Co-evolutionary pathways: How governance and conflict shape human adaptation in contested social-ecological systems

Co-evolutionary pathways: How governance and conflict shape human adaptation in contested social-ecological systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Co-evolutionary pathways: How governance and conflict shape human adaptation in contested social-ecological systems Recamar Guiñares, Joan Regina Castro, Nygiel Armada, Elin Torell This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7986678/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Managing human adaptation in contested social-ecological systems (SES) is a defining challenge of the Anthropocene. Current approaches often fail because they treat governance as a static intervention designed to manage resources, overlooking its dynamic interplay with social conflict. How governance and conflict co-evolve to produce divergent community fates remains a critical unknown. Here, using extensive survey data from small-scale fishing communities in the West Philippine Sea—a global hotspot of biodiversity and conflict—we reveal how seemingly homogenous communities facing identical pressures fracture into distinct, path-dependent trajectories. We show that this divergence is driven by a co-evolutionary feedback loop between institutional frameworks and community responses. In regions with weak institutional presence, a negative feedback loop emerges, where unresolved conflict drives individualistic ‘exit-and-ration’ strategies that further erode the potential for collective action. In contrast, where active management provides forums for negotiation, a positive feedback loop fosters a collective ‘comply-and-buffer’ strategy, reinforcing institutional legitimacy and building adaptive capacity. We further reveal that this dynamic is fueled by a crisis of economic entitlement, not resource scarcity. These findings present a generalizable model of SES change, demonstrating that adaptation is an emergent property of the co-evolution between social conflict and governance. Effective intervention must therefore shift from designing static rules to cultivating institutional processes capable of channeling conflict into constructive, adaptive pathways. Social-ecological systems Adaptation pathways Co-evolution Marine governance Resource conflict West Philippine Sea Figures Figure 1 Figure 2 Figure 3 1. Introduction The South China Sea is a global nexus of extraordinary marine biodiversity (Acosta et al., 2025; Quimpo et al., 2019 ), immense economic value (Liu, 2013 ; Ng and Tan, 2000 ; Zhong and White, 2017; Galdorisi, 2014 ; Zhang et al., 2023), and intense geopolitical conflict (Sasaki, 2025 ; Lee, 2023 ; Malayang et al., 2023 ; Tet et al., 2017; Mendoza et al., 2019 ; Zhang, 2018; Joyner, 1998 ). This vital seascape, which includes the West Philippine Sea (WPS) (Malayang et al., 2023 ), is pushed toward ecological collapse by the synergistic pressures of overfishing, habitat destruction, and militarization (Sasaki, 2025 ; Lee, 2023 ). While the dominant narrative frames this as a geopolitical crisis requiring top-down state action (Sasaki, 2025 ), this view obscures a deeper puzzle playing out at the human scale: how do local communities, sharing a common ecosystem and facing identical stressors, arrive at dramatically divergent fates? Why do some communities collapse into social fragmentation and resource degradation, while others innovate and adapt? This paper argues that the answer lies not in the nature of the environmental shock or the resources available, but in the institutional architecture that determines whether social conflict becomes a catalyst for innovation or a trigger for collapse (North et al., 2009 ). While decades of research, following Ostrom, have established that institutions are critical for managing common-pool resources (Cox et al., 2010 ; Gelcich et al., 2010 ; Ostrom, 2009 ; Dietz et al., 2003 ; Agrawal, 2001 ), much of this work treats governance as a static input for managing resources, often assuming a baseline of potential cooperation. It has not fully grappled with how governance systems must actively process, transform, and channel deep-seated social conflicts as an intrinsic part of the adaptation process, especially in highly contested environments (Folke et al., 2005 ; Armitage et al., 2009 ; Pahl-Wostl et al., 2012 ; Chaffin et al., 2014 ). The critical scientific challenge is to understand the dynamic feedback between institutional structures and social conflict, and how this interplay shapes a community's evolutionary trajectory. This study addresses this challenge by providing the first large-scale, quantitative analysis of how governance and conflict co-evolve to shape adaptation pathways in the WPS. Drawing on extensive surveys across two key Fisheries Management Areas with differing governance regimes (Binobo et al., 2024 ; Malayang et al., 2023 ; Oceana Philippines, (n.d.), we investigate how these contexts produce fundamentally different community-level outcomes. By centering the experiential knowledge of those on the front lines, we provide a political ecology perspective that links environmental change, food insecurity, and human adaptation in one of the world's most contested marine environments. To conceptualize these dynamics, we argue that local governance and social conflict co-evolve, creating distinct, path-dependent adaptation trajectories. We present this theoretical argument in our Co-evolutionary Framework (Fig. 1 ), which illustrates the two divergent pathways—a maladaptive 'vicious cycle' and an adaptive 'virtuous cycle'—that emerge from this process. The remainder of this paper uses empirical data from the West Philippine Sea to test and validate this framework, demonstrating its utility for understanding and intervening in contested social-ecological systems globally. 2. Materials and methods 2.1. Study design and data collection We employed a mixed-methods survey framework to quantify stakeholder perceptions and coping strategies in 19 coastal communities across Fisheries Management Areas (FMAs) 5 and 6 (Fig. 2 ), a critical social-ecological region of the West Philippine Sea (WPS) (Quimpo et al., 2019 ) defined by high biodiversity, significant economic dependence, and intense geopolitical pressure (Malayang et al., 2023 ). The use of a mixed-methods approach was essential to triangulate quantitative patterns of perception with the contextual nuance of lived experiences, providing a more robust understanding than either method could achieve alone. A multistage sampling design was used to survey 621 small-scale fishers. The design combined purposive sampling, through engagement with local community leaders to gain access and identify key informants with stratified random sampling across four provinces to ensure proportional representation and minimize sampling bias. A structured questionnaire, developed through expert consultation and refined via pilot testing in each FMA, was administered by trained enumerators from January to June 2024 using the Kobo Toolbox platform for electronic data capture. The instrument collected data on demographics, fishing practices, perceptions of ecosystem services and threats, and household food security experiences. All research protocols were approved by the University of Rhode Island Institutional Review Board (IRB1819-227), and all participants provided informed consent. 2.2. Statistical analysis All statistical analyses were conducted in R with a significance threshold of α = 0.05. The analytical strategy was designed to first identify underlying perceptual structures and then to test how these structures and resulting behaviors differed across key groups. This quantitative approach allows for the robust identification of divergent community-level patterns. We use this evidence not to statistically test a universal causal law, but to provide a detailed, empirically grounded illustration of the co-evolutionary dynamics that can generate path-dependent adaptation trajectories in complex systems. First, we used descriptive statistics to characterize the study population. Second, to reduce the dimensionality of the complex survey data and identify latent constructs, we employed exploratory factor analysis (EFA) with maximum likelihood estimation. This technique was applied separately to question sets related to perceived threats and food security experiences, allowing us to identify the underlying dimensions that shape fisher decision-making. Third, we used chi-square tests and analysis of variance (ANOVA) to compare threat perceptions and food consumption patterns across different demographic groups, fishing sectors (municipal vs. commercial), and the two FMAs. Finally, to visualize the primary axes of variation and identify the distinct constellations of priorities and coping strategies that characterize each FMA, we used correspondence analysis (CA), a multivariate technique ideal for exploring the relationships within categorical survey data. For this analysis, a complete-case subset of the data (n = 400) was used to ensure model robustness. Further details on all statistical procedures are provided in the Supplementary Materials. 3. Results 3.1. Profile of an economically precarious fishing community The study population (N = 621) represents a socioeconomically vulnerable community highly dependent on the marine resources of the West Philippine Sea (WPS). The majority of respondents were low-income (83.7% earning ≤ PHP 10,000/month), small-scale municipal fishers (79.2%) operating within local waters. Despite high compliance with vessel registration (99.7%), the community faces significant economic precarity, a finding consistent across demographic and household characteristics (Table 1 ). This dependence is reflected in their diet, which consists of near-daily rice consumption (mean = 6.43 days/week) and frequent fresh fish consumption, the latter being significantly correlated with income (Table 2 ). Table 1 Descriptive analysis and significance test results. Variable Category Mean SD Percentage (%) P-value Sig. Diff? 4*Age Group Under 21 0.008 0.089 0.81 9.36×10 − 1 Yes (p < 0.05) Yes 21 to 40 0.409 0.492 40.90 41 to 60 0.530 0.500 52.98 Over 60 0.089 0.284 8.86 2*Gender Female 0.206 0.405 20.61 5.77×10 − 6 Yes (p < 0.05) Yes Male 0.792 0.406 79.23 2*Location FMA 5 0.267 0.443 26.73 0.138 No FMA 6 0.731 0.444 73.11 4*Educational Attainment Elementary 0.391 0.488 39.13 1.91×10 − 9 Yes (p < 0.05) Yes Secondary 0.507 0.500 50.72 College 0.069 0.254 6.92 No Formal Education 0.010 0.098 0.97 2*Household Size Five or Fewer 0.692 0.462 69.24 8.75×10 − 2 Yes (p < 0.05) Yes More Than Five 0.308 0.462 30.76 3*Livelihood Fisher 0.884 0.320 88.41 1.14×10 − 8 Yes (p < 0.05) Yes Gleaner 0.002 0.040 0.16 Fish Seller 0.090 0.287 9.02 2*Type of Fishing Activity Municipal 0.792 0.406 79.23 9.93×10 − 9 Yes (p < 0.05) Yes Commercial 0.190 0.393 19.00 Number of Fishers (Numeric) 1.316 0.989 58.45* 0.00884 Yes (p < 0.05) Yes 7*Household Income < 5,000 0.475 0.500 47.50 7.60×10 − 5 Yes (p 50,000 0.005 0.069 0.48 2*Registration Status Registered 0.997 0.057 99.68 0.214 No Unregistered 0.005 0.069 0.48 2*Fishing Boat Ownership Owns Boat 0.738 0.44 73.75 6.45×10 − 3 Yes (p < 0.05) Yes No Boat 0.264 0.441 26.41 Notes: *Percentage for Number of Fishers represents the mean value. Significant dif- ferences (P < 0.05) are marked with a gray background. P-values indicate statistical significance: Age Group (9.36e-133), Gender (5.77e-06), Educational Attainment (1.91e- 93), Household Size (8.75e-22), Livelihood (1.14e-81), Type of Fishing Activity (9.93e-93), Number of Fishers (0.00884), Household Income (7.60e-54), and Fishing Boat Ownership (6.45e-32) show significant variation. Non-significant results include Location (0.138) and Registration Status (0.214). Significance suggests non-uniform distributions, potentially reflecting distinct demographic patterns. The tests performed include Chi-Square Test for categorical variables and ANOVA or Kruskal-Wallis Test for the numeric variable Number of Fishers across age groups. Table 2 Food consumption patterns in WPS communities (past 7 days, n = 621). Food Type Proportion Consuming (%) Mean Days Median Days SD Days Canned fish and seafood 75.4 2.73 3 1.55 Fruits 75.0 2.46 2 1.74 Canned meat (pork, chicken, beef) 56.7 1.82 1 1.36 Eggs 77.7 3.57 3 2.03 Rice, bread, camote, potato 90.8 6.43 7 1.26 Beans 37.0 1.66 1 1.45 Fresh fish and seafood 91.9 6.58 7 1.05 Vegetables 87.0 4.76 5 2.00 Mollusks and crustaceans 52.1 4.24 4 2.04 Fresh meat (pork, chicken, beef) 65.0 2.29 2 1.68 3.2. Fishers prioritize local environmental threats over geopolitical issues To understand the primary concerns of this community, we analyzed their perceptions of threats to the WPS ecosystem and their livelihoods. Factor analysis revealed two distinct perceptual dimensions: (1) immediate, tangible threats related to resource depletion and local fishing impacts, and (2) broader geopolitical threats related to territorial disputes and foreign access (Table 3 ). When asked to identify the most significant issues, a clear hierarchy emerged. A majority of fishers (64.4%) identified "illegal destructive fishing" as a primary threat. This was a significantly higher proportion than those who prioritized "territorial disputes" (53.6%; p < 0.001) or "foreign illegal fishing" (47.5%). The least-cited threat was "climate change" (15.6%) (Table 5 ). This indicates that fishers' primary concerns are focused on local, observable environmental degradation that directly impacts their catch, rather than on the more abstract geopolitical conflicts that dominate national discourse (Table 4 ). Notably, commercial fishers, who operate over larger areas, reported significantly higher perceptions of nearly all threats—including poaching and climate change—compared to their municipal counterparts (Table 5 ). Table 3 Summary of factor analysis results and response frequencies for fishing-related attitudes with significant results highlighted. Variable PA1 Loading PA2 Loading Communality (h2) Uniqueness (u2) KMO Count Percentage (%) Extremely important -0.10 0.63 0.37 0.63 0.67 430 69.2 Rich in natural resources 0.76 0.14 0.67 0.33 0.70 253 40.7 Sanctuary 0.71 0.31 0.73 0.27 0.66 316 50.9 Illegal destructive fishing 0.81 -0.27 0.59 0.41 0.60 400 64.4 Territorial disputes 0.14 0.80 0.72 0.28 0.68 333 53.6 Foreign illegal fishing -0.08 0.56 0.29 0.71 0.51 295 47.5 Loss of freedom to fish 0.40 0.10 0.19 0.81 0.78 303 48.8 Note : PA1 represents “Resource and Fishing Impacts”; PA2 represents “Territorial and Access Issues.” Bolded values indicate significant results: factor loadings ≥ 0. 4, communalities ≥ 0. 5, percentages ≥ 50%. Loadings are from the pattern matrix using principal axis factoring with oblimin rotation. KMO values indicate suitability for factor analysis (> 0.5). Data based on 621 respondents. Table 4 Endorsement rates and significant pairwise comparisons of marine threats and services. Threat/Service Count Endorsed Percentage Endorsed Significant Pairwise Comparisons Extremely important 430 69.24% vs. Rich in natural resources (p < 0.001 ∗∗∗ ) Rich in natural resources 253 40.74% – Sanctuary 316 50.89% vs. Loss of freedom to fish (p = 1.000) Illegal destructive fishing 400 64.41% vs. Territorial disputes (p = 0.0004 ∗∗ ) Territorial disputes 333 53.62% – Foreign illegal fishing 295 47.50% – Loss of freedom to fish 303 48.79% – Note: Pairwise proportion tests with Bonferroni correction. ∗∗ p < 0.01, ∗∗∗ p < 0.001. Table 5 Endorsement rates of marine threats among municipal, commercial, and mixed-sector fishers. Threat Municipal Fishers (% Endorsed, n) Commercial Fishers (% Endorsed, n) Doing Both (% Endorsed, n) Overall, Fishers (% Endorsed, n) Land Conversion 25.7% (124/482) 32.3% (41/127) 16.7% (2/12) 26.9% (167/621) Illegal/Destructive Fishing** 61.4% (296/482) 75.6% (96/127) 66.7% (8/12) 64.4% (400/621) Poaching*** 20.5% (99/482) 52.8% (67/127) 0.0% (0/12) 26.7% (166/621) Territorial Disputes** 50.0% (241/482) 67.7% (86/127) 50.0% (6/12) 53.6% (333/621) Foreign Illegal Fishing* 46.9% (226/482) 52.8% (67/127) 16.7% (2/12) 47.5% (295/621) Climate Change*** 11.6% (56/482) 30.7% (39/127) 16.7% (2/12) 15.6% (97/621) Note: Significance is based on Fisher's Exact Test comparing endorsement rates across all three fisher types. Significance levels are denoted as: * p < 0.05, ** p < 0.01, *** p < 0.001.* 3.3. A paradox of perceived abundance and experienced food insecurity We next investigated the community's experience with food security. Factor analysis identified four distinct dimensions of their food security experience, separating general "Food Security Concerns" from episodes of "Severe Food Insecurity" and "Reduced Meal Frequency." A fourth, separate factor captured perceptions of "Fish Abundance" (Table 6 ). A striking paradox emerged from these dimensions. Pearson correlation analysis revealed that fishers' perceptions of fish abundance were not significantly correlated with any of the three measures of household food insecurity (r = -0.033 to -0.049, p > 0.05). In contrast, measures of food insecurity were strongly inter-correlated; for example, "Food Security Concerns" was strongly associated with "Reduced Meal Frequency" (r = 0.697, p < 0.001) (Table 7 ). This disconnect suggests that household food security in this community is not driven by the perceived availability of the primary marine resource, but rather by the economic and access-related factors that lead to reduced food consumption. Table 6 Factor analysis of food security and fish consumption in the WPS. Item Factor Loadings Communality Uniqueness Food Security Concerns Severe Food Insecurity Reduced Meal Frequency Fish Abundance Plenty of fish − 0. 01 0. 08 0. 00 0.89 0. 80 0. 20 Fish prevents hunger 0. 00 − 0. 08 0. 01 0.84 0. 71 0. 29 Sell fish, eat leftovers 0.43 − 0. 17 − 0. 02 0. 26 0. 24 0. 76 No food (month) − 0. 01 0.96 0. 02 0. 03 0. 92 0. 08 No food (year) 0. 02 0.99 − 0. 01 − 0. 01 0. 98 0. 02 Worry food (month) 0.93 0. 04 0. 03 − 0. 01 0. 94 0. 06 Worry food (year) 0.98 − 0. 01 − 0. 01 − 0. 01 0. 95 0. 05 Eat < 3/day (month) − 0. 03 0. 01 1.02 0. 00 1. 00 0. 00 Eat < 3/day (year) 0. 05 − 0. 02 0.92 0. 00 0. 90 0. 10 Variance Explained SS Loadings 2. 04 1. 93 1. 90 1. 57 Proportion Var 0. 226 0. 215 0. 211 0. 174 Cumulative Var 0. 226 0. 441 0. 652 0. 826 Fit Statistics RMSR 0.003 RMSEA 0.011 TLI 1.000 BIC -32.15 Chi-Square p-value 0.376 Note: Loadings ≥ 0. 3 are in bold. Based on maximum likelihood estimation with oblimin rotation. Table 7 Factor correlations and significance tests for food security and fish abundance factors. Factor Pair Correlation (r) t-value df p-value Food Security Concerns vs. Reduced Meal Frequency 0.697 24.19 619 < 0.001*** Food Security Concerns vs. Severe Food Insecurity 0.184 4.65 619 < 0.001*** Severe Food Insecurity vs. Reduced Meal Frequency 0.317 8.29 619 < 0.001*** Food Security Concerns vs. Fish Abundance -0.033 -0.82 619 0.412 Severe Food Insecurity vs. Fish Abundance -0.016 -0.40 619 0.689 Reduced Meal Frequency vs. Fish Abundance -0.049 -1.22 619 0.223 Note: t-tests for correlation significance, n = 621. ∗∗∗ p < 0.001. 3.4. Divergent pressures lead to distinct regional coping strategies These underlying conditions of economic precarity and perceptual priorities set the stage for the emergence of two distinct, path-dependent adaptation trajectories, which are shaped by the co-evolution of governance and community response. Finally, we examined how these socioeconomic and perceptual pressures translate into community-level strategies. Correspondence analysis revealed a single, powerful dimension of variation that starkly separated the coping mechanisms and priorities of fishers in the two different Fisheries Management Areas (FMAs). In FMA 5, communities responded to threats and food shortages with strategies of exit and deprivation. The most characteristic responses included "Switch to other jobs" (Dim1: 3.72), "Skip meals" (Dim1: 3.44), and identifying "Loss of livelihood" as the primary threat (Dim1: 2.17). In sharp contrast, communities in FMA 6 employed strategies of resilience and financial leveraging within the existing system. Their characteristic responses included "Borrow money" to cope with shortages (Dim1: -1.71) and prioritizing the "Protection of Marine Protected Areas" to mitigate threats (Dim1: -1.99) (Table 8 , Fig. 3 ). These findings reveal that while the fishing communities appear homogenous at a demographic level, they have adopted fundamentally different pathways for adaptation and survival based on their distinct local contexts. Table 8 Correspondence analysis results: options with significant differences between FMA 5 and FMA 6. Question Option FMA 5 (%) FMA 6 (%) Dim1 Coordinate What alternative livelihoods do you suggest for those affected by WPS issues? Switch to other jobs 23.5 4.6 3.72 How do you cope with food shortages and rising food prices? Skip meals 18.1 4.2 3.44 How should the government address illegal, unreported, and unregulated (IUU) fishing and poaching in WPS? Form a Department of Fisheries 24.7 8.8 2.59 How have WPS threats affected your food and livelihood? Loss of livelihood 27.1 11.6 2.17 What can you or your group/association do to reduce threats in the West Philippine Sea (WPS)? Protect Marine Protected Areas (MPAs) 10.8 21.5 -1.99 How do you cope with food shortages and rising food prices? Borrow money from friends, neighbors, or relatives 44.6 79.8 -1.71 How do you cope with food shortages and rising food prices? Reduce expenses 91.0 47.9 1.66 What issues affect your ability to meet food needs? Fishing restrictions 18.7 31.6 -1.56 What issues affect your ability to meet food needs? Low or no income 41.6 60.9 -1.18 4. Discussion Our findings reveal a fundamental principle of social-ecological change: human adaptation in contested systems is an emergent outcome of the co-evolution between governance institutions and social conflict (Folke et al., 2005 ; Armitage et al., 2009 ; Chaffin et al., 2014 ), a dynamic that generates distinct, path-dependent trajectories. While uniformly exposed to the same macro-level pressures, the fishing communities of the West Philippine Sea have not responded monolithically. Instead, they have fractured onto two divergent pathways—an individualistic ‘exit-and-ration’ strategy and a collective ‘comply-and-buffer’ strategy. This divergence is not explained by demographics, but by a feedback loop where the local governance regime shapes how communities respond to conflict, and those responses, in turn, reinforce or undermine the governance system itself (Pahl-Wostl et al., 2012 ; Catedrilla et al., 2012 ; Tolentino-Zondervan and Zondervan, 2022; Chaffin et al., 2014 ). The Co-evolutionary dynamics of adaptation pathways Social-ecological systems are complex adaptive systems, and their change over time is rarely linear (Folke, 2006 ; Walker et al., 2004; Gunderson and Holling, 2002 ). Our results provide a clear empirical window into two such non-linear pathways. The specific trajectory a community follows is determined by how its governance institutions process the core underlying vulnerability: a crisis of economic entitlement (Sen, 1981; Devereux, 2009 ; Eakin and Luers, 2006 ). As our analysis shows, food insecurity is paradoxically disconnected from perceived resource abundance, confirming that the central problem is access, not absolute supply. This creates a constant potential for social conflict over access and distribution. The function of governance, then, is not merely to manage fish stocks, but to manage this inherent social conflict. In the less-regulated Fisheries Management Area (FMA) 5, the absence of trusted, legitimate forums for negotiation creates a maladaptive spiral. Here, conflict is unresolved, leading to rational, individualistic coping strategies: abandon the fishery or ration food. These actions, while logical for a household, are corrosive at the community level. They atomize society, destroy social capital, and further erode any basis for the collective action needed to address shared problems. This is a classic negative feedback loop: weak institutions fail to channel conflict, leading to behaviors that further weaken the potential for effective governance, locking the community into a path of declining adaptive capacity (Lebel et al., 2006 ; Walker et al., 2004; Warner, 2010). In contrast, the more actively managed FMA 6 demonstrates a virtuous cycle. The presence of management initiatives, such as Marine Protected Areas, creates a different institutional landscape. Crucially, these institutions provide a multi-level forum for negotiation and rule-making, which does not eliminate conflict but transforms its nature—from a destructive, zero-sum struggle into a productive, positive-sum negotiation over rules and access. By engaging with these rules ('comply'), fishers reinforce the system's legitimacy. This institutional stability enables them to adopt forward-looking economic strategies, such as using debt to smooth consumption ('buffer'), rather than simply exiting. This constitutes a positive feedback loop: functional governance channels conflict constructively, which builds trust and enables collective strategies, thereby strengthening the legitimacy and capacity of the governance system itself (Folke et al., 2005 ; Armitage et al., 2009 ; Pahl-Wostl et al., 2012 ). This pathway is not without peril—reliance on "distress debt" can create new vulnerabilities (Dercon and Krishnan, 2000 ; Anderson, 2010 ; Reardon and Vosti, 1995 )—but it represents a fundamentally more adaptive trajectory than the social collapse seen in FMA 5. Generalizing the mechanism: from static design to dynamic process This co-evolutionary dynamic is a core organizing principle for understanding adaptation in any contested SES, from transboundary river basins (Pahl-Wostl, 2007 ; Zeitoun and Mirumachi, 2008 ; Folke et al., 2010 ) to urban climate infrastructure disputes (Broto and Bulkeley, 2013). It explains why identical, technically sound policy interventions—like establishing a protected area—can succeed in one context and catastrophically fail in another. The success of an institution is not determined by its design on paper, but by its capacity to engage with and productively channel the social conflicts that are inherent in resource use. This has profound implications for policy. The goal of intervention must shift from designing static institutional structures (e.g., "implement co-management") to cultivating dynamic institutional processes that can perform the essential function of conflict transformation. The priority for a community on a maladaptive path like FMA 5 is not necessarily a new set of fishing rules, but rather the foundational work of building legitimate forums for dispute resolution and social safety nets that reduce the acute economic precarity driving exit strategies. For a community on an adaptive path like FMA 6, the priority is to strengthen those institutions by ensuring equitable benefit-sharing and access to fair credit, preventing the virtuous cycle from being derailed by new poverty traps. 5. Conclusion This study reveals that human adaptation in contested social-ecological systems is not a simple response to environmental pressure, but an emergent property of the co-evolution of governance and social conflict. We have identified two divergent, path-dependent trajectories—a maladaptive ‘exit-and-ration’ spiral and an adaptive ‘comply-and-buffer’ cycle—and shown that the pathway taken is determined by the capacity of local institutions to transform, rather than suppress, conflict. This finding provides a generalizable model of SES change, demonstrating that vulnerability is a dynamic process, not a static condition. The critical implication for science and policy is that building resilience requires a fundamental shift in focus: from the design of optimal rules to the cultivation of adaptive governance processes that can navigate conflict and unlock a community's capacity for collective action. Declarations Competing interests: The author has no relevant financial or non-financial interests to disclose. Consent: All participants provided written informed consent prior to the interview. Data statement: The raw data and code supporting the findings of this study are held by the USAID-funded Fish Right Program. The views expressed herein are solely those of the authors and not of the USAID. Data Availability The supporting data and code for this study are available at: https://doi.org/10.7910/DVN/MBBZIP Acknowledgments The authors would like to express their profound appreciation to all who made this research possible. We are deeply grateful for the time and knowledge shared by the fishers who participated in this study, the diligent work of our enumerators in the field, and the crucial guidance and support provided by local community leaders. Funding: This work was supported by the United States Agency for International Development (USAID) through the Fish Right Program [Cooperative Agreement -72049218CA00004], 2025. Clinical trial number: not applicable. Clinical trial number: not applicable. Conflict of Interest: The authors declare that they have no conflict of interest. Ethical approval: All research protocols were approved by the University of Rhode Island Institutional Review Board (IRB1819-227). Informed consent: All participants provided informed consent before participating in the study. References Agrawal A (2001) Common property institutions and sustainable governance of resources. World Dev 29:1649–1672. doi:10.1016/S0305-750X(01)00063-8 Anderson K (2010) Policy reforms affecting agricultural incentives: Much achieved, much still needed. 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Island Press, Washington, DC Joyner CC (1998) The Spratly Islands dispute: Rethinking the interplay of law, diplomacy, and geo-politics in the South China Sea. Int J Mar Coast Law 13:193–236. doi:10.1163/157180898X00256 Lee H (2023) The legality of militarization of the South China Sea and its legal implications. KMI Int J Marit Aff Fish 15:1–24. doi:10.54007/ijmaf.2023.15.1.1 Lebel L, Anderies JM, Campbell B, Folke C, Hatfield-Dodds S, Hughes TP, Wilson J (2006) Governance and the capacity to manage resilience in regional social–ecological systems. Ecol Soc 11:19. Liu JY (2013) Status of marine biodiversity of the China Seas. PLoS ONE 8:e50719. doi:10.1371/journal.pone.0050719 Malayang BS III, Jacinto GS, Castro JR, Subade RF, Alampay RBA, Cruz LJ (2023) Fisheries management areas in the West Philippine Sea and their heritage values. Asian J Agric Dev 20:31–52. doi:10.37801/ajad2023.20.1.p2 Mendoza R, Siriban C, Ty T (2019) Survey of economic implications of maritime and territorial disputes. J Econ Surv 33. doi:10.1111/joes.12311 Ng PKL, Tan KS (2000) The state of marine biodiversity in the South China Sea. Raffles Bull Zool 2000(Supplement No. 8):3–7. North DC, Wallis JJ, Weingast BR (2009) Violence and social orders: A conceptual framework for interpreting recorded human history. Cambridge University Press, Cambridge. doi:10.1017/CBO9780511575839 Ostrom E (2009) A general framework for analyzing sustainability of social–ecological systems. Science 325:419–422. doi:10.1126/science.1172133 Pahl-Wostl C (2007) Transitions towards adaptive management of water facing climate and global change. Water Resour Manag 21:49–62. doi:10.1007/s11269-006-9040-4 Pahl-Wostl C, Lebel L, Knieper C, Nikitina E (2012) From applying panaceas to mastering complexity: Toward adaptive water governance in river basins. Environ Sci Policy 23:24–34. doi:10.1016/j.envsci.2012.07.014 Quimpo TJR, Cabaitan PC, Go KTB, Dumalagan EE Jr, Villanoy CL, Siringan FP (2019) Similarity in benthic habitat and fish assemblages in the upper mesophotic and shallow water reefs in the West Philippine Sea. J Mar Biol Assoc UK 99:1507–1517. doi:10.1017/S0025315419000456 Reardon T, Vosti SA (1995) Links between rural poverty and the environment in developing countries: Asset categories and investment poverty. World Dev 23:1495–1506. doi:10.1016/0305-750X(95)00061-G Sasaki F (2025) Space, maritime security, and geopolitics in the South China Sea. J Indo-Pac Aff Summer 2025:53–78. Zeitoun M, Mirumachi N (2008) Transboundary water interaction I: Reconsidering conflict and cooperation. Int Environ Agreements 8:297–316. doi:10.1007/s10784-008-9083-5 Additional Declarations No competing interests reported. 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Guiñares","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYJCCA2CSvYHhA4MBSVp4DjDOIFoLBEgkALUQA8z71xgeLqi5Jyc/8+3B5oICBjnz/gX4tcjceGNweMaxYmOD23mJzTMMGIxlbjwg4ByJMwaHedgSEjdI55g/5jFgSJwhcYAYLf8S6ufPPGPYTJwW/h6Dw7xtCQkMN3igWvgbCNnCVnCYty/BcMMZsF8kjCUk8OsA2nJ482eebwny8u1ngSH2x0ZOgp+Aw4DRAWPxMDADucgiOADCTLAWFJFRMApGwSgYBWAAAPnKQl3p403XAAAAAElFTkSuQmCC","orcid":"","institution":"PATH Foundation Philippines, Inc.","correspondingAuthor":true,"prefix":"","firstName":"Recamar","middleName":"","lastName":"Guiñares","suffix":""},{"id":545896182,"identity":"e72eea58-e5cc-4b9e-bf76-aaebcfb3be20","order_by":1,"name":"Joan Regina Castro","email":"","orcid":"","institution":"PATH Foundation Philippines, 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10:49:34","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131691,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7986678/v1/6ebb0d8cba6a966abfa17aaa.html"},{"id":96247156,"identity":"0c0f8746-036a-4dc8-bc22-222d0b670a5f","added_by":"auto","created_at":"2025-11-19 07:27:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":188421,"visible":true,"origin":"","legend":"\u003cp\u003eA Co-evolutionary framework for human adaptation in contested social-ecological systems\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7986678/v1/06c25a8f83b0256fb89e697a.png"},{"id":96076972,"identity":"0abb4332-e1c8-4e4d-bb74-e74b23324c4d","added_by":"auto","created_at":"2025-11-17 10:49:34","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114653,"visible":true,"origin":"","legend":"\u003cp\u003eStudy areas. Map lines delineate study areas and do not necessarily depict accepted national boundaries\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7986678/v1/359fc2387369bfa7580d963c.jpeg"},{"id":96076973,"identity":"754f9b51-d2b9-4af7-bd73-0e059836172e","added_by":"auto","created_at":"2025-11-17 10:49:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88449,"visible":true,"origin":"","legend":"\u003cp\u003eCorrespondence analysis biplot of fisher response patterns in FMA 5 and FMA 6. The single dimension (Dim1) explains 100% of the total inertia, illustrating the clear separation in responses between the two areas\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7986678/v1/f9863f2eca88dd8086a75a41.png"},{"id":96256189,"identity":"d77c8edd-6d8f-47d6-a9ce-8a8229c79feb","added_by":"auto","created_at":"2025-11-19 07:49:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1683052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7986678/v1/016b2ff5-5ca2-417a-aa83-ae85f661ed46.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Co-evolutionary pathways: How governance and conflict shape human adaptation in contested social-ecological systems","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe South China Sea is a global nexus of extraordinary marine biodiversity (Acosta et al., 2025; Quimpo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), immense economic value (Liu, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ng and Tan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Zhong and White, 2017; Galdorisi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., 2023), and intense geopolitical conflict (Sasaki, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lee, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Malayang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tet et al., 2017; Mendoza et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang, 2018; Joyner, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This vital seascape, which includes the West Philippine Sea (WPS) (Malayang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), is pushed toward ecological collapse by the synergistic pressures of overfishing, habitat destruction, and militarization (Sasaki, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lee, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the dominant narrative frames this as a geopolitical crisis requiring top-down state action (Sasaki, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), this view obscures a deeper puzzle playing out at the human scale: how do local communities, sharing a common ecosystem and facing identical stressors, arrive at dramatically divergent fates? Why do some communities collapse into social fragmentation and resource degradation, while others innovate and adapt?\u003c/p\u003e\u003cp\u003eThis paper argues that the answer lies not in the nature of the environmental shock or the resources available, but in the institutional architecture that determines whether social conflict becomes a catalyst for innovation or a trigger for collapse (North et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While decades of research, following Ostrom, have established that institutions are critical for managing common-pool resources (Cox et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gelcich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ostrom, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dietz et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Agrawal, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), much of this work treats governance as a static input for managing resources, often assuming a baseline of potential cooperation. It has not fully grappled with how governance systems must actively process, transform, and channel deep-seated social conflicts as an intrinsic part of the adaptation process, especially in highly contested environments (Folke et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Armitage et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pahl-Wostl et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chaffin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The critical scientific challenge is to understand the dynamic feedback between institutional structures and social conflict, and how this interplay shapes a community's evolutionary trajectory.\u003c/p\u003e\u003cp\u003eThis study addresses this challenge by providing the first large-scale, quantitative analysis of how governance and conflict co-evolve to shape adaptation pathways in the WPS. Drawing on extensive surveys across two key Fisheries Management Areas with differing governance regimes (Binobo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Malayang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oceana Philippines, (n.d.), we investigate how these contexts produce fundamentally different community-level outcomes. By centering the experiential knowledge of those on the front lines, we provide a political ecology perspective that links environmental change, food insecurity, and human adaptation in one of the world's most contested marine environments. To conceptualize these dynamics, we argue that local governance and social conflict co-evolve, creating distinct, path-dependent adaptation trajectories. We present this theoretical argument in our Co-evolutionary Framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which illustrates the two divergent pathways\u0026mdash;a maladaptive 'vicious cycle' and an adaptive 'virtuous cycle'\u0026mdash;that emerge from this process. The remainder of this paper uses empirical data from the West Philippine Sea to test and validate this framework, demonstrating its utility for understanding and intervening in contested social-ecological systems globally.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study design and data collection\u003c/h2\u003e\u003cp\u003eWe employed a mixed-methods survey framework to quantify stakeholder perceptions and coping strategies in 19 coastal communities across Fisheries Management Areas (FMAs) 5 and 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), a critical social-ecological region of the West Philippine Sea (WPS) (Quimpo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) defined by high biodiversity, significant economic dependence, and intense geopolitical pressure (Malayang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The use of a mixed-methods approach was essential to triangulate quantitative patterns of perception with the contextual nuance of lived experiences, providing a more robust understanding than either method could achieve alone.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA multistage sampling design was used to survey 621 small-scale fishers. The design combined purposive sampling, through engagement with local community leaders to gain access and identify key informants with stratified random sampling across four provinces to ensure proportional representation and minimize sampling bias. A structured questionnaire, developed through expert consultation and refined via pilot testing in each FMA, was administered by trained enumerators from January to June 2024 using the Kobo Toolbox platform for electronic data capture. The instrument collected data on demographics, fishing practices, perceptions of ecosystem services and threats, and household food security experiences. All research protocols were approved by the University of Rhode Island Institutional Review Board (IRB1819-227), and all participants provided informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted in R with a significance threshold of α\u0026thinsp;=\u0026thinsp;0.05. The analytical strategy was designed to first identify underlying perceptual structures and then to test how these structures and resulting behaviors differed across key groups. This quantitative approach allows for the robust identification of divergent community-level patterns. We use this evidence not to statistically test a universal causal law, but to provide a detailed, empirically grounded illustration of the co-evolutionary dynamics that can generate path-dependent adaptation trajectories in complex systems.\u003c/p\u003e\u003cp\u003eFirst, we used descriptive statistics to characterize the study population. Second, to reduce the dimensionality of the complex survey data and identify latent constructs, we employed exploratory factor analysis (EFA) with maximum likelihood estimation. This technique was applied separately to question sets related to perceived threats and food security experiences, allowing us to identify the underlying dimensions that shape fisher decision-making. Third, we used chi-square tests and analysis of variance (ANOVA) to compare threat perceptions and food consumption patterns across different demographic groups, fishing sectors (municipal vs. commercial), and the two FMAs. Finally, to visualize the primary axes of variation and identify the distinct constellations of priorities and coping strategies that characterize each FMA, we used correspondence analysis (CA), a multivariate technique ideal for exploring the relationships within categorical survey data. For this analysis, a complete-case subset of the data (n\u0026thinsp;=\u0026thinsp;400) was used to ensure model robustness. Further details on all statistical procedures are provided in the Supplementary Materials.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Profile of an economically precarious fishing community\u003c/h2\u003e\u003cp\u003eThe study population (N\u0026thinsp;=\u0026thinsp;621) represents a socioeconomically vulnerable community highly dependent on the marine resources of the West Philippine Sea (WPS). The majority of respondents were low-income (83.7% earning\u0026thinsp;\u0026le;\u0026thinsp;PHP 10,000/month), small-scale municipal fishers (79.2%) operating within local waters. Despite high compliance with vessel registration (99.7%), the community faces significant economic precarity, a finding consistent across demographic and household characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This dependence is reflected in their diet, which consists of near-daily rice consumption (mean\u0026thinsp;=\u0026thinsp;6.43 days/week) and frequent fresh fish consumption, the latter being significantly correlated with income (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive analysis and significance test results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSig. Diff?\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4*Age Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnder 21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.36\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 to 40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 to 60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOver 60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2*Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.77\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2*Location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMA 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMA 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4*Educational Attainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElementary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.91\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Formal Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2*Household Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFive or Fewer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.75\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore Than Five\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3*Livelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFisher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.14\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGleaner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFish Seller\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2*Type of Fishing Activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMunicipal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.93\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommercial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Fishers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Numeric)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.45*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7*Household Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.60\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,000\u0026ndash;10,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,000\u0026ndash;20,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21,000\u0026ndash;30,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31,000\u0026ndash;40,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41,000\u0026ndash;50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2*Registration Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegistered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnregistered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2*Fishing Boat Ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOwns Boat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.45\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Boat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNotes: *Percentage for Number of Fishers represents the mean value. Significant dif- ferences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are marked with a gray background. P-values indicate statistical significance: Age Group (9.36e-133), Gender (5.77e-06), Educational Attainment (1.91e- 93), Household Size (8.75e-22), Livelihood (1.14e-81), Type of Fishing Activity (9.93e-93), Number of Fishers (0.00884), Household Income (7.60e-54), and Fishing Boat Ownership (6.45e-32) show significant variation. Non-significant results include Location (0.138) and Registration Status (0.214). Significance suggests non-uniform distributions, potentially reflecting distinct demographic patterns. The tests performed include Chi-Square Test for categorical variables and ANOVA or Kruskal-Wallis Test for the numeric variable Number of Fishers across age groups.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFood consumption patterns in WPS communities (past 7 days, n\u0026thinsp;=\u0026thinsp;621).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion Consuming (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean Days\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian Days\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD Days\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCanned fish and seafood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFruits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCanned meat (pork, chicken, beef)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEggs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRice, bread, camote, potato\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFresh fish and seafood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMollusks and crustaceans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFresh meat (pork, chicken, beef)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Fishers prioritize local environmental threats over geopolitical issues\u003c/h2\u003e\u003cp\u003eTo understand the primary concerns of this community, we analyzed their perceptions of threats to the WPS ecosystem and their livelihoods. Factor analysis revealed two distinct perceptual dimensions: (1) immediate, tangible threats related to resource depletion and local fishing impacts, and (2) broader geopolitical threats related to territorial disputes and foreign access (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When asked to identify the most significant issues, a clear hierarchy emerged. A majority of fishers (64.4%) identified \"illegal destructive fishing\" as a primary threat. This was a significantly higher proportion than those who prioritized \"territorial disputes\" (53.6%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or \"foreign illegal fishing\" (47.5%). The least-cited threat was \"climate change\" (15.6%) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This indicates that fishers' primary concerns are focused on local, observable environmental degradation that directly impacts their catch, rather than on the more abstract geopolitical conflicts that dominate national discourse (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, commercial fishers, who operate over larger areas, reported significantly higher perceptions of nearly all threats\u0026mdash;including poaching and climate change\u0026mdash;compared to their municipal counterparts (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of factor analysis results and response frequencies for fishing-related attitudes with significant results highlighted.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePA1 Loading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePA2 Loading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCommunality (h2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUniqueness (u2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKMO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtremely important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e69.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRich in natural resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e40.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSanctuary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e50.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIllegal destructive fishing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerritorial disputes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e53.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign illegal fishing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e47.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoss of freedom to fish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eNote : PA1 represents \u0026ldquo;Resource and Fishing Impacts\u0026rdquo;; PA2 represents \u0026ldquo;Territorial and Access Issues.\u0026rdquo; Bolded values indicate significant results: factor loadings\u0026thinsp;\u0026ge;\u0026thinsp;0. 4, communalities\u0026thinsp;\u0026ge;\u0026thinsp;0. 5, percentages\u0026thinsp;\u0026ge;\u0026thinsp;50%. Loadings are from the pattern matrix using principal axis factoring with oblimin rotation. KMO values indicate suitability for factor analysis (\u0026gt;\u0026thinsp;0.5). Data based on 621 respondents.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEndorsement rates and significant pairwise comparisons of marine threats and services.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreat/Service\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount Endorsed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage Endorsed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant Pairwise Comparisons\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtremely important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003evs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRich in natural resources (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 \u0026lowast;\u0026lowast;\u0026lowast; )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRich in natural resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.74%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSanctuary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003evs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLoss of freedom to fish (p\u0026thinsp;=\u0026thinsp;1.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIllegal destructive fishing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.41%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003evs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTerritorial disputes (p\u0026thinsp;=\u0026thinsp;0.0004 \u0026lowast;\u0026lowast; )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerritorial disputes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.62%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign illegal fishing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoss of freedom to fish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.79%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNote: Pairwise proportion tests with Bonferroni correction. \u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u0026lowast;\u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEndorsement rates of marine threats among municipal, commercial, and mixed-sector fishers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMunicipal Fishers (% Endorsed, n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCommercial Fishers (% Endorsed, n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDoing Both (% Endorsed, n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOverall, Fishers (% Endorsed, n)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Conversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.7% (124/482)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.3% (41/127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.7% (2/12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.9% (167/621)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIllegal/Destructive Fishing**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.4% (296/482)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.6% (96/127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.7% (8/12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.4% (400/621)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoaching***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.5% (99/482)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.8% (67/127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0% (0/12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.7% (166/621)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerritorial Disputes**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.0% (241/482)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.7% (86/127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.0% (6/12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.6% (333/621)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign Illegal Fishing*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.9% (226/482)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.8% (67/127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.7% (2/12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.5% (295/621)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClimate Change***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.6% (56/482)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.7% (39/127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.7% (2/12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.6% (97/621)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote: Significance is based on Fisher's Exact Test comparing endorsement rates across all three fisher types. Significance levels are denoted as: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3. A paradox of perceived abundance and experienced food insecurity\u003c/h2\u003e\u003cp\u003eWe next investigated the community's experience with food security. Factor analysis identified four distinct dimensions of their food security experience, separating general \"Food Security Concerns\" from episodes of \"Severe Food Insecurity\" and \"Reduced Meal Frequency.\" A fourth, separate factor captured perceptions of \"Fish Abundance\" (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A striking paradox emerged from these dimensions. Pearson correlation analysis revealed that fishers' perceptions of fish abundance were not significantly correlated with any of the three measures of household food insecurity (r = -0.033 to -0.049, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, measures of food insecurity were strongly inter-correlated; for example, \"Food Security Concerns\" was strongly associated with \"Reduced Meal Frequency\" (r\u0026thinsp;=\u0026thinsp;0.697, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This disconnect suggests that household food security in this community is not driven by the perceived availability of the primary marine resource, but rather by the economic and access-related factors that lead to reduced food consumption.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor analysis of food security and fish consumption in the WPS.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor Loadings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCommunality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUniqueness\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFood Security Concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSevere Food Insecurity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReduced Meal Frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFish Abundance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlenty of fish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus; 0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0. 08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0. 00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFish prevents hunger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0. 00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus; 0. 08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSell fish, eat leftovers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus; 0. 17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus; 0. 02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0. 26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo food (month)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus; 0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0. 02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0. 03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo food (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0. 02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus; 0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus; 0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorry food (month)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0. 04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0. 03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus; 0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorry food (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus; 0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus; 0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus; 0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEat\u0026thinsp;\u0026lt;\u0026thinsp;3/day (month)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus; 0. 03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0. 01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0. 00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1. 00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEat\u0026thinsp;\u0026lt;\u0026thinsp;3/day (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0. 05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus; 0. 02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0. 00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0. 10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVariance Explained\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSS Loadings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2. 04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1. 93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1. 90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1. 57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion Var\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0. 226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0. 215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0. 211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0. 174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative Var\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0. 226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0. 441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0. 652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0. 826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFit Statistics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-32.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChi-Square p-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote: Loadings\u0026thinsp;\u0026ge;\u0026thinsp;0. 3 are in bold. Based on maximum likelihood estimation with oblimin rotation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor correlations and significance tests for food security and fish abundance factors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor Pair\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation (r)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Security Concerns vs. Reduced Meal Frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Security Concerns vs. Severe Food Insecurity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Food Insecurity vs. Reduced Meal Frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Security Concerns vs. Fish Abundance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Food Insecurity vs. Fish Abundance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReduced Meal Frequency vs. Fish Abundance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote: t-tests for correlation significance, n\u0026thinsp;=\u0026thinsp;621. \u0026lowast;\u0026lowast;\u0026lowast; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Divergent pressures lead to distinct regional coping strategies\u003c/h2\u003e\u003cp\u003eThese underlying conditions of economic precarity and perceptual priorities set the stage for the emergence of two distinct, path-dependent adaptation trajectories, which are shaped by the co-evolution of governance and community response. Finally, we examined how these socioeconomic and perceptual pressures translate into community-level strategies. Correspondence analysis revealed a single, powerful dimension of variation that starkly separated the coping mechanisms and priorities of fishers in the two different Fisheries Management Areas (FMAs).\u003c/p\u003e\u003cp\u003eIn FMA 5, communities responded to threats and food shortages with strategies of exit and deprivation. The most characteristic responses included \"Switch to other jobs\" (Dim1: 3.72), \"Skip meals\" (Dim1: 3.44), and identifying \"Loss of livelihood\" as the primary threat (Dim1: 2.17). In sharp contrast, communities in FMA 6 employed strategies of resilience and financial leveraging within the existing system. Their characteristic responses included \"Borrow money\" to cope with shortages (Dim1: -1.71) and prioritizing the \"Protection of Marine Protected Areas\" to mitigate threats (Dim1: -1.99) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings reveal that while the fishing communities appear homogenous at a demographic level, they have adopted fundamentally different pathways for adaptation and survival based on their distinct local contexts.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrespondence analysis results: options with significant differences between FMA 5 and FMA 6.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuestion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFMA 5 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFMA 6 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDim1 Coordinate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhat alternative livelihoods do you suggest for those affected by WPS issues?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSwitch to other jobs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHow do you cope with food shortages and rising food prices?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSkip meals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHow should the government address illegal, unreported, and unregulated (IUU) fishing and poaching in WPS?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForm a Department of Fisheries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHow have WPS threats affected your food and livelihood?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLoss of livelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhat can you or your group/association do to reduce threats in the West Philippine Sea (WPS)?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtect Marine Protected Areas (MPAs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHow do you cope with food shortages and rising food prices?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBorrow money from friends, neighbors, or relatives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHow do you cope with food shortages and rising food prices?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduce expenses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhat issues affect your ability to meet food needs?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFishing restrictions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhat issues affect your ability to meet food needs?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow or no income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur findings reveal a fundamental principle of social-ecological change: human adaptation in contested systems is an emergent outcome of the co-evolution between governance institutions and social conflict (Folke et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Armitage et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chaffin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), a dynamic that generates distinct, path-dependent trajectories. While uniformly exposed to the same macro-level pressures, the fishing communities of the West Philippine Sea have not responded monolithically. Instead, they have fractured onto two divergent pathways\u0026mdash;an individualistic \u0026lsquo;exit-and-ration\u0026rsquo; strategy and a collective \u0026lsquo;comply-and-buffer\u0026rsquo; strategy. This divergence is not explained by demographics, but by a feedback loop where the local governance regime shapes how communities respond to conflict, and those responses, in turn, reinforce or undermine the governance system itself (Pahl-Wostl et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Catedrilla et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tolentino-Zondervan and Zondervan, 2022; Chaffin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Co-evolutionary dynamics of adaptation pathways\u003c/b\u003e Social-ecological systems are complex adaptive systems, and their change over time is rarely linear (Folke, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Walker et al., 2004; Gunderson and Holling, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Our results provide a clear empirical window into two such non-linear pathways. The specific trajectory a community follows is determined by how its governance institutions process the core underlying vulnerability: a crisis of economic entitlement (Sen, 1981; Devereux, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Eakin and Luers, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). As our analysis shows, food insecurity is paradoxically disconnected from perceived resource abundance, confirming that the central problem is access, not absolute supply. This creates a constant potential for social conflict over access and distribution. The function of governance, then, is not merely to manage fish stocks, but to manage this inherent social conflict.\u003c/p\u003e\u003cp\u003eIn the less-regulated Fisheries Management Area (FMA) 5, the absence of trusted, legitimate forums for negotiation creates a maladaptive spiral. Here, conflict is unresolved, leading to rational, individualistic coping strategies: abandon the fishery or ration food. These actions, while logical for a household, are corrosive at the community level. They atomize society, destroy social capital, and further erode any basis for the collective action needed to address shared problems. This is a classic negative feedback loop: weak institutions fail to channel conflict, leading to behaviors that further weaken the potential for effective governance, locking the community into a path of declining adaptive capacity (Lebel et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Walker et al., 2004; Warner, 2010).\u003c/p\u003e\u003cp\u003eIn contrast, the more actively managed FMA 6 demonstrates a virtuous cycle. The presence of management initiatives, such as Marine Protected Areas, creates a different institutional landscape. Crucially, these institutions provide a multi-level forum for negotiation and rule-making, which does not eliminate conflict but transforms its nature\u0026mdash;from a destructive, zero-sum struggle into a productive, positive-sum negotiation over rules and access. By engaging with these rules ('comply'), fishers reinforce the system's legitimacy. This institutional stability enables them to adopt forward-looking economic strategies, such as using debt to smooth consumption ('buffer'), rather than simply exiting. This constitutes a positive feedback loop: functional governance channels conflict constructively, which builds trust and enables collective strategies, thereby strengthening the legitimacy and capacity of the governance system itself (Folke et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Armitage et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pahl-Wostl et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This pathway is not without peril\u0026mdash;reliance on \"distress debt\" can create new vulnerabilities (Dercon and Krishnan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Anderson, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Reardon and Vosti, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1995\u003c/span\u003e)\u0026mdash;but it represents a fundamentally more adaptive trajectory than the social collapse seen in FMA 5.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneralizing the mechanism: from static design to dynamic process\u003c/b\u003e This co-evolutionary dynamic is a core organizing principle for understanding adaptation in any contested SES, from transboundary river basins (Pahl-Wostl, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zeitoun and Mirumachi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Folke et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to urban climate infrastructure disputes (Broto and Bulkeley, 2013). It explains why identical, technically sound policy interventions\u0026mdash;like establishing a protected area\u0026mdash;can succeed in one context and catastrophically fail in another. The success of an institution is not determined by its design on paper, but by its capacity to engage with and productively channel the social conflicts that are inherent in resource use.\u003c/p\u003e\u003cp\u003eThis has profound implications for policy. The goal of intervention must shift from designing static institutional structures (e.g., \"implement co-management\") to cultivating dynamic institutional processes that can perform the essential function of conflict transformation. The priority for a community on a maladaptive path like FMA 5 is not necessarily a new set of fishing rules, but rather the foundational work of building legitimate forums for dispute resolution and social safety nets that reduce the acute economic precarity driving exit strategies. For a community on an adaptive path like FMA 6, the priority is to strengthen those institutions by ensuring equitable benefit-sharing and access to fair credit, preventing the virtuous cycle from being derailed by new poverty traps.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study reveals that human adaptation in contested social-ecological systems is not a simple response to environmental pressure, but an emergent property of the co-evolution of governance and social conflict. We have identified two divergent, path-dependent trajectories\u0026mdash;a maladaptive \u0026lsquo;exit-and-ration\u0026rsquo; spiral and an adaptive \u0026lsquo;comply-and-buffer\u0026rsquo; cycle\u0026mdash;and shown that the pathway taken is determined by the capacity of local institutions to transform, rather than suppress, conflict. This finding provides a generalizable model of SES change, demonstrating that vulnerability is a dynamic process, not a static condition. The critical implication for science and policy is that building resilience requires a fundamental shift in focus: from the design of optimal rules to the cultivation of adaptive governance processes that can navigate conflict and unlock a community's capacity for collective action.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eConsent: All participants provided written informed consent prior to the interview.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData statement:\u0026nbsp;\u003c/strong\u003eThe raw data and code supporting the findings of this study are held by the USAID-funded Fish Right Program. The views expressed herein are solely those of the authors and not of the USAID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe supporting data and code for this study are available at: https://doi.org/10.7910/DVN/MBBZIP\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their profound appreciation to all who made this research possible. We are deeply grateful for the time and knowledge shared by the fishers who participated in this study, the diligent work of our enumerators in the field, and the crucial guidance and support provided by local community leaders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by the United States Agency for International Development (USAID) through the Fish Right Program [Cooperative Agreement -72049218CA00004], 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e All research protocols were approved by the University of Rhode Island Institutional Review Board (IRB1819-227).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e All participants provided informed consent before participating in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgrawal A (2001) Common property institutions and sustainable governance of resources. 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Int Environ Agreements 8:297\u0026ndash;316. doi:10.1007/s10784-008-9083-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social-ecological systems, Adaptation pathways, Co-evolution, Marine governance, Resource conflict, West Philippine Sea","lastPublishedDoi":"10.21203/rs.3.rs-7986678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7986678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eManaging human adaptation in contested social-ecological systems (SES) is a defining challenge of the Anthropocene. Current approaches often fail because they treat governance as a static intervention designed to manage resources, overlooking its dynamic interplay with social conflict. How governance and conflict co-evolve to produce divergent community fates remains a critical unknown. Here, using extensive survey data from small-scale fishing communities in the West Philippine Sea\u0026mdash;a global hotspot of biodiversity and conflict\u0026mdash;we reveal how seemingly homogenous communities facing identical pressures fracture into distinct, path-dependent trajectories. We show that this divergence is driven by a co-evolutionary feedback loop between institutional frameworks and community responses. In regions with weak institutional presence, a negative feedback loop emerges, where unresolved conflict drives individualistic \u0026lsquo;exit-and-ration\u0026rsquo; strategies that further erode the potential for collective action. In contrast, where active management provides forums for negotiation, a positive feedback loop fosters a collective \u0026lsquo;comply-and-buffer\u0026rsquo; strategy, reinforcing institutional legitimacy and building adaptive capacity. We further reveal that this dynamic is fueled by a crisis of economic entitlement, not resource scarcity. These findings present a generalizable model of SES change, demonstrating that adaptation is an emergent property of the co-evolution between social conflict and governance. Effective intervention must therefore shift from designing static rules to cultivating institutional processes capable of channeling conflict into constructive, adaptive pathways.\u003c/p\u003e","manuscriptTitle":"Co-evolutionary pathways: How governance and conflict shape human adaptation in contested social-ecological systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 10:49:29","doi":"10.21203/rs.3.rs-7986678/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f892444-2bc5-4b15-965c-0d278ac71ee7","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T05:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 10:49:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7986678","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7986678","identity":"rs-7986678","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}