Exploring Resilience to Disturbances in Coral Reef Ecosystems, with Case Studies from Ningaloo Reef, Western Australia

Exploring resilience to disturbances in coral reef ecosystems, with case studies from Ningaloo Reef, Western Australia

Anna Katrina Cresswell BSc (Hons)

This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia School of Biological Sciences Marine Science 2020 Thesis declaration

I, Anna Katrina Cresswell, certify that:

This thesis has been substantially accomplished during enrolment in this degree.

This thesis does not contain material which has been submitted for the award of any other degree or diploma in my name, in any university or other tertiary institution.

In the future, no part of this thesis will be used in a submission in my name, for any other degree or diploma in any university or other tertiary institution without the prior approval of The University of Western Australia and where applicable, any partner institution responsible for the joint-award of this degree.

This thesis does not contain any material previously published or written by another person, except where due reference has been made in the text and, where relevant, in the Authorship Declaration that follows.

This thesis does not violate or infringe any copyright, trademark, patent, or other rights whatsoever of any person.

This thesis contains published work and/or work prepared for publication, some of which has been co-authored.

Signature Date: 1/1/2020

Abstract

The discipline of disturbance ecology is increasingly relevant as climatic change and anthropogenic stressors alter the frequency, intensity and duration of disturbances to natural systems. Due to the vulnerability of corals to increased water temperatures as a result of climate change, coral reefs are now considered to be the most threatened marine ecosystem. This thesis aims to improve our understanding of the responses of key components of coral reef ecosystems to both acute (short-lived intense events) and chronic (ongoing stress) disturbances using a resilience framework. I consider resilience as a function of both resistance — the capacity of a system to absorb or withstand a disturbance; and recovery — the capacity to return to a pre-disturbance state. Several chapters of this thesis investigate resilience as a concept, while others explore key biological processes underlying the resilience of reefs or consider how to assess ecosystem responses to disturbance.

Following the introductory chapter 1 that contextualises the thesis in terms of previous work and also highlights important knowledge gaps, chapters 2 to 6 deal with data from various sources. Each chapter contributes to a deeper understanding of the dynamics of change resulting from disturbance. The specific aims are to: • Synthesise the global scientific literature on acute climatic disturbances to coral, in order to allow broad inferences on how these events influence coral dynamics (chapter 2); • Investigate a new methodology for remotely monitoring coral reproduction using satellite imagery (chapter 3); • Present new information on coral growth rates from a previously unsurveyed habitat at Ningaloo Reef, and explore a novel method for sampling coral growth (chapter 4); • Build a unique three-dimensional functional-structural coral model as a tool to investigate the interplay between functional traits, growth, competition and disturbance in coral communities and apply this model to test the influence of different frequencies and intensities of disturbance (chapter 5); • Investigate the role of management in mitigating a chronic disturbance (recreational fishing) across distinct coral reef habitats at Ningaloo Reef (chapter 6). iii

A range of approaches are used to address these aims. Science is a cumulative process and in chapters 2 and 6 meta-analyses are used to build on existing information regarding the responses of coral and fish to disturbance. In chapters 3 and 4, an examination is made of the biological processes underlying resilience using new survey methods now available through evolving technologies; and chapter 5 describes an agent-based theoretical model I have created to explore some of the mechanisms such as growth and competition which underlie disturbance dynamics.

The thesis provides new insights into the relationships between impact and recovery from acute climatic disturbances across coral reefs globally, with relevance for the study of resilience in natural systems more broadly. I demonstrate the applicability of new technologies for demographic studies of corals, providing the first estimates of coral growth rates for the Ningaloo Reef slope. Functional structural modelling is used to integrate these demographics (reproduction and growth) with competition and disturbance, and the results indicate that disturbance frequency governs simulated coral communities more than disturbance intensity. I also demonstrate the complexities of both assessing the effects of disturbances and quantifying the success of management in complex ecosystems, by demonstrating the influence of interactions between variables on the disturbance effects.

Insights from chapters 1 to 6 led me to devise a framework of identifiers to help contextualise the use of the term resilience in ecological studies and in broader scientific and policy discussions. In my chapter 7 discussion I present this framework, with multiple lines of reasoning based on the previous chapters and the available literature.

Resilience has become a conceptual cornerstone for the monitoring and management of tropical coral reefs. This thesis sits within the broader active and critical debate on how to best quantify the resilience of natural systems. Table of Contents

Thesis declaration ...... ii Abstract ...... iii Table of Contents ...... v List of Figures ...... vii List of Tables ...... xii Acknowledgements ...... xiii Authorship declaration: co-authored publications ...... xv Chapter 1 Introduction ...... 21 Chapter 2 ...... 36 Temporal dynamics in coral cover: a global analysis of impact and recovery from acute climatic disturbances ...... 37 Abstract ...... 37 Introduction ...... 38 Methods ...... 41 Results ...... 48 Discussion ...... 57 Conclusions ...... 61 Acknowledgements ...... 62 Chapter 3 ...... 64 Synthetic Aperture Radar scenes of the North West Shelf, Western Australia, suggest this is an underutilised method to remotely study mass coral spawning ...... 65 Abstract ...... 65 Introduction ...... 66 Methods ...... 67 Results and Discussion ...... 70 Conclusions ...... 75 Acknowledgements ...... 75 Chapter 4 ...... 76 Structure-from-motion reveals coral growth is influenced by colony size and wave energy on the reef slope at Ningaloo Reef, Western Australia ...... 77 Abstract ...... 77 Introduction ...... 78 Material and methods ...... 81 Results ...... 88 Discussion ...... 94

v Conclusions ...... 99 Acknowledgements ...... 99 Chapter 5 ...... 100 Frequent hydrodynamic disturbances decrease the morphological diversity and rugosity of simulated coral communities ...... 101 Abstract ...... 101 Introduction ...... 102 Materials and Methods ...... 105 Results ...... 114 Discussion ...... 119 Acknowledgements ...... 125 Chapter 6 ...... 126 Disentangling the response of fishes to recreational fishing over 30 years within a fringing coral reef reserve network ...... 127 Abstract ...... 127 Introduction ...... 128 Material and methods ...... 130 Results ...... 136 Discussion ...... 144 Conclusions ...... 149 Acknowledgements and Funding ...... 151 Chapter 7 Discussion ...... 153 References ...... 173 Chapter 2 Appendices ...... 191 Chapter 4 Appendices ...... 205 Chapter 5 Appendices ...... 213 Chapter 6 Appendices ...... 227

List of Figures

Figure 1.1. The use of the term resilience relative to all other terms (ngrams) through time according to Google Books Ngram Viewer (https://books.google.com/ngrams/). The timing of Holling’s (1973) seminal paper is indicated...... 22 Figure 1.2. Schematic illustration of the disturbance types considered in this thesis — fishing, cyclones (or severe storms), marine heat waves and associated coral bleaching, flooding events, associated with freshwater discharge onto reefs, and situations where multiple chronic and/or acute disturbances occur simultaneously — with an example of the disturbance intensity through time and a theoretical response in the ecosystem component...... 26 Figure 1.3. A section of Ningaloo Reef adjacent to Osprey Bay showing the dry arid coastline and the fringing reef structure. The major reef zones, which are a focus in chapters 5 and 6, are indicated (Photo: Nick Thake, May 2019)...... 29 Figure 1.4. Timeline showing four types of disturbance at Ningaloo Reef. Cyclones are shown in yellow; cyclone names are provided post 1970 when names were introduced. The solid yellow lines have lengths corresponding to the cyclone category (from 1 to 5) through reference to the x-axis, when this information was available (the length of yellow dashed lines is not informative). Pink bars show positive Ningaloo Nino Indices (associated with warm water temperatures) as defined in Feng et al. (2015) (fig. 2a) from 1950-2013. The length of the pink bars gives the Ningaloo Nino Index in °C through reference to the x-axis. Reports of tsunamis that likely caused an impact at Ningaloo Reef (Nott, 2000) are noted in grey, as well as heavy rainfall events likely associated with flooding and freshwater runoff onto the reef are shown in blue...... 30 Figure 1.5. Schematic summary of this thesis...... 34 Figure 2.1. Recovery trajectory shape options based on the sign (negative or positive) of linear, logarithmic and exponential model types...... 46 Figure 2.2. A schematised example of a study showing the key predictor (a-d) and response variables (1-3) considered in recovery analyses...... 47 Figure 2.3. Schematic summarising the data search and selection process. The final publications and ‘studies’ are categorised into those with data before and shortly after a disturbance (‘impact’), those with data at multiple times after the disturbance (‘recovery’) and those with the combination of data before, and at multiple times after the disturbance (‘impact and recovery’)...... 49 Figure 2.4. a) Spatial distribution of studies showing sites with the colour of the points indicating the type of disturbance. Open circles show sites with impact data and closed circles show sites with both impact and recovery data and those with only recovery data; temporal distribution of disturbances for b) impact and c) recovery data, categorised by disturbance type. A study is defined by a combination of the disturbance and study site...... 50 Figure 2.5. Violin plots showing the distribution of the coral cover across studies before and after disturbance for a) all studies (n = 380); b) temperature, n = 197, c) physical (cyclones and severe storms, n = 137), d) flooding, n = 30 and e) multiple, n = 16, disturbances. Boxplots overlaid, showing the median and interquartile ranges...... 51

vii Figure 2.6. a) The percentage cover of coral before disturbance plotted against the cover remaining post disturbance. Colours represent the different disturbance types. b) The mean impact, e, lnRR, for each disturbance type. The dashed line represents coral cover before equal to coral cover after...... 52 Figure 2.7. a) Predictions from linear models fit to the 5-year post disturbance recovery trajectories. Colour indicates a positive versus a negative trend. b) Predictions from the most likely model (based on AIC) fitted to the full length of available recovery trajectory for each study. Colours indicate the shape classification...... 53 Figure 2.8. Number of studies classified to each of the eight recovery shape based on AIC selection criteria. Black text shows the proportion of each shape, while the red text shows the mean of the relative likelihood of this shape when it was selected as the most likely (i.e. a higher value indicates that on average, confidence in the classification was higher)...... 53 Figure 2.9. Predicted relationship between the percentage coral cover remaining after disturbance, the rate of recovery and the percentage of coral cover prior to disturbance as determined from linear modelling. Different colours indicate predictions for different values of coral cover before disturbance. Grey arrows indicate the proposed mechanisms driving the relationships...... 55 Figure 2.10. Probability of positive decelerating versus positive accelerating recovery shape as determined from the binomial generalised linear model (model equation shown in italics). Different colours indicate a different amount of coral cover before disturbance. Grey arrows indicate the proposed mechanisms driving the relationships...... 56 Figure 3.1. The North West Shelf of Australia showing the region covered by the RADARSAT SAR scenes examined for this study. The coastline and isobaths from 100x1000 m are from the General Bathymetric Chart of the Oceans (GEBCO) https://www.gebco.net/ and the 20 m was copied from the Australian National Bathymetric Map Series...... 69 Figure 3.2. The RADARSAT SAR scenes from the Montebello and Barrow Islands region for a) 17, b) 19, c) 24 and d) 26 March 2001. The vectors represent the winds (ms-1) measured at Barrow Island airport at the times of the satellite passes...... 70 Figure 3.3. Predicted tide at Barrow Island and wind speed measured at Barrow Island Airport around the times of the RADARSAT SAR scenes, which are marked with vertical dashed lines. The shading indicates night, the small black boxes show the phases of the moon, and wind vectors at the times of the scenes are added at the intersections of the dashed lines with the 10 ms- 1 grid lines...... 72 Figure 3.4. a) RADARSAT SAR scene for 19:50 hours WST 19 March 2001 and b) the same scene with the yellow and green overlays showing the positions of subtidal and intertidal reefs respectively. Note, the spatial data for reef locations are smaller than the SAR scene, indicated by blue outline...... 73 Figure 4.1. a) Location of Ningaloo Reef relative to the Australian continent and b) Ningaloo Reef and North West Cape, Western Australia showing the locations of sites in three regions – Jurabi, Mangrove and Osprey – where standard photos where used to monitor growth of coral colonies (pink, n = 16) and the location of temporally replicated orthophoto plots (blue, n = 18)...... 81 Figure 4.2. Example of a tagged Acropora coral colony, showing the time-series of standard photos taken for measurement in May of a) 2017, b) 2018 and c) 2019...... 84

Figure 4.3. Example of an orthophoto time-series in May of a) 2017, b) 2018 and c) 2019, with the ruler and coral colonies that were matched over multiple years indicated in yellow...... 85 Figure 4.4. Arithmetic mean radii (AMR) for colonies as determined from tagged colony photographs and from the orthophotos. Points closer to the dashed line indicate greater similarity between the methods. Acropora are shown in blue (n = 35) and Stylophora in orange (n = 8)...... 89 Figure 4.5. Predictions of how Acropora growth rate (annual change in radius) changes with water velocity, method and initial coral radius according to the best linear mixed-effects model based AIC (Table 4.1). The interaction is evident in the different slopes of the coloured lines which represent predictions for different initial colony sizes...... 91 Figure 4.6. Predictions of how Acropora growth rate (annual change in radius) changes with a) water velocity and b) the initial radius of a colony, according to the second-best linear mixed-effects model based on AIC (Table 4.1)...... 91 Figure 4.7. Boxplots showing the distribution of growth rate for each method and for the three genera: Acropora, Platygyra and Pocilloporidae. (Heavy horizontal line: median; top and bottom of box: upper and lower quartiles; The whiskers extend to the largest and smallest value no further than 1.5 * inter-quartile; data beyond the end of the whiskers are the ‘outliers’)...... 92 Figure 4.8. Pie chart displaying the proportion of colonies from the standard photo surveys that had partial mortality (some clearly dead tissue), overgrowth by adjacent corals, physical damage (i.e. sections missing), total mortality with structure remaining (‘dead’) and total mortality where the colony was no longer present (‘dead and removed’, i.e. the tag was there but the coral had broken off from the substrate)...... 93 Figure 5.1. The five coral morphologies: encrusting, hemispherical, tabular, corymbose and branching. The top panel shows the modelled representation and the bottom panel provides an example photograph of the associated real coral morphology. The morphologies are ordered by susceptibility to hydrodynamic disturbance as calculated by their colony shape factor...... 106 Figure 5.2. a) Light level shown through a vertical cross-section of the 1 m3 modelled environment (lighter blue = greater light) with a single tabular coral shown absorbing the light that reaches it, thus creating shading below and b) a simulated coral community showing light absorption of different corals. In the simulated community in b), note the self-shading of the branching corals, the shadows of the branching corals on the tops of the tabular corals, and the very small amount of light passing through the gaps between tabular corals...... 107 Figure 5.3. a) Basal areas and b) profile areas of the five morphologies with 30 voxel radii; c) relationship between colony radius and colony shape factor (CSF) for the five morphologies. The red dashed line indicates the specified threshold of a ‘high intensity’ disturbance, where all colonies above the line will be removed, while the red dotted line indicates the same for a ‘low intensity’ disturbance...... 109 Figure. 5.4. Map of sites (indicated in pink) where coral percentage cover data and linear rugosity were obtained from the Osprey Bay region of Ningaloo Reef, overlaid on output from the SWAN model at a resolution of 30 m showing the mean modelled water velocity (m s-1) at the sea floor...... 113

ix Figure 5.5. Temporal dynamics of the six most distinct scenario outputs under fixed disturbance regimes showing the density of colonies (m-2), percentage cover and volume of each morphology over 25 years, with the right-hand panel showing a 3D representation of one randomly chosen replicate community at the end of the simulation. Ribbons indicate 95% confidence intervals across 50 replicate simulations. N = no, I = infrequent, F = frequent; L = low intensity disturbance, H = high intensity disturbance...... 115 Figure 5.6. Temporal dynamics of all scenarios over 25 years under random disturbance intensity and frequency a) total coral cover, b) linear rugosity, c) diversity by density, d) diversity by cover, e) diversity by volume for fixed (left panel) and random (right panel) scenarios. Ribbons indicate 95% confidence intervals across the 50 simulations. N = no, I = infrequent, F = frequent; L = low intensity disturbance, H = high intensity disturbance...... 116 Figure 5.7. Percentage cover of the five coral morphologies on a) the reef flat (n=20) and b) the reef slope (n=20); c) mean linear rugosity; d) mean Simpson’s Diversity Index (diversity by cover) and e) mean modelled bottom velocity from the SWAN model for sites at Ningaloo Reef, Western Australia. In the boxplots, boxes indicate interquartile ranges, horizontal lines indicate medians, points indicate outliers and the whiskers extend from the smallest value within 1.5 times the interquartile range below the 25th percentile to the largest value within 1.5 times the interquartile above 75th percentile...... 119 Figure 6.1. The Ningaloo Marine Park and Muiron Islands Marine Management Area boundaries (dotted lines) with the location of sanctuary zones (referred to as reserves in the present study) shown in green along the Ningaloo coast of Western Australia under the a) initial (1987 – 2005) and b) current (2005 - 2017) zoning schemes. Tantabiddi Well and Winderabandi Point are indicated with red markers as spearfishing is prohibited between these locations. The Osprey reserve is also indicated. In b) blue regions indicate zones on the coastal boundaries of the reserves where shore-based fishing is allowed...... 132 Figure 6.2. a) Relative fish abundance inside to outside the reserves (back-transformed weighted mean effect sizes) with 95% confidence intervals), for the six fish groups: Lethrinidae, Lethrinus nebulosus, L. atkinsoni, Epinephelinae, Epinephelus rivulatus and Scarinae. Effect sizes are significant when the confidence intervals do not overlap 1.0. Open dots correspond to non- significant effects (i.e. no effect). Sample sizes are given in Table App 6. 5.1. Triangular points show the predicted effect size when habitat was included as a moderator variable in the meta-analysis, for the habitat with the largest mean effect size (orange represents the back reef & lagoon coral, and blue represents the reef flat). b) Importance scores based on summed Akaike weights corrected for finite samples (AICc) from full- subsets analyses exploring the influence of seven variables on the overall effect size for each fish taxa: 1 is highly important while 0 is not important. Red X symbols mark the variables that were included in the most parsimonious models for each fish taxa (also see Table 6.2 2 and Figure 6.3)...... 138 Figure 6.3. Predicted relative fish abundance inside to outside reserves (back-transformed predicted weighted effect sizes) with 95% confidence intervals for the six fish groups – a) Lethrinidae; b) Lethrinus nebulosus c) L. atkinsoni; d) Epinephelinae; e) Epinephelus rivulatus; f) Scarinae – as a function of variables present in the most parsimonious models (Table 6.2) from full- subsets GAMM analysis. Ribbons and error bars represent 95% confidence intervals...... 142

Figure 6.4. Effect sizes through time for a) abundance and b) biomass from comparison pairs for the Osprey reserve and estimated density of c) abundance and d) biomass inside and outside the reserve through time. Ribbons indicate 95% confidence intervals on generalised additive models...... 143 Figure 7.1. A schematic illustrating the proposed resilience identifiers ...... 155 Figure 7.2. Schematic illustration of the different resolutions examined in this thesis, with the ecosystem level referring to a coral reef and the ecosystem components including coral and fish. The functional types in this work were morphological types, defined by morphological traits and the different taxa, which included species, genera and families in this thesis. The chapters which consider each resolution are indicated...... 158

xi List of Tables

Table 2.1. Statistics for the linear model determined to be the best fit based on AIC comparisons of different model variations testing the relationship between the 5-year recovery rate and the predictor variables in Figure 2.2: coral cover before, the impact response ratio and coral cover after disturbance. Models were weighted by the inverse of the standard error of the slopes. The p-value for the overall model was 0.003, df = 58, Multiple R2 = 0.21...... 54 Table 2.2. Results of the binomial generalised linear model of the probability of a positive decelerating versus positive accelerating recovery trajectory shape as models by coral cover before and after disturbance...... 56 Table 4.1. Point estimates, standard error, degrees of freedom and p-values according to marginal t-tests for the top two (according to AIC) linear mixed-effects models of coral growth rate as annual change in AMR (cm) for Acropora (number of annual measurements = 546). Both models included a random effect of coral ID nested in site...... 90 Table 5.1. Matrix showing the nine disturbance scenarios with combinations of disturbance frequency and intensity. Colours match those used in Figure 5.6 (N = no, I = infrequent, F = frequent; L = low intensity disturbance, H = high intensity disturbance). Columns define the frequency of high intensity disturbances and rows define the frequency of low intensity disturbance...... 111 Table 6.1. Description and summary of the seven variables used in analysis ...... 139 Table 6.2. Top Generalised Additive Mixed Models (GAMMs) for predicting the response ratio inside to outside reserves, 푬, for abundance from full subset analyses for the abundance of the six fish groups. Difference between the lowest reported corrected Akaike Information Criterion (ΔAICc), AICc weights (ωAICc), variance explained (R2) and estimated degrees of freedom (EDF) are reported for model comparison. Model selection was based on the most parsimonious model (fewest variables and lowest EDF) within two units of the lowest AICc. This model is shown in bold text...... 141 Table 7.1. Resilience identifiers for each chapter in this thesis ...... 166

Acknowledgements

I am grateful for all the help, interest and support I have received over the last 4 years. I have acknowledged specific people in individual chapters, and here I acknowledge some of the many others. This research was supported by an Australian Government Research Training Program (RTP) Scholarship. I gratefully acknowledge the financial support of the BHP-CSIRO Ningaloo Outlook Marine Research Partnership for my top-up scholarship and financial contributions that allowed field work at Ningaloo Reef for the research contained in this thesis. I am grateful for the more general support offered by CSIRO and the Ningaloo Outlook team. UWA was a lovely environment to do my PhD and I have been honoured to receive the Jean Rogerson Postgraduate Scholarship which has been invaluable to my studies.

Thanks to my supervisors, Michael Renton, Tim Langlois, Damian Thomson, Mick Haywood and Gary Kendrick. Michael, as I’ve said before, I am so grateful for your calm logic, big brain and statistical and modelling magic. You have been a rock and I am not sure how I would have got here otherwise. Tim, I am very thankful for your guidance and the opportunities you have sent my way, including encouraging me to take on the dataset that led to chapter 6 of this thesis, and connecting me to Joachim Claudet in France, thus allowing chapter 2 (as well as visits to Perpignan and Paris!). Damian and Mick, our Ningaloo field trips embodied all the reasons I wanted to do this PhD, with lots of laughing, big and small beautiful marine creatures, our favourite Osprey days, the drive back to Yardie – exhausted, sunned and smiling. Damian, thank you for always being just down the hallway, making sure I had everything I needed, and reminding me it was just a PhD. Gary, I attribute the focus on resilience in this thesis to you, as it was your thought-provoking questions and enthusiasm about this complex subject at the beginning of PhD that led me down this path.

There could be no better support team than Mum, Dad and my little dog Archie, so thank you to Susan Blackburn and George Cresswell for unwavering confidence, support and understanding, and for reading the first drafts of anything and everything. Thank you to Archie for providing extremely unergonomic warmth and love from my lap during the last two months of writing. These occurred at our family shack on the northeast coast of Tasmania, during which my PhD was solar-powered, and I thank Dad for being the on-call renewable energy electrician and getting me powered back up when things inexplicably stopped working, and Lynne Symonds for providing power from the grid when all else failed. A special thanks to my aunt, Elizabeth Ellis, for doing a final and thorough proofread of my Introduction and Discussion.

It has been a long and sometime winding road over the last 4 years and I have been very lucky to have others walking the PhD journey alongside me, in particular Anita Giraldo, Harri Davies,

xiii Libby Trevenen and Jess Stubbs. A special thanks to Harry Moore for always being confident that everything would be fine, and for making sure that I had a life outside of my PhD, whether it be searching for numbats, birds or snakes; snorkelling; picnicking; or visiting Lulani (Perth Zoo’s critically endangered baby white-cheeked gibbon).

I would also like to thank Fi, Steve, Sophie, Cam, Peter and Ruby for being my Perth family and always having an open door when I needed a home, or an escape to Margaret River; Rhiannan Longley for being my best Perth friend and housemate and sharing in many cups of earl grey with me; Harry Watson for being there for the beginning of this journey and making the move to Perth a happier one; Bev Pether for a lovely home close to uni; Jo Myers for a special friendship, burgers and Peanut play dates; Ernest Hemingway, the Stick Insect, for being my buddy through a rough patch; Mat Vanderklift for making sure I kept the ‘Ph’ in the PhD; Joachim Claudet, for patiently teaching me meta-analysis; Prospero Productions and in particular Russell Vines, for a once in a lifetime detour from my PhD, and the opportunity to explore Ningaloo Reef in a mini-submersible, and communicate with a wide audience about this special place; Shaun Wilson for providing wisdom at beercycles; Thomas Holmes for trusting me to take over the meta-analysis that forms chapter 6 of this thesis; the Coral Bay Research Station and Frazer McGregor for always having an open door to me and my research; and Georgina Gogarty, Steve Odea and Peter Turner of the Coral Bay Subsea Explorer, for hosting me to explore short-term temporal variability in fish counts.

Various people expressed interest and enthusiasm about my PhD, including, but not limited to, Mat Vanderklift, Fabio Boschetti, Becky Fisher, Shaun Wilson, Thomas Holmes, Melanie Trapon, Cindy Bessey, Brett Molony, Dirk Slawinski, Fiamma Riviera and Claire Butler, and this helped me to keep moving forward.

I acknowledge the Traditional Custodians of the land on which I work and live, and recognise their continuing connection to land, water and community. I pay respect to Elders past, present and emerging. In particular, I would like to recognise and acknowledge the Jinigudira people as the traditional custodians of the Ningaloo land and sea country. The Jinigudira, a part of the West Thalandji people, and their predecessors, have lived in this region for at least the last thirty thousand years. Having spent a lot of time on the Ningaloo coast during my PhD, it is not hard to understand that this has been a special place for a long time.

Authorship declaration: co-authored publications

This thesis contains work that has been published and prepared for publication.

Details of the work:

This Chapter is prepared for submission, journal TBA.

Title: Temporal dynamics in coral cover: a global analysis of impact and recovery from acute climatic disturbances

Authors: Anna K. Cresswell, Michael Renton, Tim Langlois, Jasmine Lynn, Damian Thomson, Joachim Claudet Location in thesis:

Chapter 2 Student contribution to work:

AKC and JC conceived the study. AKC and JL collected the data. AKC analysed the data with advice from MR and JC. AKC wrote the manuscript, which all authors provided comment on. Co-author signatures and dates:

Joachim Claudet (coordinating author) December 1st 2019

Tim Langlois

Jasmine Ly

Damian Th

Details of the work:

This Chapter was published in August 2019:

Anna K. Cresswell, Paul C. Tildesley. & George R. Cresswell (2019) Synthetic Aperture Radar scenes of the North West Shelf, Western Australia, suggest this is an underutilised method to remotely study mass coral spawning. Journal of the Royal Society of Western Australia, 102, 45-51. Location in thesis:

Chapter 3 Student contribution to work:

Anna Cresswell and George Cresswell conceived the study and analysed the data. George Cresswell and Paul Tildesley collected the data. George Cresswell created the visualisations. Anna Cresswell wrote the manuscript.

Co-author signatures and dates: 1/1/2020 ained, email approval given

Details of the work:

This Chapter was accepted to the Journal of Experimental Marine Biology and Ecology in July 2020.

Title: Coral growth rates in a high wave energy environment at Ningaloo Reef, Western Australia, using traditional and novel methods.

Authors: Anna K Cresswell, Melanie Trapon, Michael Renton, Michael D E Haywood, Ana Giraldo Ospina, Dirk Slawinski, Rachel Austin, Damian P Thomson Location in thesis:

Chapter 4 Student contribution to work:

AKC, DT, MT and MH conceptualised the study. AKC, DT, MT, MH, AG collected the data. MT, AG, RA, AKC analysed the images. AKC analysed the data with advice from MR, created the visualisations and wrote the manuscript. All authors provided comments on drafts. Co-author signatures and dates:

Melanie Trapon

Michael Haywoo

Ana Giraldo Ospi

Dirk Slawinski

Rachel Austin 19

Damian P Thoms 2/12/2019

Details of the work:

This chapter was published in Coral Reefs in June 2020:

Cresswell, A.K., Thomson, D.P., Haywood, M.D.E. et al. Frequent hydrodynamic disturbances decrease the morphological diversity and structural complexity of 3D simulated coral communities. Coral Reefs (2020). https://doi.org/10.1007/s00338-020- 01947-1

Authors: Anna Cresswell, Damian Thomson, Mick Haywood, Michael Renton Location in thesis:

Chapter 5 Student contribution to work:

MR and AKC conceptualised, designed and coded the model. DPT and MDEH provided case study data and helped AKC to analyse and interpret this data, as well as providing expertise on coral biology and physiology. AKC conducted simulations, analysed the data, created visualisations and wrote the manuscript. All authors provided comments and suggestions on drafts. Co-author signatures and dates:

Mick Haywood 19

Damian Thomson 2/12/2019

xvii Details of the work:

This work was published in June 2019:

Cresswell, A., Langlois, T., Wilson, S., Claudet, J., Thomson, D., Renton, M., Fulton, C., Fisher, R., Vanderklift, M. & Babcock, R, Stuart-Smith, R.D., Haywood, M.D.E., Depcyznski, M., Westera, M.B., Ayling, A., Fitzpatrick, B., Halford, A., McLean, D., Pillans, R., Cheal, A.J., Tinkler, P., Edgar, G.J., Graham, N.A.J., Harvey, E. & Holmes, T.H. (2019) Disentangling the response of fishes to recreational fishing over 30 years within a fringing coral reef reserve network. Biological Conservation, 237, 514-524. Location in thesis:

Chapter 6 Student contribution to work:

Thomas Holmes, Shaun Wilson and Anna Cresswell conceived the study. SKW, TJL, DPT, CJF, MAV, RCB, RDS-S, MDEH, MD, MW, AAM, BF, ARH, DLM, RDP, AJC, PT, GJE, NAJG, THH contributed data. Anna Cresswell and Thomas Holmes synthesised the data. Anna Cresswell analysed the data, created the visualisations and wrote the manuscript. JC, MR, RF and TJL provided advice for analysis. All authors provided comments on the manuscript. Co-author signatures and dates: (coordinating author, on behalf of all authors) 02/12/2019 Other authors provided email approval

Student signature Date: 12/12/2019

I, Michael Renton, certify that the student’s statements regarding their contribution to each of the works listed above are correct.

As all co-authors’ signatures could not be obtained, I hereby authorise inclusion of the co- authored work in the thesis.

Coordinating supervisor signature:

Date: 14/01/2020

Look deep into nature, and then you will understand everything better. Albert Einstein

Chapter 1 – Introduction

Chapter 1 Introduction

Disturbance ecology

Disturbances are part of the natural functioning of ecosystems and play an integral role in shaping the structure and dynamics of ecological communities. Accumulating evidence suggests that disturbances are increasing in frequency and intensity globally (Jentsch & Beierkuhnlein, 2008; Zhang et al., 2013; Steffen et al., 2015; Babcock et al., 2019). Thus, it is suggested that ‘disturbance ecology’ is a particularly valuable lens through which to understand how these changes will influence ecosystems and their components (Turner, 2010; Newman, 2019). Disturbance ecology seeks a strong mechanistic understanding of disturbances and the dynamics of change that they trigger. This understanding continues to evolve, particularly for complex ecosystems.

Disturbances can be categorised as either ‘acute’, that is, short-lived, discrete, intense events or ‘chronic’, those acting over a prolonged period (greater than the generation time of many organisms in an ecosystem) (Hughes et al., 2010). The effects of acute disturbances (e.g. wildfire, floods, extreme storms) are usually more rapid than chronic disturbances (e.g. fishing, pollution, habitat destruction) but both can cause drastic change in natural systems (Jackson et al., 2001; Halpern et al., 2008). More frequent and intense acute disturbances are now occurring, as well as increases in the number and intensity of chronic disturbances (Hughes et al., 2010). This raises serious concerns about the capacity of ecosystems to endure and/or adapt to these changes.

There is some inconsistency in the semantics of disturbance, which creates confusion and erodes comparability in the scientific literature (Bailey & Van de Pol, 2016). Most definitions of disturbance fall under one of two perspectives (Rykiel & Edward, 1985; Van de Pol et al., 2017). The first is impact-related, where a disturbance is something that may be measured by its impact or effect on the biological/ecological system of interest. The second relates to a disturbance being a disruption to the normal physical environment (e.g. weather, anthropogenic pressure), which then likely acts as a cause for an effect in a biological/ecological system (Bailey & Van de Pol, 2016; Hodgson et al., 2016; Van de Pol et al., 2017). This is clarified by Bailey and Van de Pol (2016) who explain that this amounts to asking subtly different questions: 1) ‘What is driving this observed biological impact?’ versus 2) ‘What is the biological impact of this 21 disturbance?’. In this thesis I have considered disturbance under its causal definition, as I was interested in exploring the latter question.

Resilience

In modern society, the word ‘resilience’ appears in many contexts – politics, psychology, management, physics, and, of interest here, ecology. The resilience of natural systems has been studied for more than four decades in efforts to describe and understand the responses of ecological systems to disturbance (Holling, 1973). The etymology of the term resilience has origins back to the 17th century from the Latin verb ‘resilere’ meaning, ‘to spring back’. ‘Ecology’, on the other hand, only became prominent in the second half of the 20th century (McIntosh, 1986) (Figure 1.1). The two were merged in earnest with the work of Holling (1973). In recent decades resilience has become a popular framework for guiding discussions about the future of natural systems in a rapidly changing climate (Hughes et al., 2010; Hodgson et al., 2015; O'Leary et al., 2017).

Perhaps because resilience is used in many different circumstances and contexts, defining, and indeed, quantifying, resilience for ecology has been fraught with unclear semantics, copious nuances in definition, and little consensus (Brand & Jax, 2007; Chapter 1 – Introduction Shaikh & Kauppi, 2010; Mumby et al., 2014a; Hodgson et al., 2015). The term broadly relates to the capacity of a system to absorb and/or recover from disturbances, but despite many studies highlighting the need for a common framework of resilience that can be compared between ecosystems (Hodgson et al., 2016; Ingrisch & Bahn, 2018), there are not clear units for measuring resilience (Hodgson et al., 2015). Recent scientific literature has used resilience to describe either the ability of a species, population, functional group, or ecosystem to absorb or withstand a disturbance, that is ‘resistance’; or the ability to recover to a pre-disturbance state, that is ‘recovery’; or a combination of the two (Hodgson et al., 2015; Nimmo et al., 2015). In this thesis I have considered resilience in a bivariate framework combining these two distinct, but related, properties of resistance and recovery, in line with frameworks proposed by Hodgson et al. (2015); Nimmo et al. (2015) and Ingrisch and Bahn (2018). This approach fits most closely with what has been termed ‘engineering resilience’ by some previous studies, in contrast to ‘ecosystem resilience.’ The former is more simplified in generally assuming that recovery will eventually occur (see Mumby et al., 2014a), while the latter accounts for phase-shifts to alternative states.

This engineering concept of resilience is therefore simpler than other frameworks (Nyström et al., 2000; Nyström & Folke, 2001; Mumby et al., 2014b; Boschetti et al., 2019a), but was the choice for this thesis as it offers the greatest opportunity for a definition of a resilience that may be quantified, which is arguably a requirement for the implementation of resilience for management purposes (West & Salm, 2003; Lam et al., 2017). Resistance can be measured by the change in an ecological component, e.g. abundance/ biomass of a species or functional group, from prior to after the disturbance. Recovery can be quantified through consideration of the rate or path of return to pre- disturbance conditions after the disturbance has ceased. In the context of acute disturbances, both resistance and recovery can theoretically be measured, while for chronic disturbances, there is generally no opportunity for recovery, unless the disturbance is reduced, or adaptation occurs under ongoing pressure (Nimmo et al., 2015).

Coral reefs

Coral reefs have been one of the most intensely studied ecosystems in the resilience context, in part because many have shown drastic changes, indicating low resilience, in

23 recent decades (Nyström et al., 2000; Roff & Mumby, 2012). Coral reefs are biologically productive and complex ecosystems (Connell, 1978), making them a challenging and interesting study system. Indeed, they have received more scientific attention than any other marine ecosystem in the last half-century (Mumby & Steneck, 2008). Recent repeated global bleaching events, causing mass mortality of corals (Hughes et al., 2017) has led coral reefs to be considered the most threatened marine ecosystem globally as a result of climate-related ocean change (IPCC, 2019). While corals have demonstrated some capacity to endure and recover from disturbances (Gilmour et al., 2013; Lamy et al., 2016; Cowburn et al., 2019), their resilience to the current and predicted frequency and intensity of disturbances, in particular marine heatwaves, appears to be insufficient (Hughes et al., 2003; Emanuel, 2005).

Acute climatic disturbances that can rapidly reduce live coral cover, diversity and reef structure include marine heat waves, cyclones and flooding events, which are all predicted to increase due to anthropogenic induced climate change (Emanuel, 2005; Rahmstorf & Coumou, 2011; Christensen et al., 2013; Pratchett et al., 2013; Cai et al., 2015). Chronic disturbances also threaten coral reefs, with ongoing increases in ocean temperatures and ocean acidification major concerns for the future of coral reefs, in addition to water quality, overexploitation, disease and coastal development (e.g. Jackson et al., 2001; Hughes et al., 2003; Pandolfi et al., 2005; Bruno & Selig, 2007; Lough, 2016). The disturbances that are considered in this thesis and a conceptual model of their dynamics and examples of the resulting change in ecosystems components are shown in Figure 1.2.

Early assessments suggested that in general, reefs recover from acute disturbances but not from situations where gradual declines led to their demise (Connell, 1997). More recent studies suggest that it will be the acute disturbances, overlaid on a backdrop of chronic disturbances, that ultimately cause mass collapse or entire restructuring of natural ecosystems (Fraser et al., 2014; Vasseur et al., 2014; Lawson et al., 2015; Bailey & Van de Pol, 2016; Grant et al., 2017; Babcock et al., 2019). In efforts to maintain coral reefs for their unique socio-economic and ecological values, resilience has become a paradigm in coral reef management (Hughes et al., 2005; Mumby et al., 2014a). I have adopted a resilience framework for this thesis in order to further conceptualise and understand disturbance ecology in coral reefs.

Chapter 1 – Introduction Coral reef ecosystem components

Resilience is used at multiple levels of resolution: in reference to networks of ecosystems (Nyström & Folke, 2001); ecosystems; or, more simply, ‘ecosystem components’. The last is the simplest to quantify (Nimmo et al., 2015). Corals and fish could be argued to be the cornerstone components of coral reef ecosystems, and as such, each are considered as ecosystem components in this thesis. Scleractinian corals (hard corals) are ‘reef builders’ that are foundational to tropical coral reefs, secreting calcium carbonate that over time leads to the construction of the largest living structures on Earth (Goreau, 1963). Strongly correlated with the presence of corals are the fish (Pratchett et al., 2006; Graham & Nash, 2013), which are ecologically important and have globally important socio-economic value (Cinner et al., 2012).

Both fish and coral are valuable case study components for coral reef ecosystems because they are well studied and have a diversity of different taxa and functional types. Within both coral and fish as ecosystem components, there are various resolutions that can be considered based on taxonomy or functional types: e.g. species, genera, family, or functional types. Furthermore, the components can be measured with different metrics, e.g. number of individuals, percentage cover, volume, biomass. Different taxa and functional types may have a higher resilience or adaptive capacity than others (Madin et al., 2008; Fulton, 2011; Cinner et al., 2016). Determining which ecosystem components have greater resilience than others, and why, will allow us to improve predictions for the future composition of coral reef ecosystems.

Corals have often been studied using the metric of coral cover, either as a total of all corals, or for different taxa, for example at the genus level. It is now recognised that the morphologic, demographic and life-history traits of species play a central role in coral reef resilience (Darling et al., 2013; Madin et al., 2016) and coral functional types are now often used alongside taxonomic classifications (McWilliam et al., 2020). For example, corals have a large diversity of shapes and sizes, which has been linked to their vulnerability to disturbance, i.e. different levels of resistance (Madin et al., 2014). There is also important variation in the growth rates and reproductive ability of different coral groups, which can influence their capacity to recover. The success and speed of these processes can govern whether ecosystem components are maintained in the face of

25 acute and chronic disturbances (Loya et al., 2001; McClanahan et al., 2012; Darling et al., 2013).

As with coral taxa or functional types, different fish taxa and functional types can have different levels of resilience. Additionally, different fish groups are often subjected to different types of disturbances, i.e. large-bodied fish that are targeted by fishers, versus smaller coral-associated species that directly rely on coral for food or shelter (Wilson et al., 2006; Pratchett et al., 2008). The life-history characteristics of fish can allow them different levels of resilience, for example, short-lived species have been shown to have higher resilience to fishing than longer-lived species.

Figure 1.2. Schematic illustration of the disturbance types considered in this thesis — fishing, cyclones (or severe storms), marine heat waves and associated coral bleaching, flooding events, associated with freshwater discharge onto reefs, and situations where multiple chronic and/or acute disturbances occur simultaneously — with an example of the disturbance intensity through time and a theoretical response in the ecosystem component.

Chapter 1 – Introduction Management of disturbances

For some disturbances, with fishing a good example, there are established management procedures to moderate the disturbance (e.g. total allowable catch, daily or boat catch restrictions) or to control it temporally (e.g. temporary closures, defined fishing seasons) or spatially (e.g. no-take marine reserves) (Botsford et al., 1997). On the other hand, many of the global and regional disturbances, such as acute climatic disturbances, are, aside from a reduction in greenhouse gases, largely beyond the immediate control of managers and regional policy makers (Mumby & Anthony, 2015). If the disturbances cannot be controlled or moderated, it follows to consider if the resilience of the ecosystem and/or its components may be enhanced with local management (Hughes et al., 2005; Benson & Garmestani, 2011; Morecroft et al., 2012; Emily S. Darling, 2019). Breaking resilience into resistance and recovery can be helpful in this context. Generally studies have shown that for marine heatwaves, protection (e.g. in the form of a no-take marine reserve) does not reduce bleaching response during the disturbance (i.e. does not moderate the disturbance or increase resistance), but can improve recovery (Wilson et al., 2012b; Van Oppen et al., 2017), however Mellin et al. (2016) showed that the magnitude of disturbance impacts were 30% lower and subsequent recovery was 20% faster than in adjacent unprotected habitats using a 20-year time series from Australia’s Great Barrier Reef. There is significant debate and research on how to best manage disturbances in coral reefs and increase their resilience (Hughes et al., 2003; Mumby et al., 2014a; Mumby & Anthony, 2015; Bates et al., 2019; Bruno et al., 2019).

Tools for disturbance ecology

The methods with which we can investigate resistance and recovery and begin to understand, quantify and manage resilience can come from multiple scientific approaches; e.g. laboratory experiments (Wittebolle et al., 2009), field experiments (Thrush et al., 2013), field survey programs, in particular long-term monitoring (Emily

S. Darling, 2019), and modelling (Boschetti et al., 2019a). For any approach, understanding temporal and spatial dynamics in the response to disturbance is essential. This can arguably be best achieved with long time-series data because the processes of resistance and recovery may unfold over many years. Spatiotemporal data is important for identifying whether patterns observed at one point in time hold more generally in space and time, and is generally only achieved in long-term monitoring programs (Mellin et al., 2020). Extensive spatiotemporal datasets are, however, rare for marine 27 systems, including coral reefs, which is almost certainly linked to difficulties in surveying in the marine environment and inconsistent funding. To allow broader inferences, meta-analyses are increasingly used (Koricheva et al., 2013; Hedges & Olkin, 2014). Broadly speaking, a meta-analysis can be defined as a systematic literature review supported by statistical methods where the goal is to aggregate and contrast the findings from several related studies (Glass 1976). Though coral reefs do not have many long time-series data available, they have been highly studied, and therefore the literature base is large, offering opportunities for new insights via the synthesis of data. Modelling is another tool that can be used to help piece together a clear picture of resilience, particularly where empirical data is not available. Even if empirical data does exist, modelling can explore at finer resolutions and over timeframes that are not possible to obtain with empirical data.

Ningaloo Reef

All coral reef systems are different, so while broad statements can be made about the future of coral reefs globally, not all reefs will respond equally (Roff & Mumby, 2012; Cinner et al., 2016). Ningaloo Reef, on the west coast of Australia, is one of the world’s longest and most extensive fringing coral reef systems, encompassing ~300 km of coast (2 degrees of latitude, 21°40 S to 23°34 S) (Cassata & Collins, 2008). There are various local factors which mean that there are relatively few anthropogenic disturbances compared to other reefs globally, arguably making it more feasible to disentangle the individual effects of specific disturbances. Having formed on the western side of a continent, the reef is associated with a hot arid coast: mean daily maximum temperatures on land exceed 37°C in the austral summer and annual rainfall is <50 mm (Figure 1.2; Vanderklift et al., In press). Human population and development have so far remained low (<2500 local population: 2016 census, https://www.abs.gov.au/). However, rates of visitation by tourists are considered relatively high (200,000 people/year in 2004 (CALM, 2005)). There is a multi-tiered management structure with international input from the UNESCO World Heritage Committee, as well as Commonwealth and State (Western Australia) management (CALM, 2005; Jones, 2019). Much of the reef falls within marine parks and reserves including highly- protected ‘no-take’ (IUCN Category II) reserves (34%; (CALM, 2005)). The impact of Chapter 1 – Introduction recreational fishing on Ningaloo Reef is examined in this thesis because it has been a largely consistent disturbance for >30 years.

Ningaloo Reef has not been immune to acute climatic events. Figure 1.4 shows a summary of the documented disturbance events at Ningaloo Reef, with disturbances more common in recent years; however, this is driven in part by an absence of records in the earlier years. The reef has experienced two severe marine heatwaves that have caused mortality of corals via coral bleaching, the most drastic which was associated with a strong La Nina in 2011 (Feng et al., 2015). Cyclones are common, bringing extreme wind, tidal and swell/wave conditions, which have the potential to cause significant damage (http://www.bom.gov.au/cyclone/climatology/wa.shtml).

Figure 1.3. A section of Ningaloo Reef adjacent to Osprey Bay showing the dry arid coastline and the fringing reef structure. The major reef zones, which are a focus in chapters 5 and 6, are indicated (Photo: Nick Thake, May 2019).

Figure 1.4. Timeline showing four types of disturbance at Ningaloo Reef. Cyclones are shown in yellow; cyclone names are provided post 1970 when names were introduced. The solid yellow lines have lengths corresponding to the cyclone category (from 1 to 5) through reference to the x-axis, when this information was available (the length of yellow dashed lines is not informative). Pink bars show positive Ningaloo Nino Indices (associated with warm water temperatures) as defined in Feng et al. (2015) (fig. 2a) from 1950-2013. The length of the pink bars gives the Ningaloo Nino Index in °C through reference to the x-axis. Reports of tsunamis that likely caused an impact at Ningaloo Reef (Nott, 2000) are noted in grey, as well as heavy rainfall events likely associated with flooding and freshwater runoff onto the reef are shown in blue. Chapter 1 – Introduction Aims and structure

Key questions in ecology centre on how ecological communities respond to disturbance (Allison, 2004; De Cáceres et al., 2019). This thesis aims to bring clarity and new tools to the field of disturbance ecology in coral reef ecosystems in an era of climate change. I have endeavoured to develop our understanding of the temporal dynamics surrounding disturbance, and to explore the relative contributions of resistance and recovery to the overall resilience of key ecosystem components in coral reefs (Hodgson et al., 2015). Understanding the biological processes of reproduction, growth and competition, alongside external forcing from disturbances, is essential to obtain a mechanistic understanding of disturbance ecology. These processes are examined individually and in combination in the chapters that follow (for schematised summary see Figure 1.5).

In the study described in chapter 2 the impacts to, and recovery of, coral from acute climatic disturbances were quantified and compared using the global scientific literature base, incorporating data from coral reefs spanning nearly 60 degrees of latitude. This study tackled the challenge of understanding the component processes of resilience - resistance and recovery – and their interdependencies. The study highlighted the importance of biological processes underlying resilience and some of these processes are then explored in chapters 3 and 4.

The focus of chapter 3 is the phenomenon of mass coral spawning, the reproductive process of corals that maintains reefs and provides a mechanism for recovery. This is important because reefs that have experienced disturbance can be repopulated by adjacent healthy reefs. This study explored a potential new tool to monitor this important process. I present evidence that with more readily available technology we can monitor mass coral spawning events remotely using spaceborne synthetic aperture radar.

To complement the consideration of coral reproduction in chapter 3, the study described in chapter 4 considered another key process important to maintenance and recovery of corals, namely growth. Growth data were collected over a two-year period (May 2017- May 2019) at Ningaloo Reef, in a highly exposed environment where there have not been previous studies of coral growth. This work investigated new technology (structure-from-motion photogrammetry) for studying coral growth and compared it to 31 the more traditional coral growth method (tagging and individually photographing coral colonies for measurement).

The processes of reproduction and growth explored in chapters 3 and 4 are clearly important for recovery from the disturbances examined in chapter 2, and thus chapter 5 incorporates information described in the previous three chapters, and moves into a theoretical modelling space, where fine-scale dynamics can be considered mechanistically. A three-dimensional functional-structural model of a community of corals containing five distinct coral growth forms is presented: Coralcraft. In the existing scientific literature models of communities with function and structure are largely unexplored until now. Coral reefs are architecturally complex three-dimensional habitats, yet studies of coral reefs have been primarily limited to two-dimensional surveys. Various scenarios of different frequencies and intensities of disturbance were simulated using Coralcraft, and compared with empirical data from Ningaloo Reef. The study aimed to create clarity on the trade-off between coral morphologies that can quickly occupy space (high recovery potential) versus those that are slow growing but robust in the face of disturbance (high resistance).

Chapter 6 takes a different but important focus, considering resilience to a chronic disturbance. This investigation differs from the previous chapters for several reasons: up to this point in the thesis the focus is on acute disturbances, for which both resistance and recovery can be considered. For chronic disturbances however, resistance and recovery are more difficult to distinguish, as there is often no clear end to chronic disturbances and thus no opportunity for recovery. Furthermore, the previous chapters explore disturbances that are inherently difficult to manage, as they are linked to the climate, the management of which must largely be global. Some disturbances however, can potentially be managed. This final data chapter investigates this idea, and the associated challenges of assessing resistance in a complex marine ecosystem. The effect of recreational fishing over a 30-year period was investigated at Ningaloo Reef. In this study, as in chapter 2, existing data was used via the process of meta-analysis. This allowed investigation of whether management practices can allow for recovery in a system undergoing chronic stress, and whether resistance can be increased. In this chapter the ecosystem component is fish rather than coral, but the resilience concepts remain the same.

Chapter 1 – Introduction Lastly, in chapter 7, the Discussion, I critically evaluate the concept of resilience and its use in coral reef ecology, and ecology more generally. I suggest a set of identifiers that must be established and defined in ecological studies using the term resilience for it to be a useful construct for scientific and policy discussions. I discuss some of the challenges encountered within the research for this thesis, and well as important next steps. I explore the future of coral reef systems such as Ningaloo Reef, placing the knowledge gained in the context of coral reefs globally. Finally, I reflect on the study of resilience, potential trade-offs between complexity and usability, and the future of resilience within ecology.

Figure 1.5. Schematic summary of this thesis.

Chapter 2

Science is a cumulative process (Shoemaker et al., 2003) and therefore it is not surprising that there are often numerous studies addressing the same basic questions. This chapter takes a broad look at the published literature that has documented the impact of, and/or recovery from, acute climate disturbances. Changes in coral cover due to disturbance are often particularly notable and thus provided a suitable case for investigating the two key components of resilience, that is, resistance and recovery, and the links between them. It is established that various external variables, such as geographic location and anthropogenic stressors, can influence resistance and recovery in coral reefs, but the effect of internal state variables, such as the amount of coral cover before and after a disturbance, has received less attention. The influence of these variables on the short-term dynamics surrounding disturbance is investigated in this chapter.

Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances

Temporal dynamics in coral cover: a global analysis of impact and recovery from acute climatic disturbances

Anna K Cresswell1,2,3, Michael Renton1,4, Tim Langlois1,3, Jasmine Lynn1, Damian Thomson2, Joachim.5,6 Claudet

Abstract

Acute climate disturbances threaten the long-term viability of coral reefs, and their resilience depends on both their resistance (the ability to withstand a disturbance) and recovery (the ability to return to a pre-disturbance state). We know that external variables, such as geographic location, or anthropogenic stressors, can influence resistance and recovery in coral reefs, but the effect of ‘internal state variables’, such as the amount of coral cover before and after a disturbance, has received less attention. Here, we review the global scientific literature on both the impacts and the recovery of coral from acute climatic disturbances, specifically marine heatwaves, extreme rainfall and severe storms. We quantify the mean relative change in coral cover resulting from the different disturbances and the 5-year recovery rates following disturbance. We also use a novel approach to classify recovery trajectories into flatline, linear, accelerating or decelerating ‘shapes’, finding that the most common shapes are positive accelerating and decelerating, with linear recovery rare. We show that both recovery rate and shape have meaningful non-linear relationships with the internal state variables. Higher coral cover after disturbance correlated with higher recovery, but only until a certain point;

37 this relationship reversed when coral cover after disturbance was close to pre- disturbance levels. We suggest this can be explained by higher coral cover facilitating faster growth and reproduction, but limited by space availability. This theory was supported by higher probabilities of positive decelerating recovery trajectories when coral cover after disturbance is closer to the cover before disturbance. In providing an up-to-date synthesis of acute climatic disturbances in coral reefs, we show that internal state variables are important factors for conceptualising and quantifying resilience; an important contribution in the ongoing pursuit to understand and quantify the dynamics of change in complex ecosystems.

Keywords: Climate change, coral reef ecology, disturbance ecology, extreme climatic event, meta-analysis, resilience

Introduction

Understanding the response of natural systems to disturbance is an overarching goal in ecology (De Cáceres et al., 2019), particularly as recent studies suggest that it will be the acute disturbances, or extreme events, that will ultimately drive mass restructuring of natural ecosystems (Fraser et al., 2014; Vasseur et al., 2014; Lawson et al., 2015; Bailey & Van de Pol, 2016; Grant et al., 2017; Babcock et al., 2019). The processes and factors that govern the response of natural systems to acute disturbance are numerous and complex, and thus difficult to quantify. Conservation requires a strong mechanistic understanding of how and why ecological systems change during and after disturbance (Morecroft et al., 2012). ‘Resilience’ is often discussed in the context of ecosystem response to disturbance, with one approach considering resilience to be the product of ‘resistance’, the ability of a system to absorb or withstand a disturbance, and ‘recovery’, the ability of a system to return to its pre-disturbance state (Hodgson et al., 2015). Despite much discussion and numerous theoretical studies conceptualising the ways to measure these components of resistance and recovery (Nyström & Folke, 2001; Hodgson et al., 2015; Ingrisch & Bahn, 2018), as well as individual empirical studies considering resistance and recovery (e.g. Cowburn et al., 2019), we do not have a broadly applicable understanding of the relative roles, controls on, or relationship between impact and recovery processes that is grounded in empirical data.

Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances Resilience has been a particularly popular framework for guiding discussion of the persistence of coral reefs in a rapidly changing climate (Mumby et al., 2007; Hughes et al., 2010; Mumby & Steneck, 2011; O'Leary et al., 2017). Coral reefs provide a good study system for understanding resilience to acute disturbances, because these events, while generally considered to be rare (Van de Pol et al., 2017), are not uncommon in corals reefs (Connell, 1997). Furthermore, their impacts on coral abundance are often clearly measurable by a loss in live coral cover. The negative effects of global climate change on coral reefs are increasingly being recognised, particularly via the occurrence of mass coral bleaching (Hughes et al., 2017), whereas the effects of other acute climatic disturbances have been realised for longer: severe storms, i.e. cyclones/hurricanes (Gardner et al., 2005; Beeden et al., 2015) and flooding events (Stoddart, 1969). These disturbances are intrinsically linked to the climate, and are predicted to increase in frequency, intensity, or both (Zhang et al., 2013; Cheal et al., 2017; Frölicher et al., 2018).

In coral reefs, live coral cover is understood to be a reliable indicator of overall ecosystem health. Thanks to many individual empirical studies and previous meta- analyses there is a good understanding of disturbance impacts on coral cover (Gardner et al., 2005; Bruno & Selig, 2007; Claar et al., 2018) and recovery of coral post disturbance (Baker et al., 2008; Graham et al., 2011). Previous studies have mostly focussed on the external variables that explain variability in the magnitude of impacts to coral and the rate of recovery, such as anthropogenic stressors, local management, type of reef, or geography (Connell, 1997; Baker et al., 2008; Graham et al., 2011; Claar et al., 2018). The role of the ‘internal state variables’, such as the amount of coral before and after disturbance, in governing the short-term dynamics surrounding disturbance has received less attention in quantitative syntheses. Arguably, however, a good understanding of the internal controls on impact and recovery is needed in order to disentangle the influence of external controls. Graham et al. (2011) suggested that recovery dynamics are likely to be non-linear, with recent modelling approaches using Gompertz models (MacNeil et al., 2019; Mellin et al., 2019b) to capture non-linear recovery that may be most rapid at intermediate levels of coral cover post disturbance. Most previous meta-analyses have assumed linear recovery of coral (Graham et al., 2011; Bruno et al., 2019), which is unlikely to be accurate in most cases, particularly for a percentage metric, which is bounded by a maximum of 100% cover.

39 Recent research has highlighted that internal state variables can guide management action. Various studies have discussed a coral cover of 10% as being an important threshold for allowing positive reef carbonate production (Perry et al., 2013; Perry et al., 2015; Januchowski-Hartley et al., 2017) and in influencing the abundance and diversity of fish populations (Beldade et al., 2015). Emily S. Darling (2019) recently proposed three management strategies (protect, recover, or transform) which are based in a large part upon whether a reef is above or below a proposed 10% coral cover threshold of taxa that are important for structural complexity and carbonate production. Emily S. Darling (2019) presented evidence and arguments for the importance of this threshold, but the influence of such a threshold on the short-term coral dynamics in the years before and after disturbance has not been considered.

Our goal was to synthesise a unique global dataset from the published literature on impacts and recovery of coral from acute climatic events – specifically temperature stress events, physical disturbance from cyclones and severe storms, and flooding events – that would allow us to investigate the relationships between resistance and recovery as the composites of resilience in coral reef ecosystems. We specifically wanted to assess the role of internal state variables in governing the temporal dynamics of coral in response to acute disturbance, considering their influence on the relative magnitude of coral reduction resulting from disturbance and on the recovery trajectories based on three attributes: direction (positive or negative), rate (change in coral cover per year) and trajectory shape (linear, accelerating or decelerating). We aimed to assess the predictability of different recovery outcomes using internal state variables; to understand if and how coral abundance prior to disturbance influenced the impact and recovery from disturbance; and how coral abundance remaining post-disturbance influenced recovery. We further sought to test for the importance of a 10% coral cover threshold in influencing both impact and recovery from disturbance.

Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances Methods

Data search and selection criteria

Appropriate data were obtained via electronic literature searches in Scopus®, the largest abstract and citation database of peer-reviewed literature. The literature search string used is provided in Appendix 2.1. Our focus was on peer-reviewed publications, whereas other reviews and meta-analyses have sought to include more of the grey literature (Schutte et al., 2010). Data searches were initially conducted on 15 March 2017, before being updated on 27 June 2019. Publications were selected for potential relevance firstly based on the Scopus classification of ‘subject area’ (for more details see Appendix 2.1) and then from the title and abstract, before examining the full text. Several major reviews and meta-analyses were identified in the data searches (Wilkinson, 2000; Gardner et al., 2003; Gardner et al., 2005; Bruno & Selig, 2007; Baker et al., 2008; Bruno et al., 2009; Ateweberhan & McClanahan, 2010; Schutte et al., 2010; Ateweberhan et al., 2011; Graham et al., 2011; Ateweberhan et al., 2013; Claar et al., 2018; Hughes et al., 2018) and the literature cited by these publications were examined, i.e. snowball sampling (Goodman, 1961), in order to identify any publications that may have been missed in the Scopus search.

For inclusion, a publication was required to report a measure of coral abundance in comparable units before and after a known or suspected ‘acute climatic disturbance’: temperature stress events, physical disturbance from cyclones and severe storms, and flooding/high rainfall events. While there are other key acute disturbances that affect coral reefs, including outbreaks of crown-of-thorns starfish (Pratchett et al., 2017) and coral disease (Van Woesik & Randall, 2017), we chose to focus on the disturbances intrinsically linked to a changing climate.

‘Before’ and ‘after’ measurements were required to be within ± 3 years of the disturbance. Data with replicated measurements and clear reporting of sample size (number of replicates and level of replication), mean and an appropriate measure of error associated with a mean (e.g. standard error, confidence interval, standard deviation) were required for the meta-analytical components of analyses, though publications that did not report these requirements were also collected for use in data

41 summaries. Publications that presented data only in boxplot form were not considered in the analyses as means could not be reliably extracted. Summarised data from reviews and meta-analyses were included on a case by case basis and the individual publications contributing to the syntheses were used in preference to the synthesis publication itself.

Data extraction

The data required for quantitative meta-analysis — mean, error, sample size and level (quadrat, transect, site), date of measurements and of disturbances — were extracted for all publications that met the selection criteria. The units used to measure coral abundance were recorded (e.g. total coral cover, density of coral colonies or surface area). Data were collected from text or tables, supplementary data, or in most cases, from graphs. Data in graphical form were extracted using the software DataThief (Tummers, 2006). This software allows distances within the images to be calculated using a pixel-distance ratio of the data points to the known scale of the graph axes within an image. Data were extracted at the lowest level of replication that had an appropriate error associated. If these lower replication levels did not have errors associated with them, they were combined to the next hierarchy up, usually to the publication level, and the mean and standard deviation of the group was calculated. Errors reported as standard errors and 95% confidence intervals were converted to standard deviations in the pooled database. In cases where the error was zero or too small to extract from the figures a small constant (c=0.01) was used. This constant was also added to any zero means, to allow calculation of log response ratios.

Metadata were extracted from the publications, including disturbance type, site information including country, latitude and longitude (in cases where geographical coordinates were not provided by the publication, we approximated using Google Earth from the location described in the text). We recorded sampling information including the method (e.g. diver based, aerial, manta tow) and area surveyed, where provided.

Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances Data organisation, classification and hierarchy

Publications were divided into ‘studies’ which formed the sampling units of our analyses in cases where there were data for different regions, reefs, islands/atolls, sites, reef zones or depths, and/or multiple disturbances within a publication. For time-series data, all data points following a disturbance were examined as the ‘recovery trajectory’, up until the point where a secondary disturbance occurred, at which point a new study was created, and so on. The closest data point available before the disturbance was used to quantify the amount of coral before disturbance. Any data points that were not clearly before or after the disturbance were classified as ‘during’. Studies were then classified as one of ‘Impact’ (measurement before and after a disturbance); ‘Recovery’ (≥ 3 measurements after a disturbance over ≥ 3 years); or ‘Impact and Recovery’, which had both types of data.

Impact analysis

We used a mixed-effects meta-analytical approach to assess change in coral cover resulting from disturbance across the pooled data with effect sizes based on the log response ratio of coral cover after to before disturbance for each study ( Eq. 2.1). Effect sizes were weighted by the inverse of the sum of the within- and among-study variances. Weighted mean effect sizes and associated confidence intervals were calculated using the metafor package (Viechtbauer, 2010) in the statistical program R (R Core Team, 2017) with the variance estimator set to “REML” restricted maximum- likelihood estimator (Viechtbauer, 2005).

풄풐풓풂풍 풆 = 퐥퐧(푹푹) = 퐥퐧 ( 풂풇풕풆풓 ) Eq. 2.1 풄풐풓풂풍풃풆풇풐풓풆

We used weighted random mixed-effects meta-regression models, that is, linear models that examine the influence of one or more moderator variables on the outcomes (Viechtbauer, 2010), to determine the influence of disturbance type on the magnitude of the effect size, e. We also considered coral cover before disturbance as a predictor for e.

43 Various studies have identified a cover of 10% coral as being an important threshold in determining the longevity of a reef (Perry et al., 2013; Perry et al., 2015; Januchowski- Hartley et al., 2017; Emily S. Darling, 2019). We investigated this threshold by quantifying how often coral cover was reduced from >10% to <10% as a result of disturbance, and examining whether there was any significant difference in the size of the impact between situations where coral cover was less than 10% before disturbance versus greater than 10%.

There were likely correlations among studies in the dataset, resulting from the factors such as similar locations, authors, publications or survey methods and, while we acknowledge this, there were too many to model robustly.

Recovery analysis

For time-series data with multiple disturbances we tested for whether each disturbance resulted in an impact log response ratio <-0.1, as a proxy to indicate that the disturbance had had some impact that would compromise the recovery trajectory from the primary disturbance, and if not, the secondary disturbance was ignored. This was more conservative than previous studies, e.g. Connell (1997) defined an ecologically significant decline to be 33% of the initial cover, but we wanted to ensure, as much as possible, that the effects of secondary disturbances were not influencing the recovery response from the primary disturbances.

Calculating recovery rate

We calculated the median temporal span of data following disturbance (5 years) and focussed on this period to examine recovery rate. We required at least 3 data points within this period. We fitted linear models using lm() in R (Zuur et al., 2009), with coral cover as the response variable and years since disturbance as the predictor variable, to the datapoints in the 0-5 years following disturbance for each recovery study. From these models we extracted the slope (i.e. the average annual rate of recovery, % year-1) and the standard error and the variance of the slope from the model summaries.

Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances Classifying recovery trajectory shape

We next investigated the ‘shape’ of the recovery trajectory. Our approach was to fit a set of four possible models with similar levels of complexity — null, linear, logarithmic (decelerating) and simple second order quadratic (accelerating) — to the longest time- series of data available following a disturbance, with coral cover as the response variable and years since disturbance as the predictor variable. The models were chosen to be ecologically relevant and to represent qualitatively distinct dynamics that are broadly representative of a range of possible outcomes, though we recognise that other models could have been considered. The equations and base profiles are given in Figure 2.1. We modified the model selection procedure in Thiault et al. (2017) and all four models were fit to the recovery data of each study, before examining the ‘relative likelihood’ of each model, which is proportional to the probability that the ith model minimizes the (estimated) information loss. Likelihood was based on Akaike Information Criterion (AIC) though we also considered AIC corrected for small sample sizes (AICc) and Bayesian Information Criterion (BIC) (Anderson & Burnham, 2002). The recovery trajectory was then classified as the model with the highest relative likelihood. These were further classified to one of eight possible ‘shapes’ based on whether the models were negative or positive (i.e. positive linear, negative linear, positive decelerating, negative decelerating, positive accelerating, negative accelerating). In a case where there was no strong trend in the data, i.e. a flatline, the null model would be selected in preference to a linear model based on the information criteria, as it had one less parameter. To account for this, when a null model was selected, the slope of the linear model was also examined, and if the slope of the linear model was less than 0.5 we classified this as ‘flatline’. For the positive recovery trajectories, we calculated the mean and median predicted recovery time for coral cover to return to 95% of the pre-disturbance cover.

Figure 2.1. Recovery trajectory shape options based on the sign (negative or positive) of linear, logarithmic and exponential model types.

Predicting recovery rate

We used linear models, again using lm() in R, to investigate whether disturbance type, the size of the impact (lnRR, as a proxy for resistance) and the internal state variables of coral cover before and after disturbance could help explain recovery rate (a) to d) in Figure 2.2). The continuous predictors were explored following the protocol of Zuur et al. (2010) and transformed to make their distributions less skewed where appropriate: coral cover before and after disturbance were square-root transformed while the log- response ratio (the impact) was converted back to a ratio (RR) by taking the exponential. We first tested a model with all four predictor variables, before simplifying to avoid overfitting by removing non-significant variables sequentially and testing to confirm AIC was reduced.

Trajectory shape

We trialled classification trees using the same predictor variables to classify the recover trajectory shapes but were not successful in distinguishing between shapes using the Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances trees. We next considered a generalised linear model (GLM) with the binomial response variable of positive accelerating versus positive decelerating recovery trajectory (the two most common shapes), excluding studies with the other shapes. We were specifically interested in if and how the two internal state variables – coral cover before and after disturbance –influenced the probability of each recovery trajectory shape and fit the model with only these variables. The relative likelihood of the recovery trajectory shape was used to weight the studies in the GLM.

Figure 2.2. A schematised example of a study showing the key predictor (a-d) and response variables (1-3) considered in recovery analyses.

47 Results

Data summary

The first literature search in February 2017 produced 5,436 potentially relevant publications, while a follow up search in June 2019 produced another 1,004 potentially relevant publications. 68 additional publications were identified for potential relevance from these publications. Figure 2.3 shows the results of sequentially restricting the publications to those that were relevant and satisfied the selection criteria. The final dataset is unlikely to be exhaustive, given challenges in searching such broad literature, but we endeavoured to collect as many appropriate studies as possible. 100 publications were identified with before and after measurements relating to one of the four disturbance categories. When each of these publications were partitioned to account for factors such as distinct regions, sites or depths and individual disturbances we had 394 ‘studies’ for analysis. 380 of these studies reported percentage coral cover, while other measures included number of colonies per unit area. 88 studies reported means, but not sample sizes or errors. 208 studies related to temperature stress (heat waves/ El Nino events or bleaching reports); 138 to physical disturbance (cyclones or severe storms assumed to be associated with damaging waves); 30 concerned heavy rainfall or flooding presumed to have resulting in fresh water input to the study location; and 18 studies had more than one disturbance event in between before and after measurements. Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances

Figure 2.3. Schematic summarising the data search and selection process. The final publications and ‘studies’ are categorised into those with data before and shortly after a disturbance (‘impact’), those with data at multiple times after the disturbance (‘recovery’) and those with the combination of data before, and at multiple times after the disturbance (‘impact and recovery’).

There were 90 recovery studies, 60 which were associated with data prior to the disturbance, which allowed us to explore the relationship between impact and recovery. Of these 90 recovery studies, 47 related to temperature, 34 to physical disturbance and 8 had multiple disturbances. There were no flood studies with appropriate recovery data.

The amalgamated study sites span 59 degrees of latitude from 30°S to 29°N (Figure 2.4c). The earliest disturbance in the dataset was in 1966 and the most recent was in 2018 (Figure 2.4a,b). A higher count of studies in certain years can be partly explained by the occurrence of three pan-tropical El Nino events causing mass coral bleaching in

49 1998, 2010 and 2015/6 (understandably, the last event has not yet accumulated as much literature as the former two) (Heron et al., 2017).

Figure 2.4. a) Spatial distribution of studies showing sites with the colour of the points indicating the type of disturbance. Open circles show sites with impact data and closed circles show sites with both impact and recovery data and those with only recovery data; temporal distribution of disturbances for b) impact and c) recovery data, categorised by disturbance type. A study is defined by a combination of the disturbance and study site.

Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances

Impact

On average, disturbances caused a 56% relative reduction in coral cover such that the average log response ratio, e = -0.58 ±0.08 (95%CI). There was very large heterogeneity in this effect (QT = 304004.3, df = 393, p<0.001). There were clear shifts in the distribution of coral cover towards lower covers following disturbance, and a corresponding drop in the median coral cover among studies (Figure 2.5, Figure 2.6).

Disturbance type explained a significant amount of heterogeneity (Q M =28.0, df = 3, p<0.001) but large residual heterogeneity remained (QR = 291779.3, df = 390, p<0.001). All disturbance types resulted in overall significant average reductions in coral cover (Figure 2.6). Floods caused the greatest reduction according to the mixed-model for disturbance typology (e = -1.07 ±0.29, 95%CI), following by temperature anomalies (e = -0.68 ±0.11, 95%CI), physical disturbances (e = -0.36 ±0.13, 95%CI) and multiple disturbances (e = -0.36 ±0.13, 95%CI) (Figure 2.6). There were cases when coral cover decreased following disturbance, which was most common for physical disturbances.

Figure 2.5. Violin plots showing the distribution of the coral cover across studies before and after disturbance for a) all studies (n = 380); b) temperature, n = 197, c) physical (cyclones and severe storms, n = 137), d) flooding, n = 30 and e) multiple, n = 16, disturbances. Boxplots overlaid, showing the median and interquartile ranges.

The relationship between the size of the impact (lnRR) and coral cover before disturbance was significantly negative (QM = 3.9632, df = 1, p<0.047) and indicated that for a 1% increase in coral cover there was a decline of 0.008 in the lnRR, which approximates to a 0.8% decline in the response ratio for each 1% increase in the percentage coral cover prior to disturbance.

51 There were 344 studies with coral cover >10% and 36 with coral cover <10% before disturbance. At the first measurement following the disturbances 113 (33%) studies had coral cover <10%, while 267 datasets maintained coral cover >10%.

Figure 2.6. a) The percentage cover of coral before disturbance plotted against the cover remaining post disturbance. Colours represent the different disturbance types. b) The mean impact, e, lnRR, for each disturbance type. The dashed line represents coral cover before equal to coral cover after.

Recovery direction, rate and trajectory shape

72 studies satisfied the criteria for investigating recovery rate. 47 had positive slopes and 15 were negative (Figure 2.7a), with a mean recovery rate of 1.23±0.29 (SE) % coral cover year-1.

The distribution of models classified to each recovery shape for the full length of available recovery data are shown in Figure 2.7b, which demonstrates the high degree of heterogeneity in recovery trajectory shapes. The shape selection according to relative likelihood based on AIC and BIC were near identical (Appendix 2.3) whereas the AICc selection resulted in more null and flatline models, as to be expected. Visual inspection of the fits indicated that the AIC and BIC were reasonable (Appendix 2.3) and AIC was chosen to represent the most likely model fits which are shown in Figure 2.7b. The trajectories were positive for 45 studies, negative for 17 studies and null/ flatline for 10 studies, i.e. signs of recovery, signs of decline and no detectable signs of recovery. The mean time for coral to recover to 95% of the cover before disturbance was to be 9.3±1.1 Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances years, (median 7.1 years) when 2 outliers with recovery times of >50 years were removed.

Figure 2.7. a) Predictions from linear models fit to the 5-year post disturbance recovery trajectories. Colour indicates a positive versus a negative trend. b) Predictions from the most likely model (based on AIC) fitted to the full length of available recovery trajectory for each study. Colours indicate the shape classification.

Figure 2.8. Number of studies classified to each of the eight recovery shape based on AIC selection criteria. Black text shows the proportion of each shape, while the red text shows the mean of the relative likelihood of this shape when it was selected as the most likely (i.e. a higher value indicates that on average, confidence in the classification was higher).

53 Predicting recovery rate

When coral cover prior to disturbance was <10% the mean recovery rate was smaller than for when coral cover was >10%, though not significantly (t-test, p=0.221). When coral cover after disturbance was <10% the mean recovery rate was higher than when coral cover was >10%, again not significantly (t-test, p=0.367). Of the studies that had <10% coral cover following disturbance (n = 20), 16 were classified with positive recovery shapes and three were negative and one was a null model. Of the 52 studies that had >10% cover following disturbance, 29 had positive trajectories while 14 were negative and 9 were classified as null/flatline models.

Model exploration found no significant difference in the recovery rate between physical and temperature disturbances and including this factor did not improve to model AIC (there were not enough replicates to reliably test for differences with flood or multiple disturbances). The most parsimonious model for predicting recovery rate included all other variables: coral cover before and after disturbance, and the response ratio, RR, of 2 the impact (p <0.001, df = 58, Multiple R = 0.21) (Table 2.1).

Variable Estimate Std. Error P-value Intercept 7.02 2.35 0.004

sqrt(Cbefore) 1.31 0.39 0.002 RR -9.71 2.76 <0.001

sqrt(Cafter) -0.87 0.36 0.020

The model revealed a parabolic type relationship between post-disturbance coral cover and rate of recovery, which depended on pre-disturbance coral cover (Figure 2.9). In most cases, the rate of recovery was higher for greater post-disturbance coral cover, but only up to a point, after which the relationship plateaued and started to decrease as post- disturbance coral cover approached similar levels pre-disturbance coral cover. In other words, greater post-disturbance coral cover was associated with higher recovery rates until post-disturbance coral cover was close to pre-disturbance coral cover (i.e. when Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances impact was small and thus high recovery rates were not possible). For very low coral cover before disturbance (10%) the maximum possible size of impact was small (because there is not much coral to use), and thus recovery rate was also low; in this case recovery rate just declines for higher remaining cover.

Figure 2.9. Predicted relationship between the percentage coral cover remaining after disturbance, the rate of recovery and the percentage of coral cover prior to disturbance as determined from linear modelling. Different colours indicate predictions for different values of coral cover before disturbance. Grey arrows indicate the proposed mechanisms driving the relationships.

Predicting recovery shape

Exploration using classification trees showed it was not possible to distinguish between the eight possible recovery trajectories. Positive accelerating and positive decelerating were the most common recovery trajectories and we obtained a significant binomial generalised linear model of the probability of these two shapes based on coral cover before and after disturbance (Figure 2.10, Table 2.2). In summary, this model indicated that the probability of having an accelerating trajectory decreased as the amount of space available for recovery decreased, or conversely, the probability of having a decelerating recovery trajectory increased as the amount of space available became limiting.

55 Table 2.2. Results of the binomial generalised linear model of the probability of a positive decelerating versus positive accelerating recovery trajectory shape as models by coral cover before and after disturbance.

Variable Estimate Std. Error P-value Intercept -0.53 0.15 <0.001

sqrt(Cbefore) 0.17 0.03 <0.001

sqrt(Cafter) -0.18 0.03 <0.001

Figure 2.10. Probability of positive decelerating versus positive accelerating recovery shape as determined from the binomial generalised linear model (model equation shown in italics). Different colours indicate a different amount of coral cover before disturbance. Grey arrows indicate the proposed mechanisms driving the relationships.

Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances Discussion

We captured an important relationship between recovery rate and the size of the impact as a function of the percentage coral cover before and after disturbance (internal state variables). The parabolic relationship we found between pre-disturbance coral cover, post-disturbance coral cover, and recovery rate indicates two opposing processes are occurring, which we suggest are 1) increased recovery potential with greater coral cover and 2) decreased recovery potential resulting from density-dependent space limitation, supporting a Gompertz type model of recovery (MacNeil et al., 2019; Mellin et al., 2019b). The positive accelerating and positive decelerating recovery shapes were the most common, but importantly, many trajectories (35 of 72 studies) showed no signs of recovery (flatline or null models) or signs of further decline after disturbance (negative models). Predictability between the recovery shapes was low, but, in support of the influence of space limitation, we found higher probabilities of positive decelerating recovery trajectories when coral cover after disturbance was closer to the cover before disturbance, i.e. little space for high recovery rates. We did not detect strong signals for the importance of a 10% threshold in governing recovery dynamics, rather finding that following relatively high impact disturbances, recovery rate was positively related to initial post-disturbance coral cover in a more continuous way. Though we provide quantification for the magnitude of impact and the rate and shape of recovery from disturbance, which are potentially key measurable informants of the resistance and recovery components of resilience (Nimmo et al., 2015), we were not able to clearly compare the values between different studies without having consistent information on the intensity of disturbances across the dataset. Furthermore, the two recovery metrics explored — recovery rate and recovery trajectory shape — clearly gave different information.

The relationship between the internal state variables and recovery rate expands significantly on previous work. Connell (1997) investigated both whether higher coral cover before disturbance predisposed a site to slower recovery, and if greater loss of coral was associated with slower recovery, but did not find evidence to support these hypotheses. Graham et al. (2011) showed a relationship between recovery rate and post- disturbance coral cover that is similar to that of the present study, i.e. slower for low post-disturbance cover, with highest recovery rates when the post-disturbance cover was in the bracket of 6-10%, and then slower recovery for post-disturbance covers greater 57 than this. Graham et al. (2011) suggested this relationship may have resulted from only considered recovering reefs that were therefore capable of rapid space occupation. However, in the present study we also included reefs that were showing no recovery or declines. Graham et al. (2011)’s second explanation now appears to be more likely in light of the results of our study, that is, recovery dynamics are nonlinear, with the highest recovery occurring at medium to high levels of coral loss. The modelled relationship in our study provided greater insight on this theory, showing a further dependency on the coral cover prior to disturbance and illustrate these relationships.

We hypothesise the relationship between the internal state variables and recovery rate is driven by two opposing processes: coral growth/reproductive potential and space limitation. We suggest the first is the controlling mechanism when impact is relatively high, creating enough space for high recovery rates. In this case, with higher coral cover there are more corals available that can grow and occupy space, therefore allowing higher reproductive potential and/or habitat provision for coral settlement. We suggest the second mechanism, space limitation (Connell, 1978; Hughes & Jackson, 1985), becomes the greater control on recovery rate when coral cover remains relatively high, and there is little room in the community for high recovery rates. This second mechanism was supported by higher probabilities of positive decelerating recovery trajectories when coral cover after disturbance was closer to the cover before disturbance. From these results we suggest that pre-disturbance coral cover is as a coarse proxy for the carrying capacity of the system, given that recovery is slower when post-disturbance coral cover is closer to that pre-disturbance value. The accuracy of the coral cover before disturbance in representing the carrying capacity of coral in a particular coral reef ecosystem is likely influenced by various factors including the dynamical status of coral cover prior to disturbance (Rykiel & Edward, 1985; Bahn & Ingrisch, 2018), and previous disturbances. Nonetheless, the model was surprisingly strong given high variability between studies and disturbances, and other variation presumably stemming from the type of coral and its capacity for regeneration, reproduction and growth, as well as other factors such as the legacy of previous disturbance events.

The models for recovery rate showed no significant difference in recovery between physical and temperature disturbances while the internal state variables were found to be more useful predictors. Recovery is known to vary between physical disturbances Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances that remove reef structure (e.g. cyclones) and those where the structure remains, at least in the short term, as dead coral (bleaching) (Bellwood et al., 2006; Graham et al., 2006; Graham et al., 2015). Despite this, Graham et al. (2011) also found no strong evidence for the influence of disturbance type on recovery rate. Finer temporal data than the generally annual frequency of surveys in the studies included here may allow better investigation of the different impacts and recovery resulting from different disturbances types.

Attempts to categorise differences in impact and recovery resulting from pre- and post- disturbance coral covers on either side of a 10% threshold were challenged by lower statistical power from dealing with a categorical variable. In any case, it seems that at least for recovery rate, it seems that a fixed threshold is a simplification, and rather there are complex relationships between both coral cover before and after disturbance, which then influence recovery. The amount of coral cover before disturbance was negatively correlated with the size of the impact, perhaps reflects the idea that if you have more coral, you have more coral to lose.

Understanding recovery rates is particularly important as the return time between acute climatic events decreases (Hughes et al., 2018). The 5-year mean recovery rate we calculated, 1.23% year-1, was significantly lower than that calculated by Graham et al. (2011)’s at 3.56% year-1, though that study only considered recovering reefs (i.e. positive trajectories) and the mean length examined in that case was 11.1 years. Importantly, we found signs of positive recovery only for 37 of 72 studies. While it is acknowledged that recovery following disturbance can be delayed (Wilkinson, 1999; Mellin et al., 2016), best represented by the accelerating recovery trajectory shape in the present study, it has generally been assumed that recovery will eventually occur (Nyström & Folke, 2001). However, phase shifts to non-coral dominated states have also received a lot of attention (Done, 1992; Bellwood et al., 2006; Hughes et al., 2007). The absence of positive trajectories in many of the studies examined here maybe be due to the conservative classification of a disturbance (a reduction in lnRR of <0.1). When disturbances are small, it is more likely that other trends in the ecosystem could swamp the recovery trajectory. The length of the time-series data available may not have captured the recovery in some cases, or the return time between consecutive disturbances may not have allowed for positive recovery. Hughes et al. (2018) found that the median return-time between pairs of severe bleaching events has diminished

59 steadily since 1980 and is now only six years, and therefore that the interval between recurrent bleaching is too short for full recovery of coral assemblages. We found the median time for recovery to within 95% of pre-disturbance coral cover was 7.1 years, in the cases where the recovery slope was positive. As acute disturbances become more regular they begin to merge from acute to chronic and it becomes more difficult to study the recovery trajectories relating to individual disturbances.

In this study we considered disturbance to be a cause (e.g. a cyclone occurred in the study area) (Rykiel & Edward, 1985; Van de Pol et al., 2017), rather than an effect, and therefore there were cases when a disturbance was noted by a study, but no negative effect was observed. Such an approach likely contributed to high variability in the impacts observed, but we consider it to be important, as we wanted to ask, ‘What is the biological impact of this climate extreme?’ not ‘Which climate process drives this extreme biological event?’ a distinction explored by Van de Pol et al. (2017). While we used the size of impact to quantify resistance, meaningfully interpreting this was not possible without being able to quantify the intensity of the disturbances across the dataset. Further, it is important to acknowledge that the time between before measurements of coral cover, the disturbance, and the after measurement varied from one study to another, degrading comparability with the implication that our impact results likely underestimate the true impacts. This would also add variability to the models of recovery rate and trajectory shape.

Many studies have established strong variation in the dynamics of corals among species and functional types (Hughes et al., 2012; González-Barrios & Álvarez-Filip, 2018; Wilson et al., 2019) and traits (Darling et al., 2012; Darling et al., 2013; Madin et al., 2016) and there is no doubt that complex dynamics underlie the patterns in total coral cover examined here. Some coral traits are more important for either resistance or recovery, for example the 10% threshold proposed by Emily S. Darling (2019) focussed on competitive and stress-tolerant corals. It is possible that the patterns observed here are driven by certain taxa or functional groups; for example Wilson et al. (2019) showed that coral decline and recovery were primarily driven by changes in the cover of branching coral, particularly those from the families Acroporidae and Pocilloporidae. Furthermore, dynamics can vary spatially, for example along cross-shelf gradients (Mellin et al., 2019a). Unfortunately, finer taxonomic or functional investigations were not possible across the synthesised dataset, but would be an important focus for future Chapter 2 – A global analysis of impact and recovery from acute climatic disturbances work. There was a lot of other information that was not available across all datasets (e.g. disturbance intensity, GPS locations of study sites, sampling error, exact survey dates) and we reference to Claar et al. (2018) for suggestions on how individual studies can improve their reporting to be better tailored for subsequent use in data syntheses. This should be a priority, as data synthesis and meta-analyses have much to offer, given the major issue now faced by coral reefs is intrinsically global, and therefore our understanding and reporting must also have global scale.

Conclusions

We successfully synthesised a large and unique dataset from the literature and quantified the impacts and recovery of coral from four categories of acute climatic disturbance – temperature stress, physical disturbance, flooding, and multiple disturbances. With this dataset we were able to conduct a large empirical investigation of the dynamics of coral surrounding disturbance and the links between impact and recovery. It is timely to seek to combine some of the more theoretical concepts of resilience with empirical data, however, transferring the theory to real-world systems requires various simplifications so that ecologically measurable units can be considered (Nyström & Folke, 2001). Here we were limited in our ability to understand resistance, without a quantification of disturbance intensity, and future work should seek to use disturbance intensity information to better understand resistance. The relationship between the internal state variables and recovery rate and trajectory shape is a particularly important contribution in the ongoing pursuit to understand and quantify the complexities of resilience in complex ecosystems. Updating this analysis as more data emerges over the next decade in relation to the recent global bleaching events will be important to better understand whether these processes are changing as the disturbances themselves change in frequency, intensity and duration.

61 Acknowledgements

We acknowledge financial support of the BHP-CSIRO Industry-Science Marine Research Partnership and the University of Western Australia Jean Rogerson Postgraduate Scholarship. We gratefully acknowledge inspiration and input from Gary Kendrick in conceptualising this study, and for insightful discussions on resilience. We are grateful to Meryn Scott, CSIRO Librarian, for assistance in refining the search terms for such a large literature base. We are thankful to Francesco Patrizi and Shu-Chiang Huang who volunteered to assist with data extraction. We would like to thank Harry Moore for discussion on managing global spatial data and Susan Blackburn and George Cresswell for providing comment on drafts.

Chapter 3

This chapter considers a new method to gain information on the process of reproduction of corals, and the resulting connectivity between reefs. It is critical to have a robust understanding of coral reproductive processes and how they may alter with climate change, in order to work towards understanding coral reef recovery and therefore resilience.

Chapter 3 – A novel method to monitor coral reproduction Synthetic Aperture Radar scenes of the North West Shelf, Western Australia, suggest this is an underutilised method to remotely study mass coral spawning

Anna K. Cresswell1,2,3*, Paul C. Tildesley4 and George R. Cresswell4

1 School of Biological Sciences, University of Western Australia, 35 Stirling Highway, Crawley WA, 6009, Australia 2 CSIRO Oceans and Atmosphere, Indian Ocean Marine Research Centre, 64 Fairway, Crawley, WA, 6009, Australia 3 The UWA Oceans Institute, University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia 4 formerly CSIRO Oceans and Atmosphere, Castray Esplanade, Battery Point Tas 7004, Australia (retired)

Abstract

Corals reproduce during annual ‘mass spawning’ when gametes are released into the water column in near-unison. While mass spawning events have been well studied, there remain key questions about their timing, triggers and resilience. We report the second occasion to our knowledge in which a mass spawning has been captured serendipitously with satellite-borne synthetic aperture radar (SAR). SAR can collect information through cloud and in both day and night by detecting changes in ocean surface roughness, including, as we shown here, those caused by mass coral spawning, which creates slicks or films of spawn on the sea surface. We examined four SAR scenes of coral reefs on the North West Shelf, Western Australia, from a 10-day interval bracketing the expected time of mass spawning in March 2001. The scene from 19 March 2001 shows what we classify as a snapshot of mass coral spawn slicks, from reefs extending over an area of roughly 100 km. The locations of the slicks correlated spatially with underlying carbonate reefs. We suggest SAR monitoring of coral reefs at spawning time may be an underutilised method that can provide new information on this natural phenomenon.

65 Keywords: Barrow Island, coral reproduction, coral reef, RADARSAT, remote sensing, SAR, monitoring, methodology

Introduction

Understanding coral life cycles is becoming more important as ocean warming drives mass coral mortality events (Hughes et al., 2017). Reproduction in corals is a synchronised phenomenon, known as “mass spawning”, where a large proportion of corals release gametes over only a few consecutive nights at certain times of the year (Simpson et al., 1991; Keith et al., 2016). The reproductive matter floats to the surface and in reefs of dense coral this often leads to coral spawn ‘slicks’ or ‘films’ on the water surface (Oliver & Willis, 1987). Depending on weather conditions, these slicks can persist for several days, allowing the possibility of tracking both the extent and the movement of coral embryos and larvae following a mass spawning.

Satellite-borne SAR sends microwave pulses to Earth, and based on the return of echoes back to the satellite, makes an image, which can provide information on the surface from which it was reflected (Gens, 2008). Roughened ocean surfaces scatter part of the SAR signal back to the satellite whereas a smooth ocean surface, where capillary waves are dampened, reflects the signal away (Gens, 2008). When Jones et al. (2006) detected surface slicks in a SAR scene obtained with the Canadian Space Agency’s RADARSAT of the Vulcan and Goeree Shoals in the Timor Sea on 16 April 1998 they interpreted these to be coral spawning slicks, the first documented by SAR. Their case was strong because the shapes of the slicks matched those of the underlying reefs, and the conditions were right to expect mass spawning in this region: autumn, 4½ days after full moon, shortly after sunset, an ebb tide one hour before low tide with wind speed 4.6 ms- 1.

Coral spawn slicks are composed of coral eggs and embryos and their breakdown products, and can form dense, highly viscous patches (Oliver & Willis, 1987). SAR has commonly been used to detect natural hydrocarbon seeps and oil spills in the ocean (Tian et al., 2015), the principles of which may be applied to coral slicks (Jones et al., 2006). There are challenges associated with detecting slicks, mainly linked to the wind and swell conditions at the ocean surface. Harahsheh et al. (2001) assign optimum wind Chapter 3 – A novel method to monitor coral reproduction speeds for oil slick detection to be between 3 and 6 ms-1, given that wind speeds less than this will cause minimal difference in backscatter between slicks and calm seas, and wind speeds greater are likely to cause disintegration of slicks (Ivanov, 2000; Brekke & Solberg, 2005).

In Western Australia mass coral spawning was first recorded in the Dampier Archipelago (Simpson, 1985). In this region corals generally spawn around the third quarter of the moon (i.e. one week after full moon) on neap, nocturnal, ebb tides (Rosser & Gilmour, 2008; Baird et al., 2011; Gilmour et al., 2016). In some cases where the full moon falls near the edge of the spawning window only some corals will have mature gametes and spawn, whereas others will delay spawning until the following full moon, resulting in a ‘split-spawning’ (Gilmour et al., 2016).

There are still questions about the timing of coral spawning, geographic variation in spawning season and the environmental triggers (Rosser & Gilmour, 2008; Baird et al., 2011; Gilmour et al., 2016; Keith et al., 2016). Recently, remote sensing has been recognised as an important tool for the interdisciplinary assessment of coral reef processes (Hedley et al., 2016). Here, we present four SAR scenes of the waters around the Barrow and Montebello Islands that bracket the time of an expected coral spawning event in March 2001. SAR has not previously been used in targeted studies of coral spawning events, and we discuss the utility of this method for adding to the knowledge base on coral spawning.

Methods

Four SAR scenes were obtained over a ten-day interval in March 2001 as part of Project 250 of RADARSAT-1 of the Canadian Space Agency/Agence spatiale canadienne (Parashar et al., 1993). They were initially acquired to investigate oceanic internal waves in the region (Jackson 2004, fig. 3). The scenes are “ScanSAR Wide”, i.e. 500 km square grid with a resolution of 100 m, and cover the region around the Barrow and Montebello Islands, part of a group of islands to the northwest of the Australian coast at ~21°S (Figure 3.1). The date and times of the scenes, as well as whether the satellite was in ascending or descending orbit, are as follows: 17 March (05:44, descending), 19

67 March (18:50, ascending), 24 March (05:40, descending) and 26 March (18:46, ascending). The SAR scenes were interpreted visually; generally, white areas due to high backscatter of the reflected signal indicate roughened water surfaces, and black or low backscatter areas indicate smooth surfaces (Gens, 2008). Images required interpretation with reference to additional factors as low backscatter can be indicative of windless, smooth water surfaces (Gens, 2008), oil slicks (Nunziata et al., 2013) and phytoplankton blooms (Wu et al., 2018) amongst other things (Harahsheh et al., 2001). In order to narrow the classification to coral spawn we followed the advice of Jones et al. (2006) to increase the accuracy of interpretation. Ancillary data—bathymetry, current velocities, wind speeds and directions—were investigated alongside the SAR scenes. We examined the scenes with reference to the expected time and days of coral spawning in 2001, the proximity to underlying carbonate reefs, and the states of the tides and winds. We also checked for any historical reports of oil spills at the time of the SAR scenes.

With reference to the time, moon phase and tide, a spawning event was expected 7‒10 days after the full moon on a neap, nocturnal, ebb tide (Gilmour et al., 2016). Tide data for the time of the SAR scenes was sourced for Barrow Island from http://tides.mobilegeographics.com. Tides in the region are semidiurnal and the magnitude varies around the islands with a maximum spring tide on the east coasts of > 4 m compared with <2.5 m on the west coasts of the offshore islands (Richards & Rosser, 2012).

Wind measurements from the Barrow Island Airport were obtained from the Australian Bureau of Meteorology for the times of the SAR scenes. We considered the locations of the subtidal and intertidal carbonate reefs as indicators of a coral population, overlaying spatial data from SAR scenes illustrated in fig. 3 by the Department of Environment and Conservation (2007).

All references to time are in Western Standard Time, WST (Coordinated Universal Time, UTC + 8 hours), with local time in the study region being WST-20 minutes.

Chapter 3 – A novel method to monitor coral reproduction

Figure 3.1. The North West Shelf of Australia showing the region covered by the RADARSAT SAR scenes examined for this study. The coastline and isobaths from 100x1000 m are from the General Bathymetric Chart of the Oceans (GEBCO) https://www.gebco.net/ and the 20 m was copied from the Australian National Bathymetric Map Series.

69 Results and Discussion

Figure 3.2. The RADARSAT SAR scenes from the Montebello and Barrow Islands region for a) 17, b) 19, c) 24 and d) 26 March 2001. The vectors represent the winds (ms-1) measured at Barrow Island airport at the times of the satellite passes.

The four SAR scenes (Figure 3.2) and information on wind, moon phase and time of day (Figure 3.3), suggest that a mass spawning event can be captured using SAR. The 17 March 2001 scene (Figure 3.2a), six days after the full moon, was captured at dawn on what was a strong ebb tide with Barrow Island airport recording a 9 ms-1 southwesterly wind. This is equivalent to five on the Beaufort Scale (Mather 2005), which translates to “Fresh breeze. Moderate waves (1.8 m), many whitecaps”. Although the time of the lunar month, the tide and time of day satisfied Gilmour’s et al. (2016) conditions for mass coral spawning for the region, no coral spawn slicks were detected. Chapter 3 – A novel method to monitor coral reproduction However, the strong wind and breaking waves would likely have demolished any coral spawn slicks that may have been present.

The 19 March 2001 scene (Figure 3.2b), ten days after full moon at neap tide (weak ebb), was captured at early twilight and a 6 ms-1 westerly wind was recorded, equivalent to four on the Beaufort Scale—“Moderate breeze. Small waves (1 m), some whitecaps.” Gilmour’s et al. (2016) conditions for mass coral spawning—phase of the moon, tidal conditions and time of day (early evening)—were satisfied and the wind speed fell within the optimum range for slick detection (Harahsheh et al., 2001). These factors in combination with low backscatter shown by black areas near the islands, and between there and the NW Australian coast, indicate slicks. Slicks appear to have originated from multiple reefs across the study region.

The 24 March 2001 scene (Figure 3.2c) was captured just before dawn, at new moon and low water with a 9 ms-1 southeasterly wind, equivalent to five on the Beaufort Scale, as was that for the 16 March scene. Roughened waters, some aligned with the wind direction, can be seen between the northwestern Australia coast and Barrow Island. As with Figure 3.2a, there appear to be no regions of smooth water or slicks, in keeping with the strong winds.

The 26 March 2001 scene (Figure 3.2d) was captured just after sunset at new moon at low water with a 4 ms-1 northeasterly wind, equivalent to three on the Beaufort Scale— “Gentle breeze. Large wavelets (0.6 m), crests begin to break”. The gentle breeze may be the reason for the extensive smooth areas in the southwest part of the scene. To the west of the Montebello Islands localised areas of low backscatter are likely due to calm water surfaces, as in this region coral spawning has not been documented to continue for this many days after the full moon (Simpson et al., 1991; Gilmour et al., 2016).

Figure 3.3. Predicted tide at Barrow Island and wind speed measured at Barrow Island Airport around the times of the RADARSAT SAR scenes, which are marked with vertical dashed lines. The shading indicates night, the small black boxes show the phases of the moon, and wind vectors at the times of the scenes are added at the intersections of the dashed lines with the 10 ms-1 grid lines.

The complex distribution of subtidal and intertidal reefs around Barrow and the Montebello Islands is represented in the yellow and green overlay on the 19 March 2001 scene in Figure 3.4b, as this was the scene that showed strong evidence of coral spawn slicks. There appears to be a strong relationship between the reefs and the dark pixels (low backscatter) of the SAR image. We suggest these dark pixels represent a mass coral spawning in progress on the evening of 19 March. The slicks are more distinct in some locations, particularly west of the Montebello Islands, east of Barrow Chapter 3 – A novel method to monitor coral reproduction Island and at the subtidal reefs further south. Eastward from Barrow Island is a fan of water that is smooth relative to its surroundings, as shown by the darker pixels. We speculate that this may be due to coral spawn being carried eastward by current and/or wind, though it could also be a result of smooth water in the windward lee of the land. Dark pixels on the eastern sides of two small reefs west of Barrow Island can be interpreted as having a similar origin. Along part of the southeast coast of Barrow Island there is also smooth water, possibly from coral spawn or from being in the lee of the island.

Figure 3.4. a) RADARSAT SAR scene for 19:50 hours WST 19 March 2001 and b) the same scene with the yellow and green overlays showing the positions of subtidal and intertidal reefs respectively. Note, the spatial data for reef locations are smaller than the SAR scene, indicated by blue outline.

Aerial photographic surveys are one method that has historically been used to quantify the extent of coral spawn slicks, which appear as pink or white patches on the water surface (Oliver & Willis, 1987). The advantage of SAR over photography is that it can collect data at night (coral generally spawn in the evening), and through cloud cover (Gens, 2008). However, there are potential problems associated with false positive

73 detections. Smooth water, oil spills, phytoplankton blooms (Wu et al., 2018), in particular Trichodesmium spp. slicks (Oliver & Willis, 1987), and natural carbon seeps all have the potential to give a false positive for coral spawn (Gens, 2008). Low wind speeds may cause pseudo slicks which would be indistinguishable from coral spawn, as Figure 3.2d showed, whereas high wind speeds can cause coral spawn to be quickly dispersed thus not forming slicks that can be detected by SAR (Brekke & Solberg, 2005).

Nevertheless, our study, along with that of Jones et al. (2006), suggest it would be worthwhile to trial SAR as a method for targeted studies of coral spawning on a large scale, if measures can be taken to remove the likelihood of false positives through consideration of the weather, tide, and predicted coral spawning time. Combining SAR with data such as in situ documentation of coral spawning, and monitoring of oil slicks or phytoplankton blooms that could confuse the SAR interpretation would allow this method to be used more accurately. Alternatively, or additionally, optical satellite (e.g. from Landsat (https://landsat.gsfc.nasa.gov/)) or airborne imagery, in daylight hours if cloud was absent, would help to confirm SAR identifications (Hedley et al., 2016).

SAR may be particularly useful to assess remote or difficult to access coral reefs where monitoring coral spawning can be logistically and/or economically challenging. In the years since the SAR scenes of the present study were collected, SAR technology (and remote sensing satellite technology more generally) has advanced. There are now more than 15 space-borne SAR systems in operation for a diverse range of applications, with continued advances in the capability to collect ecological information (Moreira et al., 2013). This includes the Sentinel-1 satellite (https://sentinel.esa.int/web/sentinel/missions/sentinel-1/data-distribution-schedule), which has freely distributed SAR scenes. There are various ways SAR acquisition could be adapted to directly target coral spawning. The SAR scenes interpreted in this study were the “ScanSAR Wide” option, but higher resolution, smaller scenes could be collected in the future. There are likely to be issues with satellite location lining up with the timing and location of coral spawning in cases where the satellite overpass does not align with the targeted reef(s). Ideally, several satellite passes each day could capture the evolution of coral spawn slicks. However, access to a number of SAR satellites would be required to achieve this, as the return frequency of an individual satellite is generally every few days, as was the case in the present study. With a greater number of Chapter 3 – A novel method to monitor coral reproduction satellites operating SAR, this becomes more possible. Nonetheless, targeting a large reef system such as the Great Barrier Reef, or being flexible with location, would allow higher likelihood of capturing the event.

Conclusions

Of the four SAR scenes we examined, that for 19 March 2001 captured a mass coral spawning event off north Western Australia that was synchronised at reefs extending over 100 km. This is, to our knowledge, the second time SAR has serendipitously captured mass coral spawning. This suggests that if targeted, SAR can be a method to provide important synoptic information on the timing, extent and surface longevity of coral spawn slicks, which would increase our understanding of this phenomenon. New information could be applied in many fields, including modelling studies focused on reproduction, connectivity and dispersal in coral reefs, as parameters surrounding the extent and timing of mass spawning events are important for such models (Wood et al., 2014). It could also directly benefit managers; for example, in situ monitoring in north Western Australia is used to detect spawning events on reefs in areas of dredging and industrial development, yet Styan and Rosser (2012) suggest that current practices often miss significant mass spawning events. We do not suggest SAR as a replacement to existing monitoring, rather as a complementary tool, with limitations discussed above. Greater availability of SAR, including freely available images from Sentinel-1, promises to continue to allow better connections between field observations of spawning events with satellite-borne imagery.

Acknowledgements

The SAR scenes were collected as part of Project 250 of RADARSAT-1 of the Canadian Space Agency/Agence spatiale canadienne. PCT and GRC were employed by CSIRO at that time. We acknowledge the BHP−CSIRO Industry−Science Ningaloo Outlook Marine Research Partnership and the University of Western Australia Jean Rogerson Postgraduate Scholarship for supporting AKC at the time of writing. Andrew Heyward provided expertise regarding the timing of the spawning event in this location. Dr Susan Blackburn and other anonymous reviewers provided valuable comments on the manuscript. The Royal Society of Western Australia’s editor Arthur Mory was very helpful in preparing this for publication. We thank the Australian Bureau of Meteorology for their service.

75 Chapter 4

In addition to coral reproduction explored in the previous chapter, the growth and natural mortality of organisms influences both the maintenance and recovery of ecosystems, and therefore, their resilience. This study seeks to quantify coral growth in an under-surveyed coral reef habitat and to provide a better understanding of the environmental controls on growth rate. A new method for quantifying coral growth rates is applied and compared to the more established method.

Chapter 4 – A novel method to measure coral growth Structure-from-motion reveals coral growth is influenced by colony size and wave energy on the reef slope at Ningaloo Reef, Western Australia

Anna K Cresswell1,2,3,*, Melanie Trapon2, Michael Renton1,5, Michael D E Haywood4, Ana Giraldo Ospina1,3, Dirk Slawinski2, Rachel Austin1, Damian P Thomson2 1 School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia 2 Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Crawley, WA, Australia 3 The UWA Oceans Institute, The University of Western Australia, Crawley, WA, Australia 4 Oceans & Atmosphere, Commonwealth Scientific and Industrial Research Organisation (CSIRO), St Lucia, Queensland, Australia 5 School of Agriculture and Environment, The University of Western Australia, Crawley, WA, Australia

Abstract

A variety of techniques for measuring coral growth, a fundamental biological trait, have been used to improve our understanding of coral demographics. Remaining gaps and biases in this information, in combination with technological advances, led to the present study. We assessed the capacity of structure-from-motion (SfM) technology to build image mosaics (orthophotos) of ~6 x 8 m reef plots from which individual coral colonies could be identified in repeated annual surveys and colony planar area measured. We monitored Acropora, Pocilloporidae (specifically from the genera Stylophora and Seriatopora) and Platygyra colonies over 2 years. We compared growth measurements, as change in radius, from SfM orthophotos to the more traditional method of tagging and photographing individual coral colonies. We conclude that coral growth can be measured from SfM orthophotos, with clear advantages over tagging, including large sample sizes, and speed and ease of surveying, despite some bias identified in the SfM method (growth estimates were 15% greater (~0.5 cm/year) from standard photos than from the SfM orthophotos). We documented mortality and growth rates in a high wave energy environment on the reef slope at Ningaloo Reef, Western Australia, where there were no existing coral growth or mortality estimates. The mean annual change in radial extension of Acropora colonies with no mortality was 2.92±0.06

77 (SE, n = 572) cm across the pooled data set of standard and orthophotos, substantially lower than previous estimates on similar species from the region. The growth rate of Pocilloporidae was 3.54±0.52 (SE, n =12) cm year-1 for colonies with no mortality. Robust planar growth estimates were not obtained for Platygyra due to its morphology, but we report high survivorship of the colonies: 94% with no mortality as compared with 79% for Acropora and 53% for Pocilloporidae. High sample sizes from the novel SfM methodology allowed us to demonstrate that Acropora growth rate was inversely linked to wave energy and coral size.

Keywords: 3D reconstruction, Acropora, coral reef, methodology, photogrammetry, Pocilloporidae

Introduction

Coral growth is a fundamental biological trait that is essential to the maintenance and recovery of coral reefs. However, coral growth is not simple to measure, as corals are slow growing organisms in marine environments that can be logistically difficult to sample. Various growth sampling methods have been established and used on many reefs globally, leading to important demographic information (Buddemeier and Kinzie, 1976; Hughes and Connell, 1987; Pratchett et al., 2015). Nevertheless, there are still major gaps, as well as biases, in our understanding of the spatial, temporal and taxonomic variation in coral growth rates. The importance of filling these gaps continues to be recognised (Edmunds and Riegl, 2020). Many reefs remain unsurveys for coral growth, and certain taxa are more highly studied than others, with studies tending to have focused on easier-to-survey shallow and sheltered reef environments (Pratchett et al., 2015). Given that coral growth rates vary in response to multiple factors including depth, light, water quality and temperature (Baker and Weber, 1975; Pratchett et al., 2015), it is important to target under-surveyed environments in order to better understand the drivers of these differences. Environments with high wave driven water movement are logistically challenging to survey, and, as a result, exposed reef slopes are understudied compared to shallower reef flat or lagoonal environments.

Obtaining estimates of coral growth rate requires temporal measurements of a coral colony’s linear dimensions, area, volume, or weight, such that a time-averaged growth rate can be calculated (Pratchett et al., 2015). Established methods tend to focus either Chapter 4 – A novel method to measure coral growth on measuring the mass of carbonate material that is added through time (i.e. calcification), or changes in colony dimensions (Pratchett et al., 2015). Colonies are generally tagged or stained, and GPS referenced so they can be revisited and measured. There is variation in the time, costs and logistical challenges of the different methods (Holcomb et al., 2013). Taking a time-series of photographs containing a scale bar is a common method as it is non-destructive and allows measurements to be extracted later from the images, thus reducing time and logistical investment in the field. Change in area of occupation (standardized for colony size by calculating change in ‘arithmetic mean radius’) is considered to be a robust growth metric for corals that tend to grow primarily in the horizontal plane (Pratchett et al., 2015).

Various studies have stitched together multiple photographs to render image mosaics, so-called ‘orthophotos’, in order to study the demographics of corals and other marine benthic organisms in reef areas that are larger than any single photograph can capture. For example, Wakeford et al. (2008) monitored a 4 x 8 m reef plot on the Great Barrier Reef using photo-mosaics to derive rates of growth, recruitment, mortality and interaction between corals over two decades. Hudson et al. (1982) used photo-mosaics to assess change in the cover of different coral morphologies associated with oil drilling in the Philippines. Banks et al. (1998) stitched together low-level aerial photographs to create orthophotos of a coral reef in Florida to conduct an impact assessment. New digital technology is vastly increasing our capacity to efficiently obtain high resolution underwater images. Structure-from-motion (SfM) is an emerging cost-effective computer vision technology that can create large 3D reconstructions of a reef by stitching together numerous overlapping photographs and is increasingly being used in coral reef studies (Burns et al., 2015; Ferrari et al., 2016). SfM has already been used to obtain accurate, fine-scale three-dimensional growth rates for individual colonies, but only for relatively small sample sizes (Bennecke et al., 2016; Ferrari et al., 2017). As well as three-dimensional information, SfM creates two-dimensional orthophotos, which may contain hundreds of individual corals, and potentially allow numerous colonies to be efficiently tracked and measured over time. The accuracy and precision of such an approach yet to be determined.

The logistical challenges of monitoring coral growth mean that there are still major gaps in our understanding of the factors that influence coral growth. It is recognised that coral growth varies with water movement, but much of this work has been laboratory

79 based, or in relatively calm environments (Dennison and Barnes, 1988; Jokiel, 1978; Madin et al., 2012b; Nakamura and Yamasaki, 2005). Similarly, previous work has shown that there are declines in growth and calcification rates with increasing depth, but not for all species (Barnes and Taylor, 1973; Huston, 1985). There is clearly still much to understand about the relationships between coral growth rates and environmental/physical conditions (Madin et al., 2012a).

Here we explore the utility of SfM orthophotos for temporally resurveying coral reef plots and identifying and measuring multiple individual coral colonies. We assess the accuracy, precision and challenges of a SfM approach for measuring coral growth by providing comparisons of growth measurements from two methods: the traditional method of individually tagging and photographing colonies versus SfM surveys. This allowed us to investigate coral growth rates and mortality in a high wave energy environment and assess whether variation in growth rates was linked to wave energy, coral size, depth or location. Higher water flow has been linked to faster growth rate of corals (Schutter et al., 2010), but in the present study we hypothesised such positive relations would be surpassed in the high wave energy environment on the reef slope, and therefore higher water velocities may be negatively related to growth rates. We expected coral growth may decrease with depth and either be independent of size or faster for small corals (Dornelas et al., 2017; Madin et al., 2020). We targeted the reef slope at Ningaloo Reef, Western Australia, in 5-12 m depth, which experiences very high wave driven water motion and for which there are no existing coral growth measurements. There are some measurements of coral growth from Ningaloo Reef for a handful of species and genera (see Bessey et al., 2018; Cooper et al., 2012; Simpson and Authority, 1988; Stimson, 1996; Webster et al., 2015), but all are from the back reef or lagoon in depths <5 m. We sought to build on these existing studies and quantify growth rates for three common groups: Acropora, Pocilloporidae and Platygyra, the latter two which have no prior growth estimates from the region.

Chapter 4 – A novel method to measure coral growth Material and methods

Study site

Study sites were selected in the northern region of Ningaloo Reef, an extensive fringing reef system on the coast of Western Australia (Figure 4.1). Sites were selected on the reef slope in a depth range of 5 – 12 m (n =21). This region has a well-developed reef crest, typically 0.2 – 1.0 km from the shore. A significant proportion of corals are on the reef slope at Ningaloo Reef, which runs nearly continuously for more than 250 km. This environment differs markedly from that of the reef flat, lagoon or back reef environments because of its greater depth (associated with lower light) and higher exposure to wave driven water motion, as well as likely differences in nutrient availability and water quality (Cassata and Collins, 2008; Kobryn et al., 2013).

Figure 4.1. a) Location of Ningaloo Reef relative to the Australian continent and b) Ningaloo Reef and North West Cape, Western Australia showing the locations of sites in three regions – Jurabi, Mangrove and Osprey – where standard photos where used to monitor growth of coral colonies (pink, n = 16) and the location of temporally replicated orthophoto plots (blue, n = 18).

Target genera

We chose three distinct taxa at Ningaloo Reef: Acropora, Platygyra and Pocilloporidae (specifically genera Stylophora and Seriatopora). We did not focus on finer taxonomic levels because it was not possible to confidently identify species from the SfM survey method. However, our best guess at species identification from the standard photos is that we captured mostly Seriatopora caliendrum and Stylophora pistillata (these two

81 species have very similar growth forms and are distinguished primarily via the alignment of corallites) from Pocilloporidae, while for Platygyra we think the majority of colonies were P. daedalea. We were not confident to assign any species to the Acropora that were measured. The species assignments should be interpreted with caution, noting the fluidity of current coral taxonomy and given that, due to time limitation in the field, we did not make in-water species assessments. We targeted Acropora with plating morphologies, though some of the smaller colonies were more caespitose in form. The Stylophora/Seriatopora were caespitose whereas the Platygyra were massive, with some borderline encrusting (see pg. 226 Pratchett et al., 2015). We targeted colony sizes ranging from 1 - 49 cm.

Survey methodology

Tagging

Between 11 and 23 May 2017 we deployed stainless steel, 3 x 8 cm tags with a unique four-digit number engraved adjacent to select individual coral colonies at 16 GPS referenced sites in northern Ningaloo Reef (Figure 4.1). The number of tags deployed at each site depended on the time available and weather conditions on the day of deployment. An additional 25 tags were deployed in May 2018 at 3 sites. Tags were attached using plastic cable ties and an additional cable tie was attached vertically to assist with relocation. A 30 cm ruler, with 10 cm lengths differentiated, was placed next to, or on top of, the tagged coral, ensuring the ruler was as close as possible to the planar surface of the colony (Figure 4.2). A ‘standard photo’ was then taken from the top-down perspective, perpendicular to the planar surface of the tagged coral colony and the ruler, at ~30cm above the coral using an Olympus E-PL1 camera in an Olympus PT- EP01 underwater housing, with a Zen WA-100 dome port, on automatic settings. Sites were revisited and tagged colonies rephotographed between 29 April and 14 May 2018 and again between 13 and 22 May 2019 (e.g. Figure 4.2).

Structure-from-motion

Structure-from-motion (SfM) is an emerging cost-effective computer vision technology that can create large reconstructions of sections of reef by stitching together numerous overlapping photographs. SfM surveys were conducted on the same dives as the standard photo surveys. In 2017, we installed two lengths of rebar (metal rods) of ~0.75 m length at each site. These were hammered into the seafloor to mark two ends of ~6 x Chapter 4 – A novel method to measure coral growth 8 m rectangular plots which were the target areas for the SfM surveys. The SfM surveys were conducted by a diver swimming slowly in a boustrophedon pattern over the plot marked by the rebar, at a distance of ~2 m above the seafloor with a GoPro Hero4 in a housing on automatic exposure settings set to take one photo every 0.5 seconds, such that 150-200 continuous overlapping images were collected to cover the 6 x 8 m rectangular plot. The same 30 cm ruler used in the standard photo surveys was placed in the plot prior to collecting the photographs. In subsequent years, each site was resurveyed, using the rebar as a reference to ensure the same plot was photographed. This process could be completed within 5 minutes, if the rebar was quickly located.

The SfM process aligns photos using algorithms that mathematically detect invariant features (‘key points’) that overlap in the images (Verhoeven, 2012; Westoby et al., 2012). The mosaiced overlapping sections of the images are then mapped. The resulting texture mapped surface is extracted, or ‘rendered’, into an orthophoto, similar to traditional aerial orthophotos. This process results in a continuous high-resolution image of the survey area which can be analysed for geospatial properties (Burns et al., 2015). While it is possible to measure three-dimensional information using SfM (Ferrari et al., 2017; Young et al., 2017), this was beyond the scope of the present study, and we focussed on the orthophotos, as the method could be directly compared with the standard photo method.

The majority of orthophotos were rendered using Open Drone Map, ODM [http://www.opendronemap.org], which is a freely available software, because a background aim of this work was to operationalise automatic processing of multiple sites using this freeware. However, in some cases blurry or distorted orthophotos were rendered and in these instances (14% of all orthophotos) we used the commercial program Agisoft Photoscan, which appeared to be more successful in producing orthophotos free of distortion. We tested for differences in the mean colony area measured using the two software packages by rendering orthophotos from six sites (2019 data) in both ODM and in Agisoft Photoscan. We measured the area of 34 colonies from both and a paired t-test indicated that the mean colony area was not significantly different as a result of the software used (t = -0.34, df = 33, p = 0.73), with the mean area of colonies measured using ODM on average 5.2 cm2 greater than from Agisoft Photoscan (95% confidence interval -25.4 to 35.8 cm2 difference). The settings

83 used to render the orthophotos in both ODM and Agisoft Photoscan are provided in Table App 4. 1.2.

Measurements from tags and orthophotos

We measured the two-dimensional planar area of individual coral colonies over the two- year period (May 2017 – May 2019) using the software ImageJ (Schneider et al., 2012). ImageJ allows users to measure distances and areas of unknown dimensions within images by calculating a pixel-distance ratio using an object of known length as a reference scale.

We calculated the mean ground sample distance (GSD) for a random subset (n = 8 images) of the standard photos, the ODM and the Agisoft orthophotos as the distance (m) per pixel by using ImageJ to determine how many pixels a set length of the ruler (0.3 m) covered. A smaller GSD therefore corresponds to higher image resolution.

Standard photos

The ruler was used as the reference scale before a freehand polygon was traced around the coral colony (Figure 4.2) in ImageJ for each standard photo. In this way, the same colony was measured twice, one year apart, unless it died, disappeared, or was not relocated.

Figure 4.2. Example of a tagged Acropora coral colony, showing the time-series of standard photos taken for measurement in May of a) 2017, b) 2018 and c) 2019.

Structure-from-motion and orthophotos

Time-series of orthophotos were created where the plot area could be cross-referenced between multiple years (Figure 4.3). By annotating the orthophotos with numbers indicating coral colonies that could be matched between years, the planar area of up to 50 colonies could be measured from an orthophoto time-series of one reef plot. Chapter 4 – A novel method to measure coral growth Colonies were randomly selected from those that could be clearly identified across multiple years. As for the standard photos, the ruler was used as the reference scale, and colonies were measured in ImageJ. To decrease potential issues arising from errors in the topographical rendering of the orthophoto, measurements were only taken from colonies with clearly defined edges and in locations where the ruler was at a similar depth (z-field) to the colonies of interest.

Figure 4.3. Example of an orthophoto time-series in May of a) 2017, b) 2018 and c) 2019, with the ruler and coral colonies that were matched over multiple years indicated in yellow.

Planar area was converted into arithmetic mean radius (AMR) to provide a size- independent measure of linear growth (Pratchett et al., 2015), by assuming the colonies were circular and therefore calculating AMR as

퐴푟푒푎 퐴푟𝑖푡ℎ푚푒푡𝑖푐 푚푒푎푛 푟푎푑𝑖푢푠, 퐴푀푅 = √ 휋

Survivorship

Photo time-series were created from the standard photos of individual coral colonies and we visually documented the number of colonies that had partial mortality (some clearly dead tissue), overgrowth by other corals, physical damage (i.e. sections missing), total mortality with structure remaining and total mortality where the colony was no longer present (i.e. the tag was there but the coral had broken off from the

85 substrate). This documentation was descriptive only and used to give an indication of whether the three coral groups showed different degrees of survivorship over the two- year study period.

Given that colonies were selected from the orthophotos with a bias towards those that appeared healthy across multiple years, we could not quantify mortality or partial mortality from the orthophotos. While this should be possible with sufficient image resolution, it would require a more targeted investigation.

Statistical analyses

Paired comparison of coral colony area and radius

Before considering growth rates, we first aimed to assess whether the measurements of area and associated AMR obtained from the orthophotos and standard photos were comparable. In cases where it was possible to identify a coral colony within the orthophoto as being the same colony in one of the standard photos (Acropora n = 35, Pocilloporidae n = 8, Figure App 4. 1.4), we measured the area of the colony in two ways: from the orthophoto and from the standard photo. We compared the two sets of measurements of colony area using a paired t-test for each coral group. We then converted the area measurements to AMR and conducted a paired t-test on the radii data as well. The paired t-tests tested the null hypotheses that the mean differences between the (i) areas and (ii) radii obtained from the standard photos and the orthophotos were zero.

Growth rates

We only included coral colonies that showed no signs of mortality or overgrowth by adjacent colonies. In this sense, we were trying to capture an ‘optimum’ growth rate rather than the average growth rate. Each coral taxa was considered in a separate analysis, with coral growth (annual change in AMR) as the response variable and the potential explanatory variables were method (standard photo or orthophoto), year (2017-2018 or 2018-2019), depth (5 – 12 m), region (Jurabi, Mangrove and Osprey), water velocity (23-67 cm s-1) and initial coral size (AMR 1-49 cm). Estimates of wave- induced water velocity at the seafloor for the sites were derived using the ‘simulating waves near shore’ (SWAN) model (Booij et al., 1999) on a 30 by 30 m grid encompassing the study area. Chapter 4 – A novel method to measure coral growth Data were explored following the protocol of Zuur et al. (2010) to determine the most robust approach for the modelling. Residuals was normally distributed and therefore a Gaussian error model was appropriate. Outliers were investigated but no observations were removed. Collinearity among the continuous predictor variables was assessed and was low. In a handful of cases, a colony in the standard photos was also measured in the orthophoto. To account for these being non-independent observations, we removed these observations from the orthophoto dataset. To account for repeated measures through time on the same colonies, we included a random effect of coral ID nested in site. Predictor variables were transformed to reduce the skewness where appropriate: coral size was loge-transformed. Homogeneity of variance was checked visually by plotting the residuals against the fitted values.

We used linear mixed-effects models (Zuur et al., 2009), using the nlme package (Pinheiro et al., 2019) in R (R Development Core Team, 2018). We first used ‘coplots’ to visualise the potential presence of interactions and assess whether we had sufficient replication to test these interactions (Zuur et al., 2010). We tested for significant interactions between coral size and method based on the hypothesis that there may be variation in the accuracy of the two methods for different coral sizes. We also considered a potential interaction between coral size and water velocity based on the hypothesis that water velocity may decrease growth at higher values. A full model containing all variables was fitted using maximum likelihood and then sequentially simplified: non-significant variables (p<0.05) were removed one-by-one with testing to confirm AIC was reduced in each case, otherwise the variables were retained in the model. Any models within two AIC of the best AIC were then refitted with restricted maximum likelihood in order to obtain the final test statistics. The continuous predictor variables in the final models were mean-centred so that the intercepts could be more meaningfully interpreted, i.e. as the predicted growth rates when the continuous predictor variables were at their mean, rather than zero.

This was repeated with relative change in area (as the log-response ratio) as the response variable (Appendix 4.1), as this is another way of investigating growth rates (Dornelas et al., 2017).

87 Results

Paired comparison

The GSD of the standard photos was higher than the orthophotos, as expected because the camera was higher resolution and captured images closer to the coral colonies. The mean GSD of the standard photos was 1.37x10-4 m/pixel, while it was 6.65x10-4 m/pixel for the Agisoft Photoscan orthophotos and 6.03x10-4 m/pixel for the ODM orthophotos.

Paired comparisons of the measurements of planar area for Acropora obtained using the two methods (Figure 4.4) showed that the mean area measured in the standard photos was 54.16 cm2 smaller (-120.55 – +12.24; 95% CI, i.e. between 120.55 smaller and 12.24 cm2 larger than when measured using the orthophotos), but that this difference was not statistically different (t = -1.66, df = 34, p = 0.11). Similarly, when these areas were converted to AMR, the mean radius obtained from the standard photos was 0.37 cm smaller (-0.80 – +0.05; 95% CI) than that obtained from the orthophotos, again not significantly (t = -1.77, df = 34, p = 0.09).

The mean planar area obtained for Stylophora/Seriatopora colonies from the standard photos was 43.66 cm2 smaller (-124.66 – +37.35; 95% CI) than that obtained from the orthophotos, but this difference was not found to be significant (t=-1.27, df = 7, p = 0.24). When converted to AMR, the mean radius obtained from the standard photos was 0.44 cm smaller (-1.41 – +0.53, 95% CI) than that obtained from the orthophotos, though this difference was not significant (t = -1.08, df = 7, p = 0.32).

Examination of photo time-series for Platygyra highlighted concern that the measurements obtained were particularly sensitive to the angle of the photograph given, because of the non-planar growth form of this genus (Figure App 4. 1.3). This was expected (Pratchett et al., 2015), and, in combination with the slow growth measured 0.89±0.09 (SE, n = 73) cm year-1 across the pooled dataset, led us to exclude Platygyra from further growth analyses.

Chapter 4 – A novel method to measure coral growth

Figure 4.4. Arithmetic mean radii (AMR) for colonies as determined from tagged colony photographs and from the orthophotos. Points closer to the dashed line indicate greater similarity between the methods. Acropora are shown in blue (n = 35) and Stylophora in orange (n = 8).

Coral growth rates and variability

Two models were found to explain variability within the growth rates of Acropora (Table 4.1). The best model according to AIC was coral growth ~ log(coral size) * water velocity + method with coral ID nested within site as a random effect (Figure 4.5). This model was only marginally significantly better than the same model without the interaction (p=0.042), and the difference in AIC was approximately two, so we also considered the simpler model (Figure 4.6).

Across the pooled dataset the healthy Acropora colonies had a mean growth rate of 2.92±0.06 (SE, n = 572) cm year-1 (Figure 4.7). The predicted growth rate for Acropora from the orthophotos from the top model when all other predictors were at their mean was 2.83±0.14 (SE) cm year-1, while from the standard photos the prediction was 0.42 cm higher at 3.25±0.19 cm year-1 (p=0.028), i.e. the predicted growth rate from the standard photos was 15% higher than that from the orthophotos (Table 4.1). This model further indicated that growth rate declined with increasing water velocity, and that this relationship was more negative for larger colonies, i.e. high water movement had a

89 stronger negative effect on the growth of larger colonies than on the growth of smaller colonies (Figure 4.5, Table 4.1).

The second-best model, without the interaction, showed a negative relationship between coral growth and log(coral size), -0.26±0.10 (SE, p=0.009) cm year-1, indicating that growth was slower for larger colonies (Figure 4.6). Across the observed radius range of colonies from 2 to 49 cm, this corresponds to an ~40% increase in predicted growth rate with decreasing colony size (Figure 4.6). The estimate of the slope for water velocity was also negative (-0.02±0.01, SE, p = 0.01), indicating slower growth at more exposed sites. Across the observed range of water velocity from 23-67 cm s-1, this corresponds to an ~30% increase in predicted growth rate with decreasing water velocity (Figure 4.6, Table 4.1).

Table 4.1. Point estimates, standard error, degrees of freedom and p-values according to marginal t-tests for the top two (according to AIC) linear mixed-effects models of coral growth rate as annual change in AMR (cm) for Acropora (number of annual measurements = 546). Both models included a random effect of coral ID nested in site.

MODEL 1: size + water velocity + method + size*water velocity AIC = 1883.5 Variable Estimate Std. Error df P-value mean centred intercept 2.83 0.14 389 <0.001 (log) coral size -0.32 0.10 133 0.002 water velocity -0.02 0.01 20 0.058 method (standard photo) 0.42 0.19 389 0.028 (log) coral size by -0.02 0.01 132 0.036 water velocity MODEL 2: size, water velocity, method AIC = 1885.6 Variable Estimate Std. Error df P-value mean centred intercept 2.83 0.14 389 <0.001 (log) coral size -0.28 0.10 133 0.007 water velocity -0.02 0.01 20 0.034 method (standard photo) 0.46 0.19 389 0.015 When the relative change in area, rather than the absolute change in radius, was modelled as the response variable the best model according to AIC was the same as Model 1 (Table 4.1), except for including an additional interaction between coral size and method. As for the radius model, the area model indicated that growth rate declined with increasing water velocity. This relationship was more negative from the standard photos (Figure App 4. 1.5). Small colonies measured from the standard photos increased in area the most.

Chapter 4 – A novel method to measure coral growth

Figure 4.5. Predictions of how Acropora growth rate (annual change in radius) changes with water velocity, method and initial coral radius according to the best linear mixed-effects model based AIC (Table 4.1). The interaction is evident in the different slopes of the coloured lines which represent predictions for different initial colony sizes.

Figure 4.6. Predictions of how Acropora growth rate (annual change in radius) changes with a) water velocity and b) the initial radius of a colony, according to the second-best linear mixed-effects model based on AIC (Table 4.1).

We obtained only 12 measurements of Pocilloporidae colonies that remained healthy over a year period (Table App 4. 1.2). Exploratory modelling found no significant variables, as expected with such a small sample size. On average, these Pocilloporidae

91 colonies had a growth rate of 3.54±0.52 (SE, n =12) cm year-1, with notably more variation in growth than the other taxa (Figure 4.7).

Figure 4.7. Boxplots showing the distribution of growth rate for each method and for the three genera: Acropora, Platygyra and Pocilloporidae. (Heavy horizontal line: median; top and bottom of box: upper and lower quartiles; The whiskers extend to the largest and smallest value no further than 1.5 * inter-quartile; data beyond the end of the whiskers are the ‘outliers’).

Mortality

Across both years, an average of 79% of the Acropora colonies in the standard photos and 94% of the Platygyra colonies showed no obvious mortality over a year period, while Pocilloporidae colonies showed no mortality in only 53% of cases in the standard photos (Figure 4.8). These figures are summarised by year in Table S1.2.

Chapter 4 – A novel method to measure coral growth

Figure 4.8. Pie chart displaying the proportion of colonies from the standard photo surveys that had partial mortality (some clearly dead tissue), overgrowth by adjacent corals, physical damage (i.e. sections missing), total mortality with structure remaining (‘dead’) and total mortality where the colony was no longer present (‘dead and removed’, i.e. the tag was there but the coral had broken off from the substrate).

93 Discussion

We have provided the first growth measurements for the Ningaloo Reef slope for Acropora and Pocilloporidae (Stylophora/Seriatopora), as well as documentation of mortality and partial mortality for Acropora, Pocilloporidae and Platygyra using the standard photo method. The growth measurements obtained for Acropora were significantly lower relative to other studies on similar species in the region (Bessey et al., 2018; Simpson and Authority, 1988; Stimson, 1996), though these other studies were conducted in <5 m depth. This difference may also be explained by the species composition of the colonies in our study relative to these previous studies. We showed that annual increase in AMR of Acropora colonies was correlated with colony size: growth rate was smaller for larger colonies. There was also evidence that growth rates increased with decreasing wave energy and that the growth rates of larger coral colonies were disproportionately affected by wave energy relative to smaller colonies. Lastly, the models indicated that measurements from the standard photos were 15% greater than from the orthophotos (~0.5 cm year-1), indicating a potential bias, despite the paired comparison of area and radius between the two methods finding no significant differences. Notably, the range of growth rates associated with variation in water velocity and colony size was higher (explaining increases up to 40%) than that attributed to the different methods. Nonetheless, this method bias should be considered and explored further in future studies. We suggest that SfM orthophotos can provide important demographic information for corals, in particular noting that very high sample sizes can be achieved efficiently.

The mean change in AMR for Acropora colonies that had no obvious mortality was 2.92 cm year-1 in the pooled dataset, while the best statistical model gave a growth rate of 3.25 cm year-1 for the standard photos. Previous measurements of change in Acropora AMR are higher: A. hyacinthus colonies were estimated to grow 12.9 cm year-1 at a site north of our study area, in the Dampier Archipelago (20.5°S) (Simpson, 1988); Stimson (1996) estimated extension rates of 12.4 ± 1.4 cm year-1 at one site in north western Ningaloo Reef and 10.5 ± 1.2 cm year-1 on the eastern side of North West Cape for A. spicifera; and Bessey et al. (2018) found a growth rate of 7.9 ± 3.7 cm year- 1 for A. spicifera on the Ningaloo reef flat. The composition of Acropora taxa on the reef slope is different to that in the other reef habitats (Cassata and Collins, 2008) and this is likely a key factor contributing to the different growth rates. Chapter 4 – A novel method to measure coral growth

It is probable that the deeper, darker, more highly exposed environment on the reef slope contributed to the slower growth rates obtained in the present study. Indeed, the modelling indicated that there could be as much as a 1 cm year-1 decline in annual growth for Acropora across the range of increasing water velocities, and that this difference was greater for larger colonies. The relationship with water velocity is unlikely to be linear, as previous studies have shown that water movement is important for bringing nutrients and boosting coral growth (Jokiel, 1978). In the present study however, in conditions of high water movement on the reef slope, corals may be investing more energy into building denser skeletons than into radial growth (Madin et al., 2012b).

The Acropora growth rate estimates in the present study were lower than global averages reported for tabular and caespitose Acropora in Pratchett et al. (2015) (~7.5 cm year-1), but were similar to those reported for corymbose Acropora (~3 cm year-1). While we endeavoured to target plating Acropora, many of the smaller colonies (<10 cm radius) were not clearly categorised as any one morphology. No growth measurements of Pocilloporidae exist for from the region, whereas Pratchett et al. (2015) report a mean growth rate for caespitose Stylophora from 9 records to be approximately 1.6 cm year-1 and approximately 1.8 cm year-1 for Seriatopora from 4 records in other locations. Our estimates were higher than these, at 3.54 cm year-1, however we only considered Stylophora/ Seriatopora colonies that remained healthy across the year period for this calculation, and found a high proportion of mortality and partial mortality for this group. The growth rate for Stylophora/ Seriatopora was higher than that of Acropora, but again, in terms of the overall health of the population, Pocilloporidae had higher levels of mortality than Acropora: 47% versus 21%. Changes in calcification rate or volume would be needed to more accurately compare among genera given their different morphologies (Figure App 4. 1.3). The massive growth morphology of Platygyra is not conducive to two-dimensional survey method (Pratchett et al., 2015). Using SfM at the colony level, as in Ferrari et al. (2017), offers the capacity to extend growth rates into the vertical axis. However, it was still useful to monitor the Platygyra in order to document mortality and partial mortality, finding that only 6% of colonies experienced obvious visible mortality over an annual period.

95 For the Acropora colonies where we obtained large sample sizes, allowing statistical modelling of the importance of covariates, we were able to show that coral size, water velocity and survey method were important factors. There was a negative relationship between the initial coral size and the subsequent growth rate, supporting previous work (Dornelas et al., 2017). We also detected a negative relationship between coral size and the modelled water velocity. Water flow has previously been positively correlated with coral growth, due to higher flow providing better flushing and higher nutrient supply (Osinga et al., 2011; Schutter et al., 2010). However, in the present case, where water velocities were high, it seems this positive relationship has been surpassed, and the higher wave energy is instead associated with reduced planar growth. Higher wave energy (particularly during storm and cyclonic events) would also increase mortality and partial mortality (Madin et al., 2014), but we were not able to quantify this. Future work should endeavour to better understand the mechanisms underlying this relationship.

Previous studies have shown that coral growth decreases with increasing depth (Baker and Weber, 1975; Huston, 1985; Miller, 1995). Therefore, future work should target cross-depth comparisons for the region, ideally across different reef habitats (i.e. slope, crest, reef flat, lagoon), as well surveying other coral species or genera. We did not find evidence that depth explained variation in growth rate, but we did not survey the depths <5 m in this study and our depth range only covered 7 m (5-12 m), with most sites at ~7.5 m.

There are various potential explanations for the growth estimates from the standard photos being ~0.5 cm year-1 greater than those obtained from the orthophotos. Differences in the species composition measured in the orthophotos and the standard photos is a possible contributor, as it was not possible to determine species from the orthophotos, and even from the standard photos, identifying Acropora to species level could not be done with confidence. However, given that the corals measured from the two methods came from the same 6 by 8 m plots, we think this is unlikely the main contributing factor.

The higher photo resolution in the standard photos than the orthophotos may have contributed to the difference in growth rates between the two methods. The paired comparison of area and AMR found that the measurements from the two methods were Chapter 4 – A novel method to measure coral growth statistically similar, but that the area and radii estimates were smaller in the standard photos, likely the result of higher photo resolution allowing closer tracking of the true colony edges and therefore producing slightly smaller area estimates. Another explanation of the differences between the two methods is that overgrowth of colonies may not have been so readily detected in the orthophotos, and/or the corals measured from the orthophotos may have experienced a higher degree of competition than the corals in the standard photos, which were usually clear of direct neighbouring corals. The methodological bias should be considered in future work looking to use orthophoto plots for coral demographic studies. However, while this is an important difference, we note that the differences across the range of water velocities and coral sizes were greater.

We make various suggestions which will improve future studies of coral growth and/or mortality using SfM: • Deploy multiple length references as well as ‘targets’ in the orthophoto plots so that coral colonies can be measured multiple times, allowing the error related to the ruler position to be estimated and minimised. This has become standard practice in SfM and would have improved the accuracy and precision of the present study (see http://www.agisoft.com/). On this note, it may be more efficient, and allow better understanding of the precision and accuracy of the measurements, if multiple targets were used to scale the entire orthophoto such that the corals could be measured directly from a scaled orthophoto. • Permanently mark all four corners of the orthophoto plot, or consider circular plots surrounding a central point (e.g. Bryson et al., 2017; Pizarro et al., 2017), to increase overlap between years, thus maximising possible sample sizes. • Use a single software for rendering orthophotos (Burns and Delparte, 2017). A background aim in this study was to operationalise automatic processing of multiple sites using the freeware software Open Drone Map, but in some cases, we found that Agisoft photoscan produced clearer orthophotos and therefore used this software. Though we found no significant difference in the area and

97 AMR measured from orthophotos from the two software packages, this potential source of variation should be removed where possible. • Seek to quantify 3D measures of growth using ‘digital elevation models, DEMs’ or ‘triangulated irregular networks, TINs’ (other products of SfM). Recent studies have shown this is possible if individual corals are targeted (Bennecke et al., 2016; Ferrari et al., 2017), but there are issues with obtaining true 3D measurements if SfM is conducted from above, as overhangs cannot be detected (Fukunaga et al., 2019). • Randomly select colonies in the first temporal orthophoto and follow growth and partial mortality and these are both processes for modular organisms (Dornelas et al., 2017; Madin et al., 2020). This would enable a more realistic estimation of the growth rate of the general population. In contrast, during the present study we targeted healthy colonies that could be matched across years in the orthophotos (thus yielding an ‘optimum growth rate’). • Conducting an in-water survey to identify coral species (collecting samples if necessary) at survey sites as best as possible to help interpret results. The species composition within genera is a potential explanation for differences in the growth rates obtained here in comparison to other studies and may also explain differences between the two survey methods.

The clear advantage of the orthophoto plots is the high sample sizes that can be obtained easily and efficiently (up to 50 colonies from one 6 x 8 m plot in the present study), including in difficult-to-survey environments such as the high wave energy environment of the present study. The orthophotos can be collected within a few minutes on SCUBA once there are markers in place for the plot. SfM technology is now readily available to non-experts, and orthophotos and 3D reconstructions of corals can be achieved with relative cost-efficiency (Pygas et al., 2019). The orthophotos can also provide important additional information about a site, e.g. structural complexity, community composition (including that of other prominent benthic biota e.g. algae etc.), and have the potential to provide measurements of 3D growth (Fukunaga et al., 2019). There are disadvantages however, such as the misalignment in growth rate that was identified in the present study, and in the taxonomic resolution that can be determined from the orthophotos. Chapter 4 – A novel method to measure coral growth Conclusions

Studies of demography continue to be useful, particularly considering the important role of coral growth and mortality in defining reef structure and positive carbonate budgets in the face of ocean warming, acidification and sea level rise. There are still major gaps in our understanding of coral growth across different taxa and environmental conditions, which should continue to be investigated. We showed that SfM can provide important demographic information for corals and is a promising tool for filling some of these information gaps. More work is needed in order to understand the difference in growth rates observed between the highly exposed reef slope and the shallow, higher light environments on the reef flat and lagoon.

Acknowledgements

The study was supported by the BHP-CSIRO Industry-Science Ningaloo Outlook Marine Research Partnership. AKC was supported by this partnership and a Jean Rogerson University of Western Australia PhD Scholarship. We recognise and acknowledge the Jinigudira people, as the traditional custodians of the land and sea country on which this study was conducted. We thank Cindy Bessey, Christopher Doropoulos, Dan Orr, Ryan Crossing and Nick Gust for field support; Margaret Miller for database support; and Jim Gunson for running the SWAN wave model for the study region. We are grateful to Olivia Wei An Seow for helping to process the standard photos, and to Susan Blackburn, George Cresswell, Harry Moore, Camille Mellin and Josh Madin for providing comments on the manuscript.

99 Chapter 5

Coral reefs are architecturally complex three-dimensional habitats, yet studies of coral reefs have been primarily limited to two-dimensional surveys. This chapter describes the development of a novel three-dimensional theoretical model, Coralcraft, and its application to investigating the response of coral communities to hydrodynamic disturbances. The metrics of community composition, morphological diversity and linear rugosity are considered. In the chapter a trade-off between coral morphologies that have high resistance to disturbance, but are slower to recover from it, is considered. The metric of total coral cover used in chapter 2 likely masks changes in species and traits, and this chapter describes the investigation of the dynamics of different coral morphologies.

The coral zoophyte may be leveled by transported masses swept over by the waters; yet like the trodden sod, it sprouts again, and continues to grow and flourish as before. James Dwight Dana, 1853 Chapter 5 – A model of coral growth, competition and response to disturbance Frequent hydrodynamic disturbances decrease the morphological diversity and rugosity of simulated coral communities

Anna K. Cresswell1,2,3, Damian P. Thomson2, Michael D. E. Haywood4, Michael Renton1,5

1 School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia 2 Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Crawley, WA, Australia 3 The UWA Oceans Institute, The University of Western Australia, Crawley, WA, Australia 4 Oceans & Atmosphere, Commonwealth Scientific and Industrial Research Organisation (CSIRO), St Lucia, Queensland, Australia 5 School of Agriculture and Environment, The University of Western Australia, Crawley, WA, Australia

Abstract

Scleractinian corals, which create complex three-dimensional carbonate structures, are the foundation of coral reef ecosystems. There are a variety of coral growth forms or ‘morphologies’ that have distinct advantages and disadvantages when corals compete for space and light, and when they are exposed to hydrodynamic disturbances. We developed a novel three-dimensional functional-structural model, Coralcraft, to investigate how hydrodynamic disturbances (of different frequencies and intensities) influence coral communities. Using five common coral morphologies - encrusting, hemispherical, tabular, corymbose and branching - Coralcraft tracks the temporal dynamics of simulated coral communities. Six metrics are considered: (i) the number of colonies, (ii) the percentage cover and (iii) the volume of each morphology; (iv) the structural complexity (rugosity) and (v) the total coral cover of the community; and (vi) the diversity of morphologies in the community as calculated according to metrics (i)- (iii). Frequent high intensity disturbances caused the greatest reduction to the structural complexity and morphological diversity of simulated communities, with some variation depending on the metric used to calculate diversity. Conversely, scenarios of no disturbance or infrequent disturbance (of low or high intensity) led to communities with higher structural complexity and a higher diversity of morphologies. These results indicate that disturbance frequency may play a greater role in eroding structural complexity and diversity than disturbance intensity. Importantly, given that the scientific coral reef literature generally uses percentage cover, the other metrics we

101 considered – number of colonies, volume, rugosity and diversity – captured the dynamics of impact and recovery from disturbance in subtly different ways. While there are many models for corals, Coralcraft is the first to model the temporal dynamics of three-dimensional growth, competition and response to disturbance in a community with different coral morphologies.

Keywords: Cellular automaton, functional diversity, functional-structural, simulation, individual-based

Introduction

Disturbance and competition play important roles in structuring sessile communities, yet the mechanisms that ultimately define communities through time are not well understood. Scleractinian corals (phylum Cnidaria, class Anthozoa) are sessile colonial animals that form hard ‘skeletons’ through the secretion of calcium carbonate. This makes coral reefs architecturally complex three-dimensional habitats, yet studies of coral reefs have been primarily limited to two-dimensional surveys. Corals have a large diversity of shapes and sizes, which have been linked to competitive ability, as well as vulnerability to disturbance (Madin et al., 2014). In shallow tropical oceans, corals exist in space-limited communities, where competition between coral colonies and other sessile organisms can occur via overgrowth and shading (Connell, 1983). Furthermore, one coral type may win over the other sessile organisms in a community as a result of local environmental or physical conditions (Woodley et al., 1981; Done, 1982; Connell et al., 2004), such as light, depth, wave energy, substrate type or disturbance regime (Connell et al., 1997; Darling et al., 2013), or biological factors such as herbivory (Tanner, 1995; Lirman, 2001). Our understanding of coral reef dynamics remains constrained by uncertainty around the interplay between these factors and community- level responses to disturbance events, particularly given predictions of more frequent or intense disturbances (Emanuel, 2013).

Hydrodynamic disturbances, for example from current or wave action, or in the extreme, destructive waves resulting from cyclones or severe storms, can strongly impact coral community assemblages (Done, 1983). This is in part because of strong links between coral morphology and vulnerability to hydrodynamic disturbances, with some morphologies inherently more vulnerable as a result of their size, shape, skeletal Chapter 5 – A model of coral growth, competition and response to disturbance density or strength of attachment to the seafloor (Done, 1983; Madin, 2005; Madin & Connolly, 2006; Madin et al., 2014). Community assemblage is also dependent on the recovery ability of different corals (Rogers, 1993); with corals that are more vulnerable to disturbance often capable of more rapid growth following disturbance (Darling et al., 2013). Long standing theories such as the ‘intermediate disturbance hypothesis’ (strongly inspired by coral reef studies) propose that the diversity of competing species is maximized at intermediate frequencies and/or intensities of disturbance (Hutchinson, 1961; Grime, 1973; Connell, 1978).

Empirically testing theories of disturbance and competition is difficult for long-lived, slow-growing organisms such as corals, because in situ observations must span several decades (Fox, 2013). Such long-term datasets are rare in the scientific literature, but the few that exist have guided our current understanding of the roles of competition and disturbance and include Tsounis and Edmunds (2017) at the US Virgin Islands (>30 years), Tanner (2017) at Heron Island, Great Barrier Reef (>50 years) and Lamy et al. (2016) at Moorea, French Polynesia (>30 years). Historically, studies have focused at the taxonomic level, but trait-based approaches are suggested to offer additional understanding of function and diversity on coral reefs (Madin et al., 2016; Denis et al., 2017). The number and type (species and/or trait) of coral colonies in an area or along a transect has been used to assess coral communities (Connell et al., 2004), as has size distributions of corals (McClanahan et al., 2008), or colony surface area and volume (Hoegh-Guldberg, 1988; Fisher et al., 2007). However, by far the most commonly used metric is percentage cover (a two-dimensional planar view proportion of coral surface to sea floor) (see Graham et al., 2011). Information on the linear rugosity (3D structure) of a reef patch is also a commonly monitored metric (Alvarez-Filip et al., 2011; Graham & Nash, 2013). Recently, photogrammetry, with the ability to create 3D representations of corals (e.g. Burns et al., 2015; Ferrari et al., 2017), is making fine-scale 3D assessments more accessible. A better understanding of what information to prioritise is important, given that some measures are relatively easy to gather in field studies, while others are more time-consuming, destructive to corals, or costly.

To help address the challenges of purely empirical approaches, multiple coral models, representing corals and/or reefs at various spatial and temporal scales, have been developed over the last decades (Craig, 2010). There are two-dimensional models of coral dynamics and disturbance (Langmead & Sheppard, 2004; Horwitz et al., 2017),

103 competition (Johnson & Seinen, 2002), herbivory (e.g. Mumby, 2006; Sandin & McNamara, 2012), community composition (Maguire & Porter, 1977), calcification rates and reef zonation (Graus et al., 1984). There are also 3D models of coral growth and form (Dauget, 1991; Merks et al., 2004; Sleeman et al., 2005; Chindapol et al., 2013) with Kaandorp et al. (2011) creating sophisticated 3D models capturing the emergent growth of individual corals as modular organisms at the polyp level. Other models have taken steps to integrate 3D growth with disturbance dynamics (Andres & Rodenhouse, 1993; Muko et al., 2001). However, none of the existing models fully capture the temporal dynamics of growth and competition of coral communities in a three-dimensional framework under a variety of disturbance scenarios.

To help address this gap, we developed Coralcraft, a functional-structural model with distinct coral morphologies. We wanted the model to capture the dynamic interplay of corals growing and competing (for space and light) in 3D space and perturbations from hydrodynamic disturbances. We used Coralcraft to investigate how the frequency and intensity of hydrodynamic disturbances affected simulated coral communities, in terms of the abundance of each of the five morphologies, and the structural complexity and morphological diversity of the community. We hypothesised that if there were no disturbances, morphologies capable of rapid occupation of space and overtopping of other morphologies would dominate, thus leading to low diversity, but high structural complexity and high total coral cover. Conversely, under frequent intense disturbances we expected structural complexity, morphological diversity and total coral cover would be reduced, due to the removal of complex morphologies as a result of their structural vulnerability. For infrequent disturbances we hypothesized the highest diversity, with structural complexity and total coral cover lower than if there was no disturbance. Finally, as a case study to determine whether the simulated communities could be matched to observations of real reef situations, we made a comparison of the model results to in situ observations from two hydrodynamically distinct environments at Ningaloo Reef, Western Australia.

Chapter 5 – A model of coral growth, competition and response to disturbance Materials and Methods

Model overview

The model, Coralcraft, uses a functional-structural approach combining functional components, such as photosynthesis, with a dynamic representation of the 3D structure of multiple morphologies of corals. It borrows concepts from an initial prototype in Cresswell et al. (2017). Coralcraft represents a 1 m3 volume (the modelled ‘environment’), with the bottom plane representing the seafloor. To eliminate edge effects, the vertical boundaries of the simulated environment are wrapped, which is equivalent to assuming the environment and community are replicated on all sides, while the bottom of the environment, (z = 1 plane, representing the seafloor) and the top of the environment, (z = 100 plane, representing the water surface) are limits of growth. The environment is divided into 1 cm3 ‘voxels’ which are the smallest spatial unit upon which growth and light attenuation are governed. Voxels are either empty (a water voxel) or occupied by coral (Appendix 5.1). The voxel representation suits the modular architecture of corals, which results from the iteration of a single unit, the polyp (Lartaud et al., 2015). Dynamics operate on a discrete time step representing a 1-week period.

For this study, five distinct coral morphologies were designed – encrusting, hemispherical, tabular, corymbose and branching – as representations of common morphologies on coral reefs (Pratchett et al., 2015) (Figure 5.1). The colonies were designed with a maximum radius of 49 voxels, which equates to a colony that at its largest size stretches across the width of the environment (100 voxels across), and halfway up the height of the environment. We note that there is much variability and indeed, plasticity, in coral morphologies that is currently beyond the scope of Coralcraft.

While corals are animals, they have some strong similarities to plants because their tissue contains photosynthetic zooxanthellae (phylum Dinoflagellata) that produce energy that is utilized by the coral in addition to filter feeding (Dubinsky & Falkowski, 2011). Consequently, light is an important factor in moderating coral growth and survival. While seawater is transparent to light it is subject to both refraction and attenuation and light intensity decreases exponentially with depth (Duxbury, 2018). We estimated and modelled this exponential decrease using the Beer–Lambert–Bouguer law 105 (213). Based on estimates of the amount of light lost per vertical meter in coastal areas (Jerlov, 1951; Dubinsky & Falkowski, 2011) we assumed 28% of light was lost per metre on a simulated reef section. We further assumed a constant amount of light enters the water surface, i.e. each voxel at the top (z = 100) of the environment, and then in order to reduce the light exponentially to 28% of the surface value at the seafloor, only 99.67% of light is passed to the voxel layer below, while the rest is absorbed. In order to create shading, 50% of the light that is not absorbed in a voxel is transmitted to the voxel directly below; while the remaining 50% is transmitted equally among the four voxels neighbouring the voxel directly below; and 100% of light is absorbed by a coral voxel. These rules result in a vertical light gradient and shading, with coral voxels closer to the surface and unshaded by other coral voxels receiving more light (Figure 5.2).

Figure 5.1. The five coral morphologies: encrusting, hemispherical, tabular, corymbose and branching. The top panel shows the modelled representation and the bottom panel provides an example photograph of the associated real coral morphology. The morphologies are ordered by susceptibility to hydrodynamic disturbance as calculated by their colony shape factor. Chapter 5 – A model of coral growth, competition and response to disturbance

Figure 5.2. a) Light level shown through a vertical cross-section of the 1 m3 modelled environment (lighter blue = greater light) with a single tabular coral shown absorbing the light that reaches it, thus creating shading below and b) a simulated coral community showing light absorption of different corals. In the simulated community in b), note the self-shading of the branching corals, the shadows of the branching corals on the tops of the tabular corals, and the very small amount of light passing through the gaps between tabular corals.

Major ecological processes – growth, recruitment and mortality – occur at one of three hierarchical levels: voxels, coral colonies or the coral community (Appendix 5.1). Growth occurs at the voxel level, via the occupation of adjacent voxels in the 3D Von Neumann neighbourhood (i.e. the 6 immediately adjacent cells) that satisfy a colony’s morphology conditions. All five morphologies have equal growth potential, but due to their shapes, some morphologies (e.g. branching, tabular) can more quickly occupy space, obtain greater height and create shading over a larger area, thus indirectly growing faster. Colony growth is limited to a maximum growth of six voxels per colony per timestep, a simplification to represent a limitation in the amount of resources a colony can obtain and use for growth and the increasing costs of translocation with increasing size. While somewhat arbitrary, the six-voxel growth rate cap aimed to capture the costs of being larger (e.g. moving resources) while using growth rates that were comparable to those in the literature (Pratchett et al., 2015).

Recruitment is represented as the colonisation of a randomly selected single voxel in the z=1 plane (the seafloor), which, if empty, and if there is enough light, will begin to grow, otherwise colonization is unsuccessful. In this study recruitment was set at five

107 new corals every 52 time steps (annually), based on estimates from the literature (Stobart et al., 2005; Graham et al., 2015; Turner et al., 2018).

There are three types of mortality incorporated into the model: (i) mortality due to shading, (ii) ‘background mortality’ due to factors not explicitly captured in the model and (iii) mortality due to hydrodynamic disturbance. Mortality due to shading occurs at the voxel level; an individual voxel will die if it does not absorb enough light in a time step to maintain itself, resulting in partial mortality of a colony. Thus, coral to coral competition for light in the form of shading can also indirectly influence the growth rate of the corals. To represent chronic levels of “background’ mortality due to factors such as predation and sand scouring (Rotjan & Lewis, 2008; Lenihan et al., 2011) the probability of background mortality was set at 0.1% per time step for all morphologies except encrusting, which was set at 0.5% to reflect higher exposure to sand scouring.

Mortality due to hydrodynamic disturbance was modelled using ‘Colony Shape Factor’, (CSF) of coral colonies (Madin & Connolly 2006). CSFs were calculated as a function of the basal attachment area and the profile area of a coral colony (Figure 5.3) exposed to a horizontal force, with higher CSF (higher vulnerability) related to lower basal attachment area and higher profile area. The CSFs vary for different sizes and morphologies of simulated corals, with a large CSF equating to a more vulnerable coral colony (Figure 5.3c). A hydrodynamic disturbance removes all corals above user specified threshold CSFs, with more intense disturbances thus having lower threshold CSF values.

Chapter 5 – A model of coral growth, competition and response to disturbance

Figure 5.3. a) Basal areas and b) profile areas of the five morphologies with 30 voxel radii; c) relationship between colony radius and colony shape factor (CSF) for the five morphologies. The red dashed line indicates the specified threshold of a ‘high intensity’ disturbance, where all colonies above the line will be removed, while the red dotted line indicates the same for a ‘low intensity’ disturbance.

109 Scenarios

We investigated a high intensity disturbance that would remove a large majority of corals in the community (e.g. a cyclone), and a low intensity disturbance that would remove only the more vulnerable. We selected intensities matching colony shape factors of 1.5 (high intensity) and 20 (low intensity) through reference to Figure 5.3. We defined nine disturbance scenarios with different combinations of disturbance intensity (low: CSF threshold equal to 20, or high: CSF threshold equal to 1.5) and frequency (none, infrequent (5 years, 260 time steps) or frequent (0.5 years, 26 time steps)) (Table 5.1). These nine scenarios were simulated with (i) fixed (exact fixed values in Table 5.1) and (ii) random (values randomly selected from a normal distribution surrounding the fixed values in Table 5.1) intensities and frequencies. Each combination was simulated 50 times (to account for stochasticity) and run for 100 years (5200 time steps). All simulations commenced with 10 colonies (two of each morphology) randomly located on the seafloor and occupying one voxel (for more information see Appendix 5.1).

It took approximately 200 hours to simultaneously run nine scenarios for a simulated period of 100 years on a Dell Precision 7810 desktop PC with two Xeon (E5-2630 ), 2.3 GHz processors (12 cores total) and 32 GB RAM.

Chapter 5 – A model of coral growth, competition and response to disturbance

Table 5.1. Matrix showing the nine disturbance scenarios with combinations of disturbance frequency and intensity. Colours match those used in Figure 5.6 (N = no, I = infrequent, F = frequent; L = low intensity disturbance, H = high intensity disturbance). Columns define the frequency of high intensity disturbances and rows define the frequency of low intensity disturbance.

Model outputs

Six metrics were calculated within each time step. Firstly, ‘abundance metrics’: (i) the number of colonies per m2 (density), (ii) the percentage cover and (iii) the volume per m3 of each morphology. Secondly, ‘community metrics’: (iv) total coral cover; (v) two measures of the structural complexity (rugosity) of the community; and (vi) the diversity of morphologies in the community as calculated according to metrics (i)-(iii). Different morphologies and coral sizes contribute to percentage cover, volume and structural complexity to varying extents. Percentage cover was calculated to mimic field studies where downwards facing photographs of the seafloor are taken and overlaid with points that are then used to calculate mean percentage cover of the benthic species present (Leujak & Ormond, 2007). We calculated the number of voxels that would be visible for each coral morphology when the environment was viewed from above.

111 Volume was calculated by summing the number of voxels (either alive or dead) of each morphology in the modelled environment.

Structural complexity was calculated for the coral community as (i) linear rugosity and (ii) surface rugosity. Linear rugosity is a standard metric in coral reef research (Risk, 1972; Luckhurst & Luckhurst, 1978) and is generally measured by SCUBA divers where a measuring tape is pulled flat across the reef while the chain follows the topography of the reef (Graham & Nash, 2013). Linear rugosity is then calculated as a ratio of the convoluted length of the chain to the Euclidian distance of the tape measure (Graham & Nash, 2013). Using Coralcraft, linear rugosity was calculated as the ratio of the mean of the topographical distance (chain equivalent) versus the fixed horizontal distance (tape measure equivalent) across the simulated community of corals, for every x-plane (i.e. the mean of 100 measures, see Figure App 5. 1.1). Therefore, a value of 1 indicates a perfectly flat environment, while higher values indicate greater linear rugosity (see Appendix 5.1). We also calculated the topographical surface area (the surface area of all corals and any unoccupied seafloor), and calculated surface rugosity as the ratio of this surface area to a flat unoccupied seafloor (100 x 100 cm). Topographical surface area is being used increasingly to measure of structural complexity (Burns et al., 2015) and is similar to linear rugosity in that a value of 1 indicates a perfectly flat environment, while higher values indicate greater surface rugosity.

Simpson’s Diversity Index (Simpson, 1949) was used to calculate three morphological diversity metrics based on the (i) number of colonies in the simulated area ‘diversity by density’, (ii) the percentage cover, ‘diversity by cover’, and (iii) volume, ‘diversity by volume’ of the different morphologies. This index is scaled from 0 to 0.8, where 0.8 represents the highest possible diversity, i.e. even abundance of five morphological types.

There are various user-specified parameters detailed in Table App 5. 1.1. 3D plots of the coral community, showing coral voxels coloured by (i) morphology (ii) live/ dead status and (iii) light absorption can be created using the rgl package (Adler et al., 2018) (Appendix 5.1).

Chapter 5 – A model of coral growth, competition and response to disturbance Case study: two hydrodynamically distinct reef environments at Ningaloo Reef

The simulated coral communities from the different scenarios were compared with in situ coral communities at Osprey Bay, Ningaloo Reef, Western Australia (22.2379° S, 113.8390° E). Ningaloo Reef provided a good case study for testing Coralcraft because the reef bathymetry creates distinct reef flat and reef slope zones with strong differences in wave driven water velocities (Figure. 5.4). Coral cover was estimated using photos taken along 25 m transects at 0.5 m intervals using a digital camera held perpendicular to the substrate at approximately 30-50 cm. Surveys were undertaken at 5 sites on the reef flat and 5 sites on the reef slope annually in May between 2015-2018. Percent cover at species or genus level was estimated using Transect Analysis Software (SeaGIS™) and then classified into the five morphologies or excluded from the analysis in the case of non-coral organisms or substrate types. Linear rugosity was estimated using the chain and tape method (Graham & Nash, 2013) as the ratio of the distance covered by a 10 m chain with chain links of 20 cm to a 10 m tape. Annual mean estimates of wave-induced water velocity at the seafloor for the same sites were derived using the ‘simulating waves near shore’ (SWAN) model (see Booij et al., 1999) on a 30 by 30 m grid encompassing the study area (Figure. 5.4).

Figure. 5.4. Map of sites (indicated in pink) where coral percentage cover data and linear rugosity were obtained from the Osprey Bay region of Ningaloo Reef, overlaid on output from the SWAN model at a resolution of 30 m showing the mean modelled water velocity (m s-1) at the sea floor.

113 Results

Simulation outputs

Coralcraft successfully simulated corals interacting in a community over time. Different disturbance scenarios led to markedly distinct community assemblage dynamics (Figure 5.5, Appendix 5.3, Appendix 5.4); each with differences in structural complexity and diversity metrics (Figure 5.6). In all fixed disturbance scenarios, the impact of disturbances was evident as a marked step decline in all or particular morphologies (Figure 5.5), as well as declines in the structural complexity and diversity metrics (Figure 5.6), followed by a recovery if disturbances were infrequent. Linear rugosity and surface rugosity gave similar outputs and so we focussed on linear rugosity.

Chapter 5 – A model of coral growth, competition and response to disturbance

Figure 5.5. Temporal dynamics of the six most distinct scenario outputs under fixed disturbance regimes showing the density of colonies (m-2), percentage cover and volume of each morphology over 25 years, with the right-hand panel showing a 3D representation of one randomly chosen replicate community at the end of the simulation. Ribbons indicate 95% confidence intervals across 50 replicate simulations. N = no, I = infrequent, F = frequent; L = low intensity disturbance, H = high intensity disturbance.

115

Figure 5.6. Temporal dynamics of all scenarios over 25 years under random disturbance intensity and frequency a) total coral cover, b) linear rugosity, c) diversity by density, d) diversity by cover, e) diversity by volume for fixed (left panel) and random (right panel) scenarios. Ribbons indicate 95% confidence intervals across the 50 simulations. N = no, I = infrequent, F = frequent; L = low intensity disturbance, H = high intensity disturbance. Chapter 5 – A model of coral growth, competition and response to disturbance Community metrics — total cover, linear rugosity, surface rugosity and the three diversity metrics — varied between scenarios and in general were all highest under no disturbance (Figure 5.6). The second highest levels of these metrics were achieved when disturbance was infrequent (5 yearly), but this distinction was less clear when diversity was based on percentage cover than on volume (Figure 5.6). Diversity by cover was most reduced by frequent high intensity disturbances, while diversity by volume was reduced to a similar extent by frequent disturbances of both low and high intensities. Under no disturbance (NL-NH) there was generally an equal density of morphologies and volume of each morphology through time, except for the encrusting morphologies, which were lower (Figure 5.5). As a result, this scenario maintained the highest diversity by volume relative to the other scenarios, as distinct from diversity by cover, though this diversity declined over time (Figure 5.6c-e).

Under infrequent low intensity disturbances (IL-NH) the percentage cover of encrusting, branching and corymbose colonies decreased over time while tabular and massive morphologies increased, with tabular dominating (Figure 5.5). Similar patterns were captured in the volume dynamics, however hemispherical morphologies were more abundant according to this metric. When the infrequent disturbances were of high intensity (NL-IH) the step-declines following disturbance were larger, with each disturbance leading to a significant decline in the percentage cover and volume of tabular, corymbose and branching colonies, with variable levels of recovery between disturbances. In this case tabular corals no longer dominated the community and encrusting and hemispherical morphologies were more abundant.

When both high and low intensity disturbances were set to occur infrequently in the same simulation (IL-IH), temporal dynamics were similar to the NL-IH scenario, indicating that the high intensity disturbances were governing the dynamics of the community (Figure 5.5). Total coral cover and diversity were also similar, whereas there were differences in structural complexity: the IL-IH scenario tended to have lower complexity than the NL-IH or the IL-NH scenarios.

Frequent disturbances meant recovery time was minimal (< 6 months). When the frequent disturbances were low intensity (FL-NH) tabular morphologies survived to a certain size, as governed by their CSF (Figure 5.3, Figure 5.5), while branching and corymbose were continually removed and hemispherical and encrusting dominated.

117 Under frequent high intensity disturbances (NL-FH) all complex morphologies were removed and did not recover, leaving the community dominated by hemispherical and encrusting morphologies.

All three scenarios with frequent high intensity disturbances (-FH) had low total coral cover, linear rugosity and diversity by cover (Figure 5.6), indicating that if there are frequent high intensity disturbances occurring, the effect of other additional disturbances is negligible. Frequent low intensity disturbance scenarios (FL-) also resulted in low linear rugosity.

While random scenarios generally matched the overall patterns of the fixed scenarios (Appendix 5.3, Appendix 5.4), the random nature of both the timing and intensity of the disturbances (Table 5.1) meant that the step changes resulting from disturbance were not obvious when averaged across the 50 simulations. Spawning still occurred at regular intervals, resulting in increases in the diversity by density of colonies every 52 time steps in Figure 5.6c. The random intensity of the disturbances resulted in occasional removal of morphologies that would otherwise survive in the fixed scenarios. The most marked difference was for the FL-NH scenario, where tabular cover was lower in the random simulations than in the fixed simulations. This can be explained by the fixed low intensity disturbances only removing the largest tabular corals (Figure 5.3c), while the random variations on the low intensity disturbances removed smaller tabular corals as well.

Comparison of simulation outputs to case study

Data from the SWAN model showed marked differences in the seafloor water velocities between sites on the reef flat and the reef slope at Osprey Bay, Ningaloo Reef (Figure. 5.4, Figure 5.7). There were also clear differences in the percentage cover of the five morphologies between the two zones and both linear rugosity and diversity were higher on the reef slope (Figure 5.7). The reef flat was dominated by tabular corals and best matched the three fixed scenarios with no high intensity disturbances, or the NL-NH and IL-NH random scenarios. The reef slope had a more even cover of the five morphologies, best replicated by model scenarios with fixed, infrequent high intensity disturbances, following a recovery period after disturbance. Total cover of the five morphologies was higher on the reef slope (41.9%) than on the reef flat (28.6%). These Chapter 5 – A model of coral growth, competition and response to disturbance covers were lower than those achieved in the simulations which ranged from 30-80%. At the Ningaloo Reef sites, the remaining cover was predominately turfing algae (40.5%) and sand (~12.6%) on the reef flat and coralline algae (15.1%), macroalgae (7.0%) and turf algae (31.4%) on the reef slope.

Figure 5.7. Percentage cover of the five coral morphologies on a) the reef flat (n=20) and b) the reef slope (n=20); c) mean linear rugosity; d) mean Simpson’s Diversity Index (diversity by cover) and e) mean modelled bottom velocity from the SWAN model for sites at Ningaloo Reef, Western Australia. In the boxplots, boxes indicate interquartile ranges, horizontal lines indicate medians, points indicate outliers and the whiskers extend from the smallest value within 1.5 times the interquartile range below the 25th percentile to the largest value within 1.5 times the interquartile above 75th percentile.

Discussion

Coralcraft successfully simulated coral communities over multiple generations and showed that the community composition was sensitive to both the intensity and frequency of hydrodynamic disturbances. As expected, the scenario with no disturbances had the highest total coral cover and structural complexity. Contrary to the ‘intermediate disturbance hypothesis’, this scenario also led to communities with the highest diversities, at least according to volume, rather than infrequent disturbances as we predicted. When diversity was calculated according to percentage cover, all scenarios had similar diversity, except for the frequent high intensity scenarios, which

119 were significantly lower. The different results between the three metrics of diversity should be a consideration for future studies. Frequent high intensity disturbances (half yearly), led to the lowest diversities, linear rugosity and total coral cover, as expected. Importantly however, communities subjected to frequent low intensity disturbances also had low rugosity, and in the case of diversity by volume, low intensity disturbances produced similarly low diversities as high intensity disturbances. With predicted increases in the intensity, and possibly frequency, of hydrodynamic disturbances (Emanuel, 2013), our simulation results support hypotheses that the structural complexity and morphological diversity of coral reef communities will decrease (Williams & Graham, 2019). In particular, our results suggest that increased disturbance frequency may be a more important factor than disturbance intensity.

All three abundance metrics – density of colonies, percentage cover and volume – decreased to varying degrees following disturbance, with variation linked to coral morphology and disturbance intensity, aligning with the work of Madin et al. (2014). Previous empirical studies of intense hydrodynamic disturbances have recorded selective mortality: Woodley et al. (1981) found tabular and branching morphologies were reduced by up to 99% following a cyclone, while hemispherical morphologies were reduced by only 9% (Jamaica); Hughes and Connell (1999) found a 42% reduction in tabular corals following a cyclone while hemispherical and encrusting morphologies showed little change (Great Barrier Reef). The influence of selective mortality on diversity is more complicated and although we found that morphological diversity was most commonly reduced following a disturbance, several empirical studies have found other results at the taxonomic level. For example, several cyclones (Hurricane Allen, 1980; tropical storm Klaus, US Virgin Islands, 1984) resulted in increases in species diversity because dominant species were reduced more than rarer species (Rogers, 1993). However, in Coralcraft diversity can be calculated immediately after the disturbance, while in field studies, monitoring is often annual, such that recovery may already be occurring at the time of the post disturbance diversity measurement. Nonetheless, it may be that without a clear competitive dominant coral morphology in Coralcraft, evenness is more readily achieved under no disturbance scenarios than is realistic; despite tabular corals being most abundant in the community in terms of percentage cover, other morphologies persisted in the community. Incorporating parameters for direct competitive advantage into the model, where one coral type is able to overgrow and kill another adjacent coral (Connell et al., 2004) may help address this. Chapter 5 – A model of coral growth, competition and response to disturbance Furthermore, the effect of varying the exact height and width of morphologies could be explored in more detail, as different morphology variations would be more or less competitive.

Recovery time is essential in determining long-term community dynamics. Recolonization and recovery of corals after cyclones is known to vary greatly among species (Connell et al., 1997). Disturbed reefs have been documented to become dominated by faster growing corals in multiple reef systems (Riegl & Piller, 2003; Darling et al., 2013; McClanahan & Muthiga, 2014). However, under frequent half- yearly disturbances, hemispherical and encrusting morphologies dominated, without ample time for recovery of the more structurally complex morphologies. Thus, structural complexity and diversity decreased as a result. This supports predictions that coral assemblages will tend towards less diverse and more homogeneous assemblages of species (or functional groups) that can better tolerate more frequent disturbances (Darling et al., 2013).

In the majority of scenarios, total coral cover was around 40-60%, and was generally reduced with higher frequency or intensity of disturbance, matching with observations that corals rarely cover all the available substrate and supporting the idea that recurrent disturbance is one likely mechanism behind this (Connell et al., 1997). Total coral cover was generally higher in simulations than in the case study, likely due to competition between corals and other sessile organisms that were also present at the field sites.

We were able to match the percentage cover of different morphologies in the case study to model output from certain scenarios, though in some cases only at certain time periods following disturbance. Due to the model assumptions, we would not expect exact matches for the case study. Reef flat coral communities at Ningaloo Reef were most comparable to the simulated communities resulting from no disturbance or half yearly low intensity disturbances i.e. high cover of the tabular morphology. Reef areas that experience relatively benign conditions associated with shallow, clear waters and low wave action are often characterized by low-diversity assemblages of branching or tabular morphologies, attributed to the ability of these morphologies to grow quickly and outcompete other corals through shading (Baird & Hughes, 2000). The Ningaloo reef slope coral community, with higher wave action, had greater morphological diversity and better matched scenarios with fixed, infrequent (5 yearly) disturbances

121 (both low and high intensity). However, these scenarios were highly dynamic, and the best match for the reef slope coral communities tended to be after a recovery period of 3-4 years following a disturbance. This could be interpreted to mean that the reef slope is more dynamic than the reef flat, with disturbance and recovery occurring frequently. In future work long-term empirical data could be compared with simulation outputs to investigate this further. Other factors vary between the reef flat and the reef slope, such as water depth and light, which likely influence the community assemblage. Furthermore, other sessile organisms that may be present in the communities, but are not considered in Coralcraft, such as macroalgae, turf algae and coralline algae, can be strong competitors for space (Hughes, 1989), likely affecting community dynamics in complex ways (Boschetti et al., 2019). Future iterations of the model would benefit from incorporating these other sessile organisms, given the importance of these in overall reef dynamics (Tanner, 1995; Lirman, 2001).

Coralcraft highlighted that the choice of metric matters and . marked differences between the percentage cover and volume metrics of abundance of different morphologies were evident. As an example, the percentage cover metric showed tabular morphologies clearly dominating the community in the no disturbance scenario, whereas in terms of volume, massive morphologies dominated the community. The most appropriate metric will depend on the aims and scope of a study, and the differences between metrics highlighted here should be considered when reporting one or another. For example, the density of colonies of each trait may be important in studies of genetic diversity; whereas volume may be most useful when considering rates of reef accretion or carbonate budgets (Perry et al., 2018); and percentage cover is most easily collected in field studies and is most common in the literature if comparisons are sought. Importantly, the abundance metric used also influenced the interpretation of diversity. For example, frequent high intensity disturbances decreased the diversity by cover metric the most, while diversity by volume showed that frequent low intensity disturbances led to the lowest diversity.

Disturbance dynamics were captured in the linear and surface rugosity of the community, as step declines following fixed disturbance. Thus, these metrics can be a useful and relatively simple metrics for detecting impact and recovery from hydrodynamic disturbances, though this would be complicated by underlying reef bathymetry. There was strong correlation between linear rugosity and surface rugosity, Chapter 5 – A model of coral growth, competition and response to disturbance though the surface rugosity spread across a larger range and therefore the scenarios separated more clearly, indicating that this measure contained more detailed information, as to be expected with the additional dimension. In any case though, both measures can provide information on the structural complexity of the community. However, whereas hydrodynamic disturbances clearly influence structural complexity, other types of disturbance, such as coral bleaching, may not (Graham et al., 2007). In this case using this metric alone would be misleading. We suggest that the use of multiple metrics in parallel allows a broader assessment of coral communities.

There are many simplifying assumptions in the model that should be considered when interpreting the results. Some of these assumptions relate to the morphologies, which are representations only, and do not fully represent the real diversity of morphologies, nor the plasticity of corals. For example, previous studies of hemispherical morphologies has suggested that these may be better modelled by an ‘ice-cream cone’- type shape that does not have a full attachment to the bottom due to scouring and erosion at the base (Massel & Done, 1993). The strength of the seafloor substrate is not considered here, but is known to influence vulnerability to hydrodynamic disturbance, sometimes more than a coral’s morphology (Madin, 2005). Furthermore, corals may not break at the base, as there are other fragile components, such as the branches in the branching morphology, so future work could investigate fragmentation and removal of only part of a colony.

There are also several biological aspects which are simplified, for example the process of recruitment. We specified five possible new recruits annually, which were only successful if they were randomly allocated to free space. In reality coral larvae can actively seek appropriate substrate (Babcock & Mundy, 1996). The initial proportion of recruits at the beginning of the simulations (two of each morphology) likely influenced the subsequent community dynamics. The effects of varying the proportions of different morphologies in the recruits and the total number of recruits could be explored in future studies using Coralcraft, as previous work has shown that there are links between coral fecundity and morphology and size (Álvarez‐Noriega et al., 2016). Corals can also undergo both fission and fusion (Hughes & Jackson, 1985; Raymundo & Maypa, 2004; Brito-Millán et al., 2019); we assumed hydrodynamic disturbance resulted in dislodgement, which resulted in mortality, however, this is not necessarily the case, as, particularly for branching morphologies, corals can continue growing from fragments,

123 or fragments can grow across space to fuse back together with their original colony or other colonies. These factors would likely influence dynamics and would also be an interesting consideration for planning of coral reef restoration projects using Coralcraft (Sleeman et al., 2005). Furthermore, different corals can also have intrinsically different growth rates (Pratchett et al., 2015), which has not been considered in the present simulations, but would be an important investigation point for future studies (Darling et al., 2013; Kayal et al., 2015).

The size of the environment represented in the present study (1 m3), as well as the maximum radius of the coral colonies (49 cm), limited capacity to capture some reef scenarios. For example, some massive corals can grow much larger (several meters in height and diameter), which could not be captured with these size and radius constraints. Similarly, tabular and branching corals can form large patches, though we suggest that this would be represented in the present version of the model by numerous adjacent individual corals. Coralcraft does allow a larger environment to be specified by users, however for many fine-scale investigations, coral colonies <0.5 m diameter and a 1 m3 environment should be sufficient. Coralcraft is programmed so that the sides of the environment (and the coral community) are ‘wrapped’, which essentially means the environment is replicated on all sides. With multiple replicate simulations (here 50), our approach is equivalent to sampling many small spatially independent areas/plots from a much larger site, and thus allows us to effectively and efficiently simulate a large area with homogenous environmental conditions but a heterogeneous coral community.

Other processes could be incorporated in future versions of the model: we focused on wave energy and light as key structuring elements, yet corals are mixotrophs, and get energy from both light and via filter feeding (Fox et al., 2018). Therefore, it would be interesting to consider how the availability of plankton and nutrients in combination with variability in the uptake by different coral morphologies (e.g. considering surface area) influences coral growth and the resulting community dynamics (see. Kaandorp et al., 2011). As a final note, it would be interesting to modify the model to better capture coral reef accretion over long time frames, with functionality where live corals may settle on dead corals and over time build a reef. This would require some significant modification and benefit from the incorporation of crustose coralline algae, which are known to play a critical role in reef accretion (Adey, 1998).

Chapter 5 – A model of coral growth, competition and response to disturbance Coralcraft is a powerful new modelling tool for mechanistically representing, visualising and understanding the fine-scale temporal dynamics in the morphological composition of coral communities responding to hydrodynamic disturbances. The model is an important step in modelling interactions between coral communities and their environment. Coralcraft provides the capacity to explore the effects of different disturbance scenarios by varying disturbance frequency and intensity, as well as changes to important ecological processes, such as recruitment, or environmental conditions, such as light levels. It should help to provide a more multi-faceted interpretation of traditional monitoring and field experiment data, in particular by capturing the dynamics of multiple community metrics in parallel, such as percentage cover, volume, structural complexity and diversity.

Acknowledgements

We gratefully acknowledge the support of the University of Western Australia Jean Rogerson Postgraduate Scholarship and the BHP-CSIRO Industry-Science Ningaloo Outlook Marine Research Partnership. We thank Melanie Trapon and Cindy Bessey for their involvement in data collection and for conducting the image analysis of the case study data, as well as Margaret Miller for data management. We thank Jim Gunson for running the SWAN model for the Ningaloo Reef region and Harry Moore for assistance managing this data in QGIS. We are grateful to Libby Trevenen, Christopher Doropoulos, Russ Babcock, Dirk Slawinski, and Andrew Pomeroy for fruitful discussions about the model and the specifics of coral biology, competition and mechanical vulnerability. We thank Tim Hammer for coining the name ‘Coralcraft’. We thank Susan Blackburn and George Cresswell for comments on drafts. We gratefully thank three anonymous reviewers and editor Stuart Sandin for their time investment to provide valuable and detailed comments on the manuscript, which improved it to the version presented here.

125 Chapter 6

Acute climatic disturbances occur against a backdrop of other chronic anthropogenic disturbances, with a particularly relevant one for coral reefs being fishing. In this chapter I explore the ongoing effect of recreational fishing in the Ningaloo Marine Park over a period of 30 years. The resilience components of resistance and recovery require careful consideration for this type of chronic disturbance, as the disturbance has no clear end point. I used the network of reserves to provide a reference state with which to compare fished to unfished areas and thus make an assessment of the effect of a chronic disturbance. This paves the way for the important discussion of the management of disturbances.

Chapter 6 – Disentangling the effects of a chronic disturbance Disentangling the response of fishes to recreational fishing over 30 years within a fringing coral reef reserve network

Cresswell, A.K.1,2,3, Langlois, T.J.1,3, Wilson, S.K.3,4, Claudet, J.5,6, Thomson, D.P2, Renton, M.1,7, Fulton, C.J.8, Fisher, R.3,9, Vanderklift, M.A2, Babcock, R.C.3,10, Stuart-Smith, R. D. 11, Haywood, M.D.E.10, Depczynski, M.3,9, Westera, M.12, Ayling, A.M.13 , Fitzpatrick, B.14, Halford, A. R.15, McLean, D.L.3,9, Pillans, R. D. 10, Cheal, A.J.9, Tinkler, P.16, Edgar, G. J.11, Graham, N.A.J.17, Holmes, T.H.4

1 School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia 2 CSIRO Oceans & Atmosphere, Indian Ocean Marine Research Centre, Crawley, WA, Australia 3 The UWA Oceans Institute, The University of Western Australia, Crawley, WA, Australia 4 Marine Science Program, Western Australia Department of Biodiversity, Conservation and Attractions 5 National Center for Scientific Research, PSL Université Paris, CRIOBE, USR 3278 CNRS- EPHE-UPVD, Maison des Océans, 195 rue Saint-Jacques 75005 Paris, France 6 Laboratoire d’Excellence CORAIL, Moorea, French Polynesia. 7 School of Agriculture and Environment, The University of Western Australia, Crawley, WA, Australia 8 Research School of Biology, The Australian National University, Canberra, ACT, Australia 9 Australian Institute of Marine Science, Indian Ocean Marine Research Centre, Crawley, WA, Australia 10 CSIRO Oceans & Atmosphere, Queensland Biosciences Precinct, St Lucia, Queensland, Australia 11 Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania 12 BMT, Level 4/20 Parkland Rd, Osborne Park, WA, Australia 13 Sea Research, 20 Rattray Ave, c, QLD, Australia 14 Oceanwise Australia Pty Ltd, Floreat, Perth, Western Australia 6014, Australia 15 SPC, B.P. D5 – 98848 Noumea Cedex, New Caledonia 16 School of Life and Environmental Sciences, Deakin University, Warrnambool, Victoria, Australia 17 Lancaster Environment Centre, Lancaster University, Lancaster, UK

Abstract

Few studies assess the effects of recreational fishing in isolation from commercial fishing exist. We used meta-analysis to synthesise 4,444 samples from 30 years (1987- 2017) of fish surveys inside and outside a large network of highly protected reserves in the Ningaloo Marine Park, Western Australia, where the major fishing activity is recreational. Data were collected by different agencies, using varied survey designs and sampling methods. We contrasted the relative abundance and biomass of target and non- target fish groups between fished and reserve locations. We considered the influence of, and possible interactions between, seven additional variables: age and size of reserve,

127 one of two reserve network configurations, reef habitat type, recreational fishing activity, shore-based fishing regulations and survey method. Taxa responded differently: the abundance and biomass inside reserves relative to outside was higher for targeted lethrinids, while other targeted (and non-targeted) fish groups were indistinguishable. Reef habitat was important for explaining lethrinid response to protection, and this factor interacted with reserve size, such that larger reserves were demonstrably more effective in the back reef and lagoon habitats. There was little evidence of changes in relative abundance and biomass of fishes with reserve age, or after rezoning and expansion of the reserve network. Our study demonstrates the complexities in quantifying fishing effects, highlighting some of the key factors and interactions that likely underlie the varied results in reserve assessments that should be considered in future reserve design and assessment.

Keywords: Marine protected area; MPA; fisheries; coral reef; Ningaloo; adaptive management; recreational fishing; Lethrinus

Introduction

Anthropogenic activities continue to expand worldwide, particularly in the tropics, threatening natural systems and the ecosystem services they provide (Barlow et al., 2018). As a result, ‘protected areas’ that seek to balance extractive activities with other socio-ecological values are increasingly being used to manage terrestrial and marine systems (Jenkins & Joppa, 2009; Sala et al., 2018). Many studies have assessed the conservation effects of marine reserves (reviewed by Mosquera et al., 2000; Russ, 2002), including quantitative syntheses of regional and global studies, with most finding higher abundance and size of targeted species within reserve boundaries in the case of ‘no-take’, or highly protected reserves (Lester et al., 2009). The large majority of these findings are from regions with commercial fisheries, and less is documented about the impacts of recreational fisheries, despite several studies flagging the potentially high impacts of these fisheries (McPhee et al., 2002; Coleman et al., 2004; Cowx & Cooke, 2004; Lewin et al., 2006). No-take reserves are a key tool for assessing the impacts of fishing (Ballantine, 2014) and while there are a handful of empirical studies that have demonstrated the effects of fishing, using inside outside comparisons, on targeted invertebrates (Shears et al., 2006; Babcock et al., 2007) and finfish (Denny et al., 2004) a comprehensive assessment including reserves with different characteristics over long Chapter 6 – Disentangling the effects of a chronic disturbance time frames is lacking. The magnitude of differences inside to outside reserves has been correlated with their design, in particular size and age, with larger and older reserves typically resulting in greater abundance and/or size of targeted fishes than reserves that are smaller or newly established (Claudet et al., 2008; Edgar et al., 2014). The effects of reserves vary among biomes, locations and taxa of interest (Côté et al., 2005; Claudet et al., 2010; Mora & Sale, 2011) and there are examples of reserves having negligible effects on targeted fish communities (McLaren et al., 2015). In addition to size and age of reserves, explanations for this variability include high levels of cross-boundary movement by fishes (Pillans et al., 2014) and minimal to no difference in fishing activity between reserve and fished areas due to accessibility and/or non-compliance by fishers (Bergseth et al., 2017), all of which make disentangling the true effects of fishing more complicated.

Ideally assessments of the influence of reserves are based on replicated studies across multiple comparable reserves with long time series of biological data before and after reserve establishment (Underwood, 1993; Russ, 2002; Osenberg et al., 2011). Yet such data are typically beyond the scope of single research programs, necessitating the integration of multiple datasets. ‘Adaptive management,’ involving changes to the number, size or boundaries of reserves in response to new scientific information, changes in fishing pressure or changing social attitudes (McCook et al., 2010) further complicates long-term assessments. Ongoing improvement of ecological sampling methods and technologies has resulted in new survey methods being introduced to monitoring (Goetze et al., 2015): video based methods (baited remote underwater video (BRUV) and diver operated video (DOV)) are now commonly used alongside or in place of the previously more common underwater visual census (UVC) (Mallet & Pelletier, 2014). Therefore, evaluations of reserves that have long-term datasets must have the capacity to incorporate and evolve with changes in reserve design and survey methods (Claudet & Guidetti, 2010). Other factors, including differences in habitat and benthic structure, have been shown to affect outcomes of reserve evaluation (Miller & Russ, 2014; Rees et al., 2018b) and while these factors have been studied independently, few assessments consider multiple factors simultaneously, including possible interactions (Edgar et al., 2014). Differences in fishing pressure outside of reserves will also directly impact inside to outside comparisons, yet data that quantify localised variation in fishing activity at the scale of marine parks and reserve networks are rarely available (Lewin et al., 2006).

129 Here, we synthesise a unique 30 year dataset from within a multiple-use marine park at Ningaloo Reef, Western Australia. The type of fishing activity at Ningaloo Reef (almost exclusively recreational) in combination with a highly protected and regulated network of reserves that have undergone significant expansion during the study period, offers the opportunity to advance on previous studies and inform on the potential impacts of recreational fisheries. We integrate data from numerous agencies with varied survey designs and methods, and therefore use a meta-analytical approach to compare the abundance and biomass of select targeted and non-targeted tropical reef fish inside reserves with adjacent fished areas. We tested two hypotheses: (1) the relative abundance and biomass of targeted fish taxa will be greater inside reserves than outside due to recreational fishing activity; and (2) the observed relative abundance and biomass will vary with survey method, age and size of reserve, spatial variability in fishing activity (including shore-based fishing) and/or habitat. Our study offers four main novelties. First, the effect of recreational fishing on targeted species is assessed in isolation from commercial fishing. Second, we explicitly consider potential interactions between variables. Third, the influence of changes in the reserve network is considered in the context of the increasingly common adaptive management. Fourth, we consider the influence of shore-based fishing, which has rarely been investigated. We therefore provide advances on previous work that are of importance for future planning and assessment of protected areas.

Material and methods

Study region

Data for this study are from the Ningaloo Marine Park (henceforth, the Park) on the western Australian coastline (22°S, 113°E; Figure 6.1). The Park covers the majority of Ningaloo Reef (a World Heritage site) which is a fringing coral reef almost 300 km in length. The reef encompasses a sheltered lagoon that is highly accessible by shore-based fishers and those operating recreational vessels (Smallwood & Beckley, 2012). Despite a relatively small permanent human population, this area is a popular tourism destination for recreational fishers (Sumner et al., 2002; Smallwood & Beckley, 2012; Mitchell et al., 2018). There have not been any major commercial fishing activities within the marine park since the 1970s, (for summary see pg. 78, CALM (2005) and pg. 70, DPIRD (2017)). Recreational spearfishing has additional restrictions of varying Chapter 6 – Disentangling the effects of a chronic disturbance degrees outside of the reserves, with spearfishing prohibited along a 70km stretch of coast between Tantabiddi Well and Winderabandi Point, and spearfishing for Labridae and Serranidae prohibited throughout the Park (DPIRD, 2018) (Figure 6.1).

A network of eight no-take reserves was established in April 1987 to cover 10%, ~22,400 ha, of the total Park (Figure 6.1a) (CALM, 1989). In 2005, the majority of the existing eight reserves were expanded in size and 10 new reserves were added (Figure 6.1b), increasing the reserve coverage to 88,365 ha (34% of the NMP). At the same time, three reserves, covering 1,929 ha, were established as a part of the 28,616 ha Muiron Islands Marine Management Area (MIMMA), immediately adjacent to the northern boundary of the Park (CALM, 2005). Together, the Park and the MIMMA form a continuous network of reserves (CALM, 2005). There is some variation in the regulations along the boundaries of the 21 current reserves, complicating terminology and analysis, with eight reserves allowing shore-based fishing from their coastal boundaries Appendix 6.1, Figure 6.1. According to recent classifications of marine reserves, the two forms of reserves in the present study, those with shore-based fishing prohibited and those where it is allowed, would classify as Fully Protected Areas and Highly Protected Areas, respectively (Horta e Costa et al., 2016), both of which would be expected to provide protection for fished species (Zupan et al., 2018). We explicitly include consideration of the effect of shore-based fishing in our analyses.

131

Figure 6.1. The Ningaloo Marine Park and Muiron Islands Marine Management Area boundaries (dotted lines) with the location of sanctuary zones (referred to as reserves in the present study) shown in green along the Ningaloo coast of Western Australia under the a) initial (1987 – 2005) and b) current (2005 - 2017) zoning schemes. Tantabiddi Well and Winderabandi Point are indicated with red markers as spearfishing is prohibited between these locations. The Osprey reserve is also indicated. In b) blue regions indicate zones on the coastal boundaries of the reserves where shore- based fishing is allowed.

Survey data

Data from all major research and monitoring programs surveying fish in the Park over the last 30 years (1987 – 2017) were collated (0) to create a very large synthesis of information. Three different survey methods were used to census fish: Baited Remote Underwater stereo-Video (BRUV), Diver Operated stereo-Video (DOV) and Underwater Visual Census (UVC) (Langlois et al., 2010; Murphy & Jenkins, 2010). The majority (90%) of surveys also estimated the length of fish (an in situ estimate of Chapter 6 – Disentangling the effects of a chronic disturbance total length for UVC, and fork length measured from stereo-video for DOV and BRUV), which allowed estimates of biomass using formulae from FishBase (Froese, 2018).

Data were organised hierarchically with a sample (individual UVC or DOV transect or BRUV deployment) being the lowest level of replication. Samples were classified to the next hierarchical level and termed a ‘comparison pair’, based on the criteria: (i) that there were at least two samples inside and two samples outside a given reserve, (ii) these samples were collected within 2 weeks of each other, (iii) samples were collected more than 200 m from within or outside of the reserve boundaries (excluding one reserve, the small size of which meant this was not a logical rule), (iv) samples were collected using the same survey method within one of four habitat categories (see Table 6.1). Data satisfying these conditions consisted of 4,444 samples classified into 305 relative abundance comparison pairs and 3,892 samples classified into 268 relative biomass comparison pairs. These data covered seven of the initial eight reserves and 16 of the 21 current reserves (0).

Fish groups

We consider three main fish groups common at Ningaloo Reef, at family or subfamily and species level, which differ in terms of their behaviour and representation in fisheries catch reports. This included: parrotfishes (Scarinae), which are not typically targeted by fishers in Australia, and two groups which are highly targeted by recreational fishers in the region (Ryan et al., 2013; Ryan et al., 2015) that have different behaviours; emperors (Lethrinidae; mobile roving predators) and groupers (Epinephelinae; mostly site-attached ambush predators). Previous work has indicated both Epinephelinae and Lethrinidae are vulnerable to fishing and many species in both subfamilies are targeted across the Indo-Pacific (Abesamis et al., 2014). Species level analyses included two species from Lethrinidae: the spangled emperor, Lethrinus nebulosus, which is recognised as the most highly targeted species in the region, consistently featuring at the top of the estimated catch for the bioregion over the 30-year study period, and the yellow-tailed emperor, L. atkinsoni, a species that is anecdotally retained by fishers and featured as the 6th most common species recorded in the 1998/9 catch survey, but was a minor component in subsequent surveys (Sumner et al., 2002; Ryan et al., 2015). The Chinaman Rockcod, Epinephelus rivulatus (Epinephelinae) was also considered, with catches comparable to those of L. nebulosus across the catch reports (Ryan et al., 2015). 133 Individual species were not considered from the Scarinae subfamily due to inconsistencies in the accuracy of identification of species from this family.

Meta-analysis

We used a mixed-effects meta-analytical approach to assess the effect of the reserves on fish abundance and biomass. We calculated effect sizes as log-ratios for each of the comparison pairs inside to outside the reserves (Claudet et al., 2008) (see Appendix 6.4 for formulas). A constant was added to the mean abundance (c= 0.5) and mean biomass (c = 100 g) to allow calculation of the log ratio in cases where fish were absent either inside or outside (i.e. zero values). We ran a sensitivity analysis on the value of the constant (Appendix 6.5) to determine these values. The size of the constant impacted the magnitude of the effect size, but in general did not influence the significance. Nonetheless, the exact magnitude of the overall effect size should be interpreted with caution. In cases where both the inside and outside mean count of fish were zero, the samples were excluded from the analysis. Effect sizes were weighted by the inverse of the sum of the within- and among-study variances (Appendix 6.4). Weighted effect sizes and variances were calculated using the metafor package (Viechtbauer, 2010) in the statistical program R (R Core Team, 2017) with the variance estimator set to “REML” restricted maximum likelihood estimator. Overall effect sizes were comparable for both abundance and biomass and for simplicity we presented the abundance results as these were available for a larger dataset, providing biomass results in Appendix 6.7.

Sources of variability

We considered seven variables that might mediate the response of fish abundance and biomass to the presence of the reserves (): (i) the number of years between when a sample was collected and when the zoning went into place; (ii) initial or current zoning scheme (see Figure App 6. 1.1); (iii) survey method; (iv) four coarse habitats with distinct coral and algae assemblages: ‘exposed reef slope’, ‘reef flat’, ‘back reef & lagoon coral’, and ‘lagoon algae’; (v) spatial area of a reserve; (vi) an estimate of fishing pressure outside of individual reserves; (vii) the presence of shore-based fishing zones adjacent to some reserves. Data were explored following the protocol of Zuur et al. (2010) and transformed to normalise their distribution where appropriate (see Table 6.1). Chapter 6 – Disentangling the effects of a chronic disturbance

As all effect sizes were heterogeneous (Appendix 6.6), we explored the influence of the seven variables using weighted mixed-effects categorical meta-analyses and meta- regression, considering each variable as a moderator in isolation to determine which variables explained significant heterogeneity in the overall effect size (see Appendix 6.4 for formulas). We also investigated reserve identity to allow comparison between individual reserves. Given there were correlations among the variables and potential interactions and non-linear effects, we then used weighted full-subsets generalised additive mixed modelling (FSSgam) (Fisher et al. 2018) to investigate the relative importance of each variable in explaining variability in the overall effect size for each fish group. The response variable, effect size e, was modelled with a Gaussian distribution using gam() in the mgcv package in R (Wood, 2011). Years protection and boat fishing were included as continuous smoothers in the FSSgam to allow for non- linear relationships. The distribution of reserve size was not much improved by transformation and sqrt(reserve size) was therefore included in the model set as a linear predictor. Reserve identity was highly collinear with other variables (in particular reserve size), and therefore, rather than including this as a random effect, a smoother of the mean latitude of comparison pairs was included in all models (and as part of the null model). This yielded comparable results to including reserve identity as a random effect. Interactions between the factor variables habitat and shore fishing and the continuous variables reserve size and years protection were tested. In all models the smoothing parameter was limited to a simple spline, allowing only monotonic relationships (k=3) for all continuous variables except for latitude, which was unlimited. Summed AICc weights were used as a metric of variable importance to investigate the relative importance of each predictor variable across the full set of models (Anderson & Burnham, 2002). Variables included in the most parsimonious model (fewest variables and lowest estimated degrees of freedom within two units of the AICc) were plotted to visualise the shape and direction of relationships between the variables and the effect size. We interpret results of variable importance and the top models with caution and consider the results of the mixed-effects meta-analyses and meta-regression alongside the results of the FSSgam.

Lastly, given the importance of temporal patterns in investigations of protected areas, we explicitly investigate data from the Osprey reserve (see Figure 6.1), the best temporally replicated reserve in the dataset. Using available and relatively consistently

135 collected UVC and DOV data we estimated mean fish density as count per transect area. We tested for significant linear and quadratic relationships between the density of L. nebulosus and survey year then fitted generalised additive mixed models to illustrate trends.

Results

When compared to areas open to fishing, Lethrinidae were on average 57% more abundant (78% more biomass) inside the reserves (e = 0.45±0.12, 95%CI, Figure 6.2a), however the effect was heterogeneous (QT = 2002.6, df = 301, p<0.001, Table App 6. 6.2). The most parsimonious model for Lethrinidae abundance consisted of an interaction between habitat and reserve size (Table 6.2), with the same true for biomass (Appendix 6.7). The categorical meta-analysis supported the importance of habitat for relative abundance; showing it explained significant heterogeneity among effect sizes

(QM = 39.5, df = 3, p<0.001, Table App 6. 6.2) with the most positive effect identified in back reef & lagoon coral sites with an average of 93% more Lethrinidae inside the reserves (e = 0.66±0.14, 95%CI) (Figure 6.2a, Figure App 6. 6.1) at sites in this habitat. On the reef flat Lethrinidae were 53% more abundant inside the reserves (e = 0.42±0.32, 95%CI) while there was no significant effect on the exposed reef slope and a negative effect in the lagoon algae habitat (Figure App 6. 6.1). The interaction of reserve size and habitat was evident as an increase in effect size with increasing reserve size in the back reef & lagoon coral habitat versus no clear trends in the other habitats.

Lethrinus nebulosus were on average 42% more abundant (86% more biomass) inside reserves than outside (e = 0.35 ±0.15, 95%CI, Figure 6.2a). The effect was heterogeneous (QT = 1971.1, df = 256, p<0.001, Table App 6. 6.2). The most parsimonious model included the interaction between habitat and reserve size with these two variables also having the highest variable importance across the full-subsets model set (Table App 6. 7.1, Figure 6.2b). The same was true in the biomass analysis (Appendix 6.7). Habitat explained significant heterogeneity for relative fish abundance

(QM = 32.5, df = 3, p<0.001, Figure App 6. 7.1) and L. nebulosus were on average 84% more abundant within back reef & lagoon coral sites inside the reserves (e = 0.61±0.17, 95%CI), whereas no differences were observed for the reef flat or exposed reef slope sites and a negative effect was observed for lagoon algae sites (Figure App 6. 6.1). As for Lethrinidae, the interaction of reserve size and habitat was evident by an increase in Chapter 6 – Disentangling the effects of a chronic disturbance the effect size with increasing reserve size in the back reef & lagoon coral habitat and no clear effects in the other habitats.

On average, the abundance of L. atkinsoni was 40% higher (60% more biomass) inside reserves than outside (e = 0.34±0.09, 95%CI). The effect was heterogeneous (QT = 1739.7, df = 279, p<0.001, Figure App 6. 6.2). The most parsimonious model included zoning scheme and method, which also had the highest importance according to weighted AICc (Figure 6.2b, Table 6.2). These two variables also explained significant heterogeneity according to the categorical mixed-effects meta-analyses. Predictions indicated that the BRUV method contributed the most to the positive effect size of L. atkinsoni (Figure 6.3c), though this was not significant, nor were the differences between initial and current zoning schemes, though there was a slightly higher effect size from the older zoning scheme. Multiple variables explained significant heterogeneity for L. atkinsoni according to the categorical meta-analysis and the meta- regression (Table App 6. 6.3), including habitat (QM = 14.6, df = 3, p<0.001, Table App 6. 6.3). Reef flat sites had 94% higher abundance, (e = 0.66±0.26, 95%CI) and back reef & lagoon coral sites 43% higher abundance (e = 0.36±0.12, 95%CI) inside the reserves. There were no significant effects for the other habitats (Figure App 6. 6.1). The biomass analysis for L. atkinsoni indicated that years protection may interact with habitat, and that on the reef flat the effect size was higher and showed a parabolic pattern with years protection (Figure App 6. 7.2).

137

Figure 6.2. a) Relative fish abundance inside to outside the reserves (back-transformed weighted mean effect sizes) with 95% confidence intervals), for the six fish groups: Lethrinidae, Lethrinus nebulosus, L. atkinsoni, Epinephelinae, Epinephelus rivulatus and Scarinae. Effect sizes are significant when the confidence intervals do not overlap 1.0. Open dots correspond to non- significant effects (i.e. no effect). Sample sizes are given in Table App 6. 5.1. Triangular points show the predicted effect size when habitat was included as a moderator variable in the meta-analysis, for the habitat with the largest mean effect size (orange represents the back reef & lagoon coral, and blue represents the reef flat). b) Importance scores based on summed Akaike weights corrected for finite samples (AICc) from full-subsets analyses exploring the influence of seven variables on the overall effect size for each fish taxa: 1 is highly important while 0 is not important. Red X symbols mark the variables that were included in the most parsimonious models for each fish taxa (also see Table 6.2 2 and Figure 6.3).

Chapter 6 – Disentangling the effects of a chronic disturbance

Table 6.1. Description and summary of the seven variables used in analysis

Variable Description Description of variable levels Source (transformation used in analyses) Years Years between Samples were classified to a single reserve based on their location. In (CALM, 2005) protection zoning and data cases where rezoning meant that a reserves size was increased, a (CALM, 1989) collection sample inside the old reserve area was classified as the 'initial' zoning, while if the sample were in the new area it was classified as 'current' zoning. Years protection was then calculated as the number of years between zoning and sampling. Range: 0 - 30 years from time of survey to reserve implementation to sampling. Zoning scheme Factor describing Initial 1987-2005, 8 no-take zones (Figure 6.1a) (CALM, 2005) the two major (CALM, 1989) reserve networks also see Current 2005-present, 18 no-take (excepting shore fishing) during the study Appendix 6.1 and 3 no-take zones in the Muiron Islands Marine period Management Area (Figure 6.1b)

Survey method Factor describing UVC Underwater visual census, collected along transect For more the major survey lines of set length and width (25 x 5 m, 50 x 5 m or information methods used to 100 x 10 m, 250 x 10 m). Most on SCUBA, some via on methods collect fish count snorkel. Fish counted and length estimated in situ. see and size data BRUV Baited remote underwater stereo-video 0 deployments, (30-60 minutes) point location, fish counted and length estimated post hoc from video DOV Diver operated stereo-video, collected along a transect line of set length and width (5 m in width and varying between 25 and 50 m length), fish counted and length estimated post hoc from video Habitat Factor describing Exposed reef The ocean side of the fringing reef, where the reef Classified by four major habitat slope slopes to deeper water and the majority of wave authors, (see types which have energy is received Collins et al., differences both Reef flat Shallow (~1-3m deep), shoreward from the reef 2003) in the dominant crest for tens to hundreds of meters, typically benthic dominated by the plate coral Acropora spiecifera community and on limestone bedrock wave exposure Back reef & From where the reef flat breaks into more patchy lagoon coral reef and sand environments, sheltered from wave energy and including some large coral bommies Lagoon algae Sheltered shallow water lagoon, sandy bottom often dominated by fleshy canopy forming seaweed of the genera Sargassum and Sargassopsis. Reserve size Area (ha) of each 50 – 44752 hectares (CALM, 2005) (square –root) reserve at time of Mean: 6031 ha; Median: 1756 ha (CALM, 1989) survey Boat fishing * A mean estimate Mean density of vessels observed fishing during aerial surveys in peak Smallwood (log- of the number of season in 2007. Each survey sample latitude and longitude was and Beckley transformation) vessels assigned the value of the underlying spatial data in fig. 4 of Smallwood (2012) recreationally and Beckley (2012). For surveys inside the reserves is was assumed fishing sites that fishing activity was 0. outside of Range: 0 - 0.625 vessels fishing per 9 km2 ; Mean: 0.12; Median: 0.11 reserves * Not available for the Muiron Islands Other estimates of fishing activity exist (Sumner et al. 2002) but this metric was deemed the most detailed Shore fishing Factor describing Allowed Shore fishing is allowed along the entire, or part of (CALM, 2005) whether or not a the coastal boundary of the reserve (26% of data) reserve has shore Prohibited No shore fishing is permitted anywhere in the fishing zones on reserve (74% of data) its coastal boundary

The effect size for Epinephelinae abundance was significantly negative with 9% fewer fishes inside than outside the reserves (e = -0.09±0.08, 95%CI), although this result was

139 heterogeneous (QT = 1125.7, df = 276, p<0.001, Table App 6. 6.2). Variable importance scores showed no variables with high importance relative to the Lethrinidae and L. nebulosus model sets. Reserve size and years protection were present in the most parsimonious model (Figure 6.2b, Table 6.2), while for the biomass it was method and boat fishing (Appendix 6.7). There were weak increasing trends for both reserve size and years protection, however the lack of strongly important or consistent variables in these model sets means the results should be interpreted cautiously.

On average there was no significant difference inside to outside the reserves for E. rivulatus abundance (e = -0.06±0.09, 95%CI), though the effect was heterogeneous (QT = 477.3, df = 166, p<0.001, Table App 6. 6.2 G1). Zoning scheme and boat fishing had the highest variable importance across the model set and featured in the most parsimonious model. The effect size transitioned from no effect for low boat fishing activity, to a positive effect when there was high boat fishing activity, but the confidence intervals did not show this trend to be significant. The initial reserve network (in place longer) had a more positive effect than the newer reserves, but again this was not significant (Figure 6.3e).

The control group, Scarinae, showed no significant difference inside to outside the reserves (e = -0.01±0.11, 95%CI) and this effect was heterogeneous (QT = 1701.1, df = 260, p<0.001,Table App 6. 6.2). All variables had low importance according to AICc (Figure 6.2b, Table 6.2) and while boat fishing and shore fishing appear in the most parsimonious model we interpret this with caution. In the biomass analysis the most parsimonious model had habitat only (Appendix 6.7).

In the full-subsets analysis reserve size and habitat appeared with the highest variable importance (for Lethrinidae and L. nebulosus) while other variables - survey method, years protection, zoning scheme and shore fishing - had low importance across all six fish groups. In many cases the heterogeneity statistics from the mixed-effect meta- analysis models supported the findings of the full-subsets analysis, but for some variables such as shore fishing, the meta-analysis indicated this variable was important, explaining significant heterogeneity for all fish groups, except for L. nebulosus. Chapter 6 – Disentangling the effects of a chronic disturbance

Table 6.2. Top Generalised Additive Mixed Models (GAMMs) for predicting the response ratio inside to outside reserves, 푬̅, for abundance from full subset analyses for the abundance of the six fish groups. Difference between the lowest reported corrected Akaike Information Criterion (ΔAICc), AICc weights (ωAICc), variance explained (R2) and estimated degrees of freedom (EDF) are reported for model comparison. Model selection was based on the most parsimonious model (fewest variables and lowest EDF) within two units of the lowest AICc. This model is shown in bold text.

Fish group Model ΔAICc ωAICc R2 EDF 0.31 0.14 14.6 LETHRINIDAE Habitat + Years protection by Habitat + Size by Habitat 0.00 0.28 0.13 10.7 Years protection + Habitat + Size by Habitat 0.17 0.28 0.12 9.0 Habitat + Size by Habitat 0.19 0.57 0.17 9.0 L. nebulosus Habitat + Size by Habitat 0.00 0.19 0.08 6.3 L. atkinsoni Method + Zoning scheme 0.00 0.18 0.09 9.0 Habitat + Method + Zoning scheme 0.09 0.14 0.08 7.4 Boat fishing + Method + Zoning scheme 0.71 0.10 0.08 8.0 Habitat + Method + Size 1.41 0.18 0.11 14.4 Habitat + Size + Years protection by Habitat 0.00 0.11 0.08 8.3 Years protection + Boat fishing + Size 0.92 0.08 0.08 7.3 EPINEPHELINAE Years protection + Size 1.51 0.60 0.17 8.9 E. rivulatus Boat fishing + Zoning scheme 0.00 0.16 0.03 4.0 SCARINAE Boat fishing + Shore fishing 0.00 0.11 0.03 4.2 Boat fishing + Zoning scheme 0.80 0.08 0.05 10.5 Years protection + Habitat + Size by Habitat 1.25 0.06 0.04 9.0 Habitat + Size by Habitat 1.84

141

Figure 6.3. Predicted relative fish abundance inside to outside reserves (back-transformed predicted weighted effect sizes) with 95% confidence intervals for the six fish groups – a) Lethrinidae; b) Lethrinus nebulosus c) L. atkinsoni; d) Epinephelinae; e) Epinephelus rivulatus; f) Scarinae – as a function of variables present in the most parsimonious models (Table 6.2) from full- subsets GAMM analysis. Ribbons and error bars represent 95% confidence intervals.

Chapter 6 – Disentangling the effects of a chronic disturbance The temporal investigation for the most highly targeted fish, L. nebulosus, at Osprey reserve gave results that generally confirmed what was found in the overall analysis, showing effect sizes that are mostly positive though time, with higher abundance and biomass inside than outside (Fig 4). There were no strong or significant patterns with time, except for the abundance density outside of the reserve, which had a significantly negative linear trend (P=0.032). Generalised additive model fits indicated that, particularly in the latter half of the study period, both abundance and biomass may have declined both inside and outside the Osprey reserve, while there is some indication that abundance initially increased inside of the reserve following establishment. However, confidence in these trends is low and the gam fits were not statistically different from null models (except for abundance density outside the reserve, P = 0.048).

143 Discussion

Across the 30 year synthesis higher abundance and biomass of certain targeted fish taxa inside the reserves suggests that recreational fishing can have significant effects in isolation from commercial harvest, as also shown in some previous studies (Denny et al., 2004; Shears et al., 2006; Babcock et al., 2007). We found the extent of this effect was variable among targeted taxa and influenced by a range of other factors. While our analyses revealed higher relative abundance and biomass of lethrinids (Lethrinus nebulosus and L. atkinsoni) inside reserves, no significant effect was found for the abundance of Epinephelus rivulatus, and a small negative effect was detected for the epinephelids as a group. All effects were heterogeneous, which was not surprising given the size and complexity of the synthesised dataset (including differences in size and age of reserves) and given that fish responses to reserves are known to vary with taxon- specific, ecological and zoning factors (Barrett et al., 2007; Claudet et al., 2010; Edgar et al., 2014). Here we advance previous findings with the largest meta-analysis on recreational fishing in isolation from commercial fishing, illustrating the new information that can be gained from synthesising existing data, though we do not discount the advantages of strategic and consistent monitoring data. We show that it is important for assessments of reserves to consider habitat effects, and potential interactions with factors such as reserve size or age, as well as variability in fishing activity, or differences in survey method in order to avoid oversimplified conclusions on how fish abundance and biomass respond to management.

Some previous studies in the Park have linked higher abundance and biomass of targeted species inside reserves to protection from fishing (Westera, 2003; Babcock et al., 2008; Fitzpatrick et al., 2015); though results of other studies are more equivocal (Wilson et al., 2012a; Wilson et al., 2018a). The reasons behind the disparate conclusions are unclear, but may be due to limited and/or varied spatial and temporal scales of the individual studies, different survey methodologies, the confounding influence of habitat, or high variability in target species abundance distributions. We also investigated the regulations on shore-based fishing on the coastal boundaries of reserves, with the hypothesis that this may influence the ability of the reserves to maintain higher abundance and biomass of fishes. There were mixed results with the full-subsets analysis indicating this variable had low importance, while the meta- analysis showed it did explain significant heterogeneity, and indicated effect sizes were larger (though not significantly) when shore fishing was prohibited. However, this Chapter 6 – Disentangling the effects of a chronic disturbance factor was likely correlated with other variables not available in the present study, such as accessibility to reserves. High correlation between fish recruitment and larger natural cycles (El Niño Southern Oscillation) has also been suggested as a reason for inconsistencies in fishes response to reserves (Wilson et al., 2018b). In the present study we found high variability in the relative abundance of lethrinids among the different reserves, which can partially account for the varied conclusions of previous studies at smaller spatial scales. Nonetheless, when all data were pooled the average effect was clearly positive for abundance and biomass of the three lethrinid groups. However, the magnitudes of the positive effects were small (max 57% higher inside) relative to studies in other parts of the world (Watson & Ormond, 1994; Russ et al., 2015). A significant positive response for L. atkinsoni (40% higher), similar to that of L. nebulosus (42%) was not expected, given L. atkinsoni does not feature highly in catch reports (Ryan et al., 2017), possibly indicating it may be more influenced by recreational angling than previously recognised.

Known differences in behaviour between lethrinid and epinephelids taxa did not correlate with their response to reserves as expected. Lethrinids are known to have large home ranges relative to many epinephelids, including E. rivulatus, and are therefore more likely to move across reserve boundaries (Mackie & Black, 1999; Pillans et al., 2014; Babcock et al., 2017), with the expectation that they may experience lower levels of protection than epinephelids. However, we only observed positive responses for the lethrinids. It is possible that lower counts of epinephelids than lethrinids in the dataset may have reduced the power to detect an effect in the former group, or there are other factors that have not been captured in our analyses.

The age of no-take reserves has been shown to be a significant positive correlate of relative fish abundance for targeted species (Claudet et al., 2008; Edgar et al., 2014; Zupan et al., 2018). Demonstrated increases in effect size with time help attribute positive effect sizes to the presence of a protected area, rather than other factors (Russ et al., 2015). In the present study there was negligible evidence of changes in effect sizes with age of reserve. Where relationships were present, the shape of the trend was generally parabolic, showing an increase initially, before subsequent decrease around 2005, though no relationships were significant. This was supported by examining data for L. nebulosus, from the best temporally replicated reserve, Osprey, where again no clear temporal patterns were found. Potentially of concern for managers was the

145 significantly negative decline in L. nebulosus density outside of the Osprey reserve, and a slight increase followed by a decrease inside this reserve. However, the confidence intervals on all temporal patterns were large. These findings are in contrast with previous studies, for example Russ et al. (2015) showed lethrinids continued to increase in density inside reserves in the Philippines on time scales of 8-30 years. In the present study rezoning in 2005 made temporal analyses more complex, though by including zoning scheme as a variable we partly addressed this. Effect sizes were not strongly influenced by this variable, implying that the effect sizes were broadly consistent across the initial and current reserve networks. Where zoning scheme did feature for L. atkinsoni, the older reserves had a more positive effect, as expected.

The absence of a strong temporal link with effect size must be considered when interpreting the positive effect sizes, however there are various factors which may have contributed to the absence of a strong relationship. First, while there is limited evidence of a reduction in fishing activity within the Park (Ryan et al., 2015, 2017) a shift in fishing activity to areas offshore (>100m depth) (West et al., 2015; Mitchell et al., 2018), which are not part of the current survey data, is likely. Second, the mobile behaviour of lethrinid fishes may be capping the levels of the observed effect size, if a proportion of their population is travelling further than the reserve boundaries. Pillans et al. (2014) found that approximately 60% of tagged lethrinid individuals move at scales greater than the average reserve size over a year period. Third, illegal fishing within the reserves may also limit a temporal increase in effect size, as Smallwood and Beckley (2012) found 8-12% of observed vessels were fishing inside reserves in the Park in 2007. Fourth, we do not discount that the unevenness of sampling though time, with some years being more highly sampled than others (Figure App 6. 2.1) potentially influenced our capacity to detect a trend if it were present. The analysis of L. nebulosus density at Osprey showed that the temporal patterns inside and outside reserves can be complex and not always captured by the overall effect size. Parallel declines or increases in density occurring both inside and outside are masked from the effect size. Vanderklift et al. (2019) showed parallel declines occurring inside and outside one of the Ningaloo no-take reserves, Mandu, for multiple families of fishes. Such declines have also been observed in other fisheries closures on the western Australian coast (Bornt et al., 2015).

Chapter 6 – Disentangling the effects of a chronic disturbance Though our study only had a very coarse level of habitat classification available, our results support previous studies (Miller & Russ, 2014; Rees et al., 2018a; Rees et al., 2018b), showing the importance of habitat when assessing the ability of reserves to support target species abundance. We further demonstrate interactions between habitat and reserve size, showing that conclusions on both the magnitude and direction (positive or negative) of observed effects are influenced by this interaction. In the case of L. atkinsoni biomass we also found an interaction between habitat and reserve age, though the models were not as strong. Previous studies have demonstrated the positive influence of larger and older reserves (Halpern & Warner, 2002; Claudet et al., 2008; Edgar et al., 2014; Zupan et al., 2018), however the interaction with habitat has not previously been explored. Furthermore, it is noteworthy that effect sizes were greatest in the back reef & lagoon coral habitat for L. nebulosus, while for L. atkinsoni, the effect was greatest on the reef flat, a result that may be attributed to these habitats being preferred by the adults of each species respectively (Babcock et al., 2008; Wilson et al., 2017). This is important when considering potential changes to habitat inside or outside of reserves, as Russ et al. (2015), showed that changes in benthic habitat due to disturbance could markedly influence the effect of reserves for lethrinids. We advise that reserves must incorporate adequate amounts of the essential habitats of the species or communities they are designed to protect, and assessment of reserve effectiveness must account for possible interactions between habitat and reserve size and age.

While habitat was particularly important for the lethrinid groups, it was not found to be an important predictor for Epinephelinae or E. rivulatus. Again, this was contrary to expectations given the often high site fidelity of Epinephelinae (Mackie & Black, 1999). However, the relatively coarse habitat classification available for our analyses likely did not adequately capture the habitat requirements for this group. Previous work has shown E. rivulatus is strongly associated with macroalgal habitats at Ningaloo Reef (Wilson et al., 2012a) but that variability in the quality of macroalgal habitats can be substantial and have major implications for fish abundance (Fulton et al., 2014; Wilson et al., 2014; Lim et al., 2016). Furthermore, Beckley and Lombard (2012) found that deeper habitats seaward of the reef have relatively lower spatial protection from recreational fishing, despite these habitats potentially supporting a high biomass of epinephelids (Babcock et al., 2008). It is thus plausible that habitats outside of the reserves were more appropriate for Epinephelinae, particularly prior to re-zoning in 2005, which could explain the overall negative and null effects for these groups. A much better

147 understanding of the habitat requirements, electivity and movement across seascapes by targeted taxa and appropriate ‘micro-habitat’ classifications are needed to more fully understand these results.

Where the boat fishing variable appeared in models for E. rivulatus, there were subtle positive trends in effect size as fishing activity increased, i.e. where boat fishing was most prevalent the effect size was greater. Our metric for fishing activity is unlikely to be representative across the 30 years of data, as it was an estimate from 2007 (Smallwood & Beckley, 2012), yet still showed some importance. We think this is a particularly important factor when assessing reserves, as variability in fishing activity (spatially and temporally) makes it very difficult to disentangle the true effect of the reserves if this variability is not quantified. We suggest that finer-scale spatiotemporal data on the pressures outside, and indeed inside, of reserves would clarify reserve assessments, both in the case of the present study but also more generally in any assessment of spatial protection. In the case of marine reserves, quantitative standardised data on fishing activity at the scale of individual reserves should be prioritised alongside the collection of ecological data.

Synthesizing data from multiple survey methods leads to larger datasets, and the advent of video-based methods in the last decades (e.g. BRUV and DOV) has increased the diversity of methods used to monitor fish. Contrary to expectations, in general, survey method did not strongly influence the effect size. The strongest effect sizes (Lethrinidae and L. nebulosus) were consistently detected regardless of the survey method. L. atkinsoni exhibited a more positive effect when surveyed by remote video as compared to diver-based methods, which may be partly explained by fish behaviours associated with both the attraction to bait and avoidance of divers (Watson et al., 2005; Goetze et al., 2015), particularly on SCUBA (Radford et al., 2005). On balance, we did not distinguish a single survey method as optimal, and in most cases it was appropriate to compare data from the three methods for the effect size calculation. This is likely possible because of the nature of our effect size, which, as a ratio, is more robust to different units of measurement. However, this cannot provide the same level of information as standardised temporal data on fish density. We therefore suggest that monitoring programs should prioritise resurveying existing monitoring sites with comparable methodology to build more robust time-series data, else adopt the method(s) that are best suited to surveying the taxa of interest. Chapter 6 – Disentangling the effects of a chronic disturbance Conclusions

There were two major challenges in addressing the aims of this study. The first stemmed from the nature of the available data, as while we showed that new information can be gained from collaboration and the synthesis of disparate data, a lack of consistent temporal data meant it was not possible to understand the temporal changes to the fish populations. This was demonstrated by some complex trends in the estimated fish density inside and outside the Osprey reserve that underlay the overall effect size. Therefore, the value of consistent monitoring across time and space is unequivocal, particularly given a likely increase in adaptive management complicating temporal assessments. Indeed, at Ningaloo, the management plans of a marine park in commonwealth waters directly seaward of the Ningaloo Marine Park has recently been updated. Our findings suggest that consistent monitoring, producing data that can be compared to that of the present study should be implemented for this new Park. The second challenge was explored by Underwood (1995), who argued that ecological research can better aid management if management interventions are treated as testable hypotheses. No-take marine reserves can provide experiments with which to test hypotheses regarding the effects of fishing (Langlois & Ballantine, 2005). However, our study has highlighted that variability in ‘experimental design,’ resulting from a range of complexities including spatial and temporal variability in fishing activity, shore fishing zones adjacent to no-takes areas and modifications to reserve design over time, make determining the long-term outcomes of these experiments difficult. We suggest that in order to best analyse across such complicated experimental designs it is necessary to account for (i) habitat; (ii) potential interactions between habitat and reserve size and age; and (iii) variability in fishing activity outside of reserves and compliance inside reserves. Regarding the last point, integration of the collection of fishing activity data with the collection of ecological data is likely to help interpret the true effects of reserves. The two are clearly intertwined and having data on both the pressure and the response is essential for holistic assessments of the efficacy of spatial management interventions.

149

Chapter 6 – Disentangling the effects of a chronic disturbance Acknowledgements and Funding

We thank the Department of Biodiversity, Conservation and Attractions and the Department of Primary Industries and Regional Development staff in Exmouth, in particular we are grateful for comments from Claire Smallwood with expertise on recreational fishing in the study region and Peter Barnes for expertise on the management regimes of the Ningaloo Marine Park. We would like to thank: Claire Butler for assistance and advice handling spatial data in QGIS; Emma Lawrence for discussion on the sensitivity analysis; Glenn Moore (WA Museum) for advising on the current taxonomy of Serranidae and Epinephelinae; George Cresswell, Susan Blackburn and Brett Molony for comments and editing. Field data are extremely time intensive to collect and we appreciatively acknowledge the many people who collected the data and provided logistical support for the numerous studies included in this data synthesis: UWA; Todd Bodd, Matt Birt, Brigit Vaughan, Isabella Lindgren; CSIRO; Geordie Clapin, Nicole Murphy, David Kozak, Julia Phillips, Ryan Downie, Fiona Graham, Kylie Cook, Catherine Seytre, Auriane Jones, Monique Grol, Andrea Zabala Peres, Helene Boulloche-Sabine, Lydiane Mattio, Cindy Bessey, Melanie Trapon, Doug Bearham, James McLaughlin, Ryan Crossing, Mark Wilson, Margaret Miller, Darren Dennis, David Milton, Rodrigio Bustamante, Tim Skewes; RLS; all volunteer citizen scientists involved in data collection, especially Paul Day, Kevin Smith and Ben Jones, as well as Antonia Cooper and Just Berkhout for database support; DBCA: Peter Barnes, Huw Dilley, David Lierich, Matt Smith, Teresa Edgecombe, Dani Robb, Jutta Wildforster, Joe Morgan, Shannon Armstrong, George Shedrawi; ANU: Mae Noble; AIMS: Conrad Speed, Ben Radford; Sea Research: Ian Parker, Gerry Allen, Avril Ayling

The study was possible through funding provided by numerous organisations and programs: CSIRO, the Western Australian Marine Science Institution, WA Department of Biodiversity, Conservation and Attractions, The Australian National University, the Australian Institute of Marine Science, Caring for our Country, and the Gorgon Barrow Island Net Conservation Benefits Fund, BHP-CSIRO Industry-Science Ningaloo Outlook Marine Research Partnership, AIMS-Woodside Energy, WA State NRM program and Royalties for Regions program, Coastwest, the University of Western Australia, Edith Cowan University School of Natural Sciences, the Jean Rogerson Postgraduate Scholarship

151

Clearly it’s not that simple; how could it be? I’m talking about how Nature is organised and how it changes, one of the most complex subjects imaginable. Charlie Veron ~ A Life Underwater Chapter 7 – Discussion Chapter 7 Discussion

In the previous chapters I have explored different elements of the responses of two ecosystem components — coral and fish — to a variety of disturbances. I have also investigated some of the key processes underlying maintenance and recovery in coral reefs: reproduction and growth. Via multiple approaches, including data synthesis, modelling and novel survey methods, various important insights have been achieved, contributing to our understanding of coral reef resilience and disturbance ecology.

Chapter 2 showed that while the dynamics of coral cover are highly variable, there are important relationships between impact and recovery. In particular, the recovery rate has a non-linear relationship with coral cover before and after disturbance. I suggested that this was governed by the opposing processes of increased reproductive and growth potential (arising from higher coral cover) versus space limitation. This is an important insight for studies of resilience and for developing management strategies for coral reefs. This chapter also provided an up-to-date summary of impact and recovery in coral reefs from increasingly common acute climatic disturbances.

Chapters 3 and 4 explored coral reproduction and coral growth respectively, processes likely important in governing the non-linear relationship between impact and recovery rate revealed in chapter 2. Chapter 3 suggested how we may gain more information about the phenomenon of mass coral spawning by presenting the second occasion where satellite-borne synthetic aperture radar serendipitously captured a mass coral spawning event. The continuing advances in technology offer increased power for studies of coral reefs, including remote, difficult-to-access reefs. In chapter 4 I was able to quantify growth rates for two genera of corals on the Ningaloo Reef slope, an environment with very high wave-driven water motion, thus extending our understanding of coral growth in different environments. Indeed, we found growth rates that were slower than any previous estimates for the region, perhaps explained by their location in a highly exposed, deep habitat. I was also successful in using a new method (structure-from- motion) to obtain high sample sizes to measure these growth rates.

Chapter 5 modelled the dynamics of disturbance at fine temporal scales for various disturbance scenarios, finding that disturbance frequency was a particularly important driver of coral community dynamics. The work also showed that the various metrics

153 used (coral cover, number and volume of colonies, linear rugosity and morphological diversity) each gave different, but usually complementary dynamics. The simplifications in a focus on total coral cover were illustrated; the different morphologies had distinctly different dynamics which underlay the total coral cover dynamics.

Chapter 6 focussed on a chronic anthropogenic disturbance, recreational fishing, using data from the Ningaloo Marine Park. This study demonstrated the complexities of quantifying fishing impacts, highlighting some of the key factors and interactions that likely underlie inconsistencies in the literature for reserve assessments. The results indicated that reef habitat and its interaction with reserve size, as well as the influence of spatial variability in fishing activity, were particularly important for the assessment and design of no-take marine reserves.

Resilience is relative

I encountered many of the same challenges faced by others when applying a resilience framework. These included variable definitions, high interdependencies between ecosystem components, and many unknown parameters. The widespread use of resilience in the popular media, as well as a technical term in multiple scientific fields, has led to inconsistent usage in ecological scientific literature. Hodgson et al. (2015) reasoned that resilience has come to mean so many different things in ecological studies that it must assume its broadest definition. While I generally agree with this statement, if this is to be the way forward, then the term should be clarified within individual scientific studies to achieve consistency and transparency.

The research for, and results of, chapters 1 to 6 now lead me to suggest that if resilience is to be used more broadly across systems as a useful scientific and policy term, it should be contextualised relative to a set of ‘identifiers’: (i) the focal ecosystem; (ii) the ecosystem component of interest and the resolution with which this component is examined (iii) the metric used to measure it; (iv) the type of disturbance:(v) its intensity, frequency and duration; (vi) the spatial and temporal scale of interest; (vii) the reference state or baseline; and (viii) the perspective of the user (schematised in Figure 7.1). In combination these identifiers aim to provide a framework to contextualise resilience. In the following discussion I use several lines of reasoning that draw on examples from the Chapter 7 – Discussion previous chapters and the available scientific literature to support why these are important and relevant identifiers for the study of resilience. Table 7.1 provides examples of these identifiers for each chapter of this thesis.

Figure 7.1. A schematic illustrating the proposed resilience identifiers

155 Relative to the ecosystem

The transferability of resilience measures across different ecosystems was beyond the scope of this thesis as I focussed only on coral reef ecosystems, but it is nonetheless an important consideration. Coral reefs are among the most complex ecosystems (Connell, 1978), and therefore many of the ideas explored in this thesis should be transferable to simpler systems. However, different ecosystems have inherently different dynamics that may influence the application of the bivariate resistance-recovery resilience framework. Corals have multiple characteristics that lend them to assessments of resistance and recovery. In particular, fairly stable base dynamics distinguish them from other more dynamic marine habitat formers, such as macroalgae or seagrass, which can have dramatic seasonal variation (Lüning & Tom Dieck, 1989; Wu et al., 2017).

Relative to the ecosystem component, resolution and metric

In this thesis I have used ecosystem components, as suggested by Nimmo et al. (2015) and I have considered the resistance and/or recovery of coral and fish as components of coral reef ecosystems. These ecosystem components are essentially acting as a proxy for the resilience of the ecosystem. Other approaches are more targeted at a whole- ecosystem level, seeking to capture multiple components and their interactions (Nyström et al., 2000; Nyström & Folke, 2001; Boschetti et al., 2019a). Arguably, however, measuring and communicating changes in ecosystem components of direct relevance to managers and policy makers, i.e. coral abundance, fish abundance or biomass, can be more comprehensible and applicable than abstract changes to whole- ecosystem resilience metrics (Nimmo et al., 2015).

Ecosystem components can be examined at different resolutions (Figure 7.2). The coarsest resolution of coral examined was total abundance of all live corals in chapter 2, whereas finer resolutions were considered in chapter 3 (genera) and chapter 5 (morphological types). Chapter 6 considered fish as an ecosystem component, at the resolution of both family and species.

The different ecosystem components and resolutions cannot always be examined with comparable metrics. For example, in chapter 2, the resolution of the available data constrained the metric to be percentage of total live coral cover. Both relative and absolute changes in coral cover were considered in order to quantify resistance, while Chapter 7 – Discussion recovery was quantified both using recovery rate (annual change in percentage coral cover) in conjunction with the shape of the recovery trajectory (linear, accelerating, decelerating, flatline).

The study described in chapter 5 focused on morphological types, exploring multiple metrics: diversity, number, volume and percentage cover of five coral morphologies, as well as the linear rugosity of the coral community. This examination clearly demonstrates that at the resolution of total coral cover, the dynamics of finer functional types are not captured. While this is established in the literature, Coralcraft provided fine-scale graphical illustrations of this phenomenon, from which we could better understand some of the mechanisms driving the dynamics, such as competition for space and light, and the ability to quickly occupy space.

There can be significant and strong interdependencies between the target ecosystem component (e.g. fish) and other ecosystem components (e.g. coral). In chapter 6, I have suggested that given the interaction between habitat type and the protection from fishing provided by the reserves, if the coral habitats are impacted by disturbance, this may indirectly affect the fish population, and the ability of the reserves to maintain higher abundance/biomass of targeted fishes. (Graham et al., 2007; Wilson et al., 2019). These interdependencies between ecosystem components are another key challenge for quantifying resilience.

157

Figure 7.2. Schematic illustration of the different resolutions examined in this thesis, with the ecosystem level referring to a coral reef and the ecosystem components including coral and fish. The functional types in this work were morphological types, defined by morphological traits and the different taxa, which included species, genera and families in this thesis. The chapters which consider each resolution are indicated.

Relative to disturbance

In this thesis I assumed a causal definition of disturbance, i.e. a change in the physical environment that likely, but not necessarily, causes a measurable response in the ecosystem or ecosystem components. When seeking to assess resistance to disturbance this was important in order to avoid circular reasoning, which would arise when a disturbance was defined to be an effect to the ecological system (Van de Pol et al., 2017). There remain limited insights into the mechanisms that may promote resistance (Ghedini et al., 2015), which may have in part resulted from this circularity, given that disturbances are more likely to be studied when they have an obvious effect, i.e. in systems that are not resistant. Similarly, recovery may occur so quickly that the disturbance is not registered as an effect to the ecological system. Chapter 7 – Discussion

Disturbance type

The next resilience identifiers relate to the type of disturbance and their intensity, duration and frequency. Clearly, different types of disturbances affect ecosystems and their components to different degrees. A cyclone can quickly remove physical structure, while temperature stress may cause coral bleaching, followed by death, but the structural breakdown of the coral skeletons can take years (Graham et al., 2007). Cyclones, bleaching and flooding, as the key acute disturbances examined in chapter 2, often have measurable impacts on coral abundance, but their direct effects to other ecosystem components, i.e. fish, can be more difficult to quantify. Similarly, fishing, the focus in chapter 6, may also be affecting coral, but through indirect pathways, i.e. trophic cascades (Babcock et al., 2010; Roff & Mumby, 2012; Ghedini et al., 2015). Many of these intricacies were beyond the scope of this thesis, but their importance is acknowledged, especially for ecosystem-level understanding.

A coral reef may be resilient to some disturbances but not others, or not the increasing number and intensity of disturbances that are occurring today. Coral reefs were originally thought to be temporally stable on the scale of millennia, but recent declines have shattered these paradigms (Mumby & Steneck, 2008). Potts (1986) described Indo- Pacific reefs as a “globally robust and resilient mosaic of regional and local, oceanic and shelf, systems, linked by long-distance dispersal, but buffered by substantial spatial barriers’ in contrast with Caribbean reefs which were described as less resilient. Yet, recent global mass bleaching events have had drastic consequences in all biogeographic regions (Hughes et al., 2018).

Disturbance intensity, frequency and duration

The importance of having a measure of disturbance intensity in resilience studies was evident in chapters 2, 5, and 6. Quantifying measures of resistance that could be compared across systems was not possible without a quantification of the intensity of the disturbances. In the data synthesis described in chapter 2, disturbance intensity information was not available for most of the studies. Furthermore, in some studies, the intensity of a disturbance was determined from the change in the ecosystem component (i.e. coral cover), such that a large loss in the biological component prescribed an intense disturbance. With this approach there is little separation between cause and effect (Van de Pol et al., 2017), and no room for intense disturbances that cause little

159 change in the biological component as a result of high resistance. Therefore, when seeking to quantify resistance, the disturbance intensity needs to be quantified by environmental variables (e.g. temperature, wind speed, number of fishers), not the change in the biological component.

In chapter 6, I showed that spatial data on the intensity of fishing across the network of no-take reserves in the Ningaloo Marine Park helped to quantify the effect of fishing. However, the available data was from only one time point, despite the duration of the disturbance being > 30 years. Given that fishing intensity likely varied over this period, our capacity to precisely link cause and effect was degraded. As an analogy to likely variation in the intensity of fishing pressure outside different reserves in the Ningaloo Marine Park network, we can consider a simple beaker experiment where there are three beakers: the first a control with distilled water (a no-take reserve); the second containing water with 1 ml hydrochloric acid added (e.g. recreational fishing allowed, but the area is not often visited by fishers); and the third with 10 ml hydrochloric acid added (many recreational fishers fish is the area). Without knowing the amount of acid (the intensity of disturbance) beakers two and three are considered the same. This occurs in many marine reserve assessments due to an absence of data on fishing intensity that corresponds to the level of the ecological data.

Using Coralcraft in chapter 5 allowed total control over the intensity and frequency of disturbances, as is the power of modelling. In this way it was possible to quantify and visualise both the impact – as a reduction in the number of coral colonies, cover, or volume of each morphological type – and the subsequent recovery, and reference these to the disturbance intensity and frequency.

The duration and frequency of disturbances, as well as their intensities, are important considerations underlying the definitions of acute versus chronic disturbances. I classified the disturbances examined in this thesis as either acute or chronic, though acknowledge that this is not always a clear distinction, and that it is largely subjective. For example, Connell (1997) regarded a ‘series of acute disturbances occurring so frequently that there is little time between them for recovery’ to be a chronic disturbance. Various studies now note that as recovery time-windows shorten, due to a combination of increases in disturbance frequency and duration, many disturbances previously considered acute (e.g. marine heatwaves) are likely to become chronic Chapter 7 – Discussion (Baker et al., 2008; Hughes et al., 2018). For example, the time-window between consecutive mass bleaching events has decreased from ~25-30 years a few decades ago to just 6 years (Hughes et al., 2018), and this is shorter than the mean recovery times for coral found in chapter 2 (7.1 years). Thus, the line between chronic and acute disturbances has become increasingly blurred for coral reefs.

The applicability of resistance and recovery can be confused for chronic disturbances and frequent acute disturbances as it becomes difficult to distinguish impact and recovery relating to individual disturbances, as was the approach in chapter 2. Thus, the bivariate resilience components of resistance and recovery also become less distinguishable. Some have argued that resistance is more applicable for chronic disturbances (Baker et al., 2008; Lake, 2013; Darling & Côté, 2018), though arguably either resistance or recovery may be driving a resilient system in these situations, but it is not possible to distinguish them without very fine scale spatiotemporal data. This can be exemplified by a highly resilient weed community: a chronic disturbance occurs where a gardener is continually pulling out weeds, yet when the abundance of weeds is compared before the gardener began weeding, and two weeks later, there is the same amount of weeds in the garden. We conclude that the weed community is highly resilient, but is this driven by resistance or recovery? The large pile of dead weeds in the gardener’s compost bin implies it is the later. The intensity, frequency and duration of disturbance are therefore clearly key considerations for the quantification of the relative contributions of resistance and recovery to resilience.

Relative to the reference state or baseline

Shifting baselines are known to complicate environmental assessments and management strategies (Pauly, 1995; Knowlton & Jackson, 2008; Soga & Gaston, 2018), and they also create challenges for the quantification of resilience (Bahn & Ingrisch, 2018; Yeung & Richardson, 2018; Allen et al., 2019). It is generally agreed that studies of disturbance impact and recovery should include an undisturbed reference system as a control, which allows parallel monitoring of baseline dynamics (Rykiel & Edward, 1985; Underwood, 1993; Nimmo et al., 2015; Thiault et al., 2017). However, this is rarely possible for observational studies of unplanned large-scale disturbances (characteristics which define many climatic disturbances such as those examined in

161 chapter 2), except in the few cases where there is historical monitoring data (Bahn & Ingrisch, 2018; Yeung & Richardson, 2018).

Qualifying the baseline or reference state acknowledges that resilience is impacted by, and relative to, past disturbances. Coral cover prior to disturbance was used as the reference state in chapter 2 – as other meta-analyses have done (McCrackin et al., 2017) – with the recognition that this is unlikely to represent the true baseline coral cover, as a system may be recovering from an earlier disturbance. Outputs from Coralcraft revealed how the percentage of coral cover prior to disturbance decreased with each consecutive disturbance (Figure 5.5). Furthermore, baselines are rarely constant in nature, given that the state of an ecosystem is continuously changing due to the intrinsic dynamics of species and populations (Yeung & Richardson, 2018). Another complication arises if there are multiple disturbances occurring simultaneously; distinguishing the resistance or recovery from one disturbance is confounded by the others. This last point was one of the reasons Ningaloo Reef acted as a good case study for the examination of some singular disturbances (recreational fishing) and wave- driven water movement, because, as outlined, it has few disturbances relative to other reefs globally.

As covered in chapter 6, marine reserves can be thought of as creating a reference state (Langlois & Ballantine, 2005) for fishing as a disturbance. However, as emphasised in chapter 6, and in the discussion of disturbance intensity above, neither the extent to which the marine reserve is unimpacted, nor the intensity of the pressure outside of the reserves, are easily quantifiable. Furthermore, reserves cannot provide a reference state for the other disturbances (e.g. marine heat waves or cyclones). An absence of data prior to the implementation of the marine reserves, in combination with the variety of survey methods used over the 30 years, meant that it was not possible to use a ‘before-after- control-impact’ approach, that is more powerful for isolating the dynamics of disturbance (Thiault et al., 2017). Consistent long-term monitoring data are particularly valuable for allowing this approach (Cresswell et al., 2019; Vanderklift et al., 2019).

In theory, a reference state should be obtainable using modelling. However, the start of any simulation model is often artificial unless a known baseline can be programmed. In chapter 5 we were challenged with how to begin the simulations, and started with an empty environment, i.e. bare substrate, with 10 corals recruits. The steady state or Chapter 7 – Discussion equilibrium of the no disturbance scenario is therefore likely to be a truer representation of the baseline, however in the period we examined (20 years) the system did not reach equilibrium.

Two remaining points regarding baselines are that (i) they might not be the desired state from a management perspective (Yeung & Richardson, 2018), nor (ii) obtainable (Duarte et al., 2009; Allen et al., 2019). The latter links to an issue with the bivariate framework of resistance and recovery; that is, it fails to capture situations where a regime shift to an alternative stable state occurs and no recovery that can be referenced to the pre-disturbance conditions is possible (Yeung & Richardson, 2018; Allen et al., 2019). The idea that ecosystems can have multiple stable states is well established (Done, 1992), and indeed underlies some definitions of resilience, e.g. “the resilience of a complex system is its capacity to absorb recurrent disturbances or shocks and adapt to change without fundamentally switching to an alternative stable state” (Holling, 1973; Scheffer et al., 2001; Hughes et al., 2010). This will be a key point for consideration in future development of the resilience identifiers presented here.

Relative in space and time

Our understanding of ecosystem dynamics is directly influenced by the scale at which we make ecological observations (Pandolfi, 2002; Hughes et al., 2005). Andrew and Mapstone (1987) discussed how perceptions of scale are entrenched in our perception of problems, and the resulting generation of hypotheses. Ecological research however, must expand upon the scales convenient to these perceptions, to also encompass the scales at which organisms are responding to their environment. Regarding the concept of baselines, Pimm et al. (2019) showed the longer that one studies a natural system the less certain that they become of the existence of any long-term equilibrium. Impact and recovery from disturbance can be observed at many different temporal and spatial scales: they may be viewed over the relatively short periods of time that characterise most studies, or over thousands of years through the geological record (Jackson 1992). Clearly, it is not possible to deal with all scales within an individual study, so there must be transparency and justification for the scales chosen (Andrew & Mapstone, 1987).

As well as the spatiotemporal scale of an individual study, it is important to consider the scale of its broader applicability. Andrew and Mapstone (1987) specify that results

163 cannot be applied at different scales. This is less clear for modelling and data syntheses. For example, while we worked with a spatial scale of 1 m3 with Coralcraft in chapter 5, we argue that the results have meaning at larger spatiotemporal scales because the sides of the simulated environment were ‘wrapped’ and therefore it could be seen to represent an infinitely large, but environmentally homogeneous area. Further, the model explored theoretical ideas which are less constrained by space and time. In chapter 2, the individual studies that contributed to the synthesised dataset were mostly relatively small scale – at the level of quadrats of transects, individual reefs, islands or atolls, sometimes up to biogeographic regions – yet the combination of these studies allowed inferences at a global scale. Nonetheless, careful consideration should be given to whether the questions being posited, and their results, are specific in space or time, or whether they allow more generality.

The most useful studies within the synthesised dataset in chapter 2 were those with consistent time-series data, which allowed the relationships between impact and recovery trajectories to be investigated. Likewise, much of the power of the work in chapter 6 stemmed from the length of the synthesised study (30 years), which provided greater certainty in the observed effects of recreational fishing and its constancy through time, where a single study on one reserve or at one time could not. The number of samples, and their distribution in space and time is particularly important for distinguishing the effect of a disturbance from natural variability or other disturbances. It is clear that robust interpretation comes from collecting and/or synthesising data at more than one spatial and temporal scale.

Relative to perspective and goals

The last identifier I suggest here – and perhaps the most important for improving transparency in the discussion and assessment of resilience – is the perspective of the user. This links to a number of the previous identifiers, in particular space, time and baselines. For example, a geologist will generally have a longer temporal perspective than a biologist. More broadly, some examples of user perspectives may include theoretical or empirical perspectives from scientists, managers or policy makers, or some combination. Given differences in context, goals and perspective of different users, the lack of consistency in the use of resilience is not surprising.

Chapter 7 – Discussion As an example of potential misalignments regarding resilience stemming from such differences I quote Birkeland (1997):

Although coral reefs and other plant-animal reef systems are fragile, they are resilient. Coral reefs vanished from the record during a period of about 10 million years following the late Cretaceous, but many of the same families and even some of the same genera of scleractinian coral eventually returned. A factual statement, and yet for the studies in this thesis, and for managers and policy makers concerned with the near future (e.g. 1-50 years) of coral reefs, this evolutionary span of resilience is mostly irrelevant. The user perspective will not always be straightforward to define, but some effort to give clarity on why resilience is being used, should be made by studies.

165 Table 7.1. Resilience identifiers for each chapter in this thesis

Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 (i) Ecosystem Coral reef Coral reef Coral reef Coral reef Coral reef

(ii) Ecosystem component Coral Coral Coral Coral Fish

(iii) Component resolution Total coral Total coral Coral genera Coral Species and morphologies Families of fish (coral functional types)

(iv) Component metric Percentage NA NA Percentage Relative fish cover cover; count, abundance and volume and biomass; diversity; linear rugosity (v) Disturbance type marine NA NA Severe storms/ Recreational heatwaves, hydrodynamic fishing extreme rainfall and severe storms (vi) Disturbance intensity, Varied; acute NA NA Varied scenarios Chronic frequency & duration disturbances

(vii) Temporal scale (years) 3 – 30 1 2 20 30

(vii) Spatial scale Sites to reefs to Region, Ningaloo Reef 1 m3 / infinitely Ningaloo Reef islands, in multiple reefs large but global context homogeneous area

(viii) Reference state Last NA NA Pre-disturbance No-take marine measurement abundance of reserves before each coral disturbance morphology

(ix) User perspective or goals Ecological Scientific Biological Ecological Management knowledge and monitoring/ knowledge knowledge and theory; biological theory management knowledge

Suggestions for future work

The thesis has a broad scope in that it encompasses investigations of both chronic and acute disturbances. It considers how we assess and monitor the dynamic processes of resistance and recovery of key components of coral reef ecosystems, and tackles some of the more theoretical components of the discussion of resilience. With such a broad scope there is a great deal that can be explored along each of these avenues in future investigations. Much of this has been discussed in the respective chapters, so I highlight only several key topics for future research here: Chapter 7 – Discussion

• In additional to the internal state variables considered in chapter 2, a next step would be to consider environmental variables (depth, reef type, latitude, biogeographic region) and management variables (governance, human population pressure, marine reserves) to see if there are differences in impact and recovery that can be linked to these variables, and their relative importance alongside the internal state variables. • Selecting studies from the dataset in chapter 2 that provide finer resolution functional or taxonomic information would allow an empirical investigation of the dynamics underlying total coral cover and could be linked to the modelling in chapter 5. It is clear that some disturbances (e.g. cyclones) are particularly selective (Hughes et al., 2010), disproportionately impacting some species or functional types more than others. Factors such as structural complexity (rugosity) or diversity could also be empirically investigated with such a dataset, as there are key hypotheses about the influence of these variables on coral reef resilience (Roff et al., 2015). • Coral reefs sit within a wider network of closely interlinked habitats such as mangroves and seagrass beds (Mumby & Hastings, 2008). Future work would benefit from developing comparisons between the resistance and resilience of coral and these other key marine habitat formers. • The capacity to study coral communities in three-dimensions, where historically studies have been two-dimensional (i.e. coral cover, see chapter 2) is rapidly increasing with structure-from-motion technology (see chapter 4) and with models like Coralcraft. This offers many opportunities to quantify and model the effects of disturbances on the structure and function of coral communities (Graham et al., 2015; Ferrari et al., 2016). Expanding the coral growth work in chapter 4 to capture 3D growth, using the methods of Ferrari et al. (2017), would provide important demographic information in high sample sizes. This could also help to better calibrate and validate Coralcraft. • Further exploration of if and how the bivariate resilience framework considered in this thesis can be applied to systems that undergo phase-shifts is needed (Done, 1992; Allen et al., 2019). A useful comparison of the pros and cons of resilience that is framed around phase-shifts, versus resilience that assumes recovery, as was the case in this thesis, is provided in Mumby et al. (2014a). This would require careful consideration in order to maintain a balance between complexity and useability. An additional dimension would likely be required,

167 where there are multiple bivariate frameworks for each possible phase/state of the system. • Populating the empirical dataset in chapter 2 into the existing proposed theoretical frameworks (e.g. Hodgson et al., 2015; Nimmo et al., 2015; Ingrisch & Bahn, 2018) and assessing their usability for empirical data promises to be particularly insightful for investigating the application of theoretical approaches.

Thesis challenges and limitations

I experienced significant challenges associated with the accessibility, format and quality of data used to build the datasets in both chapter 2 and chapter 6. Gaps in monitoring, different and unclear reporting of methods and results in individual studies were all frustrations that eroded comparability. Empirical observational data is associated with many unknown parameters; when many sets of these data are combined, the number of variables that may be influencing the observations increases. Furthermore, the time required for sourcing data from the literature (chapter 2) or via collaboration (chapter 6), and the subsequent data organisation, cleaning and manipulation should not be underestimated. There appeared to be little attempt to standardise methods across the studies synthesised in chapter 2, nor could this have been expected given the varied aims and locations of the studies. Moving forward however, greater consideration for future use of the data should be considered. Climate change is the major threat to coral reefs and it is intrinsically global, and therefore data also needs to be global. Data synthesis and data sharing is needed to build datasets that can answer these questions. While ‘open data’ is being promoted as the way forward for science, there remain significant obstacles (for discussion of the opportunities and challenges of open data see Gurstein, 2011; Molloy, 2011; Reichman et al., 2011).

There were also challenges and limitations of the modelling, though of a different nature. Coralcraft provided highly detailed and abundant simulation data, however, as with any modelling, one must make many assumptions in order to simply the infinitely complex ‘real world’. Therefore, it was important not to overinterpret the results. Furthermore, the best way to select where to simplify and what assumptions to make required careful thought with reference to existing literature. Given the subjective nature of making such simplifications, careful justification was then required.

Chapter 7 – Discussion Building this thesis around the concept of resilience also had challenges. The disparities in the definition and use of resilience meant that significant time and thought was needed to determine how best to employ resilience in a way that could achieve the aims of this thesis. I chose to explore and build on the simpler but more operational concepts of resilience recently suggested by Hodgson et al. (2015), Nimmo et al. (2015) and Ingrisch and Bahn (2018), rather than more complex frameworks proposed by others (Nyström & Folke, 2001; Mumby et al., 2014a; Boschetti et al., 2019a). Nyström and Folke (2001) argued that it is not enough to measure the rates of recovery of individual reefs and that multi-scale interactions must be analysed and understood. They suggested that coral reef management for conservation must capture cross-scale interactions within a matrix of reefs (Nyström & Folke, 2001). This may be the case, but while it may be possible to model (Kubicek & Borell, 2011), to measure these multi-scale changes and interactions empirically would be extremely challenging, if not impossible.

The future of Ningaloo Reef

Ningaloo Reef provided multiple case studies for this thesis and offered the opportunity to consider several disturbances in near isolation from many of the additional disturbances that occur on other reefs. However, Ningaloo Reef is not immune to the effects of disturbance, and, like all reefs globally, it faces increasing pressures from climatic change and human influence (including ecotourism, industrialisation from the oil and gas industries, pollution, fishing and development (Jones, 2019; Vanderklift et al., In press). The foundation ecosystem components considered in this thesis – coral and fish – are threatened by these factors (Jones, 2019).

While chapter 6 found higher abundance and biomass of targeted fish species inside the no-take reserves, this was not the case for all the fish groups considered, and a recent study (Vanderklift et al., 2019) found that multiple groups of fish have declined both inside and outside one of the reserves, a pattern that would be masked from the response ratio approach required by the disparate data in chapter 6. It seems likely that fishing pressure will increase at Ningaloo Reef as the number of Australian and overseas visitors increases. Indeed, the population of Western Australia is projected to more than double by 2066 (with a projected range of 3.6-5.9 million, www.abs.gov.au).

Global climate models predict that water temperatures at Ningaloo Reef will increase (Heron et al., 2017). Therefore, coral bleaching, which has so far been infrequent, with 169 the first major bleaching in 2011, is likely to become common (Vanderklift et al., In press). Changes in ocean currents, in combination with a potential reduction in abundance of coral further north (Gilmour et al., 2019; Haywood et al., 2019) may reduce the supply of coral larvae to Ningaloo Reef (Boschetti et al., 2019b), though see Underwood et al. (2013). A better understanding of coral reproduction and connectivity between reefs is therefore important, as was discussed in chapter 3.

While recent estimates of maximum vertical reef accretion potentials for coral reefs around the world show that Ningaloo Reef has among the highest reef accretion potentials in the Indian Ocean (Perry et al., 2018), it is also projected that under any other than the most optimistic climate change mitigation scenarios, accretion on Ningaloo’s reefs, like most reefs around the world, will not be able to keep up with sea level rise by 2100 (Perry et al., 2018). Such scenarios would be interesting to investigate by adapting Coralcraft.

The future of resilience

The theory of ecosystem resilience has evolved over the last 40 years (Holling, 1973), but is clearly still developing (Waller, 2001). Given predictions of more intense, longer lasting, more frequent disturbances, in combination with shifts in scale from local to global, we should expect the resilience of coral reefs to remain, indeed escalate, as a topical issue. The continuing active debate on resilience (Hodgson et al., 2016; Yeung & Richardson, 2018; Allen et al., 2019; Grafton et al., 2019; Pimm et al., 2019) reflects its importance and complexity.

Management of resilience

The resilience of many natural systems to increases in disturbance intensity, duration and frequency appears to be low (Knowlton, 2001; Steffen et al., 2015), with recovery less commonly observed in many natural systems (Scheffer et al., 2001). This is changing the ways that ecosystems are managed, with ‘resilience-based management’ now commonplace (Lam et al., 2017). Rather than manage an ecosystem state or select components of ecosystems, resilience-based management seeks to use knowledge on the current and future drivers influencing ecosystem function to prioritize, implement, and adapt management actions. Quantifying metrics for resilience-based management is crucial for it to be successfully applied. There has been discussion on the most suitable Chapter 7 – Discussion resilience metrics for coral reefs, e.g. connectivity, herbivore biomass, macroalgae or temperature variability (West & Salm, 2003; Obura & Grimsditch, 2009; McClanahan et al., 2012). Monitoring programs have historically focussed on the biological community, but need to be adapted to incorporate strategic monitoring of resilience metrics in order to inform resilience-based management (Lam et al., 2017). Linking back to chapter 6 for example, simply monitoring fish abundance/biomass was not enough. Deeper understanding of the intensity of the disturbances, the traits of the fish, and their habitat use was needed to fully interpret the results. Of course, if resilience itself is still evolving, then so too is resilience-based management.

Bruno et al. (2019) reviewed the scientific literature for support that management, for example in the form of the marine reserves explored in chapter 6, increased the resilience of coral, and found little empirical evidence for this, despite strong theoretical underpinnings. Various possible reasons for this have been suggested: the negative impacts of temperature stress events swamp the protection against local factors such as pollution or fishing; complex trophic links mean that protection does not trickle down to the coral, or that management of one set of drivers (e.g. fishing) may select species/ functional traits that are highly sensitive to other disturbances (e.g. ocean warming), thus creating what has been termed a ‘protection paradox’ (Bates et al., 2019; Bruno et al., 2019). Clearly there are many future challenges for resilience and resilience-based management.

Beyond resistance and recovery

The exploration of resistance and recovery in this thesis is a building block that may contribute to more complex or broadly applicable research efforts for resilience. I have focussed on relatively short-term resilience, with studies generally at local or regional scales. Resilience has been conceptualised as a much broader construct (Nyström & Folke, 2001; Ingrisch & Bahn, 2018; Allen et al., 2019) and alongside other constructs such as vulnerability and robustness (Mumby & Anthony, 2015; Grafton et al., 2019), which were beyond the scope of this thesis. The bivariate framework for resilience does not consider the potential shift of systems to alternative stable states, where indeed Holling (1973) seminal definition of ecosystem resilience was built on this possibility: that ecological resilience is the amount of disturbance a system can withstand before crossing a threshold and fundamentally changing. I came from the perspective of

171 seeking to provide measures of change as a step towards a quantification of resilience that is of use to managers, rather than seeking a more intrinsic property of an ecosystem. As Pimm et al. (2019) argue, measuring resilience is a necessary step to operationalise it across different ecosystems, and tipping points are extremely difficult to predict and measure.

Conclusions

Superficially, resilience has an alluring simplicity, seeming to offer answers to some of the most challenging ecological questions. However, deep complexities exist beneath the surface, as revealed in this thesis. I developed the framework of resilience identifiers to help deal with these complexities. There remains however, an important trade-off: if resilience is clearly defined relative to the identifiers above – i.e. a specific ecosystem component, measured with a set metric, in a defined period of time and space, against a specific disturbance – not all of the complexity of the resilience of the ecosystem is captured, while by trying to capture all the complexity, clarity and usability is compromised. While there are many other approaches to resilience, which I do not discount, the bivariate framework of resistance and recovery, in combination with the resilience identifiers presented here, has the advantage of relative simplicity. Resilience can offer many opportunities for intellectual advancement in theoretical discussions and for grappling with the practicalities of ecosystem management in a rapidly changing world.

References

Abesamis, R.A., Green, A.L., Russ, G.R. & Jadloc, C.R.L. (2014) The intrinsic vulnerability to fishing of coral reef fishes and their differential recovery in fishery closures. Reviews in Fish Biology and Fisheries, 24, 1033-1063. Adler, D., Murdoch, D. & and others (2018) rgl: 3D Visualization Using OpenGL. R package version 0.99.9. Allen, C.R., Angeler, D.G., Chaffin, B.C., Twidwell, D. & Garmestani, A. (2019) Resilience reconciled. Nature Sustainability, 2, 898-900. Allison, G. (2004) The influence of species diversity and stress intensity on community resistance and resilience. Ecological Monographs, 74, 117-134. Alvarez-Filip, L., Côté, I.M., Gill, J.A., Watkinson, A.R. & Dulvy, N.K. (2011) Region-wide temporal and spatial variation in Caribbean reef architecture: Is coral cover the whole story? Global Change Biology, 17, 2470-2477. Álvarez‐Noriega, M., Baird, A.H., Dornelas, M., Madin, J.S., Cumbo, V.R. & Connolly, S.R. (2016) Fecundity and the demographic strategies of coral morphologies. Ecology, 97, 3485-3493. Anderson, D.R. & Burnham, K.P. (2002) Avoiding pitfalls when using information-theoretic methods. The Journal of Wildlife Management, 912-918. Andres, N.G. & Rodenhouse, N.L. (1993) Resilience of corals to hurricanes: a simulation model. Coral Reefs, 12, 167-175. Andrew, N. & Mapstone, B. (1987) Sampling and the description of spatial pattern in marine ecology. Oceanography and Marine Biology, 25, 39-90. Ateweberhan, M. & McClanahan, T.R. (2010) Relationship between historical sea-surface temperature variability and climate change-induced coral mortality in the western Indian Ocean. Marine Pollution Bulletin, 60, 964-970. Ateweberhan, M., McClanahan, T.R., Graham, N.A.J. & Sheppard, C.R.C. (2011) Episodic heterogeneous decline and recovery of coral cover in the Indian Ocean. Coral Reefs, 30, 739-752. Ateweberhan, M., Feary, D.A., Keshavmurthy, S., Chen, A., Schleyer, M.H. & Sheppard, C.R.C. (2013) Climate change impacts on coral reefs: Synergies with local effects, possibilities for acclimation, and management implications. Marine Pollution Bulletin, 74, 526-539. Ayling, T. & Ayling, A.L. (1987) Ningaloo Marine Park: Preliminary Fish Density Assessment and Habitat Survey: with Information on Coral Damage Due to Drupella Cornus Grazing: a Report Prepared for the Department of Conservation and Land Management, Western Australia. Department of Conservation and Land Management. Babcock, R. & Mundy, C. (1996) Coral recruitment: Consequences of settlement choice for early growth and survivorship in two scleractinians. Journal of Experimental Marine Biology and Ecology, 206, 179-201. Babcock, R., Pillans, R. & Rochester, W. (2017) Environmental and individual effects on the behaviour and spawning movements of Lethrinus nebulosus on a coral reef. Marine and Freshwater Research, 68, 1422-1437. Babcock, R., Phillips, J., Lourey, M. & Clapin, G. (2007) Increased density, biomass and egg production in an unfished population of Western Rock Lobster (Panulirus cygnus) at Rottnest Island, Western Australia. Marine and Freshwater Research, 58, 286-292. Babcock, R., Bull, G., Harrison, P.L., Heyward, A.J., Oliver, J., Wallace, C. & Willis, B. (1986) Synchronous spawnings of 105 scleractinian coral species on the Great Barrier Reef. Marine Biology, 90, 379-394. Babcock, R., Haywood, M., Vanderklift, M., Clapin, G., Kleczkowski, M., Dennis, D., Skewes, T., Milton, D., Murphy, N. & Pillans, R. (2008) Ecosystem Impacts of Human Usage and the Effectiveness of Zoning for Biodiversity conservation: Broad-scale Fish Census. Final Analysis and Recommendations 2007. In: Final Analysis and Recommendations, p. 99. CSIRO Marine and Atmospheric Research, Hobart.

173 Babcock, R.C., Shears, N.T., Alcala, A.C., Barrett, N.S., Edgar, G.J., Lafferty, K.D., McClanahan, T.R. & Russ, G.R. (2010) Decadal trends in marine reserves reveal differential rates of change in direct and indirect effects. Proceedings of the National Academy of Sciences, 107, 18256. Babcock, R.C., Bustamante, R.H., Fulton, E.A., Fulton, D.J., Haywood, M.D., Hobday, A.J., Kenyon, R., Matear, R.J., Plagányi, E.E. & Richardson, A.J. (2019) Severe continental- scale impacts of climate change are happening now: Extreme climate events impact marine habitat forming communities along 45% of Australia’s coast. Frontiers in Marine Science, 6, 411. Bahn, M. & Ingrisch, J. (2018) Accounting for Complexity in Resilience Comparisons: A Reply to Yeung and Richardson, and Further Considerations. Trends in ecology & evolution, 33, 649-651. Bailey, L.D. & Van de Pol, M. (2016) Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events. Journal of Animal Ecology, 85, 85- 96. Baird, A. & Hughes, T. (2000) Competitive dominance by tabular corals: an experimental analysis of recruitment and survival of understorey assemblages. Journal of Experimental Marine Biology and Ecology, 251, 117-132. Baird, A., Blakeway, D., Hurley, T. & Stoddart, J. (2011) Seasonality of coral reproduction in the Dampier Archipelago, northern Western Australia. Marine biology, 158, 275-285. Baker, A.C., Glynn, P.W. & Riegl, B. (2008) Climate change and coral reef bleaching: An ecological assessment of long-term impacts, recovery trends and future outlook. Estuarine, coastal and shelf science, 80, 435-471. Baker, P.A. & Weber, J.N. (1975) Coral growth rate: Variation with depth. Earth and Planetary Science Letters, 27, 57-61. Ballantine, B. (2014) Fifty years on: lessons from marine reserves in New Zealand and principles for a worldwide network. Biological Conservation, 176, 297-307. Barlow, J., França, F., Gardner, T.A., Hicks, C.C., Lennox, G.D., Berenguer, E., Castello, L., Economo, E.P., Ferreira, J. & Guenard, B. (2018) The future of hyperdiverse tropical ecosystems. Nature, 559, 517. Barrett, N.S., Edgar, G.J., Buxton, C.D. & Haddon, M. (2007) Changes in fish assemblages following 10 years of protection in Tasmanian marine protected areas. Journal of Experimental Marine Biology and Ecology, 345, 141-157. Bates, A.E., Cooke, R.S., Duncan, M.I., Edgar, G.J., Bruno, J.F., Benedetti-Cecchi, L., Côté, I.M., Lefcheck, J.S., Costello, M.J. & Barrett, N. (2019) Climate resilience in marine protected areas and the ‘Protection Paradox’. Biological Conservation, 236, 305-314. Beckley, L.E. & Lombard, A.T. (2012) A systematic evaluation of the incremental protection of broad-scale habitats at Ningaloo Reef, Western Australia. Marine and Freshwater Research, 63, 17-22. Beeden, R., Maynard, J., Puotinen, M., Marshall, P., Dryden, J., Goldberg, J. & Williams, G. (2015) Impacts and recovery from severe tropical cyclone yasi on the great barrier reef. PLoS ONE, 10 Beldade, R., Mills, S.C., Claudet, J. & Côté, I.M. (2015) More coral, more fish? Contrasting snapshots from a remote Pacific atoll. PeerJ, 3, e745. Bellwood, D.R., Hoey, A.S., Ackerman, J.L. & Depczynski, M. (2006) Coral bleaching, reef fish community phase shifts and the resilience of coral reefs. Global Change Biology, 12, 1587-1594. Bennecke, S., Kwasnitschka, T., Metaxas, A. & Dullo, W.-C. (2016) In situ growth rates of deep-water octocorals determined from 3D photogrammetric reconstructions. Coral Reefs, 35, 1227-1239. Benson, M.H. & Garmestani, A.S. (2011) Can we manage for resilience? The integration of resilience thinking into natural resource management in the United States. Environmental Management, 48, 392-399. Bergseth, B.J., Williamson, D.H., Russ, G.R., Sutton, S.G. & Cinner, J.E. (2017) A social– ecological approach to assessing and managing poaching by recreational fishers. Frontiers in Ecology and the Environment, 15, 67-73.

Bessey, C., Babcock, R.C., Thomson, D.P. & Haywood, M.D.E. (2018) Outbreak densities of the coral predator Drupella in relation to in situ Acropora growth rates on Ningaloo Reef, Western Australia. Coral Reefs, 37, 985-993. Birkeland, C. (1997) Life and death of coral reefs. Springer Science & Business Media. Booij, N., Ris, R.C. & Holthuijsen, L.H. (1999) A third‐generation wave model for coastal regions: 1. Model description and validation. Journal of geophysical research: Oceans, 104, 7649-7666. Bornt, K.R., McLean, D.L., Langlois, T.J., Harvey, E.S., Bellchambers, L.M., Evans, S.N. & Newman, S.J. (2015) Targeted demersal fish species exhibit variable responses to long- term protection from fishing at the Houtman Abrolhos Islands. Coral Reefs, 34, 1297- 1312. Boschetti, F., Prunera, K., Vanderklift, M.A., Thomson, D.P., Babcock, R.C., Doropoulos, C., Cresswell, A. & Lozano-Montes, H. (2019a) Information-theoretic measures of ecosystem change, sustainability, and resilience. ICES Journal of Marine Science, Boschetti, F., Babcock, R.C., Doropoulos, C., Thomson, D.P., Feng, M., Slawinski, D., Berry, O. & Vanderklift, M.A. (2019b) Setting priorities for conservation at the interface between ocean circulation, connectivity, and population dynamics. Ecol. Appl, Botsford, L.W., Castilla, J.C. & Peterson, C.H. (1997) The management of fisheries and marine ecosystems. Science, 277, 509-515. Bradburn, M.J., Deeks, J.J., Berlin, J.A. & Russell Localio, A. (2007) Much ado about nothing: a comparison of the performance of meta‐analytical methods with rare events. Statistics in medicine, 26, 53-77. Brand, F. & Jax, K. (2007) Focusing the meaning (s) of resilience: resilience as a descriptive concept and a boundary object. Ecology and society, 12 Brekke, C. & Solberg, A.H.S. (2005) Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 95, 1-13. Brito-Millán, M., Vermeij, M.J., Alcantar, E.A. & Sandin, S.A. (2019) Coral reef assessments based on cover alone mask active dynamics of coral communities. Marine Ecology Progress Series, 630, 55-68. Bruno, J.F. & Selig, E.R. (2007) Regional decline of coral cover in the Indo-Pacific: timing, extent, and subregional comparisons. PloS one, 2, e711. Bruno, J.F., Côté, I.M. & Toth, L.T. (2019) Climate change, coral loss, and the curious case of the parrotfish paradigm: why don't marine protected areas improve reef resilience? Annual review of marine science, 11, 307-334. Bruno, J.F., Sweatman, H., Precht, W.F., Selig, E.R. & Schutte, V.G.W. (2009) Assessing evidence of phase shifts from coral to macroalgal dominance on coral reefs. Ecology, 90, 1478-1484. Burns, J. & Delparte, D. (2017) Comparison of commercial structure-from-motion photogrammety software used for underwater three-dimensional modeling of coral reef environments. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 127. Burns, J., Delparte, D., Gates, R. & Takabayashi, M. (2015) Integrating structure-from-motion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs. PeerJ, 3, e1077. Cai, W., Wang, G., Santoso, A., McPhaden, M.J., Wu, L., Jin, F.-F., Timmermann, A., Collins, M., Vecchi, G. & Lengaigne, M. (2015) Increased frequency of extreme La Niña events under greenhouse warming. Nature Climate Change, 5, 132-137. CALM (1989) Ningaloo Marine Park Management Plan 1989 - 1999. In: Department of Conservation and Land Management, p. 108. Department of Conservation and Land Management, Perth, WA, Australia. CALM (2005) Management Plan for the Ningaloo Marine Park and Muiron Islands Marine Management Area 2005-2015: Management Plan No 52. In: Department of Conservation and Land Management, Fremantle, p. 115. Western Australia Department of Conservation and Land Management, Perth, WA, Australia. Cappo, M.M. & Brown, I.I. (1996) Evaluation of sampling methods for reef fish populations of commercial, recreational interest. CRC Reef Research Technical report No. 6.

175 Cassata, L. & Collins, L.B. (2008) Coral reef communities, habitats, and substrates in and near sanctuary zones of Ningaloo Marine Park. Journal of Coastal Research, 139-151. Cheal, A.J., MacNeil, M.A., Emslie, M.J. & Sweatman, H. (2017) The threat to coral reefs from more intense cyclones under climate change. Global Change Biology, 23, 1511-1524. Cheng, J., Pullenayegum, E., Marshall, J.K., Iorio, A. & Thabane, L. (2016) Impact of including or excluding both-armed zero-event studies on using standard meta-analysis methods for rare event outcome: a simulation study. BMJ Open, 6 Chindapol, N., Kaandorp, J.A., Cronemberger, C., Mass, T. & Genin, A. (2013) Modelling growth and form of the scleractinian coral Pocillopora verrucosa and the influence of hydrodynamics. PLoS Comput Biol, 9, e1002849. Christensen, J.H., Kanikicharla, K.K., Marshall, G. & Turner, J. (2013) Climate phenomena and their relevance for future regional climate change. Cinner, J.E., McClanahan, T.R., Graham, N.A., Daw, T.M., Maina, J., Stead, S.M., Wamukota, A., Brown, K. & Bodin, Ö. (2012) Vulnerability of coastal communities to key impacts of climate change on coral reef fisheries. Global Environmental Change, 22, 12-20. Cinner, J.E., Huchery, C., MacNeil, M.A., Graham, N.A., McClanahan, T.R., Maina, J., Maire, E., Kittinger, J.N., Hicks, C.C. & Mora, C. (2016) Bright spots among the world’s coral reefs. Nature, Claar, D.C., Szostek, L., McDevitt-Irwin, J.M., Schanze, J.J. & Baum, J.K. (2018) Global patterns and impacts of El Niño events on coral reefs: A meta-analysis. PLoS ONE, 13 Claudet, J. & Guidetti, P. (2010) Improving assessments of marine protected areas. Aquatic conservation: marine and freshwater ecosystems, 20, 239-242. Claudet, J., Osenberg, C.W., Benedetti‐Cecchi, L., Domenici, P., García‐Charton, J.A., Pérez‐ Ruzafa, Á., Badalamenti, F., Bayle‐Sempere, J., Brito, A. & Bulleri, F. (2008) Marine reserves: size and age do matter. Ecology letters, 11, 481-489. Claudet, J., Osenberg, C., Domenici, P., Badalamenti, F., Milazzo, M., Falcón, J.M., Bertocci, I., Benedetti-Cecchi, L., García-Charton, J.A. & Goñi, R. (2010) Marine reserves: fish life history and ecological traits matter. Ecological applications, 20, 830-839. Coleman, F.C., Figueira, W.F., Ueland, J.S. & Crowder, L.B. (2004) The impact of United States recreational fisheries on marine fish populations. science, 305, 1958-1960. Collins, L.B., Zhu, Z.R., Wyrwoll, K.-H. & Eisenhauer, A. (2003) Late Quaternary structure and development of the northern Ningaloo Reef, Australia. Sedimentary Geology, 159, 81-94. Connell, J. (1997) Disturbance and recovery of coral assemblages. Coral reefs, 16, S101-S113. Connell, J.H. (1978) Diversity in Tropical Rain Forests and Coral Reefs. Science, 199, 1302- 1310. Connell, J.H. (1983) On the prevalence and relative importance of interspecific competition: evidence from field experiments. The American Naturalist, 122, 661-696. Connell, J.H., Hughes, T.P. & Wallace, C.C. (1997) A 30-year study of coral abundance, recruitment, and disturbance at several scales in space and time. Ecological Monographs, 67, 461-488. Connell, J.H., Hughes, T.P., Wallace, C.C., Tanner, J.E., Harms, K.E. & Kerr, A.M. (2004) A long‐term study of competition and diversity of corals. Ecological Monographs, 74, 179-210. Cooper, T.F., O’Leary, R.A. & Lough, J.M. (2012) Growth of Western Australian corals in the Anthropocene. Science, 335, 593-596. Côté, I.M., Mosqueira, I. & Reynolds, J.D. (2005) Effects of marine reserve characteristics on the protection of fish populations: a meta‐analysis. Journal of Fish Biology, 59, 178- 189. Cowburn, B., Moritz, C., Grimsditch, G. & Solandt, J.L. (2019) Evidence of coral bleaching avoidance, resistance and recovery in the Maldives during the 2016 mass-bleaching event. Marine Ecology Progress Series, 626, 53-67. Cowx, I.G. & Cooke, S.J. (2004) The Role of Recreational Fishing in Global Fish Crises. BioScience, 54, 857-859. Craig, P.D. (2010) Imposed and inherent scales in cellular automata models of habitat. Ecological Modelling, 221, 2425-2434.

Cresswell, A., Thomson, D., Trevenen, E. & Renton, M. (2017) A functional-structural coral model. 22nd International Congress on Modelling and Simulation (ed by G. Syme, Hatton Macdonald, D., Fulton, B. And Piantadosi, J.), pp. 237-243. Hobart, Tasmania, Australia. Cresswell, A., Langlois, T., Wilson, S., Claudet, J., Thomson, D., Renton, M., Fulton, C., Fisher, R., Vanderklift, M., Babcock, R., Stuart-Smith, R.D., Haywood, M.D.E., Depcyznski, M., Westera, M.B., Ayling, A., Fitzpatrick, B., Halford, A., McLean, D., Pillans, R., Cheal, A.J., Tinkler, P., Edgar, G.J., Graham, N.A.J., Harvey, E. & Holmes, T.H. (2019) Disentangling the response of fishes to recreational fishing over 30 years within a fringing coral reef reserve network. Biological Conservation, 237, 514-524. Darling, E.S. & Côté, I.M. (2018) Seeking resilience in marine ecosystems. Science, 359, 986. Darling, E.S., McClanahan, T.R. & Côté, I.M. (2013) Life histories predict coral community disassembly under multiple stressors. Global Change Biology, 19, 1930-1940. Darling, E.S., Alvarez-Filip, L., Oliver, T.A., McClanahan, T.R. & Côté, I.M. (2012) Evaluating life-history strategies of reef corals from species traits. Ecology Letters, 15, 1378-1386. Dauget, J.-M. (1991) Application of tree architectural models to reef-coral growth forms. Marine Biology, 111, 157-165. De Cáceres, M., Coll, L., Legendre, P., Allen, R.B., Wiser, S.K., Fortin, M.J., Condit, R. & Hubbell, S. (2019) Trajectory analysis in community ecology. Ecological Monographs, 89, e01350. Denis, V., Ribas-Deulofeu, L., Sturaro, N., Kuo, C.-Y. & Chen, C.A. (2017) A functional approach to the structural complexity of coral assemblages based on colony morphological features. Scientific reports, 7, 9849. Denny, C.M., Willis, T.J. & Babcock, R.C. (2004) Rapid recolonisation of snapper Pagrus auratus: Sparidae within an offshore island marine reserve after implementation of no- take status. Marine Ecology Progress Series, 272, 183-190. Department of Environment and Conservation (2007) Management plan for the Montebello/Barrow Islands marine conservation reserves 2007‒2017. Management Plan No. 55. In. Marine Parks & Reserves Authority and Western Australia Department of Environment and Conservation, Perth, WA. Depczynski, M.M., Tinkler, P.P., Cheal, A.A. & Speed, C.C. (2015) Ningaloo fish communities. Ningaloo and Outer Shark Bay Environmental Baseline Survey 2014 (ed. by K.K. Miller, M.M. Depczynski, M.M. Cappo, M.M. Wakeford, C.C. Speed, M.M. Stowar, J.J. Colquhoun, P.P. Tinkler, A.A. Cheal and R.R. Fisher), p. 121. Report prepared by the Australian Institute of Marine Science for Woodside Energy Ltd. Australian Institute of Marine Science, Townsville, Australia. Done, T. (1982) Patterns in the distribution of coral communities across the central Great Barrier Reef. Coral Reefs, 1, 95-107. Done, T.J. (1992) Phase shifts in coral reef communities and their ecological significance. The Ecology of Mangrove and Related Ecosystems: Proceedings of the International Symposium held at Mombasa, Kenya, 24–30 September 1990 (ed. by V. Jaccarini and E. Martens), pp. 121-132. Springer Netherlands, Dordrecht. Done, T.T. (1983) Coral zonation, its nature and significance. Perspectives on coral reefs- pages: 107-147. Australian Institute of Marine Science (AIMS). Doropoulos, C., Roff, G., Bozec, Y.-M., Zupan, M., Werminghausen, J. & Mumby, P. (2016) Characterising the ecological trade-offs throughout the early ontogeny of coral recruitment. DPIRD, D.o.P.I.a.R.D. (2018) Ningaloo Marine Park. Available at: https://parks.dpaw.wa.gov.au/sites/default/files/downloads/parks/2462- 13%20Spearfishing%20in%20Ningaloo%20WEB.pdf (accessed 4/11/2018 2018). DPIRD, D.o.P.I.a.R.D.W.A. (2017) Gascoyne Coast Bioregion, Status reports of the fisheries and aquatic resources of Western Australia 2016/17. Available at: (accessed Duarte, C.M., Conley, D.J., Carstensen, J. & Sánchez-Camacho, M. (2009) Return to Neverland: shifting baselines affect eutrophication restoration targets. Estuaries and Coasts, 32, 29-36.

177 Dubinsky, Z. & Falkowski, P. (2011) Light as a Source of Information and Energy in Zooxanthellate Corals. Coral Reefs: An Ecosystem in Transition (ed. by Z. Dubinsky and N. Stambler), pp. 107-118. Springer Netherlands, Dordrecht. Duxbury, A.C.B., Robert Howard; Mackenzie, Fred T. (2018) Seawater: Optical properties. In: Encyclopedia Britannica (ed. P.B. Adam Augustyn, Brian Duignan, Alison Eldridge, Erik Gregersen, J.E. Luebering, Amy Mckenna, Melissa Petruzzello, John P. Rafferty, Michael Ray, Kara Rogers, Amy Tikkanen, Jeff Wallenfeldt, Adam Zeidan, Alicja Zelazko). Encyclopædia Britannica, Inc. Edgar, G.J., Stuart-Smith, R.D., Willis, T.J., Kininmonth, S., Baker, S.C., Banks, S., Barrett, N.S., Becerro, M.A., Bernard, A.T. & Berkhout, J. (2014) Global conservation outcomes depend on marine protected areas with five key features. Nature, 506, 216- 220. Emanuel, K. (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686-688. Emanuel, K.A. (2013) Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proceedings of the National Academy of Sciences, 110, 12219-12224. Emily S. Darling, T.R.M., Joseph Maina, Georgina G. Gurney, Nicholas A. J. Graham, Fraser Januchowski-Hartley, Joshua E. Cinner, Camilo Mora, Christina C. Hicks, Eva Maire, Marji Puotinen, William J. Skirving, Mehdi Adjeroud, Gabby Ahmadia, Rohan Arthur, Andrew G. Bauman, Maria Beger, Michael L. Berumen, Lionel Bigot, Jessica Bouwmeester, Ambroise Brenier, Tom C. L. Bridge, Eric Brown, Stuart J. Campbell, Sara Cannon, Bruce Cauvin, Chaolun Allen Chen, Joachim Claudet, Vianney Denis, Simon Donner, Estradivari, Nur Fadli, David A. Feary, Douglas Fenner, Helen Fox, Erik C. Franklin, Alan Friedlander, James Gilmour, Claire Goiran, James Guest, Jean- Paul A. Hobbs, Andrew S. Hoey, Peter Houk, Steven Johnson, Stacy D. Jupiter, Mohsen Kayal, Chao-yang Kuo, Joleah Lamb, Michelle A. C. Lee, Jeffrey Low, Nyawira Muthiga, Efin Muttaqin, Yashika Nand, Kirsty L. Nash, Osamu Nedlic, John M. Pandolfi, Shinta Pardede, Vardhan Patankar, Lucie Penin, Lauriane Ribas-Deulofeu, Zoe Richards, T. Edward Roberts, Ku’ulei S. Rodgers, Che Din Mohd Safuan, Enric Sala, George Shedrawi, Tsai Min Sin, Patrick Smallhorn-West, Jennifer E. Smith, Brigitte Sommer, Peter D. Steinberg, Makamas Sutthacheep, Chun Hong James Tan, Gareth J. Williams, Shaun Wilson, Thamasak Yeemin, John F. Bruno, Marie-Josée Fortin, Martin Krkosek, David Mouillot (2019) Social–environmental drivers inform strategic management of coral reefs in the Anthropocene. Nature ecology & evolution, 1-10. Feng, M., Hendon, H.H., Xie, S.P., Marshall, A.G., Schiller, A., Kosaka, Y., Caputi, N. & Pearce, A. (2015) Decadal increase in Ningaloo Niño since the late 1990s. Geophysical Research Letters, 42, 104-112. Ferrari, R., Bryson, M., Bridge, T., Hustache, J., Williams, S.B., Byrne, M. & Figueira, W. (2016) Quantifying the response of structural complexity and community composition to environmental change in marine communities. Global change biology, 22, 1965- 1975. Ferrari, R., Figueira, W.F., Pratchett, M.S., Boube, T., Adam, A., Kobelkowsky-Vidrio, T., Doo, S.S., Atwood, T.B. & Byrne, M. (2017) 3D photogrammetry quantifies growth and external erosion of individual coral colonies and skeletons. Scientific reports, 7, 16737. Fisher, R., Wilson Shaun, K., Sin Tsai, M., Lee Ai, C. & Langlois Tim, J. (2018) A simple function for full‐subsets multiple regression in ecology with R. Ecology and Evolution, 0 Fisher, W.S., Davis, W.P., Quarles, R.L., Patrick, J., Campbell, J.G., Harris, P.S., Hemmer, B.L. & Parsons, M. (2007) Characterizing Coral Condition Using Estimates of Three- dimensional Colony Surface Area. Environmental Monitoring and Assessment, 125, 347-360. Fitzpatrick, B., Harvey, E., Langlois, T., Babcock, R. & Twiggs, E. (2015) Effects of fishing on fish assemblages at the reefscape scale. Marine Ecology Progress Series, 524, 241-253.

Fox, J.W. (2013) The intermediate disturbance hypothesis should be abandoned. Trends in Ecology & Evolution, 28, 86-92. Fox, M.D., Williams, G.J., Johnson, M.D., Radice, V.Z., Zgliczynski, B.J., Kelly, E.L.A., Rohwer, F.L., Sandin, S.A. & Smith, J.E. (2018) Gradients in Primary Production Predict Trophic Strategies of Mixotrophic Corals across Spatial Scales. Current Biology, 28, 3355-3363.e4. Fraser, M.W., Kendrick, G.A., Statton, J., Hovey, R.K., Zavala‐Perez, A. & Walker, D.I. (2014) Extreme climate events lower resilience of foundation seagrass at edge of biogeographical range. Journal of Ecology, 102, 1528-1536. Froese, R.a.D.P. (2018) FishBase. Available at: www.fishbase.org (accessed 15/1/2018 2018). Frölicher, T.L., Fischer, E.M. & Gruber, N. (2018) Marine heatwaves under global warming. Nature, 560, 360. Fukunaga, A., Burns, J.H., Craig, B.K. & Kosaki, R.K. (2019) Integrating three-dimensional benthic habitat characterization techniques into ecological monitoring of coral reefs. Journal of Marine Science and Engineering, 7, 27. Fulton, C.J., Depczynski, M., Holmes, T.H., Noble, M.M., Radford, B., Wernberg, T. & Wilson, S.K. (2014) Sea temperature shapes seasonal fluctuations in seaweed biomass within the Ningaloo coral reef ecosystem. Limnology and Oceanography, 59, 156-166. Fulton, E.A. (2011) Interesting times: winners, losers, and system shifts under climate change around Australia. ICES Journal of Marine Science: Journal du Conseil, 68, 1329-1342. Gardner, T.A., Côté, I.M., Gill, J.A., Grant, A. & Watkinson, A.R. (2003) Long-term region- wide declines in Caribbean corals. Science, 301, 958-960. Gardner, T.A., Côté, I.M., Gill, J.A., Grant, A. & Watkinson, A.R. (2005) Hurricanes and caribbean coral reefs: Impacts, recovery patterns, and role in long-term decline. Ecology, 86, 174-184. Gens, R. (2008) Oceanographic Applications of SAR Remote Sensing. GIScience & Remote Sensing, 45, 275-305. Ghedini, G., Russell, B.D. & Connell, S.D. (2015) Trophic compensation reinforces resistance: herbivory absorbs the increasing effects of multiple disturbances. Ecology Letters, 18, 182-187. Gilmour, J., Speed, C.W. & Babcock, R. (2016) Coral reproduction in Western Australia. PeerJ, 4, e2010. Gilmour, J., Cook, K., Ryan, N., Puotinen, M., Green, R., Shedrawi, G., Hobbs, J.-P., Thomson, D., Babcock, R., Buckee, J., Foster, T., Richards, Z., Wilson, S., Barnes, P., Coutts, T., Radford, B., Piggott, C., Depczynski, M., Evans, S., Schoepf, V., Evans, R., Halford, A., Nutt, C., Bancroft, K., Heyward, A. & Oades, D. (2019) The state of Western Australia’s coral reefs. Coral Reefs, Gilmour, J.P., Smith, L.D., Heyward, A.J., Baird, A.H. & Pratchett, M.S. (2013) Recovery of an isolated coral reef system following severe disturbance. Science, 340, 69-71. Goetze, J., Jupiter, S., Langlois, T., Wilson, S., Harvey, E., Bond, T. & Naisilisili, W. (2015) Diver operated video most accurately detects the impacts of fishing within periodically harvested closures. Journal of Experimental Marine Biology and Ecology, 462, 74-82. González-Barrios, F.J. & Álvarez-Filip, L. (2018) A framework for measuring coral species- specific contribution to reef functioning in the Caribbean. Ecological indicators, 95, 877-886. Goodman, L.A. (1961) Snowball sampling. The annals of mathematical statistics, 148-170. Goreau, T.F. (1963) Calcium carbonate deposition by coralline algae and corals in relation to their roles as reef‐builders. Annals of the New York Academy of Sciences, 109, 127-167. Grafton, R.Q., Doyen, L., Béné, C., Borgomeo, E., Brooks, K., Chu, L., Cumming, G.S., Dixon, J., Dovers, S. & Garrick, D. (2019) Realizing resilience for decision-making. Nature Sustainability, 2, 907-913. Graham, N. & Nash, K. (2013) The importance of structural complexity in coral reef ecosystems. Coral Reefs, 32, 315-326. Graham, N.A., Jennings, S., MacNeil, M.A., Mouillot, D. & Wilson, S.K. (2015) Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature, 518, 94-97.

179 Graham, N.A., Wilson, S.K., Jennings, S., Polunin, N.V., Robinson, J., Bijoux, J.P. & Daw, T.M. (2007) Lag effects in the impacts of mass coral bleaching on coral reef fish, fisheries, and ecosystems. Conservation biology, 21, 1291-1300. Graham, N.A.J., Nash, K.L. & Kool, J.T. (2011) Coral reef recovery dynamics in a changing world. Coral Reefs, 30, 283-294. Graham, N.A.J., Wilson, S.K., Jennings, S., Polunin, N.V.C., Bijoux, J.P. & Robinson, J. (2006) Dynamic fragility of oceanic coral reef ecosystems. Proceedings of the National Academy of Sciences of the United States of America, 103, 8425-8429. Grant, P.R., Grant, B.R., Huey, R.B., Johnson, M.T., Knoll, A.H. & Schmitt, J. (2017) Evolution caused by extreme events. Philosophical Transactions of the Royal Society B: Biological Sciences, 372, 20160146. Grime, J.P. (1973) Competitive exclusion in herbaceous vegetation. Nature, 242, 344. Gurstein, M.B. (2011) Open data: Empowering the empowered or effective data use for everyone? First Monday, 16 Halpern, B.S. & Warner, R.R. (2002) Marine reserves have rapid and lasting effects. Ecology Letters, 5, 361-366. Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., Fujita, R., Heinemann, D., Lenihan, H.S., Madin, E.M.P., Perry, M.T., Selig, E.R., Spalding, M., Steneck, R. & Watson, R. (2008) A Global Map of Human Impact on Marine Ecosystems. Science, 319, 948. Harahsheh, H., Essa, S., Shiobarac, M., Nishidaid, T. & Onumad, T. (2001) Operational satellite monitoring and detection for oil spill in offshore of united Arab Emirates. Fuel, 1, 1,500. Haywood, M., Thomson, D., Babcock, R., Pillans, R., Keesing, J., Miller, M., Rochester, W., Donovan, A., Evans, R., Shedrawi, G. & Field, S. (2019) Crown-of-thorns starfish impede recovery of coral reefs following bleaching. Marine Biology, in press Hedges, L.V. & Olkin, I. (2014) Statistical methods for meta-analysis. Academic press. Hedges, L.V., Gurevitch, J. & Curtis, P.S. (1999) The meta‐analysis of response ratios in experimental ecology. Ecology, 80, 1150-1156. Hedley, J., Roelfsema, C., Chollett, I., Harborne, A., Heron, S., Weeks, S., Skirving, W., Strong, A., Eakin, C. & Christensen, T. (2016) Remote sensing of coral reefs for monitoring and management: a review. Remote Sensing, 8, 118. Heron, S.F., Eakin, C.M., Douvere, F., Anderson, K.L., Day, J.C., Geiger, E., Hoegh-Guldberg, O., Van Hooidonk, R., Hughes, T. & Marshall, P. (2017) Impacts of climate change on World Heritage coral reefs: A first global scientific assessment. Hodgson, D., McDonald, J.L. & Hosken, D.J. (2015) What do you mean,‘resilient’? Trends in ecology & evolution, 30, 503-506. Hodgson, D., McDonald, J.L. & Hosken, D.J. (2016) Resilience is complicated, but comparable: a reply to Yeung and Richardson. Trends in ecology & evolution, 31, 3-4. Hoegh-Guldberg, O. (1988) A method for determining the surface area of corals. Coral Reefs, 7, 113-116. Holcomb, M., Cohen, A.L. & McCorkle, D.C. (2013) An evaluation of staining techniques for marking daily growth in scleractinian corals. Journal of experimental marine biology and ecology, 440, 126-131. Holling, C.S. (1973) Resilience and stability of ecological systems. Annual review of ecology and systematics, 1-23. Holmes, T.H., Wilson, S.K., Travers, M.J., Langlois, T.J., Evans, R.D., Moore, G.I., Douglas, R.A., Shedrawi, G., Harvey, E.S. & Hickey, K. (2013) A comparison of visual-and stereo-video based fish community assessment methods in tropical and temperate marine waters of Western Australia. Limnology and Oceanography, Methods, 11, 337- 350. Horta e Costa, B., Claudet, J., Franco, G., Erzini, K., Caro, A. & Gonçalves, E.J. (2016) A regulation-based classification system for Marine Protected Areas (MPAs). Marine Policy, 72, 192-198. Horwitz, R., Hoogenboom, M.O. & Fine, M. (2017) Spatial competition dynamics between reef corals under ocean acidification. Scientific Reports, 7, 40288.

Hughes, T. & Jackson, J. (1985) Population dynamics and life histories of foliaceous corals. Ecological Monographs, 55, 141-166. Hughes, T. & Connell, J. (1999) Multiple stressors on coral reefs: a long-term perspective. Limnology and oceanography, 44, 932-940. Hughes, T.P. (1989) Community Structure and Diversity of Coral Reefs: The Role of History. Ecology, 70, 275-279. Hughes, T.P., Bellwood, D.R., Folke, C., Steneck, R.S. & Wilson, J. (2005) New paradigms for supporting the resilience of marine ecosystems. Trends in Ecology & Evolution, 20, 380-386. Hughes, T.P., Graham, N.A., Jackson, J.B., Mumby, P.J. & Steneck, R.S. (2010) Rising to the challenge of sustaining coral reef resilience. Trends in ecology & evolution, 25, 633- 642. Hughes, Terry P., Baird, Andrew H., Dinsdale, Elizabeth A., Moltschaniwskyj, Natalie A., Pratchett, Morgan S., Tanner, Jason E. & Willis, Bette L. (2012) Assembly Rules of Reef Corals Are Flexible along a Steep Climatic Gradient. Current Biology, 22, 736- 741. Hughes, T.P., Baird, A.H., Bellwood, D.R., Card, M., Connolly, S.R., Folke, C., Grosberg, R., Hoegh-Guldberg, O., Jackson, J. & Kleypas, J. (2003) Climate change, human impacts, and the resilience of coral reefs. science, 301, 929-933. Hughes, T.P., Rodrigues, M.J., Bellwood, D.R., Ceccarelli, D., Hoegh-Guldberg, O., McCook, L., Moltschaniwskyj, N., Pratchett, M.S., Steneck, R.S. & Willis, B. (2007) Phase shifts, herbivory, and the resilience of coral reefs to climate change. Current Biology, 17, 360-365. Hughes, T.P., Kerry, J.T., Álvarez-Noriega, M., Álvarez-Romero, J.G., Anderson, K.D., Baird, A.H., Babcock, R.C., Beger, M., Bellwood, D.R. & Berkelmans, R. (2017) Global warming and recurrent mass bleaching of corals. Nature, 543, 373-377. Hughes, T.P., Anderson, K.D., Connolly, S.R., Heron, S.F., Kerry, J.T., Lough, J.M., Baird, A.H., Baum, J.K., Berumen, M.L. & Bridge, T.C. (2018) Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science, 359, 80-83. Huston, M. (1985) Variation in coral growth rates with depth at Discovery Bay, Jamaica. Coral Reefs, 4, 19-25. Hutchinson, G.E. (1961) The Paradox of the Plankton. The American Naturalist, 95, 137-145. Ingrisch, J. & Bahn, M. (2018) Towards a comparable quantification of resilience. Trends in ecology & evolution, 33, 251-259. IPCC, I.P.o.C.C. (2019) Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate Ivanov, A. (2000) Oil pollution of the sea on Kosmos-1870 and Almaz-1 radar imagery. Earth Observation and Remote Sensing, 15, 949-966. Jackson, J.B.C., Kirby, M.X., Berger, W.H., Bjorndal, K.A., Botsford, L.W., Bourque, B.J., Bradbury, R.H., Cooke, R., Erlandson, J., Estes, J.A., Hughes, T.P., Kidwell, S., Lange, C.B., Lenihan, H.S., Pandolfi, J.M., Peterson, C.H., Steneck, R.S., Tegner, M.J. & Warner, R.R. (2001) Historical Overfishing and the Recent Collapse of Coastal Ecosystems. Science, 293, 629. Januchowski-Hartley, F.A., Graham, N.A., Wilson, S.K., Jennings, S. & Perry, C.T. (2017) Drivers and predictions of coral reef carbonate budget trajectories. Proceedings of the Royal Society B: Biological Sciences, 284, 20162533. Jenkins, C.N. & Joppa, L. (2009) Expansion of the global terrestrial protected area system. Biological conservation, 142, 2166-2174. Jentsch, A. & Beierkuhnlein, C. (2008) Research frontiers in climate change: effects of extreme meteorological events on ecosystems. Comptes Rendus Geoscience, 340, 621-628. Jerlov, N.G. (1951) Optical studies of ocean water. Rep. Swedish Deep-Sea Exped. 3: 1-59.. 1976. Marine optics. In. Elsevier Johnson, C.R. & Seinen, I. (2002) Selection for restraint in competitive ability in spatial competition systems. Proceedings of the Royal Society of London B: Biological Sciences, 269, 655-663. Jones, A.T., Thankappan, M., Logan, G.A., Kennard, J.M., Smith, C.J., Williams, A.K. & Lawrence, G.M. (2006) Coral spawn and bathymetric slicks in Synthetic Aperture

181 Radar (SAR) data from the Timor Sea, north‐west Australia. International Journal of Remote Sensing, 27, 2063-2069. Jones, P.J. (2019) A governance analysis of Ningaloo and Shark Bay Marine Parks, Western Australia: putting the ‘eco’in tourism to build resilience but threatened in long-term by climate change? Marine Policy, 103636. Kaandorp, J.A., Filatov, M. & Chindapol, N. (2011) Simulating and Quantifying the Environmental Influence on Coral Colony Growth and Form. Coral Reefs: An Ecosystem in Transition (ed. by Z. Dubinsky and N. Stambler), pp. 177-185. Springer Netherlands, Dordrecht. Keith, S.A., Maynard, J.A., Edwards, A.J., Guest, J.R., Bauman, A.G., Van Hooidonk, R., Heron, S.F., Berumen, M.L., Bouwmeester, J. & Piromvaragorn, S. (2016) Coral mass spawning predicted by rapid seasonal rise in ocean temperature. Proc. R. Soc. B (ed by, p. 20160011. Knowlton, N. (2001) The future of coral reefs. Proceedings of the National Academy of Sciences, 98, 5419-5425. Knowlton, N. & Jackson, J.B. (2008) Shifting baselines, local impacts, and global change on coral reefs. PLoS biology, 6, e54. Kobryn, H.T., Wouters, K., Beckley, L.E. & Heege, T. (2013) Ningaloo Reef: Shallow marine habitats mapped using a hyperspectral sensor. PloS one, 8, e70105. Koricheva, J., Gurevitch, J. & Mengersen, K. (2013) Handbook of meta-analysis in ecology and evolution. Princeton University Press. Kubicek, A. & Borell, E. (2011) Modelling Resilience and Phase Shifts in Coral Reefs: Application of Different Modelling Approaches. Modelling Complex Ecological Dynamics: An Introduction into Ecological Modelling for Students, Teachers & Scientists (ed. by F. Jopp, H. Reuter and B. Breckling), pp. 241-255. Springer Berlin Heidelberg, Berlin, Heidelberg. Lake, P.S. (2013) Resistance, resilience and restoration. Ecological management & restoration, 14, 20-24. Lam, V.Y., Doropoulos, C. & Mumby, P.J. (2017) The influence of resilience-based management on coral reef monitoring: A systematic review. PloS one, 12, e0172064. Lamy, T., Galzin, R., Kulbicki, M., Lison de Loma, T. & Claudet, J. (2016) Three decades of recurrent declines and recoveries in corals belie ongoing change in fish assemblages. Coral Reefs, 35, 293-302. Langlois, T.J. & Ballantine, W.J. (2005) Marine ecological research in New Zealand: developing predictive models through the study of no‐take marine reserves. Conservation Biology, 19, 1763-1770. Langlois, T.J., Harvey, E.S., Fitzpatrick, B., Meeuwig, J.J., Shedrawi, G. & Watson, D.L. (2010) Cost-efficient sampling of fish assemblages: comparison of baited video stations and diver video transects. Aquatic biology, 9, 155-168. Langmead, O. & Sheppard, C. (2004) Coral reef community dynamics and disturbance: a simulation model. Ecological Modelling, 175, 271-290. Lawson, C.R., Vindenes, Y., Bailey, L. & van de Pol, M. (2015) Environmental variation and population responses to global change. Ecology Letters, 18, 724-736. Lenihan, H.S., Holbrook, S.J., Schmitt, R.J. & Brooks, A.J. (2011) Influence of corallivory, competition, and habitat structure on coral community shifts. Ecology, 92, 1959-1971. Lester, S.E., Halpern, B.S., Grorud-Colvert, K., Lubchenco, J., Ruttenberg, B.I., Gaines, S.D., Airamé, S. & Warner, R.R. (2009) Biological effects within no-take marine reserves: a global synthesis. Marine Ecology Progress Series, 384, 33-46. Leujak, W. & Ormond, R.F.G. (2007) Comparative accuracy and efficiency of six coral community survey methods. Journal of Experimental Marine Biology and Ecology, 351, 168-187. Lewin, W.-C., Arlinghaus, R. & Mehner, T. (2006) Documented and Potential Biological Impacts of Recreational Fishing: Insights for Management and Conservation. Reviews in Fisheries Science, 14, 305-367. Lim, I.E., Wilson, S.K., Holmes, T.H., Noble, M.M. & Fulton, C.J. (2016) Specialization within a shifting habitat mosaic underpins the seasonal abundance of a tropical fish. Ecosphere, 7, e01212.

Lirman, D. (2001) Competition between macroalgae and corals: effects of herbivore exclusion and increased algal biomass on coral survivorship and growth. Coral reefs, 19, 392-399. Lough, J.M. (2016) Coral reefs: Turning back time. Nature, 531, 314-315. Loya, Y., Sakai, K., Yamazato, K., Nakano, Y., Sambali, H. & Van Woesik, R. (2001) Coral bleaching: the winners and the losers. Ecology letters, 4, 122-131. Luckhurst, B. & Luckhurst, K. (1978) Analysis of the influence of substrate variables on coral reef fish communities. Marine Biology, 49, 317-323. Lüning, K. & Tom Dieck, I. (1989) Environmental triggers in algal seasonality. Botanica Marina, 32, 389-398. Ma, K.Y. & Craig, M.T. (2018) An inconvenient monophyly: an update on the taxonomy of the groupers (Epinephelidae). Copeia, 106, 443-456. Mackie, M. & Black, R. (1999) Research on Two Serranid Species (Serranidae: Epinephelinae) in Western Australian Waters. University of Western Australia/Fisheries Research & Development Corporation. MacNeil, M.A., Mellin, C., Matthews, S., Wolff, N.H., McClanahan, T.R., Devlin, M., Drovandi, C., Mengersen, K. & Graham, N.A. (2019) Water quality mediates resilience on the Great Barrier Reef. Nature ecology & evolution, 3, 620-627. Madin, J.S. (2005) Mechanical limitations of reef corals during hydrodynamic disturbances. Coral Reefs, 24, 630-635. Madin, J.S. & Connolly, S.R. (2006) Ecological consequences of major hydrodynamic disturbances on coral reefs. Nature, 444, 477-480. Madin, J.S., O'Donnell, M.J. & Connolly, S.R. (2008) Climate-mediated mechanical changes to post-disturbance coral assemblages. Biology Letters, 4, 490-493. Madin, J.S., Baird, A.H., Dornelas, M. & Connolly, S.R. (2014) Mechanical vulnerability explains size-dependent mortality of reef corals. Ecology Letters, 17, 1008-1015. Madin, J.S., Hoogenboom, M.O., Connolly, S.R., Darling, E.S., Falster, D.S., Huang, D., Keith, S.A., Mizerek, T., Pandolfi, J.M. & Putnam, H.M. (2016) A trait-based approach to advance coral reef science. Trends in ecology & evolution, 31, 419-428. Mallet, D. & Pelletier, D. (2014) Underwater video techniques for observing coastal marine biodiversity: a review of sixty years of publications (1952–2012). Fisheries Research, 154, 44-62. Massel, S. & Done, T. (1993) Effects of cyclone waves on massive coral assemblages on the Great Barrier Reef: meteorology, hydrodynamics and demography. Coral Reefs, 12, 153-166. McClanahan, T. & Muthiga, N. (2014) Community change and evidence for variable warm- water temperature adaptation of corals in Northern Male Atoll, Maldives. Marine Pollution Bulletin, 80, 107-113. McClanahan, T.R., Ateweberhan, M. & Omukoto, J. (2008) Long-term changes in coral colony size distributions on Kenyan reefs under different management regimes and across the 1998 bleaching event. Marine Biology, 153, 755-768. McClanahan, T.R., Donner, S.D., Maynard, J.A., MacNeil, M.A., Graham, N.A., Maina, J., Baker, A.C., Beger, M., Campbell, S.J. & Darling, E.S. (2012) Prioritizing key resilience indicators to support coral reef management in a changing climate. PloS one, 7, e42884. McCook, L.J., Ayling, T., Cappo, M., Choat, J.H., Evans, R.D., De Freitas, D.M., Heupel, M., Hughes, T.P., Jones, G.P., Mapstone, B., Marsh, H., Mills, M., Molloy, F.J., Pitcher, C.R., Pressey, R.L., Russ, G.R., Sutton, S., Sweatman, H., Tobin, R., Wachenfeld, D.R. & Williamson, D.H. (2010) Adaptive management of the Great Barrier Reef: A globally significant demonstration of the benefits of networks of marine reserves. Proceedings of the National Academy of Sciences, 107, 18278. McCrackin, M.L., Jones, H.P., Jones, P.C. & Moreno‐Mateos, D. (2017) Recovery of lakes and coastal marine ecosystems from eutrophication: A global meta‐analysis. Limnology and Oceanography, 62, 507-518. McIntosh, R.P. (1986) The background of ecology: concept and theory. Cambridge University Press. McLaren, B.W., Langlois, T.J., Harvey, E.S., Shortland-Jones, H. & Stevens, R. (2015) A small no-take marine sanctuary provides consistent protection for small-bodied by-catch

183 species, but not for large-bodied, high-risk species. Journal of Experimental Marine Biology and Ecology, 471, 153-163. McLean, D.L., Langlois, T.J., Newman, S.J., Holmes, T.H., Birt, M.J., Bornt, K.R., Bond, T., Collins, D.L., Evans, S.N., Travers, M.J., Wakefield, C.B., Babcock, R.C. & Fisher, R. (2016) Distribution, abundance, diversity and habitat associations of fishes across a bioregion experiencing rapid coastal development. Estuarine, Coastal and Shelf Science, 178, 36-47. McPhee, D.P., Leadbitter, D. & Skilleter, G.A. (2002) Swallowing the bait: is recreational fishing in Australia ecologically sustainable? Pacific Conservation Biology, 8, 40-51. McWilliam, M., Pratchett, M.S., Hoogenboom, M.O. & Hughes, T.P. (2020) Deficits in functional trait diversity following recovery on coral reefs. Proceedings of the Royal Society B: Biological Sciences, 287, 20192628. Mellin, C., Thompson, A., Jonker, M.J. & Emslie, M.J. (2019a) Cross-shelf variation in coral community response to disturbance on the Great Barrier Reef. Diversity, 11, 38. Mellin, C., Peterson, E.E., Puotinen, M. & Schaffelke, B. (2020) Representation and complementarity of the long-term coral monitoring on the Great Barrier Reef. Ecological Applications, n/a Mellin, C., Aaron MacNeil, M., Cheal, A.J., Emslie, M.J. & Julian Caley, M. (2016) Marine protected areas increase resilience among coral reef communities. Ecology letters, 19, 629-637. Mellin, C., Matthews, S., Anthony, K.R., Brown, S.C., Caley, M.J., Johns, K.A., Osborne, K., Puotinen, M., Thompson, A. & Wolff, N.H. (2019b) Spatial resilience of the Great Barrier Reef under cumulative disturbance impacts. Global change biology, 25, 2431- 2445. Merks, R.M., Hoekstra, A.G., Kaandorp, J.A. & Sloot, P.M. (2004) Polyp oriented modelling of coral growth. Journal of Theoretical Biology, 228, 559-576. Miller, K.I. & Russ, G.R. (2014) Studies of no-take marine reserves: Methods for differentiating reserve and habitat effects. Ocean & Coastal Management, 96, 51-60. Mitchell, J., McLean, D., Collin, S., Taylor, S., Jackson, G., Fisher, R. & Langlois, T. (2018) Quantifying shark depredation in a recreational fishery in the Ningaloo Marine Park and Exmouth Gulf, Western Australia. Marine Ecology Progress Series, 587, 141-157. Molloy, J.C. (2011) The open knowledge foundation: open data means better science. PLoS biology, 9, e1001195. Mora, C. & Sale, P.F. (2011) Ongoing global biodiversity loss and the need to move beyond protected areas: a review of the technical and practical shortcomings of protected areas on land and sea. Marine ecology progress series, 434, 251-266. Morecroft, M.D., Crick, H.Q., Duffield, S.J. & Macgregor, N.A. (2012) Resilience to climate change: translating principles into practice. Journal of Applied Ecology, 49, 547-551. Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I. & Papathanassiou, K.P. (2013) A tutorial on synthetic aperture radar. IEEE Geoscience and remote sensing magazine, 1, 6-43. Mosquera, I., Côté, I.M., Jennings, S. & Reynolds, J.D. (2000) Conservation benefits of marine reserves for fish populations. Animal Conservation forum (ed by, pp. 321-332. Muko, S., Sakai, K. & Iwasa, Y. (2001) Dynamics of marine sessile organisms with space- limited growth and recruitment: application to corals. Journal of Theoretical Biology, 210, 67-80. Mumby, P.J. (2006) The impact of exploiting grazers (Scaridae) on the dynamics of Caribbean coral reefs. Ecological Applications, 16, 747-769. Mumby, P.J. & Steneck, R.S. (2008) Coral reef management and conservation in light of rapidly evolving ecological paradigms. Trends in ecology & evolution, 23, 555-563. Mumby, P.J. & Hastings, A. (2008) The impact of ecosystem connectivity on coral reef resilience. Journal of Applied Ecology, 45, 854-862. Mumby, P.J. & Steneck, R.S. (2011) The resilience of coral reefs and its implications for reef management. Coral Reefs: An Ecosystem in Transition, pp. 509-519. Mumby, P.J. & Anthony, K.R.N. (2015) Resilience metrics to inform ecosystem management under global change with application to coral reefs. Methods in Ecology and Evolution, 6, 1088-1096.

Mumby, P.J., Hastings, A. & Edwards, H.J. (2007) Thresholds and the resilience of Caribbean coral reefs. Nature, 450, 98-101. Mumby, P.J., Chollett, I., Bozec, Y.-M. & Wolff, N.H. (2014a) Ecological resilience, robustness and vulnerability: how do these concepts benefit ecosystem management? Current Opinion in Environmental Sustainability, 7, 22-27. Mumby, P.J., Wolff, N.H., Bozec, Y.M., Chollett, I. & Halloran, P. (2014b) Operationalizing the resilience of coral reefs in an era of climate change. Conservation Letters, 7, 176- 187. Murphy, H.M. & Jenkins, G.P. (2010) Observational methods used in marine spatial monitoring of fishes and associated habitats: a review. Marine and Freshwater Research, 61, 236- 252. Newman, E.A. (2019) Disturbance Ecology in the Anthropocene. Frontiers in Ecology and Evolution, 7 Nimmo, D., Mac Nally, R., Cunningham, S.C., Haslem, A. & Bennett, A.F. (2015) Vive la résistance: reviving resistance for 21st century conservation. Trends in ecology & evolution, 30, 516-523. Nott, J. (2000) Records of prehistoric tsunamis from boulder deposits–evidence from Australia. Science of Tsunami Hazards, 18, 3-14. Nunziata, F., Gambardella, A. & Migliaccio, M. (2013) On the degree of polarization for SAR sea oil slick observation. ISPRS Journal of Photogrammetry and Remote Sensing, 78, 41-49. Nyström, M. & Folke, C. (2001) Spatial resilience of coral reefs. Ecosystems, 4, 406-417. Nyström, M., Folke, C. & Moberg, F. (2000) Coral reef disturbance and resilience in a human- dominated environment. Trends in Ecology & Evolution, 15, 413-417. O'Leary, J.K., Micheli, F., Airoldi, L., Boch, C., De Leo, G., Elahi, R., Ferretti, F., Graham, N.A., Litvin, S.Y. & Low, N.H. (2017) The resilience of marine ecosystems to climatic disturbances. BioScience, 67, 208-220. Obura, D. & Grimsditch, G. (2009) Resilience assessment of coral reefs: assessment protocol for coral reefs, focusing on coral bleaching and thermal stress. Citeseer. Oliver, J. & Willis, B. (1987) Coral-spawn slicks in the Great Barrier Reef: preliminary observations. Marine Biology, 94, 521-529. Osenberg, C.W., Sarnelle, O., Cooper, S.D. & Holt, R.D. (1999) Resolving ecological questions through meta‐analysis: goals, metrics, and models. Ecology, 80, 1105-1117. Osenberg, C.W., Shima, J.S., Miller, S.L. & Stier, A.C. (2011) Ecology: assessing effects of marine protected areas: confounding in space and possible solutions. Marine protected areas: a multidisciplinary approach, 143-167. Osinga, R., Schutter, M., Griffioen, B., Wijffels, R.H., Verreth, J.A., Shafir, S., Henard, S., Taruffi, M., Gili, C. & Lavorano, S. (2011) The biology and economics of coral growth. Marine Biotechnology, 13, 658-671. Pandolfi, J. (2002) Coral community dynamics at multiple scales. Coral reefs, 21, 13-23. Pandolfi, J., Jackson, J.B.C., Baron, N. & Bradbury, R. (2005) Are US coral reefs on the slippery slope to slime? Science, 307, 1725. Parashar, S., Langham, E., McNally, J. & Ahmed, S. (1993) RADARSAT mission requirements and concept. Canadian Journal of Remote Sensing, 19, 280-288. Pauly, D. (1995) Anecdotes and the shifting baseline syndrome of fisheries. Trends in ecology & evolution, 10, 430. Perry, C.T., Murphy, G.N., Graham, N.A., Wilson, S.K., Januchowski-Hartley, F.A. & East, H.K. (2015) Remote coral reefs can sustain high growth potential and may match future sea-level trends. Scientific Reports, 5, 18289. Perry, C.T., Murphy, G.N., Kench, P.S., Smithers, S.G., Edinger, E.N., Steneck, R.S. & Mumby, P.J. (2013) Caribbean-wide decline in carbonate production threatens coral reef growth. Nature communications, 4, 1402. Perry, C.T., Alvarez-Filip, L., Graham, N.A., Mumby, P.J., Wilson, S.K., Kench, P.S., Manzello, D.P., Morgan, K.M., Slangen, A.B. & Thomson, D.P. (2018) Loss of coral reef growth capacity to track future increases in sea level. Nature, 558, 396. Pillans, R.D., Bearham, D., Boomer, A., Downie, R., Patterson, T.A., Thomson, D.P. & Babcock, R.C. (2014) Multi year observations reveal variability in residence of a

185 tropical demersal fish, Lethrinus nebulosus: implications for spatial management. PLoS One, 9, e105507. Pimm, S.L., Donohue, I., Montoya, J.M. & Loreau, M. (2019) Measuring resilience is essential to understand it. Nature Sustainability, 2, 895-897. Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. (2019) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-140. Potts, D. (1986) Late Cenozoic biogeography and evolution of reef organisms. Annual Meeting of the International Society of Reef Studies (ed by. Pratchett, M., Wilson, S. & Baird, A. (2006) Declines in the abundance of Chaetodon butterflyfishes following extensive coral depletion. Journal of Fish Biology, 69, 1269- 1280. Pratchett, M.S., McCowan, D., Maynard, J.A. & Heron, S.F. (2013) Changes in bleaching susceptibility among corals subject to ocean warming and recurrent bleaching in Moorea, French Polynesia. PloS one, 8, e70443. Pratchett, M.S., Anderson, K.D., Hoogenboom, M.O., Widman, E., Baird, A.H., Pandolfi, J.M., Edmunds, P.J. & Lough, J.M. (2015) Spatial, temporal and taxonomic variation in coral growth—implications for the structure and function of coral reef ecosystems. Oceanography and Marine Biology: An Annual Review, 53, 215-295. Pratchett, M.S., Munday, P., Wilson, S.K., Graham, N.A., Cinner, J.E., Bellwood, D.R., Jones, G.P., Polunin, N.V. & McClanahan, T. (2008) Effects of climate-induced coral bleaching on coral-reef fishes. Ecological and economic consequences. Oceanography and Marine Biology: Annual Review, 46, 251-296. Pratchett, M.S., Caballes, C.F., Wilmes, J.C., Matthews, S., Mellin, C., Sweatman, H., Nadler, L.E., Brodie, J., Thompson, C.A. & Hoey, J. (2017) Thirty years of research on crown- of-thorns starfish (1986–2016): scientific advances and emerging opportunities. Diversity, 9, 41. Pygas, D.R., Ferrari, R. & Figueira, W. (2019) Review and meta-analysis of the importance of remotely sensed habitat structural complexity in marine ecology. Estuarine, Coastal and Shelf Science, 106468. R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. R Development Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Radford, C.A., Jeffs, A.G., Tindle, C.T., Cole, R.G. & Montgomery, J.C. (2005) Bubbled waters: The noise generated by underwater breathing apparatus. Marine and freshwater behaviour and physiology, 38, 259-267. Rahmstorf, S. & Coumou, D. (2011) Increase of extreme events in a warming world. Proceedings of the National Academy of Sciences, 108, 17905-17909. Raymundo, L.J. & Maypa, A.P. (2004) Getting bigger faster: mediation of size‐specific mortality via fusion in juvenile coral transplants. Ecological Applications, 14, 281-295. Rees, M.J., Knott, N.A. & Davis, A.R. (2018a) Habitat and seascape patterns drive spatial variability in temperate fish assemblages: implications for marine protected areas. Marine Ecology Progress Series, 607, 171-186. Rees, M.J., Knott, N.A., Neilson, J., Linklater, M., Osterloh, I., Jordan, A. & Davis, A.R. (2018b) Accounting for habitat structural complexity improves the assessment of performance in no-take marine reserves. Biological Conservation, 224, 100-110. Reichman, O.J., Jones, M.B. & Schildhauer, M.P. (2011) Challenges and opportunities of open data in ecology. Science, 331, 703-705. Richards, Z. & Rosser, N. (2012) Abundance, distribution and new records of scleractinian corals at Barrow Island and Southern Montebello Islands, Pilbara (offshore) bioregion. Journal of the Royal Society of Western Australia, 95, 155-165. Riegl, B. & Piller, W.E. (2003) Possible refugia for reefs in times of environmental stress. International Journal of Earth Sciences, 92, 520-531. Roff, G. & Mumby, P.J. (2012) Global disparity in the resilience of coral reefs. Trends in Ecology & Evolution, 27, 404-413.

Roff, G., Doropoulos, C., Zupan, M., Rogers, A., Steneck, R.S., Golbuu, Y. & Mumby, P.J. (2015) Phase shift facilitation following cyclone disturbance on coral reefs. Oecologia, 178, 1193-1203. Rogers, C.S. (1993) Hurricanes and coral reefs: the intermediate disturbance hypothesis revisited. Coral Reefs, 12, 127-137. Rosser, N.L. & Gilmour, J.P. (2008) New insights into patterns of coral spawning on Western Australian reefs. Coral Reefs, 27, 345-349. Rossi, P., Castagnetti, C., Capra, A., Brooks, A.J. & Mancini, F. (2019) Detecting change in coral reef 3D structure using underwater photogrammetry: critical issues and performance metrics. Applied Geomatics, Rotjan, R.D. & Lewis, S.M. (2008) Impact of coral predators on tropical reefs. Marine Ecology Progress Series, 367, 73-91. Russ, G.R. (2002) Yet another review of marine reserves as reef fishery management tools. Coral reef fishes: dynamics and diversity in a complex ecosystem, 24, 421. Russ, G.R., Miller, K.I., Rizzari, J.R. & Alcala, A.C. (2015) Long-term no-take marine reserve and benthic habitat effects on coral reef fishes. Marine Ecology Progress Series, 529, 233-248. Ryan, K., Wise, B., Hall, N., Pollock, K., Sulin, E. & Gaughan, D.J. (2013) An integrated system to survey boat-based recreational fishing in Western Australia 2011/12. Fisheries Research Division, Western Australian Fisheries and Marine Research Laboratories. Ryan, K., Hall, N., Lai, E., Smallwood, C., Taylor, S. & Wise, B. (2015) State-wide survey of boat-based recreational fishing in Western Australia 2013/14. Fisheries Research Division. Ryan, K., Hall, N., Lai, E., Smallwood, C., Taylor, S. & Wise, B. (2017) State-wide survey of boat-based recreational fishing in Western Australia 2015/16. In. Department of Primary Industries and Regional Development Rykiel, J. & Edward, J. (1985) Towards a definition of ecological disturbance. Australian Journal of Ecology, 10, 361-365. Sala, E., Lubchenco, J., Grorud-Colvert, K., Novelli, C., Roberts, C. & Sumaila, U.R. (2018) Assessing real progress towards effective ocean protection. Marine Policy, 91, 11-13. Samoilys, M., Fuentes, H., Tuwai, I., Tikomainiusiladi, B., Leqata, J., Oreihaka, E., Mobiha, A., Potuku, T., Die, D. & Connell, S. (1995) Application of underwater visual census to assessing coral reef fish stocks in the tropical Pacific. Report prepared for the Australian Centre for International Agricultural Research (ACIAR). ACIAR Project, Sandin, S.A. & McNamara, D.E. (2012) Spatial dynamics of benthic competition on coral reefs. Oecologia, 168, 1079-1090. Scheffer, M., Carpenter, S., Foley, J.A., Folke, C. & Walker, B. (2001) Catastrophic shifts in ecosystems. Nature, 413, 591-596. Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. (2012) NIH Image to ImageJ: 25 years of image analysis. Nature methods, 9, 671. Schutte, V.G.W., Selig, E.R. & Bruno, J.F. (2010) Regional spatio-temporal trends in Caribbean coral reef benthic communities. Marine Ecology Progress Series, 402, 115-122. Shaikh, A. & Kauppi, C. (2010) Deconstructing resilience: Myriad conceptualizations and interpretations. Shears, N.T., Grace, R.V., Usmar, N.R., Kerr, V. & Babcock, R.C. (2006) Long-term trends in lobster populations in a partially protected vs. no-take Marine Park. Biological conservation, 132, 222-231. Shoemaker, P.J., Tankard Jr, J.W. & Lasorsa, D.L. (2003) How to build social science theories. Sage publications. Simpson, C. (1985) Mass spawning of scleractinian corals in the Dampier Archipelago and the implications for management of coral reefs in Western Australia. West Aust Dept Conserv Environ Bull, 244, 35. Simpson, C., Pearce, A. & Walker, D. (1991) Mass spawning of corals on Western Australian reefs and comparisons with the Great Barrier Reef. Journal of the Royal Society of Western Australia, 74, 85-91.

187 Simpson, C.J. (1988) Ecology of Scleractinian Corals in the Dampier Archipelago, Western Australia. Environmental Protection Authority, Western Australia. Simpson, E.H. (1949) Measurement of Diversity. Nature, 163, 688-688. Sleeman, J.C., Boggs, G.S., Radford, B.C. & Kendrick, G.A. (2005) Using agent-based models to aid reef restoration: Enhancing coral cover and topographic complexity through the spatial arrangement of coral transplants. Restoration Ecology, 13, 685-694. Smallwood, C.B. & Beckley, L.E. (2012) Spatial distribution and zoning compliance of recreational fishing in Ningaloo Marine Park, north-western Australia. Fisheries Research, 125, 40-50. Soga, M. & Gaston, K.J. (2018) Shifting baseline syndrome: causes, consequences, and implications. Frontiers in Ecology and the Environment, 16, 222-230. Spittal, M.J., Pirkis, J. & Gurrin, L.C. (2015) Meta-analysis of incidence rate data in the presence of zero events. BMC medical research methodology, 15, 42. Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. (2015) The trajectory of the Anthropocene: the great acceleration. The Anthropocene Review, 2, 81-98. Stimson, J. (1996) Wave-like outward growth of some table-and plate-forming corals, and a hypothetical mechanism. Bulletin of Marine Science, 58, 301-313. Stobart, B., Teleki, K., Buckley, R., Downing, N. & Callow, M. (2005) Coral recovery at Aldabra Atoll, Seychelles: Five years after the 1998 bleaching event. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 363, 251-255. Stoddart, D.R. (1969) Ecology and morphology of recent coral reefs. Biological Reviews, 44, 433-498. Storlazzi, C.D., Norris, B.K. & Rosenberger, K.J. (2015) The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: Why fine-grained terrestrial sediment is bad for coral reef ecosystems. Coral Reefs, 34, 967-975. Storlazzi, C.D., Brown, E.K., Field, M.E., Rodgers, K. & Jokiel, P.L. (2005) A model for wave control on coral breakage and species distribution in the Hawaiian Islands. Coral Reefs, 24, 43-55. Styan, C.A. & Rosser, N.L. (2012) Is monitoring for mass spawning events in coral assemblages in north Western Australia likely to detect spawning? Marine pollution bulletin, 64, 2523-2527. Sumner, N.R., Williamson, P.C. & Malseed, B.E. (2002) A 12-month survey of recreational fishing in the Gascoyne bioregion of Western Australia during 1998-99. Department of Fisheries, Western Australia. Tanner, J.E. (1995) Competition between scleractinian corals and macroalgae: an experimental investigation of coral growth, survival and reproduction. Journal of Experimental Marine Biology and Ecology, 190, 151-168. Tanner, J.E. (2017) Multi-decadal analysis reveals contrasting patterns of resilience and decline in coral assemblages. Coral Reefs, 36, 1225-1233. Thiault, L., Kernaléguen, L., Osenberg, C.W. & Claudet, J. (2017) Progressive‐Change BACIPS: a flexible approach for environmental impact assessment. Methods in Ecology and Evolution, 8, 288-296. Thrush, S.F., Hewitt, J.E., Lohrer, A.M. & Chiaroni, L.D. (2013) When small changes matter: the role of cross‐scale interactions between habitat and ecological connectivity in recovery. Ecological Applications, 23, 226-238. Tian, W., Bian, X., Shao, Y. & Zhang, Z. (2015) On the detection of oil spill with China's HJ- 1C SAR image. Aquatic Procedia, 3, 144-150. Tsounis, G. & Edmunds, P.J. (2017) Three decades of coral reef community dynamics in St. John, USVI: A contrast of scleractinians and octocorals. Ecosphere, 8 Tummers, B. (2006) DataThief III. Turner, M.G. (2010) Disturbance and landscape dynamics in a changing world. Ecology, 91, 2833-2849. Underwood, A. (1993) The mechanics of spatially replicated sampling programmes to detect environmental impacts in a variable world. Australian Journal of ecology, 18, 99-116. Underwood, A. (1995) Ecological research and (and research into) environmental management. Ecological Applications, 5, 232-247.

Underwood, J.N., Wilson, S.K., Ludgerus, L. & Evans, R.D. (2013) Integrating connectivity science and spatial conservation management of coral reefs in north-west Australia. Journal for Nature Conservation, 21, 163-172. Van de Pol, M., Jenouvrier, S., Cornelissen, J.H. & Visser, M.E. (2017) Behavioural, ecological and evolutionary responses to extreme climatic events: challenges and directions. In. The Royal Society Van Oppen, M.J., Gates, R.D., Blackall, L.L., Cantin, N., Chakravarti, L.J., Chan, W.Y., Cormick, C., Crean, A., Damjanovic, K. & Epstein, H. (2017) Shifting paradigms in restoration of the world's coral reefs. Global change biology, 23, 3437-3448. Van Woesik, R. & Randall, C.J. (2017) Coral disease hotspots in the Caribbean. Ecosphere, 8, e01814. Vanderklift, M.A., Babcock, R.C., Boschetti, F., Haywood, M.D., Pillans, R.D. & Thomson, D.P. (2019) Declining abundance of coral reef fish in a World-Heritage-listed marine park. Scientific reports, 9, 1-8. Vanderklift, M.A., Babcock, R.C., Barnes, P., Cresswell, A.K., Feng, M., Haywood, M.D.E., Holmes, T.H., Lavery, P.S., Pillans, R.D., Smallwood, C.B., Thomson, D.P., Tucker, A.D., Waples, K. & Wilson, S.K. (In press) The oceanography and ecology of Ningaloo, a marine World Heritage Area. Oceanography & Marine Biology: An Annual Review, Vasseur, D.A., DeLong, J.P., Gilbert, B., Greig, H.S., Harley, C.D., McCann, K.S., Savage, V., Tunney, T.D. & O'Connor, M.I. (2014) Increased temperature variation poses a greater risk to species than climate warming. Proceedings of the Royal Society B: Biological Sciences, 281, 20132612. Verhoeven, G. (2012) Getting computer vision airborne: using structure from motion for accurate orthophoto production. RSPSoc archaeology special interest group meeting spring 2012 (ed by, pp. 4-6. Viechtbauer, W. (2005) Bias and efficiency of meta-analytic variance estimators in the random- effects model. Journal of Educational and Behavioral Statistics, 30, 261-293. Viechtbauer, W. (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw, 36, 1-48. Waller, M.A. (2001) Resilience in ecosystemic context: Evolution of the concept. American Journal of Orthopsychiatry, 71, 290-297. Watson, D.L., Harvey, E.S., Anderson, M.J. & Kendrick, G.A. (2005) A comparison of temperate reef fish assemblages recorded by three underwater stereo-video techniques. Marine Biology, 148, 415-425. Watson, M. & Ormond, R. (1994) Effect of an artisanal fishery on the fish and urchin populations of a Kenyan coral reef. Marine Ecology Progress Series, 115-129. Webster, F.J., Babcock, R.C., Van Keulen, M. & Loneragan, N.R. (2015) Macroalgae inhibits larval settlement and increases recruit mortality at Ningaloo Reef, Western Australia. PloS one, 10, e0124162. West, J. & Salm, R. (2003) Resistance and resilience to coral bleaching: implications for coral reef conservation. West, L., Stark, K., Murphy, J., Lyle, J. & Ochwada-Doyle, F. (2015) Survey of recreational fishing in New South Wales and the ACT, 2013/14. Westera, M.B. (2003) The effect of recreational fishing on targeted fishes and trophic structure, in a coral reef marine park. Edith Cowan University, Perth, Western Australia. Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J. & Reynolds, J.M. (2012) ‘Structure-from-Motion’photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314. Wilkinson, C. (2000) Status of coral reefs of the world: 2000. Wilkinson, C.R. (1999) Global and local threats to coral reef functioning and existence: review and predictions. Marine and Freshwater Research, 50, 867-878. Williams, G.J. & Graham, N.A. (2019) Rethinking coral reef functional futures. Functional Ecology, 33, 942-947. Wilson, S., Fulton, C., Depczynski, M., Holmes, T., Noble, M., Radford, B. & Tinkler, P. (2014) Seasonal changes in habitat structure underpin shifts in macroalgae-associated tropical fish communities. Marine biology, 161, 2597-2607.

189 Wilson, S.K., Graham, N.A.J., Pratchett, M.S., Jones, G.P. & Polunin, N.V.C. (2006) Multiple disturbances and the global degradation of coral reefs: are reef fishes at risk or resilient? Global Change Biology, 12, 2220-2234. Wilson, S.K., Graham, N.A.J., Holmes, T., MacNeil, M.A. & Ryan, N. (2018a) Visual versus video methods for estimating reef fish biomass. Ecological Indicators, 85, 146-152. Wilson, S.K., Robinson, J.P.W., Chong-Seng, K., Robinson, J. & Graham, N.A.J. (2019) Boom and bust of keystone structure on coral reefs. Coral Reefs, 38, 625-635. Wilson, S.K., Babcock, R.C., Fisher, R., Holmes, T.H., Moore, J.A.Y. & Thomson, D.P. (2012a) Relative and combined effects of habitat and fishing on reef fish communities across a limited fishing gradient at Ningaloo. Marine Environmental Research, 81, 1- 11. Wilson, S.K., Depczynski, M., Holmes, T.H., Noble, M.M., Radford, B.T., Tinkler, P. & Fulton, C.J. (2017) Climatic conditions and nursery habitat quality provide indicators of reef fish recruitment strength. Limnology and Oceanography, 62, 1868-1880. Wilson, S.K., Graham, N.A.J., Fisher, R., Robinson, J., Nash, K., Chong-Seng, K., Polunin, N.V.C., Aumeeruddy, R. & Quatre, R. (2012b) Effect of Macroalgal Expansion and Marine Protected Areas on Coral Recovery Following a Climatic Disturbance. Conservation Biology, 26, 995-1004. Wilson, S.K., Depcyznski, M., Fisher, R., Holmes, T.H., Noble, M.M., Radford, B.T., Rule, M., Shedrawi, G., Tinkler, P. & Fulton, C.J. (2018b) Climatic forcing and larval dispersal capabilities shape the replenishment of fishes and their habitat-forming biota on a tropical coral reef. Ecology and Evolution, 8, 1918-1928. Wittebolle, L., Marzorati, M., Clement, L., Balloi, A., Daffonchio, D., Heylen, K., De Vos, P., Verstraete, W. & Boon, N. (2009) Initial community evenness favours functionality under selective stress. Nature, 458, 623. Wood, S., Paris, C., Ridgwell, A. & Hendy, E. (2014) Modelling dispersal and connectivity of broadcast spawning corals at the global scale. Global Ecology and Biogeography, 23, 1- 11. Wood, S.N. (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73, 3-36. Woodley, J., Chornesky, E., Clifford, P., Jackson, J., Kaufman, L., Knowlton, N., Lang, J., Pearson, M., Porter, J. & Rooney, M. (1981) Hurricane Allen's impact on Jamaican coral reefs. Science, 214, 749-755. Wu, L., Wang, L., Min, L., Hou, W., Guo, Z., Zhao, J. & Li, N. (2018) Discrimination of Algal- Bloom Using Spaceborne SAR Observations of Great Lakes in China. Remote Sensing, 10, 767. Wu, P.P.-Y., Mengersen, K., McMahon, K., Kendrick, G.A., Chartrand, K., York, P.H., Rasheed, M.A. & Caley, M.J. (2017) Timing anthropogenic stressors to mitigate their impact on marine ecosystem resilience. Nature communications, 8, 1263. Yeung, A.C. & Richardson, J.S. (2018) Expanding resilience comparisons to address management needs: a response to Ingrisch and Bahn. Trends in ecology & evolution, 33, 647-649. Young, G., Dey, S., Rogers, A. & Exton, D. (2017) Cost and time-effective method for multi- scale measures of rugosity, fractal dimension, and vector dispersion from coral reef 3D models. PloS one, 12, e0175341. Zhang, X., Wan, H., Zwiers, F.W., Hegerl, G.C. & Min, S.K. (2013) Attributing intensification of precipitation extremes to human influence. Geophysical Research Letters, 40, 5252- 5257. Zupan, M., Fragkopoulou, E., Claudet, J., Erzini, K., Horta e Costa, B. & Gonçalves, E.J. (2018) Marine partially protected areas: drivers of ecological effectiveness. Frontiers in Ecology and the Environment, 16, 381-387. Zuur, A., Ieno, E.N., Walker, N., Saveliev, A.A. & Smith, G.M. (2009) Mixed effects models and extensions in ecology with R. Springer. Zuur, A.F., Ieno, E.N. & Elphick, C.S. (2010) A protocol for data exploration to avoid common statistical problems. Methods in ecology and evolution, 1, 3-14.

Chapter 2 Appendices

Appendix 2.1. Search terms used in Scopus

Title, abstract and keywords were searched for the following combinations of words and terms:

( coral* ) AND ( ( cyclone* OR hurricane* OR typhoon* OR storm* OR cyclonic OR wave* ) OR ( "heat wave*" OR heatwave* OR "temperature anomaly" OR "coral bleaching" OR bleaching OR enso OR "El Nino" ) OR ( flood* OR monsoon ) ) AND ( measure* OR change* OR impact* OR recovery OR disturbance* OR effect* OR loss* OR resistance OR persistence OR resilience ) AND NOT ( ( holocene* OR palaeo* OR paleo OR "trace metal*" OR mineralogy OR sclerochronology* OR stress AND band* OR "interglacial" OR "Sr/Ca" OR genetic* OR dredging ) ) )

The search was then further limited to the following fields: Agricultural and Biological Sciences Environmental Science Engineering Multidisciplinary Computer Science Mathematics Decision Sciences Undefined

191 Appendix 2.2. Meta-analysis effect size calculations

A log response ratio (e) (Hedges et al., 1999) was calculated for each study i as

푋̅푖,퐴+1 푐표푟푎푙푎푓푡푒푟+1 푒푖 = ln ( ) = ln(푅푅) = ln ( ) 푋̅푖,퐵+1 푐표푟푎푙푏푒푓표푟푒+1 where and 푋̅푖,퐴 and 푋̅푖,퐵 were the mean coral abundance after (A) and before (B) a given disturbance in study i. Therefore, a more negative 푒푖 implies a larger impact. A log response ratio was appropriate because it is independent of the actual unit of measurement across the different survey methods.

The variance of 푒푖 was also quantified (i.e. the within-study variance) as the sum of the variances associated with each mean in the log-ratio, given that sampling error plays an important role in introducing variability in the overall outcome of a meta-analysis: 휎2 휎2 푣 = 푖,퐴 + 푖,퐵 푖 ̅2 ̅2 (푛푖,퐴푋푖,퐴) (푛푖,퐵푋푖,퐵)

Here, 푣푖푗 is calculated from the standard deviation, 휎푖 , sample size, 푛푖, and mean coral abundance 푋̅푖 after (A) and before (B) a disturbance for study i.

Effect sizes were weighted by the inverse of the sum of the within- and among-study variances, 푤푖. The among-study variance was calculated using the metafor package (Viechtbauer, 2010) in the statistical program R (R Core Team, 2017). 1 푤푖 = 푣푖 This weighting minimized the influence of studies with low statistical power, and increased the influence of studies with high statistical power, meaning that each inside/ outside replicate did not contribute equally to the final pooled outcome. Weighted meta- analyses of this sort are considered to increase the precision and power of meta-analyses (Osenberg et al., 1999) and was an appropriate approach in the present study as there was a large distribution of sample sizes and variance associated with the inside/ outside comparison pairs.

The weighted cumulative impact, 퐸̅ , was obtained as

푛 ∑ 푖 푖=1 푤푖푗푒푖 1 퐸̅ = 푛 with associated variance, 푣̅̅̅̅ = 푛 . ∑ 푖 ∑ 푖 푖=1 푤푖 푖=1 푤푖

where 푤푖 and 푒푖 are defined above. The overall heterogeneity (푄푡) was calculated as

푛푖 2 푄푡 = ∑ 푤푖(푒푖 − 퐸̅ ) 푖=1 2 and its significance was tested against the 휒 distribution with 푛푖 − 1 degrees of freedom.

We used a weighted random-effects model to calculate 퐸̅푗 and 푄푡 using the metafor package in R through the call rma(푒푖, 푣푖, method = “REML”, data = ) following the suggestions in Viechtbauer (2005) that the REML, restricted maximum likelihood estimator for variance, strikes a good balance between unbiasedness and efficiency.

We ran weighted random mixed-effects meta-regression models, that is, linear models that examine the influence of one or more moderator variables on the outcomes (Viechtbauer, 2010), to determine the influence of disturbance type, and whether coral cover before disturbance was above or below 10% mediated the overall effect size 퐸̅푗. For a given level (L) of the factor, a weighted cumulative effect size was calculated as

푛퐿푗 ∑ 푤푖 푒푖 ̅ 푖=1 퐸퐿 = 푛퐿 ∑푖=1 푤푖

Where 푛퐿 is the number of comparison pairs in level, L, of the factor, and 푒푖 and 푤푖 are defined above. The heterogeneity of the model explained by the factor (푄푚) was calculated as

푛퐿푗 2 푄푚,푗 = ∑ 푤푖푗(퐸̅퐿,푗 − 퐸̅푗) 푖=1 2 The significance of 푄푚 was tested against the 휒 distribution with 푛퐿푗 − 1 degrees of freedom

We also considered coral cover before disturbance as a continuous predictor for e. There were likely correlations among studies in the dataset, resulting from the factors such as similar locations, authors, publications or survey methods and, while we acknowledge this, there were too many to model robustly.

193 Appendix 2.3. Classifying the shape of recovery trajectories

The following plots show the relative likelihood of each model fit to each study with the associated raw data and the most likely model (shown in blue) and second most likely model (shown in red) plotted over the top of the recovery points.

195

197

199

201

Figure App 2. 3.1 Number of studies classified to each recovery shape based on a) AIC b) BIC and c) AICc selection criteria. Black text shows the proportion of each shape, while the red text shows the mean relative likelihood of this model being the best fit relative to the other models.

203 Appendix 2.4. Distribution of predictor variables

Figure App 2. 4.1 shows the distribution of each of the variables detailed in Figure 2.2 as predictors for the direction and shape of recovery

Figure App 2. 4.1 Slope of linear models (with error bars showing standard error of the slope) plotted against a) the percentage cover of coral before disturbance, b) the percentage cover of coral following disturbance (at the start of the recovery trajectory) and c) the magnitude of the impact, or the effect size as calculated as the log response ratio. Colours indicate disturbance category. a) and b) also have panels showing the recovery speeds categorised into groups of less than and greater than 10% coral cover, before and after disturbance.

Chapter 4 Appendices

Appendix 4.1. Additional plots

Table App 4. 1.1 Software settings used to render orthophotos in Open Drone Map, ODM, and Agisoft Photoscan*

Process Settings ODM Settings Agisoft Photoscan Comments Accuracy = High Generic preselection Yes Photo Reference preselection = Yes Minimum Number of Features = 8000 alignment Reset current alignment = Yes Key point limit = 40,000 Tile point limit = 4000 Sparse point Default values selected by ODM cloud Build dense Quality = Medium Default values selected by ODM point cloud Depth filtering = Mild Depth maps quality = Medium Mesh Size = 1000000 Face count = High * = No longer Build mesh Mesh Solver Divide* = 11 Custom face count = 200,000 supported in Mesh OctTree Depth = 11 Interpolation = Enabled current version Point classes = All Depth maps quality = Medium Build tiled Face count = Low Default values selected by ODM model Custom face count = 4000 Tile size = 256 * = No longer DEM Terrain Type* = Complex Interpolation = Enabled Build DEM supported in NonForest Point classes = All current version Texture Default values selected by ODM Surface = Mesh Build Default values Blending mode = Mosaic orthophoto Hole filling = Yes

* More detail on rendering orthophotos can be found in Lechene et al. (2019) and Burns and Delparte (2017).

205

Table App 4. 1.2 Summary classified by year and genera of the tags lost versus relocated as a fraction of the total number of tags deployed (first two rows); the mortality status of tagged coral colonies (number of colonies as a fraction of the total number of colonies relocated) (subsequent rows) in May of 2017, 2018 and 2019; and the new tags deployed in 2018 (final row). In the second year the same colonies were surveyed as in the first year except for the new colonies that were added in 2018.

Year 1: 2017->2018 Year 2: 2018->2019 Acropora Platygyra Pocilloporida Acropora Platygyra Pocilloporida e e Tag and colony 48/54 22/23 13/16 59/63 23/23 7/7 relocated Colony and tag 6/54 1/23 3/16 4/63 0/23 0/7 lost No mortality 40/48 21/22 7/13 45/59 21/23 3/7 Partial 3/48 2/59 1/23 2/7 mortality Overgrown by 1/22 another coral Physical 1/59 damage Dead (100% 3/48 1/13 4/59 1/7 mortality) Dead and gone 2/48 5/13 7/59 1/23 1/7 (tag relocated, coral not present) New tags 23 2 deployed

Figure App 4. 1.1 Size frequency distributions of Acropora colonies examined.

207

Figure App 4. 1.2 The area (cm2) of tagged colonies at the start (x-axis) and end (y-axis) of monitoring years a) 1 and b) 2. Arithmetic mean radius (cm) as estimated from the 2D planar area of tagged colonies at the start and end of monitoring years c) 1 and d) 2. Points above the dotted line indicate growth has occurred.

Acropora

Stylophora

Platygyra

Figure App 4. 1.3 Example side profiles of the three genera demonstrating the potential shortcomings of two-dimensional planar area estimates

209

Figure App 4. 1.4. Example of how colonies were matched and measured between standard photos and orthophotos for the paired comparison of area and arithmetic mean radii: a) an orthophoto, b) a colony which was identified as a tagged colony, c) the same colony enlarged and d) the standard photo of the same colony.

Investigation of relative change in area

Methods We took the same approach as in Section 2.5.2 of the main manuscript to model the relative change in area, considering the same variables. However, instead of the change in radius as the response, we used the natural log response ratio of the area after one year relative to the area at the start of the year, i.e. 퐴푟푒푎푡+1 ∆푎푟푒푎= 푙표𝑔푒 ( ) 퐴푟푒푎푡

Results and Discussion

The best model according to AIC was ∆푎푟푒푎 ~ log(coral size) * water velocity + log(coral size) * method with coral ID nested within site as a random effect. This model supported the change in arithmetic mean radius model but also included the additional interaction between coral size and method.

The predicted ratio of change in area of Acropora colonies from the standard photos was significantly higher than from the orthophotos. The model further indicated that growth rate declined with increasing water velocity, though this relationship was stronger for the standard photos (Figure S2.1). Small colonies measured from the standard photos increased in area the most.

211 Table App 4. 1.3 Point estimates, standard error, degrees of freedom and p-values according to marginal t-tests for the top (according to AIC) linear mixed-effects models of the log response ratio of annual change in Area for Acropora (number of annual measurements = 546). All models tested included a random effect of coral ID nested in site.

MODEL: size + water velocity + size*method + size*water velocity Variable Estimate Std. Error df P-value mean centred intercept 0.431 0.039 389 <0.001 (log) coral size -0.743 0.025 131 <0.001 water velocity -0.005 0.002 20 0.061 method (standard photo) 1.383 0.104 389 <0.001 (log) coral size by -0.002 0.001 131 0.064 water velocity (log) coral size by 0.379 0.040 131 <0.001 method

Figure App 4. 1.5. Predictions of how Acropora growth rate (as the ratio of the change in area over a one-year period) changes with water velocity, size and survey method, according to the best linear mixed-effects model based on AIC (Table 1). Models had the log response ratio as the response variable, i.e. the y-axis shows the exponential of the response.

Chapter 5 Appendices

Appendix 5.1. Additional detail and information on the model

General

Coral growth and community development occur inside the simulated ‘environment’, which represents a section of a reef and the water column above it with flexible dimensions in x, y, z space, set to be 1 m3. The environment is divided into 1 cm3 voxels which are the lowest spatial unit upon which growth and light attenuation are governed. The voxel representation suits the modular architecture of corals, which results from the iteration of a single unit, the polyp (Lartaud et al., 2015). To eliminate edge effects, vertical boundaries of the simulated environment are wrapped, which is equivalent to assuming the environment is replicated on all sides, while the bottom of the environment, (z = 1 plane representing the seafloor) and the top of the environment, (z = 100 plane, representing the water surface) are limits of growth. The model is implemented using R (R Core Team, 2017) as we hoped this would be the most user- friendly platform for ecologists.

Coralcraft captures major ecological processes: growth, reproduction and mortality at one of three hierarchical levels: voxels, coral colonies or the coral community. In each time step key variables are calculated and tracked for each of these levels. Light is absorbed at the voxel level, allowing a coral-occupied voxel to acquire resources which are then shared at the colony level. Partial mortality, where sections of a coral colony may die (Nugues & Roberts, 2003), is represented by the death of individual voxels in a colony. These dead voxels remain in the simulated environment unless the whole colony dies in which case the colony is removed from the environment. The coral community emerges from interactions at the two lower levels (the voxel and the colony level).

Figure App 5. 1.1 summarises the progression of the model. Coral recruits are specified as one of the five possible morphologies prior to entering the simulated environment where they are randomly placed at the base of the environment in the z = 1 plane and begin capturing light energy and growing. Growth is constrained to the 3D von Neumann neighbourhood. Each voxel may grow into empty voxels in the

213 neighbourhood surrounding it, if they satisfy its morphology conditions, but will not grow into voxels already occupied with coral.

Figure App 5. 1.1 A simplified schematic representation of the model. The red cubes represent 1 cm3 voxels occupied by coral. At (a) initial recruits or new recruits from reproduction are parameterised with one of five morphologies. At (b) recruits enter the model domain and the light environment (c) where light is highest at the surface and attenuates with depth as z decreases. In (d) the amount of energy of a colony is calculated together with the availability of empty neighbouring voxels. If a colony has sufficient energy for growth and space is available, an additional voxel is added to the colony (e). The maximum number of voxels that a colony may grow in a time step is capped at six. Over multiple time-steps 3D growth is achieved (f).

The time step can be varied but is set at one week for the present study. This was chosen to meet empirical growth rate measurements of corals (0.005 – 0.07 m year-1) (Pratchett et al., 2015) so that the simulated coral grow approximately 0.005 – 0.07 m per 52 time steps (1 year).

Growth forms

The model code specifies the five possible morphologies and defines a dynamic 3D structure by specifying which voxels are occupied for any given size of a colony to a maximum radius of 49 voxels. The maximum number of voxels that a colony may grow in a time step is capped at six. This is somewhat arbitrary but was required to stop the corals growing exponentially faster as colony size increased, which was not realistic. The six-voxel growth cap aims to capture the costs of being larger (e.g. moving resources) and allowed growth rates that were comparable to those in the literature.

Light and shading

Light governs primary productivity and thus the basic structure of most shallow-water ecosystems. While seawater is transparent to light it is subject to both refraction and attenuation and light intensity decreases exponentially as it propagates (Duxbury, 2018). This exponential decrease can be estimated with a form of Beer–Lambert–Bouguer law

−풌푫 푰푫 = 푰ퟎ풆 Eq. 5.1.1 where 퐼퐷 is the intensity of light at depth D, 퐼0 is the intensity of light at the surface and k is an extinction coefficient relating to the water type (Storlazzi et al., 2015; Duxbury, 2018). Photosynthetically active radiation (PAR) is solar radiation in the visible light spectrum from 400 to 700 nm that organisms can utilize for photosynthesis (Storlazzi et al., 2015). Different wavelengths of light are attenuated at different rates, e.g. Jerlov (1951) estimated loss of light per meter of seawater in coastal areas to range from 28% for some blue green light (~400 nm) to 74% for red light (700 nm). In general, blue light penetrates the farthest in waters surrounding coral reefs while red light is absorbed the most quickly. The consequence of this is that profiles of PAR with depth will not follow the same exponential profile as described by Eq. 5.1.1. For the present version of Coralcraft we make several simplifying assumptions, assuming a constant amount of light enters the water surface and that 28% of light is lost per metre on a simulated reef section (Jerlov, 1951; Dubinsky & Falkowski, 2011). Variation in the angle of incidence due to the movement of the sun throughout a day is not considered. This equates to k = 0.329 in Eq. 5.1.1 and we program this with 99.67% of light transitioning to the 1 cm layer below. This estimate could be varied by a user to account for different water clarity (Storlazzi et al., 2015). This results in coral voxels occupying a higher z having more light available.

In order to create shading in the simulated reef, of the light that passes through a voxel, 50% is assumed to travel straight down to the voxel immediately below and the remaining 50% is spread into the voxels below the voxel’s horizontal neighbours. The effects of this are evident in areas adjacent to an occupied voxel. An occupied voxel absorbs all light entering it and thus shades the voxel below (Figure 5.2, main text). Coral voxels that are shaded may die as a result of not receiving enough light.

215 Recruitment

Corals reproduce in a phenomenon known as ‘mass spawning,’ where eggs and sperm are released into the water column annually in near-unison by a large majority of corals on a reef (Babcock et al., 1986). The resulting coral larvae will eventually attach and begin to grow on another part of the reef 4-7 days after mass-spawning. Pre and post spawning processes are complex (Doropoulos et al., 2016), and are highly simplified in Coralcraft. Recruitment is simulated in the model as the random occupation of voxels in the z=1 plane (the seafloor) with five (user-specified parameter) new corals every 52 timesteps (annually), based on some general estimates from the literature (Stobart et al., 2005; Graham et al., 2015). Recruits are successful if they are allocated to an empty voxel.

Colony shape factor

Madin & Connolly (2006) propose a colony shape factor (CSF), a dimensionless index of mechanical vulnerability to a horizontal force, to capture coral vulnerability to hydrodynamic disturbance and we calculate this for our simulated corals. There are other models (e.g. Massel & Done, 1993; Storlazzi et al., 2005) but the CSF was well suited to the requirements of Coralcraft. The formula considers both the side profile of the colony and its ‘basal attachment’ to the seafloor such that

ퟏퟔ 풉 푪푺푭 = ퟐ ∫ 풚 풘(풚) 풅풚 Eq. 5.1.2 풅|| 풅−흅 풚=ퟎ where 푑|| and 푑−are the two basal attachment diameters of a colony (here measured to be the number of voxels parallel and perpendicular, respectively, to the wave motion), and the integral is the second moment of area of the colony outline perpendicular to wave motion, where 푤(푦) is the projected width, y is the distance above the substrate and h is the height of the colony. If a hydrodynamic disturbance occurs in a given time step, the CSF of all coral colonies in the environment is calculated using , and if the disturbance intensity exceeds the mechanical threshold of the CSF of a colony, the colony is removed from the environment. The different morphologies have different basal attachment areas and profile areas which directly influences their CSF (Figure 5.3, main text). The CSF varies not only between morphologies but also for different sizes of simulated corals (Figure 5.3), which is in agreement with the empirical work of (Madin et al., 2014).

Table App 5. 1.1 Variables tracked in each time-step, relating to voxel, colony and community

Hierarchical Variable Meaning level Voxel Light uptake How much light a coral voxel absorbs in a given time step. Neighbours Six possible neighbours in the 3D Von Neumann neighbourhood are tracked for whether they are empty and available for growth or occupied by coral. Dead/ alive An occupied voxel may be alive or dead, dying if it does not acquire enough light energy in a time step. Colony Resources The amount of resources, or energy, a colony has for growth. Light absorption increases the resources of a colony, while growth and general maintenance decrease resources. Number of voxels Growth is limited to a maximum of six voxels per colony per time step, in order to growing per stop exponential increase in growth with increase in colony size. colony Community Coral colony Count of the total colonies of each morphology in the environment, i.e. per m2. morphology density Percentage cover The number of voxels of each morphology (including voxels that have died) when of each surveyed from above as a two-dimensional planar view proportion of coral voxels morphology to total area of the xy-plane of the environment. Occupied volume The total number of voxels occupied by each coral morphology (including voxels of each that have died). morphology Linear rugosity Mean increase in distance required to traverse over the top of the coral communities relative to a flat surface without corals Morphological Simpson’s diversity index calculated from the amount of each morphology and diversity the relative abundance according to number of colonies, percentage cover and volume.

Morphological diversity calculation

Simpson’s Diversity Index (Simpson, 1949) was used to calculate morphological diversity incorporating both the number of morphologies present and the relative abundance of each morphology (Eq. 5.1.3), with abundance of each morphology taken to be either the number of colonies in the simulated area (density), the percentage cover or the volume: ‘diversity by density’, ‘diversity by cover’ and ‘diversity by volume’.

푛 2 푆𝑖푚푝푠표푛′푠 퐷𝑖푣푒푟푠𝑖푡푦 퐼푛푑푒푥 = 1 − ∑ ( ) Eq. 5.1.3 푁 where n is the number of individuals displaying a specific morphology and N is the total number of individuals (colonies, or voxels in the case of percentage cover and volume). This index scales from 0 to 0.8, where 0.8 is the highest possible diversity, because we have at most five morphological types.

User-specified parameters

Various parameters can be specified according to the user’s study system and questions of interest. These include factors relating to the amount of time the model is run for and the number of simulations, the morphologies included in a given simulation, resources, background mortality, recruitment and hydrodynamic disturbance parameters (Table App 5. 1.1).

217 Table App 5. 1.1. User-specified parameters, meaning and values used in the present study

Parameter Syntax Meaning Values used in present study Simulations runs Number of times a simulation is run with identical 10 parameters to calculate mean and standard deviation of model outputs. Time steps timesteps Total number of timesteps in a given simulation. 1040 (20 years) 3

Environment ws x, y, z distances specifying the number of voxels along 1 m size each axis of the cubic environment. Light extinction k Dimensionless value used in Beer’s law to estimate 0.329 coefficient light at depth. Number of initial n.initial.colo Number and type of single voxel colonies placed in the 10, two of each Simulation colonies nies environment at the start of a simulation. morphology Initial colony init.locations Location of initial colonies in the z = 1 plane. random locations Morphologies -- Selection of the five morphologies chosen for a given all five included simulation. Initial resources start.res Energy allocated to new corals entering the 1

environment (via recruitment). Growth resource.to. Colony resources required to grow 1 voxel (a lower 1 volume value equates to a faster growth rate). Maintenance maint Energy used per voxel for maintenance in a time step. 0.1

Resources Maximum colony res.cap Maximum amount of resources a colony can 6 resources accumulate.

Background background. Probability of background mortality of a colony in a 0.001 mortality mort time step. Background background. Probability of background mortality of an encrusting 0.005 mortality mort colony in a time step. Mortality encrusting Recruitment spawn.freq Frequency (in time steps) of recruitment. 52 frequency

Number of nnewrecruits The number of coral recruits that enter the 5 recruits environment when recruitment is set to occur. Can be random or fixed. Recruit locations -- Random location in the z = 1 plane. Not all recruits will random ‘establish’ and will only be successful if they are

Recruitment randomly allocated to an empty voxel. Morphologies of randomrecru May be external (random), local (a function of the live external recruit its volume of each morphology), or a combination. (random) Disturbance dist.freq The frequency of disturbance (in time steps). infrequent=260 frequency frequent=26

Disturbance disturbance.i The intensity of a disturbance in relation to a coral low=20 intensity ntensity colonies shape factor (CSF). A smaller number high=1.5 equates to a more intense disturbance. See Figure 6.3.

Random/fixed randomdist Disturbances be fixed to occur exactly at set both trialled Disturbance frequencies and intensity levels or in a probability distribution about the specified frequency and intensity.

Linear rugosity

Linear rugosity is calculated as the percentage increase in the distance required to cover a fixed flat distance (1 m) when the community of corals must be traversed (Figure App 5. 1.1). This distance was calculated for every x integer in the simulated environment and the mean is used.

Figure App 5. 1.1 Schematics illustrating the calculation of linear rugosity from the model output. a) shows a cross section of b) with both showing the length of line (dotted) required to tranverse this section of the community, as compared to the dashed line which would be length if there were no corals present. This is calculated for all values of x from 1 to 100 and the mean value is taken. The linear rugosity index is the ratio of the dotted to the dashed line lengths.

219 Appendix 5.2. Additional plots

Figure App 5. 2.1 A randomly selected replicate of the community after 20 years in the no disturbance (NL-NH) scenario showing coral voxels coloured by a) morphology, b) light absorption and c) dead and live voxels.

Figure App 5. 2.2 Temporal dynamics of three simulations (out of 50) from the random IL-IH scenario. Note variation arising from the random nature of both the frequency and the intensity of the disturbances.

221

Appendix 5.3. All scenario outputs – fixed

223 Appendix 5.4. All scenario outputs - random

Appendix 5.5. Additional morphologies for future exploration

Figure App 5. 5.1 Additional coral morphologies designed for future studies.

225

Chapter 6 Appendices

Appendix 6.1. Additional information on individual reserves and rezoning

Maps of individual reserves can be found in CALM (1989), pgs. 55, for the 1987-2005 zoning and (CALM, 2005), pgs. 89-96, for the 2005-current zoning. Table App 6. 1.1 details individual reserves, their year of establishment, size, and the regulations on shore-based fishing on the coastal edges of the reserves. As well, the number of inside outside comparisons and the total number of surveys are given for each reserve.

Table App 6. 1.1 Features of historical and current reserves in the Ningaloo Marine Park

Total Total Year Size Shore number number Reserve Management area of Establishment (ha) fishing comparison of pairs samples 3 Mile Ningaloo Marine Park 2005 395 N 0 0 Bateman Ningaloo Marine Park 2005 1111 N 1 12 Bundegi Ningaloo Marine Park 1987/2005 696 N 3 rezoned 24 Cape Farquhar Ningaloo Marine Park 2005 5326 N 3 31 Cloates/ Dugong Ningaloo Marine Park 1987/2005 44752 P 17 268 rezoned

Gnaraloo Bay Ningaloo Marine Park 2005 1021 N 2 39 Jurabi Ningaloo Marine Park 2005 754 Y 9 78 Lakeside Ningaloo Marine Park 2005 8 N 0 0

(CURRENT) Lighthouse Bay Ningaloo Marine Park 2005 763 P 9 136

Mandu Ningaloo Marine Park 1987/2004 1349 N 12 110 rezoned Mangrove Ningaloo Marine Park 1987/2005 1135 N 27 429 rezoned Maud rezoned Ningaloo Marine Park 1987/2005 2151 P 10 133 Murat Ningaloo Marine Park 2005 490 Y 0 0 Muiron Islands Marine North Muiron 2005 828 N 4 66

Management Size

PRESENT MANAGEMENT PRESENT

– Osprey Ningaloo Marine Park 1987/2005 9513 P 7 54 rezoned

2005 2005 Pelican Ningaloo Marine Park 1987/2006 10864 P 28 454 rezoned Muiron Islands Marine South Muiron 2005 784 N 4 54 Management Size Muiron Islands Marine Sunday Island 2005 317 N 0 0 Management Size Tantabiddi Ningaloo Marine Park 2005 50 N 8 98 Turtles Ningaloo Marine Park 2005 2461 N 0 0 Winderabandi Ningaloo Marine Park 2005 5526 Y 7 52

Bundegi Ningaloo Marine Park 1987 297 N 20 333 Cloates Ningaloo Marine Park 1987 6257 N 16 186

Dugong Ningaloo Marine Park 1987 8852 N 0 0 (INITIAL)

Mandu Ningaloo Marine Park 1987 1163 N 50 1001

2005 2005

– Mangrove Ningaloo Marine Park 1987 403 N 2 14 Maud Ningaloo Marine Park 1987 1806 N 36 652 1987 1987 Osprey Ningaloo Marine Park 1987 1756 N 44 701 Pelican Ningaloo Marine Park 1987 908 N 7 66 MANAGEMENT 3 Mile Ningaloo Marine Park 2005 395 N 0 0

227 The rezoning of the eight reserves in 2005, with the addition of new reserves and the expansion of the existing reserves required that data be clearly classified to account for this. Figure App 6. 1.1 shows an example of how samples were referenced based on their spatial location to the initial or current reserves and the time period of sampling.

Figure App 6. 1.1 Example classification of ‘initial’ and ‘current’ zoning. The boundaries of the initial 1987 – 2005 reserves, Cloates and Dugong, are indicated by a green outline, while the current reserve (2005 – 2017) is shown in black which combined the two initial reserves into one larger reserve, ‘Cloates/Dugong rezoned’ (also see Table 6.1). Pre 2005, surveys were classified as inside/outside based on their spatial relation to initial reserves (green outline). Post 2005, surveys were classified as inside/outside based on their spatial relation to the current reserves (black). If an inside reserve is within the initial boundaries, but surveyed post 2005, it was referenced as initial as shown in c). If a sample was collected at a site within the new area of the rezoned reserve post the rezoning, it was referenced as current as seen in d).

Appendix 6.2. Data summary

Suitable data were identified through searches on Google Scholar and research databases compiled by the Department of Biodiversity, Conservation and Attractions. In addition, relevant researchers from universities, research institutions, industry and citizen science programs operating in Western Australia were contacted to source unpublished data. Nine major custodians contributed data to this study.

229 Table App 6. 2.1 summarises the survey methods used by each custodian, the number of samples collected, the temporal span of data, the reserves surveyed and directs to further reading for more information. Figure App 6. 2.1 graphically illustrates the important data constraints.

Table App 6. 2.1 Summary of data contributions Total Maximum numbe More temporal Data custodian r of Reserves surveyed Main survey methods information span of survey available: surveys s Australian 183 Bundegi, Bundegi rezoned, 1993 -2014 Underwater Visual (Depczynski Institute of Cloates, Cloates/Dugong Census : et al., 2015) Marine Science – rezoned, Mandu, Mangrove 50 x 5 m Woodside Energy rezoned, Maud, North (AIMS-Woodside) Muiron, Osprey, Pelican rezoned, Tantabiddi Ben Fitzpatrick 345 Mandu, Osprey 2006 - Baited Remote (Fitzpatrick et 2007 Underwater stereo-Video al., 2015) Commonwealth 18 Bundegi, Bundegi rezoned, 2006 - Underwater Visual (Babcock et Industrial and Cape Farquhar, Cloates, 2017 Census, 4 main sizes: al., 2008) Scientific Cloates/Dugong rezoned, 1) Either a singular, or Research Gnaraloo Bay, Jurabi, three, 25 × 5 m transects Organisation Lighthouse Bay, Mandu, per site. (CSIRO) Mandu rezoned, Mangrove, 2) 30 × 5 m transects Mangrove rezoned, Maud, 3) 50 × 5 m transects North Muiron, Osprey, 4) 100 × 10 m transects at Osprey rezoned, Pelican, a site Pelican rezoned, South Muiron Western 1237 Bundegi , Cape Farquhar, 2010 - Diver Operated stereo- (Wilson et al., Australian Cloates, Cloates/Dugong 2016 Video: six replicate 50 x 5 2012a; Department of rezoned, Jurabi, Lighthouse m belt transects per site; Holmes et al., Biodiversity, Bay, Mandu, Mangrove or nine replicate 30 x 5 m 2013; Wilson Conservation and rezoned, Maud, Maud belt transects per site et al., 2018a) Attractions rezoned, North Muiron, (DBCA) Osprey, Pelican, Pelican rezoned, Tantabiddi, Winderabandi Mark Westera 257 Mandu, Maud, Osprey 1999 - Baited Remote (Westera, 2000 Underwater stereo- 2003) Video, 30 minute deployments at 12 replicate locations in each zone, Underwater visual census using snorkel, 250 x 10 m transects Reef Life Survey 291 Bateman, Bundegi, Cloates, 2010 - Underwater Visual (see (RLS) Maud, Maud rezoned, 2017 Census, http://reeflife Pelican rezoned 50 x 5 m transects survey.com/fi les/2008/09/r ils-reef- monitoring- procedures.p df). Tony Ayling 60 Osprey 1987 (Ayling & Ayling, 1987) The University of 325 Cloates, Cloates/Dugong 2014 - Baited Remote (McLean et Western Australia rezoned, Mandu rezoned, 2015 Underwater stereo- al., 2016) Mangrove rezoned, Osprey, Video, generally 60 Osprey rezoned, Pelican minute deployments rezoned, Winderabandi Joint data 587 Cloates/Dugong rezoned, 2013 - Underwater Visual (Fulton et al. custodians WA Mandu, 2015 Census, 4 main sizes: 2014) Department of Mangrove rezoned, Maud, 1) Either a singular, or Biodiversity, Maud rezoned, Pelican, three, 25 × 5 m transects Conservation and Pelican rezoned, per site. Attractions, 2) 30 × 5 m transects Australian 3) 50 × 5 m transects Institute of 4) 100 × 10 m transects at Marine Science, a site Australian National University

231 In most surveys biomass had been calculated by the respective data custodians from the estimated or measured fish length. In these cases, the provided values were used (with the reasoning that different survey methods may warrant slightly different biomass calculations). In cases where length data was available but biomass had not been calculated the fish counts and length estimates were converted to biomass (kg) using constants and formulas from Fishbase (www.fishbase.org). 퐵𝑖표푚푎푠푠 = 푒ln(푎)+푏 ×ln (퐿) where L is the estimated total length of the fish and a and b are constants for the species or family in question. In cases where data provided fork length measurements, these were converted to total length using the formula from FishBase: 푇퐿 = 푐 + 푑 × 퐹퐿 where TL is estimated total fish length, FL is the measured fork length from BRUV or DOV video and c and d are parameters specific to the fish species in question.

More data was available for some NTMRs and years, with increased sampling after 2005 (Figure App 6. 2.1).

Figure App 6. 2.1 Temporal and spatial distribution of samples: a) number of samples by year, with shade indicating the type of survey method (BRUV, Baited Remote Underwater stereo-Video, DOV, Diver Operated stereo-Video and UVC, Underwater Visual Census); b) distribution of samples inside and outside reserves for each of the four major habitats; c) distribution of samples inside and outside for each reserve under both the initial and current zoning.

Appendix 6.3. Fish groups

Analyses were conducted at the family/subfamily level and species level. Investigation of patterns at the family/subfamily level allowed the inclusion of data from targeted but rare species.

Table App 6. 3.1 Total count of fish groups across all data

Taxa Total count across synthesised data LETHRINIDAE 10307 L. nebulosus 4183 L. atkinsoni 4765 EPINEPHELINAE 4012 E. rivulatus 1119 SCARINAE 44931

Genera included in the family/ sub-family analysis that were sampled in the synthesised data are summarised below.

233

Lethrinidae Epinephelinae * Scarinae • Gnathodentex aureolineatus • Aethaloperca rogaa • Calotomus carolinus • Gymnocranius euanus • Anyperodon leucogrammicus • Calotomus spinidens • Gymnocranius grandoculis • Cephalopholis spp. • Cetoscarus ocellatus • Gymnocranius griseus • Cephalopholis argus • Chlorurus bleekeri • Gymnocranius spp. • Cephalopholis boenak • Chlorurus microrhinos • Lethrinus amboinensis • Cephalopholis cyanostigma • Chlorurus sordidus • Lethrinus atkinsoni • Cephalopholis formosa • Hipposcarus longiceps • Lethrinus genivittatus • Cephalopholis miniata • Leptoscarus vaigiensis • Lethrinus harak • Cephalopholis sexmaculata • Scarus chameleon • Lethrinus laticaudis • Cephalopholis sonnerati • Scarus dimidiatus • Lethrinus lentjan • Cromileptes altivelis • Scarus flavipectoralis • Lethrinus microdon • Epinephelus spp. • Scarus frenatus • Lethrinus miniatus • Epinephelus areolatus • Scarus ghobban • Lethrinus nebulosus • Epinephelus bilobatus • Scarus globiceps • Lethrinus obsoletus • Epinephelus coeruleopunctatus • Scarus niger • Lethrinus olivaceus • Epinephelus coioides • Scarus oviceps • Lethrinus ravus • Epinephelus corallicola • Scarus prasiognathos • Lethrinus rubrioperculatus • Epinephelus fasciatus • Scarus psittacus • Lethrinus semicinctus • Epinephelus fuscoguttatus • Scarus rivulatus • Lethrinus variegatus • Epinephelus hexagonatus • Scarus rubroviolaceus • Lethrinus spp. • Epinephelus lanceolatus • Scarus schlegeli • Monotaxis grandoculis • Epinephelus macrospilos • Scarus spp. • Epinephelus maculatus • Epinephelus malabaricus • Epinephelus melanostigma • Epinephelus merra • Epinephelus polyphekadion • Epinephelus quoyanus • Epinephelus rivulatus • Epinephelus tauvina • Epinephelus tukula • Plectropomus leopardus • Plectropomus maculatus • Plectropomus spp. • Variola albimarginata • Variola louti

* Note that there is current discussion of whether Epinephelinae may be better classified as its own family, Epinephelidae (Ma & Craig, 2018), however we have chosen to name it as its subfamily here.

Appendix 6.4. Formulas used for calculating effect sizes

For each inside/ outside comparison pair a log response ratio (e) (Hedges et al., 1999) was calculated as the ratio of the mean abundance/ biomass inside to outside a reserve for comparison pair i and fish group j as

푋̅푖푗,퐼 푒푖푗 = ln ( ) 푋̅푖푗,푂

where and 푋̅푖푗,퐼 and 푋̅푖푗,푂 are the mean abundance or biomass inside (I) and outside (O) a reserve. Therefore, a positive 푒푖푗 implies a greater fish abundance/ biomass inside the reserves than outside. A log response ratio was appropriate because it is independent of the actual unit of measurement across the different survey methods.

The variance of 푒푖푗 was also quantified (i.e. the within-study variance, where a study is a comparison pair), given that sampling error plays an important role in introducing variability in the overall outcome of a meta-analysis: 휎2 휎2 푣 = 푖푗,퐼 + 푖푗,푂 푖푗 ̅2 ̅2 (푛푖푗,퐼푋푖푗,퐼) (푛푖푗,푂푋푖푗,푂) ̅ Here, 푣푖푗 is calculated from the standard deviation, 휎푖푗 , sample size, 푛푖푗, and mean 푋푖푗 for inside (I) and outside (O) for comparison pair i and fish group j.

Effect sizes were weighted by the inverse of the sum of the within- and among-study variances, 푤푖푗,. The within-study variance (where a study is a comparison pair) was the sum of the variances associated with each mean in the log-ratio. The among-study variance was calculated using the metafor package (Viechtbauer, 2010) in the statistical program R (R Core Team, 2017). 1 푤푖푗 = 푣푖푗 This weighting minimized the influence of studies with low statistical power, and increased the influence of studies with high statistical power, meaning that each inside/ outside replicate did not contribute equally to the final pooled outcome. Weighted meta- analyses of this sort are considered to increase the precision and power of meta-analyses (Osenberg et al., 1999) and was an appropriate approach in the present study as there was a large distribution of sample sizes and variance associated with the inside/ outside comparison pairs.

235 The weighted cumulative effect size for fish group j, 퐸̅푗, was obtained as

푛 ∑ 푖푗 푖=1 푤푖푗푒푖푗 1 퐸̅푗 = 푛 with associated variance, 푣̅푗 = 푛 . ∑ 푖푗 ∑ 푖푗 푖=1 푤푖푗 푖=1 푤푖푗

where 푤푖푗 and 푒푖푗 are defined above. The overall heterogeneity (푄푡) for fish group j was calculated as

푛푖푗 2 푄푡,푗 = ∑ 푤푖푗(푒푖푗 − 퐸̅푗) 푖=1 2 and its significance was tested against the 휒 distribution with 푛푖푗 − 1 degrees of freedom.

We used a random-effects model to calculate 퐸̅푗 and 푄푡,푗 using the metafor package in R through the call rma(푒푖푗, 푣푖푗, method = “REML”, data = ) following the suggestions in Viechtbauer (2005) that the REML, restricted maximum likelihood estimator for variance, strikes a good balance between unbiasedness and efficiency.

We ran mixed-effects categorical analyses and meta-regression to examine how the seven additional variables (Table 6.1, main text) mediated the overall effect size 퐸̅푗. For a given level (L) of the factor, a weighted cumulative effect size was calculated as

푛퐿푗 ∑ 푤푖푗 푒푖푗 퐸̅ = 푖=1 퐿,푗 푛퐿푗 ∑푖=1 푤푖푗

Where 푛퐿푗 is the number of comparison pairs in level, L, of the factor, and 푒푖푗 and 푤푖푗 are defined above. The heterogeneity of the model explained by the factor (푄푚) was calculated as

푛퐿푗 2 푄푚,푗 = ∑ 푤푖푗(퐸̅퐿,푗 − 퐸̅푗) 푖=1 2 The significance of 푄푚 was tested against the 휒 distribution with 푛퐿푗 − 1 degrees of freedom

Appendix 6.5. Sensitivity analysis for case of one-armed zero events

Cheng et al. (2016) define two scenarios relating to zero events: zero-event and double- zero-event. In the present study there were both double-zero-events (a zero mean abundance/biomass for both inside and outside) and zero-events (zero mean abundance/biomass for either the inside or the outside), summarised in Table App 6. 5.1. In both cases, lnR cannot be computed due to division by zero and/or logarithm of zero. The former case are often removed from meta-analyses, while the latter is often handled via the addition of a small constant (Bradburn et al., 2007; Spittal et al., 2015). The total number of comparisons (n) used in the final calculated of the weighted average effect size is given.

In the present study the species examined can be encountered infrequently on a survey. As well, one of the key species, Lethrinus nebulosus, is a schooling species which, when it occurs, can be present in larger numbers. Consequently, there are many zero counts in the data which needed to be explicitly considered.

Table App 6. 5.1 Summary of total number of comparisons and zero events

Final sample size Total number of Double-zero (n) in calculation Fish Group possible Zero-events events of mean weighted comparisons effect size

LETHRINIDAE 305 4 151 301 L. nebulosus 305 48 162 257 L. atkinsoni 305 25 101 280 EPINEPHELINAE 305 28 110 277 E. rivulatus 305 138 210 167 ABUNDANCE SCARINAE 305 44 46 261 LETHRINIDAE 268 4 49 264 L. nebulosus 268 45 143 223 L. atkinsoni 268 21 86 247 EPINEPHELINAE 268 15 87 253

BIOMASS E. rivulatus 268 112 180 156 SCARINAE 268 45 48 223

Double-zero-events were removed from analysis. For the remaining data a constant of 0.5 fish (half the smallest unit in the abundance analysis) was added to both the inside and outside mean counts, while for biomass a constant of 100 grams was added to both the inside and outside mean biomass.

Given there is no clear protocol for the size of the constant used to deal with zeros in a lnR meta-analysis, we conducted a sensitivity analysis on the value of the constant. We

237 ran the sensitivity analysis for the abundance data for the six taxa examined. We tested constant values, c, ranging from c = 0.01 to c = 1 in 0.01 increments.

Figure App 6. 5.1 Sensitivity analysis for the size of the constant added to inside/outside mean abundance showing the resulting transformed mean weighted effect size, exponential of 푬̅, and 95% confidence interval (CI) for values of constant c ranging from 0.01 to 1 in 0.01 increments. a) Lethrinidae, b) L. nebulosus, c) L. atkinsoni, d) Epinephelinae, e) E. rivulatus, f) Scarinae. The mean effect sizes are considered significant when the confidence intervals do not include one.

It is clear that the size of the constant used influenced the mean overall effect size. For example, the magnitude of 퐸̅ for L. nebulosus total abundance varied from 퐸̅ = 0.73 (0.54) when c = 0.01 to 퐸̅ = 0.28 (0.08) when c = 1. However, while the constant size impacted the magnitude of the effect size, it did not influence the significance, except for E. rivulatus, which had the highest count of zero-events, and transitioned from marginally negative to not significantly different from one. From this analysis we decided a constant of 0.5 would be an adequate, and conservative addition for the calculation of lnR in this analysis. Given the high levels of inherent variability expected in fish count data (Samoilys et al., 1995; Cappo & Brown, 1996) and additional variation from uncontrolled variables, even with addition of a constant, overall differences in abundance would have to be consistent in order to observe statistical significance. Nonetheless, we urge caution in the interpretation of the magnitude of the overall effect.

Appendix 6.6. Meta-analysis statistics

Table App 6. 6.1 Total heterogeneity statistics

ABUNDANCE BIOMASS Fish group QT df P QT df P LETHRINIDAE 2002.57 300 <0.001 2318.26 263 <0.001 L. nebulosus 1971.07 256 <0.001 2886.99 222 <0.001 L. atkinsoni 1739.71 279 <0.001 1928.65 246 <0.001 EPINEPHELINAE 1125.65 276 <0.001 1307.49 252 <0.001 E. rivulatus 477.33 166 <0.001 590.76 155 <0.001 SCARINAE 1701.09 260 <0.001 1224.57 222 <0.001

Table App 6. 6.2 and Table App 6. 6.3 summarise the results of weighted mixed-effects meta-analyses for all seven variables and for reserve identity modelled individually. Figure App 6. 6.1 shows the predicted effect size for the cases where the moderator reserve identity explained a significant amount of heterogeneity for habitat so that it can be directly compared to the overall effect sizes in Figure 6.2 (main text).

239 Table App 6. 6.2 Mixed-effects models heterogeneity statistics for abundance data

ABUNDANCE Model heterogeneity* Residual heterogeneity Fish group Qm df P Qe df P <0.00 LETHRINIDAE 39.46 3 1 1622.38 297 <0.001

<0.00 L. nebulosus 32.51 3 1 1574.07 253 <0.001 <0.00 L. atkinsoni 14.55 3 1 1614.93 276 <0.001 HABITAT EPINEPHELINAE 0.31 3 0.96 1117.33 273 <0.001 E. rivulatus 5.39 3 0.15 467.33 163 <0.001 SCARINAE 6 3 0.11 1589.43 257 <0.001 LETHRINIDAE 4.09 2 0.13 1975.79 298 <0.001 L. nebulosus 0.71 2 0.7 1946.56 254 <0.001 L. atkinsoni 7.85 2 0.02 1676.16 277 <0.001 EPINEPHELINAE 4.35 2 0.11 1094.35 274 <0.001

METHOD E. rivulatus 0.98 2 0.61 474.68 164 <0.001 SCARINAE 0.01 2 0.99 1633.03 258 <0.001 LETHRINIDAE 3.8 1 0.05 1959.06 299 <0.001

L. nebulosus 0.01 1 0.94 1970.56 255 <0.001

<0.00 L. atkinsoni 8.11 1 1 1688.35 278 <0.001 SIZE EPINEPHELINAE 1.58 1 0.21 1112.23 275 <0.001

RESERVE E. rivulatus 0.47 1 0.49 477.08 165 <0.001 SCARINAE 0.02 1 0.88 1701.09 259 <0.001

LETHRINIDAE 0 1 0.95 2001.07 299 <0.001 L. nebulosus 0.92 1 0.34 1949.89 255 <0.001 L. atkinsoni 1.86 1 0.17 1736.82 278 <0.001 EPINEPHELINAE 0.42 1 0.52 1115.08 275 <0.001

YEARS YEARS E. rivulatus 5.43 1 0.02 462.46 165 <0.001

PROTECTION SCARINAE 0.83 1 0.36 1689.03 259 <0.001 LETHRINIDAE 0.83 1 0.36 2002.31 299 <0.001 L. nebulosus 0.87 1 0.35 1961.63 255 <0.001 <0.00 L. atkinsoni 8.43 1 1 1696.16 278 <0.001

EPINEPHELINAE 1.1 1 0.29 1100.82 275 <0.001 ZONING ZONING SCHEME <0.00 E. rivulatus 13.43 1 1 440.94 165 <0.001 SCARINAE 3.82 1 0.05 1680.66 259 <0.001 LETHRINIDAE 1.15 1 0.28 1987.99 295 <0.001 L. nebulosus 0.7 1 0.4 1960.92 251 <0.001 L. atkinsoni 0.55 1 0.46 1698.56 275 <0.001

EPINEPHELINAE 0.01 1 0.92 1099.86 271 <0.001 BOAT BOAT

FISHING E. rivulatus 4.79 1 0.03 457.62 162 <0.001 SCARINAE 5.9 1 0.02 1670.41 255 <0.001 LETHRINIDAE 7.09 1 0.01 1953.98 299 <0.001

L. nebulosus 0.16 1 0.69 1966.75 255 <0.001 <0.00 L. atkinsoni 9.03 1 1 1689.1 278 <0.001

EPINEPHELINAE 3.9 1 0.05 1060.95 275 <0.001 SHORE SHORE FISHING E. rivulatus 6.16 1 0.01 461.9 165 <0.001 SCARINAE 5.59 1 0.02 1674.58 259 <0.001

<0.00 LETHRINIDAE 43.22 21 1 1746.07 279 <0.001 <0.00 L. nebulosus 48.82 21 1 1665.43 235 <0.001 L. atkinsoni 35.29 20 0.02 1494.98 259 <0.001 <0.00 EPINEPHELINAE 54.33 21 1 880.04 255 <0.001 <0.00

E. rivulatus 68.95 20 1 344.61 146 <0.001 RESERVE IDENTITY RESERVE SCARINAE 27.81 21 0.15 1484.39 239 <0.001 LETHRINIDAE 2.39 1 0.12 1994.3 299 <0.001 L. nebulosus 0 1 0.99 1962.36 255 <0.001 L. atkinsoni 5.75 1 0.02 1674.53 278 <0.001 EPINEPHELINAE 0.5 1 0.48 1115.22 275 <0.001

YEAR E. rivulatus 3.59 1 0.06 468.95 165 <0.001 SURVEY SURVEY 21.29 1 <0.00 1579.86 259 <0.001 SCARINAE 1 * Total heterogeneity is provided in Table App 6. 6.1

Table App 6. 6.3 Mixed-effects models heterogeneity statistics for abundance data

BIOMASS Model heterogeneity* Residual heterogeneity Fish group Qm df P Qe df P LETHRINIDAE 41.75 3 <0.001 2082.5 260 <0.001 L. nebulosus 29.38 3 <0.001 2743.83 219 <0.001 L. atkinsoni 17.13 3 <0.001 1714.19 243 <0.001 EPINEPHELINAE 1.21 3 0.75 1296.97 249 <0.001

HABITAT E. rivulatus 2.53 3 0.47 517.38 152 <0.001 SCARINAE 22.86 3 <0.001 1038.84 219 <0.001 LETHRINIDAE 2.46 2 0.29 2140.53 261 <0.001 L. nebulosus 0.63 2 0.73 2829.86 220 <0.001 L. atkinsoni 7.79 2 0.02 1720.61 244 <0.001 EPINEPHELINAE 12.37 2 <0.001 1258.74 250 <0.001

METHOD E. rivulatus 5.9 2 0.05 586.26 153 <0.001 SCARINAE 1.16 2 0.56 1193.63 220 <0.001

LETHRINIDAE 1.3 1 0.25 2318.18 262 <0.001

L. nebulosus 0.1 1 0.75 2886.72 221 <0.001

L. atkinsoni 1.97 1 0.16 1925.76 245 <0.001

SIZE EPINEPHELINAE 3.78 1 0.05 1307.41 251 <0.001

RESERVE E. rivulatus 1.32 1 0.25 571.52 154 <0.001 SCARINAE 0.59 1 0.44 1220.28 221 <0.001

LETHRINIDAE 0.01 1 0.94 2312.18 262 <0.001 L. nebulosus 0.71 1 0.4 2882.59 221 <0.001 L. atkinsoni 3.13 1 0.08 1924.77 245 <0.001 EPINEPHELINAE 0.12 1 0.73 1307.3 251 <0.001

YEARS YEARS E. rivulatus 5.01 1 0.03 587.67 154 <0.001

PROTECTION 0.89 SCARINAE 0.02 1 1218.81 221 <0.001 LETHRINIDAE 0.44 1 0.51 2314.92 262 <0.001 L. nebulosus 0.54 1 0.46 2883.99 221 <0.001 L. atkinsoni 9.59 1 <0.001 1925.41 245 <0.001

EPINEPHELINAE 0.04 1 0.83 1307.27 251 <0.001 ZONING ZONING SCHEME E. rivulatus 9.96 1 <0.001 587.78 154 <0.001 SCARINAE 3.09 1 0.08 1222.86 221 <0.001 LETHRINIDAE 0.18 1 0.67 2313.23 258 <0.001 L. nebulosus 0.04 1 0.84 2879.67 217 <0.001 L. atkinsoni 0.14 1 0.7 1923.96 243 <0.001

EPINEPHELINAE 0.82 1 0.37 1287.94 247 <0.001 BOAT BOAT

FISHING E. rivulatus 3.75 1 0.05 578.7 151 <0.001 SCARINAE 0.57 1 0.45 1193.33 217 <0.001 LETHRINIDAE 2.19 1 0.14 2295.06 262 <0.001 L. nebulosus 0 1 0.96 2606.71 221 <0.001 L. atkinsoni 5.25 1 0.02 1927.59 245 <0.001

EPINEPHELINAE 0.38 1 0.54 1268.07 251 <0.001 SHORE SHORE FISHING E. rivulatus 3.05 1 0.08 580.64 154 <0.001 SCARINAE 5.78 1 0.02 1168.18 221 <0.001 LETHRINIDAE 29.09 21 0.11 2019.62 242 <0.001 L. nebulosus 27.02 21 0.17 1941.89 201 <0.001 L. atkinsoni 33.93 20 0.03 1846.92 226 <0.001

EPINEPHELINAE 58.1 21 <0.001 987.83 231 <0.001 IDENTITY RESERVE RESERVE E. rivulatus 49.47 20 <0.001 503.04 135 <0.001 SCARINAE 33.11 21 0.04 1046.15 201 <0.001 LETHRINIDAE 2.39 1 0.12 1994.3 299 <0.001 L. nebulosus 0 1 0.99 1962.36 255 <0.001 L. atkinsoni 5.75 1 0.02 1674.53 278 <0.001 EPINEPHELINAE 0.5 1 0.48 1115.22 275 <0.001 E. rivulatus 3.59 1 0.06 468.95 165 <0.001

SURVEY YEAR SURVEY SCARINAE 21.29 1 <0.001 1579.86 259 <0.001 * Total heterogeneity is provided in Table App 6. 6.1

241 Figure App 6. 6.1 shows the transformed predicted effect sizes as relative fish abundance by habitat while Figure App 6. 6.2 shows this for reserve identity.

Figure App 6. 6.1 Relative fish abundance inside to outside reserves (back-transformed weighted mean effect sizes) with 95% confidence intervals) for the Lethrinidae, Lethrinus nebulosus, and L. atkinsoni, the fish groups for which habitat explained a significant amount of variation. Effect sizes are considered significant when the confidence intervals do not include one. Black dots correspond to significant effects while grey dots correspond to non-significant effects.

Figure App 6. 6.2 is included to show that there is heterogeneity between individual reserves and because it may be useful to managers of the Ningaloo Marine Park.

Figure App 6. 6.2 Relative fish abundance (Transformed weighted mean effect sizes (풆풙풑(풆)) with 95% confidence intervals (CI)), for the Lethrinidae, Lethrinus nebulosus, Lethrinus atkinsoni, Epinephelinae and Epinephelus rivulatus, the fish groups for which reserve identity explained a significant amount of variation. Results are categorised into panels based on the initial or current zoning. Point shape indicates whether shore fishing is allowed or prohibited along the coastal edge of the reserve (circle = prohibited; triangle = allowed). Effect sizes are considered significant when the confidence intervals do not include one. Black dots correspond to significant effects while grey dots correspond to non-significant effects.

243 Appendix 6.7. Biomass results

Table App 6. 7.1 Top Generalised Additive Mixed Models (GAMMs) for predicting the response ratio inside to outside reserves, 푬̅, for biomass from full subset analyses for the abundance of the six fish groups. Difference between the lowest reported corrected Akaike Information Criterion (ΔAICc), AICc weights (ωAICc), variance explained (R2) and estimated degrees of freedom (EDF) are reported for model comparison. Model selection was based on the most parsimonious model (fewest variables and lowest EDF) within two units of the lowest AICc. This model is shown in bold text.

Fish group Model ΔAICc ωAICc R2 EDF LETHRINIDAE Habitat + Size by Habitat 0 0.855 0.17399 9 Habitat + Years protection by Habitat + Size by L. nebulosus Habitat 0 0.443 0.19863 14.51 Habitat + Size by Habitat 0.857 0.289 0.17016 9 Years protection + Habitat + Size by Habitat 1.301 0.231 0.17594 10.66 L. atkinsoni Habitat + Method + Years protection by Habitat 0 0.29 0.10828 12.98 Habitat + Years protection by Habitat 0.39 0.238 0.09662 10.67 Habitat + Method + Zoning scheme 1.273 0.153 0.08162 8 EPINEPHELINAE Years protection + Boat fishing + Method 0 0.196 0.09835 7.81

Boat fishing + Method 0.516 0.152 0.09171 6.68 Years protection + Method 1.092 0.114 0.09192 7.24 Method 1.198 0.108 0.08585 5.88 Boat fishing + Method + Shore fishing 1.751 0.082 0.09098 7.61 Boat fishing + Method + Zoning scheme 1.955 0.074 0.09064 7.71 E. rivulatus Boat fishing + Method + Zoning scheme 0 0.546 0.17358 12.31 Years protection + Boat fishing + Method 1.457 0.264 0.16292 11.96 SCARIDAE Years protection + Habitat + Size by Habitat 0 0.271 0.12181 16.44 Habitat 1.354 0.138 0.05738 5 Years protection + Habitat + Size 1.406 0.134 0.09791 13.04 Habitat + Zoning scheme 1.698 0.116 0.06077 6 Habitat + Size by Habitat 1.889 0.106 0.07496 9 Habitat + Size 1.952 0.102 0.06103 6.28

Figure App 6. 7.1 a) Relative fish biomass (Transformed weighted mean effect sizes (풆풙풑(푬̅)) with 95% confidence intervals (CI)), for the relative abundance of the six fish taxa: Lethrinidae, Lethrinus nebulosus, L. atkinsoni, Epinephelinae, Epinephelus rivulatus and Scarinae. Effect sizes are considered significant when the confidence intervals do not include one. Open dots correspond to non-significant effects. Sample sizes are given in Figure App 6. 5.1. b) Importance scores (based on summed Akaike weights corrected for finite samples (AICc)) from full-subsets analyses exploring the influence of seven variables on the overall effect size for each fish taxa. Reserve size was square-root transformed and boat fishing was log-transformed in all models. Red X symbols mark the variables that were included in the most parsimonious models for each fish taxa.

245

Figure App 6. 7.2 Predicted relative fish biomass inside to outside reserves (back-transformed predicted weighted effect sizes) with 95% confidence intervals) for the six fish groups – a) Lethrinidae; b) Lethrinus nebulosus c) Lethrinus atkinsoni; d) Epinephelinae; e) Epinephelus rivulatus; f) Scarinae for abundance– as a function of variables present in the most parsimonious models (Table App 6. 7.1) from full-subsets GAMM analysis. Ribbons represent 95% confidence intervals

247