Draft abstract. Small pelagic fisheries may contribute to the resilience of coastal communities in coral reef systems by diversifying the fishery portfolio available to households. As with diversification in other risk-prone systems, the value of this portfolio effect may not be higher average returns, but lower exposure to shocks affecting a single resource base. In small-scale fisheries, where income volatility, food insecurity, governance constraints, and climate exposure often overlap, reducing risk is central to resilience. This article uses Rare’s Impact, Monitoring, Evaluation, and Learning (IMEL) Framework to illustrate how fishery portfolio composition can be interpreted alongside social resilience metrics in community-led fisheries management areas. We focus on Indonesia as an illustrative case because the available legacy data provide the clearest ABM-level overlap between catch composition and Household Survey indicators. The analysis links the contribution of small pelagic taxa to recorded catch with social equity, trust in local leadership, collective efficacy, participation, financial sufficiency, and food security. Because the IMEL Framework is being implemented in 2026, the historical data used here were not originally collected with the sampling coverage and precision required for full management use. The analysis should therefore be read as a proof of concept: it shows how the IMEL approach can connect fisheries and social information to support adaptive management, while also illustrating why fit-for-purpose sampling is necessary for future resilience monitoring.

Central claim. Small pelagic fisheries can contribute to community resilience when they expand the portfolio of resources available to coastal households, but their resilience value depends on effective, equitable, and legitimate co-management. Fishery diversification is therefore not only an ecological or economic condition; it is also a social and institutional challenge.

Introduction

Coastal communities in coral reef systems often depend on a mix of reef-associated and pelagic fisheries for food, income, and cultural continuity. This mix matters because different fisheries are exposed to different ecological, climatic, market, and governance pressures. A community that can access both reef-associated species and small pelagics may have a more diversified fishery portfolio than a community dependent on a narrower resource base.

The idea is intuitive: diversification can reduce risk. In finance, a diversified portfolio may not always produce the highest possible return, but it can reduce exposure to a single shock. A similar logic can apply to small-scale fisheries. If one resource becomes temporarily less available, less profitable, or more heavily regulated, access to another resource may help households smooth food and income. In contexts where social vulnerability and climatic risk exposure are high, risk reduction itself is a resilience outcome.

However, diversification does not automatically produce resilience. A more diversified fishery portfolio can still generate conflict, unequal benefits, overharvesting, or unstable incomes if the social and institutional foundations of management are weak. The resilience value of small pelagics therefore depends on whether communities can govern access, participate in decisions, trust local leadership, manage rules legitimately, and maintain household well-being under changing conditions.

Rare’s Impact, Monitoring, Evaluation, and Learning (IMEL) Framework provides a practical way to make this argument measurable. The framework treats resilience as multidimensional: social conditions such as equity, trust, participation, collective efficacy, financial sufficiency, and food security are measured alongside ecological and fisheries dimensions such as fisheries productivity and the protection of critical habitats. In this article, the emphasis is on social metrics, while catch composition is used to represent the fishery portfolio context in which those social conditions operate.

Why this is an Indonesia case illustration

The broader legacy dataset reviewed for this work includes catch and Household Survey data from the Philippines, Indonesia, and Mozambique. Together, these files contain 189,211 catch records across 77 ABMs and 29,023 Household Survey records, of which 15,969 could be linked to an ABM using the available matching fields.

For the article, we focus on Indonesia. This is not intended to make a general claim about all Fish Forever countries or all coral reef small-scale fisheries. Indonesia is used as an illustrative case because the available legacy data provide the clearest overlap between ABM-level catch composition and Household Survey social resilience indicators. This makes it a stronger case for demonstrating the analytical logic of the IMEL Framework.

Data-readiness framing. The analysis below demonstrates the analytical logic of the IMEL approach, but it should not be interpreted as a final assessment of resilience across sites. Historical catch and HHS collections were not designed to provide the coverage, timing, precision, or consistency required for a management-ready resilience framework. The 2026 IMEL sampling design is intended to address this gap by producing data with sufficient coverage and precision to support more reliable ABM-level interpretation and adaptive management.

The main contribution of this article is not to claim that past data can already provide a complete resilience assessment. The contribution is to show how the framework can be used: combining fishery portfolio information with social metrics to identify resilience opportunities, vulnerabilities, and management questions that future IMEL data collection can answer more rigorously.

Analytical approach

This article asks three questions:

  1. Where are small pelagics important in recorded fishery portfolios in Indonesia?
  2. What is the social resilience profile of those communities?
  3. How can combining catch composition and social metrics support adaptive management?

The analysis is descriptive and exploratory. It is not designed to estimate the causal effect of small pelagic fisheries on community resilience. Instead, it develops an empirical basis for using the IMEL Framework as a management and learning tool.

Unit of analysis. The main unit is the area-based management unit (ABM). In this analysis, an ABM refers to a discrete coastal and marine management area where local stewardship, clear governance arrangements, and inclusive decision-making are expected to support durable conservation outcomes and enhanced socio-economic and ecological resilience. ABMs can take different forms across countries and contexts, including managed access areas, reserves, OECMs, MPAs, and other related management models.

Within Rare’s IMEL Framework, the ABM is the core unit used to organize program results, assess progress, and understand change over time. This allows the framework to apply a shared measurement approach across countries while still accommodating different legal, ecological, and governance contexts. In this article, ABMs are summarized from ABM-year observations where catch and Household Survey data overlap. Catch data are used to describe fishery portfolio composition. Household Survey responses are used to estimate social resilience indicators. The ABM-year join is used for reproducibility, but the main article figures emphasize ABM-level patterns because the goal is to support management interpretation.

Data and measures

The analysis uses two data sources. The first is a catch dataset containing recorded catch weight, value, taxonomic information, date, fisher identifier, and ABM name. The second is a Household Survey dataset containing selected social and livelihood indicators. The catch data are used to characterize fishery portfolio composition. The Household Survey data are used to estimate social resilience indicators.

In Indonesia, the catch dataset contains 101,725 records across 24 ABMs, representing 1,733,133 kg of recorded catch. Strict small pelagics account for 23.4% of recorded catch weight, while the broader pelagic and mobile schooling group accounts for 54.6%. The Household Survey dataset contains 13,630 Indonesia records.

The final quality-filtered analytical sample includes 16 ABM-year observations across 9 Indonesian ABMs. For the article figures, ABM-year observations are aggregated to ABM-level summaries.

data_profile <- tibble::tibble(
  Measure = c(
    "Indonesia catch records",
    "Indonesia ABMs in catch data",
    "Indonesia recorded catch weight (kg)",
    "Strict small pelagic share of catch weight",
    "Broad pelagic/mobile schooling share of catch weight",
    "Indonesia HHS records",
    "Quality-filtered ABM-year observations",
    "ABMs in article analytical sample"
  ),
  Value = c(
    fmt_n(idn_catch_records),
    fmt_n(idn_catch_abms),
    fmt_n(idn_total_catch_kg),
    fmt_pct(idn_strict_pelagic_share, accuracy = 0.1),
    fmt_pct(idn_broad_pelagic_share, accuracy = 0.1),
    fmt_n(idn_hhs),
    fmt_n(n_analytical),
    fmt_n(n_analytical_abms)
  )
)

knitr::kable(data_profile)
Measure Value
Indonesia catch records 101,725
Indonesia ABMs in catch data 24
Indonesia recorded catch weight (kg) 1,733,133
Strict small pelagic share of catch weight 23.4%
Broad pelagic/mobile schooling share of catch weight 54.6%
Indonesia HHS records 13,630
Quality-filtered ABM-year observations 16
ABMs in article analytical sample 9

Fishery portfolio measure

Catch records were classified into three analytical groups:

  • Strict small pelagic: sardine-, anchovy-, scad-, and small mackerel-like taxa.
  • Other pelagic / mobile schooling: broader pelagic or mobile schooling taxa that may contribute to a similar portfolio logic but are not treated as the strict small pelagic group.
  • Other recorded catch: all remaining recorded catch.

This classification is intentionally practical. The purpose is not to resolve every taxonomic detail, but to distinguish the portion of the recorded fishery portfolio most directly relevant to the small pelagic resilience argument.

Social resilience measures

The Household Survey indicators were scored from 0 to 100, where higher values indicate more favorable resilience conditions. The article uses six social metrics:

social_indicator_table <- tibble::tibble(
  Dimension = c(
    "Social equity",
    "Leadership trust",
    "Collective efficacy",
    "Participation",
    "Financial sufficiency",
    "Food security"
  ),
  Interpretation = c(
    "Respondents perceive that they benefit equally from the fishery.",
    "Respondents trust local decision-makers to act in the community interest.",
    "Respondents believe their community can manage the fishery effectively.",
    "Respondents believe local participation can help maintain or improve catch.",
    "Households report being able to cover their needs.",
    "Households report not worrying about having enough food."
  )
)

knitr::kable(social_indicator_table)
Dimension Interpretation
Social equity Respondents perceive that they benefit equally from the fishery.
Leadership trust Respondents trust local decision-makers to act in the community interest.
Collective efficacy Respondents believe their community can manage the fishery effectively.
Participation Respondents believe local participation can help maintain or improve catch.
Financial sufficiency Households report being able to cover their needs.
Food security Households report not worrying about having enough food.

The six indicators are also summarized into three analytical scores:

  • Collective action score: average of social equity, leadership trust, collective efficacy, and participation.
  • Livelihoods and food security score: average of financial sufficiency and food security.
  • Social resilience score: average of all six social indicators.

These scores are used to summarize patterns for the article. They should be interpreted as analytical summaries rather than final official resilience scores.

Results

Small pelagics are unevenly distributed across Indonesian ABM portfolios

Small pelagics do not contribute equally across Indonesian ABMs. Some ABMs show a visible strict small pelagic component in recorded catch, while others are dominated by other recorded catch. This variation is useful for the article because it creates the empirical basis for asking how fishery portfolio composition relates to social resilience conditions.

catch_composition_abm %>%
  ggplot(aes(x = ma_name, y = share, fill = fishery_group)) +
  geom_col(width = 0.78, position = position_stack(reverse = FALSE)) +
  coord_flip() +
  scale_y_continuous(labels = percent_format(accuracy = 1), expand = expansion(mult = c(0, 0.02))) +
  scale_fill_manual(values = fishery_palette, drop = FALSE) +
  labs(
    x = NULL,
    y = "Share of recorded catch weight",
    fill = NULL,
    title = "Small pelagic contribution varies across Indonesian ABMs",
    subtitle = "Catch composition is summarized across years for ABMs included in the article analytical sample."
  ) +
  theme_article()
Figure 1. Recorded catch composition by Indonesian ABM in the quality-filtered analytical sample. Strict small pelagics are highlighted in blue and placed at the base of each bar so their contribution can be compared directly across ABMs.

Figure 1. Recorded catch composition by Indonesian ABM in the quality-filtered analytical sample. Strict small pelagics are highlighted in blue and placed at the base of each bar so their contribution can be compared directly across ABMs.

The figure should not be interpreted as a measure of total fishery dependence. It describes the composition of recorded catch in the available data. Still, the variation across ABMs is valuable because it shows where small pelagics are more or less prominent within the observed fishery portfolio.

Social resilience varies across dimensions and ABMs

The social resilience profile also varies across ABMs. Some communities show stronger scores for participation or collective efficacy, while others show weaker financial sufficiency or food security. This multidimensionality is central to the IMEL approach: resilience is not reduced to a single fishery production measure.

social_long_abm %>%
  mutate(ma_name = fct_reorder(ma_name, social_resilience_score, .desc = FALSE)) %>%
  ggplot(aes(x = indicator, y = ma_name, fill = score)) +
  geom_tile(color = "white", linewidth = 0.4) +
  geom_text(aes(label = round(score, 0)), size = 3, color = "gray20") +
  scale_fill_gradient(low = "#F2F2F2", high = article_blue, limits = c(0, 100), na.value = "white") +
  labs(
    x = NULL,
    y = NULL,
    fill = "Score",
    title = "Community resilience profiles differ across social dimensions",
    subtitle = "The same ABM can show stronger conditions in one dimension and weaker conditions in another."
  ) +
  theme_article(base_size = 10) +
  theme(
    axis.text.x = element_text(angle = 35, hjust = 1),
    panel.grid = element_blank()
  )
Figure 2. Social resilience indicators by Indonesian ABM. Scores are aggregated across years at ABM level; each cell reports a 0–100 score, with higher values indicating more favorable resilience conditions.

Figure 2. Social resilience indicators by Indonesian ABM. Scores are aggregated across years at ABM level; each cell reports a 0–100 score, with higher values indicating more favorable resilience conditions.

This figure is useful because it prevents overinterpreting fishery composition. Two ABMs may have similar small pelagic contribution but different social conditions. Conversely, two ABMs may have similar social resilience scores but very different fishery portfolios.

An initial IMEL lens for adaptive management

The value of this analysis is not only in describing patterns. It also shows how IMEL metrics can support adaptive management. By combining fishery portfolio composition and social resilience scores, managers can begin to distinguish between different types of situations that may require different responses.

The figure below is intentionally presented as an initial management lens, not as a definitive classification. It uses median cutoffs in the Indonesia ABM-level analytical dataset to separate ABMs into four broad signals:

  • Diversification opportunity: higher small pelagic contribution and higher social resilience.
  • Potential dependence risk: higher small pelagic contribution but lower social resilience.
  • Strong social foundation: lower small pelagic contribution but higher social resilience.
  • Foundational resilience priority: lower small pelagic contribution and lower social resilience.
typology_df %>%
  ggplot(aes(x = strict_small_pelagic_share, y = social_resilience_score)) +
  geom_vline(xintercept = pelagic_cutoff, linetype = "dashed", color = "gray45") +
  geom_hline(yintercept = social_cutoff, linetype = "dashed", color = "gray45") +
  geom_point(aes(size = hhs_n_total), alpha = 0.78, color = article_blue) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  scale_y_continuous(limits = c(0, 100)) +
  labs(
    x = "Strict small pelagic share of recorded catch weight",
    y = "Social resilience score",
    size = "HHS n",
    title = "A practical IMEL lens for adaptive management",
    subtitle = "Combining portfolio composition and social resilience helps distinguish opportunity from vulnerability."
  ) +
  theme_article()
Figure 5. Initial management-learning lens combining fishery portfolio composition and social resilience in Indonesia. Dashed lines show sample medians; the quadrants are intended as a discussion tool, not a final classification.

Figure 5. Initial management-learning lens combining fishery portfolio composition and social resilience in Indonesia. Dashed lines show sample medians; the quadrants are intended as a discussion tool, not a final classification.

This lens helps move the management question away from a narrow focus on whether small pelagics are present. A more useful question is whether communities have the social foundations needed to manage a diversified fishery portfolio fairly and adaptively.

For example, a site with high small pelagic contribution and strong social resilience may be a good candidate for more advanced adaptive management, monitoring, or value-chain work. A site with high small pelagic contribution but weaker social resilience may require more attention to participation, trust, benefit-sharing, and household risk before fishery portfolio diversification can be treated as a resilience asset.

Discussion

This article uses Indonesia as an illustrative case to demonstrate an approach that the IMEL Framework is designed to make more actionable from 2026 onward. That distinction is central. The historical data can show how catch composition and social metrics may be brought together, but they cannot fully deliver the management-ready precision and coverage that the framework now requires. The analysis should therefore be read as a foundation for future IMEL implementation, not as a final assessment of site performance.

Within that framing, the analysis supports three main points.

First, small pelagics should be understood as part of a broader fishery portfolio. Their contribution varies across Indonesian ABMs, which means their potential role in resilience is also context-specific. In some places, they may represent a meaningful additional resource base. In others, they may be marginal or not central to the recorded fishery portfolio.

Second, community resilience cannot be inferred from fishery composition alone. The social indicators show variation across equity, trust, collective efficacy, participation, financial sufficiency, and food security. These dimensions shape whether communities can convert access to multiple fisheries into more stable livelihoods and more adaptive management.

Third, the IMEL approach is useful because it links measurement to management. It allows teams to look at fishery portfolio composition alongside the social conditions that enable legitimate and adaptive co-management. It also provides a structure for integrating this social evidence with ecological and fisheries indicators, including fisheries productivity and critical habitat protection. This is especially important for economic growth strategies in small-scale fisheries, where growth that is not socially grounded can increase risk, inequality, or conflict.

The key implication is that small pelagic fisheries can contribute to resilience, but not automatically. Their contribution depends on whether diversification is governed through institutions that are trusted, participatory, equitable, and capable of adapting to change.

Conclusion

Small pelagic fisheries may help coastal communities in coral reef systems reduce risk by diversifying the fishery portfolio available for food and income. But diversification alone is not resilience. The resilience value of small pelagics depends on the social and institutional conditions that determine who benefits, who participates, how rules are adapted, and whether households are better able to withstand shocks.

By combining catch composition with social resilience metrics, Rare’s IMEL Framework provides a practical way to understand this relationship and use it for adaptive management. The analysis developed here should be treated as a proof of concept using Indonesia legacy data. Its main contribution is to show how social metrics can be integrated into fisheries strategies so that small pelagic fisheries are evaluated not only as a production opportunity, but as part of a broader community resilience agenda. As the IMEL Framework is implemented in 2026, improved sampling coverage and precision should allow this approach to move from demonstration toward more reliable management use.