Evaluation of Thermal Stress in Tropical Marine Organisms in the Context of Climate Warming

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS

Evaluation of thermal stress in tropical marine organisms in the context of climate warming

Doutoramento em Biologia Especialidade em Biologia Marinha e Aquacultura

Sara Carolina Gusmão Coito Madeira

Tese orientada por: Doutora Catarina Vinagre Professor Doutor Mário Diniz Professor Doutor Henrique Cabral

Documento especialmente elaborado para a obtenção do grau de doutor

2018

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS

Evaluation of thermal stress in tropical marine organisms in the context of climate warming

Doutoramento em Biologia Especialidade em Biologia Marinha e Aquacultura

Sara Carolina Gusmão Coito Madeira

Tese orientada por: Doutora Catarina Vinagre Professor Doutor Mário Diniz Professor Doutor Henrique Cabral

Documento especialmente elaborado para a obtenção do grau de doutor

2018

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS

Evaluation of thermal stress in tropical marine organisms in the context of climate warming

Doutoramento em Biologia Especialidade em Biologia Marinha e Aquacultura

Sara Carolina Gusmão Coito Madeira

Tese orientada por: Doutora Catarina Vinagre Professor Doutor Mário Diniz Professor Doutor Henrique Cabral Júri: Presidente: ● Doutora Maria Manuela Gomes Coelho de Noronha Trancoso, Professora Catedrática e Membro do Conselho Científico, Faculdade de Ciências da Universidade de Lisboa Vogais: ● Doutor Luís Miguel dos Santos Russo Vieira, Investigador Auxiliar, Centro Interdisciplinar de Investigação Marinha e Ambiental (CIIMAR) da Universidade do Porto ● Doutora Maria Helena Ferrão Ribeiro da Costa, Professora Catedrática, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa ● Doutora Ana Margarida da Silva Faria, Investigadora, Centro de Ciências do Mar e do Ambiente (MARE) no ISPA – Instituto Universitário de Ciências Psicológicas, Sociais e da Vida ● Doutor José Pavão Mendes de Paula, Professor Associado com Agregação, Faculdade de Ciências da Universidade de Lisboa ● Doutora Catarina Maria Batista Vinagre, Investigadora FCT “nível inicial”, equiparação a Investigador Auxiliar da carreira de investigação científica, Faculdade de Ciências da Universidade de Lisboa

Documento especialmente elaborado para a obtenção do grau de doutor

Fundação para a Ciência e Tecnologia (FCT)

2018

This study was funded by Fundação para a Ciência e Tecnologia, through a PhD scholarship SFRH/BD/92975/2013 and the research project WarmingWebs (PTDC/MAR-EST/2141/2012). Additional funding was provided by the strategic projects UID/MAR/04292/2013 granted to MARE and UID/Multi/04378/2013 granted to UCIBIO.

Nota prévia

A presente tese apresenta artigos científicos já publicados ou submetidos para publicação (capítulos 2 a 8), de acordo com o previsto no n˚2 do artigo 25˚ do Regulamento de Estudos de Pós Graduação da Universidade de Lisboa, publicado em Diário da República 2ª série – N˚57 – 23 de Março de 2015. Uma vez que estes trabalhos foram realizados em colaboração, a candidata esclarece que participou integralmente na concepção dos trabalhos, obtenção de dados, análise e discussão dos resultados, bem como na redacção dos manuscritos.

Lisboa, Março de 2018

Carolina Madeira

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“I like the scientific spirit — the holding off, the being sure but not too sure, the willingness to surrender ideas when the evidence is against them: this is ultimately fine — it always keeps the way beyond open — always gives life, thought, affection, the whole man, a chance to try over again after a mistake — after a wrong guess.”

Walt Whitman Walt Whitman's Camden Conversations

Acknowledgements

Someone once said “many times a day I realize how much my own outer and inner life is built upon the labors of my fellow people, both living and dead, both known and unknown and how I earnestly must exert myself in order to give in return as much as I have received”. I believe these words fully express the extent of my gratitude towards all the people that walked with me through the PhD journey, guiding, advising and supporting me in the ups and downs. If nothing else, this PhD pursuit has taught me that we are here for the sake of each other, human beings connected with the purpose of helping each other grow, and, in that way – helping us all evolve as a species. I will try - hoping words will not fail me, to express how thankful I am to each and everyone that made this work possible.

Firstly, I thank Fundação para a Ciência e Tecnologia for giving me a PhD grant that made my dream of pursuing a career in Science come true.

Then, I would like to thank my wonderful supervisors: Catarina Vinagre, Mário Diniz and Henrique Cabral. Your vision, hard work and ethics in science have been an inspiration to me and I’ll always admire you. You have given me all the tools I needed to accomplish this academic achievement, but, in fact, you have given me more: your time, your patience, your knowledge and life experience and your friendship. You have given me a piece of yourselves, of your lives, and for that, I cherish you. You helped me to be confident and believe in myself, knowing I had “all it takes” to get the job done. With the trust and freedom you gave me, I grew to be an autonomous researcher, yet unafraid to ask for help and guidance when I need it. That is something I will always be thankful for.

Catarina, I am so grateful you proposed me this PhD, allowing me to follow my passion for tropical marine ecology. This PhD brought me amazing opportunities, not only to work with charismatic species, but also to travel to some incredible places during fieldwork and just feel amazed every day I stepped outside and into some beautiful tropical beach! You made me reach my life goal of living in shorts and flip-flops (and we both know I am a warm-adapted organism)!! Check √ You were tireless in your efforts to provide me with everything I needed. I am proud to have been your apprentice, and your perseverance in the difficult academia world has taught me immensely.

Mário, you were a solid pillar during my lab work. Your guidance allowed me to refine my practice in molecular ecology and your scientific curiosity was a source of creativity in our

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scientific discussions. You taught me almost everything I know about biochemistry and metabolism. You are one of the most understanding and patient scientists I’ve ever met, and I knew I could always count on your wise words when the lack of motivation sometimes kicked in. I am also very thankful that you always encouraged me to go to scientific meetings and presentations, providing me with financial resources to do it. I can’t thank you enough for everything you did for me, since whenever something went wrong during work you were the first one to come up with all kinds of solutions to help me. You were always available and present during these 4 years.

Prof. Henrique, we first met probably a decade ago (already?!) when I was doing my bachelor in Marine Biology at FCUL. Since then I’ve always considered you one of my main and most impressive references in the marine scientific community. I’ve always looked up to you and I’ve always felt inspired not only by your academic and professional achievements, but also because you always had a kind word of encouragement to your students. So, having you as my supervisor is more than what I could’ve asked for! Your care and interest in helping me to get through the different stages of my PhD (from the doctoral program to practical work, to article writing and thesis writing) have been essential to smooth the path. Your advice during statistical analyses was essential for the progress of this work. I am deeply grateful for always making yourself available for meetings and scientific discussions. I cannot emphasize enough how much I have learned from you during my entire professional life. Thank you.

I also thank Guca Flores for the collaboration with Cebimar. You provided everything that was needed for fieldwork and experiments in Brazil and I can’t thank you enough for that, it absolutely saved my work at a time when things were going wrong. It was amazing to meet you and everyone at Cebimar! Thank you for a wonderful experience and for your valuable inputs and suggestions to my work.

My colleague Miguel Costa Leal, who always challenged, helped and encouraged me even at a distance. I thank you for always thinking of me when you have new ideas – I always know something productive will come out of our chats and meetings (usually one or several scientific papers in pretty good IF peer-reviewed journals)!! You gave me the incredible opportunity of having an internship at Eawag, where I learned new methods and lab techniques, which very much enriched my PhD work. I thank you for everything you taught me about statistical analyses and scientific writing, and just general biology and ecology from our scientific discussions!

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I thank Alexandra Elbakyan and the Sci-Hub team for their revolutionary project of making all scientific articles available to the public. Your project is serving the best interests of society and it should be recognized as such. You saved me on so many occasions. I don’t even know you in person, but I can honestly say you made an incredible contribution to the work of this thesis.

I would now like to thank my awesome colleagues from ECCOWEBS research group (MARE – FCUL): Vanessa Mendonça, Rui Cereja, Marta Dias, Sheila Alexandre and Pedro Fernandes. You were amazing friends and we had so many joyful moments together! I keep you all very close to my heart! Oh the fun and laughter during field trips, the same sense of wonder for nature and ecology, all the snorkeling and swimming together, the running away from crazy octopuses and jellyfish, the whining about how tiring it is to write half a dozen scientific articles in a row, all the lunches and dinners shared…it doesn’t matter what the occasion was, you guys were always there for me! You keep challenging me intellectually with all our scientific discussions, and I know I can always count on you, professionally or otherwise!! Thank you for being incredible people, I am so lucky you crossed my path!

My dear and cherished colleagues from FCT-UNL: Pedro Costa, Ana Patrícia, Carla Martins, Marta Martins, Cátia Gonçalves, Nagore Cuevas, Joana Antunes, Ana Luísa Maulvault, Eduardo Araújo and Susana Jorge. You absolutely made my days at FCT! Your companionship, understanding, friendship and scientific knowledge have helped me grow immensely. Our shared moments in the lab, at the office or during lunch times were absolutely essential to keep my mental sanity during the 4 years of PhD. Without you, this journey would have been so much poorer. You made me laugh and supported me throughout the loneliest moments! You brought me in and made me feel like family. Besides, your enthusiasm in doing outdoor activities together really gave me a sense of team building and bonding!! I thank you all so much for everything, I will never forget all these moments!!

My beloved college friends Catarina Casadinho, Lia Laporta, Tiago Veríssimo, Diogo Stilwell and Rita Borba. You always make me laugh! I love that we all trust each other to share our craziest and most creative scientific/professional ideas! You remind me of who I am and why I became a biologist in the first place! Thanks for all the insightful scientific discussions and for the incredible companions you have been along the past decade.

My friends David Avelar and Pedro Garrett from FCUL, with whom I shared so many lunch picnics at the faculty’s gardens, talking about science, family, life, research, work, human relations and everything in between! You have taught me so much about finding balance in life,

about personal growth and development and about being present and just enjoying the here and now. You have my admiration and gratitude.

My friends Yara Gutkin, Vanessa Leal, Sónia Sarmento and my cousin Vera Carmo, you girls were my solid rocks during these years! My conversations with you gave me such peace of mind and serenity! I am deeply thankful to you sisters!

My life-long friends from 9B - João Abreu, Rui Araújo, João Coelho, Rui Teixeira, Rita Prata and Catarina Martinho, for all the support and friendship, and for giving my brain a rest from science! You were essential to help me get through these years of hard work.

I thank Miguel Santos and Luis Dias for challenging me intellectually, by asking difficult scientific and spiritual questions. You helped me improve my cognitive competence by testing my argumentative skills! I am grateful that you opened my mind to new horizons. You also opened my heart and taught me so much about love, emotions, relationships and myself. You made me stronger and more confident in the person I am. You encouraged me to trust in myself and take better care of every aspect of my being. I will be eternally grateful for that.

I thank all of my family: grandparents, parents, sister, cousins, uncles and aunts! You raised me up to become a better person each and every day of my life. You showed me by example what the important things are in life. You were tireless in your understanding, love and support. It’s impossible to say in a few sentences how much you mean to me. Mom and Dad thank you for all the sacrifices you made to make me happy. You always believed in me and encouraged me to follow my dreams, motivating me in my endeavors. You listened to me in all moments: happy, boring, complaining, excited, annoyed, etc! Thank you for your love, patience and words of advice. Finally, my twin sister – I have absolutely no idea what I would do with my life or who I’d be without you. You are my twin sister and my twin soul. You are my all: my inspiration, my dearest friend, my teacher, my strength, my higher love. You and I discovered Nature together, we explored the outdoors together, we shared our love for Science and the Ocean together and we travelled through the wonders of marine biology together. You alone have probably taught me more about everything than anyone else I’ve know. Thank you for always showing me there is a way. Thank you for this lifetime of sharing together.

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TABLE OF CONTENTS

Acknowledgements………………………………………………………………………... vii ABSTRACT………………………………………………………………………………... xxi RESUMO…………………………………………………………………………………... xxiii List of symbols and abbreviations...... xxvii Chapter 1. General Introduction…………………………………………………………. 1 1.1. Global change and human impacts in the world’s oceans...... 3 1.2. Warming in tropical oceans: today and tomorrow………..…………………...….….. 5 1.3. Impacts and mechanisms of tolerance and adaptation to temperature change in marine organisms……...………………………………………………………..………… 8 1.4. General evaluation of stress and performance in tropical marine organisms under ocean warming………….………………………………………………………………… 14 1.5. Main aims of the thesis……………..………………………………………………… 22 1.6. Thesis outline……….………………………………………………………………... 23 References………………………………………………………………………………… 25 Chapter 2. Thermal acclimation in clownfish: an integrated biomarker response and multi-tissue experimental approach……………………………………………………… 37 Abstract………………………………………………………………….………………... 39 Graphical abstract……………………………………………………………………..…... 40 Highlights………….……………………………………………………………………… 40 2.1. Introduction……………………….………………………………………………….. 41 2.2. Materials and methods…………………….…………………………………………. 43 2.2.1. Test organism and acclimation procedure…...……………………………………… 43 2.2.2. Experimental design and sampling……..……………………………………………. 43 2.2.3. Biochemical biomarkers and IBR………...…………………………………………... 44 2.2.4. Data analysis……...…………………………………………………………………….. 45 2.3. Results………….…………………………………………………………………….. 45 2.3.1. Stress biomarkers activity……………………………………………………………… 45 2.3.2. Integrated biomarkers response index (IBR)………………...……………………… 47 2.3.3. Body condition and stress biomarker levels…………………………………………. 54 2.4. Discussion…………….……………………………………………………………… 56 2.4.1. Patterns of biomarker levels under elevated temperature…………………...……. 56 2.4.2. Assessment of thermal stress in different organs………………...…………………. 57 2.4.3. Influence of time of exposure in biomarker responses……...……………………… 58 2.4.4. Relation between body condition and stress responses……...…………………….. 60 2.5. Conclusions……….………………………………………………………………….. 62

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Acknowledgements………………………………………………………………………. 62 References………………………………………………………………………………… 63 Chapter 3. Comparing biomarker responses during thermal acclimation: a lethal vs non-lethal approach in a tropical reef clownfish………………………………………... 69 Abstract………….………………………………………………………………………... 71 Graphical abstract……………..…………………………………………………………... 72 Highlights……….………………………………………………………………………… 72 3.1. Introduction…….…………………………………………………………………….. 73 3.2. Materials and methods………….……………………………………………………. 75 3.2.1. Ethical statement…………...…………………………………………………………… 75 3.2.2. Housing and husbandry of fish……………………………………...………………… 75 3.2.3. Experimental design……………………………………...…………………………….. 75 3.2.4. Biomarker analyses……………………………………………………………...……... 77 3.2.5. Survival and mortality………………………………………….………………………. 78 3.2.6. Data analysis…………………………………………………………………...……….. 78 3.3. Results…….………………………………………………………………………….. 79 3.4. Discussion….………………………………………………………………………… 83 3.4.1. Stress levels under and ocean warming scenario………………...………………… 83 3.4.2. Lethal vs non-lethal approach………….…...………………………………………… 85 3.4.3. Survival under increased temperature………..……………………………………... 86 3.4.4. Study considerations and justification………...……………………………………... 87 3.5. Conclusions………………….……………………………………………………….. 88 Acknowledgements…………….…………………………………………………………. 89 References…………………….…………………………………………………………... 89 Chapter 4. Thermal stress and energy metabolism in two circumtropical decapods crustaceans: responses to acute temperature events……………………………………. 95 Abstract……………….…………………………………………………………………... 97 Graphical abstract……………………..…………………………………………………... 98 Highlights……………………………….………………………………………………… 98 4.1. Introduction…………………………….…………………………………………….. 99 4.2. Materials and methods…………………………………….…………………………. 100 4.2.1. Test organisms and acclimation procedure…………………………………………. 101 4.2.2. Experimental design and sampling………………………………………..…………. 101 4.2.3. Biochemical markers………………………………………………………………….... 102 4.2.4. Data analysis…………………………………………………………………………..... 105 4.3. Results……………………………………………………………………….……….. 106

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4.3.1. Thermal, oxidative and neurotoxic stress biomarkers……………………………... 106 4.3.2. Integrated biomarker responses (IBR) index………………………………………... 109 4.3.3. Energy metabolism and performance………………………………………………… 112 4.4. Discussion………………………………………………………………………….… 112 4.5. Conclusions………………………….……………………………………………….. 115 Acknowledgements……………………….………………………………………………. 115 References………………………….…………………………………………………...... 116 Chapter 5. Thermal stress, thermal safety margins and acclimation capacity in tropical shallow waters – an experimental approach testing multiple end-points in two common fish…………………………………………………………………………... 123 Abstract………….………………………………………………………………………... 125 Graphical abstract…………..……………………………………………………………... 126 Highlights……….………………………………………………………………………… 126 5.1. Introduction…………………………………………………………………………... 127 5.2. Materials and methods……………………………………………………………….. 129 5.2.1. Ethical statement………………………………………………………………………... 129 5.2.2. Test species and assessment of their thermal environments………………………. 129 5.2.3. Specimen collection and acclimation procedure…………………………………… 130 5.2.4. Experimental design and rationale…………………………………………………… 120 5.2.5. Experimental outcomes………………………………………………………………… 132 5.3. Data analysis…………………………………………………………………………. 134 5.4. Results………………………………………………………………………………... 135 5.4.1. Assessment of the species’ thermal environments during the juvenile phase…… 135 5.4.2. Thermal and oxidative stress biomarker analyses………………………………….. 136 5.4.3. Thermal tolerance………………………………………………………………………. 141 5.4.4. Whole body analyses…………………………………………………………………… 142 5.5. Discussion……………………………………………………………………………. 145 5.5.1. Stress biomarkers: combined influence of temperature and time of exposure….. 145 5.5.2. Species upper thermal limits, intraspecific variation and acclimation capacity.. 147 5.5.3. Whole organisms stress indicators…………………………………………………… 149 5.6. Conclusions…………………………………………………………………………... 152 Acknowledgements……………………………………………………………………….. 152 References………………………………………………………………………………… 153 Chapter 6. High thermal tolerance does not protect from chronic warming – a multiple end-point approach with a tropical gastropod 161 Abstract…………………………………………………………………………………… 163

Graphical abstract…………………………………………………………………………. 164 Highlights…………………………………………………………………………………. 164 6.1. Introduction…………………………………………………………………………... 165 6.2. Materials and methods……………………………………………………………….. 166 6.2.1. Ethical statement………………………………………………………………………... 166 6.2.2. Test species, specimen collection and acclimation procedure……………………. 167 6.2.3. Experimental design……………………………………………………………………. 167 6.2.4. Molecular biomarkers………………………………………………………………….. 168 6.2.5. Thermal tolerance………………………………………………………………………. 169 6.2.6. Data analyses……………………………………………………………………………. 170 6.3. Results………………………………………………………………………………... 171 6.3.1. Environmental temperature variation………………………………………………... 171 6.3.2. Sub-cellular stress assessment………………………………………………………… 172 6.3.3. Whole animal stress assessment………………………………………………………. 173 6.4. Discussion……………………………………………………………………………. 174 Acknowledgements……………………………………………………………………….. 179 References………………………………………………………………………………… 180 Chapter 7. Present and future invasion perspectives of an alien shrimp in South Atlantic coastal waters – an experimental assessment of functional biomarkers and thermal tolerance………………………………………………………………………….. 187 Abstract…………………………………………………………………………………… 189 Graphical abstract…………………………………………………………………………. 190 Highlights…………………………………………………………………………………. 190 7.1. Introduction…………………………………………………………………………... 191 7.2. Materials and methods……………………………………………………………….. 192 7.2.1. Ethical guidelines……………………………………………………………………….. 192 7.2.2. Animal collection and field temperature measurements…………………………… 193 7.2.3. Experimental layout…………………………………………………………………….. 195 7.2.4. Laboratory analyses……………………………………………………………………. 198 7.3. Results………………………………………………………………………………... 201 7.3.1. Characterization of rocky reefs’ thermal environments in original and newly colonized areas…………………………………………………………………………………. 201 7.3.2. Temperature effects on Lysmata lipkei………………………………………………. 203 7.4. Discussion……………………………………………………………………………. 207 7.5. Conclusions…………………………………………………………………………... 210 Acknowledgements……………………………………………………………………….. 210

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References………………………………………………………………………………… 211 Chapter 8. Environmental health assessment of warming coastal ecosystems in the tropics – application of an integrated biomarker response index……………………… 219 Abstract…………………………………………………………………………………… 221 Graphical abstract…………………………………………………………………………. 222 Highlights…………………………………………………………………………………. 222 8.1. Introduction…………………………………………………………………………... 223 8.2. Materials and methods……………………………………………………………….. 225 8.2.1. Selected coastal environment and test species……………………………………… 225 8.2.2. Experimental design……………………………………………………………………. 227 8.2.3. Sampling scheme………………………………………………………………………... 228 8.2.4. Biomarker measurements……………………………………………………………… 229 8.2.5. Data treatment and statistical analysis………………………………………………. 233 8.3. Results………………………………………………………………………………... 234 8.3.1. Multi-stress response categories IBR approach……………………………………. 234 8.3.2. Overall stress response IBR approach……………………………………………….. 238 8.3.3. Biomarker scores as a fitness index………………………………………………….. 240 8.4. Discussion……………………………………………………………………………. 242 8.5. Conclusions…………………………………………………………………………... 244 Acknowledgements……………………………………………………………………….. 246 References………………………………………………………………………………… 246 Chapter 9. Final remarks and future perspectives……………………………………… 255 9.1. Final remarks…………………………………………………………………………. 257 9.2. Future perspectives…………………………………………………………………… 262 ANNEXES…………………………………………………………………………………. 267 Annex 1: PhD outputs…………………………………………………………………….. 269 Annex 2: Supplementary material on biochemical methods used in chapters 2-8………... 275 Annex 3: Supplementary material for chapter 2………………………………………….. 281 Annex 4: Supplementary material for chapter 5………………………………………….. 291 Annex 5: Supplementry material for chapter 6…………………………………………… 309 Annex 6: Supplementary material for chapter 7………………………………………… 315

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ABSTRACT

The marine environment is already registering the impacts of climate change. The current increase in global temperature since pre-industrial times is disrupting life in the oceans, from the tropics to the poles. The key impacts for marine species as warming continues include shifting home ranges and altered life histories due to the direct effects of temperature on metabolism, life cycles and behaviour of organisms. These pervasive effects on species will also have a significant impact in the goods and services provided by the ocean to human society. However, the vulnerability of tropical marine species (e.g. reef fish and non-coral invertebrates) towards ocean warming is still far from clear, not only due to the gap of knowledge on general ecology and biology of most tropical species, but also because there is a lack of integrative approaches addressing physiological and molecular thermal compensation mechanisms, as well as a lack of adequate or optimized assessment tools for tropical habitats. Therefore, the aim of this thesis was to assess the vulnerability of marine species (fish, crustaceans and gastropods) from tropical reef environments to ocean warming (gradual temperature increase and heat waves), using molecular- (proteins and antioxidant enzymes), cellular (lipid peroxidation), tissue- (energy reserves), organism- (condition, critical thermal maxima) and sample population (acclimation rate, thermal safety margins, mortality) parameters to measure stress and performance. General methodologies included collecting animals from tropical shallow waters with hand nets, experimental assays testing acute and chronic thermal stress and thermal tolerance in the lab (following control and warming scenarios), periodic samplings of several tissue types and biomarker quantification (sample homogenization and posterior immunoassays, kinetic colorimetric assays and elemental analyses by isotope ratio mass spectrometry). Mathematical calculations were used for several performance parameters as well as stress indices. Multivariate (as well as multifactorial) statistical analyses were then performed for all datasets. Results revealed that chronically increased water temperatures (30˚C – 32˚C) elicited a time-dependent cellular stress response in all species tested, where chaperones and antioxidant enzymes showed the greatest fold-changes. The latter molecular responses were highly inducible in vital organs such as gills, liver and muscle, suggesting a relation to tissue function, metabolic- and oxygen diffusion rate which determine the flux of reactive oxygen species and damage potential in cells. Additionally, animals subjected to increased temperatures also showed poorer health status and decreased body condition with lower energy reserves when compared to control temperatures, although mortality levels remained unchanged. An acute exposure to increased thermal load revealed plastic upper thermal limits. However, thermal safety margins were generally low for shallow water species in reef environments, indicating a

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low ability to tolerate further future warming. This means that organisms will have to rely on subtidal habitats for thermal refugia as shallow water habitats (e.g. tide pools) become ecological traps where animals cannot escape heat. Overall, the results point to the importance of monitoring thermal change in the wild to provide decision makers with appropriate and detailed information to sustain conservation efforts in fragile tropical marine ecosystems.

Keywords: climate change, temperature, tropical marine organisms, physiology, environmental bio-monitoring

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RESUMO

O ambiente marinho está actualmente a sofrer transformações devido às alterações climáticas. O aumento da temperatura global que se tem verificado no último século está a alterar a vida nos oceanos, desde os trópicos até aos polos. Os impactos mais relevantes deste fenómeno vão desde alterações nos ecossistemas e biótopos, até alterações na composição e distribuição geográfica das espécies, com consequências para a biodiversidade local e regional. Tem-se observado ainda a alteração dos padrões de história vital dos organismos, devido aos efeitos directos da temperatura na sua biologia e ecologia. Tudo isto tem tido um efeito significativo a nível dos bens e serviços que o oceano providencia à sociedade humana, com perdas consideráveis a nível económico e socio-cultural. Desta forma, a protecção da biodiversidade irá deparar-se com grandes desafios nas próximas décadas, especialmente em zonas tropicais onde a susceptibilidade a fenómenos climáticos é maior, e as medidas de protecção são muitas vezes mais permissivas. Neste processo, é essencial que as medidas de protecção e adaptação climáticas incluam as reorientações ou correcções necessárias aos conceitos de conservação da Natureza e gestão das zonas costeiras já existentes. Neste contexto, é necessária investigação fundamental para compreender a vulnerabilidade de espécies marinhas tropicais (e.g. peixes de recife e invertebrados não- coralíneos) ao aquecimento do oceano. Para além de haver uma lacuna de conhecimento relativa à biologia e ecologia da maioria das espécies tropicais, não existe uma abordagem integradora que inclua a análise dos mecanismos fisiológicos e moleculares de compensação térmica, assim como não foram testadas ou optimizadas as ferramentas para fazer este tipo de avaliação em ambientes tropicais. Esta tese de doutoramento teve como ponto de partida a necessidade de responder às seguintes questões: “Em que medida é que o aquecimento dos oceanos afecta os organismos marinhos tropicais ectotérmicos?”, e, “De que forma é que se poderá monitorizar estes efeitos da temperatura?”. Para estudar a vulnerabilidade/resiliência destes organismos ao aumento de temperatura, optou-se por fazer uma avaliação holística da biologia térmica de várias espécies (de vários grupos distintos, i.e. peixes, crustáceos e gastrópodes) de ambientes de recife tropical sujeitos ao aquecimento da água do mar (crónico e agudo), medindo o nível de stress e desempenho fisiológico dos animais sujeitos à variação deste factor ambiental. Em particular, esta tese focou-se nos efeitos da temperatura a nível molecular (proteínas e enzimas antioxidantes), celular (peroxidação lipídica das membranas celulares), tecidular (reservas energéticas), organísmico (condição corporal, limites térmicos máximos), e populacional da amostra (taxa de aclimatação, margens térmicas de segurança e mortalidade) revelando os mecanismos e a capacidade dos animais para resistir aos cenários de aquecimento global actuais e previstos para o final deste século. Foi utilizada uma abordagem multidisciplinar para atingir

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os seguintes objectivos: (i) seleccionar, optimizar e validar uma série de biomarcadores para gerar uma perspectiva completa da fisiologia do stress em espécies marinhas tropicais; (ii) providenciar uma base de dados de parâmetros fisiológicos em condições óptimas para os grupos animais em estudo, para que, a partir daí se possa medir/quantificar os níveis de stress; (iii) avaliar a utilidade destes parâmetros fisiológicos na bio-monitorização da variação térmica em sistemas de recife tropical susceptíveis, como uma forma de diagnosticar a saúde do ecossistema, identificar eventos stressantes e desenhar planos preventivos para o futuro. As metodologias gerais para atingir estes objetivos incluíram a colheita no campo de animais tropicais de águas pouco profundas com redes de mão, ensaios experimentais para testar stress agudo, crónico e valor térmico crítico (após exposição a cenários de controlo e de aquecimento da água do mar), amostragens periódicas de vários tipos de tecido, e quantificação de biomarcadores (homogeneização de amostras seguida de imunoensaios, ensaios cinéticos colorimétricos e análises de elementos através de espectometria de massa de rácio de isótopos). Foram também realizados vários cálculos matemáticos para analisar os indicadores de desempenho assim como para os índices de stress. Todos os conjuntos de dados gerados foram depois analisados estatisticamente, recorrendo a técnicas multivariadas e multifactoriais. Os trabalhos realizados foram desenvolvidos ao longo de vários capítulos, como é descrito em seguida. A presente tese é então composta por nove capítulos: no primeiro capítulo é descrito o estado da arte sobre temas relacionados com as alterações climáticas em ecossistemas marinhos tropicais e conceitos de biologia térmica, assim como é referida a estrutura e os objetivos da presente tese. Nos capítulos 2-4 é analisada a biologia térmica de várias espécies criadas em cativeiro (com história térmica conhecida) sob condições de aumento de temperatura crónico e agudo (onda de calor), numa perspectiva fisiológica e metodológica. Nos capítulos 5-8, faz-se uma abordagem a animais do meio natural, que co-existem no mesmo tipo de habitat (recife rochoso tropical intertidal), e são analisados os efeitos de stress térmico crónico e agudo, agora num contexto ecológico. No capítulo 9 são apresentadas as conclusões da tese e perspectivas futuras. Mais especificamente, no capítulo 2 - Aclimatação térmica em peixes palhaço: resposta integrada de biomarcadores através de uma abordagem experimental em vários órgãos - foi estudada a forma como as temperaturas previstas para o oceano futuro afectam a fisiologia (marcadores proteicos e enzimáticos medidos em vários tipos de tecido, e medição da condição corporal) de um peixe tropical com história térmica conhecida e variação genética mínima (devido à criação em aquacultura ornamental), para depois se conjugar as respostas num índice que permite a avaliação global da saúde dos organismos. No capítulo 3 - Comparação da resposta de biomarcadores durante o processo de aclimatação térmica: uma abordagem letal vs não-letal em Amphiprion ocellaris (peixe palhaço)

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– efectuou-se um estudo de fisiologia comparativa na mesma espécie de peixe palhaço para testar a possibilidade de utilizar um método de amostragem não letal com resultados fiáveis na medição de parâmetros bioquímicos e moleculares de avaliação do stress. No capítulo 4 - Stress térmico e metabolismo energético em crustáceos decápodes com distribuição tropical: efeitos de eventos agudos de aumento de temperatura - foram investigados os efeitos de uma simulação experimental de uma onda de calor na fisiologia (biomarcadores moleculares e metabolismo energético) de duas espécies de crustáceos com histórias térmicas conhecidas, utilizando um índice integrado de resposta ao stress e fazendo uma avaliação do estado geral de saúde dos organismos. No capítulo 5 - Stress térmico, margem térmica de segurança e capacidade de aclimatação em águas tropicais pouco profundas – uma abordagem experimental multi- parâmetro em peixes comuns - foram analisados os efeitos de cenários de aumento de temperatura do oceano em duas espécies de peixes intertidais de um recife rochoso (testam-se biomarcadores moleculares, metabolismo energético, tolerância térmica, condição corporal, capacidade de aclimatação, mortalidade e margem de segurança térmica), e relacionam-se as diferenças interespecíficas com as suas histórias vitais e preferências de micro-habitat. No capítulo 6 - Tolerância térmica elevada não garante protecção contra o aquecimento crónico da água do mar: uma abordagem multi-paramétrica com uma espécie de gastrópode tropical - foi investigada a biologia térmica (efeitos subletais, tolerância térmica e margem térmica de segurança) de uma espécie de gastrópode cosmopolita que habita em zonas de intertidal rochoso sujeitas a temperaturas elevadas, e testou-se um cenário experimental de aquecimento. No capítulo 7 - Perspectiva presente e futura de expansão de uma espécie de camarão invasora na zona costeira do Atlântico Sul – uma avaliação experimental de biomarcadores funcionais e tolerância térmica - foram estudados vários cenários climáticos e as suas consequências na expansão geográfica de uma espécie de crustáceo invasor no oceano Atlântico tropical. Foi avaliada a influência que a fisiologia tem no sucesso da invasão, através da análise de parâmetros moleculares, celulares e corporais, assim como a taxa de resposta de aclimatação e a margem térmica de segurança), comparando os regimes de temperatura presentes vs previstos entre a sua distribuição geográfica original e os locais em que houve introdução da espécie. Finalmente, no capítulo 8 – Avaliação do estado de saúde de ecossistemas costeiros tropicais em aquecimento – aplicação de um índice integrado de biomarcadores - foram explorados os padrões gerais fisiológicos que emergem entre diferentes grupos animais e espécies que vivem no mesmo tipo de habitat. Fez-se a integração das características fisiológicas de diferentes níveis de complexidade biológica para diagnosticar a saúde dos recifes

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tropicais rochosos, e avaliou-se o risco ecológico do aumento de temperatura em espécies de águas pouco profundas. No geral, os resultados dos vários tipos de análises complementares (moleculares, celulares, tecidulares e organísmicas) revelaram que o aumento gradual e crónico da temperatura (30˚C – 32˚C) induziu uma resposta celular de stress dependente do tempo de exposição, em todas as espécies animais testadas. A nível molecular observou-se que as proteínas da família das chaperonas e as enzimas antioxidantes foram as que demonstraram maiores alterações, especialmente em órgãos vitais como as brânquias, o fígado e o músculo. Isto sugere uma relação com a função de cada tipo de tecido, nomeadamente com a sua taxa metabólica e taxa de difusão de oxigénio – o que, por sua vez, determina o fluxo de espécies reactivas de oxigénio nas células e a dimensão dos danos às estruturas celulares. Adicionalmente, os animais que foram sujeitos a temperaturas elevadas demonstraram um estado geral menos saudável e com menor condição corporal e menos reservas energéticas, quando comparados com os animais do controlo. Ainda assim, os níveis de mortalidade mantiveram-se iguais em ambos os casos. Uma exposição aguda a uma maior carga térmica revelou que as espécies testadas possuem plasticidade nos seus limites térmicos máximos. Contudo, as margens térmicas de segurança (ou seja, a tolerância ao aquecimento) revelaram-se baixas para espécies de águas pouco profundas em ambientes de recife, o que indica uma baixa capacidade para suportar a continuação da elevação da temperatura da água. Nestas circunstâncias, os habitats de águas pouco profundas (e.g. intertidal e poças de maré) podem tornar-se armadilhas ecológicas, onde os animais não serão capazes de escapar ao calor. Isto permite prever que estes organismos irão migrar para habitats subtidais, onde as águas são mais frias, em busca de refúgio térmico. Numa visão global dos resultados, estes apontam para a importância da bio-monitorização da carga térmica no meio natural. A recolha de dados e informação detalhada será essencial para que os decisores dêem os passos necessários para aumentar os esforços de conservação da Natureza nos frágeis ecossistemas marinhos tropicais. Ao compreendermos os mecanismos por detrás das respostas dos organismos aos desafios térmicos, a nossa capacidade de previsão e gestão, assim como de avaliação de risco ambiental tornar-se-ão mais precisas face aos cenários de alterações climáticas para o final do século.

Palavras-chave: alterações climáticas, temperatura, organismos marinhos tropicais, fisiologia, bio-monitorização ambiental

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List of symbols and abbreviations

Abs, absorbance AChE, acetylcholinesterase ACTI, acetilthiocholine iodide AMPK, adenosine monophosphate kinase ANOVA, analysis of variance AOX, antioxidant ARR, Acclimation Response Ratio ARRIVE, Animal Research: Reporting in Vivo Experiments ATP, adenosine triphosphate AVG, Aquário Vasco da Gama Avg, average BSA, bovine serum albumin C:N, carbon:nitrogen CAT, catalase CDNB, chloro-2,4-dinitrobenzene CEBIMar, Centro de Biologia Marinha CEUA, Comissão de Ética na Utilização de Animais CI, confidence interval CMIP, Coupled Intercomparison Project Phase

CO2, carbon dioxide CSR, cellular stress response CTMax, Critical Thermal Maximum CV, coefficient of variation Da, Dalton DCM, dichlorometane DGAV, Direcção Geral de Veterinária DNA, deoxyribonucleic acid DTNB, 5,5’-dithiobis-(2-nitrobenzoic acid) E, east e.g., for example EC, enzyme commission EDA, Experimental Design Assistant EDTA, ethylenediaminetetraacetic acid EFSA, European Food Safety Authority ELISA, Enzyme-linked immunosorbent assay ENSO, El-Niño Souther Oscillation Eq, equation EU, European Union

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EWCE, extreme weather and climatic events ɛ, extinction coefficient FCT, Fundação para a Ciência e Tecnologia FELASA, Federation of European Laboratory Animal Science Associations Fig., figure g, grams GHG, greenhouse gas GLM, General Linear Models GSH, reduced glutathione GST, gultathione-S-transferase H+, hydrogen ion

H2O, water

H2O2, hydrogen peroxide HCl, hydrochloridric acid HO*, hydroxyl radical hr, hour HRP, Horseradish peroxidase HSC, Heat shock conjugate Hsc70, Heat shock protein cognate 70 kDa HSD, Honest significant difference Hsp70, Heat shock protein 70 kDa Hsps, Heat shock proteins IBR, integrated biomarker response IgG, immunoglobulin G ind, individual IPCC, Intergovernmental Panel on Climate Change IUCN, International Union for Conservation of Nature and Natural Resources IWPWP, Indo-West Pacific Warm Pool KCl, Potassium Chloride kDa, kilodalton Kg, kilograms

KH2PO4, monopotassium phosphate Km, kilometre L, litre L:D, light:dark LC, lipid content LPO, lipid peroxidation

Lt, total length M, molar concentration MANOVA, Multivariate analysis of variance

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MARE, Marine and Environmental Sciences Centre MAX, maximum MDA, malondialdehyde bis(dimethylacetal) MeOH, methanol mg, milligrams MHT, Maximum Habitat Temperature MIN, minimum Min, minutes mL, mililitre mm, milimetres mM, milimolar

Mt, total wet mass M-W, Mann-Whitney N, north NA, not available or not-applicable

Na2HPO4, disodium phosphate NaCl, sodium chloride NADH, Nicotinamide adenine dinucleotide NADPH, Nicotinamide adenine dinucleotide phosphate NaOH, sodium hydroxide NASA, National Aeronautics and Space Administration NBT, nitroblue tetrazolium NE, northeast NMI, National Meteorology Institute nmol, nanomoles NOAA, National Oceanic and Atmospheric Administration of USA NW, northwest • O2 , superoxide radical ˚C, degree Celsius OCLTT, oxygen and capacity limited thermal tolerance OECD, Organization for Economic Co-Operation & Development OH, hydroxide OOH, oxide hydroxide p, p-value PBS, phosphate buffered saline PC, principal component PCA, principal component analysis PQC, protein quality control PUFAs, polyunsaturated fatty acids PVC, polyvinyl chloride

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RCP, Representative Concentration Pathways RNA, ribonucleic acid ROS, reactive oxygen species S, south SD, standard deviation SDS, sodium dodecyl sulphate SE, southeast SOD, superoxide dismutase SRES, Special Report on Emission Scenarios SST, sea surface temperature T0, 0 days of exposure T14, 14 days of exposure T21, 21 days of exposure T28, 28 days of exposure T7, 7 days of exposure TBA, thiobarbituric acid TBARS, thiobarbituric acid reactive substances TCA, trichloroacetic acid

Tdays, timepoint in days Temp, Temperature TLE, total lipid extracts TMB/E, 3,3’,5,5’-Tetramethylbenzidine for ELISA Tprotein, total protein TSM, Thermal Safety Margin Ub, total ubiquitin UCIBIO, Research Unit on Applied Molecular Biosciences USA, United States of America USP, Universidade São Paulo UV, ultraviolet W, Watt W, west WHO, World Health Organization XOD, xanthine oxidase χ2, Chi-square %C, percentage of carbon ~, approximately ∆, variation ↑ increase or up-regulation ↓ decrease or down-regulation µg, micrograms

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µg, micrograms µL, microlitres µmol, micromoles

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CHAPTER 1

General Introduction

1.1. Global change and human impacts in the world’s oceans

Global change is firmly established as a scientific reality, with a variety of challenges for humankind emerging today and in the coming decades. As the human population increases, so does our global footprint and most of the projected threats to biodiversity arising from human growth and consumption are expected to be severe (Rocha, 2017). Presently, there is no area in the world’s oceans unaffected by human influence and a significant fraction (41%) is affected by multiple pressures (Gattuso et al., 2015; Halpern et al., 2008), ranging from agriculture and industry, to fishing, aquaculture and tourism. Human activities (either land-based or ocean- based) have strongly impacted the oceans mainly by (i) altering runoff of pollutants and nutrients into coastal waters (Syvitski, 2005), (ii) removing, changing or destroying natural habitats (Jackson, 2008) and/or (iii) imposing the climate change ‘deadly trio’ – warming, ocean acidification and deoxygenation (Bijma et al., 2013; Nowicki et al., 2012). All of these anthropogenic drivers of ecological change can be observed both locally and regionally (Fig. 1.1), with strong spatial heterogeneity (Halpern et al., 2008; Ottersen et al., 2010).

Figure 1.1 Global map (A) of cumulative human impact across 20 ocean ecosystem types. (Insets) Highly impacted regions in the Eastern Caribbean (B), the North Sea (C), and the Japanese waters (D) and one of the least impacted regions, in northern Australia and the Torres Strait (E). Ecosystem types include: hard and soft bottom shallow, shelf, slope and deep; coral reefs; mangroves; surface water; deep water;

seamounts; nearshore ecosystems with assumed distributions (rocky intertidal, beach, intertidal mud, suspension-feeding reefs, salt marshes, kelp forests). Human impacts include: direct human, benthic structures, commercial shipping, ocean-based pollution, species invasion, climate change (acidification, UV, warming), fishing (artisanal, pelagic and demersal), and pollution (inorganic, organic and nutrient input). Source: Halpern et al. 2008.

As expected, coastal areas, which have been focal points for human settlements and marine resource use in the past centuries, are the most impacted by the above mentioned anthropogenic drivers of environmental change. As coastal habitats are progressively altered, fragmented or loss, the likelihood of the occurrence of abrupt ecological shifts in these areas increases (Lotze et al., 2006; Montefalcone et al., 2011). Table 1.1 presents a summary of the magnitude of degradation and biodiversity loss in coastal ecosystems already occurring in the present.

Table 1.1 Status and trends of major coastal-associated ecosystems defined by principal symptoms and drivers of degradation in the >99% of the global ocean that is unprotected from exploitation. Adapted from Jackson (2008). Ecosystem Status Symptoms Drivers Coral reefs Critically Live coral reduced 50-93%, fish populations reduced Overfishing, warming and endangered 90%, apex predators virtually absent, other megafauna acidification, run-off of reduced by 90-100%, population explosions of nutrients and toxins, invasive seaweeds, loss of complex habitat, mass mortality of species corals from disease and coral bleaching Estuaries and Critically Marshlands, mangroves, seagrasses and oyster reefs Overfishing, runoff of nutrients coastal seas endangered reduced 67-91%, fish and other shellfish populations and toxins, warming, invasive reduced 50-80%, eutrophication and hypoxia with mass species, coastal land use mortality of fish and invertebrates, loss of native species, toxic algal blooms, outbreaks of disease, contamination and infection of fish and shellfish, human disease Continental Endangered Loss of complex benthic habitat, fishes and sharks Overfishing, trophic cascades, shelves reduced 50-99%, eutrophication and hypoxia in “dead trawling, runoff of nutrients and zones” near river mouths, toxic algal blooms, toxins, warming and contamination and infection of fish and shellfish, acidification, introduced decreased upwelling of nutrients, changes in plankton species, escape of aquaculture communities species

The combination of drivers depicted in Table 1.1 has, over time undermined these ecosystems’ ecological resilience (Hernández-Delgado, 2015; Jackson, 2008; Pandolfi, 2003),

as demonstrated by numerous fisheries collapses (Blaber, 2013, 2002; Perry, 2005) and historical losses of coral reefs, mangroves and wetlands (Gable et al., 1991; Loarie et al., 2009; Munday et al., 2009; Pandolfi, 2003). Predictions for the near and long-term health of marine ecosystems are therefore concerning, if the course of human action observed until today is to remain. Even in the best scenarios, with the creation of marine protected areas and strict regulations on pollution and fishing, along with significant reduction in greenhouse gas emissions, scientists have highlighted that the effects of yesterday’s and today’s actions will continue to be felt in the future. In particular, climate change stressors (warming, acidification and deoxygenation) and their nonlinear interactions will significantly shape ecological processes in the marine realm in the decades to come (Ottersen et al., 2010).

1.2. Warming in tropical oceans: today and tomorrow

Climate monitoring over the past century has shown that the Earth is warming (Hughes, 2000). The appearance of this risk factor is a consequence of the perturbation of the carbon cycle due to the anthropogenic release of carbon dioxide and/or methane in industrialization processes and consequent burning of fossil fuels, and is major cause of concern (Beaugrand et al., 2008; Ottersen et al., 2010; Todgham and Stillman, 2013). Recorded 20th century trends have shown that global mean surface air temperatures have increased 0.6˚C since the late 19th century and by 0.2˚C to 0.3˚C over the past 50 years (Hughes, 2000). This means that the average global temperature on Earth has increased by about 0.85˚C since 1880 (Hartmann et al., 2013; IPCC, 2014; NASA, 2017). This additional heat has been mostly absorbed by the oceans: estimates are that from the surface down to 700 m, ocean temperature has increased by 0.2˚C over the last decades (Noone et al., 2013). Moreover, climate models predict that the mean annual global surface temperature will further increase between 0.3˚C to 4.8˚C by 2100 (temperature range including all Intergovernmental Panel on Climate Change, IPCC, emission scenarios, Fig. 1.2) (Collins et al., 2013; IPCC, 2014).

Figure 1.2 Global mean temperature change averaged across all Coupled Intercomparison Project Phase 5 (CMIP5) IPCC models (relative to 1986-2005) for the four Representative Concentration Pathway (RCP) scenarios: low emissions (dark blue), intermediate emissions (light blue and orange) and high emissions (red). Likely ranges for global temperature change by the end of the 21st century are indicated by vertical bars. For the highest (red) and lowest (dark blue) greenhouse gas emission scenarios, illustrative maps of surface temperature change at the end of the 21st century (2081–2100 relative to 1986–2005) are shown. Source: Collins et al. 2013.

Tropical oceans in particular provide the major storage of heat for the global climate system and are also the source of most of the atmospheric water vapor, the single most important greenhouse gas (GHG) (Herbert et al., 2010). Due to these characteristics, tropical oceans can lead to large feedbacks that enhance the global expression of climate cycles both at seasonal, interannual, interdecadal, and long-term scales (Lin et al., 2011). Numerous studies have documented the importance of several modes of sea surface temperature (SST) variability in the Pacific, such as the El Niño-Southern Oscillation (ENSO) cycle and its relationship to convection both locally and extratropically (e.g. Glynn 1988; McPhaden et al. 1998; Chiang and Sobel 2002; Abram et al. 2009). Also, interannual SST variability in the Atlantic, associated with ENSO, the Atlantic dipole and equatorial warm events are all coupled with changes in the location of the intertropical convergence zone (Clayson and Weitlich, 2007). At all of these timescales, SSTs can affect rainfall and convection in the Indian Ocean (Clayson and Weitlich, 2007).

Tropical climate trends for the past century (Fig. 1.3) suggest that the largest warming trend has occurred in the eastern tropical Atlantic (1.2˚C - 1.6˚C), whereas the eastern tropical Pacific warmed approximately by 0.8˚C to 1.0˚C, similar in magnitude to the tropical Indian Ocean and the central tropical Atlantic (Deser et al., 2010). Therefore, regional and local variability need to be accounted for when estimating impacts for marine biota and ecosystems.

Figure 1.3 Twentieth century tropical and subtropical (20˚N to 20˚S) SST anomaly trend. Note: white pixels denote insufficient data. Source: Deser et al. 2010.

Additionally, ocean isotherms in tropical areas have been fluctuating significantly, with areas of lower temperatures contracting and areas of higher temperature expanding (Arora et al., 2016). It has been observed that tropical ocean areas with SST below 25˚C change little, whereas in isotherms between 26˚C and 28˚C, the area change rate decreases with the increase in SST (Lin et al., 2011). On the contrary, the ocean areas between 29˚C and 30˚C in the global Pacific and Indian oceans have the largest increase rate and the global tropical oceans have a maximum area increase rate of 2.29 × 106 km2.decade-1 (Lin et al., 2011). This means that not only ocean temperatures are gradually rising but also that the naturally warmest ocean areas are expanding rapidly, leading to a narrower range of tropical temperatures, which are being skewed further to the warmer edge of their range. Finally, predicted future temperature anomalies for tropical regions range from 0.9˚C - 3.3˚C across scenarios for late 21st century (2081-2100) (IPCC, 2013; IPCC, 2014), which means that thermal pressure over ecosystems will continue.

Changes in ocean heat content have direct physical and biogeochemical consequences, including thermal expansion, sea level rise, increased melt water, washed nutrients and sediments, and reduced salinity (Kharin et al., 2007; Knutson et al., 2010). Rising temperatures will also increase surface ocean stratification, which in turn will affect the surface-water light regime and nutrient input from deeper layers, and thus ocean productivity (Ackerman, 2013). Extreme events (Fig. 1.4) will significantly escalate in frequency and intensity: heat waves, cold spells, storms, La Niña and El Niño, droughts and heavy rain (Ceccherini et al., 2017; Chiang and Sobel, 2002; Di Lorenzo and Mantua, 2016; Kharin et al., 2007; Sura, 2011).

Figure 1.4 Predicted shifts in extreme events. Shifts in extreme events due to changes in temperature distribution can occur in three ways a) shift of the entire distribution towards a warmer climate; b) increase in temperature variability with no shift in the mean and c) asymmetric distribution towards hot weather. Source: IPCC 2012.

All these changes in ocean temperatures and consequently in chemistry and currents mean that many organisms will find themselves in unsuitable environments, potentially testing their ability to survive.

1.3. Impacts and mechanisms of tolerance and adaptation to temperature change in marine organisms

As observed by many scientists, climate warming affects, through many different mechanisms and processes, the structure and function of marine ecosystems, setting ecological

patterns in the marine environment (Bijma et al., 2013; Crabbe, 2008; Graham et al., 2008). Observations from the field show that the ongoing ecosystem changes in response to climate warming include poleward or depth shifts in species geographical distribution (Brander et al., 2003; Grebmeier et al., 2006; Perry, 2005), population collapses or local extinctions (Pörtner and Knust, 2007), failure of large scale animal migrations (Pörtner and Farrell, 2008), changes in the seasonal timing of recurring biological events (Kerr and Dobrowski, 2013; Wiltshire and Manly, 2004) and changes in food availability and food web structure (Durant et al., 2007; Taucher and Oschlies, 2011).

For tropical marine regions in particular, the impacts of climate warming — the cradle of biodiversity — are often predicted to be small relative to those in temperate regions (Hernández-Delgado, 2015; Roberts, 2002) because the rate of climate warming in the tropics is lower than at higher latitudes (Hartmann et al., 2013). Yet, predictions based only on the magnitude of climate change may be misleading. Coral and rocky reefs for instance, are a very important component of tropical shores which harbor rich communities that are already naturally subjected to temperature regimes that fluctuate with a high frequency and magnitude (Ammar et al., 2000; Craig et al., 2001; Kennedy et al., 2013). This temperature regime coupled with a gradual temperature rise and the increase in extreme climatic events is leading to one of the most worrisome ecological phenomena: community reassembly in time and space (Groom et al., 2012). Consequently, reef fish, corals, sea urchins and coralline algae are declining (Kroeker et al., 2014) whereas algal turfs, non-calcifying macrophytes and non-calcifying zooxanthellate cnidarians are benefiting from elevated SSTs in such tropical reef systems (Johnson et al., 2012; Koch et al., 2013) (see Fig. 1.5 for detailed impacts of increasing SST in tropical marine ecosystems).

Figure 1.5 Impact chain of ocean warming for tropical coastal areas in: A) coral reefs, B) fish stocks, C) biodiversity. Adapted from Ci-Grasp 2.0 (http://www.pik-potsdam.de/cigrasp-2/index.html).

All of these observed changes are directly or indirectly derived from the core assumptions of thermal biology (i) temperature is one of the most pervasive state variables affecting biological processes, and (ii) the laws of thermodynamics define the direction and rate of biochemical processes that ultimately determine the performance of whole organisms and species fitness (Brown et al., 2004; Haynie, 2001). In fact, temperature exerts its effects at different levels of biological organization, and literature suggests a hierarchical series of tolerance (Fig. 1.6), from molecular to communities and ecosystems, with wider tolerance windows at lower levels and highest sensitivity at higher levels of biological complexity (Barry et al., 1995; Bozinovic and Pörtner, 2015; Chen and Stillman, 2012; Hofmann and Todgham, 2010; Mora and Ospína, 2001; Somero, 2010; Warren et al., 2011).

Figure 1.6 Up-scaling of temperature effects from the molecular level to communities and ecosystems (adapted from Madeira 2016).

With regard to organisms, they tend to have thermal tolerances that reflect the environment in which they are found, and the study of their thermal biology can help to identify individual and ecological characteristics of “winners” and “losers” in a climate warming context (Logan and Somero, 2010). When exposed to stressful temperature variations, and depending on the magnitude and duration of the thermal challenge, organisms must divert variable amounts of available energy away from foraging, growth and reproduction to cellular defense and maintenance of physiological homeostasis (Perry, 2005; Todgham and Stillman, 2013). Consequently, there are cascading impacts in reproductive rate, recruitment, mortality and population size and distribution, which are all dependent on temperature (e.g., Kröncke et al. 1998; Perry 2005; Pörtner and Farrell 2008).

In fact, all organisms specialize on a limited range of ambient temperatures. Usually, thermal specialists occur both at low and high latitudes, and thermal generalists appear most common at mid-latitudes (Pörtner and Knust, 2007). This pattern tracks the seasonality of ocean surface temperatures - tropical oceans are warm but show little temperature variation throughout the year and the largest seasonality in ocean surface temperatures are seen at mid-latitudes (Tewksbury et al., 2008). Thermal specialization phenomenon has traditionally been linked to tradeoffs (Table 1.2) in structural properties of molecules (e.g. enzyme, proteins, cell membrane components) and associated functional adjustments to a range of temperatures (Bijma et al., 2013). It reflects the integration of molecular functions into organisms as high-complexity systems (Huey and Berrigan, 2001).

Table 1.2 Proximate mechanisms by which thermal reaction norms differ between genotypes and the resulting tradeoffs. Source: Angilletta et al. 2003.

Change in reaction norm Proximate mechanism Tradeoff Greater performance over a broad range of temperatures ↑ concentration of all isozymes Allocation or acquisition ↑ density of organelles Allocation or acquisition ↑ number of cells Allocation or acquisition Greater performance over a narrow range of temperatures ↑ enzyme flexibility Specialist – Generalist ↑ concentration of a specific isozyme Allocation or acquisition Greater performance at extreme temperatures ↑ concentration of a specific isozyme Allocation or acquisition ↑ stability of enzymes Specialist – Generalist ↑ intracellular stabilizers Allocation or acquisition

Overall, the specialization on limited temperature ranges implies functional disturbances setting in at temperatures beyond these ranges. Therefore, thermal acclimatization between seasons or adaptation to a climate regime involves shifting thermal windows and adjusting window widths (Fig. 1.7), in accordance with ambient temperature variability (Angilletta, 2009; Angilletta et al., 2006, 2004; Pörtner and Farrell, 2008). Usually, when temperature of the environment varies spatially and temporally, one of four conditions must occur (Angilleta et al., 2006): (i) body temperature is regulated by behavior and physiology; (ii) body temperature fluctuates, but performance is not impaired; (iii) body temperature fluctuates, and performance is initially impaired but is restored later by acclimation; or (iv) body temperature fluctuates, and performance is impaired and is not restored by acclimation. However, acclimation capacity is limited to a more or less dynamic thermal niche which covers the temperature regime and determines the biogeography of a species. Overall, the relationships between energy turnover, the capacity for aerobic and anaerobic activity, and the width of thermal windows lead to an integrative understanding of specialization on climate and, of sensitivity to climate change (Previati et al., 2010).

A B C

Figure 1.7 Concept of thermal windows. A) The thermal windows of aerobic performance display optima and limitations by pejus (pejus means “turning worse”), critical, and denaturation temperatures, when

tolerance becomes increasingly passive and time-limited. Specifically, performance in animals is supported by aerobic scope, the increase in oxygen consumption rate from resting to maximal. The tolerance window for each species is described as a favorable range of temperature and this range includes an optimal zone and a suboptimal zone. The suboptimal zone begins at a temperature where the maximum of oxygen delivery cannot be increased further and aerobic scope starts to decrease (oxygen limited thermal tolerance, OLTT). When temperature fluctuates above or below the suboptimal zone, performance of the species is negatively affected. Seasonal acclimatization involves a limited shift or reshaping of the window by mechanisms that adjust functional capacity, endurance, or protection. B) Positions and widths on the temperature scale shift with life stage. C) Acclimatized windows are narrow in stenothermal species or wide in eurytherms, reflecting adaptation to climate zones. Windows still differ for species whose biogeography overlap in the same ecosystem. Warming cues start seasonal processes earlier (shifting phenology), causing potential mismatch with processes timed according constant cues

(e.g. light). Synergistic stressors like ocean acidification (by CO2) and hypoxia narrow thermal windows according to species specific sensitivities (broken lines), modulating biogeography, coexistence ranges, and other interactions further. Source: Pörtner and Knust 2007; Pörtner and Farrell 2008.

Moreover, geographic variation in temperature tolerance, or differences in the average individual tolerances between geographically distinct populations, arises due to individual variation. This variation in temperature tolerance of an organism represents both adaptation (a distinct genotype) and phenotypic plasticity, or the range of phenotypes possible for a single genotype, which can be either fixed or variable over an individual's lifespan. As consequence, populations which are likely to persist in the warmer conditions predicted by climate change are those in which: (i) individuals have high temperature tolerances (Calosi et al., 2008), (ii) individuals have the capacity, via phenotypic plasticity, to acclimate to higher temperatures (Sorte et al., 2011), or (iii) populations of tolerant individuals – those that either already have high tolerance or have high acclimatization capacity – can disperse and re-seed areas of less tolerant populations (Deutsch et al., 2008).

Therefore, the understanding of the molecular underpinnings of thermal tolerance and acclimatization, and the connection of this information with physiological and ecological patterns is of significant importance to improve biogeographic forecasting under a context of ocean warming (Hofmann, 2005). According to current knowledge, there are several responses and potential mechanisms that may provide resistance, protection and tolerance in organisms during temperature stress, including (i) the activation of the neuroendocrine/endocrine system to produce hormones (e.g. catecholamines and corticosteroids) that activate molecular cascades (Barton, 2002; Iwama et al., 1999); (ii) induction of specific metabolic pathways that lead to biochemical and physiological adjustments, namely in respiratory, cardiovascular and immune functions as well as changes in energy metabolism (Allan et al., 2006; Barton, 2002; Iwama et

al., 1999; Stillman and Somero, 1996) and (iii) behavioral thermoregulation, in which animals select and explore cooler areas in their habitats according the their thermal preferences (Díaz et al., 2015; Reiser et al., 2016). In regards to metabolic pathways modulated by temperature, the main ones include generalized responses at the cellular level (cellular stress response, CSR) involving the protection and repair of macromolecules (Buckley et al., 2006). From inducible heat shock proteins that act in the refolding of denatured structural and cellular proteins (Feder and Hofmann, 1999; Georgopoulos and Welch, 1993; Sørensen et al., 2003; Tomanek and Somero, 1999); the ubiquitin-proteasome pathway that targets irreversible damaged proteins for degradation (Hofmann and Somero, 1995; Medicherla and Goldberg, 2008; Windisch et al., 2014); the production of antioxidant enzymes that scavenge and disable harmful oxygen radicals or reactive oxygen species (ROS) (Abele et al., 2002; Lesser, 2012, 2006); the activation of energy related pathways such as gluconeogenesis and glycogenolysis to obtain extra energy for cellular defense (Iwama et al., 1999; Tomanek, 2014; Wegener, 1988); to cytoskeleton dynamics and cell signaling (Madeira, 2016; Tomanek, 2011), all of these contribute to enhanced performance and survival of organisms under challenging temperature conditions.

1.4. General evaluation of stress and performance in tropical marine organisms under ocean warming

Over the last 50 years, the study of the mechanisms to cope with environmental stress has shifted from a largely physiological science to a more integrated science of behavior, physiology, ecology, and evolution (Angilletta et al., 2006). Enhancing further progress in understanding the causes and mechanisms of thermal stress in organisms is essential for opening potential avenues for the prevention, rehabilitation and conservation of species and habitats in coastal areas currently experiencing thermal stress. The systematic investigation of thermal stress pathology requires solving several issues (Downs et al., 2000; Harper and Wolf, 2009; Solé et al., 2004), namely (i) the identification and determination of nominal levels for many molecular and cellular parameters that reflect physiological status and (ii) the development of reliable and precise technologies to determine animals’ health status, both in the laboratory and in the field. There is a need to understand how large-scale temperature ‘signals’ are downscaled to the level of the organism, ultimately defining the organism’s fundamental niche (Helmuth et al., 2010; Kearney, 2006). All of these needs have contributed to a shift from abiotic monitoring to effect (biological-based) monitoring – through assaying specific biological endpoints in living organisms (Lam, 2009). Under this context, biomarkers – “induced variations in cellular or biochemical components or processes, structures or functions that are measurable in a biological system or sample” (Henderson et al., 1987), have recently gained popularity in

environmental management and tracking of thermal change (see Fig. 1.8 on the pros and cons of biomarkers). Biological markers can contribute to quantitative risk assessment in climate warming studies by helping to: determine the forms of stress-time-response relationships in organisms; assess the biologically threshold for thermal stress in indicator species; make interspecies comparisons of stress intensity, potential acclimation and effects; resolve the quantitative relationships between interindividual variability; and identify subpopulations that are at increased risk (Ladeira and Viegas, 2016; Schulte and Mazzuckelli, 1991). In particular, biomarkers of effect (also known as biomarkers of response) measure alterations within an organism that, depending upon their magnitude can be recognized as associated with an established or possible health impairment or disease (WHO, 1993).

Figure 1.8 A comparison of the pros and cons of biomarkers and bioindicators at various biological organization levels. An antagonistic relationship exists between specificity and ecological relevance. Source: Lam 2009.

Ideally, a set of specific biomarkers should be selected to encompass effects at the whole organism, organ function, tissue metabolism, individual cells functioning and at the subcellular level (Barrett et al., 1997). To develop this kind of approach in tropical marine ecosystems, researchers need to make a concerted effort to use diagnostic tools that allow the

creation of a picture of the various stress responses, pathways and processes involved for different thermal sensitive species and animal groups. These tools will provide knowledge of the current condition of the ecosystems as well as allow the identification of time trends, which is what was attempted in this thesis (Table 1.3 provides the selected set of biomarkers used in this research with the appropriate justifications).

Table 1.3 Set of parameters selected to be used in this research to investigate the effects of increasing sea surface temperatures in tropical marine organisms: targeted biological level and temperature effects at each level, biomarker functions, biological/ecological relevance and expected responses under warming. Note: for an extensive stress metabolism review, please see Hofmann and Somero 1995, Hil and Vi 1998, Halpin et al. 2002, Mukhopadhyay et al. 2003, Rieger et al. 2005 and Yamashita et al. 2010 for heat shock proteins; Lesser 2006, 2012, Lushchak 2011 and Tomanek 2011 for oxidative stress; Wegener 1988 and Hocquette et al. 2010 for energy metabolism.

Biomarker Biological Temperature effects at each Biomarker functions Biological/ecological relevance Expected responses References level biological level under warming

Heat shock Molecular High temperature increases Intra-cellular defense; stabilize Cellular stress response; Increase at moderate Feder and Hofmann protein kinetic energy in molecules which denatured and nascent proteins; maintenance of protein pool stress levels and 1999; Tomanek 2002; 70kDa induces changes in three translocate proteins; mediate integrity; chaperones with selective decrease at extreme Werner et al. 2006; dimensional structures of proteins protein folding and inhibit incorrect value, they are known to confer stress levels or Morris et al. 2013 (the correctly folded state depends side reactions during that process; thermotolerance; indicate degree of acclimation, varies on the balance between entropy prevent formation of cytotoxic reversible protein damage; reduce with organ (responses and stabilizing interactions); aggregations; Hsps function as the cost of de novo protein are expected to be causes protein unfolding and oligomers/complexes; cellular synthesis; but can also have greater in more heat denaturation, and consequent loss protein pool and induction of Hsps deleterious effects on fitness (toxic sensitive tissues, such of biological activity. Differences are interrelated in high concentrations, high as gills and liver) in protein thermal stability energetic cost and requirement of a influence protein turnover costs sizable fraction of the cell for the cell. apparatus)

Ubiquitin Molecular Elimination of irreversible Cellular stress response; ubiquitin- Hofmann and Somero misfolded proteins; selective proteasome pathway; important in 1995; Kültz 2004; tagging of proteins for degradation cell cycle regulation, apoptosis, Medicherla and in response to specific signals; trafficking, transcription, immune Goldberg 2008; continuous protein turnover to and inflammatory responses; Windisch et al. 2014; rapidly modulate protein levels; ubiquitin reflects the structural Madeira et al. 2016 turnover of excess free forms of integrity of the protein component

proteins of cells

Superoxide Molecular The dynamic equilibrium of First line of defense against ROS, Cellular stress response; prevent the Pannunzio and Storey dismutase production and elimination of catalyzes dismutation of superoxide generation of or promote the 1998; Lesser 2006; ROS during cellular aerobic radical into oxygen and hydrogen elimination of ROS/free radicals Mukherjee 2008; •- + metabolism can be unbalanced by peroxide (2O2 + 2H  O2 + that arise from oxidative stress at Falfushynska et al.

an increase in temperature, which H2O2) high temperatures (due to higher 2014 leads to higher metabolic rates metabolism or tissue re- Catalase Molecular Antioxidant enzyme, catalyzes the Abele et al. 2002; and higher O2 consumption. In oxygenation during recovery detoxification of hydrogen peroxide Lesser 2006; consequence, perturbations in the phase); repair oxidative damage; into water and oxygen (2H O  Lushchak 2011; redox status can occur and 2 2 regulation of cellular processes; 2H O + O ) Madeira et al. 2013; oxidative stress may arise. 2 2 adaptation to internal and Madeira 2016 Oxygen free radicals generated environmental changes.

Glutathione- Molecular under such circumstances can Phase II metabolic isozyme that Antioxidant enzymes act by Pannunzio and Storey S-transferase interact with cell macromolecules detoxifies lipid peroxides (lipid- reducing ROS enzymatically. The 1998; Lesser 2006; (DNA, proteins, lipids) in OOH) by transforming them into central purposes of antioxidant Dorts et al. 2009; chemical reactions by electron alcohols (lipid-OH) defenses in biological systems are Pavlović et al. 2010; 1 exchange and have been to quench O2 at the site of Madeira 2016 implicated in cellular disfunction production and to quench or reduce or death, leading to aging and the flux of reduced oxygen disease processes. intermediates to prevent the production of HO*, the most damaging of ROS. Antioxidants act together at different levels in an

oxidative sequence.

Acetylcholin- Molecular Temperature increase can cause: Catalyzes the breakdown of Key enzyme of the nervous system; Can increase or Leticia and Gerardo esterase lower production of acetylcholine and other choline occurs at neuromuscular junctions decrease with 2008; Durieux et al. cholinesterase enzyme to save esters that function as and chemical synapses temperature stress, 2011; Lionetto et al. energy; changes in structure and neurotransmitters (cholinergic), where it terminates depending on the 2013; Vieira et al. kinetic properties of AChE – loss synaptic transmission; its inhibition species 2014; Arambourou of binding ability; production of leads to paralysis and cardio- and Stoks 2015 inhibitory compounds that respiratory failure; can have decrease AChE activity, leading significant impacts in organism to the accumulation of behavior (e.g. swimming, balance, acetylcholine and blocking spasms); early sign of behavior neurotransmission impairments

Lipid Cellular The increase in ROS levels Oxidative degradation of lipids by a One of the most relevant Increase, but depends Madeira et al. 2013, peroxidation caused by higher temperatures chain reaction (i.e., initiation, mechanisms of cellular injury; on targeted organ 2014; Falfushynska et attack polyunsaturated fatty acids propagation and termination): indicates that the integrity of al. 2014; Vinagre et (PUFAs). This elicits a self- unsaturated lipid + OH*  Lipid biological membranes has been al. 2014; Almroth et

perpetuating chain-reaction and radical + O2  Lipid peroxyl compromised – cells lose the ability al. 2015 results in the formation of lipid radical + unsaturated lipid  lipid to regulate membrane fluidity and peroxides and aldehydes, which peroxide + lipid radical  cycle tolerate thermal stress; end products have been implicated in apoptosis continues  lipid radical + lipid of lipid peroxidation (reactive induction in cells radical  non-radical species aldehydes) are mutagenic and carcinogenic

Total protein Tissue Thermal stress affects cellular Proxy of energy reserve Low protein concentrations usually Increase at moderate Alberts et al. 2002; protein pool and energy allocation accumulation - general measure of occur when there is weight loss, stress levels or Boone et al. 2002; and consumption in cells (i.e., growth, development and health, poor health status, disease or acclimation and Krieger et al. 2006; biosynthesis, maintenance and energy source that can be used in immunodeficiency decrease at extreme Fonseca et al. 2011; external work); increased aerobic metabolism when other stress levels Ferreira et al. 2015 temperature may cause a negative sources are depleted energy balance that decreases % carbon Tissue Proxy of energy reserve Temperature stress affects the Increase at moderate Elia et al. 1999; energy deposits, for example due (isotopic) or accumulation – lipid storage in ability of cells to storage fat, low fat stress levels or Lotze et al. 2006; to apoptosis of fat body cells total lipids tissues, general measure of body deposits may be a signal of poor acclimation and Nanton et al. 2007; (depletion of lipids) condition and health, main energy condition and growth, as well as depletion at extreme Hocquette et al. 2010; source in aerobic metabolism affect gonad development and stress levels Klepsatel et al. 2016; reproduction Kühnhold et al. 2016

Fulton K Individual Elevated temperatures influence Index based on morphometric Environmental stress (chronic or Potential increase at Stevenson and food consumption as well as measures (length and weight) to acute) may lead to weight changes, moderate temperature Woods 2006; Vinagre feeding conversion efficiency, measure body condition decreased growth, poor condition and decrease at severe et al. 2012; and consequently, body condition status thermal stress Hoefnagel and Verberk 2015

Critical Individual Temperature events (past and Estimation of the upper thermal Determines the organism ability to Increase with Mora and Ospína Thermal present) modulate CTMax limit based on organism loss of undergo a severe thermal challenge; increasing temperature 2001; Ospína and Maxima through molecular changes (as righting response (e.g. equilibrium) when CTMax is surpassed, visible and time of exposure to Mora 2004; Eme and previously describe), which through the use of warming ramps; behavioral changes occur – thermal stress; in Bennett 2009; influence the animal’s whole measure of whole body thermal muscular spasms, inability to swin; tropical animals usually Madeira et al. 2012;

body thermal tolerance tolerance varies with thermal history, life ranges ~ 34˚C – 40˚C Vinagre et al. 2013, stage, micro-habitat, tidal height (subtidal and intertidal) 2015, 2016 and species

Acclimation Sample Its variation under temperature Measures plasticity of thermal Allows for regional comparisons Increase with Kumlu et al. 2010; capacity population stress/acclimation may be related tolerance at varying temperatures between populations and species, it increasing temperature Donelson et al. 2011; to flexibility and changes in and time of exposure is relevant to understand different and time of exposure to Re et al. 2012; cellular stress responses and other vulnerability profiles in space/time thermal stress Noyola et al. 2015; cytoprotective mechanisms according to warming scenarios Beaman et al. 2016; Vinagre et al. 2016

Mortality Sample Hyperthermia causes tachycardia Measured as % mortality or Measures the ability to survive High mortality/low Peters et al. 1997; population and tachypnea, seizures; above a survival curves, performance stress survival, depending on Kappenman et al. certain threshold leads to organ parameter the level of thermal 2009; Loarie et al. failure, unconsciousness and stress 2009; Munday et al. death 2012; Marshall et al. 2015

Thermal Sample As elevated temperature changes Measures the warming tolerance of Allows the assessment of Decrease with Paaijmans et al. 2013; safety margin CTMax and acclimation capacity a population/species, i.e. average population vulnerability/resilience increasing Marshall et al. 2015; population of organisms, it causes shifts in thermal tolerance compared to to present and future scenarios of environmental Chapperon et al. the population thermal safety maximum habitat temperature temperature conditions temperature 2016; Vinagre et al. margin 2016

1.5. Main aims of the thesis

The predicted increase in global temperature will bring changes in ecosystems, biotopes and composition and spread of species. In this process, it is essential that climate protection and adaptation measures include the necessary amendments or reorientations to existing nature conservation and coastal management concepts. Therefore, the protection of biodiversity will face big challenges over the next decades, especially in tropical areas where vulnerability to climate change is higher and protection measures are often more permissive. Basic research is needed to fill in the knowledge gap on tropical species (including biology, ecology and physiology) to enable the development/improvement of biomonitoring programs that include keeping track of thermal change at these locations and its effects on organisms, using metrics and methods comparable to the ones already used in temperate regions.

The ideas that originated this thesis were an attempt to address the previous concerns by answering the following questions: how will ocean warming affect marine ectothermic organisms from tropical areas? and how can we monitor these temperature effects? To evaluate tropical organisms’ vulnerability/resilience to increasing temperatures, I performed holistic assessments on the thermal biology of several species, including vertebrates (fish) and invertebrates (crustaceans and gastropods), tank-raised and wild animals, and intertidal and subtidal species from the tropical Atlantic and tropical Indo-Pacific regions.

This thesis focuses on temperature effects at the molecular, cellular, tissue and organism level, uncovering physiological mechanisms and thermal limits, and predicting population ability to withstand future projected temperature scenarios. Using an integrative and multidisciplinary approach to the information generated, the main aims were to (i) select, optimize and validate a set of biomarkers/parameters to generate a full picture of the physiology of stress in tropical marine ectotherms; (ii) provide a coherent database on physiological parameters within a range of optimal conditions for the studied animal groups (which are not defined for most tropical species) so that from there stress can be measured/quantified; (iii) analyze the utility of these physiological parameters in the biomonitoring of thermal change in vulnerable tropical reef systems as a way of diagnosing these ecosystems’ health, identify stressful events and design preventive plans in the future. Based on the important gaps of knowledge outlined above, the specific research questions addressed by this thesis were:

1. What are the cellular stress response mechanisms and metabolic pathways activated in tropical marine ectotherms during a thermal challenge?

2. How can we non-invasively assess response mechanisms to temperature in tropical marine ectotherms? 3. How can we integrate molecular and physiological responses to temperature in a general measure of stress? 4. What are the upper thermal limits of each species and how do they vary in time under chronic warming conditions? 5. Are the different parameters plastic in their responses and how does plasticity influence the potential for acclimation under ocean warming? 6. Is acclimation enough to maintain health and survival of species under future warmer conditions?

In summary, the overall output of this thesis was the characterization of the vulnerability profile of tropical marine ectotherms to ocean warming. By understanding the mechanisms behind organism responses to thermal challenges, our predictive and management capacities as well as ecological risk assessment will be enhanced under climate change scenarios.

1.6. Thesis outline

Briefly, this thesis (Fig. 1.9) is comprised of 9 chapters, the first being a general view of the main themes related to climate change on tropical marine ecosystems and animal thermal biology concepts as well as the structure of the present thesis; chapters 2-4 are the starting point where I analyze the thermal biology of several captive-bred species (with known thermal history) under chronic warming and an acute thermal event (heat wave); in chapters 5-8, I move from captive-bred species to wild species co-existing in the same habitat (tropical intertidal rocky reef in Southeastern Brazil), and analyze the effects of chronic and acute thermal stress exposure in these animals under an ecological context. Chapter 9 is the final one, where I present the concluding remarks and future perspectives.

Figure 1.9 PhD workplan flowchart.

More specifically, in Chapter 2 I investigate how future predictions of ocean warming affect the physiology (molecular biomarkers in several organs and body condition) of a captive- bred tropical fish species, with known thermal history and minimal genetic variation between individuals, minimizing all sources of uncontrollable variation; and integrate responses in an index allowing the evaluation of organisms’ health. In Chapter 3 I use a comparative physiology approach in the same captive-bred tropical fish species to investigate physiological responses (molecular biomarkers) to temperature stress using lethal vs non-lethal sampling methods. In Chapter 4 I investigate the effects of a heat wave on the physiology (molecular biomarkers and energy metabolism) of two captive-bred crustacean species (with known thermal histories), using an integrated index of responses for organism health assessment. In Chapter 5 I analyze the effects of climate warming scenarios on two intertidal fish species (molecular biomarkers, energy metabolism, thermal tolerance, body condition, acclimation capacity, mortality, thermal safety margins) from a tropical rocky reef and relate them with differences in micro-habitat preferences and life-histories. In Chapter 6 I investigate the thermal biology (sublethal effects, thermal tolerance and thermal safety margin) of a cosmopolitan intertidal gastropod species from a tropical rocky reef under temperature increase in an experimental setup of a warming ocean scenario. In Chapter 7 I investigate how climate warming scenarios will affect the geographical expansion of an invasive shrimp species in a

tropical rocky reef by characterizing its physiology (assessment of molecular biomarkers, energy reserves, body condition, upper thermal limits, acclimation response ratio, mortality, thermal safety margin) and by comparing present temperature regimes and predictions between their home range and introduction locations. Finally, in Chapter 8 I explore the general physiological patterns that emerge between different tropical animal groups and species within the same habitat and integrate physiological characterizations from different complexity levels to diagnose rocky reef health and attempt an ecological risk assessment for tropical intertidal species.

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CHAPTER 2

Thermal acclimation in clownfish: an integrated biomarker response and multi-tissue experimental approach

Madeira C, Madeira D, Diniz MS, Cabral HN, Vinagre C (2016) Ecological Indicators 71: 280- 292. DOI: 10.1016/j.ecolind.2016.07.009

ABSTRACT

The effects of increased temperature were tested in Amphiprion ocellaris, using a cellular diagnostics approach (in several tissues) combined with an organismal approach (body condition). Clownfish were exposed to a one month experiment following two temperature treatments: control (26˚C) and elevated temperature (30˚C). Fish were sampled at 0, 7, 14, 21 and 28 days for 1) assessment of stress biomarkers (catalase, lipid peroxidation, glutathione-S- transferase, superoxide dismutase, acetylcholinesterase, heat shock protein 70kDa and ubiquitin - in brain, gills, liver, intestine and muscle), 2) estimation of integrated biomarker response index based on the biomarkers tested and 3) assessment of Fulton’s K index. Results show all biomarkers except acetylcholinesterase responded consistently and significantly to elevated temperature across tissue types suggesting they are suitable indicators of thermal stress in A. ocellaris. Biomarker levels were tissue-specific, and in addition, the most reactive tissues to temperature were muscle, gills and liver which suggest that highly oxygenated tissues seem to be the most responsive under thermal stress. The most responsive sampling times to increased temperature were T7 (increase) and T28 (decrease): thermal stress was observed after 7 days of exposure, then a pattern of decrease in biomarker levels towards the end of the experiment was observed, which may suggest fish were able to acclimate to exposure conditions. This indicates that A. ocellaris probably lives far from its upper thermal limit and is capable of adjusting the protein quality control system and enzymes’ activities to protect cell functions under elevated temperatures. The temperature treatment did not significantly influence body condition of the animals but biomarkers were negatively correlated to wet body weight. This suggests that thermal acclimation incurs at some energetic cost. In conclusion, these results suggest that this coral reef fish species presents a significant acclimation potential under ocean warming scenarios of +4˚C.

Keywords: coral reef fish; thermal biology; stress biomarkers; IBR indices; tropical ecosystem health

Graphical abstract

Highlights • Long term effects of elevated temperature were tested in ocellaris clownfish • Molecular biomarkers and body condition were assessed using integrated indices • The most reactive tissues to temperature were muscle, gills and liver • Thermal stress was observed after 7 days, and after 28 days organisms acclimated • Thermal acclimation incurs with some energetic costs on body condition

2.1. Introduction

Temperature fundamentally affects all aspects of physiology by influencing reaction rates, as well as the physical properties of biological molecules (Hochachka and Somero 2002). Hence, thermal stress is widely proposed as the dominant physical stress in shallow water habitats and is reported to have pervasive effects on marine fish on both temperate (Helmuth et al. 2006) and tropical shores (Firth and Williams 2009). It is hypothesized that, for a complex organism, a hierarchical series of tolerance prevails, ranging from systemic to cellular to molecular levels (Weibel et al. 1991), with highest sensitivity at the organism level and wider tolerance windows at lower levels of biological complexity (Goh and Lai 2014). Because seasonal temperature variations are minimal in tropical areas, marine fish inhabiting such habitats are expected to display narrow tolerance ranges of temperature once their thermal limits may be close to their optimal temperature (Deutsch et al. 2008). In consequence, changes in environmental temperature may lead to the poor maintenance of physiological homeostasis, resulting in stress (Long et al. 2012).

However, no specific criteria have been established as to what constitutes optimum conditions for tropical marine ecosystems, and baseline data are often not available. While there is a growing appreciation that warmer temperatures will indirectly affect tropical reef fish communities through the degradation of reef habitats from mass coral bleaching (Pratchett et al. 2008), much less is known about the direct effects that increasing water temperature will have on coral reef fish (Nilsson et al. 2010). In particular, research is needed to determine suitable tropical test species that are or relate to potential impacts on keystone species, as well as appropriate test conditions (Botté et al. 2012). Studies are necessary to characterize and shed more light on the physiological and biochemical behavior of molecules in reef fish, under normal conditions to enable further analysis of changes induced by stress exposure (Assis et al. 2012).

Recent research on cardinalfish and damselfish has shown that exposure to elevated temperatures can result in increased O2 demand and therefore increase basic maintenance costs for the organism (Clarke 2003). This can reduce aerobic scope of reef fish (Nilsson et al. 2009) and key aspects of individual performance, such as growth (Munday et al. 2008) and fecundity (Donelson et al. 2010). In fact, hyperthermic stressful conditions can trigger a cascade of metabolic alterations in individuals (Romero 2004) and when it exceeds the individual capacity for acclimation, it can lead to health disorders due to enzyme inactivation, protein unfolding and degradation, DNA damage and lipid peroxidation (Livingstone 2003). Hence, fish (like other

organisms) have evolved defense systems and cytoprotective mechanisms which are employed for the maintenance of cell viability and functional activity (Tang et al 2014).

The response to thermal stress thus comprises both enzymatic and nonenzymatic pathways in a process called cellular stress response (CSR) (Kaviraj and Gupta 2014). On one way, enzymatic pathways involve antioxidant enzymes whose primary purpose is to quench or reduce the flux of reactive oxygen species (ROS) produced during oxidative metabolism (Lesser 2011). On the other way, nonenzymatic processes involve the induction of protective proteins that act as molecular chaperones and prevent protein unfolding and aggregation which would render them nonfunctional. In the first case, the enzymes superoxide dismutase (SOD; converts - O2 to H2O2+O2), catalase (CAT; reduces H2O2 to H2O) and glutathione S transferase (GST, phase II enzyme that conjugates hydro-peroxides with glutathione) are used by cells to maintain their oxidative status and are also the biomarkers of choice to appraise oxidative stress (Di Giulio et al. 1995). In addition lipid peroxides (LPO) are used as markers of cell membrane damage (Ferreira et al. 2015a), and the activity of the enzyme acetylcholinesterase (AChE, terminates synaptic transmission) can be used as a measure of neurotoxicity (Ferreira et al. 2015a). In the second case, induced heat shock proteins (Hsps) act in the refolding of denatured structural and cellular proteins and ubiquitin (Ub) signals nonfunctional proteins for degradation in the proteasome (Coles and Brown 2003). Variations in the expression of stress proteins and antioxidants are hence used as biomarkers of effect once they are a direct measure of changes in the degree of reversible/irreversible macromolecular damage and their expression and accumulation in cells represents a quantifiable response to sublethal thermal stress exposure (Vieira et al. 2014).

However, the application of biomarkers in environmental assessment is limited without an integrated system to overcome difficulties in relating information and in categorizing the severity of stressors and induced changes in the health status of the organisms (Cravo et al. 2012). In these circumstances, a simple method is needed to summarize biomarker responses and simplify their interpretation in biomonitoring programs (Beliaeff and Burgeot 2002). One solution is the use of a methodology that integrates the responses of different biomarkers into a single value or graph, allowing information to be summarized in the form of a multivariate data set. Among these indices, the Integrated Biomaker Response (IBR, Beliaeff and Burgeot 2002) along with star plots, are amongst the most used in field and laboratory studies (Serafim et al. 2012). This should provide a more valid basis for interpretation of ecological surveys.

The main objective of this study was to measure the cellular stress response in the tropical reef fish species Amphiprion ocellaris under exposure to elevated temperature. In

particular, the research aims were to 1) evaluate the effects of elevated temperature in A. ocellaris through the quantification of molecular biomarkers i.e. CAT, LPO, SOD, GST, AChE, Hsp70 and Ub at two different temperatures (26˚C – control, and 30˚C – elevated temperature), along one experimental month (sampling at 0, 7, 14, 21 and 28 days), in several tissue types (brain, gills, liver, intestine and muscle); 2) use a multi-biomarker integrated approach (IBR and star plots) for the evaluation of the effect of elevated temperature on organismal health and ability to acclimate and 3) evaluate the effects of elevated temperature on body condition of A. ocellaris.

2.2. Materials and methods

2.2.1. Test organism and acclimation procedure

The model species used in this study was the clownfish Amphiprion ocellaris (Cuvier 1830). Juvenile fish (n=45) were obtained from Tropical Marine Centre Iberia hatchery (Loures, Portugal). Upon arrival at the laboratory fish were placed in one indoor re-circulating water system (total volume of 2,000 L), comprised of six 70 L polyvinyl tanks supplied with aerated sea water, one common sump and an external skimmer and UV filter. Inflow of clean water in each tank was 300 ml.min-1. Fish were randomly distributed across tanks (n=10 individuals per tank) and allowed to acclimate at 26±0.5˚C for 2 weeks.

2.2.2. Experimental design and sampling

All experimental procedures followed ethical guidelines in national and international legislation, namely the European Food Safety Authority recommendations (three of the authors possess FELASA level C accreditation). The experimental design (Fig. A3.1 in annex 3) consisted of two temperature treatments: 26˚C±0.5˚C (control, physiological optimum for A. ocellaris) and 30˚C±0.5˚C (elevated temperature, chosen to reflect current IPCC projections for 2100 in tropical areas (IPCC, 2013)), with the duration of one month and samplings at 0, 7, 14, 21 and 28 days. For each treatment there were 3 replicate tanks (35 x 35 x 55 cm each), with 10 individuals per tank. All tanks were provided with a filter (ELITE 9 Underwater Mini-Filter Hagen, 220L.h-1) and a thermostat heater (ELITE 200W). To keep environmental parameters constant throughout the experiment, a monitoring scheme was employed. Each tank was provided with a Petco thermometer with suction cup to monitor temperature continuously. Other parameters i.e. salinity (kept at 35), pH (kept at 8±0.01), ammonia (kept at 0 mg.L-1) and nitrites (kept under 0.3 mg.L-1), were monitored twice a week using a hand-held refractometer (Atago, Japan), a digital pH probe (model HI9025, Hanna Instruments, USA), and Tetra Test Kits (Tetra

Ammonia Test Kit and Tetra Nitrites Test Kit, USA), respectively. A period feeding regime (twice a day) was implemented. Fish were fed with fresh food (mixture of shrimp and mussels in proportion 1:1) and dry food (Tropical Super Spirulina Forte Mini Granulat). At each sampling day 5 animals from each treatment were chosen randomly (Fig. A3.2 in annex 3) sacrificed by cervical dissection and then measured and weighed. Internal organs (brain, gills, liver, intestine and muscle) were then extracted and frozen at -80˚C until further analyses (Fig. A3.3 in annex 3).

2.2.3. Biochemical biomarkers and IBR

Tissue samples were homogenized in 1ml of phosphate buffered saline (140mM NaCl,

3mM KCl, 10mM Na2HPO4, 2mM KH2PO4, pH 7.4) using a Tissue Master 125 homogenizer (Omni International, Kennesaw, USA) on ice-cold conditions. Homogenates were then centrifuged at 4˚C for 15 min at 10,000 g. The supernatant was collected, transferred to new microtubes (1.5 ml) and frozen immediately (-80˚C). Total protein determination and enzymatic assays were then carried out using procedures previously described and adapted for 96-well microplates (Madeira et al. 2015), which are extensively described in the supplementary material (see Table A2.1 in annex 2): i) Enzymatic assay of catalase (Enzyme Commision number – EC 1.11.1.6, following Johansson and Borg 1988) ii) Lipid peroxides assay using TBARS (thiobarbituric acid reactive substances), measured as MDA content (Uchiyama and Mihara 1978); iii) Enzymatic assay of glutathione S-transferase activity (EC 2.5.1.18), using reduced L- glutathione (GSH) and the substrate CDNB (1-Chloro-2,4-dinitrobenzene) originally developed by Habig et al. (1974). iv) Enzymatic assay of superoxide dismutase activity, using nitroblue tetrazolium (NBT) and xanthine oxidase (XOD) as described by Sun et al. (1988). v) Enzymatic assay of acetylcholinesterase (EC 3.1.1.7) using thiol quantification based on Ellman’s method (Ellman et al. 1961); vi) Heat shock protein 70 quantification using an indirect Enzyme Linked Immunosorbent assay (ELISA) and ubiquitin quantification using a direct ELISA (Madeira et al. 2014); vii) Total protein quantification (Bradford 1976).

To integrate results from the different biomarkers and understand global/general responses, the Integrated Biomarker Response (IBR) was calculated according to Beliaeff and Burgeot (2002); details can also be found in the supplementary data (see annex 3). Lower or

higher index core values can be translated into the impact of high temperature on organisms: higher index core values are indicative of a poorer health status (stressed organisms).

2.2.4. Data analysis

Stress biomarker data were tested for 1) outliers (box-whiskers plot, coefficient 1.5 for outliers and extremes), 2) normality through Shapiro-Wilk's test and 3) homoscedasticity through Levène's test. Logarithmic transformation was employed when test assumptions (normal distribution and homoscedasticity) were not met. Data were then analysed by factorial ANOVAs and MANOVA with biomarkers as dependent variables and temperature, time and tissue type as independent variables. This global model allowed testing for differences in temperature treatments across the entire experimental month, differences in biomarker levels in different tissues/organs and finally, it also allowed testing for possible interactions between time and temperature and tissue and temperature. To test which groups differed and which variables contributed to the significant differences detected, we performed post-hoc Tukey's honestly significant difference tests. In order to detect significant differences in IBR values obtained for different treatments, Student t-tests were employed.

To calculate body condition of the animals, Fulton’s K was directly determined from the morphometric data with the formula:

where is the total wet mass (mg) and is the total length (mm) (Ricker 1975). Student t- tests were employed to detect for differences in morphometric data and condition index between treatments. Because linear length growth is primarily skeletal growth, whereas weight growth is mainly due to protein accretion and increase in muscle mass (and these are regulated by different systems and have different energetic costs) (Einarsdóttir et al. 2014); and this study used mainly protein and enzymatic biomarkers, wet body weight was used to explore further variation in biomarker levels. Relationship between total protein (in the muscle and whole organism) and body weight as well as relationships between biomarkers with body weight were examined by regression analysis and Spearman correlation, respectively. All statistical analyses were performed using the software Statistica v12 (StatSoft Inc., USA).

2.3. Results

2.3.1. Stress biomarkers activity Biomarker basal levels in healthy animals regarding each tissue are presented in table 2.1. The use of a multivariate model where all the biomarkers tested were combined in one

analysis (MANOVA) showed significant differences for all the effects tested (temperature, tissue type and the interaction between them and with time, Table 2.2). The univariate ANOVAs allowed the identification of the variables which contributed to these overall results. Test results showed that significant variability in response to the temperature treatments in the entire experimental month could be observed for LPO, GST and Hsp70 (Table 2.2). In all of the cases studied significant differences were also detected in biomarker levels in different tissues. When it comes to the interactions between factors (Table 2.2), significant results were observed for several biomarkers in all the combinations tested. Considering temperature – time interactions, significant results were observed for all biomarkers except for SOD and AChE. Significant differences for the interaction between temperature-tissue and temperature-tissue- time were also detected (for CAT, GST, Hsp70 and CAT, LPO, GST, Hsp70, Ub respectively).

Table 2.1 Biomarker basal levels (mean±SD) in healthy A. ocellaris juveniles. NA = Non-Applicable. Abbreviations: CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase, Hsp70 – heat shock protein 70kDa, Ub – ubiquitin.

Biomarker Units Brain Gills Liver Intestine Muscle Total protein mg.ml-1 of homogenate 0.663±0.094 1.096±0.316 1.122±0.324 1.159±0.254 6.036±3.083 CAT nmol.min.mg-1 of protein 30.529±8.638 150.256±42.286 2609.876±1147.067 224.957±85.041 21.953±7.765 LPO nmol.mg-1 of protein 0.019±0.011 0.034±0.027 0.261±0.327 0.040±0.028 0.009±0.005 GST nmol.min.mg-1 of protein 25.317±12.059 123.457±50.557 774.027±271.563 74.508±29.054 15.697±9.029 SOD %inhibition.mg-1 of protein 2.132±0.674 7.860±1.562 67.937±23.648 6.893±1.686 1.048±0.156 AChE nmol.min.mg-1 of protein 229.339±44.353 NA NA NA 274.266±119.852 Hsp70 µg.mg-1 of protein 0.244±0.125 0.872±0.505 12.687±7.443 0.948±0.597 0.063±0.025 Ub µg.mg-1 of protein 0.026±0.020 0.194±0.111 3.753±1.369 0.159±0.076 0.052±0.015

Table 2.2 Compilation of statistical analyses (factorial ANOVAs and MANOVA) of temperature and between tissue variability, and interactions with time in biomarker responses in Amphiprion ocellaris. Significant results (p-value < 0.05) are marked in bold. Abbreviations: CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase, Hsp70 – heat shock protein 70kDa, Ub – ubiquitin.

CAT LPO GST SOD AchE Hsp70 Ub Wilks’s test F p F p F p F p F p F p F p F p Temperature 0.405 0.525 20.030 0.000 10.500 0.001 3.700 0.056 0.893 0.348 7.477 0.007 0.492 0.484 8.348 0.000 Tissue 1763.461 0.000 103.952 0.000 1005.483 0.000 23.920 0.000 4.846 0.031 425.117 0.000 1523.643 0.000 120.149 0.000 Temp*Time 3.727 0.006 11.420 0.000 8.214 0.000 2.332 0.057 0.080 0.988 16.760 0.000 7.463 0.000 7.621 0.000 Temp*Tissue 3.647 0.007 0.391 0.815 5.641 0.000 0.852 0.494 0.346 0.558 8.657 0.000 1.868 0.118 3.501 0.000 T*T*Ta 3.077 0.000 4.794 0.000 4.880 0.000 1.037 0.420 0.229 0.921 3.828 0.000 2.193 0.006 3.099 0.000 aT*T*T = Temperature x Tissue x Time

In addition, biomarker levels for temperature treatments in each sampling time and each organ are presented in detail in Fig. A3.4, A3.5 and A3.6 in annex 3. Overall, it is possible to observe a general trend of increase-decrease in biomarker levels at 30˚C, evident especially in CAT (gills, intestine, muscle, Fig. A3.4b, d and e in annex 3), and Hsp70 (gills, liver, intestine, Fig. A3.5b, c and d in annex 3), but also detected in Ub (brain, muscle, Fig. A3.5a and e in annex 3), LPO (intestine, muscle, Fig. A3.5d and e in annex 3), SOD (gills, Fig. A3.4b in annex 3) and GST (muscle, Fig. A3.4e in annex 3). Another observed trend was a significant decrease in biomarker levels at 30˚C towards the end of the experiment (from T21 through T28), namely in LPO (brain, gills, liver, intestine, Fig. A3.5a, b, c, d in annex 3), and also in SOD (liver, muscle, Fig. A3.4c, e in annex 3), CAT (brain, Fig. A3.4a in annex 3) and GST (gills, Fig. A3.4b in annex 3).

The most reactive biomarkers (which responded in more than one sampling time in several organs) were (in decreasing order): Hsp70 > Ub > CAT > LPO > SOD > GST > AChE (the latter with no significant differences detected for any of the sampling times). Other interesting patterns standing out in the analyses performed were that biomarkers seem to have stronger responses to the temperature treatments in the muscle, gill and liver tissues (more significant differences were found between treatments in these tissues, Fig. A3.4-A3.6 in annex 3). The least responsive tissue was the intestine. With respect to the sampling times, biomarkers seemed to be more responsive to the temperature treatments either at the beginning of the experiment (T7, generally an increase was detected) or at the end of the experiment (T28, generally a decrease was detected).

2.3.2. Integrated biomarkers response index (IBR)

Star plots for calculated scores using IBR index for biomarker responses are depicted in Fig. 2.1. Graphs show the most responsive biomarkers vary among different tissue types and different sampling times. In brain tissue (Fig. 2.1a), GST showed higher scores at 30˚C for most

sampling times (T7-T28), denoting deleterious effects of elevated temperature (check also table A3.1 in annex 3 to see a summary of the effects detected by IBR calculations). On the other hand CAT presented lower scores at 30˚C in the brain when compared to controls (in T7, T14 and T28). In gill tissue (Fig. 2.1b), the most responsive biomarkers were CAT, GST, SOD and Hsp70. Higher scores for these biomarkers were detected at 30˚C in T7 and/or T14, whereas in T21, the scores decreased to lower levels than control (pattern of increase-decrease).

Figure 2.1 Star plots for each sampling time of Amphiprion ocellaris exposed to 26˚C and 30˚C (exposure period: 0 days, 7 days, 14 days, 21 days and 28 days) in each tissue studied: a) brain, b) gills, c) liver, d) intestine and e) muscle. Biomarker scores were calculated in the IBR (Integrated Biomarker Responses) Index. Abbreviations: CAT – catalase, LPO – lipid peroxides, GST – glutathione-S- transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase, Hsp70 – heat shock protein 70kDa, Ub – ubiquitin.

In liver tissue, CAT and GST were the most responsive biomarkers, both showing deleterious effects at elevated temperature in 3 sampling times (T14, T21, T28 and T7, T14, T28, respectively). In intestine tissue, Hsp70 was the most responsive biomarker, showing a pattern of increase-decrease-increase in scores in T7, T21 and T28 (corresponding to a decrease- increase-decrease in fitness, Table A3.1 in annex 3). CAT also responded in intestine, with higher scores at elevated temperature in T7, followed by a decrease at T21 and T28. In muscle tissues, biomarkers with most increased scores at elevated temperature were Hsp70 (T7, T14) and CAT (T0, T21).

Combining all sampling times in IBR analysis in each tissue (Fig. 2.2) allowed for an overall interpretation of responses to elevated temperature in the experimental month. The brain (Fig. 2.2a) tissues were not in general very reactive to elevated temperature; in fact IBR index values are generally lower at 30˚C and no significant differences were detected (Table 2.3a). Overall higher mean scores at 30˚C were nevertheless detected for GST and Ub. In gill tissue (Fig. 2.2b), it is possible to observe that when all sampling times were combined, mean biomarker scores were similar at both temperature treatments. IBR index was higher for 30˚C from T7-T14/T28, but mean IBR values for 26˚C and 30˚C were not statistically different (Table 2.3a). In liver tissue during the experimental month, all reached higher mean scores at 30˚C and IBR values were higher in T7-T14, and then stabilized until the end of the experiment, but mean IBR values for the whole experiment were similar for both temperature treatments (Table 2.3a). In intestine tissue, mean biomarker scores (including all sampling times) were similar at both temperatures. IBR index values were higher at T7 and T28 but no significant differences were detected between temperature treatments for mean IBR values (Table 2.3a).

Figure 2.2 Star plots with mean scores for the experimental month (all sampling times combined) of Amphiprion ocellaris exposed to 26˚C and 30˚C; and IBR values for each sampling time and all times combined (mean+SD) in each tissue of Amphiprion ocellaris studied: a) brain, b) gills, c) liver, d) intestine and e) muscle. Abbreviations: CAT – catalase, LPO – lipid peroxides, GST – glutathione-S- transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase, Hsp70 – heat shock protein 70kDa, Ub – ubiquitin.

In muscle tissue, Hsp70, Ub and CAT showed higher mean scores at elevated temperature for the whole experimental month and IBR values were higher from T0-T14, followed by a decrease, especially at T28 (at 30˚C). Mean IBR values for the two temperature treatments in the whole experiment were not statistically different (Table 2.3a).

Figure 2.3 a) Star plots with mean scores for the whole animal (all tissues combined) Amphiprion ocellaris exposed to 26˚C and 30˚C in each sampling time; b) IBR mean values for each sampling time in each tissue and all tissue types combined (data is presented as mean+SD). Abbreviations: CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase, Hsp70 – heat shock protein 70kDa, Ub – ubiquitin.

In order to analyse the differences in IBR index in each sampling time for the whole organism (combining all tissues in the analysis) star plots were constructed based on mean scores for each biomarker (Fig. 2.3). Results show biomarker mean scores (for the whole organism) were coherently higher in T7 (except LPO) at 30˚C (Fig. 2.3a), which is reflected in

IBR index values in T7 (Fig. 2.3b) in each tissue and all tissues combined, where statistically significant differences were detected (Table 2.3b). In T14, mean biomarker scores show CAT, GST and LPO still respond to elevated temperature and mean IBR (for the whole body) is still higher in T14, though not statistically significant (Table 2.3b). From that sampling time on until the end of the experiment (T28), graphs (Fig. 2.3a) show biomarker mean scores for the whole organism at 30˚C decrease, reaching lower levels than at 26˚C (T21) and then seem to stabilize at T28 (mean scores are similar for both temperatures, except for Hsp70 which still shows a slight increase at 30˚C). This is also reflected in IBR values for these sampling times (Fig. 2.3b), showing mean IBR values for the whole organism are lower at higher temperatures in T21 and T28, although these differences are not significant (Table 2.3b).

Figure 2.4 Star plot with mean scores for the whole animal (all tissues combined) in the experimental month (all sampling times combined), exposed to 26˚C and 30˚C; b) IBR values for each tissue type and all tissue types combined (mean+SD) in the experimental month (all sampling times combined). Abbreviations: CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase, Hsp70 – heat shock protein 70kDa, Ub – ubiquitin.

In a final global analysis, all tissues and all sampling times were combined to calculate IBR index (Fig. 2.4). Results show that mean biomarker scores were similar for both temperature treatments, as well as mean IBR values (for the whole animal, for the whole experimental month), with no significant differences detected (Table 2.3b). It is observable however mean IBR is higher in liver and muscle, which had been previously identified as the most responsive tissues to elevated temperature.

Table 2.3 Student t-tests for differences in IBR index values between temperature treatments (26˚C vs 30˚C) in a) each tissue type analysed and whole organism (all tissue types combined) for the entire experimental month; b) each sampling time analysed and entire experimental month (all sampling times combined) for the whole organism. Significant differences (95% confidence limits) are marked in bold.

t-test df p-value a) IBR brain 0.483 8 0.642 IBR gills 0.183 8 0.859 IBR liver -1.502 8 0.171 IBR intestine 0.097 8 0.925 IBR muscle -0.848 8 0.421 IBR whole organism -0.207 8 0.841 b) IBR T0 -0.053 8 0.959 IBR T7 -2.248 8 0.050 IBR T14 -1.563 8 0.157 IBR T21 1.647 8 0.138 IBR T28 0.116 8 0.911 IBR T0-T28 -0.284 8 0.784

2.3.3. Body condition and stress biomarker levels

Length and weight of the specimens were measured (total length(mean±SD)= 3.58±0.32 cm, total mass(mean±SD)= 1.05±0.26 g at 26˚C; total length(mean±SD)= 3.49±0.40 cm, total mass(mean±SD)= 1.02±0.31 g at 30˚C) but no significant differences were detected either in length or weight between temperatures (t-value=1.756, p=0.087 and t-value=1.237, p=0.224, respectively). In addition, we calculated the body condition index Fulton’s K for all individuals (K26˚C (mean±SD) =

2.25±0.22, K30˚C (mean±SD) = 2.27±0.50) and no significant differences were detected between treatments (t-value=-1.328, p=0.190). As such, the body condition of the clownfish was relatively stable throughout the experiments in both treatments.

Weight gain/loss is especially reflected in muscle mass, but also in total organism mass and it is known to influence protein production. Differences in mean total protein between treatments were evaluated but no significant differences were detected (t-value=-1.861, p=0.068). Regression results between mean total protein and total muscle protein with body weight are presented in Fig. 2.5. Positive linear correlations were found between protein content (both in total organism and muscle alone) and wet body weight of the studied specimens.

Figure 2.5 Relationship between a) mean total protein concentration (mg.ml-1) in sample extracts (all tissues combined – brain, gills, liver, intestine, muscle) and individual wet body weight (g.ind-1) of Amphiprion ocellaris (R2=0.307, p=0.000, n=45); b) muscle total protein concentration (mg.ml-1) in sample extracts and individual wet body weight (g.ind-1) of Amphiprion ocellaris (R2=0.245, p=0.000, n=45).

Correlations between biomarker levels and wet body weight were also tested (Table 2.4) and results showed that significant correlations occurred mostly in the muscle tissue (5 out of 7 biomarkers – LPO, GST, AChE, Hsp70 and Ub were significantly correlated to wet body weight) as expected. All correlations found in this tissue (except for LPO) were negative. Biomarkers that significantly correlated more to wet body weight were Hsp70, Ub and GST (all showed significant correlations in at least 3 of the analysed tissues, Table 2.4).

Table 2.4 Spearman’s rank order correlation between biomarker levels in each tissue and total wet body weight (g.ind-1) of Amphiprion ocellaris. NA = Non-applicable (AChE was only measured in brain and muscle). Abbreviations: CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase, Hsp70 – heat shock protein 70kDa, Ub – ubiquitin.

Brain Gills Liver Intestine Muscle R p R p R p R p R p CAT 0.537 0.000 0.091 0.532 0.620 0.000 0.007 0.960 0.014 0.923 LPO 0.226 0.114 0.043 0.769 0.197 0.170 0.109 0.453 0.348 0.013 GST -0.491 0.000 -0.332 0.019 0.017 0.905 -0.255 0.074 -0.406 0.003 SOD 0.175 0.224 -0.053 0.713 0.078 0.590 0.436 0.002 0.119 0.409 AChE 0.190 0.186 NA NA NA NA NA NA -0.570 0.000 Hsp70 -0.291 0.040 -0.629 0.000 -0.195 0.176 -0.207 0.148 -0.486 0.000 Ub -0.091 0.531 -0.477 0.000 -0.534 0.000 0.003 0.982 -0.511 0.000

2.4. Discussion

In the present study the mechanisms involved in thermal stress in the tropical reef fish A. ocellaris were assessed. This analysis not only provided information about the response mechanisms inherent to high temperature exposure, but also the patterns observed at different sampling times, integrated in a multi-biomarker approach. An attemptive health assessment of organisms was also employed by looking at differences in body condition under elevated temperature exposure. The main highlights elucidated from the present study will be discussed in the following subsections.

2.4.1. Patterns of biomarker levels under elevated temperature

The results obtained from biomarker analyses show that most of the target molecules responded consistently and significantly to elevated temperature across tissue types. The most responsive target molecules were (by this order) Hsp70, Ub, CAT, LPO, SOD and GST, and showed typical biphasic responses in their levels (increase-decrease) upon stress exposure which foresees they are suitable indicators of thermal stress in A. ocellaris. They proved to be suitable once they showed sensitivity, inducibility and robustness in responses across tissue types at different temperatures. In particular, these results suggest that at 30˚C animals were pushed beyond their pejus thermal threshold (Pörtner 2010) leading to the activation of the CSR. One possible hypothesis is that an increase in temperature is coupled with an increase in hypoxia so as the metabolic rate increases (Goh and Lai 2014), it results in excess production of oxygen radicals and consequent macromolecular damage (such as partially folded or aggregated proteins and lipid peroxidation in cell membranes), explaining the initial increase of several biomarkers. Interestingly, LPO, which directly measures irreversible damage to membranes also responded significantly to an increase in temperature (mostly at 14 days), suggesting that even full functioning cell protective mechanisms cannot protect the cells from all damage.

As known, temperature affects the reactivity of molecules by affecting protein conformation, kinetic properties, and assembly (Tang et al. 2014). Once this happens, the regulation associated with prevention, repair and degradation of damaged macromolecules is subsequently triggered to avert an increase in ROS accumulation and protein aggregation, which are harmful to cell viability (Bukau et al. 2006). The fact that critical cytoprotective mechanisms for coping with temperature stress behaved differently between 26˚C and 30˚C in A. ocellaris tissues, indicates that processes, namely protein quality control (PQC,which includes Hsp70 and Ub, Bukau et al. 2006) and free radical scavenging (including CAT, SOD and GST) were

activated under elevated temperature. This suggests that fish were thermally stressed. Similar results in response to change in ambient temperature have also been observed for many other aquatic and marine species (Hochachka and Somero 2002; Hofmann et al. 2002; Place et al. 2004; Iwama et al. 2006; Todgham et al. 2007; Cui et al. 2013). In fact, since the CSR is highly conserved among taxa, it was expected that A. ocellaris would respond in the same manner, as was observed.

The only biomarker that did not significantly respond to temperature in this study was AChE. It is know that this enzyme controls nerve stimulation, and under stress it is expected to downregulate its activity which has been observed for the tropical fish Acanthochromis polyacanthus under increased temperatures (Botté et al. 2013), and could possibly lead to muscle overstimulation and consequent respiratory failure (Assis et al. 2012). However, this was not observed in the present study, probably because the temperature tested was not high enough to affect the nervous system. Nevertheless, other studies with temperate marine fish species have also reported no significant effects of temperature on cholinesterase isoforms (Beauvais et al. 2002).

Focusing on the pattern of increase-decrease observed for most biomarkers under elevated temperature exposure, several authors have suggested that, after that first short term defense activation, an organism can either: i) exhaust the cellular defence mechanisms as it enters hypoxaemia or ii) it can acclimate. Both lead to a significant decrease in biomarker levels. In this study, the second hypothesis is suggested as the most plausible, and the reasons for this will be discussed in further detail later on, as the effects of exposure time on biomarker responses are analysed.

2.4.2. Assessment of thermal stress in different organs

Results showed that i) biomarker levels (both baseline and stress) are tissue-specific and vary in order of magnitude in different tissues – liver had the highest constitutive levels; ii) the most reactive tissues to temperature were muscle, gills and liver; and iii) overall, IBR values were similar for both temperature treatments (26˚C and 30˚C) in all organs separately and in the whole organism. The occurrence of spatially asymmetric expression of biomarker levels raises several points of interest. These data may suggest that: a) each tissue has a particular metabolic activity level (and a consequent particular rate of protein synthesis and consumption) that is likely to vary and probably depends on temperature (Pavlović et al. 2010); b) the diffusion rate of oxygen and hence the build-up of ROS is strongly influenced by the tissue function (Falfushynska et al. 2014).

Focusing on the first hypothesis, in each tissue or cell, function determines which metabolic pathways are mainly being expressed, which could account for the differences in temperature sensitivity and consequent biomarker response (Kaviraj and Gupta 2014). These results are corroborated by other studies which suggest biomarker sensitivity to stress exposure needs to be correctly evaluated in different cell types and organs because they are likely to be tissue-specific (Goh and Lai 2014; Colin et al. 2015). It is expected that basal biomarker levels differ in each organ, and organs with higher basal levels are likely to respond faster and better under stress circumstances (Frederich and Pörtner 2000), as was observed in A. ocellaris in this study. In regards to the second hypothesis, the coordinated response of different cytoprotective mechanisms triggered by temperature indicates that highly aerobic tissues such as gills (breathing function) or muscle (moving/swimming function) may be more sensitive to heat- induced oxidative stress than less aerobic tissues, for instance digestive tissue as observed by Falfushynska et al (2014). This corroborates the results, given that intestine was the least responsive tissue under elevated temperature in A. ocellaris and the most responsive tissues were muscle, gills and liver. These results thus further strengthen the concept that the first line of thermal sensitivity is due to capacity limitations at higher levels of organizational complexity, i.e. the integrated function of the oxygen delivery system, before individual, molecular or membrane functions become disturbed (Goh and Lai 2014).

2.4.3. Influence of time of exposure in biomarker responses

In the present study, several biomarkers exhibited a response that was either induced or inhibited according to the sampling time. Results showed that exposure time significantly interacted with temperature responses and tissue-type, so in fact time influenced the organisms’ reaction to elevated temperature. This suggests long term exposure tests are advisable when a more comprehensive approach is required in order to integrate physical fate and changes in intensity with time (Ferreira et al 2015b). Interestingly, other studies, namely those using fish bioassays, suggest the time of exposure to a stressor should be defined based on what is being tested (e.g. behavior, feeding, survival, bioaccumulation, reproduction) (Cook et al. 2014). Time of exposure can thus be considered one of the key factors to improve ecological relevance in bioassays and should be made a priority in biomarker research.

In this study, the most responsive sampling times to increased temperature were T7 (increase) and T28 (decrease). Firstly, there’s a typical thermal stress response after 7 days of exposure (significant higher levels of biomarkers in organisms under elevated temperature), and then biomarker levels stabilize (either they show a significant decrease in comparison with

controls or no significant differences from the control, this was mainly observed for LPO, SOD, CAT and GST, in several organs). This comprehensive assessment of molecular and cellular stress responses showed that elevated temperatures are able to induce cytotoxic changes in ocellaris clownfish, which has also been observed for many other temperate (Madeira et al. 2013, 2014; Vinagre et al. 2014) and a few tropical fish species (Abbink et al. 2011; Le et al. 2011; Botté et al. 2013). These results were also corroborated by IBR index results, once at T7 there was a significantly higher IBR value at 30˚C, when compared to the control, which mirrors the higher scores obtained for biomarkers at 30˚C, coincident with their increase and explains a higher demand for cytoprotection against thermal stress. Since biomarkers are direct measures of reversible/irreversible damage to macromolecules, this suggests a decrease in cellular fitness at T7. This is in accordance with the literature, which states that, in fish that are able to acclimate to a rise in water temperature, the process usually takes about 1 week (Barrionuevo and Fernandes 1998).

Interestingly, IBR values then stabilized and no significant differences were detected between the control and elevated temperature for the remaining of the sampling times until the end of the experiment which could be indicative of acclimation. To further strengthen these results, IBR results for the entire month (all sampling times together) and entire organism (all organs together) were also indicative of acclimation, due to a lack of significant differences between IBR values at 26˚C and 30˚C.

This suggests A. ocellaris acclimated to 30˚C after one week of exposure to this temperature. To date, only a handful of studies have examined thermal acclimation in coral reef fish, but most indicate that these fish have limited capacity for acclimation to increasing temperature (e.g. Nilsson et al. 2010, Vinagre et al. 2016). For example, Pomacentrus moluccensis kept for 22 days at 32°C did not exhibit improved aerobic capacity compared with fish kept for 0 or 7days at this temperature (Nilsson et al. 2010). On the contrary, the present study shows that some juvenile coral reef fish can cope with adverse environmental conditions through acclimation (a form of plasticity), which involves the modification of physiological characteristics (Angilletta 2009). This suggests a suite of enzymes with different thermal optima, and enzymatic systems that are capable of optimizing the lipid composition of cellular and subcellular membranes (Gracey et al. 2004). In fact, the decrease in biomarker levels towards the end of the experiment suggests a decrease in ROS formation and protein and lipid damage as the organisms acclimate to elevated temperature. This is further evidence that responses to ocean warming will vary among species and that clownfish may be not be as thermally sensitive as other coral reef fish (e.g. as cardinal fish, see Nilsson et al. 2010). Furthermore, it is possible that acclimation capacity has not yet undergone selection as present

temperatures do not seem to have exceeded the thermal performance breadth of this species (Donelson and Munday 2012). In addition, many species of fish have greater tolerance for increased temperature during their early life (Wilson and Nagler 2006). As such, exposure to elevated temperatures during these times (i.e. juvenile phases) may condition enzymatic systems to operate more efficiently at elevated temperature (Angilleta et al. 2009; Nilsson et al. 2010), which seems to be the case for the juvenile A. ocellaris in this study.

Moreover these results are corroborated by other studies with tropical reef fish from Indo-Pacific regions (nine banded cardinal fish and white tailed humbug, Eme and Bennett 2009) suggesting that some reef associated species appear able to survive long term exposure to temperatures 7˚C above current mean water temperatures (26-27˚C), so long as the rate of temperature change does not exceed the fish’s acclimation rate. Exposure to 30˚C as used in this study is still below that suggested 7˚C limit for coral reef fish, which seems to fall within the tolerance zone of this clownfish species, explaining why they showed such a good acclimation potential. Likewise, Mora and Ospína (2001) have studied the CTMax (Critical Thermal Maxima) of several coral reef fish and came to the conclusion that even the least tolerant species tested had a CTMax that was 8˚C above the current mean sea temperature in the area of collection. In another study by Ospína and Mora (2004), several reef fish acclimated at 26˚C had CTMax that varied between 35.0˚C (Apogon pacifi) and 37.5˚C (Lutjanus guttatus). In a similar study, Eme and Bennett (2009), came to the conclusion that reef fish acclimated to elevated temperatures (namely, 30˚C), showed a higher CTMax when compared to organisms acclimated at 26˚C, reaching values as high as 40.25˚C (Dascyllus aruanus).

Clownfish thus seem to respond well to chronic elevated temperature corroborating the hypothesis that this species still lives far from its upper thermal tolerance limit and current global-warming trend is still unlikely to be directly dangerous for this species (Mora and Ospína 2001). It is expected that if other syntopic fish show similar tolerance and acclimation potential to those seen in this study, then tropical Indo-Pacific reef fish are more likely to be affected by the indirect consequences of increasing water temperature (e.g., reef degradation or changes in trophic structure) rather than direct temperature-induced mortality (Munday et al. 2008a). Nevertheless it is necessary to consider that even if an increase in metabolism due to an increase in temperature may not endanger the species, the combination of high temperature with other stressors (e.g. chemicals) can potentiate their bioaccumulation and therefore toxicity.

2.4.4. Relation between body condition and stress responses

One avenue of ‘calibration’ of the multibiomarker approaches is comparison with other measures of organismal condition (Halpin et al. 2002). The study of body condition in relation to thermal tolerance allows for the assessment of intra-specific variation in thermal tolerance, potential resilience to extreme thermal phenomena, and the significance of body size as a selective trait under these phenomena (Ospína and Mora 2004). In this study, no significant differences were detected either in length and weight, or in condition index of the animals between treatments. Despite biomarker activities being significantly increased by elevated temperature, thermal stress effects were not significantly detected at organismal level, probably because 30˚C was not sufficiently high to induce changes in somatic growth of the specimens. These results are in agreement with theory, which states that enzymatic activities are affected before the target sentinel experiences growth and body condition alterations (Hyne and Maher 2003). For instance, in one study, with the reef fish Acantochromis polyacanthus, authors found that acclimation of the resting metabolic rate allowed for additional energy to be made available (cytoprotective pathways are downregulated) and this energy was used to maintain body condition (Donelson et al. 2011).

Moreover, as expected, mean total protein for all tissues did not present significant changes between temperature treatments, as this is the last energy reserve to change or be used upon stress exposure (Ferreira et al. 2015b). Not surprisingly mean total protein in the muscle and whole organism was found to be related to wet body weight because during growth animals build up energy reserves and invest in protein production, mainly at the muscle level, essential for swimming and performing daily activities. Considering that survival rates are positively associated with body condition in juvenile reef fish (Holmes and McCormick 2009), these results suggest that ocellaris clownfish are still able to maintain their body condition and survive to maturity under elevated temperatures.

Finally, results from Spearman correlations show heavier animals were negatively correlated to biomarker levels. This is indicative that as wet body weight and muscle mass increased (leading to an increase in mean total protein and muscle total protein), biomarker levels decreased. This suggests the more robust animals were the less cellular stress animals seemed to suffer. Animals in better condition thus showed more ability to acclimate to quick changes in the environment. In addition it suggests that the response to thermal stress in small tropical reef fish may be affected by differences in body size, possibly due to either ontogenetic differences in physiology or to differences in the area/volume ratio (Ospína and Mora 2004), which influence the lag between water temperature and the internal temperature of the individual (Stevens and Fry 1974). Also, as water temperature increases, the rate of metabolic activity also increases and consequently so does the minimum energy needed to maintain

physiological cell functions (Donelson and Munday 2012), in which proteins play an essential role. In fact, the synthesis of proteins represents as much as 40% of all fish oxygen consumption (Miegel et al. 2010), so it is very energetically taxing. This may explain why there was a negative correlation between body weight and biomarker levels, especially in the muscle of A. ocellaris (for GST, AChE, Hsp70 and Ub) – energy may have been redirected from somatic growth pathways to cytoprotective pathways. As mentioned, these results were most evident in the muscle, and biomarker activities were proved to vary significantly between organs and tissue types, so this also suggests that the allocation of energy for somatic growth and cytoprotective processes can vary from tissue to tissue, due to redirection of energy sources to different metabolic pathways.

Correlation results are indicative that increased temperature or acclimation per se, can incur at some physiological cost as suggested by Donelson et al. (2011) for Acantochromis polyacanthus. This demonstrates that even with acclimation, warmer sea temperatures are likely to have some impacts on reef fish populations. Therefore, if a fish is unable to adapt to temperature stress then these conditions will eventually result in reduced growth, disease resistance and lower reproductive success (Meakin et al. 2014).

2.5. Conclusions

The coral reef fish A. ocellaris is sensitive to an increase in water temperature but is also capable of acclimation in the long term. This implies that if the geographic range of A.ocellaris spans temperature gradients that are greater than projected changes in ocean temperature (Munday et al. 2008b), this would indicate that this fish species possesses the capacity for local thermal acclimation and adaptation (Munday et al. 2012).

This study showed that the use of a set of biomarkers with different sensitivities and specificities can help diagnose the causes of ecological impairment (Colin et al. 2015). Multivariate techniques (that include the analysis of multiple endpoints) should therefore be used to enable the exploration of linear and non-linear relationships between biomarkers and temperature. In the future, it could be interesting to combine biomarker results with demographic studies and bio-indicator sensitive distribution models to further increase ecological relevance of physiological studies (Colin et al. 2015). As highlighted by Garcia et al. (2014), this would also add a degree of realism to spatial projections of exposure to increasing sea surface temperatures.

Acknowledgements

Annex 3 Supplementary material associated with this chapter can be found in annex 3 in the annexes section.

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CHAPTER 3

Comparing biomarker responses during thermal acclimation: a lethal vs non-lethal approach in a tropical reef clownfish

Madeira C, Madeira D, Diniz MS, Cabral HN, Vinagre C (2017) Comparative Biochemistry and Physiology, Part A 204: 104-112, DOI: 10.1016/j.cbpa.2016.11.018

ABSTRACT

Knowledge of thermal stress biology for most tropical fish species in reef ecosystems under climate change is still quite limited. Thus, the objective of this study was to measure the time-course changes of thermal stress biomarkers in the commercially exploited coral reef fish Amphiprion ocellaris, during a laboratory simulated event of increased temperature. Heat shock protein 70kDa (Hsp70) and total ubiquitin (Ub) were determined in the muscle (lethal method) and in the fin (non-lethal alternative method) under two temperature treatments (control – 26˚C and elevated temperature – 30˚C) throughout one month with weekly samplings. Results suggest that biomarker basal levels are tissue-specific and influence the degree of response under temperature exposure. Responses were highly inducible in the muscle but not in fin tissue, indicating that the latter is not reliable for monitoring purposes. Thermal stress was observed in the muscle after one week of exposure (both biomarkers increased significantly) and Ub levels then decreased, suggesting the animals were capable to acclimate by maintaining high levels of Hsp70 and through an effective protein turnover. In addition, the results show that mortality rates did not differ between treatments. This indicates that A. ocellaris is capable of displaying a plastic response to elevated temperature by adjusting the protein quality control system to protect cell functions, without decreasing survival. Thus, this coral reef fish species presents a significant acclimation potential under ocean warming scenarios of +4˚C. Monitoring of thermal stress through a non-lethal method, fin-clipping, although desirable proved to be inadequate for this species.

Keywords: biomonitoring; clownfish; coral reefs; ecophysiology; molecular biomarkers; thermal biology

Graphical abstract

Highlights • Effects of elevated temperature were tested in ocellaris clownfish • Molecular thermal stress biomarkers and survival rates were assessed • Responses were highly inducible in muscle but not in fin tissue • Thermal stress was observed after one week, followed by heat acclimation • High potential for plasticity in cytoprotective mechanisms while maintaning survival

3.1. Introduction

Increased water temperature is generally considered one of the most problematic and important stressors affecting marine organisms as a result of climate change (Vinagre et al., 2012; Munday, 2014). Temporal datasets from ocean observations have estimated a global average warming of 0.11˚C/decade in the upper 75m surface ocean layer in the period of 1970s- present (Aoki et al., 2013). In addition, projection scenarios suggest a further temperature anomaly of +3 to +4˚C in global sea surface temperature (SST) until the end of the 21st century, under business-as-usual CO2 emissions (IPCC, 2013).

Predictions for the tropics indicate that the rate of ocean warming will be slower in these latitudes when compared to more temperate ones (Collins et al., 2013; Vinagre et al., 2015). However, recent studies suggest that marine ectotherms inhabiting at these latitudes are just as vulnerable considering that metabolic rates of these animals change faster at high temperatures (Noyola et al., 2013), and temperatures in tropical regions are already quite warm (Dillon et al., 2010). In these circumstances, thermal history and thermal variation also play a pivotal role in determining thermal tolerance/susceptibility. For instance, in the Indo-Pacific region, home range of ocellaris clownfish, the Indo-West Pacific Warm Pool has an annual temperature variation of about 0.5˚C while outside the warm pool it can range up to 4.5˚C (Hartmann et al., 2013). In consequence, even a small increase in temperature can have different impacts on organisms and while some populations are locally adapted to temperature variations and can cope better with it physiologically, many others may find themselves in unsuitable environments, potentially testing their ability to survive.

Fish are especially affected by temperature variation and its rate of change in time and space, and this risk is even higher in exploited populations (Gattuso et al., 2015), such as the ones commercialized in marine ornamental reef fish trade and aquaculture. However, although there have been many studies investigating thermal stress in commercial temperate marine fish species (Vinagre et al., 2012; Madeira et al., 2013), research on tropical species has been comparatively scarce. It is generally known that fish specialize on a limited range of ambient temperatures, a phenomenon that has traditionally been linked to tradeoffs in structural properties of enzymes or membranes and associated functional adjustments to a range of temperatures (Bijma et al., 2013). Therefore, under a situation of increased suboptimal temperature, thermal acclimation is one means of coping with it (Pandolfi et al., 2011). This implies the prevention of cellular damage and maintenance of internal homeostasis, and in order to do this cells are known to display a cellular stress response (CSR). The CSR is characterized by the synthesis of several proteins, namely heat shock proteins (Hsps) and ubiquitin, both

known to confer thermotolerance (Mukherjee 2008; Madeira et al. 2016). Inducible Hsps act in the refolding of denatured structural and cellular proteins, and ubiquitin signals non-functional proteins for degradation in the proteasome (Coles and Brown, 2003).

For the evaluation of thermal stress in environmental biomonitoring, it is necessary to consider that variation in sensitivity to stress among cell types, tissues and organs needs to be adequately assessed (Angilletta et al., 2006). Traditional standardized methods usually involve the removal of organs and tissue processing (e.g. white muscle, liver, gills) resulting in the death of the individuals which are used for monitoring purposes. Interestingly, Colin et al. (2015) have highlighted the necessity of evaluating biomarker responses in novel target tissues that allow for non-lethal methodologies (e.g. scales, fins, blood, cell cultures, reproductive fluids). This is particularly important, since lethal methods should be avoided, especially in sensitive and economically vital ecosystems, like coral reefs.

In regards to this, Zhang et al. (2012) have suggested that fin-clipping, a rapid and non- lethal sampling procedure long used in fisheries management should be reliable for stress biomarker analyses. Based on the facts that fish fins contain bone as well as thin layers of tissue which are in direct contact with the exterior environment, the fin may actually represent an ideal tissue source to test physiological responses to external stimuli (Zhang et al., 2012).

Hence, in this study, the main objective was to measure the CSR to increased temperature in the Indo-Pacific commercial reef fish species Amphiprion ocellaris (false percula clownfish) and to assess the applicability of fin as a target tissue in biomonitoring. According to a recent semi-quantitative methodology developed initially by Patrick et al. (2010), A. ocellaris has been rated as a highly vulnerable species due to low productivity and high susceptibility to capture for the aquarium fishery (Maison and Graham, 2015). Adding up to this, this species is site-attached along its life cycle to its host anemone, making it fairly easy to tag and track long- term in the wild.

In order to evaluate the CSR, muscle (traditionally used in this kind of study) and caudal fin (novel target tissue) were selected as test tissues. In particular, the research aims were to 1) compare heat shock protein 70kDa (Hsp70) and ubiquitin (Ub), at two different temperatures (26˚C – control, and 30˚C – elevated temperature) along one experimental month (sampling at 0, 7, 14, 21 and 28 days) – if the biomarkers are differentially expressed after exposure to high temperature they should be reliable for thermal stress evaluation in reef fish species; 2) evaluate the adequacy of the use of a novel target tissue - caudal fin (with regenerative capacity) as a good proxy for measurements of thermal stress in tropical reef fish – if any of the biomarkers

analyzed are proven reliable in this tissue, then it should be adequate for stress biomonitoring studies using those biomarkers; and 3) analysis of survival/mortality rates under thermal stress/acclimation.

3.2. Materials and methods

3.2.1. Ethical statement

This study followed ARRIVE guidelines and EU legislation for animal experimentation (Directive2010/63/EU). Four authors (C.M., D.M., M.S.D. and C.V.) have a level C certification by FELASA (Federation of European Laboratory Animal Science Associations).

3.2.2. Housing and husbandry of fish

Juvenile Amphiprion ocellaris (n=60, mean ± sd total length of 3.54 ± 0.36 cm and 1.05 ± 0.29 g weight) were obtained from Tropical Marine Centre Iberia hatchery (Loures, Portugal) and transported to the animal research facility in individual plastic bags in styrofoam boxes, to maintain stable temperature conditions. Fish were randomly placed in a re-circulating system (total of 2,000 L) comprised of 70L white polyvinyl tanks (35  35  55 cm) (n=10 individuals.tank-1, 6 tanks in total), enriched with sand, live rock and PVC tubes for shelter. All tanks were filled with clean aerated sea water (95-100% air saturation), with a constant temperature of 26 ± 0.5°C, salinity 35‰ and pH 8 ± 0.1 (same conditions of the hatchery). Inflow of clean sea water in each individual tank was 300 mL.min-1. Each tank was provided with a filter (ELITE Underwater Mini-Filter Hagen, 220 L.h-1). Fish were allowed to acclimate for two weeks and their welfare was assessed (i.e. wounds or disease symptoms). During the acclimation and experimental trial fish were fed twice a day until satiation with frozen food (a mixture of shrimp and mussels in proportion 1:1) and commercial cyanobacterium Spirulina sp. (Super Forte Mini Granulat, Tropical®, Poland).

3.2.3. Experimental design

After the acclimation period, temperature was gradually increased (for approximately two days, at a rate of 0.10˚C.hr-1) until the experimental temperatures were reached (control 26 ± 0.5˚C; increased temperature 30 ± 0.5˚C; n=3 replicate tanks for each temperature). These temperatures were chosen to reflect (i) thermal history conditions of the specimens throughout their life cycles – animals from the hatchery were acclimated and maintained at this temperature, reflecting the field conditions at the collection time of the original brood stock

(control temperature); (ii) IPCC report of regional projections of sea surface temperature (SST) thermal anomaly for the Indo-Pacific ocean (between +3 to +4˚C by 2100 in the RCP8.5 scenario (IPCC, 2013) for the treatment temperature). This scenario – of high greenhouse gas emissions - was chosen because there is evidence suggesting that the rate of CO2 emissions since 2000 was greater than for the most fossil-fuel-intensive scenarios developed in previous climate change assessments (Raupach et al., 2007).

The experiments were carried out in shaded day light (15h light: 9h dark). Temperatures were maintained for 28 days using thermostats (ELITE® 15-Inch/200W, 200L, China). The sampling design consisted of euthanizing fish trough cervical transection at day 0, 7, 14, 21 and 28 (both temperatures). Timing of samplings and duration of trials were chosen according to their physiological, biological and ecological relevance, following Malins and Ostrander (1994) and OECD guidelines for repeated exposure toxicity studies (www.oecd.org). The rationale behind the sampling scheme was to provide information on the possible health hazards for fish likely to arise from a repeated exposure to increased temperature over a relatively limited period of time (by investigating major effects in tissues through biochemical determinations). This can be used as a first approach to a posterior long-term study (based on the results of the first, it is then possible for instance the application of these biomarkers to in situ in long-term biomonitoring).

At each time point, five specimens were randomly sampled (for each temperature treatment), and muscle and fin tissues (clip of the lower tip of caudal fin) were collected and immediately frozen at -80˚C until further analyses. The types of tissues used for analysis were selected based on literature recommendations. Fish muscle is regularly used in standardized biomarker analyses (Colin et al., 2015) and is therefore useful for comparison of results among similar species and/or with previous studies. Caudal fin tissue was selected as a non-lethal alternative method because in natural environments, caudal fins of fish species are frequently lost to some extent due to aggressive behavior, predation or diseases and normally show a fast regeneration rate (Fu et al., 2013). In addition, studies have shown that in case of fin loss, fish show immediate behavioral compensation (Wagner et al., 2009) – increase in tail beat frequency and amplitude, minimizing the effects of its loss. Finally, in poor swimmers (like A. ocellaris) the swimming performance seems to be much less affected by caudal fin loss than in strong swimmers (Fu et al., 2013).

In order to avoid natural daily biomarker variation, samples were always taken at the same period of the day. Sample sizes were calculated following a power analysis (www.statisticalsolutions.net, σ=10%, β=0.90, Δ=20-25%, α=0.05, then n=5 per treatment, for

each timepoint). The values for the parameters used were based on previous studies (Madeira, et al. 2016). To prevent any additional handling stress, the total length and weight of all fish was measured after the animals were euthanized using an icthyometer and a scale, respectively.

3.2.4. Biomarker analyses

Biochemical markers used in this study were selected to represent direct measurements of thermal damage to cellular proteins (Henkel et al., 2009) and to meet the following criteria: being quantifiable, universal within the study group, sublethal and reliable for interpretation, rendering them as effective thermal stress markers (Lewis et al., 2000).

Sample treatment Tissue samples were homogenized in 1.0 mL of phosphate buffered saline (PBS) solution (140mM NaCl, 3mM KCl, 10mM Na2HPO4, 2mM KH2PO4, pH 7.4) using a Tissue Master 125 homogenizer (Omni International, Kennesaw, USA) on ice-cold conditions. Homogenates were then centrifuged at 10,000× g (at 4˚C for 15 min). Afterwards, the supernatant was collected, transferred to new microtubes (1.5 mL) and frozen (-80˚C).

Biochemical analyses Protein determinations were carried out using procedures previously described and adapted for 96-well microplates:

Heat shock protein 70 quantification was carried out using an indirect Enzyme Linked Immunosorbent Assay (ELISA) and ubiquitin quantification was performed using a direct ELISA (Madeira et al., 2015). In both ELISAs, three replicates of 50 µL were taken from each sample, transferred to the microplates’ wells and incubated overnight at 4˚C. The microplates were washed (3×) in PBS 0.05% Tween-20 and then blocked by adding 200 µL of 1% BSA (Bovine Serum Albumin, Sigma-Aldrich, USA) in PBS. The microplates were then incubated at 37°C for 90 min. After microplate washing, the primary antibodies (anti-Hsp70/Hsc70, Acris, USA, in the first case and Ub P4D1, sc-8017, HRP conjugate, Santa Cruz, USA, in the second case), were diluted to 1.0 µg.mL-1 and 0.5 µg.mL-1, respectively, in 1% BSA, and added to the microplates’ wells (50µL each). Then the microplates were incubated again for 90 min at 37°C. After another washing stage:

i) For Hsp70 quantification, the secondary antibody (anti-mouse IgG, fab specific, alkaline phosphatase conjugate, Sigma-Aldrich, USA) was diluted (1.0 µg.mL-1 in 1% BSA) and added (50 µL) to each well followed by incubation at 37°C for 90 min. After the washing

stage, 100 µL of substrate (SIGMA FASTTM p-Nitrophenyl Phosphate Tablets, Sigma-Aldrich, USA) were added to each well and incubated for 30 min at room temperature. Fifty µL of stop solution (3N NaOH) were added to each well and the absorbance was read in a 96-well microplate reader at 405nm (BIO-RAD, Benchmark, USA). ii) For ubiquitin quantification, 100 µL of substrate (TMB/E, Temecula California, Merck Millipore) were added to each well and incubated for 30 min at room temperature. One hundred µL of stop solution (1N HCl) were added to each well and the absorbance was read in a 96-well microplate reader at 415nm (BIO-RAD, Benchmark, USA).

For quantification purposes, calibration curves were constructed using serial dilutions of purified Hsp70 active protein (Acris, USA) and of purified ubiquitin (UbpBio, E-1100, USA), respectively, to give a range from 0 to 2.0 µg.mL-1 of protein.

Total protein content was determined by the Bradford method (Bradford, 1976). A calibration curve was obtained using BSA standards (0 to 2.0 mg.mL-1). These measurements were used to normalize Hsp70 and ubiquitin results.

3.2.5. Survival and mortality

Survival/mortality was recorded along the experimental month (number of alive and dead specimens per day and per tank in each temperature treatment along the 28 days). Number of specimens was then transformed into percentage values for further analysis.

3.2.6. Data analysis

All data were tested for 1) outliers (box-whiskers plot, coefficient 1.5 for outliers and extremes), 2) normality through Shapiro-Wilk’s test and 3) homoscedasticity through Levene’s test. To rule out a possible effect of the different tanks in the results, one way MANOVAs were employed (data were pooled across time for each tissue).

To analyze differences between treatments and to compare biomarker basal levels (at control temperature) in the two tissues, two-way ANOVAs and MANOVA were employed, with temperature and time, and then with tissue and time as independent variables (respectively) and Hsp70 and ubiquitin as dependent variables. Logarithmic transformation was employed when at least one of the assumptions of normality and homoscedasticity was not verified. To test which groups differed and the variables that contributed to the significant differences detected, we performed post-hoc Tukey’s honestly significant difference tests, which perform

pairwise comparisons and cross validated the results with the univariate ANOVAs. Relationship between biomarker patterns at 30˚C as well as relationship between fin and muscle responses at 30˚C was examined by correlation analysis (Pearson or Spearman when the assumption of normality was violated).

A survival analysis along the experimental month for each temperature was performed using the log-rank (Mantel-Cox) test. Additionally, mortality rates were calculated at the end of the experiment in each tank and a Student’s t-test was employed to compare differences between temperature treatments (after testing assumptions of normality and homoscedasticity).

All results were considered to be statistically significant at p-value ≤ 0.05. All statistical analyses were carried out using Statistica software (version 12.0, StatSoft Inc., USA), except for survival analysis, which was performed using GraphPad Prism (version 5.0, (GraphPad Software, Inc., USA).

3.3. Results

Results from the ANOVAs testing for differences between tanks confirm that these had no influence in biomarker levels (F=0.459, p=0.911 in fin and F=1.753, p=0.081 in muscle). The multivariate model where the biomarkers were combined in one analysis (MANOVA, Wilks test) showed significant differences for all the effects tested (factors temperature, time and the interaction between them, Table 3.1) in the muscle and significant differences only for factor time in fin tissue. The univariate ANOVAs test results (Table 3.1) showed that: (i) the main effect of temperature in the entire experimental month was significant for muscle Hsp70, (ii) natural temporal variability could be observed in both biomarkers (Hsp70 and Ub) and both tissues (fin and muscle) tested, (iii) interactions between temperature and time were only significant again in the muscle, for both Hsp70 and Ub. These results show that temperature treatments only influenced biomarker levels in the muscle, while no differences due to high temperature were detected in fin tissue.

Table 3.1 Compilation of statistical analyses (two-way ANOVAs and MANOVA) on the effects of temperature (26˚C vs 30˚C) and time (T0, T7, T14, T21, T28) and their interactions in biomarker responses in fin and muscle tissues of A. ocellaris. Significant results (95% confidence limits) are marked in bold. Abbreviations: Hsp70 – heat shock protein 70; Ub – total ubiquitin; Temp - Temperature.

Hsp70 Ub Wilks test F p F p F p

Fin Temperature 2.002 0.164 0.021 0.882 1.685 0.198 Time 12.126 0.000 6.881 0.000 8.383 0.000 Temp × Time 0.367 0.830 0.545 0.703 0.604 0.771 Muscle Temperature 28.771 0.000 0.778 0.382 14.100 0.000 Time 24.346 0.000 25.573 0.000 21.048 0.000 Temp × Time 5.451 0.001 3.795 0.010 4.349 0.000

From the analysis of the interactions between time and temperature, the most relevant combinations were selected and are depicted in Fig. 3.1. In fin tissue (Fig. 3.1a), no significant differences were detected between temperature treatments in each time point sampled, whereas in muscle tissue (Fig. 3.1b), a general pattern of increase of Hsp70 along time was observed at 30˚C (significant for t = 7 days and t = 28 days), and a pattern of increase-decrease-increase in Ub levels along time at 30˚C was also observed (significant for t = 7 days, t = 14 days and t = 28 days).

Figure 3.1 Protein biomarkers (mean + SD for heat shock protein 70kDa – Hsp70, and for total ubiquitin – Ub) in Amphiprion ocellaris tissues under two temperature treatments (26˚C and 30˚C): a) fin, b)

muscle. Significant differences from the control group at each timepoint (Tdays) are marked with an asterisk (*) (obtained from the post hoc Tukey’s tests for the interactions between temperature and time in table 3.1). Note: for purposes of easy visualization, only combinations of interest were selected to be represented in graphs.

From the analysis of differences in biomarker basal levels in control animals (26˚C) regarding different tissues, results showed significant differences in both Hsp70 and Ub (Table 3.2), as well as significant differences in the interactions between tissue and time. Relevant combinations of interactions are depicted in Fig. 3.2. Graphs showed that fin consistently presented higher basal levels of both biomarkers along the experimental month. Significant differences were detected for Hsp70 levels at T0, T7, T14 and T28, and significant differences were also detected for Ub levels at T0, T21 and T28 (fin vs muscle at 26˚C, Fig. 3.2a-b).

Table 3.2 Two-way ANOVAs and MANOVA on differences on biomarker basal levels between tissue types (in the control experiment – 26˚C) and interactions with time in A. ocellaris. Significant results (95% confidence limits) are marked in bold. Abbreviations: Hsp70 – heat shock protein 70; Ub – total ubiquitin. Hsp70 Ub Wilks test F p F p F p Tissue 55.006 0.000 29.193 0.000 28.343 0.000 Tissue × Time 5.437 0.001 3.194 0.022 4.350 0.000

Figure 3.2 Basal (constitutively expressed) levels of protein biomarkers in Amphiprion ocellaris at control temperature (26˚C) between tissues: a) Hsp70 (heat shock protein, mean+SD) and b) Ub (total ubiquitin, mean+SD). Significant differences between tissues at each time point are marked with an asterisk (*) (obtained from the post hoc Tukey’s tests for the interactions between tissue and time in table 2). Note: for purposes of easy visualization, only combinations of interest were selected to be represented in graphs.

In order to further explore biomarker response patterns under exposure to elevated temperature, correlations between (i) Hsp70 and Ub in each tissue at 30˚C (Fig. 3.3a-b), and (ii) fin and muscle for each biomarker at 30˚C (Fig. 3.3c-d) were performed. The results showed that Hsp70 and Ub were positively correlated in each tissue, but no significant correlation was found between levels of the same biomarker in different tissues, respectively.

Figure 3.3 Correlations of biomarker response patterns (significant at p ≤ 0.05) under elevated temperature (30˚C): between Hsp70 and Ub in (a) fin, (b) muscle; and between fin and muscle tissues in (c) Hsp70, (d) Ub. Abbreviations: Hsp70 – heat shock protein 70; Ub – total ubiquitin.

Survival analysis along the experimental month using Mantel-Cox test detected no significant differences between control and elevated temperature (χ2=0.291, p=0.589; Fig. 3.4). Additionally, cumulative mortalities of A. ocellaris juveniles at the end of the experiment were 37.04±3.70% at 26˚C and 44.81±8.72% at 30˚C, and also no significant differences were detected between temperature treatments (t=-0.359, p=0.737).

Figure 3.4 Percent survival of clownfish A. ocellaris under temperature treatments (control – 26˚C, elevated temperature – 30˚C) along the experimental month (solid lines represent average % survival and dashed lines represent 95% confidence limits).

3.4. Discussion

The acclimation potential of physiological mechanisms that regulate the stress response is one of the processes that determines the vulnerability of organisms to temperature variations (Madeira et al. 2016). Hence, this study compared the cellular stress responses of A. ocellaris in muscle and fin tissues under normal and suboptimal elevated temperature conditions in order to analyze the behavior of biochemical markers.

3.4.1. Stress levels under an ocean warming scenario

Considering that thermal variation affects reactivity of molecules by affecting protein conformation, kinetic properties and assembly (Tang et al., 2014), then as expected, biomarkers measured in the muscle showed a typical conspicuous response to a temperature increase of +4˚C, as shown by MANOVA results (Table 3.1). The pattern observed (Fig.3.1b, Hsp70 and Ub increased at day 7, then Ub decreased at day 14, and finally, as Hsp70 levels raised again at day 28, Ub levels decreased) indicates that initially the CSR was activated due to protein damage (aggregation, unfolding and denaturation), suggesting the animals were under thermal stress. Later on, as the juveniles acclimated to the new temperature conditions, Hsp70 kept showing increased levels in order to maintain cellular homeostasis, while the degree of

irreversible damage (protein denaturation) started to decrease, as shown by lower Ub levels. This is a clear thermal stress (short-term defense activation) followed by heat acclimation (plasticity in cytoprotective mechanisms) response, which has also been observed in several other marine fish species – the CSR is highly conserved among taxa (e.g. seabass Dicentrarchus labrax, (Vinagre et al., 2012); guilt-head seabream Sparus aurata (Madeira et al. 2014); goby Gillichthys mirabilis (Logan and Somero, 2011)). In addition, Hsp70 and Ub showed significant correlations in stress responses (Fig. 3.3a-b) suggesting that their expressed levels are also regulated by each other responses, given that both of these biomarkers are directly involved in preventing (Hsp70) or dealing with (Ub) protein damage.

Time of exposure to elevated temperature also plays a role in the CSR, which can be induced or inhibited, mirroring cytotoxic changes which demand higher cytoprotection and posterior adjustment of thermal reaction norms to the new temperature conditions, respectively (Donelson and Munday, 2012). These results suggest that although short-term measures of metabolic responses (timeframes from hours to a few days) are expected to indicate the sensitivity of species to regional warming, given adequate time species may, in fact be able to acclimate to increasing temperature, as was also observed by Donelson and Munday (2012). This kind of reversible plasticity (acclimation) confers the capacity for individuals to remodel physiological processes repeatedly throughout their lifetime to compensate for the potentially negative effects of changing conditions. Interestingly, recent research has suggested that the capacity for reversible acclimation during an organism’s life period is in fact determined by the developmental conditions of the animal, indicating that the several forms of plasticity are mechanistically linked, for instance through epigenetic changes (Beaman et al., 2016).

In accordance with the results from the present study, a previous study with A. ocellaris, also suggested physiological acclimation under elevated temperature, with no differences detected in body condition or growth rate (Madeira et al. 2016). This may come as counterintuitive, considering that tropical species have evolved in a relatively stable thermal environment (Nilsson et al., 2009), and are therefore expected to exhibit narrower thermal reaction norms. On the other hand, studies with Indo-Pacific tropical reef fish have come to the conclusion that reef associated fish species appear able to survive long term exposure to temperatures 7˚C above mean Indo-Pacific SST (26-27˚C) (Munday, 2014).

Considering that the natural distribution of A. ocellaris is considerably broad (27˚N- 20˚S, 92˚E-140˚E; including off N. Territory Australia, NW Australia, throughout SE Asia, Andaman Sea and Indo-Australian Archipelago, and north of Ryukyu Islands (Allen, 2009)), encompassing a total temperature span (inclusive of seasons) of approximately 22-31˚C, there

can be several interesting ecological implications of the results under current and projected SST. Firstly, when the geographic range of a species spans temperature gradients (~9˚C for A. ocellaris) that are greater than projected changes in ocean temperature (~4˚C), then this is suggestive that that particular species possesses the capacity for local thermal acclimation and adaptation (Munday et al., 2012, 2008) even under warming scenarios. Secondly, it suggests that acclimation potential of some tropical reef fishes may be proportional to the seasonal variation in minimum and maximum temperatures they experience (Donelson et al., 2011b). Finally, these results also support the hypothesis that tropical reef species may be able to acclimate so long as the rate of temperature change does not exceed the fish’s acclimation rate (Eme and Bennett, 2009). All of these imply that populations of the same species inhabiting in different locations may experience different degrees of thermal stress and ability to acclimate in the face of global change and extreme events associated to it such as heat waves.

3.4.2. Lethal vs non-lethal approach

One of the objectives of this study was to determine the suitability of using an alternative approach to traditional lethal biomarker monitoring methods, by comparing Hsp70 and Ub responses in muscle tissue and fin tissue. Although there was a clear detectable response of these biomarkers in muscle, no significant differences between temperature treatments were detected in fin (Table 3.1, Fig. 3.1a). These results are unexpected, considering that fin is a very dynamic tissue which is in direct contact with external cues, such as increased water temperature, while, muscle, on the other hand, being an internal organ is dependent on the lag time between an increase in external water temperature and an increase in internal body temperature, so it would be expected that fin tissues would have a stronger and faster response. However, there can be several explanations for these results: i) Hsp70 and Ub basal levels in fin are enough to stabilize proteins under a temperature increase of +4˚C (from 26 to 30˚C); ii) temperature variation is a frequently encountered stressor (in this tissue), leading to a possible desensitization to it and lesser inducible responses in consequence (Madeira et al. 2014); iii) these biomarkers are not as responsive to high temperature in fin tissue as they are in other fish tissues, such as muscle.

In fact, results from further analyses seem to support all of the above hypotheses. The two-way ANOVAs and MANOVA testing for differences on biomarker basal levels (constitutively expressed) between tissue types in the control experiment (26˚C) and interactions with time, showed significant results for all the effects tested (Table 3.2). Further exploration of these results (Fig. 3.2) showed that fin tissue presented consistently significant higher levels of both biomarkers. This might be a form of saving energy on activating and then deactivating

CSR pathways. The strategy of keeping biomarker levels continuously high, ready for thermal protection at any instant is more efficient in this case, since fin tissues are constantly exposed to varying environmental temperatures. This is corroborated by previous studies with fish in which a rise in temperature did not induce additional production of biomarker levels in species that are used to considerable temperature variations (e.g. Diplodus spp, (Madeira et al., 2013a), Pomatochistus microps (Fonseca et al., 2011)). In addition, results from correlation analysis between stress responses in muscle and fin (Fig. 3.3c-d) detected no significant correlations between them, suggesting that they do not respond in similar ways – biomarkers responses were highly variable in fin but highly coherent in muscle. In fact, a parallel study developed by Madeira et al. (2016) evaluated these biomarkers’ responses to thermal stress in other organs of A. ocellaris, and results indicate that biomarker responses are tissue-specific, with significant differences and similar patterns detected mainly in more oxygenated tissues (muscle is more oxygenated than fin). So tissue function as well as oxygen diffusion rate, also seem to play a role in the induction of the CSR, translated by an increase in biomarker levels. In addition, it would be interesting to test further non-lethal targets such as blood in future studies, coupling biomarkers of thermal and oxidative stress as well as other related blood metabolites to see how they behave under temperature change.

The implications of these results are that fin does not seem a reliable tissue to be used in thermal stress studies which may come as a disappointment in the attempt of developing better practices and methods to be applied in monitoring programs in sensitive ecosystems like coral reefs. Nevertheless, to our knowledge, this is the very first approach of this kind in a tropical thermal biology study, and further research using other fish species and testing a wider range of varying temperatures is necessary before final conclusions can be drawn.

3.4.3. Survival under increased temperature

The acclimation ability of A. ocellaris juveniles was also evidenced by the survival analysis results (Fig. 3.4). Although non-significant, slightly lower survival rates at 30˚C were observed between day 7 and day 21 of the experiment when compared to controls, which may be due to the fact that in fish that have the capacity to acclimate, the process can take from a few days to a couple of weeks (Barrionuevo and Fernandes, 1998), so they either acclimate or die during this time, yielding generally more variable survival rates. However, overall analysis of mortality for the entire experimental month detected no significant differences between temperature treatments. These results suggest that fish display a significant acclimation potential to heat, and that the acclimation to one temperature (26˚C) does not necessarily coincide with

poor performance and fitness at a higher temperature (30˚C), which was also observed by Eme & Bennett (2009).

However, it is also worth noting that while acclimation has the potential to produce benefits to the individual, developmental trade-offs may exist (Angilletta et al., 2004). In one long-term study, testing the acclimation potential of the tropical reef fish Acanthochromis polyacanthus along its entire life cycle (Donelson et al., 2011b), researchers came to the conclusion that fish acclimated to +3˚C were smaller and poorer in condition than fish raised at present day temperatures, even if no differences in mortality rates were observed. This suggests that even with acclimation capacity, there may be significant consequences of ocean warming for future tropical reef fish populations.

3.4.4. Study considerations and justification

This section describes the possible limitations of this study in an attempt to shed light on where new efforts need to be made and highlight opportunities for new scientific challenges. Firstly, this study uses tank-raised fish, which in comparison with wild animals may have the following complications: i) possible lack of genetic variability (which may lead to reduced fitness and more exacerbated stress responses than wild populations (Brown et al., 2009)); ii) high acclimation to captivity conditions (may lead to changes in stress responses patterns – may either limit or exacerbate them); iii) possibility of using strains which are specifically resistant/vulnerable to temperature variation, depending on the original broodstock thermal history. Nonetheless, literature also states that lab experimental approaches with tank-raised animals are justified and should be employed before environmental surveys with wild animals (Colin et al., 2015). This is especially important when using species from fragile ecosystems, to avoid unnecessary impacts and to guarantee reliability of results by using tested, optimized and standardized biomarkers for the specific species and stressors under analysis.

In addition, this study only includes juvenile fish which are usually the most thermal stress resistant. According to Pörtner & Farrell (2008) theory of aerobic thermal windows, the width of thermal windows in fish varies across life stages, with eggs, early larvae and adult spawners having narrower thermal windows, followed by growing adults, while juveniles have the wider window widths, allowing them a greater scope for shifts in aerobic performance under temperature variation. So the results could be different when using larvae, adults or reproducing adults, and this variability needs to be considered – ideally thermal stress should be evaluated and compared between all life stages of one species in future studies.

Finally, the influence of another possibly confounding variable, particularly in this species, needs to be adequately addressed in future research – the social strata. As known, A. ocellaris in the wild live in unique social groups, where the largest animal develops as female, the second-largest develops as a male, and the rest of the animals remain gender neutral, in a size-based dominance hierarchy (Avise and Mank, 2009). Interestingly, a recent study with coral reef fish has shown that temperature variation affects several types of behaviors (activity, aggressiveness and boldness – which increase as a function of temperature) in these animals (Biro et al., 2010). The authors suggest that changes in energy metabolism, namely metabolic rates due to temperature variation in ectothermic organisms may contribute to a change in the animals’ temperament. This is important considering that A. ocellaris frequently employ aggressive behaviors towards conspecifics in order to maintain dominance of the social hierarchy (Mitchell and Dill, 2005). Thus, stress levels in these fish may be influenced not only directly but also indirectly by temperature through increased aggression. This acquires significant relevance as animals approach adulthood and sexual maturity. However, as in this study only juvenile fish (~3 months old) were used (during a short period of time), and A. ocellaris only reach maturity at about 8-18 months after birth (Ajithkumar and Balasubramanian, 2009), aggressive behaviour was not quantified.

3.5. Conclusions

In the wild, A. ocellaris inhabit coral reefs and associated nearby habitats (such as seagrass beds and sand flats) - either in sheltered lagoons or in outer reef slopes at a maximum depth of 15m (Maison and Graham, 2015). Different fish populations are therefore variably prone to warming: shallow semi-confined waters such as lagoons in fringing reefs are more prone to warming due to low thermal inertia, while outer reef slopes, as open systems show a higher heat capacity, and smaller changes in temperature, consequently. This implies that A. ocellaris living in different types of reefs also have different availabilities for thermal refugia, and some populations must rely on plastic responses to cope with elevated temperatures, while others may also rely on behavioral thermoregulation (Nay et al., 2015) .

In this study muscle analyses showed that juvenile clownfish possess acclimation capacity without lower survival rates, suggesting a plastic response to temperature, mainly based on effective protein turnover. This synchronization of the cell kinetics with variation in thermal energy through plasticity (a form of adaptation) has been tested in other coral reef fish and those studies have also suggested that these animals display thermotolerance and significant potential for acclimation and adaptation, thus they do not seem to be in immediate danger due to direct effects of warming oceans (Mora and Ospína, 2001).

Fin analysis did not yield reliable results for thermal stress monitoring purposes. The implications of this might be further delay in developing better tools and methodologies in monitoring programs. This is a cause for concern, given the urgency of efficient, non-invasive in situ vulnerability/resilience analysis of tropical species, and the need for ecological risk assessment of exploited populations as well as the implementation of better conservation measures. In addition, there are other factors adding to fish susceptibility/resilience to increased SST that cannot be overlooked. In particular, the influence of anthropogenic disturbances (e.g. overfishing pressure, habitat destruction and pollution), all of which are liable to produce additive and synergistic deleterious effects, will also play a relevant role in the ability of fish populations to acclimate and adapt. This is why it’s so important to improve biomonitoring methodologies and fill in the knowledge gap on thermal stress biology in tropical organisms.

Acknowledgements

Authors thank V. Mendonça for help during transport and accommodation of the fish. This work was funded by the Portuguese Fundação para a Ciência e Tecnologia (FCT) through the projects PTDC/AAG-REC/2139/201, PTDC/MAR-EST/2141/2012 granted to MARE, and project UID/Multi/04378/2013granted to Unidade de Ciências Biomoleculares Aplicadas (UCIBIO). C.M. and D.M. acknowledge PhD grants (SFRH/BD/92975/2013 and SFRH/BD/80613/2011, respectively) and C.V. acknowledges a senior position, all of these granted by FCT. The funding source had no involvement in the study design, analysis and interpretation of the data and manuscript writing, and neither in the decision of publication of this work.

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CHAPTER 4

Thermal stress and energy metabolism in two circumtropical decapod crustaceans: responses to acute temperature events

Madeira C, Leal MC, Diniz MS, Cabral HN, Vinagre C (accepted w/ minor revisions) Marine Environmental Research.

ABSTRACT

Extreme events associated with global warming, such as ocean heat waves can have contrasting fitness consequences for different species, thereby modifying the structure and composition of marine communities. Here, we examined the effects of a laboratory simulated heat wave on the physiology and performance of two Indo-Pacific crustacean species: the shrimp Rhynchocinetes durbanensis and the hermit crab Calcinus laevimanus. We exposed the crustaceans to a control temperature or to a +5˚C temperature (25˚C vs 30˚C) for two consecutive weeks, and weekly analyzed protective proteins, antioxidant activity, and lipid peroxides in muscle and visceral mass. Fulton’s K, total protein, %C and C:N ratio of muscle tissue were also analyzed at the end of the experiment. Results showed that 1) the most responsive tissues were the muscle in the shrimp species and the visceral mass in the hermit crab species; 2) biomarker responses in both species occurred mostly after 7 days of exposure; 3) temperature stress lead to an increase in biomarker levels; 4) highest biomarker fold-changes were detected in protective chaperones and antioxidants superoxide dismutase and glutathione- S-transferase; 4) integrated biomarker indices suggest poorer health status in individuals subjected to the heat wave; 5) fitness changes at the organism level were only detected in R. durbanensis; 6) mortality rates of both species remain unchanged with the heat wave. Finally, we concluded that these species are capable of physiological adjustments in response to rapid environmental changes, which ultimately confers them with enough thermal tolerance to withstand this simulated heat wave without major performance consequences.

Keywords: decapod crustaceans; marine heat waves; thermal stress; integrated biomarker index; energy reserves; tropical Indo-Pacific

Graphical abstract

Highlights • Effects of a marine heat wave were tested in two tropical crustaceans • Heat wave elicited a cellular stress response that was time-dependent • Specimens subjected to a heat wave showed poorer health condition • Heat wave did not affect mortality rates in neither species

4.1. Introduction

A body of evidence is building up concerning global increases in extreme weather and climatic events (EWCEs), including the occurrence of heat waves. Future projections show that warmer and more frequent days above 35˚C (maximum air temperature), as well as increased frequency of heat waves, have a 90% to 99% probability to occur (IPCC, 2014). Maximum daily temperatures have risen 2.3˚C worldwide between 1981 and 2000, and projections estimate an additional 3.5˚C for 2081-2100 (Kharin et al., 2007). The intensity and duration of these extreme events are also expected to increase as global warming continues (Goodess, 2012). Because EWCE events are key drivers of marine biodiversity patterns (Wernberg et al., 2016), addressing how such episodic events affect species performance is critical to further develop predictive models of species distribution and go beyond their current basis on gradual warming trends (Ummenhofer and Meehl, 2017).

Particularly, the Indo-Pacific region has been ground of some record-breaking heat waves in a very recent past (2013-2015). For instance, recorded data showed SST anomalies exceeding three standard deviations in consecutive years in the north Pacific (Di Lorenzo and Mantua, 2016). In 2015-2016, Indian and Pacific oceans experienced extremely high tropical SSTs associated to an El Niño event, which resulted in the third massive coral bleaching event recorded in history, spanning from Australia to Southeast Asia (NOAA, 2015). In the Western Indian ocean, mainly along the African coastal line, the magnitude of heats have also increased since 1996 in temperature, days, and extension area (Ceccherini et al., 2017). Finally, warm pools in the Indian and Pacific oceans are also expanding, with areas between 29˚C and 30˚C showing the largest increase rates at interdecadal timescales (Lin et al., 2013, 2011).

Marine organisms exposed to extreme or unusual environmental changes can have different responses, resulting in varying outcomes for their populations. If environmental changes are physiologically tolerable (i.e. animals have the ability to adjust their physiology to maintain homeostasis), individual acclimation and/or adaptation over generations occurs. If environmental changes are physiologically intolerable, migration or death usually occurs (González-Ortegón et al., 2013), with consequent re-distribution of species. Ectothermic animals are especially vulnerable to extreme temperatures and may therefore experience severe performance decrements during a heat wave event. Nevertheless, even ectotherms are able to modulate metabolic mechanisms and behavior to some extent to maintain homeostatic balance and promote functional capacity, cell survival, and avoid organ failure (Feder and Hofmann, 1999; Logan and Somero, 2011; Madeira et al., 2016a). In particular, physiological responses during a temperature event are induced by an increase in the flux of cellular reactive oxygen

species (ROS) in cells, usually due to a mismatch between the oxygen demand and oxygen supply system (Pörtner and Knust, 2007). This increased ROS flux causes macromolecular damage, e.g. to protein structure (Hofmann and Somero, 1995; Kregel, 2002), cell membrane integrity and stability (Vinagre et al., 2014), and DNA (Kültz, 2004). When the damage extension rises above a certain threshold, cell apoptosis and tissue necrosis can occur (Madeira et al., 2014b; Mukherjee, 2008). As a consequence, organisms may down-regulate their aerobic energy metabolism whereas other metabolic pathways are activated to respond to increased oxidative stress, or organisms may switch metabolic energy fuels (Tomanek, 2014). Specifically, cytoprotective pathways include: 1) management of damaged/denatured molecules, through increased production of heat shock proteins and proteolysis via the ubiquitin- proteasome pathway (Halpin et al., 2002; Hofmann, 2005; Hofmann and Somero, 1995; Madeira et al., 2012; Tomanek and Somero, 2002), 2) regulation of oxidative stress and detoxification of toxic byproducts by increasing antioxidant enzyme activity (with ROS scavenging function) (Lushchak, 2011; Madeira et al., 2016a; Madeira et al., 2014a, 2013b; Vinagre et al., 2014), 3) shifting from pro-oxidant NADH producing and oxidizing pathways to antioxidant NADPH producing and consuming pathways (Alberts et al., 2002; Tomanek, 2014; Wegener, 1988).

In this study, we explored the physiological responses of two Indo-Pacific crustacean species, the camel shrimp, Rhynchocinetes durbanensis, and the orange and black hermit crab, Calcinus laevimanus. In order to determine their vulnerability to heat wave events and its relation to molecular pathways of cytoprotection and acclimation, we evaluated these species’ thermal sensitivities, their ability to sustain performance, and survive periods of severe environmental stress. Decapod crustacean species are important components of circumtropical coastal habitats, not only in terms of diversity and biomass but also as key organisms in the trophic dynamics and nutrient cycling of these systems (Abele, 1974; Grilo et al., 2011; Pachelle et al., 2016). If heat waves affect these organisms’ health, growth, or survival, there may be unforeseen functional consequences for coastal populations and communities. In particular, we aimed to: 1) compare these species’ thermal stress responses at two ecologically relevant experimental temperatures (control and heat wave simulation), using a set of selected thermal, oxidative, neurotoxicity, and energy metabolism biomarkers; 2) perform a comprehensive and comparative animal health assessment between control and heat wave conditions, based on integrated biomarker indices to understand the role of thermal induced stress in possible physiological or health impairments; and 3) analyze each species performance at control and extreme temperature by measuring body condition and mortality.

4.2. Materials and methods

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4.2.1. Test organisms and acclimation procedure

Two circumtropical crustacean species commonly found in the Indo-Pacific region were used in this study: the camel shrimp Rhynchocinetes durbanensis (Gordon, 1936), and the orange and black hermit crab Calcinus laevimanus (Randall, 1840). Both species inhabit reef areas, either in biogenic (coral) reefs or rocky platforms. R. durbanensis is a subtidal species, living at depths from 2 to 40 m (Weinberg, 2005), whereas C. laevimanus is both intertidal and subtidal, and can be found at any depth from 0 to 95 m (Markham, 2003). Adult specimens (n = 30 for each species) were obtained from Opérculo, Lda® ornamental aquaculture company (Guincho, Portugal). The use of captive-bred animals is justified based on the necessity of having knowledge and control on the animals’ previous thermal histories. Upon arrival at the laboratory, animals were placed in one indoor re-circulating water system (total volume of 2,000 L), comprised of four 70 L polyvinyl tanks supplied with aerated sea water, one common sump and an external skimmer and UV filter. Crustaceans were randomly distributed across tanks (n = 7-8 individuals of each species per tank) and allowed to acclimate at 25.0 ± 0.5˚C for 2 weeks.

4.2.2. Experimental design and sampling

All experimental procedures were approved by “Direcção Geral de Alimentação e Veterinária” (DGAV) following ethical guidelines in national and international legislation. Study design followed ARRIVE guidelines and recommendations by the Federation of European Laboratory Animal Science Associations (see Festing and Altman, 2002; Hau and Schapiro, 2010). Common-garden experiments are the gold-standard design to separate environmental/laboratory controlled effects from possible differentiation among populations and animals of a single species (Gaitán-Espitia et al., 2017). Therefore, the experiments consisted of two temperature treatments using common garden conditions: 25.0˚C ± 0.5˚C (control – temperature at which the animals were reared, corresponding to mean temperatures in the Indo- Pacific region outside warm-pool areas, as shown by satellite data in www.seatemperature.org) and 30.0˚C ± 0.5˚C (simulating a +5.0˚C heat wave event), with the duration of two weeks and samplings at 0, 7 and 14 days. The duration of the experiment was based on the following explanation: present duration of heat waves in the Indo-Pacific is on average 5-7 days, but time- series trends show an increase in heat wave duration of about 1.3 days.decade-1 from 1961-2013, and if this trend is to be continued, future heat waves by the end of this century can last for up to two weeks (Zinke et al., 2015; Rohini et al., 2016; Ceccherini et al., 2017). To simulate the heat wave, we used a stepwise method to increase temperature in the treatment group. At day zero

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temperature was increased from 25.0˚C to 27.0˚C. After 24h at 27.0˚C, temperature was increased further up to 30.0˚C and then kept for two consecutive weeks. Then, on the last day, temperatures were lowered again to 25.0˚C. Animals in the control treatment were kept at 25.0˚C throughout the 14-day period. This procedure follows Collier and Waycott (2014) and Arambourou and Stoks (2015) for heat wave simulation experiments. Each treatment had 2 replicate tanks (35 x 35 x 55 cm each), with 7-8 individuals of each species per tank. All tanks were provided with a thermostat heater (ELITE 200W). To keep environmental parameters constant throughout the experiment, a monitoring scheme was employed. Each tank was provided with a Petco thermometer with suction cup to continuously monitor temperature. Other parameters, such as salinity (kept at 35), pH (kept at 8±0.01), ammonia (kept below 0.25 mg.L- 1), and nitrites (kept under 0.3 mg.L-1), were monitored twice a week using a hand-held refractometer (Atago, Japan), a digital pH probe (model HI9025, Hanna Instruments, USA), and Tetra Test Kits (Tetra Ammonia Test Kit and Tetra Nitrites Test Kit, USA), respectively. Animals were fed twice a day with fresh food (mixture of shrimp and mussels in proportion 1:1) and dry food (Tropical Super Spirulina Forte Mini Granulat), except in samplings days, when animals were not fed in the prior 24h. Environmental enrichment consisted of adding sand, live rocks, and seaweed Caulerpa taxifolia to the tanks. Mortality was recorded every day in each tank. At each sampling day, 5 animals of each species from each treatment were chosen randomly (n = 5 individuals × 3 timepoints × 2 temperatures = 30), sacrificed by longitudinal transection, and then measured and weighed. Internal organs (muscle and visceral mass) were extracted and frozen at -80˚C until further analyses.

4.2.3. Biochemical biomarkers

Total protein extraction and quantification

Tissue samples from muscle and visceral mass were homogenized in a phosphate buffered saline solution (140mM NaCl, 3mM KCl, 10mM Na2HPO4, 2mM KH2PO4, pH 7.4) using a Tissue Master 125 homogenizer (Omni International, Kennesaw, USA) on ice-cold. Homogenates were then centrifuged at 4˚C for 15 min at 10,000 g (Hitachi, Japan). The supernatant was collected, transferred to new microtubes (1.5 ml) and frozen immediately (- 80˚C). Total protein determination was performed according to Bradford (1976): Bradford reagent (Comassie Blue G250, methanol, phosphoric acid, distilled water) was added to three replicates of each sample in a 96-well microplate. Absorbance was read at 595 nm in a microplate reader (BIO-RAD, Benchmark, USA). BSA standards were used for a calibration curve (0 to 4 mg).

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Thermal stress proteins quantification

Heat shock protein 70 (Hsp70) and total ubiquitin (Ub) were quantified using an indirect Enzyme Linked Immunosorbent assay (ELISA) and a direct ELISA (respectively, and following Madeira et al. 2014a). Briefly, in both procedures, samples (three replicates of each) were incubated in 96-well plates overnight at 4˚C. On the next day, microplates were washed in PBS 0.05% Tween-20 and blocked with 1% BSA (Sigma-Aldrich, USA) in PBS and incubated at 37˚C for 90 min. Microplates were then washed again with PBS. The primary antibodies (anti-Hsp70/Hsc70, Acris, USA, for Hsp70 and Ub P4D1, sc-8017, HRP conjugate, Santa Cruz, USA, for total ubiquitin) were then added to the samples and microplates were again incubated for 90 min at 37˚C. After another washing stage: a) For Hsp quantification, the secondary antibody (anti-mouse IgG, fab specific, alkaline phosphatase conjugate, Sigma-Aldrich, USA) was added to each well followed by incubation at 37˚C for 90 min. After the washing stage, the substrate (SIGMA FAST™ p-Nitrophenyl Phosphate Tablets, Sigma- Aldrich, USA) was added to each well and incubated for 30 min at room temperature. Stop solution (3 N NaOH) was added to each well and the absorbance was read at 405 nm. b) For ubiquitin quantification, the substrate (TMB/E, Temecula California, Merck Millipore) was added to each well and incubated for 30 min at room temperature. Stop solution (1 N HCl) was added to each well and the absorbance was read at 415 nm. For quantification purposes, calibration curves were constructed using serial dilutions of purified Hsp70 active protein (Acris, USA) and of purified ubiquitin (UbpBio, E-1100, USA), respectively, to give a range from 0 to 2 μg.mL−1 of protein.

Oxidative stress enzymes

The enzymatic assay of catalase (CAT; Enzyme Comission number - EC 1.11.1.6) was carried out according to procedures described elsewhere (Aebi, 1983; Beers and Sizer, 1952; Li and Schellhorn, 2007). Activity from a standard bovine catalase solution of 1523.6 U.mL−1 was used as a positive control. Catalase activity was calculated using a molar extinction coefficient −1 −1 (at 240 nm) for H2O2 of 0.04 mM (mM cm ).

The enzymatic assay of glutathione-S-transferase (GST) activity (EC 2.5.1.18) was adapted from (Habig et al., 1974) and optimized for 96-well microplates. The substrate CDNB (1-chloro- 2,4-dinitrobenzene) was used to react with the enzyme. After reading the absorbance

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at 340 nm GST activity was calculated using a molar extinction coefficient for CDNB of 5.3 mM (mM−1 cm−1) after correction for the microplate’s wells path length.

The enzymatic assay of superoxide dismutase (SOD) activity (EC 1.15.1.1), using nitroblue tetrazolium (NBT) and xanthine oxidase (XOD), was carried out according to (Sun et al., 1988). After reading the absorbance at 560 nm, SOD activity was calculated using the equation for the % inhibition:

Oxidative damage products – lipid peroxidation

The lipid peroxides (LPO) assay was adapted from the thiobarbituric acid reactive substances (TBARS) protocol (Uchiyama and Mihara, 1978). Monobasic sodium phosphate buffer (50 mM) was added to each sample, followed by SDS 8.1%, trichloroacetic acid (20%, pH = 3.5), and thiobarbituric acid (1%). Milli-Q grade ultrapure water was added to each mixture and microtubes were then put in a vortex for 30 s and incubated in boiling water for 10 min. To stop the reaction, microtubes were placed on ice for 10 minutes. Duplicates of the supernatant of each reaction were put into a 96-well microplate and absorbance was read at 530 nm. To quantify the lipid peroxides, an eight-point calibration curve (0–0.3 M TBARS) was calculated using malondialdehyde-bis(dimethylacetal) (MDA) standards (Merck Millipore, Portugal).

Nervous system enzyme

Enzymatic assay of acetylcholinesterase (AChE; EC 3.1.1.7) was performed using thiol quantification based on Ellman’s method (Ellman et al., 1961). A reagent mix containing sodium phosphate buffer 50 mM (pH 8.0), 1 mM Ellman’s reagent (DTNB – 5,5’-dithiobis-(2- nitrobenzoic acid) in 50mM phosphate buffer) and 75 mM ACTI (acetilthiocholine iodide) in phosphate buffer 50 mM was added to each sample in a 96-well microplate. Negative controls were included. Absorbance was read at 415 nm each minute for 10 minutes. AChE activity was calculated using a molar extinction coefficient for DTNB of 0.00781εmM.

Energy biomarkers

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The C:N (carbon:nitrogen) molar ratio was used as a proxy for changes in energy metabolism/reserves, and % C (carbon) used as a proxy for lipid reserves (following Sterner and Elser 2002). Muscle pellets were lyophilized (by freeze-drying in vacuum) and grounded to a fine homogeneous powder. Samples of ~0.5 mg were loaded into tin cups and analyzed using an Elementar Isoprime continuous-flow mass spectrometer (GV Instruments) coupled to a vario PYRO cube elemental analyser (Elementar, Hanau, Germany). Reference materials (acetanilide; Stable Isotope Research Facility, Indiana University, USA) were assayed at the beginning of each run and after every 10 samples. Additionally, total protein (as described in subsection 2.3.1.) was also analyzed as a proxy of energy reserves.

4.2.4. Data analysis

Stress biomarker data were assessed for normality through Shapiro-Wilk's test, homoscedasticity through Levene's test and outliers based on graphical methods. When test assumptions (normal distribution and homoscedasticity) were not met, either a data transformation was performed or a non-parametric test was used. Factorial ANOVAs and post hoc Tukey HSD tests (whenever the null hypotheses were rejected) were carried out to detect: 1) which biomarkers (thermal, oxidative or neurotoxic stress) contributed to differentiate between temperature treatments (25˚C vs 30˚C) and exposure times (T0, T7, T14); and 2) differences between biomarker levels throughout the experiment at different temperatures.

Additionally, to integrate results from these biomarkers and understand global/general responses, the Integrated Biomarker Response (IBR) index was calculated according to (Beliaeff and Burgeot, 2002). In brief, IBR was calculated by summing up triangular star plot areas calculated for each two neighbouring data. The general mean (m) and the standard deviation (s) of all data (including all sampling times) regarding a given biomarker was calculated, followed by a standardization to obtain Y, where Y = (X - m)/s, and X is the mean value for the biomarker at a given time. Then Z was calculated using Z = -Y or Z = Y, in the case of a biological effect corresponding respectively to an inhibition or a stimulation. The score (S) was calculated by S = Z + |Min|, where S ≥ 0 and |Min| is the absolute value for the minimum value for all calculated Y in a given biomarker at all measurements made. Star plots were then used to display Score results (S) and to calculate the integrated biomarker response (IBR) as:

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where Si and are two consecutive clockwise scores (radius coordinates) of a given star plot; Ai corresponds to the area the connecting two scores; n the number of biomarkers used for calculations; and α = 2п/n. Lower or higher index core values can be translated into the impact of high temperature on organisms: higher index core values are indicative of a poorer health status (stressed organisms). IBR values were used to compare intensity of responses between the two species and between tissues in each species. Moreover, Principal Component Analysis (PCA) was also carried out for each species to detect biomarkers that correlate with temperature, time and tissue, contributing to explain the variance in the dataset.

To calculate body condition of the animals, Fulton’s K was determined from the morphometric data as follows:

where is the total wet mass (mg) and is the total length (mm) (Ricker, 1975). Student t- tests were employed to detect for differences in condition index, as well as for energy reserves (total protein, %C and C:N molar ratio) between treatments at the end of the heat wave event simulation. Relationship between %C and LPO extension at 30˚C in the muscle was also tested by Pearson correlation in both species. No statistical tests were applied to mortality because there were no dead specimens to report in neither species. All statistical analyses were performed using the software Statistica v8 (StatSoft Inc., USA). All results were considered to be statistically significant at p-value < 0.05.

4.3. Results

4.3.1. Thermal, oxidative and neurotoxic stress biomarkers

Statistical analyses showed that three (Hsp70, CAT, GST) out of seven biomarkers here tested were affected by temperature in the muscle of R. durbanensis, while none of the biomarkers showed significant responses in the same tissue in C. laevimanus (Table 4.1). On the other hand, while only two (Hsp70, Ub) out of six biomarkers responded in R. durbanensis visceral mass, five (all except for SOD) out of six biomarkers responded in this tissue in C. laevimanus (Table 4.2). Time-dependent differences between temperatures (tested as temperature-time interactions) showed identical results when testing temperature alone. The

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exception was R. durbanensis visceral mass, where Hsp70 did not show significant interactive differences despite differences in temperature alone, and GST that did not show differences when testing temperature alone, but recorded significant changes when testing interactions with time (Table 4.2).

Table 4.1 Factorial ANOVAs results on the muscle tissue of a) Rhynchocinetes durbanensis and b) Calcinus laevimanus. Effect of temperature (25˚C vs 30˚C) and its interaction with time (T0, T7, T14) on biomarker levels (thermal, oxidative and neurotoxic stress). Significant results (p-value < 0.05) are presented in bold. a) b) Temperature Temp × Time Temperature Temp × Time F p-value F p-value F p-value F p-value Hsp70 4.88 0.036 14.03 <0.001 0.40 0.528 0.72 0.493 Ub 0.66 0.423 1.00 0.382 0.01 0.967 0.01 0.992 CAT 12.51 0.001 6.10 0.007 0.02 0.869 0.01 0.993 LPO 0.00 1.00 0.07 0.929 0.00 0.984 0.00 0.999 GST 5.35 0.029 13.33 <0.001 0.04 0.829 0.12 0.879 SOD 0.47 0.496 0.14 0.868 0.63 0.432 0.22 0.800 AChE 3.42 0.076 1.21 0.315 0.18 0.671 0.46 0.632

Table 4.2 Factorial ANOVAs results on the visceral mass tissue of a) Rhynchocinetes durbanensis and b) Calcinus laevimanus. Effect of temperature (25˚C vs 30˚C) and its interaction with time (T0, T7, T14) on biomarker levels (thermal and oxidative). Significant results (p-value < 0.05) are presented in bold. a) b) Temperature Temp × Time Temperature Temp × Time F p-value F p-value F p-value F p-value Hsp70 4.92 0.036 2.48 0.104 5.34 0.029 4.59 0.020 Ub 4.34 0.048 5.66 0.009 18.30 <0.001 4.58 0.020 CAT 0.54 0.468 2.94 0.071 36.71 <0.001 9.28 0.001 LPO 0.73 0.398 0.67 0.516 11.68 0.002 3.25 0.056 GST 0.954 0.338 3.76 0.037 20.92 <0.001 12.05 <0.001 SOD 3.18 0.086 2.75 0.083 3.02 0.094 1.47 0.249

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Figure 4.1 Biomarker log2 fold-change after 7 and 14 days of exposure to heat wave simulation: A) Rhynchocinetes durbanensis muscle, B) Calcinus laevimanus muscle, C) R. durbanensis visceral mass, D) C. laevimanus visceral mass. Significant differences (p-value < 0.05) from control (T0) are presented with an asterisk (*).

While levels of Hsp70 (protein unfolding biomarker) increased after 7 days of thermal challenge in both species (3.2-fold in muscle in R. durbanensis and 1.3-fold in visceral mass in C. laevimanus, Fig.4.1A, D), Ub (protein denaturation biomarker) showed significant increases both after 7 (1.6-fold and 2.3-fold for each species, Fig. 4.1C, D, respectively) and 14 days (2.6- fold in C. laevimanus, Fig. 4.1D) of thermal challenge, but these changes were only observed in the crustaceans’ visceral mass. Focusing on AOX (antioxidant enzymes), GST, and SOD showed significant increases at T7 (0.6-fold and 2.8-fold) in R. durbanensis (in muscle and visceral mass, respectively, Fig. 4.1A, C), while CAT showed a significant decrease in the muscle of this species (-0.7-fold), also at T7. AOX enzymes in C. laevimanus only showed significant time-dependent changes with temperature in the visceral mass (Fig. 4.1D). SOD increased significantly after 7 days (2.0-fold), while GST increased only after 14 days (5.3- fold). CAT showed significant increase in fold-change at all timepoints (2.9-fold in T7 and 2.8- fold in T14). Considering the oxidation products arising from oxidative stress, LPO only showed a significant fold-change (4.2-fold) in T7 in C. laevimanus’s visceral mass. No significant changes were detected for AChE (neurotoxic stress marker). In summary, R. durbanensis showed: 1) a pattern of biomarker increase after 7 days of exposure to a higher

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temperature, but no response was detected after 14 days of exposure (in none of the tissues) – different biomarkers responded in each tissue, but the timing of responses was similar; 2) higher biomarker fold-changes in muscle tissue when compared to visceral mass; 3) Hsp70 in the muscle and SOD in visceral mass were the biomarkers with highest fold-changes; and 4) significant biomarker fold-change variation (between -0.7-fold and 3.2-fold). Finally, in C. laevimanus our results showed that: 1) muscle was irresponsive to the thermal challenge, whereas the visceral mass showed consistent biomarker significant increases after being exposed to a higher temperature (both after 7 and 14 days); 2) biomarkers Ub and CAT responded in all timepoints tested; and 3) all biomarkers that responded significantly showed positive fold-changes (that varied from 2.0-fold to 5.3-fold), and the highest fold-change was detected in GST.

4.3.2. Integrated biomarker responses (IBR) index

IBRs individually calculated for each tissue type and each timepoint (for both species, Fig. 4.2) showed that index values varied along time in muscle tissue, being higher at 30˚C in earlier timepoints (T0 and T7), and then decreasing at T14, giving overall mean IBR values that are very similar between temperatures. In visceral mass, however, IBR index core values were consistently higher at 30˚C across timepoints, and mean IBR from the whole experiment trial duration also showed increased IBR values at heat wave temperature when compared to control temperature in both species.

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Figure 4.2 Integrated biomarker response index (IBR) after experimental trials at 25˚C and 30˚C in each timepoint sampled, and mean + SD of all timepoints in muscle and visceral mass of A) Rhynchocinetes durbanensis and B) Calcinus laevimanus.

Star plots for biomarker responses (in all tissues and sampling times) show that graph area is higher at 30˚C when compared to 25˚C for both species (Fig. 4.3). This suggests consistently higher scores at 30˚C for both species, translating into higher index core values at this temperature and, therefore, poorer health status during the heat wave simulation.

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Figure 4.3 Integrated biomarker response index (IBR) and visual representation of variables in the principal components analysis carried out for each species A) Rhynchocinetes durbanensis and B) Calcinus laevimanus exposed to 25˚C and 30˚C, considering all sampling times (0, 7 and 14 days) and both tissues (muscle and visceral mass).

Figure 4.4 Factor 1 and factor 2 of the principal components analysis carried out for both tissues (muscle and visceral mass) in A) R. durbanensis and B) C. laevimanus exposed to 25˚C (control) and 30˚C (heat wave).

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PCAs (Fig. 4.3) showed that biomarkers mostly correlated to temperature were Hsp70 and GST in R. durbanensis and LPO and CAT in C. laevimanus, whereas CAT and SOD and SOD and Ub were mostly correlated to tissue type in R. durbanensis and C. laevimanus, respectively. No particular biomarker was associated with time in both species analyzed. The cumulative amount of variance explained (Fig. 4.4) was 83.87% in the shrimp species and 81.30% in the ermit crab species.

4.3.3. Energy metabolism and performance

Condition estimates and energy markers results showed that higher temperature only affected R. durbanensis, namely in Fulton’ K (increased from 25˚C to 30˚C) and %C (decreased in muscle from 25˚C to 30˚C). No significant differences were found for C. laevimanus in any of the parameters here tested (Table 4.3). Significant Pearson correlations were observed between %C and LPO extension at 30˚C in the muscle for both species (positive for R. durbanensis: R = 0.615, p-value = 0.015; and negative for C. laevimanus: R = -0.657, p-value = 0.039). Mortality was 0% for both species in both treatments.

Table 4.3 Student t-tests performed on condition parameters after exposure to a 2-week heat wave simulation experiment. Significant results (p-value < 0.05) are presented in bold. Fulton's K Total protein %C C:N molar t p-value t p-value t p-value t p-value R. durbanensis -2.513 0.017 0.527 0.601 2.497 0.018 0.474 0.638 C. laevimanus 0.806 0.908 -0.966 0.437 -1.682 0.994 0.411 0.613

4.4. Discussion

Temperature affects organisms at different levels of organization, ranging from molecular to whole-body (Dent and Lutterschmidt, 2003; Mora and Ospína, 2001). In this study, we showed for both crustacean species that temperature effects were mainly detected in the amount of protective proteins and enzyme activity levels. Performance consequences were only observed in R. durbanensis, particularly in condition and energy reserves. The general pattern of biomarker variation here analyzed showed that: 1) different species and different tissues display different thermal sensitivities, 2) biomarker changes occurred at similar timings, and 3)

biomarkers with significant log2 fold-changes showed mild to moderate (Hsp70, CAT, GST) or severe stress (Ub and LPO) in cells (cell stress degree as in Logan and Somero 2011). All of these observations suggest that an increase of +5˚C during two consecutive weeks had a significant quantifiable impact in these species’ physiology. Heat waves are generally associated

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to an increase in animals’ metabolic activity (Arambourou and Stoks, 2015), which is mostly associated with increased respiration rates as a consequence of extra maintenance, repair, and protein production (Feder and Hofmann, 1999; Tomanek, 2010). This ultimately suggests that organisms need to adjust their metabolic rates during heat stress in order to balance tissue oxygenation and energy production/expenditure (McElroy et al., 2012). The high magnitude of fold-changes detected in Hsp70 and SOD for R. durbanensis after 7 days suggest that increased temperature causes protein unfolding and cytotoxic aggregations (Madeira et al., 2015; Yamashita et al., 2010) in muscle cells, and a higher flux of superoxide radical arising from oxidative stress (Rosa et al., 2014) in visceral mass. C. laevimanus showed highest order of magnitude changes in GST (at 14 days) in the visceral mass, suggesting enzyme activity to detoxify toxic lipid peroxides that arose (at 7 days) from oxidative damage to cell membranes (Madeira et al., 2013b; Vinagre et al., 2014). Such responses to a thermal challenge have also been documented in many other crustacean species (e.g. caridean shrimps: Palaemon spp. Allan et al. 2006; González-Ortegón et al. 2013; Madeira et al. 2015; Magozzi and Calosi 2015, and Crangon crangon Reiser et al. 2014; white shrimp Litopenaeus vannamei González et al. 2010; lobster Homarus americanus Chang 2005; grapsidae crab Cyclograpsus cinereus Lardies et al. 2011). Mean IBR values were always higher at 30˚C when compared to 25˚C (except for muscle in C. laevimanus), indicating that animals exposed to the heat wave were in poorer health status (i.e., stressed) when compared to control conditions.

This heat wave simulation seemed to be perceived differently by each species. Neither species showed changes in mortality levels, suggesting that the increase in 5˚C in temperature for two consecutive weeks was not an extremely stressful one. However the imposed temperature did show a significant influence in fitness parameters of R. durbanensis. Although Fulton’s K increased during the heat wave, %C levels decreased, indicating a process of depletion of lipid reserves. Other studies have also shown that thermal stress (either warm or cold) can cause lowered energy stores (Klepsatel et al., 2016; Wen et al., 2017), even if growth increases (Arambourou and Stoks, 2015). Another aspect of the results was the significant correlation obtained between %C and LPO levels – the amount of stored lipids seems to be a key aspect in the extension of lipid peroxidation experienced by the animals under heat stress. However, opposite correlations were observed for each species (positive for R. durbanensis and negative for C. laevimanus), which makes it difficult to hypothesize which pathways are at work. We suggest two possibilities: 1) a positive correlation suggests that lipid rich tissues suffer proportionally from lipid oxidation leading to increased accumulation of LPOs, whereas 2) a negative correlation suggests that lipid rich tissues have more energy available to direct towards cytoprotective pathways, therefore avoiding cellular damage and leading to low levels

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of LPOs. However, this is an issue that requires further research before any conclusions can be drawn.

All of these differences in heat wave responses detected between species from the same reef environment can be associated with i) differences in the kinetics of responses, ii) differences in temperature sensitivity and iii) differences in the habitats they explore (and consequently in life histories and activity levels). For instance, the shrimp R. durbanensis is a strictly subtidal animal, while the hermit crab C. laevimanus is both intertidal and subtidal, which means that hermit crabs are more versatile and are commonly used to explore steep thermal gradients (Helmuth and Hofmann, 2001; Madeira et al., 2015; Somero, 2002). This is a very significant feature in intertidal reefs where there are topographically complex microhabitats including rock pools, crevices, boulders, and emergent platforms (Lathlean et al., 2017). Consequently, hermit crabs responses may be more plastic, mediated by internal changes that can include – but are not limited to – higher basal biomarker levels and inducible responses (Madeira et al., 2017b, 2014a), sooner activation of energy saving cellular pathways (e.g. metabolic depression) (Madeira et al., 2015) and changes in mitochondrial densities and in the oxygen supply system (e.g. hemolymph hematocrit) (Abele et al., 2002; Verberk et al., 2016). All of these responses allow hermit crabs to optimize metabolism in varying thermal windows. Therefore, higher acclimation to temperature variation could be expected for C. laevimanus when compared to R. durbanensis.

The absence of mortality during the heat wave here simulated for both species can be associated with several reasons. It is possible that the thermal challenge was not intense or prolonged enough to cause mortality, even though this was the first time that these animals were exposed to 30˚C (both crustaceans were bred, reared, and maintained at 25˚C). Previous studies have shown that animals have the ability to withstand temporal spikes in temperature (Arambourou and Stoks, 2015) until a certain physiological threshold is achieved, above which growth is severely affected and mortality levels can rise up to 100% of the population. Additionally i) the animals may have originated from strains resistant to temperature variation, depending on the original broodstock thermal history; ii) high acclimation to captivity conditions may limit stress response patterns (Madeira et al., 2017a) (as domestication affects thermal physiology and the more generations have been kept in captivity, the stronger this effect may be due to inbreeding and lower genetic diversity); 3) both species are native to reef environments that naturally experience dynamic temperature variations over short periods, even at subtidal depths (Leichter et al., 2006). For instance, it is know that sub-surface temperatures can vary as a result of diurnal warming in shallow waters up to 10m depth (Brown, 1997; Craig et al., 2001), and due to tidal forcing and internal waves at greater depths (20-30m) (Lee et al.,

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1999). As the high specific heat and thermal conductivity of water ensure that marine ectotherms will have body temperatures equivalent to that of their surroundings (Feder and Hofmann, 1999), then inhabitants of these environments are likely adapted to the kind of thermal challenge tested here.

Additionally, when geographical distributions of species span temperature gradients that are greater than projected changes due to climate warming, those particular species may be genetically wired for thermal acclimation and adaptation (Munday et al., 2012, 2008). This seems to be the case for the species under study considering that their natural distributions are broad - from eastern Africa, to India, Southeast Asia and Western Pacific islands (R. durbanensis, Grave 2010; C. laevimanus, Lemaitre and McLaughlin 2017). Also, SST variability exhibited along the coast lines of the Indo-Pacific region where these species inhabit ranges from about 22˚C to 31˚C. Interestingly, the capacity to acclimate to altered conditions in such cases is likely to be similar among most of the natural populations of those species along latitudinal gradients, if there are no restrictions to gene flow, as was observed for the crab Petrolisthes violaceus along the Pacific coast of Chile (Gaitán-Espitia et al., 2017). This means that even tank-raised animals may share this capacity with their wild counterparts, inherited from the original broodstocks, which would explain our results.

4.5. Conclusions

The main conclusions to retrieve from this study are that both animals displayed a plastic response under heat wave conditions with no mortality, suggesting that the physiological adjustments they employed conferred them thermal tolerance. Furthermore, the application of integrated indices, combined with whole body measurements, yielded reliable results for thermal stress monitoring purposes. By optimizing tools and methodologies with tank-raised animals, we can then implement them in field studies with natural populations and fill the existing knowledge gap on tropical thermal stress biology. This provides a first step on improving ecological risk assessment of vulnerable populations and it will allow the implementation of better conservation measures in already fragile tropical communities.

Acknowledgements

Authors thank Aquário Vasco da Gama (public aquarium) for providing live rock and seaweeds, and thank everyone involved in maintenance of the experimental tanks. This study had the support of the Portuguese Foundation for Science and Technology (FCT) through the WarmingWebs project (PTDC/MAR-EST/2141/2012), the “Investigador FCT” position granted

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to C.V. and the strategic projects UID/MAR/04292/2013 granted to MARE and UID/Multi/04378/2013 granted to UCIBIO. C.M. was funded by a PhD grant (SFRH/BD/92975/2013), and M.C.L. by a post-doc grant (SFRH/BPD/115298/2016), all granted by FCT.

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CHAPTER 5

Thermal stress, thermal safety margins and acclimation capacity in tropical shallow waters – an experimental approach testing multiple end-points in two common fish

Madeira C, Mendonça V, Leal MC, Flores AAV, Cabral HN, Diniz MS, Vinagre C (2017) Ecological Indicators 81: 146-158, DOI: 10.1016/j.ecolind.2017.05.050

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ABSTRACT

Tropical organisms are predicted to be among the most impacted by increasing sea surface temperatures, particularly those from intertidal habitats. In this study, a complete thermal biology assessment was conducted for two widespread tropical Atlantic shallow reef fish: Abudefduf saxatilis (damselfish) and Scartella cristata (blenny), which make extensive use of tide pools. The main objectives were to measure the time-course changes during one month in i) thermal and oxidative stress biomarkers (in gills, muscle and skin), ii) upper thermal limits, acclimation capacity and thermal safety margins and iii) body size, condition and energy reserves (total protein and lipid contents), under two temperature treatments (control – mean summer water temperature, and elevated temperature – +3˚C, as projected by climate warming scenarios for the end of this century). Results from biomarker analyses suggest that under increased temperature, both species displayed a typical response of physiological stress characterized by the activation of molecular chaperones and antioxidant protection. Both species presented a significant acclimation potential in the long term, as shown by increased critical thermal maxima values at higher temperature. However, these species may already be at risk during summer heat waves, as thermal safety margins for both species were low. Additionally, despite acclimation, some energetic tradeoffs may exist, since specimens from both species showed smaller body sizes at higher temperature (even though maintaining body condition). Finally, temperature treatments had a significant influence not only in the total amount of energy reserves (lipid contents) but also in their rate of deposition or depletion (total proteins and lipid contents). This is the first multi-end-point holistic approach to assess the impact of warming in shallow tropical water fish and it highlights the high risk that intertidal organisms are facing in both present and future sea surface temperature conditions.

Keywords: tropical fish; biomarkers; thermal tolerance; ocean warming; intertidal; environmental biomonitoring

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Graphical abstract

Highlights • Thermal stress was evaluated in two tropical fish species using multiple end-points • Chaperones, catalase, ubiquitin and lipid peroxides were the most responsive markers • Gills proved to be the best tissue to monitor cellular stress responses • Both fish showed acclimation ability but narrow thermal safety margins • The two species showed smaller sizes at high temperature • Juvenile fish stages may be at risk in tide pools under warming scenarios

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5.1. Introduction

In recent years, considerable attention has been given to the potential impacts of climate warming on marine ectotherms. These organisms have to adapt not only to the rising mean temperatures (expected to increase by up to + 3˚C by the end of this century (IPCC, 2013)) but also to more pronounced short-term outlier temperature events (i.e., heat waves) which are expected to increase in frequency and result in short-term selection events (Culumber and Monks, 2014).

Temperature annual fluctuations are generally small in tropical latitudes due to the absence of a cold season (McGregor and Nieuwolt, 1998). A temperature variation of just a few degrees thus represents a proportionally large change for organisms adapted to such relatively stable environment (Donelson et al., 2011b). In addition, organisms in these geographical regions may be already operating close to or at their physiological limits (Blaber, 2002). The accuracy of estimates of biotic changes in the tropics in response to climate change is, therefore, dependent on the understanding of how thermal stress affects animal physiology (Klepsatel et al., 2016).

Rocky intertidal habitats in particular are among the aquatic environments most vulnerable to temperature rise because they typically display low thermal inertia (Vinagre et al., 2013b). Fish, as ectotherms and inhabitants of these systems, are expected to be significantly affected by temperature variation. In fact, the temperature range experienced by a population can influence the width of the fitness of the thermal reaction norm and the amount of plasticity expressed (see Angilletta et al., 2003, 2006 for a review). For instance, water temperature experienced by larval stages of coral reef fish is known to influence their pelagic larval duration, daily growth and size at settlement (McLeod et al., 2015). Acute and chronic exposures to temperature change were also shown to alter the shape of thermal performance curves of mitochondrial respiration in fish (Schulte et al., 2011).

The temperature tolerance window for each species is usually described as a range of performance breadth and includes an optimal and suboptimal zone (Pörtner and Farrell, 2008). Above or below that range, physiological performance is negatively affected. In consequence, the shifting of thermal regimes due to climate change forces fish to perform at suboptimal temperatures or at temperatures beyond their thermal windows (Huey et al., 2009; McDonnell and Chapman, 2016). However, fish can only explore their passive range of tolerance for a limited time (Madeira et al., 2012a) because long-term processes such as growth are gradually reduced (Pörtner and Knust, 2007). The knowledge on how these processes are affected is thus

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of major importance because growth, for instance, affects time to maturation, fecundity, feeding behavior and recruitment (Houde, 1987), and ultimately determines the populations’ productivity and stability (Kappenman et al., 2009).

The ability of fish to respond to temperature dynamics relies mainly on the co- ordination of internal components (e.g. specific responses of different organelles, cells and tissues) and processes (e.g. intra and extracellular acid base chemistry) to maintain physiological homeostasis (Somero, 2010). A disruption of these components sets the physiological limits for the whole organism (Shultz et al., 2014). The temperature at which stress begins to occur is largely dependent on a fish’s prior exposure history, and acclimation may be one way of dealing with stressful conditions (Bevelhimer and Bennett, 2000). This process comprises regulated responses that can change the phenotype (Angilletta et al., 2006) and allow the organism to match its physiology to the current environment (Angilletta, 2009). Whereas cellular and tissue components can acclimate at different rates (Colin et al., 2015), complete acclimation at the whole-animal level to increased temperature usually involves a change in acute thermal tolerance (measured as critical thermal maxima, CTMax) (Peck et al., 2014), which is a common metric to evaluate whole animal acclimation (Mora and Ospína, 2001). On the other hand, the focus on molecular and cellular responses provides important mechanistic information on thermal compensation of these functions and is essential to build a solid basis for comparative physiology approaches (Peck et al., 2014). For instance, biochemical measurements of the cellular stress response (CSR) in several animal tissues are typically used for this purpose. In particular, under hyperthermia, the CSR is promptly activated in cells and it involves the prevention and repair of macromolecular damage (Feder and Hofmann, 1999; Kültz, 2004). There are different thresholds for the induction of different components of the CSR, which have the potential to differentiate intensity of stress responses (mild, moderate and severe) and cellular pathways involved in physiological mitigation of the experienced stressor (Logan and Somero, 2010). For instance, at mild levels of heat stress, there is a perturbation of protein structure and cytosolic aggregation of proteins which leads to the induction of molecular chaperones (heat shock proteins – Hsps), such as Hsp70. Hsps not only refold proteins that have unfolded, but they are also believed to act as sensors of cellular redox changes (Kalmar and Greensmith, 2009) and have been reported to activate reactive oxygen species (ROS) scavengers (such as catalase, superoxide dismutase and peroxidases) to reduce the flux of ROS produced during oxidative metabolism (Mocanu et al., 1993; Madeira et al., 2013). Hsps also inhibit the depolarization of the mitochondrial membrane to maintain its integrity (Snoeckx et al., 2001). Moderate levels of heat stress are usually associated with protein denaturation, and proteolysis through the ubiquitin-proteasome pathway may be initiated to remove proteins that cannot be rescued through activities of chaperones (Tomanek and Somero, 2002; Logan and

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Somero, 2011). Finally, at severe levels of heat stress, whole cells may become sufficiently damaged (e.g. due to DNA damage or lipid peroxidation in cell membranes, see Madeira et al., 2013, 2014, 2016) to trigger the induction of apoptotic pathways (Logan and Somero, 2011). Therefore, this type of approach is concerned with establishing a pathological profile that has resulted from the interaction of an organism with a defined stressor and the degree of stress experienced.

Despite the thorough knowledge of the thermal biology of marine temperate organisms, our understanding of thermal stress in tropical fish in thermally dynamic environments remains scarce due to a lack of studies that integrate organismal and subcellular indicators. In order to address this gap in eco-physiological research that is critical for monitoring and conservation purposes, the main aim of this study was to investigate the thermal physiology of two tropical Atlantic rocky reef fish species (Abudefduf saxatilis and Scartella cristata) during one month. In particular, the objectives were to: i) assess sublethal effects of increased temperature at a molecular and cellular level through characterization of biomarkers of biological effects (heat shock protein 70 kDa – Hsp70, ubiquitin – Ub, catalase – CAT, lipid peroxides – LPO, glutathione-S-transferase – GST, superoxide dismutase – SOD and acetylcholinesterase - AChE) in several tissue types (gills, muscle and skin); ii) estimate the upper thermal limits (CTMax), thermal safety margins (i.e., warming tolerance) and acclimation capacity of thermal tolerance of each species, as well as their intraspecific variation, and iii) evaluate the effects of thermal stress/acclimation on the performance of these species (e.g. growth, body condition, energy reserves), under control (mean summer water temperature at the collection time – 29˚C) and a warming scenario (future mean summer water temperature – 32˚C).

5.2. Materials and methods

5.2.1. Ethical statement

The authors declare that the study design followed ARRIVE guidelines and that the use of animals in experimental trials followed the Portuguese and Brazilian legislation for laboratory animal science (Decreto-Lei n˚ 113/2013 and Lei n˚ 11.794/2008, respectively). Authorization document 0421/000/000/2013 from the Portuguese authorities (DGAV) and 13.1.981.53.7 from the Brazilian authorities (CEUA, USP - Ribeirão Preto). Three authors (C.M., M.S.D., C.V.) have a level C FELASA certification (Federation of European Laboratory Animal Science Associations).

5.2.2. Test species and assessment of their thermal environments

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The tropical fish species tested were Abudefduf saxatilis (Pomacentridae, Linnaeus, 1758) and Scartella cristata (Blenniidae, Linnaeus, 1758). These two demersal fish may share a common habitat during the juvenile phase in the tidal pools of shallow reefs. As fish grow, A. saxatilis migrates to the shallow subtidal (0-20m depth) where it schools in groups up to several hundreds of individuals (Allen, 1991) and S. cristata, as an intertidal species, remains in tidal pools but has also been observed to occur in the shallow subtidal (0-10m depth) (Robins and Ray, 1986) (see Table A4.1 in annex 4 in the annexes section for full details on each species’ characteristics).

To assess the local summer thermal environments of these species, data on water temperature at the collection site were recorded with Hobo V2 probes from 8 replicate pools sampled continuously over 56 days (from December to February) at 2h intervals during the summer of 2015/6. Probes were glued to the rocks during low tide with Tubolit® (epoxy polyamide cement) and were removed after data collection using a chisel.

5.2.3. Specimen collection and acclimation procedure

Wild juvenile specimens (A. saxatilis: n = 120, mean ± SD total length = 3.82 cm ± 1.23 cm, mean ± SD total weight = 1.30 g ± 1.00 g, and S. cristata: n = 120, mean ± SD total length = 4.44 cm ± 1.20 cm, mean ± SD total weight = 1.29 g ± 1.18 g) were collected using hand nets in shallow waters (<50 cm) of Southeast Brazil (23˚49’42’’S, 45˚26’29’’W, Barequeçaba beach, São Sebastião, São Paulo) and placed in plastic containers with aerated sea water. The animals were then transported to the research facilities (Cebimar – Universidade de São Paulo, S. Sebastião) and placed into aquaria upon arrival. They were allowed to acclimate at 29.0˚C ± 0.5˚C for two weeks. Fish welfare was assessed regularly (i.e. daily, for wounds or disease symptoms) and the animals were fed once a day ad libitum with frozen shrimp.

5.2.4. Experimental design and rationale

The experimental trials consisted of subjecting the animals to two temperature conditions over a one month period. The temperatures chosen (29.0˚C ± 0.5˚C for control and 32.0˚C ± 0.5˚C for elevated temperature treatment) reflect: i) current mean summer water temperature at the collection site and time (as measured by Hobo V2 probes and as measured in a previous study (Vinagre et al., 2015) and ii) future mean summer water temperature according to IPCC regional projections of sea surface temperature (SST) anomalies for the tropical/subtropical Atlantic (+ 3˚C at the end of this century in the RCP8.5 scenario (IPCC,

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2013)), respectively. In the latter, temperature was increased slowly during two days at a rate of 0.10˚C.hr-1 from 29˚C until 32˚C were reached.

The experimental apparatus consisted of two systems of 290 L each. Each system was comprised of four tanks (two larger plastic tanks 60 × 30 × 25 cm and two smaller glass tanks 25 × 25 × 25 cm), one seawater deposit, one sump and a UV filter (see Fig. A4.1 in annex 4). Temperature treatments were applied to each tank individually (using thermostats from Eheim® Jager Heater 150W, Germany and thermometers from Petco, UK). Specimens from A. saxatilis were randomly placed in the larger plastic tanks (n = 30 individuals.tank-1, two replicate tanks per temperature treatment), while S. cristata specimens were randomly placed in the smaller glass tanks (n = 30 individuals.tank-1, two replicate tanks per temperature treatment). All tanks were enriched with live rocks and filled with clean aerated seawater (95-100% oxygen saturation) and salinity 35. Temperatures were maintained for 28 days. Experiments were carried out in shaded day light (14L:10D).

For the purpose of physiological monitoring through biochemical determinations (molecular biomarkers, total protein and lipid contents), the timing of samplings and duration of trials was chosen following OECD guidelines for repeated exposure toxicity studies (www.oecd.org). Five fish were sampled by cervical transection at day 0, 7, 14, 21 and 28, always at the same period of the day (for both temperature treatments, total n = 50 specimens of each species for molecular analyses) (see Fig. A4.2 in annex 4). Sample sizes were calculated using a power analysis (www.statisticalsolutions.net, σ = 10%, 1-β = 0.90, Δ = 20-25%, α = 0.05). Gills, muscle and skin tissues were selected for analysis (based on literature recommendations, see Newman, 2014), and were removed from the organisms and immediately frozen at -80˚C (see Fig. A4.3 in annex 4).

To quantify the organisms’ upper thermal limits, the CTMax dynamic method was employed. This method has been widely used for the determination of thermal limits of ectothermic vertebrates and invertebrates (Barnes et al., 2010; Vinagre et al., 2013; Kaspari et al., 2015). It is determined by exposing the organisms to a constant thermal ramp until a critical point is reached (e.g. loss of balance, Lutterschmidt and Hutchison, (1997)). In fish, loss of equilibrium was defined as the point when individuals could not swim straight and started moving in an angled position. These criteria are the same followed by Madeira et al. (2012) and Vinagre et al. (2016).

In this study, CTMax was estimated for a subset of individuals of each species (see Fig. A4.2 in annex 4) after 7 days and 28 days of exposure to each temperature (total n = 45 for A.

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saxatilis and n = 61 for S. cristata). Although the experiment was sequential, different organisms of each species were tested in each CTMax trial, i.e. no organism was exposed to more than one CTMax trial (all individuals that were subjected to a CTMax trial were excluded from the remaining experiment).

All organisms were subjected to a thermostatic bath with a constant rate of water temperature increase of 1˚C/15 min, with constant aeration. Their behavior was observed, until they reached the end-point. This temperature ramp is consistent to what can be found in tide pools during summer days, following recommendations of Vinagre et al. (2015) for the use of ecologically realistic warming ramps. The temperature at which each animal reached its end- point was measured with a digital thermometer.

5.2.5. Experimental outcomes

Molecular biomarkers

Biomarkers have been typically used in environmental toxicological studies once they represent detectable biochemical and tissue-level changes that indicate altered physiology under stressful conditions (Hook et al., 2014). Carefully selected biomarkers (quantifiable, universal within the study group, sublethal and reliable for interpretation) may therefore be the best approach to identify an early response to stress as they are much more sensitive than whole- animal responses (Smit et al., 2009). For purposes of biomarker measurements, tissue samples were homogenized in 1.0 mL of phosphate buffered saline (PBS) solution (140mM NaCl, 3mM

KCl, 10mM Na2HPO4, 2mM KH2PO4, pH 7.4) using a Tissue Master 125 homogenizer (Omni International, Kennesaw, USA) on ice-cold. Homogenates were centrifuged at 10,000 × g (at 4˚C for 15 min) and the supernatant transferred to new microtubes (1.5 mL) and frozen (-80˚C). Protein quantification and kinetic assays were then carried out using colorimetric methods previously described and adapted for 96-well microplates (Madeira et al., 2015), which are extensively described in the supplementary material (see Table A2.1 in annex 2 in the annexes section): i) Heat shock protein 70 quantification using an indirect Enzyme Linked Immunosorbent assay (ELISA) and ubiquitin quantification using a direct ELISA (Madeira et al., 2017a); ii) Enzymatic assay of catalase (Enzyme Commision number – EC 1.11.1.6, following Johansson and Borg (1988)); iii) Lipid peroxides assay using TBARS (thiobarbituric acid reactive substances), measured as malondialdehyde (MDA) content (Uchiyama and Mihara, 1978);

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iv) Enzymatic assay of glutathione S-transferase activity (EC 2.5.1.18), using reduced L- glutathione (GSH) and the substrate CDNB (1-Chloro-2,4-dinitrobenzene) originally developed by Habig et al. (1974); v) Enzymatic assay of superoxide dismutase activity, using nitroblue tetrazolium (NBT) and xanthine oxidase (XOD) as described by Sun et al. (1988); vi) Enzymatic assay of acetylcholinesterase (EC 3.1.1.7) using thiol quantification based on Ellman’s method (Ellman et al., 1961); vii) Total protein quantification (Bradford, 1976). A calibration curve was obtained using BSA standards (0 to 2.0 mg.mL-1). These measurements were used to normalize the other biomarker results.

Upper thermal limits, acclimation capacity and thermal safety margins

The upper thermal limits (CTMax) for each species were calculated as the arithmetic mean of the collective thermal points at which the end point is reached (Mora and Ospína, 2001). To determine the intraspecific variability of the CTMax, the coefficient of variation (in percentage) was calculated for each species as:

The acclimation capacity (i.e., the change in a physiological parameter that allows an organism to adapt to new environmental conditions) was determined by comparing CTMax values after short term and longer term exposure to both temperature conditions (CTMaxT28 –

CTMaxT7 at each temperature and CTMax32˚C – CTMax29˚C at 7 and 28 days). Thermal safety margins (i.e., the comparison of the maximum temperature that an organism can endure with the maximum environmental temperature) for each species were calculated as the difference between mean CTMax and Maximum Habitat Temperature (MHT) (determined through field measurements) as they provide an estimate on how close these species live to their thermal limits.

Size, body condition and energy reserves

The total length and weight of all individuals was measured either after the animals were euthanized (for molecular analyses) or at the end of CTMax experiments with an ichtyometer and a scale, in order to minimize handling stress. Body condition of the animals was calculated by Fulton’s K index, from morphometric data as follows:

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where is the total wet mass (mg) and is the total length (mm) (Ricker, 1975).

In addition, mean total protein (from gills, muscle and skin) and total lipid content (from skeletal muscle) were measured proxies of energy reserves. Total protein determination was performed by the Bradford method (Bradford, 1976), as described in the previous section. The protocol for lipid extraction was adapted from Ladd and Sachs (2015) and consisted in lyophilizing (by freeze-drying) muscle pellet samples to a fine homogeneous ground powder. Lipids from ~10-30 mg of tissue were extracted with 2:1 dichloromethane:methanol (DCM:MeOH), vortexed for 5 seconds and sonicated for 10 min in an ice-cold water bath. Samples were then frozen at -20˚C overnight to allow the settlement of tissue particles in the solvent. On the next day, the solvent (supernatant) from each sample was transferred to new glass vials (previously combusted at 450˚C for 5h) with glass pipettes. The resulting total lipid extracts (TLE) were then evaporated to dryness under a stream of N2 on a turbovap system. All the previous extraction steps were repeated at least 3× for each sample. Lipid content in each sample was then calculated as (and normalized afterwards per mg of dry muscle):

5.3. Data analysis

As a standard procedure, all data were tested for 1) outliers (box-whiskers plot, coefficient 1.5 for outliers and extremes), 2) normality through histograms and Shapiro-Wilk’s test, and 3) homoscedasticity through Levene’s test. When at least one of the assumptions of a specific test (e.g. normality or homoscedasticity) did not occur, either a data transformation (e.g. logarithmic) was employed or a non-parametric test was used (this applies to all the statistical analyses performed).

Firstly, a factorial MANOVA was applied to overall biomarker response data to check for differences between species and between tissue types. Post-hoc Tukey’s honestly significant difference (HSD) tests were performed to factor “Tissue type” to check which tissues were statistically different from each other. Secondly, to analyse changes in biomarker behavior between temperature treatments in each of the three tissues, factorial ANOVAs were also employed for each species, with temperature and time as independent variables and the set of biomarkers as dependent variables. To test which groups were different and verify the variables that contributed to the significant results, post-hoc Tukey’s HSD tests were performed. Principal

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Components Analysis (PCA) was also employed to explore which variables contributed most to the differences detected between temperature treatments and species (after testing for collinearity with a chi-square test for independence between explanatory variables). Differences in CTMax values were explored by a factorial ANOVA, with temperature, time and species as grouping factors, followed by post-hoc unequal N HSD tests. A Mann-Whitney U test was employed to test for differences in CTMax intraspecific variation between species.

As temperature increase is also expected to influence organisms on a whole body level in the longer term, Student t-tests were performed on morphometric data (length and weight) and on condition estimates (Fulton’s K) after 28 days of exposure to the temperature treatments. Since mean total protein (from the 3 tissues) and lipid contents (from muscle) were measured as proxies of energy reserves (which serve as respiratory substrates during oxidative metabolism, Farrell, 2011); and these are associated with changes in body mass, differences between temperature treatments after 28 days of exposure were also analysed in these cases. This was done by applying general linear models (GLMs, separate slopes design), testing the main effects of temperature on the dependent variables (mean total protein and lipid contents) with body mass as a covariate, for each species. Moreover, the relationship between lipid contents and lipid peroxidation in the muscle was analysed through Spearman correlation, in order to understand the extent of macromolecular damage (extensive lipid peroxidation damage to cell walls may be associated to a temporary or permanent loss of the capacity for cellular lipid storage, due to apoptosis (Klepsatel et al., 2016)). Additionally, the upper thermal limits were analysed in relation to body size (length), using regression analysis. All results were considered to be statistically significant at a p-value ≤ 0.05 and all statistical analyses were carried out in Statistica software (version 13.0, Statsoft Inc. USA).

5.4. Results

5.4.1. Assessment of the species’ thermal environments during the juvenile phase

The collection of environmental temperature data measurements are summarized in Table 5.1 (see also Fig. A4.4 in annex 4). The higher mean monthly temperature recorded during the summer months was 29.32˚C ± 0.45˚C, observed in February while the lowest was observed in January (25.72˚C ± 0.65˚C). Average of minimum and maximum temperatures during the whole summer season ranged from 18.43˚C ± 1.93˚C to 37.60˚C ± 2.62˚C, with the absolute maximum temperature record of 41.50˚C during December in three of the tidal pools sampled. Average daily temperature variation (24h, inclusive of dark/light and tidal cycles) during the summer was 4.78˚C ± 1.06˚C at the sampling site.

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Table 5.1 Mean (± SD), maximum (Max) and minimum (Min) values of water temperature as well as daily water temperature variation at the collection site (˚C). These data were collected from 8 replicate tidal pools, sampled continuously during the summer season of 2015/6 (from December to February).

December January February Summer season Average temperature (˚C) 28.24 ± 0.57 25.72 ± 0.65 29.32 ± 0.45 26.95 ± 0.41 Max temperature (˚C) 37.50 ± 2.71 36.37 ± 3.68 36.57 ± 2.54 37.60 ± 2.62 Min temperature (˚C) 20.57 ± 1.03 18.78 ± 2.44 23.83 ± 8.47 18.43 ± 1.93 Daily temperature variation (˚C) 5.76 ± 1.56 3.62 ± 0.85 5.54 ± 1.33 4.78 ± 1.06

5.4.2. Thermal and oxidative stress biomarker analyses

Significant differences in overall biomarker responses were detected between species (F = 183.220, p-value < 0.0001) as well as between tissue types (F = 46.510, p-value < 0.0001). Post hoc Tukey HSD tests for differences between tissues showed that they were all significantly different from each other (gills vs muscle, p-value < 0.0001, gills vs skin, p-value < 0.0001 and muscle vs skin, p-value < 0.0001).

Furthermore, significant differences were also detected for the effects temperature, time and the interaction between them in biomarker responses in both fish species and in all tissues tested (Tables 5.2, 5.3). Univariate analyses showed that all biomarkers were highly responsive to an increase in temperature after one month of exposure, especially in the gills (all biomarkers responded significantly in both fish species, except for GST in A. saxatilis) (see also Tables A4.2 and A4.3 in annex 4 for biomarker basal levels). The muscle tissue showed slightly different responses for each species: i) in A. saxatilis (Table 5.2), thermal stress biomarkers (Hsp70 and Ub) did not respond, however oxidative stress and neurotoxicity biomarkers (CAT, LPO, GST and AChE, respectively) showed significant differences between temperature treatments; in ii) S. cristata (Table 5.3) both thermal stress (Hsp70) and oxidative stress biomarkers (LPO, GST) showed significant differences, but no differences could be detected in neurotoxicity (AChE) under elevated temperature after one month. Skin was the least responsive tissue in both fish species, where only LPO showed differences in S. cristata, and LPO, GST and SOD in A. saxatilis (Tables 5.2, 5.3). Test results also showed significant natural temporal variability in molecular markers along the experiment trials, which was coherent for both species tested in all tissues (Tables 5.2, 5.3, and see also Tables A4.2 and A4.3 in annex 4 for average biomarker basal levels in both species).

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Table 5.2 Compilation of statistical analyses (factorial ANOVAs) on the effects of temperature (29˚C vs 32˚C) and time (T0, T7, T14, T21, T28) and their interactions in biomarker responses in gills, muscle and skin tissues of A. saxatilis. Variables that were log transformed in order to meet the assumptions: Ub, GST and SOD in gills and muscle, Hsp70 and Ub in skin. Abbreviations: Hsp70 – heat shock protein 70; Ub – total ubiquitin; CAT – catalase; LPO – lipid peroxides; GST – glutathione-S-transferase; SOD – superoxide dismutase; AChE – acetylcholinesterase; Temp - Temperature. NA - non-applicable (note: AChE was only measured in muscle). Hsp70 Ub CAT LPO GST SOD AChE F p-value F p-value F p-value F p-value F p-value F p-value F p-value Gills Temperature 5.753 * 10.201 ** 32.398 *** 163.199 *** 1.891 ns 4.865 * NA NA Time 7.364 *** 7.909 *** 6.169 *** 22.962 *** 2.258 ns 4.102 ** NA NA Temp x Time 7.241 *** 4.894 ** 2.688 * 23.139 *** 2.673 * 3.865 ** NA NA Muscle Temperature 1.004 ns 3.612 ns 54.369 *** 97.144 *** 7.383 ** 3.569 ns 6.807 ** Time 1.000 ns 6.647 *** 24.452 *** 15.809 *** 14.532 *** 0.442 ns 5.807 *** Temp x Time 0.318 ns 5.152 ** 13.467 *** 21.305 *** 2.784 * 2.476 ns 0.882 ns Skin Temperature 5.899 * 1.210 ns 1.205 ns 49.640 *** 4.440 * 5.250 * NA NA Time 4.597 ** 18.479 *** 47.156 *** 47.761 *** 13.875 *** 9.327 *** NA NA Temp x Time 6.068 *** 4.397 ** 37.506 *** 8.647 *** 4.306 ** 4.877 ** NA NA Level of significance: * stands for p-value ≤ 0.05, ** stands for p-value ≤ 0.01, and *** stands for p-value ≤ 0.001, ns stands for non-significant (p-value > 0.05).

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Table 5.3 Compilation of statistical analyses (factorial ANOVAs) on the effects of temperature (29˚C vs 32˚C) and time (T0, T7, T14, T21, T28) and their interactions in biomarker responses in gills, muscle and skin tissues of S. cristata. Variables that were log transformed in order to meet the assumptions: Ub and SOD in gills, Hsp70 and GST in muscle, Ub, LPO and GST in skin. Abbreviations: Hsp70 – heat shock protein 70; Ub – total ubiquitin; CAT – catalase; LPO – lipid peroxides; GST – glutathione-S- transferase; SOD – superoxide dismutase; AChE – acetylcholinesterase; Temp - Temperature. NA - non-applicable (note: AChE was only measured in muscle). Hsp70 Ub CAT LPO GST SOD AChE F p-value F p-value F p-value F p-value F p-value F p-value F p-value Gills Temperature 9.459 ** 5.495 * 11.318 ** 14.846 *** 7.310 0.010 7.546 ** NA NA Time 1.750 ns 1.827 ns 1.617 ns 11.433 *** 3.018 0.029 2.188 ns NA NA Temp x Time 4.312 ** 2.643 * 4.976 ** 10.274 *** 2.883 0.035 4.374 ** NA NA Muscle Temperature 18.653 *** 1.970 ns 2.518 ns 12.746 *** 8.886 ** 1.203 ns 3.000 ns Time 17.379 *** 6.667 *** 5.283 ** 15.748 *** 12.010 *** 1.166 ns 9.205 *** Temp x Time 8.181 *** 0.855 ns 1.513 ns 8.477 *** 0.974 ns 0.949 ns 1.210 ns Skin Temperature 0.357 ns 0.641 ns 1.111 ns 6.784 * 1.474 ns 2.154 ns NA NA Time 2.502 ns 14.647 *** 3.327 * 18.387 *** 10.750 *** 4.771 ** NA NA Temp x Time 12.006 *** 4.091 ** 3.913 ** 11.350 *** 2.742 * 4.375 ** NA NA Level of significance: * stands for p-value ≤ 0.05, ** stands for p-value ≤ 0.01, and *** stands for p-value ≤ 0.001, ns stands for non-significant (p-value > 0.05).

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Considering the 2-factor (temperature-time) interactions, both gills and skin showed significant interactions for all biomarkers tested in both species, while in the muscle this was less evident, with differences only detected in Hsp70 and LPO in S. cristata, and Ub, CAT, LPO, GST in A. saxatilis (Tables 5.2, 5.3). However, when only selecting the interactions of interest (29˚C vs 32˚C in each sampling time, Table 5.4), the pattern of responsiveness of each tissue was similar to the one previously described when testing temperature alone (gills > muscle > skin).

Table 5.4 Interactions between temperature and time based on post-hoc Tukey HSD test results. For purposes of visualization, only combinations of interest were selected - variation in biomarkers’ levels in comparison to control (29˚C) within each time point (T7 – seven days of exposure; T14 – fourteen days of exposure; T21 - twenty-one days of exposure; T28 – twenty eight days of exposure). Red arrows () indicate significant stressful responses (increase in biomarker fold-change) and green arrows () indicate significant acclimation responses (decrease in biomarker fold-change); hyphen (-) indicates no change in comparison to control. Numbers before arrows indicate biomarker fold change in comparison to control (29˚C within that time-point). Abb: Hsp70 – heat shock protein 70 kDa; Ub – total ubiquitin; CAT – catalase; LPO – lipid peroxidation; GST – glutathione-S-transferase; SOD – superoxide dismutase. For additional information – full results presented in bar graphs, see figures A4.5 to A4.8 in annex 4 in the annexes section. T7 T14 T21 T28 29 vs 32˚C 29 vs 32˚C 29 vs 32˚C 29 vs 32˚C A. saxatilis Gills 6.5Ub 6.5CAT - 5.1Hsp70 5.0CAT 23.2LPO 5.8CAT 17.0LPO 6.2LPO 3.4GST 6.1SOD Muscle 4.1 Ub 3.5CAT - 2.3LPO 19.8CAT 21.9LPO 10.1LPO Skin 25.7CAT 0.46Ub 0.21CAT 0.23CAT 24.3LPO 0.23GST S. cristata Gills 6.6LPO - 18.4Hsp70 - 9.4LPO 8.2CAT 6.3GST 7.2SOD Muscle 0.10Hsp70 - 0.09Hsp70 0.12Hsp70 0.14LPO Skin - 0.05Hsp70 12.1Hsp70 9.8LPO

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Overall, it is possible to observe several patterns in the post-hoc Tukey HSD tests on the interactions between temperature and time (Table 5.4, see also Fig. A4.5 to A4.8 in annex 4): i) biomarkers show a stronger response in gills with a clear trend of increase at 32˚C through time (as shown by biomarker fold-change and more significant interactions found in this tissue for both species); ii) biomarkers in muscle tissue from A. saxatilis showed an identical pattern to the one observed in the gills, while in muscle tissue from S. cristata only Hsp70 showed a significant pattern that decreased through time at 32˚C; iii) biomarkers in skin tissue of both species showed a pattern of fluctuation through time at 32˚C (increase-decrease in A. saxatilis, and decrease-increase in S. cristata). In addition, the most reactive biomarkers (which responded in more than one sampling time in several tissue types) under elevated temperature exposure were (in decreasing order): CAT > LPO > Ub > GST > SOD > Hsp70 > AChE for A. saxatilis and Hsp70 > LPO > CAT > GST > SOD > Ub > AChE for S. cristata. Finally, biomarkers with higher fold changes were LPO in A. saxatilis and Hsp70 in S. cristata.

With respect to sampling times (see Fig. A4.5 to A4.8 in annex 4), biomarkers showed different response patterns according to each fish species. In A. saxatilis, biomarkers seemed to be more responsive either at the beginning of the experiment (T7 and T14, generally an increase in levels was detected, except for T14 in skin) or at the end of the experiment (T28, an increase in levels was also detected, except for skin). On the other hand, in S. cristata, biomarkers were more responsive towards the end of the experiment (T21, when there was generally an increase in biomarkers levels, and T28, when both increases and decreases were observed, depending on the tissue type).

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Figure 5.1 Ordination diagram of the first two axes of the principal components analysis of all investigated biomarkers (from all timepoints in all tissues combined) in both fish species at control (29˚C) and elevated temperature (32˚C): A) projection of cases on the factor plane, B) projection of variables on the factor plane (see factor loadings in Table A4.4 in annex 4). Percent of variance explained is shown in each graph associated to the correspondent principal component extracted from the analyses.

Principal Components Analysis (PCA) was applied to further explore the differences in thermal and antioxidant defense proteins and enzyme activities when combining all tissues, between the two investigated species and temperature treatments (χ2 = 0.000, p-value = 1.000 for independence between explanatory variables). Results (Fig. 5.1) showed that variance explained by PC1 and PC2 was 57.65% and 22.96%, respectively, yielding a cumulative percent of variance explained of 80.61%. According to factor loadings (see Table A4.4 in annex 4), PC1 was mostly correlated with GST, Ub, SOD and LPO while PC2 was correlated with CAT and Hsp70 (Fig.5.1B). Results suggest a certain degree of separation between species in biomarker data along PC1, considering both temperature treatments (Fig. 5.1A). Along PC2 it is possible to observe a certain separation of temperature groups (Fig. 5.1A).

5.4.3. Thermal tolerance

Results from factorial ANOVAs (Table 5.5) testing the effects of temperature, time and species on CTMax values showed significant differences for all the effects tested, except for interactions time-species and temperature-time-species.

Table 5.5 Factorial ANOVAs on the main and interactive effects of acclimating temperature, time and species on CTMax values. Significant results (95% confidence limits) are presented in bold.

CTMax

F p-value Temperature 38.726 <0.0001 Time 5.081 0.026 Species 94.473 <0.0001 Temp x Time 43.485 <0.0001 Temp x Species 8.533 0.004 Time x Species 1.348 0.248 Temp x Time x Species 0.185 0.668

Test results show that CTMax values were significantly higher for individuals exposed to 32˚C when compared to control individuals. In addition, when comparing both species, A. saxatilis overall average CTMax estimates were significantly higher than for S. cristata, with mean ± SD values of 39.17˚C ± 0.85˚C and 37.82˚C ± 0.79˚C, respectively.

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Moreover, intraspecific CTMax variation, as % CV for the tested fish was generally low and similar for both species (mean ± SD 0.89% ± 0.69% and 1.21% ± 1.20% for A. saxatilis and S. cristata, respectively, M-W U = 8.000, p-value > 0.05).

Figure 5.2 Critical Thermal Maxima (CTMax) after 7 days and 28 days at two different acclimating conditions predicting exposure to present average summer temperatures (29˚C, control) and future average summer temperatures (32˚C): A) A. saxatilis, B) S. cristata. Significant differences (p-value ≤ 0.05) are presented with an asterisk (*).

Fig. 5.2 shows CTMax values for both fish species after short term and longer term acclimation to both experimental temperatures. The graphs show that while in short term acclimation (7 days) there are no significant differences in CTMax values between temperature treatments, after 28 days of exposure significant differences could be detected in CTMax with both species showing an increasing trend in CTMax after longer term acclimation to a higher temperature. Acclimation capacity therefore ranged from 0.22˚C to 2.24˚C (for A. saxatilis) and from -0.39˚C to 1.31˚C (for S. cristata) when comparing both temperature treatments within each timepoint; and from -1.44˚C to 0.57˚C (for A. saxatilis) and from -1.0˚C to 0.70˚C (for S. cristata) when comparing short term with longer term exposure within each temperature. Thermal safety margin (mean CTMax – MHT, where MHT = 41.50˚C) for each species was - 2.33˚C and -3.68˚C for A. saxatilis and S. cristata, respectively.

5.4.4. Whole body analyses

The increase in experimental temperature was also expected to influence body condition of the fish species to some degree. Length and weight (mean ± SD) of the specimens were measured as proxies of body size (A. saxatilis: length 47.8 mm ± 8.77 mm at 29˚C and 44.08 mm ± 6.70 mm at 32˚C; weight 2.09 g ± 1.18 g at 29˚C and 1.58 g ± 0.71 g at 32˚C; S. cristata: length 50.72 mm ± 12.29 mm at 29˚C and 42.84 mm ± 7.26 mm at 32˚C; weight 1.76 g ± 1.30 g

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at 29˚C and 0.98 g ± 0.50 g at 32˚C). Significant differences were detected in body size between temperature treatments in both fish species (A. saxatilis: t = 4.31, p-value = 0.044 for length and t = 5.91, p-value = 0.019 for weight; S. cristata: t = 8.63, p-value = 0.005 for length and t = 8.84, p-value = 0.004 for weight) (see Fig. A4.9 in annex 4 for average size shift from the beginning to the end of the experiment for both fish species). In addition, body condition was analysed for both species (Fulton’s K index) but no significant differences were found between temperature treatments after one month of exposure (A. saxatilis t = 0.695, p-value = 0.489; S. cristata t = -0.239, p-value = 0.811).

GLMs on the main effects of temperature on mean total protein and on lipid contents, with fish body mass as a covariate are represented in Fig. 5.3. The models tested for differences between slopes and intercepts as well as overall regression significance.

Figure 5.3 Differences in the relationship between mean total protein (all tissues combined, mg.mL-1) and body mass with temperature of A) A. saxatilis and B) S. cristata; and differences in the relationship

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between lipid contents (muscle, %.mg-1 of dry tissue) and body mass with temperature of C) A. saxatilis and D) S. cristata. N = 20-25 specimens in each regression analysis.

Results from the GLMs regarding mean total protein suggest that: (i) there is a significant variation of mean total protein with body mass for both species (test of whole model, F = 31.311, p-value < 0.0001 for A. saxatilis and F = 6.712, p-value < 0.0001 for S. cristata); (ii) there are no significant differences between intercepts for both species (F = 0.087, p-value = 0.768 for A. saxatilis and F = 0.263, p-value = 0.610 for S. cristata), which indicates that in both temperature treatments there is the same amount of protein for the same amount of body mass, however (iii) significant differences between slopes were found for both species (F = 44.211, p- value < 0.0001 for A. saxatilis and F = 10.005, p-value < 0.0001 for S. cristata), indicating that there is a different behavior (rate of increase of total protein per unit of body mass) in the relationship, according to temperature variation.

GLM results regarding lipid content suggest that: (i) there is a significant variation of lipid content with body mass only for S. cristata (test of whole model F = 1.844, p-value = 0.152 for A. saxatilis and F = 8.075, p-value < 0.0001 for S. cristata); (ii) in the case of S. cristata, significant differences were detected both between intercepts (F = 9.218, p-value = 0.003) and slopes (F = 7.925, p-value = 0.001), showing that at 32˚C there is a higher lipid content per unit of body mass (higher intercept). However, as body mass increases at higher temperature, the rate of decrease of lipid content is more pronounced than at control temperature. Furthermore, a correlation analysis was undertaken in order to verify whether the decrease of lipid content was related to lipid peroxidation (and consequent cell apoptosis). This possibility was not backed by results, since no significant relationship was found between lipid contents and lipid peroxidation in muscle tissues of both fish species (A. saxatilis: R = -0.037, p- value = 0.794; S. cristata: R = -0.230, p-value = 0.107).

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Figure 5.4 Regression analysis between body size (length in mm) and CTMax (˚C) in A) A. saxatilis (N = 46, R = -0.442, p-value = 0.002) and B) S. cristata (N = 61, R = -0.359, p-value = 0.005). Linear trend represented with 95% confidence limits.

The temperature-size relationship was assessed through regression analyses testing the ability of body length to estimate CTMax values (Fig. 5.4) and results were significant for both species. Graphs show a decreasing pattern of CTMax values as body size increases.

5.5. Discussion

Predicted increases in mean sea surface temperature as well as predicted increased frequency, duration, intensity and spatial extent of heat waves will impose both long and short- term thermal stress on tropical marine fish. Therefore, in the present study the mechanisms involved in sublethal, as well as whole body responses to increased temperature were evaluated in two tropical rocky reef fish. The main highlights elucidated from this study are discussed in the following subsections.

5.5.1. Stress biomarkers: combined influence of temperature and time of exposure

The molecular analyses performed allowed the identification of biomarker response mechanisms and patterns under increased temperature. This foresees their suitability as markers for monitoring temperature-induced molecular changes in these species, which can be highly useful in assessing the health of organisms in their environment. In particular, results from temperature-time interactions showed that the most responsive biomarkers were CAT, LPO and Ub in A. saxatilis and Hsp70, LPO and CAT in S. cristata. These results suggest that at 32˚C the intrinsic thermal sensitivities of the cell (e.g. protein thermal stability) lead to sufficient thermal

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damage that required the activation of protective processes. Molecular chaperones increase when there is protein unfolding and aggregation (Yamashita et al., 2010; Madeira et al., 2012) but when thermal stress causes irreversible damage to proteins that molecular chaperones cannot remedy, this leads to proteolysis via the ubiquitin-proteasome pathway (Hofmann and Somero, 1995; Medicherla and Goldberg, 2008), which is indicative of increased protein turnover at higher temperature. Furthermore, one type of stress response may be coordinated with other cytoprotective responses (Madeira et al., 2013a). In this case, a significant increase in CAT activity was also observed in both species suggesting temperature induced ROS production and activation of ROS scavangers (antioxidants). This may be explained by the fact that an increase in temperature increases metabolic rates (Dillon et al., 2010), which means that more oxygen must be delivered to cells to maintain aerobic metabolism (Donelson and Munday, 2012). In consequence, increased ROS production is thought to be due to increased oxygen demand at higher temperatures, which leads to accelerated mitochondrial respiration (Abele et al., 2002). Oxidative stress arises from the inability of the antioxidant defense system to avoid damage caused by ROS, and although both tropical fish species were able to increase CAT (and other antioxidants) activity, this was insufficient to minimize peroxidative damage as shown by increased LPO levels. This significant increase in LPO levels (indicating injury of plasma membranes and loss of membrane integrity with possible apoptosis (Lesser, 2006)) may just be a reflection of increased metabolism at higher temperature or indicate a limited plastic response (Madeira et al., 2014b) of these tropical fish. These differences in biomarkers’ responsiveness are noteworthy because their fold-change is indicative of stress severity (Tomanek and Somero, 1999; Tomanek, 2002). Combined biomarker results thus suggest a response typical of moderate to severe stress exposure in both A. saxatilis and S. cristata.

These results also suggest that it is important to use a full set of biomarkers from different cellular pathways to adequately assess thermal stress in natural populations. CAT and LPO proved to be valuable in providing extra information on the CSR, in addition to traditional thermal stress markers Hsp70 and Ub. Furthermore, it indicates that some components of the CSR may be more sensitive to the relative duration of heating (e.g. time spent above threshold temperature, CAT and LPO) while other may be more sensitive to thermal load (Hsp70 and Ub) which has also been suggested by Logan and Somero (2011).

In this study, the most responsive tissues were (in decreasing order) the gills, muscle and skin in both species. Results showed that biomarker levels (both baseline and stress) are tissue-specific and vary in order of magnitude in different tissues, which was also observed in other marine fish and invertebrates (e.g. Gillichthys mirabilis, Buckley et al., 2006; Sparus aurata, Madeira et al., 2014b; Amphiprion ocellaris, Madeira et al., 2016, 2017; Veretillum

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cynomorium, Madeira et al., 2015; Palaemon elegans, Madeira et al., 2016). Differences in responses observed among tissues have been proposed to be related to the differences in oxygen supply and oxygen delivering systems, as well as the predominant type of metabolism/function in that tissue (aerobic vs anaerobic) (Falfushynska et al., 2014; Pavlović et al., 2010). For instance, the gills which have breathing function and are thus highly vascularized and aerobic tissues, continually exposed to the fish’s external environment (Harper and Wolf, 2009), appear to be more sensitive to heat induced stress than less aerobic tissues (such as skeletal muscle – which has a low partial pressure of oxygen and most energy is obtained anaerobically through conversion of pyruvate into lactate (Almeida-Val et al., 2011); and skin which has mainly an osmorregulatory function and acts as a barrier against pathogens (Harper and Wolf, 2009)). Therefore thermal and oxidative stress levels observed for each tissue seem to be directly related to the diffusion rate of oxygen and consequent build-up of ROS, leading to different sensitivities and biomarker responses.

Another highlight from the results was the influence of time of exposure in each species responses. While A. saxatilis was more responsive either at the beginning or end of the experiment (at 7 and 28 days, increase in biomarker levels in gills and muscle), S. cristata responded towards the end of the experiment (at 21 and 28 days, increase-decrease in biomarker levels). These differences in thermal stress responses (intensity and timings) between species are probably associated with differences in their life style (and particularly in activity), possibly mediated through factors such as mitochondrial density, erythrocyte size and haematocrit levels and associated physiological costs (Wells and Baldwin, 1990; Clarke and Johnston, 1999; Shultz et al., 2014). Thus, proximal causes of differences in physiology (anatomical and physiological organizations) as well as physiological optimization at the species level (in adaptation to specific micro-habitats or niches) may play a role in explaining response patterns between different taxa (Clarke and Johnston, 1999). For instance, the exploration of different micro-habitats within the tide pools (A. saxatilis explores the upper and middle part of the pools, actively swimming most of the time, while S. cristata remains closely associated to the bottom) influences the animals’ behaviors and activity levels, influencing their energy requirements and leading to different thermal compensatory reorganization of metabolism (McDonnell and Chapman, 2016). This type of differentiation in biomarker responses between species that occupy different thermal niches in the intertidal has also been observed in other studies of crabs, shrimp, fish and snails (Madeira et al., 2014a, 2013b, 2012; Nguyen et al., 2011).

5.5.2. Species upper thermal limits, intraspecific variation and acclimation capacity

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Results show that CTMax values were significantly affected by temperature, time and species. Both species displayed noteworthy tolerance to high temperatures, in particular, A. saxatilis (CTMax range = 37.86˚C – 40.10˚C, from control to increased temperature treatment respectively) when compared to S. cristata (CTMax range = 37.13˚C – 38.45˚C, from control to increased temperature treatment respectively). In accordance, rocky reef fish collected from the same area of this study and acclimated at 26˚C showed CTMax values ranging from 34˚C (Scartella cristata) to 40˚C (Bathygobius soporator) in the case of intertidal species, and from 34.5˚C (Parablennius marmoreus) to 36˚C (Malacoctenus delalandii) in subtidal fish (Vinagre et al., 2016b). In addition, other studies with tropical reef fish species have also shown notable tolerance to high temperature, with CTMax values ranging from 35˚C (Apogon pacifi) to 37.5˚C (Lutjanus guttatus) in present day temperature acclimated fish (Ospína and Mora, 2004); and CTMax approaching or even exceeding 40˚C (Dascylus aruanus) in fish acclimated to future temperature conditions (+4˚C) (Eme and Bennett, 2009).

The significant differences in mean CTMax between the two species, with A. saxatilis showing higher thermal tolerance may be explained by different hypotheses: (i) both species inhabit the intertidal as juveniles, which are shallow habitats with low thermal inertia, but while A. saxatilis mainly explores the upper part of tide pools, subjected to wider temperature change, S. cristata remains close to the bottom, which is a more thermally stable micro-environment (especially in large pools at lower intertidal levels) (Vinagre et al., 2015), (ii) different geographic distributional areas (A. saxatilis prefers subtropical habitats while S. cristata prefers tropical habitats) imply that these species have developed different physiological and cellular adaptations to different overall thermal ranges (Deutsch et al., 2008; Tewksbury et al., 2008; Madeira et al., 2012b) – for instance strictly tropical species usually show more limited thermal tolerance than more widely distributed species (Nguyen et al., 2011), (iii) mean genetic distances and evolutionary histories could also account for differences in thermal tolerances (Dent and Lutterschmidt, 2003).

Considering that the thermal history of the specimens used in this study was identical (they were all collected from the same local populations and then subjected to the same captivity conditions) CTMax intraspecific variability was generally low for both species. Mora and Ospína (2001) and Schaefer and Ryan (2006) had previously observed identical results under similar circumstances. In addition, site fidelity (to the tide pool or rocky reef) and low dispersal from each group of individuals may also be at play (Vinagre et al., 2016, 2015). Both species showed a significant CTMax acclimation ability but only in longer term (28 days), not in the short term trials (7 days). A. saxatilis increased its CTMax by 2.24˚C, and S. cristata by 1.32˚C in comparison to the control temperature. Previous studies with tropical fish show an

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acclimation capacity in their CTMax temperatures ranging from 0.8˚C to 1.8˚C in long term trials (Vinagre et al., 2016b). The higher CTMax acclimation capacity of A. saxatilis may be related to its temperature regime once it prefers subtropical temperatures (although it also inhabits the tropics) (Allen, 1991), while S. cristata mostly prefers tropical latitudes (where seasonality lacks) (Robins and Ray, 1986). Thus, S. cristata may not be able to substantially increase its CTMax because this species already lives near the upper thermal limit of its performance window. Additionally, quicker heat shock and oxidative stress response in A. saxatilis may have contributed to a bigger change in metabolic thermal sensitivity and higher CTMax acclimation (Oksala et al., 2014).

Results show both fish species have mean CTMax values (39.37˚C for A. saxatalis, 37.82˚C for S. cristata) close to the summer average maximum water temperature in the intertidal (37.60˚C). However, during heat waves tidal pools can reach an absolute maximum temperature of 41.50˚C for several days. This suggests that survival and fitness of these species in response to acute temperature events may already be under pressure at present-day temperatures. This is reflected in the thermal safety margin, calculated for both species, which was shown to be negative (-2.33˚C and -3.68˚C for A. saxatilis and S. cristata, respectively). Another important implication of these results is that tidal pools may be acting as ecological traps (sensu Hale and Swearer, 2015) for early life stages of fish during warm days, as observed for various tropical fish species in the Marshall Islands tidepools (Hiatt and Strasburg, 1960), possibly hampering the completion of the life cycle of these organisms. This is likely to have an effect on the genetic variation and evolutionary potential in response to temperature stress selection. Thus, resilience to these extreme events can play an important role in evolutionary response to climate change.

5.5.3 Whole organism stress indicators

Indicators of performance, as well as thermal limitation such as growth, body condition or survival reflect the aerobic scope and the shape of the temperature-dependent performance window (Barton, 2002; Pörtner and Farrell, 2008; Pörtner and Knust, 2007), and therefore their analysis becomes useful in the context of thermal biology studies. Results from this research showed that after one month of exposure to increased temperature, significant differences were detected in body size between treatments (body size was smaller at high temperature), however body condition index showed no differences for both species. This suggests that although fish were able to maintain their body condition, slower growth occurred at high temperatures, which is probably a trade-off arising due to energy expense in physiological thermal stress responses (Angilletta et al., 2003; Donelson et al., 2011b). In addition, other studies have suggested that at

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warm temperatures, cells begin to lose shape and volume which may explain smaller sizes at higher temperatures (e.g. due to disruption of cell membranes and of the linkage between cytoskeleton and extracellular matrix) (Windisch et al., 2014). In corroboration of the present study, another two studies with coral reef fish showed that fish acclimated to a higher than normal temperature were on average smaller than fish kept at present day temperatures (Acantochromis polyacanthus, (Donelson et al., 2011b); Amphiprion melanopus, (Nowicki et al., 2012)), and that if fish can only partially compensate for temperature effects, then growth would be affected by a change in metabolic scope (cardinal and damselfish, (Nilsson et al., 2010)). So there may be a critical temperature threshold below which a temperature increase has positive effects on growth and protein turnover rates, while negative effects may result when there is a temperature increase above such a threshold (Nakano et al., 2004). Therefore, these results indicate that: i) there may be thermal constraints on body size (Angilletta et al., 2004), ii) growth rate is sensitive to temperature change (Donelson et al., 2011b) and iii) thermal/oxidative stress metabolism may also be related to specific variations in body architecture (Clarke and Johnston, 1999).

The fact that fish from the 32˚C treatments in this study were shorter on average also has ramifications for individual fitness, including reduced size at maturity, which in turn may reduce reproductive output (Johnston and Leggett, 2002). Alternatively, maturity may be delayed until a size threshold is reached, which could increase the likelihood of mortality before maturity (due to predation) and reduce the size of the breeding population (Donelson et al., 2011b; Morita et al., 2005). This demonstrates that even with acclimation, warmer sea temperatures are likely to have significant impacts on reef fish populations.

During growth, healthy animals invest in protein production and build up energy reserves (Deane and Woo, 2009), so the quantification of energy substrates in the context of thermal biology provides a valuable tool for organisms’ health assessments. Results from the GLMs testing for significant differences in the relationship between body size and mean total protein/lipid contents according to temperature treatments showed some interesting trends. First, as expected, protein content significantly increased with body mass. Also, although there was an overall equal amount of mean total protein in both treatments, temperature was found to significantly affect the rate at which the increase occurred - animals at 32˚C showed a higher increase in total protein per unit of body mass when compared to control animals, in both species. This is suggestive of a faster metabolism at higher temperatures (Dillon et al., 2010; Agostini et al., 2013), and may also explain why the animals were able to maintain their body condition. In regard to the relationship between body mass and lipid contents, the model used showed that the regression was significant for S. cristata but not for A. saxatilis. One possible

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explanation for this is that although the chemical composition of muscles is relatively constant, the lipid part is highly variable, namely between species, and it is dependent on the ability of the muscle to store energy as fat (Elia et al., 1999). For instance, animals with higher activity levels and swimming (such as A. saxatilis when compared to S. cristata), and thus a higher glycolytic activity, generally display a reduced development of intramuscular fat during growth, and increasing muscularity dilutes the final fat content of the muscle (D’Souza et al., 2003; Hocquette et al., 2010). To add up to this, the expression of muscular fat content in many species displays a period at the beginning of life in which muscular fat content does not increase (Hocquette et al., 2010) in the first stages of growth, which seems to be the case for A. saxatilis. Additionally, in S. cristata it was observed that at 32˚C, although there was an overall higher lipid content, there was also a higher rate of lipid loss in the muscle as body mass increased, when compared to control animals. In corroboration, previous studies have identified through gene ontology analysis that lipid metabolism is significantly affected by temperature stress (Smith et al., 2013; Windisch et al., 2014). Thus, several explanations for these results are proposed: i) a higher overall lipid content at 32˚C can be explained by the induction of the pentose-phosphate pathway during heat stress which leads to the production of NADPH, which can then be used in fatty acid biosynthesis (Xie, 2015); ii) however, an intact heat-shock response (increase in heat shock proteins) seems to be essential to protect ectotherms from a temperature-dependent body fat decline during longer exposure times (Klepsatel et al., 2016) (which seems to hold true for A. saxatilis, as there was an increase in Hsp70 and no changes in lipid contents in the muscle). However, we observed the opposite for S. cristata. Hsp70 response in the muscle of S. cristata occurred in the opposite direction (significant decrease, less induction), which suggests a metabolic suppression in this tissue in order to save energy (Baler et al., 1992; Iwama et al., 1999; Lund and Tufts, 2003) and concomitantly observed a higher rate of decrease in lipid contents in the muscle.

Notwithstanding, results from this study showed a non-significant correlation between the formation of lipid peroxides due to oxidative stress (in cell membranes – phospholipid bilayer) and lipid accumulation in skeletal muscle (in adypocites mainly located in the connective tissue surrounding the fibers (Nanton et al., 2007)) in both fish species. This suggests that although there was a measurable disruption of phospholipid bilayer in cell membranes (LPO levels) caused by ROS, which may likely have induced a certain degree of apoptosis (Lesser, 2006), this does not seem to have occurred in a sufficient scale to have also caused a complete depletion of muscle lipid reserves nor completely limit the capacity of accumulating intramuscular fat.

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Finally, results from regression analyses between thermal tolerance and body size showed a decreasing linear trend of CTMax values as body length increased, for both species. Thermal limits had already been previously correlated with body size and age (Lutterschmidt and Hutchison, 1997; Madeira et al., 2012b) and those studies have similarly shown that within a species, smaller animals have a higher temperature tolerance than larger conspecifics, as observed in this study and as predicted by the principle of oxygen and capacity limitation (Pörtner and Knust, 2007). In addition, it suggests that responses to thermal stress in small tropical fish may be affected by differences in body size, possibly due to either ontogenetic differences in physiology or to differences in the area/volume ratio, which influence the lag between water temperature and the internal temperature of the individual (Ospína and Mora, 2004; Madeira et al., 2016). Also, considering that as the animals grow they move from the intertidal to the subtidal zone, which is a more thermally stable environment, then it could be expected that CTMax decreased as body size increased.

5.6 Conclusions

The research conducted revealed that at projected future temperatures, both A. saxatilis and S. cristata displayed a typical response of moderate to severe heat stress exposure (at a sublethal molecular level). Nevertheless, both species presented a significant acclimation potential in the long term, as shown by increased CTMax values at higher temperature, after long term exposure. However, energetic tradeoffs seem to exist. This was especially evident in whole body analyses, showing smaller body sizes at higher temperature, with possible consequences on other stages of these species’ life cycles. In spite of the potential for adaptive plasticity, these species may already be at risk during heat waves in summer, once their thermal safety margins were shown to be negative in face of the maximum intertidal temperature recorded in the collection area. This suggests that tide pools in tropical locations may be acting as ecological traps for juvenile fish stages, and that these animals may be at risk. These findings support the need for regular monitoring of these habitats and show the importance of thermal biology studies in the context of ecological health assessments under climate change.

Acknowledgements

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The funding source had no involvement in the study design, collection and analysis of data or in the decision to publish the work. Authors would like to thank everyone involved in the field work and maintenance of the experimental tanks.

Annex 4. Supplementary material associated with this chapter can be found in annex 4 in the annexes section.

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CHAPTER 6

High thermal tolerance does not protect from chronic warming – a multiple end-point approach with a tropical gastropod

Madeira C, Mendonça V, Flores AAV, Diniz MS, Vinagre C (2018) Ecological Indicators 91: 626-635, DOI: 10.1016/j.ecolind.2018.04.044.

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ABSTRACT

Animal physiology and ecology are affected by temperature and this is particularly relevant in the tropics, where organisms are already living on the warm edge of their thermal windows. Here, we present data on sublethal effects of temperature (using molecular biomarkers), thermal tolerance and warming safety margins of a gastropod (Stramonita haemastoma) from tropical rocky shores, under an experimental setup of a business-as-usual warming scenario. Heat shock response, protein damage, antioxidant activity and lipid damage were all evaluated once a week during one month of exposure at a control temperature, and at an experimental temperature of plus 3˚C. Significant increase of heat shock protein response, lipid peroxides and catalase at the elevated temperature suggest the activation of cytoprotective pathways as response to an increased thermal load. Duration of exposure also had a significant influence in the animals’ responses, since whole body thermal tolerance only showed acclimation potential in the short term, but not in the longer term. Thermal safety margin was low for this species, suggesting a narrow ability to tolerate further warming. Hence, enduring thermal stress, as predicted if present day warming trends are not reversed, may compromise populations of tropical marine snails.

Keywords: tropical gastropod; ocean warming; cellular stress response; thermal tolerance; rocky shore; ecophysiology

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Graphical abstract

Highlights • Physiological responses to warming were analyzed in a tropical intertidal gastropod • Significant heat shock and antioxidant responses during warm-acclimation • High CTMax, with acclimation capability in the short-term but not in the long-term • Low warming tolerance: narrow margin to tolerate further increase in sea temperature

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6.1. Introduction

Predicting how organisms and populations respond to climate warming is a foremost concern of global change biologists (Marshall et al., 2015). Rising sea surface temperature (SST) influences the physiology, ecology and evolution of marine organisms (Díaz et al., 2015; Nguyen et al., 2011) and extinction risk analyses suggest that communities and populations from coastal regions in the tropics are particularly vulnerable to climate warming (Finnegan et al., 2015). In fact, there is significant empirical evidence that marine animals in tropical areas are increasingly susceptible to environmental warming because they have evolved in a relatively stable and non-seasonal environment and the current temperatures they encounter are already close to their thermal limits (Deutsch et al., 2008; Huey et al., 2009; Tewksbury et al., 2008). These limits may be surpassed very soon considering the global average temperature has already risen ca. 0.74˚C over the past century (1906–2005) and is expected to increase between 1.4˚C and 5.8˚C over the remainder of this century (Hartmann et al., 2013; IPCC, 2013).

Shallow water and intertidal ecosystems are prone to enormous thermal variability in time and space (Helmuth and Hofmann, 2001), and therefore ectotherms inhabiting these ecosystems constitute important models to address the impacts of climate change. Since these organisms live at the interface of land and sea, they are subjected both to fluctuating air and aquatic conditions (Helmuth et al., 2010; Rubal et al., 2013). Their physiological responses to physical stress are unique, revealing specific adaptive responses and strategies to deal with changing environmental temperatures (Helmuth and Hofmann, 2001; Somero, 2002). Among invertebrates, gastropods in particular are an ideal group to study. They are accessible, numerous, slow-moving, easy to collect and suitable for experimental manipulation (Hammond and Synnot, 1994). Additionally, as an extremely diversified group (Schilthuizen and Rutjes, 2001) with a variety of clades prevailing in harsh intertidal conditions, including tropical rocky shores (Flores et al., 2015; Garrity, 1984), they are also excellent sentinel candidates, prone to noticeable die-off under extreme thermal conditions (Williams and Morritt, 1995). Given temporal heterogeneity in the velocity of temperature change in such environments (including short-time scales due to tidal cycles and diurnal and nocturnal variations as well as long timescales due to climate related gradual increase in SST) (Lima and Wethey, 2012), remarkably different scenarios may arise, and selection associated with temperature extremes is expected to play a major role on the thermal biology of species inhabiting these ecosystems (Marshall et al., 2015).

Vulnerability to thermal stress has been suggested to be associated with the aerobic capacity of the animal, thermal stress unbalancing the oxygen supply versus demand

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(Deschaseaux et al., 2010). Thus, thermoregulation mechanisms constitute a crucial strategy to ameliorate the impact of stressful temperatures (Harley et al., 2009; Madeira et al., 2012; Pörtner and Farrell, 2008). In particular, the most important mechanisms involved in the maintenance of physiological homeostasis within the temperature range of a species are heat shock proteins, the anti-oxidative system and the mechanisms of alterations in general energetic metabolism (Axenov-Gribanov et al., 2014; Cooke et al., 2014; Farrell, 2009; Feder and Hofmann, 1999; Pörtner and Farrell, 2008; Somero, 2010). These defence mechanisms allow an organism to overcome the negative consequences of thermal stress, including the accumulation of free radicals, protein degradation and energy depletion (Abele et al., 2002; Hofmann and Somero, 1995; Tomanek, 2010). In this sense, studying the activation of cellular and biochemical stress responses can be a critical point for defining the thermal window and thermal tolerance of a species (Deschaseaux et al., 2010). These biological responses can be used as stress biomarkers providing early warning signals of environmental deterioration and indicating occurrences of adverse ecological consequences (Wu et al., 2005). It is crucial that under such context, biomarkers’ initial induction, maximum induction, adaptation and recovery are fully studied and understood (Wu et al., 2005) so that they can be effectively applied in biomonitoring programs at field sites.

The main aim of the present study was to investigate the thermal physiology of a tropical Atlantic rocky reef gastropod species, Stramonita haemastoma, throughout one month. In particular, the objectives were to: i) assess sublethal effects of increased temperature at a molecular and cellular level by analyzing biomarkers of biological effects (heat shock protein 70 kDa – Hsp70, ubiquitin – Ub, catalase – CAT, lipid peroxides – LPO, glutathione-S- transferase – GST, superoxide dismutase – SOD and acetylcholinesterase - AChE) in foot muscle tissue; ii) estimate the upper thermal limits (Critical Thermal Maximum - CTMax), thermal safety margins (i.e., warming tolerance) and acclimation capacity of thermal tolerance of this species, as well as its intraspecific variation, under control (mean summer water temperature at the collection site and time – 29˚C) and warming scenarios (future mean summer water temperature – 32˚C) and iii) evaluate the dynamics of time-scales of the responses in a context of constant thermal fluctuations (i.e. rocky intertidal) vs gradual temperature increase due to climate change.

6.2. Materials and methods

6.2.1. Ethical statement

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This study complied with all the ethical guidelines for animal experimentation, namely Portuguese and Brazilian legislation (Decreto-Lei n˚ 113/2013 and Lei n˚ 11.794/2008, respectively), and experimental protocols were approved by Direcção Geral de Alimentação e Veterinária (Portuguese licensing body, authorization document 0421/000/000/2013) and by Comissão de Ética no Uso de Animais (USP - Ribeirão Preto, Brazilian licensing body, authorization document 13.1.981.53.7). The study design followed ARRIVE guidelines and three authors have level C FELASA certification (Federation of European Laboratory Animal Science Associations).

6.2.2. Test species, specimen collection and acclimation procedure

Gastropod mollusk Stramonita haemastoma (Linnaeus 1767, Muricidae) was selected as biological model. This species is widely distributed throughout tropical areas in the Southeast Pacific, Atlantic Ocean and the Mediterranean (Houart and Gofas, 2009). It inhabits all kinds of hard substrates (Sept, 2016) and in southeastern Brazil it is commonly found in i) shallow subtidal waters and ii) inside tide pools during low tide. Snails were sampled from mid to low tide pools (< 2 m from the water line and < 0.5 m in elevation over basaltic rock formations) in the rocky platform zone of Barequeçaba Beach, São Sebastião, located in the southeastern coast of Brazil (23˚49’42’’S, 45˚26’29’’W). The local thermal environment was assessed during 3 months of the warm season (December 2015 to February 2016) by i) analyzing data from the brazilian National Meteorology Institute (www.inmet.gov.br) for air temperature and ii) collecting in situ data with Hobo V2 probes (continuous sampling over 56 days at 2h intervals) for tide pool temperature (eight probes in eight replicate pools sampled) and for subtidal water temperature (two probes, ~1 m depth). Adult animals were collected by hand (total n = 120, mean ± SD total length = 3.54 ± 0.76 cm, mean ± SD total weight = 9.32 ± 5.35 g) and placed in plastic containers with aerated sea water. The total number of animals collected was higher than the determined sample sizes, allowing for accidental death during transport and acclimation to captivity conditions. Animals were taken to experimental facilities at the CEBIMar – Universidade de São Paulo (São Sebastião), and allowed to acclimate at 29.0˚C ± 0.5˚C for two weeks. They were fed once a day ad libitum with frozen shrimp (Costa Sul Congelados®).

6.2.3. Experimental design

Chronic heat stress experiments were performed following a scenario of increased summer SSTs in rocky shore subtidal shallow waters, where S. haemastoma seeks thermal refugia during high summer temperatures. Gastropods were subjected to either one of the following temperature treatments for one month: i) current mean summer water temperature at

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the collection site/time (control, 29˚C) and ii) future mean summer water temperature (32˚C) according to IPCC regional projections of the SST anomaly for the tropical/subtropical Atlantic (+ 3˚C at the end of this century in the RCP8.5 scenario (IPCC, 2013)) (see Fig. A5.1 in annex 5). The experimental trials in captivity were performed in two separate semi-open aquaria systems of 200 L each. Each of the systems consisted of 2 replicate glass tanks (25 × 25 × 25 cm), one seawater deposit, one sump and a UV filter. Specimens were randomly allocated into tanks (n = 30 individuals.tank-1), which were then randomly attributed to control and temperature treatment (2 replicate tanks per temperature). Temperature treatment of 32˚C was attained by gradually raising water temperature from 29˚C at a rate of 0.10˚C.h-1 during two days. All tanks were filled with clean aerated seawater (95-100% oxygen saturation) and salinity 35. Live rocks were used as environmental enrichment. Temperatures were maintained for 28 days using thermostats (Eheim® Jager Heater 150W, Germany). Experiments were carried out in shaded day light (14L:10D).

6.2.4. Molecular biomarkers

Molecular biomarkers have been unanimously adopted as early warning signals of individuals’ physiological stress (Wu et al., 2005). They are defined as any measurable molecular response (Colin et al., 2015) and have significantly contributed to the mechanistic understanding of how stressors affect organisms (Kidd et al., 2007). Timing of samplings for biomarker analyses was chosen based on OECD guidelines for repeated exposure toxicity studies (www.oecd.org) (see Fig. A5.2 in annex 5). Sample sizes were based on previous studies (Madeira et al., 2016, 2014). Five gastropods were randomly sampled from replicate tanks at day 0, 7, 14, 21 and 28, always at the same period of the day (for both temperature treatments, total n = 50 specimens). Foot muscle tissue was removed from the organisms and immediately frozen at -80˚C until further analyses. Tissue samples were homogenized in 1.0 mL of phosphate buffered saline (PBS) solution (140mM NaCl, 3mM KCl, 10mM Na2HPO4, 2mM

KH2PO4, pH 7.4) using a Tissue Master 125 homogenizer (Omni International, Kennesaw, USA) on ice-cold. Homogenates were then centrifuged at 10,000 × g (at 4˚C for 15 min). Afterwards, the supernatant was collected, transferred to new microtubes (1.5 mL) and frozen (- 80˚C).

Protein quantification and kinetic assays were then carried out using colorimetric methods previously described and adapted for 96-well microplates (Madeira et al., 2015) (see Table A2.1 in in annex 2 in the annexes section for detailed discription):

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i) Hsp70 quantification using an indirect Enzyme Linked Immunosorbent assay (ELISA) and Ub quantification using a direct ELISA (Madeira et al., 2017); ii) Enzymatic assay of CAT (Enzyme Commision number – EC 1.11.1.6) (Johansson and Borg, 1988); iii) LPO assay using TBARS (thiobarbituric acid reactive substances), measured as malondialdehyde (MDA) content (Uchiyama and Mihara, 1978); iv) Enzymatic assay of GST activity (EC 2.5.1.18), using reduced L-glutathione (GSH) and the substrate CDNB (1-Chloro-2,4-dinitrobenzene) (Habig et al., 1974); v) Enzymatic assay of SOD, using nitroblue tetrazolium (NBT) and xanthine oxidase (XOD) (Sun et al., 1988); vi) Enzymatic assay of AChE (EC 3.1.1.7) using thiol quantification based on Ellman’s method (Ellman et al., 1961); vii) Total protein quantification (Bradford, 1976). A calibration curve was obtained using BSA standards (0 to 2 mg.mL-1). These measurements were used to normalize the other biomarker results.

6.2.5. Thermal tolerance

Acute heat stress experiments to determine thermal tolerance were performed following a scenario of increasing water temperature in tide pools. The rationale was that tide pools are where specimens from this species can get trapped during a heat wave event, with no possibility of escaping until the next high tide. The rocky platform at the study site has a basaltic constitution, so it possesses a strong heat absorbing capacity, and temperature is frequently higher inside tide pools (underwater, especially in small ones) when compared to air temperatures (see Fig. A5.4 in annex 5). However, since basaltic rock can reach really high temperatures under solar radiation, getting out of the tide pools to escape the heat wave is not an option for this species. In fact, no specimens were observed in the rocky platform outside tide pools during low tide. To quantify the specimens’ upper thermal tolerance, the CTMax dynamic method was employed. CTMax method has been widely used for the determination of thermal limits of ectothermic vertebrates and invertebrates (Barnes et al., 2010; Kaspari et al., 2015; Vinagre et al., 2013). It is typically determined by a process of water heating at a constant increasing temperature rate up to the thermal point (experimental end-point) at which the locomotory activities become disorganized and an animal loses the ability to escape from conditions that will promptly lead to its death (Díaz et al., 2015). In this study, CTMax was estimated for a subset of individuals from each temperature treatment after 7 days and 28 days of exposure (~10-15 individuals per temperature per timepoint, total n = 42, this sample size is similar to those used by other researchers (Madeira et al., 2012; Mora and Ospína, 2001;

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Vinagre et al., 2013)). All individuals that were subjected to a CTMax trial were then excluded from the remaining experiment. Animals were placed in a transparent container and allowed to attach to its walls and move around; inactive specimens were removed and discarded from the experiment. The remaining organisms were then subjected to a thermostatic bath with a constant rate of water temperature increase of 1˚C per 15 min, with constant aeration, until they reached the end-point (foot detachment from surface). This temperature ramp is consistent to what can be found inside tide pools during summer days, following recommendations for the use of ecologically realistic warming ramps (Vinagre et al., 2015). The temperature at which each animal reached its end-point was measured with a digital thermometer (RISEPRO®). Every 10 min, containers were tipped over to identify which organisms remained attached and which had reached CTMax, by losing their attachment (Sorte and Hofmann, 2005).

The upper thermal limits for this species were calculated using the equation:

Where Tend-point is the temperature at which the end-point was reached for any given individual, and n stands for sample size.

To determine the intraspecific variability of the CTMax, the coefficient of variation (in percentage) was calculated as:

The acclimation capacity was determined by comparing CTMax values after short term and longer-term exposure to both water temperature conditions (CTMaxT28 – CTMaxT7 at each temperature and CTMax32˚C – CTMax29˚C at 7 and 28 days).

Thermal safety margin (i.e. warming tolerance) was calculated as the difference between mean CTMax and MHT (Maximum Habitat Temperature) as it provides an estimate on how close this species lives to its thermal limits. MHT was determined through field measurements in the studied rocky platform (absolute maximum temperature value recorded during the summer), using probes as described previously.

6.2.6. Data analyses

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As a preliminary exploratory approach, all data were tested for 1) outliers (box-whiskers plot, coefficient 1.5 for outliers and extremes), 2) normality through histograms and Shapiro- Wilk’s test and 3) homoscedasticity through Levene’s test. When at least one of the assumptions of a specific test (e.g. normality or homoscedasticity) did not occur, either a data transformation (e.g. logarithmic) was employed or a non-parametric test was used (this applies for all the statistical analyses performed). To rule out a possible effect of the different tanks and interaction between tank and time in the results, 2-way MANOVAs were employed.

Main temperature effects (present day vs. predicted from warming modeling) and acclimation capacity (as time-dependent temperature effects) were tested in S. haemastoma at both the subcellular (molecular biomarkers in the muscle) and individual levels (thermal tolerance - CTMax) through 2-way ANOVAs. Post hoc Tukey’s honestly significance difference (HSD) tests or post hoc unequal N HSD tests were also applied where appropriate to analyze which groups were different. All results were considered to be statistically significant at a p-value < 0.05. All statistical analyses were carried out in Statistica software (version 13.0, Statsoft Inc. USA).

6.3. Results

6.3.1. Environmental temperature variation

Results obtained from meteorological datasets and from probes at the collection sites (Fig. 6.1) show that during the sampled summer months (December to February), mean monthly temperatures varied from 28.73˚C to 29.97˚C in the subtidal (underwater), from 25.72˚C to 29.32˚C in the tide pools (underwater) and from 26.60˚C to 28.13˚C in outside air. February was the warmest month recorded in the data loggers. Daily temperature variation (average maximum – average minimum) was about 2.31˚C ± 2.17˚C in the subtidal, 4.78˚C ±1.06˚C in tide pools and 9.35˚C ± 3.52˚C in outside air. The mean maximum temperatures recorded during these months ranged from 28.27˚C to 30.45˚C in the subtidal, from 36.37˚C to 37.50˚C in tide pools and from 32.73˚C to 34.52˚C in outside air. The absolute maximum temperature values were higher in tide pools when compared to the other areas of the rocky platform, with 41.50˚C (MHT in this study) measured underwater in three of the tide pools sampled, compared to a maximum of 32.10˚C in the subtidal (underwater) and 37.80˚C in outside air.

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Figure 6.1 Average (AVG) and maximum (MAX) temperatures measured during the warm season (2015/6) (December, January and February in the collection area) in the subtidal, tide pools and outside air. More detailed information can be found in the electronic supplementary material (Fig. A5.4 in annex 5).

6.3.2. Sub-cellular stress assessment

Statistical tests ruled out differences between replicate tanks and interaction between tanks and time in biomarker levels (Wilk’s test, F=0.349, p-value=0.557 and F=0.898, p- value=0.474, respectively).

Results from factorial ANOVAs showed that there was a significant effect of temperature (Table 6.1), as well as a significant interactive effect of temperature and time on biomarkers Hsp70, CAT and LPO (Table 6.1, Fig. 6.2). Interactions of interest between temperature and time were selected (29˚C vs 32˚C within each timepoint) and are presented in Fig. 6.2. Overall, biomarkers in the muscle of S. haemastoma were more responsive to a temperature increase after 7 and 14 days of exposure (Hsp70 and LPO). However, one of the measured biomarkers only showed a detectable response after 28 days of exposure to high temperature (CAT). The selected combination of interactions showed no significant effects in Ub, GST, SOD and AChE levels.

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Table 6.1 Summary results of factorial ANOVAs showing main effects of temperature (29˚C, 32˚C) and interactive effects of temperature and time (T0, T7, T14, T21, T28) on selected biomarkers from muscle tissues of S. haemastoma. Significant results (p-value < 0.05) are presented in bold.

Hsp70 Ub CAT LPO GST SOD AChE F p F p F p F p F p F p F p Temperature 21.009 <0.0001 0.002 0.966 4.033 0.051 46.536 <0.0001 1.705 0.199 1.636 0.208 0.111 0.741 Temp x Time 6.263 <0.0001 0.137 0.967 3.744 0.011 21.669 <0.0001 1.898 0.129 0.715 0.586 3.285 0.020

Figure 6.2 Interactive effects of temperature and time on biomarker levels (mean + SD) based on post- hoc Tukey HSD tests: a) thermal stress markers Hsp70 and Ub; b) oxidative stress markers CAT and LPO; c) oxidative stress markers GST and SOD and d) neurotoxicity marker AChE. For purposes of visualization, only combinations of interest were selected (29˚C vs 32˚C within each sampling time). Significant results are presented with an asterisk (*).

6.3.3. Whole animal stress assessment

CTMax values estimated for this gastropod species ranged from 41.9˚C to 43.2˚C. Intraspecific CTMax variation, as % CV was generally low (mean ± SD 2.47% ± 0.32%). The factorial ANOVA testing for effects of temperature and time on whole body thermal tolerance showed no significant effects of temperature (F = 2.929, p-value = 0.093) but detected a

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significant interaction of temperature and time on CTMax (F = 7.048, p-value = 0.010). Results from post-hoc Tukey HSD tests (visual representation in Fig. 6.3) for interactive effects of temperature and time on CTMax values showed that while in short term acclimation (7 days) there were significant differences in thermal tolerance between temperature treatments, after 28 days of exposure no differences could be detected in CTMax. Additionally, significant differences in CTMax were detected within the same temperature treatment (at 32˚C, there was a decrease in thermal tolerance from 7 to 28 days of exposure). Acclimation capacity ranged from -0.28˚C to 1.31˚C within each timepoint (29˚C vs 32˚C) and from 0˚C to -1.58˚C within each temperature treatment (T7 vs T28). Warming tolerance (i.e. thermal safety margin, mean CTMax – MHT, Maximum Habitat Temperature, where MHT = 41.50˚C) for S. haemastoma was 0.66˚C.

Figure 6.3 CTMax after 7 days and 28 days of exposure to two different acclimating conditions predicting exposure to present average summer water temperatures (29˚C, control) and future average summer water temperatures (32˚C, +3˚C). Significant differences are presented with an asterisk (*).

6.4. Discussion

On rocky shores subjected to extreme temperatures, organisms’ adaptation to changing environmental temperatures is particularly important in order to reduce the likelihood of reaching stressful or lethal body temperatures (Harley et al., 2009). This is especially true for small, slow-moving ectotherms such as gastropods, which cannot readily escape unfavorable

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conditions. As a typically mid-low intertidal and subtidal species (Ramírez et al., 2009), Stramonita haemastoma showed to rely on physiological mechanisms to maintain homeostasis under increased temperatures. Results from this study suggest that the changes (significant increase) detected in Hsp70, CAT and LPO levels during warm-acclimation likely reflect a response to changes in the generation of oxygen free radicals within tissues (Pannunzio and Storey, 1998), and could have roles that range from “signaling” (adaptive physiological, Hsp70, CAT) to “pathological” (maladaptive unwanted, LPO) (Zorov et al. 2014). These types of molecular responses have also been observed for many other invertebrate species (including gastropods) and vertebrate species (Helmuth and Hofmann, 2001; Hofmann and Somero, 1995; Madeira et al., 2016, 2015, 2014, 2013, 2012). For instance, in one study (Axenov-Gribanov et al., 2014) researchers performed a cellular and metabolic assessment of thermal stress responses in the gastropod Benedictia limnaeoides ongurensis and observed a significant activation of anti-oxidative enzymes, protein denaturation, depletion of energetic metabolites and enhanced lipid structural disruptions. In another study (Deschaseaux et al., 2010), the authors showed that thermal stress in gastropod egg masses caused a decrease in the total antioxidant capacity as lipid peroxidation increased in embryos. Studies with limpets at field sites also showed increased levels of Hsps during warmer periods (Lottia digitalis (Halpin et al., 2002), Patella spp. (Harley et al., 2009)).

Interactive effects between temperature and time on biomarker levels showed an activation of the cellular stress response (CSR) in foot muscle through an enhancement in protein protection (increase of molecular chaperone Hsp70) after 7 and until 14 days of exposure, simultaneously with increased levels of LPO at these timings. Only at the end of the experiment (28 days), an increase in antioxidant defenses was detected (CAT). Considering that each biomarker or response mechanism can be associated to a specific degree of stress severity (Logan and Somero, 2011), and that temperature has a very strong and positive correlation with cellular levels of ROS and macromolecular damage (Paital, 2016), these results are interesting. Heat shock response for instance is quite plastic and induces thermotolerance, such that cells exposed to an initial mild heat stress can later be tolerant to temperatures that were stressful prior to the first heat exposure (Parsell and Lindquist, 1993). The most important functions performed by Hsp70 under heat stress are minimizing the aggregation of non-native proteins (Feder and Hofmann, 1999) and disassembling misfolded proteins (Georgopoulos and Welch, 1993). Therefore, the detection of increased levels of Hsp70 in S. haemastoma exposed to high temperature is indicative of the presence of abnormal proteins, but at this point the damage is still reversible (Baler et al., 1992; Parsell and Lindquist, 1993; Tomanek and Somero, 2002), which is also suggested by the lack of changes in ubiquitin levels. However, the rise in lipid peroxidation levels suggests cell membrane disruption with possible induction of apoptosis

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(Mukherjee, 2008). Aditionally, high LPO is often associated with accelerated biochemical pathways in cells (Madeira et al., 2013) and consequent malfunction of the mitochondrial complex enzymes, leading to a less efficient oxidative phosphorylation state/system which results in reduced ATP generation (Paital, 2016). Thus, progressive physiological and energetic depression may be observed as lipid peroxidation rate increases (Axenov-Gribanov et al., 2014), which may be temporary or permanent depending on the duration and intensity of stress. This type of response, with a predominance of oxidizing, catabolic and proteolytic processes is a well known response to thermal stress (Grieshaber et al., 1994) and is corroborated by previous ecophysiological studies with marine and aquatic animals (Abele et al., 2002; Bijma et al., 2013; Pörtner and Knust, 2007).

Notwithstanding, the concomitant increase of several biomarker levels at higher temperature and variable timings could also have alternative explanations: i) it could indicate a metabolic compensation process is taking place (Dong et al., 2011) and/or ii) it can reflect a preparative defense strategy for the recovery once the thermal stress is removed (Hermes-Lima et al., 1998). Therefore, when interpreting thermal stress results, it is necessary to take into account that although the main pillar of the metabolic theory of ecology proposes a universal temperature dependent model (as temperature increases so does metabolic rate, Brown et al., 2004); controversy exists when it comes to organisms from naturally stressful thermal environments (e.g. intertidal habitats, Marshall and McQuaid, 2011). It could be argued that the theory does not account for thermal compensation of metabolism over the time of exposure to temperature change (Clarke, 2004). In fact, several studies with rocky intertidal invertebrates have found that metabolic rate was actually lowered rather than raised over the range of higher temperatures the animals experienced (e.g. Echinolittorina malaccana, Marshall and McQuaid, 2011; 17 species of calcified invertebrates, Watson et al., 2014; Pachygrapsus marmoratus, Fusi et al., 2015). Aditionally, it has been observed that while metabolic depression and reduced oxygen consumption are taking place during an environmental stress, there is still an increase in anti-oxidant defenses as a strategy to deal with oxidative stress that will take place during the arousal phase after the stress is gone (Hermes-Lima et al., 1998). Therefore, an increase in oxidative stress biomarkers may not always be related to increased respiration rates, but to a switch from temperature dependent metabolism to temperature insensitive metabolism (showing an intrinsic capacity for metabolic depression/compensation) as a form of saving energy and overcoming the higher physiological maintenance costs of living at high temperatures (Marshall and McQuaid, 2011; Lesser, 2016; Watson et al., 2014).

Results from the present study also suggest that as animals adjust their physiology to their environment, different cellular responses are taking place, and some response mechanisms

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may be more sensitive to the heat load experienced while others may be more sensitive to the duration of exposure to elevated temperature (Wu et al., 2005).

Field and experimental studies have provided significant evidence that physiological tolerances reflect the environment of the species (Harley et al., 2009). The present study corroborates those findings since thermal tolerance of S. haemastoma was shown to be particularly high, as expected for an intertidal animal. CTMax values ranged from 41.9˚C to 43.2˚C. These values are within the range of upper thermal limits found for other species of intertidal mollusks (between 41˚C - 52˚C in animals kept underwater (Nguyen et al., 2011)). Since heat tolerance of marine intertidal animals is related to their vertical distribution along the shore (Stillman and Somero, 1996) and considering S. haemastoma inhabits mid-low intertidal zones, its CTMax values are closer to the lower edge of the range, while upper intertidal species have thermal limits approaching or surpassing 50˚C. A high upper thermal limit is an important feature for intertidal gastropods, once it allows them to maintain mobility at temperatures almost up to the lethal level and resist predation or seek cooler microhabitats (behavioral thermoregulation) (Stirling, 1982). Additionally, CTMax intraspecific variation was low, which is probably due to the fact that not only these animals were collected in the same area but were allowed to acclimate to captivity conditions, and therefore have similar thermal histories, which can account for less variability in measured upper thermal limits.

As mentioned earlier, how animals deal with changing environmental conditions depends on the intensity of the changes and how long animals are exposed to these changes (Hoefnagel and Verberk, 2016; Rezende et al., 2014). The present study tested whether short and prolonged exposure to higher temperatures could increase upper thermal limits (in comparison to control temperatures) and results suggest a significant acclimation of CTMax values after 7 days but not after 28 days of exposure to increased temperature. This seems to suggest that whelks acclimate only through a short period of exposure to high temperature. It is likely that a one week warm-acclimation reduces thermal sensitivity because either the increase in oxygen demand with warming occurs at a physiologically manageable rate (controllable adjustments in the oxygen delivery system) (Verberk et al., 2011) or metabolic depression minimizes energy loss (Marshall and McQuaid, 2011), which results in higher CTMax at 32˚C when compared to control. This higher CTMax may also be related to biomarker results, since there was a significant response at 7 days and it is known that heat shock response increases thermotolerance (Zerebecki and Sorte, 2011). However, a more prolonged and continuous period of heat stress evidenced a limited capability for thermal adaptation in the long run for S. haemastoma. In fact, looking only at CTMax variation at 32˚C, it is possible to observe a significant decrease from 7 to 28 days of exposure to this temperature. This could be due to the

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disappearance of heat hardening (Sherman and Levitis, 2003) (acute short-lived response to transient fluctuation in environmental temperature) from 7 to 28 days, which lead to a decreased CTMax accordingly. The duration of the heat hardening process is closely related to alterations in energy consumption during stress (Sherman and Levitis, 2003; Sokolova, 2013) and so food quality and nutrient intake also play a part on acquiring and losing tolerance to stress (Rabasa and Dickson, 2016). Generally, during prolonged stress, three changes occur in energy metabolism i) energy mobilization for the activation of cytoprotective pathways (Angilletta et al., 2003), ii) consumption of energy stores (e.g. releasing of stored sugars, fats and proteins) as the stress continues, leading to decreased weight gain and higher susceptibility to disease (Krieger et al., 2006; Klepsatel et al., 2016; Kühnhold et al., 2016) and iii) draining of energy stores during chronic stress (Klepsatel et al., 2016), when the organism need for energy becomes greater than its ability to produce or save it, which may result in significant weight loss and possible health impairments (Rabasa and Dickson, 2016; Windisch et al., 2014). Therefore, in future studies, it would be interesting to analyse how energy (e.g. by measuring available energy – total carbohydrates, lipids and proteins; energy consumption – electron transport system activity; and/or cellular energy allocation, Ferreira et al., 2015) is changed during thermal stress/acclimation to confirm these mechanisms. Therefore, since thermal stress is expected to be very energetically taxing for an organism, tradeoffs may exist, and important biological and ecological processes can be affected (Angilletta et al., 2003; Angilletta, 2004). In particular, decreased growth and body condition can have cascading effects on reproduction (e.g. affecting time to reach sexual maturity, size at maturity and egg quality, behavior and performance (Huey and Berrigan, 2001; Peters et al., 1997)).

As one study (Nguyen et al., 2011) pointed out, adaptations that improve survival in short term acute stresses or regular cyclic stresses (e.g. those that occur in the intertidal zone), such as cellular stress responses might not be sufficient or may even reduce the fitness of organisms facing long term thermal stress. Hence, organisms that developed high acute heat tolerances, such as intertidal species, may in fact be more sensitive to chronic warming rates (Nguyen et al., 2011). This may be because while some biological responses are clearly reversible upon abatement of short term stressful conditions (e.g. enzyme induction, lysosomal integrity) (Bálint et al., 1995; Chandravathy and Reddy, 1994; Hole et al., 1995), other responses (e.g. cell damage, pathological symptoms, deformities) may be enhanced under long term stressful conditions and are usually permanent and not recoverable (Dwivedi et al., 1997).

Nevertheless, the thermal safety margin (TSM, i.e., warming tolerance) for S. haemastoma was quite low but still showed a positive value, even after warm-acclimation. Low values of TSM in rocky shore animals suggest that stress is likely ameliorated during extreme

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heating events by behavioral thermoregulation (Marshall et al., 2015). The risk of mortality therefore is not simply a matter of habitat temperature, but rather a combination of microclimatic conditions coupled with an organism’s ability to access them. What this means is that for species already living quite close to their upper thermal limits, as is the case for S. haemastoma, although they may not be immediately in danger, their window of ability to tolerate further warming during the present century is rather narrow under IPCC worst case business-as-usual scenarios. That may just be enough to trigger significant impacts in animal survival, growth performance and reproduction and ultimately, migration or local extinction of these animals. Considering that this species is a highly efficient carnivore that preys on other mollusks (limpets, mussels, vermitid gastropods), crustaceans (barnacles) and bryozoans (Rilov et al., 2002), the ecological implications in the event of their death or migration may be significant for the ecosystem. Namely, there could be an impact in the equilibrium between trophic levels, as the removal of a predator could lead to an increase in the number and biomass of grazers, which in turn could lead to lessened algae biomass in intertidal environments (O’Connor et al., 2013). Therefore, significant change in organism assemblages as well as community shifts could be foreseen in such cases.

In conclusion, gastropods from a tropical rocky shore presented early warning signals of impaired health during chronic thermal stress experiments, which points to the impacts of future SST increase in natural populations in the wild. Future environmental health assessments should therefore include approaches that analyse multi-biomarker (from molecular to whole-organism) responses of animals. The present research showed that an approach integrating physiologival measurements of several levels of biological complexity showed satisfactory results, producing sensitive and cost-effective assessments, which could be incorporated as descriptors of environmental status in future coastal zones biomonitoring. Finally, the physiological responses to increased temperature and TSMs of this sentinel species indicate that there may be a mismatch between tolerance and performance for warm-adapted species in rocky intertidal habitats. This means that, given a certain pattern of temporal variation of temperature (both at short-term – i.e. tidal cycles, diurnal vs nocturnal; and long-term – gradual increase in SST), the range of tolerance may be wide, however, the temperature range and amount of time the organisms have for fitness-enhancing processes (e.g. growth and reproduction) can, in contrast be narrow. The consequences of this are that these species need to have increased efficiency during limited times of optimal conditions or they may be significantly at risk of local extinction in such ecosystems.

Acknowledgements

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This study had the support of the Portuguese Foundation for Science and Technology (FCT) through the WarmingWebs project (PTDC/MAR-EST/2141/2012), the “Investigador FCT” position granted to C. Vinagre and the strategic projects UID/MAR/04292/2013 granted to MARE and UID/Multi/04378/2013 granted to UCIBIO. C.M. and V.M. acknowledge PhD grants (SFRH/BD/92975/2013 and SFRH/BD/109618/2015, respectively) also granted by FCT. Authors would like to thank everyone involved in the field work and maintenance of the experimental tanks.

Annex 5. Supplementary material associated with this chapter can be found in annex 5 in the annexes section.

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CHAPTER 7

Present and future invasion perspectives of an alien shrimp in South Atlantic coastal waters – an experimental assessment of functional biomarkers and thermal tolerance

Madeira C, Mendonça V, Leal MC, Diniz MS, Cabral HN, Flores AAV, Vinagre C. Submitted.

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ABSTRACT

Climate change, particularly ocean warming, is thought to benefit the spread of invasive species due to their increased tolerance to temperature fluctuations when compared to native species. However, the physiological tolerance of invasive species as a potential mechanism driving invasion success has been overlooked. Here, we experimentally evaluated the physiological responses of a recent invader in the Southern Atlantic, the caridean shrimp Lysmata lipkei, under an ocean warming scenario. Adult shrimps were collected from rocky shores in southeastern Brazil and subjected to experimental trials under a control and a +3˚C scenario temperature. Molecular biomarkers (in gills and muscle), upper thermal limits, acclimation response ratio, thermal safety margins, mortality, body condition and energy reserves were all measured throughout one month of experimental trials. Results suggest that higher temperatures elicit physiological adjustments at the molecular level, which underpin the high thermal tolerances here observed. Additionally, results show that this invasive shrimp has significant acclimation capacity, without negative performance consequences under an ocean warming scenario. Thermal safety margins were low for intertidal habitat but considerably high for subtidal habitat. We conclude that this shrimp possesses the ability to continue its invasion in subtropical waters of the Atlantic Ocean (mainly in subtidal habitats) both under present conditions and future climate warming scenarios.

Keywords: tropical shrimp; invasive species; warming oceans; rocky reef; thermal biology

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Graphical abstract

Highlights • We investigated if physiological tolerance influences peppermint shrimp invasion success • Physiological biomarkers of invasive shrimp were analysed under warming scenarios • Cytoprotective molecular mechanisms underpin high thermal tolerances for this species • Invasive shrimp showed high acclimation ability without negative performance • Thermal safety margin suggests further potential for invasion of South Atlantic Ocean

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7.1. Introduction

The increase in mean global sea surface temperature and frequency of extreme events have been causing alterations in species distributions (Loarie et al., 2009; Hoefnagel and Verberk, 2016), which concomitantly creates gaps for new colonizations, particularly by invasive species (Madeira et al., 2012). Temperature is one of the main physical environmental parameters acting as an ‘ecological filter’ to invasion by non-native species (Crowl et al. 2008). Consequently, it is important to study the thermal biology of invasive species to elucidate the physiological mechanisms driving the establishment of non-native species in new locations as global warming continues (Kelley 2014). The physiological tolerance hypothesis of successful invasions (Zerebecki and Sorte 2011) suggests that the latitudinal range width occupied by a species predicts performance breadth for habitat temperatures and physiological thermotolerances, and ultimately indicates a species ability to become invasive in other regions. In particular, the different thermal environments to which a species may be subjected to during a process of invasion affects biochemical interactions and cellular metabolism (Tomanek and Somero 2002; Angilletta et al. 2006) and, ultimately, organism performance (Pörtner and Farrell 2008).

However, there is still a scarcity of studies on how physiology shapes biogeographical distributions of invasive species (Somero 2002; Cortes et al. 2016). Previous studies with marine species have shown a significant effect of water temperature on heat shock protein expression (Hofmann and Somero 1995; Tomanek and Somero 2002; Shabtay and Arad 2005; Clark et al. 2008; Madeira et al. 2015b), immune and antioxidant functions (Pannunzio and Storey 1998; Pérez-Casanova et al. 2008; Madeira et al. 2013), including scavenging capacity (Yuan et al. 2013), metabolite synthesis (Barton 2002; Viant et al. 2003) and metabolic performance (Magozzi and Calosi 2015; Payne et al. 2016), physio-ecological short-term processes (e.g. rates of ingestion, defecation, respiration and excretion) (Yuan et al. 2009, 2013), and in long-term processes such as growth (Angilletta 2004; Rushworth et al. 2011; Ye et al. 2011). As such, the successful establishment of non-indigenous species will require them to tolerate a possibly different range of temperatures and rate of changes in the new environment through physiological adjustments such as the ones mentioned above (Kelley 2014). In other words, a high acclimation and adaption ability are required for a species to be able to invade a new habitat or geographical area. In particular, Kelley (2014) and Lejeusne et al. (2014) proposed that, when compared to native species, invasive species i) display higher resistance to acute thermal stress due to broad geographical thermal widths, ii) show lower rates of oxygen consumption with consequent greater resistance to environmental stresses (better respiratory performance and more efficient metabolism), and iii) have higher survival rates

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under chronic stress due to higher acclimation capacity. Thus, more thermotolerant invasive species might have a clear adaptive advantage in a scenario of climate warming (González- Ortegón et al. 2013). The identification of phenotypes (mainly through the distinction of specific physiological traits) that commonly occur among successful invaders can help to recognize biological characteristics that may predispose a species to become established if introduced (Kolar and Lodge 2001). In doing so, by using methods that integrate measures of several biological complexity (Chapperon et al. 2016), one can predict the long term performance of organisms under different thermal regimes to ultimately avert such species from becoming introduced or, at least, minimize their expansion from where the initial introduction took place (Kelley 2014).

The caridean shrimp Lysmata lipkei (taxonomically described by Okuno and Fiedler, 2010) is native from the Indo-West Pacific (namely Japan) and has recently invaded the tropical and subtropical western Atlantic (Pachelle et al. 2016). Since this species is fairly recent to science, it is useful to gather specific information on its thermal biology and physiology to monitor the expansion of its populations in the wild. Additionally, experimental trials are needed to understand this species’ ability to colonize new areas under climate change scenarios. Here, we investigated the physiological responses of the invasive shrimp Lysmata lipkei in southeastern Brazilian rocky shores to chronic and acute heat stress events in present environmental conditions and according to future sea warming projections. We followed a multi-parameter approach (molecular, cellular and whole-body) as it is useful to detect metabolic alterations that constitute early signs of impairment in individuals and populations (Colin et al. 2015). Specifically, we i) performed a comparative assessment of biochemical changes (using a set of selected biomarkers) that modulate stress responses in different tissues (gills and muscle) throughout exposure time (one month); ii) estimated the upper thermal limit, acclimation ability and thermal safety margins of this shrimp species in the new colonized environment; and iii) analyzed parameters of performance, such as growth, body condition, accumulation of energy reserves, and mortality rates under control and increased temperature.

7.2. Materials and methods

7.2.1. Ethical guidelines

The experiments here performed were approved by the competent authorities both in Portugal and Brazil (Direcção Geral de Alimentação e Veterinária and Comissão de Ética no Uso de Animais, respectively, which provided the authorization documents 0421/000/000/2013 and 13.1.981.53.7). The experimental design followed ARRIVE guidelines and

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recommendations by the Federation of European Laboratory Animal Science Associations (see Festing and Altman, 2002; Hau and Schapiro, 2010).

7.2.2. Animal collection and field temperature measurements

The biological model used in this study was the shrimp species Lysmata lipkei (Lymastidae, Okuno and Fiedler, 2010). Lysmata shrimps are suitable for experimental manipulation because their general biology, ecology, and behavior are well studied and because they are easily reared and maintained in captivity. In particular, Lysmata lipkei was described as a native species of the Indo-Pacific ocean (Okuno and Fiedler 2010), and it has been reported as an invasive species in the eastern Brazilian coast (Pachelle et al. 2016), both in intertidal and subtidal environments (~15m depth) (Fig. 7.1).

Wild specimens (n = 120, mean ± SD total length = 4.04 cm ± 1.06 cm, mean ± SD total weight = 1.01 g ± 0.83 g) were collected with hand nets in shallow waters (<50 cm) of intertidal rocky reefs in Southeastern Brazil (23˚49’42’’S, 45˚26’29’’W, Barequeçaba Beach, São Sebastião, São Paulo, Fig. 7.1). Animals were transported to the marine field station (CEBIMar - Universidade de São Paulo, S. Sebastião) in plastic containers with aerated sea water. Upon arrival they were allowed to acclimate for two weeks at local seawater temperature (29.0˚C ± 0.5). Shrimp welfare was assessed daily (e.g. parasites, lesions, feeding behavior) and animals were fed once a day ad libitum with frozen food.

The thermal environment of this species was assessed at the initial introduction locations (northeastern Brazil – Ceará and Rio Grande do Norte, Pachelle et al. 2016) and at the study location (S. Sebastião, Brazil). The temperature at this species’ original geographic occurrence (Japan) was also analyzed. Data were collected from several sources, including: i) scientific studies; ii) sea temperature database (satellite sea surface temperature – SST – data available from www.seatemperature.org); and iii) Hobo V2 probes placed at the study site (S. Sebastião), glued to rocks of 8 replicate tide pools during low tide, using epoxy polyamide cement Tubolit®. In situ temperature data was recorded continuously during the warm season months (from December to February, 56 days) in 2015/16. Measurements were taken at intervals of 2 hours, 24/7. After data collection, probes were removed using a chisel.

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Figure 7.1 (a) Original geographical distribution range of the caridean shrimp Lysmata lipkei according to Okuno and Fiedler (2010). The locations shown are the sampling sites from which specimens were collected and first taxonomically identified in 2010. (b) L. lipkei known occurrences in Brazilian coastal waters: NE Brazil (in the states of Ceará – Camocim and Icapuí, and Rio Grande do Norte – Areia Branca), where the first introductions of this species were observed (Pachelle et al. 2016), and SE Brazil where shrimps were collected for this study (São Sebastião, São Paulo State). This image was prepared using GoogleEarth® images.

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7.2.3. Experimental layout

The experimental trials in captivity were performed in two separate semi-open aquaria systems of 200 L each. Each of the systems consisted of 2 replicate glass tanks (25 × 25 × 25 cm), one seawater deposit, one sump and a UV filter (see Fig. A6.1 in annex 6). After acclimation, L. lipkei specimens were randomly allocated into experimental tanks (n = 30 individuals.tank-1), which were then randomly attributed to control and temperature treatment (2 replicate tanks per temperature). The total number of animals used in the experiment trials included extra spare individuals in addition to the final sample sizes, allowing for accidental death, as well as possible unexpected problems during lab assays. Temperature was gradually increased at a rate of 0.10˚C.hr-1 until experimental temperatures were reached: a) control temperature (29.0˚C ± 0.5˚C), reflecting current mean summer water temperature at the collection site, and b) thermal challenge (32.0˚C ± 0.5˚C), reflecting projected future mean summer water temperature according to the IPCC modeling of sea surface temperature (SST) anomalies for the tropical/subtropical Atlantic (+ 3.0˚C at the end of this century in the RCP8.5 scenario, IPCC (2013)). All tanks were filled with clean aerated seawater (95-100% oxygen saturation) and salinity 35. Environmental enrichment was achieved by adding small cobbles to tanks. Temperatures were maintained for 28 days using thermostats (Eheim® Jager Heater 150W, Germany). Experiments were carried out in shaded day light (14L:10D). Sampling was performed at several timepoints (Fig. 7.2 and see also Fig. A6.2 in annex 6) by euthanizing the individuals through longitudinal transection. Sample sizes were similar to those used by Madeira et al. (2012b, 2016) and Vinagre et al.(2013): i) For molecular biomarker analyses, animals were sampled once a week, at day 0, 7, 14, 21 and 28. At each of these timepoints, five individuals were randomly sampled from the two replicate tanks for each temperature (5 individuals × 5 timepoints × 2 temperatures, total n = 50 specimens). Gills and muscle tissue were collected separately and immediately frozen at -80˚C until further analyses. This timeline follows OECD recommendations for repeated toxicity studies. ii) For upper thermal limits determination, a subset of individuals was randomly sampled from each temperature treatment after 7 and 28 days of exposure, and subsequently subjected to CTMax (Critical Thermal Maximum) trials (13-14 individuals × 2 timepoints × 2 temperatures, total n = 54). CTMax is a dynamic method widely used for the determination of thermal limits in ectothermic animals (Kaspari et al. 2015). This method exposes animals to a constant thermal ramp until they reach a critical end-point (i.e., loss of righting response, Lutterschmidt and

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Hutchison 1997). Shrimps were subjected to a thermostatic bath with a constant rate of water temperature increase of 1˚C per 15 min and constant aeration (see Fig. A6.3 in annex 6). Their behavior was observed until they reached the end-point. This temperature ramp is consistent to what can be found in the intertidal during summer days, following recommendations of Vinagre et al. (2015) for the use of ecologically realistic warming ramps. The temperature at which each animal reached its end-point was measured with a digital thermometer. All individuals subjected to one CTMax trial were then removed from the remaining experiment. iii) For assessment of body condition, morphometric measurements of the animals (total length and weight, 26 individuals × 2 timepoints × 2 temperatures, total n = 104) were taken using an icthyometer and a scale, respectively. Determination of energy reserves was performed using the same muscle samples referred in point i). Sampling times for whole-body assessments were performed at the beginning (0 days) and end (28 days) of the experiment. Mortality was also recorded along the experimental month (number of live and dead animals per day and per tank in each temperature treatment along the 28 days).

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Figure 7.2 Experimental approach (diagram constructed using Experimental Design Assistant, https://eda.nc3rs.org.uk/).

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7.2.4. Laboratory analyses

Molecular biomarkers

Molecular biomarkers were chosen based on their role on the cellular stress response (CSR): i) heat shock protein 70 kDa (Hsp70) is a chaperone with an adaptive value, repairing damaged proteins upon thermal stress (Feder and Hofmann 1999; Coles and Brown 2003; Hofmann 2005; Madeira et al. 2012a); ii) ubiquitin (Ub) targets irreversibly damaged proteins for proteasome degradation (Logan and Somero 2011; Tang et al. 2014); iii) anti-oxidant enzymes (catalase – CAT, glutathione-S-transferase – GST, superoxide dismutase – SOD) neutralize reactive oxygen species (ROS) and oxidation products (e.g. lipid peroxides) that arise due to higher metabolic rates at higher temperatures (Lushchak 2011; Vinagre et al. 2012), iv) lipid peroxides (LPO) are markers of cell membrane damage and apoptosis (Pannunzio and Storey 1998; Logan and Somero 2011); v) acetylcholinesterase (AChE) is a neurotoxicity marker, it ends synaptic transmission at cholinergic synapses by catalyzing the breakdown of acetylcholine. When its activity is disrupted or inhibited, it can lead to paralysis and possible cardio-vascular failure (Assis et al. 2012; Singh et al. 2013).

Samples (approximately 200-250 mg of gills and muscle) were placed in 1.5 mL microtubes and homogenated in 1 mL of phosphate buffered saline (PBS) solution (140 mM

NaCl, 3 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4, pH 7.4) to extract the most soluble cytosolic proteins, using a Tissue Master 125 homogenizer (Omni International, Kennesaw, USA) on ice-cold. The crude homogenates were then centrifuged for 15 min at 10,000 × g (4˚C). Afterwards, the supernatant was collected, transferred to new microtubes, and frozen at - 80˚C until further biochemical analyses. Protein quantification and kinetic assays (Table 7.1) were then carried out using colorimetric methods previously described and adapted for 96-well microplates (Madeira et al. 2015a) (Table A2.1 in annex 2 shows a detailed description of each method used). Total protein in tissue samples was quantified using the Bradford method (Bradford 1976) and used to standardize all biomarker results.

Table 7.1 Methods used for biomarker analyses. Abb: Hsp70 – heat shock protein 70; Ub – total ubiquitin; CAT – catalase; LPO – lipid peroxides; GST – glutathione-S-transferase; SOD – superoxide dismutase; AChE – acetylcholinesterase; ELISA – Enzyme Linked Immunosorbent Assay; EC – Enzyme Comission number. Biomarker Type of analysis EC References Hsp70 protein quantification, indirect ELISA - Madeira et al. 2017 Ub protein quantification, direct ELISA - Madeira et al. 2017

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CAT enzymatic/kinetic assay (activity) 1.11.1.6 Johansson and Borg 1988 LPO quantification of malondialdehyde - Uchiyama and Mihara 1978 GST enzymatic/kinetic assay (activity) 2.5.1.18 Habig et al. 1974 SOD enzymatic/kinetic assay (activity) 1.15.1.1 Sun et al. 1988 AChE enzymatic/kinetic assay (activity) 3.1.1.7 Ellman et al. 1961

Energy reserves

Despite some uncertainties regarding the correlation of the elemental composition and the biochemical make-up of organisms, the ratio between particular elements provides important information on organismal physiological condition (Harris et al. 2000). For example, the C:N ratio assesses elemental shifts that are associated with changes in the main organic components, such as proteins (N rich) and lipids (C rich) (Vollenweider 1985). Therefore, the C:N ratio is here used as a proxy of changes in energy metabolism/reserves, and % C used as a proxy of lipid reserves (following Sterner and Elser 2002). Muscle samples were lyophilized (by freeze- drying in vacuum) and grounded to a fine homogeneous powder. Samples of ~0.5 mg were loaded into tin cups and analyzed using an Elementar Isoprime continuous-flow mass spectrometer (GV Instruments) coupled to a vario PYRO cube elemental analyser (Elementar, Hanau, Germany). Reference materials (acetanilide; Stable Isotope Research Facility, Indiana University, USA) were assayed at the beginning of each run and after every 10 samples. Additionally, total protein was also analyzed as a proxy of energy reserves, since proteins can serve as respiratory substrates when other energy sources are depleted (for instance due to stress) (Alberts et al. 2002). Total protein was extracted as described in the previous section (Bradford 1976).

Data analyses

Some calculations were performed previously to the statistical analyses as follows: i) Biomarker results were all standardized by the total protein in each sample.

ii) The upper thermal limits for each species were calculated using the equation:

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where Tend-point is the temperature at which the end-point was reached for any given individual, and n stands for sample size. To determine the intraspecific variability of the CTMax, the coefficient of variation (CV, in percentage) was calculated for each species:

The acclimation capacity was determined by: i) comparing CTMax values after short term and longer term exposure to both temperature conditions (CTMaxT28 – CTMaxT7 at each temperature and CTMax32˚C – CTMax29˚C at 7 and 28 days); ii) acclimation response ratio (ARR, (Claussen 1977)) as ΔCTMax / Δtemperature. Thermal safety margin (TSM, i.e. warming tolerance) was calculated for the intertidal and subtidal environments as the difference between mean CTMax for this species and MHT (Maximum Habitat Temperature) determined through field measurements for the intertidal and through satellite data for the subtidal) - this provides an estimate on how close this species lives to its thermal limits. iii) Body condition was calculated by Fulton’s K index, from morphometric data, as follows:

where is the total wet mass (mg) and is the total length (mm) (Ricker 1975).

To calculate the C:N ratio of each muscle sample, %C and %N were firstly divided by the molar weight of each element (12.0107 g.mol-1 for carbon and 14.0067 g.mol-1 for nitrogen), and then the molar ratio was calculated. Additionally, mortality records were transformed into percentages (%) values for further analysis.

An exploratory analysis was performed on the datasets to assess for: i) outliers (box- whiskers plot, coefficient 1.5 for outliers and extremes), ii) normality through histograms and Shapiro-Wilk’s test, and iii) homoscedasticity through Levene’s test. When, at least, one of the assumptions of a specific test (e.g. normality or homoscedasticity) were not met, either a data transformation (e.g. logarithmic) was performed or a non-parametric test was used (this applies to all the statistical analyses performed). The principal objective of the statistical analyses was to evaluate the main temperature effects (present day vs predicted from warming modeling) and/or acclimation capacity (as time-dependent temperature effects) on the several physiological parameters measured (from molecular to cellular to individual level).

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Factorial ANOVAs were carried out to detect: a) which biomarkers contribute to differentiate between temperature treatments (29˚C vs 32˚C), exposure times (T0, T7, T14, T21, T28), and tissue types (gills and muscle) (following statistical approaches by e.g Whitehead and Crawford 2005 and Madeira et al. 2016); b) differences between biomarker levels throughout the experiment at different temperatures. For simplicity, the results only show significant differences from control (29˚C) at each time-point. Moreover, Principal Component Analysis (PCA) was also carried out for each tissue to detect biomarkers that correlate with temperature and time and that contribute to explain the variance in the dataset. Upper thermal limits data were analysed with a two-way ANOVA (temperature – 29˚C vs 32˚C, and time – T7 vs T28, as categorical predictors, and CTMax as dependent variable). Tukey’s post-hocs for unequal N were carried out to detect differences between all groups. Fulton’s K condition index, total protein, % C, and C:N ratio were also analysed with factorial ANOVAs (temperature - 29˚C vs 32˚C, and time – T0 vs T28, as factors). No tests were performed on mortality because there were no dead individuals to report. All results from the previous tests were considered statistically significant at a p-value <0.05 and all statistical analyses were carried out using Statistica software (version 13.0, Statsoft Inc. USA).

7.3. Results

7.3.1. Characterization of rocky reefs’ thermal environments in the shrimp’s native home range and colonized areas in Southwestern Atlantic Ocean

The average water temperature in L. lipkei’s original distribution range (warm temperate and subtropical waters of Japan, from the Boso Peninsula towards southwest to the Ryukyu islands; Okuno and Fiedler 2010) is 24˚C (Mann and Lazier 2006). Satellite data from several coastal cities in this region showed that the average SST throughout the year (Fig. 7.3A) ranges from 19.9˚C (February) to 28.1˚C (August), with maximum coastal water temperatures reaching 30˚C during summer.

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Figure 7.3 a) Present SST temperatures (˚C) in L. lipkei original distribution range (Japan, data from the locations where these shrimps were first caught and described: Okinawa, Boso Peninsula, Ryukyu Islands) and at the first introduction locations in Brazil (Ceará and Rio Grande do Norte states, in the northeastern coast); b) Present and projected temperatures for 2100 (+3˚C) in São Sebastião (SP, Brazil) coastal waters – new colonized environment. Note: present monthly average sea surface temperatures were calculated from satellite data for the past 5 years.

The Brazilian coastal environments colonized by this shrimp species show similar temperature ranges that vary with latitude (Castro et al. 2006). Each year, from October to March (warm/wet season), an average water temperature of 22˚C – 27˚C is usually observed (Miloslavich et al. 2011). In northeastern Brazilian coast, where the biological invasion was first observed in 2016, average water temperature ranges from 22˚C to 28.5˚C (Bourlès et al. 1999). In southeastern Brazil, where this study was conducted, average water temperature is 24˚C, with a 7˚C to 10˚C variance over the continental shelf (Lentini et al. 2001). Based on satellite data archived data from the past 5 years, average sea surface temperatures along the northeastern Brazilian coast vary from 26.6˚C to 28.9˚C throughout the year, reaching maximum temperatures of 29.7˚C (Fig. 7.3A). At our study location, S. Sebastião (Fig. 7.3B), temperature ranges approximately from 24˚C to 26˚C during the warm months (from December to March). March is usually the warmest month, with maximum water temperatures approaching 29˚C. In a recent study, mean SST for the summer months in the same area of this study was 26˚C, and data collected from tide pools during 2014 and 2015 showed a maximum temperature record of 41˚C (Vinagre et al. 2016). In another study, average water temperature in S. Sebastião during December (2014) and January (2015) was 29˚C (Vinagre et al. 2015).

Temperature data collected in this study from field probes in S. Sebastião (Southeastern Brazil) showed a mean daily minimum and maximum temperatures ranging from 18.43˚C to

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37.60˚C (check also Fig. A6.4 and Table A6.1 in annex 6 for detailed data). There was an absolute maximum record of 41.50˚C (MHT) in three of the tide pools sampled. Mean monthly temperatures varied between 25.72˚C to 29.32˚C, and February was the warmest month recorded in the data loggers. Monthly temperature variation (average maximum – average minimum) was 16.60˚C ± 2.74˚C, while average daily temperature variation was 4.78˚C ± 1.06˚C.

According to regional predictions (IPCC 2013), South American water temperatures will rise 3˚C until 2100. Therefore, the expected mean summer water temperatures for 2100 at the field site using the previous measurements as reference range from 28.7˚C to 32.3˚C. Nevertheless, such temperatures can already occur in present days for a limited period of time (days or weeks) during extremely hot warm seasons, as shown by maximum water temperatures from both field and satellite data.

7.3.2. Temperature effects on Lysmata lipkei

Stress assessment at lower levels of biological organization

Overall, Lysmata lipkei showed a significant physiological response to a temperature increase of +3˚C during the experimental period. A significant response to the temperature challenge was observed for biomarkers Hsp70, CAT, GST and SOD (Table 7.2). Significant differences in biomarker levels between tissues were also detected (see Table A6.2 in annex 6 for average basal biomarker levels in gills and muscle), as well as significant interaction between factors for some of the biomarkers (Table 7.2). In particular, significant temperature- time interactions were observed for all biomarkers. The temperature-tissue-time interaction was also significant but only for Ub and SOD. Based on the PCA (Fig. 7.4, see table A6.3 in annex 6 for factor loadings), Hsp70 and SOD were the biomarkers in the gills mostly correlated to temperature variation, while no particular biomarker was associated with time. In the muscle tissue, Ub and CAT were correlated with temperature and AChE and GST were the biomarkers most correlated with time. Additionally, the variance explained by PC1 and PC2 in the gills (60.02% and 19.30%, for PC1 and PC2, respectively) was higher than in the muscle (31.85% and 23.48% for PC1 and PC2, respectively).

Table 7.2 Compilation of statistical analyses (factorial ANOVAs) showing main effects of temperature (29˚C, 32˚C) and tissue (gills, muscle) and their interactive effects, as well as interactions with time (0, 7, 14, 21 and 28 days) on selected molecular biomarkers from the species L. lipkei. Significant results (p- value < 0.05) are indicated with an asterisk (*). Abb: Hsp70 – heat shock protein 70kDa; Ub – ubiquitin;

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CAT – catalase; LPO – lipid peroxides; GST – glutathione-S-transferase; SOD – superoxide dismutase; AChE – acetylcholinesterase; Temp – Temperature; T*T*T – Temperature × Time × Tissue. Hsp70 Ub CAT LPO GST SOD AChE F p F p F p F p F p F p F p Temperature 21.9 *** 0.9 ns 16.2 *** 0.8 ns 10.0 ** 7.9 ** 3.544 ns Tissue 181.2 *** 82.6 *** 218.7 *** 21.1 *** 104.4 *** 169.8 *** NA NA Temp*Time 4.3 ** 3.1 * 4.5 ** 10.9 *** 2.7 * 3.0 * 2.676 * Temp*Tissue 3.1 ns 1.1 ns 0.7 ns 0.6 ns 0.4 ns 0.0 ns NA NA T*T*T 1.6 ns 4.6 ** 2.0 ns 1.3 ns 1.4 ns 2.3 * NA NA Levels of significance: * p < 0.05, ** p < 0.01, and *** p < 0.001, ns: non-significant (p > 0.05).

Figure 7.4 Ordination diagrams of the first two axes of the PCA carried out for each sampled tissue of Lysmata likpei exposed to 29˚C and 32˚C considering all sampling times (0, 7, 14, 21 and 28 days of exposure): a) gills and b) muscle.

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Significant biomarker changes under increased temperature in each sampling time and each organ are presented in Fig. 7.5 (significant differences in levels were obtained from post- hoc Tukey HSD tests following ANOVAs on table 7.2). Shrimps were more responsive to a temperature increase after seven days of exposure, when biomarkers significantly increased their levels (all biomarkers in gills, and CAT and AChE in muscle). After the first week of exposure, biomarker levels returned to control levels and no significant differences were observed between 29˚C and 32˚C at 14 and 21 days of exposure. Nevertheless, at the end of the experiment (28 days), a significant change was detected again, coherently in both tissues, and characterized by an increase in LPO levels. A common pattern was therefore conspicuous for both tissues: biomarkers that significantly responded to the temperature challenge showed the same pattern/direction of response (increase) and similar response timings. Nevertheless, biomarker response in the gills showed slightly higher fold-changes than in the muscle tissue (see also Fig. A6.5 and A6.6 in annex 6).

The most responsive biomarkers in both tissues were CAT and LPO. Hsp70, Ub, GST and SOD only showed changes in gill tissue. Almost all biomarkers showed a rapid response to an increased thermal load, except for LPO, which took 28 days to show a significant change.

Figure 7.5 Variation in biomarkers’ levels in comparison to control (29˚C) within each time point (T7, T14, T21, and T28 days of exposure). Arrows () indicate significant responses (p-value ≤0.05) to the thermal challenge (increase in biomarker levels). Numbers before arrows stand for fold-change values.

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Abb: Hsp70 – heat shock protein 70 kDa; Ub – total ubiquitin; CAT – catalase; LPO – lipid peroxidation; GST – glutathione-S-transferase; SOD – superoxide dismutase; AChE – acetylcholinesterase

Stress assessment at higher levels of biological organization

Lysmata lipkei showed mean CTMax values ranging from 36.6˚C to 38.4˚C at control and increased temperature, respectively, with significantly higher CTMax at 32˚C than at control temperature (F = 49.60, p-value <0.0001). A significant interaction between acclimation time and temperature was observed for CTMax (F = 11.68, p-value = 0.001): acclimation time affects the thermal tolerance plasticity (Fig. 7.6). Particularly, significant differences were found for CTMax between 29˚C and 32˚C both after 7 days and 28 days of acclimation time, but the degree of plasticity (i.e., environmentally induced phenotypic changes) was higher after longer acclimation times. An acclimation capacity of 0.87˚C was recorded after 7 days and 2.51˚C after 28 days, with an overall ARR of 0.6. A negative TSM of -3.99˚C was recorded in the intertidal environment and a positive TSM of 9.45˚C was recorded in the subtidal environment.

Figure 7.6 Critical Thermal Maxima (CTMax) measured in Lysmata lipkei after 7 days and 28 days at two different acclimating conditions predicting exposure to present average summer water temperatures (29˚C, control) and future average summer water temperatures (32˚C) in intertidal rocky reefs of Southeastern Brazil. Significant differences (p-value < 0.05) are indicated with an asterisk (*).

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The condition indicators analyzed for L. lipkei exposed to different temperature conditions only showed significant differences for total protein (Table 7.3), with lower protein content at 32˚C when compared to the 29˚C treatment. A significant temperature-time interaction was also solely observed for total protein, particularly at T28 (p-value = 0.031). All other condition indicators (Fulton’s K, % C, and C:N) were similar between experimental treatments and throughout the experiment. Mortality rate was 0% throughout the experiment and across experimental treatments.

Table 7.3 Factorial ANOVAs showing the main effects of temperature (29˚C vs 32˚C) and interactive effects with time (T0 vs T28) on condition indicators for L. lipkei. Significant results (p-value < 0.05) are presented in bold. Abb: Temp – Temperature. Fulton's K Total protein %C C:N molar ratio F p-value F p-value F p-value F p-value Temperature 0.069 0.793 5.639 0.022 1.240 0.272 0.608 0.440 Temp x Time 0.720 0.408 4.784 0.043 0.034 0.854 0.072 0.792

7.4. Discussion

Understanding how an invasive species might respond to variations in environmental temperature can provide notable insights about the patterns and processes of species invasions under climate change scenarios (Zerebecki and Sorte 2011). In this study, we showed that the recently invasive shrimp species Lysmata lipkei in the southern Atlantic displays a significant ability to acclimate to chronic elevated temperature, which is supported by the absence of mortality during the temperature stress experiments. Overall, results from the different molecular, cellular and whole-body analyses here performed suggest that: i) this species is sensitive enough to elicit a detectable response to high temperature (in accordance with other studies with caridean shrimps, Allan et al. 2006; González-Ortegón et al. 2013; Reiser et al. 2014; Magozzi and Calosi 2015); and ii) the intensity of stress and duration of exposure are key features determining the onset of biochemical and physiological adjustments (as also observed by Buckley et al. 2004; Schulte et al. 2011 and Madeira et al. 2016 for several ectotherms). Additionally, no variations were detected in condition indices and lipid reserves between treatments, suggesting that shrimps maintained their weight and condition, and possibly maintained foraging activity and food conversion efficiency.

The first thing to consider when analyzing this shrimp’s ability to acclimate to sea warming predictions in its new environments in the Southern Atlantic Ocean is the similarity of temperature variations in this region when compared to its original distribution range in Japan.

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This has also been observed for another invasive decapod, Hemigrapsus sanguineus, whose niche in the new geographic range is very similar to that occupied in their native home range (Lohrer et al. 2000). All the temperature datasets here analysed show that the average yearly temperature range in the invaded areas in NE and SE Brazil (about 22˚C – 28˚C) is very similar to that of their native range in Japan (about 20˚C – 28˚C), with mean maximal temperatures approaching 30˚C in all locations analyzed. In situ data also showed that these shrimps experience wide temperature variations in the invaded area, with average minimum and maximum daily temperatures ranging from 24.03˚C to 31.91˚C. Tide pools in S. Sebastião area are particularly vulnerable to high temperatures because they are typically smaller in area and volume when compared to tide pools from other regions of southeastern Brazil and from other tropical areas in the Southwestern Atlantic (Pastro et al. 2016). These organisms also inhabit the intertidal and shallow subtidal in their original geographical area (Okuno and Fiedler 2010), which suggests that they are likely equipped with adaptive mechanisms that may allow them to invade these extremely variable environments (Ravaux et al. 2016). This eurythermality trait has been theorized as a mechanism driving successful species invasion (McMahon 2002; Zerebecki and Sorte 2011; Kelley 2014), and it is likely an essential aspect for the establishment of thriving self-sustaining populations in new habitats.

Molecular mechanisms underpinning L. lipkei eurythermality varied with thermal load and time, as also observed for other decapod crustaceans (e.g. Tavares-Sánchez et al. 2004; Qian et al. 2012; Vinagre et al. 2014; Madeira et al. 2015b; Rodríguez-Fuentes et al. 2017). Biomarkers Hsp70 and SOD in gills, and Ub and CAT in muscle were mostly associated with temperature, while damage to lipids (LPO) was mostly associated with the duration of exposure. These results suggest that chaperone and antioxidant defenses can play an important role during thermal stress by stabilizing damaged proteins (Tomanek 2010) and protecting cells from the toxic effects of ROS (Lesser 2006), thereby allowing the proper management of cellular functioning and the maintenance of homeostasis across environmental temperature changes (Farcy et al. 2009). This seems to be enough to promote survival and avoid general metabolic failure (Halpin et al. 2002; Lesser 2006; Yamashita et al. 2010; Madeira et al. 2013) in this shrimp species. The secondary effects of increased respiration rate and aerobic metabolism at higher temperature, which are usually translated in increased ROS flux and consequent lipid damage to cellular membranes (Pannunzio and Storey 1998), seem to be time dependent and occur on a small scale in this species, considering no changes were detected in growth, lipid reserves or mortality after chronic warming. All of these are strong indicators that L. lipkei possesses high acclimation ability, which may afford this species a relatively high degree of environmental independence (Miao and Tu 1995).

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The activation of these cellular stress responses has been previously linked to the acquisition and shifts in organismal thermal tolerance (Feder and Hofmann 1999; Whitley et al. 1999; Madeira et al. 2015b; Rahlff et al. 2017). Our study corroborates these findings since CTMax values for L. lipkei were considerably high (between 36.6˚C to 38.4˚C) among ectothermic animals, and are in line with upper thermal limits found for other tropical shrimps (e.g., 39.7˚C for Palaemon northropi, 36˚C for Litopenaeus vannamei, 34.7˚C for Hippolyte obliquimanus, (Ravaux et al. 2016) and 39˚C for Pachygrapsus transversus (Vinagre et al. 2016)), as well as for other invasive crustaceans (e.g. 33˚C for Palaemon macrodactylus, Lejeusne et al. 2014; 35˚C for Carcinus maenas, Cuculescu et al. 1998). In fact, an integrative study of the physiology of invasive vs native species found that the mean upper thermal limits for invasive decapods was around 35˚C (Kelley 2014), so L. lipkei’s CTMax values are higher than average. This ultimately suggests that their high thermal tolerance may have been crucial in the invasion of Southern Atlantic coastal habitats. Additionally, these values are greater than the maximum water temperature for S. Sebastião coastal area, which is 30.1˚C (data from the CEBIMar-USP meteorological station, Vinagre et al. 2015), although tide pools can reach 41.5˚C during heat waves. As shown by the TSM values calculated in this study for the intertidal and subtidal (-3.99˚C and 9.45˚C, respectively), we can expect L. lipkei to expand further south and more into subtidal habitats along the Brazilian coast. By the year 2100, models predict that water temperatures in the Southern Atlantic will increase by 3˚C (IPCC 2013), resulting in a summer average of 27˚C to 29˚C in subtidal areas and an average temperature of 32˚C in the intertidal area of S. Sebastião, which is still well below the CTMax measured for this species. The acclimation capacity of L. lipkei, i.e. higher plasticity expressed at higher temperatures and after longer exposure times, also evidences that not only this species is able to accommodate future temperature changes, but also that there may be an influence of thermal history (for instance season or thermal niche occupied) in the upper thermal limits, which has already been documented for crustaceans (Ober et al. 2016). Interestingly, and according to Ravaux et al. (2016), the ARR value of 0.6 obtained for L. lipkei does represent a genuine acclimatory capacity.

Although it is generally stated that acclimation imposes metabolic costs to organisms (Angilletta et al. 2004) as it depends upon energy that the animal might otherwise use for a different function, these costs are difficult to quantify (Tomanek and Somero 2002). The quantitative assessments performed in this study, however, suggest that acclimation did not impose energy costs for this invasive shrimp. No differences were detected either in mortality, condition, and growth or lipid reserves after the experimental trials. This indicates that L. lipkei adults were able to maintain homeostasis likely by sustaining their energy balance and oxygen consumption rate, keeping them within a stable range, as also observed for other invertebrate

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species (see Kühnhold et al. 2016). Verberk et al. (2016) explained this balance based on the fact that organisms depend on a constant and sufficient flux of O2 from their environment to their metabolizing tissues in order to ensure an adequate ATP supply to cover all the physiological demands and still maintain the energy status (Rodríguez-Fuentes et al. 2017). Notwithstanding, it should also be considered that food was not a limiting factor in the present study, and the animals could have maintained their energy status by increasing food intake. According to Clark et al. (2013), acclimation in aquatic ectotherms typically occurs within 1-3 weeks, as also observed in this study. Nevertheless, it is worth noting that organismal-level responses, such as metabolic rates, and long term processes, such as growth and reproduction (Pörtner and Knust 2007), may not have the same time-course as cellular-level responses (Rahlff et al. 2017). Longer experimental trials, including all the life-cycle stages, may be necessary to detect changes at higher levels of biological complexity.

7.5. Conclusions

The main conclusion from this study is that Lysmata lipkei possesses a significant acclimation potential that might enable this species to continue its invasion to tropical and subtropical areas along the Southwestern Atlantic coast. Additionally, results from this study suggest that non-indigenous species such as this shrimp species have specific traits that make them successful invaders, namely: i) thermally tolerant phenotypes (with high upper thermal limits), and ii) strong ability to acclimate and make physiological adjustments while maintaining performance. The recognition of such phenotypic traits can help scientists and managers to identify species that have the physiological ability to tolerate both present and future abiotic environments in particular geographic regions. This should be an important tool in predicting which areas are susceptible for invasions or range expansions and develop preventive measures to avoid the introduction of non-native species or allow better management measures in locations where these invaders have already settled.

Annex 6. Supplementary material associated with this chapter can be found in annex 6 in the annexes section.

Acknowledgements This study had the support of the Portuguese Foundation for Science and Technology (FCT) through the WarmingWebs project (PTDC/MAR-EST/2141/2012), the “Investigador FCT” position granted to C.V. and the strategic projects UID/MAR/04292/2013 granted to MARE and UID/Multi/04378/2013 granted to UCIBIO. C.M. and V.M. acknowledge PhD grants

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(SFRH/BD/92975/2013 and SFRH/BD/109618/2015, respectively), and M.C.L. acknowledges a post-doc grant (SFRH/BPD/115298/2016), all granted by FCT. The funding source had no involvement in the study design, collection and analysis of data or in the decision to publish the work. Authors would like to thank everyone involved in the field work and maintenance of the experimental tanks.

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CHAPTER 8

Environmental health assessment of warming coastal ecosystems in the tropics – application of an integrated biomarkers response index

Madeira C, Mendonça V, Leal MC, Diniz MS, Cabral HN, Flores AAV, Vinagre C (2018) Science of the Total Environment 643: 28-39, DOI: 10.1016/j.scitotenv.2018.06.152.

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ABSTRACT

According to climate science, ocean warming is one of the current and future greatest threats to coastal ecosystems. Projection scenarios for the end of this century show that tropical intertidal ecosystems are particularly at risk. In this study we optimized and tested a holistic method for bio-monitoring present and projected thermal pressure in such ecosystems, in order to assess organism vulnerability to ocean warming. Several species representative of different animal groups (fish, crustaceans and gastropods) were collected in the field and subjected to an experimental trial for 28 days, testing two temperatures: control (present seawater summer temperature) and elevated temperature (+3˚C, projected seawater temperature anomaly for 2100). Muscle samples were collected weekly to quantify several biomarkers of: i) macromolecular damage (protein unfolding and denaturation and lipid peroxidation), ii) reactive oxygen species (ROS) scavengers (antioxidant enzymes), and iii) body condition (energy reserves and body mass). These biomarkers were combined in integrated biomarker response (IBR) indices, either in three separate stress response categories (as previously defined) or in a unique combined analysis of overall physiological performance. Results suggest that temperature affected the IBRs using both approaches, with increasing temperatures significantly impairing organisms’ overall health. Biomarkers of lower biological complexity levels (molecular and cellular) indicated deleterious effects of temperature, whereas biomarkers at higher complexity levels (tissue and whole-body) suggested maintenance of performance after chronic exposure. The main advantage of using an index that combines all biomarkers with different biological complexity levels is that it is highly efficient in providing solid information on physiological performance of organisms and their vulnerability to climate warming. This feature is essential for implementing adequate tools in monitoring programs to properly address environmental issues and allow managers and stakeholders to take the appropriate preventive and conservation measures in fragile coastal systems.

Keywords: tropical rocky reef, ocean warming, physiological performance, stress biomarkers, IBRs, environmental health assessment

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Graphical abstract

Highlights • Chronic exposure to elevated temperature tested in tropical rocky reef animals • Markers of macromolecular damage, ROS scavenging and body condition assessed with IBRs • Markers at molecular and cellular level showed deleterious effects in fitness (protein unfolding and denaturation, and cell membrane lipid peroxidation) • Markers at tissue and whole body level showed positive or no effects in fitness (energy balance and body mass were maintained) • IBRs are efficient holistic methods to monitor climate change induced thermal change in natural populations

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8.1. Introduction

The growing need to evaluate the quality of marine ecosystems exposed to anthropogenic pressures has led to the implementation of international policies with the ultimate aim of assessing, protecting, and restoring these ecosystems (Colin et al., 2015; Devin et al., 2014; Henderson et al., 1987; Hook et al., 2014). Vulnerability research under climate change has therefore acquired significant importance in recent years. However, the complex, nonlinear, and multiplicity of dynamics of natural systems make the assessment of ecological vulnerability quite challenging. As a consequence, it has been suggested that such an evaluation should be conducted at small geographical scales and be context-specific (Beroya-Eitner, 2016).

Several approaches have been employed in monitoring programs to provide the necessary information to assess vulnerability/resilience of marine ecosystems and to ensure the maintenance of biodiversity and the integrity of marine communities. For instance, changes in community structure and measures of contamination are often used to indicate ecosystem health status. However, these responses are manifestations of damage rather than prognostic indices (Knap et al., 2002). Therefore, the combination of precocity and relevance in monitoring tools is essential and should enable the necessary management actions to be taken upon the slightest indication of environmental deterioration. To address this issue, assessments at the organism level have been recommended (Lam, 2009; Lam and Gray, 2003). The multi-biomarker approach attempts to perform such assessments by measuring physiological changes at several levels of biological complexity (i.e., molecular, cellular, tissue-level), which underlie posterior effects at more complex levels (i.e. organisms and populations), and for which causality can be established with environmental factors (Knap et al., 2002; Marigómez et al., 2013). In particular, the integration of biomarkers in health indices have provided coherent mechanistic information concerning biological responses to environmental stress (Beliaeff and Burgeot, 2002), have succeeded in identifying temporal and spatial fluctuations as well as the magnitude of change in the health status of ecosystems (Broeg and Lehtonen, 2006; Lehtonen et al., 2014), and have allowed for comparisons of populations inhabiting different locations (Lehtonen et al., 2014). Additionally, indices facilitate the communication of results from complex physiology- environment interactions to managers and the public (ICES, 2007).

Several of these integrated biomarker indices (IBRs) have been widely used for biomonitoring the effects of chemical pollution in aquatic systems, including freshwater, brackish, and marine (Broeg and Lehtonen, 2006; Dagnino et al., 2007; Devin et al., 2014; Hook et al., 2014; Schulte and Mazzuckelli, 1991). However, this approach has not been properly explored in the context of global warming. To our knowledge, there are no defined sets of biomarkers,

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mathematical algorithms, or standardized procedures to apply these indices to evaluate the effects of increasing sea surface temperatures (SST) in coastal areas predicted to be most affected by gradual temperature rise and extreme weather events.

As shown by several studies, the increase of ocean temperatures is expected to be more severe in the tropics and subtropics (Deser et al., 2010; Hughes, 2000; Roberts, 2002; Tewksbury et al., 2008), when compared to temperate areas. In the past few decades, the resilience of coastal tropical marine ecosystems has been undermined, as demonstrated by numerous fisheries collapses (Blaber, 2002; Perry, 2005) and historical losses of coral reefs, mangroves, and wetlands due to climate related events (Loarie et al., 2009; Munday et al., 2009; Pandolfi et al., 2011). Tropical climate trends from the past decades suggest that the largest ocean warming has occurred in the tropical Atlantic (0.8˚C-1.6˚C, Deser et al., 2010) and climate models predict further temperature increase (Collins et al., 2013). The Southeastern Brazilian coastline in the tropical/subtropical Atlantic, in particular, is expected to be one of the most significantly affected regions in the world by climate warming, with scenarios of air temperature rise from +2˚C to 3˚C (in the best case scenario) to +3˚C to 6˚C in the worst case scenario (Marengo, 2007) by the end of this century. Additionally, as Vincent et al. (2005) confirmed, the increase in frequency of hot nights and warm days is already a reality.

Among the coastal habitats in Southeastern Brazil, shallow waters are expected to experience the impacts of climate warming more rapidly, as noted by Vinagre et al. (2018). Intertidal platforms make the transition between terrestrial and marine realms and are regularly subjected to varying temperature regimes. Several features make them very useful study sites: (i) they are easily accessible (Nguyen et al., 2011); (ii) they host diverse biological communities (Somero, 2002; Vinagre et al., 2013) including important commercial species of fish, crustaceans, and gastropods (Barry et al., 1995; Nagelkerken and Simpson, 2013; Tomanek, 2002); (iii) they are essential to the proper ecological functioning and cycling of nutrients (Lathlean et al., 2017), and, most especially, (iv) they have low thermal inertia which renders them ideal natural laboratories for climate change research (Madeira et al., 2017b, 2012; Vinagre et al., 2018). Therefore, species inhabiting these habitats are suitable models for ecophysiological studies and for the determination of organisms’ acclimation and adaptation potentials under thermal stress (Beaman et al., 2016; Helmuth and Hofmann, 2001).

The aim of this study was to optimize, standardize, and compare different integrated biomarker approaches to be employed in the monitoring of climate-change induced thermal stress in tropical/subtropical coastal regions. We (i) applied a series of physiological biomarkers (including molecular, cellular, tissue and organism level to different target species, from

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different taxonomical groups, e.g. fish, crustaceans and gastropods) from a rocky reef platform in Southeastern Brazil under present and future summer seawater temperatures; (ii) adapted an integrated biomarker response index and applied it to our biomarker datasets, either under three categories of stress responses (macromolecular damage, oxidative stress and changes in body condition) or under one unique overall category (physiological performance) and compared the advantages and disadvantages of each approach; (iii) we calculated and compared index values in present vs future ocean temperatures and based on these results we performed an assessment of environmental health and thermal vulnerability of intertidal habitats of Southeastern Brazilian coast to climate warming conditions.

8.2. Materials and methods

The authors of the present study declare that all conducted field collections and experimental as well as laboratorial procedures followed European and Brazilian legislation. Guidelines concerning the use of animals in science were strictly followed and all experiments were properly authorized by legal authorities.

8.2.1. Selected coastal environment and test species

The selected field site was located in Southeastern Brazilian coast, in shallow waters of São Sebastião, State of São Paulo (23˚49’42’’S, 45˚26’29’’W). This field site represents a coastal environment highly representative of this geographical region: it makes the transition between Atlantic rainforest (which is typical of the eastern region and covers 48% of the terrestrial biome along the coastal area of Brazil, Prates et al., 2010) and the marine realm, and is under the influence of a tropical-subtropical climatic regimen (Miloslavich et al., 2011).

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Table 8.1 Characterization of the species and respective samples collected for the present study. Quantitative measurements are presented as mean±SD. Species Taxonomic group Sample size Length (cm) Weight (g) Description References Abudefduf Pisces 60 4.59±7.96 1.83±0.99 Subtropical; demersal; transient in tide pools; shallow Cérvigon, 1994; Emery, 1978; Feitoza et saxatilis subtidal; lives in schools; preferred temperature 23˚C- al., 2003; Fishelson, 1969; IUCN, 2017; 26˚C; commercial (fisheries and ornamental); Lieske and Myers, 1994 omnivorous; IUCN least concern Scartella cristata Pisces 60 4.67±1.07 1.37±1.0 Tropical; demersal; resident in both tide pools and Bath, 1990; Cérvigon, 1994; CITES, 2013; shallow rocky areas; preferred temperature 22˚C-26˚C; Claro, 1994; Feitoza et al., 2003; IUCN, commercial (ornamental); herbivorous/omnivorous; 2017; Pastro et al., 2016; Richards, 2013; IUCN least concern Robins and Ray, 1986 Lysmata lipkei Crustacea 60 3.36±0.59 0.60±0.29 Subtropical; benthic; resident in intertidal and shallow Anker and De Grave, 2016; Bauer, 2004; reefs; preferred temperature ~24˚C; commercial IUCN, 2017; Okuno and Fiedler, 2010 (ornamental); omnivorous; IUCN not evaluated Stramonita Mollusca 60 3.54±0.76 9.32±5.35 Tropical; benthic; resident in intertidal and subtidal; Houart and Gofas, 2009; IUCN, 2017; haemastoma preferred temperature ~25˚C; commercial (fisheries); Leal, 2003; Ramírez et al., 2009 carnivorous; IUCN not evaluated

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In total, 4 common shallow water species were tested (Table 8.1): the fishes Abudefduf saxatilis (Linnaeus 1758) and Scartella cristata (Linnaeus 1758), the peppermint shrimp Lysmata lipkei (Okuno and Fiedler 2010), and the gastropod mollusk Stramonita haemastoma (Linnaeus 1767). All the selected species have commercial interest (either for fisheries and/or ornamental aquaria) and live in close association with the seafloor, having preferred temperatures varying between the tropical and subtropical regimes. According to the IUCN, they are either least concern or have not been categorized yet. Specimens from these species were collected using hand nets in shallow water (<50 cm) and placed in plastic containers with aerated seawater. The animals were then transported to the research facilities (Cebimar – Universidade de São Paulo, S. Sebastião) and placed into aquaria upon arrival. They were allowed to acclimate to captivity conditions at local summer seawater temperature (29.0˚C ± 0.5˚C) for two weeks. Animal welfare was assessed regularly (i.e. daily, for wounds or disease symptoms) and the animals were fed once a day ad libitum with frozen shrimp.

8.2.2. Experimental design

The study was designed to test the effects of chronic exposure to elevated temperature in several species of a coastal ecosystem in one of the world areas predicted to be most affected by the gradual increase of ocean temperatures. In order to do this, two experimental temperatures were chosen: control (29.0˚C±0.5˚C), corresponding to present mean summer water temperature at the collection site, as described in previous studies from data collected with field probes during the past years (Madeira et al., 2017b; Vinagre et al., 2018, 2015); and future mean summer water temperature (32.0˚C±0.5˚C), according to IPCC(2013) and Marengo (2007) projections for Southeastern Brazilian coast. In order to attain experimental temperatures after acclimation, temperature was increased gradually during two days at a rate of 0.10˚C.hr-1. The organisms were randomly placed in two re-circulating water systems, of 350 L volume each, one system for each experimental temperature (Fig. 8.1).

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Figure 8.1 Experimental setup (not to scale).

Each system was comprised of 8 tanks (2 larger plastic tanks of 45 L and dimensions 60 × 30 × 25 cm, and 6 smaller glass tanks of 15 L and dimensions 25 × 25 × 25 cm), one seawater deposit of 36 L and one sump of 125 L as well as a UV filter. Experimental temperature was the same for all tanks within one system, but was applied individually to each tank, using thermostats. There were two replicate tanks per species in each system. Specimens from A. saxatilis were randomly placed in the 2 larger plastic tanks (as they require more swimming space, n = 15 individuals.tank-1), while specimens from the other species (S. cristata, L. lipkei and S. haemastoma) were randomly placed in the other 6 smaller glass tanks (n = 15 individuals.tank-1). All tanks were enriched with live rocks and were filled with clean aerated seawater (95-100% oxygen saturation) and salinity 35. Temperatures were maintained for 28 days and experiments were carried out in shaded day light (14L:10D). All animals were fed once a day with commercial frozen shrimp, except in the 24h prior to sampling.

8.2.3. Sampling scheme

The sampling design consisted of euthanizing the animals at day 0, 7, 14, 21, and 28 either by cervical or longitudinal transection (for vertebrates and invertebrates, respectively, according to Hau and Schapiro, 2010). These timings are consistent with OECD guidelines for

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prolonged and repeated exposure toxicity studies, and were adapted to aquatic animals. At each timepoint (Fig. 8.2), five individuals of each species were randomly sampled for each temperature condition (n = 50 specimens from all timepoints for both treatments for each species, to be used in biochemical analyses). These sample sizes were based on previous studies performed by Madeira (2016). White muscle was collected from each specimen and immediately frozen at -80˚C until further analyses. To prevent any additional handling stress, the total length and weight of all individuals was measured at the end of each sampling using an icthyometer and a scale.

8.2.4. Biomarker measurements

The biomarkers used in the present study were chosen based on several biological complexity levels (Fig. 8.2) as well as their role in the physiological response to stress, as follows: i) Molecular biomarkers: these are important components of primary (hormone-related) and secondary (metabolism and molecular cascades-related) stress responses (Farrell, 2011). The markers chosen all have significant roles in the cellular stress response (CSR, Barton, 2002; Iwama et al., 1999) and participate in the main molecular pathways modulated by temperature, namely protein quality control (heat shock proteins – Hsp70 with chaperoning function, Feder and Hofmann, 1999; Yamashita et al., 2010; and ubiquitin – Ub which signals denatured proteins for degradation in the proteasome, Hofmann and Somero, 1995); and reactive oxygen species (ROS) scavengers (anti-oxidant enzymes, Lesser, 2006: catalase – CAT, glutathione-S- transferase – GST and superoxide dismutase – SOD).

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Figure 8.2 Timeline and sampling scheme of the experiment. ii) Cellular biomarkers: these may serve as indicators of alterations to cellular structures and components, function, and apoptosis (Kültz, 2004). Herein we selected lipid peroxides – LPO, which are the end product of phospholipid bilayer destruction due to ROS action and are markers of membrane damage and consequent cell death (Lesser, 2012; Madeira et al., 2013). iii) Tissue biomarkers: allow the detection of changes in general metabolism and accumulation of energy resources as well as histopathological alterations (Madeira et al., 2014b; Rabasa and Dickson, 2016). In this study, energy reserves were used as markers, and total proteins and lipids were quantified in the muscle of the animals tested. iv) Organism biomarkers: the inclusion of whole body measurements gives a picture of terciary responses to stress, such as growth and performance (Barton, 2002). We used body mass here as a measure of body condition.

For measuring biomarkers, muscle samples were homogenized in 1.0 mL of phosphate buffered saline (PBS) solution (140mM NaCl, 3mM KCl, 10mM Na2HPO4, 2mM KH2PO4, pH 7.4) using a Tissue Master 125 homogenizer (Omni International, Kennesaw, USA) on ice-cold. Homogenates were centrifuged at 10,000 × g (at 4˚C for 15 min) and the supernatant transferred to new microtubes (1.5 mL) and frozen (-80˚C). Several assays (summarized in Table 8.2) were

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then carried out to quantify the different biomarkers. Since these methods require long and extensive descriptions, these are given in detail in annex 2 in the annexes section rather than in the main text:

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Table 8.2 Methods used for biomarker analyses. Abb: Hsp70 – heat shock protein 70; Ub – total ubiquitin; CAT – catalase; GST – glutathione-S-transferase; SOD – superoxide dismutase; AChE – acetylcholinesterase; LPO – lipid peroxides; Tprotein – Total proteins; Tlipids – Total lipids; BM – Body mass; ELISA – Enzyme Linked Immunosorbent Assay; LC – Lipid Content; C – Carbon. Biomarker Type of analysis units References Hsp70 protein quantification, indirect ELISA µg.mg-1 of protein Madeira et al. 2017 Ub protein quantification, direct ELISA µg.mg-1 of protein Madeira et al. 2017 LPO quantification of malondialdehyde nmol.mg-1 of protein Uchiyama and Mihara 1978 CAT enzymatic/kinetic assay (activity) nmol.min.mg-1 of protein Johansson and Borg 1988 GST enzymatic/kinetic assay (activity) nmol.min.mg-1 of protein Habig et al. 1974 SOD enzymatic/kinetic assay (activity) % inhibition.mg-1 of protein Sun et al. 1988 Tprotein direct quantification mg.ml-1 of homogenate Bradford 1976 Tlipids direct quantification or isotopic elemental composition %LC or %C. mg-1 of muscle Ladd and Sachs 2015 BM fresh weight measurement g.ind-1 Stevenson and Woods 2006

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8.2.5. Data treatment and statistical analyses

All data were first explored by descriptive statistics and were then tested for normality (Shapiro-Wilk’s test) and homoscedasticity (Levene’s test). In order to analyze complex multidimensional biomarker data in an integrated way, IBR indices were applied following Beliaeff and Burgeot (2002) and Broeg and Lehtonen (2006). In summary, the general mean (m) and the standard deviation (s) of all sampling times combined regarding a given biomarker were calculated, followed by a standardization to obtain Y, where Y = (X - m)/s, and X is the mean value for the biomarker at a given time. Then Z was calculated using Z = -Y or Z = Y, in the case of a biological effect corresponding respectively to an inhibition or a stimulation. The score (S) was calculated by S = Z + |Min|, where S ≥ 0 and |Min| is the absolute value for the minimum value for all calculated Y in a given biomarker at all measurements made. Star plots were then used to display Score results (S) and to calculate the integrated biomarker response (IBR) as:

Where Si and are two consecutive clockwise scores (radius coordinates) of a given star plot; Ai corresponds to the area the connecting two scores; n the number of biomarkers used for calculations; and α = 2п/n.

Since the IBR is obtained by summing up all the parameters, to allow a correct and more accurate comparison, IBR was divided by the number of sampling times assessed and presented as IBR/n (Broeg and Lehtonen, 2006). The IBR calculations were always performed with the same order of parameters for all sampling times. By using this method it is possible to get an overall state of organisms under elevated temperature exposure.

Two approaches were taken for index calculations: i) Biomarkers were divided in three stress response categories: i) macromolecular damage (Hsp70, Ub and LPO), ii) ROS scavenging (CAT, GST and SOD), and iii) body condition (Tprotein, Tlipids and BM). IBRs were calculated separately for each defined category (for each

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timepoint, for both temperature treatments for all species). Then, to test main and interactive effects of the species, stress response category and temperature treatment on IBR values, a factorial ANOVA was performed, followed by post-hoc Tukey tests. To test overall differences (combining data from T0-T28 days) between control and elevated temperature in each stress response category for each species, parametric Student t-tests or non-parametric Mann-Whitney U tests were applied to IBR values (depending on whether data followed assumptions of normality and homoscedasticity). ii) Biomarkers were all grouped together in one unique category – physiological performance – for the index calculations. IBRs were calculated for each timepoint, for both temperature treatments for all species. To test main and interactive effects of the species and temperature treatment on IBR values, a factorial ANOVA was performed, followed by post-hoc Tukey tests. To test overall differences (combining data from T0-T28 days) between control and elevated temperature in each species physiological performance, Student t-tests or Mann-Whitney U tests were applied to IBR values.

Additionally, we also analyzed biomarker scores as a fitness index, which was calculated according to the equation ˚ ˚ (Ferreira et al., 2015), where E denotes effect, S29˚C denotes score at 29˚C (control) and S32˚C denotes score at 32˚C (elevated temperature) for a given biomarker. Values that differed in ≥ 0.5 or ≤ -0.5 points from the control score were considered to be from an animal with a higher or lower fitness, respectively. Overall, score values for each biomarker (combining all species and all timepoints) were compared between 29˚C and 32˚C with a Student’s t-test or the non-parametric equivalent Mann-Whitney test.

All statistical analyses were carried out using Statistica v8.0 (Statsoft, USA) and all tests were considered to be statistically significant at p-value < 0.05.

8.3. Results

8.3.1. Multi-stress response categories IBR approach

The factorial ANOVA showed that all the independent variables tested (species, stress response type and temperature) had significant main effects in IBR values (Table 8.3). Post hoc tests showed that significant differences contributing to these results were detected between S. cristata and L. lipkei (p-value = 0.001) for species; between macromolecular damage and body

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condition (p-value < 0.001), and between ROS scavenging and body condition (p-value = 0.005) for stress response category, and between both temperature treatments.

Table 8.3 Factorial ANOVA on the main and interactive effects of independent factors species, stress response type and temperature on the dependent variable IBR index. Statistical significance was considered at p-value < 0.05. Significant results are presented in bold. F MS p-value Species 5.30 13.53 <0.001 Stress response type 9.29 23.78 0.002 Temperature 42.52 108.56 <0.001 Species × Stress response type 2.49 6.37 <0.001 Species × Temperature 1.37 3.50 0.027 Stress response type × Temperature 63.52 162.16 0.255 Species × Stress response type × Temperature 3.60 9.19 0.003

Star plots for calculated scores using IBR indices in three categories of stress responses are pictured in Fig. 8.3-8.5. In all categories analyzed, the most sensitive species to an increase in temperature was S. cristata, which can be observed by the larger graphic areas in the star plots at 32˚C when compared to 29˚C (Fig. 8.3B, 8.4B, and 8.5B). Nevertheless, all species showed statistically significant differences between mean IBRs at control vs elevated temperature (when all sampling times were combined) for all three response categories, except A. saxatilis in the body condition category, where no differences were detected.

IBRs calculated for each sampling time independently were very coherent for all species in categories of macromolecular damage and ROS scavenging (Fig. 8.3-8.5, bar graphs), showing consistently higher IBR values at 32˚C when compared to 29˚C, from T0 to T28 days. On the contrary, for the category of body condition, results showed the opposite trend, with IBR decreasing along the entire experimental time, with biomarkers presenting lower scores at 32˚C in all the timepoints tested. In general, T7 and/or T28 were the most responsive sampling times, with bigger differences between 29˚C and 32˚C in all categories of responses and species analyzed.

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Figure 8.3 Results from the first category of stress responses – macromolecular damage. Star plots with mean scores (and respective IBRs below) for the experimental month (all sampling times combined, T0- T28) for the 4 species exposed to 29˚C (control) and 32˚C (stress temperature); and specific IBR values for each sampling time and all times combined (mean+SD) in muscle tissue of: A) Abudefduf saxatilis, B) Scartella cristata, C) Lysmata lipkei, D) Stramonita haemastoma. Statistically significant differences between mean IBRs (when all sampling times were combined) are shown with an asterisk (*). Abb: Hsp70 – heat shock protein 70 kDa; Ub – total ubiquitin; LPO – lipid peroxides.

Considering each stress response category separately, we observed that the most responsive biomarkers to elevated temperature concerning macromolecular damage were Ub (higher changes in scores when compared to control especially in S. cristata and S. haemastoma, Fig. 8.3B and D) and LPO (higher scores when compared to control especially in S. cristata and L. lipkei, Fig. 8.3B and C). In stress response category of ROS scavenging, the most responsive biomarkers were CAT and GST (in A. saxatilis, and L. lipkei, Fig. 8.4A and C), while SOD was the most responsive in S. haemastoma (Fig. 8.4D).

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Figure 8.4 Results from the second category of stress responses – ROS scavenging. Star plots with mean scores (and respective IBRs below) for the experimental month (all sampling times combined, T0-T28) for the 4 species exposed to 29˚C (control) and 32˚C (stress temperature); and specific IBR values for each sampling time and all times combined (mean+SD) in muscle tissue of: A) Abudefduf saxatilis, B) Scartella cristata, C) Lysmata lipkei, D) Stramonita haemastoma. Statistically significant differences between mean IBRs (when all sampling times were combined) are shown with an asterisk (*). Abb: CAT – catalase; GST – glutathione-S-transferase; SOD – superoxide dismutase.

Finally, in the last stress response category, body condition, the markers that showed stronger responsiveness to the temperature treatment were Tlipids in fish (Fig. 8.5A and B) and Tprotein in invertebrates (Fig. 8.5C and D), while body mass seemed to the less affected by an increase in temperature (Fig. 8.5A, C and D).

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Figure 8.5 Results from the third category of stress responses – body condition. Star plots with mean scores (and respective IBRs below) for the experimental month (all sampling times combined, T0-T28) for the 4 species exposed to 29˚C (control) and 32˚C (stress temperature); and specific IBR values for each sampling time and all times combined (mean+SD) in muscle tissue of: A) Abudefduf saxatilis, B) Scartella cristata, C) Lysmata lipkei, D) Stramonita haemastoma. Statistically significant differences between mean IBRs (when all sampling times were combined) are shown with an asterisk (*). Abb: Tprotein – total protein; Tlipids – total lipids.

8.3.2. Overall stress response IBR approach

In this approach, all biomarkers measured were combined in IBR calculations to give one unique measure of physiological performance. Factorial ANOVA (Table 8.4) showed a significant main effect of temperature in index values, characterized by an increase in IBR from 29˚C to 32˚C. No significant effect of species or interaction between temperature and species in IBR was detected.

Table 8.4 Factorial ANOVA on the main and interactive effects of independent factors species and temperature on the dependent variable IBR index. Statistical significance was considered at p-value < 0.05. Significant results are presented in bold.

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F MS p-value Species 0.96 6.96 0.421 Temperature 57.93 417.58 <0.001 Species × Temperature 2.38 17.20 0.087

Star plots and index values show that increased temperature did not affect all the species tested. In fact, no significant differences in mean IBR values from all sampling times combined (T0-T28) were observed either in A. saxatilis (mean index core values were similar, Fig. 8.6A) and L. lipkei (due to a high standard deviation, Fig. 8.6C). Higher index values at 32˚C when compared to control were always found when we assess IBR results at each timepoint for all the four species (except for T21 for A. saxatilis and T28 for L. lipkei, Fig. 8.6A and C). These higher values were either relatively constant throughout the entire experimental month (A. saxatilis and S. haemastoma, Fig. 8.6A, and D, respectively) or showed an increasing trend along time (S. cristata, Fig. 6B), or a slightly decreasing trend along time (L. lipkei, Fig. 8.6C), so there is not an evident pattern of a specific most responsive sampling timepoint during the month of temperature treatment.

The most responsive biomarkers were CAT and GST in A. saxatilis; Ub, GST and SOD in S. cristata; Tprotein in L. lipkei and Hsp70, SOD and Tprotein in S. haemastoma. The markers least influenced by elevated temperature were body mass (in A. saxatilis and L. lipkei), Tprotein (S. cristata) and Tlipids (S. haemastoma).

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Figure 8.6 Results from combining all biomarkers into only one unique response category – physiological performance. Star plots with mean scores (and respective IBRs below) for the experimental month (all sampling times combined, T0-T28) for the 4 species exposed to 29˚C (control) and 32˚C (stress temperature); and specific IBR values for each sampling time and all times combined (mean+SD) in muscle tissue of: A) Abudefduf saxatilis, B) Scartella cristata, C) Lysmata lipkei, D) Stramonita haemastoma. Abb: Hsp70 – heat shock protein 70 kDa; Ub – total ubiquitin; CAT – catalase; LPO – lipid peroxides; GST – glutathione-S-transferase; SOD – superoxide dismutase; Tprotein – total protein; Tlipids – total lipids.

8.3.3. Biomarker scores as a fitness index

When analyzing biomarker scores as a signal for biological effect in fitness, results showed that biomarkers with higher scores at 32˚C when compared to 29˚C were also the ones denoting deleterious effects in fitness (E ≤ -0.5, in red in Fig. 8.7).

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Figure 8.7 Fitness index presented as a color hitmap: scores were compared for biomarkers between control organisms and those exposed to 32˚C. Red denotes deleterious effects and green denotes positive effects, while yellow denotes no detected effect; values are ±0.5 (or higher) from control’s score values.

Overall, biomarkers denoting greater deleterious effects in fitness were Hsp70, Ub, CAT and GST, while biomarkers denoting positive effects in fitness after chronic exposure to elevated temperature were energy reserves (Tproteins and Tlipids), for all species in general.

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Figure 8.8 Biomarker scores (mean+SD) for all sampling times and for all species combined at 29˚C vs 32˚C. Abb: Hsp70 – heat shock protein 70 kDa; Ub – total ubiquitin; LPO – lipid peroxides; CAT – catalase; GST – glutathione-S-transferase; SOD – superoxide dismutase; Tprotein – total protein; Tlipids – total lipids; BM – body mass.

The overall biomarker change between temperatures, measured as score values is shown in Fig. 8.8. All biomarkers showed to be significantly affected by temperature, except for body mass, where there were no detected significant differences. The most responsive biomarkers in decreasing order were as follows: GST > Ub > CAT > Hsp70 > LPO > SOD > Tlipids > Tprotein > BM.

8.4. Discussion

Accurate predictions of the impacts of climate change in marine populations and ecosystems require the assessment of environmental health as well as understanding of vulnerability and resilience of organisms (Hook et al., 2014; Montefalcone et al., 2011; Vinagre et al., 2016). The ability of organisms to withstand and mitigate the impacts of climate change has two essential components, including an adaptive (genotype dependent) and an acclimation component (phenotypic plasticity, physiology dependent) (Madeira, 2016). In this study, we used an integrated approach to evaluate the acclimation potential of several tropical/subtropical shallow water species inhabiting in a common coastal habitat in southeastern Brazil, an area predicted to be highly impacted by increasing temperature. Our results showed that the future

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predicted scenario of +3˚C warming in sea temperature elicited significant physiological responses in all animal groups tested (fish, crustaceans and gastropods).

Overall, the three stress response categories approach to IBR suggests that index values were significantly affected by temperature, and also by the species and response category tested. In contrast, the approach for overall physiological performance only showed significant differences for factor temperature. These results confirm the adequacy of the use of IBR indices beyond the assessment of chemical pollution, and are a first step in applying these indices in tropical/subtropical field animals for the bio-monitoring of climate-change induced thermal change. Interestingly, such results also imply that there is a need to account for confounding factors depending on which anthropogenic factor or combination of factors we intend to evaluate (Beliaeff and Burgeot, 2002; Vinagre et al 2012).

In both approaches, stress responses were generally characterized by a consistent increase of macromolecular damage biomarkers (molecular and cellular level) suggesting protein unfolding and denaturation (Madeira et al., 2012; Medicherla and Goldberg, 2008) as well as cell membrane destruction (Madeira et al., 2014a, 2013), since Ub and LPO were amongst the most responsive biomarkers (higher scores at 32˚C when compared to control). The induction of protective pathways against ROS damage also occurred at 32˚C, with significant enzymatic activity of CAT and GST, which is consistent with previous studies on the thermal biology of marine animals and oxidative stress in marine environments (e.g. Dent and Lutterschmidt, 2003; Lam et al., 2005; Lesser, 2006; Madeira et al., 2015; Newton et al., 2010; Somero, 2002; Vinagre et al., 2014). Markers at the tissue level, namely energy reserves, showed responses in both directions (increase or decrease), depending on sampling times and species, which suggests that in some animals, enhancement of defenses at lower levels of biological complexity may have some energetic tradeoffs, while in others, energy accumulation is unaffected or even improved (Klepsatel et al., 2016; Kühnhold et al., 2016; Rabasa and Dickson, 2016). This suggests that an increase in temperature may have a positive effect on metabolism until a certain threshold is surpassed and then the animals begin to be negatively affected (see oxygen limited thermal tolerance, OLTT theory by Pörtner and Farrell, 2008; Pörtner and Knust, 2007). Nevertheless, lipid reserves seemed to be affected mostly in fish (slight increase at 32˚C), while protein reserves seemed to be mostly affected in invertebrates (decrease in shrimps and slight increase in gastropods). These findings are supported by results of the marker at whole body level, body mass, which seemed to be generally unaffected by higher temperatures (no significant changes in overall scores between temperatures). Several explanations are proposed: either the animals were able to maintain energy balance and performance (Huey and Berrigan, 2001) or longer exposure times are needed before temperature

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effects in this parameter can be observed. It is also possible that the abundant food during the experiment allowed animals to compensate energy tradeoffs from cytoprotection and therefore no changes were detected at whole-body level. The analysis of mean biomarker scores corroborate the previous trends, showing that overall, the most responsive biomarkers were GST and Ub (showing greater deleterious effects in the fitness hitmap), while body mass was non-responsive at elevated temperature. It indicates that an environmental change in temperature was met by physiological adjustments to maintain internal homeostasis and that these can be persistently induced over time under chronic exposure until either: i) acclimation is achieved (Donelson and Munday, 2012) or ii) acclimation is not achieved and there is metabolic depression (Sokolova, 2013). Although the induction and increased concentration of certain CSR components for long periods can have cytotoxic effects (Iwama et al., 1999) and lead to general inflammation and decreased health status, here, we suggest that there may have been acclimation. Not only because body condition was maintained, but also because results from the fitness index suggest that markers related to condition, such as energy reserves had positive effects in fitness after animals were exposed to elevated temperature. Nevertheless it is also worth noting that longer exposure times may be needed to detect decreases in processes such as growth, especially when food is not a limiting factor.

The main differences observed in the two different IBR approaches taken (using three stress response categories vs a combined unique category of physiological performance) were detected in the patterns of the timing of responses and in the final assessment of the most heat- vulnerable/resilient species. Firstly, when using 3 separate categories of stress responses, changes in biomarkers were mainly evident either at the beginning (T7) or end (T28) of the experiment. It shows that physiological adjustments are readily induced upon an environmental change and can be adjusted along time to match present stress conditions, which is suggestive of phenotypic plasticity in physiological traits (Relyea, 2002; Schulte et al., 2011). This information seems to be lost in the most holistic approach which integrates all biomarkers at once in the IBR, since no specific pattern in timings of responses was detected. Finally, in the three category approach, the most sensitive species to increased temperature was S. cristata, while in the whole performance approach it was both S. cristata and S. haemastoma, so there was some level of coherence in the results. The least sensitive species were A. saxatilis and L. lipkei. While all of these species are well-adapted to warm shallow waters, it is not surprising that A. saxatilis, a wide-spread species in tropical and subtropical regions, and L. lipkei, an invasive species, are the least sensitive, since it is well-known that species with such characteristics are particularly resilient to environmental changes.

8.5. Conclusions

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IBRs may represent an effective method to be applied in general ecosystem health assessments in areas projected to be significantly affected by gradual increase in temperature. This study also opens up new opportunities to IBR application in monitoring the biological effects of extreme weather events such as marine heat waves. The use of an index with markers that measure responses at all levels of biological organization (from molecular to organism) addresses the main issue of low biological or ecological relevance that has been pointed out for analyses that only use molecular data (in spite of their high specificity) (Lam, 2009), while still being relatively easy to determine, and maintain a feasible cost-to-benefit balance.

It is important to test further species and organism groups, especially highly vulnerable ones that are at risk of local or regional extinction (e.g. tropical scleractinian corals and seagrasses). There is also the need to integrate thermal stress biomarkers with the ones used for chemical pollution, since there is some overlap between the two, so they need to be well characterized under both conditions separately and synergistically to understand confounding effects.

Finally, the main pros and cons of evaluating physiological performance under three or only one combined category of stress responses are: i) The holistic version that combines all biomarkers in one IBR is more integrative, and more straightforward to interpret, which means that it is also more objective in terms of addressing monitoring needs and supporting conservation actions, so it can be considered to be highly effective in practical terms. On the other hand, it may leave some loose ends on mechanistic understanding of thermal sensitivity or vulnerability of species because it loses information in regards to specific biomarker or response timing patterns as some responses may overlap, or overrule each other; ii) On its turn, the 3-category stress responses approach is very useful to distinguish between components of physiological responses and biological complexity levels, while also giving a general picture of overall organism health. On the downside, it does not solve the problem of the difficulty in analyzing multidimensional sets of biomarker data (Devin et al., 2014), especially if IBRs are to be used in field monitoring, where a significant amount of sites and species are going to be analyzed.

Overall, this study contributes with significant insights for improving tropical marine ecosystem bio-monitoring in the face of ocean warming and increasing demands from the public

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for governmental bodies to keep track and act upon ecosystem change due to anthropogenic pressure.

Acknowledgments

This study had the support of the Portuguese Foundation for Science and Technology (FCT) through the WarmingWebs project (PTDC/MAR-EST/2141/2012), the “Investigador FCT” position granted to C.V. and the strategic projects UID/MAR/04292/2013 granted to MARE and UID/Multi/04378/2013 granted to UCIBIO. C.M. and V.M. acknowledge PhD grants (SFRH/BD/92975/2013 and SFRH/BD/109618/2015, respectively), and M.C.L. acknowledges a post-doc grant (SFRH/BPD/115298/2016), all granted by FCT. The funding source had no involvement in the study design, collection and analysis of data or in the decision to publish the work. Authors would like to thank everyone involved in the field work and maintenance of the experimental tanks.

Annex 2. Supplementary material for this chapter can be found in annex 2 in the annexes section.

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CHAPTER 9

Final remarks and future perspectives

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9.1. Final remarks

The overall goal of the present thesis (see annex 1 for PhD outputs) was to increase the understanding of the role that temperature change plays in shaping the ecophysiology of tropical marine ectotherm organisms, and the ecological implications of thermal stress and/or acclimation of these organisms for future conservation efforts of tropical ecosystems’ biodiversity. The necessity of monitoring thermal change in highly vulnerable biomes requires an improvement not only in the methods available but also in the basic knowledge of eligible indicator species. Therefore, in order to tackle new questions on the thermal biology of tropical reef organisms, the first component of this thesis was dedicated to the optimization of methods and metrics already used in environmental monitoring in temperate regions and to the development of an integrated index to analyse multiple physiological endpoints holistically (Chapters 2-4). The methods optimized and developed were associated to the use of molecular biomarkers and whole-body performance measurements under chronic stress (Chapter 2) and acute stress events (Chapter 4); and non-lethal or less invasive sampling of fragile tropical organisms (Chapter 3). The work in these previously mentioned chapters was carried out using captive-bred animals with controlled thermal histories, and all tested conditions were simulated in the laboratory. After this methodological validation, in Chapters 5-7, field work was carried out in a shallow water tropical reef environment and these methodologies were tested in wild animals, while also characterizing in situ temperature regimes and using lab experiments to simulate projected regional ocean warming scenarios for the end of this century. In these chapters, the methods were cross validated, and new parameters directly important for ecological risk assessments were added (e.g. acclimation response ratio and thermal safety margins). Finally, in Chapter 8, all the parameters that were obtained from different biological complexity levels were integrated in a single value or graph to give a general measure of stress or health condition of the animals. In this way, comparison between tropical animals from different animal groups and micro-habitats is possible, and can likely be extrapolated for other tropical marine reef systems, allowing for local and regional comparisons, both intra- and inter- populations and species.

Six research questions were proposed in the introductory chapter (Chapter 1). Here, I briefly summarize the answer to each specific question in the context of the observed results:

1. What are the cellular stress response mechanisms and metabolic pathways activated in tropical marine ectotherms during a thermal challenge? The methods used allowed taking several snapshots of spatial and temporal functioning of the cellular machinery under thermal challenges, allowing the creation of a picture of the

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physiological adjustments taking place during the stress/acclimation process. Firstly, there was an activation of the protein quality control system, through the induction of Hsp70 and ubiquitin, then, as metabolic rate increases and the ROS flux is higher, antioxidant enzymes also showed an increased activity. Despite some minor cell membrane damage due to lipid peroxidation, vital organ integrity is maintained. Energy storages (lipids and proteins) were changed when exposed to chronic warming, probably due to energy mobilization for cytoprotective pathways (e.g. cytoskeleton adjustments, gene expression regulation, chaperoning, acid-base balance), evidencing some energetic tradeoffs of thermal stress responses. However, longer experimental periods are likely needed before a consistent trend of change can be detected in energy reserves as well as in body condition. Such physiological adjustments may well act as a first buffer to the impacts of warming as they allow the metabolism of the animal to match the current environmental conditions to maintain internal homeostasis.

2. How can we non-invasively assess response mechanisms to temperature in tropical marine ectotherms? Preliminary results from a small number of fish fin tissue samples showed promising results regarding biochemical markers of thermal stress however the fin-clipping method proved unreliable after more samples were added to the analyses. The successful use of a non-lethal method for the simultaneous measurement of multiple physiological related parameters will require testing more species (e.g. scaled fish vs bare skinned fish) and other tissues or body fluids (e.g. fish blood or crustacean heamolymph). New tools may be needed and methodologies will require the development of new technology that allows for smaller samples. Biosensors measuring volatile components that are released from the animals’ bodies as a result of metabolism also seem to be a fruitful area of research with a promising future.

3. How can we integrate molecular and physiological responses to temperature in a general measure of stress? In this thesis I used an integrated index of responses that had been previously developed by other authors to quantify organism responses to chemical contamination, and adapted it to the parameters used in my research. The main advantage of using this index is that it takes a set of multidimensional data from which is hard to grasp all the response patterns, and normalizes all the physiological parameters to one scale, scores them according to the level of changes detected (which can be visualized in a star plot) and gives a final index value that can be directly translated to the animals’ health condition. Although the concept and mathematical algorithms used in the index are quite simple and straightforward, it is the first time that this kind of approach is used in thermal stress monitoring. This brings a significant improvement to the

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ecophysiology field under the climate change context in the sense that it is now possible to use this index to categorize vulnerability levels for different animal groups or key species from tropical habitats providing a fast response to management needs. Additionally, stress indices can be used as a line of evidence in climate change impacts research, to determine the implications of warming for broader ecosystem health. Their use should be sistematyzed by developing specific software for biomonitoring purposes. It is expected that this methodological development will aid in ecological risk assessements and decision making towards the protection of tropical reef environments.

4. What are the upper thermal limits of each species and how do they vary in time under chronic warming conditions? The determination of upper thermal limits for wild animals in this thesis showed that average values varied between ~ 37˚C (in shrimp), 39˚C (in fish) and up to 43˚C (in gastropods). Upper thermal limits showed variation with environmental temperature and time, displaying both negative and positive acclimation ability. Species with higher CTMax (S. haemastoma) showed less ability to acclimate their upper thermal limits. In species where acclimation ability was positive (A. saxatilis, S. cristata and L. lipkei), it ranged from 0.8˚C to 2.5˚C on average.

5. Are the different parameters measured plastic in their responses and how does plasticity influence the potential for acclimation under ocean warming? One has to be cautious when inferring about plasticity of physiological traits, especially when it comes to field animals, since their genetic variability was not tested. Additionally, since I used different organisms at each sampling timepoint, samples were independent rather than repeated measures, so again I was evaluating different genotypes. However, when testing captive-bred animals (A. ocellaris, R. durbanensis and C. laevimanus) with genetic similarity, the analysed markers showed variation both with temperature and time which points to the plastic character of these traits. Likely, this can be extrapolated to tropical marine ectotherms in the field, and our results suggest that plasticity may play a significant role in the acclimation of organisms to ocean warming. In fact, the changes detected in cellular stress responses as well as the ability of animals to accommodate a change of up to 2˚C in their CTMax values is a key feature for animals to be able to withstand varying temperature conditions and regimes.

6. Is acclimation enough to maintain health and survival of species under warming conditions? Data from this thesis suggests that poorer health status derived from energetic tradeoffs due to thermal cytoprotection do occur but seem to be limited to a short timeframe if food is not a limiting factor. Acclimation was enough to guarantee survival of animals under conditions of

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+3˚C to +4˚C of chronic warming as well as heat waves. However, it is worth noting that survival does not equal thriving, and in tropical shallow water environments, in particular, thermal safety margins were low, which indicates that despite their acclimation ability, animals will likely be forced to migrate to deeper waters or new habitats in order to escape excessive temperatures. This may lead to shifs in community composition with significant ecological consequences (e.g. changes in trophic web structure, nursery grounds, recruitment).

A summary of the main findings of this thesis are pictured in Fig. 9.1 bellow:

Figure 9.1 Summary of the general response patterns of marine tropical ectotherms to increasing ocean temperatures, unraveled in this thesis. From the present decade until 2100, the IPCC predicts a gradual ocean warming with temperature anomalies in the range of +3˚C to +6˚C, under business as usual scenarios. The overall analysis of physiological adjustments of tropical reef associated organisms revealed that responses occur at several biological levels of complexity, with easily and rapidly detectable responses at a sublethal level (molecular and cellular damage; tissue biochemical composition varies – in fat and protein), and slower, but still significant changes at individual (size shifts) and whole sample levels (significant acclimation ability, maintaining performance as shown by unchanged mortality levels –

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however, thermal safety margins are low, suggesting low tolerance to further warming at the end of the 21st century). Favorable phenotypes for populations under warming conditions should display: high CTMax (with acclimation ability), larger thermal windows (distribution including tropical and subtropical), high potential for thermal compensation of metabolic rate or switiching to aestivation mode under extremely high temperatures and lower maintenance costs of metabolism.

In summary, the following main conclusions from the present thesis are highlighted: i) Time-dependent protection mechanisms against thermal challenges were differentially induced according to: distinct organ types, distinct species and animal groups, distinct microhabitats explored and animal life-style; ii) There was a time-, environmental- and size-dependent shift in species upper thermal limits; iii) Energy reserves were changed upon stress and slower growth occurred under chronic warming; iv) All animals tested were sensitive to warming but survival was unaffected; v) Acute and chronic exposure revealed similar thermal stress responses, but chronic exposure allowed the recognition of acclimation in the long term; vi) Animals in shallow tropical reefs may already be at risk in present summer temperatures. Further chronic temperature rising and low thermal safety margins in face of maximum habitat temperatures coud lead, on one hand to migration of native species to seek thermal refugia or even their local extinction, on the other hand to a facilitation of the expansion of introduced eurythermal species. vii) There is a need to improve management plans in tropical regions that take into account the vulnerability of species towards climate change, contributing to the sustainability of marine resources and biodiversity protection.

Finally, I outline some management recommendations based on the findings of this four-year research: • Continuous biomonitoring of thermal stress in situ in tropical areas should be implemented, in order to categorize and map the areas of occurrence of vulnerable species, most critical timings (according to abiotic changes or specific life cycle events) and prioritize actions for the maintenance of functional biodiversity in these habitats; • Restrictions to other potential sources of anthropogenic pressure in tropical shallow water areas should be enforced (animal communities in these habitats are already under the influence of a combination of stressors and may not be able to accommodate further changes from intense human use of coastal areas); • Protection of thermal refuge areas (such as the subtidal) and maintenance of microhabitat

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heterogeinity so that animals may persist in their current environment by using behavioural thermoregulation; • Management of invasive organisms – non-native species with economic interest should be harvested (either for food or ornamental aquaculture) as a means of protecting the environment as well as providing an alternative resource of food/income for local communities.

The conclusions of this study should also have broad implications for all researchers working on ocean warming, habitat quality assessment and tropical eco-physiology.

9.2. Future perspectives

Tropical reef systems provide a wealth of ecosystem services, including the provision of coastal protection, commercial fishing, tourism, animal protein, sand production and the highest biodiversity in the oceans. Many of these services are ultimately founded on the healthy functioning of reef communities. Eco-physiological studies in a context of climate change can therefore provide valuable information to help scientists understand the dynamics of reef animals and habitats and help stakeholders improve conservation efforts, while also assuring the sustainability of stocks of explored species, either for human food consumption or ornamental aquarium trade. The present study was a first step in filling the knowledge gap on thermal stress physiology of tropical marine ectotherms, and it is essential that further efforts are made in order to address other remaining questions and continue the research developed in this thesis.

Firstly, from an ecological perspective, it would be interesting to perform comparative physiology assessments that evaluate thermal stress in tropical organisms under parallel, comparable conditions: rocky vs biogenic reefs, intertidal vs subtidal habitats, and analyze specific groups of animals (e.g. same family) from different tropical oceans (Atlantic, Indian, Pacific) for the same habitat type. This should help us understand how tropical organisms “filter” the environmental signal of “warming” through the modulation of their physiological responses by understanding different spatial and temporal scales. In order to achieve this, long- term metadata are necessary. The integration of such physiological metadata with temperature data (both at the niche and habitat level) will allow the identification of the directions of ecological responses, and increase our predictive power of the effects of climate change on ecosystem health. This will require an improvement in bioinformatic programs to search for patterns in large metadatasets. Additionally, we need to assess species and ecosystems vulnerability by using response-based frameworks that compile information on specific species traits and characteristics that can be associated with different types of responses that may be beneficial or detrimental for species in a context of climate warming (e.g. including physiology

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adjustments and tolerance, migration, adaptive evolution or use of microrefugia).

Secondly, from a methodological perspective, if an integrated index of vulnerability to warming was to be implemented in monitoring programs, other parameters could be included, such as markers of tissue inflammation (determined by histopathological approaches, e.g. hyperemia, macrophage infiltration, atrophy or dystrophy, microhaemorrhages, fluid retention, apoptosis and necrosis), behavior markers (e.g. activity levels, feeding, aggressiveness) and performance markers (e.g. swimming velocity). At a molecular level, there are several other interesting biomarkers that could be explored, such as oxidative stress damage to DNA (e.g. 8- hydroxyguanosine), indicators of immunocompetence (e.g. phenoloxidase activity in invertebrates), indicators of aerobic and anaerobic metabolism (e.g. adenosine monophosphate activated protein kinase – AMPK activity; mitochondrial aldehyde dehydrogenase – ALDH2 activity and lactate concentration) and indicators of energetic changes in cells (e.g. adenylate energy charge) just to name a few. Immunohistochemical approaches to biomarkers (by selectively imaging them in tissues and cells) would also be of value. But perhaps the most promising in the present scientific momentum are the ‘omics’ approaches. Better than selecting biomarkers based on prior literature or trial and error, the study of whole genomes, whole proteomes or metabolomes, allows for the complete screening and detection of relevant changes in molecules under certain environmental contexts, while placing them in the whole picture of the physiology/metabolism context that would otherwise be hard to grasp. However, there are obstacles that we, as researchers need to overcome: there is hardly any available complete or partial profile of either of these ‘omics’ for marine ectotherms. This major issue makes it especially difficult for these technologies to be applied in the marine ecophysiology scientific field. Notwithstanding, such techniques would certainly bring major advances in the understanding of the regulation and underpinnings behind stress responses.

Thirdly, from an evolutionary perspective, several questions regarding the ability of tropical marine ectoherms to adapt and thrive in a changing ocean remain to be answered. Climate change induced warming implies prolonged seasonal warming periods, including short episodes of extreme heat and subsequent cooling. Therefore, under natural conditions, animals are often warmed slowly and dynamically (i.e. discontinuously), which has seldomly been tested under laboratory experimental conditions. The role of trans-generational plasticity in producing pre-adapted progeny that exhibits traits associated with increased fitness both in optimal and stressful conditions should be further investigated under this context. Additionally, the clarification of the role of parental effects (e.g. mitochondrial DNA inherited from the female) and other epigenetic phenomena (DNA methylation) in the maintenance and viability of populations in a warming ocean would help to untangle species potential for adaptation. These

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effects should be identified across marine taxa in fragile tropical reef environments. If this information was combined with demographic studies, the added ecological relevance and spatial realism would help improve climate projections and management programs.

Lastly, from a socio-economical perspective, acclimation capacity and range of optimal and suboptimal temperatures should be defined for the main keystone and commercial species both in rocky reef and coral reef environments. This should include studies with all the life stages (temperature ranges for larval migration, juvenile growth and adult reproduction) and seasons (dry and wet). This understanding would also be of value in modeling trophic web changes under ocean warming – knowing which species are and are not resilient in elevated temperature conditions allows the determination of the probability of trophic web collapse in the future ocean. Fisheries’ dependent human populations in the tropics, which are highly vulnerable, could then be advised and protected from famine and further poverty, as preventive plans could be designed based on these studies. The characterization of phenotypes for marine tropical organisms that survive or are adapted to chronic warming or extreme climatic events should also be very helpful in conserting efforts for coral reef rehabilitation – for example to produce corals in aquaculture and later release them at wild sites as well as determining the likelihood of disease outbreaks. Additionally, the combined effect of environmental variables should be assessed in wild populations under the appropriate time-frames since organisms can respond in different ways when exposed to several stress factors that can have additive, synergistic or antagonistic effects.

All of the suggested research guidelines outlined above will help tackle the challenges of biodiversity conservation under a changing ocean. Ecosystems are naturally dynamic entities and we must understand that they are going to continue to change in order to reach new equilibrium stages and domains as our climate changes (even if we were to stop emissions today and even if we were to implement worldwide networks of marine protected areas). Therefore, our main efforts should perhaps not focus on keeping habitats and biomes exactly as they are today (static or in agreement with what we, humans, know about them), but rather in managing them in the best way possible to allow them to maintain their ecological roles and resilience in the face of change.

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ANNEX 1

PhD outputs

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Articles in international peer-reviewed journals

Madeira C, Madeira D, Cabral HN, Diniz MS, Vinagre C (2016) Thermal acclimation in clownfish: an integrated biomarker response and multi-tissue experimental approach. Ecological Indicators 71: 280-292, DOI: 10.1016/j.ecolind.2016.07.009. Madeira C, Madeira D, Diniz MS, Cabral HN, Vinagre C (2017) Comparing biomarker responses during thermal acclimation: a lethal vs non-lethal approach in a tropical reef clownfish. Comparative Biochemistry and Physiology, Part A 204: 104-112, DOI: 10.1016/j.cbpa.2016.11.018. Madeira C, Mendonça V, Leal MC, Flores AAV, Cabral HN, Diniz MS, Vinagre C (2017) Thermal stress, thermal safety margins and acclimation capacity in tropical shallow waters – an experimental approach testing multiple end-points in two common fish. Ecological Indicators 81: 146-158, DOI: 10.1016/j.ecolind.2017.05.050. Madeira C, Mendonça V, Flores AAV, Diniz MS, Vinagre C (2018) High thermal tolerance does not protect from chronic warming – a multiple end-point approach with a tropical gastropod, Stramonita haemastoma. Ecological Indicators 91: 626-635, DOI: 10.1016/j.ecolind.2018.04.044. Madeira C, Mendonça V, Flores AAV, Diniz MS, Cabral HN, Vinagre V (2018) Environmental health assessment of warming coastal ecosystems in the tropics – application of an integrated biomarker response index. Science of the Total Environment 643: 28-39, DOI: 10.1016/j.scitotenv.2018.06.152. Madeira C, Diniz MS, Cabral HN, Vinagre C (accepted w/ minor revisions) Thermal stress and energy metabolism in two circumtropical decapods crustaceans: effects of acute temperature events. Marine Environmental Research. Madeira C, Mendonça V, Diniz MS, Cabral HN, Flores AAV, Vinagre C (submitted) Present and future invasion perspectives of an alien shrimp in South Atlantic coastal waters – an experimental assessment of functional biomarkers and thermal tolerance.

Conference abstracts Madeira C, Mendonça V, Leal MC, Flores AA, Cabral H, Diniz MS and Vinagre C (2018). Environmental health assessment of warming coastal ecosystems in the tropics – application of an integrated biomarker response index. Front. Mar. Sci. Conference Abstract: IMMR'18 | International Meeting on Marine Research 2018. doi: 10.3389/conf.FMARS.2018.06.00039 Madeira C, Madeira D, Diniz MS, Cabral H and Vinagre C (2018). Comparing biomarker responses during thermal acclimation: a lethal vs non-lethal approach in a tropical reef

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clownfish. Front. Mar. Sci. Conference Abstract: IMMR'18 | International Meeting on Marine Research 2018. doi: 10.3389/conf.FMARS.2018.06.00040

Posters presented in scientific meetings (national and international) 2018. Madeira C, Madeira D, Diniz MS, Cabral HN, Vinagre C. Comparing biomarker responses during thermal acclimation: a lethal vs non-lethal approach in a tropical reef clownfish. International Meeting on Marine Research 4th-6th July. Peniche, Portugal. 2018. Madeira C, Mendonça V, Leal MC, Flores AAV, Cabral HN, Diniz, MS, Vinagre C. Environmental health assessment of warming coastal ecosystems in the tropics - application of an integrated biomarker response index. International Meeting on Marine Research 4th-6th July. Peniche, Portugal. 2018. Madeira C, Mendonça V, Leal MC, Flores AAV, Cabral HN, Diniz MS, Vinagre C. Integrated index of stress responses to a future marine heat wave in tropical intertidal organisms. 3rd Internacional Congress of CiiEM - Research & Innovation in Human and Health Sciences. 20th-22nd June. Caparica, Portugal. 2018. Madeira C, Mendonça V, Leal MC, Flores AAV, Cabral HN, Diniz MS, Vinagre C. Integrated index of stress responses to a future marine heat wave in tropical intertidal organisms. 4th Climate Change Symposium, 4th-8th June. Washington, DC, USA. 2017. Madeira C, Leal MC, Cabral HN, Vinagre C, Diniz M. Monitoring the effects of a heat wave on tropical crustaceans using an integrated index of stress. 16th SPECO National Congress on Ecology, 9th-10th November, Lisbon, Portugal. 2016. Madeira C, Mendonça V, Flores AAV, Cabral HN, Diniz MS, Vinagre C. Thermal stress biomarkers in the widespread sergeant-major fish: temperature as a major environmental stressor in a warming ocean. MARES Conference 2016: Marine Ecosystems Health and Conservation, February 1st-5th, Olhão, Portugal. 2015. Madeira C, Madeira D, Diniz MS, Cabral HN, Vinagre C. Linking temperature disturbances to stress biomarker levels in ocellaris clownfish: an experimental approach to a warming tropical environment. 1st International Congress of CiiEM – from basic sciences to clinical research, 27-28th November. Caparica, Portugal.

Oral communications in scientific meetings (national and international) Madeira C, Mendonça V, Leal MC, Flores AAV, Cabral HN, Vinagre C, Diniz MS (2018, abstract accepted) Ecophysiology of recently introduced shrimp species in the tropical Atlantic enlightens present and future invasion potential of tropical regions under ocean warming. 9th YOUMARES Congress: The Oceans – Our Research, Our Future, 11th-14th September. Oldenberg, Germany.

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Madeira C, Mendonça V, Leal MC, Flores AAV, Cabral HN, Diniz MS, Vinagre C (2018) Present and future invasion perspectives of an alien shrimp in South Atlantic coastal waters - a physiology assessment under ocean warming scenarios. International Meeting on Marine Research, 4th-6th July. Peniche, Portugal. Madeira C, Mendonça V, Leal MC, Flores AAV, Cabral HN, Vinagre C, Diniz M (2017) Thermal stress and acclimation capacity in tropical oceans – an experimental approach with multiple endpoints in intertidal organisms. 16th SPECO National Congress on Ecology, 9th- 10th November, Lisbon, Portugal. Madeira C, Madeira D, Cabral HN, Diniz MS, Vinagre C (2017) Thermal acclimation in clownfish: an integrated physiological response to a warming ocean. Quintas do MARE, 25th May, ISPA (Instituto Superior de Psicologia Aplicada), Lisbon, Portugal. Madeira C, Madeira D, Cabral HN, Diniz MS, Vinagre C (2016) Monitoring stress in warming tropical oceans: linking temperature changes to biomarker levels in ocellaris clownfish. MARES Conference 2016: Marine Ecosystems Health and Conservation, February 1st to 5th, Olhão, Portugal.

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ANNEX 2

Supplementary material on biochemical methods used in chapters 2-8

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Table A2.1 Methodology details for laboratorial procedures. Biomarker Method used Detailed description

CAT Enzymatic assay Three replicates of 10 μL were taken from each diluted sample (1:10) and transferred to the microplates' wells. Then, 100 μL of assay buffer (100 mM potassium phosphate), 30 μL of methanol and 20 μL of 0.035 M hydrogen peroxide were added to each well, following a 20 min incubation in the shaker. Another 30 μL of potassium hydroxide 10 M and 30 μL of Purpald 34.2 mM were added and samples were left to incubate again for 10 min in the shaker. Reactions were finalized with 10 μL of potassium periodate 65.2 mM and absorbance was read at 540 nm. Activity from a standard bovine catalase solution of 1523.6 U·mL−1 was used as a positive control. Formaldehyde standards were used to produce a calibration curve. Catalase activity was calculated considering that one unit of catalase is defined as the amount that will cause the formation of 1.0 nmol of formaldehyde per minute at 25 °C.

LPO TBARS method Five microliters of each sample were added to 45 μL of 50 mM monobasic sodium phosphate buffer. Then 12.5 μL of SDS 8.1%, 93.5 μL of trichloroacetic acid (20%, pH1/43.5) and 93.5 μL of thiobarbituric acid (1%) were added to each microtube. To this mixture, 50.5 μL of Milli-Q grade ultrapure water was added and the microtubes were put in a vortex for 30 s. The microtubes' lids were punctured with a needle and the microtubes were incubated in boiling water for 10 min. Straight after, they were placed on ice for a few minutes to cool and 62.5 μL of Milli-Q grade ultrapure water and 312.5 μL of n-butanol pyridine (15:1, v/v) were added. Then the microtubes were placed in a vortex and centrifuged at 10000 ×g for 5 min. Duplicates of 150 μL of the supernatant of each reaction were put into a 96-well microplate and absorbancewas read at 530 nm. To quantify the lipid peroxides, an eight-point calibration curve (0–0.3 mM TBARS) was calculated using malondialdehyde bis (dimethylacetal) standards (from Merck).

GST Enzymatic assay Three replicates of 20 μLwere taken from each diluted sample (1:10) and transferred to the microplates' wells. In each well we added 180 μL of a reagent mix containing: 200 mM reduced L-glutathione, 100 mM CNDB and buffer Dulbecco (Sigma Aldrich, USA). After reading the absorbance at 340 nm, GST activity was calculated using a molar extinction coefficient for CDNB of 0.0053εmM.

SOD Enzymatic assay Three replicates of 10 μLwere taken from each diluted sample (1:10) and transferred to the microplates' wells. In each well we added 240 μL of a reagent mix containing: EDTA 3 mM, xanthine 3 mM, NBT 0.75 mM and XOD 100 um. Negative controls of mix without sample were included. After reading the absorbance at 560 nm, SOD activity was calculated using the equation for the % inhibition:

Abs560 = ((Abs560/min negative control – Abs560/min sample) / (Abs560/min negative control)) × 100

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AChE Enzymatic assay Three replicates of 50 μLwere taken from each diluted sample (1:10) and transferred to the microplates' wells. In each well we added 250 μL of a reagent mix containing: sodium phosphate buffer 50 mM (pH 8.0), 1 mM Ellman’s reagent (DTNB – 5,5’-dithiobis-(2-nitrobenzoic acid) in 50mM phosphate buffer) and 75 mM ACTI (acetilthiocholine iodide) in phosphate buffer 50 mM. Negative controls were included by using 50 μL of buffer instead of sample. Absorbance was read at 415 nm each minute for 10 minutes. AChE activity was calculated using a molar extinction coefficient for DTNB of 0.00781εmM.

Hsp70 Indirect ELISA In both ELISAs, three replicates of 50 μLwere taken from each diluted sample (1:10), transferred to the microplates' wells and incubated overnight at 4°C. The microplates were washed (3×) in PBS 0.05% Tween-20 and then blocked by adding 200 μL of 1% BSA (Bovine Serum Albumin, Sigma-Aldrich, USA) in PBS. Ub Direct ELISA The microplates were then incubated at 37°C for 90 min. After microplate washing, the primary antibodies (anti-Hsp70/Hsc70, Acris, USA, in the first case and Ub P4D1, sc-8017, HRP conjugate, Santa Cruz, USA, in the second case), were diluted to 1 μg/mL and 0.5 μg/mL, respectively, in 1% BSA, and added to the microplates' wells (50 μL each). Then the microplates were incubated for 90 min at 37 °C. After another washing stage:

a) For Hsp quantification, the secondary antibody (anti-mouse IgG, fab specific, alkaline phosphatase conjugate, Sigma-Aldrich, USA) was diluted (1 μg/mL in 1% BSA) and added (50 μL) to each well followed by incubation at 37 °C for 90 min. After the washing stage, 100 μL of substrate (SIGMA FAST™ p-Nitrophenyl Phosphate Tablets, Sigma- Aldrich, USA) was added to each well and incubated for 30 min at room temperature. Fifty microliters of stop solution (3 N NaOH) was added to each well and the absorbance was read in a 96-well microplate reader at 405 nm (BIO-RAD, Benchmark, USA).

b) For ubiquitin quantification, 100 μL of substrate (TMB/E, Temecula California, Merck Millipore) was added to each well and incubated for 30 min at room temperature. One hundred microliters of stop solution (1 N HCl) was added to each well and the absorbance was read in a 96-well microplate reader at 415 nm (BIO-RAD, Benchmark, USA).

For quantification purposes, calibration curves were constructed using serial dilutions of purified HSP70 active protein (Acris, USA) and of purified ubiquitin (UbpBio, E-1100, USA), respectively, to give a range from 0 to 2 μg·mL−1 of protein.

Total protein Bradford method 200 μL of Bradford reagent (Comassie Blue G250, methanol, phosphoric acid, distilled water) were added to three replicates of 10 μL of diluted samples (1:10). Absorbance was read at 595 nm. BSA standards were used for a calibration curve. Total protein measurements were used to normalize all biomarker levels (Hsp70, total ubiquitin, antioxidant enzymes and LPO).

Total lipids %C Muscle samples were lyophilized (by freeze-drying in vacuum) and grounded to a fine homogeneous powder. Samples of ~0.5 mg were loaded into tin cups and analyzed using an Elementar Isoprime continuous-flow mass spectrometer (GV Instruments) coupled to a vario PYRO cube elemental analyser (Elementar, Hanau,

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Germany). Reference materials (acetanilide; Stable Isotope Research Facility, Indiana University, USA) were assayed at the beginning of each run and after every 10 samples. Direct quantification Lyophilization (by freeze-drying) of muscle pellet samples to a fine homogeneous ground powder. Lipids from ~10-30 mg of tissue were extracted with 2:1 dichloromethane:methanol (DCM:MeOH), vortexed for 5 seconds and sonicated for 10 min in an ice-cold water bath. Samples were then frozen at -20˚C overnight to allow the settlement of tissue particles in the solvent. On the next day, the solvent (supernatant) from each sample was transferred to new glass vials (previously

combusted at 450˚C for 5h) with glass pipettes. The resulting total lipid extracts (TLE) were then evaporated to dryness under a stream of N2 on a turbovap system. All the previous extraction steps were repeated at least 3× for each sample. Lipid content in each sample was then calculated as (and normalized afterwards per mg

of dry muscle):

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ANNEX 3

Supplementary material for chapter 2

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Figures

Figure A3.1. Experimental setup (not to scale).

Figure A3.2. Timeline and sampling scheme of the experiment. At each time point (T = days) 5 individuals were randomly sampled from the 3 tanks for each temperature.

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Figure A3.3. Fish were caught with aquarium hand nets and were sacrificed by cervical dissection. Internal organs were extracted and placed in microtubes, then frozen at -80˚C.

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Figure A3.4. Enzymatic biomarkers activities (mean+SD for catalase – CAT, and glutathione-S- transferase – GST, and mean±SD for superoxide dismutase - SOD) in Amphiprion ocellaris tissues under two temperature treatments (26˚C and 30˚C): a) brain, b) gills, c) liver, d) intestine, e) muscle. Significant differences from the control group (obtained from post hoc Tukey HSD tests for selected interactions temperature × time based on ANOVAs in table 2.2 in chapter 2) are marked with a colored asterisk (*) (blue for CAT, green for GST and black for SOD).

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Figure A3.5. Protein biomarkers (mean+SD for heat shock protein 70kDa – Hsp70, and total ubiquitin – Ub) and lipid peroxides (LPO, mean±SD) in Amphiprion ocellaris tissues under two temperature treatments (26˚C and 30˚C): a) brain, b) gills, c) liver, d) intestine, e) muscle. Significant differences from the control group (obtained from post hoc Tukey HSD tests for selected interactions temperature × time based on ANOVAs in table 2.2 in chapter 2) are marked with a colored asterisk (*) (purple for Hsp70, yellow for Ub and orange for LPO).

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Figure A3.6. Biomarker acetylcholinesterase (AChE) activity (mean+SD) in Amphiprion ocellaris tissues under two temperature treatments (26˚C and 30˚C): a) brain, b) muscle. No significant differences were detected between the two treatments (obtained from post hoc Tukey HSD tests for selected interactions temperature × time based on ANOVAs in table 2.2 in chapter 2).

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Tables

Table A3.1. Scores comparison for biomarkers between control organisms from the species Amphiprion ocellaris and those exposed to 30˚C. Red boxes denote deleterious effects and green boxes denote positive effects. ↗ - denotes increase (in fitness), ↘ denotes decrease (in fitness); values are ±0.5 from

control’s score values. Calculations were perfomed according to the equation ˚ ˚

(Ferreira et al. 2015a), where E denotes effect, S26˚C denotes score at 26˚C (control) and S30˚C denotes score at 30˚C (elevated temperature). CAT LPO GST SOD AChE Hsp70 Ub BRAIN T0 ↘ ↗ ↗ T7 ↗ ↗ ↘ ↘ T14 ↗ ↘ ↗ T21 ↘ ↗ ↗ ↘ T28 ↗ ↘ ↗ ↘ T0-T28 ↗ ↘ GILLS T0 ↘ ↗ T7 ↘ ↘ ↘ ↘ T14 ↘ ↘ ↘ T21 ↗ ↗ ↗ ↗ ↗ T28 ↘ ↘ ↗ T0-T28 LIVER T0 ↗ ↘ ↗ ↘ ↘ T7 ↘ ↘ ↘ ↘ T14 ↘ ↘ ↗ T21 ↘ ↘ ↗ T28 ↘ ↗ ↘ ↗ ↘ T0-T28 ↘ INTESTINE T0 ↗ T7 ↘ ↗ ↘ ↘ ↘ T14 ↗ T21 ↗ ↗ T28 ↗ ↘ T0-T28 MUSCLE T0 ↘ ↘ ↘ T7 ↗ ↘ ↘ T14 ↘ ↘ ↘ T21 ↘ ↗ ↗

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T28 ↗ ↗ ↗ T0-T28 ↘ ↘

IBR calculations

In order to understand global responses of A. ocellaris to elevated temperature, the integrated biomarker response (IBR) was calculated according to Beliaeff and Burgeot (2002), posteriorly changed by Broeg and Lehtonen (2006) and described in detail in Ferreira et al (2015a and b), as follows:

The IBR was calculated by summing up triangular star plot areas calculated for each two neighbouring data. To calculate IBR integrating all biomarkers, the general mean (m) and the standard deviation (s) of all data (including all sampling times) regarding a given biomarker was calculated, followed by a standardization to obtain Y, where Y = (X - m)/s, and X is the mean value for the biomarker at a given time. Then Z was calculated using Z = -Y or Z = Y, in the case of a biological effect corresponding respectively to an inhibition or a stimulation. Regarding the biological effect, biomarkers can either increase or decrease depending on the intensity of the stressor, and with organisms’ strategy as well. In theory, organisms tend to spend more energy in order to deal with a stressor, but an opposite strategy can also be used (Ferreira et al. 2015a). As such: i) The activity of biomarkers CAT, GST and SOD can be induced in order to cope with the formation of lipid peroxides or inactivated by ROS-mediated denaturation; ii) LPO increases under stress due to damage to cellular membranes caused by ROS, but it can decrease if there is acclimation, when enzyme activities and lipid content of cellular membrane are adjusted to the new environmental conditions; iii) Hsp70 can be induced to prevent protein unfolding and aggregation, and Ub can be induced to signal denatured proteins to be degraded in the proteasome, or they can be downregulated if these cytoprotective systems are exauhested and there is a stress induced metabolic depression; iv) AChE is expected to decrease under severe stress exposure, leading to failed synaptic transmission and consequent muscle overstimulation, ending in cardiorespiratory colapse, but if the temperature increases only slightly at a managable rate, then AChE could in fact increase. For these reasons, their kinetics must be followed through time to consider their biological effect.

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The score (S) was calculated by S = Z + |Min|, where S ≥ 0 and |Min| is the absolute value for the minimum value for all calculated Y in a given biomarker at all measurements made. Star plots were then used to display Score results (S) and to calculate the integrated biomarker response (IBR) as:

Since the IBR is obtained by summing up all the parameters, to allow a correct and more accurate comparison IBR was divided by the number of sampling times or number of tissue types assessed and presented as IBR/n (Broeg and Lehtonen, 2006). By using this method it is possible to get an overall state of organisms under elevated temperature exposure.

Analysing the scores as a fitness index (see table A3.1), values that differed in 0.5 from the control score were considered to be from an animal with a higher or lower fitness (higher or lower scores, respectively). The IBR calculations were always performed with the same order of parameters for all sampling times.

References Beliaeff, B., Burgeot, T., 2002. Integrated biomarker response: a useful tool for ecological risk assessment. Environ. Toxicol. Chem. 21:1316–1322 Broeg, K., Lehtonen, K.K., 2006. Indices for the assessment of environmental pollution of the Baltic Sea coasts: Integrated assessment of a multi-biomarker approach. Mar. Pollut. Bull. 53:508–522 Ferreira, N.G.C., Cardoso, D.N., Morgado, R., Soares, A.M.V.M., Loureiro, S., 2015a. Long-term exposure of the isopod Porcellionides pruinosus to nickel: Costs in the energy budget and detoxification enzymes. Chemosphere 135: 354–362 Ferreira, N.G.C., Morgado, R., Santos, M.J.G., Soares, A.M.V.M., Loureiro, S., 2015b. Biomarkers and energy reserves in the isopod Porcellionides pruinosus: The effects of long-term exposure to dimethoate. Sci. Total Environ. 502: 91–102

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Supplementary material for chapter 5

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Figures

Figure A4.1 The experimental apparatus (not to scale) consisted of two systems of 290 L each (herein only one system is represented). Each system was comprised of 4 tanks (2 larger plastic tanks 60 × 30 × 25 cm and 2 smaller glass tanks 25 × 25 × 25 cm), one seawater deposit, one sump and a UV filter. Temperature treatments were applied to each tank individually using thermostats. Juvenile specimens from A. saxatilis were randomly placed in 2 larger plastic replicate tanks for each temperature (n = 30 individuals.tank-1), while S. cristata juveniles were randomly placed in 2 smaller glass tanks for each temperature (n = 30 individuals.tank-1). All tanks were enriched with live rocks and were filled with clean aerated seawater (95-100% oxygen saturation) and salinity 35.

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Figure A4.2 Timeline and sampling scheme of the experiment. For molecular biomarker and lipid content analysis, 5 individuals were randomly sampled for each temperature treatment (for both species). Sampling was done once a week (at 0, 7, 14, 21 and 28 days) and morphometric data on these specimens was also collected. For the CTMax determinations, 10-15 individuals for each temperature treatment (for both species) were used and experiments were performed after short term (7 days) and longer term (28 days) acclimation. Again, morphometric data was also collected for these specimens.

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Figure A4.3 During sampling, specimens from both fish species were caught with aquarium hand nets and were euthanized by cervical dissection. Tissues for analysis (skeletal muscle, skin and gills) were collected, placed in eppendorfs and then immediately frozen at -80˚C.

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Figure A4.4.Overall temperature frequency distributions at tide-pools continuously monitored at Brazil. Data was obtained from 8 replicate pools sampled continuously over 56 d at 2h intervals during the summer of 2015/6.

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Figure A4.5 Protein biomarkers (mean + SD for heat shock protein – Hsp70, and total ubiquitin – Ub) under two temperature treatments (29˚C and 32˚C) in: A) gills, B) muscle and C) skin. Numbers next to letters stand for (1) Abudefduf saxatilis and (2) Scartella cristata. Significant differences from the control group are presented with a colored asterisk (*) (blue for Hsp70 and green for Ub).

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Figure A4.6 Enzyme biomarker and lipid peroxides (mean + SD for CAT – catalase, and LPO – lipid peroxides) under two temperature treatments (29˚C and 32˚C) in: A) gills, B) muscle and C) skin. Numbers next to letters stand for (1) Abudefduf saxatilis and (2) Scartella cristata. Significant differences from the control group are presented with a colored asterisk (*) (orange for CAT and blue for LPO).

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Figure A4.7 Enzyme biomarkers (mean + SD for GST – glutathione-S-transferase, and SOD – superoxide dismutase) under two temperature treatments (29˚C and 32˚C) in: A) gills, B) muscle and C) skin. Numbers next to letters stand for (1) Abudefduf saxatilis and (2) Scartella cristata. Significant differences from the control group are presented with a colored asterisk (*) (green for GST and purple for SOD).

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Figure A4.8 Enzyme biomarker AChE (acetylcholinesterase, mean + SD) under two temperature treatments (29˚C and 32˚C) in muscle of: A1) Abudefduf saxatilis and A2) Scartella cristata. No significant differences in these temperature-time interactions.

Figure A4.9 Average size shift from the beginning to the end of the experiments (mean + SD) under two temperature treatments (29˚C and 32˚C): A1) Abudefduf saxatilis length (mm), A2) Scartella cristata length (mm), B1) Abudefduf saxatilis weight (g), B2) Scartella cristata weight (g).

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Tables Table A4.1 Main characteristics of the fish species used in this study. Domain Specific traits Abudefduf saxatilis Scartella cristata References

Zander 1986 Shallow reefs; juveniles stay in tide pools, near to Inhabit shallow rocky areas and tide pools. They Robertson et al. 1993 caves, shipwrecks, and among sargassum and Habitat hide in empty Strombus gigas shells, holes or Cervigón 1994 other free objects where they can be protected. troughs of rocks, or between algae Molina et al. 2006 Juveniles also live in mangrove forests and reefs

Feitoza et al. 2003 Domain Demersal Demersal Wingerter 2012 Tropical (preferred) and subtropical, Western Atlantic: Bermuda, Florida (USA), and northern Geographic location Gulf of Mexico to Brazil. Eastern Atlantic: Masuda et al. 1984 and environment Mauritania and the Canary Islands to about Tropical and subtropical (preferred), western Scott & Scott 1988 South Africa. Also known in the southern parts Atlantic from Canada to Uruguay in South Bath 1990 Distribution of Mediterranean, including the following most America, the Caribbean, as well as the coast of Masuda & Allen 1993 southern localities of the northern coast: West Africa south to Angola Molina et al. 2006 Torremolinos and Taramay near Motril (Spain),

Sicily, Kyra Island (Gulf of Aigina) and near Palaea Epidavros, Peloponnes (Greece). Northwest Pacific: Japan and Taiwan. Allen 1991 Depth (m) 0-20 0 - 10 Lieske & Myers 1994

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Molina et al. 2006 Wingerter 2012 Sedentary, non-migratory after post larval Sedentary, non-migratory after post-larval Fishelson 1969 Home range settlement settlement Wingerter 2012 Breder & Rosen 1966 Reproductive biology Oviparous (egg-laying), multiple spawners Oviparous (egg-laying), parental care Wingerter 2012 Throughout the year in tropical areas; warm- Throughout the year in tropical areas; spring Bessa et al. 2007 Spawning period season in subtropical areas and summer in subtropical areas Richards 2013 Few hundred up to a thousand or more eggs (male cares for the nest which may have Alshuth et al. 1998 Fecundity 200,000 eggs/spawning event multiple clutches of eggs from different females Wingerter 2012 at the same time)

Reproduction Molina et al. 2006 Egg duration (days) 4-5 8 Wingerter 2012 Larval length at hatching Alshuth et al. 1998 2.4 2.5 - 3 (mm) Wingerter 2012

Maturation age (weeks) ? 18 Wingerter 2012

Maturation size (cm) 8-10 ? Molina et al. 2006

Pelagic stage lasting 18-27 days and a post larval Larval duration (days) ? Molina et al. 2006 pelagic stage lasting 55 days Maximum size reported (cm Claro 1994 23; 0.2 12; ? Life span and kg) Cervigón 1994 Maximum age recorded ? ?

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(years) Duplication time (years) 1.4 - 4.4 1.25 www.fishbase.org Molina et al. 2006 Optimal temperature (˚C) 23-26 22.2 – 25.6 Mendes et al. 2009 Critical Thermal Maximum 39.17 37.82 present study Critical Thermal Minimum ? ? Temperature range Temperature for spawning Molina et al. 2006 24 25.5 activity (˚C) Wingerter 2012 Temperature for hatching ? 25.5 Wingerter 2012 (˚C) Feeds heavily on epilithic (especially filamentous) algae; in the course of grazing it ingests a very wide variety of items included in Emery 1978 Benthic and pelagic algae, pelagic tunicates, small Feeding the epilithic algal matrix (e.g., detritus and Wilson 2009 crustaceans and fish, plankton and shrimp larvae microorganisms). Further, it may target certain Wingerter 2012 Trophic small sessile invertebrates (e.g., Aiptasia spp. characterization anemones) Randall 1996 Omnivorous (plants/detritus + animals) (troph. Dominici-Arosemena Trophic level Herbivorous/Omnivorous 2.2 - 2.79) & Wolff 2005

Dispersal capacity and genetic Greater similarity among contiguous populations Faith et al. 2004 The eastern Atlantic and Brazillian populations Genetic traits differentiation between that become more differentiated with distance; Molina et al. 2006 may represent separate species populations differences between insular and continental IUCN 2016

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populations; phenetic divergences seem to suggest restrictions to the genetic flow in the South Atlantic population. Emery 1978 Fisheries minor commercial of no interest Robins & Ray 1986 Commercial Emery 1978 Aquarium commercial commercial importance Wingerter 2012 freshmarine.com Price (US $) 5 - 10 25 – 60 miniwaters.fish IUCN 2016 Conservation IUCN Red List Least concern Least concern CITES 2013

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Table A4.2 Average biomarker basal levels (mean ± SD) in control A. saxatilis juveniles. NA = Non- Applicable. Abbreviations: Hsp70 – heat shock protein 70kDa, Ub – ubiquitin, CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase. Biomarker Units Gills Muscle Skin Total protein mg.ml-1 of homogenate 0.29 ± 0.10 4.12 ± 1.51 0.28 ± 0.15 Hsp70 µg.mg-1 of protein 3.86 ± 2.32 0.34 ± 0.21 4.49 ± 2.99 Ub µg.mg-1 of protein 0.14 ± 0.13 0.01 ± 0.008 0.04 ± 0.03 CAT nmol.min.mg-1 of protein 81.12 ± 40.78 5.42 ± 3.91 5.82 ± 2.10 LPO nmol.mg-1 of protein 0.04 ± 0.01 0.01 ± 0.002 0.04 ± 0.02 GST nmol.min.mg-1 of protein 52.84 ± 26.18 8.44 ± 2.96 34.71 ± 24.02 SOD % inhibition.mg-1 of protein 3.72 ± 2.28 0.19 ± 0.07 1.64 ± 1.60 AChE nmol.min.mg-1 of protein NA 32.26 ± 23.02 NA

Table A4.3 Average biomarker basal levels (mean ± SD) in control S. cristata juveniles. NA = Non- Applicable. Abbreviations: Hsp70 – heat shock protein 70kDa, Ub – ubiquitin, CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase. Biomarker Units Gills Muscle Skin Total protein mg.ml-1 of homogenate 0.12 ± 0.09 1.74 ± 0.82 0.68 ± 0.52 Hsp70 µg.mg-1 of protein 3.97 ± 3.00 0.30 ± 0.21 0.37 ± 0.23 Ub µg.mg-1 of protein 0.41 ± 0.22 0.03 ± 0.01 0.07 ± 0.05 CAT nmol.min.mg-1 of protein 36.75 ± 18.54 1.38 ± 0.87 4.45 ± 2.68 LPO nmol.mg-1 of protein 0.42 ± 0.28 0.03 ± 0.01 0.14 ± 0.13 GST nmol.min.mg-1 of protein 210.58 ± 119.30 17.14 ± 10.27 59.35 ± 44.02 SOD % inhibition.mg-1 of protein 11.74 ± 7.59 0.37 ± 0.20 1.63 ± 1.22 AChE nmol.min.mg-1 of protein NA 196.60 ± 94.28 NA

Table A4.4 Factor loadings of PCA of all investigated biomarkers (from all timepoints in all tissues combined) in both fish species at control (29˚C) and elevated temperature (32˚C). Abbreviations: Hsp70 – heat shock protein 70kDa, Ub – ubiquitin, CAT – catalase, LPO – lipid peroxides, GST – glutathione-S- transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase. Factor 1 (PC1) Factor 2 (PC2) Hsp70 -0.201202 -0.796821 Ub -0.937293 -0.037024 CAT 0.037030 -0.806358 LPO -0.837979 -0.056685

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GST -0.976628 0.032875 SOD -0.901944 -0.096487 AChE -0.561237 0.476777

References Alshuth, S.R., Tucker Jr., J.W. and Hateley, J. 1998. Egg and Larval Development of Laboratory-Reared Sergeant Major, Abudefduf saxatilis (Pisces, Pomacentridae). Bulletin of Marine Science, 1: 121-133. Bath, H., 1990. Blenniidae. p. 905-915. In J.C. Quero, J.C. Hureau, C. Karrer, A. Post and L. Saldanha (eds.) Check-list of the fishes of the eastern tropical Atlantic (CLOFETA). JNICT, Lisbon; SEI, Paris; and UNESCO, Paris. Vol. 2. Bessa, E., Ferraz Dias, J. and De Souza, A.M. 2007. Rare Data on a Rocky Shore Fish Reproductive Biology: Sex Ratio, Length of First Maturation and Spawning Period of Abudefduf saxatilis (Linnaeus, 1758) with Notes on Stegastes Variabilis Spawning Period (Perciformes: Pomacentridae) in São Paulo, Brazil. Braz. J. Oceanogr. 55, 3: 199-206. Breder, C.M. and D.E. Rosen, 1966. Modes of reproduction in fishes. T.F.H. Publications, Neptune City, New Jersey. 941 p. Cervigón, F., 1994. Los peces marinos de Venezuela. Volume 3. Fundación Científica Los Roques, Caracas,Venezuela. 295 p. CITES, 2013. Appendices I, II and III valid from 12 June 2013. UNEP. Claro, R., 1994. Características generales de la ictiofauna. p. 55-70. In R. Claro (ed.) Ecología de los peces marinos de Cuba. Instituto de Oceanología Academia de Ciencias de Cuba and Centro de Investigaciones de Quintana Roo. Dominici-Arosemena, A. and M. Wolff, 2005. Reef fish community structure in Bocas del Toro (Caribbean, Panamá): Gradients in habitat complexity and exposure. Caribbean J. Sci. 41(3):613-637. Emery, A.R., 1978. Pomacentridae. In W. Fischer (ed.) FAO species identification sheets for fishery purposes. West Central Atlantic (Fishing Area 31). FAO, Rome. Vol. 4. pag.var. Faith, D.P., C.A.M. Reid and J. Hunter, 2004. Integrating phylogenetic diversity, complementarity, and endemism for conservation assessment. Conser. Biol. 18(1):255-261. Feitoza, B.M., L.A. Rocha, O.J. Luiz-Júnior, S.R. Floeter and J.L. Gasparini, 2003. Reef fishes of St. Paul's Rocks: new records and notes on biology and zoogeography. Aqua J. Ichthyol. Aquat. Biol. 7(2):61-82. Fishelson, L. 1969. Behaviour and Ecology of a Population of Abudefduf saxatilis (Pomacentridae, Teleostei) at Eilat (Red Sea). Animal Behaviour 18: 225–237. IUCN. 2016. Abudefduf saxatilis (Damsel Fish, Pilotfish, Sargeant Major). Accessed April 4th 2017. http://www.iucnredlist.org/details/188581/0 Lieske, E. and R. Myers, 1994. Collins Pocket Guide. Coral reef fishes. Indo-Pacific & Caribbean including the Red Sea. Haper Collins Publishers, 400 p. Masuda, H. and G.R. Allen, 1993. Meeresfische der Welt - Groß-Indopazifische Region. Tetra Verlag, Herrenteich, Melle. 528 p.

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Masuda, H., K. Amaoka, C. Araga, T. Uyeno and T. Yoshino, 1984. The fishes of the Japanese Archipelago. Vol. 1. Tokai University Press, Tokyo, Japan. 437 p. (text). Mendes, T.C., Villaça, R.C., Ferreira, C.E.L., 2009. Diet and trophic plasticity of an herbivorous blenny Scartella cristata of subtropical rocky shores. Journal of Fish Biology (2009) 75, 1816–1830 doi:10.1111/j.1095-8649.2009.02434.x Molina, W. F, Shibattta, O.A. and Galetti, P.M., Jr. 2006. Multivariate Morphological Analyses in Continental and Island Populations of Abudefduf saxatilis (Linnaeus) (Pomacentridae, Perciformes) of Western Atlantic, Pan-American Journal of Aquatic Sciences 2: 49-56 Randall, J.E., 1996. Caribbean reef fishes. Third Edition - revised and enlarged. T.F.H. Publications, Inc. Ltd., Hong Kong. 3nd ed. 368 p. Richards, W.J. 2013. Early stages of Atlantic Fishes: An identification guide for the Western Central North Atlantic. Volumes I & II. CRC Press, Taylor and Francis Group. Boca Raton, FL, USA, 1312 pp. Robertson, D. R., Schober, U.M., and Brawn, J.D. 1993. Comparative Variation in Spawning Output and Juvenile Recruitment of Some Caribbean Reef Fishes. Marine Ecology Progress Series Mar. Ecol. Prog. Ser. 94: 105–13. Robins, C.R. and G.C. Ray, 1986. A field guide to Atlantic coast fishes of North America. Houghton Mifflin Company, Boston, U.S.A. 354 p. Scott, W.B. and M.G. Scott, 1988. Atlantic fishes of Canada. Can. Bull. Fish. Aquat. Sci. 219:731 p. Wilson, S.K., 2009. Diversity in the Diet and Feeding Habits of Blennies. pp.139-162. In Patzner, R.A., E.J. Gonçalves, P.A. Hastings and B.G. Kapoor (eds.) The biology of blennies. Enfield, NH : Science Publishers, 482 p. Wingerter, K., 2012. Aquarium Fish: Reconsidering the Molly Miller Blenny. Advanced Aquarist, volume XI. http://www.advancedaquarist.com/2012/9/fish, accessed 04-04-2017 Zander, C.D., 1986. Blenniidae. p. 1096-1112. In P.J.P. Whitehead, M.-L. Bauchot, J.-C. Hureau, J. Nielsen and E. Tortonese (eds.) Fishes of the North-eastern Atlantic and the Mediterranean, volume 3. UNESCO, Paris.

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Supplementary material for chapter 6

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Figures

Figure A5.1 The experimental trials in captivity were performed in two separate semi-open aquaria systems of 200 L each. Each of the systems consisted of 2 replicate glass tanks (25 × 25 × 25 cm), one seawater deposit, one sump and a UV filter. Specimens were randomly distributed into tanks (n = 30 individuals.tank-1). There were 2 replicate tanks per temperature treatment. All tanks were filled with clean aerated seawater (95-100% oxygen saturation) and salinity 35. Live rocks were used as environmental enrichment. Temperatures were maintained for 28 days using thermostats (Eheim® Jager Heater 150W, Germany). Animals were fed once a day ad libitum with frozen shrimp.

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Figure A5.2 Timeline and sampling scheme of the experiment. For molecular biomarker analysis, 5 individuals were randomly sampled for each temperature treatment. Sampling was done once a week (at 0, 7, 14, 21 and 28 days) and morphometric data on these specimens was also collected. For the CTMax determinations, 10-15 individuals for each temperature treatment were used and experiments were performed after short term (7 days) and longer term (28 days) acclimation. Again, morphometric data was also collected for these specimens.

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Figure A5.3 Average size shift from the beginning to the end of the experiments (mean + SD) under two temperature treatments (29˚C and 32˚C): A) length (mm), B) weight (g).

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Figure A5.4 Temperature measurements (˚C) from the study area during the summer of 2015/6 (continuous sampling from December 2015 to February 2016): A) air temperature (data obtained from the brazilian National Meteorological Institute, www.inmet.gov.br), B) tide pool water temperature, measured in 8 replicate pools (8 underwater probes), C) subtidal water temperature, measured at ~1 m depth (2 underwater probes).

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ANNEX 6

Supplementary material for chapter 7

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Figures

Figure A6.1 The experimental trials in captivity were performed in two separate semi-open aquaria systems of 200 L each. Each of the systems consisted of 2 replicate glass tanks (25 × 25 × 25 cm), one seawater deposit, one sump and a UV filter. Specimens were randomly distributed into tanks (n = 30 individuals.tank-1). There were 2 replicate tanks per temperature treatment. All tanks were filled with clean aerated seawater (95-100% oxygen saturation) and salinity 35. Live rocks were used as environmental enrichment. Temperatures were maintained for 28 days using thermostats (Eheim® Jager Heater 150W, Germany). Animals were fed once a day ad libitum with frozen food.

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Figure A6.2 Timeline and sampling scheme of the experiment. For molecular biomarker and energy reserves analyses, 5 individuals were randomly sampled for each temperature treatment. Sampling was performed once a week (at 0, 7, 14, 21 and 28 days) for biomarkers, and at the beginning and end of the experiment for energy reserves. Morphometric data on these specimens was also collected after euthanasia to minimize handling stress. For the CTMax determinations, 10-15 individuals for each temperature treatment were used and experiments were performed after short term (7 days) and longer term (28 days) acclimation. Morphometric data was also collected for these specimens.

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Figure A6.3 CTMax experimental setup. The specimens were randomly placed in 40.5L white plastic tanks (with water from the home tank), which were transferred to a thermostatized bath, connected to a heated/refrigerator circulator (MultiTemp III, Pharmacia Biotech). Each tank had a lid to prevent evaporation and was equipped with an aeration system. Note: not to scale.

Figure A6.4 Overall temperature frequency distributions at tide-pools continuously monitored at S. Sebastião, S. Paulo, in Southeastern Brazil. Data were obtained from 8 replicate pools sampled continuously over 56 d at 2h intervals during the summer of 2015/6.

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Figure A6.5 Biomarker behaviour along time (one month) under two temperature treatments (29˚C and 32˚C): A) Hsp70 (heat shock protein 70 kDa) and Ub (ubiquitin); B) CAT (catalase) and LPO (lipid peroxides), C) GST (glutathione-S-transferase) and SOD (superoxide dismutase). Numbers next to letters stand for (1) gills and (2) muscle. Significant differences from the control group are presented with colored asterisks (*).

Figure A6.6 Biomarker AChE (acetylcholinesterase, mean + SD) under two temperature treatments (29˚C and 32˚C) in muscle of Lysmata lipkei.

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Tables

Table A6.1 Mean (± SD), maximum (Max) and minimum (Min) values of water temperature as well as daily water temperature variation at the collection site in S. Sebastião (S. Paulo, Southeastern Brazil) (˚C). These data were collected from 8 replicate tidal pools, sampled continuously during the summer season of 2015/6 (from December to February). December January February Summer season Average temperature (˚C) 28.24 ± 0.57 25.72 ± 0.65 29.32 ± 0.45 26.95 ± 0.41 Max temperature (˚C) 37.50 ± 2.71 36.37 ± 3.68 36.57 ± 2.54 37.60 ± 2.62 Min temperature (˚C) 20.57 ± 1.03 18.78 ± 2.44 23.83 ± 8.47 18.43 ± 1.93 Daily temperature variation (˚C) 5.76 ± 1.56 3.62 ± 0.85 5.54 ± 1.33 4.78 ± 1.06

Table A6.2 Average biomarker basal levels (mean ± SD) in control L. lipkei specimens. NA = Non-Applicable. Abbreviations: Hsp70 – heat shock protein 70kDa, Ub – ubiquitin, CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase. Biomarker Units Gills Muscle Total protein mg.ml-1 of homogenate 1.181 ± 0.953 4.072 ± 0.679 Hsp70 µg.mg-1 of protein 0.047 ± 0.026 0.016 ± 0.011 Ub µg.mg-1 of protein 0.042 ± 0.022 0.023 ± 0.005 CAT nmol.min.mg-1 of protein 1.975 ± 1.521 0.349 ± 0.115 LPO nmol.mg-1 of protein 0.029 ± 0.023 0.005 ± 0.001 GST nmol.min.mg-1 of protein 40.998 ± 17.054 19.377 ± 6.092 SOD % inhibition.mg-1 of protein 39.530 ± 19.253 7.942 ± 4.274 AChE nmol.min.mg-1 of protein NA 19.506 ± 7.581

Table A6.3 Factor loadings of PCA for all biomarkers (from all timepoints) in each tissue (gills and muscle) at control (29˚C) and elevated temperature (32˚C). Abbreviations: Hsp70 – heat shock protein 70kDa, Ub – ubiquitin, CAT – catalase, LPO – lipid peroxides, GST – glutathione-S-transferase, SOD – superoxide dismutase, AChE – acetylcholinesterase. Gills Muscle Factor 1 (PC1) Factor 2 (PC2) Factor 1 (PC1) Factor 2 (PC2) Hsp70 -0.721386 0.619807 0.277426 0.516229 Ub -0.829986 -0.396690 0.483328 -0.060575 CAT -0.884664 -0.293094 0.869067 -0.145863

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LPO -0.690976 -0.063790 0.602275 0.572364 GST -0.799861 -0.333434 0.648144 -0.436260 SOD -0.701584 0.644535 0.497780 0.473443 AChE NA NA 0.364647 -0.781044

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