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Article

Mangrove Forests in Ecuador: A Two-Decade Analysis

by
Ramiro Morocho
1,*,
Ivonne González
1,
Tiago Osorio Ferreira
2 and
Xosé Luis Otero
3,4
1
Departamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, San Cayetano s/n, Loja 1101608, Ecuador
2
Luiz de Queiroz College of Agriculture, University of Sao Paulo (Escola Superior de Agricultura Luiz de Queiroz), Av. Padua Dias 11, Piracicaba, Sao Paulo 13418-900, Brazil
3
CRETUS, Departamento de Edafoloxía e Química Agrícola, Facultade de Bioloxía, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
4
Rede de Estacións Biolóxicas da Universidade de Santiago de Compostela, Rede de Estacións Biolóxicas da USC, Casa do Hórreo, Rúa da Ribeira, 1–4, 15590 A Graña–Ferrol, Spain
*
Author to whom correspondence should be addressed.
Forests 2022, 13(5), 656; https://doi.org/10.3390/f13050656
Submission received: 21 February 2022 / Revised: 12 April 2022 / Accepted: 20 April 2022 / Published: 23 April 2022
(This article belongs to the Special Issue Mangrove Wetland Restoration and Rehabilitation)
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Abstract

:
Mangroves are one of the most important ecosystems especially due to the services they provide, but in contrast are one of the most threatened by human activities at a global level. In Ecuador, mangrove forests are currently fragile and threatened due to the great anthropic pressure, which has largely reduced the area they occupy. However, there is already evidence that certain actions are contributing both to their conservation and the recovery of the lost mangrove area. In this study, we assessed the multitemporal dynamics of changes in mangrove cover in four coastal provinces of the country over a period of 20 years (1998–2018) based on remote sensing data analyzed using GIS tools. Our results showed that the area affected by mangrove forest destruction reached its maximum during the 1998–2010 period, when 4.56% (194.57 km2) of the mangrove forest was lost. This situation especially affected the provinces of El Oro and Guayas. The main cause for the loss of mangrove cover was the expansion of shrimp farms, followed by agriculture and construction. However, a slight recovery of ~2.9% has been observed, although loss remains constant. Mangrove ecosystem conservation policies, mainly applied to zones within protected areas; the establishment of use and custody agreements and the halt of shrimp farm expansion; the development of mangrove forests on areas with sediment deposits; and natural mangrove recovery processes are key factors for mangrove restoration. These results suggest that it is possible to continue restoring mangrove cover and thus maintain some of the main ecosystem services they provide for the benefit of humans.

1. Introduction

Mangrove forests are located at the confluence of land and sea in the world’s subtropical and tropical regions [1,2,3,4]. They possess characteristics that make them structurally and functionally unique [1]. These forests are hot spots for biodiversity, and provide important and valuable ecosystem services, including coastal protection and fish production, and they are also efficient carbon sinks [3,5,6,7]. Thus, mangroves provide vital climate change mitigation and adaptation services [2].
Mangroves are among the most productive coastal ecosystems [8,9], contributing to the subsistence of forest-dependent communities and their livelihoods [10].
However, despite the great importance of these unique coastal ecosystems, their destruction and loss have intensified worldwide in the last decades [11]. Mangroves have experienced significant losses, globally declining from 136,798 km2 in 2000 to 135,882 km2 in 2016 [12]. Since then, global mangrove deforestation has remained, although at much lower rates, between 0.16% and 0.39% per year [13]. Studies suggest that they could have originally occupied over 200,000 km2 and that considerably more than 50,000 km2, or one quarter, of the original mangrove cover have been lost because of human activities [14].
Losses occur especially in developing countries, where more than 90% of the world’s mangroves are located [14]. In fact, destruction and degradation are reaching alarming levels in tropical regions, where these ecosystems flourish. Areas where dramatic changes are taking place include countries in Southeast Asia, such as Indonesia, Bangladesh, India, Vietnam, Singapore, Philippines, Thailand, and Myanmar; and in Africa, such as Nigeria, Tanzania, and Guinea-Bissau. Meanwhile, in the Americas, substantial changes are evident in French Guyana, Brazil, México, Perú, Colombia, Venezuela, and Ecuador [1,12,15,16].
Multiple forces drive global mangrove loss, of which nature and dynamics can vary dramatically across regional and local contexts [17]. The greatest driver for mangrove forest loss is the conversion to aquacultural, agricultural, and urban land uses [8]. In Ecuador, mangroves have been explored for timber, charcoal, and tannins, but the most frequent impact has been the conversion of mangrove land to aquaculture, salt production, and agriculture [18].
Mangrove forests in Ecuador are concentrated around river estuaries [18]. The major areas are located along the estuaries of rivers Mataje-Santiago-Cayapas, Muisne, Cojimíes, Chone, Guayas, and Jubones-Santa Rosa-Arenillas [19]. Based on the monitoring of mangrove forests carried out by the Centro de Levantamientos Integrados de Recursos Naturales—CLIRSEN, Instituto Ecuatoriano Forestal y de Áreas Naturales y Vida Silvestre—INEFAN, and other authors, their area has declined from 2022 km2 in 1969 to 1485 km2 in 1999 [20].
According to the national classification of ecosystems [21], in Ecuador, we found two vegetation formations: the Chocó Equatorial mangrove (Esmeraldas) and the Jama-Zapotillo mangrove (Manabí, Guayas, and El Oro). The Chocó Equatorial area has a pluvial bioclimate with humid ombrotype, while the Jama-Zapotillo area has a xeric bioclimate with dry ombrotype [21,22]. Mangrove forests are mainly composed of well-known major mangrove species: Rhizophora mangle L., R. racemose G. Mey, R. × harrisonii Leechm., Laguncularia racemosa (L.) C.F. Gaertn. var. racemosa, L. racemosa var. glabriflora (C. Presl) Stace, and Avicennia germinans (L.) L. [20,23,24]. Rhizophora mangle L. accounts for between 80% and 90% of all mangrove forests, while the remaining 10% consists mainly of Avicennia germinans (L.) L. and Laguncularia racemose (L.) [25,26].
Due to the relevant role that mangrove forests play in the conservation of biodiversity and coastal productivity, coastal protection, and in the fight against climate change, it is essential to understand how the area they cover has changed over time [4]. In this sense, remote sensing technologies constitute efficient, fast, and reliable tools for its study. Remote sensing plays an important role in coastal monitoring in many tropical regions, where detailed estimates of changes in forest cover are still necessary due to the impact of these changes on sustainable development and on the environment [27,28].
This study aimed to assess the spatiotemporal dynamics of mangroves in Ecuador. We performed a two-decade assessment using GIS and remote sensing technology. Thus, we detected, identified, mapped, and tracked mangrove conditions and changes [29]. Our results contribute to a better understanding of the current status of mangroves. Moreover, analyzing changes in land use provides valuable insights about deforestation hotspots. Finally, these data contribute to the analysis of the effectiveness of mangrove conservation efforts.

2. Materials and Methods

2.1. Defining the Potential Area Occupied by Mangrove Forests

This study focused on mangrove forests along the Ecuadorian Pacific coastline, located in the provinces of Esmeraldas, Manabí, Guayas, and El Oro. To define the study area, we used the following criteria: (I) previous mangrove cover (official cartography from the Ministry of Water, Environment and Ecological Transition—MAATE), (II) topography (flat coastal plains), (III) distance from coastline (up to 4 km), and (IV) tidal range (up to 3 m) [25]. We combined these criteria through the map overlay technique in order to determine suitable areas for potential mangrove growth (Figure 1).

2.2. Data Sources and Preprocessing

The climatic characteristics of the study area made it difficult to capture clear images across the whole area, especially by presence of cloud cover. In this context, national studies carried out by state agencies constitute a good option to provide baseline data about the previous extent of mangrove forests [30]. Therefore, different data sources were combined for this study: (i) official cartography, (ii) medium-resolution imagery, and (iii) high-resolution imagery.

2.2.1. Official Cartography

Some researchers have used maps from official agencies as a reference for mangrove extent when high quality imagery was not available [30,31,32]. In Ecuador, the Ministry of Water, Environment, and Ecological Transition (MAATE) is responsible for generating land cover and land use maps within the context of the Land Cover/Land Use of Continental Ecuador Project. Historical land cover maps corresponding to the years of 2000, 2008, and 2016 at a scale of 1:100,000 were obtained from the Interactive Environmental Map web platform. In the case of the first year of analysis for the province of Guayas, we used this information from MAATE because no good quality satellite images were found for visual interpretation.

2.2.2. Medium-Resolution Imagery

For this study, satellite images from Landsat and Sentinel scenes were obtained from the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov/ accessed on 16 January 2018) (Table 1). L1TP products with the lowest possible percentage of cloud cover (less than 20%) were selected [33]. Visual interpretation was performed based on RGB composites from these images. RGB composites help distinguish the different types of land cover and land use. Satellite data with medium spatial resolution, such as those from Landsat and Sentinel, provide adequate spatial details for mapping mangrove areas [34] at the national scale [29].

2.2.3. High-Resolution Imagery

From 2010 to 2014, the Ministry of Agriculture developed a Project called SIGTIERRAS which generated aerial photograph imagery at a national level. Aerial photographs from SIGTIERRAS are orthorectified and have a 1 m spatial resolution. These orthophotographs were downloaded from the SIGTIERRAS website (http://www.sigtierras.gob.ec/descargas/ accessed on 16 January 2018), considering that the aerial photography can provide suitable information for highly detailed mapping in small and narrow coastal environments [29,34]. The information for the second year of analysis for all provinces was obtained from the interpretation of these photographs.

2.3. Classification of Images

The classification strategy applied in this study is an adaptation of the classification system proposed by MAATE within the Land Use/Land Cover of Continental Ecuador Project. We employed the first level of this classification system to define general categories that represented land cover patterns in the study area (Table 2).
The following pictures illustrate the land use and land cover categories in the study area (Figure 2).
After establishing the classification scheme, we digitized LULC maps for two periods (1998–2010; 2010–2018). We used a frequently applied method consisting of visual interpretation followed by on-screen digitizing [29]. This method avoids errors that are generated when using automatic classification methods. On-screen digitizing is a way to trace features from images. This is one of the most accurate techniques for characterizing land cover [35]. For the visual interpretation of satellite images, we created false color composites. For Landsat imagery, we applied an RGB composite using near infrared (NIR), shortwave infrared (SWIR), and red bands. For Sentinel 2A imagery, we applied a band composite using vegetation red edge, shortwave infrared (SWIR), and red bands (Figure 3). These composites are capable of distinguishing mangrove forest from the surrounding objects more clearly [36].
LULC maps of each province were generated for three different periods which were established according to data availability (Table 3).
Based on the map for the first year (Y1), the FAO interdependent classification method [37] was applied to generate maps for the second and third years (Y2 and Y3). This approach consists of comparing polygons in the first map with images from subsequent dates, modifying only those segments where changes are detected [38]. According to Ramírez and Zubieta [39], interdependent classification is an accurate technique since it reduces classification and position errors.

2.4. Accuracy Assessment

We compared maps Y1, Y2, and Y3 with imagery hosted by Google Earth Engine and high-resolution Google Earth (GE) imagery, where available. Some studies have shown that high-resolution images from GE have an overall positional accuracy close to 1 m, sufficient for deriving ground truth samples [40]. We determined a minimum sample size of 50 points for each LUC category, as recommended by Congalton and Green [41] for classifications with less than 12 categories. A total of 4200 sample points were randomly generated for the entire area and study periods (Table 4). Accordingly, a confusion matrix technique was applied to assess the accuracy of the classification. Overall accuracy and the kappa coefficient were calculated to verify map classification [42].

2.5. Detection of Changes

Map overlay between any two points in time generates a cross tabulation matrix, where rows show the categories from an initial time point, columns show the categories from a subsequent time point, and entries show the size of the area that transitioned from the initial to the subsequent category during the time interval. Entries on the diagonal indicate persistence of land categories, while entries off the diagonal show changes in land use categories [43].

3. Results and Discussion

3.1. Accuracy Assesment

Table 5 summarizes the results from accuracy assessment. For all classifications, the overall accuracy was above 80%. Kappa coefficient values were above 0.75 for all classified maps. Rwanga and Ndmbuki [44] classified kappa values of 0.61–0.8 as substantial and 0.81–1 as almost perfect agreement. Thus, the classifications were accurate enough to perform further analysis.
We obtained good results with the visual interpretation method. This method usually presents less classification errors than automatic classification [38]. However, this approach has some limitations such as the differences among interpreters’ perceptions and problems during digitization derived from image quality (atmospheric conditions, brightness, stereoscopic effects, cast shadows, contrast, mixed pixels, and geometric resolution) [45]. It is important to note that Guayas Y1 map obtained from the MAATE database has an acceptable accuracy, even though it was generated through automated classification. Regardless of the accuracy, we must point out that classified maps also have uncertainty, thus the findings we describe in the next sections must be considered as estimates instead of exact measurements.

3.2. Changes in Mangrove-Covered Area

We analyzed the quantity of LULC types in all LULC categories for each study period (Figure 4). Mangrove and shrimp farming were the dominant LULC types in the study area. These categories represented more than 50% of the total area in each province.
Mangroves in Ecuador have been suffering a degradation process since the 1960s [17,46]. According to CLIRSEN [47], in 1987, mangroves covered an area of 1751 km2. The present results show that the area covered by mangroves in 1998 was 1484 km2, suggesting a loss of 267 km2. By 2010, this area had slightly increased to 1580.9 km2, which entails the recovery of at least 96.9 km2 of mangrove. Finally, by 2018, the mangrove-covered area was 1645.2 km2, indicating an increase of 64.3 km2 in mangrove cover. Overall, the analysis of net changes in mangrove cover showed a slight increase (2.9%) between the initial and final periods (Table 6).
In Ecuador, different plans have been implemented since 1949 to allow for the sustainable exploitation of mangrove forests, particularly focusing on timber use for shipbuilding, railways, and exports [48].
Historical mangrove degradation has been mainly linked to the expansion of shrimp aquaculture [49]. Shrimp farms were established on the tidal flats where mangrove forests grow, causing mangrove deforestation [50]. As reported by CLIRSEN [47], the uncontrolled expansion of the shrimp farming industry in Ecuador within less than 30 years, is the main cause for mangrove alteration and loss [15,51]. It is worth considering that, despite the great social, economic, and environmental value of this ecosystem for the population, shrimp farming is the activity that generates the highest revenue in Ecuador followed by the oil industry. Shrimp farming reached a total revenue of USD 3823.53 million in 2020, accounting for 25.53% of the country’s non-oil exports and 18.90% of the total exports of Ecuador [52].
The present study confirmed that the expansion of shrimp farming has slowed down in recent years. This result is in line with findings by Hamilton et al. [50] who considered the following periods: (i) 1970–1990, rapid deforestation; (ii) 1990–2000, stabilization period; and (iii) 2000–2010, recovery period. The period analyzed in this study matches the two latter periods proposed by Hamilton et al. [25]; therefore, a positive balance in mangrove recovery was observed.
Our data suggest that conservation policies implemented 20 years ago have allowed the recovery of around 161.20 km2 of mangrove forests in Ecuador. On the other hand, it is worth noting that during the first period, the area covered by shrimp farms increased by 6.5%, while during the second period, this area started to gradually decrease (−1.3%; Figure 4 and Table 6). Despite the efforts made to implement sustainable policies for mangrove management, the development of shrimp farming has led to illegal land concessions, authorizations for mangrove clearing without meeting the legally established requirements, and land-buying from local communities [48].
It is also worth pointing out that in all periods, the area covered by water bodies gradually decreased from 557.0 km2 (1998) to 556.6 km2 (2010) and finally, to 565.6 km2 (2018). This suggests that, given the mangroves’ sediment retention dynamics, new mangrove-covered spaces may have been generated, contributing to the increase in their cover area [53].
“Natural vegetation” gradually decreased by 0.8% from 1998 (15.5%) to 2018 (14.7%). This result suggests that anthropogenic activities also affected other type of vegetation besides mangrove.
A particular case was found in the coastal region of the Esmeraldas province, where a net 9% increase in mangrove-covered area was observed between 1998 and 2018. The built-up land cover category showed a 3% increase within the same period. Cropland was the category with the largest net loss, with almost 10%, while shrimp farming and natural vegetation have decreased by 1%. An example of these occupation dynamics and the abandonment of shrimp farms can be found in the Bocana de Limones sector (Figure 5).
Net change data suggested that conservation policies in Esmeraldas have contributed to mangrove recovery and to halting the expansion of shrimp farming. This could also be due to the existence of three protected areas in this region: the Manglares Cayapa Mataje Ecological Reserve, Manglares del río Muisne, and Manglares del río Esmeraldas Wildlife Refuges. Moreover, the existence of 18 agreements for the sustainable use of mangroves by users with ancestral rights has been reported, although only five of them were still in force by 2019. Similar trends have been observed in mangrove forests within protected areas in other regions of the world, such as the Saadani National Park in Tanzania [54] or the Futian Mangrove National Nature Reserve and the Mai Po Marshes Nature Reserve in China [55,56].
Mangrove forests in El Oro occupied around 234 km2 in 1987. In the same year, shrimp farms covered 297 km2 [47]. According to our data, in 1997, there were nearly 164 km2 of mangrove forests and 411 km2 of shrimp ponds. This means that between 1987 and 1997, El Oro lost approximately 30% of its mangrove cover. Conversely, during the same period, the area occupied by shrimp farms almost doubled. From 1997 to 2018, our data showed a slightly increasing trend in both mangrove (1.9%) and shrimp farms (0.7%) (Figure 4). Thus, it could be suggested that these categories have become stable in El Oro.
In Guayas, the mangrove-covered area barely increased during the first period (0.26%), while the area covered by shrimp farms experienced a significant growth of 10.4%. (5.5% (Figure 4). During the second period, the mangrove-covered area increased 1.38%, (8.6%) while shrimp farms had a minimum decrease of 2.04%. This result appears to be related to a positive impact of the executive decrees No. 1391 and No. 852, issued by the Government of Ecuador in 2008 and 2016, respectively. These decrees regulate shrimp enterprises expansion [51]. In Manabí, a positive balance in mangrove cover was observed for both periods (with a 2.6% net increase; Figure 4). While only three sustainable use agreements are in force in this province, it is worth noting the creation of the Islas Corazón y Fragatas Wildlife Refuge in 2002, mainly aimed at the protection and restoration of the mangrove ecosystem.

3.3. Interactions between Anthropogenic Activities and Mangroves

In line with previous studies, the net change data showed that land use in the region under analysis has undergone little change. However, cross-tabulation analysis (Table 7) revealed that mangrove was the category with the highest values for swap and total change indicators. This means that approximately 19% of mangrove cover has been subject to permanent loss and gain processes (swap P1 and swap P2). A similar pattern of results worldwide was mentioned by De Lacerda et al. [57]. They indicate that despite a slight reduction in forest loss rates, mangrove clearing and fragmentation continues.
Given the fragility of mangrove forests, constant alterations can negatively impact the health of the mangrove not only in terms of cover but also of water, soil, and fauna [12]. Therefore, programs should be developed to monitor the restoration process to avoid changes in land use, considering that mangrove recovery can require at least five years. Mangroves may recover after storms with minimal or no intervention, as long as elevation and hydrology have not been deeply affected (for example, due to sediment loss) [58]. In many aspects, mangroves act like weed species and can grow quickly in an intertidal environment with few competitors. Overall, their natural recovery is often faster than any other marine ecosystem. Initial recovery can be observed within 3–5 years, when new generations of mangroves are able to take hold, although the full recovery of an ecologically functional forest can take longer [59].
The provinces with the highest degree of mangrove losses during the study periods were Guayas > El Oro > Esmeraldas > Manabí (Table 8), contrasting with the findings by CLIRSEN [47], which evidenced that the provinces with the greatest loss were El Oro > Guayas > Manabí > Esmeraldas. Gross losses highlighted the existence of some areas subject to deforestation. Generally, these areas lack any legal protection or custody.
The analysis of mangrove gross losses allowed detection of deforestation hotspots. The rate of deforestation in these hotspots was higher in Manabí and El Oro during P1. In P2, we found slower loss rates in all the provinces, except for Guayas (Figure 6).
Although shrimp farming has declined since the 1990s, mangroves face different anthropic pressures such as wood extraction, oil-palm production, coastal development, and urban sprawl [17,20,60]. Conversely, our study revealed that other anthropic activities, although to a smaller degree, have generated major changes in mangrove cover. These activities consist mainly of the development of human settlements and touristic infrastructure. Moreover, change indicators warn about continued changes over time, leading to potentially irreversible transformations of mangrove forests [61].
As shown in Table 7, these categories have contributed to mangrove loss and gain in the province of Esmeraldas. As for shrimp farming, a pattern of permanent occupation followed by abandonment of the ponds could be observed in association with natural mangrove recovery, mainly in the Muisne and Esmeraldas River estuaries. In Esmeraldas, the Cayapas-Mataje protected area was the location with the greatest mangrove forest recovery (Figure 7).
As for the province of Manabí (Figure 8), gains greatly exceeded losses, mainly in the Chone river estuary (in the south of the province) and in the Chamanga area (in the north of the province), where three custody agreements exist. Although recovery in the Chone estuary has been remarkable, the pressure from shrimp farming continued to gradually degrade mangroves.
The same trend could be observed in the province of Guayas (Figure 9), with the constant occupation and abandonment of areas dedicated to crops and shrimp farming activities; however, it is also worth noting the recovery of mangrove areas, often related to conservation and recovery initiatives through the establishment of agreements for the sustainable use and custody of mangroves (AUSCM). This was the case of the Guayas river estuary, which comprises a large portion of the Gulf of Guayaquil and Puná Island.
Finally, in the case of El Oro province (Figure 10), the continuous interspersion between shrimp farming and mangrove recovery areas was much more evident, although the latter slightly predominated, mostly owing to areas under the protection of AUSCM. This process was the most notable in the Jambelí archipelago.
Generally, the interactions occurring to the detriment of the mangrove ecosystem and resulting in a decrease in its area were closely linked mainly to shrimp farming; however, they have been proven possible to control through the design and enforcement of adequate conservation and sustainable use policies, as well as with the establishment of protected areas that include representative sites within these ecosystems.
The analysis of changes also revealed that other processes were also contributing to mangrove degradation, although to a smaller extent; for example, anthropic activities such as crop cultivation (such as African oil palm), urban expansion, and construction of port and touristic infrastructures. Coastal erosion processes were also evidenced, altering mangrove vegetation and accelerating water flow on the soil surfaces, in turn resulting in transport of sediments away from the area. When this situation is sustained over time, it can lead to loss of mangroves, changes in sediment movement pattern, and loss of land to sea [62] (Figure 11).
Natural vegetation category is the result of changes in areas originally covered by mangrove, but which could not be restored and have instead become part of neighboring ecosystems, such as brush. In this analysis, low vegetation refers to land with low plant cover density, resulting from the transition or lag period from mangrove conversion to aquaculture. After mangroves are cut, these areas are often left for a certain period before they are turned into ponds. During this lag period, new vegetation such as shrubs, grasses, and even bare lands takes over the former mangrove area [63]. Among the actions that best represented a contribution to the increase in areas covered by mangroves was the establishment of national protected areas within the National System of Protected Areas (Sistema Nacional de Áreas Protegidas, SNAP) or of areas under sustainable use and conservation agreements, such as the Áreas de Uso Sustentable y Custodia de Manglar (AUSCM).
While mangrove loss has been constant, actions aiming at the protection and restoration of this ecosystem have proven to be efficient to maintain and increase its cover area. Among these actions, it is worth highlighting the agreements known as AUSCM established in the four studied provinces, which by 2018 covered around 592.08 km2 [51,64] out of the 1618.35 km2 of mangroves in the country, i.e., around 37% of the total mangrove cover. The situation is different in areas where no protection or sustainable use figures have been implemented: in these areas located near estuaries, around shrimp pools, or near the AUSCM, the constant loss of mangrove cover is significant.
Overall, the four main estuaries in the provinces of Manabí (Chone river and Cojimíes) and Esmeraldas (Mataje and Esmeraldas), in the north of Ecuador, lost around 209.50 km2 of mangrove forests between the arrival of aquaculture and the moment of maximum deforestation, while 179.80 km2 have been lost between the arrival of aquaculture and the most recent survey. This amounts to a 37% decrease in mangrove cover until the moment of maximum mangrove loss. Excluding the Cayapas-Mataje ecological reserve located in the Mataje estuary, mangrove loss in the three remaining estuaries reached 83% by 2000 and is currently at 69%.
According to several authors, communities play a major role in mangrove restoration [65,66]. Studies carried out in the south of Thailand also demonstrated that community-managed protected areas have been more effective for mangrove protection than protected areas under national management. Moreover, similar cases exist, e.g., in Indonesia, Philippines, and Cambodia, where community management has yielded positive results in terms of mangrove cover [67,68].
Similarly, the existence of protected areas such as those belonging to the National System of Protected Areas (SNAP) contributed to the conservation and restoration of the mangrove ecosystem, since the different conservation areas include extensive mangrove forests, which in 2012 accounted for 46.5% of mangroves present in the country, i.e., approximately 730.71 km2 [51].

4. Conclusions

The spatiotemporal analysis of the evolution of the area covered by mangrove forests in Ecuador determined that:
  • The highest mangrove destruction rate in the country was reached during the 1998–2010 period, resulting in a loss of 194.57 km2, which amount to 4.56% of the total mangrove area. The most affected provinces were El Oro and Guayas, and shrimp farming activity was the main cause of mangrove loss.
  • Since the 2010–2018 period, a gradual recovery of occupied areas has been observed, especially in the northern province of Esmeraldas and in the southern province of El Oro. This recovery is probably related to the regulation of deforestation, mangrove conservation and restoration initiatives implemented in the Cayapas Mataje ecological reserve, and the implementation of areas under sustainable use and conservation agreements (Áreas de Uso Sustentable y Custodia de Manglar, AUSCM), mainly in the province of El Oro.
  • Infrastructure building, agricultural land use, and construction and maintenance of shrimp farming infrastructure are currently the main causes related to the destruction and loss of new mangrove areas; this process is widespread among all the provinces in the country but has been especially evident in the province of El Oro.
  • The remaining mangrove-covered areas are still subjected to deforestation processes; however, the rate at which these processes occur has been shown to have slowed down compared to two decades ago.

Author Contributions

Conceptualization, I.G. and R.M.; methodology I.G. and R.M.; software, I.G.; validation, I.G. and R.M.; formal analysis, I.G. and R.M.; investigation, I.G. and R.M.; writing—original draft preparation, I.G., R.M., X.L.O. and T.O.F.; writing—review and editing, T.O.F. review and editing, I.G., R.M. and X.L.O.; supervision, X.L.O.; project administration, I.G. and R.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study is part of the program for consolidation of competitive research groups (Axudas consolidación e estruturación de unidades de investigación competitivas do SUG del Plan Galego IDT, Ambiosol Group ref. 2018-PG036), and Cross-Research in Environmental Technologies, CRETUS (Xunta de Galicia GRUP2015/02, ref. 2018-PG100).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available at https://earthexplorer.usgs.gov; http://ide.ambiente.gob.ec/mapainteractivo/; http://www.sigtierras.gob.ec/descargas/ accessed on 16 January 2018.

Acknowledgments

The authors acknowledge the helpful comments received from Alejandro Gómez Pazo (Universidad de Santiago de Compostela) on previous versions of this paper. The authors would also like to thank Jhoony Chalán, Luis Armijos, Fernando Villavicencio, Belén Cañar, and Saymar Gómez for their contribution to pre-processing of the information; Christine Francis for translation and Elsa Morocho and David Coleman Parsons for proofreading and translation.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Potential distribution of mangrove forests along the Ecuadorian coastline.
Figure 1. Potential distribution of mangrove forests along the Ecuadorian coastline.
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Forests 13 00656 g002a 550Forests 13 00656 g002b 550
Figure 2. Representation of land use and land cover categories: (a) water; (b) shrimp farming; (c) built-up area; (d) cropland; (e) mangrove; (f) natural vegetation; and (g) bare land.
Figure 2. Representation of land use and land cover categories: (a) water; (b) shrimp farming; (c) built-up area; (d) cropland; (e) mangrove; (f) natural vegetation; and (g) bare land.
Forests 13 00656 g002aForests 13 00656 g002b
Forests 13 00656 g003 550
Figure 3. (a) Landsat RGB image combines the near infrared (NIR), shortwave infrared (SWIR), and red bands, (b) sentinel 2A RGB image combines the vegetation red edge, shortwave infrared (SWIR), and red bands. These composites display mangrove forests in a reddish-brown color.
Figure 3. (a) Landsat RGB image combines the near infrared (NIR), shortwave infrared (SWIR), and red bands, (b) sentinel 2A RGB image combines the vegetation red edge, shortwave infrared (SWIR), and red bands. These composites display mangrove forests in a reddish-brown color.
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Forests 13 00656 g004 550
Figure 4. Percent cover of each LULC type by province during Y1, Y2, and Y3 (see Table 3). LULC types are: water (W), shrimp farming (S), built-up land (BU), cropland (C), mangrove (M), natural vegetation (V), and bare land (BL).
Figure 4. Percent cover of each LULC type by province during Y1, Y2, and Y3 (see Table 3). LULC types are: water (W), shrimp farming (S), built-up land (BU), cropland (C), mangrove (M), natural vegetation (V), and bare land (BL).
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Figure 5. Land occupation and abandonment of shrimp farms in the Bocana de Limones sector, in the Esmeraldas province. (a) In the 1998 satellite image, shrimp ponds are shown in dark color and mangroves are shown in reddish-brown; (b) in the 2010 satellite image, some abandoned pools with recovering mangroves can be observed; (c) finally, in the 2018 satellite image, abandoned shrimp farms covered by mangroves can be observed on the left and right sides.
Figure 5. Land occupation and abandonment of shrimp farms in the Bocana de Limones sector, in the Esmeraldas province. (a) In the 1998 satellite image, shrimp ponds are shown in dark color and mangroves are shown in reddish-brown; (b) in the 2010 satellite image, some abandoned pools with recovering mangroves can be observed; (c) finally, in the 2018 satellite image, abandoned shrimp farms covered by mangroves can be observed on the left and right sides.
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Forests 13 00656 g006 550
Figure 6. Mangrove loss rates by province. P1: 1998–2010, P2: 2010–2018.
Figure 6. Mangrove loss rates by province. P1: 1998–2010, P2: 2010–2018.
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Figure 7. Mangrove losses and gains in the Esmeraldas province between 1998 and 2016.
Figure 7. Mangrove losses and gains in the Esmeraldas province between 1998 and 2016.
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Figure 8. Mangrove losses and gains in the Manabí province between 1998 and 2016.
Figure 8. Mangrove losses and gains in the Manabí province between 1998 and 2016.
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Figure 9. Mangrove losses and gains in the Guayas province between 1984 and 2016.
Figure 9. Mangrove losses and gains in the Guayas province between 1984 and 2016.
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Figure 10. Mangrove losses and gains in the province of El Oro between 1997 and 2018.
Figure 10. Mangrove losses and gains in the province of El Oro between 1997 and 2018.
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Figure 11. Intensity of annual change from mangrove to other cover categories (period 2010–2018), represented according to the following equation: Rtin = transition area from i to n during time period n/duration of the period area of category i to time n × 100.
Figure 11. Intensity of annual change from mangrove to other cover categories (period 2010–2018), represented according to the following equation: Rtin = transition area from i to n during time period n/duration of the period area of category i to time n × 100.
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Table
Table 1. Medium-resolution imagery used in this study.
Table 1. Medium-resolution imagery used in this study.
No.NameProvincePath-RowDateResolution
1LANDSAT 5Esmeraldas010-0598 March 199830
2LANDSAT 5011-0593 April 199930
3LANDSAT 5011-06019 June 199830
4LANDSAT 7010-0594 March 201730
5LANDSAT 7011-05922 January 201730
6LANDSAT 7011-06022 January 201730
7LANDSAT 5Manabí011-06019 June 199830
8LANDSAT 5011-06116 April 199830
9LANDSAT 8011-06017 April 201630
10LANDSAT 8011-06117 April 201630
14LANDSAT 8Guayas010-06227 February 201830
15LANDSAT 8011-0616 May 201730
16LANDSAT 8011-06213 October 201730
17LANDSAT 5El Oro010-06220 February 199830
18LANDSAT 5011-0623 April 199930
19LANDSAT 5011-0626 October 199730
20SENTINEL 2T17MNS22 April 201820
21SENTINEL 2T17MPS22 April 201820
Table
Table 2. Classification system with the land use and land cover categories (LULC) applied in this study.
Table 2. Classification system with the land use and land cover categories (LULC) applied in this study.
MAATE
Classification System		Adapted CategoryDescription
ForestMangroveTrees and shrubs in the coastal intertidal zone
Shrub and grassNatural vegetationForests and shrubs in flooded zones
Agricultural landCroplandCrops, pastures for livestock, arable areas, logging
Anthropic areasBuilt-up areaArtificial structures (buildings, roads, coastal infrastructure)
Shrimp farmingActive and inactive shrimp ponds
Water bodiesWaterRivers and estuaries
Other landsBare landSandy and rocky areas
Table
Table 3. Years analyzed by province.
Table 3. Years analyzed by province.
ProvinceYear 1 (Y1)Year 2 (Y2)Year 3 (Y3)
Esmeraldas199820102017
Manabí199820102017
Guayas200020112016
El Oro199720112018
Table
Table 4. Details of sample points by province and LUC.
Table 4. Details of sample points by province and LUC.
ProvinceMangroveShrimp FarmingBuilt-Up LandCroplandNatural VegetationWaterBare LandTotal
El Oro50505050505050350
Esmeraldas50505050505050350
Guayas50505050505050350
Manabi50505050505050350
Total2002002002002002002001400 *
* Total of sample points generated per year.
Table
Table 5. Accuracy assessment and kappa coefficient values.
Table 5. Accuracy assessment and kappa coefficient values.
Province/YearOverall
Accuracy		Kappa
Coefficient
El Oro Y183.820.76
El Oro Y289.770.85
El Oro Y387.870.82
Esmeraldas Y184.310.81
Esmeraldas Y285.980.82
Esmeraldas Y385.820.82
Guayas Y181.250.76
Guayas Y283.070.79
Guayas Y380.100.75
Manabí Y184.730.80
Manabí Y287.050.83
Manabí Y386.130.82
Table
Table 6. Changes in land cover and use-type cover in the three study years.
Table 6. Changes in land cover and use-type cover in the three study years.
Land Cover/Use Type199820102018
km2%km2%km2%
Water557.010.0556.610.0565.610.1
Shrimp farming1301.723.31662.529.81594.128.5
Built-up area172.23.1237.54.3250.44.5
Cropland1070.419.2634.111.4595.110.7
Mangrove1484.026.61580.928.31645.229.5
Natural vegetation864.615.5853.615.3821.514.7
Bare land136.32.461.01.1114.02.0
Table
Table 7. Indicators of change in the different land use categories *.
Table 7. Indicators of change in the different land use categories *.
CategoriesPersistence P1Persistence P2Loss P1Loss P2Gain P1Gain P2Total Change P1Total Change P2Swap P1Swap P2Net Change P1Net Change P2
Water9.017.082.263.551.623.743.897.293.247.100.640.19
Shrimp farming22.5420.802.0110.979.239.6711.2420.654.0319.347.221.30
Built-up land3.033.940.220.531.440.781.661.310.441.061.220.25
Cropland9.058.9111.613.173.022.4014.645.576.054.808.590.77
Mangrove24.6325.154.564.995.516.2210.0811.219.139.980.951.24
Natural vegetation5.987.422.472.343.771.766.244.094.943.511.310.58
Bare land0.310.292.310.880.851.853.162.731.701.761.460.97
Total 74.5473.5825.4626.4225.4626.4250.9152.8429.5247.5421.395.30
* Values are expressed as percentage of the landscape for each category at the initial time point for each period. P1: 1998–2010, P2: 2010–2018.
Table
Table 8. Mangrove gross losses and gains by province *.
Table 8. Mangrove gross losses and gains by province *.
ProvinceYear 1–Year 2 (P1)Year 2–Year 3 (P2)
LossGainLossGain
El Oro37.6349.708.7312.16
Esmeraldas25.6995.636.2213.48
Guayas119.65120.6772.23120.20
Manabí11.6017.276.3412.13
* Estimated lost and gained areas in km2 for Periods 1 and 2.
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Morocho, R.; González, I.; Ferreira, T.O.; Otero, X.L. Mangrove Forests in Ecuador: A Two-Decade Analysis. Forests 2022, 13, 656. https://doi.org/10.3390/f13050656

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Morocho R, González I, Ferreira TO, Otero XL. Mangrove Forests in Ecuador: A Two-Decade Analysis. Forests. 2022; 13(5):656. https://doi.org/10.3390/f13050656

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Morocho, Ramiro, Ivonne González, Tiago Osorio Ferreira, and Xosé Luis Otero. 2022. "Mangrove Forests in Ecuador: A Two-Decade Analysis" Forests 13, no. 5: 656. https://doi.org/10.3390/f13050656

APA Style

Morocho, R., González, I., Ferreira, T. O., & Otero, X. L. (2022). Mangrove Forests in Ecuador: A Two-Decade Analysis. Forests, 13(5), 656. https://doi.org/10.3390/f13050656

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