Research

INTRODUCTION

Human societies are innately connected to, shaped by, and dependent on the natural world around them. The concept of social-ecological systems (SES) emerged from this thinking as a way to frame the interconnectedness between people and nature (Berkes and Folke 1998, Charles 2012). Within a coupled SES, the social dimension (including cultural, economic, political, and managerial facets) and the ecological dimension (including climate) interact through dynamic linkages, so that change in one dimension affects the other (Perry et al. 2010, Folke et al. 2016). Understanding the interactions and feedbacks between social and ecological dimensions and how they affect the potential for sustainability is the foundation of SES research (Ostrom 2009, Kittinger et al. 2013).

The ways in which livelihoods are constructed within SES exemplify the linkages between social and ecological dimensions, particularly in the context of rural livelihoods that utilize natural resources such as fisheries. Fish-based livelihood activities and the benefits people derive from them are shaped by processes of environmental and social change (Hicks et al. 2016, Robinson et al. 2019, Golden et al. 2021, Gutierrez et al. 2023). Change in SES is perpetual as complex factors across multiple scales influence both natural systems and human activity simultaneously. How people perceive change and adapt their livelihoods to navigate fluidly changing conditions impacts the SES in which they are embedded (Schwarz et al. 2011). Adaptations can help create or reinforce positive synergies between social and ecological dimensions, or alternatively they can lead to a series of adverse trade-offs or unintended rebound effects where some social or ecological benefits are achieved at the expense of others (McShane et al. 2011, Finkbeiner et al. 2018).

Livelihoods research approaches have become a common analytical tool to understand localized human-environment interactions (e.g., Scoones 2009), including in coastal and marine SES where aquatic foods support an array of fish-based livelihoods (e.g., Ferrol-Schulte et al. 2013). More than just income generating activities, livelihoods in this case can be understood to encapsulate the grouping of assets, activities, and access to these that together compose the living gained by individuals or households (Ellis 2000). Through an intimate understanding of the diverse ways in which people build their lives, a central function of livelihoods approaches is to guide policies and investments at the intersection of rural development and resource management in a way that supports sustainable livelihoods (Chambers and Conway 1992, Allison and Ellis 2001). A livelihood is considered sustainable when the standard of living in relation to assets and well-being and can be maintained, vulnerability to external shocks and trends can be reduced, and livelihood activities do not push natural systems beyond their thresholds (Allison and Horemans 2006).

In the Pacific Islands, where life and society are uniquely connected to the ocean (e.g., Hauʻofa 2008), maintaining the vital contribution of aquatic resources to livelihoods and food security is a point of emphasis in regional policy (e.g., FFA and SPC 2015, SPC 2015). Coastal communities throughout the region derive immense economic, nutritional, and cultural value from marine ecosystems through fish-based livelihood activities (Gillett and Fong 2023). Aquatic foods are an irreplaceable component of regional food security, economic prosperity, and spiritual well-being (Charlton et al. 2016, Farmery et al. 2020). Yet, social and ecological contexts are evolving in the Pacific Islands. For example, integration with global markets have regional food systems in transition (Andrew et al. 2022), and in some places per capita coastal fisheries production is in decline (Barnett 2011, Gillett and Fong 2023).

Numerous projects have been implemented in the Pacific Island coastal fisheries sector to support sustainable livelihoods by enhancing, diversifying, or transforming household livelihood activities. (Roscher et al. 2022a). Livelihoods projects are broadly a form of intervention designed by external agencies or organizations to resolve a problem or improve a situation. Some have been able to achieve outcomes that embody sustainable livelihoods, yet the body of evidence both in the Pacific and globally is inconsistent (Roscher et al. 2022b). Many have struggled to benefit people in the way they intend simply because they are not adapted to local contexts and therefore do not fit with the way people live their lives (O’Garra 2007). Projects not adapted to local contexts typically fail to account for important SES dynamics, which experiences have demonstrated can result in adverse trade-offs or unintended rebound effects within and between social and ecological dimensions (Wright et al. 2015, Pomeroy et al. 2017). Common examples with rich scholarship exist on both sides of the spectrum in the global fisheries literature, such as the trade-offs and rebound effects of projects designed for conservation or, alternatively, from rural development projects (e.g., Diedrich and Aswani 2016, Connell 2018, Gill et al. 2019).

Despite decades of rural livelihoods research, there is still limited knowledge of how livelihoods-focused projects can improve the lives of people while also supporting sustainable fisheries. In part, this relates to a lack of understanding of how people’s adaptations to the change promoted within livelihood projects will impact the SES. We help address this gap using an exploratory approach in a Pacific Island case study setting by presenting fishers with a series of hypothetical fishing and non-fishing scenarios. The scenarios simulate potential changes resulting from projects aiming to either enhance existing livelihood activities or transform into alternative livelihood activities. We query participants how and why they would adapt their portfolio of livelihood activities in response to each scenario with a particular focus on changes to fishing activities. We then examine the relationship between different socioeconomic and livelihood indicators on these adaptations and explore potential trade-offs and rebound effects from them. How fishers respond to hypothetical change can serve as a proxy for how they may respond to synonymous change in reality.

METHODS

Research location and sampling approach

We conducted research from February to April 2023 under ethics protocol 2021/297 using a mixed-methodological approach in nine communities, including five islets, around the northern half of Langalanga Lagoon in Malaita Province, Solomon Islands (Fig. 1). The SES encompassing the lagoon and the communities that call it home exemplify the contemporary contexts of many peri-urban settings throughout the Pacific. Traditional practices mix with modernity as society navigates rapidly changing social and ecological conditions. Although not everyone is a fisher, fish remain an important source of food and income for many (e.g., Sulu et al. 2015). Supporting fish-based livelihoods for food and income security is therefore highlighted as a priority within the current Solomon Islands national fisheries policy (MFMR 2019). Through this policy emphasis, several fish-based livelihoods projects have been implemented in the lagoon, making it an ideal location for our enquiry.

Communities were purposively sampled to include respondents from the two primary language groups in this section of the lagoon: the Langalanga and the Kwaraʻae. Although closely related in some respects, they are distinct in several fundamental aspects of culture and tradition. The Langalanga “saltwater people” have lived on small islets, many of which they built, off the coast of mainland Malaita for hundreds of years (Guo 2003). They are the traditional custodians of coastal aquatic resources that form the basis of their subsistence economy. A defining cultural feature of the Langalanga is their manufacture of shell wealth that they produce for themselves and also trade to other groups within Malaita (including Kwaraʻae) and further afield in the region (Connell 1977, Goto 1996a, Guo 2006). Shell wealth is used by many groups in the Solomon Islands for bride price, body decoration, and compensation among other uses (Guo 2006). Conversely, the “bush people” from Kwaraʻae communities hold customary tenure on the fertile hinterlands of mainland Malaita’s interior and have therefore formed their subsistence economy around horticulture. The systems of barter and exchange are a prominent feature of traditional practices in Malaitan society and have been evolving through centuries (e.g., Ross 1978).

The research team consisted of four people, three of which (one woman and two men) are from Langalanga and therefore both familiar with the customs as well as fluent in Pijin (a lingua franca in the Solomon Islands) and local dialects. Prior to conducting research in each community, the research team visited with respective community leaders to provide information about the research and obtain their approval to proceed with research activities. Our research focused on adults that go fishing regularly (i.e., at least once per week), who were identified using a mixture of purposive and snowball sampling techniques to ensure adequate coverage of our predictor variables (Henry 1990). Initially, local leaders identified women and men from their community who they know to go fishing regularly. Subsequently, fishers were identified via snowball sampling where respondents would suggest more contacts until the same names were repeatedly nominated (Verma et al. 2017). This sampling method has been used previously in similar contexts where respondents are selected based on particular characteristics and where access to such populations can be difficult (e.g., Blythe et al. 2017).

Data collection

We collected data in two parts, a brief survey followed by a participatory activity. The survey helped describe the socioeconomic context of the participant and took approximately 5 minutes to complete. Subsequently, we conducted a participatory activity that included multiple components and took 45–60 minutes to complete. We provide a brief overview of this activity below, refer to Appendix 1 for the data collection documents.

We carried out a livelihoods mapping exercise to understand the range of fishing and non-fishing activities performed over the past year that bring food or money into the household. The time frame of a year was chosen to account for seasonal fluctuations in livelihood activities. We also accounted for the activities performed at the house or in the community that do not bring food or money into their household but do require time. The categories of fishing activities we distinguished include: “glean,” “handline,” “net,” and “spear”. Additionally, we distinguished 10 different non-fishing categories of livelihood activities, including: “casual work,” “formal employment,” “forage,” “garden,” “home activities,” “livestock,” “other” (e.g., remittances), “shell wealth production,” “small businesses,” and “trades” (e.g., carpentry). Participants could identify as many or as little categories of livelihood activities as necessary, yet all participants engaged in at least one category of fishing activities.

As participants described the activities they perform, the facilitator organized them into one of the livelihood categories that took a bit of interpretation. For example, the marketing of foods from the sea or garden was not regarded as its own activity, but as an extension of the primary activity itself. However, if the individual was marketing value-added food products (e.g., cassava pudding), or other non-food goods (e.g., clothing, cookware), it was considered a small business if they owned the market stall. If they did not own the stall, but helped for any sort of compensation, it was considered casual work.

We conducted a ranking exercise to understand which categories of fishing and non-fishing activities were the most important for income and food. Participants placed pebbles next to the activities that were the most, second most, and third most important sources of income, and then repeated the process for food. However, in peri-urban settings like Langalanga Lagoon, most food is purchased from the store and not produced at home. We therefore instructed participants to only consider activities where they produce the food themselves in their rankings. We also instructed participants to consider activities that produce goods they sell, trade, or barter with as income.

Finally, we conducted a scoring exercise to understand the time requirements of each category of livelihood activities. Participants were given 30 pebbles, each pebble represented a day of time in an average month of the year. Participants scored each category of fishing and non-fishing livelihood activities they engaged in, as well as one score for all household activities, according to how much time they spend performing them so that the activities that require more time received more pebbles. Participants were asked to include the time spent marketing fresh foods from the sea or garden in their calculations for how much time they spend conducting the actual fishing or gardening activity. All mapped categories of livelihood activities received at least one pebble, because all livelihood activities take at least some time.

Hypothetical scenarios

Participants responded to four hypothetical fishing and non-fishing scenarios that exemplify possible changes induced by different types of livelihoods projects in the region. For each scenario, we provided participants time to re-think and re-score how much time they spend performing any of their livelihood activities, including starting new ones or stopping existing ones, and then prompted them to provide an explanation for why they did or did not make any changes. We noted if the participant spent more, the same, or less time doing the activity that the scenario was framed around, as well as how time was re-scored across all activities. Once time score changes and explanations were recorded for each scenario, we re-set time scores to baseline.

Scenarios one and two: livelihood enhancements within and outside of fishing

Increasing the efficiency of the small-scale fishery sector is often emphasized as a poverty alleviation tactic within development projects (e.g., Gillett 2010, Purcell et al. 2017). Yet, overly efficient fishing practices also pose an obvious and substantial risk of promoting resource overexploitation. Efficiency increases should lead to proportional decreases in resource use in order to be sustainable (see Binswanger 2001). Scenarios one and two explored this relationship both within and outside of the fishery sector.

For scenario one, we queried participants about how they would respond to a hypothetical doubling in their catch from the fishing activity that had been scored as the most time consuming. For example, if a participant scored the activity of gleaning with the most pebbles of any fishing activity, the scenario was based on a doubling of their typical gleaning catch in the same amount of time. Scenario two related to this same efficiency improvement, but to a pre-existing livelihood activity outside of fishing that was scored as the most time consuming. If gardening was scored with the most pebbles outside of fishing during the baseline activity, this scenario was framed around a hypothetical doubling of the crop production from the garden in the same amount of time. In figures and tables, we refer to scenario one as “double catch” and scenario two as “double production.”

Scenarios three and four: willingness and ability to engage in an alternative livelihood opportunity

Alternative livelihoods are often utilized as a mechanism for conservation by encouraging people to forego resource extractive activities for those that have less impact (Roe et al. 2015, Wright et al. 2015). However, in order to achieve long-term conservation objectives, people must be willing to adopt the alternative livelihood being promoted and have equitable access to it (Wright et al. 2015). Scenarios three and four explored dimensions of willingness and access to engage in alternative livelihoods.

Based on the longstanding plans to develop a tuna processing plant in Bina Harbor (e.g., SIG 2022), scenario three queried participants how they would respond to a hypothetical opportunity for employment at this plant. We asked if participants would be interested in either part- or full-time work, and if yes, how they would re-score their time to account for this new livelihood activity. Part-time employment meant that participants re-scored seven pebbles, while full-time meant they re-scored 15 pebbles. Scenario four builds on this example to understand impacts of inequitable access. We based the scenario on the condition that the participant was interested in the opportunity but did not get employment. Further, fishing taboos have been established adjacent to the plant, which has led to a 50% catch decline from their most time intensive fishing activity. In figures and tables, we refer to scenario three as “new opportunity” and scenario four as “half catch.”

Socioeconomic and livelihood indicators

We accounted for 17 socioeconomic and livelihood indicators that could be associated with fishers’ livelihood decisions in each scenario (e.g., Allison and Ellis 2001, Cinner et al. 2009, Dacks et al. 2018). Socioeconomic indicators collected during the survey included the participants’ gender, age, language group, connectivity (island vs mainland), household size, ownership of expensive fishing assets (engine and/or net), satisfaction with financial and health situations, and the materials used to construct the roof and walls of the house. Livelihood indicators collected during the survey and the participatory activity included percent of catch sold, total time spent fishing, the importance of fishing for food and income, as well as the three properties of livelihood diversity as typified in Roscher et al. (2022a). The properties of diversity include the balance of time allocation across all livelihood activities, the variety of fishing activities employed, and the number of livelihood sectors engaged in (i.e., disparity of economic activities). For the full descriptions of each variable, including how they were calculated and their summary statistics, refer to Appendix 3.

Connectivity and the percent of catch sold were used to explore the effects of market proximity, which has been demonstrated to drive fishery resource exploitation (e.g., Brewer et al. 2009). It is also established in the global literature that wealth status is related to fishers’ decisions to exit a declining fishery (e.g., Cinner et al. 2009, Daw et al. 2012). We examined this relationship in two ways. Financial and health satisfaction served as a proxy to understand the participants’ subjective perceptions of material well-being (e.g., Weeratunge et al. 2014, Coulthard et al. 2017). Additionally, ownership of expensive fishing assets and materials used to construct the roof and walls of the house were used as proxies for participants’ objective material wealth.

Data analysis

We annotated data in a (Microsoft Excel) spreadsheet to be exported into RStudio where all quantitative analyses and visualizations were conducted (RStudio Team 2020). We conducted qualitative analysis of the scenario response explanations following an inductive coding process (Saldaña 2021). Response explanations were repeatedly combed through, and codes were constructed and refined into thematic categories that reflected the diverse rationales behind participants (non)adaptations. This method enabled the data to speak for itself rather than beginning the analysis with a set of codes established a priori.

We calculated the weighted average of baseline time scores based on the methods of Sulu et al. (2015). Time scores were converted to ranks so that for each participant the activity with the most pebbles (i.e., consumed the most time) received a rank of 1 (r = 1), the activity with the second most pebbles received a rank of 2, so on and so forth. If two (or more) activities were scored with the same number of pebbles, they were both given the same rank, and the next highest time score would return to the natural progression. For each participant, the number of ranks depended on how many livelihood categories they engage in. Each category of activities was then weighted according to the frequency of times it was employed among all participants, as follows:

(1)

Where Wt = weighted average time score rank for a given category of livelihood activities

tr = the product of the time score rank for a given category of livelihood activities and the count of how many times it was ranked in that position

f = total count of participants conducting a given category of livelihood activities

We employed binomial logistic regression models to analyze the relationship between scenario responses (dependent variables) and our socioeconomic and livelihood indicators (predictor variables). Significant variables indicated a difference in how people responded to each scenario. The reference category for each dependent variable was set so that interpretations could be framed around the response of interest. For scenarios one and two, we predicted the possibility of participants saying they would invest “more” time into the activity that experienced an efficiency increase. Scenario three predicted the possibility of participants saying they were “interested” in a new livelihood opportunity, and scenario four predicted the possibility of participants saying they would fish “less” in response to a halving of their typical catch.

Prior to fitting each of the four models, we tested the associations between the scenario responses (dependent variables) and the socioeconomic and livelihood indicators (predictor variables) following the methods of d’Armengol et al. (2018). We used Fishers exact tests to test associations with categorical predictor variables, and two-sample t-tests for continuous predictor variables. We performed a Shapiro-Wilk test on each of the continuous predictor variables to check for normality of the data, and a Wilcoxon rank sum test as the non-parametric alternative in instances where the predictor variable was not normally distributed (see Appendix 3). We checked data for collinearity by calculating a variance inflation factor (VIF) of each response variable and all potential predictor variables. No VIF was < 2, so all associated predictor variables were included in the model fitting (e.g., Brewer et al. 2022).

Binary logistic regressions were subsequently fitted with predictor variables that showed associations (p ≤ 0.1 for Fishers exact tests and t-tests) to examine their individual and aggregated effects on each dependent variable (i.e., scenario). In some instances, we included predictor variables that did not have a significant association in the initial testing if they were of interest and there was an anticipated relationship. We employed a stepwise model selection procedure to fit each model, which adds and removes each predictor variable until a minimum Akaike’s information criterion score is achieved. For each scenario, the final model equations were defined as:

(2)

Where Y = scenario 1–4

Χ₁ to Χ_n = each predictor variable included in the final model

β₀ = model intercept

β₁ to β_n = coefficient of each predictor. Odds ratios were calculated by exponentiating each and interpreting the probability of the occurrence of the non-reference category of the response.

Methodological assumptions and limitations

The method enabled participants to express their different realities through evaluations of how each scenario would impact them, yet it struggled to capture much of the contextual complexity that influenced individual responses. For example, people utilize the same fishing method to catch different aquatic foods for different purposes throughout the year (Roscher et al. 2023), and what they target, how much time they spend conducting it, or its importance for food or income would fluctuate accordingly. Participants often qualified their responses to the scenarios by remarking how different contexts within the scenario would change how they respond to it. Part of this complexity relates to our unit of analysis. Our research focuses on the individual and how they negotiate change, which allows us to explore important social dynamics (e.g., gendered dimensions). In reality, these decisions are not made in isolation from the rest of the household or community.

Another form of complexity that was hard to capture related to the connections between livelihood activities. For example, there is a clear connection between gleaning for shells, which we count as a livelihood activity, and the manufacture and sale of shell wealth, which we count as a different livelihood activity. Logically, the species composition and the quantity of shells collected would influence what shell wealth could be produced and the time spent producing it. Although years of heavy exploitation mean some shells are sourced from elsewhere in the archipelago and purchased at markets (Goto 1996a). The same could be said for the time spent marketing foods from the sea or garden. Yet, participants would often change the time scores for the livelihood activity included in a specific scenario, but much fewer considered the consequences of that change and made additional changes to other livelihood activities. We therefore must acknowledge that these scenarios and peoples’ responses to them are oversimplifications of what would happen in reality.

RESULTS

There were 84 research participants (43 women, 41 men) from nine communities, between the ages of 20 and 71 years old (mean = 43.3, standard deviation = 12.9). Nearly a quarter of participants (24%) live on one of the five sampled islets, with the rest living in one of the four study communities on the coastline of mainland Malaita. Most participants were from the Langalanga dialect group (71%), including all of the participants living in communities on islets. Participants fished for an average of 9 days per month and sold just more than 50% of their catch. Most had a high degree of disparity in their portfolio of livelihood activities. Of the 10 livelihood sectors we distinguish (excluding home activities), 45% engaged in six or seven categories compared to only 24% that engaged in three or four categories. Similarly, 70% of participants engaged in a high variety of fishing activities in the past year (i.e., at least three activities). However, just under 40% and 25% identified fishing as the most important source of home-produced food or income, respectively. The complete set of socioeconomic and livelihood indicators, including their summary statistics, are available in Appendix 3.

Baseline livelihood activities and time scores

Fishing with a handline (92% of all participants) and gleaning and collecting (89%) had the highest participation rates within the fishing sector (Fig. 2A). These two activities also had the lowest weighted time ranks (i.e., take the most time on average) among the categories of fishing activities, although their ranking of 3.76 and 4.52 are not among the most time-intensive categories across all livelihood sectors (Fig. 2B). Spearing was conducted by more than half of the participants (62%) and had the next lowest time rank among fishing categories (5.20). The use of a net was the least common fishing activity (45%) and had the highest time rank among fishing activities (5.45), meaning participants typically spend less time doing this activity than other fishing activities.

All participants performed activities in their home or their community, and this category ranked as the second most time-intensive (2.31; Fig. 2B). Nearly all participants engaged in gardening (92%; Fig. 2A), either purely for subsistence or for both income and subsistence, and on average spend a lot of time conducting these activities (2.56). Most participants operated a small business (81%) such as selling baked or value-added foods (e.g., cassava pudding), firewood, or betel and tobacco. The manufacturing of shell wealth was conducted by over half of participants (56%), but it is more time intensive than any fishing activity (3.26). The two livelihood categories with the lowest participation rates were professional trades such as carpentry (8%), and formal employment such as teaching (5%). Although only a few people had formal employment, those that do typically ranked it as their most time-intensive category (1.25). The full set of activities described within each livelihood category, including gender disaggregated participation data, is available in Appendix 2.

Responses to hypothetical scenarios

Scenarios one and two: livelihood enhancements within and outside of fishing

For nearly 80% of women and men, the fishing activity that scenario one was framed around was gleaning and handlining, respectively. In total, approximately 60% of participants increased their time spent conducting the fishing activity experiencing a doubling of catch (S1, Fig. 3), while the remainder were relatively split between decreasing their time spent on the fishing activity (25%) or fishing the same time (18%). Participants reallocated a total of 179 time pebbles in response to a catch doubling, which resulted in a net surplus of nearly 50 pebbles in the scenario fishing activity by men and another 40 by women (S1, Fig. 4). Time spent gardening was most frequently adjusted, followed by time spent on home and community activities, and in both categories there was a net loss of pebbles for women and men. Participants also frequently moved pebbles that were allocated to other fishing activities, leading to a net loss of 14 pebbles for men and 2 for women. Four participants ceased a different fishing activity to focus on the scenario fishing activity.

For 55% of the participants, the activity that scenario two was framed around was gardening, followed by manufacturing shell wealth (23%), small businesses (13%), casual work (6%), and professional trades (3%). Contrary to scenario one, 56% of participants deceased their time spent conducting the non-fishing activity experiencing a doubling of production (S2, Fig. 3). A total of 222 time pebbles were reallocated, which led to a net deficit of 87 pebbles in the scenario activity for women but a net surplus of 28 pebbles for men (S2, Fig. 4). Home and community activities had the most pebbles adjusted, resulting in a net gain for both women (45 pebbles) and men (5 pebbles). In all other categories of livelihood activities there was a net loss of pebbles for men and a net gain for women, including fishing activities where men had a net loss of 20 pebbles and women had a net gain of 9 pebbles.

Scenarios three and four: willingness and ability to engage in an alternative livelihood opportunity

In response to an opportunity for formal employment outside of the small-scale fishing sector, approximately 66% of participants were interested (S3, Fig. 3). Slightly more than half of interested participants indicated they would take a full-time position and reallocated 15 pebbles while the remainder took part-time employment and reallocated 7 pebbles. In total, 637 time pebbles were reallocated, which resulted in a surplus of nearly 400 pebbles in the scenario activity for women and 243 for men (S3, Fig. 4). All categories of livelihood activities had net losses of pebbles, including fishing activities, which had the highest net losses of 131 pebbles for women and 92 for men. Of these participants, 17 ceased one or more fishing activity to focus on the new opportunity, and another six left the fishing sector by ceasing all fishing activities. Many participants also moved pebbles from gardening (-71 for women and -35 for men) and home and community activities (-77 for women and -11 for men).

Unable to access the new opportunity and faced with the halving of their typical catch, 68% of participants decreased their time spent conducting the fishing activity experiencing a catch loss, compared to just 10% that increased their time (S4; Fig. 3). Participants reallocated a total of 169 time pebbles in response to a 50% catch loss which resulted in a net deficit of 70 pebbles in the scenario fishing activity by women and another 50 by men (S4; Fig. 4). Five participants, mainly women, ceased the scenario fishing activity. Time spent gardening was most frequently adjusted, leading to a net gain of 39 pebbles for women and 32 for men, followed by shell wealth production (+22 pebbles for women but -9 for men). Participants also frequently shifted pebbles allocated to other fishing activities, which led to a net gain 13 pebbles for men but a net loss of one pebble for women.

Response explanations

Response explanations were thematically organized into six categories (Table 1). In all four scenarios, the most common category of response related to economics and opportunities. Many of these responses were driven by the desire to increase income or time efficiency. However, income maximization was not the sole motivation behind people’s actions; many participants referenced other motivations such as life satisfaction and enjoyment or food security. Approximately 30% of the responses to a doubling of production from a non-fishing activity (S2) referenced life satisfaction and enjoyment as their motivation and another 20% referenced this categorical driver in response to a doubling of their catch (S1) or a new livelihood opportunity (S3). Food security was most frequently mentioned in response to a catch loss (S4; 30%). Though not as frequent, a moderate number of participants referenced their families and communities in their response explanations, including 14% in response to a doubling of their catch (S1). The category of health and safety was also moderately referenced, particularly in response to a new opportunity outside of the fishing sector (S3; 11%). Environmental concern was the least referenced categorical driver of response explanations across all four scenarios.

Influence of socioeconomic and livelihood indicators

Four binary logistic regressions were used to analyze the relationship between relevant predictor variables and the response of interest in each scenario (Table 2). Scenario one analyzed the probability of investing or not investing more time into a fishing activity that doubled in production. There was a significant positive association between this response and people from the Kwaraʻae language group who were nearly four times more likely to fish more compared to people from the Langalanga language group. There was also a significant positive association between investing more time into fishing and age (for every year of age, the odds of increasing time spent fishing increased by 5%), and a moderate positive relationship between investing more time fishing and having roofs made with modern building materials. Those who responded to an efficiency gain within the fishery by investing more time were significantly associated with those who responded to an efficiency gain outside of the fishery by investing more time (S2). Regarding the probability of investing or not investing more time into a non-fishing activity that doubled in production, women were over six times more likely to not invest more time compared to men.

Scenario three analyzed the probability of being interested in a new alternative opportunity outside of fishing, and again there was a significant positive association with women who were over three times more likely to be interested. Conversely, there was a significant negative association with being interested and age (for every year of age, the odds of being interested in the alternative opportunity decreased by 5%). There was also a moderate positive relationship with participants who have modern housing materials on their roofs. When faced with a halving of the typical catch (S4), only a few variables were moderately associated with the decision to invest or not invest more time into fishing. People from mainland communities were approximately three times more likely to decrease their time spent fishing compared to people from island communities. Additionally, those who already spend more time fishing were more likely to fish less in response to a halving of their catch (for every additional day a participant fishes, the odds of decreasing their time spent fishing in response to the catch loss increased by 11%).

DISCUSSION

Adaptations to each of the four scenarios demonstrated complexity in how different people within a geographically confined space perceive and react to the same change and highlighted some of the numerous trade-offs or rebound effects that can emerge through various forms of livelihoods projects. Scenarios framed around livelihood enhancements suggested efficiency improvements in the fishing sector are more likely to lead to overexploitation compared to analogous improvements in other economic sectors. Further, scenarios framed around an alternative livelihood opportunity exhibited gendered and age-related differences in willingness to engage in the new opportunity, the possibility of reinforcing vulnerabilities for the already vulnerable, or contributing to deleterious large-scale processes such as the ongoing regional food system transition. Findings showcase the utility of hypothetical scenarios to uncover insights into local contexts and emphasize the need to deliberately investigate these contexts before implementing projects, imposing management regulations, or enacting policies to minimize the risk of adverse impacts.

Potential trade-offs and rebound effects emerging through hypothetical scenarios

Profitability at the expense of sustainability

The tendency to invest more time into a fishing activity with enhanced efficiency corroborates an economic theory from the late 19th century known as Jevons’ paradox (see Alcott 2005). Instead of reducing resource use, the theory proposes that efficiency gains have a rebound effect where the gains as well as additional input (i.e., time) would be invested back into the fishery because of the reduced cost of input per unit output (i.e., catch). This pattern would ultimately increase resource consumption and incentivize fishing practices that exceeds the long run sustainable catch limits, thus resulting in a trade-off where poverty reduction objectives are achieved at the expense of resource sustainability. There are abundant examples illustrating how technological innovations in fisheries incite this paradox across economic sectors and geographies (e.g., Gillett et al. 2008, Eigaard et al. 2014), including from development projects within the small-scale fisheries sector in Malaita. For example, Akimichi (1991) documented the decline of rabbitfish populations in the shallow sea-grass beds of Lau Lagoon after coolers with ice blocks were distributed for coastal fisheries development.

The contrasting results between efficiency improvements inside the fishery versus analogous improvements outside of the fishery may, in part, allude to the intangible rewards that people realize through fishing such as satisfaction and self-realization (e.g., Pollnac et al. 2001, Pollnac and Poggie 2008), or the cultural significance of fishing within the lagoon (e.g., Goto 1996b). The prevalence of responses emphasizing life enjoyment and satisfaction, or family and community, highlight that these can be powerful motives in the Pacific context (e.g., Govan 2011). Several response explanations explicitly mentioned the enjoyment of fishing, such as this 27-year-old woman, “I enjoy fishing, so I would glean more and [earn more income] to support my little family’s needs ...” Even within the fishing sector, adaptations reflected personal preferences for activities that require little financial investment or physical exertion or could be done more easily such as gleaning or handlining.

Indicators related to increasing the time spent on an enhanced fishing activity include people from the Kwaraʻae language group, older people, and to a lesser extent, those with houses made of modern materials (i.e., tin). For an efficiency improvement outside of the fishery, men were especially prone to increase time spent on the enhanced activity compared to women. Unsurprisingly, almost everyone adapting to an efficiency improvement by investing more time into the enhanced activity was at least partially motivated by economic opportunities and the idea of increasing their incomes, but explanations referencing food security were also common. Findings corroborate global scholarship that show factors such as gender, age, ethnicity, and wealth status influence how people adapt to environmental change in a small-scale fisheries context (e.g., Bailey 1982, Pollnac et al. 2001, Cinner et al. 2009). They also support Lau et al.’s (2019) finding from Papua New Guinea that people assign the highest importance to ecosystem services that directly support livelihoods and material well-being.

Gendered and age-related patterns in (non)adaptations to new opportunities

Scenarios framed around an opportunity for employment outside the fishing sector show that two out of three people would be willing to adopt an alternative livelihood. In doing so, nearly everyone adapted by shifting time out of the fishery sector, including a substantial amount of people that either ceased at least one fishing activity or left the fishery completely. The prevalence of this adaptation demonstrates the alluring potential of alternative livelihoods that can also provide a secure income as an approach to achieve conservation objectives by diverting effort away from the fishery. However, both older people and men were significantly associated with not being interested in the alternative livelihood.

Results support the prevailing insight that older people exhibit more rigidity in their willingness to adapt to change, including alternative livelihoods outside of the fishery (e.g., Bailey 1982). They also reflect the evolving norms around traditional divisions of labor in contemporary Pacific society (Cohen et al. 2016), and question the notion that women are more risk averse than men regarding their willingness to engage in new opportunities, as argued by Lawless et al. (2019). Our finding aligns with Locke et al. (2017) who found women in communities close to urban centers (such as those in Langalanga Lagoon) display a greater willingness to take the lead on doing new things. Yet, women were also significantly more likely to shift time away from an enhanced non-fishing activity into other activities, often by reinvesting their time back into household activities. This willingness to take on additional activities away from the household coupled with the frequent adaptation to reinvest efficiency gains back into the household to “... catch up on home activities” (54-year-old female), highlights the risk of increasing labor burdens for women through alternative livelihoods. This can ultimately lead to a trade-off where women’s agency and immediate freedom is jeopardized to accommodate intensified time and labor demands (Lawless et al. 2019).

Risk of new opportunities reinforcing pre-existing vulnerabilities

Faced with the inability to access an opportunity for employment that also led to a catch loss through increased restrictions, most people shifted time away from the impacted fishing activity. Although insignificant, there was a predictable moderate relationship between shifting time away from the fishing activity experiencing a catch loss and living on mainland Malaita. Communities on the mainland would have greater access to infrastructure, amenities, and economic or subsistence opportunities, all of which would encourage a higher mobility of fishing labor.

Evidence from Sulu et al. (2015) suggests fishing livelihoods are comparatively more important for those living on the islets, and this notion was verified by the finding that people there were twice as likely to score fishing as their most important source of food produced at home. As one 49-year-old male living on an islet explained, “Fishing is my main source of food and income, I have no choice but to increase [effort] to address the loss.” This lack of alternative subsistence and economic options beyond fishing helps to demonstrate the probability that if access to an emerging opportunity was limited and increased fishing restrictions were put in place, people living on islets could be disproportionately impacted if they are excluded. Understanding how people live and make a living within the wider context of society should therefore be prioritized to inform decision making in a way that avoids reinforcing vulnerabilities for the already vulnerable.

Contributing to a food system transition that undermines public health

When presented with a hypothetical opportunity for alternative employment, people’s adaptations and explanations indicated they prioritized income security over food security. More than half of the total time shifted into the new activity originated from a primary producing activity such as fishing or gardening. As one 40-year-old male explained, “With a secure income, I will just buy more food from the shops to make up for lost garden production.” Tendencies to forego food production activities highlight the growth of the cash economy, particularly in areas like Langalanga Lagoon with access to urban markets. Associated to this integration with the cash economy is a food system in transition from local indigenous foods to imported processed foods (Andrew et al. 2022). The ubiquity of imported foods has grown substantially in recent decades as they outcompete traditional foods in price and availability (Coyne 2000).

Time flowed back into primary producing activities when faced with the inability to access the alternative livelihood opportunity that also led to a catch loss. Commonly motivated by food security needs, most adapted by shifting time away from the impacted fishing activity into different fishing or gardening activities. Adaptations to catch losses reveal the importance of local food production as a fallback option during times of hardship, as was recently experienced during the COVID-19 crisis (Farrell et al. 2020, Ferguson et al. 2022, Eriksson et al. 2023). It is therefore critical to consider how projects promoting alternative livelihoods or implementing fishery restrictions impact the production of and access to locally produced and nutritionally valuable foods. Overreliance on imported processed foods can negatively rebound by reducing food security and sovereignty, with knock on consequences for public health (Thaman 1982, Hughes and Lawrence 2005, Ferguson et al. 2022).

Implications of findings and conclusions

The variation in how people adapted to each scenario, including how they allocated their time and how they rationalized their decisions demonstrates some of the complexity in how people make choices as they navigating change. People adapt to change based on their unique needs, desires, and capabilities, and these adaptations also carry their own consequences. Responses and explanations to the scenarios illustrated how everyone’s circumstances are different, and these differences make it very unlikely that a livelihoods project will be able to benefit everyone equally. Combined with the inherent tensions of aligning conflicting rural development and resource sustainability objectives, it may very well be that trade-offs and rebound effects from livelihoods projects are inevitable.

Some scholarship has argued that the risk of seeking win-win SES outcomes that uniformly benefit both people and planet feeds a cycle of optimism and disenchantment that benefits neither of them (e.g., McShane et al. 2011). The pragmatic approach through this perspective would be to identify and acknowledge the likely trade-offs or rebound effects of different choices during the planning phase to help inform decision making (Hirsch et al. 2011, Howe et al. 2014, Finkbeiner et al. 2018). Through a process of identification and negotiation, difficult choices can then be made in partnership with beneficiary communities in a transparent manner that includes careful consideration of how the most vulnerable could be impacted. Explicitly acknowledging trade-offs may elicit uncomfortable conversations, but it would also manage realistic expectations as well as bring clarity into what matters to whom and why, and what should not be traded off.

Generating insights into what factors predict certain trade-offs, for example, by simulating the proposed change, represents one way to uncover some of the diverse contexts that shape people’s adaptations to social and ecological change. The knowledge generated through this approach provides valuable insights into how people may adapt to similar changes in reality, thereby building on the stream of research that has utilized scenarios to anticipate behavior to environmental change (e.g., Cinner et al. 2009, Shaw et al. 2009). This represents an important contribution to understand how policies or investments may impact the SES they are embedded within, which may be able to help expose potential adverse trade-offs or unintended rebound effects pre-emptively. Yet, a more robust qualitative tool is needed to more meaningfully unpack why certain factors are associated to different adaptations. This is one area where future research would be well-positioned.

Although the extrapolation of specific findings from this research are inhibited by their localized nature, lessons from this research carry significant practical applications for development practitioners, resource managers, and policy makers seeking to facilitate development or conservation to integrate into their planning. Methods that can produce learning into local contexts before implementing projects, imposing regulations, or enacting policies can help identify relevant leverage points or strategies that minimize the risk of inciting maladaptive responses to change. In this way, efforts to support sustainable livelihoods both within the Pacific Islands and further abroad can be better positioned to contribute to policy goals that emphasize this objective such as the Solomon Islands national fisheries policy (MFMR 2019), and to benefit people in the way they intend.

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