Among the Calamianes Group of islands in Palawan, Philippines, Busuanga is the largest and is divided into two municipalities: “Busuanga” in the west and “Coron” in the east. In Busuanga, our study sites include barangays Concepcion, New Busuanga, Quezon, Salvacion, and Turda. In Coron, our sites include barangays Borac, Barangay 5, Decalachao, San Jose and Tagumpay (Figure 2). Busuanga Island is diverse in natural resources. The pristine mangrove areas and existing MPAs are considered the strengths of Busuanga Island due to their diversity. However, the richness and vastness of its natural resources are slowly vanishing and exploited due to upland human-related activities which affects the coastal ecosystems. Unsustainable human activities occurring simultaneously in marine ecosystems of Busuanga are threatening the island’s biodiversity (C3 Philippines personal communication, February 3, 2021).

Almost 70% of protected mangroves in the Philippines are found in Palawan (74,267 has). In Busuanga Island, there are 24 true and 28 associate mangrove species, with the most abundant mangroves Rhizophora spp. and Xylocarpus granatum. However, these mangroves have been heavily exploited, especially those within easy access to roads for charcoal, fuel and building materials. Busuanga Island seagrass beds are dense and speciose (up to 8 species of seagrass) and serve as feeding grounds for dugong (PCSD, 2006), but have been declining since the 1980s (Tamondong et al., 2021).

Our place-based social vulnerability assessment for fishing communities adapted several vulnerability assessment tools and approaches (Allison et al., 2009; Mamauag et al., 2013; Orencio and Fujii, 2013; Jacinto et al., 2015; Licuanan et al., 2015; Quiros et al., 2018).

We chose criteria based on availability of data, the literature, and the ease of explaining criteria to non-specialist stakeholders (Licuanan et al., 2015). We used four criteria: ecosystem, socio-economic, fisheries, and governance (Figure 1). Variables for these criteria were modified from tools to assess fisheries ecosystem vulnerabilities to climate change impacts, and we kept the number of criteria low, for ease of uptake by stakeholders (Mamauag et al., 2013; Jacinto et al., 2015; Licuanan et al., 2015). The scale of assessment is the coastal community or the barangay, the smallest political unit in the Philippines. We limited our study to 10 barangays from two municipalities (Busuanga and Coron) within Busuanga Island.

Scoring was based on a simple, semi-quantitative approach, where scores ranged from 1 to 5, with 1 or 2, categorized as low, 3 to 4 categorized as medium, and 5 as high (Tables 1–3; Mamauag et al., 2013; Licuanan et al., 2015). Threshold values were based on Philippines specific conditions from the literature (Tables 1–3; Licuanan et al., 2015). The numerical values for each criteria were summed, then converted to a rank system with point class intervals of low, medium, or high (Table 4; Jacinto et al., 2015).

We gathered field data and engaged in participant observation between February 2019 and October 2020 in 10 barangays in Busuanga Island, Palawan Province in the Philippines.

We define Exposure as actual and perceived threats that result in the loss or degradation of blue carbon ecosystems (Table 1, Figure 2). We gathered qualitative data from fishers’ perceptions to assess the exposure of environmental resources (i.e., coastal ecosystems and fishing grounds) to threats (i.e., land use changes due to development) (Jacinto et al., 2015) and context specific socio-economic criteria (Quiros et al., 2018). Threats to blue carbon ecosystems in Coron are due to illegal and unsustainable forest practices, illegal cutting of mangroves, changes in land use, and improper waste disposal (Abrenica et al., 2013). In Busaunga, the major threats are unsustainable agricultural and forest practices, timber poaching, and conflicting knowledge about marine protected areas (Bautista et al., 2017). Coastal development has a significant negative impact on seagrass condition (Quiros et al., 2017), while it is the biggest threat to mangroves (Spalding et al., 2010).

Socio-economic context influences a barangay’s exposure (Quiros et al., 2018). For the first Exposure variable, a weighted distance to the towns (Table 1 and Supplementary Table 3) is a proxy for urbanism (Birkmann et al., 2014; Quiros et al., 2018). We used the distance to Coron, the major town of Busuanga Island and the capital of the Coron municipality, and the distance to Salvacion, the capital of Busuanga municipality for this calculation.

The second Exposure variable is the presence of tourism defined by combining expert opinion and counting the number of registered establishments from the Coron Tourism Office (Table 1 and Supplementary Table 3). Many development initiatives, road building and land conversion projects are due to tourism development, which in some cases leads to environmental degradation and loss of access to natural resources (Shah et al., 2000).

The third and fourth Exposure variables use qualitative data from household surveys about the perceived condition of blue carbon ecosystems, rating seagrass and mangroves separately on a 5-point scale (low, medium, high exposure). Low exposure of blue carbon ecosystems is defined as widespread, dense coverage, medium exposure is patchy and/or decreasing coverage, and high exposure is sparse coverage (Table 1 and Supplementary Table 3).

We choose not to include physical variables like wave exposure, temperature, or sea level height as Exposure variables because these physical stressors operated at spatial scales larger than the barangay, and other studies found the same exposure level for adjacent barangays (Licuanan et al., 2015). Instead, we used local perceptions of mangrove and seagrass ecosystem condition as a basis to compare Exposure across different barangays, in addition to the socio-economic context of urbanism and tourism. The benefits of this type of analysis are to manage for the social vulnerabilities of individual barangays, while their Exposure to larger scale physical variables remains the same.

Sensitivity variables fall under ecosystem, socio-economic and fisheries criteria (Figure 1 and Table 2). We collected field data and conducted spatial analysis on seagrass and mangrove fisheries and habitats separately, and for socio-economic variables, we obtained household interview data and barangay statistics.

Adaptive capacity variables fall within ecosystem, socio-economic, fisheries, and governance criteria (Table 3 and Figure 2). For fisheries, socio-economic, and governance criteria, we used household and fisher interviews, and barangay statistics, while for ecosystem criteria, we used field survey data and spatial analysis of seagrass and mangroves.

We limited our spatial analysis of seagrass and mangroves within the boundaries of the 10 barangays in Busuanga Island. We used Barangay boundaries from the latest Environmentally Critical Areas Network (ECAN) reports (Abrenica et al., 2013; Bautista et al., 2017), but when they did not overlap with the mapped barangay boundaries from household interviews, we adjusted the barangay boundaries to encompass the individual households surveyed.

Busuanga Island has two municipal capitals, Coron and Salvacion, which correspond to the municipalities of Coron and Busuanga, respectively. To calculate the impact of urban centers on each of the 10 barangays, we calculated the weighted average distance to the nearest municipal capital, using human population as the weighted measure. Urban living increases Exposure due to overcrowded living conditions, lack of services for adequate housing, nutrition and healthcare (Baker, 2012). The population of Coron is projected at 18,883 (a) in 2020, while the population in Salvacion is projected at 3,639 (b) in 2020 (Abrenica et al., 2013; Bautista et al., 2017). To calculate the “weight” of each population center, we divided the population of that capital by the total population of both municipal capitals. Coron had a weight of 0.84 (a/(a + b)) while Salvacion had a weight of 0.16 (b/(a + b)). We then multiplied the distance from each barangay center to each municipal capital and its weight to get the weighted average distance. We calculated the distance between barangays and municipal capitals using the main transportation network roads.

To calculate the coastline covered by blue carbon ecosystems, we used a hybrid approach, using remotely sensed data, on the ground assessments and expert opinion. For mangrove cover, we used the Mangrove Vegetation Index (MVI), implemented in Google Earth Engine to create a mangrove extent map of the Philippines (Baloloy et al., 2020). For seagrass cover, we used a linear spectral unmixing method on Landsat 8 images, with pure spectra or endmembers from August to December 2019. To validate the remotely sensed seagrass images, we used ground assessments of seagrasses and expert opinion to increase the reliability of the maps, because accuracy depends on the environmental conditions of the study area (Veettil et al., 2020). We did not validate the mangrove coverage map because it was well validated with field data and drone images, with an accuracy ranging from 94% in Calauit Island, to 96% in Binguan, Coron and 100% in Sagrada and Bugtong, Busuanga (Baloloy et al., 2020).

To calculate the proportion of mangroves covering each barangay’s coastline, we overlaid Busuanga’s MVI map on the Philippine Barangay boundary map and calculated the length of coastline with mangroves (considering a 100-m buffer distance) using ArcGIS 10.7.1. We divided the length of the mangrove forest by the total length of each barangay’s coastline to get a proportion of mangroves covering the coastline. This approach however ignores the width and hence the area, for simplicity.

To calculate the proportion of seagrass covering the reef flat, we obtained the coral reef base layer from UNEP (UNEP-WCMC et al., 2018) and overlaid it with the validated seagrass map. To calculate the proportion of the reef flat (area covered by the coral reef) covered by seagrasses, we divided the seagrass area in each barangay by the reef flat area.

To calculate the patchiness versus connectivity of seagrass and mangroves along the coastline, we created a continuous grid of 500-meter cells of mangrove forest and seagrass beds, averaging around 500 – 1000 meters from the coastline, with a maximum of 2.5 km from the coastline because some riverine mangrove forests were distributed inland. We used the focal statistics function of ArcGIS to calculate the contiguous area of 3 cells with seagrass or mangroves, separately. We divided the focal analysis score per barangay by the number of 500-meter cells covered by that barangay’s coastline to create a ratio of connectivity for seagrass and mangroves along the coast. We rated small, fragmented habitats with focal analysis ratios of less than 25% as patchy, ratios of between 25% and 60% as medium patchiness, and ratios greater than 60% as contiguous.

To calculate presence of adjacent habitat, the same 500-meter cells were assigned a connectivity score between zero and two, with one habitat (seagrass, mangrove or coral) found present in a cell, given a score of zero, two habitats (seagrass/mangrove, seagrass/coral or coral/mangrove), given a score of one, and all three habitats present, given a score of two. Like the patchiness ratio, we divided the total cells in that barangay’s grid by the cumulative connectivity score to get a connectivity score. We rated low connectivity as a score of less than 1, medium connectivity with a score between 1 and 1.5, and high connectivity if the score was greater than 1.5.

Mangroves covered greater than 50% of all barangay coastlines, while seagrasses covered around 50% and greater of barangays’ coastlines (Figure 2). Mangrove forests did not stay within barangay boundaries but extended beyond individual barangay coastlines into adjacent barangays. Patchiness of seagrass and mangrove habitat was low, with at least 40% of the coastline with contiguous habitat. Blue carbon ecosystems were relatively well-connected with adjacent habitat such as coral reefs and other mangroves and seagrasses. These results show that blue carbon ecosystems are relatively intact in Busuanga Island. Perceived conditions of seagrasses and mangroves varied, ranging from low exposure to high exposure of both habitats. The results of our spatial analysis (Figure 2) and household interviews concerning perceived condition of blue carbon ecosystems (Figure 3 and Supplementary Tables 3, 4) showed that in some locations, past blue carbon ecosystems were more extensive than current conditions, with households referring to significant damage due to typhoon Yolanda/Haiyan in 2013.

Our exposure variables were based on the perceived condition of blue carbon ecosystems and the degree of indirect threats or perturbations from the socio-economic influence of urbanism and tourism. Peri-urban barangays in Busuanga are Salvacion and New Busuanga, and urban barangays in Coron are Barangay Poblacion, Barangays 1 through 6, and Tagumpay. Barangays with the greatest exposure were more urbanized and exposed to tourism (Barangay 5 and Tagumpay), while rural barangays and barangays with little or no tourism had less exposure (Quezon and Borac). Rural barangays influenced by tourism (Concepcion, New Busuanga and San Jose) had greater exposure than rural barangays that not influenced by tourism (Figures 3, 4, Table 5, and Supplementary Tables 3, 4, Figure 5A).

The most sensitive barangays were urban barangays Barangay 5 and Tagumpay, and the least sensitive was rural barangay Quezon. Borac, Turda and Quezon had lower blue carbon ecosystem sensitivity than Barangay 5, Tagumpay and Concepcion. Quezon had lower fisheries and socio-economic sensitivity than the other barangays. Barangays with high sensitivities had coastal fringing mangroves, seagrass beds with low seagrass cover, reliance on nearshore seagrass and mangrove fisheries catch, and more tourism income. Barangays with low sensitivities had extensive seagrass and mangroves along their coastlines, low human population density and alternative incomes to fishing (Table 5, Figures 3, 4 and Supplementary Table 5, 6, Figure 5B).

Blue carbon ecosystem sensitivity was medium to high due to relatively low seagrass percent cover in some barangays and the presence of scrub-fringing mangroves, which is the mangrove forest type more sensitive to changes. Seagrass sensitivity was higher in monocultures and lower in multi-species seagrass meadows. Urban barangays like Barangay 5 and Tagumpay had higher seagrass sensitivity due to degraded seagrass habitat. Rural barangays like Quezon and Turda had less seagrass sensitivity due to high seagrass percent cover and diverse seagrass species present (Figure 3 and Supplementary Tables 5, 6, Figures 1A, 3B).

Mangrove sensitivity was defined by mangrove forest type with scrub-fringing mangroves, the most sensitive but also the most common mangrove forest type found in Salvacion, Barangay 5, Tagumpay and Turda. Rural barangays like Concepcion, New Busuanga, Quezon and Borac had less mangrove sensitivity because they were dominated by riverine-basin-fringing forests, the least sensitive mangrove forest type. The mangrove types with medium sensitivity were the riverine-fringing and scrub fringing mangroves found in Decalachao and San Jose (Figure 3 and Supplementary Tables 5, 6, Figures 1A, 3B).

Socio-economic sensitivity was medium for most barangays. The exceptions were due to greater reliance on tourism income and greater population densities in urbanized barangays. The rural barangay, San Jose also had high socio-economic sensitivity due to a greater reliance on tourism income. Socio-economic sensitivity ranged from a minimum sensitivity score of 3 for Decalachao, with low population density, low reliance on tourism income and low reliance on fisheries, to a maximum sensitivity score of 13 for Barangay 5, with high population density, high reliance on tourism income but low reliance on fisheries (Figure 3 and Supplementary Figures 1B, 3B, Tables 5, 6).

Blue carbon ecosystem fisheries are highly sensitive fisheries due to their nearshore catch composition and low catch per unit effort, with mangrove fisheries having a slightly lower sensitivity due to greater catch per unit effort. Fisheries sensitivity was high for all barangays except for Quezon, due to its high catch per unit effort for both seagrass and mangrove catch. Since there was minimal natural seagrass habitat in Borac and no seagrass fisheries, we assigned Borac’s seagrass fishery and seagrass species sensitivity variables with the lowest possible score, 1 (Figure 3 and Supplementary Figures 1B, 3B, Tables 5, 6).

The barangay with the greatest adaptive capacity was Quezon, while the barangays with the least adaptive capacity were Concepcion and Turda. Borac and Tagumpay had relatively lower blue carbon ecosystem and fisheries adaptive capacity than the rest of the barangays, while Barangay 5, Tagumpay and Decalachao had relatively lower socio-economic and governance adaptive capacity than the rest of the barangays. Barangays with low adaptive capacity did not have alternative livelihoods to fishing, had fishers with low education and a high average fishing experience per fisher. Barangays with high adaptive capacity had high connectivity between seagrass and mangrove patches, the presence of adjacent habitats, had fishers with other sources of income, and the presence of NGOs and POs (Table 5, Figures 3, 4, and Supplementary Tables 7, 8, and Figure 5C).

Mangrove adaptive capacity was higher than seagrass adaptive capacity (Supplementary Figure 4C). Mangrove adaptive capacity was medium to high due to medium to high connectivity of mangrove patches along the coast and the presence of adjacent habitat (Figure 4 and Supplementary Figures 2A, 3C, 4C). Seagrass adaptive capacity was low in Enhalus acoroides dominated meadows, and medium in mixed meadows with Enhalus-Thalassia dominated and Thalassia-Cymodocea-Halodule seagrasses. Seagrass adaptive capacity was medium to low, due to medium to low connectivity of seagrass patches along the reef flat and the predominance of Enhalus and Enhalus-Thalassia dominated seagrass beds, which are climax species and need more time to grow and recover from loss (Bjork et al., 2008). While Borac does not have a significant amount of naturally occurring seagrass, remote sensing analysis predicted a small patch of seagrass, so we were able to assign a low adaptive capacity score for Borac’s seagrass habitat extent (Figure 3 and Supplementary Figures 2A, 3C, 4C).

Each barangay’s socio-economic adaptive capacity was constrained by low fisher education (<10 years of education), but adaptive capacity increased with diversified livelihoods (Muallil et al., 2011; Licuanan et al., 2015) (Figure 3 and Supplementary Figures 2B, 3C, 4C). Among socio-economic variables, education had the lowest scores across all barangays (Supplementary Table 8). Decalachao had the lowest socio-economic adaptive capacity score because fishers did not have other sources of income besides fishing and had low education levels (Table 5 and Supplementary Table 8). We found a low proportion of households with salaried income in the urban barangays, Barangay 5 and Tagumpay. Rural barangays New Busuanga, Borac and Turda also had little or no households with salaried income (Figure 3 and Supplementary Figures 2B, 3C, 4C).

The fisheries sector variables had the lowest adaptive capacity due to high average fishing experience. However, the presence of alternative livelihoods to fishing helped to increase adaptive capacity. The exceptions were Concepcion and Decalachao, where fishers had few alternative livelihoods to fishing. Across all barangays, average fishing experience was high (>20 years of fishing experience) showing a low likelihood of exiting the fishery, therefore a lack of adaptive capacity (Figure 3 and Supplementary Figures 2A, 3C, 4C).

Governance adaptive capacity was medium due to the presence of NGOs and POs (especially for Concepcion, New Busuanga, and Salvacion), which increased the information available to communities and the capacity for community organization and action. The exceptions were the urban barangays of Barangay 5 and Tagumpay, and Turda because they had lower numbers of POs (Figure 3 and Supplementary Figures 2B, 3C).

Overall, rural barangays had less exposure and lower sensitivity to blue carbon ecosystem loss than urban barangays. Across all barangays, diversified livelihoods increased adaptive capacity. The barangays with the highest exposure and sensitivity were the urban barangays of Barangay 5 and Tagumpay, while the barangays with lowest exposure and sensitivity were rural Quezon and Borac. All barangays had medium overall adaptive capacity. The lowest overall vulnerability was Quezon, followed by Borac, and the highest overall vulnerabilities were Barangay 5 and Tagumpay (Table 5 and Figure 4).

Our analyses revealed a range in coastal barangay social vulnerabilities, showing the complex relationship between blue carbon ecosystems and human communities, even within one island. We found that seagrass and mangrove ecosystems in Busuanga Island were relatively intact. This is a good sign for coastal communities in Busuanga.

The main factors contributing to community vulnerability in other contexts were food security factors, followed by economic/livelihood, policy and institutional factors (Orencio and Fujii, 2013). Our work differed from previous studies because it combined methodologies that were used to examine climate change vulnerabilities of fisheries (Mamauag et al., 2013; Licuanan et al., 2015) with an examination of social vulnerability (Cutter et al., 2008; Ekstrom et al., 2015; Quiros et al., 2018). While there are vulnerability studies on mangroves that combine environmental criteria with human management criteria, these focused mostly on physical processes (Ellison, 2015). Multi-criteria vulnerability studies on seagrass are scarce, with most focusing on environmental criteria (Waycott et al., 2007) or compiling expert knowledge from workshops (Grech et al., 2012; Tan et al., 2018). Our study is novel because it used vulnerability analysis on empirical data for both mangroves and seagrasses and human communities, highlighting these links in the social-ecological system.

Healthy blue carbon ecosystems can mitigate against social vulnerabilities in human communities by being less sensitive to threats and better able to recover from loss. Our spatial analysis revealed healthy blue carbon ecosystems, as measured by proportion of coastline covered, low patchiness and high continuity of mangroves along the coastline, the presence of adjacent habitat, and type of seagrass bed and mangrove forest present. Interestingly, mangrove fisheries occurred in both riverine-basin-fringing forests (least sensitive) as well as the scrub-fringing (most sensitive) mangrove forests. Certain mangrove forest types are more sensitive than others, and certain spatial contexts (low connectivity with other mangrove forest habitats and limited extent) result in more sensitive mangrove ecosystems. For sensitive mangroves like scrub-fringing mangroves or mangroves with low connectivity and low extent along the coastline, managers can impose risk averse policies such as limiting use by fishers and coastal developers.

Seagrass habitat sensitivity ranged from low to high, which is evidence that among blue carbon ecosystems in Busuanga, seagrasses were more vulnerable. Field collected data and local perceptions showed there were greater negative changes to seagrasses. Seagrass habitat percent cover within quadrats ranged from low (11% in Concepcion) to high (95% in Quezon). This data corroborated with local perceptions of changes to seagrass with 77% of Concepcion respondents saying seagrass in their barangay was patchy, and 80% of Quezon respondents saying seagrass in their barangay was widespread. While the link between tourism and urbanism’s effect on blue carbon ecosystems is indirect and our purpose was not to describe the mechanism, we must note that Concepcion is one of the barangays with growing tourism development and relatively close to a peri-urban town, Salvacion, while Quezon is a remote, rural barangay with very little tourism development (Quevedo et al., 2021). This is evidence of tourism’s indirect impact on seagrass ecosystems. Furthermore, threats in Busuanga such as unsustainable agricultural and forest practices (Bautista et al., 2017) also play a role in blue carbon ecosystem health, but we did not investigate this relationship.

Busuanga households diversified their income sources beyond fishing, increasing their adaptive capacity. They engaged in farming, tourism, construction, transportation and salaried employment including working for pearl farms, schools, the service industry and retail. Salaried jobs mitigate a barangay’s sensitivity to blue carbon ecosystem loss because salaried jobs do not rely on the health of the habitat, unlike tourism or fishing. One cause for concern was the low proportion of households with salaried income in the urban barangays, Barangay 5 and Tagumpay, which is opposite of what one may expect from an urban area, which should provide more reliable employment.

Education is a limiting criteria for socio-economic adaptive capacity. Poor educational attainment of fishers limits the livelihoods available to them in the future (Pauly, 1997). The rural barangays San Jose and Quezon do not have high schools. Residents attend high schools in neighboring barangays, making it more difficult to travel to school, especially during the rainy season with rough roads connecting barangays.

Tourism in Busuanga island is largely nature-based tourism, relying on healthy coastal ecosystems. With the degradation or loss of blue carbon ecosystems, the very base upon which Busuanga Island’s tourism relies on is endangered. Tourism is the leading source of livelihoods in Coron (Abrenica et al., 2013), while in Busuanga, access to tourism is a problem (Bautista et al., 2017). Quevedo et al. (2021) found greater perceived tourism benefits in urban versus rural dwellers; these benefits were moderate overall, with slightly positive socio-cultural impacts and slightly negative economic and environmental impacts. In general, urban barangays had greater reliance on tourism income and hence, greater sensitivity, but greater reliance on tourism was also found in the rural barangay San Jose. These findings show that sensitivities are not only based on the rural-urban gradient but also on other aspects of the socio-economic context, such as tourism development.

Our research revealed the highly sensitive nature of the seagrass and mangrove fisheries sectors. Seagrass fisheries are very important for coastal communities as evidenced by the high participation in gleaning activities around the world (Cullen-Unsworth et al., 2014; Quiros et al., 2018; Nordlund et al., 2018; Unsworth et al., 2018). However, seagrass and mangrove fisheries are highly sensitive and inherently vulnerable fisheries due to their low catch per fisher per day (Mamauag et al., 2009; Muallil et al., 2011). Seagrass and mangrove fisheries are largely unregulated in Busuanga Island. While gleaners are required to register as fishers, most are not registered and since most do not use boats, their gleaning activities go unseen by natural resource managers. This is cause for concern because Siegel et al. (2019) found that fisheries regulations increase socio-economic adaptive capacity due to more environmental monitoring and adaptive management.

Another issue with blue carbon fisheries is the low catch rate, which is an indicator of fishing effort and fishing pressure (Licuanan et al., 2015) on the seagrass and mangrove ecosystems. A policy intervention is establishing equitable fisheries regulations for blue carbon ecosystem fisheries. An exception to the low catch rates was the Quezon mangrove fishery which relies on Quezon’s riverine-basin-fringing mangrove forest, which extends further west to Buluag’s extensive riverine-basin-fringing mangrove forest and is bordered to the north by the mangroves of Calauit Island. Quezon’s rich mangrove forest connected with the mangroves of neighboring barangays and islands provided the community with excellent catch.

Seagrass and mangrove fisheries have low adaptive capacity due to the high average fishing experience per fisher (Muallil et al., 2011). While more than 20 years fishing experience shows a decreased likelihood to exit the fishery (Muallil et al., 2011), we found that fishers exit the fishery when seagrass and mangrove habitat is degraded (Barangay 5 and Tagumpay), due to either low catch per unit effort or poor quality (“dirty”) catch. Fishers in these barangays reported completely leaving the fishery and only glean recreationally in distant islands where the habitat is not degraded. Alternative incomes in retail, tourism and construction are available to fishers in these urban barangays.

Access to information and community organization help improve governance (Orencio and Fujii, 2013; Ekstrom et al., 2015). Busuanga island has a healthy mix of NGOs dedicated to blue carbon ecosystems and relatively abundant community organizations (POs). Notable exceptions with low NGO and POs presence were urban Barangay 5 and Tagumpay, and rural Turda. We suggest a policy intervention to establish NGO programs in these urban barangays for blue carbon ecosystem management, hopeful that ease of access to these areas will make starting projects possible. Establishing NGO programs in remote Turda, however, will be a challenge. Since POs are not as dependent as NGOs on outside influence and funding, capacity building to enhance PO activities can be led by local barangay officials in both urban and rural settings.