UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE
PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA
CENTRO DE BIOCIÈNCIAS
VALORAÇÃO DE SERVIÇOS
ECOSSISTÊMICOS COSTEIROS EM
CENÁRIOS DE MUDANÇAS CLIMÁTICAS
Nadia Selene Zamboni
Orientadora: Dra. Adriana Rosa Carvalho
UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA
CENTRO DE BIOCIÈNCIAS
Tese de Doutorado
VALORAÇÃO DE SERVIÇOS ECOSSISTÊMICOS COSTEIROS EM CENÁRIOS DE MUDANÇAS CLIMÁTICAS
Nadia Selene Zamboni
Orientador: Dra. Adriana Carvalho
Natal, Rio Grande do Norte, Brasil 2020
UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA
CENTRO DE BIOCIÈNCIAS
Nadia Selene Zamboni
VALORAÇÃO DE SERVIÇOS ECOSSISTÊMICOS COSTEIROS
EM CENÁRIOS DE MUDANÇAS CLIMÁTICAS
Tese apresentada à Universidade Federal do Rio Grande do Norte, como parte das exigências do Programa de Pós-Graduação em Ecologia, para obtenção do título de Doutora.
Orientadora Dra. Adriana Rosa Carvalho
Natal, Rio Grande do Norte, Brasil 2020
UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA
CENTRO DE BIOCIÈNCIAS
Nadia Selene Zamboni
VALORAÇÃO DE SERVIÇOS ECOSSISTÊMICOS COSTEIROS
EM CENÁRIOS DE MUDANÇAS CLIMÁTICAS
Tese apresentada à Universidade Federal do Rio Grande do Norte, como parte das exigências do Programa de Pós-Graduação em Ecologia, para obtenção do título de Doutora.
______________________________________________________________ Dra. Adriana Rosa Carvalho (UFRN)- ORIENTADORA
_______________________________________________________________ Dr. Fúlvio Aurélio de Morais Freire (UFRN)- Examinador Interno
_______________________________________________________________ Dr. Eurico Mesquita Noleto Filho (UNESP)- Examinador Externo ao Programa
_______________________________________________________________
Dr. Carlos Henrique Figuereido Lacerda (Instituto Coral Vivo)- Examinador Externo à Instituição
_______________________________________________________________ Dr. Jorge Luiz Rodrigue Filho (UDESC)- Examinador Externo à Instituição
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
Zamboni, Nadia Selene.
Valoração de serviços ecossistêmicos costeiros em cenários de mudanças climáticas / Nadia Selene Zamboni. - Natal, 2020. 147 f.: il.
Tese (Doutorado) - Universidade Federal do Rio Grande do Norte. Centro de Biociências. Programa de Pós-graduação em Ecologia.
Orientadora: Dra. Adriana Rosa Carvalho.
1. serviços ecossistêmicos - Tese. 2. Capital natural - Tese. 3. Ecossistemas costeiros - Tese. 4. InVEST - Tese. 5. Economia ecológica - Tese. 6. Vulnerabilidade costeira - Tese. I.
Carvalho, Adriana Rosa. II. Universidade Federal do Rio Grande do Norte. III. Título.
RN/UF/BSCB CDU 574.5 Elaborado por KATIA REJANE DA SILVA - CRB-15/351
"Os que decidem sobre o amanhã,
devem avaliar o impacto no futuro"
AGRADECIMENTOS
Primeiro quero agradecer à Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) pelo suporte financeiro dado durante estes 4 anos de Doutorado e
incrível experiência no Brasil.
Agradeço a minha orientadora Adriana Carvalho por toda a sabedoria, confiança
e paciência depositada em mim. Por todo o seu apoio, proteção e flexibilidade durante
momentos difíceis e a sua parceria ao longo do desenvolvimento da tese.
Também agradeço ao pessoal do Laboratório de Geotecnologias Aplicadas,
Modelagem Costeira e Oceânica - GNOMO da UFRN, especialmente a Venerando
Amaro, Fátima Alves e Mattheus Prudêncio, por toda ajuda substancial que recebi durante
o Doutorado, desde o material fornecido até o suporte técnico e metodológico. Graças à
colaboração deles, foi possível superar diversos obstáculos na aprendizagem de várias
ferramentas de sistemas de informação geográfica e sensoriamento remoto. Foi uma
grande parceria que concluiu na elaboração de trabalhos conjuntos.
Fico grata ao conselho gestor e ao Núcleo IDEMA da Reserva de
Desenvolvimento Sustentável Ponta do Tubarão, por ter me acolhido, e aceito o meu
projeto para ser elaborado, em parte, dentro desta área protegida.
Finalmente, quero agradecer a minha família, amigos e colegas pela companhia
maravilhosa (longe e perto) que tive o prazer de disfrutar durante todo o meu tempo em
Natal. São inumeráveis a experiencias vividas, todo o aprendizado e o carinho recebido.
Fico muito feliz de ter ganhado tantos amigos e belas anedotas que ficarão na minha
"O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Código de Financiamento 001”
SUMÁRIO
RESUMO... i
ABSTRACT... iii
INTRODUÇÃO GERAL... 1
REFERÊNCIAS BIBLIOGRÁFICAS... 6
CAPITULO I - Global Distribution and Value of Coastal-Marine Ecosystem Services Abstract... 14
Introduction... 15
Material and Methods... 17
Literature Search... 17 Data Analysis... 19 Results... 23 Discussion... 37 Conclusions... 42 Acknowledgments... 43 References... 43 Supplementary Material... 55
CAPITULO II - Effects of Land Use Changes on Blue Carbon Services in Northeast Brazilian Mangrove Areas Abstract... 58
Introduction... 60
Study Area and Methods... 63
The Mangrove Areas Surveyed... 63
Data Acquisition and Digital Image Processing... 65
LULC Mapping and Generation of Future Mangroves Conversion Scenarios... 67
Estimation of Coastal Blue Carbon Ecosystem Services... 69
Results... 71
Future Mangrove Conversion Scenarios by 2035 and 2050... 75
Blue Carbon Ecosystem Services Estimates... 77
Discussion... 81
Dynamic of the Blue Carbon Ecosystem Services... 81
Valuation of the Blue Carbon Ecosystem service... 83
Caveats of the Study... 86
Conclusions... 87
Acknowledgments... 88
References... 88
CAPITULO III - Coastal Protection Service Delivery in Northeast Brazil in Face of Sea Level Rise Abstract... 103
Introduction... 105
Material and Methods... 107
Study Area... 107
Coastal Vulnerability Modelling... 110
Acquisition of input data for Coastal Vulnerability Modelling... 112
Economic Estimation of Losses and Costs from Shoreline Retreat... 117
Results... 117
Multitemporal Analysis of Shoreline Dynamics and Changes Forecast... 117
Coastal Vulnerability... 120
Analysis of Socioeconomic Losses and Costs... 121
Discussion... 122
Conclusions... 126
Acknowledgments... 127
References... 127
RESUMO
O cenário de mudanças climáticas e crescimento populacional nas zonas costeiras vem gerando perda de habitat e de serviços ecossistêmicos (SE) para populações litorâneas. Atualmente há pouco entendimento sobre o efeito destes fatores na distribuição e no valor dos SE de ambientes costeiros ao longo do tempo. Para entender a dinâmica de perda no suprimento de SE costeiros e sua correspondente perda em valores econômicos, nesta tese propusemos abordar estes assuntos ao longo de três capítulos. Os principais objetivos foram (1) mapear o viés de ambientes e SE costeiros mais valorados no mundo, bem como a distribuição de benefícios econômicos por eles gerados em relação à alguns indicadores de desenvolvimento humano e socioeconômico (CAP 1 - Global Distribution and Value
of Coastal-Marine Ecosystem Services); (2) avaliar os efeitos das conversões de terra em
um período futuro de 33 anos, sobre a dinâmica de prestação de serviços de Carbono Azul Costeiro por manguezais no Nordeste do Brasil (CAP 2– Effects of Land Use Changes on
Blue Carbon Services in Northeast Brazilian Mangrove Areas); e (3) avaliar a
vulnerabilidade e as mudanças temporais na paisagem costeira, medindo as taxas de erosão/acreção na costa e as perdas e custos econômicos da retração de longo prazo da linha de costa e perda de manguezais no Nordeste do Brasil (CAP 3- Coastal Protection
Service Delivery in Northeast Brazil in Face of Sea Level Rise). Os resultados obtidos no
Capítulo 1 indicam que a) os ecossistemas costeiros mais quantificados e valorados no mundo são zonas húmidas e manguezais; b) os SE mais valorados são recreação, pesca comercial e proteção costeira; c) os métodos de valoração mais utilizados são disposição à pagar e valores de mercado; d) os benefícios econômicos médios anuais totais dos SE costeiros observados em todo o mundo variaram de US$1,100 a US$77 bilhões; e e) existe uma relação positiva entre os valores económicos dos SE e os indicadores de desenvolvimento humano e socioeconômico (Produto Interno Bruto e Índice de Desenvolvimento Humano). Os resultados obtidos no Capítulo 2 indicam que a área e o carbono total armazenado dos manguezais do nordeste brasileiro podem diminuir em quase 35%, e 22.2% respectivamente até 2050, impulsionado em grande parte pela expansão de atividades salineiras e carcinicultura. O total de Sequestro Líquido de Carbono (SLC) poderia atingir cerca de 17.3 tCO2e/há/ano, e o Valor Presente Líquido
(2017) é de US$2,044.3/ha. Embora aparentemente não ocorram grandes variações nos valores de SLC entre períodos ao longo de 33 anos, valores altos de emissões de carbono
(1,351.2 tCO2e/ha) poderiam ser observados durante o período 2017-2035. Os resultados obtidos no Capítulo 3 indicam que um terço da linha de costa está em processo de erosão e as previsões são de uma perda de 630,000 m2 de linha de costa até 2026. Os manguezais reduzem em 20% os níveis de vulnerabilidade costeira moderada-alta dos efeitos do aumento do nível do mar, enquanto protegem uma quarta parte da população total exposta. Caso a área total de manguezais for perdida, provavelmente essa proporção aumentará para metade da população, causando custos de mais de US$6.1bilhões (R$32.4 bilhões).
Palavras-chave: capital natural; economia ecológica; ecossistemas costeiros; InVEST;
ABSTRACT
The current scenario of climate change and population growth in coastal areas has led to loss of habitat and ecosystem services (ES) for coastal populations. Currently, there is little understanding about the effect of these drivers on the distribution and value of ES in coastal environments over time. In order to understand the dynamics of loss in the supplying of coastal ES and their corresponding loss in economic values, in this thesis we proposed to address these issues over three chapters. The main objectives were (1) to map the bias of the most valued coastal environments and ecosystem services in the world, as well as the distribution of economic benefits generated by them in relation to some indicators of human and socioeconomic development (CH 1 - Global Distribution and
Value of Coastal-Marine Ecosystem Services); (2) to assess the effects of land
conversions in a future period of 33 years, on the dynamics of Coastal Blue Carbon services provision by mangroves in Northeast Brazil (CH 2– Effects of Land Use Changes
on Blue Carbon Services in Northeast Brazilian Mangrove Areas); and (3) evaluate the
vulnerability and the temporal changes in coastal landscape by measuring the erosion/accretion rates in the shoreline and the economic losses and costs from long-term retreats in the coastline and mangroves loss in Northeast Brazil (CAP 3- Coastal
Protection Service Delivery in Northeast Brazil in Face of Sea Level Rise). The results
obtained in Chapter 1 indicate that a) the most quantified and valued coastal ecosystems worldwide are wetlands and mangroves; b) the most valued ES are recreation, commercial fishing and coastal protection; c) the most widely used valuation methods are willingness to pay and market values; d) The total annual average economic benefits of ES observed worldwide ranged from US$1,100 to US$77 billion; and (e) there is a positive relationship between the ES economic values and the human and socioeconomic development indicators (Gross Domestic Product and Human Development Index). The results obtained in Chapter 2 indicate that the forest area in northeastern Brazil can decrease by almost 35% and the total carbon stored by 22.2% by 2050, driven in large part by the expansion of salt and shrimp farming activities. The total Net Carbon Sequestration (NCS) could reach about 17.3 tCO2e/ha/year, and the Net Present Value (2017) is US $ 2,044.3/ha. Although there are apparently no major variations in the NCS values between periods over 33 years, high values of carbon emissions (1,351.2 tCO2e/ha) could be observed during 2017-2035. The results obtained in Chapter 2 indicate that one third of
coastline is under erosion process and the prediction is of losing 630,000 m2 of shoreline until 2026. Mangroves reduce in 20% the moderate-high vulnerability levels of coastal areas from sea-level rise effects, while protecting a fourth part of the total exposed population. Probably this proportion could increase to half the population if total mangrove area were lost, causing costs of more than USD6.1billions (BRL32.4 billions).
Keywords: coastal ecosystems; ecological economics; InVEST; natural capital; vulnerability.
INTRODUÇÃO GERAL
Vários autores tem definido o conceito de Serviços Ecossistêmicos (SE)
(COSTANZA et al., 1997; MEA, 2005; BOYD e BANZHAF 2007; FISHER et al. 2009;
LIQUETE et al., 2013; PASCUAL et al., 2017; DÍAZ et al., 2018), considerando-os como
contribuições ou benefícios da natureza, processos ecológicos ou aspectos dos ecossistemas
que garantem o bem-estar humano. Este termo surge pela primeira vez na década de 80
(EHRLICH e EHRLICH, 1981; EHRLICH e MOONEY, 1983), ganhando diversas
contribuições com o tempo (COSTANZA et al., 1997; DAILY, 1997; MASOOD e
GARWIN, 1998), e despertando o interesse não só da comunidade científica como também
de políticos e gestores (BRAAT e GROOT, 2012; COSTANZA e KUBISZEWSKI, 2012;
BURKHARD et al., 2013). Além disso, o esgotamento do capital natural gerado pela
crescente perda de SE levou à necessidade de incluir este termo nos estudos de economia
ecológica (COSTANZA et al., 1991).
Segundo HAINES-YOUNG e POTSCHIN (2012), os SE não podem ser avaliados ou
mapeados, sem antes ser descritos e medidos. Por isso, a categorização dos SE são
necessários para possibilitar discussões, análises, modelagem e valoração (COSTANZA et
al., 2017). Entre as diferentes formas de classificação dos SE sugeridas por diferentes autores
e iniciativas internacionais destacam-se COSTANZA et al. (1997), DE GROOT et al. (2002),
MEA (2005), BOYD e BANZHAF (2007), FISHER e TURNER (2008), TEEB (2010).
Baseado em um dos sistemas de classificação mais utilizados proposto pela Millenium
Ecosystem Assessment (MEA, 2005), e modificada pela The Economics of Ecosystems and Biodiversity (TEEB, 2010), os SE podem ser categorizados em serviços de provisão
(exemplo: alimento e água), regulação (exemplo: regulação da qualidade do ar e controle de
(exemplo: manutenção da diversidade de plantas e animais).
Entre os ecossistemas, os costeiro-marinhos estão entre os mais produtivos e
diversificados do mundo (MARTÍNEZ et al., 2007; WILKINSON, 2008). A extensa
distribuição dos mesmos, leva à existência de uma grande variedade de características
geomorfológicas, regimes climáticos e biomas (MARTÍNEZ et al., 2007). Também fornecem
vários SE tais como proteção costeira, controle da erosão, provisão de alimento e água,
regulação de inundações, controle de doenças, ciclagem de nutrientes que mantêm as
condições para a vida, serviços culturais e recreativas, entre muitos outros (WATTAGE,
2011; SPALDING et al., 2013).
Apesar de todos os benefícios que estes sistemas oferecem ao ser humano, existem
vários fatores de origem natural e/ou antropogênico que impactam no suprimento de SE, em
escala espacial e temporal (FRIESS et al., 2015), como por exemplo, os efeitos do aumento
do nível do mar (RANASINGHE, 2016) ou das mudanças no uso da terra (LOTZE et al.,
2006; SEP, 2013; PAWAR, 2016). As zonas costeiras são altamente vulneráveis aos efeitos
destes fatores, levando a uma queda global dos ecossistemas costeiros (MEA 2005; UNEP,
2006; FAO, 2007; BECK et al., 2011; BURKE et al., 2011; SILLIMAN et al., 2009;
SPALDING et al., 2010) e afetando um grande número de benefícios (BARBIER et al., 2014;
SPALDING et al., 2014; CHUAI et al., 2016).
Para poder avaliar o impacto destes múltiplos fatores, é de grande relevância o
desenvolvimento de estudos de avaliação e mapeamento dos SE costeiros. Para isto, podem
ser utilizadas técnicas de modelagem que incorporem aspectos dos impactos de fatores,
modelando a provisão de SE baixo diferentes cenários (FRIESS et al., 2015). Contudo, a
escassez de dados espaciais e a dificuldade que significa capturar informação de ambientes
(BURKHARD e MAES, 2017), faz deste tipo de trabalhos, um grande desafio.
O “mapeamento” representa o delineamento espacial dos ecossistemas, bem como a quantificação de sua condição e dos serviços que eles fornecem (MAES et al., 2016). O
mapeamento dos SE é essencial para entender como os ecossistemas contribuem para o
bem-estar humano e apoiar políticas e tomada de decisões em relação ao impacto sobre os recursos
naturais (BRAAT e GROOT, 2012; BURKHARD e MAES, 2017). Além disso, os mapas são
uma ferramenta muito poderosa para processar dados e informações complexas da
quantificação de SE em diferentes escalas espaciais e temporais, apoiando assim o
gerenciamento ambiental e de recursos, bem como o planejamento da paisagem
(CROSSMAN et al., 2014). Por outra parte, a “avaliação” dos ecossistemas e os SE que eles
fornecem implica numa tradução dos dados espaciais e indicadores em informação
compreensível para as políticas e tomada de decisão, através do uso de mapas, indicadores,
narrativas e gráficos (MAES et al., 2016).
O mapeamento e avaliação dos SE em escala de paisagem é recomendável, já que as
métricas de paisagem possuem o poder de apoiar na identificação e no monitoramento de
características espaciais de paisagens que têm implicações sobre o desempenho da
biodiversidade (BURKHARD e MAES, 2017); e permitem compreender a variabilidade
espacial na produção e fluxo de SE (BALMFORD et al., 2011). Este tipo de estudos são
necessários para detectar as muitas causas da perda de biodiversidade que operam em grandes
escalas espaciais, tais como conversão e fragmentação de habitat, superexploração e mudança
climática (JONES, 2011).
A estimativa do valor econômico dos SE é um outro aspecto importante no
planejamento de conservação e gestão baseada em ecossistemas (TALLIS et al., 2008;
políticas e decisões locais, nacionais e globais (TURNER et al., 2007). Além disso, sabendo
que a existência de risco só se constitui quando há valoração de algum bem, material ou imaterial (NICOLODI e PETERMANN, 2010), este tipo de estudos permite sensibilizar aos humanos a não danificar os processos ecológicos necessários para apoiar o fluxo contínuo de
SE dos quais o bem-estar das gerações presentes e futuras dependem (MEA, 2005b) e,
portanto, assegurar o desenvolvimento sustentável (TURNER et al., 2010).
Os ecossistemas costeiros estão entre os ecossistemas mais valiosos do mundo por
causa dos vários e importantes SE que eles fornecem (CAMACHO-VALDEZ et al., 2013).
Segundo RAO et al. (2015), o valor global dos SE costeiros para ecossistemas costeiros
específicos em 2013 variou de 0.5 – 2,530 US $/ha/ano. Outros dados indicam que os
ambientes costeiros e oceanos juntos contribuem com cerca de 40% do valor econômico total
da biosfera (COSTANZA et al., 2014). Embora existem vários trabalhos que têm resumido
e compilado estudos de valoração relacionados a diferentes ambientes costeiros-marinhos no
mundo, e as mudanças que eles sofreram por fatores naturais e antropogênicos (BROWN e
HAUSNER, 2017; DEWSBURY et al., 2016; SAGEBIEL et al., 2016; LIQUETE et al.,
2013), ainda são escassos os estudos que analisem a distribuição espacial dos SE costeiros e
os benefícios económicos proporcionados por suas funções ecossistêmicas em escala
espaço-temporal frente aos efeitos das mudanças climáticas e pressão antrópica.
Diante do acima mencionado somado ao atual cenário de mudanças climáticas e
avanço da urbanização em zonas costeiras, o objetivo desta tese de doutorado encontra-se
contido em três capítulos: no primeiro capítulo intitulado “Global Distribution and Value of
Coastal-Marine Ecosystem Services”, foi realizada uma revisão bibliométrica de estudos de valoração económica dos SE costeiros-marinhos no mundo, com o objetivo de fornecer uma
benefícios econômicos e a relação desses valores com alguns indicadores de
desenvolvimento humano e socioeconômico.
No segundo capitulo intitulado “Effects of Land Use Changes on Blue Carbon Services in Northeast Brazilian Mangrove Areas”, foram desenvolvidos cenários futuros de conversões de manguezais, utilizando taxas de conversão históricas obtidas a partir da análise de imagens
híbridas de sensoriamento remoto. Também foram estimados os estoques e emissões de
carbono, sequestro líquido total de carbono e valor presente líquido, para avaliar os efeitos
das conversões de terras durante um período futuro de 33 anos (2017-2050), na dinâmica dos
sistemas de manguezais e nas mudanças na prestação de serviços de Carbono Azul Costeiro
na região nordeste do Brasil.
Finalmente, no terceiro capitulo intitulado “Coastal Protection Service Delivery in Northeast
Brazil in Face of Sea Level Rise”, foi realizada uma análise de vulnerabilidade costeira na região nordeste do Brasil para compreender a importância dos manguezais como prestadores
do serviço de proteção costeira frente aos efeitos do aumento do nível do mar. Foram
analisadas as mudanças temporais na paisagem costeira no que se refere a processos de
erosão/acreção na costa, para estimar as perdas e custos econômicos que representaria uma
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Global distribution of studies and values of coastal-marine ecosystem
services: a systematic review
Nadia Selene Zamboni1*, Eurico Mesquita Noleto Filho1, Adriana Rosa Carvalho1 1Ecology Department, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte,
Brazil.
Abstract
In face of the accelerated transformation of coastal-marine ecosystems and the declining on
the ecosystem services (ES) around the world it is necessary the understanding of the
economic value and the distribution of ES worldwide, in order to support ecosystem
management and conservation policies. The aim of this study is to provide an overview
regarding the ES from coastal-marine systems and determine the global distribution of the
economic benefits and the relation of these values to human and socio-economic
development indicators. We reviewed articles from the Web of Science platform and built
thematic maps and percentage graphs regarding valuation studies of coastal-marine
ecosystems and their ES around the world. We applied ANOVAs and multiple regression
models (GLM) in order to understand the relation between the economic values of SE and
human and socio-economic development indicators (such as GDP, HDI and population
density). It was found that the most quantified and valued ecosystems are wetlands and
mangroves. Recreation, commercial fishing, coastal protection and habitat are the most
valued services. The most widely used valuation methods are willingness to pay and market
values. The average annual economic values ranged from US $ 1,100 to US $ 77 billion.
Finally, there is a positive relationship between the economic values of the SE and the GDP
Key-words: coastal-marine ecosystems; conservation policies; ecological economics; natural capital.
Introduction
Coastal and marine ecosystems (CME) are the most productive, diverse and valuable
ecosystems in the world (Martínez et al. 2007; Wilkinson 2008). They provide a wide variety
of ecosystem services, clustered in provisioning, regulating, cultural and supporting
categories (Millenium Ecosystem Assessment, 2005). Through this supply, CME contribute
withmore than 40% of the total economic value of the biosphere, reaching an estimated value
of US$49.7 trillion (Costanza et al. 1997). Furthermore, the extensive distribution of the
marine and coastal environments worldwide leads to a variety of geomorphological features,
weather regimes and biomes (Martínez et al. 2007), generating an ample array of population
settlement and commerce opportunities.
Despite the indubitable importance and value of the goods and services of these
ecosystems, they are seriously threatened by climate change and anthropogenic disturbances
(Spalding et al. 2014; Bindoff et al. 2019). Properly managing CME, integrating economic
approach and methodologies to support policies for the sustainable use of resources and
services, is currently a great concern among decision-makers. The valuation and accounting of ecosystem services values in “dollar language” has gained momentum in science and policy (Lopes and Videira 2016) to state cost effective and sustainable conservation policies
and manage ecosystems judiciously (Brander et al. 2006; Ten Brinck 2011; Patterson and
Since ecosystem services have simultaneous market and non-market values, different
methods of economic valuation can be used in order to measure their benefits in monetary
terms (Remoundou et al. 2009). Market pricing methods are one of the most frequently and
widely reported worldwide, since it can value most services and are often low cost and
convenient/fast (Groot et al 2012; Salem and Mercer 2012; Valdez and Luna 2012;
Chaikumbung et al. 2016; Himmes-Cornell et al 2018). However, there are several other
well-recognized value assessment methodologies, although few studies have synthesized all
of them together (Schuhmann and Mahon 2015; Barbier 2016; Dewsbury et al. 2016;
Sagebiel et al. 2016; Vegh et al. 2014).
Much of the summarized and compiled valuation studies focus on specific CME such
as coral reefs, mangroves, beaches and dunes aiming to identify the main ecosystem services,
the natural and anthropogenic drivers of changes and the economic benefits they provide
(Brown and Hausner, 2017; Dewsbury et al., 2016; Sagebiel et al., 2016; Liquete et al., 2013).
In addition, poor research on the valuation of goods and ecosystem services in developing
countries is also noticeable in the literature, despite these nations contain much part of the
world's biodiversity and natural resources (Christie et al., 2008; Fazey et al., 2005; Georgiou
et al., 2006). The experience in the markets is even more restricted in these countries, since
much of the people depend on limited subsistence livelihoods (Kenter et al., 2011).
Therefore, it is expected to find lower ecosystem services values in countries with poor
economies and less human well-being.
In this context, the understanding of socio-environmental interactions and the
relationship between ecosystems and human well-being becomes relevant in the quest to
achieve sustainable development, especially in marine environments, where most of the
world's natural capital is concentrated (Li and Fang 2014). The Millenium Ecosystem
Development Index (HDI), with a weighted average of different indicators such as Gross
Domestic Product (GDP). Since the HDI is an aggregate measure that includes the per capita
GDP (Anand and Sen 1992), it is expected that both indicators behave in the same way in
relation to the ecosystem services values.
Given that reviews increasingly focus on methods or specific habitats or countries,
there are few studies reviewing coastal marine ecosystem services on a global scale (Costanza
et al. 1997; Barbier et al. 2011; Vo et al. 2012). Under the hypothesis that the distribution of
studies is mainly concentrated on those coastal ecosystem services that are valued in markets
and in more economically stable countries, in this study we intend to determine the spatial
distribution and focus of the scientific literature on coastal ecosystem services in the world.
Thus, the purpose of this paper is to provide a review of scientific papers that quantified and
valued coastal ecosystem services around the world to relate global distribution of the
economic benefits from these services and socioeconomic indicators. In face of the
accelerated transformation on CME and their services worldwide, this review provides a
valuable understanding on which ecosystem services are the most valuable, the economic
valuation methods used and the distribution of their economic benefits into a social context.
The global appraisal stands out what information is missing and how much it represents in
terms of ecosystem service and is useful to support ecosystem management actions.
Material and methods
Literature Search
Several studies integrating worldwide coastal-marine ecosystems, the ecosystem
services that they provide and their corresponding economic benefits were gathered from an
methods were also recorded in order to identify the most commonly used strategies to value
ecosystem services. Studies were sourced using the scientific platform Web of Science
(http://wokinfo.com/). We searched for studies in the English language that were published
from 1980. We did not extend the review before 1980, because the modern-day concept of “ecosystem services” did not appear until the mid-1980s (Ehrlich and Mooney 1983), from the notion of “environmental services” that had emerged in the previous decade (Wilson and Matthews 1970). The search was completed in December 2017, and consisted on the combination of the keywords: valu* OR econom* AND “coastal ecosystem services” OR “marine ecosystem services” (where the asterisk is a replacement for any possible ending from the root of the respective term; the quotation marks indicate that the term was used as
a whole, and not as an isolated word; the search operator AND retrieves documents that
contains both terms; and the search operator OR retrieves documents that contains at least
one of the terms).
We selected all the articles published in academic journals, since they have usually
undergone a rigorous peer-review process. The publications obtained from the keyword
searches were manually filtered by screening titles and abstracts and reading the entire body
of the studies when they fit the search. We discarded reviews of previous studies as this study
attempts to compile case studies that contain original results and that have valued, quantified,
ranked or listed coastal or marine ecosystem services. The number of studies selected at
various stages is shown in the flow diagram in Figure 1. The number of publications finally
included in the meta-analysis was 61.
Data such as year of the study, geographic coordinates, country where the study area
is located and status of protection were collected from each article. Also, the type of
coastal-marine ecosystem was identified and the ecosystem services were listed. Finally, in those
economic value from each ecosystem service were recorded. Economic values presented in
original currency were converted to values of United States Dollars (USD) referent to 2017.
Figure 1. Selection of studies for inclusion in the meta-analysis.
Data analysis
We used VOSviewer 1.6.15 software (Van Eck and Waltman 2010) to visualize a
network of keywords co-occurrence in order to identify the main topics related to coastal and
marine ES covered in the selected articles. Keywords are extracted by the software from the
frequent terms that meet the minimum occurrence of 5 were included in the network
co-occurrence map. The strength of each link or connection between terms (co-co-occurrence link)
is also recorded by the software. This link strength indicates the number of publications in
which two terms occur together, and is represented by a positive numerical value. The higher
this value, the stronger the link.
There is also the total link strength attribute that indicates the total strength of the
co-occurrence links of a key-word with others. For this, the software identifies terms from the
bibliographic data using natural language processing algorithms and then connects them by
co-occurrence using a fractional counting method. This method means that a co-occurrence
between key-words is assigned fractionally to each of the co-occurrence links, with the
overall weight of the key-words co-occurrence being equal to one (Van Eck and Waltman
2014). The software also assigns closely related key-words (nodes) into clusters by applying
a weighted and parameterized variant of modularity-based clustering and using a smart local
moving algorithm (Waltman and Van Eck 2013; Waltman et al. 2010).
Based on the literature reviewed, choropleth maps were developed using ArcGis 10.3
software (ESRI, 2014) to show the number of articles produced and the total average
economic benefits obtained from coastal and marine ES worldwide. Additionally, the
percentage of the ES that contribute the most to these benefits were estimated and mapped.
All the ES were registered following the classification of MEA (2005a) (Table 1).
Chi-square tests were applied to compare differences between the proportions of ES
valued, category of ES valued and economic valuation methods used throughout the review.
On the other hand, a Bayesian Multinomial model was performed to calculate the probability
that groups (ES valued, quantified and ranked per ecosystems; categories of ES valued per
is equal to 0. For this purpose, it was used a boolean variable that counts the number of simulations in which the difference among groups are ≥ 0 or < 0, representing a Bayesian analogue to the classical approach p-value (Gelman et al. 1996; Kéry and Royle 2016). Non
informative prior was used in all models (Jefreys 1961).
Two-way analysis of variance (ANOVA) was applied using “R” software in order to
test if there are significant differences between the ES valued around the world and according
to the country development level. The economic benefits were used as response variables
and the categories of ES were used as predictor variables. To perform the parametric analysis,
the response variable was logarithmized so that the data met the premise of normal
distribution.
Finally, multiple regression models were applied in order to test how the total average
economic values of the ES are affected by socio-economic indicators such as GDP and HDI,
population density and number of scientific papers about the valuation of coastal and marine
ES. To perform the regressions, we normalized the response variable by applying logarithm.
We also applied logarithm on the predictor variables (GDP, HDI and population density) and
square root on the predictor variable (number of scientific papers) to avoid overdispersion of
regression residuals.
The GDP and density predictor variables were significantly correlated (Pearson: n =
68; r = 0.91, P <0.05), therefore, we avoided considering both in the model through a subset.
Since the values of the response variable are continuous (U$), the models assumed a Gaussian
distribution. First, we build a saturated model considering all possible combinations between
variables, including interactions. Then, we performed data dredging using the dredge
through the AICc values, and excluding the subset (GDP and population density). A total of 33 models were tested, and we only accept models with ∆ ≤ 2 (Burnham & Anderson, 2002).
Table 1. List of the most relevant ecosystem services identified throughout the review and categorized
according to the classification of the Millennium Ecosystem Assessment (2005)
* They are non-use values, which refers to the non-use satisfactions as preservation benefit (Walsh et al. 1984).
CATEGORIES OF ECOSYSTEM SERVICES
Provisioning Regulating Supporting Cultural Existence and bequest values* EC O SY ST EM SE R V IC ES
Aquaculture production Biological control Biodiversity Amenity Existence Biomass Carbon sequestration Habitat Culture benefits Bequest Commercial fishing Climate regulation Nutrient cycling Education Future values Energy Coastal protection Soil Formation Identity
Fodder Disturbance prevention Recreation
Food Erosion control Recreational fishing
Fuel Pollination Scientific research
Genetic resources Waste treatment Spiritual benefits
Harvesting Water quality Tourism
Pharmaceutical resources Raw materials
Seaweed farming Timber
Results
A total of 1,438 articles about economic valuation and coastal and marine ecosystem
services (ES) were yielded from the research in Web of Science over the past 30 years, of
which 233 publications (among articles, articles for symposium and editorial material) met
all the required criteria. There is a clear trend of increasing in the number of studies over
time and markedly from 2009 (Figure 1). Based on the density of articles, the countries that
appeared most frequently were the United States (47), China (22) and the United Kingdom
(20) and the other 64 countries were mentioned in less than fifteen publications each one
(Figure 2).
Figure 2. Number of publications about economic valuation and coastal-marine ecosystem services from 1980
There were 5,322 different keywords used in the 233 publications selected. The
cluster co-occurrence network (Figure 4) shows the first 20 most frequent keywords
represented by the size of the circles, and the strength of the links represented by the thickness of the lines, with the thickest having a link strength of 33.88 and the thinnest of 0.20. These
keywords and its respective total link strength values are also presented in Table 2.
Three main clusters were formed according to different colors. The red cluster focuses
on the management of ES (the two most frequent keywords) and the valuation in its different
forms. This grouping also included the two most studied and probably most valued
ecosystems that are wetlands and mangroves. The blue cluster refers to the conservation of
biodiversity (the following most frequent terms) strongly related to the topics of ecology and
marine protected areas. Finally, the green cluster (constituted by the less frequent key-words)
represents issues addressed around climate change.
Figure 4. Network visualization co-occurrence cluster map for key-words analysis on coastal and marine
ecosystem services recorded in the articles from 1980 to 2017 reviewed. The size of the circles indicates the frequency of occurrence of key-words. The thickness of the lines indicates the strength of the link between terms
Table 2. The first trend key-words on coastal and marine ecosystem services.
Figure 5 shows that wetlands, mangroves and reefs (including artificial reefs, oyster
reefs and coral reefs) were the ecosystems that appeared the most throughout the review
(n=132). The highest proportion of ES from wetlands and mangroves were valued, quantified
and ranked; and for reefs, most of the ES were ranked. No statistical differences were
observed for each of the rest of the ecosystems between the three evaluation categories.
Key-words Occurrences Total Link Strength
Ecosystem services 283 219.00 Management 193 158.00 Conservation 138 125.00 Biodiversity 130 112.00 Valuation 95 85.00 Climate-change 69 60.00 Impacts 54 45.00 Fisheries 51 43.00 Economic Valuation 50 40.00 Restoration 46 39.00 Coastal 45 38.00 Values 39 37.00 Contingent valuation 42 37.00
Marine protected areas 36 35.00
Sea-level rise 42 35.00 Ecosystems 44 35.00 Wetlands 36 34.00 Ecology 39 33.00 Mangroves 39 32.00 Resilience 34 28.00
Statistical differences were observed between the ES valued throughout the review
(X-squared = 136.72, df = 16, p-value < 2.2e-16). However, recreation, commercial fishing,
coastal protection and habitat seem to stand out. Significant differences were also observed
between categories of ES (X-squared = 10.016, df = 3, p-value = 0.01843).
Figure 5. Coastal ecosystem services valued, quantified and ranked per ecosystems in papers around the world
from 1980 to 2017 (based on 132 articles). “Ranked” implies in attributing value of importance to each ES enumerated for the ecosystem. Different black letters represent differences among categories valued, quantified and ranked. Different red letters represent differences within each category valued, quantified and ranked.
Wetlands and mangroves were the ecosystems that contributed the most to all the
categories of ES valued (Figure 6). Regulation and support services were more valued on
wetlands ecosystems, while provision services stood out in mangroves. No statistical
Figure 6. Categories of coastal ecosystem services valued according to the ecosystems in papers around the
world from 1980 to 2017 (based on 146 articles). Different black letters represent differences among the ES categories. Different red letters represent differences within each ES category.
Statistical differences were observed between economic valuation methods used
throughout the reviewed studies (X-squared = 497.74, df = 16, p-value < 2.2e-16). However,
Figure 7. Economic valuation methods (%) applied throughout the reviewed studies from 1980 to 2017 for the
estimation of ecosystem services values. CV- Contingent Valuation; MP- Market Prices; BT- Benefit Transfer; RC- Replacement Cost; DCA- Damage Cost Avoided; TCM- Travel Cost Method; HPM- Hedonic Prices Method; OC- Opportunity Cost.; Other methods (Choice experiment, Funds for conservation, Abatement cost, Emergy values, Public spending, Social carbon cost, Science catalogue, Fuzzy math, Ecological value method).
More than 40% of the ecosystems registered in this review (mostly wetlands and
mangroves) were found in a non-protected status followed by 32.5% in non-reported status
(Figure 8). No statistical differences were observed for each of the rest of the ecosystems
between the protection status categories, and few differences between ecosystems were
Figure 8. The protection status of coastal ecosystems reported throughout the reviewed studies from 1980 to
2017. Different black letters represent differences between the protection status categories. Different red letters represent differences within each protection status category.
The total annual average economic values obtained from the sum of all the valued ES
of each country worldwide were between USD1.1 thousand and USD77 billion per year. The
highest values were found in China, some countries of Europe and Indo-Pacific islands,
Australia and the United States. On the other hand, the lowest values were registered in
islands from Oceania, Mozambique (Africa) and small countries in Europe (S1 Figure).
In the American continent, a great contribution of regulation ES such as coastal
protection service was registered over the highest economic values observed in the United
States and Belize. In Europe, the highest values are given by the cultural ES of recreation.
This latter is also an important ES in Africa, as well as the regulation ES of carbon
sequestration, that contributes the most in the economic values of most African countries. In
Asia and Oceania was registered a high input of regulation and provision ecosystem services
Figure 9. Contribution (%) of coastal ecosystem services to the total average economic values per country according to the review from 1980 to 2017. A-Amenity; AqP-Aquaculture Production;
Bi-Biodiversity; C-Culture; CF-Commercial Fishing; CP-Coastal Protection; CR- Climate Regulation; CS- Carbon Sequestration; DP- Disturbance Prevention; EC-Erosion Control; En-Energy; Ex- Existence of all service’s benefits; F-Fodder; Fd-Food; Fu-Fuel; GeGenetic Resources; H-Habitat; Har- Harvesting; LP- Litter Production; NC-Nutrient Cycling; PS- Physical Space; R-Recreation; RF- Recreational Fishing; RM-Raw Materials; SR- Scientific Research; Tm-Tourism; WQ-Water Quality; WS-Water Supply; WT-Waste Treatment; Others (rest of ecosystem services valued).
The absolute economic values of the averages obtained per ES categories ranged from
USD9.4to USD2.4 billion, with the first value referring to the ES category of “Support” and the last to “Provision” (S1 Table). However, there are no statistical differences between the mean economic values of the different services (Table 3, Figure 11).
Table 3. ANOVA performed among the mean economic values of different coastal ES categories registered
throughout the studies reviewed from 1980 to 2017.
Figure 11. Mean economic values of coastal ES categories registered throughout the studies reviewed from
1980 to 2017. Bars represent the standard error.
In general, there is a large discrepancy of economic values in the ES of “Support”, “Regulation” and “Provision” and “All categories together” across countries worldwide. On the other hand, “Cultural” ES show more homogeneous values around the world compared to the others ES. Developing countries have lower mean economic values of the coastal ES
Df SS F-value P-value
ES categories 4 26.71 0.5071 0.7305 Residuals 151 1,988.5
values (regardless of the category of ES) when compared to developed countries (Table 4;
Figure 12).
Table 4. ANOVA of mean economic values of coastal ES categories performed among developing and
developed countries.
Figure 12. Mean economic values of the coastal ES categories according to the level of development of the
countries, using data of papers from 1980 to 2017. Bars represent the standard error.
After the application of logarithm on the predictor variables, we found that GDP and
population density were significantly correlated (Pearson: n = 68; r = 0.91, P <0.05), so we
avoided considering both in the model through a subset (S2 Figure).
Df SS F-value P-value
ES categories 4 26.71 0.5459 0.7022 Country development level 1 140.89 11.5154 0.0008 ES categories: Country development level 4 61.28 1.2522 0.2915
A total of 33 models were tested. From those, three models were selected to explain
the overall average economic value of coastal ecosystem services (USD) from the 68
countries recorded (Table 5). First the model with GDP, HDI and the GDP: HDI interaction
showed the best fit, followed by the model with density, HDI and the interaction density:
HDI. The third model that best explained the economic value of coastal ecosystem services
used GDP and HDI variables. The HDI factor was present in all models, showing Relative
Importance of 1, followed by GDP (0.65), GDP: HDI (0.41), and finally, by density and density: HDI (0.35). Any models selected the factor “number of scientific articles”.
The results obtained on the multiple regression models revealed that practically all
variables behave positively to the response variable, with the exception of HDI of model 1.
But considering the large standard error in model 1 [SE = 9.16], it would be expected the
neutrality of the variable. Therefore, GDP, HDI and population density are positively related
MODELS PARAMETERS DESCRIPTION Parameters GDP + HDI + GDP:HDI dnst + HDI + dnst:HDI GDP + HDI Mean of ß Relative Importance
k 5 5 4 ß Int. 12.33 (±2.7) 16.53 (±1.5) 14.41 (±2.23) 14.30 (±0.23) ß dnst --- 1.25 (±0.44) --- 0.44 (±0.62) 0.35 ß GDP 1.21 (±0.44) --- 0.79 (±0.22) 0.68 (±0.57) 0.65 ß HDI −4.44 (±9.16) 1.90 (±6.66) 3.89 (± 2.31) 0.22 (±5.42) 1 ß dnst: HDI --- 2.16 (±1.77) --- 0.75 (±1.2) 0.35 ß GDP:HDI 1.78 (±1.6) --- --- 0.73 (±1.08) 0.41 ∆ AICc 0 0.32 1.07 Wi 0.293 0.250 0.172 AdjR2 0.3591 0.3561 0.3260
Table 5. Best models fitted for Total Average Economic Value (USD) from 68 countries considering GDP, HDI, population density (dnst) and number of published scientific
articles. To each predictor variable is also presented the mean of the ß coefficient through the models and their Relative Importance. (k, number of parameters; ß, regression coefficient of predictor variables; ∆AICc, differences in the value of AICc in relation to the best model; Wi, model weight; AdjR2, adjusted R2, and (±values) mean Standard
Discussion
In the context of global change in coasts and oceans, and the consequent loss of
ecosystem services (ES) and socioeconomic costs for the population, identifying the
distribution of the coastal and marine ecosystems (CME), the ES they provide and the
respective economic benefits worldwide, becomes crucial for governance and
decision-makers. Furthermore, there is a need to understand the relation between coastal ES values
and socioeconomic indicators. Thus, in this study we present an overview of the global
knowledge about CME by uncovering a great advance of studies regarding coastal ES
values and its relation with the Gross Domestic Product (GDP) and the Human
Development Index (HDI) around the world.
It is probably due to the popularization of the term “ecosystem services” and the knowledge about “ecological economics” in the 1980s (Ehrlich and Mooney 1981, 1983; Jansson 1984), that in our review we recorded articles from 1987 onwards. However,
there was an abrupt and exponential increase in studies from 2009, may be as a result of
initiatives like Economics of Ecosystems and Biodiversity (TEEB) undertaken between
2007 and 2010, that aimed incorporate ES into decision making (TEEB 2010) and bring
ES to the media and business segments (Costanza et al., 2014; Groot et al., 2010).
In this review we revealed that studies on coastal and marine ES address three
main co-occurring topics: “management”, “conservation of biodiversity” and “climate change”. Management and conservation are terms that have grown into an ecosystem-based approach that deals with conservation and natural-resource decision-making by
integrating natural and social systems (McLeod et al., 2005; Leslie and McLeod, 2007).
Besides, ES can be seen as a policy tool to protect biodiversity (Liquete et al. 2016). Some
Belize, Brazil and Thailand (Barbier et al. 2008; Jordan et al. 2012; Scyphers et al. 2014;
Arkema et al. 2015;Scherer and Asmus 2016).
On the other hand, “economic valuation” of ES permits understanding the contributions that they provide to human well-being (Schuhmann and Mahon 2015).
Faced with climate change, special interest on the cumulative economic loss of ES has
increased in the global community due to the costs that represent in terms of damage,
repair and replacement costs (Bartelmus, 2009; Narayan et al. 2017; Runting et al. 2017). However, little mention of the terms “climate change” and “sea-level rise” were recorded throughout the review, which leads to a scarce publication of global reports to unravel the
loss of coastal ES and economic cost that these processes pose to the population, mainly
in coastal areas. It is also surprising that anthropogenic factors such as land conversion,
population and economic growth have not been mentioned more frequently as drivers of
coastal ES loss among studies.
Wetlands and mangroves were the most quantified and valued ecosystems
worldwide, as have been reported by several authors (Brander et al., 2006; Groot et al.,
2012; Barbier, 2013; Chaikumbung et al., 2016). The great concern about these systems
may be due to the large area they occupy (Lead et al. 2018), the high contribution they
provide annually in goods and ES (about USD26.3 trillion - Costanza et al. 2014), and
the high percentages of area loss in recent years (50% of saltmarshes and 35% of
mangroves - Barbier et al. 2011). Regulating and supporting are the services most valued
on these ecosystems as was also registered by Groot et al. (2012) and Barbier et al. (2013),
probably due to the various environmental and socio-economic biases that the loss of
services related to coastal protection and water and climate regulation services can
This review also highlighted that other coastal environments such as reefs,
seagrasses and estuaries are less quantified and valued despite the valuable ecosystem
services they provide (about USD 22 trillion - Costanza et al. 2014) and the great loss
area that has also suffered in the last decades (Brown et al 2006; Waycott et al. 2009;
Barbier et al. 2011). Therefore, the subsidy of further research on these and others poorly
studied CME such as dunes and beaches, becomes important especially in the face of
growing uncertainty on the protection and threats to coastal ES. In this context, we
observed a high percentage of unprotected study areas that may be mirroring the slow
growth of the global rate of marine protected area (MPA), when compared with the
advance of biodiversity threats. Furthermore, the extent of coverage by MPA is still
limited (Cullis-Suzuki and Pauly 2010; Mora and Sale 2011), and it is likely that the
proportion of the world's MPA remains unknown (Martínez et al. 2007).
Among the economic valuation methods recorded throughout the review, the
contingent valuation method by means of the willingness to pay technique prevailed
among the study cases. This method is usually the most applied for both use and non-use
values (Vo et al. 2012; Mehvar et al. 2018), since it is able to value ES not negotiable on
markets (Birol et al. 2006; Norton and Hynes 2014; Sagebiel et al. 2016). Another
valuation method that appeared very frequently and is widely reported worldwide, is the
market prices approach (Salem and Mercer 2012; Valdez and Luna 2012; Chaikumbung
et al. 2016). Beyond valuing most services, some authors reported lower values generated
by market prices than other approaches such as contingent valuation (Brander et al. 2006;
Ghermandi 2015; Groot et al. 2012).
The total value of ecosystem services for the 68 countries registered here (USD
207.1 billion) represents up to 0.1% of global value estimated to world ES (global value
