To generate hypothetical interventions, a theory (or theories) of change can be developed that describes how the behavioral drivers identified in Step 1 can be addressed to ultimately reduce illegal fishing. Developing theories of change helps ensure that those designing a hypothetical intervention share an underlying understanding of the relevant context and various dynamics at work. It is an important step, because interventions that seem generalizable may fail to remain relevant in a particular fishery upon closer examination of the theory of change (Margoluis et al., 2013).

Theories of change can be informed by a literature review, information gathered in Step 1, and local knowledge. Several types of interventions have been shown, either empirically or via modeling studies, to be effective for reducing illegal behaviors (e.g., Hamilton and Axelrod, 1981; Tsebelis, 1989; Sutinen and Kuperan, 1999; Fehr and Gächter, 2002; Gezelius, 2002, 2004; Andreozzi, 2004; Fowler, 2005; Velez et al., 2005; Sumaila et al., 2006; Tyler, 2006; Lopez et al., 2012; Kraak et al., 2014; von Essen et al., 2014). In addition, certain principles for changing behavior have been articulated based on experiments and field experience (e.g., Dolan et al., 2010; Ayal et al., 2015; Samson, 2017). Thus, there is an abundant and growing body of research that can be drawn from to identify a compelling theory of change and develop hypothetical interventions.

## Step 3: Experimentally Test Hypothetical Interventions

Once potential interventions have been developed to address a particular behavior associated with illegal fishing, the next step is to experimentally test the efficacy of those interventions. To do so, artefactual field experiments [wherein samples are drawn from the population that would be used in a larger field study (Velez et al., 2006; Fehr and Leibbrandt, 2011; Lopez et al., 2012; Gneezy et al., 2016)], or laboratory experiments that use samples of individuals from each target actor group can be conducted to test the effectiveness of each

intervention – compared to each other as well as a control treatment if possible – at modifying attitudes and changing behaviors. If it is not possible to test experimental interventions on the target audience (due to concerns about influencing the target audience ahead of the full implementation of the intervention, for example), then an alternate but highly similar audience can be used (Samson, 2017). In addition, given available time and resources, preliminary experiments with small numbers of participants can be conducted in a separate community to test intervention variations and refine experimental protocols before implementing the full-scale experiment in the target community. Behavior change might not be captured as a change in actual illegal fishing, but rather through an analogous behavior in a simulation; for example, through reductions in cheating behaviors in a game-based experiment. Common pool resource (CPR) games are often used to test the effects of hypothetical interventions on participants in a simulated fishery. Experimental interventions could include mock fishing scenarios, simulated framing and messaging campaigns, the provision of feedback on the effects of illegal fishing, or many other types of interventions designed to address drivers identified in surveys and interviews. Experimental treatments that simulate real world variations and challenges can be used to test the robustness of interventions to some extent.

## Step 4: Pilot Interventions Based on the Mechanisms Identified

Once interventions have been tested experimentally, those with demonstrated efficacy (in the experimental setting) at shifting behaviors in the desired direction should be piloted with small groups under real-world conditions in the target community. This step is crucial because behavioral interventions that are effective in experimental settings may not be effective in real-world settings, where efforts may be undermined by countless confounding factors that were not accounted for in the experiment. For example, it is easy to convey information, such as the aggregate catch of all fishers, to the participants in an experiment. It may, however, be significantly more difficult to get that same information to all members of a fishing community within the requisite amount of time. Similarly, experimentation may reveal that allowing participants to generate their own fishing rules is effective in reducing illegal behaviors, but it might not be politically possible to implement an intervention that involves having fishers re-design the actual regulations with which they must comply. The purpose of the pilot stage, therefore, is to clarify how the interventions that worked in the experimental phase can be implemented in the real world, and to identify problems that arise in the field before they are implemented at a larger scale, requiring more resources and capacity.

Depending on the intervention and the results of the experiments in step 3, piloting may require multiple "conditions" to test different versions ("treatments") of each intervention in the real-world environment. Depending on available resources, each version of the intervention should be replicated on multiple sample populations as well, if possible. In addition, control groups should be included (Martin et al., 2012; Monitoring Design Before-After Control-Impact, 2017). At this step, the efficacy of the interventions can be measured directly with surveys conducted before and after interventions are carried out, and with direct observations of behavior, although it is likely that only short-term process indicators can be measured during this pilot phase (e.g., immediate reductions in the presence of non-permitted fishers on the water, or indicators of earlier steps in the theory of change, such as shifts in knowledge or attitudes). Longerterm outcome indicators (e.g., community-level metrics such as reductions in seasonal catch above quotas or official reports on the prevalence of illegal fishing) should be used to measure success of the large-scale implementation (in the next step).

## Step 5: Scale-Up Tested Interventions, and Set Up Systems to Monitor, Evaluate, and Adjust

If the pilots demonstrate efficacy, and after any necessary modifications are made to the design based on the results, then the interventions are ready to be implemented on a broad, community-wide scale.

A deliberate scaling strategy should be developed, rather than simply depending on the effectiveness of the intervention to result in broader uptake (Battista et al., 2017). A key step will be to determine what scaling up interventions will entail in terms of actual logistics and policy. These will vary greatly among interventions: for example, they may be minimal if the intervention consists of participation in a CPR game that illustrates dynamics that lead to illegal fishing in order to change beliefs about illegal fishing. Some interventions can be scaled very simply, for example by increasing the number of monitors showing aggregate catch at landing sites. However, some interventions such as campaigns to change social norms or the use of more participatory processes to increase the legitimacy of regulations may require many more resources, policy changes, and even the formation of new relationships or changes in existing relationships.

These kinds of changes may lead to unforeseen challenges. For example, if interventions involve the implementation of a program that rewards community members who report illegal fishing, conflicts may form between adjacent areas of the community that failed to emerge in the pilot stage (e.g., if one part of the community has a disproportionately large number of illegal fishermen).

Thus, except in limited circumstances, scaling up interventions from pilots to the entire community is likely to lead to unexpected dynamics (World Health Organization and ExpandNet, 2011; Management Systems International, 2012; Sutton and Rao, 2014; Battista et al., 2017). This is generally an unavoidable part of changing a social and economic system. When challenges do arise, it is critical to document them and any changes made to the intervention in order to accurately

evaluate its efficacy in the following step. Changing interventions prematurely will confound efforts to analyze the efficacy of the intervention, but may be necessary depending on context.

Evaluation of the performance of behavior change interventions is critical, from beginning to end (Woodhouse et al., 2016; Battista et al., 2017). Measurable indicators of the desired behavior changes should be chosen and evaluated prior to implementation to generate baseline data. Many of the same indicators used for evaluating the efficacy of interventions in experiments and pilot projects can be used to assess the efficacy of community-wide interventions. These can include process indicators that reflect intermediate changes that precede actual outcomes such as reductions in illegal fishing (e.g., increased cooperation in reporting and monitoring programs, or the purchase of legal fishing gears). In addition, indicators of actual outcomes (i.e., reductions in illegal fishing behavior) should also be measured at the community-wide scale to ensure illegal fishing has actually been reduced. It is important to measure changes in similar communities not receiving interventions, if possible.

Reductions in illegal fishing can often be measured using standard fisheries data collection methods, including catch logs, reports from patrols, at-sea or electronic observer data, etc. In some cases, new data streams will be required to measure behavior change; for example, individual catch records would be necessary for measuring the efficacy of an intervention aimed at changing individual fishing or reporting behavior. These can be complemented by social science techniques, e.g., surveys with fishermen and community members regarding the frequency and intensity of the illegal fishing taking place. Progress should be measured against specific, quantitative objectives for each indicator that are established before implementation. If these objectives are not being met, project managers can then investigate what is undermining the intervention's effectiveness, and propose solutions to get them back on track.

## DISCUSSION AND CONCLUSION

There is an urgent need to reduce illegal fishing, especially in fisheries where illegal fishing may be threatening livelihoods and food security in dependent communities, and where resources for bolstering surveillance and enforcement are limited. Moreover, corruption, lenient penalties, and other problems constrain the efficacy of enforcement systems in many fisheries around the world. There is evidence that behavioral factors related to perceptions and beliefs, self-interest, and information or the lack thereof can also drive illegal fishing activities. Hence, interventions aimed at addressing these drivers can supplement enforcement efforts or prompt compliance where enforcement is inadequate to reduce illegal fishing activity. Furthermore, such interventions may prove to be even more effective if implemented in concert with each other, so that multiple drivers of the behavior are targeted at once. For example, efforts to improve access to information can be paired with messaging/framing campaigns targeted toward relevant perceptions and beliefs in order to increase knowledge while simultaneously generating motivation, a technique which has proven effective for eliciting change (Butler et al., 2013; Sutton and Rao, 2014).

Significant research has been conducted on behavioral science-based interventions that may be useful to marine conservation and management (Reddy et al., 2017), and there is much room to improve outcomes through the application of these lessons. However, as behavior change is contextand actor-dependent, a systematic process for developing solutions to illegal fishing tailored to specific fishery contexts is needed.

We posit that effective interventions to reduce illegal fishing behaviors in any setting can be developed and implemented by using the 5-step process described here, which is derived from well-established methods that have been used to change behavior in many other sectors. This process entails developing a deep understanding of the target communities and includes steps to characterize illegal fishing behaviors and their drivers, develop theories of how undesirable behaviors can be changed, test potential interventions with experiments, pilot and adjust interventions, and fully implement interventions, as well as to monitor, evaluate, and adjust interventions to ensure their ongoing effectiveness.

We are not recommending that conventional, commandand-control regulations and enforcement systems designed to deter illegal fishing, where they already exist, be replaced by a behavioral approach. Instead, we seek to highlight the conditions under which deterrence methods can be complemented through the application of behavioral science, and suggest approaches to improve compliance. Behavioral science-based interventions can improve the efficacy and uptake of conventional regulations, improving the levels of compliance and deference, as well as their resilience over the long term (Tyler, 2006). Combining behavioral interventions with increased surveillance to increase detection probability, for example, may be highly effective for reducing illegal fishing because both the monetary and non-monetary drivers of the illegal behaviors are targeted (Hønneland, 2000; Sumaila et al., 2006; Velez et al., 2006). Furthermore, efforts to reveal and reduce corruption, which can themselves benefit from the lessons of behavioral science, can go a long way toward reducing the systemic drivers of illegal fishing (Becker and Stigler, 1974; Hønneland, 1999, 2000; Sutinen and Kuperan, 1999; Dietz et al., 2003; Sumaila et al., 2006; Tyler, 2006; Hauck, 2007; Österblom et al., 2011; Sundström, 2012; von Essen et al., 2014). For these reasons, behavioral interventions may be particularly useful in small-scale and low-governance fisheries that lack resources for high levels of surveillance and enforcement.

## AUTHOR CONTRIBUTIONS

WB contributed to the development of the novel method presented in the manuscript and also was the lead author of the

manuscript. RR-C was the lead in developing the novel method presented in the manuscript, and provided multiple reviews and edits of the manuscript text. SS contributed to the background literature review presented in the manuscript, and also provided a number of complete reviews. JF, ME, and DL-K contributed to the development of the novel method presented in the manuscript and the initial draft. RF contributed to development of the novel method presented in the manuscript and the initial draft, and provided multiple reviews and edits.

## REFERENCES

## ACKNOWLEDGMENTS

We would like to thank Drs. Maria Alejandra Velez, and Andreas Leibbrandt, as well as Anthony Rogers, for their guidance and feedback on the development of our 5-step process for designing, testing, and implementing behavioral science-based interventions for illegal fishing. This work would not have been possible without the support of donors and supporters of Environmental Defense Fund. The views expressed in this publication are solely those of the authors and have not been reviewed by the funding sources or affiliated institutions.

Analytical and Political Challenges in Building Governance Mechanisms, eds E. Brousseau, T. Dedeurwaerdere, P. A. Jouvet, and M. Willinger (Oxford: Oxford University Press), 223–248.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Battista, Romero-Canyas, Smith, Fraire, Effron, Larson-Konar and Fujita. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Toward Ecosystem-Based Assessment and Management of Small-Scale and Multi-Gear Fisheries: Insights From the Tropical Eastern Pacific

Pilar Herrón1,2,3,4 \*, Gustavo A. Castellanos-Galindo1 , Moritz Stäbler1 , Juan Manuel Díaz5,6 and Matthias Wolff1,2

 Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany, 2 Faculty of Biology and Chemistry, University of Bremen, Bremen, Germany, 3 CEMarin – Corporation Center of Excellence in Marine Sciences, Bogotá, Colombia, Fundación Ecomares, Cali, Colombia, 5 Department of Geography, Universidad Nacional de Colombia, Bogotá, Colombia, Fundación Marviva, Bogotá, Colombia

#### Edited by:

Beatrice Irene Crona, The Royal Swedish Academy of Sciences, Sweden

### Reviewed by:

Konstantinos Tsagarakis, Hellenic Centre for Marine Research (HCMR), Greece Michele Gristina, National Research Council (CNR), Italy

> \*Correspondence: Pilar Herrón<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Marine Fisheries, Aquaculture and Living Resources, a section of the journal Frontiers in Marine Science

Received: 06 November 2018 Accepted: 01 March 2019 Published: 19 March 2019

#### Citation:

Herrón P, Castellanos-Galindo GA, Stäbler M, Díaz JM and Wolff M (2019) Toward Ecosystem-Based Assessment and Management of Small-Scale and Multi-Gear Fisheries: Insights From the Tropical Eastern Pacific. Front. Mar. Sci. 6:127. doi: 10.3389/fmars.2019.00127 Small-scale fisheries (SSF) remain a largely under-assessed and overlooked sector by governments and researchers, despite contributing approximately 50% to global fish landings and providing food and income for millions of people. The multi-species, multigear and data-poor nature of SSF makes implementation of traditional single-species management approaches – like catch-quotas or size limits – particularly challenging and insufficient. A more holistic approach is thus required, which demands assessment of ecological impacts. Here we carried out an estimation of selected ecological indicators of the impact of fisheries (mean length, maximum body size, mean trophic level, trophic and spatial guilds, threatened species and landed by-catch) based on the nominal catch of different gears in three representative SSF along the Colombian Pacific using landings data collected in multiple years (2011–2017). Results showed that taxonomic, size-based, functional and conservation features of the nominal catch vary greatly with geographical location and gear type used. Overall, handlines and longlines tend to select larger sizes and higher trophic levels than nets, but they also catch a higher proportion of intrinsically vulnerable species and species of conservation concern. This challenges the idea that more selective gears have overall lower ecological impacts. In contrast, nets target a wider size range – although focusing on small or medium sized fish – and include a higher diversity of trophic and spatial guilds, which could arguably be considered a more "balanced harvest" type of fishing that retains ecosystem structure and functionality. Bottom trawls, though, exhibited a relatively high percentage of landed by-catch, an undesirable feature for any fisheries in terms of sustainability. We propose that the assessment of a suite of ecological indicators, like those implemented here, should be included as part of periodic evaluations of multi-gear and multispecies SSF in tropical coastal areas, as a practical step toward ecosystem-based fisheries management.

Keywords: Colombia, ecological indicators, ecosystem approach to fisheries, gear-based management, tropical fisheries, catch composition

## INTRODUCTION

fmars-06-00127 March 16, 2019 Time: 17:4 # 2

Small-scale fisheries (SSF) are widely recognized for their contribution to nearly half of global landings and for the multiple socio-economic benefits they provide to coastal communities (Andrew et al., 2007; Béné et al., 2010; FAO, 2015). However, this fisheries sector remains largely under-assessed and overlooked by governments and researchers (Salas et al., 2007, 2019; FAO, 2015; Purcell and Pomeroy, 2015). Management of SSF in tropical developing countries is generally constrained by insufficient government funding, lack of political will, open access regimes, multiple and scattered landing sites and low participation of resource users in decision making (Andrew et al., 2007; Salas et al., 2007; Kato et al., 2012). Traditional management approaches like catch-quotas and size limits for target species exhibit several practical difficulties when tried to be implemented in multi-gear and multi-species tropical SSF (Salas et al., 2007; Purcell and Pomeroy, 2015). Furthermore, the establishment of catch-quotas, one of the most common management measures, depends on reliable assessments of the target stock size and condition of main target species but these type of assessments are often hindered by low quality of the data available, high uncertainties underlying length-frequency catch data and lack of knowledge of basic growth and reproduction features of target species (Froese, 2004; Cope and Punt, 2009; Ramírez et al., 2017; Herrón et al., 2018).

In the past two decades a shift in fisheries management has been observed from a single-species approach – in which the main objective was to obtain maximum sustainable yields (MSY) of target species – to a more holistic approach that also considers the impacts of fishing at the community and ecosystem level, for which two main frameworks are commonly used: the Ecosystem Approach to Fisheries – EAF (Garcia, 2003) and the Ecosystem-Based Fisheries Management – EBFM (Pikitch et al., 2004). Both frameworks take into account the undesired effects of fishing on ecosystems due to the inherent selectivity of the fisheries for a particular size range and/or taxonomic group; these effects may include impacts on biodiversity, taxonomic composition, population abundance, size structure, trophic structure and trophic dynamics of biological communities (Pauly, 1984; Jennings and Kaiser, 1998; Arias-González et al., 2004; Pikitch et al., 2004). To detect such impacts, several ecological indicators have been proposed based on empirical or modelderived evidence of their potential to adequately inform of fishing impacts. These indicators often relate to basic ecosystem's attributes such as: species richness and diversity, biomass, relative abundance of specific target or non-target groups, size structure, trophic level, structure and dynamics of the food web (Rochet and Trenkel, 2003; Fulton et al., 2005; Jennings, 2005; Jennings and Dulvy, 2005; Link, 2005; Shin et al., 2005). Current scientific advice for fisheries management in the European Union, for example, incorporates assessment of indicators such as: mean length of the fish community, proportion of predatory fish in the community, catch-based marine trophic index, proportion of discards in the fishery, among others ["IndiSeas" project – Coll et al. (2016)]. Other approaches to holistically assess fisheries and examine fishing impacts at the ecosystem level are mass-balanced trophic models, which require knowledge of trophic relations, as well as detailed data on diet composition and fishing effort that are not always available for coastal tropical systems [but see for example: Bacalso and Wolff (2014); Rehren et al. (2018), and Tesfaye and Wolff (2018)].

Here we examine the composition of the nominal catch of the multi-gear and multi-specific SSF of the Colombian Pacific coast to assess geographic or gear-related differences in selected indicators used as proxies of the potential ecological impacts of current fishing practices. Our analyses used a unique set of landings data from recent years (from 2011 to 2017) collected at three coastal zones of the Colombian Pacific with different environmental, socio-economic and fisheries management regimes. Finally, we discuss the potential benefit of implementing a periodic monitoring of ecological indicators to assess and manage SSF under an ecosystembased approach.

## MATERIALS AND METHODS

## Study Area

The Colombian Pacific coast is part of the tropical eastern Pacific region and it is located in the western side of the country bordering with Panama (7◦ 130 2100N, 77◦ 530 2500W) and Ecuador (1◦ 270 4800N, 78◦ 510 4300W), and stretching for approximately 1,300 km (Correa and Morton, 2010) (**Figure 1**). The northern coastal sub-region extends for approximately 335 km of coastline south of the Panama border and is characterized by rocky and sandy shores, and relatively small mangrove forests [ca. 50 km2 , Velandia and Díaz (2016)]. This sub-region has a narrow continental shelf (1–15 km) and a low human population density [6 people∗km−2 , DANE (2011)]. In contrast, the central coastal sub-region of Buenaventura, which encompasses approximately 150 km of coastline south and north of the city of Buenaventura, is dominated by mangrove forests [ca. 220 km2 ; Mejía-Rentería et al. (2018)], alluvial plains, river deltas and estuaries. These seascapes are also the dominant ones in the remaining Colombian Pacific southern coast up to the border with Ecuador. The Buenaventura sub-region has a wider continental shelf (32–52 km) and a higher human population density [70 people∗km−2 , DANE (2011)] mainly due to the presence of the main city port of the entire Colombian Pacific (Buenaventura city).

Within the northern sub-region, there are two management areas declared in recent years: (1) an Exclusive Artisanal Fisheries Zone or ZEPA, for its Spanish acronym, and (2) a regional marine protected area (Tribugá – Integrated Regional Management District or DRMI for its Spanish acronym), declared recently by the Colombian fisheries authority and by the regional environmental authority, respectively (AUNAP, 2013; CODECHOCO, 2014) (**Figure 1**). These two management zones cover ca. 1,600 km2 of coastal and marine habitats (Velandia and Díaz, 2016) and complement conservation efforts by the adjacent National Natural Park Utría established in 1987 (PNN, 2006), which includes a marine area of ca. 132 km2 . Current fishing practices inside the marine area of the National Park are

similar to those within the DRMI (PNN, 2011) and therefore we considered the Park's area as part of the same coastal zone, referred to hereafter as Tribugá.

## Fishing Gears

At least 13 different main types of fishing gears have been reported in the Colombian Pacific SSF (Saavedra-Diaz, 2012) and eight of those are used by fishers at some or all of the three coastal zones studied here. These eight gears are: handlines, longlines (bottom), gillnets (including lobster nets), bottom trawls, purse seines, beach seines, cast nets and spear guns. Cast nets are mostly used to collect bait (such as sardines or anchovies) used in longlines or handlines and therefore the catch derived from this gear is rarely landed. Spear guns are used by a very low number of fishers while beach seines are more commonly used by family groups in the coastal communities. However, these two gears (spear guns and beach seines) contributed to <1% of the nominal catch recorded within each zone (**Figure 2**) and therefore were not included in further analyses. The main characteristics of the five gears that account for most of the catch are summarized in **Table 1**, including a sub-classification of gillnets based on the net material and on their mesh size. Given that lobster nets are a type

Buenaventura. Location of sampled landing sites, National Natural Parks and mangrove forests within the three coastal zones are also shown.

of gillnet targeted on a specific taxonomic group (Palinuridae) and include the use of bait, we treat them here as a separate type of gear. Detailed technical specifications of these gears and how they are used in the Colombian Pacific can be found in Saavedra-Diaz (2012) and Puentes et al. (2014).

TABLE 1 | Characteristics of gear types and subtypes that contribute most to total SSF landings at the three zones of the Colombian Pacific included in the present study.

## Data Collection

In the ZEPA and Tribugá coastal zones, a community-based fisheries monitoring program was implemented from 2011 to 2016 by the regional non-government organization (NGO) MarViva Foundation1 . Local observers were trained and hired to collect data at landing sites within each coastal community (Díaz et al., 2016; López-Angarita et al., 2018). Monthly visits were made by staff from the NGO to verify data quality and species identification. Data gathered through this monitoring program and used in the present study include data from nine landing sites located in ZEPA (2011–2013) and nine landing sites located in Tribugá (2011–2013 and 2016). At the Buenaventura coastal zone a similar community-based monitoring scheme was adopted by the authors of this study to collect data from August 2016 to July 2017 at three representative landing sites (**Figure 1**). Data gathered at landing sites included: date, common name of landed species, weight landed per taxa to the nearest 0.05 kg, catch status (e.g., whole, gutted), fishing gear type and fishing method. Also, total length of fish (or disk width in rays) and total length of invertebrates to the nearest 0.5 cm were measured in a representative sample of the catch (20–30%). All fish species were identified to the lowest taxonomic level possible following identification guides available for the region (Keen, 1971; Fisher et al., 1995; Acero, 2004; Marceniuk and Acero, 2009; Robertson and Allen, 2015).

Taking into account the collective ownership and management of the land occupied by Afro-descendent communities along the Pacific coast of Colombia (Law 70 of 1993), formal agreements with the Boards of the Community Councils Los Riscales, Los Delfines, Cupica, Juradó, Cajambre and Bazán-Bocana (in charge of the coastal areas where this study took place) were made by either MarViva Foundation or by the first author, whereby written informed consent was obtained. Additionally, meetings with fishers' representatives (locally elected leaders of fishers associations) were held at each coastal community to explain the objectives and methods of the project prior to the beginning of field activities. Approval from an external ethical committee was not required by local legislation for research collecting fisheries data at landing sites.

1www.marviva.net

TABLE 2 | Size-based, functional and conservation indicators used here for the assessment of the catch of SSF in the Colombian Pacific.

Rationale for its use as proxies of ecological fishing impacts and related literature are also indicated.

## Data Processing and Analyses

Considering that 80% of fish were not landed whole, but gutted (42.6%), beheaded (2.2%), gutted and beheaded (31.4%) or as trunks (3.4%), weight corrections factors based on FAO (2000) were applied to landed weight for more accurate estimates of live weight removed per taxon. For some taxonomic families of smallsized species of relatively low market value (e.g., Acanthuridae and Muraenidae) there was partial or no data available on conversion factors. We assigned a conversion factor of 1.1 to those cases, being this value the most common reported as conversion factor for gutted weight across taxa (FAO, 2000). Large sting rays (Hypanus spp.) that could not be weighted were measured and disk-widths were later converted to total weight based on literature values for the two species involved (Ehemann et al., 2017). A table with all correction factors used per taxon is included as part of the (**Supplementary Table S1**). Landings data converted to live weight is technically known as "nominal catch" (FAO, 2018) which does not include discarded specimens (live or dead) that are not brought to landing sites. For practical reasons we will refer here to the nominal catch as "catch." After weight conversion was performed, as described above, relative weight per taxa (species, genus, or family) was calculated based on the catch (kg) per taxon divided by the total catch (all taxa combined) within each coastal zone.

To explore potential inter-annual differences in the catch composition of the coastal zones of Tribugá and ZEPA, we carried out cluster and non-metric multi-dimensional scaling analyses (nMDS) to compare relative weight within landing sites among years of those species that contributed to 95% of the catch at each landing site.

Size-based, functional and conservation indicators related to the composition of the catch were estimated and assessed among coastal zones and fishing gears. A list of the selected indicators is presented in **Table 2**, along with a brief description and the rationale behind their current global use as proxies of ecological fishing impacts. Mean total length (cm) in the catch was estimated across taxa for each gear within each coastal zone and visualized through violin plots. Maximum body size (cm), trophic level, trophic guild and spatial guild were assigned to all species registered in the landings based on data available on FishBase (Froese and Pauly, 2017), the Smithsonian Tropical Eastern Pacific Fish Guide (Robertson and Allen, 2015) and SeaLifeBase (Palomares and Pauly, 2018). Additionally, published values from local studies (Criales-Hernandez et al., 2006; Castellanos-Galindo et al., 2017) were used to assign trophic levels to some invertebrate species for which information could not be found on international databases. Trophic guilds categories used were: herbivore, invertivore, omnivore, piscivore and planktivore, while categories of spatial guilds used were: demersal, bentho-pelagic and pelagic.

Conservation threat status was assigned to species based on regional and national assessments that follow the International Union for the Conservation of Nature's (IUCN) Red List

standards (IUCN, 2017). National assessments used are those carried out in Colombia in recent years for marine fish species (Chasqui et al., 2017), marine invertebrates (Ardila et al., 2002) and reptiles (Morales-Betancourt et al., 2015); the last one was included taking into account the rare occurrence of some species of sea turtles in the catch. Information on regional assessments was based on Polidoro et al. (2012). The categories used are: Not Evaluated (NE), Data Deficient (DD), Least Concern (LC), Near Threatened (NT), Vulnerable (VU), Endangered (EN), and Critically Endangered (CR). Definitions and criteria used for each category can be found at www.iucnredlist.org.

An additional classification of the taxa registered in the catch was made based on their current use or importance to fishers and markets. Three categories were considered for this purpose: "commercial," for those species of commercial interest, "local use" for those species that are not sold to external markets but are locally consumed or used as bait, and "by-catch" for those species that are not intentionally targeted and are usually discarded before reaching the landing site. However, when the size of the individuals was not so small (approximately >25 cm) or when fishers did not carry out the sorting process of the catch while they were on-board, some of that by-catch made it to the landing site and we will refer to that portion of the catch as "landed bycatch." In the case of bottom trawlers, fishers generally brought the last haul completely unsorted and separated from the rest of the catch, so we could use that haul to estimate landed by-catch. The classification of species in the above mentioned categories was based on Díaz et al. (2016) and on interviews made to local fishers of the coastal zone of Buenaventura by the first author (unpublished data).

Mean trophic level (MTL) of the catch for each gear category (g) at each coastal zone was estimated using the formula described by Pauly et al. (1998):

$$MTL\left(\mathcal{g}\right) = \sum\_{s=i}^{n} W \mathcal{ig}^{\*} TL i / \sum W \mathcal{g}^{\*}$$

Where Wig is the biomass (total weight) of species i caught by gear g, and TLi is the trophic level of species i for n species. In a similar way we estimated mean maximum body size (MBS) per gear type at each zone, replacing TL in the previous formula for MBS. Generalized linear models (GLMs), using a logarithmic link function and a quasi-poisson distribution, were used to assess differences in mean length, MTL and mean MBS among gear types and zones. When statistical differences were detected within either factor or their interaction, pairwise comparisons were carried out using the "emmeans" R Package, based on least-square means and adjusted p-values following Tukey tests (R Core Team, 2017; Russell, 2018).

## RESULTS

A total of 40,035 one-day fishing trips were sampled accounting for 1,823.2 tons of estimated biomass in the catch and 515,243 specimens measured. The proportion of the catch contributed by each fishing gear differed among ZEPA, Tribugá and TABLE 3 | Total biomass in the nominal catch (i.e., live weight converted from landed weight, as described in section "Materials and Methods") of SSF, number of fishing trips sampled and estimated percentage of trips sampled per gear type at landing sites of the three coastal zones of the Colombian Pacific included in the present study (n.d., no data available on total number of trips).

Buenaventura (**Figure 2**). Hook-based gears contributed the most to the catch in ZEPA and Tribugá, while net-based gears dominated in Buenaventura. The relative contribution made by each gear to the total biomass was similar to the proportion of fishing trips per gear in ZEPA and Tribugá, but not so in Buenaventura, where a very large biomass contribution was made by purse seine nets despite the relatively low number of fishing trips recorded for that gear type (**Figure 2**). Total biomass (kg), number and estimated percentage of fishing trips sampled per gear type at each zone are presented in **Table 3**.

## Taxonomic Composition of the Catch

179+ species belonging to 80 families were identified as part of the catch of the SSF of the three coastal zones of the Colombian Pacific. However, this number of species is probably an under-estimation of the richness of the catch considering that 66 common names of mostly rare species (i.e., low relative abundance in the catch) were not assigned to any taxonomic category and 31 of them were only identified to genus or family level, resulting in a total of 276 different common names registered in the catch of the three coastal zones.

95% of the biomass in the catch was accounted for by 24 families and 72 species (**Figure 3** and **Supplementary Figure S1**). Mackerels, tunas and bonitos (Scombridae) contributed between 20 and 30% of the annual catch at all zones, indicating an overall importance of this family in the SSF of the entire Colombian Pacific coast. Jacks (Carangidae), cusk eels (Ophidiidae), groupers (Serranidae) and snappers (Lutjanidae) were also important

in the landings of ZEPA and Tribugá while in Buenaventura, catfishes (Ariidae), whiptail stingrays (Dasyatidae) and drums or croakers (Sciaenidae) followed Scombridae in the relative abundance ranking (**Supplementary Figure S1**). A higher number of species (41) accounted for 95% of the catch in Tribugá than in Buenaventura and ZEPA (35 species each) (**Figure 3**). The distribution of the relative abundance of species shows a more even pattern in the catch of Tribugá than that of ZEPA, where two dominant species (Thunnus albacares – Scombridae and Brotula clarkae – Ophidiidae) contributed to 35% of the catch. Invertebrate species, mainly shrimps (Penaeidae) and lobsters (Palinuridae), were abundant in Buenaventura, but not so in ZEPA or Tribugá. Additionally, several shark species were relatively abundant in ZEPA and Tribugá compared to Buenaventura (**Figure 3**). A complete list of the taxa recorded in the catch of each zone, with their absolute and relative weight, is included in the (**Supplementary Table S2**).

Results from the cluster and the nMDS analyses showed that there were not distinctive inter-annual differences related to species composition of the catch at the landing sites sampled in ZEPA and Tribugá (**Supplementary Figure S2**). Based on records of daily fishing activity in Tribugá and ZEPA collected by MarViva during their monitoring program (Díaz et al., 2016) and the information available from the Colombian fisheries authority2 , there was no evidence of changes in fishing effort made by the small-scale fleet in those areas during the past 10 years. We thus used the combined catch data for all years of each of these coastal zones for subsequent analyses.

## Size-Based Indicators of the Catch

Overall, most specimens at all zones were <100 cm of total length (**Figure 4**) with longlines in ZEPA capturing on average larger size classes (**Table 4**), even though the largest specimens were caught by handlines in Tribugá (e.g., the sailfish species Istiophorus platypterus reaching >400 cm TL; **Supplementary Figure S3**). In contrast, bottom trawls in Buenaventura exhibited a high relative abundance of small-sized individuals with a narrow unimodal distribution of length. The catch of this gear was composed mainly of the target small shrimps species Pacific seabob – Xiphopenaeus riveti and titi shrimp – Protrachypene precipua, and other non-target small-sized invertebrates and juvenile fish of several species (**Figures 3**, **4**). Lobster nets and gillnets in Buenaventura had most of their catch toward the lower side of the overall length range observed in this study (**Figure 4** and **Table 4**). Results from the GLM conducted with the entire catch (all species included) showed that mean length in the catch within the same gear type was statistically different among zones, with ZEPA showing higher mean length in the catch than Tribugá and Buenaventura for gillnets and longlines, and also for handlines when compared to Tribugá (p < 0.001 in all cases). Mean length was also statistically different among gears within the same zone: in Buenaventura, mean length of purse seines was higher than that of all other gears whereas mean length of bottom trawls was lower than all the other gears (p < 0.001 in all cases). In Tribugá and ZEPA, longlines had a significantly higher mean length in their catch compared to handlines and gillnets (p < 0.001 in all cases).

Handlines and longlines showed the largest maximum body size (MBS) of the species in the catch, with mean values above 130 cm in all cases (**Figure 5** and **Table 4**). Mean MBS of the entire catch of handlines was statistically higher (p < 0.001) in ZEPA than in Tribugá. In the case of longlines, Buenaventura showed higher mean MBS than ZEPA and Tribugá, related to the high relative abundance of stingrays (Hypanus spp.) in the catch of longlines of that central coastal zone, although the mean MBS was only statistically different when compared to ZEPA (p = 0.02). On the other hand, gillnets appear to target species of medium MBS at all three zones with mean values close to 80 cm and statistical differences found between Tribugá and Buenaventura (p = 0.01). Bottom trawls had a significantly

2http://sepec.aunap.gov.co/

maximum length observed at each zone is included as part of the Supplementary Figure S3.

TABLE 4 | Mean estimated values ± standard deviation for total length, maximum body size and trophic level of the catch at the three geographical zones studied here: Buenaventura, Tribugá, and ZEPA.

Estimated values are shown for both the total catch (all species included) and for the fish portion of the catch, excluding invertebrates and reptiles (i.e., sea turtles).

smaller mean MBS than all other gears except for lobster nets (p < 0.01 in all cases).

Paired comparisons based on the entire catch within each coastal zone revealed that in Buenaventura, longlines had higher MBS than all other gears, except for purse seines (p < 0.001 in all cases), while bottom trawl had lower MBS than all other gears except for lobster nets (p < 0.01 in all cases); none of the other paired comparisons was statistically significant in Buenaventura. In ZEPA, handlines exhibited a significantly higher MBS than longlines and gillnets (p < 0.001 in both cases). In Tribugá mean

MBS of gillnets was lower than that of handlines and longlines (p < 0.001 in both cases), but mean MBS values of the two hookbased gears, i.e., longlines and handlines, were not significantly different between each other.

For bottom trawls, gillnets and lobster nets in Buenaventura invertebrates accounted for 34, 8, and 56% of the catch, respectively. To assess the influence of shrimp and other small invertebrates on the estimates of size-based indicators, we estimated mean length and mean MBS for the fish portion of the catch, i.e., excluding all invertebrates and other non-fish species (i.e., sea turtles) from the data set prior to analyses. As expected, values of both size-based indicators, especially of MBS, increased for bottom trawls, lobster nets and gillnets in Buenaventura (**Table 4**) but had no effect in other gears of that coastal zone nor in the estimates derived from Tribugá and ZEPA, where invertebrates and reptiles accounted for only 0.03 and 0.01% of the catch, respectively. GLMs conducted for the fish portion of the catch showed the same statistical differences in total length among zones and/or gears observed previously for the whole catch, except for the difference between gillnets and lobster nets in Buenaventura which was not significant this time (p = 0.98). In contrast, the results of the pairwise comparisons of MBS values based on the fish portion of the catch showed that differences among zones or gears previously observed for the entire catch were no longer significant. In particular, mean MBS of the fish caught with gillnets was not statistically different between Buenaventura and Tribugá (p = 0.12) and within Buenaventura mean MBS of longlines and lobster nets were not statistically different (p = 0.34) from each other.

## Functional Indicators of the Catch

Mean trophic level of the entire catch (all species included) was very similar across gears and zones, with mean values lying above 3.5 for all cases except for bottom trawls in Buenaventura that exhibited the lowest mean value, while handlines and purse seines exhibited the highest values (**Figure 6** and **Table 4**). Statistically significant differences among gears within the same coastal zone were only found between MTL of handlines and longlines within ZEPA (p < 0.01).

Following the rationale explained above for size-based indicators and considering the general positive relationship between a species body size and its trophic level (Romanuk et al., 2011), we also estimated MTL for the fish portion of the catch only. Similarly to the findings related to mean length

of the Colombian Pacific: ZEPA, Tribugá, and Buenaventura (abbreviation: Bventura).

and MBS, there was an increase – although relatively smaller – in the estimated values of MTL for bottom trawls, gillnets and lobster nets in Buenaventura (**Figure 6** and **Table 4**). The small increase resulted in the difference between MTL of gillnets from Buenaventura and gillnets from Tribugá being no longer significant (p = 0.24).

Results of the relative abundance of trophic guilds corroborated that high trophic level guilds (piscivores and invertivores) are dominant in the catch of most gears across zones, except for bottom trawls, the gear that showed the highest diversity of trophic guilds (**Figure 7**). Also worth noting is the higher relative abundance of invertivores in the catch of longlines in Buenaventura compared to that of Tribugá and ZEPA for the same gear, where piscivores accounted for more than 90% of the catch.

In terms of spatial guilds, demersal species were dominant in Buenaventura for all gears except for purse seines, contrasting with the results from ZEPA and Tribugá where pelagic species had a higher relative abundance in the catch of gillnets and handlines while longlines caught more demersal and bentho-pelagic species (**Figure 7**). The overall proportions of species belonging to different spatial guilds was similar between ZEPA and Tribugá for the same type of gear: gillnets, handlines or longlines.

## Conservation Indicators of the Catch

Based on the regional assessment of IUCN's Red List, the three coastal zones have Least Concern (LC) as the predominant category of the biomass in the catch (54–73%), while threatened categories (Vulnerable – VU, Endangered – EN and Critically Endangered – CR) represented less than 1% of the biomass. The relative weight of species classified as Near Threatened (NT) was higher in ZEPA than in the other two zones with handlines being the gear that contributed most to that difference (**Table 5**). When the same analysis was based on the national assessment (Colombian's red lists assessments), Not Evaluated (NE) and Near threatened (NT) were the dominant categories in the catch of all zones – with ZEPA exhibiting the highest relative abundance of NT species – while Data Deficient (DD) and LC had overall low values. Based on the national assessments, the relative weight

and Buenaventura.

TABLE 5 | Proportions of threatened or non-threatened taxa in the catch of SSF of the Colombian Pacific.

Species classification were based on regional assessments (Polidoro et al., 2012) (Reg) and national assessments produced by the Colombian government (Col) for marine fish species (Chasqui et al., 2017), marine invertebrates (Ardila et al., 2002) and reptiles (Morales-Betancourt et al., 2015). Categories used are: Not Evaluated (NE), Data Deficient (DD), Least Concern (LC), Near Threatened (NT), Vulnerable (VU), Endangered (EN), and Critically Endangered (CR), based on: www.iucnredlist.org. Taxa which could not be identified to species level and therefore could not be assigned to a specific category where classified as "unknown."

of species under category VU was higher in the catch of ZEPA and Buenaventura, mostly due to the presence of species caught with longlines (e.g., stingrays). Overall, the relative abundance of threatened or near threatened categories in the catch was higher when based on national assessments than when the analysis was based on IUCN's regional assessments.

Landed by-catch species, those that are not commercialized or locally used, were only a conspicuous proportion of the catch of bottom trawls where they accounted for >30% of the catch (**Table 6**). For the rest of the gears, landed by-catch was below 3% and more than 75% of the catch corresponded to commercially important species. In ZEPA and Tribugá, 20% of the catch of gillnets is locally consumed or used as bait, instead of sold to local or external markets.

## DISCUSSION

Our results showed clear differences in the catch composition among the three coastal zones and, particularly, between the northern rocky-dominated coast (Tribugá and ZEPA) and the central estuarine and mangrove-dominated coast (Buenaventura)

"Commercial" refers to species that are usually sold to local or external markets, "local use" to those species that are not sold but are locally consumed or used as bait, and "landed by-catch" to those species that are not intentionally targeted and that are usually discarded. Taxa which could not be identified to species level and could not be assigned to a specific category where classified as "unknown".

of the Colombian Pacific. Some of the observed differences were related to the interaction between gear type and geographical location of the coastal zones.

Despite being in a traditionally data-poor tropical SSF context, our data sets, produced by a non-government organization and by an academic research project, included higher sampling frequency, sample size and geographic coverage than normal government fisheries data (Ramírez et al., 2017; Herrón et al., 2018). Community-based fishing monitoring schemes (as those followed in the present study) are therefore useful and likely more effective and less expensive ways of monitoring fisheries resources in typical SSF like the ones evaluated here. Overcoming some limitations in these schemes like the correct differentiation of certain species and common names within certain taxonomic groups, e.g., groupers, sharks, is something that will require further attention in the future (Castellanos-Galindo et al., 2018).

## Size-Based and Functional Indicators

In the management areas established in the northern coastal zones, a higher selectivity of fishing gears has been promoted based on the assumption that gillnets tend to catch a higher proportion of immature fish and have higher by-catch rates than hook-based gears (Vieira et al., 2016; Ramírez-Luna and Chuenpagdee, 2019). Our results confirmed a lower mean length in the catch of gillnets when compared to longlines and handlines in ZEPA and Tribugá. However, fisheries selectivity is influenced not only by the gear used but also by spatial and temporal patterns of resource distribution (Maunder et al., 2014; Sampson, 2014). Therefore, the observed differences reflect not only the inherent selectivity of gears but also the location of the fishing grounds used by each gear. Particularly in ZEPA, longline fishers use deeper grounds located at greater distances from the shore, whereas gillnets tend to fish in areas closer to shore (Velandia and Díaz, 2016). A higher abundance of larger/older individuals in deeper habitats has been widely reported for many fish species and has been attributed to ontogenetic changes, although recent evidence indicate that this "deepening" could also be associated to increased fishing pressure in shallower areas (Frank et al., 2018). Distance to shore and depth of fishing grounds could also explain the higher mean length observed in the catch of purse seines in Buenaventura compared to other gears in the same zone (**Figure 4**). Purse seines are used at fishing grounds located further offshore (8–10 nautical miles) than other gears (unpublished data) and target mostly pelagic species (**Figure 3**). Bottom trawls – which exhibited the lowest mean length in the catch of all zones even when invertebrates are removed – have the smallest mesh size of all nets (0.500) and are used in near-shore, shallow waters (unpublished data), targeting mainly two small-sized shrimp species (X. riveti and P. precipua). Continued monitoring of mean length in the catch complemented by spatial analyses of fishing grounds could provide more information regarding the factors explaining the observed differences and the potential long-term impacts of different gears on the size-structure of fish and invertebrate communities.

Our results of maximum body size (MBS) in the catch indicate that longlines and handlines are targeting larger bodysized species that are more vulnerable to overfishing due to their life history characteristics (Jennings et al., 1998; Cheung, 2007) (e.g., sailfish, tunas, and sharks, **Figure 3**), while bottom trawls are targeting species that could potentially withstand more fishing pressure and/or recover more rapidly (e.g., shrimps, other small invertebrates and small-sized fish species). Particularly in Buenaventura, longlines had a significantly higher MBS than the rest of the gears, probably linked to the fact that large-sized stingrays of the genus Hypanus were an important part of the catch of this gear (**Figures 3**, **5**).

Targeting a relative high proportion of small-sized specimens has been suggested as a way of improving overall yields while maintaining the structure of the natural ecosystem, under the concept of "balanced harvest" (Kolding et al., 2015b), an approach that contradicts traditional management measures like imposing size limits for target species to avoid fishing immature individuals thus preventing growth and recruitment overfishing (Beverton, 1992; Myers and Mertz, 1998; Froese, 2004). Despite being more aligned to the principles of EBFM, critics of the balanced harvest approach have also argued that there are many practical difficulties of implementing such harvest scheme, particularly a drastic shift in consumers' seafood preferences toward new species and sizes (Charles et al., 2015; Froese et al., 2015; Garcia et al., 2015).

Similarly to size-based indicators, MTL of the catch has been used as an indicator of ecological fishing impacts as it is expected to decrease with increasing fishing pressure [Pauly et al., 1998; Jennings et al., 2002; Pinnegar et al., 2002; Gascuel et al., 2016, but see Sethi et al. (2010)]. However, MTL has been criticized as an indicator of ecosystem condition since it can be largely influenced by external economic factors, such as market demands (for species and sizes) and by environmental variability that alters the dynamics of primary productivity and the recruitment of planktivore species (Caddy et al., 1998; Caddy and Garibaldi, 2000; Branch et al., 2010). Nevertheless, MTL may still be a

suitable indicator for the state of a fishery system, if fishing pattern and external factors remain constant over time and only fishing effort increases (Shannon et al., 2014). Our estimates of MTL were fairly similar across gears and coastal zones and showed that SSF in the Colombian Pacific are extracting mainly high trophic level species of the system. This is corroborated by the high proportion of piscivores and invertivores in the catch of most gears across all zones, with the exception of bottom trawls that exhibited the highest diversity of trophic guilds in the catch (**Figure 7**).

These results go in line with a worldwide pattern of fishing that has focused on high trophic levels (Kolding et al., 2015a). MTL values observed here (overall mean: 3.9) are higher than MTL values reported in tropical SSF of the Western Indian Ocean [2.3–3.6, Rehren et al. (2018); Tuda et al. (2016)], the Caribbean [3.3–3.5, Arias-González et al. (2004)], the Indo-Pacific [2.4–3.7, Bacalso and Wolff (2014)] and other localities in the tropical eastern Pacific [2.5–2.9, Zetina-Rejon et al. (2003) and Díaz-Uribe et al. (2007)]. However, values of trophic level per species used in this study correspond to the adult phase of the species (FishBase, Froese and Pauly, 2017) and do not necessarily correspond to the actual trophic level of the size classes harvested per species. This can impose biases in the estimates of mean MTL of the catch (Caddy et al., 1998; Reed et al., 2016). In the future, local studies on the diet composition of target species should be conducted and used to estimate trophic levels per size class of main target species.

Differences observed in the proportion of spatial guilds across zones and gears seem best explained by location and habitat type. In ZEPA and Tribugá, coastal zones characterized by narrow continental shelves and few estuaries, pelagic and bentho-pelagic species dominated the catch (**Figure 7**). In contrast, fishing gears in the mangrove-dominated and estuarine area of Buenaventura caught mainly demersal species, except for purse seines, the only gear that operates further off-shore. Therefore, observed differences in proportions of spatial guilds do not seem to offer at this point an unequivocal indication of potential geographical or gear-based differences in fishing impacts but future assessments of temporal trends of this indicator might indicate changes in fishing effort or in the natural abundance of the resources (Caddy, 2000; Pitcher and Preikshot, 2001; Link et al., 2002).

## Conservation Indicators

Based on regional assessments of the threatened status of species (Polidoro et al., 2012), most of the catch of SSF in the Colombian Pacific does not currently face major extinction risks, which could be interpreted as a sign of a sustainable fishery. However, the diagnosis is different when the national assessments are used (Ardila et al., 2002; Morales-Betancourt et al., 2015; Chasqui et al., 2017), since a large proportion of the biomass in the catch corresponds to Nearly Threatened (NT) species (**Table 5**). Based on the national red lists, longlines' catch is conformed partly by species classified as Vulnerable (VU) in Buenaventura (37%) and in ZEPA (17%), mainly attributed to the presence of rays, stingrays and sharks. However, national assessments of commercially important species have generally been based on stock assessments with limited landings time-series or with poor spatial coverage. This could impose biases and is a common situation in data-limited tropical SSFs assessments (Costello et al., 2012; Ramírez et al., 2017; Herrón et al., 2018). On the other hand, the high proportion of Not Evaluated (NE) species in the catch of SSF, based on national assessments (**Table 5**), highlights the need to collect data on the status of natural populations based also on fishery-independent surveys.

By-catch and discards have also been considered to be meaningful indicators of the potential ecosystem impacts of fishing (Fulton et al., 2005; Link, 2005). They are increasingly being monitored and regulated in fisheries of developed countries [e.g., Landings:Discards ratio from the IndiSeas project, Coll et al. (2016)]. The high proportion of landed by-catch of bottom trawls observed here (36%) suggests a higher ecosystem impact of this fishing gear compared to other gears currently used. Bottom trawling has long been identified as a fishing method that can cause a variety of ecological impacts, such as: reduced abundance of non-target species, reduced diversity of the benthic community, sediment resuspension, disruption of nutrients cycles, changes in primary productivity, destruction of habitat and changes in trophic dynamics of the demersal and benthic communities (Collie et al., 2000, 2017; Olsgard et al., 2008; Dell et al., 2013). Fisheries authorities in Colombia banned the use of bottom trawls more than 10 years ago (INCODER, 2004) but fishers continue to use it since there is low enforcement capacity and high market demand for the main target species (small-sized shrimp species). On the other hand, a recent study on the effects of small-scale bottom trawling in similar estuarine environments in Brazil found that observed differences in the structure of macrofaunal communities seemed to be more related to natural variability than to the degree of trawling impact (Ortega et al., 2018). These authors discussed whether those communities could be adapted to a highly dynamic and frequently disturbed estuarine environment, which could also be the case of the benthic communities in Buenaventura that have sustained a bottom trawl fishery for more than 30 years. Specific studies on the dynamics of the catch of bottom trawls involving on-board monitoring and surveys of natural benthic communities will provide valuable inputs for management decisions regarding the continuation of the ban currently established on this gear or, perhaps, a transition toward fishing effort regulation.

## CONCLUSION

Analyses of the catch through the lens of ecological indicators provide alternative paths for the assessment and monitoring of SSF that complement the traditional single-species assessment methods and provide insights into potential ecological impacts of fishing. Observed differences in taxonomic composition of the catch and in the proportion of gears used among coastal zones most likely reflect the deep knowledge of small-scale fishers about the temporal and spatial distribution of resources (Saavedra-Diaz, 2012; Purcell et al., 2018). Hook-based gears (handlines and longlines) tend to catch larger sizes and higher trophic levels than nets, but they also include a higher proportion of species

that are more vulnerable to fishing impacts and/or have higher conservation concerns. These findings challenge the generalized notion that more selective gears have overall lower ecological impacts. In contrast, net-based gears catch wider size ranges – although tend to focus on small-size classes – and include a wider representation of species, trophic and spatial guilds, which could arguably be considered a more "balanced harvest" type of fishing that retains ecosystem functionality (Garcia et al., 2015). Using the data presented here, a preliminary snap-shot assessment of the gears (**Supplementary Table S3**) suggests that there is not one ideal or "green" fishing gear since each gear harvests a specific size and/or functional component of the system and therefore will affect that component more severely than other gears. The rapid assessment also shows that the same type of gear can have different ecological impacts when used in different environmental contexts, e.g., the differences in the proportion of trophic and spatial guilds in the catch of longlines in Buenaventura compared to that in ZEPA.

Ecological indicators to assess the impacts of fisheries are most useful when assessed on a temporal timeframe and used simultaneously, taking into account that no single indicator can adequately inform on its own about the status or trends of a complex ecological system (Link et al., 2002; Shin et al., 2010; Coll et al., 2016). Additionally, the criteria to assess the degree of ecological impact of the gears must be aligned with fisheries management and conservation objectives that sometimes have conflicting long-term goals (Link, 2002). For example, targeting large individuals is usually considered a sound fisheries management measure on the basis of avoiding juveniles in the catch and allowing individuals to reproduce prior to being harvested. However, fish species that attain large body sizes are generally those that are more fecund (Barneche et al., 2018) and more vulnerable to overfishing compared to small-sized fish species, potentially facing higher extinction risks (Jennings et al., 1998; Cheung et al., 2005; Cheung, 2007).

In order to better inform management decisions related to ecological impacts imposed by different fishing gears, medium to long-term monitoring of the relative effort of each gear and of the metrics associated to ecological indicators is needed. We propose that simple ecological indicators, such as those used in this study, be included as part of annual assessments of multi-gear SSF in tropical countries where data and management capacities are limited. In this way, a systematic evaluation of the potential

## REFERENCES

impacts of fishing at the community and ecosystem level could be developed and facilitate the transition toward EBFM.

## DATA AVAILABILITY

The datasets generated for this study are available on request to the corresponding author.

## AUTHOR CONTRIBUTIONS

PH, GC-G, and MW conceived and designed the analyses. PH and JD collected the data. PH, GC-G, and MS performed the analyses and produced the final figures. All authors discussed the results and contributed to the writing of the final manuscript.

## FUNDING

MarViva Foundation received financial support from USAID (Cooperation Agreement #49-2012 of their BIOREDD+ Program), AUNAP (Cooperation Agreement #65-2012), the European Union (Cooperation Agreement #75-2013), and INVEMAR (Cooperation Agreement #009-2015). PH received financial support from CEMarin (Call # 5 of 2015) and from Rufford Foundation (RSG 19522-1).

## ACKNOWLEDGMENTS

We thank the local communities and fishers from the municipalities of Juradó, Bahía Solano, Nuquí, and Buenaventura in the Colombian Pacific coast for allowing and supporting data collection at different landing sites between 2011 and 2017. We thank to C. A. Puentes for producing the map for **Figure 1**.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars. 2019.00127/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Herrón, Castellanos-Galindo, Stäbler, Díaz and Wolff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Connecting a Trophic Model and Local Ecological Knowledge to Improve Fisheries Management: The Case of Gulf of Nicoya, Costa Rica

Astrid Sánchez-Jiménez1,2 \*, Marie Fujitani3 , Douglas MacMillan4 , Achim Schlüter3 and Matthias Wolff1 \*

#### Edited by:

Beatrice Irene Crona, The Royal Swedish Academy of Sciences, Sweden

#### Reviewed by:

Sanja Matic-Skoko, Institute of Oceanography and Fisheries, Croatia Oscar Sosa-Nishizaki, Ensenada Center for Scientific Research and Higher Education (CICESE), Mexico

#### \*Correspondence:

Astrid Sánchez-Jiménez<EMAIL_ADDRESS><EMAIL_ADDRESS>Matthias Wolff<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Marine Fisheries, Aquaculture and Living Resources, a section of the journal Frontiers in Marine Science

Received: 04 November 2018 Accepted: 01 March 2019 Published: 22 March 2019

#### Citation:

Sánchez-Jiménez A, Fujitani M, MacMillan D, Schlüter A and Wolff M (2019) Connecting a Trophic Model and Local Ecological Knowledge to Improve Fisheries Management: The Case of Gulf of Nicoya, Costa Rica. Front. Mar. Sci. 6:126. doi: 10.3389/fmars.2019.00126 1 Resource Management, Theoretical Ecology and Modelling, Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany, 2 Centro de Investigación en Ciencias del Mar y Limnología (CIMAR), Universidad de Costa Rica, San José, Costa Rica, 3 Institutional and Behavioural Economics, Social Sciences, Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany, 4 Durrell Institute of Conservation and Ecology (DICE), School of Anthropology and Conservation, University of Kent, Canterbury, United Kingdom

Trophic models of the Ecopath with Ecosim (EwE) type and local ecological knowledge (LEK) have widely been applied to fisheries assessment and management. However, there are no specific methodologies describing how LEK from local fishers can be incorporated with the scientific data from the models in the context of ecosystembased fisheries management. To our knowledge this is the first contribution exploring a systematic integration of LEK with EwE modeled output. An EwE food web model of the Nicoya Gulf ecosystem constructed 20 years ago and recently revisited by the authors and collaborators, was used in workshops to stimulate discussion among local stakeholders regarding changes in the marine ecosystem. For this study, 58 artisanal fishers were recruited to eight workshops. To assess the LEK, we documented the discussions, and the qualitative data were analyzed with quantitative frequency of responses to identify trends. Next, we systematically compared the changes in the fishery over time through an analysis of similar, complementary, and contradictory information across knowledge systems. In general, the analysis across systems reflected changes in species composition of the catches, paralleled by a harvest reduction in high-trophic-level species, as well as economic losses due to a shift in harvesting lowvalue species and due to an increase in operational costs. Particularly, we identified (1) similar pieces of information that delivered the same message, providing robust evidence of changes in the social–ecological system; (2) information complementary to each other, which together provided a broader picture (descriptors and attributes) of the changes of some fishing resources; and (3) conflicting pieces of information that indicated mismatches between sources of knowledge, which might suggest the cause of management problems. This study demonstrated how integrating knowledge systems can enhance our understanding of the state and changes in ecosystems, helping to improve fisheries management. We also found that an EwE model can be

**58**

an effective communication tool to be used with fishers and to promote discussion and engagement. Our aspiration is to bring new and replicable tools to the policy interface in Latin-American fisheries, based on both stakeholder participation (including LEK) and the best scientific information available.

Keywords: small-scale fisheries, ecosystem-based approach for fisheries management, Ecopath with Ecosim, trophic model, local ecological knowledge, connecting knowledge systems, Gulf of Nicoya, science–policy interface tools

## INTRODUCTION

In recent years, resource managers have increasingly focused on fishery-driven changes in ecosystem structures and trophic relations, since fishery-induced changes in biomass at one trophic level have been shown to affect the whole ecosystem (Pauly et al., 2000). Food web models of the Ecopath type have been used as efficient tools for presenting the interactions and changes in the food web as a result of fishing (Christensen and Pauly, 2004), and have also allowed managers to predict tentative changes in the future with the Ecosim tool (Pauly et al., 2000; Christensen and Walters, 2004). If one seeks to identify realistic measures for fisheries management, it is important to understand both the ecosystem context, in which the fishing activities take place, and the preferences or motivations underlying the fishers' behavior (Bacalso et al., 2013). While modeling tools have widely been applied for ecosystem-based fisheries assessment (Christensen and Walters, 2005), there are as of yet very few examples in the literature of their application with fishers (Power et al., 2004), despite the fact that their participation and knowledge in the decision-making process have often been shown to enhance sustainable management (Paramor et al., 2005; Pita et al., 2010; Mackinson et al., 2011; Msomphora, 2015; Leite and Pita, 2016). There are quite a few studies that are using Ecopath with Ecosim (EwE) with local fishers and other stakeholders in surveys (Paramor et al., 2005; Bacalso et al., 2013) as well as in workshops (Power et al., 2004; Paramor et al., 2005; Armada et al., 2018). The need to create synergies across scientific and local knowledge systems is engrained in the objectives of the Sustainable Development Goals (SDGs) and other international initiatives but has been limited in the global science–policy interface (Mackinson, 2001; Tengö et al., 2014), including the Latin-American tropical context (Salas et al., 2007; Begossi, 2015; Saavedra-Díaz et al., 2015).

Understanding environmental problems and their impacts on the ecosystem and its services, is crucial for societal decisions and for the development of adaptation strategies (Dietz, 2013). However, assessment may be difficult and complex, and any decision can present uncertainties (Kenter et al., 2011). Scientific data, if communicated adequately, have the potential to inform people and to help individuals in the process of weighting alternatives (Dietz, 2013). It has been shown, however, that scientific knowledge and the perception of local stakeholders are often disconnected in public decision-making, which may eventually lead to management measures that are not supported by or complied by the local stakeholders (Mackinson et al., 2011; Dietz, 2013; Msomphora, 2015). As Mackinson et al. (2011) point out, the scientific findings and the perspectives of people need to be integrated in decision-making if the desired outcome is to utilize the best scientific information available and to harmonize it with the public's beliefs about how the world works to improve the management of resources. The same principles should apply for the management of fisheries as well (Mackinson et al., 2011; Saavedra-Díaz et al., 2015; Fujitani et al., 2018).

Approximately 95% of catches in Costa Rica come from the Pacific Coast, and 8% of landings come from the artisanal fleet (Biomarcc-SINAC-GIZ, 2013). In particular, several important fishing grounds are within the Gulf of Nicoya (GoN; Wolff et al., 1998), a tropical estuary at the Pacific coast of the country (**Figure 1**). The GoN supports around 2,000 small-scale artisanal fishers, who mainly use nets to fish and fish to support their families (Pachecho-Urpí et al., 2013; Ross-Salazar, 2014). The gulf has exhibited different forms of fishery management, including spatial and temporal closures (Salas et al., 2007), such as a yearly fishing closure of 3–4 months, for the protection of small pelagic species' reproductive peak events: Whiteleg shrimps (Litopenaeus spp.), small pelagic fish species (Cupleiformes), snapper (Lutjanus spp.), and corvinas (Cynoscion spp. and other species); and feeding grounds for barracudas (Sphyraena ensis; Proyecto Golfos, 2012). Other management tools used are licenses, fishing permits, and gear restrictions (Salas et al., 2007), especially in the so-called "marine responsible fishing areas" (AMPRs for its Spanish acronym). The latter were implemented in response to local initiatives of co-management (García Lozano and Heinen, 2016).

Despite the management measures in place, challenges remain, such as fishing without a license, the use of prohibited mesh sizes by gillnet fishers (<3 inches; Ross-Salazar, 2014), the illegal use of "rastras" (a type of artisanal trawling), and the bycatch associated with both rastras and semi-industrial trawling. Fishing inside no-take areas or during closures is also a recurrent problem (Proyecto Golfos, 2012). The limited participation of governmental institutions in activities of surveillance and control of illegal fishing complicates the situation and contributes to an increasing number of fishers and fishing intensity on resources (Salas et al., 2007). The control of minimum catch sizes for target species has legally been implemented to protect juveniles. However, while this management measure can potentially be controlled at the market, it has been observed that the great variety of species caught has made enforcement difficult or compliance confusing to fishers (Purcell and Pomeroy, 2015).

For Costa Rica, there are ecological data and analyses (e.g., growth, catches, fishing gears, recruitment, reproduction, and diets analysis) that come from traditional fisheries stock

composition and fishing gears used (Marín-Alpízar and Vásquez, 2014). The three focal points selected for this study along the gulf were Isla Chira (inner area), Costa de Pájaros (inner-intermediate area), and Paquera-Tambor (outer area).

assessments, and some of this information has been summarized in recent technical reports (Biomarcc-SINAC-GIZ, 2013; Marín-Alpízar and Vásquez, 2014). However, as Marín-Alpízar and Vásquez (2014) stated, over the years, few efforts have been made to develop a holistic description and understanding of the ecosystem (Wolff et al., 1998; Alms and Wolff, 2019). On the other hand, the lack of coordinated effort between fishery stakeholders, researchers, and authorities/decision makers aggravates the situation (Proyecto Golfos, 2012). Many management initiatives do not work because stakeholders are not (or do not feel) involved in the processes (Msomphora, 2015). Moreover, the dynamics of fishing efforts are driven either by ecological (catch composition, environmental changes, etc.) or social drivers (fishers' behavior and cultural and economic aspects; Naranjo-Madrigal and Bystrom, 2019), which are not well understood. The disconnection between different social and ecological systems, scattered sources of knowledge, and the lack of inclusiveness is usually reflected in poor policy measures (Mackinson, 2001; Dietz, 2013). In general terms, overfishing is a major threat in the GoN engrained in the high fishing effort and great diversity of fishing gears employed (Pachecho-Urpí et al., 2013). The overexploitation of fishery resources is also a result of the poor coordination among different actors of society and the corresponding systematic integration of their knowledge (Mackinson, 2001; Salas et al., 2007).

A portrayal and modeling of the GoN ecosystem and its fisheries was described about 20 years ago by Wolff et al. (1998), who used a functional trophic modeling (EwE) approach to integrate ecological and fisheries data of the gulf. Recently, the gulf has been revisited by the authors and collaborators to update the model with current data (Alms and Wolff, 2019) and to contrast the state of the gulf ecosystem between these two decades. The basic input parameters gathered to create the model (Alms and Wolff, 2019) are from the Costa Rican monthly artisanal landing statistics (Incopesca), for the main target groups of fisheries, for each zone of the GoN. These statistics are considered the best scientific information available. When comparing the system models 20 years apart, some changes could clearly be distinguished: (1) species composition of the catches has changed significantly over the years, paralleled by a harvest reduction in high-trophic-level species; and (2) economic losses have increased due to a shift in harvesting low-value

species and due to an increase in operational costs. The models have facilitated a connection between the available fisheries' and ecological data (Wolff et al., 1998; Alms and Wolff, 2019).

While the EwE models are based on available ecological and fisheries information (Pauly et al., 2000) and thus appear as a promising starting point for taking management decisions in the GoN (Alms and Wolff, 2019), it is also important to evaluate and consider the state of the ecological knowledge of local stakeholders with regard to the fishing system in the Gulf, as a means to link and better understand the social and ecological drivers involved in the dynamics of fishing efforts and environmental decisions of artisanal fishers (Naranjo-Madrigal and Bystrom, 2019). The concept and applications of local ecological knowledge (LEK) seem important to be included in this context (Begossi, 2015). There are various cases in the literature that attempt to connect local and scientific knowledge, under the assumption that different knowledge systems can contribute to an enriched picture, which is useful for the sustainable management of ecosystems (Mackinson, 2001; Mackinson et al., 2011; Tengö et al., 2014). Synergies across knowledge systems can improve the understanding of environmental conditions, its changes, and possible adaptation strategies (Mackinson et al., 2011; Tengö et al., 2014).

The aim of this paper was to use scientific information of EwE in participatory workshops to integrate fisher's LEK with modeled output for the GoN, with the premise that connecting these two knowledge systems can enhance our understanding of the state and changes in the ecosystem and help to improve fisheries management. For the purpose of this study on LEK, fishers were asked about how they perceive the state and use the local natural/fishing resources, which of the resources are important for their livelihoods, and how the ecosystem and target resources have changed over the course of time (1990s– 2010s). Similar applications and definitions of LEK are found in other studies that manage fisheries in the Latin-American region (Begossi, 2015). Particularly, EwE results were used to stimulate discussion among local stakeholders regarding observed changes in the marine ecosystem. The discussions were documented and the qualitative data were analyzed and presented by frequency of responses to identify the predominant topics and trends while assessing LEK. Systematically and qualitatively, we compared the changes in the fishery over time through an analysis of similarities, complementarities, and contradictions across knowledge systems (Tengö et al., 2014). The outcomes of this study are expected to contribute to a new narrative of decisionmaking in fisheries management, based on both stakeholder participation and scientific evidence.

## MATERIALS AND METHODS

## Study Site

The GoN (**Figure 1**), located on the Pacific coast of Costa Rica, has an area of 1,550 km2 , a length of 80 km, and a width of 50 km. It is considered one of the largest tropical estuaries in Central America (Wolff et al., 1998). The Gulf contains several islands and important spots of biodiversity. Of the 214 species of fish (Proyecto Golfos, 2012), more than 50 are commercially important (Lobo-Calderón et al., 2012). Proyecto Golfos (2012)summarizes a list of coastal marine species reported in the GoN, including 200 species of polychaetes, 10 species of stomatopods, 95 species of decapods, 37 species of copepods, five species of cetaceans, and breeding areas for hammerhead sharks (Sphyrna lewini), among other species. The presence of welldeveloped mangrove areas, corals in the outer Gulf (Proyecto Golfos, 2012), nesting beaches for the Pacific ridley sea turtle (Lepidochelys olivacea), and feeding grounds for hawksbill turtles (Eretmochelys imbricata) and green turtles (Chelonia mydas) also brings conservation value to the Gulf (CREMA, 2014).

Because of the high productivity of the GoN, it is considered one of the most important estuaries in the region (Wolff et al., 1998; Alms and Wolff, 2019), and the fisheries are considered as the main economic activity (Wolff et al., 1998) on which more than 60% of the gulf's population depend (Marín-Cabrera, 2012). Costa Rica's artisanal fishers use mainly fishing nets, but also bottom and drifting longlines and handlines (Marín-Cabrera, 2012). An additional 10% of the gulf's fishers generate their income from manual shellfish harvesting (Marín-Cabrera, 2012). Moreover, two semi-industrial fleets operate in the gulf (sardine purse seiners and shrimp trawlers; Ross-Salazar, 2014).

According to its bathymetry and oceanographic conditions, the GoN can be divided into three different sectors: the inner, the middle, and the outer Gulf (Marín-Alpízar and Vásquez, 2014; **Figure 1**), named zones 201, 202, and 203, respectively, by the Costa Rican institute of fisheries, Incopesca. The division is based on species composition and the fishing gears used. Some species have reproductive sites in the inner and middle areas of the GoN (e.g., croakers and shrimps) from where they migrate to external areas when maturing; there are movements in the opposite direction of certain species as well (Proyecto Golfos, 2012). The inner gulf is defined as an area of priority by Incopesca, because of its importance in the reproduction and nursery of commercially important fish species and shrimps (Marín-Cabrera, 2012; Proyecto Golfos, 2012).

Considering the diverse conditions within the gulf, for this study, three focal points were selected along the GoN (**Figure 1**): (1) Isla Chira (north internal region, 10◦ 050 33,8400N–85◦ 090 01,0700W); (2) Costa de Pájaros (north intermediate region, 10◦ 060 02,6600N–84◦ 590 42,6800W); and (3) Paquera-Tambor (southwest external region, 9◦ 490 05,5300N– 84◦ 560 00,5100W). These three points were considered to geographically represent the diversity of extended artisanal fisher communities located along the GoN and at the same time to effectively embody common aspects among the sites, for example, similar target resources such as small pelagics (sardines, anchovies Centengraulis mysticetus, Ophistonema spp.), shrimps (white shrimp Litopenaeus spp., Pacific sea bob shrimps ∼titi Xiphopenaeus riveti), snapper (spotted rose snapper Lutjanus guttatus, Colorado snapper Lutjanus colorado), and corvinas/croakers (whitefin weakfish ∼queen Cynoscion albus, Tallfin croaker ∼agria Micropogonias altipinnis, weakfish ∼aguada Cynoscion squamipinnis, and various species; Alms and Wolff, 2019) and the generalized use of gillnet as a fishing gear.

## Study Overview

fmars-06-00126 March 21, 2019 Time: 16:26 # 5

First, we conducted a pre-workshop where a questionnaire was applied, and artisanal gillnet fishers were recruited to these workshops. Next, we conducted workshops using the information from the EwE model of the GoN as input for discussions on the changes in the fishing system over time, and for assessing LEK. Data from the workshops were analyzed by coding the most frequent words used by respondents into individual themes to identify trends and relevant topics. Finally, we qualitatively compared the state of the resources according to the EwE model output and LEK of the fishers. Evidence from the model and LEK was compared in terms of the interactions found across both sources of knowledge, as information that is similar, complementary, or contradictory to each other (Tengö et al., 2014).

The core of the study was conducted from May to July 2017, as this is a 3-month period of fishing closure in the inner and intermediate zones of the gulf, created for the protection of the reproductive peak events of target resources (small pelagic fish species, shrimps, snapper, and corvinas). Given that fishers from internal zones are usually not involved in any fishing activity during the closure, it was expected that fishers would have more availability to participate and engage. Before running the official survey, exploratory visits to the study sites were performed during January to March 2017 to establish contacts with community leaders and fishing association presidents.

In each community, we sought and obtained authorization from the presidents of the fishing associations for conducting the interviews and workshops. In the current research, we asked participants for prior informed consent and explained to them the project and the use of the data. Their responses were voluntary and confidential, and all people involved in the study had the possibility to drop out at any time. Data handling took place in an anonymized form, and it was made sure that it is impossible to identify particular individuals. The focus of the study was the small-scale artisanal fishers who used gillnet gears and owned a fishing license from Incopesca. Those fishers that worked for someone who had a license were also considered. We were interested in people who were living in the area for more than 5 years and who have about 5 years of experience as a fisher. A list of fishers' names matching these criteria was provided by the presidents of the fishing associations, so it was possible to contact participants during the study in their houses or at the local fish market.

## Pre-workshop Questionnaire Administration

The questionnaire was pretested with the fisher community leaders to measure the performance of the instrument. This allowed us to reformulate some sections for language, precision, and clarity. We avoided unfamiliar technical terms such as fishing effort, marine resource, and sustainable fishing and used instead clear expressions for the study population. During the questionnaire administration, a minimum of 10 people were interviewed per community in the expectation that at least 5 participants would be attending the workshops. The conversations were facilitated in small groups (Macmillan et al., 2002).

The general characteristics of the artisanal gillnet fishers who were interviewed and their availability to be part of a workshop were examined via the questionnaire (**Supplementary Material**). The first part of the questionnaire was designed to obtain information on the socioeconomic and demographic characteristics and fishing practices of the respondents. The second part of the questionnaire identified the willingness of the fishers to participate throughout the entire process. Each interview session lasted an average of 30 min (Leite and Gasalla, 2013) with 86 questionnaires performed; 34.5% of the fishers interviewed were from Costa de Pájaros, 32.8% from Isla Chira, and 32.8% from Paquera-Tambor. In this study, we presented results for the three sites and for the fishers who met our selection criteria and attended the workshops (N = 58). Most of the attendees were male (82.8%) with an average age of 41 years (**Supplementary Material**); the age of the majority (24.1%) range from 26 to 32 years.

## An Ecopath With Ecosim Model as a Means to Assess the Local Ecological Knowledge

To assess the local fishers' knowledge about changes in the fishing ecosystem over time, workshops were held in each focal area (**Figure 1**). Eight workshops were conducted, and 58 fishers participated.

In order to adequately communicate information to the fishers, the understanding of the EwE model output by the moderators was essential. Our interdisciplinary team (EwE modeling, behavioral economics, and fisheries ecology) met to address this aspect and to plan the sessions that would be presented in the workshops. The lectures and activities were pretested in a pilot workshop (Leite and Gasalla, 2013) with artisanal fishers in the gulf, and it was found that the simulations of the models needed to be translated into basic graphs and illustrations to facilitate participants' interaction, as other authors have also pointed out (Armada et al., 2018). In the official workshops, the major findings of the study were explained by a facilitator and an assistant using visual aids (graphs and illustrations). Likewise, the names of the species were presented to the fishers using common names and images to confirm recognition (Leite and Gasalla, 2013). The lecture slides used in the workshops are provided in the **Supplementary Material**.

For the purpose of this paper, the following changes in the fishery over time (1990s–2010s; Alms and Wolff, 2019) were explained to the participants: (1) an overall increase of 20.9% in the total fishing catches, for both artisanal and semi-industrial catches; (2) a severe decrease in the catches of shrimps and corvinas (60 and 35%, respectively) in the 2010s; (3) changes from shrimp-dominated catches (1990s) to sardine-dominated catches and small demersal fish (e.g., small corvinas and small sharks, among others).

The reduction in catches of corvinas and shrimps led to additional changes (Alms and Wolff, 2019): (4) a decline in the commercial value of the catches (almost 50%) compared to

that in the 1990s. Although shrimps still represent the largest contribution (39%) of the total value, an important percentage of the economic value (35%) is currently provided by species of lower commercial price, as in the case of small demersal (28%; e.g., small corvinas) and small pelagic species such as sardines and anchovies (7%). Other species of higher trophic levels only contribute small percentages, such as snappers (9%), large corvinas (9%), and catfish (5%). (5) Eighty percent of corvinas caught in the 2010s had not reached the size of maturity and were thus small individuals of lower market prices. (6) There are changes in corvinas and shrimps in terms of the species composition of the catches. While the capture of whitefin weakfish and tallfin croaker has increased, the catches of other corvina species have diminished by 35%. Likewise, there was a severe decrease in the catches of coastal shrimp species, including the white shrimp and the Pacific sea bob shrimp ∼titi.

An overview of the impact of different fishing fleets on the species was presented to the fishers. The artisanal gillnet fleet seems to have the strongest negative impact on different species in the ecosystem, together with predatory species, large drums, mackerel, barracuda, and catfish (Alms and Wolff, 2019). Also, the concepts of food webs, ecosystem, and trophic level (Christensen and Walters, 2004) were described to the participants, with specific examples of existent species in the GoN. This allowed our team to introduce the antecedents and logic of the trophic model of the GoN (Alms and Wolff, 2019), including the EwE 6.4.4 software used to create it (Christensen and Walters, 2004). Subsequently, we encouraged the participants to reflect on the information received and on the changes in the fishery system, to discuss it in groups (Power et al., 2004), and to make a presentation with an overview of their perceptions to the rest of the participants. Attendees were stimulated to participate equally during the activities. Their opinions and comments were registered through photographs, notes, recording relevant discussions, and collecting the materials used in the presentations (Saavedra-Díaz et al., 2015). Workshops were conducted at sites of easy access to participants, such as community centers, restaurants, and schools (Sánchez-Jiménez et al., 2014), and were based on previously published methodologies for consultation processes (Power et al., 2004; Paramor et al., 2005; Sánchez-Jiménez et al., 2014; Armada et al., 2018).

## Analysis of Data From Workshops and Interactions Across Ecopath With Ecosim and Local Ecological Knowledge

The data of the questionnaires were used to create a respondent's profile in terms of socioeconomic and demographic characteristics and fishing practices of the participants (**Supplementary Material**). Later, the qualitative data from the workshops were coded into individual themes according to the most frequent words used by respondents (Paramor et al., 2005) and presented with the percentage (%) of people referencing key subjects along with specific quotes (Ward et al., 2017). The most recurrent topics mentioned were catch, abundance of fishing resources (mainly shrimps, corvinas, and large predatory species), fishing effort, economic efficiency, and impact of fishing fleets on the marine resources.

Then, we used the key themes identified to explore interactions between the two sources of knowledge (scientific and local). We followed a modified version of the concepts described in Tengö et al. (2014) and focused on the state and changes over time of the resources of shrimps, corvinas, sardines, and large predators. Systematically and qualitatively, we compared (Gilchrist et al., 2005) the changes in the fishery over time through an analysis of similar, complementary, and contradictory information across knowledge systems. Similarity in information is understood here as two sources of knowledge that provide the same message with the exact same or very similar words. We defined complementary as different pieces of information that are unique to each knowledge system, but when combined can better describe a situation or enrich a message. Information that is contradictory to another piece of information presents opposite messages on the same topic.

For the analysis of similarities, complementarities, and contradictions across knowledge systems, descriptors and attributes (information derived from the LEK and the model) were used to describe the state and changes of resources (Mackinson et al., 2011). Attributes such as biomass, catch per unit of effort (CPUE), and abundance were used to create a descriptor for the state of the marine resources. CPUE, an index of the amount of fish caught per unit time spent fishing using a particular gear, was calculated from data reported in workshops, using the kilograms of catch per day with one gillnet (**Supplementary Material**). Economic descriptions were elicited using attributes like profits, commercial importance, and prices. Other descriptors such as fishing effort and the impact of fishing gears on species were included.

## RESULTS

The respondents' profiles are summarized in tables (**Supplementary Material**), revealing that the income for most of the interviewees lays between C//200,000 and 300,000 Costa Rican colones (1 EUR: 611 Costa Rican colones as of survey year); 36.8% said they earn C//200,000. This amount oscillates around the minimum monthly salary for Costa Rica in 2017 (256,000: EUR 419.10). Importantly, most respondents (81%) depend solely on fishing, while the remaining (17.5%) combine fishing with other activities, specifically tourism, which suggests that there is a vast number of fishers with monthly salaries below the minimum. In the range of C//80,000 to 110,000 falls the basic expenses (monthly bills and food) of 23.6% interviewees, but it is noteworthy that the expenses varied among fishers, and, for example, 18.2% require a minimum of C//200,000 to 300,000 to cover their basic needs. The balance between income and expenses seems to be influenced by the number of family dependents (between 0 and 7 with an average of 3.3 dependents) and other expenditures beyond the basics (e.g., formal or informal loans to buy a fishing boat).

Gillnet is the primary fishing activity for 66.7% of the respondents, while 26.3% interlaces gillnet with hook fishing and

7% interchanges it with line fishing. Corvina fishing is the main activity for most of the respondents and usually is combined with other activities such as fishing for shrimps (20%) and snappers (15%). Additionally, the results of the interviews indicated that 40.4% of the people are fishing 6–9 h per day almost daily (56.9%).

Discussions in workshops concerning changes in the fishing ecosystem over time were used to understand the LEK. The qualitative data helped to identify dominant topics and trends in relation to abundance of fishery resources (shrimps, corvinas, and large predatory species), fishing effort, profitability, and impact of fishing fleets on the marine resources. Then, the key themes identified in the workshops were used to examine the interactions across LEK and the food web EwE model. We focused on the state and changes over time of the resources of shrimps, corvinas, sardines, and large predators, and we compared the information of both systems of knowledge considering the similarities, complementarities, and contradictions.

## Trends With Shrimps and Corvinas (Local Ecological Knowledge)

The resources of shrimps and corvinas were indicated as highly abundant in the past, while their abundance has decreased over the course of time (**Figure 2A**). This is nicely summarized by the following statements from a fisher: "In old times, there were more croakers, you could hear them everywhere" (Isla Chiraworkshop, 17th June 2017). A participant in the same workshop specified that at around 1993, "a shrimp fever took place all over the island" because of its great abundance and the high fishing activity. In the case of corvinas, data for the 2010s show that certain species of this family were not present any longer, while others had recovered. A significant reduction in catches of corvinas was reported, ranging from 70–90 kg in the 1990s to only 20–30 kg in the 2010s, for a single day of fishing. A reduction was also reported for shrimps, from 30–39 kg in the past to only 5–9 kg today per day of fishing.

Participants indicated that shrimps and corvinas were geographically widely distributed in the past, contrasting with the restricted distribution reported for present times (**Figure 2A**): "In the past you could fish shrimps and corvinas everywhere, now you have to go to deeper waters and different places to find them" (Isla Chira-workshop, 8th June 2017). With respect to shrimps, their current distribution is restricted. Low abundance for certain species of shrimps was also mentioned.

In terms of commercial aspects, the answers of the participants reflect a medium use intensity of commercial species and low to medium prices of corvinas/shrimps in the earlier 1990s, and a greater commercial use and higher prices for both groups at the end of the 1990s and during the 2010s (**Figure 2B**). Fishers reported that at the beginning of the 1990s, and in earlier times, shrimps were so abundant that they were also used as bait. The valuable corvinas were directly sold or were exchanged for plantains, a crop grown in other distant areas, and particularly difficult to find on the islands. In the 2010s, despite the increased commercial use and higher prices for both species, the profits were reported as relatively low. If one considers the reported size reductions of corvinas and shrimps caught (**Figure 2C**), low incomes can be more easily explained, since small specimens do not receive as high prices in the market as large ones. In line with their perceptions, only 20–29 individual shrimps were needed in the 1990s to make a kilogram, while 40–49 individual shrimps are needed in currently. Corvinas were commonly around 2 kg in size in the 1990s, while currently, one to four individuals are necessary to obtain a kilogram of corvina.

In the 1990s, the most commonly used gillnets mentioned by fishers had mesh sizes of 3.5 and 3.0 inches (**Figure 2D**). During the 2000s, 2.75 and 2.5 inches were the most commonly used gillnet mesh sizes (especially after 2005), even though they are illegal. In general, illegal fishing, through the use of gillnets with mesh sizes of 2.5 inches and "rastras" (a type of artisanal trawling), was widely reported in the early 2010s up to now. Another widely used fishing gear reported during the 2010s is the semi-industrial trawl, labeled as a non-selective method with high levels of bycatch.

As stated by fishers, in the 1990s, there were almost no fishing regulations compared to the 2000s (**Figure 2D**), when Marine Areas of Responsible Fishing (AMPRs) and minimum landing sizes (larger than the size at first maturity) were created with the aim of sustaining the fishing resources and ecosystems. However, the fishers still expressed that there are high levels of illegal fishing and confusion with regard to the legal size limits of species, since there are multiple species being caught, each with different size regulations.

## Trends With Other Commercial Species and Large Predatory Species (Local Ecological Knowledge)

For several large predators, the fishers' reports point to a great decrease or absence in the waters of the gulf (**Figure 3**). This is also the case for the sierra fish (ray, Pristis spp.) and hammerhead sharks (Sphyrna lewini); however, some fishers still indicate the presence of sierra fish in coastal waters. Barracuda (Sphyraena ensis) was mentioned as a highly abundant species in the 1990s and a low-abundant species in the 2010s. In the case of jacks (Caranx sp.), the reports went from high to low abundance. In the case of groupers, the picture is diffused, with the presence and recovery of the cabrilla species (Epinephelus spp.) barely mentioned. Also, one non-commercial species for which information was provided is the bottlenose dolphin (Tursiops truncatus), reported as practically absent at present. As indicated by the fishers consulted, mackerel (Scomberomorus sierra) was classified as almost absent in the areas where they fished in the 2010s. Participants mentioned that the gillnet fishing sector generates less profits out of the harvest of this species. For the 1990s, it was suggested that there was a high abundance but low commercial importance of mackerel for gillnet fisheries as well (**Figure 4D**).

Species of catfish (Bagre panamensis) were reported as present in the 1990s and of high commercial importance, while they were almost absent and slightly commercially important in the 2010s (**Figure 4A**). Snappers (Lutjanus spp.) were identified as highly abundant and profitable in the past. However, catch went down from 20 to 29 kg in the early 1990s to 5–9 kg per

(D) mackerel (% of responses).

day in the 2010s (**Figure 4B**). Moreover, fluctuations in the market have been influencing low profits associated with the commercial importance of snappers, according to the majority of fishers consulted.

We found an interesting case with sardines (Opisthonema spp.), since according to the fishers, its catches were abundant in the past (1990s), but the current state, in the 2010s, is unknown for participants (**Figure 4C**). Fishers mentioned that Incopesca authorities suggested to significantly reduce sardine fishing. Some people assumed that the management measure was proposed due to a decrease in the catches of the species; however, there was no agreement and certainty among participants regarding this

affirmation. Currently, sardines are widely used as bait by fishers; therefore, some other fishers consider that catches of sardines remain as abundant as in the past.

## Similarities Across Systems of Knowledge

Similar information (**Table 1**) is reflected in the reports that indicate peaks in the catches of shrimps and corvinas in the 1990s with a severe decrease (60 and 35%, respectively) in the 2010s. The catches of shrimp and corvinas were widely distributed in the 1990s along the gulf, predominantly in the inner area (Zone 201). A restricted distribution of catches is suggested for both species in the 2010s, with most of the catch originating in the outer part of the gulf (Zone 203). In the case of corvinas, the current dependence on only two species (compared to seven species in the 1990s) and smaller specimens reveals changes in the species composition of the catches and a decrease from large to small demersal individuals. For both sources of knowledge in the 2010s, fishing effort of corvinas increased mainly in the intermediate area, while the fishing effort for shrimps decreased.

The model and LEK agreed that the first signals of stock reductions of large drums and shrimps were observed in the 1990s, associated to a growing commercial importance of the species at the end of the decade. At the same time, the increasing pressure of gillnet fishing on the resources, explained by the expanding illegal fishery (80% of gillnet fleets), negatively impacted the resources in the 2010s. The impact of semiindustrial trawling on shrimps and corvinas due to bycatch was identified in both knowledge systems.

## Complementarities Across Systems of Knowledge

It was possible to identify descriptors of abundance by integrating different attributes from the two systems of knowledge (from LEK and EwE), such as biomass (from EwE), CPUE (from LEK), and subjective abundance (from LEK). Attributes such as profits, commercial importance, and prices were used as descriptors of profitability (the **Supplementary Material** contains details of the attributes and descriptors used). The combination of data thus provides evidence of changes in the abundance of shrimps and corvinas from a high (1990s) to a low abundance (2010s), and economic changes from high (1990s) to low profitability (2010s) of its fishing activity (**Table 2**).

The two sources of information combined also suggest significant reductions in the harvest of high-trophic-level species (mackerel, barracudas, sharks, and rays). Specifically, in the 2010s, local fishers report a decrease in the catches of large predators including the absence of certain groups such as sharks and sierra fishes. Similarly, for the description of the profitability of shrimp and corvina fisheries, there is an accordance between both knowledge systems, which point to the economic losses in the 2010s paralleled by a shift in the harvest from high- to low-value species (**Table 2**).

According to the EwE model, shrimps accounted for the vast majority of the total value of artisanal and semi-industrial fleets in the 1990s and still represented the largest contribution to the artisanal fleets in the 2000s but experienced a strong decline in the total catch and value. The two systems of knowledge integrated suggest that the inshore resources have been decreasing, greatly affecting the artisanal fishers, since this fleet is not able to compete with the semi-industrial fishing fleet, which has larger vessels and capacities to go fishing further out to sea. For the artisanal fishery, small corvinas represent the largest contribution in the catch, and currently, just two species are sold as a high-quality product: the whitefin weakfish (Cynoscion albus) and the Stolzmann weakfish (Cynoscion stolzmanni). The rest of the species are small and sold in low-value classes.

Finally, it was also found that the temporal impact (1990s vs. 2010s) of different fishing fleets on some fishing resources is better explained when the two knowledge systems are complementary, as it presents an overall picture. That is the case of information about the impact of the semi-industrial fleet on shrimps. The data for the 1990s were obtained by combining LEK and the EwE knowledge. For the 1990s, the model alone shows the impact of different fishing fleets on large predators, as well as a negative impact by artisanal gillnet, longline, and semiindustrial shrimp fleets. The details of the gillnet fleets' impact on shrimps and corvinas were provided by the fishers since the model does not differentiate at that level. Fishers identified a growing commercial importance at the end of the 1990s with the extensive use of gillnets with mesh sizes of 3 and 3.5 inches, and a continued increase in gillnet impact on the resources in the 2010s, especially illegal fishing with mesh sizes of 2.5 and 2.75 inches.

## Contradictions Across Systems of Knowledge

There are also contradictory pieces of information between the EwE and LEK (**Table 3**). For example, despite the significant increase in the commercial importance of corvinas reported at the end of the 1990s by local fishers, they indicated that the fishing effort on this resource was low during that time. This is counter to the model, which indicates a high fishing effort since the 1990s.

The model shows considerably higher catches of sardines in the 2010s than in the 1990s, with a change from shrimpdominated catches in the 1990s to sardine dominated catches, and a peak of catches in the early 2000s. The model also detects a declining trend in the catch of sardines since 2008. In this regard, the LEK is unclear about the current state of the sardine catches, with some fishers believing that its catches are abundant as it is commonly seen and used as a bait to fish corvinas.

## DISCUSSION

There are few efforts attempting to address the lack of systematic integrated information on small-scale fisheries (Salas et al., 2007). This also applies to the fisheries of the GoN, Costa Rica, where a dissociation between sources of knowledge, mainly from local stakeholders, scientists, and decision makers, has been identified. The approach followed in this case study, examined the potential of using the scientific findings of an EwE model to stimulate discussion among fishers regarding observed changes in the marine ecosystem, and from this, obtain insight on the LEK. The

 1| Similarities across two systems, local ecological Knowledge (LEK; this study) and scientific knowledge (EwE; Alms and Wolff,

2019).

fmars-06-00126 March 21, 2019 Time: 16:26 # 11

TABLE

TABLE 2| Complementarities across two systems, LEK (this study) and scientific knowledge (EwE; Alms and Wolff, 2019).

fmars-06-00126 March 21, 2019 Time: 16:26 # 12

the attributes).

TABLE 3 | Contradictions across two systems, LEK (this study) and scientific knowledge (EwE; Alms and Wolff, 2019).

two knowledge systems (the scientific and the local) were later compared systematically and qualitatively to identify similarities, complementarities, and contradictions across information. The aim was to integrate the two sources of information to enhance our understanding of the state and changes in the ecosystem and to help improve fisheries management. Additionally, we identified other advantages resulting from this systematization related with the engagement of the fishery participants.

## Connecting Systems of Knowledge

Multiple examples in the academic literature show how synergies across knowledge systems have allowed for a better understanding of ecosystem and natural resource management (Mackinson, 2001; Gilchrist et al., 2005; Leite and Gasalla, 2013; Beaudreau and Levin, 2014; Danielsen et al., 2014; Fujitani et al., 2018). As a first step, the scientific findings of the EwE model of the GoN were presented and discussed among local fishers, illustrating a process of integration of knowledge through validation, whereby one knowledge system is incorporated into another (Tengö et al., 2014; Msomphora, 2015). Since it has been demonstrated that both types of knowledge can augment each other, the next step taken was to assess interactions across systems (Mackinson et al., 2011), as an example of cross-fertilization of knowledge (Tengö et al., 2014).

The analysis of the interactions across systems of knowledge demonstrates that the fishers' perceptions on the past and current state of the fisheries–ecosystem presented strong similarities with the core findings of the EwE model, especially in relation to the following aspects: (1) there was a severe decrease in the catch of shrimps and corvinas in the 2000s, paralleled by (2) significant changes in the species composition of the catches of corvinas over the years and a reduction in their sizes. (3) The first signs of reduction in the catches of large drums and shrimps were visible especially at the end of the 1990s, due to growing commerce and use of gillnets. (4) The usage of gillnets increased in the 2010s, and illegal fishing was indicated as a predominant activity. (5) In the 2010s, fishing efforts toward corvinas increased in area 202, and fishing efforts toward shrimps decreased.

In general, a growth in the use of gillnets was widely reported from the early 2000s up to now by both sources of information. This trend may be related to the greater intensity of fishing that has most probably led to a depletion of larger specimens in the population, resulting in the predominance of smaller individuals in the catches, a process described as fishing down the web (Pauly et al., 1998; Alms and Wolff, 2019). Like the example above, we found similar pieces of information that delivered the same message, helping to foster stronger confidence in the conclusions and providing robust evidence of changes in the social–ecological system of the Gulf.

There is information identified as complementary to each other, because together they provide a broader picture (descriptors and attributes) of the changes of certain fishing resources. The examination across the model and the LEK revealed that it was possible to generate descriptors of abundance and profitability for specific fishing resources using and combining attributes informed by both sources of knowledge (Mackinson, 2001). When combined, the following changes in the fishing system were highlighted: (1) changes over time in the abundance of shrimps and corvinas from high to low abundance; (2) significant reductions in the harvest of hightrophic-level species (mackerel, barracudas, sharks, and rays); (3) economic losses due to a shift to harvesting low-value species (predominance of small individuals and changes from shrimp- to sardine-dominated catches); and (4) revenues decreased due to higher operational costs to catch offshore resources—the inshore resources diminished, and the artisanal fishers were not able to compete for the more distant resources with the semi-industrial fishing fleet because of limitations in vessels and transportation capacities (Alms and Wolff, 2019).

Characteristics such as abundance, biomass, catch per unit effort and profits, commercial importance, and prices seem to be adequate attributes when comparing changes in a fishery system, according to both the local fishers and the scientific perspectives. With respect to the temporal changes (1990s–early 2000s vs. early 2000s–2010s) caused by different fishing fleets on some fishing resources, most of the information was provided by the model,

suggesting that there are elements of information more suited to a specific knowledge system. However, when systematic scientific knowledge is complemented with available LEK, it is possible to see an overall and enriched image (Tengö et al., 2014).

Contradictory pieces of information, on the other hand, were detected among the EwE model and the fishers' perceptions: (1) reduction in the catches of sardines since the late 2000s was identified by the model but not by the fishers. (2) Despite the reduction in catches, the high prices in the market for large pelagic fish make the activity profitable for the semi-industrial fleet, as discussed above. However, this does not apply for the artisanal fleet that operates with smaller vessels with restricted ranges and lower fishing capacities. (3) Increase in the fishing effort toward corvinas was not detected by local stakeholders but was shown by the model data. The latter result suggests that fishers possibly perceive a decrease in fishing efforts as an explanation of the reduction in the catches of corvinas over time.

Some authors raise the point that it is important not to ignore the disagreements between different sources of knowledge provided by diverse systems and stakeholders, since doing so could lead to an artificial consensus (Leite and Gasalla, 2013). The conflicting information between LEK and the EwE model found in this case study has generated new insights with the potential to be acknowledged at the decision-making level. We suggest that the mismatches between LEK and scientific knowledge could indicate the source of management and enforcement problems. For instance, in the case of the sardine, local stakeholders perceive abundant catches from the early 2000s up to now, while the model shows a declining trend in the catches since 2008. During the focal groups' activities, it was often stated that sardines are the only realistic alternative to fishing corvinas with handlines (the second most common activity in the artisanal fishing sector besides gillnet fishing). As a result, a conflict arises since reducing or avoiding fishing sardines, as authorities recommend, is difficult to enforce, and artisanal fishers do not see a need to reduce sardine fishing.

As Tengö et al. (2014) identified, and in light of the interactions between the EwE model and the LEK described so far, it is possible to note how using multiple evidence types can generate different valid and useful knowledge, but together improve our understanding of the state and changes in the fisheries systems, as well as the implications for the well-being of humans. Another area where connecting systems of knowledge would potentially be fruitful is in relation to the control of minimum catch sizes for target species. This regulation has been implemented in the GoN as an output control measure to protect juveniles with possibilities to enforce it at the market. Fishers mentioned this topic during the group discussions and expressed confusion concerning the legal-size limits of some species of corvinas, due to the large number of other species caught and the different size regulations in place for each one. The control of catch sizes is possibly creating a regulatory environment where the burden of compliance is high, yet the reason for complex regulations is unclear for some fishers. Thus, there seems to be a need for better communication with local fishing stakeholders about the reasoning behind ecosystem-based management alternatives.

## General Conclusion and Management Implications for the Gulf of Nicoya

It has been broadly recommended that scientists engage more directly with fishery management (Mackinson et al., 2011; Purcell and Pomeroy, 2015), combined with the use of LEK as a source of information (Fischer et al., 2015). However, the integration of systems of knowledge is complex and requires specific strategies of communication and methods (Leite and Gasalla, 2013). Despite the abundant literature on the importance of LEK in fisheries (Fischer et al., 2015) and the multiple examples using food web models in management schemes (Pauly et al., 2000; Christensen and Walters, 2004, 2005), there are no specific methodologies describing how LEK can be systematically incorporated with scientific data in the context of ecosystembased fisheries management (Leite and Gasalla, 2013). The method we propose is, to our knowledge, the first contribution exploring a systematic integration of a food web model of the EwE type with the assessment of fishers' LEK. As we have demonstrated, using multiple types of evidence together, can expand our understanding (Tengö et al., 2014) of the state and changes in ecosystems, helping to improve fisheries management.

As Armada et al. (2018) stated, we also found that an EwE trophic model can be an interactive and effective communication tool to use with artisanal fishers to promote initial discussions on changes in the fishing ecosystem over time and, from there, to gather their perceptions about these multifaceted and sensitive topics. After the workshops, our team learned that information of this kind should be presented using basic images and graphics, moving gradually from the most basic to the most intricate information.

The primary purpose of this study was the systematic connection of the two systems of knowledge, to improve fisheries management. The added advantage from integrating the information was the engagement from those using the resources and who participated in the workshops. As of yet, there are only very few practical applications of a food web model to engage stakeholders for fisheries management with EwE modeled output (Power et al., 2004; Paramor et al., 2005; Bacalso et al., 2013; Armada et al., 2018), and to our knowledge, there are no reports of this kind in the Latin American region or in the GoN. Most participants stressed the importance of the current participatory process regarding the information received and how they relate to it, and they also mentioned the need for considering their opinions and perceptions. This could include making regulations more transparent and potentially more streamlined. The representation of local stakeholders in the management process can potentially help contribute to levels of trust, two-way knowledge exchange, and legitimacy in developing sustainable fishing options (Power et al., 2004; Pita et al., 2010; Mackinson et al., 2011; Msomphora, 2015; Leite and Pita, 2016; Fujitani et al., 2017).

Fishery management in the GoN has rarely followed an ecosystem approach, and the interaction between species and the effect of the different fishing fleets (and fishing effort) on the ecosystem has not been adequately considered (Alms and Wolff, 2019). We note that the authorities' suggestions of reducing or