1 UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE
DEPARTAMENTO DE ECOLOGIA
PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA
USO DE INDICADORES ECOLÓGICOS E SOCIOECONÔMICOS PARA AVALIAR MUDANÇAS NA PESCA
LUDMILA DE MELO ALVES DAMASIO
NATAL/RN JANEIRO DE 2020
2 LUDMILA DE MELO ALVES DAMASIO
USO DE INDICADORES ECOLÓGICOS E SOCIOECONÔMICOS PARA AVALIAR MUDANÇAS NA PESCA
Tese apresentada como requisito obrigatório para a defesa do título de doutor em Ecologia pelo Programa de Pós-Graduação em Ecologia da Universidade Federal do Rio Grande do Norte - UFRN.
Orientadora: Dra. Priscila Fabiana Macedo Lopes Coorientadora: Dra. Maria Grazia Pennino
NATAL/RN JANEIRO DE 2020
3 Universidade Federal do Rio Grande do Norte - UFRN
Sistema de Bibliotecas - SISBI
Catalogação de Publicação na Fonte. UFRN - Biblioteca Setorial Prof. Leopoldo Nelson - -Centro de Biociências - CB
Damasio, Ludmila de Melo Alves.
Uso de indicadores ecológicos e socioeconômicos para avaliar mudanças na pesca / Ludmila de Melo Alves Damasio. - Natal, 2020.
112 f.: il.
Tese (Doutorado) - Universidade Federal do Rio Grande do Norte. Centro de Biociências. Programa de Pós-graduação em Ecologia.
Orientadora: Profa. Dra. Priscila Fabiana Macedo Lopes. Coorientadora: Profa. Dra. Maria Grazia Pennino.
1. Pesca artesanal - Tese. 2. Socioeconômico - Tese. 3. Indicadores ecossistêmicos - Tese. I. Lopes, Priscila Fabiana Macedo. II. Pennino, Maria Grazia. III. Universidade Federal do Rio Grande do Norte. IV. Título.
RN/UF/BSCB CDU 639.2
4 Agradecimentos
O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Código de Financiamento 001 (This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001).
Agradeço à minha família pelo incentivo, carinho e compreensão sempre, especialmente meus pais. O Rafa também merece um destaque especial por todo o companheirismo, apoio e muito amor.
Às minhas orientadoras Priscila e Maria, vocês foram super importantes na minha formação e aprendi muito com as duas. Muito obrigada mesmo!
Ao Villasante por me receber em Santiago e pelas contribuições no terceiro capítulo.
Aos amigos da pós: Rosemberg, Lenice, Mona, Nat, Júlia, Ana, Clara, Carol e Iohara (e muitos outros que não cabem aqui) que além da amizade se tornaram colaboradores e sempre ajudaram em algum momento de sufoco. Agradecimento especial à Mona pois sempre esteve super disposta a ajudar e ouvir todas as minhas reclamações e principalmente dar boas risadas de tudo.
À todos os pescadores que participaram das entrevistas e contribuiram para a construção dessa tese, espero que os resultados possam ajudá-los a melhor manejar sua atividade para que ela ainda atenda a gerações e gerações futuras.
5 Sumário RESUMO... 05 ABSTRACT ... 06 INTRODUÇÃO GERAL ... 07 OBJETIVO GERAL ………...……….. 11 OBJETIVOS ESPECÍFICOS ……….... 11 REFERÊNCIAS ………... 12
CAPÍTULO 1 - Use of trophic level ecosystem indexes to evaluate fisheries time series in Brazil ……….………... 14
CAPÍTULO 2 - Small changes, big impacts: geographic expansion in small-scale fisheries...………... 45
CAPÍTULO 3 - Adaptive factors and strategies in small-scale fisheries economy …... 77
6 Resumo
Peixes e frutos do mar obtidos por meio da pesca extrativa são um dos principais serviços ecossistêmicos de uso direto fornecido pelos oceanos, o que justificaria a necessidade de se regulamentar e manejar adequadamente a pesca. Esta regulamentação e este manejo, a princípio, deveriam ser feitos a partir de dados multiespecíficos, capazes de retratar uma atividade que envolve aspectos ecológicos, econômicos e sociais. Na realidade brasileira, no entanto, dados pesqueiros tendem a ser poucos ou inexistentes, e abordagens multidisciplinares ainda são raras. Neste contexto, esta tese teve como objetivo utilizar indicadores e variáveis ecológicas, sociais e econômicas para avaliar, as mudanças na pesca e seus impactos na renda dos pescadores, ocorridos nos últimos 20 anos, usando duas escalas espaciais e temporais: 1) macroescala: todo o Brasil, com o objetivo de avaliar mudanças globais em uma longa série temporal (1950-2010); 2) escala regional: oito comunidades pesqueiras do Rio Grande do Norte e Ceará, para avaliar mudanças mais específicas nos últimos 20 anos (1994 – 2014). As análises foram feitas por meio de um conjunto de diferentes ferramentas metodológicas e estatísticas robustas e capazes de lidar com limitações temporais e espaciais nos dados (entrevistas com dados de memória de pescadores, Modelos de Distribuição de Espécies, e análise de convergência indicadores ecossistêmicos). Para os dados em macroescala, utilizamos especialmente a reconstrução das capturas pesqueiras, disponível na base de dados do Sea Around Us. Para os dados em escala regional, utilizamos informações obtidas por meio de entrevistas semiestruturadas, os mesmos dados de reconstrução de capturas, dados de primeira revenda de pescado (banco de dados também fornecido pelo Sea Around Us) e dados ambientais (bioOracle). Estas duas escalas de análise foram organizadas em três capítulos distintos, cujos objetivos principais eram: 1) descrever e analisar a situação de exploração dos recursos pesqueiros no Brasil a partir de uma perspectiva ecossistêmica; 2) investigar mudanças na distribuição espacial da pesca de pequena escala ao longo da região equatorial brasileira entre 1994 e 2014 e os fatores ecológicos e socioeconômicos que influenciaram essa mudança; e 3) verificar os fatores adaptativos e as estratégias que ajudam a explicar porque alguns pescadores obtêm rendimentos mais baixos do que outros. No primeiro capítulo, identificamos que a diferença entre as ecorregiões Nordeste/Amazônia/Leste e Sudeste/Rio Grande é notável. As regiões Sudeste e Rio Grande tiveram os piores índices, indicando que a pesca não está sendo realizada de maneira sustentável enquanto que as demais regiões apresentaram índices melhores. O
7 MTI variou entre as regiões e entre a pesca artesanal e industrial, mas o fenômeno “fishing down marine food web” não foi observado em todas as regiões, apenas na região Rio Grande. Por outro lado a expansão geográfica ocorreu de forma semelhante em todas as regiões e para os dois tipos de pesca. Já no segundo capítulo, foi observado que uma mudança relevante na distribuição espacial da pesca foi detectada e demonstrou que a pesca tem se movido principalmente de águas rasas para mais profundas. Embora as espécies-alvo tenham permanecido as mesmas em 1994 e 2014, a abundância dessas espécies diminuiu significativamente ao longo do tempo, o que afetou negativamente a renda dos pescadores. Por fim, o terceiro capítulo mostrou que os pescadores mais pobres em 1994 continuam sendo os mais pobres em 2014. Embora diferentes variáveis expliquem a renda nos 2 anos - Estado, barcos grandes e motorizados e equipamento compressor em 1994 e barcos grandes e uso do anzol e linha em 2014, as 3 principais mudanças relacionadas à pesca foram feitas tanto por pescadores que perderam rendimentos quanto por pescadores que tiveram aumento nos rendimentos. Os resultados encontrados aqui certamente ajudam a compreender melhor as mudanças e dinâmicas da pesca e dos estoques pesqueiros do Brasil, ao longo dos últimos 60 anos, mas especialmente na região nordeste e podem ajudar a subsidiar decisões de manejo da pesca. Especificamente, agora pode-se concluir que a pesca não está sendo feita de maneira sustentável, vide a expansão geográfica da pesca que está acontecendo, e mesmo isto não está sendo suficiente para manter os rendimentos dos pescadores, fazendo com que este grupo já vulnerável fique mais vulnerável ainda.
8 Abstract
Fish and seafood obtained through extractive fishing are one of the main direct-use ecosystem services provided by the oceans, which would justify the need to properly regulate and manage fishing. This regulation and this management, in principle, should be made from multispecific data, capable of portraying an activity that involves ecological, economic and social aspects. In Brazilian reality, however, fishing data tend to be few or nonexistent, and multidisciplinary approaches are still rare. In this context, this thesis aimed to use ecological, social and economic indicators and variables to evaluate, changes in fisheries and their impacts on fishermen's incomes, which have occurred over the past 20 years using two spatial and temporal scales: 1) macro-scale: all over Brazil, with the objective of evaluating global changes in a long time series (1950-2010); 2) regional scale: eight fishing communities in Rio Grande do Norte and Ceará, to assess more specific changes in the last 20 years (1994 - 2014). The analyzes were made using a set of different methodological tools and robust statistics capable of dealing with temporal and spatial limitations in the data (interviews with fisher's memory data, Species Distribution Models, and convergence analysis of ecosystem indicators). For the macro-scale data, we use specially the reconstruction of the fishing catches, available in the Sea Around Us database. For the data on a regional scale, we use information obtained through semi-structured interviews, the same data from the reconstruction of catches, data from first resale of fish (database also provided by Sea Around Us) and environmental data (bioOracle). These two scales of analysis were organized into three distinct chapters, whose main objectives were: 1) describe and analyze the situation of exploitation of fisheries resources in Brazil from an ecosystem perspective; 2) investigate changes in the spatial distribution of small-scale fisheries across the Brazilian equatorial region between 1994 and 2014 and the ecological and socioeconomic factors that influenced this change; and 3) verify adaptive factors and strategies that help explain why some fishers earn lower incomes than others. In the first chapter, we identified that the difference between the Northeastern / Amazon / Eastern and Southeastern / Rio Grande ecoregions is remarkable. The Southeastern and Rio Grande regions had the worst rates, indicating that fishing is not being carried out in a sustainable manner while the other regions had better rates. MTI varied between regions and between artisanal and industrial fishing, but the “fishing down marine food web” phenomenon was not observed in all regions, only in the Rio Grande region. On the other hand, geographical expansion occurred in a similar way in all regions and for both
9 types of fishing. In the second chapter, it was observed that a relevant change in the spatial distribution of fishing was detected and demonstrated that fishing has moved mainly from shallow to deeper waters. Although the target species remained the same in 1994 and 2014, the abundance of these species decreased significantly over time, which negatively affected the fisher's income. Finally, the third chapter showed that the poorest fishers in 1994 remain the poorest in 2014. Although different variables explain the income in the 2 years - State, large and motorized boats and compressor equipment in 1994 and large boats and use of the hook and line in 2014, the 3 main changes related to fishing were made both by fisher who lost income and by fisher who had an increase in income. The results found here certainly help to better understand the changes and dynamics of fisheries and fisheries stocks in Brazil, over the last 60 years, but especially in the Northeastern region and can help to subsidize fisheries management decisions. Specifically, it can now be concluded that fishing is not being done in a sustainable way, see the geographical expansion of fishing that is happening, and even this is not being enough to maintain the fisher's incomes, making this group already vulnerable even more vulnerable.
10 Introdução geral
Os oceanos fornecem serviços e bens ecossistêmicos importantes para o bem-estar humano, incluindo serviços de provisão (ex., alimentos e medicamentos), regulação (ex., moderação das flutuações climáticas e proteção contra inundação e erosão) e serviços culturais (ex., estéticos e recreação) (Doyen et al., 2012; Worm et al., 2006). O fornecimento de frutos do mar proveniente de capturas da pesca selvagem é um dos benefícios diretos mais importantes que os humanos retiram do oceano, tanto pela relevância dos peixes como proteína animal consumida regularmente ao redor do mundo e pela economia que os mesmos movimentam, quanto também pelo seu papel na segurança alimentar e modos de vida de populações extratoras (FAO, 2019; Kleisner et al., 2014).
Sobreexploração, poluição, perda e destruição de habitats, impactos diretos e indiretos das mudanças climáticas, entre outros fatores, têm causado impactos negativos no ecossistema marinho e consequentemente nos estoques pesqueiros. A sobre-exploração, especificamente, causada pelo crescimento desordenado do setor pesqueiro em desacordo com o aconselhamento científico, levou 60% dos estoques pesqueiros marinhos à situação de explotação total (FAO, 2016). A simplificação nas teias tróficas marinhas, resultante da diminuição dos estoques pesqueiros, e em alguns casos da extinção de espécies, diminuem a resiliência dos ecossistemas marinhos aumentando sua vulnerabilidade (Hughes et al., 2005).
No Brasil o atual cenário pesqueiro é preocupante devido a múltiplos fatores. Um deles deve-se às características do litoral brasileiro, extenso e variado, que por vezes dá a falsa impressão de se tratar de um mar produtivo, embora o país tenha limitado potencial pesqueiro. Na maior parte da costa a temperatura média anual é alta, ultrapassa os 20oC, criando um ambiente de baixa concentração de nutrientes e consequentemente baixa produtividade (MMA, 2006a).
Até recentemente, especialmente para as espécies exploradas pela pesca de pequena escala, a pesca era mais restrita às regiões costeiras e às proximidades das vilas de origem, devido à inacessibilidade de pesqueiros distantes ou de dificil acesso. Com a melhora da tecnologia, os pesqueiros mais distantes passaram a ser acessados com mais frequência e facilidade (Berkes et al., 2006). A pesca industrial também passou por processo semelhante, mas em momento anterior e a partir de pesqueiros que tendem a
11 ser originalmente mais distantes da costa, em função das características dos estoques explorados. Essa expansão das áreas de pesca, comumente chamada de expansão geográfica, é uma estratégia comumente utilizada pelos pescadores quando ocorre a depleção dos recursos disponiveis mais próximos da costa. Embora intuitiva, a expansão geográfica nem sempre é facilmente detectada, especialmente quando são analisados apenas os dados de desembarque pesqueiro. Esta dificuldade se dá porque apenas as espécies capturadas e seus totais desembarcados são avaliados, sem levar em consideração o local de captura. Ao fim, a expansão geográfica mascara as depleções regionais, pois as quantidades desembarcadas podem ser mantidas estáveis por mais longos períodos de tempo, adiando a tomada de decisões (Myers and Worm, 2003).
Nesse sentido, a avaliação e compreensão das mudanças é uma questão fundamental no contexto do manejo da pesca. Embora restritos e muitas vezes incompletos, os dados que geralmente estão disponíveis para gestão, são séries temporais de dados de captura e esforço (Pauly and Zeller, 2003). Entretanto, as abordagens convencionais para gestão da pesca utilizando estes dados, focam apenas na produção das espécies-alvo, não considerando o impacto da pesca nas espécies não-alvo, nos habitats marinhos e negligenciando os fatores humanos (sociais, econômicos, culturais e institucional) que afetam esta gestão (FAO, 2008).
As análises multiespecíficas, como as feitas por meio de indicadores, são então necessárias para detectar mudanças ao longo do tempo nos ecossistemas marinhos (Pincinato, 2010). Existem muitos indicadores ecossistêmicos disponíveis com vários propósitos, mas de maneira geral os indicadores incorporam várias definições de ecologia e podem ser usados para diversos fins, dependendo dos objetivos buscados e dos dados disponíveis (Dale and Beyeler, 2001; FAO, 1999; Niemi and McDonald, 2004). Muitos países em desenvolvimento, como o Brasil, dependem de políticas públicas pobres ou ausentes em relação ao setor pesqueiro, o que resulta na falta de coleta de dados e, consequentemente, em poucos estudos necessários para o gerenciamento eficaz da pesca (Béné et al., 2010; FAO, 1999). Nestes contextos, o uso de indicadores, especialmente aqueles que não usam muitos dados, podem contribuir para uma melhor compreensão dos impactos da pesca em um determinado ecossistema (Nash et al., 2016; Rochet and Trenkel, 2003).
Um indicador multiespecífico amplamente utilizado é o nível trófico médio (MTL) das capturas. Este índice mede a mudança no nível trófico médio de
12 desembarques pesqueiros por região (Baeta et al., 2009; Freire and Pauly, 2010; Pauly, 1998). A análise desse índice pode ter duas interpretações: a diminuição do MTL pode estar relacionada ao fenômeno “fishing down marine food web” (a captura intensa dos predadores e suas consequentes depleções induzindo à pesca de níveis tróficos inferiores) ou ao fenômeno “fishing through marine food web” (adição sequencial de níveis tróficos inferiores, resultando em um declínio da média do nível trófico, apesar da pesca dos predadores se manter) (Essington et al., 2006; Pauly, 1998). Há ainda vários índices derivados do MTL, com destaque para o Fishing in Balance (FIB). Este indicador avalia se as mudanças nos níveis tróficos são compensadas por alterações das capturas: quando a pesca é voltada para níveis tróficos mais baixos espera-se maior produção biológica (Pauly et al., 2005).
Outra alternativa de análise multiespecífica é o uso de Modelos de Distribuição de Espécies (Species Distribution Models – SDMs). Os SDMs utilizam dados gerados por meio da captura da produção pesqueira, espacialmente coletados e dados ambientais relacionados às espécies capturadas, para fornecer um panorama claro da distribuição espacial dos recursos pesqueiros. Esta técnica permite uma abordagem ecossistêmica integral para o manejo de diferentes espécies simultaneamente e pode ser usada para identificar tendências na distribuição e abundância de peixes (Walters, 2003). O componente espacial, utilizado neste modelo, carrega muitas informações pois a escolha de um pesqueiro envolve diversos fatores como a distribuição das espécies-alvo, a distância da costa, os recursos técnicos disponíveis para chegar ao local de pesca com segurança e os custos associados a viagens adicionais, bem como fatores sociais e regras formais e informais de acesso (Daw, 2008; de Castro and Begossi, 1995; Pet-Soede et al., 2001). Todas essas informações estão de alguma forma resumidas em um componente espacial presente em todos os dados da pesca comercial, os quais tendem a ser infelizmente ignorados e quase nunca coletados (Booth, 2000). Identificar mudanças na distribuição espacial da pesca pode auxiliar no entendimento das dinâmicas da pesca de modo geral.
Por fim, após identificar mudanças e padrões na pesca é de suma importância avaliar o impacto econômico causado pelas mudanças nos ecossistemas. Estes impactos econômicos podem ser macro, especialmente em países com economias altamente dependentes da pesca. Por outro lado, também podem ser impactos locais, especialmente na vida das famílias que dependem do recurso pesqueiro. Para isso é
13 necessário entender a participação da renda proveniente da pesca, dentro da economia familiar do pescador de pequena escala e dos fatores que influenciam o seu rendimento. A pesca de pequena escala, especificamente, é uma atividade comercial cujo trabalho do pescador está associado à informalidade, sazonalidade de espécies, riscos de vida e uma cadeia de valores complexa (Garcia et al., 2018). Apesar desta ser a pesca com o maior número de pessoas diretamente envolvidas, é também a mais negligenciada, tanto no que diz respeito à atividade quanto aqueles que a exercem, os pescadores. Os tomadores de decisão do setor pesqueiro tendem a dirigir seus esforços para a pesca industrial. Subsídios, por exemplo, tendem a beneficiar muito mais a pesca industrial (Abdallah and Sumaila, 2007; Sumaila et al., 2016). Estes subsídios negligenciam o papel da pesca de pequena escala no fornecimento de alimentos, empregos e segurança alimentar para muitas famílias, principalmente em países em desenvolvimento, contribuindo assim para o aumento da pobreza entre os pescadores da pesca de pequena escala (Béné et al., 2007; FAO, 2008).
Da mesma forma que uma parte dos conservacionistas segue ignorando as consequências da ações humanas em função de fatores econômicos, os tomadores de decisão ligados ao desenvolvimento econômico por vezes não conseguem prever os impactos no meio ambiente, contribuindo para a persistência da pobreza (Barrett et al., 2011). Embora em muitas teorias a pobreza seja vista como um fator socioeconômico, ela pode comumente estar associada a aspectos ambientais, uma vez que a persistência da extrema pobreza e a perda de biodiversidade parecem estar interligadas em alguns locais (Cinner et al., 2009). A persistência do indivíduo na pobreza pode ser caracterizada como armadilhas da pobreza. Esta pode ser definida, de maneira simples, como manter-se pobre ou empobrecer-se ainda mais ao longo do tempo (Azariadis and Stachurski, 2005). As armadilhas da pobreza são situações em que as pessoas não conseguem mobilizar os recursos necessários para superar situações crônicas de baixa renda. Frequentemente armadilhas são associadas à degradação de recursos, portanto escapar das mesmas exigirá dos governos e atores envolvidos investimentos significativos em melhorias de governança ambiental e social e construção de uma infraestrutura social e fisica para auxiliar as pessoas nessa situação (Cinner et al., 2009; Cinner et al., 2008).
A pesca é uma atividade complexa que tem um número de efeitos diretos nos ecossistemas marinhos, desempenha um papel comercial importante além de atuar na
14 segurança alimentar, emprego e renda para milhões de pessoas no mundo todo. A sustentabilidade da pesca é indispensável para que esta atividade continue desempenhando suas funções. Para isso o manejo pesqueiro deve garantir que as capturas sejam ecologicamente sustentáveis ao longo do tempo e os benefícios para os pescadores e comunidades sejam maximizados. No entanto o manejo eficaz requer informação, como a falta de informação é um problema crônico da pesca metologias que trabalham com poucos dados devem ser discutidas e testadas para auxiliar os tomadores de decisão. Nesta tese indicadores e variáveis ecológicas, sociais e econômicas foram utilizadas para avaliar as mudanças na pesca e seus impactos na renda dos pescadores usando os dados disponíveis. Para lidar com a limitação de dados foram utilizadas ferramentas metodológicas e estatísticas robustas capazes de lidar com as limitações dos dados. Espera-se poder contribuir para a conservação dos recursos pesqueiros, visando não só a proteção do recurso, mas a sustentabilidade das comunidades pesqueiras estudadas.
15 Objetivo geral
Esta tese teve por objetivo usar indicadores e variáveis ecológicas, sociais e econômicas para avaliar mudanças na pesca e seus impactos, ocorridas nos últimos 20 anos, no Brasil e na região equatorial brasileira. Para a análise em macroescala, foram utilizados dados dos estados costeiros do Brasil. Para a região equatorial, foram amostradas oito comunidades pesqueiras do Rio Grande do Norte e do Ceará, como estudos de caso. Para isto foram utilizadas informações obtidas através de entrevistas semiestruturadas, da reconstrução dos dados de captura (Freire et al., 2015), dados de primeira revenda do pescado (banco de dados do Sea Around Us) e dados ambientais.
Objetivos específicos
1. Investigar tendências temporais das capturas pesqueiras no Brasil através de indicadores ecossistêmicos;
2. Analisar a distribuição espacial da pesca de pequena escala na região equatorial do Brasil e quais fatores influenciam essa distribuição;
3. Verificar qual o impacto das mudanças ocorridas na pesca de pequena escala na renda dos pescadores;
4. Investigar quais fatores determinam a renda dos pescadores de pequena escala nos anos estudados.
16 Referências
Azariadis, C., Stachurski, J., 2005. Chapter 5 Poverty Traps, in: Handbook of Economic Growth. Elsevier, pp. 295–384. https://doi.org/10.1016/S1574-0684(05)01005-1 Baeta, F., José Costa, M., Cabral, H., 2009. Changes in the trophic level of Portuguese
landings and fish market price variation in the last decades. Fish. Res. 97, 216– 222. https://doi.org/10.1016/j.fishres.2009.02.006
Barrett, C.B., Travis, A.J., Dasgupta, P., 2011. On biodiversity conservation and poverty traps. Proc. Natl. Acad. Sci. 108, 13907–13912.
https://doi.org/10.1073/pnas.1011521108
Béné, C., Hersoug, B., Allison, E.H., 2010. Not by Rent Alone: Analysing the Pro-Poor Functions of Small-Scale Fisheries in Developing Countries. Dev. Policy Rev. 28, 325–358. https://doi.org/10.1111/j.1467-7679.2010.00486.x
Béné, C., Macfadyen, G., Allison, E.H., 2007. Increasing the contribution of small-scale fisheries to poverty alleviation and food security. Food and Agriculture
Organization of the United Nations.
Berkes, F., Mahon, R., McConney, P., Pollnac, R., Pomeroy, R., 2006. Gestão da pesca de pequena escala: diretrizes e metodos alternativos. Editora Furg, Rio Grande. Booth, A., 2000. Incorporating the spatial component of fisheries data into stock
assessment models. ICES J. Mar. Sci. 57, 858–865. https://doi.org/10.1006/jmsc.2000.0816
Cinner, J., Fuentes, M.P.B., Randriamahazo, H., 2009. Exploring social resilience in Madagascar’s Marine Protected Areas. Ecol. Soc. 14, 41.
Cinner, J.E., Daw, T., McCLANAHAN, T.R., 2008. Socioeconomic Factors that Affect Artisanal Fishers’ Readiness to Exit a Declining Fishery. Conserv. Biol. 23, 124–130. https://doi.org/10.1111/j.1523-1739.2008.01041.x
Dale, V.H., Beyeler, S.C., 2001. Challenges in the development and use of ecological indicators. Ecol. Indic. 1, 3–10. https://doi.org/10.1016/S1470-160X(01)00003-6 Daw, T.M., 2008. Spatial distribution of effort by artisanal fishers: Exploring economic
factors affecting the lobster fisheries of the Corn Islands, Nicaragua. Fish. Res. 90, 17–25. https://doi.org/10.1016/j.fishres.2007.09.027
de Castro, F., Begossi, A., 1995. Ecology of fishing on the Grande River (Brazil): technology and territorial rights. Fish. Res. 23, 361–373.
https://doi.org/10.1016/0165-7836(94)00343-U
Doyen, L., Thébaud, O., Béné, C., Martinet, V., Gourguet, S., Bertignac, M., Fifas, S., Blanchard, F., 2012. A stochastic viability approach to ecosystem-based fisheries management. Ecol. Econ. 75, 32–42.
https://doi.org/10.1016/j.ecolecon.2012.01.005
Essington, T.E., Beaudreau, A.H., Wiedenmann, J., 2006. Fishing through marine food webs. Proc. Natl. Acad. Sci. 103, 3171–3175.
https://doi.org/10.1073/pnas.0510964103
FAO, 2019. The State of World Fisheries and Aquaculture 2018. FAO, Rome.
FAO (Ed.), 2016. Contributing to food security and nutrition for all, The state of world fisheries and aquaculture. Rome.
FAO, 2008. Small-scale capture fisheries – a global overview with emphasis on developing countries. A preliminary report of the Big Numbers Project. FAO. FAO (Ed.), 1999. Indicators for sustainable development of marine capture fisheries,
FAO technical guidelines for responsible fisheries. Food and Agriculture Organization of the United Nations, Rome.
17 Freire, K.M.F., Pauly, D., 2010. Fishing down Brazilian marine food webs, with
emphasis on the east Brazil large marine ecosystem. Fish. Res. 105, 57–62. https://doi.org/10.1016/j.fishres.2010.02.008
Garcia, F., Abdallah, P., Sachsida, A., 2018. Avaliação de efeitos do programa PROFROTA Pesqueira sobre indicadores do mercado de trabalho, Texto para Discussão. IPEA, Brasilia.
Hughes, T., Bellwood, D., Folke, C., Steneck, R., Wilson, J., 2005. New paradigms for supporting the resilience of marine ecosystems. Trends Ecol. Evol. 20, 380–386. https://doi.org/10.1016/j.tree.2005.03.022
Kleisner, K., Mansour, H., Pauly, D., 2014. Region-based MTI: resolving geographic expansion in the Marine Trophic Index. Mar. Ecol. Prog. Ser. 512, 185–199. https://doi.org/10.3354/meps10949
MMA, 2006. Programa REVIZEE: avaliação do potencial sustentável de recursos vivos na Zona Econômica Exclusiva do Brasil – Relatório Executivo. Ministério do Meio Ambiente, Brasília.
Myers, R.A., Worm, B., 2003. Rapid worldwide depletion of predatory fish communities. Nature 423, 280–283. https://doi.org/10.1038/nature01610 Nash, K.L., Bijoux, J., Robinson, J., Wilson, S.K., Graham, N.A.J., 2016. Harnessing
fishery-independent indicators to aid management of data-poor fisheries: weighing habitat and fishing effects. Ecosphere 7, e01362.
https://doi.org/10.1002/ecs2.1362
Niemi, G.J., McDonald, M.E., 2004. Application of Ecological Indicators. Annu. Rev. Ecol. Evol. Syst. 35, 89–111.
https://doi.org/10.1146/annurev.ecolsys.35.112202.130132
Pauly, D., 1998. Fishing Down Marine Food Webs. Science 279, 860–863. https://doi.org/10.1126/science.279.5352.860
Pauly, D., Watson, R., Alder, J., 2005. Global trends in world fisheries: impacts on marine ecosystems and food security. Philos. Trans. R. Soc. B Biol. Sci. 360, 5– 12. https://doi.org/10.1098/rstb.2004.1574
Pauly, D., Zeller, D., 2003. The global fisheries crisis as a rationale for improving the FAO’s database of fisheries statistics. Fish. Cent. Res. Rep. 11, 1–9.
Pet-Soede, C., Van Densen, W.L.T., Hiddink, J.G., Kuyl, S., Machiels, M.A.M., 2001. Can fishermen allocate their fishing effort in space and time on the basis of their catch rates? An example from Spermonde Archipelago, SW Sulawesi,
Indonesia. Fish. Manag. Ecol. 8, 15–36. https://doi.org/10.1046/j.1365-2400.2001.00215.x
Pincinato, R.B.M., 2010. Análise ecológica e econômica da pesca marinha por meio de indicadores multiespecíficos (Dissertação). IO- USP, São Paulo.
Rochet, M.-J., Trenkel, V.M., 2003. Which community indicators can measure the impact of fishing? A review and proposals. Can. J. Fish. Aquat. Sci. 60, 86–99. https://doi.org/10.1139/f02-164
Sen, A., 1981. Poverty and famines: an essay on entitlement and deprivation. Clarendon Press ; Oxford University Press, Oxford : New York.
Walters, C., 2003. Folly and fantasy in the analysis of spatial catch rate data. Can. J. Fish. Aquat. Sci. 60, 1433–1436. https://doi.org/10.1139/f03-152
Worm, B., Barbier, E.B., Beaumont, N., Duffy, J.E., Folke, C., Halpern, B.S., Jackson, J.B., Lotze, H.K., Micheli, F., Palumbi, S.R., Sala, E., Selkoe, K.A., Stachowicz, J.J., Watson, R., 2006. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–90. https://doi.org/10.1126/science.1132294
18 Capítulo 1 – Desvendando o passado para prever o futuro da pesca brasileira
19
Capítulo 1
Submitted to Fish and Fisheries (Decision: resubmit a revised manuscript)
Title 1: Use of trophic level ecosystem indexes to evaluate fisheries time series in Brazil Title 2: The past and the future of Brazilian fisheries through an ecosystem indicator
lens
Title 3: Unraveling the past to predict the future of Brazilian fisheries through ecosystem indexes
Running title: Past and future of Brazilian fisheries
Ludmila de Melo Alves Damasio – Graduate Program in Ecology - Federal University of Rio Grande do Norte, Campus Universitário Lagoa Nova 59078-900, Natal RN , Brazil.
Maria Grazia Pennino - Instituto Español de Oceanografía. Centro Oceanográfico de Vigo, Subida a Radio Faro, 50-52 36390 Vigo (Pontevedra) Vigo, Spain.
Fishing Ecology Management and Economics (FEME) - Universidade Federal do Rio Grande do Norte – UFRN. Depto. de Ecologia, Natal (RN), Brazil.
Statistical Modeling Ecology Group (SMEG). Departament d'Estadística i Investigació Operativa, Universitat de València. C/Dr. Moliner 50, Burjassot. 46100 Valencia, Spain.
Kátia Meirelles Felizola. Freire – Department of Fisheries Engineering and Aquaculture – Federal University of Sergipe, Rua Marechal Rondon, s/n, Jardim Rosa Elze, 49100-000, São Cristóvão, SE, Brasil.
Priscila Fabiana Macedo Lopes – Department of Ecology, Federal University of Rio Grande do Norte, Campus Universitário Lagoa Nova 59078-900, Natal RN , Brazil. Fishing Ecology Management and Economics (FEME) - Universidade Federal do Rio Grande do Norte – UFRN. Depto. de Ecologia, Natal (RN), Brazil.
20 Abstract
Ecosystem indicators can be used to monitor trends over time and, therefore, provide early warning signals of changes to the environment and to support management even in data-poor areas. This study describes and analyzes the exploitation status of fisheries resources in Brazil from an ecosystem perspective, by investigating the possible influences of environmental variables on the trends observed in fishing indicators. The Brazilian ecoregions were analyzed for their fishing history (1950-2010) and environmental data (Sea Surface Temperature and Sea Surface Salinity) to understand changes and convergences over time in four ecosystem indicators: the pelagic-demersal ratio (P/D), the Marine Trophic Index (MTI), the Fishing in Balance Index (FiB), and the Regional Marine Trophic Index (RMTI). The MTI varied among regions and between artisanal and industrial fishing, but fishing down marine food web phenomenon was not observed in all regions. The FiB remained above zero, which indicates an expansion of fishing. This was confirmed by the RMTI. No significant correlations were observed between environmental variables and these indicators. Subsidies for fishing provided in a disorderly manner, without technical, economic or social criteria, may have been responsible for both the growth and decline of fish production in the 1970s and 1990s, respectively. The use of fishing indicators and the analyses of their trends over time, such as analyses of convergence, especially of indicators that do not require plenty of data, point towards negative scenarios. Knowing these scenarios could contribute to the development of tailore-made management measures to avoid fishery and fish collapse.
Key-words: ecosystem index, FiB, MTI, P/D, RMTI, trophic level, Brazilian fisheries
Table of contents Introduction
Material and methods Background context Study area
Landing data
Trophic level of landed species and genus Ecosystem indicators
Environmental data Statistical analysis Results
Pelagic/demersal ratio (P/D) Marine Trophic Index (MTI)
21 Fishing in Balance Index (FIB)
Regional Marine Trophic Index (RMTI) Normalized cross-correlation
Analysis of convergence Discussion
References Introduction
Wild-capture fisheries, in addition to providing nutritional benefits, are a source of livelihood and income for millions of people worldwide (FAO, 2016). As the demand for fish has increased in recent years and fishing technologies have advanced, marine fisheries have been rapidly expanding and developing (Rosenberg, 2014; Swartz et al., 2010). As a consequence, there is evidence that commercial fisheries have overexploited stocks of only target species, but also biological communities, ecological processes and entire ecosystems (Agardy, 2000; Fulton et al., 2004b; Jennings & Kaiser, 1998; Pauly et al., 1998).
Given the socioeconomic importance of fisheries and the potential negative effects they may cause, the sustainability of the world’s fisheries is a major concern (Doyen et al., 2012). However, conventional approaches to fisheries management have not been truly effective because, for the most part, they only consider the impact of fishing on target species and neglect the impacts on non-target species, marine habitats and humans (FAO, 1999). This neglect has fueled calls for the Ecosystem Approach to Fisheries (EAF) (Cury and Christensen, 2005; Garcia et al., 2003; Jennings, 2005). These calls have been made openly and in important international events and agreements, such as in the Convention on Biological Diversity (1992), the Jakarta Mandate on Marine and Coastal Biological Diversity (1995), the Kyoto Declaration on the Sustainable Contribution of Fisheries to Food Security (1995), the Reykjavik’s Declaration (2001), and the World Summit on Sustainable Development (2002) (Cury et al., 2005; Garcia et al., 2003; Shin and Shannon, 2010). One way to apply an EAF approach is to use ecosystem indicators that describe and simplify the interactions between fisheries and marine ecosystems (Pauly and Watson, 2004).
Ecosystem indicators have several purposes and embody various definitions of ecology, such as trophodynamic interactions. They can be used to monitor trends over time, provide early warning signals of changes to the environment, and support management, depending on the objectives sought and available data (Dale and Beyeler, 2001; FAO, 1999; Niemi and McDonald, 2004). In many developing countries, poor or absent public policies regarding the fishing sector result in a lack of data collection and, consequently, studies that are necessary for effective fisheries management (Béné et al., 2010; FAO, 1999). In such cases, the use of indicators, especially of those that are not data-intensive, could contribute to better understanding the impacts of fishing on a given ecosystem (Nash et al., 2016; Rochet and Trenkel, 2003).
22 Despite its large territory, general economic relevance and extensive coast, Brazil is one such case where the government has historically played a negative role in fishing governance by overlooking data collection and research, and subsidizing investments to increase fishing efforts (De Azevedo and Pierri, 2014). As a consequence, the country, which has never been a major fishing player, faces collapsing stocks and quickly deteriorating marine habitats (Ruffino et al., 2016). Assessing and understanding the changes Brazilian ecosystems have undergone is a key issue of fisheries management. These changes have to be addressed using the limited data available, such as a reconstructed time series of landings provided by the Sea Around Us for the period 1950-2010 (Freire et al., 2015). With the use of standard indicators and the support of new statistical tools (Pennino, Bellido, Conesa, Coll, & Tortosa-Ausina, 2017), limited data are able to undergo multi-specific analyses that are necessary to detect changes over time in marine ecosystems (Pauly & Zeller, 2003; Pincinato, 2010).
This study describes and analyzes the exploitation of marine fisheries resources in Brazil from an ecosystem perspective, by investigating temporal trends of fishing indicators and assessing possible relationships between such trends and environmental variables. Using state-of-the-art statistical tools, these results represent new information and paint a portrait of current and future scenarios of Brazilian ecosystem dynamics as a consequence of fisheries.
Material and methods Background context
The commercial fisheries industry in Brazil dates back to the country’s colonial period (Abdallah, 1998; Diegues, 1973; Paiva, 2004). However, it was not until 1938 that the country issued its first significant legal act intended to regulate and promote this activity: the Fishing Expansion Law (Decree-Law n. 291 of 1938). With the creation of this law, the government started financing fishing development and associated infrastructure, which included establishing fishing schools. The decrees and laws that followed over the following decades took the same developmental approach, including a law to establish fishing as a base industry (Decree-Law n. 58.696 of 1966) and a law to grant it fiscal incentives (Decree-Law n. 221 of 1967) (Abdallah, 1998; Giulietti and Assumpção, 1995; Paiva, 2004; Ramalho, 2014; Silva, 2014).
The first Decree-Law concerned with the rational use and conservation of fishing resources was issued in 1971 (Decree-Law n. 68.459 of 1971). From that year on, following the growth of global environmental activism, concerns about the sustainability of resources in general became more prevalent, especially after the establishment of the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA) in 1989. IBAMA centralized the activities related to fisheries and created several fishing regulations, such as the establishment of closed seasons for some species in 1991 and benefits for fishers during closed seasons. The establishment of IBAMA did not lead to the end of fiscal incentives. In fact, they still continue in present day through tax cuts or tax exemptions on diesel oil since 1988 (Abdallah, 1998; De Azevedo and Pierri, 2014; Giulietti and Assumpção, 1995; Moreno, 2015; Ramalho, 2014).
23 Study area
The Brazilian coast extends for more than 8500 km along the Atlantic Ocean, covering an Exclusive Economic Zone (EEZ) of 3.5 million km² (MPA, 2011). Such an extensive coastline presents a great diversity of environments that influence fishing characteristics. The Brazilian coast encompasses eight marine ecoregions: Amazonia, Northeastern Brazil, Fernando de Noronha and Rocas Atoll, Sao Pedro and Sao Paulo Islands, Eastern Brazil, Trindade and Martin Vaz Islands, Southeastern Brazil, and Rio Grande (Figure 1) (Spalding et al., 2007). The oceanic islands and the atoll are not considered in this study due to the lack of reliable data on fishing.
Figure 1. Study area highlighting the ecoregions defined by Spalding et al. (2007).
In the Amazonia ecoregion, where the continental shelf ranges from 180 km to 320 km wide, artisanal fishing predominates. The Northeastern and Eastern ecoregions together, where artisanal fishing is also more important, have narrower platforms that vary from 10 km to 100 km. Industrial fishing predominates in both the Southeastern and Rio Grande ecoregions, which are 50 km to 230 km wide, and in which upwelling occurs in specific areas (Cergole, Ávila-da-Silva & Rossi-Wongtschowski, 2005; MMA, 2006b).
Landing data
Landing data (kg), for each coastal state of Brazil, were extracted from the Brazilian reconstruction of landing statistics for the period 1950–2010 (Freire et al., 2015). Only landings identified at genus or species level were considered for the analyses of ecosystem indicators. Although the dataset also included subsistence and
24 recreational landings, only landing data from artisanal and industrial fisheries were used in this study because of the study’s focus on commercial fisheries.
Analyses were carried out according to marine ecoregion (Spalding et al., 2007). Two states, Bahia and Rio de Janeiro, belong to two different ecoregions. The landings of the state of Rio de Janeiro, which is equally included between two ecoregions, were divided in half: 50% of the landings were attributed to the Eastern ecoregion and 50% to the Southeastern ecoregion. With regards to the state of Bahia, 70% of its coast is in the Eastern ecoregion, and the remaining 30% belongs to the Northeast ecoregion. Landings were divided according to this distribution. These approximations were necessary due to the impossibility of assigning landings to specific ports.
Trophic level of landed species and genus
The Trophic Level (hereafter TL) of each species and genus were extracted primarily from FishBase for fish (Froese & Pauly, 2018) and SeaLifeBase for crustaceans and mollusks (Palomares and Pauly, 2018). When information on the TL of a given species was not available in these electronic databases, the scientific literature was consulted. For cases where catches were identified to the genus level, the TL of a genus was the result of the average for all species of the same genus found in Brazil. Was not possible to identify TL for two species (Lithodes murragi and Lithodes
santolla). However, as the reported catches of these species were insignificant (less than
10 tonnes for the whole period studied), they were discarded from the analyses. Ecosystem indicators
Four different ecosystem indicators were assessed to evaluate the overall fishing status of the Brazilian ecoregions:
Pelagic/demersal ratio (P/D) - All species landed were divided into two groups according to their habitat: demersal and pelagic. This information was obtained from the FishBase and SeaLifeBase databases, for fish and invertebrates, respectively. Species classified by FishBase into reef-associated, bathypelagic, epipelagic and benthic were reclassified only as pelagic or demersal, depending on their association with the substrate. The ratio between the landings (in weight) for pelagic and demersal species was then calculated. Pelagic/demersal ratio synthesizes the structure and functioning of the ecosystem. An increase in the P/D ratio of landings could imply an increase in fish abundance due to predatory decrease or environmental change. The decline of top predator stocks due to overfishing leads to an expansion in biomass of pelagic fishes. (De Leiva Moreno, Agostini, Caddy, & Carocci, 2000; Pennino & Bellido, 2012). Marine Trophic Index (MTI) - The MTI was developed based on the assumption that a decline of the mean trophic level of fisheries catches is generally due to a fishery-induced reduction of the biomass beginning from the higher trophic levels towards the lower ones, which first results in reduced biodiversity of vulnerable predators (Pauly et al., 1998; Pauly & Christensen, 1995). This index tracks changes in TL and is calculated for each year k as a combination of fisheries landings and TL for each species landed:
25 where Yik refers to the reported landings of the species i in the year k. Changes in this index could provide useful indications of changes in the abundance and landings of species with high TL. High TL species are usually the preferable targeted species, thus giving origin to the expression ‘fishing down food webs’. However, a declining TL could also be due to the sequential addition of low TL species, which is known in the literature as ‘fishing through food webs’ (Essington et al., 2006; Pauly et al., 1998).
Fishing in Balance Index (FiB) - The FiB is a more robust index than the MTI, as it takes into account the energy exchange, in addition to analyzing species TLs. This indicator assesses whether changes in TLs are offset by changes in catches, given that a higher biological production is expected when fishing is focused on low TLs (Pauly, Watson, & Alder, 2005). The FiB is computed as:
FiBk = log [Yk (1/TE)mTLk] – log[Y0 (1/TE)mTL0]
where Yk corresponds to landings in year k, TE is the energy transfer efficiency between TLs (here set at 0.1 following Pauly et al., 2000), mTLk is the mean TL of landings in year k and 0 refers to any year used as a baseline to standardize the index. A property of the FiB index is that it remains constant, with values close to 0, when the fishery is in balance, i.e., any change in the trophic level corresponds to changes in landings. When FiB values are higher than 0 they indicate an expansion of the fishing activities. This expansion could be due to an expansion of the geographical area fished and/or due to the exploitation of new or lightly exploited stocks. Values below zero indicate that the ecosystem is being harmed by the excessive removal of biomass.
Regional Marine Trophic Index (RMTI) - The RMTI was conceived as an indicator to correct some of the limitations of the previous trophic-based indicators. When analyzed together, the MTI and FiB illustrate changes in the average TL of catches over time and provide an indication of geographic expansion or contraction throughout the fishing region. However, it is difficult to simultaneously evaluate the joint message of two indicators that represent different aspects of a process (see Branch et al., 2010). Therefore, the RMTI was introduced to correct the MTI for situations where geographic expansion really occurs (when fishing expands to new fishing grounds) (Kleisner et al., 2014). To calculate RMTI, it is first necessary to define the potential catch of a region, which is the highest level of capture possible in a region and is calculated based on the annually reported MTI. If, in a given year, the annual catch is higher than the corresponding potential catch, an increase in fishing in another region is considered to have occurred during that year. This process is repeated to detect multiple expansion periods by calculating new potential landings for each new region (Kleisner et al., 2014).
Environmental data
In addition to technological aspects (e.g., gear, boat, etc.), the composition of landings depends on a range of environmental factors, including chlorophyll–a, rainfall, and light penetration, among others. Here, however, due to the absence of data with broad temporal and geographical coverage, the environmental data included only SST
26 (Sea Surface Temperature) and SSS (Sea Surface Salinity). Nevertheless, SST and SSS are strongly related to primary production and can provide an approximation of the oceanographic dynamics of an ecosystem since their variability could modify species distribution and trophic conditions (Pennino, Muñoz, Conesa, López-Quίlez, & Bellido, 2013).
These variables were acquired from two sources: SST and SSS from The Nucleus of European Modelling of the Ocean (NEMO) for Brazil as a whole, and only SST from the NOAA Database Extended Reconstructed Sea Surface Temperature V3b (ERSST) for each ecoregion (Smith et al., 2008).
Statistical analyses
To determine the level of correlation between the environmental variables and reported landings and ecosystem indices, a normalized cross-correlation was used. This analysis searches for temporal correlations and verifies whether or not the series are correlated with the previous year or whether this correlation follows a given time lag, for example every 2 years. In this case we used a time lag of five years. We tested for correlations between the time series of environmental variables and time series of landings, and between the time series of ecosystems indicators (P/D, MTI and FiB) for each ecoregion and for type of fishing (artisanal and industrial). The cross-correlations were computed using the “ccf” function of the “stats” package of the R software ( R Team, 2018).
Following the approach by Pennino et al., (2017), an analysis of convergence was applied to verify whether the ecosystem indicators calculated for the Brazilian fisheries in each ecoregion converged, i.e., whether they were all increasing, decreasing or stabilizing. Specifically, the MTI, FiB and P/D ratio were analyzed using three different temporal windows by dividing the time series into equal parts (1950-1970, 1971-1991 and 1992-2010) to better assess possible ecosystem changes. This analytical strategy involves three steps: (1) evaluation of the external shape of the distribution of indicators of interest using a kernel smoothing; (2) assessment of convergence or/and divergence of the indicator patterns via transition probability matrices; and (3) long-term prediction of the indicators’ trends by evaluating their ergodic distribution (see Pennino et al., 2017 for more details about this methodology).
Analysis in this method is focused on the shape of distributions. If the values tend to be more concentrated around a certain value, convergence is achieved. On the contrary, there is divergence if the values are spread across a wider range.
Results
Between 1950 and 2010, fish production in Brazil averaged 591.689 tonnes per year, increasing from 161.255 tonnes in 1950 to 720.501 tonnes in 2010. The highest production was in 1984, when 1.010.903 tonnes were harvested. In the period between 1967 and 1973, fish production increased by 77%. The upward trend continued between 1973 and 1985, growing by about 16%, but by 1990 had dropped by 47%, which was followed by a partial recovery (an increase of 39%) between 1990 and 2010 (Figure 2).
27 Between 1950 and 2010, the Rio Grande and Southeastern ecoregions caught 18% and 39%, respectively, of all the seafood commercially produced in Brazil. In general, throughout the whole time series period, landings followed a growing trend in all regions and in Brazil as a whole, with the exception of the Rio Grande ecoregion, which landed less in 2010 than it did in 1950.
Industrial fishing in Brazil grew by 444% from 1950 to 2010. Of that growth, 360% occurred between 1950 and 1963. Throughout the time series, artisanal fishing grew by 280% in total, 100% of which occurred in the same first 13 years of the time series. Amazonia is the ecoregion that had the largest growth of industrial fishing: 17493% between 1950 and 2010, whereas in the Rio Grande ecoregion there was a decrease of 2.6%. Even with such an increase, the Amazonia ecoregion only contributed with 2.2% of the landings of industrial fishing in Brazil, whereas the Rio Grande ecoregion landed the highest proportion (22.3%) of the total industrial landings during those years.
The artisanal fishery scenario is slightly different. Here, the Northeastern ecoregion grew the most between 1950 and 2010, followed by the Eastern ecoregion (1061% and 634%, respectively). The Northeastern ecoregion alone was responsible for 35.4% of all the fish landed by artisanal fishery in the period studied. The Rio Grande ecoregion was responsible for only 11.7% of all fish caught by the artisanal fishery in Brazil, ranking last among all regions. A decrease in artisanal landings in the latter region of 33.5% was reported from 1950 to 2010.
28 Figure 2. Evolution of fish landings in Brazil and its ecoregions between 1950 and 2010 and respective timeline of relevant events. Blue lines represent industrial fisheries, black lines correspond to artisanal fisheries and red lines are the total reported landings.
29 Pelagic/demersal ratio (P/D)
The pelagic/demersal ratio varied similarly in industrial and artisanal fisheries in almost all ecoregions (Figure 3). The most striking case was the industrial fisheries of the Northeastern ecoregion, where the focus changed to pelagic species after 1990 and reached a peak in 2009, when pelagic landings were almost four times higher than demersal landings. In the other ecoregions, industrial and artisanal fisheries were proportionally different in their preferences, with the highest proportion of pelagic in industrial fisheries.
30 Figure 3. Pelagic/Demersal ratio (P/D) for reported landings in Brazil and its ecoregions from 1950 to 2010. The blue lines represent industrial fisheries, black lines the artisanal fisheries, and red lines represent total fishing in each ecoregion.
31 Marine Trophic Index (MTI)
The Marine Trophic Index fluctuated over time, with values always above 3 and with the highest observed values in industrial fisheries (Figure 4). The only exception is the Amazonia ecoregion, where artisanal fisheries had higher MTI values. The Northeast ecoregion showed most of the variation in the MTI. In the first few years the MTI was 3.9 but it reached its lowest value (3.5) in 1988 and rose again to 4.2 in 2007 (Figure 4). The period in which the MTI decreased in this region corresponds to the beginning and development of the industrial lobster fishery, which has a low trophic level. The industrial exploitation of lobsters in this region began in 1956 and continued until the 1980s, when lobsters were the main reported catch. However, lobster landings declined, especially from the 1990s onwards, reaching their lowest level in 2008.
The MTI values increased in the period studied in both artisanal and industrial fisheries, the only exceptions being artisanal fishing in the Rio Grande and Eastern ecoregions. Despite fluctuations over time, the value of MTI in 2010 is lower than the MTI value calculated for 1950. Variation in MTI values do not follow any decadal pattern and/or pattern among regions. The variation in MTI ranged from 0.01 to 0.70 per decade.
32 Figure 4. Marine Trophic Index (MTI) for reported landings in Brazil and its ecoregions from 1950 to 2010. The blue lines represent industrial fisheries, black lines represent artisanal fisheries and red lines represent the total fisheries in each region. Note the different scale for Amazonia and Northeastern.
33 Fishing in Balance Index (FIB)
The FiB values were mostly above zero and the time series showed an increasing pattern, especially in the early years, between 1950 and 1970 (Figure 5). Rio Grande was the only ecoregion that showed negative values of FiB and had a decreasing trend, which indicates that the ecosystem has been damaged by excessive removal of biomass. Major growth changes observed in the Amazonia and Northeastern ecoregions were mainly due to industrial fishing.
34 Figure 5. Fishing in Balance Index (FiB) for landings in Brazil and its ecoregions from 1950 to 2010. The blue lines represent industrial fisheries, black lines represent artisanal fisheries and red lines represent the total fisheries in each region. The black and red lines are superimposed in the Amazonia ecoregion.
35 Regional Marine Trophic Index (RMTI)
There were geographical expansions of fishing in the Brazilian ecoregions (Figure 6), thereby confirming what FiB values had suggested. The only exceptions were industrial fisheries in the Northeastern ecoregion and artisanal fisheries in the Eastern ecoregion. In general, fishing expanded to three new geographical regions, and in the Southeast and Rio Grande ecoregions this expansion began as early as the 1960s.
36 Figure 6. Regional Marine Trophic Index (RMTI) for landings in Brazil and its ecoregions from 1950 to 2010. The lines in different colors indicate added fishing regions.
37 Normalized cross-correlation
Considering correlations above 0.30%, overall temperatures tend to influence landings and the indexes in Brazil more than salinity. Temperatures influence artisanal and industrial FiB as well as artisanal and industrial landings and artisanal MTI. Analyzing by ecoregion, for which only sea surface temperature data (SST) is available, temperatures influence all indices and landings in the Amazonia, Northeastern and Eastern ecoregions, but do not influence any of the indices in the Southeastern and Rio Grande ecoregions (see Table 1 supplementary material).
Analysis of convergence
The time evolution of the P/D ratio, MTI and FiB index show a convergent pattern along the entire time series for artisanal fisheries, whereas for industrial fishing they show a clear divergent trend (Figure 7). MTI for industrial fisheries showed a bimodal distribution for the period 1950-1970 (Figure 7) with a lower peak below the MTI average (centered around 2.5) and another one centered at 3.5 but which became convergent over the years. Thus, even though Brazilian industrial fisheries are currently similar across ecoregions, this was not always the case, as some regions had more heterogeneous MTI, with lower values, such as the Amazonia and the Northeastern ecoregions did in the first years (Figure 7). Predictions from the MTI index show that the probability density corresponding to the ergodic distribution will be more evenly distributed, thus suggesting that differences within the groups of ecoregions considered will persist over time. However, the majority of the ecoregions will be concentrated in higher levels of the MTI (see Supplementary material, table 2). This persistence in the higher limits highlights a trend of increasing prevalence of species with high trophic levels in the reported landings in the long run, if conditions remain the same. This prediction does not take into account the possible collapse of species.
Even though the FiB for industrial fisheries in the first period was concentrated in lower values, the probability density spread over a wider range in more recent years (Figure 7). This implies that FiB tends to become gradually more different for Brazilian industrial fisheries and the opposite for artisanal fisheries. However the value always remains above zero, indicating geographic expansion of the species but as well as in the MTI, this analysis also does not take into account the possible collapse of species or even the inexistence of new fishing areas for expansion. The predictions show that the probability density will be distributed with values higher than 0 for all fisheries (Supplementary material, table 3). It is worth noting that FiB values for artisanal fisheries will remain lower than for industrial ones.
38 Figure 7: Gaussian kernel smoothing of the Pelagic/Demersal ratio (P/D), Mean Trophic Index (MTI) and Fishing in Balance (FiB) in: 1950–1970, 1971–1991, and 1992–2010. Blue lines represent industrial fisheries, black lines correspond to artisanal fisheries, and red lines are artisanal and industrial fisheries combined.
39 Discussion
In general, fishing yields from both types of fishing have increased in almost all regions in Brazil despite a sharp fall in landings in the mid-1980s-1990s. The difference between the Northeastern/Amazonia and Southeastern/Rio Grande ecoregions is remarkable. The ecosystem indicators captured these differences. The greatest variations of MTI and FiB were in the Northeastern and Amazonia regions and the Northeastern region also had the highest P/D ratio variation. Overall, the patterns of convergence and divergence indicators for industrial and artisanal fisheries were very different. The exception is geographic expansion, which was observed in the RMTI and occurred in a similar way in all regions and for both types of fisheries.
The increased production that was observed in the 1970s can be explained by policy changes that began in 1966 (Abdallah, 1998; Paiva, 2004), which included the recognition of fisheries as a base industry, thereby granting industry players access to bank loans and subsidies in the form of fiscal incentives. The latter included tax exemptions for individuals engaged in fishing activities, as well as tax exemptions on the purchase of fishing products and gear, among others (Freire et al., 2015; Silva, 2014). The subsequent fall in production in the mid-1980s-1990s may be explained by the same policies, given that these policies were created and implemented in a disorderly manner, without technical, social or economic criteria to back them up. The emblematic example of this lack of criteria for policy development is the sardine collapse that occurred in the 1980s in southeastern Brazil. The production of sardine plummeted from 230,000 tonnes in 1973 to 33,000 tonnes in 1990 (Freire & Pauly, 2010). Other consequences of government policies developed during that period include the increase of specialized fleets which target few stocks, a boost in industrial fisheries to the detriment of small-scale fisheries, and over-investment in shore-based infrastructure. No major investment was made until the 1990s in research and survey for fish stocks (Giulietti and Assumpção, 1995; Ramalho, 2014; Silva, 2014).
The negative consequences of subsidies and how they may be harmful to fish stocks, especially with respect to overcapacity and overfishing, are widely recognized worldwide (Harper et al., 2012; Sumaila et al., 2016). Subsidies have been addressed in a number of meetings since the 1990s. Among the UN Sustainable Development Goals, for example, Goal 14 (Conserve and sustainably use the oceans, seas and marine resources) specifically suggests that certain forms of fisheries subsidies which contribute to overcapacity and overfishing should be banned, and that subsidies that contribute to illegal, unreported and unregulated fishing should be eliminated (UN, 2017).
The increase in MTI in this study goes against patterns found in other regions (Arancibia & Neira, 2005; Baeta, Costa, & Cabral, 2009) and in the world’s oceans as a whole, whereby MTI is declining at a rate of about 0.1 per decade (Bhathal & Pauly, 2008; Pauly et al., 1998). Still, the growing MTI identified here should be analyzed carefully and in conjunction with other indexes, since this increase seems to be associated with geographical expansion, as shown by the FiB and the RMTI indexes. The increase in MTI is more likely due to the exploitation of high trophic level species in new areas rather than due to a possible recovery of fish stocks in areas previously
40 exploited. The largest national effort to measure the status of marine fish stocks occurred between 1995 and 2005 and analyzed 153 fish stocks in Brazil. Among these 11% were not exploited, 4% were under-exploited, 23% were fully exploited, 33% were over-exploited and 29% were not conclusively evaluated (MMA, 2006a). Since then, no such research on fish stocks has been carried out in Brazil and even the most basic data on fishing landings are not being collected at the national level (Ruffino et al., 2016).
The expansion of fisheries that we observed in Brazil, indicated by a FiB above 0 and by RMTI results, is possibly another consequence of direct government support to the fishing sector together with the implementation of technological advances (Abdallah and Sumaila, 2007). With larger and more modern fleets, fishers can now reach areas previously inaccessible for fishing. Most government investments to the fisheries sector goes to industrial fisheries, which likely explains why FiB was higher for this sector. However, artisanal fishers have also been accessing more affordable technologies, such as GPS and simple sonars. If such expansions are indeed a consequence of bad and ugly subsidies (Sumaila et al., 2010), it would be advisable to redirect policies to support good subsidies, such as the establishment of a fishing statistics program, marine protected areas, research, and sustainable fishing alternatives.
Although this study did not reveal any high correlations between environmental variables and ecosystem indexes, it is not possible to dismiss the impacts of temperature or salinity on fish stocks. It is well established that environmental variations cause changes in marine species (Campos et al., 2010; Priscila F. M. Lopes et al., 2018). Given that Brazilian indexes are not correlated with environmental variables, it is possible that the impact of fishing on this ecosystem masks other effects.
Fishing pressure and environmental changes can cause the same impacts on fish populations, which include, among others, decreased abundance, early maturation, and decreased body size in adults (Anderson et al., 2008; Cheung et al., 2013; Kuparinen et al., 2016; Rochet, 1998; Sharpe and Hendry, 2009). Dealing with such impacts requires extensive knowledge of what is happening with the overall fishing status and marine ecosystems. Indicators, such as the ones used here, are handy for supplying important information to fill some of these gaps.
The time series chosen for this study shows that industrial fisheries began in the Amazonia and Northeastern ecoregions in the 1950s, but in other ecoregions, such as the Rio Grande and Southeast regions, they may have started as early as the 1910s with the introduction of bottom trawling and other more effective fishing equipment (Afonso, 2013). The comparison of indicators shows that in the regions where industrialization began earlier both industrial and artisanal fisheries have been showing signs of fishing collapse over the last years. Different indicators of these ecoregions tend to converge and these regions should be used to highlight how the unplanned industrialization of fisheries, mainly through government subsides, is not sustainable in the long run and has direct negative consequences on fish stocks.
Accessing the status of marine ecosystems and the impact of environmental and fisheries variables on these ecosystems and fish stocks can be difficult and prone to uncertainty, especially in data-poor areas. However, part of the difficulties can be overcome with the use of ecosystem indicators, as demonstrated in this study. With little
