries, in all grid cells i, and during all time periods t and then converted into combined aboveground and belowground carbon (CC). This conversion from CMD to CC* was conducted using the mangrove biomass to carbon ratio of 1: 0.464 [7, 32]. The. 464 value is approximately constant in the literature with other values expressed between. 45-.50 [12, 33]. All equations are shown in the format: CC = M_it * (Biomass: Carbon) * CMB. Equation 1 depicts the combined function.

(1)

CC* = combined carbon, t = tonnes, ha = hectare, M_it = Mangrove density at grid location i and time slice t on a scale of 0 to 1, |Lat| = absolute latitude.

Other studies also address the mangrove biomass question from a latitude perspective using a linear modeling [12]. This research reports that 75 percent of the variance in AGMB within mangroves can be explained solely by latitude (Equation 2). By using an AGMB: BGMB ratio of 1: 0.82 CMB was calculated across all estuaries, in all grid cells i, and during all time periods t and then converted into CC using the. 464 value from Equation 1.

(2)

CC = combined carbon, t = tonnes, ha = hectare, M_it = Mangrove density at grid location i and time slice t on a scale of 0 to 1, |Lat| = absolute latitude.

The mangrove ecosystem along the Ecuadorian coast is dominated by three species: Rhizophora mangle, Laguncularia racemosa, and Avicennia germinans. Aside from general species availability in the region, a variety of geophysical factors influence the species’ distributions in each estuary, including but not limited to: soil and water salinity, nutrient availability, tidal dynamics, wave-action tolerance, and geomorphologic processes. Five zonation classes have been traditionally used to characterize entire mangrove systems [34, 35]. However, we move past these generic classes. Using spatial proxies for salinity, tidal inundation, and wave-action tolerance, a species likelihood model was constructed within the grids for each of the six studied estuaries.

First, using a priori knowledge of mangrove species distribution in the estuaries from previous studies and those published in the literature, we also utilize a set species likelihood for each of the three mangrove species [31, 36–38]. Red mangrove dominates the coast, therefore M_Rit was set at 0.90. White and black mangroves make up the majority of the remainder of the mangrove stands and thus both given M_(s)it weights of 0.05 (Equation 3).

(3)

CC’ = combined carbon, t = tonnes, ha = hectare, M_it = Mangrove density at grid location i and time slice t on a scale of 0 to 1, BM_(s) = combined biomass of species (s), _R = Rhizophora mangle, _B = Avicennia germinans_{, W} = Laguncularia racemosa

Three distance measurements were used as estimations for biogeographic factors of species distribution: distance to landward edge (salinity and freshwater input), distance to water (tidal inundation), and distance to ocean (wave action tolerance). Each of these measurements was carried out by extracting appropriate information from the land cover database and utilizing simple estuarine level distance analyses. The landward edge was defined as the furthest inland extent on all sides of the estuary that was not in contact with the ocean. The water layer was defined as all rivers and channels digitized from 5 m resolution data. The distance to ocean measurement was determined using the inlets to the estuary.

Using the three distance parameters, the likelihood of each of the three species was determined in each grid cell in each time period based on the tolerance of that species for the environmental parameter. Wave action drives a significant amount of species distribution at the water-edge of an estuary. Because of the extensive network of prop roots, the Rhizophora mangle shields species that are not as well suited for the immediate coastal or streamside habitat whereas Laguncularia racemosa and Avicennia germinans exist at higher elevations within the estuary, thus separated from immediate wave-action and frequent inundation [16, 35, 39].

Salinity also drives species distribution, furthering zonation by each species’ salinity tolerance. Rhizophora mangle are typically the most salt-tolerant while Laguncularia racemosa and Avicennia germinans thrive in more freshwater, upstream environments [15, 16, 35, 39]. The three distance parameters were classified for each of the species and weighted to create a theoretical species distribution across each estuary. By compiling and normalizing the species likelihood layers in each estuary grid cell, the output provides values representing the potential percentage of the 1 ha grid cell covered by Rhizophora mangle, Laguncularia racemosa, and Avicennia germinans as well as the area without mangrove cover. These data were then combined with LC derived mangrove percentage data at the grid level. When mangrove was present the species likelihood model was applied to give a likelihood of each species type in each grid. For example, a grid cell classified as 100 percent mangrove from RS methods could then have a secondary likelihood classification of. 9. 05. 05, meaning that the mangroves present during the survey have a 90 percent likelihood of being Rhizophora mangle, a 5 percent chance of being Laguncularia racemosa, and a 5 percent chance of being Avicennia germinans. The likelihood of each species’ presence in each grid cell i, at time t is expressed below (Equation 4).

(4)

CC^ = combined carbon, t = tonnes, ha = hectare, M_it = Mangrove density at grid location i and time slice t on a scale of 0 to 1, nM_(s)it = normalized presence likelihood of species (s) at grid location i and time slice t, BM_(s) = combined biomass of species (s), _R = Rhizophora mangle, _B = Avicennia germinans_{, W} = Laguncularia racemosa.

In the largest review of mangrove allometry [17], biomass is seen to be highly species-specific as opposed to site-specific, therefore making existing allometric equations viable in Ecuador. Using forest structure dynamics from similar mangrove stands and their derived allometric relationships, the living biomass of the stands in this study are estimated [17, 40, 41].

In summary, the first method of calculating CC is a latitude based function of AGMB [11] combined with BGMB measures derived from field measures to obtain CMB [17] (Equation 1). The second method is also a direct latitude to CMB conversion function that is established in the literature [12, 14] (Equation 2). The third method is species specific and used a priori knowledge of mangrove species distribution in Ecuadorian estuaries from previous studies that emphasize the dominance of Rhizophora mangle within Ecuador estuaries to obtain CMB (Equation 3) [31, 36, 38]. The final method utilized is a species likelihood model constructed for this paper to obtain CMB (Equation 4). This final model relied on the principle of mangrove zonation that is well documented in the literature [39, 42]. These four methods all result in differing measures of CC.

Carbon change maps were created for each time period in each estuary using all four carbon methods described above. By combining the carbon change grids with the land cover classes at each survey period, cells were given a land cover conversion value indicating the type of transition responsible for the carbon losses or gains. All possible transitions were noted to account for reforestation or afforestation. Upon completion, the magnitude of carbon change and CC transitions were reported at the estuary and national level and were also extrapolated to other selected major mangrove holding nations. Finally we compare our CC findings with those of IPCC compliant data for 2000.

Results

At the initial pre-aquaculture survey the CC stocks across all estuaries analyzed are calculated to be 18,754,752 t C ± 27 percent in total (Fig. 3). By 2011, the CC in these pre-existing forests had diminished by 8,813,841 t C ± 27 percent (Table 1) to 9,940,912 t C ± 27 percent. This equates to a CC loss of 47 percent from pre-aquaculture to 2011. The majority of the CC losses occurred between 1970 and 1990 and losses appear to have stabilized by the 2000s and remain stable to the present. Losses in El Oro province, around Grande Estuary, and in Cojimíes account for most of this change with CC losses of 3,585,069 t C ± 27 percent and 2,218,212 t C ± 27 percent respectively (Fig. 3). The areas of highest CC loss as a percentage of original stock are Chone and Cojimíes, with losses of 76 percent and 80 percent respectively (Fig. 3). The Ramsar site and protected forests of Cayapas-Mataje lost the least of their pre-aquaculture CC holdings with only 22 percent of their initial CC lost (Fig. 3).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 3. CC levels pre-aquaculture, CC losses from pre-aquaculture to 2011, CC lose attributable to aquaculture, and CC gains due to afforestation or reforestation).

All values reported are mid-range values with the error bars representing the minimum and maximum calculated values under equations 1–4.

https://doi.org/10.1371/journal.pone.0118880.g003

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. CC losses from areas delineated as mangrove forest in the initial survey.

https://doi.org/10.1371/journal.pone.0118880.t001

Of the 8,813,841 t C lost across all estuaries pre-aquaculture to present 7,014,517 C t ± 27 percent, or 80 percent, can be attributed to direct displacement of mangrove forests by shrimp aquaculture (Fig. 3) with approximately 34,500 ha of mangrove converted during the analysis period. Disregarding the federally protected Cayapas-Mataje estuary in which only 29.5 percent of mangrove loss is attributable to aquaculture, the CC loss due to this singular land use transition accounts for between 84 percent and 95 percent of all losses across all other estuaries. The most rapid losses of CC appear to occur at the initial period of shrimp aquaculture arrival within each estuary with greater than 72 percent of losses occurring between surveys one and three in each estuary.

Throughout the study period, particularly since 2000, there has been limited afforestation or reforestation within the estuaries resulted in CC increases (Fig. 3). By 2011, a maximum potential of 1,709,079 ± 43 percent t C were added to the estuaries analyzed. However, this estimate is assuming complete maturity of the stands and is therefore an overestimation at the current juvenile stage of growth. The juvenile status was verified utilizing the date a stand first appears in a survey as well as being field verified in the respective estuaries. Almost all of this regrowth occurred in areas outside of the mangrove to shrimp conversion areas in the Chone and Muisné estuaries since 2000. At stand maturity (20–30 years) if undisturbed, this additional CC stock will offset 19 percent of the documented CC losses. The Chone and Muisné estuaries have experienced the largest reforestation/afforestation, with both having a maximum potential of 16 percent of their base level CC stocks replenished. The Cojimíes estuary has experienced the smallest level of recovery with, at most, a 5 percent addition from base CC levels.

Discussion

When comparing our Ecuadorian CC data to IPCC GPG Tier 1 1 km² compliant carbon data based on GLC 2000 land cover classes, substantial differences occur at the estuarine level despite the country-wide value matching almost identically [43] (Table 2). Within Cayapas-Mataje, Chone and El Oro the IPCC results are in relatively close agreement with our CC findings (Table 2). The major differences between the two sets of results are in the neighboring estuaries of Muisné and Cojimíes. Within these estuaries the IPCC estuarine living carbon estimates are 2.16 and 4.08 times larger than our estimations. Part of this may be due to the differing scales of analysis causing forest vegetation on the estuarine edge to be included in the IPCC compliant data but this may not occur in our more resolute data. However, our results indicate this would result in a nominal amount of difference as most of the disagreement is in the central region of the estuaries.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 2. Mangrove CC levels vs. IPCC CC levels.

https://doi.org/10.1371/journal.pone.0118880.t002

The differences between our findings and the IPCC compliant data primarily occur due to the land cover classification schemes employed by the IPCC authors (GLC 2000 based on SPOT data) in which substantial broadleaved evergreen forests are shown to be present within Muisné and Cojimíes estuaries. The IPCC compliant methods do not contain a mangrove classification, so tropical evergreen broadleaf acts as the substitute. Our remote sensing surveys, field verification, and aerial imagery show little or no evidence of the existence of these forests as of 2000. Our data shows these forests, which are former mangrove areas, have been converted to aquaculture at earlier periods and hence the CC levels are substantially depleted. Within Chone estuary, the GLC 2000 herbaceous class is incorrectly shown to be dominant in the inner estuary. However, this only causes a slight over-estimation of living carbon as herbaceous cover has a far lower ecosystem carbon value than the misapplied evergreen forests depicted in the estuaries to the north. Within Chone our analysis again depicts these herbaceous regions as shrimp aquaculture. Within the Gulf of Guayaquil, on Isla Puna, the underestimation of carbon in the IPCC compliant data is also caused by classification differences, with our analysis finding some fringe mangrove in areas classified as water in the GLC 2000 dataset.

For the reasons above the IPCC compliant C grids within Ecuadorian estuaries should be treated with caution. Further research is needed to ascertain if this is a global problem or other nations follow the Ecuadorian pattern of large errors in GLC derived C measures at the estuarine level that combine into accurate C estimates at the national scale. For example, in Ecuador the GLC derived C underestimation and overestimation errors at the estuarine level essentially cancel themselves and sum to a national error rate of close to zero (Table 2). Despite summing to zero in the Ecuador example, the C errors are significantly large at the estuarine level that it cannot be assumed that all other nations over and under estimations will sum to zero as they do in the Ecuadorian example.

Our findings indicate that living carbon in Ecuador’s mangrove forests has been substantially impacted by shrimp aquaculture expansion and that greater than 80 percent of mangrove carbon losses are a direct result of land use conversion to shrimp aquaculture. In estuaries where this conversion has occurred the IPCC compliant grids may be in substantial error at the estuarine level despite being in overall agreement nationally.

Supporting Information

Acknowledgments

The authors with to thank Lucas Oshun, Ramon Cedeno Loor, and the staff of Global Student Embassy for assistance and accommodation in Ecuador. Thank you to Sarita Collins and her extended family for providing assistance in the field and translation assistance. Thank you to Clay Harris, Shauna Kiernan, Mike Mitchell, Leonardo Caeua, Ronald Zembranom, and Erica Moldenhauer for all their hard work in the field. Such analysis as these rely on freely available remote sensing data from NASA, USGS, and the government of Japan.