Managing a transboundary fishery in the Amazon: The ornamental silver arawana case.

The Amazon basin is home for the silver arawana (Osteoglossum bicirrhosum), a fish better known in the international aquarium business as the “dragon fish” due to its phenotypic characteristics that makes it resemble a Chinese dragon, which has turned it into a highly popular pet in Asian countries. In such countries, this fish is believed to bring good luck and prosperity. Although not famous for its good looks, it is still an interesting fish that presents parental care by the male.

Silver arawana (Osteoglossum bicirrhosum). Image from http://www.acuamanus.com.ar

At the tripoint where the borders of Colombia, Brazil and Peru meet in northwestern Amazonia, the ornamental fishing of the silver arawana is a highly important economic activity for many riverine communities of the three countries. However, the management policies vary greatly between countries to the point where what Brazil implements is exactly the opposite of what the others do. In Brazil its ornamental fishery is forbidden in order to protect its stocks, but the adults can be exploited by the commercial fishery for consumption. It is a large fish (up to 1 m) and widely appreciated in the Brazilian Amazon diet. In fact, the silver arawana is the third species (in biomass) landed in Tefé, a Brazilian town near (~600 km) the study sites, where its demand keeps growing. On the other hand, Colombia and Peru legally permits the silver arawana live exploitation at the fish larval stage (3-5 cm) for the aquarium trade millionaire business. These countries regulate the ornamental fishery by imposing closed seasons (Colombia also imposes a quota). It does not mean though that Colombia and Peru actually sit down to agree on dates and areas to be closed; they make their decisions without talking to each other. In common, however, we have that both countries chose to ignore the recommendations done by researchers on the best period to close the fishing in order to protect the reproductive period of the species. Instead, they established closed periods that satisfy the market stakeholders’ (middlemen and exporters) interest.  What is already bad gets worse when fishers and middlemen disrespect an already inappropriate closed season, which is easy to do under institutional weakness. 

In a region where countries boundaries are not more than an abstract concept for people and for the fish they depend upon, it is easy to imagine that larval fish are also illegally collected in Brazil. In 2014, for instance, around 17% of the fish gathered by one of the study sites in Colombia (the middle Putumayo-Iça river) came from Brazilian grounds. Brazilian farmers open lakes that are inside their properties to Colombian fishers for an average of USD 4,400. In return, a lake can yield up to 30,000 silver arawana larvae worth more than USD 13,300. Where poverty reigns, such easy profits are hard to pass. 

Of the total volume of fish caught in Colombia or Brazil, 17% were marketed by Peruvian (illegally) and 83% by Colombian middlemen to the export firms of each country respectively. We can all see that managing this fishery is a herculean task because of its high profitability and the fact that it is performed in the middle of a very isolated region. However, it would be helpful if countries could talk for once and agree on their best policies, ideally considering the results provided by research. Countries and their managers would do even better if they could also address the consumer markets, namely the United States, Asia and Europe. One recommendation would be to call for the importing countries and regions to demand that the exporting ones comply with national and international laws, regulations and advice on fishery sustainability. One way to do that would be do demand fish traceability, where we could know if the fish are being harvested with environmentally friendly methods and subjected to better post-capture practices  that decrease mortality rates. No obstacle should be used as an excuse to not move towards an integrative management approach, under shared monitoring programs, management and market goals. Perhaps, existent programs, such as the Amazon Cooperation Treaty of 1978, can be used as a kick start to get the ball rolling.  Who knows, maybe this fish will be the key to unlock a true dialogue about sustainability in South America. 

By Adriana Maldonado

References

Maldonado et al. 2017. Transboundary fisheries management in the Amazon: Assessing current policies for the management of the ornamental silver arawana (Osteoglossum bicirrhosum). Marine Policy 76: 192-199.

It's all about Bayes in data-poor fisheries modelling

It is widely recognized that marine resources, although renewable, are not endless and need to be properly managed if their contribution to the nutritional, economic and social well-being of a fast growing human population have to be sustained. In this sense, maintaining long-term marine fisheries sustainability entails not only socio-political significance, but also economic and ecological importance.

So far, most of the fish stock assessments* performed rely on methods proposed in the first half of the twentieth century. Although robust, these methods are clearly outdated and inflexible because they demand big data set and they do not take into account the interactions that exist between the biological, physical and anthropic components.

However, most of the ongoing fisheries research has to face the lack of regular data, making conventional stock assessment tools usually not applicable to data-poor situations. This is particularly a concern for developing countries, where fisheries tend to be poorly documented and inadequately managed due to limited funding for monitoring and data analyses. Still, in such countries fisheries play a major role in food security and well-being for the poor.

 One alternative that has been adopted to overcome this issue is to use more flexible models. Nevertheless, one might still wonder: could more flexible quantitative methods still perform well and be reliable under limited information? The answer to this crucial question is: Yes, it could! And indeed they do it very well!

A paper published very recently has used a set of different statistical approaches in order to extend the scope of data-poor fisheries. The study was carried out in Rio Grande do Norte state - a small coastal state on the Brazilian northeast - where the authors studied the endangered lane snapper (Lutjanus synagris) that is caught by the artisanal fleet (Fig. 1).

Figure 1: (A) Typical artisanal fleet that operates with bottom-set gillnets along Rio Grande do Norte state and (B) an example of an adult lane snapper (Photo of the fishing fleet: Marcelo Nóbrega; Photo of the lane snapper: Garcia Jr. et al., 2010)

   The new ingredient of this paper was based on the legacy left by the English Reverend Thomas Bayes (and also by Pierre S. Laplace, although this one is not properly recognized) (Fig. 2), and which has come to be known as Bayesian reasoning. Through Bayes theorem one might update her initial belief about something with new information, and thus get a new and improved belief. In essence everybody could consider herself a Bayesianist, as our updated knowledge is always initially shaped according to our initial beliefs. 

Figure 2: Thomas Bayes (left) and Pierre-Simon Laplace (right) (References: Wikipedia).

  Specifically, the authors of this paper addressed Bayesian reasoning into two important fisheries modelling issues, namely the estimation of mean size at which a species reaches first maturity (L50) and the prediction of a species’ spatial distribution. By knowing the L50 of a fish stock (or fish population) we can divide it into juveniles and adult individuals and therefore propose policies to restrict the fishing of juveniles, i.e., when a fish mean length is smaller than the estimated L50. Moreover, knowing the spatial distribution of juveniles and adults gives an overview of where each one occurs preferentially. This information helps subsidize management policies based on the use of the space by the fish, as it allows for instance the protection of specific fishing grounds,  where juveniles aggregate. 

     In this sense, by including Bayesian reasoning in these two issues, it was shown that it is a useful model for data-poor situations. This happened primarily because of the Bayesian properties, which permits that a researcher not only handles smaller datasets than usual, but also incorporates any available information to the data. The latter case is of particular interest, once it allows the results to adapt themselves automatically as we acquire more data. Thus, even if someone has a small dataset, she can either use the literature or use her own information to accumulate data and produce more reliable results.

  Last, but not least, by using Bayesian models the researchers were able to quantify the uncertainty of the predictions in a straightforward way.  In fisheries science, we do not control the data, we typically only observe it, and such observation is usually incomplete, which leads to several error sources (imperfect observations, inappopriate sampling procedures, model structure and parameter definition, etc.). This is where uncertainty emerges. Thus, if one desires to provide an accurate picture of the investigated phenomenon, it is of fundamental importance to account accurately for these multiple uncertainty sources in the models. Also, by quantifying the uncertainties someone has greater control of the quality of her results and, consequently, more freedom to make specific decisions upon the evaluated process.  For instance, in fisheries management, questions like ''what is the probability that the current catch levels of a given fishing resource remains sustainable'' or "what is the probability that area A has greater conservation potential than area B'' can only be answered under the Bayesian paradigm. Therefore, knowing that uncertainty is inherent to all scientific realms, its inclusion in the decision-making process is not only desirable, but essential.

By Marie-Christine Rufener

*a stock assessment is an evaluation of a given fish population. It can provide multiple information, such as the total biomass or the total number of individuals in that population, but it can also bring information on age estructure, fecundity, proportion of males and females, etc.

References
Garcia Jr., J.; Mendes, L. F.; Sampaio, C. L. S. & Lins, J. E. 2010. Biodiversidade marinha da Bacia Potiguar: ictiofauna. Rio de Janeiro, Série Livros 38, 195p.

Web references
https://upload.wikimedia.org/wikipedia/commons/thumb/9/91/Pierre-Simon-Laplace_(1749-1827).jpg/170px-Pierre-Simon-Laplace_(1749-1827).jpg accessed on March, 2017


https://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif accessed on March, 2017