Tese De Doutoramento

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS DEPARTAMENTO DE BIOLOGIA ANIMAL

Contribution to the development of biotic integrity assessment tools for Portuguese estuaries based on benthic communities

Paula Maria Chainho de Oliveira

Doutoramento em Biologia Especialidade de Ecologia Aplicada

2008

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS DEPARTAMENTO DE BIOLOGIA ANIMAL

Contribution to the development of biotic integrity assessment tools for Portuguese estuaries based on benthic communities

Paula Maria Chainho de Oliveira

Tese orientada por: Professora Catedrática Maria José Costa Eminent Scholar Daniel M. Dauer

Doutoramento em Biologia Especialidade de Ecologia Aplicada

2008

To my daughter

Para a minha filha

Acknowledgments

ACKNOWLEDGMENTS/ AGRADECIMENTOS

This thesis represents a very important period of my life and I could not have accomplished this challenge without the support of those who funded the work, advised me, helped me with many tasks and provided me encouragement and emotional reassurance to continue in difficult moments. Therefore, I would like to thank all those who contributed at different stages, especially:

A realização desta tese representou um período muito importante da minha vida e não teria sido possível concretizar este desafio sem o apoio daqueles que financiaram a investigação, os que me orientaram, os que ajudaram em algumas tarefas e todos os que me apoiaram nos momentos mais difíceis. Assim, quero agradecer a todos os que contribuíram nas diferentes fases do processo, em especial:

À Professora Maria José Costa, por ter aceite a orientação desta tese, por ter acreditado que seria capaz de concretizar este desafio até ao fim e por ter sido pragmática em todos os momentos em que tal foi necessário;

Professor Daniel Dauer for being my “scientific father” along these six years. Our enthusiastic discussions were fundamental for my scientific growth and your advice was always helpful in all stages of this thesis. I will always be grateful for your warmth welcome in the U.S.A., the full integration in the Lab family and the friendship and all the support that you gave me.

Instituto de Oceanografia and the Benthic Ecology Laboratory for having hosted this study and the projects that gave support to it/Ao Instituto de Oceanografia e ao Benthic Ecology Laboratory, por terem acolhido este doutoramento e os projectos no âmbito dos quais foi desenvolvido;

Ao Lino Costa, por estar sempre presente quando foi preciso, tanto no aconselhamento científico como nas dificuldades e necessidades do dia-a-dia, e no entusiasmo face a novos desafios.

À Luísa Chaves, com quem partilhei lutas comuns ao longo dos nossos doutoramentos. A tua presença preserverante nas campanhas de amostragem, projectos, relatórios intermináveis, artigos, dificuldades e alegrias foi sempre um alento para continuar. A tua amizade para além das paredes do IO é o mais importante, que fica para além da tese.

i Mike Lane, whose friendship was one of the best “consequences” of this Ph.D. Your support continued much beyond the time that I spent in the U.S.A. I would like to thank you for all your help with the statistical analysis, revisions, english lessons, scientific discussions, political discussions, movie sessions, holyday trips and little nothings that we shared every day.

À Gilda Silva, pelas experiências partilhadas na identificação dos “bichos”, pela ajuda na formatação e revisão da tese, por estar sempre pronta a ajudar nas tarefas do dia-dia, mesmo nos dias de mau humor, por saber sempre onde estão as coisas e por partilhar uma memória comum de quase 20 anos.

Ao Nuno Prista, pelos desafios diários ao conhecimento que me motivaram a não ficar apenas pelas coisas mais fáceis e já dominadas, pela ajuda no trabalho de campo, e por partilhar ideologias e frustrações.

À Ana Luisa, pelas viagens intermináveis ao Mondego, pela ajuda com a formatação e revisão da tese e por todo o apoio nas pequenas-grandes tarefas do quotidiano no laboratório.

À Carmen, Elsa, Pedro Raposo, Isabel Domingos, Carlos Assis, Sílvia, João Paulo, Bernardo, Jorge, Zé Jacinto, Zé Loff, Carolina, Obadias, Alberto, André, Sérgio e restantes colegas do laboratório e da sala dos doutorandos, pelo companheirismo de todos os dias, pelas ajudas em tarefas diversas e por me terem animado nas fases mais difíceis.

Heidi, Bud, Sharon, Cory, Ryan, Colin e Charles, for having included me so promptly in the Benthic Lab. family and for all the support you gave me during my visits to Norfolk.

Ging, Antone and my “niece” Ema, for having provided the feeling of being home during all those months spent in a foreign country. I also thank all the philipine family and latin family, who always welcomed me as a member of the “tribe”.

Ao Sr. Manuel Pata e António pelo apoio nas saídas de campo do Mondego e pelas histórias da pesca do bacalhau. Ao Francisco Ferreira e tripulação do Mor, pelo entusiasmo com que sempre se empenharam nas nossas saídas do Tejo. Aos Srs. Ilídio Viola, Vitalino Cruz e António pelo apoio nas calmas subidas e descidas do Mira, que se tornou o meu local de estudo favorito por essa “partilha alentejana”.

À Rita Vasconcelos, Manuel Cabral, Susana França, Catarina Vinagre, Sérgio Rodrigues e Tadeu Pereira, pela ajuda no trabalho de campo e pelos momentos bem passados a bordo e nas noites de laboratório.

À Carla Azeda e Carla Barrinha por terem sido umas verdadeiras “máquinas de triagem”, durante longos dias de laboratório. Sem a vossa colaboração não teria sido possível ter tantos dados.

Acknowledgments

To Professor Jean-Claude Dauvin for having received me at the Wimereux Marine Station and for all the help with invertebrate taxonomy.

Ao João Castro, por me ter acolhido na estação biológica de Sines e ter partilhado algumas dicas muito úteis para a identificação dos invertebrados.

Ao Professor Eugénio Sequeira, por ser o modelo daquilo que eu gostava de conservar quando for mais velha: motivação, ideologia e muita energia. As suas histórias da vida foram uma inspiração semanal para acreditar que vale a pena insistir na ciência.

Aos companheiros de direcção e aos assessores da LPN, por me terem poupado nas fases mais difíceis da tese e pela preocupação permanente em que a LPN não adiasse ainda mais a finalização desta tese difícil de sair.

Ao Rodrigo Leão, por ter composto as melodias que intensificaram os momentos mais difíceis, mas também os de maior satisfação.

Aos meus amigos Paula Pires, Ana Grave, Luís Soares, Barbosa e Fátima, que suportaram as minhas ausências prolongadas e se mantiveram sempre ali, no lugar onde se encontram os amigos.

À Paula, por me ter sempre mostrado que é possível fazermos mesmo aquilo que pensamos, à partida, não estar ao nosso alcance. Ao longo destes anos, fizeste-me sempre acreditar que valia a pena tentar coisas novas. Tens sido também tu que me mostras a dura realidade, tal como ela é e me impeles a enfrentar as dificuldades sem meter a cabeça na areia.

Aos meus avós, que tão bem souberam manter a “sabedoria dos velhos” e uma família unida. Esta tese também é um tributo aos vossos sacrifícios ao longo da vida, que nos permitiram fazer escolhas que nunca estiveram ao vosso alcance.

À minha família interminável, em especial aos primos que mantém o clã sempre unido. Um agradecimento especial ao Julieu, por estar sempre lá e acreditar que as escolhas malucas da prima fazem sentido neste mundo voraz.

À minha irmã, Samuel e Tiago, que são uma das partes mais importantes daquele “ninho familiar”, que me faz sempre querer voltar quando estou longe.

Aos meus pais que, apesar de nem sempre acreditarem que as minhas escolham são as certas, continuam a apoiar-me incondicionalmente e a permitir a procura dos sonhos. Sei que estarão sempre aí.

Às minhas gatas Milady, Lua e Maria, companheiras de longas horas de computador, que tanto me mimaram no trabalho solitário de pensar e redigir a tese.

iii Ao Luis, companheiro de todas as lutas, cuja paciência interminável me permitiu arrastar a finalização desta tese, e realizar outros sonhos paralelos. Este também é um projecto teu, pois sempre acreditaste que valia a pena e apoiaste-me em todos os momentos mais difíceis. O amor não se lê só nas palavras, às vezes mora nos silêncios mas acima de tudo nas acções.

À minha filha Filipa, que representou a motivação e inspiração decisivas na recta final da tese. Espero que um dia venhas a ter a possibilidade de realizar desafios tão ou mais aliciantes que este.

A presente tese foi financiada pela Fundação para a Ciência e a Tecnologia e ao ESF no âmbito do III Quadro Comunitário de Apoio, através da atribuição de uma bolsa de doutoramento (SFRH/BD/5144/2001) e do projecto EFICAS (POCI/MAR/61324/2004). O trabalho realizado beneficiou ainda do apoio financeiro do Instituto de Ambiente, através do projecto QUERE, e do apoio logístico facultado pelo Instituto Hidrográfico e Instituto da Água, através do projecto EMINAG.

Resumo

RESUMO

A importância da avaliação e monitorização do estado ecológico dos ecossistemas aquáticos está actualmente vertida num conjunto de regulamentos ambientais, cuja publicação em diversos países do Mundo teve lugar, de forma mais acentuada, ao longo das últimas décadas. Na Europa, a aprovação da Directiva-Quadro da Água (DQA) em 2000, que estabelece um quadro de acção comunitária no domínio da política da água, representou um marco importante para o estabelecimento de novos critérios de qualidade da água, ao introduzir o conceito de estado ecológico. A DQA tem como principal objectivo alcançar o bom estado ecológico de todas as massas de água até 2015 e requer o desenvolvimento de um conjunto de indicadores específicos para cada tipo de massas de água, processo que está a ser levado a cabo pelos diversos Estados Membros. Apesar de alguns países europeus já incluírem a monitorização de elementos biológicos nos seus programas nacionais, através de indicadores desenvolvidos especificamente para esse fim, outros iniciaram esse processo apenas com a implementação da DQA e carecem de instrumentos específicos adaptados às características dos seus ecossistemas. No caso português, apesar de terem sido conduzidos alguns estudos de avaliação de impacte ambiental e monitorização com base em indicadores biológicos, os mesmos restringiam-se a projectos específicos e não tinham o carácter sistemático requerido pela DQA. Com o início do processo de implementação da DQA em Portugal, e visto não existirem índices específicos para os sistemas de águas de transição nacionais, tem vindo a ser testado um conjunto de indicadores desenvolvidos para outros sistemas estuarinos. No entanto, algumas especificidades dos estuários portugueses são passíveis de limitar essa aplicação, nomeadamente o facto de Portugal se encontrar numa zona de transição do ponto de vista biogeográfico e por isso apresentar diferentes composições faunísticas ao longo do gradiente latitudinal. Para além disso, a maioria dos estuários portugueses apresenta caudais de descarga irregulares ao longo do ano, o que provoca alterações frequentes das condições ambientais e consequentemente condiciona a ocorrência de espécies com diferentes níveis de tolerância ao stress ambiental. Este tipo de estuários, que podem ser enquadrados na designação de estuários poiquiloalinos (que apresentam variações das condições de salinidade), tem sido muito pouco estudado, comparativamente aos estuários homioalinos (que apresentam condições de salinidade estáveis). Os estuários portugueses são ainda afectados pela ocorrência de fenómenos climáticos extremos, como as cheias e as secas, cuja frequência tende a aumentar, como consequência das alterações climáticas. Para além disso, estão sujeitos a pressões antropogénicas significativas, pelo que nenhum dos estuários poderá ser utilizado para estabelecer condições de referência.

v Tendo em conta este cenário, o presente estudo teve como principal objectivo testar se as características dos estuários portugueses condicionam a utilização de algumas ferramentas comummente aplicadas na avaliação do estado ecológico deste tipo de sistemas, com base em comunidades de macroinvertebrados bentónicos. Os sistemas estuarinos estudados foram o Mondego, o Tejo e o Mira, que foram incluídos no tipo A2 no âmbito do exercício de tipologia da DQA, mas que apesar disso apresentam características hidromorfológicas bastante distintas.

A presente tese encontra-se organizada em seis capítulos, quatro dos quais consistem em artigos científicos publicados e aceites em revistas científicas indexadas, precedidos de uma introdução geral e finalizando com algumas considerações finais.

O Capítulo 1 consiste numa introdução geral, que sistematiza as principais características dos estuários e os constrangimentos que as condições ambientais estuarinas e as pressões antropogénicas colocam às comunidades de macroinvertebrados bentónicos. Neste capítulo é ainda efectuado um enquadramento geral sobre as políticas e regulamentos que requerem a utilização de instrumentos de avaliação ambiental com base em comunidades bentónicas, com particular relevância para a DQA. São indicadas algumas das características das comunidades de macroinvertebrados bentónicos que os tornam bons indicadores de qualidade ambiental, assim como uma revisão sumária das principais métricas e índices utilizados. Este capítulo inclui ainda uma síntese actualizada do estado do conhecimento relativo às comunidades de invertebrados dos estuários portugueses, nomeadamente no que diz respeito à sua utilização como indicadores de qualidade e, em particular, nos aspectos relevantes para a aplicação das metodologias propostas pela DQA. É identificado o estado actual do processo de implementação em Portugal e as lacunas que levaram à realização do presente estudo. Finalmente, são apresentados os objectivos da tese e as principais questões às quais se pretendeu responder.

No Capítulo 2 são identificadas as relações entre os gradientes ambientais estuarinos e os padrões de distribuição temporal e espacial das comunidades de macroinvertebrados bentónicos do estuário do Mondego. Esta análise tem como base um conjunto de dados recolhidos ao longo de todo o gradiente salino do estuário e com uma frequência sazonal, de forma a cobrir as variações correspondentes ao ciclo hidrológico. Por terem sido realizadas amostragens após um período de cheia, este estudo permite avaliar o impacto deste tipo de evento extremo sobre as condições ambientais do estuário e as consequências ao nível da estrutura das comunidades bentónicas.

No Capítulo 3 é testada a suficiência taxonómica no exercício da tipologia (identificação de tipos de massas de água), tendo como base as comunidades de macroinvertebrados bentónicos do estuário do Mondego. O conceito de suficiência

Resumo taxonómica, ou seja, a identificação dos espécimes a um nível taxonómico superior à espécie, tem sido utilizado no âmbito da avaliação dos impactes das pressões antropogénicas sobre as comunidades bentónicas, como forma de reduzir as necessidades em termos de especialistas com conhecimentos detalhados em termos taxonómicos, permitindo reduzir o tempo de trabalho laboratorial e, simultaneamente, aumentar a replicação espacial e temporal. Para além de diferentes níveis taxonómicos, foram ainda testadas diferentes taxocenoses, afim de verificar se os agrupamentos espaciais de estações de amostragem coincidiam com as diferentes comunidades bentónicas identificadas através da utilização de todos os taxa.

No Capítulo 4 foi analisada a influência das variações sazonais nas comunidades bentónicas do estuário do Mondego, na utilização dos índices propostos para avaliação do estado ecológico dos estuários portugueses, no âmbito da DQA. Foram aplicados os índices de diversidade de Margalef e Shannon-Wiener, o índice AMBI e o método das curvas ABC, propostos pelo grupo de trabalho português (Bettencourt et al., 2004). Para averiguar quais dos índices respondem mais eficazmente aos diferentes níveis de stress antropogénico, foram utilizadas as medições de variáveis físico-químicas indicadoras de poluição. Foram testadas algumas metodologias diferentes para utilização destas variáveis na determinação das condições de referência.

No Capítulo 5 foram utilizadas duas abordagens multimétricas (B-IBI e TICOR) para avaliar a qualidade ecológica de estuários incluídos no mesmo tipo da DQA (A2), mas com diferentes características e níveis distintos de pressões antropogénicas, nomeadamente os estuários do Mondego, Tejo e Mira. As comunidades bentónicas foram classificadas em diferentes classes de qualidade, de acordo com a DQA e a eficiência das classificações obtidas foi analisada por comparação com uma classificação prévia, tendo por base os resultados de variáveis físico-químicas indicadoras de stress antropogénico. O comportamento das métricas incluídas nos índices utilizados nos diferentes estuários foi analisado, tendo em conta as especificidades ecológicas de cada estuário e os pressupostos assumidos para a inclusão das métricas nos índices.

A integração dos resultados obtidos nos diferentes capítulos da tese foi efectuada no Capítulo 6, através de um conjunto de considerações finais. A principal conclusão da tese é a de que, efectivamente, as características dos estuários portugueses condicionam a utilização dos instrumentos existentes para a avaliação do estado ecológico com base em comunidades de macroinvertebrados bentónicos. As classificações obtidas com os índices testados reflectem não apenas as diferenças nos níveis de degradação ambiental, mas também variações espaciais e temporais, o que indicia a necessidade de adaptar essas metodologias antes do seu uso sistemático em estuários portugueses.

vii No estuário do Mondego foram identificados três grandes grupos espaciais, essencialmente determinados por um forte gradiente longitudinal de salinidade. As comunidades bentónicas apresentaram variações sazonais bastante acentuadas, particularmente influenciadas pelas alterações dos caudais dulciaquícolas afluentes ao estuário e às variações salinas daí decorrentes. As alterações mais acentuadas verificaram-se no período de Inverno, cujas colheitas foram efectuadas após uma cheia, tendo como consequência uma redução drástica das densidades e número de espécies, ao que se seguiu uma recuperação significativa no período da Primavera. As variações sazonais observadas, quer em termos salinos, quer em termos das comunidades de macroinvertebrados bentónicos, permitiram classificar este estuário como poiquiloalino, dificultando a aplicação do sistema de Veneza para estratificação longitudinal do estuário em diferentes zonas salinas, sem modificações prévias, ao contrário do que acontece em estuários homioalinos. Estas conclusões parecem ter aplicação em estuários semelhantes, como é o caso do estuário do Mira, cujas variações salinas são igualmente bastante acentuadas entre épocas do ano distintas.

Diferentes classificações são obtidas quando se utilizam os dados das comunidades bentónicas correspondentes a diferentes épocas do ano e, para além disso, as classificações obtidas pelos vários índices diferem, revelando um baixo nível de concordância entre os mesmos. O período estival parece ser o mais adequado à monitorização do estado ecológico, uma vez que os índices parecem responder de uma forma mais evidente ao nível de pressão humana. Verificou-se uma maior correlação entre as respostas dos índices e as variáveis indicadoras de eutrofização, sendo os índices de biodiversidade os que revelaram maior capacidade de resposta preditiva à presença de poluição e o método ABC o que se revelou menos eficaz na correspondência entre a degradação do meio e a resposta biológica. Estes resultados sugerem que este último método não deverá ser utilizado em estuários sujeitos a elevados níveis de stress natural. As respostas discriminantes dos índices aos diferentes níveis de degradação ambiental foram mais eficientes durante o período estival, indicando a necessidade de estratificação temporal para uma melhor aplicação destes métodos.

Ambos os índices multimétricos (TICOR e B-IBI) se revelaram bastante eficazes na separação entre locais classificados acima ou abaixo da categoria de Bom estado, mas incapazes de diferenciar entre outras classes de qualidade (Excelente/Bom, Moderado, Mau/Medíocre). Para além disso, os resultados dos índices não revelam as diferenças nos níveis de degradação identificadas entre os diferentes estuários, o que demonstra que as características hidromorfológicas poderão dificultar o processo de classificação dentro de estuários pertencentes ao mesmo tipo. As métricas integradas nos índices multimétricos testados requerem alguns ajustes, que podem passar pela selecção de diferentes métricas e

viii

Resumo pela alteração dos limites das classes de qualidade para estuários ou tipos de habitats diferentes.

Apesar disso, os índices multimétricos revelaram-se mais robustos a possíveis erros de classificação do que métricas individuais, uma vez que integram diferentes tipos de resposta das comunidades bentónicas. Os estuários com características poiquiloalinas mais marcadas, tais como o Mondego e o Mira, são aqueles em que a aplicação dos índices existentes parece ser mais problemática, uma vez que as respostas das comunidades às pressões antropogénicas se confundem com uma adaptação a condições extremamente variáveis, incluindo a ocorrência de eventos extremos, como as cheias e as secas. Neste tipo de estuários, as comunidades bentónicas são dominadas por espécies oportunistas e caracterizadas por uma menor diversidade de espécies. No entanto, estas especificidades do ponto de vista ecológico, tais como a dominância de taxa monotípicos, permitem a utilização de abordagens alternativas para a tipologia, tais como a suficiência taxonómica, apesar da utilização de diferentes taxocenoses ter-se revelado menos promissora. Não foram encontradas situações de referência nos três estuários estudados, mas as variáveis físico-químicas, em especial a concentração em nutrientes, parecem ser bons indicadores de referência para testar a eficiência dos índices biológicos.

No último capítulo foram ainda sugeridas possíveis de linhas de investigação a seguir, tendo em conta as questões levantadas pelo presente estudo. A identificação de condições de referência parece ser um dos principais desafios no âmbito da implementação da DQA, uma vez que todos os estuários portugueses se encontram, pelo menos, minimamente intervencionados. A implementação de medidas de recuperação do bom estado de um estuário piloto onde isso seja viável, tal como o Mira, parece ser um caminho promissor. A selecção de métricas a serem integradas nos índices é outro dos desafios importantes, sendo necessário testar a sua utilização nos diversos estuários portugueses. A realização de estudos de longa duração é outro dos requisitos essenciais para determinar uma correcta estratificação temporal dos programas de monitorização, de forma a ter em conta os possíveis efeitos de variações interanuais. Por último, recomenda-se um estudo mais detalhado das diferenças biogeográficas nas comunidades de macroinvertebrados dos estuários portugueses, tendo em conta que Portugal é uma zona de transição entre diferentes regiões climáticas. Diferenças acentuadas na composição taxonómica poderão influenciar o desempenho dos índices em diferentes regiões do país.

PALAVRAS CHAVE: invertebrados bentónicos; indicadores biológicos; estuários portugueses; variações sazonais; Directiva-Quadro da Água

Summary

SUMMARY

Benthic macroinvertebrate communities have been widely used as indicators for assessing and monitoring human impacts over aquatic ecosystems because they respond predictably to many kinds of natural and anthropogenic pressures. They are also included in the biological indicators of ecological status required by the Water Framework Directive (WFD). The major objective of the present study was to investigate if the characteristics of the Portuguese estuaries constrained the use of existing assessment tools for evaluating ecological status based on benthic invertebrate communities.

Seasonal samples collected along the estuarine gradient of the Mondego estuary showed considerable changes in the benthic community composition between seasons and a drastic reduction on the number of taxa and abundances after a flood, although maintaining a consistent spatial pattern of aggregation of stations along seasons. Nevertheless, these changes influenced the results obtained with the application of indices proposed for use in Portuguese estuaries in the aim of the WFD, requiring a temporal and spatial stratification.

Two multimetric indices were applied in the Mondego, Tejo and Mira estuaries during the dry period, when indices respond better to pollution indicative variables, indicating that none of these estuaries are in a Good status. The indices tested are efficient in separating between benthic communities above and below Good status, but not accurate enough for discrimination of other quality classes, as required by the WFD and multimetric indices are more robust than single metrics. These indices are more adequate for estuaries less affected by natural variation, such as the Tejo estuary. On the other hand, ecological adaptations in highly dynamic estuaries as the Mondego and Mira estuaries support the use of higher taxonomic levels in the typology process. Adaptation and validation of metrics and thresholds of existing indices are recommended for its future application under the implementation of the WFD.

KEY WORDS: benthic invertebrates; biological indicators; Portuguese estuaries; seasonal variations; Water Framework Directive

LIST OF PAPERS

This thesis comprises the papers listed below, each corresponding to a Chapter, from 2 to 5.

Chainho, P., Costa, J.L., Chaves, M.L., Lane, M.F., Dauer, D.M. & Costa, M.J. 2006. Seasonal and spatial patterns of distribution of subtidal benthic invertebrate communities in the Mondego River estuary, Portugal. Hydrobiologia 555: 59–74.

Chainho, P., Lane, M.F., Chaves, M.L., Costa, J.L., Costa, M.J. & Dauer, D.M. 2006. Taxonomic sufficiency as a useful tool for typology in a poikilohaline estuary. Hydrobiologia 587: 63-78.

Chainho, P., Costa, J.L., Chaves, M.L., Dauer, D.M. & Costa, M.J. 2007. Influence of seasonal variability in benthic invertebrate community structure on the use of biotic indices to assess the ecological status of a Portuguese estuary. Marine Pollution Bulletin 54: 1586– 1597.

Chainho, P., Costa, J.L., Chaves, M.L., Costa, M.J. & Dauer, D.M. (accepted) Use of multimetric indices to classify estuaries with different hydromorphological characteristics and different levels of human pressure. Marine Pollution Bulletin.

The leading author of the papers comprised in this thesis was responsible for sample collection and processing, laboratory procedures and species identifications, as well as data analysis and manuscript writing.

TABLE OF CONTENTS

CHAPTER 1

General Introduction...... 3

CHAPTER 2

Seasonal and spatial patterns of distribution of subtidal benthic invertebrate communities in the Mondego River, Portugal – a poikilohaline estuary...... 37

CHAPTER 3

Taxonomic sufficiency as a useful tool for typology in a poikilohaline estuary ...... 63

CHAPTER 4

Influence of seasonal variability in benthic invertebrate community structure on the use of biotic indices to assess the ecological status of a Portuguese estuary ...... 91

CHAPTER 5

Use of multimetric indices to classify estuaries with different hydromorphological characteristics and different levels of human pressure...... 119

CHAPTER 6

Final Remarks ...... 151

Appendix 1

Chapter 1

General Introduction

General introduction

Chapter 1

General Introduction

To develop or not to develop a biotic index? – the beginning of this study

The decision to begin a study on benthic assessment tools was greatly influenced by my previous experience with environmental impact assessment and monitoring studies, using benthic communities as one of the biological components. Working on Portuguese estuaries, I soon realized that both spatial and temporal heterogeneity was a key attribute, not only between estuaries with different location, but also within each estuary.

As every researcher with some basic knowledge on ecology, I started by looking at the composition of the benthic communities and the distribution of different benthic species, as a result of the influence of environmental factors. Diversity indices were the very basic start to a series of attempts to synthesize the vast and complex information of the benthic communities, but the lack of reference studies on Portuguese estuaries made this approach very subjective. The next step was the use of conceptual ecological models, such as the Pearson & Rosenberg (1978) succession model, based on the knowledge of species’ tolerance to organic pollution, and the Abundance and Biomass Curves (ABC) method (Warwick 1986), which is based upon ecological relationships between stress and relative body size and abundance patterns and therefore needs no independent reference conditions. Finally, I came across multimetric indices, which integrated different types of attributes of the benthic communities and combined them into a final result.

The Benthic Index of Biotic Integrity (B-IBI) developed by Weisberg et al. (1997) was my most important inspiration for deciding to accept the challenge of developing a thesis on benthic indicators of biotic integrity. At the time I wrote my working proposal, there was no previous record of the application or development of benthic multimetric indices in the Portuguese estuaries and the available studies on benthic communities were mainly focused on the zonation of communities along environmental gradients (e.g. Andrade, 1986; Costa, 1988; Moreira et al., 1993) and particular aspects of the ecology of a few species (e.g. Queiroga, 1990; Marques et al., 1994; Guerreiro, 1998). Some work had been done on the effects of pollution on benthic communities, but was tipically restricted to narrow areas of a particular estuary and/or to a single collection (e.g. Rodrigues, 1992; Marques et al., 1993; Mucha & Costa, 1999).

All these factors, combined with the discussion and approval of the European Water Framework Directive that included macroinvertebrate communities as indicators of ecological status in estuaries, triggered the work that has resulted in this thesis. At first, my proposal pointed towards the development of a B-IBI for Portuguese estuaries, based on the knowledge

3 developed in the USA on identifying habitat types and selecting specific benthic attributes for each of those habitat types. Nevertheless, the fact that Portuguese estuaries are characterized by high heterogeneity of environmental conditions and faunal composition, a single index was very unlikely to be useful for different estuaries. A short time after starting data compilation I had to recognize that testing the applicability of existing tools and identifying major problems with their use across different estuaries would be a more important contribution than developing a totally new index.

The final result, presented as a collection of articles published in international research journals, is intended to be a contribution to the very challenging discussion on the adequacy of existing indices to assess estuaries with very particular characteristics, such as the Portuguese estuaries. During the development of this thesis, the investigation on the use of benthic indices in Portuguese estuaries flourished and several other teams and researchers published very important contributions. I truly hope that this thesis may represent an additional block in building solutions for assessing the benthic status in Portuguese estuaries and others with similar characteristics.

Estuaries – diversity of classifications

There is no single definition of estuary, although many authors refer to it as an intermediate zone linking freshwater and marine systems (e.g. McLusky, 1971; Nybakken, 1993) or simply the saline mouth of a river where it meets the sea (Day, 1981a). Nonetheless, one of the most accepted definitions of an estuary was provided by Pritchard (1967) as a semi-enclosed coastal body of water, which has a free connection with the open sea, and within which sea water is measurably diluted with freshwater derived from land drainage. Additional important attributes of estuaries that serve as the basis for further estuarine classification include patterns of salinity distribution influenced by interactions between physiography, annual freshwater discharge patterns, and tidal amplitude.

Day (1981b) classifies estuaries as (1) normal estuaries, if there is an increase in salinity from the river head towards the sea, (2) hypersaline estuaries, if salinity increases from sea water values at the mouth to hypersaline values in the upper reaches, and (3) closed or blind estuaries, if they are temporarily closed by a sand bar across the sea mouth due to decrease on freshwater imputs, thus not having tidal fluctuations. Normal estuaries can also be divided in different categories, according to variation in salinity in a vertical water column, as (1) well-mixed, when no variation in salinity is observed along the vertical column, (2) partially stratified, when there is a halocline between the upper and lower portions of the water column but differences in salinity are lower then 3 parts per thousand and (3) stratified, when differences in salinity between surface and bottom are higher than 3 parts

4 General introduction

Chapter 1 per thousand. In spite of this general classification, any given estuary may show variations in stratification conditions as a function of factors that influence turbulence, such as longitudinal distance along the estuary, season of the year or even the phase of the tidal cycle (Officer, 1983).

The salinity regime is influenced by freshwater discharge, thus there are many differences between estuaries with different climatic regimes. In temperate zones there is more rainfall during winter than summer but rivers flow permanently throughout the year, maintaining estuaries permanently opened to tidal influence (McLusky & McIntyre, 1995). In these estuaries, fluctuations in salinity may occur frequently, but extreme values are seldom registered. In contrast, in regions under sub-tropical influences, such as some areas in South Africa and Australia, rainfall occurs only during the winter and for a short period and there is a long dry period, during which freshwater flow may cease, interrupting the contact with the sea for part of the year. These are often referred as temporarily-open estuaries and salinity is normally very stable during the closing period, although it may register extreme fluctuations during the rainfall period (Teske & Wooldridge, 2001).

In addition to salinity fluctuations, other variables create significant constraints to the estuarine environment (Day, 1981b; 1981c; Vernberg, 1983):

(1) temperature ranges are greater than in the adjacent marine and freshwater ecosystems;

(2) turbidity levels often make estuaries light-limited due to upstream inputs and re- suspension of fine deposits, e.g. in the turbidity maximum zone;

(3) dissolved oxygen levels near the bottom are often lower than in rivers and the sea, due to high allocthonous inputs and water column stratification. Reduction in oxygen saturation is increased during stratification, since the bottom layer does not have significant exchanges with the water surface;

(4) sediment type can vary greatly along the estuarine gradient, with coarser sediments in the upper reaches, very fine sediments in the middle estuary, where the highest deposition rates occur and sandier sediments near the mouth, resulting from marine inputs with the tidal movements. In highly hydrodynamic estuaries, sediment composition can be very unstable between seasons and re-suspension of the surface layer occurs with the tidal cycle.

5 Estuaries – natural challenges for estuarine communities

High instability in environmental conditions is often postulated as the main reason for having fewer species in estuaries than in the adjacent ecosystems (rivers and sea) (Sanders, 1968; Boesch, 1972; Wolff, 1973). Sanders (1968) developed a theory based on the stability or predictability of the environment and on its geological history to explain lower diversity such as in estuaries and brackish waters, supported by the following elements:

(1) environmental factors are, in general, unstable and unpredictable;

(2) speciation is less probable than in more stable environments;

(3) extinction is more probable than in more stable environments;

(4) estuaries are geologically ephemeral phenomena, thus increasing the likelihood of extinction of brackish-water populations;

(5) the number of species adapted to the estuarine environment is thus lower than either in freshwater and marine ecosystems;

(6) the unstable and unpredictable nature of brackish waters prevents colonization by most freshwater and marine species.

Moreover, Kinne (1971) indicates that environments with pronounced salinity fluctuations do not promote evolutionary processes because instability acts as a brake to speciation. Therefore, fewer representatives of phyletic groups are likely to successfully colonize the estuarine environment. On the other hand, those species that are able to adapt to brackish waters often undergo ecological expansion as a result of reduced interspecific competition and can therefore be very abundant.

Regardless of all constraints of the estuarine environment, there are also advantages for resident organisms, such as (1) shelter against the action of wind waves and oceanic currents; (2) high availability of food provided by river, saltmarsh and ocean inputs; (3) and food particles readily available through sinking and downward transport by turbulent water movements (Wolff, 1983). Estuaries are among the most productive natural systems, mainly because of the rich supply of organic material from detritus and algae and nutrient input from rivers (e.g. Day, 1981c; Wolff, 1983; Levin et al., 2001).

Estuarine species are not evenly distributed along the estuarine salinity gradient and Remane (1934) proposed a first species distribution model, known as the “paradox of brackish water”. He found that the lowest number of species was not recorded halfway between freshwater and marine salinity, but displaced towards the freshwater boundary (Figure 1.1). He assumed that this asymmetric distribution was due to different tolerances of freshwater

6 General introduction

Chapter 1 and marine species to salinity variations. While the number of freshwater species decreases drastically with a slight increase in salinity, a higher number of marine species are more tolerant to salinity decrease. The number of species showed two peaks corresponding to freshwater and marine salinities. This model was later corroborated and adjusted by other authors (e.g. Wolff, 1973; Boesch et al., 1976; Nybakken, 1993).

Figure 1.1. Remane curve (after Remane, 1934), showing quantitative relations between freshwater, brackish and marine invertebrate species. The relative number of species is indicated by the vertical extension of the respective areas.

The longitudinal distribution of species along the salinity gradient of estuaries has also been used to divide estuaries into different regions and most differences in the established boundaries resulted of different authors having used different biological elements as a reference (e.g. diatoms, macroinvertebrates). Dahl (1956) questioned the salinity boundaries proposed by several authors and the Remane curve and firstly introduced the concept of poikilohaline and homoiohaline waters, the first referring to estuaries with very unstable salinity conditions and the latter to waters with higher stability of salinity values.

7 Several authors proposed classification systems based on salinity classes, with most differences concerning the boundaries between freshwater, brackish water and sea water. Redeke (1935) placed the upper limit of brackish waters at salinity 0.21, while most authors (e.g. Ekman, 1953; Kinne, 1971) identified this boundary at salinity 0.5 (Table 1.1). On the other hand, Ekman (1953) considered that only salinities above 34.0 would correspond to seawater, while most classifications would place that boundary at salinity 30.0 (Table 1.1). The Venice System identifies five salinity regions along the estuary, as summarized in Table 1.1, and is the most widely used classification system (Anonymous, 1959).

Table 1.1. Classification of different estuarine regions, based on the longitudinal salinity gradient Redeke (1935) Ekman (1953) Venice System (1959) Kinne (1971)

Freshwater (<0.21) Freshwater (0.0–0.5) Limnetic (<0.5) Oligohalinicum (0.5–5.0)

Oligohaline (0.2–1.8) Oligohaline brackish (0.5–3.0) Oligohaline (0.5–5.0) Horohalinicum (5.0–8.0)

Mesohaline (1.8–18.0) Mesohaline brackish (3.0–10.0) Mesohaline (5.0–18.0) Mesohalinicum (8.0–18.0)

Polyhaline (18.0–30.0) Polyhaline brackish (10.0–17.0) Polyhaline (18.0–30.0) Polyhalinicum (18.0–30.0)

Sea water (>30.0) Oligohaline seawater (10.0–30.0) Euhaline (>30.0) Thalassicum (30.0–40.0)

Mesohaline seawater (30.0–34.0)

Polyhaline seawater (>34.0)

An estuary can also be seen as an ecological boundary between rivers and the sea, which motivated Attrill and Rundle (2002) to investigate if estuaries would fit into either an ecotone or ecocline model, with an (1) ecotone being an area of rapid change between two different and relatively homogeneous community types and an (2) ecocline being a gradient zone environmentally more stable with a relatively homogenous unique community. They concluded that estuaries represent a two-ecocline model, with fauna inhabiting the mid- estuary being either freshwater or marine species at the edge of their range, rather than true estuarine species. Therefore, they question the existence of true estuarine species, similarly to other authors that support the theory that species living in estuaries were mainly recruited from the sea (e.g. Vernberg, 1983).

While most freshwater and marine species are typically stenohaline, withstanding a very narrow salinity gradient, species occurring in estuaries are characteristically euryhaline, i.e. capable of living along a wide salinity range (Remane & Schlieper, 1971). The critical salinity boundary of 5.0-8.0 was indicated by Remane (1934) for euryhaline estuarine organisms and is still accepted. One major adaptation of these organisms to salinity fluctuations is the osmotic regulation of body fluids, by actively controlling water losses and

8 General introduction

Chapter 1 gains when exposed to higher or lower salinities, respectively or even capable of regulating the cellular uptake of specific ions (Vernberg, 1983). Besides physiological adaptations, organisms can also develop morphological adaptations to salinity, such as reduced sizes, reduced surface in contact with the water and body surfaces less permeable, by increased calcium deposits in the exoskeleton or increased mucous secretion. Exposure to salinity fluctuations can also be avoided by burrowing behaviours, since in general salinity is more stable in the sediments than in the water column (Sanders et al., 1965; Wolff, 1973). Salinity is also very important in invertebrate species recruitment, since sharp reductions in salinity may reduce growth rates and activity in invertebrate larvae. Migration is a very common strategy for organisms that do not have other means to adapt to stressful conditions that occur in estuaries during certain periods of the year.

For sedimentary benthos sediment texture is obviously a major factor influencing faunal distribution in estuaries (e.g. Carriker, 1967; Gray, 1974; Boesch, 1977; Wolff, 1983; Kennish, 1986; Mannino & Montagna, 1997). Sediment characteristics are primarily a function of flow, since the transport and deposition of sediment particles from rivers into estuaries are regulated by freshwater flow (Day, 1981b). The middle part of the estuary, where maximum flocculation occurs normally consists of finer sediments than the uppermost and lower areas, but sediment composition varies greatly with local hydrodynamic conditions. Seasonal or even daily changes of the sediment composition, due to fluctuations in flow and tidal movements can be regarded as physical disturbances and affect the colonization by benthic invertebrate species. There is a close relationship between sediment grain size and the trophic structure of benthic communities and, in general, suspension feeders are more common in coarser sandy sediments while deposit feeders seem to have a preference for muddy sediments (Carriker, 1967; Rhoads & Young, 1970; Gray, 1974).

Seasonal fluctuations in abundance and composition can be observed due to recruitment pulses that occur during spring and autumn for most species, but also to the occurrence of extreme environmental conditions such as low temperatures, floods and droughts (Alden et al., 1997; Attrill & Power, 2000; Salen-Picard & Arlhac, 2002). Benthic communities inhabiting estuaries with seasonal floods and/or droughts will change (1) due to pulses of organic matter during floods that stimulate an increase in abundance of opportunistic species (Salen-Picard & Arlhac, 2002), (2) changes in the water and sediment quality conditions, such as higher concentrations of contaminants during droughts (Attrill & Power, 2000; Grange et al., 2000), (3) disappearance of all but highly euryhaline species (Chainho et al., 2006), and (4) potential colonization by alien species that are, in general, much more tolerant to salinity fluctuations than native species (Lee & Bell, 1999; Paavola et al., 2005).

9 Temperature is also an important factor influencing estuarine organisms’ life cycles, since slight changes in temperature often initiate the breeding response, interacting with other factors, such as seasonal changes in temperature, day length and food abundance (Sastry, 1975).

Another well known adaptation of estuarine organisms is the capacity to regulate their metabolic rate to very low levels in oxygen-deficient habitats (Vernberg, 1983; Hylland et al., 1997). Moreover, some benthic species, such as polychaetes and bivalves, can improve the levels of oxygenation of hypoxic sediments, by increasing water exchanges through their burrowing activities, phenomena known as bioturbation (Levinton, 1995). Infaunal bioturbation activity also influences biogeochemical processes in the sediment and redox conditions (Rosenberg, 2001) and modifies the characteristics of the substratum (biogenic habitat modifiers) (Constable, 1999). The level of oxygen in the sediment determines the vertical distribution of infaunal species, with 95% living in the first 5 cm (Dauer et al., 1987).

Invertebrate estuarine communities also developed special reproductive strategies to adapt to high hydrodynamic conditions, such as (1) gametal or larval release synchronized with the tidal cycle, (2) mobile species migrate to release gametes or larvae in locations compatible with the larval tolerances (Dauer et al., 1980), and (3) some species have benthic lecitothrophic larvae that benefit from parental care (Vernberg, 1983).

Estuaries – anthropogenic challenges for estuarine communities

Estuaries are particularly exposed to human pressure, since the majority of the urban areas are located in estuaries and coastal areas, and rivers and estuaries have always been exploited as sources of food and used as transport ways and disposal receptors. The study of estuarine ecology is closely related to increasing awareness on the deleterious impacts of human activities upon estuarine health, as shown by the pioneer work of Alexander et al. (1935 in McLusky, 1999), a detailed study of the Tees estuary in the UK, reporting extreme degradation of a large extension of that estuary. Some well known anthropogenic pressures are urban and industrial sewage discharge, agriculture and urban runoff, navigation, dredging, fisheries, aquaculture, wetland occupation, invasive species and climate change. Impacts on estuarine organisms are very complex in nature because the effects are often a result of interactions between different stressors, confounding the interpretation of data and the effects of specific pollutants. Nevertheless, some specific pressures and their impacts on the benthic fauna are well documented:

(1) Organic enrichment – estuaries are naturally rich in organic matter, mainly resulting allocthonous detrital input as well high levels of autochthonous primary production. Human activities, such as sewage discharge and aquaculture increase the natural

10 General introduction

Chapter 1

organic load with labile organic matter with a low C:N ratio and a high content of nutrients when compared to natural sources (Kristensen, 1990; Holmer & Kristensen, 1994). The effects of organic enrichment on benthic invertebrate communities have been extensively studied and Pearson & Rosenberg’s (1978) model is widely used to explain the succession of species along an enrichment gradient. These authors found that an undisturbed macrobenthic community maintains relatively high species richness and biomass as well as moderate total abundance. A slight increase of organic inputs favours the occurrence of a higher species number, with higher abundance and biomass and a peak of opportunistic species occur with high organic inputs. These opportunistic species are small-bodied and have high growth rates, explaining a biomass peak. Ultimately, when extreme pollution levels occur, abundance, biomass and number of taxa decrease abruptly and heavily polluted areas may be azoic (Pearson & Rosenberg, 1978; Rakocinski et al., 2000).

(2) Eutrophication – According to the OSPAR Convention (OSPAR, 1997), an eutrophication process is “the enrichment of water by nutrients causing an accelerated growth of algae and higher forms of plant life to produce an undesirable disturbance to the balance of organisms present in the water and to the quality of the water concerned, and therefore refers to the undesirable effects resulting from anthropogenic enrichment by nutrients”. Eutrophication has become a significant problem in many estuaries and coastal areas over the last four decades (Bricker et al., 2003), mainly due to sewage discharges an agriculture runoff. Associated with algal blooms are low dissolved oxygen levels that are the result of particulate matter deposition and stimulated microbial decomposition of organic matter. Such decreased oxygen levels may be a direct cause of death or severe disturbance of benthic invertebrate fauna (Flindt et al., 1997).

(3) Non-nutrient pollutants – The most common groups of contaminants found in estuaries include heavy metals (e.g. Cu, Cr, Hg, Ni, Pb, Zn), Polynuclear Aromatic Hydrocarbons (PAHs) Polychlorinated Biphenyls (PCBs) and some pesticides (e.g. aldrin, DDT, dieldrin, hexachlorobenzene). While pesticides are mainly from diffuse sources, all others can be either from point source discharges (e.g. industrial activities) or diffuse origins (motorway lixiviation). The presence of these chemicals in the water and sediments can affect the reproduction, development, and, ultimately, survival of living resources and have been referred to as toxic chemicals, or chemical contaminants. Several authors have shown deleterious effects of these contaminants on the benthic fauna, including (1) decreased growth rates (Levin et al., 1996), (2) lower fertility (Zulkosky et al., 2002), (3) carcinogenic and mutagenic sublethal toxic effects (Kennish, 1992) and bioaccumulation along the benthic food

11 web (Costello & Read, 1994). On the other hand, below certain concentrations, chemicals may induce competitive advantages to opportunistic species (Boesch & Rosenberg, 1981 in Rakocinski et al., 2000; Beeby & Richmond, 2001). The toxicity of sediments with chemical contaminants is often measured through acute toxicity tests with amphipod species, based on survival and reproduction rates (e.g. Costa et al., 1998; Casado-Martinez et al., 2007).

(4) Physical impacts – The alteration/destruction of wetlands for urban purposes is a major cause of habitat loss in estuaries, especially saltmarsh areas, reducing some specific habitats colonized by benthic invertebrates. Changes in the sediment composition may also occur in the course of dredging activities, normally performed for sand extraction and maintenance of the navigations channels. This can lead to replacement of coarser sediments by fine deposits at the extraction site and to colonization by benthic communities which are different from those in the original deposits (Hall, 1994), more often dominated by opportunistic polychaetes (Seiderer & Newell, 1999; Van Dalfsen et al., 2000). Intensive bottom fishing activities, especially by gears that dig into the sea-bed or bottom trawling have major impacts upon benthic communities, such as changes in the ratio of major groups and disappearance or fragile species (Kaiser & Spencer, 1996; Hill et al., 1999).

Ecological assessment and environmental policies - The Water Framework Directive

In the last two decades, environmental indicators have become a very important component of national and international environmental regulations, mainly through the obligation of developing environmental impact assessment studies for most projects with potential environmental impacts and environmental policies that require monitoring of the biological components (e.g. EEA, 2001; OECD, 2001; EPA, 2003). Setting environmental objectives for estuaries has become a worldwide concern, as shown by the framework for water quality management defined by the Australian and New Zealand Governments (ANZECC, 2000), the South African National Water Act (Act 56 of 1998), the North American Clean Water Act (Gibson et al., 2000) and the European Water Framework Directive (WFD) (2000/60/CE).

The WFD sets the achievement of good ecological status and good chemical status for surface waters by 2015 as its major objectives. The successful implementation of this directive depends on an integrated approach to water problems, supported by some fundamental concepts, such as (1) a single approach of water protection for all waters, including surface waters and groundwater, (2) achieving Good status for all waters by a set

12 General introduction

Chapter 1 deadline, (3) water management based on river basins, (4) a combined approach of emission limit values and quality standards, (5) using water pricing as an incentive for better use, (6) getting citizens involved more closely and (7) streamlining legislation.

The assessment of ecological status requires the development of adequate tools, based on the identification of surface water types, the definition of type-specific reference conditions, and the classification of all water bodies within five ecological quality classes. A common implementation strategy for the WFD was agreed between European Member States and several working groups developed guidance documents on diferent aspects of the WFD, including the assessment of ecological status in transitional waters (i.e. estuaries).

Typology

The process of typology was established with the purpose of assigning water bodies to a physical type, in order to ensure that valid comparisons of its ecological status can be made. For each type, reference conditions must be described as these form the ‘anchor’ for classification of the water bodies’ status or quality. The identification of water types according to the WFD, can follow two different approaches, designated by system A and system B. If system A is used the type must first be assigned to an Ecoregion, as indicated in the WFD and then described according to mean annual salinity and mean tidal range. In case of using system B, at least the same degree of differentiation must be achieved as if system A was used. System B uses a series of obligatory (e.g. tidal range and salinity) and optional factors (e.g. mean substratum composition, current velocity) in order to classify surface waters into types. Within each type, water bodies have to be identified, as the basic management units of the WFD. The subdivision of water types into smaller water bodies depends upon the pressures and resulting impacts, because areas that show different quality status, although included in the same water type, will have to be managed separately.

Reference conditions

The reference condition is a description of the biological quality elements that exist, or would exist under no or very minor disturbance from human activities, therefore corresponding to a High status. Reference condition standards are used to assess the ecological quality by comparing each situation against these standards. Reference conditions are characterized for each water type, based on the biological elements considered by the WFD for transitional waters, namely phytoplankton, other aquatic flora (macroalgae and angiosperms), benthic invertebrate macrofauna and fish fauna. Some methods indicated by the WFD to define reference conditions are (1) using existing pristine conditions, (2) using

13 historical, paleontological and other available data with sufficient level of confidence about the values for the reference condition, (3) using modelling techniques, either predictive models or hindcasting methods or (4) expert judgement when other methods are not possible. Reference conditions must summarise the range of possibilities and values for the biological quality elements over periods of time and across the geographical extent of the type, i.e. reflect the natural variability of the characterization elements.

Classification of quality status

The assessment of chemical status is based on a list of priority substances for which Environmental Quality Standards (EQSs) are set at the European level. There are only two categories for chemical status: Good and Bad. On the other hand, there are three groups of quality elements to be considered in the assessment of ecological status: (1) biological elements (listed in the reference conditions), (2) hydromorphological elements supporting the biological elements (i.e. tidal regime and morphological characteristics) and (3) chemical and physical-chemical elements supporting the biological elements, these last including general physical-chemical elements (e.g. temperature, salinity, nutrient concentrations) and specific non-priority substances identified as being discharged in significant quantities. Biological elements must be assigned into five different quality classes (High, Good, Moderate, Poor and Bad), by comparing it against the reference conditions for that type and expressed as an Ecological Quality Ratio (EQR) that varies between 0 (Bad status) and 1 (High status). Although different methods can be used by different countries to classify the ecological status, the classifications have to be comparable. To ensure that different methods produce similar classifications, all European countries are participating in an intercalibration process, in which the results obtained using different methods are compared within the same water types.

The integration of biological criteria in the assessment and definition of water quality standards was one of the major changes introduced by the WFD to European legislation on water issues. As pointed out by Dauer (1993), the use of biological elements is very important because (1) they are direct measures of the condition of the biota, (2) they may uncover problems undetected or underestimated by other methods, and (3) such criteria provide measurements of progress of restoration efforts. However, biological criteria should not replace toxicity and chemical assessment methods, but complement the information produced by those, serving as independent evaluations of the quality of marine and estuarine ecosystems (Dauer, 1993).

14 General introduction

Chapter 1

Benthic invertebrates as indicators

Benthic macroinvertebrate communities (here defined as organisms retained on a 0.5 mm screen) have been widely used as indicators for assessing and monitoring human impacts over aquatic ecosystems. Several characteristics of these communities make them respond predictably to many kinds of natural and anthropogenic pressures:

(1) Most benthic invertebrate species have limited mobility and are less able to avoid potential harmful conditions than mobile species, thus reflecting local conditions (Gray, 1979);

(2) Benthic species show very diverse physiological tolerances, life strategies and feeding modes, being sensitive to different types of stress (Pearson & Rosenberg, 1978; Bilyard, 1987);

(3) Life cycles, longevity and recruitment potential of most species allow the community structure to integrate and reflect disturbances over a relatively long period of time (Paul et al., 2001);

(4) Many benthic organisms live in the sediment-water interface, where contaminants can accumulate over long periods (Gray & Pearson, 1982);

(5) Benthic organisms are a very important component of estuarine ecosystems, closely coupled with the pelagic food web, constituting a link for the transport of contaminants to higher trophic levels (Pearson & Rosenberg, 1978; Herman et al., 1999).

Because of these characteristics, they have been often used as a proxy for assessment of the habitat quality or biological integrity, commonly defined as “the capability of supporting and maintaining a balanced, integrated, adaptive community of organisms having a species composition, diversity and functional organization comparable to that of the natural habitat of the region” (Karr & Dudley, 1981). Impacts on aquatic ecosystems may be measured at different levels of biological organization, which can include several components of the ecosystem (e.g. estuarine food web), certain communities (e.g. benthic infaunal macroinvertebrates), a few indicator species (e.g. pollution indicator species) or even populations (e.g. population dynamics of keystone species).

A good indicator should be (1) applicable to many areas/situations and scales of measurement, (2) repeatable and reproducible by others besides its authors, (3) sensitive to pressures acting on the system, responding in a predictable manner, but be relatively insensitive to expected (natural) sources of interference, (4) operationally simple (e.g. not require excessive data collection skills), (5) predict changes that can be mitigated through a

15 correct management, (6) integrative and cover key ecological gradients, (7) scientifically reliable, and (8) the benefits of the information provided by the indicator should outweigh the costs of usage (Dale & Beyeler, 2001; Niemeijer & Groot, 2007).

Several methods have been used to study benthic communities, incorporating the simultaneous responses of many species rather than using a single species as indicator. In general, most approaches are based on the description and quantification of benthic communities’ patterns and the correlation of those patterns with environmental conditions. Single or multiple attributes of the benthic communities have been used to measure deviations from the normal patterns in undisturbed conditions, but the most commonly used are abundance, biomass, species richness and diversity and the balance between pollution- sensitive and pollution-tolerant species (e.g. Pearson & Rosenberg, 1978; Warwick, 1986; Weisberg et al., 1997; Borja et al., 2000). With the availability of computational tools, the analysis of more complex data sets was possible, through methods such as classification and ordination techniques, which allow identifying general patterns of the benthic communities and relating them to environmental patterns. These techniques are often used to examine spatial patterns of distribution and changes occurring along time, associated to disturbance events (Gray & Pearson, 1982). Although multivariate analysis is very useful for describing strong pollution gradients and/or changes in the spatial and time distribution patterns, it is often difficult to interpret it into understandable results for management purposes.

The need to communicate complex patterns of the structure of benthic communities in a management perspective was one of the major reasons for the development and successful widespread use of biotic indices or indices of biotic integrity. Indices simplify the characterization of the overall state of the ecosystem by reducing it to a single number, quantify the deviations to reference conditions and facilitate communication on environmental issues to stakeholders and policy makers (Aubry & Elliott, 2006). Diversity indices are among the most used in ecology and environmental assessment and are often included in multimetric indices. The most commonly used indices of diversity are the Shannon-Wiener index (Pielou, 1969), which incorporates species richness and evenness, Sander’s rarefaction technique (Sanders, 1968), based on an estimate of the number of species among 100 individuals (ES100), later modified by Hulbert (1971), Margalef and Simpson’s diversity indices that measure the probability that two individuals randomly selected from a sample will belong to the same species (Simpson, 1949). The reduction of the complexity of species composition into a single number, although very appealing, does not incorporate other important information of the benthic community, such as the sensitiveness to stress, trophic interactions or life cycles, since all species are given equivalent weights.

Warwick (1986) developed the Abundance-Biomass Comparison method (ABC) that assumes that in undisturbed communities the biomass curve will lie above the abundance

16 General introduction

Chapter 1 curve, while in highly stressed communities the biomass curve will lie below the abundance curve. Since the interpretation of the results was mainly graphical, Clarke (1990) proposed the W-statistic as a measure that expresses the degree to which the biomass curves lie above the abundance curves. Other indices, such as the AZTI Marine Biotic Index (AMBI) (Borja et al., 2000) and BENTIX (Simboura & Zenetos, 2002) are based on the classification of species into different ecological groups, according to their sensitiveness/tolerance to pollution. The number of ecological groups varies according to each index (five for the AMBI and two for the BENTIX). AMBI has been changed to incorporate Shannon-Wiener diversity and species richness into a final classification, based on the classification of status by measuring deviations from reference conditions using factorial analysis, therefore, this approach is called Multivariate or M-AMBI (Muxika et al., 2007). Similar to M-AMBI, the Community Disturbance Index (CDI) also derives classifications using a multivariate model constrained by reference conditions (Flåten et al., 2007). The Benthic Quality Index (BQI) incorporates a measure of species’ tolerance to disturbances, but also Hulbert’s diversity index (Rosenberg et al., 2004).

Moreover, there are indices that consider information on the feeding habitat of invertebrate species, such as the Infaunal Trophic Index (ITI), in which species are assigned to four different trophic groups (i.e. suspension feeders, carrion feeders, surface deposit feeders and sub-surface deposit feeders) and the result of the index is based on the relative contribution of each group (Dauvin et al., 2007). Information on the taxonomic value of the benthic communities has also been incorporated in assessment tools, such as taxonomic distinctness, a measure that captures phylogenetic diversity, represented by taxonomic distances between every two pairs of individuals (Clarke & Warwick, 1998). The “complexity” of the ecological species at a given location can also be used as an indicator by determining the exergy, a thermodynamically based index that measures the maximum amount of useable work that can be extracted when a system is brought into equilibrium with a reference state (Marques et al., 1997). One major problem of most of these indices concerns its application to estuaries because of the high level of natural stress in this transitional environment. Species adapted to the high natural stress in estuaries are also commonly identified as opportunists under disturbances caused by pollution (Borja & Muxika, 2005; Chainho et al., 2006; Chainho et al., 2007).

17 of the benthic invertebrate assemblages. In some multimetric indices developed for North American estuaries, such as the B-IBI and the MAIA index, habitat specific thresholds and temporal stratification applications are indicated as a solution to encompass natural differences along the estuarine gradient and eliminate noise introduced by seasonal changes in the communities (Weisberg et al., 1997; Van Dolah et al., 1999; Llansó et al., 2002).

As pointed out by Diaz et al. (2004), a plethora of indices are continuously being developed, with no apparent justification. This author argues that the refinement and adaptation of the existing indices to different regions from those for which they were developed should be the priority.

Portuguese estuaries

Before the publication of the Water Framework Directive, available studies on benthic communities in Portuguese estuaries were mainly focused on the zonation of communities along environmental gradients (e.g. Andrade, 1986; Marques & Bellan-Santini, 1987; Costa, 1988; Quintino & Rodrigues, 1989; Quintino et al., 1989; Moreira et al., 1993) and particular aspects of the ecology of a few species (e.g. Queiroga, 1990; Marques et al., 1994; Guerreiro, 1998). Some work had been done on the effects of pollution on benthic communities, but often restricted to narrow areas of a particular estuary and/or to a single collection (e.g. Rodrigues, 1992; Marques et al., 1993; Mucha & Costa, 1999). Some of these studies were conducted as part of environmental impact studies, often related to dredging operations needed for maintaining navigation conditions in estuaries (e.g. Costa et al., 1999; Carvalho et al., 2001; Rodrigues & Quintino, 2002;), but also for monitoring purposes (Pereira et al., 1997; Silva et al., 2006). Long term data sets are available only for the Mondego (e.g. Dolbeth et al., 2007) and Tejo estuaries (e.g. Silva, 2006; Silva et al., 2006), making it difficult to interpret variations in benthic communities related to inter-annual changes in the environmental conditions. With the process of implementation of the WFD the investigation on assessment of the ecological status in Portuguese estuaries was encouraged and several teams and researchers got involved in the implementation process, publishing very important contributions.

The Portuguese working group for transitional waters compiled relevant data for Portuguese estuaries into a database, although for most estuaries data on hydromorphological, physical chemical and biological elements were not available for oligohaline and tidal freshwater stretches (Bettencourt et al., 2004). System B was used as a typology tool for Portuguese transitional waters and in addition to the obligatory factors (i.e. latitude/longitude, salinity and tidal range), mixing conditions, wave exposure and depth were also used as optional factors in the identification of water types. Portuguese estuaries

18 General introduction

Chapter 1 were classified into two different water types, namely type A1 that included Minho, Lima, Douro and Leça estuaries, characterized by mesotidal and stratified conditions, and type A2, which included well-mixed estuaries with irregular river discharge, namely Ria de Aveiro, Mondego, Tejo, Sado, Mira, Arade and Guadiana estuaries (Bettencourt et al., 2004). Type A2 is unique in the European context, but it includes estuaries with very distinct characteristics, For instance, the area of these estuaries varies between 17 Km2 and 2200 Km2 and the average freshwater flows may range from 10 m3.s-1 to 400 m3.s-1 (Mira and Tejo estuaries, respectively) (Ferreira et al., 2003). Estuaries were further divided into water bodies, taking natural and human components into account and the number of water bodies identified in each estuary varied between three (Lima, Douro, Mondego, Mira and Guadiana estuaries) and six (Sado estuary) (Ferreira et al., 2005). Nevertheless, Teixeira et al. (in press) concluded that six sectors would be needed in the Mondego estuary to encompass the benthic communities’ variability and define habitat specific reference conditions. The Shannon- Wiener diversity index, the Margalef species richness index (Legendre & Legendre, 1976), the AMBI index (Borja et al., 2000) and the ABC method (Warwick, 1986) were selected to assess the benthic condition in the Portuguese estuaries and a combination of two or three indices (TICOR approach) is proposed to classify the benthic status according to the requirements of the WFD (Bettencourt et al., 2004).

The efficiency of these indices was tested in the Mondego estuary by Salas et al. (2004), showing that AMBI was more accurate in separating between degraded and undegraded conditions. These authors recommend the complementary use of different methods, since single indices did not always show predictable responses to anthropogenic stress. However, when tested in other locations, these indices and others, such as taxonomic distinctness and exergy were not sensitive enough to classify benthic status into five different categories, as required by the WFD (Salas et al., 2005). Moreover, Carvalho et al. (2006) corroborated the usefulness of AMBI to identify the responses of benthic communities along gradients of organic enrichment in the Óbidos coastal lagoon. Conversely, Quintino et al. (2006) concluded that variability of environmental indicators and indices within stations could be as large between stations when using AMBI, BQI, and the United Kingdom ecological quality ratio – the latter derived for benthic communities, abundance/richness and biomass/richness ratios. Those authors emphasized the need for further testing and validating indices that were developed elsewhere before applying it to Portuguese estuarine systems.

19 FRAMEWORK AND OBJECTIVES OF THIS STUDY

Available comprehensive scientific data on benthic invertebrate communities of Portuguese estuaries was very scarce in the beginning of the present study, either because data sets did not include information on different spatial units (i.e. habitats) along the estuarine gradient, or collections were obtained in a specific season, thus not including information on time variability of the benthic communities. These aspects are particularly important in the aim of the implementation of the European Water Framework Directive that requires a systematic assessment of the ecological quality based on biological elements, including benthic invertebrates, taking into account the natural variability (spatial and temporal). Specific features of the Portuguese estuaries and the current state of knowledge on the respective benthic communities represented a challenge and a reason for developing the present research, as follows:

(1) Portugal is a transition area since, although included in the subtropical sub province (Strait of Gibraltar to Finisterre) of the Lusitanian climatic province, for most taxonomic groups there are differences between the northern and southern assemblages (OSPAR Commission, 2000). Cape Carvoeiro is indicated as a transition area for coastal marine ecosystems (Hayden et al., 1984) and the Tejo estuary is also pointed out as a biogeographical boundary between Mediterranean, temperate and sub-tropical Atlantic realms (INAG, 2001);

(2) Most Portuguese estuaries have irregular river discharge, altering regularly the environmental conditions and constraining the occurrence of benthic species with different levels of tolerance to natural stress. This type of estuary is known as poikilohanine (variable salinity conditions) and is poorly known when compared to homiohaline estuaries (stable salinity conditions). Although all influenced by irregular river discharge, poikilohaline estuaries have very distinct morphological and hydrological characteristics;

(3) Portuguese estuaries are strongly affected by extreme climatic events, such as floods and droughts, whose frequency is expected to increase with climate change;

(4) Most Portuguese estuaries are under severe anthropogenic stress and none can be considered as a pristine transitional area, hence reference conditions cannot be derived based on existing sites;

(5) The WFD requires the identification of water types that are ecologically meaningful and further definition of type specific reference conditions, against which the classification of the ecological status of water bodies must be done. The WFD is

20 General introduction

Chapter 1

particularly demanding for countries with no systematic monitoring of the biological components, as is the case of Portugal.

(6) A considerably high number of indices to assess benthic invertebrate status are available for marine environments but very few were developed specifically for estuarine conditions. No specific indices have been developed for Portuguese estuaries.

Considering this particular context, the main objective of this study was to investigate if the characteristics of the Portuguese estuaries constrained the use of existing assessment tools for evaluating ecological status based on benthic invertebrate communities. To test this hypothesis, several different questions were formulated:

(1) What are the major environmental gradients influencing the spatial distribution of benthic invertebrate communities in Portuguese estuaries?

(2) Do seasonal variations in the environmental conditions change those spatial patterns of distribution?

(3) How do seasonal patterns in macroinvertebrates influence the results of benthic indices?

(4) Are there significant differences in the classifications obtained with indices when applied during different seasons?

(5) Do benthic invertebrate communities show different responses to natural and anthropogenic sources of stress?

(6) Which environmental variables best reflect the responses of the benthic communities to human stressors?

(7) Can higher taxonomic levels and taxocenes be used in the typology process?

(8) Do hydromorphological differences in estuaries included in the same type influence the use of indicators?

(9) Are multimetric indices better than individual metrics?

(10) Are available indices adequate to assess ecological status in Portuguese estuaries with the accuracy required by the WFD?

Three different Portuguese estuaries were selected to test the assessment tools and answer these questions, namely the Mondego, Tejo and Mira estuaries. Seasonal samples were collected in the Mondego estuary to investigate aspects related to seasonal variations, while

21 only summer collections took place in the other estuaries, allowing for comparisons of different levels of human pressure and different hydromorphological characteristics.

THESIS OUTLINE

This thesis was divided in six different chapters, including a General Introduction, four papers published or in press in scientific journals included in the science citation index and some Final Remaks, as it follows:

- Chapter 1 is a general introduction that summarises and organizes the major characteristics of estuaries and the constraints that these particular environments present to benthic invertebrate communities. This introduction also gives an overview of policies and regulations that require the implementation of benthic assessment tools, with particular emphasis for the European Water Framework Directive. Some advantages of using benthic invertebrates as indicators of environmental quality are indicated and a summary revision of the major attributes and indices used is also presented. This chapter also includes an update on the current knowledge on the benthic communities of Portuguese estuaries and some of the major findings on the use of benthic assessment tools in these estuaries, in the context of the implementation of the WFD. The objectives of the thesis and the questions behind the definition of these objectives conclude this chapter.

- Chapter 2 consists of a paper that identifies seasonal and spatial patterns of distribution of subtidal benthic invertebrate communities in the Mondego River estuary. This paper examines the adaptations associated with benthic communities in estuaries with highly variable environmental conditions, including the occurrence of floods, addressing questions 1, 2 and 5. It was published in the journal Hydrobiologia, referenced as Chainho, P., Costa, J.L., Chaves, M.L., Lane, M.F., Dauer, D.M. & Costa, M.J. 2006. Seasonal and spatial patterns of distribution of subtidal benthic invertebrate communities in the Mondego River estuary, Portugal. Hydrobiologia 555: 59–74.

- Chapter 3 consists of a paper on the use of taxonomic sufficiency in Mondego River estuary for typology purposes. Different water body types are identified using the Venice salinity system and ecological consistency of that identification is evaluated by analysing the spatial patterns of distribution of benthic invertebrate communities among different seasons. Different taxonomic levels and taxocenes are used to test for their ability to discriminate between water body types previously defined, addressing questions 2 and 7. This paper was published in Hydrobiologia, referenced as Chainho, P., Lane, M.F.,

22 General introduction

Chapter 1

Chaves, M.L., Costa, J.L., Costa, M.J. & Dauer, D.M. 2006. Taxonomic sufficiency as a useful tool for typology in a poikilohaline estuary. Hydrobiologia 587: 63-78.

- Chapter 4 consists of a paper that examines the influence of seasonal variability in the benthic invertebrate communities of the Mondego estuary and how those changes might influence the use of biotic indices. The methods proposed by the WFD Portuguese working group for transitional waters are tested to see how robust the results are when no temporal stratification is applied, addressing questions 3, 4 and 6. This paper was published in Marine Pollution Bulletin, referenced as Chainho, P., Costa, J.L., Chaves, M.L., Dauer, D.M. & Costa, M.J. 2007. Influence of seasonal variability in benthic invertebrate community structure on the use of biotic indices to assess the ecological status of a Portuguese estuary. Marine Pollution Bulletin 54: 1586–1597.

- Chapter 5 consists of a paper that compares the results obtained by two different multimetric indices, TICOR approach and B-IBI, when applied to three Portuguese estuaries with different hydromorphological characteristics and different levels of human pressure. The results of these indices are tested for consistency with a priori status categories based physical-chemical criteria, addressing questions 8, 9 and 10. This paper is in press in Marine Pollution Bulletin, referenced as Chainho, P., Costa, J.L., Chaves, M.L., Costa, M.J. & Dauer, D.M. In press. Use of multimetric indices to classify estuaries with different hydromorphological characteristics and different levels of human pressure. Marine Pollution Bulletin.

- Some final remarks are presented in Chapter 6, integrating the results obtained in each chapter and presenting the major conclusions of the study. Relevant questions that arose from this thesis are also formulated, as guidance for the development of possible future studies.

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28 General introduction

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Levin, L.A., Boesch, D.F., Covich, A., Dahm, C., Erséus, C., Ewel, K.C., Kneib, R.T., Moldenke, A., Palmer, M.A., Snelgrove, P., Strayer, D. & Weslawski, J.M. 2001. The function of marine critical transition zones and the importance of sediment biodiversity. Ecosystems 4: 430–451. Levin, L., Caswell, H., Bridges, T., Di Bacco, C., Cabrera, D. & Plaia, G. 1996. Demographic responses of estuarine polychaetes to pollutants: life table response experiments. Ecological Applications 6: 1295–1313. Levinton, J. 1995. Bioturbators as ecosystem engineers: control of the sediment fabric, inter– individual interactions, and material fluxes. In Jones, C.G. & Lawton, J.H. (Eds.), Linking Species and Ecoystems, pp. 29–36. Chapman & Hall, New York, U.S.A. Llansó, R.J., Scott, L.C., Hyland, J.L., Dauer, D.M., Russel, D.E. & Kutz, F.W. 2002. An estuarine Benthic Index of Biotic Integrity for the Mid-Atlantic Region of the United States. II. Index development. Estuaries 25: 1231-1242. Mannino, A. & Montagna, P.A. 1997. Small-scale spatial variation of macrobenthic community structure. Estuaries 20: 159–173. Marques, J.C. & Bellan-Santini, D. 1987. Crustacés amphipodes des côtes du Portugal: faune de l'estuaire du Mira (Alentejo, côte sud-ouest) [Amphipod crustaceans of the Portuguese coasts: Fauna of the Mira Estuary (Alentejo, South-West coast)]. Cahiers de Biologie Marine 28: 465–480. Marques, J.C., Martins, I., Teles-Ferreira, C. & Cruz, S. 1994. Population dinamics, life history and reprodution of Cyathura carinata (Krøyer) (Isopoda, Anthuridae) in the Mondego estuary. Journal of Crustacean Biology 14: 258–272. Marques, J.C., Maranhão, P. & Pardal, M.A. 1993. Human impact assessment on the subtidal macrobenthic community structure in the Mondego estuary (western Portugal). Estuarine, Coastal and Shelf Science 37: 403–419. Marques, J.C., Pardal, M.A., Nielsen, S.N. & Jørgensen, S.E. 1997. Analysis of the properties of exergy and biodiversity along an estuarine gradient of eutrophication. Ecological Modelling 102: 155–167. McLusky, D.S. 1971. Ecology of estuaries. Heinemann, London, U.K. McLusky, D.S. 1981. The Estuarine Ecosystem. John Wiley & Sons, New York, U.S.A. McLusky, D.S. 1999. Estuarine benthic ecology: a European perspective. Australian Journal of Ecology 24: 302–311. McLusky, D.S. & McIntyre, A.D. 1995. Northern and southern estuaries and coastal areas. Netherlands Journal of Aquatic Ecology 29: 469–471. Moreira, M.H., Queiroga, H., Machado, M.M. & Cunha, M.R. 1993. Environmental gradients in a southern Europe estuarine system: Ria de Aveiro, Portugal: implications for soft bottom macrofauna colonization. Netherlands Journal of Aquatic Ecology 27: 465–482.

29 Mucha, A.P. & Costa, M.H. 1999. Macrozoobenthic community structure in two Portuguese estuaries: relationship with organic enrichment and nutrient gradients. Acta Oecologica 20: 363–376. Muxika, I., Borja, A. & Bald, J. 2007. Using historical data, expert judgement and multivariate analysis in assessing reference conditions and benthic ecological status, according to the European Water Framework Directive. Marine Pollution Bulletin 55: 16– 29. Niemeijer, D. & de Groot, R.S. 2007. A conceptual framework for selecting environmental indicator sets. Ecological Indicators 8: 14–25. Nybakken, J.W. 1993. Marine Biology: an ecological approach. Harper Collins College Publishers, New York, U.S.A. OECD. 2001. Environmental indicators: towards sustainable development. Organisation for Economic Co-operation and Development, Paris, France. Officer, C.B. 1983. Physics of estuarine circulation. In Ketchum, B.H. (ed.). Ecosystems of the World. Estuaries and enclosed seas, pp. 15–41. 26. Elsevier Science Publishing Company, Amsterdam, The Netherlands. OSPAR. 1997. The common procedure for the identification of the eutrophication status of the OSPAR maritime area. OSPAR Agreement 1997-17, London, U.K. OSPAR Commission. 2000. Quality status report 2000. OSPAR Commission, London, U.K. Paavola, M., Olenin, S. & Leppäkoski, E. 2005. Are invasive species most successful in habitats of low native species richness across European brackish water seas? Estuarine, Coastal and Shelf Science 64: 738–750. Paul, J.F., Scott, K.J., Campbell, D.E., Gentile, J.H., Srobel, C.S., Valente, R.M., Weisberg, S.B., Holland, A.F. & Ranasinghe, J.A. 2001. Developing and applying a benthic index of estuarine condition for the Virginian Biogeographic Province. Ecological Indicators 1: 83–99. Pearson, T.H. & Rosenberg, R. 1978. Macrobenthic succession in relation to organic enrichment and pollution of the marine environment. Oceanography and Marine Biology: an Annual Review 16: 229–311. Pereira, C.D., Gaudêncio, M.J., Guerra, M.T. & Lopes, M.T. 1997. Intertidal macrozoobenthos of the Tagus estuary (Portugal): the Expo'98 area. Publicaciones Especiales del Instituto Español de Oceanografía 23: 107–120. Pielou, E.C. 1969. An introduction to mathematical ecology. John Wiley & Sons, New York, U.S.A. Pritchard, D.W. 1967. What is an estuary: a physical viewpoint. American Association for the Advancement of Science 83: 3–5. Queiroga, H. 1990. Corophium multisetosum (Amphipoda: Corophiidae) in Canal de Mira, Portugal: some factors that affect its distribution. Marine Biology 104: 397–402.

30 General introduction

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Quintino, V., Elliott, M. & Rodrigues, A.M. 2006. The derivation, performance and role of univariate and multivariate indicators of benthic change: case studies at differing spatial scales. Journal of Experimental Marine Biology and Ecology 330: 368–382. Quintino, V. & Rodrigues, A.M. 1989. Environment gradients and distribution of macrozoobenthos in three Portuguese coastal systems: Obidos, Albufeira and Alvor. In Ryland, J.S. & Tyler, P.A. (eds.), Reproduction, Genetics and Distributions of Marine Organisms, pp. 441–450. Olsen & Olsen, Fredensborg, Denmark. Quintino, V., Rodrigues, A.M. & Gentil, F. 1989. Assessment of macrozoobenthic communities in the lagoon of Obidos, western coast of Portugal. Scientia Marina 53: 645–654. Rakocinski, C.F., Brown, S.S., Gaston, G.R., Heard, R.W., Walker, W.W. & Summers, J.K. 2000. Species-abundance-biomass responses by estuarine macrobenthos to sediment chemical contamination. Journal of Aquatic Ecosystem Stress and Recovery 7: 201–214. Redeke, H.C. 1935. Acartia (Acanthacartia) tonsa Dana ein neuer Copepode des Niederländischen Brackwassers. Archives Néerlandaises de Zoologie 1: 315–329. Remane, A. 1934. Die Brackwasserfauna. Zoologischer Anzeiger 7: 34–74. Remane, A. & Schlieper, C. 1971 Physiology of brackishwater. Wiley Inter-science, New York, U.S.A. Rhoads, D.C. & Young, D.K. 1970. The influence of deposit feeding organisms on sediment stability and community trophic structure. Journal of Marine Research 28: 150–78. Rodrigues, A.M. 1992. Environmental status of a multiple use estuary, through the analysis of benthic communities: the Sado Estuary, Portugal. Doctoral Thesis, University of Sterling, Scotland. Rodrigues, A.M. & Quintino, V. 2002. Estuarine sediment assessment prior to dredging: integration of benthic ecotoxicology studies. Journal of Coastal Research 34: 306–317. Rosenberg, R. 2001. Marine benthic faunal successional stages and related sedimentary activity. Scientia Marina 65: 107–119. Rosenberg, R., Blomqvist, M., Nilsson, H.C., Cederwall, H. & Dimming, A. 2004. Marine quality assessment by use of benthic species-abundance distributions: a proposed new protocol within the European Union Water Framework Directive. Marine Pollution Bulletin 49: 728–739. Salas, F., Marcosa, C., Neto, J.M., Patrício, J., Pérez-Ruzafa, A. & Marques, J.C. 2006. User- friendly guide for using benthic ecological indicators in coastal and marine quality assessment. Ocean and Coastal Management 49: 308–331. Salas, F., Neto, J.M., Borja, A. & Marques, J.C. 2004. Evaluation of the applicability of a marine biotic index to characterize the status of estuarine ecosystems: the case of Mondego estuary (Portugal). Ecological Indicators 4: 215–225.

31 Salas, F., Patrício, J., Marcos, C., Pardal, M.A., Pérez-Ruzafa, A. & Marques, J.C. 2005. Are taxonomic distinctness measures compliant to other ecological indicators in assessing ecological status? Marine Pollution Bulletin 52: 817–829. Salen-Picard, C. & Arlhac, D. 2002. Long-term changes in a Mediterranean benthic community: relationships between the Polychaete assemblages and hydrological variations of the Rhône River. Estuaries 25: 1121–1130. Sanders, H.L. 1968. Marine benthic diversity: a comparative study. American Naturalist 102: 243-282. Sanders, H.L., Mangelsdorf Jr., P.C. & Hampson, G.R. 1965. Salinity and faunal distribution in the Pocasset River, Massachusetts. Limnology and Oceanography 10: 216–229. Sastry, A.N. 1975. Physiology and ecology of reproduction in marine invertebrates. In Vernberg, F.J. (ed.), pp. 279-300. Physiological Ecology of Estuarine Organisms. University of South Carolina Press, Columbia, U.S.A. Seiderer, L.J. & Newell, R.C. 1999. Analysis of the relationship between sediment composition and biological community structure in coastal deposits: implications for marine aggregate dredging. ICES Journal of Marine Science 56: 757–765. Silva, G. 2006. Structure and dynamics of soft-bottom benthic macroinvertebrates communitites: a case study in the Tagus estuary. Master Thesis, Universidade de Lisboa, Portugal. Silva, G., Costa, J.L., Almeida, P.R. & Costa, M.J. 2006. Structure and dynamics of a benthic invertebrate community in an intertidal area of the Tagus estuary, Western Europe: a six year data series. Hydrobiologia 555: 115–128. Simboura, N. & Zenetos, A. 2002. Benthic indicators to use in ecological quality classification of Mediterranean soft bottoms marine ecosystems, including a new biotic index. Mediterranean Marine Science 3/2: 77–111. Simpson, E.H. 1949. Measurement of diversity. Nature 163: 688. Teixeira, H., Salas, F., Borja, Á, Neto, J.M. & Marques, J.C. In press. A benthic perspective in assessing the ecological status of estuaries: the case of the Mondego estuary (Portugal). Ecological Indicators. Teske, P.R. & Wooldridge, T.H. 2001. A comparison of the macrobenthic faunas of permanently open and temporarily open/closed South African estuaries. Hydrobiologia 464: 227–243. Van Dalfsen, J.A., Essink, K., Toxvig Madsen, H., Birklund, J., Romero, J. & Manzanera, M. 2000. Differential response of macrozoobenthos to marine sand extraction in the North Sea and the western Mediterranean. ICES Journal of Marine Science 57: 1439–1445. Van Dolah, R.F., Hyland, J.L., Holland, A.F., Rosen, J.S. & Snoots, T.R. 1999. A benthic index of biological integrity for assessing habitat quality in estuaries of the south-eastern U.S.A. Marine Environmental Research 48: 269–283.

32 General introduction

Chapter 1

Vernberg, W.B. 1983. Responses to estuarine stress. In Ketchum, B.H. (ed.). Ecosystems of the World. Estuaries and enclosed seas, pp. 43–63. Elsevier Science Publishing Company, Amsterdam, The Netherlands. Warwick, R.M. 1986. A new method for detecting pollution effects on marine macrobenthic communities. Marine Biology 49: 728–739. Weisberg, S.B., Ranasinghe, J.A., Dauer, D.M., Schaffner, L.C., Diaz, R.J. & Frithsen, J.B. 1997. An estuarine Benthic Index of Biotic Integrity (B-IBI) for Chesapeake Bay. Estuaries 20: 149–158. Wolff, W.J. 1973. The estuary as a habitat. An analysis of data on the soft-bottom macrofauna of the estuarine area of the rivers Rhine, Meuse, and Scheldt. Zoologische Verhandelingen 126: 1–242. Wolff, W.J. 1983. Estuarine benthos. In Ketchum, B.H. (ed.). Ecosystems of the World. Estuaries and enclosed seas, pp. 151–182. Elsevier Science Publishing Company, Amsterdam, The Netherlands. Zulkosky, A.M.,Ferguson, P.L. & McElroy, A.E. 2002. Effects of sewage-impacted sediment on reproduction in the benthic crustacean Leptocheirus plumulosus. Marine Environmental Research 54:615-619.

Chapter 2

Seasonal and spatial patters

Seasonal and spatial patterns

Chapter 2

Seasonal and spatial patterns of distribution of subtidal benthic invertebrate communities in the Mondego River, Portugal – a poikilohaline estuary

ABSTRACT

The use of benthic assemblages to assess ecological quality of estuarine environments is a major tool for the implementation of the Water Framework Directive (2000/60/CE) for European aquatic ecosystems. Benthic communities show spatially heterogeneous distributions and experience seasonal variations due to both natural and anthropogenic stresses. The major goal of this study was to quantify the relationships between environmental gradients and the spatial and temporal patterns of the benthic communities along a Portuguese estuary. Seasonal and spatial variations relating macrobenthic communities and measures of water and sediment quality along the northern branch of the Mondego River estuary were examined at seven sampling stations from July 2000 to June 2001. Cluster analysis of biological data indicated three major groups of communities based on spatial distribution patterns: (1) a lower sector with stronger marine influence and dominated by Streblospio shrubsolii and Cerastoderma glaucum; (2) a middle sector with dominance of S. shrubsolii and Corophium multisetosum; (3) and an upper sector where C. multisetosum dominates a community characterized by a lower number of species. Canonical correspondence analyses of biological and environmental data determined a major salinity gradient influencing benthic communities. Seasonal changes of benthic communities were mainly determined by freshwater input and salinity changes that imposed a strong decrease in densities and number of species during winter, followed by a recovery during spring. Benthic ecological structure and contaminant levels indicated that the Mondego northern branch is moderately disturbed, although opportunistic species dominated the benthic community, suggesting that natural and anthropogenic sources of stress may be acting together. The Mondego River estuary, a poikilohaline-type estuary, characterized by strong seasonal changes in water flow and salinity, cannot be consistently stratified into salinity regions based upon the Venice classification system. Biotic communities, exemplified here by the benthic communities, are seasonally displaced, compared to a homiohaline-type estuary where the Venice system can be applied without modification. Future identification of reference conditions and design of monitoring programs cannot be accomplished without understanding how interactions between biotic and physical-chemical dynamics differ between homiohaline and poikilohaline estuaries. Results obtained in this study could be used to assist future assessments in other Portuguese estuaries.

KEY WORDS: benthic condition, spatial patterns, seasonal variations, environmental gradients, Mondego River estuary, Venice salinity system.

37 INTRODUCTION

Benthic invertebrate communities have been widely used as indicators of the ecological status of coastal and estuarine ecosystems (e.g. Pearson & Rosenberg, 1978; Hily et al., 1986; Dauer, 1993; Weisberg et al., 1997; Borja et al., 2003) and were included in the biological elements indicated by the Water Framework Directive (WFD) (2000/60/EC) for use in environmental monitoring. Benthic infaunal species live in the sediments and show relatively low mobility, being exposed to stress due to contaminants, low dissolved oxygen, limiting nutrient levels and physical disturbances (Dauer et al., 1992; Weisberg et al., 1997; Cowie et al., 2000). Benthic communities include species with different life cycles and specific tolerances to stress events, which make them suitable to be classified into different functional groups that reflect the magnitude of disturbances (e.g. Bilyard, 1987). They also play an important role in the chemical fluxes of the water/sediment interface (Aller & Aller, 1998; Aller et al., 2001) and are one of the main compartments of aquatic food webs, being effective indicators of impacts at higher levels of biological organization (Bilyard, 1987; Alden et al., 1997).

In spite of all the advantages mentioned above some biological characteristics of benthic communities have to be taken into account when interpreting results of benthic condition assessment. Benthic communities show high spatial heterogeneity in estuaries that are related to the influence of natural gradients of different factors contributing to the overall distribution of species. Many benthic species occur along a wide spectrum of an estuarine environment while some others are confined to a narrower habitat, according to their tolerance to environmental variables such as salinity, sediment type, depth, etc. In addition to spatial patterns, temperate estuarine invertebrate communities also show important temporal variations related to seasonal and interannual changes. Seasonal fluctuations in abundance and composition can be due to recruitment pulses that occur during spring and autumn for most species, but also to the occurrence of extreme environmental conditions such as low temperatures, floods and droughts (Alden et al., 1997; Attrill & Power, 2000; Salen-Picard & Arlhac, 2002). Freshwater flow variability is one of the main factors influencing the high temporal and spatial changes in physical, chemical and biological conditions in estuaries, particularly in rivers that show strong seasonal changes (Kimmerer, 2002). These hydrodynamic fluctuations have an important effect on the erosion and depositional cycles, influencing the sediment composition and therefore the colonization by particular benthic communities. In addition, widely varying salinity patterns in an estuary will alter local benthic community composition due to seasonal flow patterns (Boesch, 1977b) or extreme episodic storm events (Boesch et al., 1976a, b).

38 Seasonal and spatial patterns

Chapter 2

The Mondego River estuary, located in the western Atlantic Portuguese coast is divided into two branches that diverge 7.5 km upstream from the river mouth and have different hydrographic characteristics (Figure 2.1). In the southern branch the water circulation is mainly driven by tidal excursion and the only freshwater input comes from the Pranto River, a small tributary. The northern branch receives most of the freshwater input and is strongly influenced by seasonal water flow fluctuations (Flindt et al., 1997). These two branches were originally in contact through a small channel in the upstream area but the southern branch became gradually silted up and the connection occurs only during strong spring tides (Cunha & Dinis, 2002). The southern branch subsystem has been widely studied concerning physical and chemical variables that influence ecological processes. These studies identified an eutrophication gradient as one of the main factors influencing benthic communities (Flindt et al., 1997; Marques et al., 1997; Lillebø et al., 1999; Martins et al., 2001; Cardoso et al., 2002). In contrast few studies have been carried out in subtidal communities of the northern branch (Marques et al., 1993; Pardal et al., 1993) and they did not cover the region upstream of the link between the southern and northern branches.

FRANCEFRANCE

A L A G G SPAIN U U SPAIN

T T R

O R P O

Figueira da Foz

1 2 3

Montemor-o-Novo 4 7 Atlantic Ocean

5 6

2 Km

Figure 2.1. Location of sampling stations selected in the northern branch of the Mondego River estuary.

Understanding the spatial and temporal variations of the benthic communities is a basic tool for discriminating between pollution induced changes and natural variations (Boesch, 1973; Holland et al., 1987) and the first step towards the development of environmental indicator tools for Portuguese estuaries. Assessment of the benthic condition

39 of the northern branch of the Mondego River estuary can give a reasonable representation of both natural and man-made impacts in the entire river basin since it receives most of the freshwater generated upstream. This paper gives an overview of the spatial distribution of benthic communities in the northern branch of the Mondego River estuary, identifying the main environmental gradient generating the distribution of those communities. Seasonal changes were also analyzed by examining variations in the dominant species. These findings emphasize the ecological importance of the poorly understood biotic differences between homiohaline and poikilohaline estuaries.

METHODS

Study area

The Mondego River estuary is located on the Portuguese Atlantic coast (40º08’ N; 8º50’ W), a temperate-warm region influenced both by Atlantic and Mediterranean climates (Figure 2.1). This region is characterized by a rainfall period that extends from November to May and a drought period of very low water flow between June and October (Loureiro et al., 1986). River flow data for the period between 1987 and 1997 measured at the Coimbra dam indicated an annual mean flow of 812 m3 s−1. Maximum flows were measured from December to March and minimum flows occurred between June and October (Figure 2.2). Average monthly flows varied between a maximum value of 167 m3 s−1 in January and a minimum average flow of 15 m3 s−1 in September. The Mondego River flow regime is very irregular and important daily changes can occur due to the action of dams controlling the discharges. This estuary is affected by a mesotidal semi-diurnal regime and is normally totally mixed, except for periods of extreme floods or droughts when it can be only partially mixed (Cunha & Dinis, 2002). During the last decades the Mondego River estuary was severely altered by the construction of dams located upstream, the drainage of some mudflat areas, embankment of the river margins, dredging activities to maintain a navigation channel and intensive agriculture use. Sediment grain size has a very heterogeneous spatial distribution along the estuary, much coarser in the upstream areas where it consists mainly of very coarse sand and some marginal locations covered with fine sand. The lower section has a dominant composition of medium to fine sand although there are small areas near the river banks were sandy mud sedimentation occurs (Cunha & Dinis, 2002).

40 Seasonal and spatial patterns

Chapter 2

Sampling design

A total of seven sampling stations were selected along the longitudinal extension of the northern branch of the estuary (Figure 2.1) in an attempt to cover the salinity range between the vicinity of the river mouth and the upper tidal reaches. Three benthic invertebrate samples were taken at each station using a modified van Veen grab (0.05 m2) and their contents were fixed and preserved with 4% buffered formalin. Grab contents were sieved in the laboratory using a 500 µm mesh and preserved in 70% alcohol. All samples were sorted using a microscope and invertebrates were identified to the highest possible taxonomic separation. Bottom dissolved oxygen (DO) (mg l−1), water temperature (ºC) and salinity were measured in situ using a Data Sonde Surveyor 4 and a Sechii disc was used to measure transparency (m). Additional water samples were collected and frozen for subsequent analysis of nitrates (mg l−1), nitrites (mg l−1), phosphates (mg l−1) and ammonia (mg l−1). The analyses were made by the laboratory of the Portuguese Environmental Institute using methods certified by the Portuguese Quality Institute. At each station, another grab sample was taken and frozen for sediment grain size, total organic content (TOC) and heavy metals analysis. Sediment grain size composition was determined using an AFNOR type sieve battery (0.063 mm; 0.25 mm; 0.5 mm; 2 mm; 9.25 mm) after drying the sediment (60ºC) for a period of 48 h. Samples were classified using the Roux (1964) scale. TOC was obtained as the difference between dry weight, measured after drying the sample at 60ºC during 24 h and ash weight, obtained after ignition at 480ºC for a period of 12 h. Heavy metal concentrations (mg kg−1) in the sediment (arsenic, chromium, lead, cooper and zinc) were also determined by the Portuguese Environmental Institute, using certified methods.

750 )

-1 500 .s 3

250 Flow (m Flow

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months

Figure 2.2. Average month flows measured at Coimbra dam between 1987 and 1997 (source: Portuguese Water Institute).

41 Seasonal variations were assessed by repeating the surveys every 3 months, namely in July (summer) and October 2000 (autumn) and in February/ March (winter) and June (spring) 2001. All samples were collected on low tide, during spring tide periods. The winter cruise took place immediately after a flood event and Stations 1 and 7 could not be sampled due to the strong currents impeding the use of the collecting devices.

Data analysis

Spatial patterns

Stations were classified into spatial groups using the ‘‘mean variance per comparison’’ technique described by Williams & Stephenson (1973). By applying this technique it was possible to group stations into spatial groups independent of the effects associated with collections conducted during different seasons. The technique estimates the variance between stations and between sampling events by calculating the Euclidean distance between stations and sampling events (over all species) after stations and sampling events were centred to their respective means. The variance estimates were used as a measure of dissimilarity between stations for cluster analyses in order to assign the spatial groups. A flexible sorting strategy was used for the cluster analysis with an intensity coefficient or value of –0.25 (Boesch, 1977a). Dissimilarity coefficients were calculated using a program written in the SAS/IML® matrix programming language while the dendrograms for this analysis were produced using PROC CLUSTER of the SAS/Stat® software package. All species counts were standardized to have an overall mean value of zero and a standard deviation of one prior to conducting this analysis. The analysis was conducted using data collected during only three seasons (summer, autumn and spring) due to the missing observations in winter. An overall test for a significant difference in species composition between site groups was accomplished using a MANOVA (Wilk’s λ) while pairwise comparisons between individual site groups were made using an F-test on pairwise squared Mahalanobis distances. Descriptive measures determined for the benthic communities of each group identified by the cluster analysis were the average density, the Shannon-Wiener diversity index (H’ loge), the number of species (considering each taxon as a species), the Simpson’s dominance index (λ') and the top 10 dominant species. Similarity percentage breakdown procedure (SIMPER) (Clarke & Warwick, 1994), included in the PRIMER software package was used to determine the contribution of individual taxa towards the dissimilarity between and similarity within the groups identified by Cluster analysis.

42 Seasonal and spatial patterns

Chapter 2

Seasonal variation

Multivariate ordination techniques were applied to analyze spatio-temporal variation in the species density reduced data set and identify the relations between environmental and biological data, using CANOCO 4.5 software. Relations between environmental and biological data were analyzed by performing a Canonical Correspondence Analysis (CCA). CCA constrains the axes to be linear combinations of the environmental variables (ter Braak & Šmilauer, 2002).

The species data matrix was reduced by eliminating taxa that occurred in less than five samples and accounted for less than 0.1% of the total abundance, to obtain an interpretable ordination diagram. A matrix of explanatory variables was also constructed to determine the variation in the species data that was related to environmental factors. The reduced environmental matrix excluded variables that showed collinearity (silt and clay) and variables which registered values under the detection limit (nitrates, nitrites, phosphates and ammonia). Metals were also excluded because they never exceeded Long et al.’s (1995) effects range-median (ERM) or Crommentuijn et al.’s (2000) maximum permissible concentrations (MPC) and any differences found between stations would not have a biologically meaningful interpretation. All other variables measured were included, namely dissolved oxygen, water temperature, salinity, transparency, depth, sediment type (stones, coarse sand, medium sand, fine sand) and total organic content.

Selection of variables was based on stepwise multiple regressions and the statistical significance of the variables added to the analysis was tested using a Monte Carlo permutation test (499 unrestricted permutations) (ter Braak & Verdonschot, 1995). Decision about the ordination model to use in the constrained analysis (CCA) was based on length of gradient calculated by a previous Detrended Canonical Correspondence Analysis (DCCA). Since the longest gradient was 4.05 the CCA was based on a unimodal model. DCCA was also used to obtain the ordination of species and samples, since an ‘‘arch effect’’ was apparent in the initial canonical correspondence analysis (CCA) (Gauch, 1982).

Seasonal variations were analyzed by plotting major taxonomic groups and ecological groups for each station group-season combination based on he classification of Borja et al. (2000). These authors assigned taxa to one of five ecological groups, according to their sensitivity to an increasing stress gradient (I to V). Contributions from individual taxa to dissimilarities between seasons were estimated using the similarity percentage breakdown procedure (SIMPER). Densities of the dominant species were plotted for each season.

43 Results

Benthic macrofauna general characterization

A total of 38 394 invertebrate specimens were collected and 84 taxa identified (Appendix 1). The highest densities were registered during spring at station 9 (56 613 ind m-2) while the lowest values were observed during winter at station 4 (160 ind m-2). The 10 dominant species accounted for 93% of the total average density with the amphipod Corophium multisetosum Stock, 1952 (47%) and the polychaete Streblospio shrubsolii (Buchanan, 1890) (21%) being the two dominant species. The dominance of C. multisetosum was due to the high numbers collected during spring at the three upstream stations. S. shrubsolii was the most abundant species during all other seasons. Dominant species found during summer and autumn were typical estuarine surface and subsurface deposit feeders (e.g. S. shrubsolii, Spio martinensis Mesnil, 1896, Chaetozone setosa Malmgren, 1867) but some freshwater invertebrates such as insect larvae (Ephemeroptera and Diptera) were collected during winter and spring, mainly in the upstream stations.

Spatial patterns in benthic communities

Cluster analysis indicated three main groups of stations in the northern branch of the Mondego River estuary (Figure 2.3). There was a significant difference in species composition across all site groups (Wilk’s λ=0.063; DF=28:33; P<0.0001) and between each of the individual site groups (Table 2.1). Spatial Group A consisted of stations 1 and 2, which are located in the lower sector of the estuary. These stations are characterized by medium sand sediments, a stronger tidal influence and a salinity decrease associated with flood events that occur primarily during winter. This group had the lowest dissolved oxygen concentrations, as well as higher levels for TOC, ammonia, lead, copper and zinc concentrations (Table 2.2). This may be due to the spatial group’s proximity to the urban area of Figueira da Foz that has harbour facilities, sewage outfalls and a large bridge with automobile traffic. Lead and zinc concentrations exceeded Long et al.’s (1995) Effects Range-Low (ERL) at the station located closer to the bridge during winter. The benthic community identified for this group was dominated by some polychaete and bivalve species. The spionid polychaete S. shrubsolii showed the higher average abundance (29%), followed by the suspension feeding bivalves Cerastoderma glaucum (Poiret, 1789) (25%) and Scrobicularia plana (da Costa, 1778) (10%) (Table 2.2). Group B clustered stations 3, 4 and 5, located in the middle sector of the estuary. At this location salinity decreased during winter and spring but tidal influence maintained relatively high salinities during the drought period. It is a transition zone for sediment that changes gradually from medium to coarse sand moving upstream. The lower

44 Seasonal and spatial patterns

Chapter 2 average percentages of TOC were found in this spatial group (Table 2.2). The benthic community was dominated by only two species, S. shrubsolii (48%) and the amphipod C. multisetosum (37%) (Table 2.2). The two uppermost stations formed Group C, characterized by lower salinities, higher oxygen levels and coarser sand. Chromium and zinc measured at these sites exceeded Crommentuijn et al.’s (2000) Negligible Concentration (NC) values and Long et al.’s (1995) ERL values and higher nitrates and TOC levels were also measured, probably due to intensive agriculture use in this area. The benthic community was strongly dominated by C. multisetosum (77%), mainly because of high numbers collected during spring. Oligochaetes (10%) and an introduced bivalve species Corbicula fulminea (Müller, 1774) (8%) were also abundant. Although in low numbers, insects larvae (Ephoron virgo (Olivier, 1791) and Chironomidae) occurred in this area associated with higher winter and spring freshwater input (Table 2.2).

All seasons 2.0

1.5 β

1.0

0.5 Flexible Beta Distance ( = -0.25)

0.0 1 2 34 5 67

Group A Group B Group C

Figure 2.3. Cluster analysis of the density data set collected in the northern branch of the Mondego River estuary. Stations were grouped into spatial groups independent of the effects associated with collections conducted during different seasons using the “mean variance per comparison” method described by Williams & Stevenson (1973).

SIMPER analysis showed higher dissimilarities between Groups A and C (91%) and higher closeness of stations of Group B to A than to C. S. shrubsolii gave the highest

45 contribution to the dissimilarities between the lower and the middle sector of the estuary (16%), except for the spring assemblage. Although dominant in both assemblages this spionid occurs with lower density at Group A stations. On the other hand, C. glaucum, the polychaete S. martinensis and the gastropod Hydrobia ulvae (Pennant, 1777) were found with much higher densities in Group A and explained 14%, 7% and 6% of the dissimilarity between Groups A and B, respectively. The high density of C. multisetosum in the upstream stations accounted for 27% of the dissimilarity between Groups B and C. Oligochates, S. shrubsolii and C. fulminea were also important in separating these two groups and explained 15%, 15% and 12% of the dissimilarities between Groups B and C, respectively. Ecological groups obtained by classifying taxa according to their sensitivity to pollution (Borja et al., 2000) were used as a measure of the ecological structure of the assemblages showing an average density domination of 18 taxa belonging to Group III, which includes species tolerant to organic enrichment. Over 92% of the organisms collected during the study were classified in this group, while only two taxa, Oligochaeta and Capitella capitata, or about 5% of the total number of organisms were classified into Group V (Figure 2.4), considered as first-order opportunistic species.

Table 2.1. Pairwise comparisons of species composition between site groups. Shown are the pairwise squared Mahalanobis distances between site groups, the F values and their associated probabilities.

Site Group A C 15.37 16.07 B 3.26 3.41 0.0007 0.0005

22.74 A 4.02 <0.0001

46 Seasonal and spatial patterns

Chapter 2

Seasonal variation in benthic communities

Major variations were found in winter assemblages that showed a reduced number of species and extremely low abundances (Table 2.2). In contrast, higher numbers and stronger dominance were registered during spring in all station groups with increased densities in Group C. As previously mentioned the ecological structure of the Mondego River estuary was numerically dominated by Group III species (Figure 2.4). However, some variations of the specific composition occurred between seasons. Summer and autumn registered a higher heterogeneity of the ecological groups represented in Group A, mainly because of the occurrence of rare species of marine influence. Spatial Group C registered the highest numerical representation of Group V during summer and winter, due to the density of oligochaetes (Figure 2.4). Spring showed higher homogeneity due to the strong numerical abundance of Group III species that lowered the relative contribution of other groups. SIMPER analysis showed which species contributed the most to the seasonal variations within each station group:

Group A – higher dissimilarities were found between winter assemblages and all other seasons. Only four taxa were collected and with very low numbers in this period, namely S. shrubsolii, Hesionidae, the isopod Cyathura carinata (Kroyer, 1847) and Turbellaria. C. glaucum, S. plana, S. martinensis, Mediomastus fragilis Rasmussen, 1973 and C. setosa were the species which best discriminated between summer and all other seasons, showing higher densities during summer, decreasing towards winter (Figure 2.5). S. shruboslii was the best discriminating species (>20%) between spring and other seasons due to the higher densities found in this period (Figure 2.5). Summer was also characterized by the presence of rare species such as Eumida sanguinea (Örsted, 1843), Glycera gigantea de Quatrefages, 1866, Nephtys cirrosa Ehlers, 1868, Nephtys hombergii Savigny, 1818 and Modiolus barbatus (Linnaeus, 1758), that were absent during other seasons. Some other species revealed a relatively important presence during summer and autumn but were absent from the winter and spring assemblages such as Heteromastus filiformis (Claparède, 1864) and C. setosa.

47 Table 2.2. Descriptive biological and environmental parameters of groups of stations identified in the Mondego River estuary (A, B and C). Maximum and minimum average values seasonally determined (summer – S; autumn – A; winter – W; spring – Sp) are presented for each group, concerning density, Shannon-Wiener diversity, number of species and Simpson’s dominance. Top ten dominant species are listed with the respective contributions to total density

Group A Group B Group C (Stations 1 and 2) (Stations 3, 4 and 5) (Stations 6 and 7) Biological parameters Density 380 W - 9420 Sp 504 W - 9651 Sp 433 W - 39720 Sp (ind m-2) Diversity 1.00 W - 2.24 A 0.70 Sp - 1.52 W 0.63 Sp - 1.27 S/W N Species 4 W - 40 S 14 W - 38 S 8 W - 14 Sp Dominance 0.31 S/A - 0.04 W/Sp 0.24 W - 0.67 Sp 0.34 S - 0.72 Sp Streblospio shrubsolii (29%) Streblospio shrubsolii (48%) Corophium multisetosum (77%) Cerastoderma glaucum (25%) Corophium multisetosum (37%) Oligochaeta (10%) Scrobicularia plana (10%) Oligochaeta (3%) Corbicula fulminea (8%) Hydrobia ulvae (7%) Spio martinensis (2%) Ephoron virgo (2%)

Dominant Spio martinensis (6%) Hydrobia ulvae (2%) Nemertea (1%) species Chaetozone setosa (5%) Nemertea (1%) Cyathura carinata (<1%) Mediomastus fragilis (5%) Hediste diversicolor (1%) Chironomidae (<1%) Angulus tennuis (3%) Gammarus subtypicus (1%) Gammarus subtypicus (<1%) Heteromastus filiformis (2%) Capitella capitata (1%) Boccardiella ligerica (<1%) Oligochaeta (1%) Corbicula fulminea (1%) Saduriella losadai (<1%)

Environmental parameters Depth (m) 3.00 - 5.50 0.80 - 5.00 3.50 - 6.00 Salinity 7.00 - 40.20 7.00 - 31.60 2.00 – 14.20 DO (mg l-1) 3.40 - 9.00 6.40 - 9.50 6.40 - 9.60

-1 NO3 (mg l ) 1.00 - 4.70 2.50 - 5.40 3.20 - 5.40 -1 NO2 (mg l ) < 0.05 0.05 - 0.10 0.05 - 0.07 -1 NH4 (mg l ) 0.08 - 0.33 0.08 - 0.23 0.08 - 0.20 As (mg kg-1) 1.20 - 9.00 0.90 - 4.60 1.00 - 13.00 Pb (mg kg-1) 2.40 - 92.00 4.00 - 28.30 2.40 - 40.00 Cu (mg kg-1) 1.30 - 28.00 0.60 - 9.00 0.70 - 23.00 Cr (mg kg-1) 2.40 - 59.00 5.30 - 44.00 3.00 - 70.00 Zn (mg kg-1) 9.40 - 184.00 13.00 - 71.00 12.00 - 115.00 TOC (%) 0.40 - 9.90 0.20 - 4.00 1.20 - 10.10 Sediment Medium sand Coarse-Medium sand Coarse sand type

48 Seasonal and spatial patterns

Chapter 2

Figure 2.4. Ecological groups (Borja et al., 2000) identified seasonally for groups of stations defined in the Mondego River estuary.

Group B – high dissimilarities between seasons were found, with the lowest dissimilarities registered between summer and autumn assemblages (57%). S. shrubsolii gave the highest contribution to dissimilarities between all seasons, except for spring, due to density changes. A higher number of species was collected during autumn and summer (Table 2.2). C. multisetosum occurred only during winter and spring (Figure 2.5) and contributed most to the dissimilarities between these seasonal assemblages (30%). S. martinensis, C. setosa, G. gigantea and the bivalve Angulus tenuis (da Costa, 1778) occurred only during summer and autumn periods, showing the same pattern as Group A, although with lower densities. Chironomidae occurred only during the winter period.

Group C – upper sector assemblages showed a higher homogeneity between seasons, except for winter and spring (80% dissimilar) due to the contribution of C. multisetosum (60%) that registered a strong numerical increase during spring (Figure 2.5). This species accounted for the highest contribution to the dissimilarities between spring and other seasons (>50%). Oligochaetes and C. fulminea were also abundant in Group C, except for the winter period (Figure 2.5). Chironomidae occurred exclusively during winter and spring and E. virgo occurred only during spring.

Figure 2.5. Seasonal variations on the densities of the eight dominant species in the benthic community of the northern branch of the Mondego River estuary (S – summer; A – autumn; W – winter; Sp – spring). Standard error is indicated.

50 Seasonal and spatial patterns

Chapter 2

Relationships between environmental and biological variables

After data reduction 28 taxa were retained, that accounted for more than 99% of the total abundance.

The constrained ordination (CCA) of species density with stepwise forward selection of environmental variables retained only three environmental variables (P<0.05): salinity, medium sand and TOC. The ordination plot showed an apparent arch effect on species and samples, suggesting the need for detrending. DCCA of species abundance data produced an ordination in which the first four axes were statistically significant (P<0.01), with respective eigenvalues of 0.66, 0.14, 0.06 and 0.03. The first two axes explained 34.9% of the total variance in species data and 56.8% of the total variance on the species–environment relation. Salinity presented a negative correlation with the first axis (–0.90) and it was the variable with the highest explanatory power related to this axis (Figure 2.6). Medium sand content also explained some of the variation on the first axis, with a correlation of –0.67. In spite of explaining some of the variance in species distribution, TOC showed low correlations with the first two axes (<0.30) (Figure 2.6).

Salinity and sediment grain size established the main gradient separating species that apparently have an optimal distribution in lower salinities and coarser sands, such as C. multisetosum, C. fulminea, E. virgo, Chironomidae, Alkmaria romijnii (Grube, 1863) and Oligochaeta (Figure 2.6). These species were mainly identified in the Group C assemblage. The ordination indicates a medium location of S. shrubsolii, C. carinata, Hediste diversicolor (O.F. Müller, 1776), Saduriella losadai Holthuis, 1964, Nemertea, Lekanesphaera hookerii (Leach, 1814) and Websterinereis glauca (Claparède, 1870) along the saline gradient. Some of these species occurred with higher densities in Group B stations but all of them demonstrated tolerance to changes occurring along the estuary since they showed a wide distribution along the three different regions identified. In the DCCA plot the remaining species were located closer to the positive end of the salinity and sediment vectors, associated with spatial Group A stations. A small part of the species variation was still explained by the second axis (6.2%), separating species characteristic of Groups A and C assemblages from Group B. Although the correlation of TOC to this axis was not very high, this variable apparently explains part of the differences mentioned, since the Group B stations registered lower values. DCCA ordination of species and samples also revealed temporal differences in similarity within groups, separating the plots of the same stations in different seasons (Figure 2.6).

51 3 Sarm Wgla

Mpal Hdiv Chir Sshr Neme Ms W4 Ccap Sp3 A4 S3 Sp4 Smar S4 A3 Lhok W3 S7A5 W5 Hulv A6 Sp5 S5 A2 Sp1 A7 Sp6 Sp2 S6 Sal S2 S1 W6 Hesi Olig Aten Arom Cful Npul Cmul 0 Ncir Ggig W2 Sp7 Hfil Spla Mbar Cgla A1 Ccar Cset Mfra TOC Slos Nhom Esan

Evir -2 -10 5

Figure 2.6. DCCA of benthic invertebrate density for the stations sampled in the Mondego River estuary during summer (S), autumn (A), winter (W) and spring (Sp). Taxa data were square root transformed. Environmental variables - Salinity (Sal), Medium sand (Ms) and Total Organic Content (TOC) - were plotted on the ordination as arrows. See Appendix 1 for taxa abbreviations.

DISCUSSION

The identification of the spatial and seasonal patterns of change in the benthic communities of the Mondego River estuary may be an important contribution to the development of biological criteria to assess the environmental condition of Portuguese estuaries. The study design led to the identification of three spatial groups with distinct environmental and biological characteristics in the northern branch of this estuary, instead of the five classes established by the Venice system. That system, based on the identification of salinity classes, is widely accepted and indicated by the WFD to identify different water types. Salinity decreases moving upstream from spatial Group A near the mouth of the River to the spatial Group C upstream and is coupled with a transition from medium to coarse sands upstream. Several sources of anthropogenic disturbance were identified in Group A, such as a well developed urban area with harbour facilities that require periodic dredging of the access

52 Seasonal and spatial patterns

Chapter 2 channel, dumping and a bridge with high traffic levels. This sector showed organic enrichment and higher levels of heavy metals were measured in the bridge vicinity. Lower organic enrichment was found in spatial Group B while the Group C stations showed higher levels of organic matter and nutrient concentrations, probably related to nutrient loads from intensive agriculture.

Benthic communities presented similar patterns of longitudinal change along the estuary. The lower sector assemblages were dominated by the surface deposit feeder S. shrubsolii and some suspension feeding (C. glaucum) and deposit-feeding (S. plana) bivalve species. Group B showed a stronger dominance of S. shrubsolii populations, shared with the amphipod C. multisetosum. The last species mentioned accounted for almost 80% of the upper sector assemblages due to high densities found during spring. DCCA identified a saline gradient influencing species distribution. Seasonal differences found in community composition and structure, were apparently also related to salinity changes caused by high freshwater input from upstream. Although salinity values were obtained with a single measurement for each season, daily and monthly flows measured upstream strongly indicate that they were representative of seasonal changes (Figure 2.2). Colonization of Group A stations by marine species was observed during the drought season while Group C stations were colonized by freshwater species during higher flow periods. Species with higher tolerances to salinity changes such as S. shrubsolii, H. diversicolor, C. carinata and S. losadai showed a wider distribution and persistence between seasons. These results are consistent with previous studies in the Mondego River estuary (Marques et al., 1993; Pardal et al., 1993), although the number of species and densities collected during this study were much higher. The higher densities in the present study are probably due to the use of a 1 mm mesh sieve during previous studies, compared to the use of a 0.5 mm mesh sieve in the present study.

Salinity effects may be acting together with physical disturbances caused by the strong currents occurring during the rainfall period and consequent alterations of the erosion– deposition cycles. Winter communities were very impoverished both numerically and in terms of the number of species and stronger changes occurred in the lower sector of the estuary, where most stenohaline species were found. In the middle sector lower salinities persisted for a longer period and the benthic community was apparently adapted to this saline regime showing lower changes between seasons. S. shrubsolii, a typical estuarine species dominated the community over all seasons, except for spring when C. multisetosum increased its density. This polychaete is classified as an opportunistic species that colonises organically enriched sediments (Pearson & Rosenberg, 1978; Sardá & Martin, 1993). This change in the dominance may be due to competition as both species occupy similar spatial niches (surface feeders dwelling in the upper 1 cm of the sediment) and occur in high densities. Previous studies carried on Portuguese estuaries showed the prevalence of this amphipod in salinities

53 ranging from 2.5 to 10 and coarser sediment (Queiroga, 1990). Cunha et al. (2000) concluded that C. multisetosum abundance may be associated with increased freshwater inflow following rainy periods and subsequent decreasing abundance with the higher summer temperatures, since it is a cold-temperature species, with its southern limit in Portugal. During spring, C. multisetosum seems to replace the dominant spionid S. shrubsolii in Group B, since increasing densities occur in the lower sector for this species in the same period. The benthic community of the upper sector seems to be more stable with lower fluctuations in specific composition and densities, except for the spring boom of C. multisetosum.

The AZTI Marine Biotic Index (AMBI) approach of Borja et al. (2000) produces an overall evaluation of benthic community condition, relative to anthropogenic stress, by scoring benthic species based upon relative tolerance to pollution. Using this approach the Mondego River estuary would be considered moderately disturbed since Group III species are dominant. These species are tolerant to excess organic matter and occur in normal conditions but increase their numbers when stimulated by organic enrichment. Based on AMBI values, winter communities in the upper sector would be considered severely degraded due to the dominance of oligochaetes and summer and autumn communities in Group A would be less disturbed since a higher number of pollution sensitive species occur (e.g. A. tenuis, Nephtys spp., Glycera spp., Owenia fusiformis Delle Chiaje, 1842, Eteone picta de Quatrefages, 1866, E. sanguinea, Diopatra neapolitana Delle Chiaje, 1841). Pollution sensitive species did occur in all sectors but generally in low densities and with great variation between seasons. These results suggest caution in applying and interpreting the AMBI approach when strong seasonal patterns occur. Nevertheless, benthic communities are still under seasonal physical and physiological stress that favours the settlement of species with opportunistic life histories and/or wide tolerance to changes in environmental conditions, such as S. shrubsolii (Sardá & Martin, 1993; Rossi & Lardicci, 2002). The present data demonstrate the ability of this species to colonize and produce high-density local populations in the lower sector of the Mondego River between winter and spring. Lardicci et al. (1997) indicated peaks of fecundity occurring every 2 months for this species when food availability is not a limiting factor.

Estuaries may be classified in many ways including geomorphology (e.g. drowned river valleys, fjords, etc.), degree of saline stratification (e.g. highly stratified, partially stratified, etc.), and tidal mixing (e.g., macrotidal, microtidal, etc.) (Lauff, 1967; Elliott & McLusky, 2002). Major variations in amount of freshwater flow may be due to strong seasonal differences in rainfall and such differences can be amplified by dam placement. Boesch (1977b) states that poikilohaline estuaries, where strong seasonal changes in salinity and water flow occur at specific locations, cannot be assessed using the Venice system. He also states that euryhaline marine species are displaced as an effect of poikilohalinity, allowing

54 Seasonal and spatial patterns

Chapter 2 the domination of estuarine endemic species. The dominance of S. shrubsolii in Group B during summer and autumn and in Group A during winter and spring indicates an agreement of the Mondego River estuary results with poikilohalinity effects theory. Apparently, stronger freshwater inputs displace euryhaline species found in the lower sector of the estuary during lower flow periods, allowing the dominance of the spionid S. shrubsolii. At the same time, this estuarine endemic species is displaced from the middle sector by freshwater species such as the amphipod C. multisetosum. The Mondego River estuary, a poikilohaline type estuary, characterized by strong seasonal changes in water flow and salinity, cannot be consistently stratified into salinity regions based upon the Venice classification system as indicated by the WFD. Biotic communities, exemplified here by the benthic communities, are seasonally displaced, compared to a homiohaline-type estuary where the Venice system can be applied without modification. Future identification of reference conditions and design of monitoring programs cannot be accomplished without understanding how interactions between biotic and physical-chemical dynamics differ between homiohaline and poikilohaline estuaries (Elliott & McLusky, 2002). Our results indicate that seasonal and spatial stratification may be necessary to be able to separate natural and anthropogenic stresses. In addition, further research is necessary in understanding the relative roles of river flow versus groundwater as sources of pollutants. For example, during low river flow seasons, groundwater flow may become relatively much more important than during times of high river flow. Understanding ecological patterns, particularly where spatial and/or temporal variation, is great, also requires a comprehensive appreciation of the interactions of geomorphology, hydrology, and climatology.

ACKNOWLEDGMENTS

This study was financially supported by two Ph.D. fellowships (SFRH/BD/5144/2001 and SFRH/ BD/6365/2001) granted by FCT (Science and Technology Foundation) and ESF in the aim of the III European Community Support Framework. Financial support was also given to a project sponsored by Instituto de Ambiente. The SAS/IML program used here was written by Dr. Raymond W. Alden III of the University of Nevada, Las Vegas. We would also like to thank Professors Jean-Claude Dauvin and João Castro for the help with the identification of some invertebrate species. Finally, we thank Gilda Silva and Nuno Prista for their valuable comments and contributions to the final draft of this manuscript.

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57 Hily, C., le Bris, H. & Glémarec, M. 1986. Impacts biologiques des emissaires urbains sur les ecosystemes benthiques. Océanis 12: 419–426. Holland, A.F., Shaughnessy, A.T. & Hiegel, M.H. 1987. Longterm variation in mesohaline Chesapeake Bay macrobenthos: spatial and temporal patterns. Estuaries 10: 227–245. Kimmerer, W.J., 2002. Effects of freshwater flow on abundance of estuarine organisms: physical effects or trophic linkages?. Marine Ecology Progress Series 243: 39–55. Lardicci, C., Ceccherelli, G. & Rossi, F. 1997. Streblospio shrubsolii (Polychaeta: Spionidae): temporal fluctuations in size and reproductive activity. Cahiers de Biologie Marine 38: 207–214. Lillebø, A.I., Pardal, M.A. & Marques, J.C. 1999. Population structure, dynamics and production of Hydrobia ulvae (Pennant) (Mollusca: Prosobranchia) along an eutrophication gradient in the Mondego estuary (Portugal). Acta Oecologica 28: 289– 304. Lauff, G.H., 1967. Estuaries. American Association for the Advancement of Science, Washington, D.C., U.S.A. Long, E.R., McDonald, D.D., Smith, S.L. & Calder, F.D. 1995. Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments. Environmental Management 19: 81–95. Loureiro, J.J., Almeida, M.C., Machado, M.L. & Teixeira, E. 1986. Bacia hidrográfica do rio Mondego. In D.G.R.A.H., (Ed.) Monografias Hidrológicas dos principais cursos de água de Portugal continental, pp. 240–278. Lisboa, Portugal. Marques, J.C., Maranhão, P. & Pardal, M.A. 1993. Human impact assessment on the subtidal macrobenthic community structure in the Mondego estuary (western Portugal). Estuarine, Coastal and Shelf Science 37: 403–419. Marques, J.C., Pardal, M.A., Nielsen, S.N. & Jørgensen, S.E., 1997. Analysis of the properties of exergy and biodiversity along an estuarine gradient of eutrophication. Ecological Modelling 102: 155–167. Martins, I., Pardal, M.A., Lillebø, A.I., Flindt, M.R. & Marques, J.C. 2001. Hydrodynamics as a major factor controlling the occurrence of green macroalgal blooms in a eutrophic estuary: a case study on the influence of precipitation and river management. Estuarine, Coastal and Shelf Science 52: 165–177. Pardal, M.A., Marques, J.C. & Bellan, G. 1993. Spatial distribution and seasonal variation of subtidal polychaete populations in the Mondego estuary (western Portugal). Cahiers de Biologie Marine 34: 497–512. Pearson, T.H. & Rosenberg, R. 1978. Macrobenthic succession in relation to organic enrichment and pollution of the marine environment. Oceanography and Marine Biology. Annual Review 16: 229–311.

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Chapter 2

Queiroga, H. 1990. Corophium multisetosum (Amphipoda: Corophiidae) in Canal de Mira, Portugal: some factors that affect its distribution. Marine Biology 104: 397–402. Rossi, F. & Lardicci, C. 2002. Role of the nutritive value of sediment in regulating population dynamics of the depositfeeding polychaete Streblospio shrubsolii. Marine Biology 140: 1129–1138. Roux, R.M., 1964. Les sediments de l’étange de Reine. Recueil des Travaux de la Station Maritime d’Endoume 35: 257–285. Salen-Picard, C. & Arlhac, D. 2002. Long-term changes in a Mediterranean benthic community: relationships between the Polychaete assemblages and hydrological variations of the Rhône river. Estuaries 25: 1121–1130. Sardá, R. & Martin, D. 1993. Populations of Streblospio Webster (Polychaeta: Spionidae) in temperate zones: demography and production. Journal of the Marine Biological Association of the United Kingdom 73: 769–784. Weisberg, S.B., Ranasinghe, J.A., Dauer, D.M., Schaffner, L.C., Diaz, R.J. & Frithsen, J.B. 1997. An estuarine Benthic Index of Biotic Integrity (B-IBI) for Chesapeake Bay. Estuaries 20: 149–158. Williams, W.T. & Stephenson, W. 1973. The analysis of three-dimensional data (sites x times x species) in marine ecology. Journal of Experimental Marine Biology and Ecology 11: 207- 227.

Chapter 3

Taxonomic sufficiency

Chainho, P., Lane, M.F., Chaves, M.L., Costa, J.L., Costa, M.J. & Dauer, D.M. 2006. Taxonomic sufficiency as a useful tool for typology in a poikilohaline estuary. Hydrobiologia 587: 63-78.

Taxonomic sufficiency

Chapter 3

Taxonomic sufficiency as a useful tool for typology in a poikilohaline estuary

ABSTRACT

Taxonomic sufficiency has been used mainly to assess benthic condition, based on the assumption that taxa can be identified to a taxonomic level higher than the species level without losing the ability to detect changes related to pollution stress. Identifying taxa to a higher level reduces the expertise and time needed to identify organisms and consequently allows increased spatial and temporal replication. The usefulness of taxonomic sufficiency for typology (identification of water body types) was examined using the benthic communities of the Mondego River estuary (Portugal). Benthic samples were collected seasonally along the Northern branch of the Mondego River estuary from July 2000 to June 2001 and several environmental parameters were measured simultaneously. Cluster analysis of species data indicated three major ecological groups, mainly related to a saline gradient along the estuary. The same groups were found when taxa were aggregated to higher taxonomic levels (genus, family, order, class), except for the phylum level. The overall spatial pattern was driven by: (1) the dominance of bivalves and the occurrence of rare marine species in the Lower Estuary; (2) the dominance of polychaetes in the Middle Estuary; (3) and the dominance of arthropods in the Upper Estuary. The ability of different taxocenes to discriminate the three ecological groups was also examined. Mollusca and Bivalvia were the only taxocenes producing the same groupings, although other taxocenes (Annelida, Polychaeta, Spionidae, and Arthropoda) showed a significant ability to discriminate between all three groups. Compared to using all taxa identified to the lowest possible taxonomic level, our results indicate that for typology (1) several higher taxonomic levels were sufficient (2) while few taxocenes alone were sufficient.

KEY WORDS: typology, Water Framework Directive, Venice system, salinity gradient, taxonomic levels, taxocenes.

63 INTRODUCTION

The concept of taxonomic sufficiency, first introduced by Ellis (1985), refers to taxonomic identification to the highest possible level that retains taxonomic accuracy and sufficient biological information to assess environmental stress effects. Since then, several authors examined the effects of reducing the taxonomic resolution, mainly concerning environmental impact assessment and monitoring studies (Warwick, 1988; Baldó et al., 1999; Gomez Gesteira et al., 2003; Terlizzi et al., 2003), but also in studies of biodiversity and conservation (Bianchi & Morri, 2000).

The cost effectiveness of monitoring programs using benthic communities has been presented as a major reason for using taxonomic sufficiency since it provides a significant reduction in costs due to high taxonomic identification expertise (Ferraro & Cole, 1995; Pagola-Carte et al., 2002). Moreover, some biological considerations have been proposed to support taxonomic sufficiency, mainly the assumption that biological responses to stress comply with a hierarchical structure. Species are the most sensitive taxa but as the level of stress increases the adaptability of lower taxonomic levels is exceeded (first individual, than species, genus, etc.). Therefore, impacts resulting from increasing levels of stress are shown at higher levels of taxonomic organization, reducing the taxonomic resolution needed to identify their effects in benthic communities (Ferraro & Cole, 1990). Additional support to taxonomic aggregation is given by the frequent level of redundancy of biological data, which allows the same inference to be drawn at different levels of taxonomic identification (Ferraro & Cole, 1992, 1995).

Regardless of the advantages, taxonomic sufficiency has been also a controversial topic among the scientific community, particularly because it might generate losses of ecological information (Maurer, 2000), for example by incorporating both monotypic and polytypic taxa to the same level (May, 1990). Olsgard et al. (1998) also emphasized that the assumption underlying most assessment studies, namely that faunal patterns are a function of changes in environmental conditions may not be true at higher taxonomic levels. Inherent in these arguments is the understanding that higher taxonomic levels cannot be used as surrogates of species diversity without having a previous knowledge of each system. The usefulness of this method also depends on the objective of the study, locations chosen, variables measured, analytical procedures and sample size (Ferraro & Cole, 1990).

Although several monitoring studies indicate little loss of information when using higher taxonomic levels to assess marine benthic communities (Dauvin et al., 2003), only a single study has examined estuarine benthic communities (De Biasi et al., 2003). The European Water Framework Directive (WFD) (2000/60/EC) defines achieving good water

64 Taxonomic sufficiency

Chapter 3 quality status of all European water bodies by 2015 as a major goal, requiring the development of adequate assessment tools, based on the identification of surface water types, the definition of type-specific reference conditions, and the classification of all water bodies within five ecological quality classes.

The exercise of typology aims to separate water bodies into different type units, based on their physical and biological characteristics, assuring that physical typology is as simple as possible but ecologically relevant (Vincent et al., 2002). As a first step, water bodies are assigned to a surface water category, namely rivers, lakes, transitional waters, coastal waters or artificial water bodies. These categories are further divided into types using the obligatory factors of latitude, longitude, tidal range and salinity to define transitional and coastal water types. The WFD indicates that the Venice system salinity classes (Anonymous, 1959) should be used for typology. Optional factors that are more appropriate to the ecological characteristics of each region (e.g. depth, current velocity, mean substratum composition) should also be used if the ecological separation cannot be achieved only with the obligatory factors (Vincent et al., 2002). Using the WFD typological criteria in a poikilohaline estuary is a challenge since salinity is highly variable, making the use of the Venice system questionable. The aim of the present study was to test taxonomic sufficiency for typology in a poikilohaline estuary, using the benthic communities of the Mondego River estuary, given that previous studies focused mainly on the assessment of the benthic condition. Because benthic communities of most European estuaries are poorly known, a substantial effort is necessary to define meaningful water types; therefore, taxonomic sufficiency could be a useful tool to reduce the identification effort and thereby allow an increase in the sampling effort.

METHODS

Study area

The Mondego River estuary is located in the western coast of Portugal (40º08’ N; 8º50’ W) and it can be considered a poikilohaline estuary with high seasonal changes in freshwater flows and salinity, as well as daily changes related to tides (Chainho et al., 2006). The Mondego River estuary is divided into two branches with different hydrographic characteristics, namely higher freshwater discharges in the northern branch and a strong tidal influence in the southern branch. According to the criteria of the WFD this estuary is included in the Atlantic/North Sea eco-region complex, is fully to partially mixed (during periods of strong floods or droughts), mesotidal (2–4 m), with a residence time of two days in the

65 northern branch and nine days in the southern branch (Flindt et al., 1997) and with intertidal areas significantly reduced by channelization, particularly in the northern branch.

Sampling

A total of seven sampling stations were selected along the saline gradient of the northern branch of the Mondego River estuary (Figure 3.1). Three benthic invertebrate samples were taken at each station using a modified van Veen grab (0.05 m2). Grab contents were fixed and preserved with 4% buffered formalin, sieved using a 500 µm mesh and preserved in 70% ethanol. All samples were sorted and identified to the lowest possible taxonomic level. Several environmental variables were measured including water depth, bottom dissolved oxygen, water temperature, transparency and nutrients concentration, as well some sediment variables such as sediment grain size and total organic content. The methods used to determine these parameters are detailed in a previous study of Chainho et al. (2006). Sampling surveys were conducted nearly every three months, specifically in July (summer) and October 2000 (autumn), February/March (winter) and June (spring) 2001, in order to assess seasonal variations. The winter cruise took place immediately after a flood event and stations 1 and 7 could not be sampled at that time due to strong currents. Further details are described in the methods section in Chainho et al. (2006).

FRANCEFRANCE

A L A G G SPAIN U U SPAIN

T T R

O R P O

Figueira da Foz

1 2 3

Montemor-o-Novo 4 7 Atlantic Ocean

5 6

2 Km

Figure 3.1. Location of sampling stations in the northern branch in the Mondego River estuary, Portugal.

66 Taxonomic sufficiency

Chapter 3

Data analysis

General characterization

The redundancy of biological data was examined by determining the percentage of monotypic and polytypic taxa (i.e. genus, family, order, class and phylum) at each sampling station. A rank correlation coefficient (Spearman) was used to measure relationships between the species data and higher taxonomic levels using the RELATE routine of the PRIMER 5.0 software package (Clarke & Warwick, 1994). Taxa were aggregated to different taxonomic levels for each sampling station and their respective similarity matrices were calculated using Bray–Curtis similarities on log transformed data. Pairwise comparisons were made between the species similarity matrix and all the others and rank correlation values (ρ) were calculated for each comparison. When ρ=1, a perfect match between similarity matrices is obtained whereas when ρ=0 matrices do not have any relation (Clarke & Gorley, 2001). A Monte Carlo permutation test (999 permutations) was used to test the significance of the correlation coefficients (P<0.01). These coefficients (ρ) were further used as similarity measures in a triangular matrix of q values calculated between all pairs of data sets and a cluster analysis was conducted to group different taxonomic levels using group average linking.

Typology

Different water body types were identified in the Mondego River estuary based on the salinity classes (Venice system) and compared to the ecological groups identified using the benthic community data. Spatial groups of stations based on the abundance of benthic invertebrates were identified using the ‘‘mean variance per comparison’’ technique described by Williams & Stephenson (1973). By applying this technique it was possible to group stations into spatial groups independent of the effects associated with collections conducted during different seasons. The technique estimates the variance between stations and between sampling events by calculating the Euclidean distance between stations and sampling events (over all species) after stations and sampling events were centered to their respective means. These variance estimates (the centered Euclidean distances) were used as a measure of dissimilarity between stations for cluster analyses to define the spatial groups. A flexible sorting strategy was used for the cluster analysis with an intensity coefficient or value of – 0.25 (Boesch, 1977a). Dissimilarity coefficients were calculated using a program written in the SAS/IML® matrix programming language while the dendrograms for this analysis were produced using PROC CLUSTER of the SAS/Stat® software package. All species counts were standardized to have an overall mean value of zero and a standard deviation of one prior to conducting this analysis. The analysis was performed using data collected during only three

67 seasons (summer, autumn and spring) due to the missing observations in winter. The average density, the Shannon-Wiener diversity index (H’ loge), the total number of taxa, the number of taxa included in major taxonomic groups and the six most abundant species (dominants) were used as descriptive measures of the benthic communities of each group identified by the cluster analysis.

Multivariate analysis of variance (MANOVA) was used to test for significant overall differences in centroids (Huberty, 1994; Johnson & Wichern, 1998), between the ecological groups identified. The statistical test used for the MANOVAs was Wilk’s λ (Huberty, 1994) which were conducted using SAS software’s MANOVA procedure. If a MANOVA was significant, pairwise Wilk’s λ were conducted between ecological groups.

In order to examine the influence of seasonality stations were also classified into spatial groups for each seasonal data set independently using Bray–Curtis similarities on square root transformed data. Similarity coefficients were used to produce hierarchical cluster dendrograms, using group average linking.

Descriptive discriminant analysis was used to determine if there was a significant separation between ecological groups and to describe which taxa showed the best discriminant ability. Discriminant analysis uses a set of response variables, in this case counts of individual taxa, to create linear composites or linear discriminant functions (LDFs) of these variables that describe separation between groups (Huberty, 1994). Two-dimensional plots showing group centroids and their associated 95% confidence ellipses for the LDFs developed were produced to identify specific taxa that were important in separating the groups. LDF axes were labeled using the names of those taxa with a significant ANOVA between ecological groups and with a high between-group structure r value (≥|0.80|). Order of the taxa along the axes was based on the magnitude of the between group structure r values (loadings). A multivariate strength-of-association index (1–Wilk’s λ) (Huberty, 1994) was used to determine the relative distances between groups of stations. Only taxa with more than a single count were used in the analyses and all analyses were conducted using log transformed data. A P value of 0.05 was used as the statistical test criterion for all discriminant analyses which were conducted using SAS© software’s CANDISC procedure.

Taxonomic sufficiency

New data matrices were produced by aggregating species data to higher taxonomic levels and into different taxocenes, as listed in Table 3.1. Taxocenes were chosen because of their general dominance of temperate estuarine macrobenthic communities.

The experimental design applied in this study aimed to determine if there were significant differences between pre-established ecological groups using all available taxa and

68 Taxonomic sufficiency

Chapter 3 to identify which, if any, taxonomic levels and taxocenes provided sufficient information to generate the same groups with significant differences. Therefore, all analyses conducted for the species data set were also performed for each taxonomic level and taxocene, namely cluster analysis using the mean variance per comparison approach, MANOVA and descriptive discriminant analysis. Only taxonomic levels and taxocenes that produced the same clusters and revealed significant differences between centroids were considered adequate for typology in the Mondego River estuary.

Table 3.1. Levels of aggregation used for taxonomic levels and taxocenes and number of taxa included in each level Taxonomic levels Taxocenes Species 85 Annelida 43 Genus 76 Polychaeta 39 Isopoda 4 Family 63 Spionidae 7 Insecta 6 Order 36 Oligochaeta 4 Mollusca 9 Class 9 Arthropoda 28 Bivalvia 6 Phylum 6 Amphipoda 9 Gastropoda 2

RESULTS

General characterization

A total of 39 835 specimens were collected in the northern branch of the Mondego River estuary and 85 different taxa were identified, comprising 76 genera, 63 families, 36 orders, 9 classes and 6 phyla (Table 3.1). Cluster analysis of the correlation coefficients between matrices of similarity obtained for different taxonomic levels showed very high correlations between the species matrix and those obtained using genus, family and order levels (ρ>0.90; P<0.001). Correlation between species level and class level matrices was lower (ρ=0.80; P<0.001), as well as with the phylum matrix (ρ=0.57; P<0.001) (Figure 3.2 a). Over 80% of the genera identified were monotypic, as well as 74% of families and 58% of orders. On the other hand over 50% of classes and phyla included more than 3 species (Figure 3.2b).

Annelids included the highest number of species (43) (Table 3.1) and accounted for 38% of the total abundance, due to the major contribution of the polychaete Streblospio shrubsolii (Buchanan, 1890) (22%). The amphipod Corophium multisetosum Stock, 1952 was the dominant species, accounting for 40% of the total abundance.

a) 80 Correlations with species data

Genus (ρ=0.996) 85 Family (ρ=0.963) Order (ρ=0.939) Class (ρ=0.804) Phylum (ρ=0.568) 90

100 Species Genus Family Order Class Phylum

Figure 3.2. Taxonomic similarity. a) Cluster analysis of the similarity between different taxonomic levels, obtained by group average linking. A rank correlation coefficient (Spearman) was used to compare similarity matrices (Bray-Curtis on square root transformed data) obtained for species and other taxonomic levels and the coefficients of pairwise tests (ρ) were used as similarity measures in the cluster analysis (P<0.01). b) Number of species (%) included in each level of taxonomic aggregation (n.i. non identified to the species level).

Typology

Cluster analyses extracting seasonal effects, using species level abundance data, identified three major ecological groups in the Mondego River estuary (Figure 3.3) and there was a significant difference between the groups (MANOVA, F=8.15; P<0.001). Pairwise Wilk’s λ tests also indicated significant differences between all ecological groups (P<0.05).

70 Taxonomic sufficiency

Chapter 3

a) All seasons 2.0

β 1.5

1.0

0.5

Flexible Beta Distance ( 0.0 = -0.25) 1 2 3 4 567

Figure 3.3. Taxonomic composition of the three spatial groups identified in the Mondego River. a) Similarity dendrogram of stations when seasonal effects are removed using the “mean variance per comparison”, applied to Euclidean distances, using a flexible beta distance, method described by Williams & Stevenson (1973) (from Chainho et al., 2006). b) Percent composition of major taxonomic groups in each of the seven stations. Salinity classes of the Venice system corresponding to the mean annual salinity in each station are indicated below clusters (see legend of Figure 3.4).

The Lower Estuary community type included stations 1 and 2 and was characterized by a numerical dominance in density of polychaetes (52%) and bivalves (41%), showing the highest number of taxa and diversity (Figure 3.3, Table 3.2). These stations had sediments consisting mainly of medium sand (Table 3.2). Stations 3, 4 and 5 were identified as the Middle Estuary community type dominated in density by polychaete species (53%), mainly S. shrubsolii and characterized by medium to coarse sand sediments. The amphipod C. multisetosum was also dominant (36%) due to the high numbers registered during spring. The Upper Estuary community type (stations 6 and 7) was largely dominated in density by amphipods (80%) and consisted mainly of coarse sediment (Figure 3.3, Table 3.2). Considering

71 the average annual salinity values there is an overlap of salinity classes and of the ecological groups identified. Only 16 taxa occurred across all ecological regions half of which were annelids.

Table 3.2. Biological and environmental descriptive parameters of the regions identified in the Mondego River estuary. Maximum, minimum and average density values (ind m-2) seasonally determined (winter – W; spring – Sp) are presented for each group. Shannon-Wiener diversity, total number of taxa and number of taxa included in different taxocenes are indicated for each group. Six most abundant species (dominants) are listed with their respective contributions to total density and the indication of the Class in bold (A- Amphipoda; B- Bivalvia; G- Gastropoda; I- Insecta; N- Nemertea; O- Oligochaeta; P- Polychaeta). Salinity and depth (m) ranges and sediment type are also indicated. Lower Estuary includes stations 1 and 2, Middle Estuary includes stations 3, 4 and 5 and Upper Estuary includes stations 6 and 7. For station locations see Figure 3.1.

Lower estuary Middle estuary Upper estuary Mean (380 W) 7 162 (9 497 Sp) (651 W) 5 761 (9 702 Sp) (453 W) 14 927 (39 550 Sp) Density Diversity 1.6 ± 0.7 1.1 ± 0.5 0.9 ± 0.3 N Taxa 60 57 21 Polychaetes 32 21 5 Oligochaetes 3 4 4 Amphipods 4 7 2 Isopods 4 4 3 Insects 0 4 3 Gastropods 2 1 1 Bivalves 6 5 1 Other 9 10 2 Groups Streblospio shrubsolii (30%)P Streblospio shrubsolii (47%)P Corophium multisetosum (78%)A Cerastoderma glaucum (25%)B Corophium multisetosum (36%)A Corbicula fulminea (8%)B Dominant Scrobicularia plana (10%)B Spio martinensis (2%)P Tubificoides sp. (4%)O Species Hydrobia ulvae (6%)G Hydrobia ulvae (2%)G Nais sp. (4%)O Spio martinensis (6%)P Tubificoides sp. (1%)O Echytraeus sp. (2%)O Chaetozone setosa (5%)P Tetrastemmatidae (1%)N Ephoron virgo (2%)I Depth 3.0 - 5.5 0.8 - 5.0 3.5 - 6.0 Salinity 7.0 – 40.0 7.0 - 31.6 2.0 - 14.2 Sediment Medium sand Coarse-Medium sand Coarse sand type

Strong seasonal changes were observed in the Mondego River estuary benthic communities, with the lowest number of species and abundances found during winter and the

72 Taxonomic sufficiency

Chapter 3 highest abundance observed during spring, associated with salinity changes along seasons (Table 3.2). Cluster analysis of seasonal data showed that three major ecological groups were again obtained with different similarity levels for different sampling events (Figure 3.4). Stations 1 and 2, located in the Lower Estuary always grouped together, except for winter when station 1 could not be sampled (Figure 3.4). Stations 3 and 4 were always included in the same group, as well as stations 6 and 7 but station 5 switched between groups over sampling periods (Figure 3.4).

Summer and autumn assemblages showed a higher similarity between the Lower and Middle Estuary benthic communities with salinity ranging from euryhaline to mesohaline (Figure 3.4). Winter and spring assemblages showed a higher level of separation between different ecological groups and included station 5 in the Upper Estuary, due high abundances of the amphipod C. multisetosum. Salinity values were much lower during these seasons, especially during winter when all stations were classified as oligohaline, except for the Lower Estuary that was mesohaline. Discriminant analysis showed significant separation of the Lower

Estuary group along the first LDF (λ1=111.2; F=8.15; P<0.001) (Figure 3.5a). Separation along this axis was due mainly to the occurrence of rare species of polychaetes with marine affinity (r>0.99) such as Diopatra neapolitana Delle Chiaje, 1841, Eteone picta de Quatrefages, 1866, Eumida sanguinea (Örsted, 1843) and Pectinaria koreni (Malmgren, 1866) in the Lower estuary and to a lesser extent to higher abundances of some common species in this group of stations, namely the bivalves Cerastoderma glaucum (Poiret, 1789), Scrobicularia plana (da Costa, 1778) and Angulus tenuis (da Costa, 1778) and the polychaetes Heteromastus filiformis (Claparède, 1864), Owenia fusiformis Delle Chiaje, 1842, Mediomastus fragilis Rasmussen, 1973 and Spio martinensis Mesnil, 1896. The Middle and Upper Estuary overlapped along the first LDF but were significantly separated along the second LDF (λ2=12.7; F=2.97; P<0.05). The polychaetes S. shrubsolii (r=–0.91), Scoloplos armiger (Müller, 1776) (r=–0.84) and Hediste diversicolor (O.F. Müller, 1776) (r=–0.82) discriminated best the Middle Estuary while the Upper Estuary was characterized by a highly diverse group of taxa including oligochaetes (Tubificoides sp., Limnodrilus hoffmeisteri Claparède, 1862, Echytraeus sp. and Nais sp.), isopods (Saduriella losadai Holthuis, 1964 and Cyathura carinata (Kroyer, 1847)), insects (Ephoron virgo (Olivier, 1791) and Chironomidae), the bivalve Corbicula fulminea (Müller, 1774) and the amphipod C. multisetosum (Figure 3.5a).

67 6 5 5 4 4 n 0.5-5.0/6.0 Oligohaline □ 3 3 Winter Autum 2 2 1 1

20 40 60 80

r 20 40 60 80 y Similarit y Similarit 100 d during winter). Stations were grouped into spatial 100 ures were obtained by calculating the Bray-Curtis

of benthic invertebrates collected seasonally in the 5.0/6.0-18.0/20.0 Mesohaline

■ 7 67 cated below cluste i d 56 5 in e r a n r o 4 4 i 18.0/20.0-30.0 Polyhaline ■ stat h 3 3 Spring Summe eac in 2 ed in 1 >30.0 Euryhaline 12 ■ ty obta

0 0

20 40 60 80 ini 20 40 60 80

Similarity Similarity 100 100 Figure 3.4. Cluster analysis of the abundance data Mondego River estuary (stations 1 and 7 were not sample sal groups by group average linking and similarity meas coefficient using log transformed data. Salinity classes of the Venice system corresponding to measures

74 Taxonomic sufficiency

Chapter 3 ) 7.40 c 5.04 troids and Lower Estuary taxonomic s). 2.68 Middle 0.32 Estuary Capitellidae, Nephtyidae, Mysidae, Glyceridae, Glyceridae, Mysidae, Nephtyidae, Capitellidae, Scrobicularidae, Tellinidae, Cardiidae, Hydrobiidae, Oweniidae, Onuphidae, Cirratullidae, Mytilidae, Phyllodocidae Pectinariidae, -2.04 Upper Estuary

Corophiidae -4.40

d)order; e)class). Groups’ cen 2.84 1.28 4.40 -1.84 -3.40 -0.28 Echytraeidae, Orbinidae Echytraeidae,

12.00 b) 8.40 Lower Estuary 4.80 1.20 ies (a)species; b)genus;.c)family; nt functions. See Appendix 1 for species abbreviations (continue tified in the northern branch of Mondego River estuary using Middle Estuary -2.40 Upper Estuary Polydora, Glycera, Angulus, Cerastoderma, Scrobicularia, Cerastoderma, Angulus, Glycera, Polydora, Eteone, Chaetozone, Mediomastus, Diopatra, Modiolus, Prionospio, Pectinaria, Owenia, Heteromastus, Eumida, Hydrobia, Solen, GastrosaccusCapitella, Spio,

-6.00

3.60 5.40 1.80 0.00

-1.80 -3.60

Boccardiella, Limnodrilus Boccardiella, Scoloplos, Hediste, Streblospio Hediste, Scoloplos, Gammarus, Saduriella, Ephoron, Ephoron, Saduriella, Gammarus,

Lower Estuary G.gig, P.cil, N.pul, S.mar, C.cap, H.ulv, G.spi H.ulv, C.cap, S.mar, N.pul, P.cil, G.gig, S.mar, M.fra, M.bar, C.set, S.pla, C.gla, A.ten, C.gla, S.pla, C.set, M.bar, M.fra, S.mar, D.nea, E.pic, E.san, H.fil, N.hom, O.fus, P.kor, P.cir P.kor, O.fus, N.hom, H.fil, E.san, E.pic, D.nea, -1.44 3.92 9.28 14.64 20.00 Upper Middle Estuary Estuary

-6.80

Turb, Nais, C.mul Nais, Turb, 4.24 2.28 6.20 -1.64 -3.60 0.32

levels that produced the same cluster as obtained for all spec Figure 3.5. Discriminant analysis of the ecological groups iden 95% confidence ellipses are plotted on the first two discrimina

Tubi, S.los, E.vir, L.hof, Echy, Chir, C.car, C.ful, C.car, Chir, Echy, L.hof, E.vir, S.los, Tubi, H.div S.arm, S.shr,

) 3.20 ma e Upper Estuary 2.20 Insecta, Crustacea, Crustacea, Insecta, Oligochaeta, Turbellaria Oligochaeta, 1.20 0.20 Middle Estuary Lower Estuary -0.80 Polychaeta, Gastropoda Polychaeta, -1.80 2.40 0.96 0.24 1.68 -0.48 -1.20 y using taxonomic levels that produced the pecies; b)genus;.c)family; d)order; e)class). 5.00 d) 3.36 Lower Estuary 1.72 Middle Estuary -0.08 Capitellida, Mysidacea, Mesogastropoda, Mysidacea, Capitellida, Oweniida, Mytiloidea, Cirratullida, Cardioidea, Eunicida Solenoidea, Phyllodocida, Upper Estuary -1.56 Amphipoda

-3.20

2.04 0.88 3.20 -1.44 -3.40 -0.28 Orbiniidea spd,Tubificina T Isopoda, Figure 3.5. (continued) Discriminant analysis of the ecological groups identified in northern branch of the Mondego River estuar same cluster as obtained for all species (a)s Groups’ centroids and 95% confidence ellipses are plotted on the first two discriminant functions. See Appendix 1 for species abbreviations.

76 Taxonomic sufficiency

Chapter 3

Taxonomic sufficiency

Taxonomic levels

Cluster analysis conducted on abundance data sets aggregated to taxonomic levels higher than the species level produced the same clusters as using taxa identified to the species level, except at the phylum level (Table 3.3). Wilk’s λ statistics revealed an overall difference between centroids of the different regions identified in the Mondego River estuary for all taxonomic levels (F values significant at P<0.001). Pairwise tests between centroids also indicated significant differences between ecological regions for all taxonomic levels (F values significant at P<0.01). Both LDFs provided a significant separation between regions for all taxonomic levels, except for the class level (P<0.01).

All centroids were well separated along the first LDF for data aggregated at higher levels except at the class level for which the Lower and Middle Estuary overlapped along the first LDF, but were separated along the second LDF (Figs. 5b–e).

All discriminant plots showed a better separation of the Lower Estuary along the first LDF, except for the class level. At this taxonomic level the first LDF explains the separation between the Upper Estuary and the other two groups, mainly because of the occurrence of common groups of taxa (i.e. polychaeta and gastropoda) in the Lower and Middle Estuary (Figure 3.5e). Although MANOVA and discriminant analyses indicated significant differences between groups for all taxonomic levels, the relative degree of differences between groups decreased at higher taxonomic levels as indicated by both the multivariate strength-of- association index (1–Wilk’s λ) (Table 3.3) and by the reduction in magnitude of the scales in the axes of the discriminant plots observed in Figure 3.5. A similar pattern was observed for all taxonomic levels with respect to taxa that showed higher discriminatory ability. Several bivalve and polychaete taxa discriminated better the Lower Estuary, some polychaete taxa discriminated the Middle Estuary and a very diverse group of taxa (e.g. insects, oligochaetes, crustaceans) discriminated better the Upper Estuary. Of all taxocenes tested only Mollusca and Bivalvia indicated the same ecological groups as obtained using species. Cluster analysis of all other taxocenes showed different hierarchical structures (Table 3.3).

All taxocenes tested showed significant overall differences between centroids of the three regions identified in the Mondego River estuary (F values significant at P<0.01), although only Annelida, Polychaeta, Spionidae, Arthropoda, Mollusca and Bivalvia were significantly different between all pairwise groups tested (F values significant at P<0.05) (Table 3.3). There was no overlap of the group ellipses along both LDFs of taxocenes representing the Phylum level (i.e. Annelida, Artropoda, Mollusca) but below that taxonomic level only bivalves showed no overlap. Polychaeta species provided good discrimination between the Lower Estuary and the other groups along the first LDF but there was some

77 degree of overlap of the Middle and Upper Estuary, resulting mainly from the occurrence of Nereid species (i.e. H. diversicolor, Websterinereis glauca (Claparède, 1870)) in both groups. Amphipoda and Oligochaeta discriminated only the Upper Estuary, Gastropoda only the Middle Estuary, while Isopoda and Insecta provided sufficient information to separate the Middle Estuary from the Upper Estuary (Table 3.3). Both LDFs were significant for the taxocenes that separated all three groups (F values significant at P<0.01) with the first LDF explaining a higher percentage of variance (>65%).

Table 3.3. Discrimination ability of different taxonomic levels and taxocenes determined by Wilk’s λ test on the centroides defined for each region

Taxocenes Similar cluster Lower Estuary Middle Estuary Upper Estuary 1- λ Species − 0.999 Genus Yes 0.998 Family Yes 0.993 Order Yes 0.974 Class Yes 0.835 Phylum No 0.660 Annelida No 0.946 Polychaeta No 0.898 Spionidae No 0.597 Oligochaeta No 0.587 Arthropoda No 0.872 Amphipoda No 0.448 Isopoda No 0.295 Insecta No 0.356 Mollusca Yes 0.876 Bivalvia Yes 0.829 Gastropoda No 0.408

Upper solid lines represent no differences between the Lower and Middle Estuary and lower solid lines represent no differences between the Lower and Upper Estuary. The first column identifies which taxonomic levels and taxocenes produced the same cluster obtained using all species. The strength of association between groups for each taxonomic level and taxocene is shown in the right column (1- Wilk’s λ)

Taxocenes Spionid polychaetes were important in discriminating between spatial groups for discriminant analyses conducted on both the Annelida and Polychaeta. S. martinensis (r=0.86) and Polydora ciliata (Johnston, 1838) (r=0.97) were among the group of species that best discriminated the Lower Estuary (higher correlations with the first LDF). Boccardiella ligerica (Ferronnieère, 1898) (r=–0.96) and S. shrubsolii (r=0.95) showed the best discriminating

78 Taxonomic sufficiency

Chapter 3 ability between the Upper and Middle Estuary, along the second LDF. The discriminant analysis using only this family revealed some degree of overlap along both LDFs, although all groups were significantly different (Figure 3.6c). That overlap was mainly related to the occurrence of S. shrubsolii, across all groups, although with different abundances, and the occurrence of S. martinensis in the Lower and Middle Estuary but with significantly higher abundance in areas of higher salinity (Table 3.2). The results of the cluster analysis for this group identified very similar groups of stations, with a single shift in positions of stations 2 and 3.

Mollusks and bivalves showed very similar discriminatory ability between groups, as shown by the ellipse plot (Figs. 3.6a–b) and the strength of association levels which were higher than 0.80 for both taxocenes (Table 3.3). Species best separating different regions were all correlated with the first LDF. C. fulminea was negatively correlated to the first LDF while all other bivalve species showed significant high positive correlations with that axis (F values significant at P<0.05). Isopods (C. carinata; S. losadai), amphipods (C. multisetosum), insects (E. virgo; Chironomidae) and mysids (Gastrosaccus spinifer (Goës, 1864)) contributed to the discriminant ability of arthopods, although none of those groups, when tested, discriminated all regions when used independently (Table 3.3).

2.00 c) ecies 1.24 c) is also taxocenes S.mar, S.shr, P.cil S.shr, S.mar, 0.48 Lower Estuary Middle Estuary -1.04 -0.28 Upper Estuary B.lig -1.80 0.48 1.60 1.04 -0.08 -1.20 -0.64

3.20 ) b 2.00 Lower Estuary 0.80 Middle Estuary A.ten, C.gla, S.pla, M.bar , S.mar on the first two discriminant functions. See Appendix 1 for sp )Mollusca; b)Bivalvia). The discriminant plot of family Spionidae tified in the northern branch of Mondego River estuary using Upper -1.60 -0.40 Estuary C. ful -2.80 0.60 1.80 1.20 0.00 -0.60 -1.20

) 3.60 a 2.32 Lower 1.04 Estuary Middle Estuary H.ulv, A.ten, C.gla, S.pla, M.bar, S.mar, Nudi S.mar, M.bar, S.pla, C.gla, A.ten, H.ulv, -1.52 -0.24 Upper Estuary C.ful -2.80 0.52 1.80 1.16 -0.12 -1.40 -0.76 shown. Groups’ centroids and 95% confidence ellipses are plotted abbreviations. that produced the same cluster as obtained for all species ( a Figure 3.6. Discriminant analysis of the ecological groups iden

80 Taxonomic sufficiency

Chapter 3

DISCUSSION

The Mondego River estuary is a poikilohaline estuary, characterized by strong seasonal changes in water flow and salinity which presents some challenges in the identification of saline regions based upon the Venice classification system as indicated by the WFD (Chainho et al., 2006). This type of salinity regime associated with strong tidal and seasonal changes has been already recognized in Portuguese systems by Moreira et al. (1993) in Ria de Aveiro. In poikilohaline estuaries benthic communities are seasonally displaced and estuarine species are dominant (Boesch, 1977b), as confirmed in the Mondego where the numerical dominance of S. shrubsolii and C. multisetosum was observed throughout most of the year.

The results of seasonal cluster analysis showed a consistent pattern of aggregation of stations among seasons, despite strong changes observed in the structure of the benthic community within each group. The only exception was station 5, which is apparently located in a transition zone and changed groups in different seasons. The overall station group consistency supports the definition of three main ecological types in the northern branch of the Mondego River estuary, namely the Lower Estuary, the Middle Estuary and the Upper Estuary.

Nevertheless, within each of these three station groups the Venice salinity classes varied greatly between seasons, ranging from oligohaline to polyhaline in some areas of the estuary, depending upon the freshwater flow regime. This scenario seems to fit into the ecocline concept introduced by Boesch (1977b) and further discussed by Attrill & Rundle (2002), defined as a boundary of progressive change between two systems, freshwater and marine. The latter proposed a two ecocline model, with two overlapping salinity gradients, one from upriver to mid-estuary for freshwater species and another extending from the sea to the mid-estuary for marine species, whose associated benthic communities change location along the estuary in relation to changes in freshwater flow. Furthermore they suggest that truly estuarine endemic species do not exist. In the Mondego, seasonal changes in freshwater flow act as an environmental gradient and euryhaline estuarine species and freshwater species shift their distributions in relation to the associated changes in salinity. Although insect species extended their distribution downstream in the Mondego River estuary during high flows and marine species of polychaetes included in families such as Phyllodocidae, Glyceridae and Cirratulidae moved upstream during low freshwater flow periods, as indicated in the two-ecocline model, estuarine endemic species were also observed, such as C. multisetosum and S. losadai. Previous studies carried out in Portuguese estuaries showed that the amphipod C. multisetosum occurs preferentially in salinities ranging from 2.5 to 10

81 (Queiroga, 1990), which is in agreement with the movements of this species upstream and downstream in the Mondego River, following salinity changes.

Using average annual salinities and benthic community groups obtained independently of seasonal effects appears to be a reasonable approach to define water body types in the Mondego River estuary, since there was a good correspondence between both approaches. The Lower Estuary had an average salinity in the euryhaline class and a fauna characterized by the dominance of marine bivalve and polychaete species. The Middle Estuary had an average salinity in the polyhaline class and a fauna dominated by polychaetes, mainly the tolerant spionid S. shrubsolii. The Upper Estuary had an average salinity in the mesohaline class and a fauna dominated by the estuarine endemic amphipod C. multisetosum. Average annual salinities in the oligohaline and tidal freshwater range were not observed although salinity measured at some stations was in the oligohaline class during winter and spring. Discriminant analysis showed that rarer species also played an important role in separating ecological groups, since some of them are found exclusively at one station group, such as the marine polychaetes (e.g. D. neapolitana, E. picta) in the Lower Estuary. On the other hand, estuarine tolerant species (e.g. S. shrubsolii) provided good discrimination of the Middle Estuary, while a highly diverse group of taxa including freshwater species discriminated the Upper Estuary from the other groups. Overall our findings suggest that an ecocline model for explaining benthic community composition may be appropriately applied to the Mondego River estuarine gradient provided the model allows for the inclusion of estuarine endemic species, as suggested by Boesch (1977b).

Techniques used to test the hypotheses of obtaining the same ecological groups with taxonomic levels higher than the species showed that only the phylum level produced significantly different results. The class level provides less discriminating ability between ecological groups, since both LDFs are necessary to separate all groups and the location of centroids and ellipses indicate that groups are closer. This is mainly related to the occurrence of common groups such as polychaetes and gastropods in the Lower and Middle Estuary. On the other hand, when taxa were grouped to genus, family and order levels good discriminating ability between ecological groups was observed, with centroids and ellipses exhibiting very similar relative position and shape when compared to those of species level. Correlations between similarity matrices of different taxonomic levels corroborated these results by showing a higher closeness of genus, family and order levels to the species matrix.

As pointed out by Ferraro & Cole (1992, 1995), a high number of monotypic taxa increases the probability of taxonomic sufficiency at taxonomic levels higher than species, mainly because of redundancy in their responses to pollution. As shown in our results over 70% of genera and families were monotypic and although the percentage was lower for orders, those that were polytypic included mainly species found in the same ecological groups. For

82 Taxonomic sufficiency

Chapter 3 example, the order Phyllodocida, which included 22 different species, was collected almost exclusively in the Lower Estuary.

Most studies concerning taxonomic sufficiency for detecting pollution impacts concluded that family level was a good surrogate for species, since there was no loss of information in the benthic responses to pollution stress (Warwick, 1988; Baldó et al., 1999; Dauvin et al., 2003; De Biasi et al., 2003; Gomez Gesteira et al., 2003; Terlizzi et al., 2003) or loss of statistical power to detect differences between degraded and undegraded locations (Ferraro & Cole, 1990, 1992; 1995). The present study indicates that, for typological purposes in poikilohaline estuaries, order can be used without significant loss of information. Even so, family level is most likely the best compromise since most taxonomic manuals are more adequate for identifications at this level and ecologists with taxonomic training are familiar with the procedures. These findings corroborate the suggestion of De Biasi et al. (2003) to use family level to assess spatial patterns in the Magra estuary (Italy).

Although some taxocenes demonstrated the ability to discriminate between the pre- established ecological groups (e.g. arthropods and annelids), only mollusks and bivalves identified the same groups using cluster analysis. The discrimination of the Upper Estuary was mostly related to the high abundance of C. fulminea, an introduced species that can easily adapt to freshwater environments and reach very high densities. Although a density of 830 ind m–2 was recorded for this species in the Upper Estuary, there is no information about its occurrence in other Portuguese estuaries since most studies did not cover upstream areas.

Only 16 species occurred across all ecological groups, half annelid species, indicating the adaptive abilities of this group. This low number of species seems to agree with the arguments of Kinne (1971) stating that environments with pronounced salinity fluctuations do not promote evolutionary processes because instability acts as a brake to speciation. Nevertheless, family Spionidae, which included the highest number of species among all families identified in the Mondego River Estuary, showed a discriminating ability almost as good as other taxocenes. This family seems to have a very high plasticity with species occurring along the entire estuary, which is why, despite the overlap observed along LDF axes in the ellipse plots, it showed significant differences between all ecological groups and the results of cluster analysis were very similar to those using species. These results suggest some caution when using taxocenes because the loss of ecological information might lead to different conclusions obtained with different analytical procedures.

The dominant species S. shrubsolii showed a very wide distribution occurring in all ecological groups, S. martinensis was very common in the Middle and Lower Estuary and B. ligerica was collected exclusively in the Upper Estuary. The other species of this family were rare (i.e. P. ciliata, Prionospio malmgreni Claparède, 1870, Prionospio cirrifera Wirén,

83 1883 and Pygospio elegans Claparède, 1863), occurring mainly in the Lower Estuary. Spionids are often classified as opportunists and used as pollution indicators (e.g. Weisberg et al., 1997; Borja et al., 2000; Eaton, 2000). In addition, spionid species exhibit a diverse variety of reproductive patterns including monotely, polytely, broadcast spawning, internal and external brooding, and poecilogony (Gudmundsson, 1985; Blake & Arnofsky, 1999). Feeding behaviours include deposit feeding, suspension feeding, switching between deposit and suspension feeding interface feeding) (Dauer et al., 1981), as well as, commensal and predatory behaviours (Williams, 2001; 2002). It is useful to make a distinction between opportunist species and stress tolerant species (Gray, 1979). Spioinds can generally be considered to be opportunistic species and some species may also be stress tolerant. However, there is little physiological data to distinguish between species that are opportunistic, stress tolerant to natural stresses and stress tolerant to anthropogenic stress. These patterns are very similar to those obtained in the James River, Chesapeake Bay, a homiohaline estuary (Dauer, unpublished data) where spionids also revealed good discriminating ability between salinity classes, showing habitat specific distributions of species in this family and high adaptability to different conditions.

CONCLUSIONS

This study demonstrates the effective application of the concept of taxonomic sufficiency in the identification of water body types in a poikilohaline estuary. Although taxonomic sufficiency has been widely tested for detecting pollution impacts, showing that the family level provides enough information to separate between degraded and undegraded sites, there was no previous substantial scientific basis for the use of taxa aggregated to higher taxonomic levels for typology. The present study shows that taxa identified to the family level discriminate the same ecological types as using the species level, mainly because of the high number of monotypic families occurring in poikilohaline estuaries.

Taxocenes demonstrated less ability to identify water body types when used separately, although mollusks and bivalves have identified the same types and annelids have shown a habitat specific distribution, in particular the family Spionidae. The use of these taxocenes needs further investigation namely on the effects of natural versus human induced stress since spionids and also some bivalve species are very well adapted to strong environmental changes. The same methodology should be applied to other estuaries with similar seasonal hydrologic changes but lower human pressure. Future typological studies should also include regions that are farther upstream in order to clarify application of the WFD to estuarine gradients.

84 Taxonomic sufficiency

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REFERENCES

Anonymous, 1959. Venice system. 1959. Final resolution of the symposium on the classification of brackish waters. Archivio di Oceanografia e Limnologia 11 (suppl): 243– 248. Attrill, M.J. & Rundle, S.D. 2002. Ecotone or ecocline: ecological boundaries in estuaries. Estuarine, Coastal & Shelf Science 55: 929-936. Baldó, F., García-Martín, S.F., Drake, P. & Arias, A.M. 1999. Discrimination between disturbed coastal ecosystems by using macrobenthos at different taxonomic levels. Bolletín del Instituto Español de Oceanografía 15: 489-493. Bianchi, C.N. & Morri, C. 2000. Marine biodiversity of the Mediterranean Sea: situation, problems and prospects for future research. Marine Pollution Bulletin 40: 367-376. Blake, J.A. & Arnofsky, P.L. 1999. Reproduction and larval development of the spioniform Polychaeta with application to systematics and phylogeny. Hydrobiologia 402: 57-106. Boesch, D.F. 1977a. Application of numerical classification in ecological investigations of water pollution. EPA Report 600/3-77-033, U.S. EPA, Corvallis, Oregon, U.S.A. Boesch, D.F. 1977b. A new look at the zonation of the benthos along the estuarine gradient. In Coull, B. (Ed.) Ecology of Marine Benthos, pp.245-266. University of South Carolina Press, Columbia, U.S.A. Borja, A., Franco, J. & Pérez, V. 2000. A Marine Biotic Index to establish the ecological quality of soft bottom benthos within European estuarine and coastal environments. Marine Pollution Bulletin 40: 1100–1114. Chainho, P., Costa, J.L., Chaves, M.L., Lane, M.F., Dauer, D.M. & Costa, M.J. 2006. Seasonal and spatial patterns of distribution of subtidal benthic invertebrate communities in the Mondego River estuary, Portugal. Hydrobiologia 555: 59-74. Clarke, K.R. & Gorley, N. 2001. PRIMER v5: user manual/tutorial. PRIMER-E-Ltd. Plymouth Marine Laboratory, U.K. Clarke, K.R. & Warwick, R.M. 1994. Change in Marine Communities: an Approach to Statistical Analysis and Interpretation. Natural Environmental Research Council, Plymouth Marine Laboratory, United Kingdom. Dauer, D.M., Maybury, C.A. & Ewing, R.M. 1981. Feeding behavior and general ecology of several spionid polychaetes from the Chesapeake Bay. Journal of Experimental Marine Biology and Ecology 54: 21-38. Dauvin, J.C., Gomez Gesteira, J.L. & Fraga, M.S. 2003. Taxonomic sufficiency: an overview of its use in the monitoring of sublittoral benthic communities after oil spills. Marine Pollution Bulletin 46: 552-555.

85 De Biasi, A.M., Bianchi, C.M. & Morri, C. 2003. Analysis of macrobenthic communities at different taxonomic levels: an example from an estuarine environment in the Ligurian Sea (NW Mediterranean). Estuarine, Coastal and Shelf Science 58: 99-106. Eaton, L. 2000. Development and validation of biocriteria using benthic macroinvertebrates for North Carolina estuarine waters. Marine Pollution Bulletin 42: 23-30. Ellis, D. 1985. Taxonomic sufficiency in pollution assessment. Marine Pollution Bulletin 16: 459. Ferraro, S.P. & Cole, F.A. 1990. Taxonomic level and the sample size sufficient for assessing pollution impacts on the Southern California Bight macrobenthos. Marine Ecology Progress Series 67: 251-262. Ferraro, S.P. & Cole, F.A. 1992. Taxonomic level sufficient for assessing a moderate impact on macrobenthic communities in Puget Sound, Washington, USA. Canadian Journal of Fisheries and Aquatic Sciences 49: 1184-1188. Ferraro, S.P. & Cole, F.A. 1995. Taxonomic level sufficient for assessing pollution impacts on the Southern California Bight macrobenthos revisited. Environmental Toxicology and Chemistry 14: 1031-1040. Flindt, M.R., Kamp-Nielsen, L., Marques, J.C., Pardal, M.A., Bocci, M., Bendoricchio, G., Salomonsen, J., Nielsen, S.N. & Jørgensen, S.E. 1997. Description of three shallow estuaries: Mondego river (Portugal), Roskilde Fjord (Denmark) and the lagoon of Venice (Italy). Ecological Modelling 102: 17–31. Gomez Gesteira, J.L., Dauvin, J.C. & Fraga, M.S. 2003. Taxonomic level for assessing oil spill effects on soft-bottom sublittoral benthic communities, Marine Pollution Bulletin 46: 562-572. Gray, J.S., 1979. Pollution-induced changes in populations. Philosophical Transactions of the Royal Society of London series B, 286: 545-561. Gudmundsson, H. 1985. Life history patterns of polychaete species of the family Spionidae. Journal of the Marine Biological Association of the UK. 65: 93-111. Huberty, C.J. 1984. Applied Discriminant Analysis. John Wiley and Sons, Inc. New York, NY. Johnson, R.A. & Wichern, D.W. 1998. Applied Multivariate Statistical Analysis. Prentice-Hall Inc., Upper Saddle River, NJ. Kinne, O. 1971. Salinity. In Kinne (Ed.), Marine ecology, a comprehensive integrated treatise on life in the oceans and coastal waters, pp. 683-1244. Vol. 1(2). Wiley, London. Maurer, D. 2000. The dark side of taxonomic sufficiency (TS). Marine Pollution Bulletin 40: 98- 101. May, R.M. 1990. Taxonomy as destiny. Nature 347: 129-130. Moreira, M.H., Queiroga, H., Machado, M.M. & Cunha, M.R. 1993. Environmental gradients in a southern Europe estuarine system: Ria de Aveiro, Portugal: implications for soft bottom macrofauna colonization. Netherlands Journal of Aquatic Ecology 27: 465-482

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Olsgard, F., Somerfield, P.J. & Carr, M.R. 1998. Relationships between taxonomic resolution, macrobenthic community patterns and disturbance. Marine Ecology Progress Series 172: 25-36. Pagola-Carte, S., Urkiaga-Alberdi, J., Bustamante, M. & Saiz-Salinas, J.I. 2002. Concordance degrees in macrozoobenthic monitoring programmes using different sampling methods and taxonomic resolution levels. Marine Pollution Bulletin 44: 63-70. Queiroga, H., 1990. Corophium multisetosum (Amphipoda: Corophiidae) in Canal de Mira, Portugal: some factors that affect its distribution. Marine Biology 104: 397–402. Terlizzi, A., Bevilacqua, S., Fraschetti, S. & Boero, F. 2003. Taxonomic sufficiency and the increasing insufficiency of taxonomic expertise. Marine Pollution Bulletin 46: 556-561. Vincent, C., Heinrich, H., Edwards, A., Nygaard, K. & Haythornthwaite, J. 2002. Guidance on typology, reference conditions and classification systems for transitional and coastal waters. CIS working group 2.4. Common implementation strategy of the Water Framework Directive, European Commission, Brussels. Warwick, R.M. 1988. The level of taxonomic discrimination required to detect pollution effects on marine benthic communities. Marine Pollution Bulletin 19: 259-268. Weisberg, S.B., Ranasinghe, J.A., Dauer, D.M., Schaffner, L.C., Diaz, R.J. & Frithsen, J.B. 1997. An estuarine Benthic Index of Biotic Integrity (B-IBI) for Chesapeake Bay. Estuaries 20: 149–158. Williams, J.D. 2001. Reproduction and larval development of Polydora robi (Polychaeta: Spionidae), an obligate commensal of hermit crabs from the Philippines. Invertebrate Biology 120: 237-247. Williams, J.D. 2002. The ecology and feeding biology of two Polydora species (Polychaeta: Spionidae) found to ingest the embryos of host hermit crabs (Anomura: Decapoda) from the Philippines. Journal of Zoology 257: 339-351. Williams, W.T. & Stephenson, W. 1973. The analysis of three-dimensional data (sites x times x species) in marine ecology. Journal of Experimental Marine Biology and Ecology 11: 207- 227.

Chapter 4

Seasonal variability in benthic indices

Chapter 4

Influence of seasonal variability in benthic invertebrate community structure on the use of biotic indices to assess the ecological status of a Portuguese estuary

ABSTRACT

The present study focused on the use of benthic invertebrate communities to assess the ecological quality of a Portuguese estuary characterized by strong seasonal changes and with eutrophication problems. Seasonal benthic samples were collected during a flood year and the methodology proposed by the WFD Portuguese group was used to classify benthic assemblages into five different quality classes. Factor analysis was applied to classify stations based on their physical-chemical status. Different classifications were obtained with different indices and among seasons and there was low agreement between indices and index-season interactions. Diversity indices were better correlated to eutrophication related variables than AMBI and ABC method. Predictable responses of benthic indices to anthropogenic stress symptoms were stronger during the dry period.

KEY WORDS: estuarine invertebrates, Water Framework Directive, Mondego River estuary, biotic indices, DIN, physical-chemical classification.

91 INTRODUCTION

The achievement of good ecological quality status in coastal and transitional waters is one of the main goals of the European Water Framework Directive (WFD) (2000/60/EC). The implementation of the WFD requires the development of assessment tools adequate for all European aquatic systems, based on (1) the identification of surface water types, (2) the definition of type-specific reference conditions and (3) the classification of all water bodies within five ecological quality classes. The recognition that reference conditions are rare among transitional European waters and that historical data and paleontological studies are lacking for most water bodies (invalidating modeling) indicates that expert judgment is the best available tool. There is no consensus among experts on using physical-chemical criteria to define reference conditions for the biological elements. For example, some authors use contaminant concentrations to separate reference and degraded conditions (e.g. Weisberg et al., 1997; Llansó et al., 2002b), in contrast others state that biological elements should be used initially to identify dysfunctions in the ecosystem and abiotic elements only when biotic degradation is detected (Borja & Heinrich, 2005). Indices are useful tools to communicate with managers because they reduce complex scientific data, integrate different types of information, and produce results that can be easily interpreted in the perspective of water quality management (Wilson & Jeffrey, 1994). Nonetheless, there is no consensus among managers and scientists concerning which of the numerous existing indices should be used. New indices are continuously being developed, but as pointed out by Diaz et al. (2004), a major effort should be focused on updating the available tools, in order to improve their performance in different ecosystems. Several classification tools have been developed in Europe over the last years using macroinvertebrate communities as indicators of anthropogenic disturbances in marine and transitional waters (e.g. Warwick, 1986; Majeed, 1987; Grall & Glémarec, 1997; Borja et al., 2000; Simboura & Zenetos, 2002; Rosenberg et al., 2004), but none seems to fulfill adequately all the requirements of the WFD. Recent developments on the implementation of the WFD indicate a tendency to combine different types of metrics such as composition, abundance and sensitivity into a final result (Vincent et al., 2002), aiming to integrate a wider range of benthic community responses. Molvaer et al. (1997 in Vincent et al., 2002) proposed a combination of the Shannon-Wiener and Hulbert (HS100) diversity indices with total organic carbon as a classification system for marine and transitional waters in Norway. In Spain, Borja et al. (2004) developed the Ecological Quality Ratio, which combines species richness, Shannon-Wiener diversity index and the AZTI Marine Biotic Index (AMBI). Moreover, the Portuguese WFD working group also proposed a classification system for marine and transitional waters, based on the combination of two or three indices (Shannon-Wiener diversity index, Margalef species richness, AMBI and ABC

92 Seasonal variability in benthic indices

Chapter 4 method), depending on the type of data available (Bettencourt et al., 2004). As indicated by the WFD guidance documents, the definition of reference conditions and subsequent classification have to incorporate the natural variability of aquatic systems and minimize intrinsic variability of biological elements, by choosing compartments (e.g. areas, seasons, biological attributes) that allow valid comparisons between biological communities of the same type. Ultimately, Member States are allowed to exclude a quality element, if natural variability, other than seasonal, does not permit the definition of reliable reference conditions (Vincent et al., 2002).

Estuaries are highly dynamic environments, mainly influenced by the hydrological regime and, as described by Boesch (1977), there is a biotic change along the estuarine complex-gradient that results in major spatial differences. Nevertheless, these spatial patterns can be significantly altered over time, mainly in poikilohaline estuaries, characterized by strong seasonal changes in freshwater discharges (Boesch, 1977; Chainho et al., 2006). Several authors have investigated the major factors producing seasonal variations in benthic communities (e.g. Lopez-Jamar et al., 1986; Alden et al., 1997; Sardá et al., 1999; Ducrotoy & Ibanez, 2002; Salen-Picard & Arlhac, 2002; Reiss & Kröncke, 2005a). In temperate regions there is a general pattern of a decline in benthic community condition (abundance and/or diversity) over autumn and winter and a recovery in summer, after spring recruitment (Lopez-Jamar et al., 1986; Alden et al., 1997). This pattern is normally associated with environmental conditions that determine food availability (Marsh & Tenore, 1990) and such conditions are fundamentally modulated by freshwater flows and the occurrence of extreme conditions, such as floods and droughts. Benthic communities inhabiting estuaries with seasonal floods and/or droughts will change (1) due to pulses of organic matter during floods that stimulate an increase in abundance of opportunistic species (Salen-Picard & Arlhac, 2002), (2) changes in the water quality conditions, such as higher concentrations of contaminants during droughts (Attrill & Power, 2000; Grange et al., 2000), (3) disappearance of all but highly euryhaline species (Chainho et al., 2006), and (4) potential colonization by alien species that are, in general, much more tolerant to salinity fluctuations than native species (Lee & Bell, 1999; Paavola et al., 2005).

Most of the indices currently used to assess the benthic status were developed, applied and/or validated for coastal marine ecosystems that generally have much less seasonal variation than estuarine ecosystems. The applicability of benthic community indices to USA monitoring programs addressed spatial and temporal variability by defining different thresholds for each habitat type and selecting an index period, respectively. Several indices and subsequent adaptations to different biogeographic regions are calculated using only summer collections (Weisberg et al., 1997; Paul et al., 2001; Smith et al., 2001; Llansó et al., 2002b), because the difference in benthic community metrics should be maximal during the

93 summer period due to increased water temperatures, water column stratification, occurrence of bottom low dissolved oxygen conditions, and salinity (Alden et al., 1997). On the other hand, in Europe no recommendations were proposed regarding either the modeling of seasonal variability or the selection of a favorable period to apply the available classification tools. Benthic indices are known to differ in the effects of seasonality, especially recruitment events (Reiss & Kröncke, 2005b); however, few studies have examined how benthic indices respond to ecosystems with strong seasonality. The main objective of the present study was to examine how seasonal variations in benthic subtidal communities influence the results obtained when using the assessment tools proposed for implementing the WFD in Portugal, namely the Shannon-Wiener, Margalef, AMBI and ABC method indices. More specifically, this paper addresses the following questions: (1) which environmental variables best reflect the responses of the benthic communities to human stressors; (2) how do seasonal patterns in macroinvertebrates influence the results of the indices; (3) which season(s) display better correlations between stressors and the classifications obtained with the indices.

Methods

Study area

The Mondego River estuary is divided in two branches with different hydrographic characteristics, the northern branch, mainly influenced by freshwater discharges of the Mondego River and the southern branch, that drains the Pranto River and is more influenced by tidal cycles since freshwater flow is low. It is a mesotidal and well-mixed estuary, with a very irregular hydrological regime, namely low water flow during the dry period and strong freshwater discharges during the rainy period. An average annual river flow of 80 m3 s-1 has been registered in the Mondego River estuary but the sampling survey was carried out during a flood year with an average annual river flow of 187 m3 s-1. Maximum river flows over 1000 m3 s-1 were measured in December 2000 and January 2001, corresponding to the biggest flood of the last two decades (www.inag.pt, October 2003). Eutrophication is considered a major problem in this estuary that was classified as a Potential Problem Area under the OSPAR Convention because of the detection of eutrophication symptoms such as high nutrient concentrations and shifts in macroalgae and seaweed species (Zostera spp. to Ulva spp.) in the southern branch, mainly as a consequence of hydrodynamic characteristics (OSPAR Commission, 2003). Benthic communities are dominated by opportunistic species, mainly bivalves and polychaetes in higher salinity areas, and amphipods and oligochaetes in upstream areas (Chainho et al., 2007).

94 Seasonal variability in benthic indices

Chapter 4

Sampling

Seasonal sampling surveys (July and October 2000, February and June 2001) were conducted in the Mondego River estuary, western Portugal (Figure 4.1). Ten sampling stations were distributed along the saline gradient of the northern branch and southern branch of the Mondego River estuary (Figure 4.1). Three benthic invertebrate samples were taken at each station using a modified van Veen LMG grab (0.05 m2) and grab contents were fixed and preserved with 4% buffered formalin, sieved using a 500 µm mesh and preserved in 70% ethanol. All samples were sorted and identified to the lowest possible taxonomic level, in order to determine the number of taxa and their respective abundances. Biomass of species per sample was also determined as ash free dry weight, after ignition at 450ºC. Several environmental variables were measured (1) in water: bottom dissolved oxygen, temperature, transparency, salinity, nutrients concentrations (NO3, NO2, NH4, P); and (2) in the sediment: sediment grain size, heavy metals concentrations (As, Cr, Cu, Pb, Zn) and total organic content. Dissolved inorganic nitrogen (DIN) was calculated using N-NO3, N-NO2 and N-NH4.The methods used to determine these parameters are detailed in a previous study by Chainho et al. (2006).

FRANCEFRANCE

A L A G G SPAIN U U SPAIN

T T R

O R P O

Figueira da Foz

1 2 8 3 Montemor-o-Novo 9 4 7

Atlantic Ocean 10 5 6

2 Km

Figure 4.1. Location of sampling stations in the Mondego River estuary.

95 Data analysis

Identification of habitat types

The Mondego River estuary was entirely included in type A2 , i.e., needing further separation in water bodies representing homogenous units for which specific environmental objectives must apply (Bettencourt et al., 2004). The delimitation of water bodies has to account not only for hydromorphological and biological characteristics, but also for differences in human pressures (Vincent et al., 2002). Since salinity is the major environmental variable influencing the distribution of benthic communities in the Mondego River estuary (Chainho et al., 2006), habitat types were identified according to their salinity measures (minimum, maximum, mean and standard deviation) as suggested by Bald et al. (2005). A cluster analysis was conducted using software SPSS 13.0 to group stations using data standardized by subtracting the mean and dividing by the standard deviation. Stations were grouped using the Ward’s minimum variance hierarchic method and Euclidean distances were used as a dissimilarity measure (Hair et al., 1998).

Benthic invertebrate classification and seasonal variations

The benthic invertebrate condition of transitional waters was assessed by calculating the Shannon-Wiener diversity index (H’), the Margalef species richness (Legendre & Legendre, 1976) (D), the AMBI index (Borja et al., 2000) and the ABC method (W) (Warwick, 1986), following the methodology suggested by the WFD Portuguese working group (Bettencourt et al., 2004) (Table 4.1). All indices were calculated for each sampling station and for each season, using the abundance per replicate. Calculations were done using PRIMER 5 software package and AMBI index software 3.0. Because AMBI is the only index with higher values corresponding to worse quality, the reciprocal 1/AMBI was used when comparing to other indices and environmental variables. Variation of the different indices, species richness (S) and abundance (N) among seasons was assessed using the adjusted coefficient of variation: Cv=(SD/M)100, with SD the standard deviation and M the mean.

Reference conditions and physical-chemical classification

Most Portuguese estuaries have been subject to increased human pressures particularly in recent decades and there is no historical data available on ecological conditions prior to anthropogenic impacts. When no reference sites exist some authors propose the use of reference physical-chemical data, using multivariate analysis (Borja et al., 2003; Bald et al., 2005), similar to applications in North American estuaries (Weisberg et al., 1997; Van Dolah et al., 1999; Llansó et al., 2002a). In this study, reference conditions for

96 Seasonal variability in benthic indices

Chapter 4

High and Bad status for the parameters Secchi disc transparency (T), dissolved oxygen (O2), ammonia (NH4) and nitrates (NO3), were those defined by Borja et al. (2004) and Bald et al. (2005) (Table 4.2). These authors used low or background levels of contamination to estimate virtual reference concentrations, weighted by salinity so that different reference conditions were defined for each of the Venice system (1959) salinity classes. An additional variable was considered, namely chlorophyll a (Chl a) and reference concentrations followed criteria established by Borja et al. (2004). For Chl a, the same reference concentrations for High and Bad status were considered across all salinity classes because no dilution factors are known for this parameter.

One of the major problems arising from classification procedures is to integrate information of several variables into a final status. A factor analysis was used to reduce the number of variables, as suggested by Bald et al. (2005). Factor analysis is a data reduction method that creates a smaller and entirely new set of variables that become surrogates of the original set of variables, retaining their nature and character (Hair et al., 1998). A separate factor analysis was conducted using software SPSS 13.0, for stations included in different salinity habitat types and two virtual stations representing reference conditions for each variable (High and Bad status) were included in each data set (Bald et al., 2005). Factors were extracted with a component analysis conducted on a data set previously log10 (x+1) transformed. A scree test criterion was used to determine the number of factors extracted, by plotting the number of factors against eigenvalues and identifying the inflection point after which the proportion of unique variance is higher than common variance (Hair et al., 1998). Factors were rotated using a Varimax rotation to redistribute the variance by factors and obtain a meaningful pattern of variable loadings by maximizing the sum of variances of the factor matrix. The amount of variance accounted by the factor solution for each variable was assessed by examining the communalities. The adequacy of the factor analysis to combine variables into a new structure represented by factors was tested using a Bartlett test of sphericity, a statistical test for the presence of correlations among variables (Green, 1979; Hair et al., 1998). A numerical value of 1, following the derivation of the Ecological Quality Ratio (EQR), as defined in the WFD, was assigned to the distance between both virtual reference stations (High and Bad status). The range values for the physical- chemical status classification (PC-EQR) were: High, 0.83–1; Good, 0.62–0.82; Moderate, 0.41– 0.61; Poor, 0.20–0.40; and Bad, <0.20 (Bald et al., 2005).

) -1 High g l µ

( 4.0 4.0 4.0 4.0 30.0 30.0 30.0 30.0 a (2005) (for

Chl – number of N 0.1 – 1.0 0.1 – 1.0 et al. -0.1 – 0.1 -1.0 – -0.1 -1.0 – -0.1 (Bi- pi )/50(S-1) ABC-method Σ ) -1 W = mol l µ 78.7 58.7 30.1 10.7 87.2 28.9 218.9 163.0 ( 3 NO (2004) and Bald – number of species; ) S -1 et al. mol

µ 5.7 4.7 3.3 2.3 63.4 50.4 31.8 19.2 ( 4

AMBI NH 0.0 – 1.2 1.2 – 3.3 3.3 – 5.0 5.0 – 6.0 6.0 – 7.0 status status 4,5(%GIV)+6(%GV)}/100 Bad High AMBI={0(%GI)+1,5(%GII)+3(%GIII)+ (%) (%) 2 2 x included in the WFD Portuguese working group approach 81.6 86.6 93.7 98.6 41.6 46.6 53.7 58.6 O

O N e

> 4.0 > 4.0 < 2.5 < 2.5 – relative abundance of the ith species; 2.5 – 4.0 Margalef – relative biomass of the ith species - for acronyms see methods section pi 2.0 2.0 2.0 8.0 0.5 0.5 0.5 2.5 Bi T (m) D = (S-1)/log T(m)

i , 2004) ( p 2 log i et al. p

Σ > 4.0 3.0 – 4.0 2.0 – 3.0 1.0 – 2.0 0.0 – 1.0 2.7 2.7 11.5 24.0 32.5 11.5 24.0 32.5 Salinity H’ = - Shannon-Wiener

Salinity

status, weighted by salinity, based on the criteria defined Borja

Bad Adapted from Bettencourt individuals; GI to GV – ecological groups; individuals;

Calculation Classification High Good Moderate Poor Bad Oligohaline Mesohaline Polyhaline Euhaline Oligohaline Mesohaline Polyhaline Euhaline Table 4.2. Concentrations of pollution indicative variables (average values) for virtual stations corresponding to and abbreviations see data analysis section) Table 4.1. Calculation and thresholds used for each inde

98 Seasonal variability in benthic indices

Chapter 4

Relationship between the benthic invertebrate and physical-chemical condition

The best indicators of benthic condition are expected to respond predictably to environmental impacts, thus showing increasing or decreasing values according to different pollution levels. Correlations between the results of the four indices tested in different seasons and variables representing environmental impacts were performed to investigate the ecological consistency between indices and variables. Total phosphorus and heavy metals were not included in the analysis since concentrations never exceeded 0.1 mg l-1 P and Long et al’s (1995) effects range medium, respectively. A pairwise Kendall correlation coefficient was used to test for significant correlations (P<0.05) between indices and variables with every possible seasonal combination (all seasons, combinations of 3 seasons, combinations of 2 seasons and individual seasons). The best combinations of indices, environmental variables and seasons were assessed by identifying the number of correct correlations according to an expected response. All indices were expected to be positively correlated with transparency and dissolved oxygen and negatively correlated with nutrients and chlorophyll a. Correlations were calculated using software SPSS 13.0.

RESULTS

Identification of habitat types

Cluster analysis using salinity attributes identified four major groups of stations in the Mondego River basin, corresponding to different classes of the Venice system (oliogohaline, mesohaline, polyhaline and euhaline) as indicated in Figure 4.2. Higher salinity levels were measured during the dry period (July and October) and lower values were registered during winter, corresponding to a flood period. The highest salinity range over seasons was recorded in stations included in the euhaline salinity class (33.2), whereas the lowest range occurred in the oligohaline stations (12.2) (Table 4.3). Groups of stations defined by salinity are consistent with those identified when using the benthic communities, as shown by Chainho et al. (2006; 2007) with station 5 corresponding to a transitional area.

Benthic invertebrate classification and seasonal variations

A total of 38 718 specimens were collected in the Mondego River estuary and 92 taxa were identified (see Appendix 1). The polychaete Streblospio shrubsolii (Buchanan, 1890) and the amphipod Corophium multisetosum Stock, 1952 were the dominant species in the Mondego River estuary, although there are significant differences in the composition of the

99 benthic communities among different habitat types (Table 4.3). The highest densities were found in the oligohaline, mainly due to the abundance of C. multisetosum during spring, and the lowest abundances occurred in the mesohaline. Fewer specimens were collected during winter, except for the polyhaline communities (southern branch), where the lowest densities were registered during spring. The number of species identified in each habitat type also varied between seasons and the lowest number was always found during winter and spring, while summer and autumn registered the highest number of species (Table 4.3).

40 Salinity

Euclidean distance 10

0 10 9 8 1 234 567

Polyhaline Euhaline Mesohaline Oligohaline Figure 4.2. Groups of stations obtained for the Mondego River estuary, using a cluster analysis based on salinity (mean, minimum, maximum and standard deviation). Stations were grouped using the Ward’s minimum variance hierarchic method and Euclidean distances were used as a dissimilarity measure.

The results of the biological indices indicated different classifications between sampling events (Table 4.4). AMBI classified most stations in a Good status, but stations 6, 9 and 10 changed between Poor and Good, while stations 3, 5, 7 and 8 changed between Moderate and Good. Only three stations maintained the same classification throughout the year, namely Good status (1, 2, and 4). Classifications obtained with Shannon-Wiener diversity index were more heterogeneous and none of the stations maintained the same classification across seasons. Some stations changed from classifications below Good status to Good status (1, 2, 3, 8), while other stations, although changing classifications between seasons, never achieved more than the Moderate status (4, 5, 6, 7, 9 and 10). According to

100 Seasonal variability in benthic indices

Chapter 4 the Portuguese guidelines for classification on transitional waters (Bettencourt et al., 2004), only three different classes apply for the Margalef and W-statistic, namely Bad/Poor, Moderate and Good/High classes. Classifications obtained in the Mondego River estuary when calculating the Margalef species richness were also very heterogeneous along seasons and only stations 6, 7 and 8 had a single classification throughout the year. Station 1 changed between Moderate and Good/High, while the remaining stations changed between Bad/Poor and Moderate. Most stations were classified in the Moderate status when using the W-statistic, four of which maintained that status along seasons (2, 4, 5 and 9) and some others changed to Good/High during some seasons (1, 6, 7 and 8). Station 3 changed between the worst and the best status, while station 10 was never classified above Moderate (Table 4.4). In general, stations were better classified during summer/autumn using Shannon-Wiener and Margalef indices, but during the same period the worst classifications were obtained when using AMBI. There was a very low agreement between classifications given by different indices to the same stations during the same season. Station 1 was classified in a Good status by all indices during summer and station 10 was included in the class Moderate by all indices during autumn. For all other stations different classifications were given by different indices, when considering a specific season.

The results of the adjusted coefficient of variation demonstrated that Shannon- Wiener and Margalef indices were more variable among seasons, when compared to the other two indices used, and showed a variation very similar to the number of species, higher during summer and autumn (Figure 4.3). Total abundance was also highly variable over time. (Table 4.3; Figure 4.3).

Physical-chemical classification

Factor analysis provided a representation of the position of each station relative to reference conditions for High and Bad status (Figure 4.4). Only the first three factors were used to define the multidimensional space for every salinity-habitat type, based on a scree test and the cumulative variation explained. Although analyses were conducted separately for each salinity habitat type, all results are presented in common plots (Figure 4.4). More than 85% of the total variation was explained by factors F1 to F3 for all salinity habitat types (Table 4.5). For oligohaline, mesohaline and polyhaline stations, factor analysis seems to be a good representation of the overall structure of stations according to physical-chemical parameters, corroborated by the results of the Bartlett test of sphericity (P<0.01) (Table 4.6). The results of this test for euhaline stations (Table 4.6) indicated no significant correlations between variables and the derived factors (P=0.169), suggesting that factor analysis is less appropriate for reducing the number of variables within this salinity stretch.

101

(66%)

inants) are (15%) ding to the O

O (6%) (26 S) (35 127 Sp) ter parameters 38

sp. (4%) 13 463

sp. (2%) Oligohaline

(13 W) sp. (4%) S; autumn - A; winter – W; (963 W) Corophium multisetosum Streblospio shrubsolii Corbicula fulminea Tubificoides Nais Echytraeus 0.8 – 6.0 2.0 – 14.2 2.0 – 14.2 71.8 – 113.7 2.2 – 12.8 50.5 – 87.1 56.0 – 94.8 1.8 – 24.0 Coarse sand

P P

(61%)

G P

(3 568 S) (5%) (12%) (26 S) (5%) 40

1 640

ed in the Mondego River estuary, accor Mesohaline

(9 W) (287 W) Streblospio shrubsolii Spio martinensis Tetrastemmatidae n.i. (6%) Hydrobia ulvae Capitella capitata Hesionidae n.i. (2%) 1.5 – 5.0 2.0 – 31.6 0.8 – 1.5 80.3 – 112.0 2.2 – 9.4 41.0 – 75.8 44.3 – 79.1 1.3 – 14.3 Coarse-Medium sand

) seasonally determined (summer -

B P -2

B )

(3%) O

G (20%)

(3% (4%) (4 418 A) (39 A) xa is indicated for each group. Six most abundant species (dom 53 (23%) e indication of the taxon in bold. Deph, physical-chemical wa 2 822

sp. (32%)

Polyhaline

gochaeta; P- Polychaeta). See methods section for abbreviations (22 W/Sp) (1 984 Sp) Medium sand Tubificoides Hydrobia ulvae Streblospio shrubsolii Scrobicularia plana Cerastoderma glaucum Chaetozone setosa 0.8 – 4.0 10.0 – 34.1 0.3 – 1.5 57.8 – 109.2 6.1 – 106.7 16.1 – 51.6 28.3 – 124.3 0.7 – 37.

iptive parameters for habitat types identifi B

B P

) (25%)

P (30%)

(5% (10%) (9 497 Sp) (41 S) (6%) (6%) 60

6 315 Euhaline

(3 W)

(380 W) Medium sand Streblospio shrubsolii Cerastoderma glaucum Scrobicularia plana Hydrobia ulvae Spio martinensis Chaetozone setosa 3.0 – 5.5 7.0 – 40.2 1.1 – 2.7 45.8 – 113.8 2.2 – 18.3 16.1 – 75.8 26.7 – 79.1 0.4 – 7.2

) ) ) ) -1 -1 -1 -1 g l

µ mol l mol l

mol l µ µ

( µ ( ( a 3 4 (%) 2

Mean Density N. Taxa Dominant Species Depth range (m) Salinity range T (m) O NH NO DIN ( Chl Sediment type ranges and sediment type are also indicated. salinity classification. Maximum, minimum and mean density values (ind m salinity classification. Maximum, minimum and mean density values (ind Table 4.3. Biological and environmental descr (A- Amphipoda; B- Bivalvia; G- Gastropoda; N- Nemertea; O- Oli spring – Sp) are presented for each group. The total number of ta listed with their respective contributions to total density and th

102 Seasonal variability in benthic indices

Chapter 4

The communalties for each variable indicated transparency, ammonia and chlorophyll a as variables most correlated with the factor solution for oligohaline, mesohaline and polyhaline stations (Table 4.6). Loadings of variables in factors show different relationships between variables in different salinity habitat types. For instance, dissolved oxygen achieves very positive loadings on factor 1 for polyhaline stations, whereas for other salinity stretches the same variable is negatively correlated with this factor (Table 4.6). The opposite was observed for transparency and chlorophyll a, with negative loadings in factor 2 for mesohaline and polyhaline stations and lower positive loadings for the oligohaline stretch. Physical-chemical conditions seem to be relatively stable over time, among the class ranges used, since classifications for each station varied only between Good and High over seasons, except for euhaline stations (stations 1 and 2) that varied between Poor and High between sampling events (Table 4.4).

Table 4.4. Range of classifications obtained for each sampling station in the Mondego River estuary across seasons, based on the results of biological indices and on the physical-chemical ecological quality ratio (PC-EQR) determined by the factorial analysis. Shannon- Stations Margalef AMBI ABC method PC- EQR Wiener

1 Moderate-Good Moderate-Good/High Good Moderate-Good/High Poor-High

2 Poor-Good Bad/Poor-Moderate Good Moderate Moderate-High

3 Poor-Good Bad/Poor-Moderate Moderate-Good Bad/Poor-Good/High High

4 Bad-Moderate Bad/Poor-Moderate Good Moderate Good-High

5 Bad-Moderate Bad/Poor-Moderate Moderate-Good Moderate Good-High

6 Bad-Poor Bad/Poor Poor-Good Moderate-Good/High High

7 Poor-Moderate Bad/Poor Moderate-Good Moderate-Good/High Good-High

8 Poor-Good Moderate Moderate-Good Moderate-Good/High Good-High

9 Bad-Moderate Bad/Poor-Moderate Poor-Good Moderate Good-High

10 Poor-Moderate Bad/Poor-Moderate Poor-Good Bad/Poor-Moderate Good-High

Relationship between the benthic invertebrate and physical-chemical condition

Kendall correlations between the benthic indices and physical-chemical variables indicated few significant relationships for most variables (Figure 4.5). DIN was the variable most correlated with differences found in the benthic condition (Figure 4.5a). For both DIN and chlorophyll a, all significant correlations of environmental variables with biological indices were the expected, namely a decline of the benthic condition as a response to an increase of the concentrations of the pollution indicative parameters. For these variables, respectively 47% and 17% of the total number of tests showed an expected significant

103 correlation with benthic indices, except for combinations with winter samples, which were not calculated since no significant correlations were found between the results of physical- chemical variables and benthic indices during that season. For O2, NO3, NO4, significant correlations included both predictable (correct) and opposite to predictable (incorrect) responses of benthic indices (Figure 4.5a). For NO3, the number of incorrect significant correlations was higher than the correct ones and for NH4 and PC-EQR all significant correlations were contrary to the predicted response (incorrect). Transparency is not represented in Figure 4.5 because no significant correlations were obtained between this variable and the results of the benthic indices.

H' D AMBI W S N

Figure 4.3. Boxplot of the adjusted coefficient of variation for the different biotic indices studied, species richness and abundance along season in the Mondego River estuary. The mean, quartiles and extreme values and indicated in each box. See methods section for abbreviations.

Margalef and Shannon-Wiener were the indices that showed the highest number of correct significant correlations with pollution indicative variables, 21% and 19% of the total number of tests, respectively (Figure 4.5b). Moreover, both indices showed a higher number of correct significant correlations when considering only variables with no unpredictable correlations (DIN and chlorophyll a), being correlated with those variables in most sampling seasons or combinations of seasons (89% and 78% of the total number of tests, respectively) (Figure 4.6).

104 Seasonal variability in benthic indices

Chapter 4

Bad 4.00 in the Bo (H) and Bm High 2.00 mesohaline (m) and Hp ■ Hm Ho Be 0.00 F1 Bp polyhaline (p); ● He -2.00 euhaline (e); ■ ) -4.00 b

2.00

3.00 1.00 0.00 -1.00 -2.00 F3 within the multidimensional space defined by factor analysis 4.00 ird (F3) factors (b). Location of stations corresponding to a Bo Bm 2.00 Hp Hm Ho Be F1 0.00 Bp He -2.00 ) -4.00 a

2.00 1.00 0.00

-1.00 -3.00 -2.00 F2 oligohaline(o). ● (B) status are identified for each salinity habitat type, indicated by the symbols: Figure 4.4. Distribution of the Mondego River estuary stations first (F1) and second (F2) factors (a) in the th

105

0.19 0.20 0.64 -0.45 -0.07

F2 0.22 0.41 0.21 0.09 -0.63 Euhaline F1 0.04 0.02 0.55 0.01 -0.57 = 14.1; p 0.169) 2 (X Euhaline 0.98 0.81 0.90 0.97 0.85 Com 1.6/ 31.9% 1.5/ 61.9% 1.4/ 90.4% F3

0.35 0.13 0.96 -0.24 -0.20 F2 0.12 0.03 0.51 -0.73 -0.23 F1 Polyhaline 0.11 0.45 0.19 0.01 -0.54 = 36.1; p 0.000) 2 Polyhaline 2.2/ 43.7% 1.6/ 75.7% 0.7/ 89.3% (X 0.90 0.84 0.97 0.83 0.92 Com ch factor (F1, F2 and F3) of the factorial analysis, for each F3 Communalties for each variable are indicated (Com), as well

0.99 -0.01 -0.23 -0.24 -0.32 for each factor in the factorial analysis F2 0.24 0.55 -0.79 -0.17 -0.17 3.2/ 64.7% 0.8/ 80.8% 0.7/ 94.1% Eigenvalues/Cumulative percentage Mesohaline F1 Mesohaline 0.33 0.65 0.11 -0.46 -0.18 <0.01) = 26.1; p 0.004) 2 P (X 0.92 0.84 0.97 0.86 0.98 Com F3

1.05 0.14 -0.14 -0.16 -0.12 F2 0.18 0.22 0.93 2.6/ 52.2% 1.3/ 77.9% 0.6/ 90.8% -0.07 -0.36 Oligohaline F1 Oligohaline 0.07 0.44 0.33 -0.32 -0.08 = 26.3; p 0.003) 2 (X

0.99 0.78 0.96 0.79 0.98 Com 1 2 3

Factors a

3 4 2

T O NH NO Chl Table 4.5. Eigenvalues and cumulative percentages obtained salinity habitat type identified in the Mondego River estuary Table 4.6. Loadings of each pollution indicative variable in ea salinity habitat type identified in the Mondego River estuary. the results of Bartlett test sphericity (

106 Seasonal variability in benthic indices

Chapter 4

Nearly 7% of the tests for AMBI were significantly correlated with pollution-indicative variables as expected, although this index was also unpredictably correlated with some of the variables (Figure 4.5b).

a) 50 40 N = 36 30 20 10

% sig. correlations sig. % 0 O2 NH4 NO3 DIN Chl a PC-EQR

b) 50 N = 54 40 30 20 10

% sig. correlations sig. % 0 H' D AMBI W Correct Incorrect

Figure 4.5. Percentage of correct (expected) and incorrect (opposite to expected) significant correlations (Kendall coefficient of correlation P<0.05) obtained between pollution indicative variables and biological indices when different combinations of seasons were tested. The total number of tests is also indicated (N). a) percentage of significant correlations for each variable; b) percentage of significant correlations for each biological index.

AMBI responded predictably to variations in the concentrations of DIN in summer and when summer/autumn results were considered together, while a significant correlation with chlorophyll a was obtained only for spring values. No significant correlations were found between the ABC method and DIN and chlorophyll a (Figure 4.6), which was significantly correlated only with NO3, when considering all pollution indicative variables (Figure 4.5b). Figure 4.7 shows that correct significant correlations were found between the results of benthic indices and the variables DIN and chlorophyll a when using data of all seasons together, different season combinations and most single seasons. No significant correlations were registered during winter or any season combinations that included winter, except for all seasons. Benthic indices responded predictably to DIN concentrations when using all seasons,

107 all seasons except winter, combinations of two seasons excluding winter and every single season, except winter. Nevertheless, significant correlations for chlorophyll a were obtained only when using a combination of three seasons (summer, autumn and spring) and autumn and spring data, combined and separately.

100 N = 9 DIN Chl a 75

% sig. correlations 0 H' D AMBI W

Figure 4.6. Number of significant correct (expected) correlations (Kendall coefficient of correlation P<0.05) obtained between DIN and chlorophyll a and biological indices when different combinations of seasons were tested. The total number of tests (N) is also indicated.

100 DIN Chl a N = 4 75

25 % sig. correlations 0 All S/A/Sp S/A S/Sp A/Sp S A Sp

Figure 4.7. Number of significant correct (expected) correlations (Kendall coefficient of correlation P<0.05) obtained between DIN and chlorophyll a and biological indices, for different combinations of seasons (All – all seasons data; S – Summer; A – Autumn; Sp – Spring). The total number of tests (N) is also indicated.

Summer showed the highest number of significant correlations with DIN when considering a single season, while summer-autumn was the best two-season combination for the same variable. On the other hand, best correlations with chlorophyll a were found in autumn as a single season, and autumn-spring when considering a two-season combination. Correlations between indices showed that only the results of Margalef and Shannon-Wiener

108 Seasonal variability in benthic indices

Chapter 4 indices were correlated to each other in all seasons and combinations of seasons (P<0.05). The highest correlation between these indices was obtained in autumn (r=0.778; P<0.01). AMBI and Shannon-Wiener results were also correlated when summer-autumn combination was considered (r=0.337; P<0.05).

DISCUSSION

Estuaries are particularly challenging due to strong spatial, seasonal and interannual variations of environmental characteristics that influence benthic communities, mainly hydrological conditions that change based on the volume of freshwater discharges. These changes are particularly severe in southern European estuaries, where the annual dry period is extended, causing higher fluctuations in the salinity regime (Elliott & McLusky, 2002). Several studies have shown that benthic communities experience seasonal changes in abundance and biomass, mainly related to recruitment events (e.g. Lopez-Jamar et al., 1986; Alden et al., 1997; Sardá et al., 1999; Ducrotoy & Ibanez, 2002; Salen-Picard et al., 2002; Reiss & Kröncke, 2005a). Some benthic indices, particularly diversity indices, are greatly affected by such seasonal changes (Salas et al., 2004; Reiss & Kröncke, 2005b). In addition, such seasonal changes also result in changes in species composition, greatly affecting indices that include compositional metrics such as pollution sensitive and pollution indicative categories. In the Mondego estuary, extreme events such as floods dramatically change environmental conditions and consequently benthic community structure (Chainho et al., 2006).

The present study was carried out during a flood year, when the strongest flood over a twenty years period occurred, which seems to have a direct effect on the benthos. Biotic classifications of condition varied greatly dependent upon the index or approach used. Similar to previous studies, diversity indices were more variable than AMBI and ABC method (Salas et al., 2004; Reiss & Kröncke, 2005b), since the former rely on species composition and abundance, while the latter reflect mainly the balance between pollution indicative and pollution sensitive species. Salas et al. (2004) corroborated these results in the Mondego River estuary, Portugal, based in benthic samples collected fortnightly over a year, in the southern branch of that estuary. In that study, AMBI showed less temporal variability when compared to diversity indices, although seasonal variations are documented for the abundance and composition of the benthic subtidal communities in the Mondego River estuary (Marques et al., 2002; Chainho et al., 2006). The AMBI index seems to be appropriate for all European coastal environments (Borja et al., 2003) but its application to systems with strong

109 seasonality and/or naturally high levels of stress, such as transitional or estuarine systems, is problematic, as shown in the present study.

The WFD sets good ecological status as a major target and requires the implementation of appropriate measures to water bodies that are classified below Good status. Therefore, as emphasized by Quintino et al. (2006), the critical boundary between Moderate and Good status will determine the need for applying remediation measures that account for large costs and consequently must be carefully defined. For ecosystems lacking reference conditions or historical data, such as the Portuguese estuaries, best professional judgement must be used to approximate reference conditions. Professional judgement can be used to develop a priori criteria to determine conditions indicative of minimal anthropogenic impacts – thereby allowing determination of metrics and values for biological thresholds indicative of the boundary between Moderate and Good status. In the development of marine and estuarine benthic indices, such a priori criteria have primarily been either (1) physical- chemical (sediment contaminant and/or low dissolved oxygen levels, e.g. Weisberg et al., 1997); (2) species responses to a known physical, chemical or organic gradient (e.g. Pearson & Rosenberg, 1978; Rakocinski et al., 2000; Smith et al., 2001), or (3) best professional judgement based upon empirical observations (e.g. Borja et al., 2000; Eaton, 2001).

The methodology proposed by Bald et al. (2005) to identify habitat types based on the salinity Venice system seems to be adequate for the Mondego estuary, since the groups of stations identified are ecologically meaningful over time, despite changes occurring in the benthic communities (Chainho et al., 2006; 2007). Additionally, these groups are coincident with water bodies identified by Ferreira et al. (2007) using a method that accounts for environmental variables and human pressure, as recommended by the WFD.

The structure identified by the factor analysis is mainly driven by variables related to eutrophication processes, such as nitrogen related variables and chlorophyll a, corroborating previous studies that identify eutrophication as a major problem, at least in the southern branch of the Mondego estuary (Martins et al., 2001; Marques et al., 2003). Based on the physical-chemical EQR values, all oligohaline, mesohaline and polyhaline stations achieve Good status over all seasons and euhaline stations change between classifications below and above Good, showing that although environmental conditions are also highly variable along the year, they still meet the criteria required for the Basque Country (Borja et al., 2004; Bald et al., 2005). Factor analysis was developed to reduce the complexity inherent in interpreting the results of different physical-chemical variables, by identifying relationships between variables and representing it by a single value, but it does not seem to cope with extreme events. For instance, the correlation between dissolved oxygen and other variables was contradictory, which is apparently related to the fact that this variable was never limiting in terms of negative effects on the biota (concentrations were always above 3.0 mg l-1). High

110 Seasonal variability in benthic indices

Chapter 4 levels of dissolved oxygen may not be an indicator of Good status since, as indicated by Ferreira et al. (2002), oxygen saturation may occur in the Mondego southern branch associated to macroalgae blooms, during spring and summer. However, such macroalgae blooms are less likely during years of high flow (Martins et al., 2001) and would not explain the high levels of dissolved oxygen found in the present study.

No significant correlations were found between physical-chemical EQRs and the results of biological indices, indicating that the benthos is not responding predictably to a combined effect of all variables. On the other hand, DIN and chlorophyll a displayed significant correlations with all biological indices, except for the ABC method, and all correlations were indicative of degradation of the benthic community with increasing concentrations. Considering that these two variables are good predictors of degradation in the Mondego estuary, in particular DIN as a primary symptom of eutrophication (OSPAR Commission, 2003), it may be concluded that Margalef and Shannon-Wiener indices respond better to variations in nutrient impacts than AMBI and that W-statistics does not show a predictable response to anthropogenic pressures. This last method has been widely applied, but Dauer et al. (1993) have draw attention to the fact that its use in estuaries is limited by the presence of many species adapted to high levels of natural stress, exhibiting the same response as to environmental degradation, namely the numeric dominance of short-lived opportunistic species. This author also emphasized the influence of large-sized non- indigenous species, such as Corbicula fluminea (Müller, 1774), that can greatly affect the results of the ABC method. Although the use of multiple indices is recommended to account for different responses of the benthic community to stress and to create a more robust approach, the present results indicate in creating combined metric or index approaches: (1) the ABC method is weakly related to the eutrophication related stress and should not be used, and (2) the Margalef and Shannon-Wiener indices are redundant and only one should be used.

The predictability of responses of benthic indices to pollution-indicative variables also depends on the season considered, as shown by the results of the correlations obtained for different seasons and combinations of seasons. A more predictable response is obtained during the dry period, while all combinations that included winter data showed no significant correlations between biological and physical-chemical indicators. Flows can have direct effects on benthic organisms, by physical displacement, but may also affect the benthos by indirect paths, such as altering intermediate abiotic and biotic variables (Hart & Finelli, 1999). These complex interactions are hardly predictable, as shown by the results obtained in this study. Data from summer and/or autumn provided the strongest and most ecologically meaningful relationships between benthic community structure and eutrophication indicators, specifically DIN concentrations, in the Mondego estuary. This conclusion is consistent with

111 temporal stratification applications in North American estuaries (Weisberg et al., 1997; Van Dolah et al., 1999; Llansó et al., 2002a), although Alden et al. (1997) suggested that spring could be included with summer when a combination of two seasons would increase statistical power. Finally, thresholds of benthic indices used to define ecological status should be calibrated for hydrographically and/or biogeographically different estuarine or transitional ecosystems.

ACKNOWLEDGMENTS

This study was financially supported by two Ph.D. fellowships (SFRH/BD/5144/2001 and SFRH/ BD/6365/ 2001) granted by FCT (Science and Technology Foundation) and ESF in the aim of the III European Community Support Framework. Project QUERE granted by Instituto de Ambiente and project EFICAS (POCI/MAR/61324/ 2004) granted by FCT. We would like to thank Ana Luisa Rego and Sérgio Rodrigues for their support to the field work.

REFERENCES

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113 Grange, N., Whitfield, A.K., De Villiers, C.J. & Allason, B.R. 2000. The response of two South African east coast estuaries to altered river flow regimes. Aquatic Conservation: Marine and Freshwater Ecosystems 10: 155-177. Green, R.H. 1979. Sampling design and statistical methods for environmental biologists. Willey-Interscience, London, U.K.. Hair, J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. 1998. Multivariate data analysis. Prentice Hall, New Jersey, U.S.A. Hart, D.D. & Finelli, C.M. 1999. Physical-biological coupling in streams: The pervasive effects of flow on benthic organisms. Annual Review of Ecology and Systematics 30: 363-395. Lee, C.E. & Bell, M.A. 1999. Causes and consequences of recent transitions to fresh water by saltwater animals. Trends in Ecology and Evolution 14 : 284-288.

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Martins, I., Pardal, M.A., Lillebø, A.I., Flindt, M.R. & Marques, J.C. 2001. Hydrodynamics as a major factor controlling the occurrence of green macroalgal blooms in a eutrophic estuary. A case study on the influence of precipitation and river management. Estuarine, Coastal and Shelf Science 52: 165-177. OSPAR Commission. 2003. OSPAR Integrated Report 2003 on the Eutrophication Status of the OSPAR Maritime Area Based Upon the First Application of the Comprehensive Procedure. Eutrophication Series, London, U.K. Paavola, M., Olenin, S. & Leppäkoski, E. 2005. Are invasive species most successful in habitats of low native species richness across European brackish water seas? Estuarine, Coastal and Shelf Science 64: 738–750. Paul, J.F., Scott, K.J., Campbell, D.E., Gentile, J.H., Srobel, C.S., Valente, R.M., Weisberg, S.B., Holland, A.F. & Ranasinghe, J.A. 2001. Developing and applying a benthic index of estuarine condition for the Virginian Biogeographic Province. Ecological Indicators 1: 83- 99. Pearson, T.H. & Rosenberg, R. 1978. Macrobenthic succession in relation to organic enrichment and pollution of the marine environment. Oceanography and Marine Biology: An Annual Review 16: 229–311. Quintino, V., Elliott, M. & Rodrigues, A.M. 2006. The derivation, performance and role of univariate and multivariate indicators of benthic change: Case studies at differing spatial scales. Journal of Experimental Marine Biology and Ecology 330: 368-382. Rakocinski, C.F., Brown, S.S., Gaston, G.R., Heard, R.W., Walker, W.W. & Summers, J.K. 2000. Species-abundance-biomass responses by estuarine macrobenthos to sediment chemical contamination. Journal of Aquatic Ecosystem Stress and Recovery 7: 201–214. Reiss, H. & Kröncke, I. 2005a. Seasonal variability of infaunal community structures in three areas of the North Sea under different environmental conditions. Estuarine, Coastal and Shelf Science 65: 253-274. Reiss, H. & Kröncke, I. 2005b. Seasonal variability of benthic indices: An approach to test the applicability of different indices for ecosystem quality assessment. Marine Pollution Bulletin 50: 1490-1499. Rosenberg, R., Blomqvist, M., Nilsson, H.C. & Dimming, A. 2004. Marine quality assessment by use of benthic species-abundance distributions: A proposed new protocol within the European Union Water Framework Directive. Marine Pollution Bulletin 18 : 490-495. Salas, F., Neto, J.M., Borja, A. & Marques, J.C. 2004. Evaluation of the applicability of a marine biotic index to characterize the status of estuarine ecosystems: The case of Mondego estuary (Portugal). Ecological Indicators 4: 215-225. Salen-Picard, C. & Arlhac, D. 2002. Long-term changes in a Mediterranean benthic community: Relationships between the Polychaete assemblages and hydrological variations of the Rhône river. Estuaries 25: 1121-1130.

115 Sardá, R., Pinedo, S. & Martin, D. 1999. Seasonal dynamics of macroinfaunal key species inhabiting shallow soft-bottoms in the Bay of Blanes (NW Mediterranean). Acta Oecologica 20: 315-326. Simboura, N. & Zenetos, A. 2002. Benthic indicators to use in ecological quality classification of Mediterranean soft bottom marine ecosystems, including a new biotic index. Mediterranean Marine Science 3: 77-111. Smith, R.W., Bergen, M., Weisberg, S.B., Candien, D., Dalkey, A., Montagne, D., Stull, J.K. & Velarde, R.G. 2001. Benthic response index for assessing infaunal communities on the Southern California mainland shelf. Ecological Applications 11: 1073-1087. Van Dolah, R.F., Hyland, J.L., Holland, A.F., Rosen, J.S. & Snoots, T.R. 1999. A benthic index of biological integrity for assessing habitat quality in estuaries of the southeastern USA. Marine Environmental Research 48: 269-283. Vincent, C., Heinrich, H., Edwards, A., Nygaard, K. & Haythornthwaite, J. 2002. Guidance on typology, reference conditions and classification systems for transitional and coastal waters. CIS working group 2.4. Common implementation strategy of the Water Framework Directive. European Commission, Brussels. Warwick, R.M. 1986. A new method for detecting pollution effects on marine macrobenthic communities. Marine Biology 49: 728-739. Weisberg, S.B., Ranasingue, J.A., Dauer, D.M., Schaffner, L.C., Diaz, R.J. & Frithsen, J.B. 1997. An estuarine benthic index of biotic integrity (B-IBI) for Chesapeake Bay. Estuaries 20: 149-158. Wilson, J.G. & Jeffrey, D.W. 1994. Benthic biological pollution indices in estuaries. In Kramer, K.J.M. Biomonitoring of coastal water and estuaries, pp 311-27. CRC Press, Baton Rouge, USA.

116

Chapter 5

Multimetric indices in different estuaries

Chapter 5

Use of multimetric indices to classify estuaries with different hydromorphological characteristics and different levels of human pressure

ABSTRACT

The assessment of estuaries based on benthic communities is widely used to determine impacts caused by human pressure and is one of the required tools for the implementation of the European Water Framework Directive (WFD). Our study compared multimetric approaches (B-IBI and TICOR) to assess the benthic condition of three Portuguese estuaries (Mondego, Tejo, and Mira rivers) with different levels of natural and human induced stress. Benthic community condition was classified into quality status categories of the WFD and compared for consistency with a priori status categories based physical-chemical criteria. Both multimetric indices discriminated equally well between locations classified above or below the Good status category but were unable to provide good separation between other quality classes (High/Good, Moderate, Poor/Bad). Metrics included in these indices are greatly affected by natural stress and we recommend the development of habitat-specific thresholds to increase the discriminatory ability of any benthic condition index.

KEYWORDS: benthic indices; estuaries; environmental assessment; natural stress; anthropogenic stress; European Water Framework Directive

119 INTRODUCTION

On a global scale, increased awareness of the biotic impacts of coastal ecosystem pollution has stimulated efforts to develop integrative approaches to the environmental assessment of ecological integrity that are reliable and robust. Such approaches must include methods for habitat or water type classification and data reduction/simplification through the use of multimetric indices. Biological criteria for identifying impaired waters have been developed since 1972 in the USA to meet the requirements of the Clean Water Act (Gibson et al., 2000). Likewise, the European Water Framework Directive (WFD) was approved in 2000 (2000/60/EC) to ensure a comprehensive approach across all European aquatic systems in order to achieve Good ecological status and Good chemical status of all water bodies by 2015. The achievement of these major quality objectives is supported by the analysis of pressures and impacts upon the aquatic ecosystems. The development of assessment tools based on benthic invertebrates is widely accepted because of several characteristics that make these communities suitable indicators of environmental health (e.g. Dauer, 1993). A plethora of benthic community assessment indices (Diaz et al. 2004) has resulted in confusion in responding to legislative requirements for environmental protection and/or restoration. Diaz et al. (2004) reviewed 64 benthic indices and concluded that more emphasis should be given to validation and refinement of existing indices rather than proposing and propagating new indices. By setting a very demanding timetable, the WFD implementation process has encouraged European countries with less experience in using biological assessment tools to adapt existing ones consistent with the recommendations of Diaz et al. (2004).

Numerous papers have been published over the last five years on the use of indices to assess the benthic status of estuarine and marine water bodies, with diversity indices (e.g. Shannon-Wiener, Hulbert), the AMBI index, the ABC method and the BQI index being the most tested across countries (e.g. Borja et al., 2003; Bettencourt et al., 2004; Solis-Weiss et al., 2004; Marín-Guirao et al., 2005; Muxika et al., 2005; 2007; Reiss & Kröncke, 2005; Labrune et al., 2006; Quintino et al., 2006; Salas et al., 2006; Zettler et al., 2007). Some problems in using these indices have been identified: (1) different conclusions of environmental condition when applying different indices to the same data (Salas et al., 2004; Labrune et al., 2006; Quintino et al., 2006; Chainho et al., 2007), and (2) ecological condition classifications contrary to a priori expectations based on known pressures thus raising issues of the validity and/or appropriate application of any given index without further calibration or modification (Salas et al., 2004; Solis-Weiss et al., 2004; Marín-Guirao et al., 2005; Dauvin et al., 2007). Some of the reasons indicated for the incongruence between indices and their inability to detect pressures are (1) lack of sensitivity to different kinds of stressors and/or multiple

120 Multimetric indices in different estuaries

Chapter 5 stressors (Solis-Weiss et al., 2004; Labrune et al., 2006, Quintino et al., 2006), (2) inability to detect subtle changes (Quintino et al., 2006), (3) inability to separate natural variability from anthropogenic stress (Borja & Muxika, 2005; Reiss & Kröncke, 2005; Labrune et al., 2006; Quintino et al., 2006; Chainho et al., 2007; Zettler et al., 2007) and (4) insufficient information concerning the tolerance and/or life history strategies of species in relation to different types of pollution (Marín-Guirao et al., 2005).

Most authors recommend the use of multiple methods based on different assumptions or data analysis approaches in order to more robustly encompass the diverse responses of the benthic communities to stressors (e.g. Dauer et al., 1993; Van Dolah et al., 1999; Bettencourt et al., 2004; Borja & Muxika, 2005; Salas et al., 2006; Flåten et al., 2007). Multimetric indices, i.e. indices that combine metrics into a single index value, should be more accurate and robust in assessing benthic community condition compared to single metrics. Weisberg et al. (1997) developed a benthic index of biotic integrity (B-IBI) to assess benthic community quality in the Chesapeake Bay, with metrics and thresholds selected according to their ability to discriminate between samples declared degraded or undegraded based upon specific criteria including levels of bottom dissolved oxygen, sediment contaminants and total organic carbon of the sediment. Metrics representative of diversity, abundance, biomass, feeding guilds, and functional groups relative to pollution sensitivity were used to characterize benthic condition in distinct habitat types. Similar approaches in developing IBI indices occurred in other regions in the USA (e.g. Van Dolah et al., 1999; Llansó et al., 2002). The calibration and validation of the IBI approach requires large databases, which has been indicated as a major problem for its use in regions with little information on benthic communities (Salas et al., 2006).

In Portugal, four indices (Shannon-Wiener, Margalef, AMBI and ABC-method) were selected to assess the benthic condition and a combination of two or three indices (TICOR approach) is proposed to classify the benthic status according to the requirements of the WFD (Bettencourt et al., 2004). Presently it is unresolved and/or unreasonable to expect that any single metric or simple index can be both representative (able to measure status and trends that are relevant to policy decisions) and sensitive (reflects response to management actions) (Borja & Dauer, in press) while also being broadly applicable at large biogeographical scales. These concerns also apply to other indices that initially were considered of wide application, such the AMBI (Borja et al., 2003; Quintino et al., 2006). This index was initially developed for marine environments and its application to estuaries is problematic when species adapted to the high natural stress in estuaries are also found in coastal regions (Borja & Muxika, 2005; Chainho et al., 2006). Such species may be inappropriately classified based upon knowledge of their distribution at the tail ends of their ecological niches in coastal waters. Indeed, for species that are widely distributed over different habitat types, it is reasonable to expect

121 that sensitivity to natural and/or anthropocentric stresses could be habitat-dependent and not necessarily a constant characteristic. The authors of AMBI recommend that this problem can be addressed by changing the thresholds of quality classes instead of modifying the assignation of species to ecological groups, since the latter would not allow comparisons across different areas and impact types (Borja & Muxika, 2005). Likewise, the evaluation of benthic condition based on diversity indices requires the definition of reference values for each habitat type, since the number of species varies greatly along the salinity gradient (e.g. Remane & Schlieper, 1971), with sediment type (e.g. Warwick et al., 1991) and presence of aquatic vegetation (Borja & Muxika, 2005), among other variables. Other factors, such as the presence of non-indigenous species might also influence the results of the assessment, as emphasized by Dauer et al. (1993) and Chainho et al. (2007).

The selection of criteria to identify the ecological status of estuaries where no reference conditions are available is an additional question that needs to be addressed. In the B-IBI approach, only benthic metrics that showed significant differences between reference and impacted locations were selected to integrate the index. Reference sites were chosen by eliminating locations with known pressures (high development levels and point sources), high contaminant levels (heavy metals and PAHs), low dissolved oxygen, high organic content and high sediment toxicity (bioassay survival) (Weisberg et al., 1997). The WFD also requires a Good chemical status of all water bodies, although the definition of criteria is still under discussion. Consistent with the objectives of the WFD, the objective of the present study is to evaluate the efficacy of these two approaches (B-IBI and TICOR) in assessing the benthic ecological condition in ecosystems with high levels of natural variability and anthropogenic stress.

METHODS

Study area

Three estuaries located along the western coast of Portugal were used as case studies (Figure 5.1). All estuaries were included in type A2 – mesotidal, well mixed estuaries with irregular river discharge, in the aim of the typology process of the WFD (Bettencourt et al., 2004), although having different hydrological characteristics, as shown in Table 5.1.

122 Multimetric indices in different estuaries

Chapter 5

2 Km Odemira FRANCE – IBERIAN PENINSULA A in the Mondego (B), Tejo

SPAIN

L L

L V.N. Milfontes

A A A

G G G

U U U

T T T

R R R

O O O P P P B D –MIRA C D 2 Km Peninsula (A). Stations sampled 5 Km Barreiro Montemor-o-Novo V. F. Xira V. Lisboa Almada Cascais Figueira da Foz –TEJO – MONDEGO C B

Figure 5.1. Location of the study areas in the Iberian (C) and Mira (D) estuaries are indicated.

123 The Mondego River estuary is divided into two branches with different hydrological characteristics, the northern branch with stronger freshwater discharges of the Mondego River and the southern branch that drains the Pranto River and is mainly influenced by tidal excursion (Ferreira et al. 2003). An average annual river flow of 80 m3 s-1 has been registered in the Mondego River estuary but river flow values between 4 and 1800 m3 s-1 have been measured, during drought and flood situations, respectively (www.inag.pt, August 2007). The Tejo River estuary is one of the largest European estuaries (320 km2), registering an annual average flow of 400 m3 s-1, with monthly discharges that may vary from 100 to 2200 m3 s-1. Two distinct areas can be identified in this estuary, a large and shallow upper region, characterized by extensive mudflats and salt marsh cover, and a deeper and narrower lower region (Ferreira et al., 2003). The Mira River estuary is a coastal plain estuary, with a narrow channel shape. Freshwater flow in the Mira River estuary also shows marked seasonal changes, with an average annual flow of 10 m3 s-1, but ranging between 0 and 500 m3 s-1 (Blanton et al., 2000). Summer and winter salinities are shown in Figure 5.2, to illustrate seasonal variations occurring in different estuarine areas.

Table 5.1. Major hydrographic characteristics of the Mondego, Tejo and Mira estuaries and catchments land use (Nb – Northern branch; Sb – Southern branch) Mondego Tejo Mira Area (km2) 9 320 3 Volume (106 m-3) 21 2 200 17 River flow (m3 s-1) 80 400 10 2 (Nb) Residence time (days) 19 14 9 (Sb) Population (thousands) 693 2 810 24 Industry units (number) 277 294 51 Irrigated areas (ha) 102 700 276 105 12 030

Human pressure varies among the studied estuaries, with higher levels of population, industry and agriculture use in the Tejo and Mondego estuaries than in the Mira River estuary (Table 5.1). Eutrophication is considered a major problem in the Mondego River estuary, and the southern branch was identified as a problem area under the designation of vulnerable zones, while the Tejo and Mira estuaries were considered non-problem areas (Ferreira et al., 2003). Contamination by heavy metals is well documented in the Tejo River estuary, both in the sediments (e.g. Caçador et al., 1996) and accumulated by different levels of the food

124 Multimetric indices in different estuaries

Chapter 5 web (França et al., 2005). Negligible concentrations have been measured in the Mondego River estuary (Chainho et al., 2006), while in the Mira River estuary some areas registered contamination by heavy metals, although in lower levels than those found in the Tejo River estuary (data obtained from the Portuguese Water Institute monitoring program).

Mondego 40 30 20 Salinity 10 0 MS MS MS FS FS FS SC FS FS FS

Tejo 40 30 20 Salinity 10 0 MS MS CS MS FS SC SC MS FS SC FS SC FS SC SC CS SC SC SC SC

Mira 40 30 20 Salinity 10 0 CS P SC P SC SC SC SC SC SC MS Summer Winter

Figure 5.2. Summer and winter salinity values registered in the Mondego, Tejo and Mira estuaries. Dominant sediment type is indicated for each location (P – Pebbles; CS – Coarse sand; MS – Medium sand; FS – Fine sand; SC – Silt & clay). Stations are ordered from upstream to downstream locations (left to right).

The Mondego River estuary is characterized by profound hydromorphological changes that occurred over the last 50 years, with the construction of the Low Mondego irrigation

125 system. The Tejo River estuary is also highly modified by embankments and the installation of harbour infrastructures, while the Mira River estuary has only minor hydromorphological changes, mainly embankments along the urban areas of Odemira and Vila Nova de Milfontes. Freshwater discharge is regulated by dams located upstream in all river basins.

Sampling

Although benthic field surveys were conducted in different seasons in all estuaries, only summer data (July 15 through September 30) was used in this study, consistent with the development and application of the B-IBI method (Weisberg et al., 1997). Sampling surveys took place in the years 2000 in the Mondego River estuary and 2003 in the Tejo and Mira estuaries. Ten sampling stations were selected in the Mondego River estuary, 20 stations in the Tejo River estuary and 11 stations in the Mira River estuary in order to be representative of the salinity gradient and different sediment types (Figure 5.1). Three benthic invertebrate samples were taken at each station using a modified van Veen LMG grab (0.05 m2) and grab contents were fixed and preserved with 4% buffered formalin, sieved using a 500 µm mesh and preserved in 70% ethanol. All samples were sorted and identified to the lowest possible taxonomic level, in order to determine the number of taxa and their respective abundances. Biomass of species per sample was also determined as ash free dry weight, after ignition at 450ºC. Several environmental variables were measured (1) in water: bottom dissolved oxygen, salinity, nutrients concentrations (NO3, NO2, NH4, P), and (2) in the sediment: sediment grain size, total organic carbon and heavy metals concentrations (As, Cr, Cu, Pb, Zn, Hg, Ni, Cd). Methods used to determine these parameters are detailed in a previous study by Chainho et al. (2006) for the Mondego River estuary. Chemical analyses were done by Instituto de Ambiente and Instituto Hidrográfico, using certified methods.

Data analysis

Physical-chemical status

The WFD sets the objectives of Good ecological status and Good chemical status for all European surface waters. The Directive also states that biological elements and hydromophological, chemical and physical-chemical elements supporting the biological elements have to be used in the assessment of ecological status (Ecostat, 2003). In order to classify a water body as in Good status, physical-chemical conditions have be within a certain range that ensures ecosystem functioning at that specific water type and meet the Ecological Quality Standards (EQS) for specific pollutants (Ecostat, 2003). EQS are still under development, as well as type specific thresholds for other physical-chemical elements. In this

126 Multimetric indices in different estuaries

Chapter 5 study we used criteria already developed and applied in other countries for dissolved oxygen, nutrient enrichment and contamination by heavy metals, since no specific criteria have been developed for Portuguese transitional waters. Only three quality classes were considered, namely High/Good, Moderate and Poor/Bad, since available thresholds are not always adequate for a classification into five quality classes, as required by the WFD. Dissolved oxygen was classified according to the criteria recommended by Bald et al. (2005) (Table 5.2). These authors used background concentrations to estimate reference conditions, weighted by salinity so that different reference conditions were defined for each of the Venice system (1959) salinity classes (Table 5.2).

Table 5.2. Criteria used to classify the chemical status based on the concentration of heavy metals in the sediment, nutrient concentrations in water and percentage of bottom dissolved oxygen (ERM – effects range-median; ERL – effects range-low)

Heavy Nutrients Classification Dissolved oxygen (%) metals (µmol N/L)

Oligohaline Mesohaline Polyhaline Euhaline

High/Good < ERL a < 7.0 > 68.2 > 73.2 > 80.4 > 85.2

Moderate ERL – ERM b 7.0 – 71.0 54.9 – 68.2 59.9 – 73.2 67.0 – 80.4 71.9 – 85.2

Poor/Bad > ERM c > 71.0 < 54.9 < 59.9 < 67.0 < 71.9

a no more than two chemicals exceed ERL b more than two chemicals exceed ERL, but none exceeded ERM c more than one chemical exceed ERM

Dissolved inorganic nitrogen (DIN) was used as an indicator of nutrient enrichment, following the criteria defined by the United States National Estuarine Eutrophication Assessment (NEEA) (Bricker et al., 2003). Contamination by heavy metals was assessed based on Long et al.’s (1995) effects range-medium (ERM) and effects range-low (ERL) concentrations. Only locations where no more the two metals exceeded the ERL concentrations were considered at High/Good status for heavy metals (Table 5.2). For DIN and heavy metals, the same reference concentration thresholds for High/Good, Moderate and Poor/Bad status were considered across all salinity classes because no dilution factors are known for these parameters.

127 Benthic invertebrate status

The benthic invertebrate condition of transitional waters was assessed using two different multimetric approaches (Tables 5.3 and 5.4), the TICOR methodology suggested by the WFD Portuguese working group (Bettencourt et al., 2004) and the B-IBI (Weisberg et al., 1997). The TICOR approach recommends a combination of two or three of the following four indices, depending on the available data: (1) the Shannon-Wiener diversity index (H’) (log2),

(2) the Margalef species richness (D) (loge) (Legendre & Legendre, 1976), (3) the AMBI index (Borja et al., 2000), and (4) the ABC-method (Warwick, 1986). The ABC-method was excluded because previous studies showed an apparent lack of predictable response of this index to pollution-induced stress in estuaries with strong seasonal changes (Chainho et al., 2007). All indices were calculated for each sampling station based on the benthic invertebrate abundance data of all replicates, using PRIMER 5.0 software package and AMBI 3.0 index software. Bettencourt et al. (2004) provide a table with a qualitative approach on how to determine the ecological status based on the combination of results of two or three different indices, but not all possible combinations are presented. This problem was addressed by converting threshold values for each index into a numerical Ecological Quality Ratio (EQR), defined as the ratio between reference and observed values of the relevant biological quality elements, varying between 0 and 1. The overall classification for each location was obtained by averaging EQR values obtained for the different indices and assigning it to a quality class according to the following ranges: High, >0.80; Good, 0.61-0.80; Moderate, 0.41-0.6; Poor, 0.21-0.4; Bad, ≤0.20. B-IBI metrics were calculated using abundance and biomass datasets, according Weisberg et al. (1997) and Alden et al. (2002). Since benthic invertebrate species found in the Portuguese estuaries differ significantly from those occurring in the Chesapeake Bay, some of the metrics had to be changed or suppressed: (1) pollution-indicative taxa were those included in ecological groups EG IV and EG V of the AMBI list, (2) pollution-sensitive taxa were those included in EG I and EG II of the AMBI list, and (3) several metrics that could not be calculated were not used, i.e. sediment depth related metrics, tolerance score and Tanypodinae to Chironomidae percent abundance ratio.

Thresholds used to classify sampling locations are indicated in Table 5.3. Quality classes were reduced to three different classifications, namely High/Good, Moderate and Poor/Bad, since for Margalef and B-IBI indices, thresholds for all five quality classes are not available.

A Kendall correlation coefficient was used to test for significant correlations between the numerical results of benthic indices (Shannon-Wiener, Margalef, AMBI, EQR and B-IBI) and the reciprocal 1/AMBI was used because AMBI is the only index with higher values corresponding to worse quality.

128 Multimetric indices in different estuaries

Chapter 5

Table 5.3.Thresholds of indices used to classify benthic invertebrate communities

Shannon-Wiener (H’) Margalef (D) AMBI EQR B-IBI

High/Good > 3.0 > 4.0 < 3.3 > 0.6 ≥ 3.0

Moderate 2.0 – 3.0 2.5 – 4.0 3.3 – 5.0 0.4 – 0.6 2.7 – 2.9

Poor/Bad < 2.0 < 2.5 > 5.0 < 0.4 < 2.6

Comparison between benthic and physical-chemical classifications

The WFD requires the identification of the ecological status of a water body based on the “one out all out” principle, which means that both biological and physical-chemical elements must be at least in Good status in order to achieve the environmental objectives for surface waters (Ecostat, 2003). Therefore, the comparison between physical-chemical and benthic invertebrates classifications was made considering two categories of results: (1) above Good status, which included stations classified as High and Good, and (2) below Good status, which included stations classified as Moderate, Poor and Bad. Physical-chemical and benthic classifications were also compared to check the agreement found for the three classes defined (Poor/Bad, Moderate and Good/High). A G-test of independence with Williams’ correction was used to investigate if the level of agreement between biological and physical-chemical classifications was independent of the index used (P<0.05). A pairwise Kendall correlation coefficient was used to test for significant correlations between the results of the benthic indices and pollution indicative variables (dissolved oxygen, transparency, nutrients, total organic content and heavy metals).

RESULTS

Physical-chemical status

Dissolved oxygen was consistently high in all estuaries studied (Figure 5.3) and low oxygen levels (below 50% saturation) were measured only in the upstream stations of the Mira River estuary, close to sewage point-source discharges. Dissolved inorganic nitrogen (DIN) concentrations showed moderate nutrient enrichment in all three estuaries (Figure 5.3) and some stations located in the upstream area of the Tejo River estuary registered concentrations above 71 µmol l-1, corresponding to a Poor/Bad status. The lowest nutrient concentrations were measured in the middle Tejo estuary and lower Mira estuary. In the

129 Mondego estuary, higher concentrations where found in the southern branch, when compared to the northern branch. Contamination by heavy metals was not detected in the Mondego River estuary since all measured metals registered concentrations below ERL values (Figure 5.3). The Mira River estuary was moderately contaminated (Figure 5.3), mainly due to concentrations of As, Cu, Cd and Ni above ERL values, but low concentrations of all metals were measured in the uppermost and downstream stations. On the other hand, the Tejo River estuary showed some highly contaminated areas since 30% of the sampling stations were classified as in Poor/Bad status (Figure 5.3), mainly near industrial areas.

Concentrations of Hg and Zn exceeded ERM values, while all other heavy metals, except for Ni, exceeded ERL values (Figure 5.3). According to the WFD, the general classification of the physical-chemical status has to be based on the poorest status obtained for any physical-chemical element. The general physical-chemical status in each studied estuary was based on that requirement and, as shown in Figure 5.3, most locations are at a Moderate status, with some areas of the Tejo and Mira estuaries classified as in a Poor/Bad status. There was a single station, located near the mouth of the Mira River estuary that met the objective of Good status for all physical-chemical elements considered.

Benthic invertebrate status

Both methods used to identify the ecological status based on the benthic communities classified most stations below Good status. TICOR classified 20%, 25% and 0% of the stations located in the Mondego, Tejo and Mira River estuary stations, respectively, as Good in status, while the B-IBI classified 20%, 15% and 9% of stations as Good in status (Figure 5.4). The B-IBI classified most stations in all three estuaries as Poor/Bad (70%, 75%, 91%, respectively for the Mondego, Tejo and Mira River estuary stations) while the TICOR approach classified most stations as Moderate in status (50%, 45%, 55%, respectively for the Mondego, Tejo and Mira River estuary stations) (Figure 5.4). Among TICOR component indices, AMBI identified a higher number of stations as Good in status in the Mondego and Mira estuaries than the Shannon- Wiener and Margalef indices (Figure 5.4).

Kendall coefficient of correlation showed that Shannon-Wiener and Margalef indices were highly correlated in the Mondego (r=0.689; P<0.0001) and Tejo estuaries (r=0.731; P<0.0001) and were more correlated to EQR values than the AMBI in all estuaries (Table 5.4). All indices were correlated to each other in the Tejo estuary, including the B-IBI, in contrast to the Mira estuary, where significant correlations were found only between the diversity indices and the EQR value.

130 Multimetric indices in different estuaries

Chapter 5

f Poor /Bad Moderate Nutrients High/Good Physico-chemical status Physico-chemical Mondego Tejo Mira 0% 20% 40% 60% 80% 100% 0% Tejo Mir a 80% 60% 40% 20% 100% Mondego Mira estuaries based on physical-chemical elements. The results o lved oxygen, nutrients and heavy metals) are also shown. Heavy metals Heavy Dissolved oxygen Dissolved 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% Tejo Mir a Tejo Mir a Mondego Mondego

Figure 5.3. Classifications obtained in the Mondego, Tejo and the different types of pollution indicative elements (disso

131

TICOR

100%

80% 60%

40% 20%

0% Mondego Tejo Mira

Shannon-Wiener, Margalef & AMBI 100%

80%

60%

40%

20%

0% H' D A MBI H' D A MBI H' D A MBI Mondego Tejo Mira

B-IBI

100%

80%

60% 40%

20% 0% Mondego Tejo Mira High/Good Moderate Poor/Bad

Figure 5.4. Classifications obtained in the Mondego, Tejo and Mira estuaries using TICOR and B-IBI approaches. The results of the different TICOR metrics (Shannon-Wiener, Margalef and AMBI indices) are also shown.

132 Multimetric indices in different estuaries

Chapter 5

Table 5.4. Significant correlations (Kendall coefficient of correlation obtained between different biological indices and between indices and pollution indicative variables for each estuary. The total number of stations is indicated (N). H’ – Shannon-Weiner Index, D – Margalef Index, AMBI –AZTI Marine Biotic Index, EQR – Ecological Quality Ratio, B-IBI – Benthic Index of Biotic Integrity, DIN – Dissolved Inorganic Nitrogen

Mondego Tejo Mira

(N=10) (N=20) (N=11)

H' - D 0.689** 0.731*** -

H' - AMBI - 0.480** -

H' - EQR 0.689** 0.850*** 0.673**

H' - B-IBI - 0.517** -

D - AMBI - - -

D - EQR 0.733** 0.754*** 0.564*

D - B-IBI - 0.343* -

AMBI - EQR 0.556* 0.484** -

AMBI - B-IBI - 0.378* -

EQR - B-IBI - 0.466** -

H' - DIN -0.867** - -

D - DIN -0.733** - -

EQR - DIN -0.556* -0.326* -

* p<0.05; ** p<0.01; *** p<0.001

The benthic species composition was different between estuaries, as shown in Table 5.5, with more than twice the number of taxa identified in the Tejo compared to the Mondego and Mira estuaries. Conversely, mean density in the Mondego River estuary was more than twice that found in the other two estuaries, while the mean biomass was very similar in the Mondego and Tejo estuaries, but much lower in the Mira River estuary (Table 5.5). Polychaetes and amphipods were the dominant groups in all three estuaries, although different species dominated in each estuary and bivalves were better represented in the Mondego River estuary (Table 5.5). These differences were also reflected in the composition and density of the pollution indicative, pollution sensitive and tolerant species, as shown in Figure 5.5, representing the density and number of species of each EG identified according to

133 the AMBI list of species (Borja et al., 2000). Tolerant species (EG III) were dominant in all estuaries, with higher densities in the Mondego and Mira estuaries than in the Tejo. Nevertheless, the number of taxa identified as pollution sensitive (EG I and II) in the Tejo River estuary was higher than all other EGs within the same estuary and compared to all EGs of the remaining estuaries (Figure 5.5).

Table 5.5. Biological descriptive parameters for the Mondego, Tejo and Mira estuaries. The mean density (ind m-2) and biomass (g m-2) and the total number of taxa are indicated for each estuary. Ten most abundant taxa (dominants) are listed (see Appendix 1 for complete list of taxa) with their respective contributions to total density and the indication of the Class in bold (A- Amphipoda; B- Bivalvia; G- Gastropoda; I- Insecta; Is- Isopoda; N- Nemertea; O- Oligochaeta; P- Polychaeta; n.i. – not identified)

Mondego River estuary Tejo River estuary Mira River estuary

Mean 4 582 2 104 1 969 Density

Mean 2.57 2.73 0.44 Biomass

N. of Taxa 71 143 68

Streblospio shrubsolii (35%)P Chaetozone setosa (26%)P Corophium orientale (27%)A Corophium multisetosum (16%)A Streblospio shrubsolii (17%)P Boccardiella redeki (13%)P Hydrobia ulvae (13%)G Corophium acherisicum (10%)A Leptocheirus pilosus (12%)A Tubificoides sp. (6%)O Tubificoides sp. (10%)O Alkmaria romijni (10%)P

Dominant Scrobicularia plana (4%)B Tetrastemmatidae n.i. (5%)N Pisione remota (7%)P species Corbicula fulminea (4%)B Polydora cornuta (4%)P Hediste diversicolor (6%)P Cerastoderma glaucum (3%)B Corbula gibba (4%)B Streblospio shrubsolii (6%)P Chaetozone setosa (2%)P Boccardiella redeki (2%)P Chironomidae n.i. (5%)I Echytraeus sp. (2%)O Cossura coasta (2%)P Cyathura carinata (3%)Is Spio martinensis (2%)P Limnodrilus hoffmeisteri (1%)O Corophium acherisicum (2%)A

Comparison between benthic and physical-chemical classifications

Using our criteria defined to classify physical-chemical elements, only one station located in the Mira River estuary was classified as in Good status, while the benthic indices indicated a higher number of stations above Good status. The level of agreement of the benthic indices and the physical-chemical classification is not significantly independent of the index used when considering only stations classified above or below Good status neither when all classes were considered, as shown by the result of the G-tests of independence (Table 5.6).

134 Multimetric indices in different estuaries

Chapter 5

10 Number of species 0 EG I EG II EG III EG IV EG V

4000 ) 2 3000

2000

1000 Density (ind/m Density 0 EG I EG II EG III EG IV EG V

Mondego Tejo Mira

Figure 5.5. Number and density of taxa included in each Ecological Group (I, II, III, IV and V), as defined by Borja et al. (2000), for Mondego, Tejo and Mira estuaries.

A gradient of agreement between AMBI (lowest agreement) and B-IBI (highest agreement) is clear when below or above Good status is considered. When using two status categories (above or below Good), the agreement between biological and physical-chemical results was highest for the B-IBI (87.8%). The TICOR approach resulted in a 78.1% level of agreement while the lowest agreement level was obtained using the AMBI index (65.8%). When all quality classes were considered (Poor/Bad, Moderate and Good/High), the best agreement between physical-chemical and biological classifications was obtained with the TICOR approach (53.7%), and the worst when using B-IBI (26.8%) (Table 5.6).

The level of agreement when considering all classes was much lower for all indices and there was a higher percentage of stations matching the physical-chemical classification using TICOR than any of the component indices alone (Table 5.6). AMBI and B-IBI were the only indices that classified correctly the single station identified as in a Good physical- chemical status.

135 Table 5.6. Level of agreement (%) between status categories determined by the biological indices/approaches compared to status categories determined by physical- chemical elements. Data for the three estuaries were combined. A. Two status categories, i.e., stations classified above or below Good status. B. Three status categories, i.e., stations classified as High/Good, Moderate and Poor/Bad. Results of a G-Williams test conducted to investigate if the frequency of agreement is independent of the index used are also shown

Shannon Margalef AMBI TICOR B-IBI -Wiener

A. Above/below Good status 75.6% 85.4% 65.8% 78.1% 87.8% (GW = 7.141; p = 0.128; df = 4)

B. All classes 46.9% 41.5% 48.8% 53.7% 26.8% (GW = 6.986; p = 0.137;df = 4)

Kendall correlations between the benthic indices and physical-chemical variables indicated significant correlations in the Mondego and Tejo River estuaries, with Shannon- -Wiener, Margalef and EQR results correlated with DIN concentrations (P<0.01) (Table 5.4).

DISCUSSION

Indices of biotic integrity have long been recognized as useful tools to measure biological responses to pollution, identify the need to apply mitigation measures and evaluate the efficiency of those measures, mainly because these indices: (1) are documented to accurately reflect both watershed level stressors and resulting exposure variables (Dauer et al., 2000; Salas et al., 2006), (2) reduce large amounts of data to a meaningful single value, and (3) are valuable tools to communicate complex data to a general audience (Weisberg et al., 1997; Aubry & Elliott, 2006).

Nevertheless, the efficiency of indices depends on a predictive understanding of the responses of the component metrics of the index to environmental stress and disturbance, at multiple spatial and temporal scales (Andersen, 1997), that might vary considerably between the region for which they were developed and other regions where they might be used.

The present study compared two multimetric indices, B-IBI and TICOR approach, in estuaries with different hydromorphological characteristics and different human pressures, namely the Mondego, Tejo and Mira estuaries. B-IBI was developed using a priori specific physical-chemical criteria (i.e., bottom-water dissolved oxygen concentrations, levels of

136 Multimetric indices in different estuaries

Chapter 5 sediment contaminants, and/or total organic carbon of the sediment) to: (1) identify samples considered undegraded, degraded or indeterminate, (2) select metrics based upon significant differences, in an ecologically meaningful manner, between undegraded and degraded data, and (3) determine thresholds for scoring each metric, using only data from samples declared undegraded. As such, the B-IBI approach relies upon reference or minimally-impacted conditions. In contrast, the TICOR approach is based solely upon best professional judgment to determine thresholds for each of the component indices and the combined index.

Physical-chemical reference conditions

Most studies recently published on the efficiency of benthic indices to separate between degraded and undegraded conditions are based on prior knowledge of pressures acting over different locations (Salas et al., 2004; 2006; Labrune et al., 2006; Quintino et al., 2006) or using physical-chemical indicators (Weisberg et al., 1997; Paul et al., 2001; Marín- -Guirao et al., 2005). The latter approach was used in the present study since no reference conditions were available and the results indicate that none of the estuaries seems to meet the WFD objective of Good physical-chemical status. Eutrophication problems in the Mondego River estuary and contamination by heavy metals in the Tejo River estuary are well documented, but the Mira River estuary has been characterized as a relatively pristine ecosystem (Marques et al., 1993; Carvalho et al., 2005). Nevertheless, physical-chemical elements measured during the present study showed significant concentrations of nutrients and metals, as well as the occurrence of low oxygen levels in some areas of the Mira River estuary. These results confirm the effect of known pollution sources, such as the Odemira sewage discharge, the irrigation perimeter of the Mira catchment area and mining activities that used to occur in upstream areas.

In determining between quality classes using physical-chemical indicators, specific threshold values separating quality classes are assumed to have a meaningful relationship to pollution-induced alterations in benthic populations. While the use of ERM and ERL values is widely accepted as a reference for heavy metals contamination (e.g. Weisberg et al., 1997; Mucha et al., 2003), criteria for dissolved oxygen and eutrophication descriptors still lack consensus among the scientific community. According to the criteria of the United States National Estuarine Eutrophication Assessment (NEEA) or the OSPAR Comprehensive procedure, oxygen concentrations ranging between 2 mg l-1 and 5-6 mg l-1 may cause stress responses in invertebrate fauna (Bricker et al., 2003; OSPAR Commission, 2003). The application of either the NEAA or OSPAR dissolved oxygen criteria to our study would have produced very similar classifications to ours, but using the criteria of Weisberg et al. (1997) would have resulted in all stations classified as undegraded, since dissolved oxygen concentrations were consistently

137 high along time (unpublished data). On the other hand, the OSPAR Commission criteria for DIN, based on background concentrations, are much less stringent than the NEEA criteria, since concentrations of 44 µmol l-1 and 34 µmol l-1were defined for the Mondego and Tejo estuaries, respectively, as corresponding to non-problem areas in terms of eutrophication (OSPAR Commission, 2003). The five uppermost stations of the Mondego River estuary and eight stations of the Tejo River estuary would have been classified as Good for nutrients if the OSPAR criteria had been applied. No background concentrations are indicated for the Mira River estuary. These background concentrations, defined as the lowest winter concentrations measured in those estuaries, have to be used with some caution since the oldest monitoring records were registered in the early 80’s, when estuaries were already under considerable human pressure. Bricker et al. (2003) consider that nutrients are not a robust descriptor of eutrophication in estuaries and use the NEAA approach to identify problem and non-problem areas, based on several indicators of pressure, state and response of the aquatic system (Bricker et al., 2003). Based on the NEAA methodology, the Mondego River estuary was identified as a potential problem area by Ferreira et al. (2003), while the Tejo and Mira estuaries were considered non problem areas. Nevertheless, this evaluation was based on data from only the lower Mira River estuary. In spite of the lower level of human pressure in the Mira River basin when compared to the Mondego and Tejo river basins (Table 5.1), this estuary is also subject to nutrient enrichment, at least in upstream areas, because of a low flow, particularly during the dry period.

Biological indices

Diversity

Diversity was an attribute considered in both B-IBI and TICOR, but the former considered habitat specific thresholds, while the latter adopted reference values defined for marine communities in Norway. Diversity indices are, in general, good indicators of change in the community structure but their use as an indicator depends on the natural heterogeneity of the communities studied, since diversity is an ambiguous concept and there is no single definition of high and low diversity. When no reference conditions are available, diversity can only be useful for comparing communities from different locations and/or tracking temporal changes a benthic community at a specific site.

The Shannon-Wiener diversity index is very popular among ecologists as a measure of variety and abundance of organisms, since it incorporates a species richness component, but also a species evenness component (Magurran, 2005), while the Margalef index is considered a species richness index (Read et al., 1978). In this study, the Shannon-Wiener and Margalef indices showed highly correlated results, thus their simultaneous use as responsive metrics is

138 Multimetric indices in different estuaries

Chapter 5 redundant and overemphasizes the diversity component of TICOR, as indicated by the correlation levels of these indices and the EQR values in all estuaries. As pointed out by some authors (Izsák, 2007; Chainho et al., 2007), the use of correlated indices does not contribute additional information, therefore only one of these indices should be used. The Shannon- Wiener is more widely used and has been included in the majority of papers published on comparing different indices to assess ecological status (e.g. Salas et al., 2004; Labrune et al., 2005; Marín-Guirao et al., 2005; Quintino et al., 2006). Thus it is more suitable for comparisons across estuaries.

Redundancy is also not considered by the metric selection criteria used in B-IBI, since all candidate metrics that showed significant differences between degraded and reference locations and were ecologically meaningful were retained in the index (Weisberg et al., 1997; Van Dolah et al., 1999; Llansó et al., 2002). Paul et al. (2001) developed a multimetric index for the Virginian Province, conceptually similar to the B-IBI, but using stepwise discriminant analysis to select the best subset of metrics for separating between degraded and reference sites. Some problems concerning the use of this statistical method, such as failure to select the best subset of variables of a given size, sampling error capitalization and the use of incorrect number of degrees of freedom have also been identified and Hulberty (1989) suggests a previous elimination of variables with no predictive validity and variables highly correlated with other variables as an alternative for reducing the number of metrics. An alternative approach to minimize redundancy in multimetric indices would be to combine the results of related metrics (e.g. diversity and dominance indices) into a single value prior to combining it with other metrics, to balance the contribution of different attributes of the benthic community. The indication of the logarithmic base and the abundance data type (abundance per replicate, density, etc.) used to calculate diversity indices must be specified, in order to assure their correct use based on specific thresholds, such as those defined in B-IBI and TICOR.

Pollution-indicative and pollution-sensitive taxa

Biotic indices have long included information on the tolerance and sensitiveness of species to pollution. The B-IBI includes pollution-sensitive and pollution-indicative metrics using either abundance or biomass estimates while the TICOR approach includes the abundance of taxa placed into five ecological groups corresponding to different tolerance levels (using the AMBI index). For the B-IBI a two-step procedure was used to determine which species to include in the pollution-sensitive and pollution-indicative metrics (Weisberg et al., 1997). First a candidate list of species for both metrics was developed using the literature and prior knowledge of each candidate species. Second, for the pollution-sensitive metric a

139 candidate species had to have a higher abundance in reference samples compared to degraded samples while candidate species for the pollution-indicative metric had to have a higher abundance in degraded samples compared to reference samples. The assignation of species by the AMBI index was based on literature addressing the sensitivity/tolerance of taxa to organic enrichment (Borja et al., 2000).

In the present study, the AMBI index seems to overestimate the benthic status in all estuaries, contributing to the overall higher category obtained with TICOR. The tendency for overestimation when using AMBI has been mentioned by other authors. Marín-Guirao et al. (2005) and Quintino et al. (2006) attributed AMBI’s overestimation of ecological status to the development of the AMBI based on the species’ tolerance to organic pollution, which might make this index less sensitive to other types of pollution such as metal contamination and physical disturbance. The weight given to dominant species has also been indicated as leading to misclassification, since diversity and the number of species are not considered (Labrune et al., 2006). The effect of the dominance of tolerant species is reflected in the three different estuaries studied, as mentioned before by Dauvin et al. (2007). Tolerant species such as Streblospio shrubsolii (Buchanan, 1890) and Corophium spp. (both in EG III) are dominant in the Mira and Mondego River estuaries, therefore constraining the AMBI results towards a general classification of stations into Good status. In contrast, the more balanced distribution of species by ecological groups in the Tejo River estuary gives more weight to relative abundance of species included in other groups. On one hand, the higher species richness in the Tejo River estuary seems to reflect a higher diversity of habitat types, as shown by different combinations of sediment types and salinity classes. On the other hand, the higher number of sensitive species (EG I and EG II) also indicates that these species find favourable conditions for their settlement in the Tejo River estuary, regardless of the overall highest pollution levels. Furthermore, the number of species and their distribution among ecological groups is very similar in the Mondego and Mira estuaries, but density and biomass are considerably higher in the Mondego River estuary, which seems to be an indication of nutrient enrichment.

Both estuaries are under severe natural stress caused by seasonal and daily changes in salinity and freshwater flow and periodically affected by floods and droughts. Extreme events have severe impacts on benthic communities and affect the results obtained when using biotic indices (Chainho et al., 2006; 2007) in the Mondego River estuary, where the number of taxa and their respective abundances decreased significantly after a flood. Droughts also influence the estuarine water quality because freshwater flow is reduced and temperatures increase, lowering dissolved oxygen levels and increasing salinity (Attrill & Power, 2000), which corresponds to what was observed in the Mira River estuary during summer. The ecological process of community succession, widely documented after Pearson & Rosenberg

140 Multimetric indices in different estuaries

Chapter 5

(1978), can be greatly affected by natural environmental variability (Rakocinski et al., 2000), such as salinity fluctuation in estuarine environments or even be interrupted by disturbance events (Boesch et al., 1976). Ritter et al. (2005) show that a salinity-stressed estuary is in a constant state of early to intermediate succession and there is no climax community but a constant replacement of tolerant species, according to the existing environmental conditions. The distribution of species by ecological groups in the Mondego and Mira estuaries is consistent with Ritter et al.’s (2005) characterization of a salinity-stressed estuary, i.e. dominance of tolerant species and low representation of pollution sensitive and pollution indicative species. In contrast, a higher number of pollution sensitive species was identified in the Tejo River estuary when compared to tolerant species, but pollution indicative species assigned to ecological group IV were very representative, giving some evidence of the presence of different successional states.

Multimetric indices

As emphasized by Borja & Dauer (in press), the use of indices to identify impacts of human pressure over ecosystem function requires that these indices are appropriately applied in space and time and generate results that are interpreted in an acceptable manner. In the present study we tested two multimetric approaches developed for different biogeographical regions, including one proposed for assessing the ecological status of Portuguese transitional waters (TICOR). A previous study by Chainho et al. (2007) pointed out some problems related to the applicability of TICOR without prior temporal stratification due to strong seasonal changes in the Mondego estuary. In this study, only summer data was considered and all different salinity habitats were covered in each estuary.

141 nutrient related ones, corroborating the results of Chainho et al. (2007). Nevertheless, in the Mira River estuary there is no apparent response of the indices to stressors and only AMBI and B-IBI identified the only station in a Good status. The low diversity and richness found in this estuary is apparently the reason for the worse classification by B-IBI and TICOR, when compared to the Mondego and Tejo estuaries.

All indices were correlated to each other in the Tejo estuary, while in the Mondego and Mira estuary only some TICOR components were related. Having no evidence that this fact relies on different pressure levels, it seems likely to be related to differences in hydrographical characteristics. The Tejo estuary is one of the largest European estuaries, with a water volume and residence time much higher than the other estuaries studied, acting as a buffer that reduces variations in parameters such as salinity, temperature and sediment composition. In contrast, the Mondego and Mira estuaries register strong variations in environmental conditions, not only across seasons but also daily variations associated with the tidal cycle. In the Mira estuary salinity amplitudes of 25 units were registered between seasons and measurements along the tidal cycle showed variations up to 15 units during a flood period (unpublished data). As pointed out by Ritter et al. (2005), frequent disturbances in environmental conditions, such as salinity changes, prevent the establishment of equilibrium species and the benthic communities remain in a constant state of early to intermediate succession. This seems to explain the strong correlation between all indices tested in the Tejo estuary, similarly to what had been found in the Chesapeake Bay (Borja et al., in press), since communities characterized by high diversity and pollution-sensitive species were identified in some locations, whereas low diversity and pollution-tolerant species are found in other locations.

CONCLUSIONS

The WFD guidance document on ecological status states that the mismatch between the biological and physical-chemical monitoring results may be an indication that the biological methods used are not sensitive to the effects of anthropogenic changes, emphasizing the need to improve biological methods (Ecostat, 2003). The present study showed that hydromorphological differences between estuaries included in the same WFD type (A2) may confound and complicate the classification process, both for physical-chemical and biological elements.

The use of physical-chemical parameters to define reference conditions still lacks consensus among experts and the benthic communities do not always show predictable responses to all types of stressors. Both the B-IBI and the TICOR approach seem to be efficient

142 Multimetric indices in different estuaries

Chapter 5 in discriminating between locations above or below Good status, as required by the WFD, but were much less efficient in discriminating other quality classes, indicating the need for further adaptation and validation of metrics. The use of diversity can be misleading in estuaries with a natural low diversity, as stressed by Puente et al. (in press), but this problem could be addressed by defining different thresholds for each habitat type, following the approach used in the B-IBI. Metrics related to the tolerance of species to pollution stress also need some adjustments in estuaries with strong natural pressures, since the dominance of tolerant species often overestimates the ecological status, especially in the case of the AMBI. This could imply the development of separate sets of metrics and thresholds for the Tejo estuary and for the Mondego and Mira estuaries, since benthic communities are under different levels of natural stress.

The future use of indices to classify the benthic status in Portuguese estuaries will require a better understanding of the spatial and temporal patterns of the invertebrate communities. In estuaries with almost no knowledge available on the benthic community patterns, such as the Mira estuary, a greater monitoring effort will be needed before being able to adapt existing benthic indices to an acceptable level of confidence. Ferraro et al. (1991) concluded that the effect of natural disturbances on the benthos may sometimes be greater than the effect of wastewater discharge and recommend long term studies (≥6 years) to reliably discriminate between them. The monitoring frequency required by the WFD for the benthic fauna (every 3 years) seems consequently inappropriate for a consistent assessment of the benthic status of estuarine systems with similar characteristics to Mondego and Mira River estuaries.

AKNOWLEDGEMENTS

This study was financially supported by two Ph.D. fellowships (SFRH/BD/5144/2001 and SFRH/ BD/6365/2001) granted by FCT (Science and Technology Foundation) and ESF in the aim of the III European Community Support Framework, project QUERE granted by Instituto de Ambiente, and projects ERIC (FCT/P/MAR/15263/1999) and EFICAS (POCI/MAR/61324/2004) granted by FCT. We would like to thank Ana Luisa Rego, Sérgio Rodrigues, Nuno Prista, Rita Vasconcelos, Manuel Cabral and Tadeu Pereira for their support to the field work.

143 REFERENCES

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Chapter 6

Final Remarks

Chapter 6

Final Remarks

The implementation of the Water Framework Directive required the development of ecological assessment tools in European countries. Some of these countries have well developed scientific backgrounds and monitoring programs that include biological elements. In contrast, in some other countries these elements were not included in the monitoring programs and no specific tools were available for assessment of the ecological status when the WFD was agreed upon, such is the case for Portugal. Therefore, the implementation of this Directive was particularly demanding and the most feasible solution to accomplish a very demanding timetable was an adaptation of existing tools. The main objective of this study was to investigate if the characteristics of the Portuguese estuaries constrained the use of existing assessment tools for evaluating ecological status based on benthic invertebrate communities.

The development and use of benthic biotic indices is grounded in several ecological assumptions of the responses of benthic communities to human induced stress. For instance, it is assumed that in degraded conditions (1) species diversity is lower (e.g. Green, 1979); (2) opportunistic species are more representative than pollution-sensitive species (e.g. Weisberg et al., 1997; Borja et al., 2000; Paul et al., 2001); (3) abundance is dominated by small- bodied species (e.g. Warwick, 1986); (4) there is a higher deposit feeders/suspension feeders ratio (e.g. Dauvin et al., 2007), deep-burrowers are less abundant (e.g. Rosenberg, 2001); and (5) there is a lower phylogenetic diversity (Clarke & Warwick, 1998). However, particularly in estuaries, there are also natural factors of stress that may produce similar responses in benthic communities, making difficult to separate between natural and anthropogenic sources of stress. Moreover, the natural spatial and temporal gradients of environmental conditions within estuaries confounds and potentially constrains the use of tools developed for other regions, e.g. coastal ecosystems.

Spatial and seasonal patterns

Benthic communities show high spatial heterogeneity in estuaries related to the influence of natural gradients of a diversity of factors. Estuarine benthic communities include both eurytopic species with broad habitat distributions and stenotopic species with narrow habitat distributions. In addition to spatial patterns, temperate estuarine invertebrate communities also show important temporal variations related to seasonal and interannual changes. The Mondego River estuary was used as a case study to investigate the patterns of distribution of the benthic invertebrate communities in a poikilohaline estuary, i.e. an estuary with great changes in environmental conditions, particularly in salinity. Three major groups of communities were identified, based on spatial distribution patterns: (1) a lower sector with

151 stronger marine influence dominated by the polychaete Streblospio shrubsolii (Buchanan, 1890) and the bivalve Cerastoderma glaucum (Poiret, 1789); (2) a middle sector dominated by S. shrubsolii and the amphipod Corophium multisetosum Stock, 1952; (3) and an upper sector where C. multisetosum dominated a community characterized by a lower number of species and the presence of an introduced bivalve species, Corbicula fulminea (Müller, 1774), in considerably high numbers (Chapter 2). These spatial groups were mainly determined by the salinity gradient, although salinity changed dramatically across seasons. In spite of considerable differences in the species composition when compared to other Portuguese estuaries, the patterns of distribution of major taxonomic groups in the Mondego are very similar to those found in the Minho (Sousa et al., 2007), Ria de Aveiro (Moreira et al., 1993) and Mira estuaries (Andrade, 1986), with a transition between the numerical dominance of bivalve, polychaete and amphipod species, from downstream to upstream stations (Chapters 2 and 3).

Species composition changes between seasons were mainly related to variations in freshwater flows. The impacts upon benthic communities were even more dramatic during extreme events, such as floods, with a significant decrease in the number of taxa and their respective abundances, as observed in the Mondego estuary (Chapter 2). Environmental conditions during the flood period resulted in considerable differences (e.g. lower salinity, different sediment composition, higher nutrient concentrations) and most species were classified as opportunists (Chapters 2 and 4). These results corroborated other studies indicating that benthic communities inhabiting estuaries with seasonal floods and/or droughts will change (1) due to pulses of organic matter during floods that stimulate an increase in abundance of opportunistic species (Salen-Picard & Arlhac, 2002); (2) changes in the water quality conditions, such as higher concentrations of contaminants during droughts (Attrill & Power, 2000; Grange et al., 2000) and (3) potential colonization by alien species that are, in general, much more tolerant to salinity fluctuations than native species (Lee & Bell, 1999; Paavola et al., 2005). In general, the consequences of floods are more dramatic than droughts because the first occur in a short period of time with great intensity and have immediate effects (e.g. mortality) on the benthic communities, while the latter occur during a prolonged period with more gradual effects (e.g. colonization by marine species).

In spite of the strong changes observed in the structure of the Mondego estuarine benthic community, the pattern of aggregation of stations based upon their similarity was consistent over seasons, except for a transition station that changed groups in different seasons (Chapter 3). The overall station group consistency supports the definition of three main ecological types in the northern branch of the Mondego River estuary, namely the Lower Estuary, the Middle Estuary and the Upper Estuary. Nevertheless, within each of these three station groups the Venice salinity classes varied greatly between seasons, ranging from oligohaline to polyhaline in some areas of the estuary, depending upon the freshwater flow regime (Chapter 3). This scenario seems to fit into the ecocline concept introduced by Boesch

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(1977) and further discussed by Attrill & Rundle (2002), defined as a boundary of progressive change between two systems, freshwater and marine. The latter proposed a two ecocline model, with two overlapping salinity gradients, one from upriver to mid estuary for freshwater species and another extending from the sea to the mid-estuary for marine species, whose associated benthic communities change location along the estuary in relation to changes in freshwater flow. In the Mondego estuary, seasonal changes in freshwater flow act as an environmental gradient and euryhaline estuarine species and freshwater species shift their distributions in relation to the associated changes in salinity.

In conclusion, the Venice system cannot be used to stratify poikilohaline estuaries into salinity regions without modification, such as the methodology suggested by Bald et al. (2005) using different salinity measures (minimum, maximum, mean and standard deviation). This methodology was successfully applied to the Mondego estuary, producing habitat types that were ecologically meaningful (Chapter 4).

Influence of seasonal variability in the use of benthic indices

Most of the indices currently used to assess benthic status were developed, applied and/or validated for coastal marine ecosystems and their application to estuarine ecosystems that have strong temporal and spatial heterogeneity must be cautiously considered and rigorously validated. Several USA monitoring programs addressed temporal variability by selecting summer as the period for application of indices (e.g. Weisberg et al., 1997; Paul et al., 2001; Smith et al., 2001; Llansó et al., 2002). In temperate regions, the measured response of the benthos as indicated by benthic community metrics should be maximal during this period due to the biological responses to the effects of increased water temperatures, water column stratification, and occurrence of bottom low dissolved oxygen conditions (Alden et al., 1997). In Europe no recommendations were proposed regarding either the modelling of seasonal variability or the selection of an optimal time period to apply the available classification tools. There are known responses of benthic indices to some seasonal cycles, especially recruitment events (Reiss & Kröncke 2005), but few studies have examined how benthic indices respond in ecosystems with strong seasonality. Seasonal samples collected in the Mondego estuary showed that indices proposed for use in Portuguese estuaries (Shannon-Wiener and Margalef diversity indices, ABC method and AMBI) generate different results when applied in different seasons. Similar to previous studies, diversity indices were more variable than the AMBI index and the ABC method (Salas et al., 2004; Reiss & Kröncke, 2005), because the former relies on species richness and abundance, while the latter reflect mainly the balance between pollution indicative and pollution sensitive species (Chapter 4).

These results confirmed the need for temporal stratification and the identification of the best period for application of indices was determined based on the predictability of responses of benthic indices to physical-chemical pollution-indicative variables, because no

153 reference conditions were available. The critical boundary between Moderate and Good status was used as a criterion to examine the predictability of responses because remediation measures are required by the WFD for all water bodies below Good status. Following the method proposed by Bald et al. (2005), factor analysis was used to reduce the complexity inherent in interpreting the results of different physical-chemical variables, by identifying relationships between variables and representing them by a single value (Ecological Quality Ratio). Nevertheless, this method of representing a combined effect of all variables does not adequately characterize extreme events because no predictable responses of the benthic indices were obtained in the Mondego estuary (Chapter 4). In contrast, DIN and chlorophyll a displayed significant correlations with all biological indices, except for the ABC method, and all correlations were indicative of degradation of the benthic community with increasing concentrations (Chapter 4). Data from summer and/or autumn provided the strongest and most ecologically meaningful relationships between benthic community structure (Chapter 4) and eutrophication indicators - specifically DIN concentrations (Chapters 4 and 5), consistent with temporal stratification applications in North American estuaries (Weisberg et al., 1997; Van Dolah et al., 1999; Llansó et al., 2002).

Adequacy of existing indices to classify the benthic status of Portuguese estuaries

According to the TICOR approach proposed for application in Portuguese estuaries, two or three of four indices (Shannon-Wiener and Margalef diversity indices, the ABC method and the AMBI index) should be used to assess ecological status, depending on the type of data available (Bettencourt et al., 2004). The results obtained in the Mondego estuary showed that no predictable responses were obtained when using the ABC method, not even during the best season for application of the indices (summer/autumn) (Chapter 4). For that reason, this index was not selected when testing the use of multimetric indices (Ticor and B-IBI) in different estuaries during the dry period.

Three estuaries located along the western coast of Portugal and included in type A2 – mesotidal, well mixed estuaries with irregular river discharge – were used as case studies. Although in the same type in the aim of the typology process of the WFD, these estuaries have different hydrological characteristics. The Tejo estuary is the largest Portuguese estuary, with higher river flow, longer residence time and higher levels of human pressure than the Mondego and Mira estuaries. These characteristics strongly influence environmental conditions as seen by nutrient enrichment measured in the Mira estuary in spite of the lower level of human pressure in this River basin when compared to the Mondego and Tejo river basins (Chapter 5).

154 Final Remarks

Chapter 6

2006) or using physical-chemical indicators (Weisberg et al., 1997; Paul et al., 2001; Marín- -Guirao et al., 2005). The latter approach was used in the present study since no reference conditions were available and the results indicate that none of the estuaries seems to meet the WFD objective of Good physical-chemical status. Eutrophication problems in the Mondego River estuary and contamination by heavy metals in the Tejo River estuary are well documented, but the Mira River estuary has been characterized as a relatively pristine ecosystem (Marques et al., 1993; Carvalho et al., 2005). Nevertheless, physical-chemical elements measured during the present study showed significant concentrations of nutrients and metals, as well as the occurrence of low oxygen levels in some areas of the Mira River estuary (Chapter 5). The use of physical-chemical indicators to identify different levels of pressure seems promising, although needing some improvement on the identification of relationships between pressures and impacts in estuaries with different hydrologic conditions, such as those studied here (Chapters 4 and 5).

The classifications obtained in the Mondego, Tejo and Mira estuaries using different benthic indices show that the level of agreement between physical-chemical classifications and benthic classifications were not significantly different among indices used (Chapter 5). Some attributes included in both multimetric approaches (TICOR and B-IBI), such as diversity indices and pollution-indicative and pollution-sensitive taxa were examined in more detail. Although diversity indices showed predictable responses to pollution indicative variables, the definition of thresholds for ecological classification must be calibrated by the natural heterogeneity of the communities studied. This requires a very detailed knowledge of the diversity patterns along the estuarine gradient, which might vary significantly across estuaries, as indicated by differences found between the three studied systems. Moreover, the use of more than one diversity measure can be redundant (Izsák, 2007) and overemphasize the weight of this ecological feature in the final result of multimetric approaches (Chapter 4 and 5). In the view of the wider use of the Shannon-Wiener index, which has been included in the majority of papers published on comparing different indices to assess ecological status (e.g. Salas et al., 2004; Labrune et al., 2005; Marín-Guirao et al., 2005; Quintino et al., 2006), this index seems to be more suitable for comparisons across estuaries. The use of information on the sensitiveness/tolerance of invertebrate taxa to pollution through indices such as the AMBI also revealed some problems related to the high levels of natural stress, that favour the dominance of opportunistic species, such as S. shrubsolii and Corophium spp. and the results obtained in this study indicate an overestimation of the benthic status when using this index (Chapters 4 and 5), as it was found in other locations (Marín-Guirao et al., 2005; Quintino et al., 2006). For both type of metrics (diversity and sensitiveness/tolerance), the selection of species from a candidate list based on confirmed differences in abundance between reference samples and degraded samples and the definition of habitat specific thresholds, such as proposed in the B-IBI (Weisberg et al., 1997) seems preferable.

155 None of the indices tested in Portuguese estuaries seems to be sensitive to smaller differences in status, because the discrimination between stations above or below Good physical-chemical status level was higher than when three classification levels (Poor/Bad, Moderate and Good/High) were used (Chapter 5), as previously found by Quintino et al. (2006). This indicates the need for a geographical adaptation of both indices (see e.g. Van Dolah et al., 1999; Salas et al., 2006, Blanchet et al., in press; Borja & Dauer, in press).

Ecological adaptations of invertebrates in stressed estuaries and how they affect the use of benthic assessment tools

The application of a single index across a strong environmental gradient and/or numerous habitat types is difficult and confounded by our ability to separate the effects of natural and anthropogenic stresses. All indices tested in this study are strongly influenced by natural stress, limiting their use without modification (Chapters 4 and 5) and the results obtained, compared to the known levels of pressure over the estuarine systems suggested that natural and anthopogenic sources of stress may be acting together (Chapters 2, 3, 4 and 5). Diversity is an ambiguous concept and there is no single definition of high and low diversity but, in general, highly stressed environments show lower diversity (e.g. Sanders, 1968; Kinne, 1971). This requires the identification of natural patterns of variation of the number of species and their abundance along estuarine gradients and across estuaries.

The ecological process of community succession, widely documented after Pearson & Rosenberg (1978), can be greatly affected by natural environmental variability (Rakocinski et al., 2000), such as salinity fluctuation in estuarine environments, or even be interrupted by disturbance events (Boesch et al., 1976). Ritter et al. (2005) show that a salinity-stressed estuary is in a constant state of early to intermediate succession and there is no climax community but a constant replacement of tolerant species, according to the existing environmental conditions. The distribution of species by ecological groups in the Mondego and Mira estuaries is consistent with Ritter et al.’s (2005) characterization of a salinity-stressed estuary, i.e. dominance of tolerant species and low representation of pollution sensitive and pollution indicative species. In contrast, a higher number of pollution sensitive species was identified in the Tejo River estuary when compared to tolerant species, but pollution indicative species were also very representative, giving some evidence of the presence of different successional states (Chapter 5).

Succession in benthic communities is likely to be frequently interrupted in highly dynamic estuaries (e.g. the Mondego and Mira estuaries) characterized by strong seasonal fluctuations in the environmental conditions and drastic changes induced by the occurrence of extreme events (floods and droughts), benthic communities’ succession processes are likely to be frequently interrupted. It is important to understand how resilient are benthic communities, and how will they evolve after these short-term perturbations and to which

156 Final Remarks

Chapter 6 extent induced changes in the benthic community will affect the assessment of ecological status. For instance, Ferraro et al. (1991) concluded that the effect of natural disturbances on the benthos may sometimes be greater than the effect of wastewater discharge and recommend long term studies (≥6 years) to reliably discriminate between them. The monitoring frequency required by the WFD for the benthic fauna (every 3 years) seems consequently inappropriate for a consistent assessment of the benthic status of estuarine systems with similar characteristics to Mondego and Mira River estuaries.

Even indicators based on the functional structure of the benthic communities, such as the ABC method, have a limited use in naturally stressed estuaries, because of the presence of many species adapted to high levels of natural stress, exhibiting the same response as to environmental degradation, namely the numeric dominance of short-lived opportunistic species (Dauer et al., 1993), as it was found in the Mondego estuary (Chapter 4).

All indices were correlated to each other in the Tejo estuary, while in the Mondego and Mira estuary only some TICOR components were related (Chapter 5). Having no evidence that this fact relies on different pressure levels, it seems likely to be related to differences in hydrographical characteristics.

High levels of natural stress, such as those found in the Mondego and Mira estuaries, seem to act as a brake to speciation, as referred by Kinne (1971) and fewer representatives of phyletic groups are evolved in such way to allow their successful colonization of the estuarine environment. A much higher number of species was found in the Tejo estuary than in the Mondego and Mira estuaries, these last characterized by a high number of monotypic taxa (Chapters 3 and 5). As pointed out by Ferraro & Cole (1992, 1995), the dominance of monotypic taxa increases the probability of taxonomic sufficiency at taxonomic levels higher than species, mainly because of redundancy in their responses to pollution. The Mondego estuary benthic community showed over 70% of monotypic genera and families. As a consequence, for typological purposes taxonomic levels up to the order can be used without significant loss of information in poikilohaline estuaries (Chapter 3). Moreover, the family level is most likely the best compromise since most taxonomic manuals are more adequate for identifications at this level and ecologists with taxonomic training are familiar with the procedures. The use of different taxocenes for typology was also tested, demonstrating less ability to identify water body types. Nevertheless, mollusks and bivalves have identified the same types as all species and annelids have shown a habitat specific distribution, in particular the family Spionidae (Chapter 3). These findings were confirmed by results obtained in the Mira estuary on the use of different taxonomic levels and taxocenes (unpublished data).

157 CONCLUSIONS

As a general conclusion of this study, there is strong evidence that the characteristics of Portuguese estuaries constrain the use of existing assessment tools for evaluating ecological status based on benthic invertebrate communities. The application of the indices proposed for Portuguese estuaries in the aim of the WFD (TICOR) and a multimetric approach developed for North American estuaries (B-IBI) seem to be efficient in discriminating between locations above or below Good status, as required by the WFD, but are much less efficient in discriminating other quality classes. Nevertheless, the classifications obtained do not reflect only differences on the level of degradation, but also spatial and temporal variations indicating the need for further adaptations before a standard use in Portuguese estuaries.

Some other questions were addressed in the present study and the major conclusions can be summarized as follows:

1. Spatial patterns of distribution of the benthic communities in Portuguese estuaries are mainly determined by the salinity gradient, as in estuarine systems of other regions, but seasonal variations in freshwater flows alter those patterns dramatically, especially during the occurrence of extreme events. These constrains require spatial and temporal stratification of sample collection for monitoring purposes, since the derivation of reference conditions applicable to all estuarine gradient and different seasons does not seam feasible;

2. Seasonal variations on the composition of benthic communities change the results of benthic indices and the respective classification of the benthic ecological status. The results of this study point towards a more efficient use of benthic indices during the dry period;

3. The response of benthic invertebrate communities to natural and anthropogenic sources of stress is very similar and there seems to be a better discrimination in estuaries with lower variations in environmental conditions, such as the Tejo estuary. For that reason, indices and metrics developed for homiohaline estuaries seem to be more appropriate for use in this estuary than in others with dominant poikilohaline characteristics, such as the Mondego and Mira estuaries, for which metrics and thresholds need some adjustments before use;

4. Existing indices seem to be efficient in separating between benthic communities above and below Good status, but not accurate enough for discrimination of other quality classes, as required by the WFD. Consequently, novel combinations of metrics and class boundaries should be proposed and validated;

158 Final Remarks

Chapter 6

5. Multimetric indices seem to be more robust to possible misclassifications than individual metrics, but these metrics require further adaptation and validation before inclusion in the method selected for future use in Portuguese estuaries;

6. No reference conditions based upon pristine conditions were found in the three estuaries studied, but physical-chemical variables and nutrient concentrations in particular seem to be a good surrogate for testing the efficiency of benthic indices;

7. Speciation processes seem to be affected in estuaries with strong environmental fluctuations, which show a dominance of monotypic taxa, supporting the use of higher taxonomic levels in the typology process;

FUTURE RESEARCH LINES

A scientific study is never the final answer for a question, but solely the “tip of the iceberg” that generates working hypotheses. This study provided some evidence on the problems and possible solutions for the application of benthic indices in Portuguese estuaries, but also raised new challenges for future work, such as:

- Identification of reference conditions – The development of a method based on existing reference conditions, as B-IBI, require the selection of habitat specific reference sites. Since all Portuguese estuaries are, at least, minimally impacted, a selection of a representative number of reference sites does not seem feasible in a short-term period. Selecting a pilot estuary with lower human pressure, such as the Mira estuary, and applying mitigation measures that reduce significantly the current impacts could serve as a case study to understand the natural evolution of benthic communities that are under natural stress and use it as a reference for estuaries with similar characteristics (e.g. Mondego estuary);

- Selection of metrics – the selection of metrics to be included in benthic indices to be used in Portuguese estuaries might imply that different metrics and thresholds are used in different habitats, within and among estuaries. A comprehensive examination of the responses of different metrics of communities exposed to diverse natural and anthropogenic levels of stress is needed;

- Long term studies – the definition of monitoring programs in the aim of the WFD require a better understanding of inter-annual changes on the benthic communities, to determine their resilience and the length of the recovery period before they reach equilibrium, after being disturbed by natural events (e.g. floods and droughts). A temporal stratification of the monitoring programs might not be compatible with the monitoring frequency indicated by the WFD;

159 - Biogeographical differences and climate changes - Portugal is a transition area, with a recognized boundary between assemblages northern to Cape Carvoeiro southern to the Tejo estuary. With increased effects of the climate change on the latitudinal distribution of species, a gradual change of the composition of benthic communities is also expected. A possible effect of these changes on the use benthic assessment tools might require particular concern, since strong differences in the taxonomic composition across different regions might influence the performance of indices.

The first two questions are currently being addressed by several studies developed by different Portuguese research teams in different estuaries and also by the Portuguese working group. These challenges might also be partially carried out through the ongoing intercalibration process, in which different countries are adjusting class boundaries by comparing classifications obtained for the same water types using different methods. The third question requires a national monitoring/research policy that favours long term monitoring/research studies, promoting the compilation of a thorough and systematic database on the benthic communities of all Portuguese estuaries. The last question requires that taxonomic skills are recognized as an important scientific competency and some funds are allocated for training taxonomists publishing taxonomic compendiums on the Portuguese fauna.

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Appendix 1

List of taxa identified in the Mondego, Tejo and Mira estuaries

Mondego Tejo Mira Mondego Tejo Mira Phyllum Cnidaria Family Cirratulidae Class Anthozoa Aphelochaeta sp. X X Order Actiniaria Caulleriella AX Family Actiniidae Caulleriella alata (Southern, 1914) X Actinia equina (Linnaeus, 1758) X Caulleriella bioculata (Keferstein, 1862) X Phyllum Plathyelmintha Chaetozone gibber Woodham & Chambers, 1994 X Class Turbellaria Chaetozone setosa Malmgren, 1867 (Cset) X X X Turbellaria n.i. (Turb) X X Cirratulus sp. X Phyllum Nemertea (Neme) Tharyx AX Family Tetrastemmatidae Timarete tentaculata (Treadwell, 1941) X Tetrastemmatidae n.i. X X X Family Cossuridae Family Tubulanidae Cossura coasta Bogdanos & Fredj, 1983 X X Tubulanidae n.i. X Family Dorvilleidae Phyllum Annelida Staurocephalus kefersteini McIntosh, 1869 X Class Oligochaeta (Olig) Family Eunicidae Family Echytraeidae Marphysa sanguinea (Montagu, 1815) X Echytraeus sp. (Echy) X X X Family Glyceridae Family Naididae Glycera alba (O.F. Müller, 1776) X X Chaetogaster sp. X Glycera convoluta Keferstein, 1862 X Nais sp. (Nais) X X X Glycera gigantea de Quatrefages, 1866 (Ggig) X X Naididae wncs X Glycera tesselata Grube, 1863 X Paranais sp. X Glycera tridactyla Schmarda, 1861 X Family Tubificidae (Tubi) Family Goniadidae Brachiura sowerbyi Beddard 1892 X Glycinde nordmanni (Malmgren, 1865) X Limnodrilus hoffmeisteri Claparède, 1862 (Lhof) X X X Goniada emerita Audouin & Milne-Edwards, 1833 X Tubifex tubifex (Müller, 1774) X Goniada maculata Örsted, 1843 X Tubificidae wcs X X Goniada norvegica Audouin and Milne-Edwards, 1833 X Tubificidae wncs X Family Hesionidae Tubificoides sp. X X X Hesionidae n.i. (Hesi) X Uncinais uncinata (Ørsted, 1842) X Ophiodromus flexuosus (Delle Chiaje, 1825) X Class Polychaeta Psamathe cirrata Keferstein, 1862 X X Family Ampharetidae Family Lumbrineridae Alkmaria romijni (Grube, 1863) (Arom) X X X Lumbrineris coccinea (Renier, 1804) X Melinna palmata Grube, 1870 X X Scoletoma impatiens (Claparède, 1868) X Family Aphroditidae Family Nephtydidae Aphroditidae n.i. X Nephtys caeca (Fabricius, 1780) X Family Capitellidae Nephtys cirrosa Ehlers, 1868 (Ncir) X X Capitella capitata (O. Fabricius, 1780) (Ccap) X X X Nephtys hombergii de Lamarck, 1818 (Nhom) X X X Heteromastus filiformis (Claparède, 1864) (Hfil) X X X Nephtys hystricis McIntosh, 1900 X X Mediomastus fragilis Rasmussen, 1973 (Mfra) X X Nephtys incisa Malmgren, 1865 X X Notomastus linearis Claparède, 1870 X Nephtys longosetosa Oersted, 1842 X Notomastus profundus Eisig, 1887 X Nepthys pulchra Rainier, 1991 (Npul) X Mondego Tejo Mira Mondego Tejo Mira Family Nereididae Family Sabelariidae Alitta succinea (Frey & Leuckart, 1847) X Sabellaria alveolata (Linnaeus, 1767) X Hediste diversicolor (O.F. Müller, 1776) (Hdiv) X X X Sabellaria spinulosa Leuckart, 1849 X Neanthes fucata (Savigny, 1818) X Sabellariidae n.i. X Websterinereis glauca (Claparède, 1870) (Wgla) X Family Sabellidae Family Onuphidae Jasmineira candela (Grube, 1863) X Diopatra neapolitana delle Chiaje, 1841 (Dnea) X X X Family Saccocirridae Family Opheliidae Saccocirrus papillocercus Bobretzky, 1872 X Ophelia radiata (Delle Chiaje, 1828) X Familidae Serpulidae Family Orbinidae Pomatoceros triqueter (Linnaeus, 1767) X Scoloplos armiger (Müller, 1776) (Sarm) X X Family Sigalionidae Family Oweniidae Sthenelais boa (Johnston, 1833) X Myriochele heeri Malmgren, 1867 X Family Spionidae Owenia fusiformis Delle Chiaje, 1842 (Ofus) X Aonides oxycephala (Sars, 1862) X Family Pectinariidae Boccardiella ligerica (Ferronnieère, 1898) (Blig) X Pectinaria koreni (Malmgren, 1866) (Pkor) X Boccardiella redeki (Horst, 1920) X X Family Pholoidae Malacoceros fuliginosus (Claparède, 1868) X Pholoe minuta (Fabricius, 1780) X X Polydora ciliata (Johnston, 1838) (Pcil) X X Family Phyllodocidae Polydora cornuta Bosc, 1802 X Eteone picta de Quatrefages, 1866 (Epic) X Polydora websteri Hartman in Loosanoff & Engle, 1943 X Eumida sanguinea (Örsted, 1843) (Esan) X X Prionospio cirrifera Wirén, 1883 (Pcir) X X X Family Pilargiidae Prionospio malmgreni Claparède, 1870 X X Pilargiidae n.i. X Pygospio elegans Claparède, 1863 X Sigambra constricta (Southern, 1921) X Scolelepis bonnieri (Mesnil, 1896) X Sigambra tentaculata (Treadwell, 1941) X Scolelepis cantabra (Rioja, 1918) X Family Pisionidae Spio martinensis Mesnil, 1896 (Smar) X X X Pisione remota (Southern, 1914) X X Streblospio shrubsolii (Buchanan, 1890) (Sshr) X X X Family Poecilochaetidae Family Syllidae Poecilochaetus serpens Allen, 1904 X Autolytus sp. X Family Poligordiidae Exogone sp. X Polygordiidae n.i. X Parapionosyllis cabezali Parapar, San Martín & Moreira, XX 2000 Family Polynoidae Pionosyllis pulligera (Krohn, 1852) X Harmothoe benthophila Hardy & Gunther, 1935 X Sphaerosyllis bulbosa Southern, 1914 X Harmothoe impar (Johnston, 1839) X Sphaerosyllis taylori Perkins, 1981 X Family Sabellariidae Sphaerosyllis torulosa X Sabellaria alveolata (Linnaeus, 1767) X Family Terebellidae X Sabellaria spinulosa Leuckart, 1849 X Lanice conchilega (Pallas, 1766) X Sabellariidae n.i. X Class Hirudinea Family Protodriliidae Hirudinea n.i. X Protodrylus hatscheki Pierantoni, 1908 X Mondego Tejo Mira Mondego Tejo Mira Phyllum Arthropoda Gitana sarsi Boeck, 1871 X Class Crustacea Family Amphitoidae Order Ostracoda Amphitoe sp. X Ostracoda n.i. X Family Aoridae Order Tanaidacea Leptocheirus pilosus Zaddach, 1844 X X Family Paratanaidae Family Atylidae Heterotanais oerstedi (Kroyer, 1842) X X X Atylus massiliensis Bellan-Santini, 1975 X Leptochelia dubia Kroyer, 1842 X Family Corophiidae Leptochelia savignyi Kroyer, 1842 X Corophium acherusicum Costa, 1851 X X X Family Tanaidae Corophium multisetosum Stock, 1952 (Cmul) X X Tanais dulongii (Audouin, 1826) X X Corophium crassicorne Bruzelius, 1859 X Order Mysidacea Corophium orientale Schellenberg, 1928 X X Family Mysidae Family Gammariidae Acanthomysis longicornis (Milne-Edwards, 1837) X Gammarus subtypicus Stock, 1966 X X Gastrosacus spinifer (Goës, 1864) (Gspi) X Gammaridae n.i. X Leptomysis gracilis (GO Sars, 1864) X Family Haustoridae Mesopodopsis slabberi (van Beneden, 1861) X Haustorius arenarius (Slabber, 1769) X Neomysis integer (Leach, 1814) X Family Hyalidae Order Isopoda Hyale pontica Rathke, 1837 X Family Anthuridae Family Ischyroceridae Cyathura carinata (Kroyer, 1847) (Ccar) X X X Ericthonius punctatus (Bate, 1857) X Family Chaetiliidae Jassa ocia (Bate, 1862) X Saduriella losadai Holthuis, 1964 (Slos) X X Family Melitidae Family Cirolanidae Abludomelita obtusata (Montagu, 1813) X Eurydice naylori Jones & Pierpoint 1997 X Melita obtusata (Montagu, 1813) X Eurydice spinigera Hansen, 1890 X Melita palmata (Montagu, 1804) (Mpal) X X X Family Gnathiidae Family Oedicerotidae Paragnathia formica (Hesse, 1864) X X X Pontocrates altamarinus (Bate & Westwood, 1862) X Family Idoteidae X X Family Phoxocephaliidae Family Sphaeromatidae Metaphoxus pectinatus (Walker, 1896) X Lekanesphaera hookeri (Leach, 1814) (Lhok) X Family Pontoporeiidae Lekanesphaera rugicauda (Leach, 1814) X Bathyporeia sarsi Watkin, 1938 X Order Amphipoda Family Urothoidae Amphipoda sp1 X Urothoe brevicornis Bate, 1862 X Amphipoda sp2 X Urothoe intermedia Bellan-Santini & Ruffo, 1986 X Family Ampeliscidae Order Cumacea Ampelisca diadema (Costa, 1853) X Family Bodotriidae Ampelisca lusitanica Bellan-Santini & Marques, 1987 X Bodotria scorpioides (Montagu, 1804) X Ampelisca spinifer Reid, 1951 X Cumopsis goodsir (Van Beneden, 1861) X Family Amphilochidae Iphinoe tenella Sars, 1878 X Amphilochus brunneus Della Valle, 1893 X Order Astacidea Amphilochus manudens Bate, 1862 X Family Diogenidae Amphilochus neapolitanus Della Valle, 1893 X Diogenes pugilator (Roux, 1829) X Mondego Tejo Mira Mondego Tejo Mira Order Cirripeda Class Bivalvia Family Chtamalophiidae Family Anomiidae Chthamalus montagui Southward, 1976 X X Anomia ephippium Linnaeus, 1758 X Order Decapoda Family Cardiidae Family Crangoniidae Acanthocardia paucicostata (Sowerby G.B. II, 1841) X Crangon crangon (Linnaeus, 1758) X X Cerastoderma edule (Linnaeus, 1758) X Family Processidae Cerastoderma glaucum (Poiret, 1789) (Cgla) X X X Processa canaliculata Leach, 1896 X X Parvicardium exiguum (Gmelin, 1791) X Family Palaemonidae Family Corbiculariidae Palaemon longirostris H. Milne-Edwards, 1837 X Corbicula fulminea (Müller, 1774) (Cful) X X X Family Pirimelidae Family Corbulidae Pirimela denticulata (Montagu, 1808) X Corbula gibba (Olivi, 1792) X X Family Portunidae Family Hiatellidae Carcinus maenas (Linnaeus, 1758) X X X Hiatella arctica (Linnaeus, 1767) X Class Insecta Family Lucinidae Order Colembola Dictyota divaricata J.V. Lamouroux, 1809 X Colembola n.i. X Loripes lacteus (Linnaeus, 1758) X Order Diptera Family Mactridae Family Ceratopogonidae Spisula solida (Linnaeus, 1758) X Ceratopogonidae n.i. X X Family Mytilidae Family Chironomidae Modiolus barbatus (Linnaeus, 1758) (Mbar) X X Chironomidae n.i. (Chir) X X X Musculus costulatus (Risso, 1826) X Family Rhagionidae Mytilus edulis Linnaeus, 1758 X Rhagionidae n.i. X Mytilus galloprovincialis Lamarck, 1819 X Order Ephemeroptera Family Nuculidae Ephemeroptera n.i. X Nucula hanleyi Winckworth, 1931 X Family Caenidae Nucula nucleus (Linnaeus, 1758) X Caenis sp. X Nucula turgida Marshall 1875 X Family Polymitarcidae Family Ostreidae Ephoron virgo (Olivier, 1791) (Evir) X Ostrea edulis Linnaeus, 1758 X Order Plecoptera Family Pectinidae Family Leuctridae Chlamys septemradiata (Müller O.F., 1776) X Leuctridae n.i. X Chlamys varia (Linné, 1758) X Phyllum Mollusca Family Pholadidae Class Gastropoda Barnea candida (Linnaeus, 1758) X Family Hydrobiidae Family Sareptidae Hydrobia ulvae (Pennant, 1777) (Hulv) X X Yoldiella sp. X Family Nassariidae Family Scrobiculariidae Hinia reticulata (Risso, 1826) X X Scrobicularia plana (da Costa, 1778) (Spla) X X X Family Rissoidae Abra alba (Wood W., 1802) X X Rissoa parva (da Costa, 1778) X Family Solenidae Solen marginatus Pulteney, 1799 (Smarg) X X Mondego Tejo Mira Family Tellinidae X X Angulus tennuis (da Costa, 1778) (Aten) Family Veneridae Ruditapes decussatus (Linnaeus, 1758) X Tapes rhomboides (Pennant, 1777) X Venerupis senegalenis (Gmelin, 1791) X Venerupis saxatilis (Fleuriau de Bellevue, 1802) X Venus casina Linnaeus, 1758 X Class Polyplacophora Family Ischnochitonidae Lepidochitona cinerea (Linnaeus, 1767) X Class Opisthobranchia Opisthobranchia n.i. (Nudi) X Phyllum Echiura Class Ophiuroidea Family Ophiuridae X X Ophiuridea n.i. Family Ophiolepidae Ophiolepidae n.i. X