https://orcid.org/0000-0001-6252-6890
Abstract
Assessing fish stocks harvested by small-scale fisheries is challenging. The lack of official fisheries data constrains the proper management of such fisheries. Thus, alternative sources of information are crucial to enrich data-poor fisheries. Here, we evaluated different sources of data for the mullet (Mugil liza) fishery, one of the most important but overexploited fisheries in Brazil. We gathered three alternative sources of catch data by artisanal fisheries: 14 years of self-reported catches by artisanal fishers across 24 municipalities; 16 years of catches by traditional beach seines mined from news outlets; and 13 years from a single community monitoring their beach seine catches. We tested whether alternative data sources follow the same trends of landing reports from systematic, official monitoring of the industrial fleet. We fitted Bayesian time-series models to test if environmental changes and stock abundance can predict these data. We found that only self-reported catches matched the official reporting trends, thereby improving our understanding of changes in the mullet stock. These findings reveal that self-reported catches by fishers provide reliable additional data useful for management. Self-reporting data are cost-effective, deals with the complexity of small-scale fisheries, and welcomes fishers as key stakeholders in management practices.
Introduction
Effective fish-stock management depends on the challenging task of gathering reliable data. The assessments of commercially valuable fish stocks in developed countries are often based on detailed longitudinal datasets (e.g. de Lestang et al., 2012). Fisheries scientists can therefore fit population-dynamics models to these data to estimate stock trends, to determine stock status and to recommend sustainable harvesting quotas. However, gathering reliable data to evaluate population trends and status can be both expensive and time-consuming (Bland et al., 2015). Hence, the assessment of many harvested fish stocks remains data-limited, thus potentially imprecise and biased (Free et al., 2020), making these stocks vulnerable to overexploitation (Costello et al., 2012).
Small-scale fisheries in developing countries are difficult to track and manage, thus invariably data-deficient. Artisanal fishers typically use small vessels varying in technological capability, fishing gear, and target species. Landings can also occur anywhere along the coast rather than being concentrated at large, regional fishing ports. These factors often result in the misconception that small-scale fisheries account for small and widespread catches with negligible consequences to the fish stocks (Pauly, 2006). Partially as a consequence, small-scale fisheries are often overlooked by governments, while catches continued to increase and have been underreported worldwide (Zeller et al., 2015; Pauly and Zeller, 2016; Canty et al., 2019). Small-scale fisheries can collectively reach operational scales similar to industrial fisheries (Alfaro-Shigueto et al., 2010), but because they tend to be concentrated in nearshore habitats they present potential threats to sensitive coastal ecosystems and vulnerable species (Shester and Micheli, 2011). Neglecting underreported small-scale fisheries can, therefore, misinform fish-stock management (Rudd and Branch, 2017), and mask the risks for biodiversity and successful conservation (Alfaro-Shigueto et al., 2010).
Recent effort lies in finding and validating alternative sources to compensate for the absence of official data on small-scale fisheries. To cite a few, historical ecology, ethnoecology, and citizen science emerge as potential alternatives. Historical abundances and distribution of marine species can be reconstructed from logbook data and catch records (e.g. Lotze and Worm, 2009). Historical baselines in fisheries can be identified from media reports (e.g. Francis et al., 2019). Catch reports can also be estimated by accessing traditional knowledge (e.g. Castello, 2004; Damasio et al., 2015), or from self-management practices such as local community records of their own catches (Mion et al., 2015). Recently, the widespread adoption of technologies such as GPS, mobile phones, and social media has facilitated the collection, processing and sharing of information on fisheries (e.g. Venturelli et al., 2017; Sbragaglia et al., 2020).
These alternative sources of information can be integrated with quantitative techniques to enrich data on small-scale fisheries (Kindsvater et al., 2018). In Brazil, small-scale fisheries are overlooked, even though they provide livelihoods for many people. However, since 2011, due to political and economic changes, there has been no official fishery statistics for large or small-scale fisheries in Brazil (MPA, 2011). This lack of official monitoring poses a significant challenge for fisheries management and hinders the reversal of declining fish stocks. A remarkable example is the mullet (Mugil liza) fishery.
The mullet fishery is one of the most important target species of the Brazilian fisheries. Every year during the mullet season (from May to July), both large and small-scale fisheries target the southern mullet stock along the south and southeast of Brazil. Large-scale fisheries are dominated by the purse seine fleet, while small-scale fisheries are more diverse, comprising gillnets, drift nets, beach seines, and even artisanal cast net fishers. Almost 10000 fishers rely on this fishery, with an estimated annual receipt of over US$9 million (MPA/MMA, 2015). However, the mullet stock in southern Brazil has been considered threatened by overexploitation since 2004. A management plan for sustainable fishing of mullet stocks was only established in 2015, proposing catch quotas, specific fishing periods for the industrial fleet and artisanal fisheries, spatial restrictions, and satellite vessel-tracking systems for the industrial fleet (MPA/MMA, 2015). A recent study combined data from multiple fishing grounds and of different fishing gear in a stock assessment (Sant’Ana et al., 2017). More than just raising a flag about the sustainability of the mullet stock, this assessment raised concerns about a data crisis, as some data from mullet fisheries are intermittent and unreliable (Sant’Ana et al., 2017). Therefore, the reliability of alternative datasets must be assessed.
Here, we evaluated the validity of three alternative sources of data on artisanal mullet fisheries in southern Brazil. We tested whether data from (i) self-reported catches by artisanal fishers, (ii) catches by traditional beach seines mined from news outlets, and (iii) catches from detailed logs of a beach seine fishing community were correlated with the official landing reports of the industrial fleet, which was considered to be the most systematic in the historical dataset. All these data sources covered fisheries harvesting the same stock, but each fishery has different dynamics in terms of spatial scale, catchability and number of fishers involved. We then modelled how sensitive these data sources are to changes in environmental conditions and stock abundance, using sea-surface temperature (SST) and the catches per unit effort (CPUE) of the industrial fleet as explanatory variables, respectively. Finally, we used our fitted model—accounting for the influence of SST and CPUE of the industrial fleet—to forecast catches after 2013—when official systematic monitoring of the industrial fleet became only partial, but still provided CPUE for part of the total catches, as a sub-sample. A high correlation between official and non-official data would suggest that alternative data could fill the gaps necessary to model population dynamics and generate predictions that can inform decision-makers.
Material and methods
Mullet
The mullet (Mugil liza) is a pelagic species that migrate to the sea to reproduce, but depend on estuaries and lagoons to feed and grow (Menezes et al., 2003). The southern stock shoals migrate from Uruguay and Argentina towards the waters of southern and south-eastern Brazil (Sadowski and Dias, 1986; Mai et al., 2014). This reproductive migration occurs from May to July. The peak of spawning occurs along with the southern Brazilian states (Rio Grande do Sul, Santa Catarina, and Paraná) and is associated with the decrease of sea-water temperature and frequent southern winds (Herbst and Hanazaki, 2014; Lemos et al., 2014). Thus, fishers are fishing the same cohort within a given year. Across years, fishers are fishing different cohorts but at the same stage—i.e. sexually mature mullets migrating to spawn (Garbin et al., 2014). Most of the mullet catches (ca. 45%) occur along the coast of Santa Catarina (Lemos et al., 2016), where the mullet fishery is a recognized feature of cultural heritage (state law number 15.922, 2012). Rules defining fishing zones can vary with vessel size and location, but most catches occur in the sea water after mullet shoals leave the estuaries and lagoons. In most estuarine systems, only traditional artisanal cast net fishers are allowed (e.g. Peterson et al., 2008; Santos et al., 2018). Such cultural aspects are strengthened by media coverage in newspapers and on TV, reporting the start of the mullet season every year, and following catches in each season.
Datasets
We assessed four datasets of mullet fisheries in the state of Santa Catarina. First, we assessed the industrial and artisanal fisheries datasets that comprised part of the data used in a recent mullet stock assessment (see Sant’Ana et al., 2017). Data from the industrial fleet comprise official landing reports from systematic monitoring conducted by the Fishery Studies Group from the University of Vale do Itajaí (UNIVALI/GEP; available at http://pmap-sc.acad.univali.br/). Landings from purse seine, bottom gillnets and trawlers were fully assessed from 2000 to 2012, but only partially reported after 2013. From this source of data, we only assessed data from purse seines, which contribute to the majority of industrial fleet catches.
Artisanal fisheries data came from three sources: self-reporting catches, media reports, and logs of a beach seine fishing community. The first source was an official effort maintained since 2003 by the Federation of Fishermen of Santa Catarina (FEPESC) that integrates catches self-reported by fishers. From 2003 to 2016, fishers from 24 municipalities reported catches from beach seines and small vessels—lengths varying from ∼5 to 10 m, powered by ∼10 to 50 HP engines—along the coast of the state of Santa Catarina, southern Brazil. In each municipality, individual fishers reported daily catches directly to the FEPESC during the mullet season, from May to July. These raw data were provided by FEPESC upon request.
The second alternative source of data came from data mining media reports of beach seines found in local newspapers at the Florianópolis Public Library, Santa Catarina. We focused on the newspaper with the highest state-wide circulation—Diário Catarinense—searching for articles on beach seining catch reports. We manually searched for articles in hard copies of newspapers published from 2000 to 2016, in every issue from the first day of April to the last day of July, encompassing the mullet season in southern Brazil (Vieira and Scalabrin, 1991; Lemos et al., 2016). When available, we retrieved information on catch sizes (number of fish or biomass in tons), date, and location.
The last alternative source of data were daily logs maintained and provided by fishers from a traditional beach seine fishing community at the Estaleirinho beach, in the state of Santa Catarina (−27.048 S, −48.587 W). This community self-organized in maintaining daily records of beach seine catches from 2004 to 2016.
Data analysis
As the industrial fleet and self-reporting data were reported in terms of weight, to correlate all datasets, we converted the number of fish captured in each of the alternative datasets to catch in metric tonnes, considering each mullet to weigh 1.6 kg on average. This figure was based on 11 newspaper articles that reported both the number of fish and tonnes captured. Although this simplified conversion presumes a time-invariant individual average weight, the mean size of mullets caught by the industrial fleet in the last decade was consistent across years (SEAP/PR and MMA, 2018). Then, we summarized catch data to tonnes per year, using only catches that occurred from May to July, when mullet migrate northwards to spawn—the peak of the mullet season (Herbst and Hanazaki, 2014; Lemos et al., 2016). To check for correlation between the three alternative catch datasets, we used Pearson correlation tests, truncating the time series from 2004 to 2012, the period when all datasets overlapped.
Because all datasets reported catches from the same stock, trends in all sources should depend on the same changes in environmental conditions and stock abundance. Thus, we also fitted models to account for artisanal fisheries trends related to changes in SST and stock abundance. We obtained SST data from the National Oceanic and Atmospheric Administration (NOAA) Environmental Research Division’s Data Access Program server (ERDDAP). We chose SST data to match time (mullet season, from May to July) and space (the coastline of the state of Santa Catarina) of all the four sources of mullet catch data. We calculated the mean daily SST in grid cells of 0.25° latitude by 0.25° longitude (∼25 km2), ranging from latitude −25.875 to −28.875 and longitude −47.875 to −49.875. For each cell, we calculated the mean SST per day, from May to July (92 days). Then, we calculated the spatial and temporal suitability for each year, considering the number of days at the optimal condition (19–21°C; see Lemos et al., 2016). Mullet migration is regulated by SST; sea-water temperature triggers the ovarian maturation by affecting vitellogenesis, which stimulates the spawning migration of the species (Whitfield et al., 2012; Lemos et al., 2014). Hence, the amount of mullet available for fishing may depend both on the time window of optimal SST conditions and the extent of the coast under optimal conditions. To account for this, we summarized the influence of SST in both space and time by summing the daily sea-surface area within the optimal conditions for each year (hereafter optimal SST). Next, we used CPUE from the industrial purse seine fleet as our proxy for trends in stock abundance. CPUE of the industrial fleet was calculated as catches over the number of fishing trips and converted to tonnes·year−1. Due to our short time series, and the absence of significant technological innovations or changes in how the industrial fleet behaves, we assumed a constant catchability throughout the study period.
Trends in the artisanal catch data were related to optimal SST and CPUE of the industrial fleet using Bayesian structural time-series models, a useful class of forecasting time-series models that generalizes many standard time-series processes in a Bayesian framework to account for uncertainty (Scott and Varian, 2014). We fitted models using the annual total catch of each alternative dataset as the response variable, and optimal SST and CPUE from the industrial purse seine fleet as predictors. Since all datasets had different start dates, we adjusted predictors to match the length of each response until 2012. In addition, we included terms for autoregressive and for a local linear trend (moving average) in the models. Autoregressive terms aim to account for the dependence of stock abundance on the abundance of previous years. Here, we used only a 3-year-lag because of our short time series. However, as mullet reach sexual maturity at around 6 years (Garbin et al., 2014), other values should be tested when longer time series become available in the future. The local linear trend was included as a proxy for stochastic or unknown processes that could cause further fluctuations in stock abundance.
Combining either predictors, autoregressive or local linear trends resulted in seven models for each of the three artisanal fishing data. Model terms were combined as follows (i) predictors + autoregressive + local linear trend; (ii) predictors + autoregressive; (iii) predictors + local linear trend; (iv) autoregressive + local linear trend; (v) predictors, (vi) autoregressive; and (vii) local linear trend. This set of models allow us to test the effect of SST and our proxy of stock size (CPUE of the industrial fleet) on catch data of the three alternative small-scale datasets, accounting for time-dependence and/or stochastic processes in stock abundance. In all cases, we ran a Markov Chain Monte Carlo (MCMC) algorithm for 10000 iterations and discarded the first 1000 as burn-in. We then compared models by their ability to fit response variables using cumulative absolute prediction errors calculated step-by-step for the observation in the step ahead. The smallest cumulative errors indicate good candidate models. In addition, we used the best model to forecast catches from 2013 to 2016, when only a subset of the industrial fleet landings was reported. If observed empirical data fell outside the forecasted 95% credible interval, then we considered the model to be inadequate to predict trends in catches. For chosen models including optimal SST and CPUE of the industrial fleet, the relative importance of each variable was calculated from posterior probability distributions for their coefficients.
We carried out all the analyses in the R environment (R Development Core Team, 2019). We used the corrplot R package (Wei and Simko, 2017) to test the correlation between datasets and the bsts R package (Scott, 2019) to fit Bayesian structural time-series models.
Results
The industrial fleet landed a total of 39555 t (metric tonnes) of mullet in the state of Santa Catarina, from 2000 to 2016 (mean = 2326 ± 1409 SD t per year). Fishing operations had a median fleet size of 82 (± 31.14 SD) and a median of 139 (± 56.56 SD) fishing trips per year. From 2000 to 2012, when the industrial fleet was systematically assessed, the average catch per unit of effort (CPUE) was 16.27 t (± 5.87 SD) per fishing trip, ranging from 6.92 in 2002 to 27.71 t per fishing trip in 2007.
Self-reported data to FEPESC amounted 1316 monthly reports from 2003 to 2016, comprising catches landed in 24 municipalities in the state of Santa Catarina, including 24 fishing communities in the Santa Catarina Island (Figure 1). Fishers self-reported a total of 16224.9 t of mullet landed (mean = 1158.9 ± 811.3 SD t per year), ranging from 425.7 in 2013 to 3543.2 t in 2016. Media reports comprised 288 newspaper articles published from 2001 to 2016, of which 70 reported mullet beach seines (median = 3.5 ± 2.6 SD, ranging from 1 to 11 newspaper articles per year). We found no media reports of mullet beach seining published in 2000. From 2001 to 2016, newspaper articles reported a total of 885.4 t of mullet landed (mean = 55.3 ± 46.4 SD t per year), ranging from 4.9 in 2014 to 154 t in 2003. Throughout the years, most of the newspaper articles were published in May (36%; n = 25) and June (60%; n = 42). Only one catch was reported in April (25 April 2003) and two in July (05 July 2001 and 17 July 2002). Catches from 11 municipalities were reported in newspaper articles. Most of the catches reported occurred in Santa Catarina Island (64%; n = 45), with other municipalities ranging from 1 to 6 reports. Fishers from the beach seine fishing community registered 273 beach seines from 2004 to 2016. During this period, fishers landed 109.5 t (mean = 5.1 ± 8.9 SD t per year), ranging from 0.6 in 2011 to 34.66 t in 2016.
Spatial and temporal scales of the alternative catch data sources. Each map indicates fishing spots in the state of Santa Catarina. The self-reporting dataset comprises mullet catches from 24 municipalities, from 2003 to 2016 (each orange dot is a report in a given month in each year); the media reports dataset was mined from 70 media reports from 2001 to 2016 (each dark blue dot is a media report); the community self-monitoring dataset is a daily log of a traditional, artisanal beach seine fishing community (light blue dot) from 2004 to 2016. Density plots on the left side of the panels show the distribution of self-reports and media reports along the coast of the state of Santa Catarina. Bar plots show the number of self-reports, newspaper articles and successful beach seines reported by year.
Catches reported by the self-reporting approach correlated with catches from the industrial fisheries fleet (r = 0.77, p < 0.05). All other correlation coefficients were not statistically clear (Figure 2). The validity of this self-reporting approach was reinforced when accounting for spatial and temporal suitability of environmental conditions and stock abundance (Table 1). All artisanal fisheries datasets were predicted by optimal SST and stock abundance, but we found industrial fleet CPUE had the highest posterior inclusion probability (0.67) in the model for the self-reporting data. We found higher coefficients and posterior inclusion probabilities for the proxy of stock abundance than for SST, but this relationship reverses in the beach seine fishing community dataset (Table 1). These models with autoregressive terms and predictors accumulated errors at a lower rate than other candidate models (Figure 3). When forecasting with models including both autoregressive terms, and optimal SST and CPUE, catches from 2013 to 2016 were mostly well-predicted (Figure 4). This was found for self-reporting (Supplementary Figures S1–S3 and Supplementary Table S1) and media reports (Supplementary Figures S4–S6; Supplementary Table S2) datasets, with values falling within the credible interval, with only one marginal value. The beach seine fishing community dataset was, however, poorly predicted (Supplementary Figures S7–S9; Supplementary Table S3).
Correlation between official and alternative mullet catch data (tonnes·year−1) in southern Brazil. The official catches come from a systematic monitoring of the industrial fleet, while the alternative sources include self-reporting by artisanal fishers, data mined from media reports, and community-based self-monitoring from a traditional beach seine fishing community. For comparison, the datasets were truncated to the period during which data were available from all sources (from 2004 to 2012). Orange line and black dots indicate the only significant correlation, between the official data (industrial fleet) and self-reported catches.
Cumulative absolute one-step-ahead errors for each set of candidate Bayesian structural time-series models. Lines show the cumulative absolute errors for each model, colour coded by the combination of terms—linear trend (moving average), autoregressive (AR), and predictors (PRED)—included in the model. Small cumulative errors indicate good candidate models. In models with predictors, annual total catch of each alternative data from artisanal fisheries were fitted as the response variable (see original series in Figure 4) to the number of days in optimal SST (19–21°C) and CPUE of the purse seine industrial fleet as predictors.
Catch time series of mullet fisheries in southern Brazil. Background histograms show the number of days (maximum = 92) in optimal conditions of mean daily SST from 19°C to 21°C. Temperatures from 19°C to 21°C are considered the best conditions for the mullet catches. (a) Data generated by an official landing monitoring program of the industrial fleet. Solid line and points show catch per unit of effort (CPUE), calculated as tonnes by fishing trips. Landings were only partially assessed after 2013, represented by the grey dashed line in panels (a)–(d). In panels (b)–(d), lines and squares show annual catches from 2000 to 2016, colour coded by each alternative data source. Lines and diamonds in black show the predicted catches and shaded areas the credible interval estimated from each Bayesian model (see the full posterior distribution in Supplementary Figures S3, S6, and S9). (b) Catch data from an integrated self-reported approach conducted by fishers from 24 municipalities in the state of Santa Catarina, southern Brazil; (c) Catch data mined from media reports in the state of Santa Catarina; (d) Catch data from self-organized and maintained daily records by fishers of only one traditional beach seine fishing community.
Coefficients and inclusion probabilities (in brackets) from Bayesian structural time-series models with the lowest accumulated error for each dataset.
| Dataset | Intercept | SST | CPUE |
|---|---|---|---|
| Self-reporting | 819.945 (0.376) | 10.038 (0.124) | 52.061 (0.67) |
| Media reports | −16.665 (0.182) | 1.041 (0.19) | 3.52 (0.529) |
| Beach seine community | 7.603 (0.305) | 0.155 (0.189) | 0.275 (0.139) |
| Dataset | Intercept | SST | CPUE |
|---|---|---|---|
| Self-reporting | 819.945 (0.376) | 10.038 (0.124) | 52.061 (0.67) |
| Media reports | −16.665 (0.182) | 1.041 (0.19) | 3.52 (0.529) |
| Beach seine community | 7.603 (0.305) | 0.155 (0.189) | 0.275 (0.139) |
Coefficients and inclusion probabilities (in brackets) from Bayesian structural time-series models with the lowest accumulated error for each dataset.
| Dataset | Intercept | SST | CPUE |
|---|---|---|---|
| Self-reporting | 819.945 (0.376) | 10.038 (0.124) | 52.061 (0.67) |
| Media reports | −16.665 (0.182) | 1.041 (0.19) | 3.52 (0.529) |
| Beach seine community | 7.603 (0.305) | 0.155 (0.189) | 0.275 (0.139) |
| Dataset | Intercept | SST | CPUE |
|---|---|---|---|
| Self-reporting | 819.945 (0.376) | 10.038 (0.124) | 52.061 (0.67) |
| Media reports | −16.665 (0.182) | 1.041 (0.19) | 3.52 (0.529) |
| Beach seine community | 7.603 (0.305) | 0.155 (0.189) | 0.275 (0.139) |
Discussion
We found that self-reported catches of mullet by small-scale fisheries can enrich formal fisheries data. The self-reported catches are mostly explained by our proxy of stock abundance—the CPUE from the industrial data—but also by greater extents in space and time of optimal SST—an important trigger for the mullet migration. Modelling self-reported catches led to accurate predictions, following the same trends of the industrial fleet official data. Combined, these findings demonstrate that self-reported catches can provide robust information about mullet stocks and compensate the lack of institutional data collection at the regional scale.
Mullet fisheries have noticeable variations in space and time. The industrial fleet can follow the migrating shoals northwards while optimal climatic conditions push the migration (Lemos et al., 2016), and still concentrate landings at large, regional fishing ports. Conversely, small-scale fisheries must wait for the migrating shoals to approach the coast (Herbst and Hanazaki, 2014). When assessing coastal fishing communities, conventional monitoring systems in developing countries fail to address such spatiotemporal variability of small-scale fisheries (Gill et al., 2019). Alternative catch data from small-scale fisheries can capture such variation at the fine scale and provide valid information on mullet stocks, as well as details on fishers’ behaviour and the fisheries dynamics. Among the three alternative catch data sources we explored here, self-reporting showed the most similar pattern to the official data. Self-reported catch data better accounted for the variations in space and time, as it relies on reports from fishers using beach seines and small vessels that together account for a significant fishery harvest along the 500 km coast of the state of Santa Catarina. In contrast, our findings suggest neither newsworthy catches nor fine-scale data from a single beach seine fishing community can capture the variation in space and time of mullet fisheries.
Media reports provide limited information to be used as a proxy of stock abundance. Despite covering catches during a similar period and along with a similar extent of the coast than the self-reporting dataset, media reports are only a small sample of all beach seines. Catches around more urbanized and accessible areas may be disproportionally more reported in the media. Also, not every catch is newsworthy, with unusually large catches more readily making headline news. Media reports may also be biased by logistical limitations (e.g. reporters are close to specific fishing spots) or editorial decision (e.g. publish only large or early catches). Hence, media reports are unlikely to represent a reliable sample of mullet catches. But media reports might still play a role in reinforcing the regional cultural relevance of mullet fisheries and shaping peoples’ perceptions of wildlife (Silk et al., 2018; Francis et al., 2019).
The detailed catch logs from a single community also present a key limitation: catch data are highly spatially constrained. Although a local community can provide detailed, fine-scale temporal data, it reports only the catches of a single fishing spot, making this data source unable to account for spatial variation. That is, catch data from a fine spatial scale (here a single community) might be disconnected from environmental processes at a large scale. However, this bias could be reduced by incorporating networks of multiple, local communities along the coast into a single, integrated, citizen-science project. A citizen-science approach could broaden the spatiotemporal scale of this alternative data source, especially if coupled with new technologies and information systems, such as webcams in fishing spots, and online social networks and reporting apps whereby fishers could share information on fishing activities along the coast (Devos et al., 2019).
Self-reporting by small-scale coastal fisheries provides the most reliable information, partially because self-reporting includes dozens of fishing communities across a large geographical range and can effectively account for environmental conditions that influence stock abundance. Therefore, self-reporting data coped with the challenges of sampling small-scale fisheries (e.g. variation in landing spots and fishing gear), often overlooked by fisheries management and stock-assessment models. This is notably important for the maintenance of the mullet fishery in southern Brazil, which since 2004 has been under threat due to poor management, lack of law enforcement and intermittent data collection (Abreu-Mota et al., 2018). Despite such difficulties, fishers maintained their continuous self-report monitoring since 2003 providing long-term data. Fine spatial and temporal-scale data provide sufficient resolution for improved detection of changes in mullet abundance and distribution, and fisheries effort at local and regional spatial scales. This is of particular relevance given climate change, since increasing SST might disrupt the timing and routes of the mullet migration, forcing changes in fishers’ behaviour and in their ability to follow high biomass concentrations. These fine-scale data will also help to identify local changes in mullet abundance and the consequences for their trophic interactions. As mullet is a key prey species for top predators, such as the bottlenose dolphins Tursiops truncatus gephyreus (Teixeira et al., 2021), this needs to be accounted for in fisheries management and in conservation plans.
We recommend implementing policies that encourage the self-reporting by artisanal fishing communities and include self-reporting data into a more comprehensive, dynamic, and integrated management approach that could lead to a very-welcomed paradigm shift in fisheries management. For instance, the cultural aspects of the mullet fishery are the key to this paradigm shift and to the success of self-reporting, or even other alternative approaches relying on data from artisanal communities. Reinforcing such cultural aspects will likely motivate fishers to engage in data collection (Newman et al., 2017), thereby facilitating their inclusion in more sustainable and participative management plans. Therefore, given that the mullet is a culturally important species in southern Brazil, by integrating alternative data sources, managers not only benefit from a more detailed description of the fishing system, but this also welcomes fishers to become key stakeholders in co-management schemes (Freitas et al., 2020), boosting their sense of stewardship and their role in the species conservation for future generations.
Machado, A. M. S., Hettwer Giehl, E. L., Fernandes, L. P., Ingram, S. L., and Daura-Jorge, F. G. 2021. Alternative data sources can fill the gaps in data-poor fisheries. – ICES Journal of Marine Science, xx: xx–xx.
Acknowledgements
We thank Ivo da Silva and the collaborators of the Federation of Fishermen of Santa Catarina (FEPESC) for sharing the data; the staff from the Public Library of the State of Santa Catarina for the help with newspapers archives; Leonardo Viera and João Campos for sharing the detailed logs from Estaleirinho beach seines. We thank Mauricio Cantor for thoroughly reviewing the manuscript text. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) - Finance Code 001 (A.M.S.M.; E.L.H.G). Figures were designed with icons from flaticon.com.
Author contributions
F.G.D.J. and A.M.S.M. conceived the study; L.P.F. performed data mining on newspapers; A.M.S.M. and E.L.H.G. explored and analysed the data; A.M.S.M., E.L.H.G. and F.G.D.J. interpreted the data; A.M.S.M., F.G.D.J. and S.N.I. wrote the original draft of the manuscript. All authors reviewed the manuscript and gave final approval for publication
Data and code availability
The raw data underlying this article were provided upon request. Data will be shared on request to the corresponding author upon permission of the third parties. The formatted dataset and R code used here are available at https://github.com/machadoams/mullet-alternative-data.
