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Investigating the Effects of Organic Pollutants on Amphibian Populations in the UK

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Investigating the Effects of Organic Pollutants on Amphibian Populations in the UK Investigating the effects of organic pollutants on amphibian populations in the UK A thesis submitted for the degree of Doctor of Philosophy in the Faculty of Science and Technology, Lancaster University Alternative format thesis March 2016 Rebecca Jane Strong (MSc) Lancaster Environment Centre Abstract Amphibians are undergoing dramatic population declines, with environmental pollution reported as a significant factor in such declines. Technologies are required that are able to monitor populations at risk of deteriorating environmental quality in a rapid, high-throughput and low-cost manner. The application of biospectroscopy in environmental monitoring represents such a scenario. Biospectroscopy is based on the vibrations of functional groups within biological samples and may be used to signature effects induced by chemicals in cells and tissues. Here, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with multivariate analysis was implemented in order to distinguish between embryos, whole tadpoles at an early stage of development and individual tissues of late-stage tadpoles of the common frog collected from ponds in the UK with varying levels of water quality, due to contamination from both urban and agricultural sources. In addition, a Xenopus laevis cell line was exposed to low-levels of fungicides used in agriculture and assessed with ATR-FTIR spectroscopy. Embryos, in general did not represent a sensitive life stage for discriminating between ponds based on their infrared spectra. In contrast, tadpoles exposed to agricultural and urban pollutants, both at early and late stages of development were readily distinguished on the basis of their infrared spectra. ATR-FTIR spectroscopy also readily detected fungicide- induced changes in X.laevis cells, both as single-agent and binary mixture effects. Data reported in this study confirm the use of ATR-FTIR spectroscopy as a sensitive technique capable of detecting small changes in cellular groups, and as such represents a valuable starting point for its use in the monitoring of amphibian populations. However further research is needed in order to overcome confounding factors existent in natural populations of complex organisms. II Acknowledgements This research would not have been possible without the assistance of many individuals. I would like to thank my supervisors: Professor Frank Martin, Dr Crispin Halsall, Professor Richard Shore and Professor Kevin Jones. Both Frank and Crispin offered me guidance and support throughout my PhD for which I am extremely grateful. Lab A20 welcomed me from the start, providing me with training and friendship. It has been a pleasure to work with you all and I would like to thank all of my laboratory colleagues and friends, past and present for making it so: Kelly Heys, Holly Butler, Lara Heppenstall, Liz Hill, Adil Ahmadzai, Junyi Li, Georgios Theophilou, Blessing Obinaju, Alana Mitchell, Simon Fogarty, Valon Llabjani and Julio Trevisan. Thanks also to Debra Hurst for providing technical support. I am also grateful to the farmers and Rangers at Crake Trees, Whinton Hill and Pennington Flash for allowing me to collect samples from their ponds and taking an interest in the research I was carrying out. Finally, I would like to thank my husband Jim for his endless support and patience, particularly when times have been tough, and to my family and friends outside of Lancaster for their encouragement, cups of tea and biscuits. Declaration I declare that this thesis is my work and has not been submitted for the award of a higher degree or qualification at this university or elsewhere. III Contents Title page I Abstract II Acknowledgements III Declaration III Contents IV List of tables and VI figures List of VII abbreviations Chapter 1. General Introduction 1 Chapter 2. Biospectroscopy as a tool to monitor subtle effects of environmental stress on the early life stages of the Common frog: a pilot study. 64 Rebecca J. Strong, Crispin J. Halsall, Martin Ferenčík, Kevin C. Jones, Richard F. Shore and Francis L. Martin Manuscript for submission Chapter 3. Using biospectroscopy to monitor amphibian health: a three year study. 107 Rebecca J. Strong, Crispin J. Halsall, Kevin C. Jones, Richard F. Shore and Francis L. Martin Manuscript for submission IV Chapter 4. Biospectroscopy reveals the effect of varying water quality on tadpole tissues of the common frog (Rana temporaria). 154 Rebecca J. Strong, Crispin J. Halsall, Martin Ferenčík, Kevin C. Jones, Richard F. Shore and Francis L. Martin Environmental Pollution 213 (2016): 322-337 Infrared spectroscopy detects changes in an amphibian Chapter 5. cell line induced by fungicides: comparison of single 205 and mixture effects. Rebecca J. Strong, Crispin J. Halsall, Kevin C. Jones, Richard F. Shore and Francis L. Martin Aquatic Toxicology 178 (2016): 8-18 Chapter 6. Discussion 236 Bibliography 245 Appendix List of publications from collaborative research 277 Conference abstracts V List of tables and figures Table1. Examples of endpoints in amphibians commonly measured following exposure to environmental contaminants.......................................................................5 Table 2. List of commonly used fungicides in the UK and their mechanism of action.............................................................................................................................11 Figure 1. Simplified life cycle of the common frog, Rana temporaria......................14 Figure 2. Rana temporaria spawn (A) and tadpoles (B)............................................20 Figure 3. Rana temporaria photographed breeding....................................................21 Figure 4. Schematic of a Michelson interferometer.....................................................25 Figure 5. Representative raw FTIR spectra.................................................................26 Figure 6. Vibrational modes in infrared spectroscopy.................................................27 Figure 7. Representative spectrum of the fingerprint region with chemical peaks tentatively assigned.......................................................................................................28 Figure 8. Sampling modes in FTIR spectroscopy........................................................30 Figure 9. Overview of commonly used pre-processing steps in IR spectroscopy.......32 Figure 10. Example outputs following PCA-LDA analysis.......................................37 Table 3. A list of commonly used methods for classification of datasets generated from IR spectroscopy....................................................................................................38 Figure 11. Overview of the aims and objectives of the thesis.....................................42 Figure 12. Approximate locations of the study sites on a map of the UK...................43 Figure 13. A workflow of the procedure used to obtain ATR-FTIR spectra of tissue samples of Rana temporaria tadpoles..........................................................................45 Figure 14. Flow diagram of conclusions, limitations and future research needs.......236 VI List of abbreviations Al: Aluminium AMPA: Aminomethylphosphonic acid ANN: Artificial neural networks ANOVA: Analysis of variance ATR: Attenuated total reflectance BCI: Body condition index Ca: Calcium Cl: Chloride CYP: Cytochrome P-450 CYP51: Sterol-14α-demethylase DCM: Dichloromethane DMSO: Dimethyl sulfoxide DNA: Deoxyribonucleic acid ESI+: Electrospray positive FBS: Fetal bovine serum Fe: Iron FFS: Forward feature selection FPA: Focal plane array FSH: Feature selection histogram FTIR: Fourier-transform infrared GC-MS: Gas chromatography–mass spectrometry HNO3: Nitric acid HSI: Hepatosomatic index HW: Head width ICP-OES: Inductively coupled plasma optical emission spectrometry VII IR: Infrared IRE: Internal reflection element K: Potassium kNN: k-nearest neighbours LC-MS: Liquid chromatography–mass spectrometry LDA: Linear discriminant analysis LDC: Linear discriminant classifier LoQ: Limits of quantification Low-E: Low-emissivity MeOH: Methanol Mg: Magnesium MRM: Multiple reaction monitoring MS-222: Tricaine methanesulfonate MSMA: Monosodium methyl arsenate Na: Sodium NO3-N: Nitrate OP: Organophosphorus OWC: Organic wastewater contaminant PAH: Polycyclic aromatic hydrocarbon PBDE: Polybrominated diphenyl ether PC: Principal component PCA: Principal component analysis PCB: Polychlorinated biphenyl PLS: Partial least squares PLS-DA: Partial least squares-discriminant analysis QCL: Quantum cascade lasers VIII QDA: Quadratic discriminant analysis QoI: Quinone outside inhibitor RNA: Ribonucleic acid SG: Savitzky-Golay SHE: Syrian Hamster Embryo SIMCA: Soft independent modelling of class analogies SO4-S: Sulphate SNR: Signal to noise ratio SVL: Snout to vent length SVM: Support vector machines TBP: Tributylphosphate TCEP: Tris(2-chloroethyl)phosphate TCPP: Tris(1-chloro-2-propyl)phosphate TEP: Triethylphosphate TON: Total organic nitrogen UPLC: Ultra performance liquid chromatograph UV: Ultraviolet IX Chapter 1. General Introduction Contents 1. Introduction ................................................................................................................ 3 2. Threats to amphibian health from pollution ............................................................... 4 2.1 Pollution from urban environments ...................................................................... 8 2.2. Pollution from agriculture ..................................................................................
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