Mapping Tourism Pressure in the Sunda-Banda Seascape (Indonesia) MPA Network and Implications for MPA Management

FINAL

By Tamera Husseini

Supervised by David Gill (Duke University), Dominic Andradi Brown (WWF-US) and Gabby Ahmadia (WWF-US)

April 2020

Masters project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University

Executive Summary

In the Indonesian archipelago – an epicenter of marine tropical biodiversity – local communities rely on marine resources to provide food and support livelihoods. The government has implemented an extensive network of Marine Protected Areas (MPAs) to protect its marine resources. As tourism increasingly plays a role in Indonesia’s economy, the relationships between tourism, local communities and conservation is becoming increasingly relevant for effective MPA management. The WWF works closely with managers in Indonesia’s Sunda-Banda Seascape (SBS) MPA-network to support ecological and social monitoring efforts. Using location data sourced online, this study spatially mapped the distribution of tourism activities (dive centers, dive sites, hotels, homestays, liveaboards, and sea/air ports) in and around 10 MPAs in the SBS and modeled tourism pressure using economic “gravity” theory. Generalised linear mixed-effects models were then used to explore the effects of tourism pressure on fish biomass data collected by WWF. The results of this study aim to provide insight into the intersection between tourism and MPA management, particularly in light of communities potentially shifting from fisheries-based to tourism-based livelihoods.

1. Introduction

1.1 Project Background

Located within the renowned Coral Triangle, the Indonesian archipelago is an epicenter of marine tropical biodiversity (Allen, 2008; Allen & Werner, 2002; Roberts, 2002). More than 2,100 species of reef fish (Allen & Werner, 2002) and more than 500 coral species (Veron et al., 2009) can be found within Indonesia’s waters. Marine ecosystems and biodiversity provide ecosystem services, food and economic resources to local communities. The Indonesian government has made protecting the nation’s marine resources a high priority, utilizing Marine Protected Areas (MPAs) as a key conservation strategy. In conjunction, the government has integrated its coastal and marine resources into its efforts to boost economic growth through tourism development.

In the Sunda-Banda Seascape, an ecoregion located in Southeast Indonesia (Wang et al., 2015), the World Wildlife Fund (WWF), together with other local and international actors, support the government’s conservation and management efforts by conducting annual social and ecological monitoring of the region’s MPA-network. These data are used to help inform adaptive decision-making around MPA management. With WWF’s support, this study aims to use that data to better understand the implications of tourism for marine management by modelling the effects of tourism pressure on ecological conditions in the SBS MPA-network.

1.2 Social and Political Drivers

Local communities across Indonesia rely on marine resources to provide food, livelihood and ecosystem services. Indonesian coral reefs provide an estimated economic benefit of $1.6 billion per year (Burke et al., 2012), yet, across the region, the marine environment is faced with threats from overfishing, destructive fishing practices, coastal pollution and climate change (Burke et al., 2012). To protect its marine resources, the government has developed policies and institutions to promote the implementation and expansion of MPA networks across the nation (Muawanah et al., 2018). In 2009, Indonesia announced a target of 20 million ha of MPA area by 2020 (Yudhoyono, 2009). In recent years the government has shifted the focus MP Draft Mas

of its objectives from biodiversity conservation to an emphasis on sustainable fisheries management (Setyawan et al. 2017; Muawanah et al., 2018).

The ecological benefits of MPAs are well studied in the literature. Studies have shown that MPAs with strong protections that prohibit commercial activity (i.e. no-take) increase fish biomass and diversity (Lester & Halpern, 2008; Edgar et al., 2014; Sala & Giakoumi, 2017). Evidence also shows that adequately protected MPAs can promote spill-over effects, whereby larvae are dispersed to areas outside of the MPA (Goni et al., 2008; Harrison, 2012). Despite the clear ecological benefits of MPAs, concerns exist about the social implications of MPAs, which are less well understood. Managers must also consider the needs of communities who rely on those resources for food and income to avoid negatively impacting social-wellbeing (Charles & Wilson, 2008; Sowman & Sunde, 2018; Stevenson et al. 2013).

Tourism is becoming a greater consideration for the management of MPAs throughout Indonesia (Kurniawan et al., 2016; Kinseng et al., 2018). Since the expansion of international tourism during the latter part of the twentieth century, Indonesia has situated itself as a global tourist destination. Indonesia’s current political regime has committed to reinvigorating growth in the tourism sector (Guild, 2019) and aims to have tourism represent 8% of the nation’s overall GDP by 2020 (Invest Islands, 2018). As a result, the number of foreign visitors has increased from 9.4 million in 2014 to 15.81 million in 2018 (BPS, 2019). In 2016, the President announced 10 New Bali’s Project, an initiative that intends to replicate the economic effects of Bali in 10 new tourist hubs (Invest Islands, 2018).

Indonesia’s attractive coastal landscapes and unique wealth of marine biodiversity offer potential for marine tourism development which therefore an integral part of the country’s plans for tourism development (The Jakarta Post, 2017). Marine tourism includes a suite of leisure, recreation and tourism activities that take place in coastal and offshore zones as well as associated development and supporting infrastructure (Orams 1999; Hall 2001). It creates opportunity for sustainable economic alternatives for local communities such scuba diving, snorkeling, recreational fishing, and yachting operations as well as through the hotel industry (Orams, 1999, Borches, 2009).

When adequately planned, managed and aligned with appropriate principles, tourism can provide benefits for marine conservation as well (Agardy, 1993). Tourism grounded in conservation principles can encourage a switch from consumptive to non-consumptive economic use of resources (Wilson & Tisdell, 2003) and raise environmental awareness and

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education (Diedrich, 2007). It can also enable self-financing mechanisms for conservation by generating sufficient revenue to cover operational and managerial costs (Borches, 2008). Spalding et al. (2017) found that 30% of the World’s reefs are of value in the tourism sector, estimating a total value of US$ 36 billion. Paradoxically, the economic success of tourism relies on environmental integrity satisfying tourist needs, eventhough tourism may itself be the cause of environmental degradation (Holden, 2008). Therefore, tourism can provide motivation and incentivization for conservation and proper management.

When left unchecked, however, tourism can have negative consequences for the marine environment. An influx of marine tourism can introduce new threats as well as increase and compound existing threats. Tourism can lead to increased demand on resources, physical damage to coral reefs caused by boat anchors (Lloret et al., 2008) or reckless tourists and divers (e.g. Hawkins et al., 1999; Davis & Tisdell, 1995), disturbance to wildlife (e.g. Lamb et al., 2014; Giglio et al., 2017), and increased pollution (e.g Edinger et al.,1995). Although trade- offs exist, proper management can allow for both conservation and use benefits (Dixon et al., 1993; Lopes et al., 2015).

Much like terrestrial national parks, MPAs commonly allow for tourism and conservation to be linked. The social and environmental implications of tourism in and around MPAs are increasingly being studied in the literature (e.g. Miller 1993; Petrosillo et al.2007; Fabinyi, 2008). Some studies have examined tourism around MPAs in Indonesia from both ecological and social perspectives (e.g. Kurniawan et al. 2016; Salm, 2007). In the Sunda-Banda Seascape (SBS), tourism has not been extensively studied. The ecological and social monitoring work conducted by WWF offers a unique opportunity to study the effects of tourism pressure on the ecological conditions in MPAs across the seascape.

1.3 Aims and Objectives

To explore how tourism can affect ecological conditions in MPAs, this study aims to spatially model tourism pressure in and around MPAs in the SBS using a tourism “gravity” metric based on the methods used by Cinner et al. (2018) and Threlfall (2018). Cinner et al. (2018) used this novel approach to study the effects of human pressure on coral reef fish both in- and outside of MPAs.

The concept of gravity is an established economic principle that models the interaction between two places as a function of mass (e.g. population size) and distance, where mass is positively

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related and distance is inversely related (Anderson, 2010). Cinner et al. (2018) conceptualized that the interaction between human markets and coral reef fish as a function of human population size divided by the time it takes to travel to the reef squared. Their results revealed that fish biomass inside protected ares declined along a gradient of human impacts in surrounding areas. A subsequent master’s thesis by Threlfall (2018) adapted Cinner et al.’s methods to produce a tourism gravity metric for Indonesia’s Bird’s Head Seascape (BHS) MPA-network, located to the north of the SBS.

In the SBS, local communities attracted by a more lucrative source of income are expected to shift from fisheries-based to tourism-based economy. As a result, fisheries are expected to experience reduced fishing pressure. Additionally, tourism may be providing a greater incentive for better resource protection. In this case, fish biomass is expected to increase in areas with higher tourism gravity. Alternatively, it is possible that the negative impacts of tourism pressure are exceeding the ecological and infrastructural capacity in and around MPAs and therefore fish biomass is decreasing in areas with high tourism gravity.

The objectives of this study are to: (1) geospatially map tourism activities (dive centers, dive sites, hotels, homestays, liveaboards and entry ports) in the SBS, (2) derive a tourism gravity layer to indicate tourism pressure around SBS MPAs and (3) use a mixed-model to statistically analyse the relationship between tourism pressure and ecological indicator data collected by WWF.

2. Methods

2.1 Study Site

The SBS is an ecologically distinct region in South-East Indonesia, at the center of the Coral Triangle (Wang et al. 2015)(Fig. 1). The region has been identified by the government as a marine conservation priority (Setyawan et al. 2017). It encompasses 151 million ha, includes approximately 5,000 islands and extends across seven provinces. Throughout the seven provinces 36% of all villages are located in coastal area (Setyawan et al. 2017). More than 2.4 million households in the SBS rely on access to marine resources for their livelihood and needs

(Burke, 2012). The SBS-network of MPAs consists of 85 MPAs1, covering a total area of 9.6 million ha (Setyawan et al., 2017). The MPAs vary in the in the level of management capacity and only a few MPAs have implemented zoning strategies (no-take zones vs. multi-use zones) (Setyawan et al., 2017). The MPAs used in this study are identified in Table 1.

Figure 1. The Sunda Banda Seascape (SBS) region, located in Central and Eastern Indonesia (Source: Setyawan et al., 2017).

Table 1. Name, area and province of the MPA’s that were used for this study.

MPA Area (m2) Province 1 Teluk Lasolo 81800 Southeast Sulawesi

1 As of December 2017. MP Draft Mas

2 Kepulauan Wakatobi 139000 Southeast Sulawesi 3 KKPD Sulawesi Tenggara 150000 Southeast Sulawesi 4 Kabupaten Seram Bagian Timur 9901 Maluku (Koon) 5 Kabupaten Maluku Tenggara (Kei 150000 Maluku Kecil) 6 Maluku Tenggara Barat (Yamdena) 783800 Maluku 7 Selat Pantar Alor (Alor) 276600 East Nusa Tenggara 8 Flores Timur 150000 East Nusa Tenggara 9 Komodo National Park 173300 East Nusa Tenggara 10 Teman Laut Banda 2500 Maluku

2.2 Tourism Data Collection

Methods for collecting tourism data were adapted from Threlfall (2018). To map tourism in the SBS, the locations of six tourism variables – ports of entry, dive centers, dive sites, hotels, homestays, and liveaboards – were extracted through systematic online searches. Locations were mapped and saved as point shapefiles in ArcGIS Pro v.2.4.3 using the WGS 1984 Geographic Coordinate System and projected in Albers South Asia Equal Area Conic. Given

the expanse of the SBS, data collection was limited to within 35km around MPA boundaries. 2

Ports of entry (airports and seaports) were identified and their GPS coordinates were extracted using Google Maps. Dive centers were located through systematic web searches and their locations extracted through Google Maps. Dive site locations were less readily available, therefore, in order to extract their locations, map images were sourced and digitised using Arc GIS Pro. Maps were sourced primarily from dive center websites as well as from Wormald (2015). In addition, all dive centers found during data collection were contacted directly via email to request information on dive site locations. Only a few dive centers responded with

information. The locations of hotels and homestays3 were pulled from interactive maps found on hotel search engine sites, TripAdvisor.com and Booking.com, as well as Google Earth.

2 This buffer distance was based on the median distance between dive centers and dive sites in Komodo, Wakatobi and Alor found during the initial stages during data collection. It was assumed that any tourism activity beyond this distance would have a negligible effect on the MPA of interest. 3 Homestays are a form of accommodation offered by locals who open their homes to visitors

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Liveaboards4 were found through systematic online searches; however, because they are not fixed to a single location, they were recorded based on visitation of MPAs.

2.3 Ecological Data Collection

From 2012 to 2017, WWF and their partners collected ecological monitoring data within the MPAs at the sites shown in Fig. 2, with the exception of Teman Laut Banda. At each monitoring site, fish were surveyed using underwater visual survey methods at 3m and 5m depths along five replicate 50 m belt transects. The number of individuals counted per site were aggregated and converted to mean density (kg/ha). Ecological monitoring data for Teman Laut Banda was provided by the Coral Triangle Center. The sites were surveyed by the Coral Triangle Center in 2012 and 2015- 2018 using a similar protocol as other sites.

Figure 2. Locations of ecological monitoring sites. Key: (1) Komodo National Park (2) Flores Timur (3) Selat Pantar Alor (4) Teluk Lasolo (5) Sulawesi Tenggara (6) Kepulauan Wakatobi (7) Teman Laut Banda (8) Kabupaten Seram Bagian Timur (Koon) (9) Kabupaten Maluku Tenggara (Kei Kecil) (10) Maluku Tenggara Barat (Yamdena)

4 Liveaboards are chartered boats or yachts that provide over-night boarding and usually offer scuba diving. Liveaboards in the SBS typically operate throughout the regions, stopping-off at various MPAs although some may stay within a single MPA.

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2.4 Tourism Gravity Analysis

Tourism pressure was spatially modelled by adapting the economic theory of “gravity” based on methods used by Cinner et al. (2019) and Threlfall (2018). The gravity theory draws on the analogy of Newton’s Law of Gravitation to illustrate how market flows are a function of size and distance to market (Anderson, 2010). The theory states that a mass of goods supplied at an origin, i, is attracted to a mass of demand for goods at destination, j, but the potential flow is reduced by the distance between them, such that:

푀푎푠푠푖 × 푀푎푠푠푗 퐺푟푎푣푖푡푦 = 2 퐷푖푠푡푎푛푐푒푖푗

For the purposes of modelling tourism gravity, the formula was modified such that:

푇표푢푟푖푠푚 "푀푎푠푠" 푇표푢푟푖푠푚 퐺푟푎푣푖푡푦 = 퐷푖푠푡푎푛푐푒2

A “mass” was derived for each tourism variable type based on the total number of variable points found during data collection. Mass was calculated as the proportion of one tourism point out of the total number of variable points in the region. In other words, one divided by the total number of points in the SBS, multiplied by 100 to give a percentage. For example, a total of 56 dive centers was found in the SBS, so the mass for a single dive center is calculated as 1/56*100 which is 1.8. Each tourism variable point was assigned a mass as shown in Table 2. Because liveaboard data did not have a spatial component, the mass for liveaboards was calculated differently. The number of liveaboards entering each MPA was divided by the total number of all liveaboards operating in the region and given as a percentage. This percentage was applied uniformly across the MPA.

Table 2. “Mass” of each tourism variable at each location

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Tourism Variable Total Mass Dive Center 54 1.8 Dive Sites 270 0.4 Hotels 194 0.5 Homestays 46 2.2 Ports 33 2.5 Liveaboards 167 -

Using a function coded in R (R Core Team, 2019), gravity was calculated using the Tourism Gravity formula for each given tourism variable. The function first applies a raster grid layer with 1.5 x1.5 km cells to each MPA. The total mass within each grid cell is then summed based on the number of points within the cell. The function then iterates through each cell and uses the Tourism Gravity formula to calculate the gravity of each cell. Finally, the gravity of all the cells were combined additively to produce a single gravity layer for a given tourism variable. This process was repeated for each tourism variable. Each tourism variable gravity layer was weighted using a relative multiplier, shown in Table 3. Multipliers were chosen based on the relative expected impact of each variable. The weighted gravity layers were combined additively to give a final gravity layer for each MPA. For example, hotels are expected to hold more guests and therefore have a greater impact than homestays. The gravity values at each monitoring site location (Fig. 2) were extract from the final gravity layer (Appendix A).

Table 3 Multipliers used on each tourism variable gravity layer

Tourism Variable Weight Dive Center 20 Dive Sites 10 Hotels 15 Homestays 10 Ports 20 Liveaboards 10

2.5 Statistical Analysis

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To test the relationship between tourism gravity (explanatory variable) and fish biomass (response variable) at monitoring sites, a combination of both linear regression models and random effects models were used. Both gravity and fish biomass were log transformed to reduce heteroskedasticity in the models. Three separate linear regressions were used to test the effect of tourism gravity on total fish biomass, herbivorous fish species biomass and key commercial fish species biomass. Mixed effects models were used to account for some of the biophysical and landscape conditions known to be correlated with fish biomass.

Random effects included in the model were net primary productivity (NPP), mean wave energy, zone (no-take vs. multi-use), reef and land area within 10km, distance to shore and population within 10km, and MPA. NPP, mean wave energy, land and reef area and population were taken for each site from the Marine Socio-Environmental Covariates (MSEC) data set. The data set can be accessed through the National Socio-Environmental Synthesis Center (SESYNC) online shiny app (Yeager et al., 2017).

3. Results

3.1 Summary of Tourism Variables

Tourism data collection resulted in 54 dive centers, 270 dive sites, 194 hotels, 46 homestays and 33 ports across all the MPAs studied, and 167 liveaboards operating in the SBS (Fig. 3 and Fig. 4)(see Appendix B for more detailed maps). The highest number of points across all tourism variables were found in/around Komodo National Park. Alor and Wakatobi also have relatively high numbers of dive centers and dive sites, but significantly less in the way of hotels and homestays when compared to Komodo. Virtually no tourism variables were found in Flores Timur, Koon and Yamdena although a few liveaboards were found to operate in these areas, particularly Yamdena. Only one dive center was found on Kei Kecil, and according to online sources they are the only dive operators on Kei Kecil. As a result, sourcing dive site locations in Kei Kecil was difficult as is reflected in the data.

Figure 3. Tourism variable locations found within 35km of MPA Boundaries. Key: (1) Komodo National Park (2) Flores Timur (3) Selat Pantar Alor (4) Teluk Lasolo (5) Sulawesi Tenggara (6) Kepulauan Wakatobi (7) Teman Laut Banda (8) Kabupaten Seram Bagian Timur (Koon) (9) Kabupaten Maluku Tenggara (Kei Kecil) (10) Maluku Tenggara Barat (Yamdena). MP Draft Mas

Figure 4. The number tourism points found in or around MPAs, broken down by tourism variable type.

The spatial distribution of tourism variables also varied among MPAs (Fig. 5a). In Komodo,

the majority of tourism variable points (70%) were located 5km beyond the MPA boundary5. All tourism variables associated with Sulawesi Tenggara were located beyond 5km from the MPA boundary. Conversely, effectively all points in Wakatobi (99%) and Alor (100%) were found inside the MPA or within 1km or the boundary. Across the SBS, the majority of dive sites (81%) were within MPAs whereas the majority of hotels (71%) were found beyond 5km from MPA boundaries (Fig. 5b). Dive centers and homestays were more evenly spread with 51% of dive centers and 41% of homestays being further than 5km from MPA boundaries.

5 Points were located 5km beyond the MPA boundary but within the 35km search radius.

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A B

Figure 5. (a) The proportion of all tourism points found within a given MPA, 1km from the boundary, 5km from the boundary or beyond 5km (b) The proportion of all dive centers, dive sites, hotels and homestays found within an MPA, 1km from the boundary, 5km from the boundary or beyond 5km.

3.2 Analysis

The final tourism gravity layer for each MPA is shown in Figure 6 (see Appendix B for more detailed maps). The highest gravity was found in Komodo National Park. Overall tourism pressure in Flores Timur (Fig 6B) and Koon (Fig 6G) is very low and, in contrast to the other MPAs, appears uniformly across the region. This is as a result of tourism pressure being influenced predominantly by liveaboards visiting the MPA and the unique way in which liveaboard mass was applied in the model. Very few other tourism variables where found in these areas. In addition to Flores Timur and Koon, Sulawesi Tenggara (Fig 6D), Yamdena (Fig 6H) and Kei Kecil (Fig 6I) also showed relatively low levels of tourism gravity. Although no tourism variables were found for Teluk Lasolo during data collection, its proximately to Sulawesi Tenggara means that there is some overlap in the tourism pressure from Sulawesi Tenggara (Fig 6D).

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Figure 6 Tourism Gravity Pressure. Key: (A) Komodo National Park (B) Flores Timur (C) Selat Pantar Alor (D) East Sulawesi Tenggara and Teluk Lasolo (E) Wakatobi (F) Teman Laut Banda (G) Koon (H) Yamdena (I) Kei Kecil. Note: Maps are not to scale.

The results of the statistical analysis revealed no significant relationship – positive or negative – between tourism gravity and fish biomass (Fig. 7 & Fig 8). Under the linear regression model, there was no significant relationship detected between tourism gravity and total fish biomass

(R2 =0.005, d.f = 131., p >0.1), herbivorous fish biomass (R2 = 0.006, d.f. = 131, p > 0.1) or

key commercial fish (R2 = 0.002, d.f. = 129, p >0.1 ). The introduction of error terms into the model did not change the outcome of the model as the random effects model also revealed no significant relationship between tourism and total fish biomass, herbivorous fish species biomass and key commercial fish species.

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Figure 7 The effect of tourism gravity on total fish biomass (herbivorous and key commercial species).

Figure 8 The effect of tourism gravity on total fish biomass per MPA.

4. Discussion

4.1 Distribution of Tourism Activity

The results of the statistical analysis did not yield any insight into the impact of tourism pressure on ecological conditions in the SBS; none the less, the gravity model has demonstrated potential as a spatial tool for planning and management. Several notable spatial patterns were detectable through mapping of tourism variables and modelling tourism gravity. Broadly, the level of tourism development in the SBS appears roughly along on a gradient from West to East.

MPAs located in the far eastern region of the SBS, such as Yamdena, Kei Kecil and Koon, have little to no tourism development. They are far removed from existing development and require long journeys which may be a barrier to development. Moreover, the closest towns and ports to these MPA’s are not immediately on the MPA boarders. For example, on Yamdena6, Saumlaki is the nearest hub to the MPA boarder and is 20km (as the crow flies) over land with limited transportation infrastructure to connect them. The Banda Islands – also known as the ‘Spice Islands’ are a notable exception. The Banda Islands’ history as a former Portuguese, Dutch and English colony and sole location of nutmeg production (UNESCO, 2015) has given the islands greater recognition.

On the Western side of the SBS, Komodo has experienced the most visible level of tourism development. Although the National Park has existed since the 1980’s, it has benefited from active development and promotion since the mid-2000s as the result of a joint venture between The Nature Conservancy and an Indonesian tourism operator (Borches, 2009). It is likely that Komodo’s proximately to Bali has driven some of the development and tourism influx.

Tourism development in Komodo is concentrated around the port town of Labuan Bajo, located approximately 20km by sea from the MPAs boarders. Of the MPAs that were studied, overall tourism gravity was highest for Komodo. The gravity modeling results for Komodo were perhaps one of the most interesting. During statistical analysis it was revealed that the monitoring sites, which were located inside the MPA, did not overlap with areas of high tourism pressure so gravity values were lower than expected. In contrast, the monitoring sites with the highest gravity values were located within Alor (Fig 8), where tourism gravity was

6 Yamdena is both the common name of the MPA and the name of the island were the MPA is located. MP Draft Mas

lower than Komodo but was concentrated within the MPA boarders. The distinction between these two spatial patterns may be important consideration for future MPA planning. Planners and managers may choose to develop tourism hotspots geographically separate from MPAs thereby potentially creating a buffering effect between tourism hubs and the MPA.

Flores Timur, Sulawsi Tenggara and Teluk Lasolo remain virtually untapped as tourism resources. Based on spatial data alone, the reasons for this are less clear along. Flores Timur is located between Komodo and Alor. While tourism development appears to be advancing across the Lesser Sunda from Bali in the West to Alor in the East, Flores Timur appears to have been omitted. Similarly, Sulawesi Tenggara is approximately 20km off the coast of Kendari City, the province capital. Hotels and homestays were prevalent in Kendari but no dive centers or dive sites were found.

4.2 Applications of Tourism Gravity

It is difficult to assess the effect burgeoning tourism will have on MPAs based on spatial analysis alone. The ecological and social intersection with tourism around MPAs has been increasingly taken up in literature (e.g. Miller, 1993; Chen et al. 2010; Oracion, 2005; Papageorgiou, 2016). Some clear themes have arisen discussing best management practices including social and ecological study, policy analyses, planning and public education. Tourism gravity may, therefore, be more effective when complementing other assessments such carrying capacity assessments or environmental impact assessment, which examine economic, ecological and social outcomes more broadly (Holden, 2008).

As Indonesia proceeds with its ambitious tourism development, it will need to continue closely monitoring its MPA networks to ensure conservation goals and social needs are being met (Pendleton et al., 2018). Going forward, Komodo and Wakatobi may be of particular interest in the SBS as both have been earmarked for further development under the 10 New Bali’s initiative (Invest Islands, 2019). Wakatobi already has well established dive operations so it is logical that supporting development (e.g. hotels) will soon catch up. Komodo, which already has a burgeoning tourism hub will need to be careful of unsustainable impacts and exceeding ecological capacity.

4.3 Limitations

Several challenges were faced during tourism data collection. Data gaps were evident especially for the more remote parts of the SBS. For example, there were online references to

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dive sites in Koon, Kei Kecil and possibly Yamdena but their locations were not available so the data could not be included in the tourism gravity model. In Kei Kecil, for example, there is only one dive center so it was assumed that there are established dive sites as well. The dive center was contacted via email but declined to share this information. This also suggests, however, that these sites are not heavily visited and so it is fair to assume that not a lot of information was lost from the model by omitting these dive sites.

Liveaboard data collection also proved challenging. In their study of the SBS, Threlfall (2018) was able to track the dive sites visited by liveaboards in the SBS. Ideally, this method would also be used for the SBS. Typically, information listed for liveaboard online was not consolidated to a single booking website and itinerary information was inconsistent across websites. Moreover, liveaboards found to be operating in the SBS did not generally publish dive site locations.

Collecting data online also required a certain amount of interpretation, in particularly, when distinguishing between hotels and homestays. In reality, accommodation is not limited to two discrete choices: accommodation choices range from large resort chains, hostels and bungalows to guesthouses, Airbnb’s and homestays. Ultimately data collection required a systematic search strategy with well-defined variables and accepting a certain amount of unavoidable information loss.

As mentioned previously, the gravity model has a lot of built-in flexibility. While this may be advantageous to the user, the parameters of the model are often presumptive and subjective. The values used for weighting the tourism level gravity layers (Table 3) were based on speculation and logic rather than scientifically tested knowledge. This seems unavoidable however, as no literature currently exists to support model parameter setting.

It is also worth noting that not all points within a given tourism variable are equal. For example, Komodo, Wakatobi and Alor have a similar number of dive sites, however, Komodo has 2 to 3 times as many dive centers. This suggests that dive sites in Komodo are visited more frequently than dive sites in other MPAs and so treating them equally in the model may not be an accurate reflection of events on the ground.

4.4 Further Work

The WWF has an interest in pursuing the tourism gravity model further. The full capability of the tourism gravity model have not been explored and, as previously discussed, the gravity

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model function has a lot of built-in flexibility. For example, the multipliers used to weight the individual tourism variable gravity layers can be adjusted based on the level of impact each variable is expected to have. Adjusting the model parameters of the gravity model may yield different results which may prove more insightful.

There is also opportunity to collect more tourism data and expand the tourism gravity model within the SBS. Although Bali is located within the SBS it was excluded from this study due to the complex level of tourism around the island. Equally, the level of complexity in and around Bali makes it an important and interesting addition. Monitoring data is available for Nusa Penida MPA, located to the southeast of Bali making it a suitable MPA of interest.

4.5 Conclusion

As noted by Threlfall (2018) in her own tourism gravity study, the Tourism Gravity model offers a method of quantifying and understanding the spatial variability of tourism pressure. The model can provide policy-makers and managers with an easily reproducible means of identifying areas potentially vulnerable to high tourism pressure. It can also inform the need for further research, management and monitoring. Although statistical analysis yielded no evidence of a significant relationship between fisheries biomass and tourism pressure, the outcomes of this study are far from conclusive. As tourism development takes a deeper hold in the SBS, more investment in research, planning and management will necessary to ensure that tourism pressure remains beneficial to the ecological and social conditions in and around MPAs.

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Acknowledgements

Many thanks to my supervisor David Gill for his continued guidance and support and for fostering a positive working environment within the Gill lab. Many thanks to my supervisors at WWF-US, Dominic Andradi-Brown (WWF-US) and Gabi Ahmadia (WWF- US), for giving me the opportunity to carry out this project and for their unwavering support throughout. Special thanks to Brian Free (WWF-US) and Jesse Cleary (Duke University) for lending me their time and expertise in GIS. Special thanks to Siobhan Threlfall (formerly of James Cook University) for sharing her advice and insight into her work on tourism in the BHS. I would also like to dedicate this project to the entire WWF-US Ocean’s Team and the various WWF-IN team members, particularly Estradivari and Amkieltiela, whose work has made this project possible.

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Appendix A

Table A1. Tourism gravity values extracted for monitoring sites within MPAs of interest.

Site ID Site Name MPA Gravity 1000 Ternate A Alor 445.28 1001 Ternate B Alor 458.34 1002 Aimoli A Alor 433.98 1003 Aimoli B Alor 437.51 1020 Mahi A Alor 429.02 1021 Mahi B Alor 428.51 1022 Pulau Pura A Alor 442.88 1023 Pulau Pura B Alor 447.72 1024 Desa Ombay Alor 439.93 1025 Bangkalan Alor 439.93 1026 Desa Pandai A Alor 429.09 1027 Desa Pandai B Alor 428.56 1028 Baulaung A Alor 427.63 1029 Baulaung B Alor 427.62 1030 Batang A Alor 426.88 1031 Batang B Alor 426.82 1032 Lapang Alor 426.69 1033 Lemma Alor 426.61 1034 Beang Onong Alor 426.61 1035 Kangge B Alor 426.45 1036 Kangge A Alor 426.45 1037 Pulau Kambing B Alor 426.42 1038 Pulau Kambing A Alor 426.42 1039 Pulau Rusa B Alor 426.40 1040 Pulau Rusa A Alor 426.40 1041 Tanjung Soyang Alor 426.45 5002 Clown Fish Valley Alor 445.35 5003 Pulau Pura (The Boardroom) Alor 447.13 5004 Pulau Ternate Alor 448.12 7 Gunung Api Timur Laut Banda 365.32 8 Gunung Api Selatan Laut Banda 107.31 12 Wali Laut Banda 58.66 20 Mangku Batu Laut Banda 83.27 1044 Desa Balaweling A Flores Timur 5.68 1048 Pulau Tiga A Flores Timur 5.68 1049 Pulau Tiga B Flores Timur 5.68 1050 Lamawala A Flores Timur 5.68 1051 Lamawala A Flores Timur 5.68 1052 Tanjung Naga A Flores Timur 5.68

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1053 Tanjung Naga B Flores Timur 5.68 1054 Latto A Flores Timur 5.68 1055 Latto B Flores Timur 5.68 1056 Ile Padung A Flores Timur 5.68 1057 Ile Padung B Flores Timur 5.68 1058 Lamaojan Flores Timur 5.68 1059 Batu Payung B Flores Timur 5.68 1060 Batu Payung A Flores Timur 5.68 1061 Koten B Flores Timur 5.68 1062 Koten A Flores Timur 5.68 1063 Koli Dateng B Flores Timur 5.68 1064 Koli Dateng A Flores Timur 5.68 1065 Pulau Mas B Flores Timur 5.68 1066 Pulau Mas A Flores Timur 5.68 1067 Desa Aran B Flores Timur 5.68 1068 Desa Aran A Flores Timur 5.68 1069 Adonara B Flores Timur 5.68 1070 Adonara A Flores Timur 5.68 5001 Kroko Flores Timur 5.68 China Shop/ Pulau Gili Lawa TNK05 Laut Taman Nasional Komodo 100.29 TNK06 Pulau Tatawa Besar Taman Nasional Komodo 100.89 TNK07 Pulau Punya Taman Nasional Komodo 99.18 TNK08 Karang Makasar Taman Nasional Komodo 96.35 TNK11 Depan Germany Flag Taman Nasional Komodo 95.07 TNK12 Torolongkoe Taman Nasional Komodo 94.65 TNK13 Letuho Taman Nasional Komodo 91.52 TNK15 Yellow Wall Taman Nasional Komodo 99.70 TNK19 Loh Baru Taman Nasional Komodo 92.47 TNK20 Pulau Bero/Depan Kerora Taman Nasional Komodo 94.01 TNK21 Pulau Kambing Taman Nasional Komodo 95.09 KKP Pulau Koon dan Pulau KOE01 Neiden Koon 5.68 KKP Pulau Koon dan Pulau KOE02 Neiden Koon 5.68 KKP Pulau Koon dan Pulau KOE03 Neiden Koon 5.68 KKP Pulau Koon dan Pulau KOE05 Neiden Koon 5.68 KKP Pulau Koon dan Pulau KOE06 Neiden Koon 5.68 KKP Pulau Koon dan Pulau KOE07 Neiden Koon 5.68 KKP Pulau Koon dan Pulau KOE08 Neiden Koon 5.68

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KKP Pulau Koon dan Pulau KOE09 Neiden Koon 5.68 KKP Pulau Koon dan Pulau KOE10 Neiden Koon 5.68 STR12 Saponda Darat Sulawesi Tenggara Sultra MPA 1.60 STR13 Pulau Hari Sulawesi Tenggara Sultra MPA 1.61 STR15 Labotaone Sulawesi Tenggara Sultra MPA 1.54 STR16 Tambolosu Sulawesi Tenggara Sultra MPA 1.40 STR17 Wawosungguh Sulawesi Tenggara Sultra MPA 1.51 STR18 Pulau Lara Sulawesi Tenggara Sultra MPA 1.87 STR20 Soropia Sulawesi Tenggara Sultra MPA 2.81 STR25 Lemo bajo Teluk Lasolo MPA 0.25 STR26 Pulau Karama Teluk Lasolo MPA 0.15 STR29 Pulau Bahulu Teluk Lasolo MPA 0.12 STR30 Pulau Bahulu Teluk Lasolo MPA 0.14 STR31 Pulau Bahulu Teluk Lasolo MPA 0.10 STR32 Pulau Sisi Teluk Lasolo MPA 0.09 STR33 P. Labengki Besar Teluk Lasolo MPA 0.11 STR34 P. Labengki Kecil Teluk Lasolo MPA 0.10 STR35 Gosong Labengki Teluk Lasolo MPA 0.08 STR36 Labengki Teluk Lasolo MPA 0.07 5251 ZPL KrgKapota 1 Wakatobi 11.31 5252 ZPL KrgKapota 2 Wakatobi 11.26 5253 ZPL KrgKapota 3 Wakatobi 11.16 5254 ZPR KrgKapota 4 Wakatobi 11.20 5255 ZPL KrgKaledupa 1 Wakatobi 11.34 5256 ZPL KrgKaledupa 2 Wakatobi 11.29 5257 ZPB KrgKaledupa 3 Wakatobi 11.33 5258 ZPB KrgKaledupa 4 Wakatobi 11.45 5259 ZPL KrgKaledupa 5 Wakatobi 11.49 5260 ZPL KrgKaledupa 6 Wakatobi 11.49 5261 ZPR KrgOtiolo 1 Wakatobi 11.29 5262 ZPR KrgOtiolo 2 Wakatobi 11.33 5263 ZPB KrgKaledupa 7 Wakatobi 11.38 5264 ZPB KrgKaledupa 8 Wakatobi 11.45 5265 ZPB KrgGurita 1 Wakatobi 21.81 5266 ZPB KrgGurita 2 Wakatobi 21.81 5267 ZPR Matahora 1 Wakatobi 43.24 5268 ZPL Waetuno 1 Wakatobi 76.60 5270 ZPL Matahora 3 Wakatobi 16.85 5271 ZPB Darawa 1 Wakatobi 13.02 5272 ZPL Kaledupa 1 Wakatobi 14.15 5273 ZPB Kaledupa 2 Wakatobi 16.65 5274 ZPL Darawa 2 Wakatobi 14.04

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5275 ZPL SMA Tomia Wakatobi 215.37 5277 ZPR PSawa 1 Wakatobi 48.35 5278 ZPL PSawa 2 Wakatobi 45.24 5279 ZPL Ndaa 1 Wakatobi 16.62 5280 ZPL Ndaa 2 Wakatobi 11.76 5281 ZPL Koromaha 1 Wakatobi 11.53 5282 ZPL Koromaha 2 Wakatobi 11.85 5283 ZPR Koromaha 3 Wakatobi 11.57 5284 ZPB Cowo-cowo Wakatobi 10.71 5285 ZPB Kentiole Wakatobi 10.51 5286 ZPB KrgKoko 1 Wakatobi 10.53 5287 ZPB KrgKoko 2 Wakatobi 10.48 5290 ZPB Anano Wakatobi 14.10 5292 ZPL Runduma 2 Wakatobi 10.64 5293 ZPL Palahidu Wakatobi 12.30 5294 ZPR KrgBante Wakatobi 13.97 Yamdena - Maluku Tenggara 5025 Pulau Nusnitu Barat 15.60 Yamdena - Maluku Tenggara MTBE01 Tanjung Nuskei Barat 15.72 Yamdena - Maluku Tenggara MTBE02 Wotuwawan - Pulau Ngolin Barat 15.63 Yamdena - Maluku Tenggara MTBE03 Sukler Barat 15.62 Yamdena - Maluku Tenggara MTBE05 Sabal Barat 15.62 Yamdena - Maluku Tenggara MTBE06 Welutu - Pulau Sera Barat 15.64 Yamdena - Maluku Tenggara MTBE09 Ujung Wuryaru Barat 15.60 Yamdena - Maluku Tenggara MTBE10 Nuswotap Barat 15.60 Yamdena - Maluku Tenggara MTBE11 Wulmali Barat 15.60 Yamdena - Maluku Tenggara MTBE12 Ungar Barat 15.59 Yamdena - Maluku Tenggara MTBE13 Labobar Barat 15.59 Yamdena - Maluku Tenggara MTBE14 Tenaman Barat 15.59 Yamdena - Maluku Tenggara MTBE15 Tanjung Itain Barat 15.59 Yamdena - Maluku Tenggara MTBE16 Tanjung Adodo Barat 15.58 Yamdena - Maluku Tenggara MTBE17 Welmasa Barat 15.58 Yamdena - Maluku Tenggara MTBE18 Tanjung Pulau Maru Barat 15.58

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Yamdena - Maluku Tenggara MTBE19 Wermatan Barat 15.58 Yamdena - Maluku Tenggara MTBE20 Frinun Barat 15.59 Yamdena - Maluku Tenggara MTBE21 Farnusan Barat 15.59 Yamdena - Maluku Tenggara MTBE22 Meti Rotan Barat 15.59 Yamdena - Maluku Tenggara MTBE23 Nukaah Barat 15.58 Yamdena - Maluku Tenggara MTBE24 Adodo Fordata Barat 15.58 Yamdena - Maluku Tenggara MTBE25 Sofianin Barat 15.58 Yamdena - Maluku Tenggara MTBE26 Klaan Barat 15.58 Yamdena - Maluku Tenggara MTBE27 Tutun Watidal Barat 15.59 Yamdena - Maluku Tenggara MTBE28 Mitak Barat 15.59 Yamdena - Maluku Tenggara MTBE29 Tanjung Botanio Barat 15.60

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Appendix B – Additional Maps

Figure B1. Raw tourism data (left) and final tourism gravity map (right) for Kabupaten Maluku Tenggara (Kei Kecil).

Figure B2. Raw tourism data (left) and final tourism gravity map (right) for Selat Pantar Alor.

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Figure B3. Raw tourism data (left) and final tourism gravity map (right) for Komodo National Park.

Figure B4. Raw tourism data (left) and final tourism gravity map (right) for Wakatobi.

Figure B5. Raw tourism data (left) and final tourism gravity map (right) for Flores Timur.

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Figure B6. Raw tourism data (left) and final tourism gravity map (right) for Flores Timur.

Figure B7. Raw tourism data (left) and final tourism gravity map (right) for Kabupaten Seram Bagian Timur (Koon)

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Figure B8. Raw tourism data (left) and final tourism gravity map (right) for Teluk Lasolo and Sulawesi Tenggara.

Figure B9. Raw tourism data (left) and final tourism gravity map (right) for Maluku Tenggara Barat (Yamdena).