Drivers of Change in Mudflat Macroinvertebrate Diversity
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Drivers of change in mudflat macroinvertebrate diversity Shannon M. White School of Biological Sciences University of Portsmouth The thesis is submitted in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy of the University of Portsmouth. October 2018 Declaration Whilst registered as a candidate for the above degree, I have not been registered for any other research award. The results and conclusions embodied in this thesis are the work of the named candidate and have not been submitted for any other academic award. Dedication To Mom and Dad, thank you for your constant love and support through all of my academic endeavors. Acknowledgements This project would not have been possible without the support of many, many people. First, I would like to thank my supervisors Dr Gordon Watson and Dr Roger Herbert for the opportunity to pursue this research and for willing to get their hands dirty on ‘ragworm day’. I’d especially like to thank Dr Watson for the many additional opportunities he has given me to further branch out in the field, from conference organizing committee member to CoCoast field assistant. I would like to thank David Clare, who inspires me daily with his quest for knowledge. Who as a partner has been ever-supportive and patient with me, and as a fellow benthic ecologist has shared valuable insights and through our many helpful discussions has helped to shape the project into what it is today. Thank you for helping me to keep perspective and helping me to grow as a scientist. Thank you to those who helped me with field work and IMS lab work, including the technical staff at IMS, Marc Martin, Graham Malyon, and Jenny Mackellar, I couldn’t have done this without you. Also, Teri Charlton, Kirstie Lucas, Ian Hendy, Joanne Younger, David Clare, and the many work experience and undergraduate students who helped me move mud around. Thank you also to my IMS postgrad family for their friendship and support. Thank you to Martin Devonshire and Peter Coxhead who introduced me to the world of biophysics during the energy reserve analysis and particular thanks to Martin for helping me with the method development. Thank you to Linley Hastewell for showing me how to use the mastersizer for sediment analysis and for carrying out LOI analyses for some of our survey samples. Jonathan Richir, thank you for helping us to successfully meet our tight survey schedule, for sharing tips on mapping as well as using the water quality database, and for your helpful insights on trace element pollution analyses. Thank you Martin Schaefer for sharing useful mapping resources and insights and to Paul Mayo for sharing GAMM code. To all of the agencies and individuals who provided datasets for the analysis or who took the time to meet with me to discuss potentially useful resources: Natural England, Environment Agency, Nigel Thomas, Sam Stanton, Chichester Harbour Conservancy, Rob Clark (IFCA), Sloyan Stray (MMO), Nick Randall (Queen’s Harbour master), and others. I would like to acknowledge the processing and identification of the faunal core samples by Hebog Environmental and MESL for the experimental and survey work, respectively. Thank you to David Clare, Gina Kolzenburg, and Thien Nguyen for helping with formatting of the thesis and to Michael Spence for discussing stats with me for my corrections. Abstract Global biodiversity loss is an internationally recognized problem that has consequences for ecosystem functioning and provision of ecosystem services and warrants investigation into drivers of change in biodiversity. In the context of natural variability, multiple anthropogenic stressors, and a changing climate, the identification of drivers of change is a complex issue. Here, an integrated approach (analysis of long-term datasets and experimental simulation) was employed to investigate drivers of change in intertidal mudflat macroinvertebrate diversity, using the Solent, on England’s south coast, as the study system. A model was developed to analyze survey datasets from the 1970s-2010s following a review process. Comparisons of the spatio-temporal patterns of change in diversity across an interconnected three-harbour system revealed differences at the harbour and within harbour scales, suggesting the relevance of local conditions for driving change versus dominance by a regional driver. Further, direct relationships were identified between diversity and within-harbour environmental conditions. In the context of a changing climate, temperature was investigated as a driver of change. The absence of a direct relationship between a regionally derived climate index and diversity and identification of the interaction of local seasonal water temperatures with local environmental conditions have highlighted the relevance of local context for predicting the way in which climate change effects may manifest. The results suggest the potential for macroalgal cover to act as a driver in this system, as direct relationships as well as relationships modified by the preceding seasonal temperatures were identified with respect to diversity, though the data available to test these relationships were limited. The effects of discrete temperature events were also investigated as a driver of change by simulating heat waves in a large outdoor mesocosm system designed to preserve natural sediment temperature profiles, solar and tidal cycles, and faunal densities. Community composition effects were not identified overall or for the abundance of shallow dwelling organisms that may be more vulnerable to extreme temperatures at the sediment surface. For the polychaete Alitta virens and the bivalve Cerastoderma edule, which exhibit different burrowing abilities, neither species exhibited higher mortality as a result of the heat wave simulations performed. Changes in energy reserves, however, suggested sublethal effects for both, which has implications for their vulnerability to the increased frequency, intensity, and duration of these events predicted for the future. The findings across these studies highlighted the relevance of local context to the patterns of change, suggesting that this must be accounted for in making predictions for how broad-scale climate change will drive change in biodiversity. For intertidal organisms potentially living close to their physiological limits, minimizing local anthropogenic stressors could benefit the current macroinvertebrate communities in the face of a changing climate. Abbreviations DAIN: Dissolved Inorganic Available Nitrogen Ea: Energy Available EA: Environment Agency ETCCDI: CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices GAMM: Generalized Additive Mixed Model HW: Heat wave SSSI: Site of Special Scientific Interest STW: Sewage Treatment Works TEPI: Trace Element Pollution Index TT-DEWCE: Task Team on Definition of Extreme Weather and Climate Events WMO: World Meteorological Organization Contents Tables ............................................................................................................ iii Figures ........................................................................................................... v Chapter 1. General Introduction ..................................................................... 8 Chapter 2. Spatio-temporal patterns in diversity .......................................... 15 2.1 INTRODUCTION ............................................................................ 16 2.2 METHODS ...................................................................................... 22 2.2.1 Spatio-temporal analysis of diversity ........................................ 22 2.2.2 Environmental drivers of diversity............................................. 33 2.3 RESULTS ....................................................................................... 42 2.3.1 Faunal datasets ........................................................................ 42 2.3.2 Baseline model results ............................................................. 46 2.3.3 Environmental modeling results ............................................... 54 2.4 DISCUSSION ................................................................................. 64 Chapter 3. Temperature as a driver of change ............................................ 70 3.1 INTRODUCTION ............................................................................ 71 3.2 METHODS ...................................................................................... 76 3.2.1 Environmental data .................................................................. 76 3.2.2 Model summary ........................................................................ 79 3.3 RESULTS ....................................................................................... 83 3.3.1 Effects of regional air temperature extremity ............................ 83 3.3.2 Interaction of local water temperature with local environmental conditions .............................................................................................. 85 3.4 DISCUSSION ................................................................................. 92 Chapter 4. Heat waves as a driver of change .............................................. 98 4.1 INTRODUCTION ............................................................................ 99 4.2 METHODS .................................................................................... 104 4.2.1 Characterization of intertidal mudflat temperature variability .. 104