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Fishy Business: Exploring the relationship between international aid, mangrove deforestation, and endangerment of livelihoods in coastal Ecuador

Summary

Mangrove forests have historically provided a reliable source of food, shelter, and fuel to residents of Ecuador’s traditional fishing communities. Mangroves were the primary economic engine of these communities, offering numerous livelihood options due to the wide range of goods and services they provide. From 1970 to 2009, deforestation occurred in the majority of Ecuador's mangrove forests. Those forests that remain are too few and too fragmented to provide the goods and services available from a large healthy forest. Most of these losses were caused by the conversion of mangrove-estuarine environments to commercial shrimp farms. In comparison to mangrove forests, commercial shrimp aquaculture provides relatively few jobs, a net loss of seafood protein within the estuary and surrounding waters, and the food produced is exported internationally and unavailable to local residents. Commercial shrimp farms also bring numerous human health issues to the regions in which they are located. For these reasons, shrimp farming in coastal Ecuador causes environmental and ecological disruption of the mangrove-estuarine environment that in turn decreases levels of food security and the livelihood options available to the local population. Many environmental groups and researchers contend that international development banks and bilateral aid have been the primary financiers of commercial aquaculture projects that contributed to mangrove deforestation and the resultant environmental, ecological, and socioeconomic degradation in coastal Ecuador and other mangrove regions. We utilize Geographic Information Systems, remotely sensed satellite imagery, aerial photography, topographic maps, and Project Level Aid data to evaluate the relationship between international aid and mangrove deforestation in Ecuador's coastal provinces from 1970 to the present.

Key words: mangrove, deforestation, Ecuador, international aid, aquaculture, livelihoods, food security.

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1. INTRODUCTION

Shrimp farming and wider aquaculture practices have been promoted by the

international aid community as a way for poor coastal regions in developing nations to promote

development (Rivera-Ferre 2009), increase livelihood options (Lewis et al. 2003), and increase

food security (United States Agency for International Development 2003). The view of shrimp

farming as a means to alleviate food security problems and promote development is not without

critics. Even within the United Nations (UN) the claim that the shrimp farming increases food

security and livelihood options is disputed (Barraclough and Finger-Stich 1996; FAO Fisheries

and Aquaculture Department 2005). Indeed, shrimp farming is often viewed as making its

profits at the expense of local communities (Batagoda 2003a). It is the direct and indirect

displacement of mangrove forests that bring into question the economic role of shrimp farming

in promoting development.

Ecuador has been at the forefront of the commercial shrimp farming expansion. Shrimp

farming occurs in Ecuador's coastal estuaries, the location of Ecuador's mangrove forests. The

estuarine land-water interface is the primary location of mangrove forests and also the preferred

economic location for commercial shrimp farms. As of 2006, Ecuador has more shrimp farm

acreage than mangrove forest acreage (CLIRSEN 2007), whereas in 1970 no commercial shrimp

farms existed. Mangrove losses caused by shrimp farm expansion can be subdivided into direct

displacement of mangrove and indirect losses. Direct displacement of mangrove is when

mangroves are deforested and replaced by shrimp farms. Indirect losses are when shrimp

farming alters the biogeochemical properties of an estuary to such an extent that mangrove

forests are no longer able to exist within a particular estuary. Indirect losses usually occur when

an estuary has exceeded its carrying capacity for shrimp farms. It should also be noted that the

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direct displacement of mangrove by shrimp farms is disputed. It is claimed that aquaculture is

only a minor constituent of mangrove deforestation and that most aquaculture is actually located

on the terrestrial portion of the land and water interface (Diana 2009) and only plays a small role

in mangrove deforestation. The actual amount of mangrove loss due to shrimp farming activity

within Ecuador remains a point of debate, with no robust quantitative landuse analysis

undertaken to establish a baseline or rate of change over time of the mangrove cover within

Ecuador. Even the current remaining level of mangrove forest cover is not well established

within the literature.

Conflicting estimates of current Ecuadorian mangrove acreage exist within the literature.

The International Society for Mangrove Ecosystems (ISME) atlas depicts Ecuador as having

246,900ha (Spalding, Blasco, and Field 1997) of mangrove as of 1996. The IMSE database

clearly omits mangroves in and around Esmeraldas, Esmeraldas Province and clearly over-

estimates mangrove cover in and around the Chone Delta in Manabí province, Isla de Puna in

Guayas Province, and southern El Oro province on the Peruvian Border (Bodero 1993). A

second estimate of mangrove forest as of 1992 was 162,000ha (Bodero 1993). Other peer-

reviewed estimates of mangrove cover are 177,600ha and 162,000 as of 1991 (Harcourt and

Sayer 1996; Parks and Bonifaz 1994). Within Ecuador, The Instituto Geográfico Militar (IGM)

estimates mangrove coverage at 148,483ha as of 1999 (CLIRSEN 2007).

Estimated rates of mangrove deforestation within Ecuador also vary greatly, as they are

usually based on one or many of the above sources and hence have differing temporal

resolutions. The amount of mangrove present before the advent of shrimp farming in Ecuador is

also difficult to establish. First, such estimates assume knowledge of historic landcover. Second,

shrimp farming arrived in differing regions at differing times. The Centro de Levantamientos

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Integrados de Recursos Naturales por Sensores Remotos (CLIRSEN) estimates mangrove cover

in 1969 as totaling 202,201ha (CLIRSEN 2007) whereas Parks estimates 203,700ha (Parks and

Bonifaz 1994) as of 1979. It is quite clear from even a cursory visual analysis of mangrove

forest remote sensing data collected between 1969 and 1979 that mangrove forest area did not

increase in Ecuador during this period. Results from this analysis actually suggest a substantial

decrease in mangrove acreage during the 1970s.

This paper utilizes remote sensing techniques within a Geographic Information Systems

(GIS) environment to produce a cross-sectional longitudinal change analysis focusing on

estuaries within each of Ecuador's four coastal provinces. The areas of each estuary covered by

mangrove forest, shrimp farms, and other uses are calculated for each longitudinal period.

Annual amounts of mangrove forest loss and annual rates of mangrove forest loss are then

calculated for each cross section. These data are aggregated to the national level and correlated

to the Project Level Aid (PLAID) database funding commitments flowing into Ecuador during

the analysis period. Differing donor populations as determined by donor type, donor, and a

subset of donors that traditionally support aquaculture projects are extracted from PLAID for

analysis.

Results of this paper include: levels of mangrove cover for each longitudinal cross-

section, cross-sectional rates and amounts of mangrove deforestation for each longitudinal data

point, the cross-sectional rate of shrimp farm growth for each longitudinal data point, and most

importantly, the statistical relationship between differing aid populations (types of aid) and the

deforestation occurring within Ecuador's mangrove environments. The independent variables are

the differing aid subsets at the national level and the dependent variables are the differing rates of

mangrove deforestation at the national scale. This paper offers insights into how aid may

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influence landuse, and how altering landuse may impede the goals of development aid, as well as

answering questions regarding rates of mangrove deforestation in Ecuador's major estuaries.

Section 2 discusses the relationship between mangroves forests and economic

opportunity and food security. Section 3 examines the growth of commercial aquaculture and

shrimp farming, and reviews the evidence for aid funding shrimp farms that cause mangrove

deforestation. It discusses the landuse change methodology, the fusion of PLAID and landuse

change data, and the statistical correlations derived between aid and deforestation at the national

level. Section 4 introduces each study area and discusses the data sources and techniques

utilized in each area. Section 5 presents the results of the landuse change analysis and of the

national level correlations between aid and deforestation. Section 6 is a discussion of the

implications of these findings and future research needs.

2. THE ROLE OF MANGROVE FORESTS

All mangrove forests provide plant products, protect shorelines, provide food and habitat

for animals, improve water quality, process nutrients, and trap sediments (Ewel, Twilley, and

Ong 1998). Mangrove forests also play an important global role as a sink for carbon. Each

hectare of mangrove forests contains 700 tons of carbon per meter of sediment (Ong 2002), and

Aquaculture aid in Ecuador = shrimp farm aid

Shrimp farm aid in Ecuador = Increase in semi-intensive shrimp farms

Semi-intensive farming occurs in estuaries = Mangrove deforestation

Mangrove deforestation = loss of local livelihood options / decreased food security

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sediment in mangrove forests is often many meters thick. Additional global mangrove forest

functions include providing habitat to numerous endangered species and to pollinating bees and

bats.

(a) Traditional uses of mangrove

Traditional livelihood uses of mangrove forests include utilizing them for renewable

timber resources. Timber from mangrove forests is used in boat and home construction, in bark

based dyes (Armitage 2002; Blanchard and Prado 1995; Warne 2007; Macnae 1968), in roof

thatching (Macnae 1968), to provide fuel and heat in the form of charcoal, and for processing

sewage (Tomlinson 1986). Mangrove forests also provide food resources to traditional

communities in the form of fish (numerous species), crab, shrimp, clam, sea snail, eel, (Armitage

2002), wild honey, edible plants (Warne 2007), and are a prime habitat for nypa palms that

provide sugar (Armitage 2002) and alcohol. Other traditional uses include the utilization of

mangrove litter for animal feed, medical plants, (Warne 2007), and tourism & recreation

(Batagoda 2003b). Furthermore, mangrove forests have been used to raise species such as

shrimp for hundreds of years in a traditional farmed system (Macnae and Kalk 1962; Naylor et

al. 1998). Traditional usage of mangrove forest is typically conducted in a sustainable manner

allowing for harvesting of differing products throughout the year (Armitage 2002). For all of

these reasons one author has called mangroves an entrepreneur’s dream (Tomlinson 1986), as

they produce raw materials from sea-water and other sources and pass on these goods to

traditional communities.

The economic value of mangrove forests is difficult to calculate because it involves

placing a numeric value on a long-term communal resource. Despite this problem, there have

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been attempts to calculate the direct economic benefit of mangrove forests to traditional

communities. It has been argued that the estuarine mangrove ecosystem has one of the highest

natural economic values per hectare of any ecosystem (Costanza et al. 1997), and that the rapid

pace of mangrove deforestation and estuarine disturbance is due to the slow realization of this

fact by economists (Blaber 2007). The direct economic benefit of a preserved mangrove forest

in Sri Lanka is estimated to be $12,229 per year per hectare (Batagoda 2003) and has been

calculated to be as high as $751,368 per hectare when illegally destroyed in Costa Rica

(Tomlinson 1986). The estimated 1994 worldwide value of a mangrove swamp was $9990 –

$19,580 (Costanza et al. 1997) per hectare with estuaries at $22,832 per hectare, exclusive of

mangrove forest (Costanza et al. 1997). Mangroves forests offer substantial economic benefits

when compared to traditional commercial crops or even high yield aquaculture practices.

Literature on livelihoods in Ecuador, though limited, supports the view of mangrove

forests providing numerous goods and services when utilized in a traditional manner. Within

Ecuador, mangroves have traditionally been used for timber, charcoal, and tannins (Spalding,

Blasco, and Field 1997). Mangroves are also used to shelter homes from the strong coastal wind

and flood events, in addition to providing materials such as timber and poles for the construction

of homes (Ocampo-Thomason 2006). In parts of coastal northern Ecuador the mangrove forests

still power entire communities by providing jobs, income and a stable supply of food (Ocampo-

Thomason 2006; Veach 1996). It is calculated that 85% of rural residential households around

San Lorenzo, Esmeraldas depend economically on traditional use of the mangrove for fishing or

the collection of cockles and crab (Ocampo-Thomason 2006). Other Ecuadorian uses and

benefits of mangrove discussed in the literature include water purification, stabilizing sediment,

and the provision of edible plant species (Parks and Bonifaz 1994).

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(b) Mangroves and fisheries

One of the most contested and important facets of mangrove forests are their role in

enhancing fisheries. Approximately 75 percent of the world’s commercial fish are over-

exploited (Deutsch et al. 2006) and in short supply. It is argued that mangrove forests play an

important role in fisheries sustainability and food security by sustaining commercial wild fish

populations. Therefore, it can be inferred that mangrove deforestation contributes to fisheries

decline. Mangroves support offshore fisheries by providing habitat for juveniles and adult fish

species and allow for productive trophic exchange. Mangroves in Florida are shown to provide

habitat, shelter, and food sources for animals at the base of the food chain that power the entire

Gulf of Mexico ecosystem (Gore 1977; Odum and Heald 1972). Within Florida, 90 percent of

commercial fish species are reliant on mangrove habitat for their existence at some point in their

life cycles (Gore 1977). The same pattern exists worldwide. Mangrove forests in Sulawesi,

Indonesia provide important spawning ground and habitat to the most important regional aquatic

life including commercially and locally important fish, shrimp, crab, and mollusks (Armitage

2002).

This view of mangroves as supporting fisheries is not without critics. It has been

described as dogma that mangrove forests support fisheries (Blaber 2007). The thesis of this

argument is that the majority of studies that equate mangrove losses to fisheries decline show

correlation but not causation, and are plagued by problems of auto-correlation because

commercial over-fishing and mangrove depletion occurred on a similar temporal scale.

However, numerous other counter-perspectives that advocate the importance of mangrove to off-

shore fisheries began to appear at the same time as Blaber’s work (Chong 2007; Frias-Torres et

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al. 2007; Granek and Frasier 2007; Koenig et al. 2007; Lugendo et al. 2007; Nagelkerken 2007;

Shervette et al. 2007). For example, it is estimated that the 567,000 ha of mangrove forests in

Malaysia sustain more than half of Malaysia's annual fish catch totaling 1.28 million tons,

through larval retention, trophic supply, and habitat support (Chong 2007). That equates to

annual offshore fishery catch in excess of 2250 tons annually that is dependent on each hectare

of mangrove. Even the limited research in coastal Ecuador points to the importance of

mangroves in sustaining regionally important commercial fish species (Shervette et al. 2007).

Although Blaber (2007) contests the relationship between the decline of fisheries and

mangrove deforestation on a global scale, his stance is unequivocal when dealing with traditional

fishing communities and their relationship to mangrove.

“It is important to distinguish between ‘fisheries within mangrove

systems’, usually of an artisanal or subsistence nature in developing countries,

and ‘offshore (of mangroves) fisheries’ that are usually commercial or industrial

concerns. In the former case, the activities by traditional or artisanal fishermen

may be long-established and are totally dependent on the existence of the

mangrove system” (Blaber 2007).

Within Ecuador, only one peer-reviewed study examines the relationship between

mangrove and fish species. This analysis compares fish populations in a mangrove swamp at the

mouth of the Rio Palmar to the mangrove-free river mouth of Rio Javita, approximately 1.5

miles from the mouth of the Rio Palmar. This study inventoried 36 fish species in 16 families

within the Rio Palmar and Rio Javita. Twenty-one of these species occurred only in the Rio

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Palmar. Not only was specie variety greater in the mangrove area, but more importantly, 9 of the

12 economically important centropomid, gerreid, and engraulid fish species occurred only in the

mangrove habitat, despite the altered state of this habitat (Shervette et al. 2007). This research

was conducted in an inter-tidal environment that had already been stripped of almost its entire

historic mangrove. According to Landsat imagery of the area, the inter-tidal mouth of the Rio

Palmar had 20 times more area under aquaculture than what remained as mangrove. Yet despite

the fact that only a small portion of Rio Palmar's mangrove remains, it still appears to sustain

more biodiverse fish populations that the nearby river in which no mangroves remain. The

results indicate that mangrove in Ecuador plays an important role in sustaining local and

regionally important fish species. Other research in Ecuador points to artisanal fisherman fishing

for shrimp and other organisms for hundreds of years, noting that the entire lifecycle of shrimp in

Ecuador’s coastal waters is reliant on mangrove (Cuoco 2005).

(c) Mangrove summary

Mangroves are the foundation of one of the most biologically diverse and economically

rewarding ecosystems on the planet. Using the metric of biological species richness or the

metric of economic return, mangrove forests have historically been under-valued. Mangroves

sustain fisheries, provide economic opportunities to local populations, provide a secure supply of

food and protein to local residents, purify water, protect coastlines from natural disasters, provide

habitat, and help manage atmospheric carbon levels. These functions of mangrove provide both

local and global benefits. Still, it is at the regional scale, especially in traditional fishing

communities, that mangroves are most beneficial. During times of food stress, mangrove habitat

provides a ready source of freely available protein. During times of fuel shortages, mangroves

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provide the fuel necessary for hot sanitary water or cooking in the form of charcoal. Mangroves

provide the homes and boats for coastal populations to earn a living. Perhaps most importantly,

mangroves stabilize the shoreline by providing the solid ground for the local populations to

inhabit in a swamp environment. Therefore, it is traditional local populations that benefit most

from mangrove and thus local populations are most adversely affected by their removal. The

process of mangrove deforestation in much of the developing world, and particularly in Ecuador,

is now driven by the growth in aquaculture and in Ecuador aquaculture is shrimp farming.

3. THE GROWTH OF AQUACULTURE

By any unit of measure, and when viewed from either side of the scientific debate, the

growth of commercial aquaculture has been remarkable and rapid. In 1970, it is estimated that

less than 4 percent of the seafood consumed worldwide was reared in a farmed environment. By

2009, aquaculture accounted for over one-third of the entire amount of seafood consumed

worldwide. Aquaculture is estimated to remain the most rapidly increasing food production

system through 2025 (Diana 2009), with growth rates continuing to exceed 10% annually.

Within many developing nations, the aquaculture growth rate since 1970 has actually exceeded

10% (FAO Fisheries and Aquaculture Department 2005). As a comparison, farmed meat growth

averages 2.8 percent for the same period (FAO Fisheries and Aquaculture Department 2005;

United Nations Committee on World Food Security 2003). Within 50 years from its inception,

commercial aquaculture production will surpass wild catch as the primary source of seafood.

By 2004, seafood exports contributed $71.5 billion to developing countries’ economies,

more than coffee, tea, bananas, rice, and meat combined. Forty-three percent of these exports

were derived from aquaculture (Diana 2009). Aquaculture production in Ecuador mirrors the

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global trend. In 1970, aquaculture production in Ecuador was estimated at 50 tons per year. By

2007, it had grown to 171,020 tons, (Figure 1) with a value of $763 million (Figure 2). If it

follows current trends, shrimp aquaculture will soon surpass wild catch in tonnage and due to the

high dollar value of shrimp compared to other fish species it may already have passed wild-catch

in dollar value. Shrimp is typically double the nearest fish export in terms of value per pound or

product. For example, the February 2010 price for frozen Ecuadorian Shrimp is $3.45 - $4.00

per pound at New York market prices (NOAA Fisheries: Office of Science and Technology

2010) whereas Ecuador's next major fish export, frozen Tilapia, wholesales in the USA for

approximately $2.00 per pound (FAO Fisheries and Aquaculture Department 2010a).

FIGURE 1

FIGURE 2

The preferred shrimp specie in most Latin America shrimp farms, including in Ecuador is

P.vannamei. P.vannamei has numerous properties that make it desirable when compared to

other shrimp species in a farmed environment (Briggs et al. 2004; Wyban and Sweeney 1991).

P. vannamei shrimp are native to the tropical Pacific coast of the Americas between Mexico and

northern Peru, existing in the +20°C isotherm (Wyban and Sweeney 1991). The entire Ecuador

Pacific coastline falls into this isotherm. As commercial shrimp farming requires water and

nutrient exchange between the farm and the estuary on a regular basis, shrimp farms are located

in locations preferred by wild P.vannamei.

Commercial shrimp farms dot the Ecuadorian coastline from the Colombian border in the

north to the Peruvian border in the south. Preferred locations are in the sheltered estuaries

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around San Lorenzo, Guayaquil, Puna, and Chone. This brings shrimp farming into direct

competition with mangrove forests that are located in the same isotherm and estuarine

environment that is now the preferred location commercial shrimp farms.

3.1 Shrimp farm financing and international aid

Shrimp farms, shrimp hatcheries and shrimp nurseries are capital-intensive operations

that require high levels of initial investment (Rajitha, Mukherjee et al. 2007). Some of the initial

funding undoubtedly comes from within the host nation and from local communities investing in

pond construction, but much of the investment in shrimp farms originates with international aid

agencies and international financial institutions. The United Nations Committee on World Food

Security reported as late as 2003 that commercial aquaculture has a role to play in eliminating

hunger and malnutrition, and this role will be particularly beneficial to artisanal fishing

populations (United Nations Committee on World Food Security 2003), a belief that has been

widely held in the development community for decades (Rivera-Ferre 2009). Such statements

and related policy goals likely explain the headline rational for development bank investment

and bilateral aid directed towards commercial aquaculture.

Public Citizen1

1 Public Citizen is a national, nonprofit consumer advocacy organization founded in 1971 to represent consumer interests in Congress, the executive branch and the courts.

cites the ‘development trilogy’ of international financing institutions—

direct investment; bilateral aid, and multilateral support and technical assistance—as the driving

force behind aquaculture development in developing countries (Public Citizen’s Food Program

2005). Public Citizen list the primary developmental bank assistance as originating from the

World Bank, the International Monetary Fund (IMF), the International Bank for Reconstruction

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and Development (IBRD), the Asian Development Bank, the African Development Bank, and

the Development Bank of Japan. The same source lists primary bilateral aid as originating from

the Overseas Economic Cooperation Fund, the Japan International Cooperation Agency, the

United States Agency for International Development (USAID), the Canadian International

Development Agency, the Commission of the European Community, the European Investment

Bank, and the Norwegian Agency for International Development. Public Citizen lists

multilateral support as coming from the UN Food and Agricultural Organization (FAO) and the

United Nations Development Program (UNDP). Most of this aid and investment is officially

given as a means of improving food security.

Public Citizen's claims that development banks and bilateral assistance are the key

supporters of aquaculture globally are supported by researchers (Rivera-Ferre 2009; Nash 1987;

Shehadeh and Orzeszk 1997). Nash (1987) shows that from 1978 to 1983, three development

banks—the World Bank, the African Development Bank, and Inter-American Development

Bank contributed nearly $190 million to aquaculture. This accounts for 51.5 percent of the total

funding over that period, as total “external assistance to aquaculture” is given as $368 million

(non-adjusted USD). With the addition of contributions from the UN System and UN Trust

Funds, multilateral donors accounted for 61.6 percent of the non-domestic funding for

aquaculture. Twenty-six percent of outside contributions to aquaculture came from bilateral

sources, particularly the European Economic Community and Japanese bilateral assistance.

Other donors comprised only 12.5 percent of aid to aquaculture worldwide. An even higher

percentage of official aid to aquaculture in the period 1988 to 1995 came from development

banks. The FAO reports that during this period, 69 percent of the $995 million (adjusted 1997

USD) of international funding for aquaculture came from development banks. Another 17

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percent of aid to aquaculture in these years came from bilateral sources and only 7 percent from

other multilateral sources. Figure 3 summarizes this information. Similar data for the periods of

1970 to 1978, 1983 to 1988, and 1995 to the present seem to be unavailable in the literature or in

FAO documentation. The unavailability of documentation on international assistance to fisheries

after 1995 may be due to the adoption of the 'Code of Conduct for Responsible Fisheries' in this

year (FAO 2010) and resultant changes in how aquaculture was viewed and supported

internationally.

FIGURE 3

The PLAID database now available to researchers allows for a narrowing-down of the aid

flows into Ecuador by sector, year, and donor and potentially by location (Nielson, Powers, and

Tierney 2010). PLAID reveals that 7123 aid commitments were made to Ecuador from 1970 –

2006, totaling $23,398,638,395 (Figure 4) in 2000 USD. Using a combination of PLAID and

Organization for Economic Development and Assistance (OECD) project codes the amount of

aid moving into fisheries and agro-industry can be determined. The fisheries and agricultural

sectors are the typical classification that direct aid to aquaculture falls under although it should

be noted that the development of homes, roads, and other infrastructure associated with

aquaculture would likely fall into other economic and social classifications. The fisheries and

agricultural sectors of aid into Ecuador show 942 commitments comprising $2,330,058,049

(Figure 5). The primary donors during this period are shown in Figure 6.

FIGURE 4

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FIGURE 5

FIGURE 6

Other accounting of the foreign aid going to support shrimp aquaculture shows $1.8

billion flowing into Ecuadorian, Honduran, Nicaraguan, Mexican, and Brazilian aquaculture in a

twenty-year period starting in 1980 from various aid agencies and investment banks (Public

Citizen’s Food Program 2005). The UN estimates it gave $8.9 million to support aquaculture

between 1987 and 1997, and that this was only 10% of public sector lending to aquaculture

during this period. This would assume total investment of almost $1 billion annually into

aquaculture for this 10-year period.

International support of aquaculture through development assistance and aid has not

ceased even as the detrimental environmental effects of aquaculture have become clearer. In

2002, USAID directed $26 million to projects that include aquaculture in the relatively pristine

aquaculture-free zone of the Ecuadorian / Colombian border under the stated intent of improving

quality of living along Ecuador’s northern border (United States Agency for International

Development 2003). As late as 2006, a USAID sponsored report promoted aquaculture in Iraq

as an opportunity for private sector employment and economic growth. Ironically, this report

notes that $26,000 is the typical investment required to start a pond, but this can drop to only

$11,000 if conducted in a semi-intensive manner in the marshes at the intersection of the Tigris

and Euphrates Rivers (The Louis Berger Group and Group 2006). The same document points

out that these marshes have decreased to less than 15% of their 1970 area in the last 25 years. It

can be stated that aquaculture in developing countries is built on a foundation of investment and

aid from developed countries, particularly through international financial and aid institutions.

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3.2 Shrimp farm lifecycle.

The length of shrimp farm productivity is dependent on the practices and location of the

farm. The typical shrimp farm lifespan is given as 5-10 years (Naylor et al. 1998) in an

intensive system or 7-15 years in a less intensive system, with high yields usually followed by a

dramatic collapse (Paez-Osuna 2001). Bacterial contamination of the sediment and viral

diseases are the primary reasons for pond abandonment and production collapses. There is

general agreement that abandoned farms rarely regain their productivity after a collapse. Most

authors also contend that abandoned farms are not rehabilitated back into mangrove ecosystems,

nor are they converted to other agricultural uses (Naylor et al. 1998; Paez-Osuna 2001), although

again this fact is disputed (Diana 2009).

Ecuadorian shrimp farms follow the general trends noted in the literature. Many

abandoned farms are located along the entire coast, are still visible on the landscape many years

after abandonment (Figure 7), and despite resurgence in shrimp farm output in the last 5 years,

few if any ponds appear to have been returned to aquaculture or mangrove forest. As previously

noted, mangrove forests continue to give a high level of economic goods and services for as long

as they remain in existence, whereas shrimp farming typically gives a high level of production

for a relatively short period before being abandoned due to disease.

3.3 Aquaculture / shrimp farming summary

FIGURE 7

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It is uncontestable that aquaculture and shrimp farming has grown in developing

countries, including Ecuador, since 1970. The use of aquatic resources to raise seafood, as

opposed to merely catching seafood, mirrors processes that have been and are occurring with the

terrestrial food supply. Unlike land-based grazing practices, or even offshore aquaculture, the

preferred shrimp farming environments are in a very limited geographic area within tropical

estuaries. Shrimp farms and aquaculture now provide vast economic returns to local and

national economies in the developing world. Despite these economic returns, protein supply, and

livelihood options offered by shrimp farming, questions remains about the long-term economic

returns.

Shrimp farming, and aquaculture in general, appears to be directly financed by

international aid, international financing institutions, and multi-national corporations. Such

institutions are generally viewed as providing aid, development assistance, or investment that

benefits the receiving nation or regions. However, the questions raised about the potential local

benefits of aquaculture also bring in to question the motives and practices behind such financing.

Commercial aquaculture is concerned with making profits not with reducing global or local food

insecurity (Deutsch et al. 2006). The commercial shrimp industry actually makes its profit at the

expense of local communities (Batagoda 2003a, 2003b) and regional food security.

The methods employed by shrimp aquaculture during the shrimp lifecycle, the shrimp

utilized in the farming system, the feed and other inputs utilized by shrimp farms, and most

importantly the location of growout ponds, may all have wider ecological and socioeconomic

consequences that are not fully accounted for beyond the economic inputs and outputs of

standalone shrimp farm economics. Nature provides numerous subsidies to shrimp farming,

including clean water to maintain the growout ponds, processing of waste and other chemicals

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exported from the farms, fishmeal and feed for the shrimp, and a nutrient rich estuarine

environment for the growout ponds. Without accounting for such natural subsidies, alterations in

local livelihoods, and the loss in mangrove forests and estuarine environment caused by shrimp

farming, the true cost / benefit analysis of shrimp farming remains incomplete. Shrimp farms

also do not generate meaningful employment, do not produce food for local consumption, are

abandoned in a toxic condition typically within a decade, and compete for and displace

mangrove forests that are traditionally the foundation of the local communities economy and

food supply.

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4. METHODOLOGY AND SITE SELECTION

Only Esmeraldas Province, Manabí Province, Guayas Province, and El Oro Province

border the Pacific Ocean within the Costa region of Ecuador (Figure 8). Due to the rapid

increase in elevation moving eastward from the Pacific coast, only these four coastal provinces

have the potential for mangrove growth. Although Los Rios is generally assigned to the Costa

region due to its lowland location, it has no saltwater inputs to sustain mangrove forests. The

province of Galápagos does have a Pacific coastline but due to the frigid Humboldt Current, no

mangroves exist within the archipelago. The research area for this study thus comprises the

areas within these four coastal provinces capable of sustaining mangrove forests and suitable for

commercial shrimp farming. The combined coastline of these provinces varies from a high

estimate of 4957 kilometers (World Resources Institute 2007) to a minimum of 2237 kilometers

(Central Intelligence Agency 2008).

FIGURE 8

In further delineating study areas, it is assumed that nowhere with elevations above 6m

receive saltwater input from the ocean. This estimate allows for localized regional topography

that may magnify the estuarine tidal ranges beyond the regional average maximum of 3m. To

obtain topography below 6m, a 30m horizontal resolution Digital Elevation Model (Souris 2008)

was derived using elevation data from the IGM topographic database of 30 million elevation

points within Ecuador. By extracting all areas within this DEM of elevations below 6 meters, we

are able to narrow the potential mangrove area down to 3547 km2 of coastal Ecuador. It is in this

21

3547 km2, that mangroves forests have the potential to occur. From within the 6m contour, the

major estuary for each province was selected (Figures 8 & 9). For Esmeraldas, Manabí, and El

Oro province, it is the entire major estuary within the province. For Guayas, Isla de Puna was

selected over the entire Guayas estuary due to the size and complexity involved it digitizing

shrimp farms for the entire Guayas basin.

FIGURE 9

4.1 Site selection and sensor information

For each study area, the Landsat archives at the Global Land Cover Facility (GLCF) and

the Global Visualization Viewer (GLOVIS) were processed to determine the first appearance of

shrimp farming within each estuary. If this date could be determined from the Landsat archives,

then the next earliest Landsat image with suitable atmospheric conditions was selected as the

baseline dataset for that estuary. For Chone Estuary and Grande Estuary, shrimp farms are

clearly visible in the earliest Landsat images available. For these areas aerial photography in

conjunction with topographic maps were obtained from the IGM in Quito and used as a baseline.

All Landsat was obtained from GLOVIS or the GLCF and the Nature Conservancy donated

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery.

Datasets utilized for mangrove and shrimp farm delineation include Landsat 1, Landsat 5

Thematic Mapper (TM), Landsat 5 Multi-Spectral Scanner (MSS), Landsat 7 Enhanced

Thematic Mapper (ETM+), ASTER, local aerial photography, and IGM topographic maps.

22

Mangrove forests, shrimp farms, and other estuarine areas were delineated for each study

site and at each imagery period. Topographic maps were scanned at 300DPI and georeferenced

using the graticule intersections. Aerial photography was scanned at 300DPI and georeferenced

using landmarks such as docks and road intersections from the temporally closest topographic

map. Landsat and ASTER imagery are both pre-georeferenced. Mangroves, shrimp farms, and

other areas were delineated by heads-up digitizing from the topographic maps and aerial

photographs. These digitized vector files were then converted to 1m resolution raster datasets

carrying differing attributes for each landuse. Mangrove delineation from ASTER and Landsat

used a combination of supervised classifications based on field data from known mangrove areas

and manual digitizing.

Shrimp farms were all manually digitized due to the difficulty of obtaining unique

spectral signatures from the water in the shrimp pond and the surrounding estuary water. As

shrimp farm and estuarine waters undergo a nearly continuous interaction as part of the draining

and filling of shrimp farms, a recently filled farm has an identical signature to the rest of the

estuary. Again, all data was rasterized to 1m meter resolution with associated attributes attached

to each cell. Although areas are reported in meters squared, the resolution of the input data

varies from ~2m2 for the analog aerial photography to 30m2 for most of the Landsat information.

Hence, the landuse area accuracy is actually closer to ±30m2 relative accuracy than reported

units in m2. As the study areas combine to 5,000,000m2 the data difference in reporting at the

meter level as opposed to the 30-meter level will result in nominal fractional changes to any

areas or percentages. Meter reporting was selected for ease of understanding of results.

23

4.3 Chone Estuary

The first study area is the Rio Chone estuary between Chone and Bahia de Caraquez, in

the province of Manabí. Figure 10 is an aerial view of the Chone estuary. It is the largest

estuary wholly in Manabí province. The Chone estuary has been identified as a habitat of

primary concern within Ecuador and has special legislation recognizing it as such (Coello,

Proafio-Lerowr, and Donald Robadue 1993). The estuary is now dominated by commercial

aquaculture with mangroves only remaining in a replanted island (Isla Corazon) and as fringe

forest along the banks of the shrimp farms. The area is a popular regional tourist destination

with Bahia de Caraquez as the largest city on the southern side estuary with a population of

approximately 30,000 people (Brinkhoff 2010).

FIGURE 10

The first shrimp farms in the Chone estuary appeared between 1968 and 1977. Mangrove

deforestation and shrimp farm growth was calculated for 1968, 1977, 1984, 1991, 1998, 2001,

2004, and 2006. The 1968 estuarine use was derived from 1:60,000 black and white aerial

photography and 1:24,000 topographic maps. The 1977 estuarine use was derived from 1:60,000

black-and-white aerial photography. The 1984 estuarine use was derived from Landsat 5 MSS.

The 1991 and 1998 estuarine use was derived Landsat 5 TM. The 2001 and 2004 estuarine use

was derived from Landsat 7 ETM+, and the 2006 estuarine use was derived from the ASTER.

The estuary size used for the analysis was 87,437,964m2.

24

4.3 Mataje-Cayapas

Figure 11 depicts the second region; it is located on the Colombian border in northern

Esmeraldas Province in and around the Mataje-Cayapas reserve. It is the largest estuary in

Esmeraldas Province. Within this estuary are the tallest known mangroves in the world with an

average height of 50m and reported maximum of 64m. Traditional mangrove uses still dominate

in this area and the forest is considered Ecuador’s most pristine mangrove environment

(Ocampo-Thomason 2006) and potentially the most pristine along the entire Pacific coast of the

Americas (Wetlands International 2004). The almost entirely Afro-Ecuadorian population

within the 44,000-km2 area and surrounding towns relies on the mangrove forest for their

income, with over 85% of households garnering their livelihood from the traditional uses of the

forest (Ocampo-Thomason 2006). This region consists of pristine estuary environments,

freshwater and inter-tidal flooded wooded wetlands, and wooded peatlands (Wetlands

International 2004). Recent aerial surveys of the area suggest that farms are beginning to appear

in isolated parts of the region. The area is difficult to access with the first road connecting it to

the rest of Ecuador finished only in 1999. The largest city on the estuary is San Lorenzo with a

population of 19,200 (Brinkhoff 2010). The majority of the area is uninhabited or inhabited with

very low population densities. The estuary can be considered a traditional fishing community.

This area of Ecuador borders a region of Colombia that has high levels of drug trafficking, past

kidnappings including those of US citizens, and is utilized as safe area for Colombian FARC

rebels.

25

FIGURE 11

The first shrimp farms appeared around 1985. Mangrove deforestation and shrimp farm

growth was calculated for 1985, 1997, 2001, and 2008. The 1985 and 1997 estuarine use was

derived from Landsat 5 TM. The 2001 estuarine use was derived Landsat 7 ETM+, and the 2008

estuarine use was derived from ASTER. The estuary size used for the analysis was

507,144,504m2.

4.4 Rio Hondo Estuary

Figure 12 depicts the third estuary; the study area is located on Isla Puna in Guayas

Province and is the major estuary on the island. Puna is at the mouth of the Guayas River, which

is the largest estuary in Ecuador. Guayaquil is the largest city in Ecuador with a population of

2,286,000 (Brinkhoff 2010) and a high population density. Despite being in close proximity to

Guayaquil, Isla Puna is sparsely populated due to a lack of fresh water sources and limited

municipal power. The two largest towns on the island (Puna and Bajada) have populations in the

low thousands. The area is visited by residents of Machala and Guayaquil as a day trip

destination. No large urban centers exist on the island. The Guayas delta could not be analyzed

as a whole due to data limitations so Rio Hondo was selected as a sample of this area.

FIGURE 12

26

The first shrimp farms appear on Landsat imagery between 1973 and 1978. Mangrove

deforestation and shrimp farm growth was calculated for 1973, 1985, 1990, 1997, and 2006. The

1973 estuarine use was derived from Landsat 1. The 1985 and 1990 estuarine use was derived

Landsat 5 TM. The 1997 estuarine use was derived from Landsat 7 ETM+, and the 2008

estuarine use was derived from ASTER. The estuary size used for the analysis was

312,956,725m2.

4.5 Grande Estuary

Figure 13 depicts the third region; it is located on the Peruvian border south of the city of

Machala. Machala is the largest city on the northern end of the estuary with a population of

250,000; the border city of Huaquillas with a population of 49,000 is located at the southern end

of the estuary. Some population pressure is possible on the northern end of the estuary although

the majority of the estuary is made up of many sparsely populated small islands between these

two cities.

FIGURE 13

The first shrimp farms appear between 1968 and 1985. Mangrove deforestation and

shrimp farm growth was calculated for 1968, 1985, 1990, and 2006. The 1968 estuarine use was

derived from 1:25,000 topographic maps. The 1985 and 1990 estuarine use was derived Landsat

27

5 TM. The 2008 estuarine use was derived from ASTER. The estuary size used for the analysis

was 175,919,482m2.

4.6 Integration of PLAID with mangrove change data

PLAID data was queried to return all data on aid to Ecuador from 5 years before the first

baseline landuse change level to the most current landuse change measure in 2008. The PLAID

annual data for total aid to Ecuador and each subset was then further adjusted to allow for three

minus one-year, four minus one-year, and five minus one-year lags. Therefore, the 1975 aid

level in the four year minus one lag analysis is actually the average aid for 1970, 1971, 1972, and

1973 combined. This is done to allow aid to have time to alter the environment. For example, it

is unlikely that an aquaculture related project funded in 1975 would be seen on the 1975

remotely sensed imagery. Indeed the imagery may have been captured before the aid was even

allocated depending on the month of aid issue and the month of image acquisition. Furthermore,

if, for example, aid were allocated in December 1974 and the 1975 image was taken in January,

then the results of all aid allocated in 1974 would not be visible on the 1975 image. Thus, aid

allocated in the year immediately preceding the date of the image is not included in the moving

averages. Thus, the minus one facet of the lag allows for this and the three, four, and five minus

one year moving average allows time for aid projects to be undertaken.

In addition to total aid, 7 subsets of the PLAID data were constructed to allow for

differing donor and aid populations to be represented in the analysis. These are:

28

(i) Multilateral donors

Multilateral donors are the reported as being the primary donors to aquaculture.

(ii) Bilateral donors

Bilateral donors are reported as only play a small role compared to multilateral donors in

supporting aquaculture. This variable is a control for (i)

(iii) Multilateral and bilateral aquaculture supportive donors (Public Citizen’s Food

Program 2005; Rivera-Ferre 2009b; FAO Fisheries and Aquaculture Department 2010b; Nash

1987; Shehadeh and Orzeszk 1997; Nielson, Powers, and Tierney 2010). These identified

aquaculture donors are reflected in the PLAID database as IMF, IFC, IDA, JPN, USA, CAN,

EC, UNDP and NOR. FAO is lacking from the PLAID database and the Asian Development

Bank does not contribute to Ecuador.

(iv) The top 10 (9) nation state importers of seafood (FAO Fisheries and Aquaculture

Department 2009) based on dollar value of seafood imports. These are identified as Japan,

United States, Spain, France, Italy, UK, Germany, Denmark, and South Korea. Chinese donor

data is omitted from PLAID and is thus excluded despite being in the top 10 nations importing

seafood.

29

(v) Nation state importers of seafood outside of the top 10 (FAO Fisheries and

Aquaculture Department 2009) based on dollar value of seafood imports. These are identified as

all bilateral donors excluding Japan, United States, Spain, France, Italy, UK, Germany,

Denmark, and South Korea. This can be considered a control group for (iii) and (iv).

(vi) All aid coded within PLAID as fisheries aid. Shrimp farming aid is commonly

tagged as going into the fisheries sector. Direct aid to aquaculture to construct farms is likely to

be classified as fisheries aid. Additionally, the UN FAO and that tracks worldwide aquaculture

production classify aquaculture as a subset of fisheries.

(vii) All aid coded within PLAID as agro-industrial aid. Shrimp farming aid is

commonly tagged as going into the agro-industrial sector. Shrimp aid tagged as agro-industrial

is more commonly indirect shrimp aid such as scientific support related to feed, disease

prevention, shrimp farm operation, and best practices.

4.7 Correlations

A correlation matrix between all measures of deforestation and all the above listed

measures of aid was then calculated for Ecuador at annual intervals using the standard Pearson

product moment correlation coefficient below.

30

where

n = observation year

x = aid

y = mangrove deforestation rate for that year.

Within the analysis n is the number of observations as longitudinal data points. For

example n1973 is the longitudinal data point of 1973. The aid values are the independent

variables x. Therefore, in the correlation matrix the average of all aid to Ecuador for 1969, 1970,

and 1971 would be represented by the 1973|3 record or x1973. This is a moving aid average (USD

2000) of the three years preceding 1972. The dependent variable in this example is y1973. This is

the percentage of mangrove deforested in Ecuador during 1973 for all mangrove forests. The

baseline for the percentage change in y is the amount of mangrove forest existing in the previous

year, as derived from the previous longitudinal data point. Mangrove deforestation between

longitudinal data points is assumed to be linear.

The correlations are then repeated for the data with Mataje-Cayapas removed from the

analyses. Mataje-Cayapas has local conditions that have prevented significant foreign

investment in shrimp farming from 1970 to present. It is only in the past ten years that northern

31

Esmeraldas Province has become economically integrated with the rest of Ecuador. The lack of

roads into the province until the late 1990s would have made the import of feed, heavy

equipment, and larvae difficult. The refrigerated export and storage of shrimp would also have

been difficult with no road connection to other parts of Ecuador. This area has other geopolitical

security reasons related to its border location that would also make infrastructure investment

unlikely.

It was anticipated that higher correlation coefficients between aid and mangrove

deforestation would occur when examining aid from donors that traditionally support

aquaculture and countries that traditionally consume and import aquaculture products, as

compared with the correlation between total aid and mangrove deforestaion. It was also

expected that lower correlation coefficients between aid and mangrove deforestation would be

found when considering aid from donors that do not traditionally import aquaculture products.

Higher correlation coefficients were expected to be obtained from multilateral donors than from

bilateral donors. Higher correlation coefficients were also expected when Mataje-Cayapas was

excluded from the analysis than when it was included. Finally, it was anticipated that agro-

industrial and fisheries aid would be more strongly correlated with mangrove deforestaion the

sum of all aid.

Four steps were taken to correct for the potential of temporal auto-correlation. First, all

dollar values within the correlation matrix are adjusted to year 2000 USD. This adjustment

accounts for the fact that mangrove deforestation longitudinally increases throughout the study

and appears to very strongly correlate with increasing total aid, when in reality, much of the

increase in aid is merely due to inflation. Secondly, a correlation of aid from donors unlikely to

have any role in aquaculture development is correlated to mangrove deforestation. However,

32

adjusted for inflation, little or no correlation would be expected between these variables.

Therefore, the normalization to 2000 USD allows for increased confidence in relationships

established between donors that support aquaculture and deforestation. Thirdly, correlations are

conducted as independent observations on a year-to-year basis. This control negates the

potential of aid increasing and mangrove deforestation increasing from its baseline level on

similar temporal scales and this appearing as a correlation within the matrix. It enforces on y the

condition that the actual estuary cross-section is independent longitudinally even when the

longitudinal data point is located in the same estuary. Therefore, n is merely year and not

year/estuary. Finally, the r-squared value for the correlation between time elapsed and total aid

was calculated. Assuming mangrove deforestation increases over time and assuming aid

increases over time, then a correlation between the two may be due to the longitudinal nature of

the study as opposed to a relation between aid and mangrove deforestation. This maximum r-

squared value of time and aid to Ecuador is .1, reflecting that aid to Ecuador is weakly related to

year of analysis.

5. RESULTS

Landuse change results are presented in map format.

Figure 14.1 = Chone Estuary, Mangrove and Shrimp Farm Longitudinal Changes Map.

Figure 14.2 = Chone Estuary, Mangrove and Shrimp Farm Displacement Type Map.

Figure 15.1 = Mataje-Cayapas, Mangrove and Shrimp Farm Longitudinal Changes Map.

33

Figure 15.2 = Mataje-Cayapas, Mangrove and Shrimp Farm Displacement Type Map

Figure 16.1 = Rio Hondo Estuary, Mangrove and Shrimp Farm Longitudinal Changes Map.

Figure 16.2 = Rio Hondo Estuary, Mangrove and Shrimp Farm Displacement Type Map.

Figure 17.1 = Grande Estuary, Mangrove and Shrimp Farm Longitudinal Changes Map.

Figure 17.2 = Grande Estuary, Mangrove and Shrimp Farm Displacement Type Map.

FIGURE 14

FIGURE 15

FIGURE 16

FIGURE 17

5.1 Chone Delta

The Chone Delta baseline mangrove level was 42,377,182 m2 of mangrove in 1968. By

2006 only 14,654,255 m2 of mangrove remained. The mangrove area dropped as low as

10,354,875 in 2001. During a field visit to this area in 2003, a group of local fisherman

explained how artisanal fisherman had been replanting mangrove in their traditional fishing

grounds over the last 3 years. This re-growth around Isla Corazon in the center of the study area

is now maturing and clearly visible on satellite imagery. Without this replanting, only 24% of

the baseline mangrove forest would have remained in the estuary. Even with this replanting only

34

35% of the baseline mangrove remains. During the analysis period, shrimp farm acreage

increased from 0m2 to 51,919,128m2. By 2006, the shrimp farm area of the Chone estuary was

354% of mangrove area. Hence by 2006, shrimp farms actually cover more of the estuary than

all other landcover combined, including water within the estuary.

35

Year Mangrove m2 Shrimp m2 Other m2 Total m2 1968 42377182 0 45060782 87437964 1977 38504938 3316503 45616523 87437964 1984 21714748 37391428 28331788 87437964 1986 19960580 36576975 30900409 87437964 1991 11630191 49131153 26676620 87437964 1998 10559916 50599127 26238921 87397964 2001 10354876 51173336 25909752 87437964 2004 13395938 50570559 23471467 87437964 2006 14654255 51919128 20864611 87437994

5.2 Mataje-Cayapas

Mataje-Cayapas was the exception when compared with other areas. Until the very end

of the study period, it showed little or no mangrove loss or shrimp farm growth. This may be

due to the relative isolation of Mataje-Cayapas and the lack of an overland transportation route to

the port of Guayaquil for much of the study period. Mataje-Cayapas is listed a preserve within

Ecuador, and this may have contributed to its lack of shrimp farm growth, but Chone is also

listed as a protected area and the results are inverse of Mataje-Cayapas. Within Mataje-Cayapas,

mangrove forest remained relatively constant, dropping only 8% during the study period. As of

2008, shrimp farm coverage remains below 10% of the mangrove area. The trend from 1997 to

present represents increased shrimp farm growth, although not yet at the expense of mangrove

forests. The increase in shrimp farm growth may be due to the opening of the first secure

overland route between the rest of Ecuador and Mataje-Cayapas around 1997. Recent USAID

documents (United States Agency for International Development 2003) point to this area as

36

currently receiving aquaculture aid and the recent growth of shrimp farms may reflect this

pattern.

Year Mangrove m2 Shrimp m2 Other m2 Total m2 1986 351176117 268832 155699555 507144504 1991 347557595 3953072 155633827 507144494 1997 337196053 14248896 155699555 507144504 2001 326951039 24493921 155699555 507144515 2008 323444109 28000860 155699555 507144524

5.3 Rio Hondo

Rio Hondo experienced substantial shrimp farm gains, yet mangrove losses are not at the

level anticipated from such aquaculture growth. This appears to be a case of shrimp aquaculture

occupying space outside of but adjacent to mangrove forest (Diana 2009). Although the

mangrove forest remains intact, it has still lost 16% of its total area. Shrimp farming has

increased from 0m2 to 121,213,127m2 during the study period, indicating a heavy level of

investment in shrimp aquaculture.

Year Mangrove m2 Shrimp m2 Other m2 Total m2 1973 99358069 0 213598655 312956724 1985 99480043 27913983 185562698 312956724 1990 105453162 75786981 131716582 312956725 1997 93129610 100765563 119061551 312956724 2000 89600063 109248017 114108644 312956724 2006 88217931 121213127 103525666 312956724

37

5.4 Grande Estuary

Grande Estuary experienced a 47% loss of mangrove from baseline to 2006. During this

time, shrimp aquaculture expanded from 0m2 to 68,726,632m2. By 2006, shrimp farm area was

148% of the remaining mangrove area. Similar to Chone, Grande Estuary now has more area in

shrimp farms than water within the estuary.

Year Mangrove m2 Shrimp m2 Other m2 Total m2 1968 87808362 0 88111120 175919482 1985 75172130 39965184 60782168 175919482 1990 63290338 52983727 59645417 175919482 2006 46592557 68726632 60600293 175919482

5.5 Overall Change in Ecuador

The rate and amount of mangrove loss within Ecuador as a whole are shown in Figure 18.

Results for all study areas combined and all study areas excluding Mataje-Cayapas are included.

Over the entire study period, mangrove loss averages approximately 1% per year with a

maximum rate of 1.71% annually. Excluding Mataje-Cayapas, the peak is 2.25% with an

average rate slightly above 1%. Although somewhat moderated by the conservative rates of

mangrove loss in Mataje-Cayapas, the overall national rate of mangrove loss still reaches 18% of

the baseline. Excluding Mataje-Cayapas, the mangrove loss rate is 35% of the baseline.

FIGURE 18

38

5.6 Correlations

Correlation Matrix.

x1 - x8 MD% (y1) MDES% (y2)

ALL 3/4/5 n

-.311 -.285 -.234 .107 .187 .259

33 33 33 33 33 33

DSA 3|4|5 n

-.209 -.226 -.235 .301 .328 .321

33 33 33 33 33 33

DIA 3|4|5 n

-.397* -.455* -.456* .050 -.026 -.075

34 34 33 34 34 33

DNIA 3|4|5 n

-.384 -.441* -.431* .050 -.026 -.075

30 29 28 30 29 28

MLT 3|4|5 n

-.239 -.211 -.134 0.46 .161 .293

33 33 33 33 33 3

BLT 3|4|5 n

-419* -.477** -.541** -.006 -.095 -104

30 29 29 30 29 29

AGRO 3|4|5 n

-0.17 -0.42 .074 .433* .504* .543**

33 33 33 33 33 33

FISH 3|4|5 n

.652** .777*` .694* .396* .463* .338

28 28 29 28 28 29

MD% - Mangrove deforestation percentage as a percentage of previous year forest

coverage.

MDES% = MD% excluding Mataje-Cayapas.

ALL = All Aid

DSA = Donors that traditionally support aquaculture.

DIA = Donors within the top 9 of consumption of imported aquaculture.

DNIA = Donors outside the top 9 of consumption of imported aquaculture.

39

MLT = Multilateral donors

BLT = Bilateral donors.

AGRO =Agro-industrial aid

FISH = Fisheries aid

n = Number of years in correlation.

3|4|5 = Indicates 3 minus 1, 4 minus 1, or 5 minus 1-year average aid lag.

* Statistically significant at .005 level (2-tailed)

** Statistically significant at .001 level (2-tailed)

No relationships between international aid and deforestation in Ecuador are present when

examining the variables of all aid into Ecuador, all aid from donors that traditionally support

aquaculture, or multilateral donors. A moderate inverse relationship exists when bilateral aid is

examined for all study areas. A weak to moderate relationship between agro-industrial aid and

mangrove deforestation is observed when Mataje-Cayapas is excluded. A moderate to strong

relationship is observed between aid coded as aid to fisheries and mangrove deforestation, with

and without Mataje-Cayapas included, respectively.

The hypothesis that multilateral aid has a strong relationship to deforestation is not

supported. The hypothesis that bilateral aid would have a weaker relationship to deforestation

than multilateral aid is evinced in the matrix, but the lack of significance in the bilateral aid

correlations causes this relationship to remain unsubstantiated by the data. Hypotheses that aid

from donors that support aquaculture or donors that import aquaculture products would have

40

higher correlations to mangrove deforestation also remain unsubstantiated. Indeed, aid from

donors that not import aquaculture and aid from donors that do actually have nearly identical

negative correlations with mangrove deforestation. Some of these correlations are also

significant. This may be a facet of both of these populations being a subset of bilateral donors,

and the fact that bilateral donors have a moderate significant inverse relationship to mangrove

deforestation.

Therefore, the only hypotheses about relationships between specific aid categories and

mangrove deforestation that appear to be substantiated at any significant level are the

relationships involving aid classified as agro-industrial and aid classified as fisheries. The agro-

industrial relationship is only present when Mataje-Cayapas is removed from the matrix and

even then, the relationship is only moderate. Correlation between fisheries aid and mangrove

deforestation is weak to moderate when Mataje-Cayapas is removed from the analysis, with two

of the three results showing significance. When the entire study area is examined, fisheries aid is

moderately to strongly correlated to mangrove deforestation, with significance at all levels.

Slightly over half of the loss of mangrove forest in Ecuador on a year-to-year basis can be

explained by its relationship to fisheries aid (Figure 19).

FIGURE 19

If the claims by Public Citizen and other researchers that shrimp farming is supported by

foreign aid are true, then it is in the fisheries aid sector that the most likely relationship between

aquaculture aid and mangrove deforestation would likely be found. This analysis indeed shows a

41

positive, significant, and strong correlation between fisheries-specific aid and mangrove

deforestation within Ecuador.

Examining fisheries aid into Ecuador is the most detailed level of analysis possible for

extracting aquaculture aid information from the PLAID database. Although aquaculture is an

activity code level of PLAID code falling within the fisheries sector, less than 8% of the

Ecuadorian records have this specific a level of coding. Projects in the PLAID database do have

long descriptions, in addition to codes, but again the percentage of entries for aid to Ecuador that

have meaningful long descriptions is too small to allow for extraction of aquaculture-specific

information. As PLAID continues to develop its database with more long descriptions added, the

number of donors expanded, and increased ability to parse out specific aid projects information

at the activity level, then the potential for enhanced results will increase.

6. DISCUSSION

Utilizing GIS in conjunction with aid databases such as PLAID allows for the

development of geospatial methods of aid analysis that have the potential to contribute to landuse

and aid theory. Although limited in the quantity of temporal data sources and cross-sectional

references, this paper does present a framework for integrating GIS and international aid data to

examine pathways between international aid and mangrove deforestation. In this methodology,

aid is the independent variable used to explain changes in the dependent variable of mangrove

loss over a period of almost forty years, encompassing four major estuaries within Ecuador.

While international funding of shrimp farms (almost all of Ecuador's aquaculture activity) itself

may not result in negative returns to local populations, shrimp farming in Ecuador has

42

historically resulted in mangrove deforestation that in turn may cause regional hardships. When

changes, such as mangrove deforestation, that can produce negative socio-economic outcomes

are indirectly promoted by activities funded through development aid, then a potential pathway

from development aid to localized economic hardship exists.

Although not depicting causation, the positive and significant correlation between

fisheries aid and mangrove deforestation as well as between agro-industrial aid and mangrove

deforestation requires further research and exploration. These results do require further

verification within and outside of Ecuador. Widening the study beyond Ecuador to account for

specific conditions in other localities would allow for more confidence in the correlations.

Measures of regional isolation and the development of other variables affecting aid deployment,

such as the presence of an estuaries investment climate or a protected status, would allow for a

hedonic model that explains the relationship between aid and mangrove deforestation beyond a

mere correlation analysis. The use of more years of remote sensing data would also add an extra

level of confidence to these findings. Additionally, increasing the resolution of aid databases

would allow for more confidence in the aid designations and the regional appropriation of

activity specific aid.

Finally, despite the appearance of a potential relationship between aquaculture-specific

aid and mangrove deforestation within Ecuador, it should be noted that the majority of the

anecdotal evidence on the relationship between aquaculture donors and mangrove deforestation

from groups such as Public Citizen and the research community is not present in this limited

analysis. Furthermore, the full story of international aid and its relationship to mangrove

deforestation through investment in commercial aquaculture may never be fully answered.

Efforts to increase the detail level of databases such as PLAID by including all donors,

43

identifying the exact purpose of each donation, and adding long descriptions that allow for

regionalization of aid commitments continue. However, the regionally housed historic aerial

photographs and topographic maps containing pre-1980 landuse data are becoming more

difficult to obtain with each new aerial survey or topographic map creation.

44

Figure 1 - Ecuadorian Aquaculture Output (1970 - 2007), t.

Figure 2 - Ecuadorian Aquaculture Output (1970 - 2007), USD 2000.

0

50000

100000

150000

200000

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

Ecuador Aquaculture Output (t)

Production (t)

Trend

0

200,000,000

400,000,000

600,000,000

800,000,000

1,000,000,000

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

Ecuador Aquaculture Output ($)

Production ($)

Trend

45

Figure 3 - Aquaculture Support Summary.

1977 to 1983 (Nash 1987)

US Dollars

Percent of Total Funding

Total Assistance from Development Banks $ 189,894,000 51.5 UN System and Trust Funds $ 37,141,000 10.1

Total Assistance from all Multilateral Sources $ 227,035,000 61.6 Total Assistance from Bilateral Sources $ 95,173,000 25.9 Other Donors $ 45,859,000 12.5 Total External Assistance to Aquaculture $ 368,067,000 100

1988 to 1995 (Shehadeh and Orzeszko 1997)

1997 US Dollars

Percent of Total Funding

Development Banks $ 686,550,000 69 Bilateral Sources $ 169,150,000 17 Multilateral Sources $ 69,650,000 7 Total Official Aid between 1988 and 1995 (1997 USD) $ 995,000,000 100

46

Figure 4 - Total Aid Commitments to Ecuador 1970 - 2006 (USD 2000).

Year Commitments (USD 2000) 1970 212087822 1971 129035200 1972 192428350 1973 356248446 1974 474602362 1975 234818100 1976 485697699 1977 429646903 1978 319844285 1979 694423156 1980 392179888 1981 634033786 1982 547315405 1983 486874444 1984 585091056 1985 953366212 1986 1008525454 1987 862899518 1988 529646444 1989 680672144 1990 943506806 1991 547326340 1992 580913437 1993 277325593 1994 1451399023 1995 353985113 1996 524958336 1997 587945129 1998 420599916 1999 273931581 2000 893498148 2001 1161816713 2002 791527838 2003 1156120169 2004 409809630 2005 1110854470 2006 1703683479

47

Figure 5 - Fisheries and Agro-industrial Commitments to Ecuador 1970 - 2006 (USD 2000).

Year Commitments (USD 2000) 1970 62577835 1972 223489 1973 181895946 1974 164166 1975 240056 1976 76328818 1977 134445319 1978 43949447 1979 12663109 1980 1109036 1981 7282600 1982 16548814 1983 5678860 1984 104408903 1985 365788361 1986 164746017 1987 328706371 1988 126117381 1989 54134781 1990 10515977 1991 20943142 1992 25063096 1993 30042340 1994 147959720 1995 50488724 1996 37321232 1997 150428772 1998 35799626 1999 5979695 2000 7921163 2001 17867088 2002 23377500 2003 23050587 2004 25367553 2005 21469678 2006 9452848

48

Figure 6 - Donor Commitments > $1,000,000, 1970 - 2006 (USD 2000).

Donor Commitments (USD 2000) IADB 8286225271 CAF 5013572354 IBRD 4776345799 USA 1625837578 IMF 1431113892 JPN 1096049034 ESP 637685205 DEU 503564257 ITA 346767183 FRA 286239607 EC 276634677 IFC 206991612 BEL 206480584 CHE 185655520 GBR 177478622 NLD 171506864 CAN 106352599 IDA 93725922 IFAD 83877530 GEF 56284541 SWE 34064343 GFATM 26757878 NOR 24217355 DNK 14048642 UNICEF 10343239 LUX 9333750 FIN 9029516 UNDP 8381723 UNFPA 7384192 KOR 7358662 AUT 3301556 AUS 1527636 IRL 1005825

49

Figure 7 - Abandoned Shrimp Farms, Chone, Ecuador.

50

Figure 8 - Ecuador Provinces.

51

Figure 9 - Study Sites2

2 In this paper, San Lorenzo is named Mataje-Cayapas, Bahia is named Chone Estuary, Guayas is named Rio Hondo Estuary and located slightly to the south of this location, and El Oro is named Grande Estuary.

52

Figure 10 - Chone Estuary.

53

Figure 11 - Mataje-Cayapas.

54

Figure 12 - Rio Hondo Estuary.

55

Figure 13 - Grande Estuary.

56

Figure 14.1 = Chone Estuary, Mangrove and Shrimp Farm Longitudinal Changes Map.

57

Figure 14.2 = Chone Estuary, Mangrove and Shrimp Farm Displacement Type Map.

58

Figure 15.1 = Mataje-Cayapas, Mangrove and Shrimp Farm Longitudinal Changes Map.

59

Figure 15.2 = Mataje-Cayapas, Mangrove and Shrimp Farm Displacement Type Map

60

Figure 16.1 = Rio Hondo Estuary, Mangrove and Shrimp Farm Longitudinal Changes Map.

61

Figure 16.2 = Rio Hondo Estuary, Mangrove and Shrimp Farm Displacement Type Map.

62

Figure 17.1 = Grande Estuary, Mangrove and Shrimp Farm Longitudinal Changes Map.

63

Figure 17.2 = Grande Estuary, Mangrove and Shrimp Farm Displacement Type Map.

64

Figure 18 - Annual Mangrove Loss.

Year Mang Loss

Per Year Mang Loss

Per Year % Mang Loss Per Year

(minus Mataje) Mang Loss Per Year

(minus Mataje) % 1973 1139663 0.5086 1139663 0.5086

1974 1137122 0.5100 1137122 0.5100

1975 1137122 0.5126 1137122 0.5126

1976 1137122 0.5153 1137122 0.5153

1977 1330049 0.6058 1330049 0.6058

1978 3452251 1.5820 3452251 1.5820

1979 3452251 1.6074 3452251 1.6074

1980 3452251 1.6337 3452251 1.6337

1981 3452251 1.6608 3452251 1.6608

1982 3452251 1.6889 3452251 1.6889

1983 3452251 1.7179 3452251 1.7179

1984 1698560 0.8600 1698560 0.8600

1985 1893318 0.9669 1893318 0.9669

1986 3193804 0.5859 2653726 1.3685

1987 3515935 0.6488 2867842 1.4994

1988 3515935 0.6531 2867842 1.5223

1989 3515935 0.6574 2867842 1.5458

1990 4774059 0.8985 4125966 2.2589

1991 4177081 0.7933 3233884 1.8114

1992 4681155 0.8961 2852648 1.6273

1993 4681155 0.9042 2852648 1.6542

1994 4681155 0.9124 2852648 1.6821

1995 4681155 0.9208 2852648 1.7108

1996 4681155 0.9294 2852648 1.7406

1997 4635738 0.9290 2348007 1.4581

1998 4699629 0.9507 2083880 1.3132

1999 4640599 0.9477 2024851 1.2930

2000 4640599 0.9568 2024851 1.3099

2001 2180855 0.4540 970921 0.6365

2002 512904 0.1073 5878 0.0039

2003 512904 0.1074 5878 0.0039

2004 972228 0.2037 465202 0.3069

2005 1125337 0.2363 618311 0.4092

2006 1527998 0.3216 1020972 0.6785

65

Figure 19 - Mangrove Deforestation / Fisheries Aid Scatter Plot.

66

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