The vulnerability factors of illegal, unreported, and unregulated (IUU) fishing in municipality of Alicia, Zamboanga Sibugay, Philippines

J. Bio. & Env. Sci. 20 2 5 106 | Fernandez et al. RE RERE RESEARCH SEARCHSEARCH SEARCH PAPER PAPERPAPER PAPER OPEN ACCESS OPEN ACCESSOPEN ACCESS OPEN ACCESS The vulnerability factors of illegal, unreported, and unregulated (IUU) fishing in municipality of Alicia, Zamboanga Sibugay, Philippines Judy Ann H. Fernandez *1 , Shekinah L. Ogoc 1 , Angelica M. Darunday 1 , Norlika D. Moti 1 , Hilly Ann Roa-Quiaoit 1 , Maria Theresa M. Mutia 2 , Frandel Louis S. Dagoc 1 , Armi G. Torres 1 1 Department of Environmental Science, Schoo l of Interdisciplinary Studies, Mindanao State University, Iligan Institute of Technology, Tibanga, Iligan City, Philippines 2National Fisheries Research and Development Institute, Freshwater Fisheries Research and Development Center (NFRDI-FFRDC), Department of Agriculture, Butong, Taal, Batangas, Philippines Article published on May 09, 2025 Key words: Vulnerability, SDG 14.4, Fisheries management, Region 9 Abstract Illegal, Unreported, and Unregulated (IUU) fishing continues to seriously threaten the sustainability of marine resources in Alicia, Zamboanga Sibugay, Philippines. However, nothing has been done yet to evaluate the IUU fishing in Alicia. This paper presents the first ever assessment of the vulnerability of coastal waters of Alicia to IUU fishing, using the Philippine IUU Fishing Index and Threat Assessment Tool (I-FIT) developed by DA-BFAR and USAID. The assessment focused on eight indicators with a scaling score from 1 (low vulnerability) to 4 (very high vulnerability) and a data quality score of 1-3 (low -high data quality). Alicia obtained a vulnerability score of 2.63, indicating a moderate risk to IUU fishing. This score is higher than the national average of 2.53. The key vulnerability factors include rich fishing grounds in the municipality that attract legal and illegal fishers, increasing prices of illegally caught fish, and a very low budget allocation for fisheries and coastal resource management. Yet, these are alleviated mainly by other factors such as welldefined jurisdictional borders and a combined enforcement pact with neighboring municipalities, as well as the absence of powerful groups defending illegal fishers. The data quality score is 3, denoting high data reliability. Recommendations include intensifying habitat protection, raising funding allocation, increasing enforcement operations, providing livelihood programs, monitoring, engaging community participation, and conducting regular vulnerability assessments. This study aligns with the Sustainable Development Goal Target 14.4, which seeks to end illegal, unreported, and unregulated (IUU) fishing. * Corresponding Author: Judy Ann H. Fernandez  [email protected] Journal of Biodiversity and Environmental Sciences (JBES) ISSN: 2220-6663 (Print) 2222-3045 (Online) Vol. 26, No. 5, p. 106-118, 2025 http://www.innspub.net J. Bio. & Env. Sci. 20 2 5 107 | Fernandez et al. Introduction The fishery sector is crucial to economic growth through its contributions to GDP, employment, trade, and livelihoods (Ibrahim et al., 2024). Fisheries is one of the largest and oldest primary industries worldwide, currently, 59.6 million people are being employed in fisheries all over the globe (FAO, 2018). However, one of the pressing problems is Illegal, unregulated, and unreported fishing posing a global threat to food security, maritime livelihoods, and fisheries sustainability, threatening ocean sustainability, national security, and the safety and human rights of fishing communities (Mackay et al., 2020; Auld et al., 2023). It is a very contentious point for the United Nations, as it affects numerous dimensions of the Sustainable Development Goals (Auld et al., 2023). Three UN Specialised Agencies - the Food and Agriculture Organization (FAO), International Labour Organization (ILO), and the International Maritime Organization (IMO) - have developed instruments relating to sustainable harvesting, working conditions, and vessel safety on board fishing vessels (Auld et al., 2023). What is shared among these instruments is the incorporation of port state measures for enforcement. Illegal, Unreported, and Unregulated (IUU) fishing poses significant challenges in both inshore and high seas ecosystems (Chen et al., 2024), affecting finfish and aquatic invertebrates alike (Temple et al., 2022). This practice has far-reaching economic, social, and environmental consequences (Fujii et al., 2021; Chen et al., 2024), including the depletion of natural resources and the degradation of ecosystems (Setianti et al., 2024). IUU fishing often results in criminal activities and its impact varies from immediate to long-lasting. Key environmental issues linked to IUU fishing include operating without permits, targeting protected species, using prohibited gear, and exceeding catch limits stated in RA 8550, as amended by Republic Act No. 10654 or the Fisheries Code of the Philippines. Estimates suggest that IUU fishing contributes to the annual capture of 11-26 million tons of fish, with a value between USD 10-23 billion (FAO, 2016). The Philippines is a prime contributor in terms of fish production to the world. In the year 2018, 4.36 million MT had been acquired from aquaculture, municipal, and commercial fisheries productions. The Philippines placed 13th for its fish production and was also in fourth place when it comes to seaweed production in the world (FAO, 2020). Fishery exports earned US $1.6 billion. The Philippine fisheries sector is a significant contributor to the economy, supporting nearly 2 million fisherfolks (TahiLuddiN and Terzi, 2021). As a member of the UN, the Philippines is committed to attaining the SDGs, including Target 14.4. Moreover, the government has made significant efforts to address this problem through legislative reforms, capacity-building initiatives, and collaboration with international organizations and neighboring countries. Despite various national and local efforts to combat IUU fishing activities, the practice continues to thrive, driven by factors such as regulatory challenges, economic pressures, sociocultural aspects, and weak monitoring, control, and surveillance (Wilcox et al., 2021). One of the most significant and impactful legislation in terms of Illegal, Unreported, and Unregulated (IUU) fishing is RA 10654, or the Amended Fisheries Code of the Philippines. The Amended Fisheries Code of the Philippines was enacted in 2015 and strengthened the original Fisheries Code, RA 8550, by having heavier penalties against IUU fishing. Based on the 2021 assessment on IUU fishing in the Philippines, only 160 municipalities and cities were assessed. The majority of the 160 LGUs fall within the 2.00-3.00 range (DA-BFAR, 2022). Eight LGUs score below 2.00, indicating a lower risk, while two LGUs fall within the 3.50-4.00 range, reflecting a higher risk. Overall, the prevalence scores are closely aligned with the vulnerability scores, indicating that IUU fishing in municipal waters is strongly linked to these areas’ susceptibility to such activities. This correlation allows LGUs and their stakeholders to effectively identify and address the risks and vulnerabilities associated with IUU fishing (DA-BFAR, 2022). J. Bio. & Env. Sci. 20 2 5 108 | Fernandez et al. As of the 2023 report, no assessment (0%) has been conducted on Illegal, Unreported, and Unregulated (IUU) fishing in the Zamboanga Peninsula (Region 9), in contrast to the significant progress observed in other regions (DA-BFAR, 2021). In response to this gap, the present study was conducted to assess the vulnerability factors contributing to IUU fishing within the municipal waters of Alicia, Zamboanga Sibugay, located in Region 9, Philippines. The assessment employed the eight standardized indicators outlined in the Philippine IUU Fishing Index and Threat Assessment (I-FIT) Tool, developed by the Department of Agriculture–Bureau of Fisheries and Aquatic Resources (DA-BFAR) in 2022. Materials and methods Study area The study was conducted within the municipality of Alicia, located in the southwestern part of Zamboanga Sibugay, approximately 7°30' North and 122°56' East on the island of Mindanao, Philippines. The municipality relies heavily on fishing and aquaculture, with a 98 km coastline. Its fisheries production drives its economy, contributing significantly to economic growth. The study focused on the 20 coastal barangays of the municipality, which include: (1) Bagong Buhay, (2) Bella, (3) Conception, (4) Dawa-Dawa, (5) Gulayon, (6) Ilisan, (7) Kawayan, (8) Lambuyogan, (9) Lapaz, (10) Lapirawan, (11) Litayon, (12) Naga-Naga, (13) Pandan-Pandan, (14) Poblacion, (15) Sta. Maria, (16) Sto. Niño, (17) Talaptap, (18) Tampalan, (19) Tandiong Muslim, and (20) Timbang-Timbang (Fig. 1). Research design This study employed a mixed-method approach, using quantitative and qualitative research to objectively assess the vulnerability to illegal, unreported, and unregulated fishing risk in Alicia, Zamboanga Sibugay. Entry protocol Ethical considerations were fundamental to this research due to the sensitivity of illegal fishing activities. All participants provided informed consent, ensuring they were fully aware of the study's purpose, their rights as respondents, and their voluntary participation. Ethical guidelines for research involving vulnerable populations were strictly adhered to, and the cultural sensitivities of the study area were respected. Fig . 1. Map showing the study area comprising 20 coastal barangays in Alicia, Zamboanga Sibugay, Philippines J. Bio. & Env. Sci. 20 2 5 109 | Fernandez et al. Prior to the study, robust entry processes were implemented to ensure the safety and accuracy of the researchers' data. A request letter was made to the Municipal Agriculture Office for a study permit, and the proposal was presented to the local mayor for approval. Following permission, collaborations with local government officials and stakeholders were also facilitated. A permit to conduct the study was obtained from the municipality. Data collection Measurement of vulnerability to IUU fishing using the I-FIT Tool The study was conducted for three months, starting October 2024 to December 2024, in 20 coastal barangays of Alicia, Zamboanga Sibugay for assessment year 2024. To determine the susceptibility of Alicia’s municipal waters to Illegal, Unreported, and Unregulated (IUU) fishing, the Philippine IUU Fishing Index and Threat Assessment Tool (I-FIT) was used in this study. Established by the USAID Fish Right Program and the Department of Agriculture–Bureau of Fisheries and Aquatic Resources (DA-BFAR), the I-FIT tool measures three dimensions: the incidence of IUU fishing, the susceptibility of areas to IUU, and the performance of responses. This research focused only the Vulnerability factors, which are quantified through eight indicators. Each of the indicators is rated on a scale of 1 (Low vulnerability) to 4 (Very High vulnerability), with a composite maximum score of 32, as shown in Fig. 2. Data for this component were gathered through secondary data from the Municipal Agriculture Office (MAO), key informant interviews with fishers and enforcement teams, and focus group discussions (Table 1). Table 1. Vulnerability factors indicators Indicators Methods/Data source Fisheries resource availability and coastal habitat quality (V1) Secondary Data/Municipal Agriculture office Ex - vessel selling price of species commonly targeted by illegal fishers (V2) Key informant interview to fishers Overcapacity of fishers (V3) Secondary Data/Municipal Agriculture Office Physical configuration of shorelines and islands (V4) Focus Group Discussion/ Secondary Data/ Municipal Agriculture Office Weather and ocean condition impacts on seaborne interventions (V5) Key informant interview and Secondary Data/Enforcement Teams LGU budget allocation for fisheries and CRM (V6) Secondary Data/ Municipal Agriculture Office Clear boundaries and jurisdiction for enforcement ( V7) Secondary Data/Municipal Agriculture Office Illegal fishers are supported by influential third - party people or groups (V8) Key informant interview/Enforcement teams Good (low) Bad (Very High) Fig. 2. IUU fishing index rating Data quality A data quality rating precedes every indicator, scored between 1 (low data quality) and 3 (high data quality), that captures the completeness and reliability of the underlying data (Fig. 3). A rating of 1 implies low data quality, meaning that the information available is incomplete or unreliable. A rating of 2 implies moderate data quality, whereas a rating of 3 implies high data quality, with both accuracy and completeness. These ratings are useful inputs for local government units, where gaps in existing data collection activities can be identified and inform the strengthening of monitoring and reporting systems. Low Medium High Fig. 3. Data quality score Population and sample size A social survey using purposive sampling was used to collect information on the vulnerability factors that lead to illegal, unreported, and unregulated (IUU) fishing in Alicia coastal waters. Respondents were J. Bio. & Env. Sci. 20 2 5 110 | Fernandez et al. people directly engaged in or impacted by local fishing operations, including fishers, owners of fishing vessels, and fish vendors. For a representative of the target population, the sample size was calculated using Slovin's formula with a 5% margin of error. ݊ = ܰ 1 + ܰ (݁)ଶ Where: n = sample size N = population size e = margin of error The population of fisherfolk is 3,385. Therefore, the sample size for this study is 358. Data analysis IUU fishing index score Each vulnerability factor indicators was given a score of 1 to 4 by the participants. The scores were averaged to get the score for a given indicator. The average of all 8 indicators (V1-V8) was computed to compute the overall vulnerability score. Table 2. Fisheries resource availability and coastal habitat quality (V1) Score Description 1 (low) Little or no known or perceived ecological richness or limited occurrence of fishing activities in the area. 2 (Moderate) Known fishing grounds for small pelagic fishes, but the poor condition of coastal habitats 3 (High) Perceived ecological richness through existing or starting marine protected areas and/ or known/perceived presence of commercially important species in the area 4 (very high) Existence of thriving marine protected areas, critical habitats, spawning grounds, traditional/ popular fishing grounds, and/ or known perceived presence of commercially important species in the area (e.g., Lapu-Lapu, maya-maya, lobster, Sardinas, Galunggong, etc.) Table 3. Data quality score (V1) Score Description 1 (Low) Data source is based only on perception 2 (Medium) Mixed data source from perceptions and some from scientifically-collected survey / monitoring data 3 (High) Score is based on fisheries and coastal habitat survey data collected using scientific methods Fisheries resource availability and coastal habitat quality (V1) The vulnerability and data quality score with corresponding descriptions are shown in Tables 2 and 3. The data used for this indicator were derived from the fisheries production reports of the Municipal Agriculture Office (MAO) and further cross-checked by focus group discussions (FGD). Ex-vessel selling price of species commonly targeted by illegal fishers (V2) Vulnerability and data quality scores with descriptions of this indicator are presented in Tables 4 and 5. These ex-vessel selling prices were the prices that illegal fishers received directly for their catch. The price of commonly targeted species by illegal fishers were computed from price monitoring of illegal fishers through key informant interviews. To calculate the percentage change for each species: 1. ܲ݁ݎܿ݁݊ݐܽ݃݁ ܫ݊ܿݎ݁ܽݏ݁ = ቀ௉௥௜௖௘ ௜௡ ଶ଴ଶସି௉௥௜௖௘ ௜௡ଶ଴ଶଷ ௉௥௜௖௘ ௜௡ ଶ଴ଶଷ ቁݔ100 2. ܶ݋ݐ݈ܽ ܣݒ݁ݎܽ݃݁ ܲ݁ݎܿ݁݊ݐܽ݃݁ = ቀ∑ ௉௘௥௖௘௡௧௔௚௘ ூ௡௖௥௘௔௦௘ ே௨௠௕௘௥ ௢௙ ௧௔௥௚௘௧௘ௗ ௦௣௘௖௜௘௦ቁݔ100 Table 4. Ex-vessel selling price of species commonly targeted by illegal fishers (V2) Score Description 1 (Low) Less than 2 % increase in the average ex-vessel price of fish or a decrease in price 2 (Moderate) 2% to 5% increase in ex - vessel price 3 (High) 6% to 10% increase in ex - vessel price 4 (Very high) The average ex - vessel price of fish is more than 10% higher than last year. Table 5. Data quality score (V2) Score Description 1 (Low) Data source is based only on perception 2 (Medium) Mixed data source from perceptions and some from scientifically-collected survey /monitoring data 3 (High) Score is based on price monitoring data Overcapacity of fishers (V3) The vulnerability and data quality scores with the corresponding descriptions for V3 are shown in Tables 6 and 7. The fishers' density in the J. Bio. & Env. Sci. 20 2 5 111 | Fernandez et al. municipality was employed as a proxy to estimate the overcapacity of fishers. It is computed using the estimated fisher population in the municipality divided by the estimated length of the coast in kilometers, as indicated by the formula below. Total Fisher Density=(Estimated number of fisheers)/(Total length of coastal area (km)) Table 6. Overcapacity of fishers (V3) Score Description 1 (low) Less than two fishers per km of coastline 2 (Moderate) 3 to 30 fishers per km of coastline 3 (High) 31 to 70 fishers per km of coastline 4 (very high) More than 70 fishers per km of coastline Table 7. Data quality score (V3) Score Description 1 (Low) Based only on rough estimates of number of fishers and coastline length 3 (High) Based on actual number of fishers and measured length of coastline from GIS or other means Physical configurations of shoreline and islands (V4) Coastal areas with continuous and simple coastline are easier to patrol over areas with complex geological characteristics. This indicator scores the physical configuration of shoreline and islands in Alicia. The vulnerability scores with the corresponding descriptions are presented in Table 8. The score was calculated based on the existing maps and accessible information of Alicia's municipal waters, consisting of a large mainland with two outlying islands. Table 8. Physical configurations of shoreline and island (V4 Score Description 1 (Low) Single, continuous, and simple coastline 2 (Moderate) Single coastline with complex features (i.e., large embayment 3 (High) Large mainland with some smaller outlying islands 4 (Very high) Multiple small islands with no known large islands Weather and ocean condition impacts on seaborne interventions (V5) The success of seaborne enforcement operations is sometimes impacted by the weather and ocean conditions. This indicator gauges the percentage of unsuccessful operations due to unsafe weather or sea conditions for the assessment year 2024. This is estimated using the formula below: Patrol abortion Rate (%)={(Number of aborted seaborne operations)/(Total number of scheduled and unscheduled operations)} × 100 The vulnerability and data quality scores with corresponding descriptions are presented in Tables 9 and 10. If there were no enforcement operations or no active enforcement team, a score of 4 (very high) was assigned. Table 9. Weather and ocean condition impacts on seaborne interventions (V5) Score Description 1 (Low) Less than 5% of enforcement operations were cancelled due to weather conditions. 2 (Moderate) 5% to 25% of enforcement operations were cancelled due to weather conditions 3 (High) 26% to 50% of enforcement operations were canceled due to weather conditions 4 (Very high) More than 50% of enforcement operations were canceled due to weather conditions. Table 10. Data quality score (V5) Score Description 1 (Low) Data source is from perception of less than 2 people 2 (Medium) Data is from more than 2 people and perceptions derived from a systematic survey or focused group discussion 3 (High) Seaborne operation/ fisheries law enforcement operation records and reports LGU budget allocation for fisheries and CRM (V6) The vulnerability and data quality scores with corresponding descriptions for this indicator are shown in Tables 11 and 12. Recognizing that larger budget allocations is crucial for providing more response measures to combat IUU fishing, this indicator was measured by the total budget allocation for fisheries and coastal resource management (CRM) divided by the total municipal water area in km 2, as presented in the formula below: J. Bio. & Env. Sci. 20 2 5 112 | Fernandez et al. Total budget per km 2 = (∑Budget for fisheries and CRM)/(Area of Municipal water in km²) Table 11. LGU budget allocation for fisheries and CRM (V6) Score Description 1 (Low) Higher than P100,000/km 2 of municipal waters 2 (Moderate) P50,000 - P100,000/km 2 of municipal waters 3 (High) P1,000 - P49,999/km 2 of municipal waters 4 (Very high) Less than P1,000/km2 of municipal waters Table 12. Data quality score (V6) Score Description 1 (Low) The data source is from rough estimates (not based on a document) 2 (Medium) Mixed data sources (i.e., from official documents and some estimates) 3 (High) Computed mostly from financial documents Clear boundaries and jurisdiction for enforcement (V7) This vulnerability indicator examines the status of the delineation of municipal waters and/or agreements with adjacent LGUs on access to fishing or water boundary jurisdiction. Its vulnerability and data quality scores with corresponding descriptions are shown in Tables 13 and 14. The data for this indicator refers to the adopted municipal boundary delineation from NAMRIA. In addition, the LGU Alicia has a joint agreement with the adjacent three LGUs (Talusan, Olutanga, and Mabuhay) to address IUU in their municipal waters. Table 13. Clear boundaries and jurisdiction for enforcement (V7) Score Description 1 (Low) Municipal waters officially delineated (NAMRIA Approved) and/ or fishing agreements with adjacent municipalities reached (e.g., with joint resolution or ordinance) 2 (Moderate) Unofficial boundaries (i.e., no ordinance or resolution), but the boundaries are recognized by adjacent fishers (i.e., no conflicts with fishers from adjacent LGUs) 4 (Very high) No boundary delineation attempts or agreements with adjacent municipalities, but with conflicts with fishers from adjacent LGUs (e.g., illegal fishing by fishers from adjacent LGUs) Table 14. Data quality score (V7) Score Description 1 (Low) Data source is from perceptions only 3 (High) Score is backed up by official documentation (e.g., ordinance or resolution) Illegal fishers supported by third-party influential people or groups (V8) The vulnerability and data quality scores with corresponding descriptions are outlined in Tables 15 and 16. Data were gathered by interviewing enforcement teams about the presence or absence of incidents in which someone or influential people groups intervened, resulting in the release without due process during the assessment year in 2024 This indicator is measured using only two choices: 1 for no incidents or 4 for at least one case during the assessment year. Table 15. Illegal fishers supported by third-party influential people or groups (V8) Score Description 1 (Low) No, there have been no incidents during the assessment year when someone intervened after an apprehension or enforcement operation that resulted in the release of the apprehended vessel and/ or fisherfolk without due process 4 (Very high) Yes, there was a t least one case during the assessment year when someone intervened after an apprehension or enforcement operation that resulted in the release of the apprehended vessel and/ or fisherfolk without due process. Table 16. Data quality score (V8) Score Description 1 (Low) Based only on perceptions or experience without any documentation 3 (High) Based on experience with some documentation of the incident and the intervention Results and discussion Fisheries resource availability and coastal habitat quality (V1) The municipality of Alicia has established the Tantanang Bay Marine Fish Sanctuary, located in Tangalay, Kawayan. It is a growing marine protected area managed by the Municipality of Alicia. Fish sanctuaries (known as 'no-take zones') J. Bio. & Env. Sci. 20 2 5 113 | Fernandez et al. are critical for creating marine conservation programs since they can conserve and grow fish stocks in tropical artisanal and temperate industrial fisheries (Solandt et al., 2022). The fisheries production in Alicia indicates the existence of commercially important species such as groupers, big-eyed scad, Crevalle, garfish, hardtail, milkfish, threadfin breams, round scad, sardines, rabbitfish, skipjack, slipmouth, snapper, spanish mackerel, sting ray, striped mackerel, trevalle, and tuna and other assorted fishes, including parrotfishes, juvenile rabbitfish, Mackerel Scad, Big eye Scad, Lobster, Mullet (Table 17). The total fish production amounted to 1,163,038.04 kilograms in Alicia, and the species abundance as shown in Table 17. Other aquatic products include seaweeds, shells, sea cucumbers, lobsters, octopuses, squids, shrimps, mud crabs/blue crabs, and cuttlefish. The Philippines' rich marine resources and expansive coastlines make it an ideal environment for a thriving seafood industry (Galang, 2024). However, the abundance of marine resources in 6 fishing grounds in Alicia attracts legal and illegal fishers to adopt faster and profitable methods, specifically using destructive fishing methods, potentially leading to coral bleaching in the southern Mindanao, where Alicia is located, particularly in the Zamboanga and Sibuguey Bay (NOAA, 2025) which may lead to the degradation of marine species and the deterioration of the marine ecosystem. This indicator has a score of 4, indicating a very high vulnerability to IUU fishing due to rich fisheries resources and presence of marine protected area).This also implies that IUU fishing is driven not only by poverty, low registration of fishers and fishing vessels, repeat offenders, lack of patrols and surveillance, but also by economic opportunities for the fishers survival and consumption (Widjaja et al., 2020; Lubchenco and Haugan, 2023). Because this information is supported by secondary data from MAO and verified using FGD, the data quality is rated 3 (high). The score is based on data acquired through scientific surveys of fisheries production and coastal habitats. Table 17. Monthly production of fish catch Months Monthly production (kg) 1. January 157,793.13 2.February 111,829.01 3. March 149,550.73 4. April 91,624.20 5. May 84,936.39 6. June 85,338.05 7. July 83,668.16 8. August 109,219.91 9. September 57,030.84 10. October 94,387.19 11. November 64,330.43 12. December 73,330 Total 1,163,038.04 Ex-vessels selling price of species commonly targeted by illegal fishers (V2) Data on the fish species commonly targeted by illegal fishers were obtained through a focus group discussion (FGD) with the Municipal Agriculture Office (MAO). According to the MAO, illegal fishing practices are generally indiscriminate, capturing various fish species without selection. Meanwhile, ex-vessel selling price data for these species were collected through key informant interviews (KII) with local fishers. Data from 2023 and 2024 were compared across 25 fish species frequently targeted by illegal fishers to assess price changes (Table 18). The analysis revealed an average percentage increase of 33.59% in ex-vessel prices. This significant price increase falls under the score of 4 (very high), as it exceeds the threshold of over 10% higher than last year. Since this score is based on mixed data source from perceptions and some scientifically-collected survey/ monitoring data. Table 18. Species prices usually targeted by illegal fishers Targeted species 2023 price 2024 price % change 1. Rabbitfish 80 100 2 2. Grouper 180 200 11.1 3. Ornate Emperor 170 200 17.6 4. Parrotfish 80 100 2 5. Whiteleg shrimp 40 60 50 6. Goatfishes 70 80 14.2 7. Unicorn fishes 70 80 14.2 8. Slipmouth 20 30 5 9. Squid 150 200 33.3 10. Indian Mackerel 180 200 11.11 11. Big - eyed scads 180 200 11.11 12. Snapper 170 200 17.65 13. Fusilier 70 100 42.86 J. Bio. & Env. Sci. 20 2 5 114 | Fernandez et al. 14. Acetes 160 200 2 15. Rabbitfish 100 170 70 16. Lobster 900 1700 88.89 17. Shortfin scad 100 100 0 18. Triggerfish 60 80 33.33 19. Moray eel 20 40 100 20. Surgeonfish 80 90 12.5 21. Butterfish 50 70 40 22. Threadfin 60 100 66.6 23. Silverside 50 70 40 24. Milkfish 100 120 20 25. Bali Sardinella 25 30 20 Total average percentage 33.59 Overcapacity of fishers (P3) In Alicia's 98-kilometer coastline, 1,773 fishers depend on the sea as the main source of their livelihood, bringing the fisher density of 18 individuals per kilometer. This places Alicia's municipal waters at a moderate level of fishing pressure with a score of 2, an evident balance between use of resources and sustainability. Yet, with increasing population and increased demand for fish, the region is highly susceptible to overfishing and exploitation, especially without strict regulation. Several studies have confirmed that fishery overcapacity increases competition among fishers for declining stocks and tends to lead them into illegal practices to maintain their catch (Nedumpara et al., 2024). The quality of data for assessing overcapacity of fishers is high with a score of 3 as this was computed based on the actual number of fishers and the measured length of coastline from GIS or other means. This data indicates the reality today and is an urgent early warning, calling stakeholders to take proactive and adaptive management approaches to preserve Alicia's abundant marine environment for future generations. Physical configurations of shorelines and islands (V4) Alicia remains vulnerable to (IUU) fishing due to its complex coastal geography, which presents significant challenges for monitoring and enforcement. The municipality comprises a large mainland and two smaller outlying islands locally known as Sinking Island, a tourist destination, and Litayon Island, inhabited by local communities as illustrated in Fig. 4. Based on these geographic characteristics, Alicia is classified under category of high vulnerability to IUU fishing with a score of 3, indicating a large mainland with some smaller outlying islands that increases the difficulty of effective fisheries management and surveillance. Fig. 4. Physical configurations of shorelines and islands in Alicia