Our study identified nine principal components (PCs) consisting of seventeen variables that influence fishers’ social vulnerability: each has eigenvalue > 1 (Fig. S1), and together explained 69.44% of the data’s variance (Table 3). Their ‘cardinality’, positive or negative loadings, reflects increased or decreased vulnerability. The sum of PCs’ weights produces the SoVI score of the fishers’ community in the Indramayu sub-district, i.e. +1.76. The positive sign indicates the high-level of their social vulnerability to Rob flooding.
Each PC’s value (column ‘weight’) reflects its contribution to the community’s overall vulnerability: the higher the value the greater its contribution. For example, external support and local government’s mitigation capacity (weight 0.16), and local knowledge of Rob flooding (weight 0.16) are PCs with the greatest contribution to the overall SoVI. The nine PCs reflect fishers’ local characteristics: mostly held low-level education with insufficient income to afford basic livelihoods and recover from disasters, hence perpetual-poverty. Nonetheless, they insist on maintaining the farming-livelihood, becoming an intra-generational culture, and equipping them with vast experience. Poverty means they cannot afford higher education for their children, resulting in generations unable to acquire new knowledge and technology to mitigate current climate-related hazards. These characteristics influence their overall vulnerability to Rob, thereby informing government authorities on improving disaster prevention and mitigation strategies. Our key findings are discussed further below, structured around the nine PCs.
External support and local government’s mitigation capacity and efforts (PC-1)
This is the most significant component representing 11.21% of total variations in farmers’ social vulnerability (Table 3) comprising four variables: fishers without the ‘standard operating procedure’ (SOP) (V1 74.1%); fishponds without evacuation routes (V2 77.8%), fishers without ‘early warning system’ (EWS) (V3 83.3%), and without Rob counseling (V4 77.8%) (Table 3). All variables were positively loaded contributing to a high social vulnerability. To address this, the local community requires external support including training, technology, finance, ambulance, and labour force from State and other agencies. Our findings show the governments’ failure to provide these.
The National government and the district’s head who chairs the regional agency (‘the Agency’) are responsible for coordinating disaster relief. Interviews of local government officials confirmed that low community engagement was the main weakness within Rob mitigation and adaptation efforts, leading to insufficient new knowledge and technology transfer and weak social capital. This confirms earlier studies (Tran et al. 2017; Gathongo and Tran 2019) which highlight that when social capital, including community organizations, is weak a disaster’s impact on the social-ecological system is severe due to the community’s inability to mobilize local resources in response. Moreover, the Agency’s limited personnel, facilities, and training resulted in personnel with low competency, only rescuing communities suffering disasters but not acting pre-emptively. Therefore the system’s sensitivity and exposure to Rob remains high for the majority of fishers. Interviews revealed that fishers responded to Rob in three ways: (i) leave to chance, (ii) harvest the fish as small tidal surges arise, and (iii) elevate the ponds with sand, together showing a short-term plan. Similar to Jozaei and Mitchell (2018)’s argument, external support and local government’s mitigation efforts are critical in building communities’ long-term adaptive capacity, including Early Warning System (EWS) availability.
The EWS development should include five inter-related aspects: (i) economic sustainability, (2) community organizations and leaders, (3) indigenous communities involvement, (4) gender and cultural diversity, and (5) effective institutional arrangements (Mohanty et al. 2019). In our study 83.3% of fishers said there is no EWS (Table S1), contributing to the community’s high vulnerability level. Interviews confirmed the lack of community organizations, local leaders, disaster management capacity building, governments’ mitigation efforts, and the Agency personnel’s knowledge on social vulnerability. Together these minimized communities’ social learning of knowledge and skills essential for building adaptive capacity.
Given the lack of external support and the government’s mitigation efforts, in agreement with Moreno et al. (2019) we suggest it is critical that the community develops its resilience by strengthening ‘activation capacity’, linking a network of local resources promptly during and post-disturbance. The community’s activation capacity is low (i.e. high vulnerability) reflected in the positive values of emergency response (V1), evacuation route (V2), EWS (V3), and local knowledge (V5) (Table 3). In this setting, the activation of local resources often requires external support (e.g., an effective government institutional organization and its mitigation and adaptation measures) resulting in a vulnerability trap loop. In this regard, the government needs to improve the adoption of the Hyogo Framework for Action (HFA) 2005–2015 (United-Nations-Office-for-Disaster-Risk-Reduction 2007) in developing an early warning system, support local livelihoods (Mohanty et al. 2019) and operations with disaster managers (Moreno et al. 2019), allow the projection of the future state of the SES system (Ferro-Azcona et al. 2019), as well as increase community awareness of the critical importance of the early warning system (Mashi et al. 2019).
Local-knowledge of tidal (Rob) flooding (PC-2)
This PC relates to whether fishers perceived Rob as a disaster and their knowledge and skills to respond. It comprised three positively loaded variables; inundated fishponds (V5), economic loss (V6), and farmers’ perception of Rob (V7), constituting 10.98% of SoVI’s variations, meaning high and increasing social vulnerability (Table 3). 32.4% of fishers only considered Rob as a disaster when large, as small was used as a harvesting technique (V7); 67.6% considered all scales of Rob as disasters (Table S1).
In general fishers have vast experience in fish farming with traditional harvesting systems working sufficiently well to address the re-occurrence of small-scale Rob. They used flood water as a source to provide feedstock, such as the small prawns that accompany it, and harvested fish immediately to reduce the risk of loss. To an extent, local knowledge catalyzes social resilience: since Rob has occurred for generations fishers carry memories of past events, developing the capacity to respond and adapt to hazards, including the use of the event for their benefit. However, traditional harvesting and protection systems are insufficient to address large-scale Rob. Since only modest protection for fishponds was provided by creating sand embankments severe Rob affected 68.5% of fishponds (inundated, V5) and 71.3% of farmers (economic losses, V6) (Table S1). Other forms of local knowledge and mitigation efforts by farmers were minimal: only 27.8% planted mangroves for flood protection (Table S1), 59.3% increased the pond embarkment height, 12% provided pond nets, 4.6% created water tunnels, 0.9% modified their ponds, 2.8% conducted early harvests, and 20.4% made no mitigation efforts (Table S7). This data highlights the critical need for external support (PC-1) to assist local fishers to improve their mitigation capacity to respond to large-scale Rob flooding.
Income, expenditure, family size, and seasonal expenditure (PCs 3, 4, 5, and 6)
These four PCs are closely related. The third, comprising 8.73% of SoVI variations, includes four negatively loaded variables (Table 3): farmers earning > IDR 12 million/harvest (V8), fishpond yielding > 1 tonnes/harvest (V9), farmers who did not rent their ponds (V10), and polyculture fishponds (V11). Only 35.2% of farmers earn over 12 million Rupiah per harvest (> US$ 805, V8) (Table 3), the remainder earning less. The government places households in the Indramayu district earning below 1,899,228 Rupiah/month (under 24 Million Rupiah/year) as under the poverty line (Statistics-Indonesia 2020b). Since most fishponds are harvested twice annually, i.e. ≈ 24 M Rupiah/year, most farmers’ (64.8%)’ income is insufficient to meet basic needs. Moreover, 45.4% of fishponds are rented, reducing overall harvest net income. The inundation of severe Rob and low-crop yields (75.9% farmers harvested < 1 tonnes/harvest, Table 3, S-1) led farmers further into debt. Contrary to findings from earlier studies (Cutter et al. 2003; Tran et al. 2017) where yields enabled farmers to recover from the damage. This Evidence explains income as a principal component influencing social vulnerability has also been identified by previous studies in developing countries (Tran et al. 2017).
The fourth component, expenditure, comprises 8.49% of our SoVI variations (Table 3). 65.7% of farmers (Table 3) developed their ponds themselves their income being insufficient to justify hiring additional labour. When recurring Rob either reduced profits or increased losses, farmers incurred debt to re-establish damaged ponds leading to recurring poverty.
The fifth, family size, comprises 7.77% of our SoVI variations (Table 3) with 33.3% fishers having more than four dependants (Table 3). Dependants include people less than 5 and above 65 years-old, school-age of 6–17 years, none of whom contribute to family income, i.e., the non-workforce (Statistics-Indonesia 2018a). Whilst an elderly person living alone is vulnerable to disaster (Mohanty et al. 2019), we found no evidence of a single elderly person in the study villages. A higher number of dependants reduces fishers’ financial resources and their ability to recover from disasters, and hence the low level of resilience and adaptive capacity. Whilst most fishers support less than four family members directly many support others indirectly.
The sixth PC, seasonal expenditure, explains 7.73% of our SoVI variations (Table 3). During harvest time 63.9% of fishers (Table 3) needed additional five to ten people weekly, the daily expenditure of 100,000 to 150,000 Rupiah (i.e. US$ 6.7–10) (Table S6) being a large financial burden. As most farmers incur debt when establishing ponds they have the little financial capacity to recover from Rob disasters, leading them to incur more debt, often resulting in bankruptcy and poverty. With the lens of ‘adaptive capacity’ applied to the capacity of response to long-term or sustainable adjustments to social-ecological system changes (Gallopin 2006), this situation explains a poverty trap and there is a clear indication that fish pond farming has been unable to improve the communities’ livelihood.
Level of education and fish farmer experience (PCs 7 and 8)
These two PCs are closely related. The level of education (PC-7) comprises 5.31% of our SoVI variance (Table 3). This finding resonates with studies in other developing countries where education was identified as a PC influencing social vulnerability (Nguyen et al. 2017; Dintwa et al. 2019; Aksha et al. 2019) but differs from the USA’s study (Cutter et al. 2003) and China (Lixin et al. 2014) where education was not identified. A 32.4% of our study’s fishers have no formal education (V15) and 25% only primary, 14.8% secondary levels, 24.1% high levels (Table S3). For instance, many farmers could neither read nor write, requiring the first author to read the questions and write the respondents’ responses. This condition constrains fishers’ ability to understand early-warning information and access recovery information during disasters.
The eighth component, fishers’ experience, comprises 5.09% of our SoVI variations (Table 3), with 63.9% having more than ten years of experience and the rest greater than five years (Table 3). Their occupation began when they were 12–15 years of age as they did not participate in formal schooling. Their techniques and methods of farming are largely informed by and acquired through experience inherited from previous generations, and hence their adaptive capacity to a tidal (Rob) flooding disaster is also formed in the same way. The memory of past events, which for generations equipped fishers with the ability to respond to repeated hazards, is challenged by contemporary environmental and climate change. With limited education, to mitigate and adapt to environmental changes fishers need external support from relevant state and non-state agencies to access external sources of information and techniques on resilient farming and technology. Moreover, the role of volunteers and environmental NGOs needs to be encouraged to enhance the local capacity to mitigate and adapt to disaster, with the latter advocated by the United Nations International Strategy for Disaster Reduction (United-Nations-Office-for-Disaster-Risk-Reduction 2007).
The last PC comprises 4.09% of SoVI variations (Table 3). Small relative to other PCs, this component influences fishers’ social vulnerability less significantly. 83.3% of them are ‘native’ (Table 3), a term describing fishers born locally, holding local identity cards, inhabiting the area, and farming pond-fish for generations. Known as ‘orang Indramayu’ they speak the local ‘Dermayon’ language (ethnic population detailed in the Materials and methods section). The rest (16.7%) are migrant seasonal fishers who, when interviewed, stated that they were not local.
Similar to previous studies (Cutter et al. 2003; Aksha et al. 2019; Rabby et al. 2019), our research revealed that native fishers employed seasonal fishers which indirectly improved the overall village economy, although the ratio between the two types of fishers is different. The percentage of native fishers in our study is larger, an essential social capital that strengthens the community’s collective capacity to respond to disasters as they were more active in disaster relief activities compared with migrant fishers (Tables S1). As well as a shared language and local customs, inhabitating the villages and sharing an occupation for generations contributed to the development of stronger bonds around shared farming activities that become culture-based. Even when experiencing harvest failure fishers return to the same livelihood with 83.3% confirming no plan to change occupation for five years, and 42.6% with intention to open new fishponds (Table S1). These findings inform the government when establishing its mitigation and adaptation plan: communities wish to continue working where they are most comfortable culturally, with valuable inter-generational experience but require protection from Rob. With a small-percentage of migrant fishers, this PC did not influence local social vulnerability significantly, contrary to other studies where the components of language and cultural diversity contrained access to disaster relief resources for certain ethnic groups (Cutter et al. 2003). Nonetheless, similar to our finding, an Australia case revealed strong bonds amongst community members as a key enabling factor for the activation of local resources when disasters strike (Hanson-easey et al. 2018).
Discussion summary: theoretical contribution and policy recommendations
As discussed earlier, the phenomenon of tidal (Rob) flooding which occurs regularly, sometimes three times a year, provides the social-ecological system with regular exposure. The destruction of mangrove forests as natural tidal protection as a consequence of uncontrolled aquaculture land expansion activities toward the sea causes infiltration properties is highly impacted on coastal vulnerability to erosion rate and tidal inundation flooding. (Dasanto 2010; Suroso and Firman 2018; Maryanto et al. 2018). From the conceptual lens of vulnerability, resilience, and adaptive capacity, the repeated occurrence of Rob provides the social-ecological system with regular exposure. Government authorities should be able to anticipate and modify the system’s sensitivity, thereby increasing its resilience. However, the SoVI score of + 1.76 indicates not only the high-level of fisher vulnerability but also the government’s failure to develop the community’s adaptive capacity. While our PCs of education, ethnicity, family size, and local knowledge are similar to those identified in Bangladesh, Mozambique, and Nepal (Blythe et al. 2015; Aksha et al. 2019; Rabby et al. 2019), external support and local government’s mitigation efforts, income disparity, and exposure to Rob are unique to our study. While government authorities should address all nine-identified PCs to improve future mitigation and adaptation efforts, our policy recommendations (summarised in this section’s last paragraph) focus on these uniquely-identified PCs.
Our findings contribute to the literature by highlighting the significant role of a community’s memory of past events, i.e. regular exposure to Rob flooding. This collective memory, accumulated intra-generationally, enabled community members to develop their capacity to respond and adapt to disasters. This includes their traditional knowledge to use small-scale Rob as a means of harvesting, a source of feed-stock, and to sustain the fish farming occupation. Nonetheless, these alone are insufficient to respond to severe hazards exacerbated by climate change. Therefore community experience and skills developed through the system’s repeated exposure to disasters must be complemented by the acquisition of new knowledge and technology to address the increased frequency and intensity of Rob flooding. Here external support and local government’s mitigation efforts are crucial but were largely absent in our case study.
‘Sensitivity’ to hazards, another vulnerability element besides ‘exposure’, was largely unaddressed by the government, consequently reducing the community’s overall capacity to cope with and moderate the impact of disasters. The local government’s limited human resources and finance enabled them to focus only on disaster relief. The development of the long-term capacity to modify the system’s sensitivity (including fishponds, fishers’ knowledge, and training, income, and organizational structure) is lacking. Our findings are similar to previous studies in Vietnam with Tran et al. (2017) showing the relationship between livelihood-diversity, income, and vulnerability, and Nguyen et al. (2017) which highlighted variables of poverty, income, family size, community organization, and education, as PCs influencing social vulnerability. Relevant to this is ‘activation capacity’, the ability of community members to activate and mobilize local resources to respond to hazards promptly and moderate the disaster’s impact. Activation capacity is influenced by the availability of social networks, community organizations, and local leaders, mostly missing in our study area.
In summary, related to critically needed of external support, our policy recommendations are as follows. Government authorities should improve local activation capacity by deploying sufficient resources; finance, knowledge and technology, physical, institutional, and infrastructure. Specifically, this support needs to be directed towards training local leaders and developing community organizations and cooperatives. The latter, known locally as BumDes (village economic unit) and available in many parts of the district but not within the five villages studied, are essential to building a sustainable local economy which addresses the perpetual poverty. This recommendation is in line with a Mozambique study that argued that focusing on building human capital could contribute towards minimizing the negative impact on coastal communities’ shrimp farming (Blythe et al. 2015). Utilizing our SoVI, governments should prioritize intervention on PCs with the highest contribution to overall SoVI, including the need for early warning systems and farming standard operating procedures. Moreover, government authorities should utilize our SoVI and other relevant-studies to inform government-sponsored studies across Indonesia’s coastal villages, and in particular our measured variables for adaptation to future SoVI development. This is because whilst each community is unique, coastal communities face similar socio-economic challenges. Being amongst the poorest in Indonesia, South East Asia, and Sub-Sahara Africa generally, they are continuously threatened by climate-related disasters and are heavily dependent on coastal resources where the majority of inhabitants are fish or shrimp farmers (Suroso and Firman 2018; Krishnan et al. 2019). Hence, our findings hold global policy relevance contributing empirical evidence on which PCs should be prioritized for intervention to improve relevant mitigation and adaptation strategies, and extends evidence from previous studies including those from Bangladesh, India, Iran, Mozambique, the Philippines, Vietnam, Nepal, China and Bostwana (Guleria and Edward 2012; Lixin et al. 2014; Blythe et al. 2015; Tran et al. 2017; Aksha et al. 2019; Dintwa et al. 2019; Hadipour et al. 2019; Krishnan et al. 2019; Rabby et al. 2019).