Water Quality Assessment of Tallebudgera Creek

Agent-based modelling of short-term juvenile bull shark movement in a semi-enclosed Gold Coast estuary

Jonas Brandi Mortensen

B.Sc. (University of Copenhagen)

School of Environment

Science, Environment, Engineering and Technology

Griffith University

Submitted in fulfilment of the requirements of the degree of

Master of Philosophy

December 2011

STATEMENT OF ORIGINALITY

This work has not previously been submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published by another person except where due reference is made in the thesis itself.

......

Jonas Brandi Mortensen

14th December 2011

II ACKNOWLEDGEMENTS

First and foremost I would like to extend my warmest thanks to my Principal Supervisor, Professor Joe Lee, for believing in me and allowing me to undertake this research project under his supervision. Thank you for your guidance and support over the past two years, especially here in the last few dramatic months. Also thanks to Dr Guy Castle for acting as my associate supervisor, and guiding me through the tricky waters of my Confirmation paper back in the early days. Special thanks goes out to Jonathan Werry for helping me establish this project with the Australian Rivers Institute, while also helping me with the shark catching effort and supplying me with the necessary equipment needed for the acoustic tracking campaign.

My most sincere thanks goes out to the DHI for supplying me with a free MIKE software license over the course of my study, but most importantly I would like to thank Flemming T. Hansen for all the ABM support he has provided me over the past two years, and for allowing me to stay in his office for three months while teaching me how to use the MIKE ABM module. I would also like to extend a special thanks to Michael Pothoff for his technical ABM troubleshooting, and most importantly; for fixing that bug in the software that prevented me from completing ABM simulations! Furthermore I would like to thank Anders Erichsen and Thomas Uhrenholdt, as well as the many other DHI employees who helped me with technical advice, while also making my stay in Hørsholm a pleasant one.

My warmest thanks and gratitude goes out to the Queensland Urban Fish Habitat Management Research Program and the Gold Coast City Council for funding part of my research, as well as the Danish State Government for sponsoring my tuition fees and living costs for the duration of this project. Also I would like to direct a special thanks to the Bureau of Meteorology (BOM), the Queensland Department of Resource Management (DERM) as well as the Environmental Ecosystem Health Program (EHMP) of Southeast Queensland for providing me with essential input data free of charge.

Furthermore, I would like to direct my warmest thanks to the following people for aiding me with the many various aspects of this research project:

 Associate Professor Charles Lemckert - For allowing me to use two very expensive Acoustic Doppler Current Profilers free of charge. It goes without saying that without

III your help the available validation data of the hydrodynamic model would have been quite lacking.  Paul Maxwell - For helping me with the historical EHMP data, and assisting me in the shark tracking campaign during the latest of hours, while being eaten alive by mosquitoes. Also thanks for the many good laughs in lab.  Johan Gustafson - For his help with constructing a mounting frame for the two ADCP‟s, as well as the actual deployment and retraction of the instrument, plus his ongoing moral support throughout the thesis.  Geoffrey Turner, Ian Underhill and Malcolm Duncan - For allowing me into the Engineering bay, as well as provide excellent guidance and assistance in the construction of a mountable hydrophone rig for the Portunus.  Joshua Reinke - For continuing the water quality campaign during my time away from the Gold Coast, while aiding me on several occasions with the actual shark tracking.  Simon Kerville and Alan Richards - For tirelessly helping me out with essential lab equipment for chlorophyll analysis, as well as saving me from trouble in the field.  All the many volunteers who have been helping me with the shark tracking campaign and other assorted field work; Ciaran Morris, Alf Okkels Jakobsen, Sophie Olsson- Pons, Thomas Baatz Andersen, Sarah Richmond, Carl Brown-Kenyon, Barbro Haug- land, Sameer Shah and Louise Pointon.

Last but not least, I would like to thank my brother, Simon Brandi Mortensen, for providing me with a place to call home these past three years that I have spent in Australia, while also guiding me through the many pitfalls of hydrodynamic modelling and Matlab scripting. I can honestly say that your constant moral support has been the glue that held me together through the hardest of times, and I will be forever grateful to you.

Finally, my warmest of thanks goes out to my family and friends back home in Denmark for believing and supporting me throughout this entire venture. Your continuous motivational support has been invaluable and keeps pushing me forever forward.

IV ABSTRACT

This project investigated the value and future potential of a coupled Eulerian-Lagrangian agent-based modelling approach as an alternative method of investigating the movement and habitat use of juvenile bull shark Carcharhinus leucas in small peri-urban estuaries. Through the use of the MIKE21 modelling suite (DHI), a depth-averaged two-dimensional hydrodynamic model was developed and implemented as a means to capture the spatio-temporal variation in hydrodynamics of the semi-enclosed Tallebudgera Creek estuary. This system provides a suite of habitats comprising artificial residential canals, polyhaline and brackish creek sections in a peri-urban setting. The hydrodynamic model served as the dynamic foundation of a spatially heterogeneous agent-based model (ABM) developed for juvenile C. leucas. The movement formulation of juvenile C. leucas was represented as a kinesis search for optimal conditions, while a random walk model served as a control.

The hydrodynamic model performed satisfactorily in terms of capturing the variations of key physical conditions of Tallebudgera Creek. Modelled values of surface elevation and flow dynamics were in good agreement with measured data sets. Simulated mean levels of salinity and temperature were likewise in good agreement with measured means; however, model analysis revealed a high sensitivity to increased freshwater influxes, and a delay in model response time.

Three neonate and juvenile individuals of C. leucas were captured and attached with acoustic tags for tracking of movement in Tallebudgera Creek. Short-term continuous tracks of a juvenile C. leucas were successfully collected as a means to relate observed movement to outputs of the hydrodynamic model and measurements of water quality, while consecutive data- points of animal position served as validation data for the agent-based model.

Analysis of C. leucas track data revealed a high site preference for the middle reach of the system over the course of the tracking campaign, even during periods when salinity levels were < 1 PSU. However, an avoidance of high salinities > 27 PSU was evident. Significant movement of the animal in a downriver direction only occurred after a period of increased flow velocities and turbidity, suggesting that these parameters may play an important role in directing shark movement in conjunction with salinity.

V The agent-based models in their current developmental stage performed unsatisfactorily in capturing observed movement, and their predictive ability was generally poor. The current ABM formulation of C. leucas movement is therefore insufficient to capture the observed pattern of behaviour. However, unforseen technical difficulties originating from the narrow and shallow nature of the Tallebudgera Creek system prevented a full assessment of the ABM results.

Despite current technical issues that were impracticable to be resolved under the available timeframe, this study represents a first attempt to construct and implement agent-based modelling to investigate bull shark movement and habitat use in a spatially and temporally dynamic hydrologic environment. It is predicted that once these technical difficulties are overcome, agent-based modelling as a research tool holds great promise for future investigation of the habitat ecology of C. leucas to benefit its conservation and management.

VI TABLE OF CONTENTS

Statement of originality ...... II Acknowledgements ...... III Abstract ...... V List of figures ...... XI List of tables ...... XVI Ethics approval ...... XVIII

Chapter I: General introduction and research aims ...... 1 1.1. General introduction ...... 2 1.2 Research questions ...... 4 1.3 Research aims ...... 5 1.4 Hypothesis ...... 5

Chapter II: Water quality assessment of tallebudgera creek ...... 6 2.1 Introduction ...... 7 2.1.1 Aims & purpose ...... 9 2.2 Methodology ...... 9 2.2.1 Sampling design ...... 9 2.2.2 Temperature ...... 11 2.2.3 Salinity ...... 11 2.2.4 pH ...... 11 2.2.5 Turbidity ...... 11 2.2.6 Dissolved Oxygen ...... 12 2.2.7 Chlorophyll ...... 12 2.2.8 Sonde calibration & measurement techniques ...... 13 2.2.9 Data analysis ...... 13 2.3 Results ...... 14 2.3.1 Temperature ...... 14 2.3.2 Salinity ...... 16 2.3.3 Dissolved Oxygen ...... 17

VII 2.3.4 pH ...... 19 2.3.5 Turbidity ...... 21 2.3.6 Chlorophyll a ...... 22 2.4 Discussion ...... 23

Chapter III: Hydrodynamic modelling of Tallebudgera Creek ...... 26 3.1 Introduction ...... 27 3.1.1 Aims & purpose ...... 28 3.2 Methodology ...... 28 3.2.1 Equational Framework ...... 28 3.2.2 Transport equations ...... 29 3.2.3 Heat exchange ...... 30 3.2.4 Model domain ...... 32 3.2.5 Input parameters ...... 34 3.2.5.1 Offshore boundary data ...... 35 3.2.5.2 Freshwater discharge ...... 35 3.2.5.3 Meteorological data ...... 35 3.2.5.5 Precipitation ...... 36 3.2.5.6 Wave radiation stress ...... 36 3.2.5.7 Initial conditions ...... 37 3.2.6 Calibration parameters ...... 37 3.2.7 Validation data ...... 39 3.2.7.1 Historical data ...... 39 3.2.7.2 ADCP data ...... 39 3.2.7.3 Temperature/Salinity data ...... 41 3.2.8 Calibration & data analysis ...... 42 3.3 Results ...... 43 3.3.1 Surface elevation validation ...... 43 3.3.2 Flow velocity validation ...... 45 3.3.3 Salinity validation ...... 48 3.3.4 Temperature validation ...... 53 3.4 Discussion ...... 58

VIII Chapter IV: Short-term movement of a juvenile bull shark in Tallebudgera Creek ...... 65 4.1 Introduction ...... 66 4.1.1 Aims & purpose ...... 68 4.2 Methodology ...... 68 4.2.1 Catching and tagging ...... 68 4.2.2 Acoustic telemetry and tracking methods ...... 70 4.2.3 Hydrophone calibration and shark position ...... 71 4.2.4 MIKE21FM data ...... 73 4.2.5 Data Analysis ...... 73 4.3 Results ...... 75 4.3.1 Track I ...... 76 4.3.2 Track II ...... 79 4.3.3 Track III ...... 83 4.3.4 Track IV ...... 87 4.3.5 Track V ...... 91 4.3.6 Track VI ...... 95 4.3.7 Collective results ...... 99 4.4 Discussion ...... 105

Chapter V: Agent-based modelling of a juvenile bull shark on a short spatio-temporal scale ...... 110 5.1 Introduction ...... 111 5.1.2 Aims & purpose ...... 112 5.2. Methodology ...... 113 5.2.1 The coupled Eulerian-Lagrangian model framework ...... 113 5.2.2 The random walk model ...... 114 5.2.2.1 Random walk movement equations ...... 115 5.2.3 The kinesis model ...... 115 5.2.3.1 Kinesis movement equations ...... 116 5.2.4 Model structure ...... 120 5.2.5 Model setup & calibration effort ...... 121 5.2.6 Data analysis ...... 124 5.3 Results ...... 126 5.3.1 Track I ...... 126

IX 5.3.2 Track II ...... 129 5.3.3 Track III ...... 134 5.3.4 Track IV ...... 138 5.3.5 Track V ...... 141 5.3.6 Track VI ...... 143 5.4 Discussion ...... 145 5.4.1 Technical restrictions ...... 145 5.4.2 Theoretical limitations ...... 146 5.4.3 Concluding remarks ...... 148

Chapter VI: General discussion and future directions ...... 150 6.1 Summary ...... 151 6.2 Evaluation of the hydrodynamic model ...... 151 6.2.1 Ecological modelling ...... 152 6.3 Agent-based modelling ...... 152 6.3.1 Future modelling directions ...... 154 6.3.2 Possibilities for conservation management ...... 155 References ...... 157 Appendix I ...... 162 Temporal validation of Salinity and Temperature across all stations ...... 162 Spatial validation of Salinity and Temperature across all sampling days ...... 167 DVD Appendix...... 179

X LIST OF FIGURES

Figure 2.2.1: Satellite imagery depicting the lower to middle reaches of the Tallebudgera Creek estuary, and the adjoining artificial canals. Plotted using a MGA-56 projection. Data source: Google Earth...... 8 Figure 2.2.2: The location of water quality stations throughout Tallebudgera Creek; see colour legend for station type ...... 10 Figure 2.3.1: A) Seasonal variation in temperature throughout the course of 2010. B) Spatial variation in temperature throughout the course of 2010. The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study ...... 15 Figure 2.3.2: A contour plot of the spatiotemporal variation of temperature throughout the sampling period. The data set depicted contains only the measurements conducted by this study ...... 15 Figure 2.3.3: A) Seasonal variations in salinity throughout the course of 2010. Note the abrupt fluctuations between sample days. B) Spatial variation in salinity throughout the course of 2010, showing a general decline of salinity proportional to distance from river mouth. Note the higher level of variation, which is indicated by the increased width between the 20th and 80th percentile, as the distance from the river mouth increases. The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study...... 16 Figure 3.3.4: A contour plot of the spatiotemporal variations of salinity throughout the sampling period. The data set depicted contains only the measurements conducted by this study...... 17 Figure 2.3.5: A) Seasonal variations in dissolved oxygen throughout the sampling period. Note the relative small difference between the 20th and 80th percentile levels across sampling days. B) Spatial variations in dissolved oxygen across the tidal-dominated part of the system. Note the slight increase of dissolved oxygen concentrations with increased distance to the river mouth. The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study...... 18 Figure 2.3.6: A contour plot of the spatiotemporal variations of dissolved oxygen throughout the sampling period. The data set depicted contains only the measurements conducted by this study...... 19 Figure 2.3.7: A) The seasonal variations of pH throughout the sampling period. Note how the fluctuations in pH tend to follow the same general trend as salinity (Figure 2.3.3A). B) Spatial variations in pH throughout the course of 2010, showing a general decline of pH levels proportional to distance from the river mouth. The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study...... 20 Figure 2.3.6: A contour plot of the spatiotemporal variation of dissolved oxygen throughout the sampling period. The data set depicted contains only the measurements conducted by this study...... 20 Figure 2.3.7: A) The seasonal variations of turbidity throughout the sampling period. B) Spatial variations in turbidity throughout the course of 2010, showing a general increase of turbidity levels proportional to distance from the river mouth. Note that the scale of the y-axis differs between A) and B). The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study...... 21 Figure 2.3.8: A contour plot of the spatiotemporal variations of turbidity throughout the sampling period. The data set depicted contains only the measurements conducted by this study...... 22 Figure 2.3.8: A) Seasonal variations in chlorophyll a throughout the sampling period of 2010. Note that chlorophyll a concentration only exceeded 20 μg/l on one occasion. B) Spatial variations in chlorophyll a throughout the course of 2010, showing a weak trend of increased chlorophyll a concentrations upstream. The data set depicted is the assimilated product of EHMP measurements along with measurements made by this study...... 23

XI Figure 3.2.1: Illustration showing the bathymetry of the Model domain, plotted in a MGA-56 projection. The area inside the black box is the part of the non-tidal dominated freshwater zone that is included in the model setup (see main text for further explanation). The red box marks the area shown in Figure 3.2.3 below...... 33 Figure 3.2.2: An example of the flexible mesh of the model. The green markers each indicate a smaller sub-area within the mesh. The figure is plotted using MGA-56 projection coordinates (unit: meters)...... 34 Figure 3.2.3: Model domain of the created offshore Spectral Wave model. The red box marks the Tallebudgera Creek entrance. The figure is plotted using MGA-56 projection coordinates (unit: meters)...... 37 Figure 3.2.4: The deployment locations of ADCP's and CTD recorders in the middle and upper reaches of the system (MR & UR). Blue stations mark the 2007 GCCM stations. The figure is plotted using MGA-56 projection coordinates (unit: meters)...... 40 Figure 3.2.5: Picture of the constructed wooden frame with the ADCP mounted on it. All metal used was either lead or stainless steel in order to reduce any effect on the internal compass...... 41 Figure 3.3.1: Modelled surface elevation (red line) plotted against measured values (blue line) during the period of 29/3-2007 to 8/4-2007 at the MR-2007 calibration point...... 43 Figure 3.3.2: Modelled surface elevation (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the MR-ADCP calibration point...... 44 Figure 3.3.3: Modelled surface elevation (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the UR-ADCP calibration point...... 45 Figure 3.3.4: Simulated current speed (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the UR-ADCP calibration point...... 46 Figure 3.3.5: Simulated current speed (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 27/9-2010 at the UR-ADCP calibration point...... 46 Figure 3.3.6: Simulated current directions (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the UR-ADCP calibration point...... 47 Figure 3.3.7: Simulated current speed (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the MR-ADCP calibration point...... 47 Figure 3.3.8: Simulated current directions (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the UR-ADCP calibration point...... 48 Figure 3.3.9: Simulated salinity (red line) plotted against measured values (blue line) during the period of 20/1-2010 to 1/2-2010 at the UR-CTD calibration point...... 49 Figure 3.3.10: Simulated salinity (red line) plotted against measured values (blue line) during the period of 11/2-2010 to 29/3-2010 at the MR-CTD calibration point...... 50 Figure 3.3.11: Simulated salinity (red line) plotted against measured values (blue line) during the period of 11/2-2010 to 29/3-2010 at the UR-CTD calibration point...... 50 Figure 3.3.12: (Left) Seasonal variation in station-averaged salinity (blue line) plotted against corresponding simulated values (red line). (Right) Annually averaged salinity for each measuring station in relation to river mouth distance, plotted against corresponding simulated values. EHMP data is omitted from the two measured data sets...... 51 Figure 3.3.13: Simulated temperature (red line) plotted against measured values (blue line) during the period of 20/1-2010 to 1/2-2010 at the MR-CTD calibration point...... 54 Figure 3.3.14: Simulated temperature (red line) plotted against measured values (blue line) during the period of 20/1-2010 to 1/2-2010 at the UR-CTD calibration point...... 54 Figure 3.3.16: Simulated temperature (red line) plotted against measured values (blue line) during the period of 11/2-2010 to 29/3-2010 at the MR-CTD calibration point...... 55

XII Figure 3.3.17: (Left) Seasonal variation in station-averaged temperature (blue line) plotted against corres-ponding simulated values (red line). (Right) Annually averaged temperature for each measuring station in relation to river mouth distance, plotted against corresponding simulated values. EHMP data is omitted from the two measured data sets ...... 56 Figure 3.4.1: Simulated current speeds at a model bottleneck during low tide (top), incoming tide (middle) and high tide (bottom). The bottleneck is located ~3 km upriver from the river mouth, while the first ADCP calibration point is located ~3 km further upriver from the bottleneck...... 60 Figure 4.2.1: Picture of the deployed gill net. Due to the narrow nature of the system, the gill net was often capable of stretching almost across the full width of the estuary...... 69 Figure 4.2.2: Juvenile bull shark suspended in the specially designed harness, while water flow across the gills was maintained by directing the boat against the current. Note the combined acoustic/roto- tag attached to the shark’s dorsal fin (red arrow)...... 70 Figure 4.2.3: The resulting surface plots from each calibration. Note the clear differences in distances at maximum signal strength and maximum gain (16 and 36 respectively), between the three calibrations. See main text for further information on the associated accuracy levels of each calibration...... 72 Figure 4.3.1: Recorded animal locations during the track undertaken on the 28/01-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_1_28012010" in the Track_Videos subfolder...... 77 Figure 4.3.2: Proportion of time spent at various temperature (top left), salinity (top right), current speed regimes (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 28/01-2010...... 78 Figure 4.3.3: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 28/01-2010. 79 Figure 4.3.4: Recorded animal locations during the track undertaken on the 03/02-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_2_03022010" in the Track_Videos subfolder...... 81 Figure 4.3.5: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 03/02-2010...... 82 Figure 4.3.6: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 03/02-2010. 83 Figure 4.3.7: Recorded animal locations during the track undertaken on the 27/02-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_3_27022010" in the Track_Videos subfolder...... 85 Figure 4.3.8: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 27/02-2010...... 86 Figure 4.3.9: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 27/02-2010. 87 Figure 4.3.10: Recorded animal locations during the track undertaken on the 06/03-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_4_06032010" in the Track_Videos subfolder...... 89

XIII Figure 4.3.11: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 06/03-2010...... 90 Figure 4.3.12: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading (bottom right) for the track conducted on the 06/02-2010...... 91 Figure 4.3.13: Recorded animal locations during the track undertaken on the 20/03-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_5_20032010" in the Track_Videos subfolder...... 93 Figure 4.3.14: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 20/03-2010...... 94 Figure 4.3.15: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 20/03-2010. 95 Figure 4.3.16: Recorded animal locations during the track undertaken on the 21/03-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_6_21032010" in the Track_Videos subfolder...... 97 Figure 4.3.17: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 21/03-2010...... 98 Figure 4.3.18: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 21/03-2010. 99 Figure 4.3.19: Total amount of recorded animal locations during all six track undertaken from the 28/01-2010 to the 21/03-2011, plotted using an MGA-56 coordinate projection. Note the concentration of registrations in the middle reach. See main text for explanatory details...... 102 Figure 4.3.20: Total proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted from the 28/01/2010 to the 21/03/2010...... 103 Figure 4.3.21: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading (bottom right) for the track conducted on the 20/03-2010...... 104 Figure 4.3.22: Frequency of animal heading relative to current direction during below-mean flow velocities (red), and above-mean flow velocities (blue)...... 104 Figure 4.4.1: Mean current speeds as predicted by the HD model, during the tracking campaign period, spanning from 28/01/2010 to 21/03/2010. Note the patches of reduced flow velocities within the middle reach area (marked by red arrows)...... 107 Figure 4.4.2: Mean salinity as predicted by the HD model, during the tracking campaign period, spanning from 28/01/2010 to 21/03/2010. Note how the area normally utilised by the animal (marked by the black box) lies within the 7-14 PSU range...... 108 Figure 5.2.1: Two-dimensional view (longitudinal and latitudinal) of a shark’s sensory range. The sensory range is independent of the Eulerian model grid, and is often estimated from the body length of the animal and model timestep. Adapted from Goodwin et al. (2006) ...... 114 Figure 5.2.2: Plot A: and plotted against the variable X with optimum , and and . Plot B: and plotted against the variable X with optimum , and and ...... 119

XIV Figure 5.2.3: The weighted functional response of (left) and (middle) plotted separately and combined (right). The optimum values depicted ( , and ) are for illustration purposes only...... 120 Figure 5.2.4: The artificial channel forcing shown for the middle reach of the model domain. Should an agent move more than 10 metres away from the channel forcing, it will automatically shift its heading towards the channel forcing in the next timestep...... 123 Figure 5.2.5: Simplified schematic of the middle reach area, highlighting the essence of the proposed distance estimation methodology. Note the potential for a huge bias in the Euclidian distance estimation (as illustrated), compared to distance estimation using the transect line as a reference. . 125 Figure 5.3.1: The best-fitted simulated agent locations as predicted by the KSTC-NC model vs. observed animal locations throughout the track conducted on 28/01 – 2010. Displayed agent locations correspond to the time of each animal registration, while agent locations in the time between animal registrations are not displayed...... 128 Figure 5.3.2: The start (point of release) and end locations of simulated agents predicted by the KSTC-NC model vs. the first registered animal location on the track conducted on 28/01 – 2010. Displayed start/end agent locations correspond to the specific agents that make it through the simulation without getting stuck on a land value. The lack of release points in the upper reach is due to the fact that agents released in this section did not make it through the entire simulation...... 129 Figure 5.3.3: The best-fitted simulated agent locations as predicted by the KSTC model vs. observed animal locations throughout the track conducted on 03/02 – 2010. Note how the simulated agent follows the tide downriver...... 131 Figure 5.3.4: The best-fitted simulated agent locations as predicted by the KSTC-NC model vs. observed animal locations throughout the track conducted on 03/02 – 2010. Note how the agent has very limited longitudinal travel...... 131 Figure 5.3.4: KSTC-model results (median, 10th and 90th percentile values) of distance (top left), depth (top right), salinity (middle left), temperature (middle right), swimming velocity (bottom left) and current speed (bottom right) versus corresponding track values (black line) for the 3rd of February 2010 plotted on a temporal axis...... 132 Figure 5.3.5: The start (point of release) and end locations of simulated agents predicted by the KS model vs. the first registered animal location on the track conducted on 03/02 – 2010. Displayed start/end agent locations correspond to the specific agents that made it through the simulation without getting stuck on a land value...... 134 Figure 5.3.5: The best-fitted simulated agent locations predicted by the KS model vs. observed animal locations throughout the track conducted on 03/02 – 2010. Note the similarity between the best-fitted movement locations predicted by the KS model versus the RW model depicted in Figure 5.3.6...... 135 Figure 5.3.6: The best-fitted simulated agent locations predicted by the KS model vs. observed animal locations throughout the track conducted on 03/02 – 2010. Note the similarity between the best-fitted movement locations predicted by the RW model versus the KS model depicted in Figure 5.3.5...... 136 Figure 5.3.7: KS-model results (median, 10th and 90th percentile values) of distance (top left), depth (top right), salinity (middle left), temperature (middle right), swimming velocity (bottom left) and current speed (bottom right) versus corresponding track values (black line) for the 27th of February 2010 plotted on a temporal axis...... 137 Figure 5.3.8: The start (point of release) and end locations of simulated agents predicted by the KS model vs. the first registered animal location on the track conducted on 27/02 – 2010. Displayed start/end agent locations correspond to the specific agents that made it through the simulation without getting stuck on a land value...... 138

XV Figure 5.3.9: The best-fitted simulated agent locations predicted by the KSTC-ZS model vs. observed animal locations throughout the track conducted on 06/03 – 2010...... 139 Figure 5.3.10: The start (point of release) and end locations of simulated agents predicted by the KS model vs. the first registered animal location on the track conducted on 06/03 – 2010. Displayed start/end agent locations correspond to the specific agents that made it through the simulation without getting stuck on a land value...... 141 Figure 5.3.11: The best-fitted simulated agent locations predicted by the KSTC-ZS model vs. observed animal locations throughout the track conducted on 20/03 – 2010...... 142 Figure 5.3.12: The best-fitted simulated agent locations predicted by the KSTC-ZS model vs. observed animal locations throughout the track conducted on 20/03 – 2010...... 144

LIST OF TABLES

Table 3.2.1: The values and user-specified settings of the enabled modules in the final model setup.. 38 Table 3.3.1: Calculated results of the Quality index for each measuring station over the course of 2010 vs. corresponding simulated values. See Appendix I for the plotted data sets...... 52 Table 3.3.2: Calculated results of the Quality index for each sampling day in relation to distance from the river mouth vs. corresponding simulated values. See Appendix I for the plotted datasets...... 52 Table 3.3.3: Calculated results of the Quality index for each measuring station over the course of 2010 vs. corresponding simulated values. See Appendix I for the plotted data sets...... 57 Table 3.3.4: Calculated results of the Quality index for each sampling day in relation to distance from the river mouth vs. corresponding simulated values. See Appendix I for the plotted data sets...... 57 Table 4.3.1: Details of the tagged sharks. Note that MN1 (male neonate), despite being of greater length than FN1 (female neonate), is a whole kilogram lighter than his female counterpart...... 75 Table 5.2.1: The model calibration parameters and their final values adopted in the three types of movement models...... 121 Table 5.3.1: Primary model results from the five applied model templates for the simulation covering the track that was conducted on the 28/01 - 2010. RW = Random Walk Model, KS = Kinesis Search for Optimal Salinity, KSTC = Kinesis Search for optimal salinity, temperature and current flow, KSTC-NC = KSTC with the contribution of current flow on agent movement removed, KSTC-ZS = KSTC with an optimum salinity of 0 PSU...... 127 Table 5.3.2: Primary model results from the five applied model templates for the simulation covering the 12-hr period prior to the track conducted on 28/01 - 2010...... 129 Table 5.3.3: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 03/02 - 2010...... 130 Table 5.3.4: Primary model results from the five applied model templates for the simulation covering the 12-hr period prior to the track that was conducted on 03/02 - 2010...... 133 Table 5.3.5: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 27/02 - 2010...... 134 Table 5.3.6: Primary model results from the five applied model templates for the simulation covering the 12-hr period prior to the track that was conducted on 27/02 - 2010...... 138 Table 5.3.7: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 06/03 - 2010...... 139

XVI Table 5.3.8: Primary model results from the five applied model templates for the simulation covering the 12-hr period prior to the track that was conducted on 06/03 - 2010...... 140 Table 5.3.9: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 20/03 - 2010...... 142 Table 5.3.10: Primary model results from the five applied model templates for the simulation covering the 12-hr period prior to the track that was conducted on 20/03 - 2010...... 143 Table 5.3.11: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 21/03 - 2010...... 143 Table 5.3.12: Primary model results from the five applied model templates for the simulation covering the 12-hr period prior to the track that was conducted on 21/03 - 2010...... 144

XVII ETHICS APPROVAL

All work associated with catching, handling/tagging and tracking of live bull sharks were approved by the Animal Ethics Committee of Griffith University (ENV/1709/AEC 2010).

The fishing effort in this study was conducted under the Queensland Department of Fisheries General Fisheries Permit No. 90306

XVIII

CHAPTER I

GENERAL INTRODUCTION AND RESEARCH AIMS

1 1.1. GENERAL INTRODUCTION

Nearly 90% of Australia‟s population lives within 100 km of the coastline, and the coastal population in many countries are currently growing at a fast rate (Martínez et al. 2007). The increased population growth and urbanisation has created a significant pressure on the various, and often fragile, coastal environments through the alteration of hydrology and sedimentation, with increased nutrient and pollution loads from concentrated run-off in urbanised areas with a high proportion of impervious surfaces (Lee et al. 2006, Halpern et al. 2008). Significant proportions of the natural habitats on urbanising coasts and estuaries have been replaced by artificial waterways such as canals, with > 65% of global seagrass and wetland habitat destroyed by human impact (Lotze et al. 2006). The bull shark Carcharhinus leucas is one of the few shark species that relies on riverine environments during specific stages of its life cycle (Pillans et al. 2005, Werry et al. 2011) and might therefore be particularly vulnerable to environmental changes due to urbanisation of coastal environments (Martin 2005).

Several studies have documented that C. leucas utilises riverine environments as a nursery for neonate and juveniles (Pillans et al. 2005, Simpfendorfer et al. 2005, Werry et al. 2011). While sub-adults and adults move much more freely in between the coastal and estuarine environments, neonate and juvenile bull sharks tend to stay in the river system (Werry 2010). Thus, to study the impacts of increased urbanisation on the life cycle of C. leucas, one must first understand how juvenile C. leucas utilise the often highly fluctuating riverine environment through their movement behaviour.

While recent acoustic telemetry studies provide increased knowledge and understanding of the physical, chemical and biological drivers of the observed movement patterns of juvenile C. leucas on local and regional scales (Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010), what drives C. leucas movement behaviour on a fine spatio-temporal basis is still poorly known. In order to develop effective management and conservation strategies for this highly mobile and potentially dangerous species, it is imperative that a detailed understanding of its short-term movement behaviour and habitat selection pattern in different stages of its life cycle is established (Heithaus et al. 2001). The advancement of acoustic technology in the last decade has enabled significant insight into the short-term movement patterns as well as fine-scale identification of habitats that potentially offer advantages to

2 shark survival and growth (Sundström et al. 2001), but an in-depth understanding of short- term movement behaviour on an individual-based level is yet to be established.

Previous studies have identified several drivers and hypotheses linked to the observed movement patterns of C. leucas. These range from 1) salinity preferences due to energetic costs associated with osmoregulation (Heupel & Simpfendorfer 2008, Simpfendorfer et al. 2008); 2) freshwater influx and diel variations in the flow regime as a result of tidal movements (Ortega et al. 2009); 3) specific habitat usage to reduce energy costs associated with movement (Werry 2010); 4) changes in the distribution of prey as a result of tidal/freshwater interaction (Ortega et al. 2009); and 5) change in movement patterns due to seasonal changes in temperature (Heupel & Simpfendorfer 2008, Ortega et al. 2009). Furthermore, the movement of C. leucas has been correlated with environmental parameters such as turbidity, pH and dissolved oxygen (e.g. Ortega et al. 2009), but so far no underlying physiological mechanism has been proposed to explain the observed correlations.

These initial findings help identify important drivers for the movement patterns of C. leucas; however, they are limited in their ability to test proposed theories due to the obvious difficulties in creating a manipulative study framework, where confounding factors can be reduced to a minimum. In addition, there are noticeable issues involved with accurately estimating various environmental parameters at the actual location of the shark and its immediate surroundings in real time. Previous studies have normally followed a general linear model (GLM) approach in order to estimate the effects of parameters such as temperature, salinity, pH, turbidity and dissolved oxygen at the location of the shark (Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010). However, due to the non-uniform advection/dispersion and transformation of these parameters across a complex spatial domain, these estimates might prove to be too limited in their accuracy.

Recent advances in computational fluid dynamics now allow researchers to apply models simulating hydrodynamic processes on a sufficiently detailed scale that is meaningful to fish, while laboratory studies have identified and defined many sensory abilities of fish capable of distinguishing elements of hydrodynamic fields (Coombs et al. 2001, Krother et al. 2002). In addition, advanced numerical ecological models have been integrated into hydrodynamic models and developed to accurately simulate important biological parameters such as primary production, nutrient cycling, chlorophyll concentration and dissolved oxygen (Szylkarski et al. 2004). The development and application of these techniques enable researchers to

3 dynamically simulate entire ecosystems on a relatively fine scale, and thus create a model foundation for the implementation of aquatic agent-based models capable of simulating individual fish behaviour in a dynamic environment. The agent-based modelling approach allows the researcher to design, apply and test detailed theories of individual movement behaviour through biologically meaningful equations describing the movement and interaction rules for the organism of interest. Previous studies in developing agent-based models for fish behaviour range from migration patterns of commercially important species of stream fish (Railsback et al. 1999) such as brown and rainbow trout (Van Winkle et al. 1998), and juvenile salmon migration (Goodwin et al. 2006), to schooling behaviour (Reuter & Breckling 1994) and bioenergetic growth models of individual fish cohorts (Humston et al. 2004). Developing a dynamic agent-based model for an euryhaline apex predator, such as C. leucas, is however yet to be attempted. It is believed that through this research approach it is possible to gain valuable insight into the biology and behaviour of C. leucas, thus allowing resource managers to improve their management decisions in regards to this species in the face of intense coastal urbanisation.

1.2 RESEARCH QUESTIONS

This study will seek to address the following questions based on the in-depth investigation of a juvenile specimen of C. leucas:

1. Do spatio-temporal changes in water quality parameters affect the short-term movement patterns of the test subject, or is it largely driven by a physical habitat preference?

2. If so, can this behaviour be explained by an instinctive drive within the animal to lower energy costs during non-foraging periods?

3. Finally, can this behaviour be recreated as an emergent behaviour through an agent-based model by developing equations that describes the energetic benefits of locating optimal habitat in terms of water quality and other physical characteristics of the system?

4 1.3 RESEARCH AIMS

This study will apply an advanced numerical Eulerian 2D model framework approach to create a dynamic virtual representation of the chosen study site, Tallebudgera Creek, southeast Queensland, Australia, to simulate the advection/dispersion of the essential physical parameters on a fine spatio-temporal scale. Short-term tracks of the juvenile C. leucas specimen will be compared and analysed in relation to the physical parameters provided by the hydrodynamic model and used as base to develop an Eulerian-Langrangian coupled agent-based model framework (ABM) capable of testing different theories of this particular individual's movement behaviour. The ultimate aim of this study is to replicate and predict the movement of the juvenile C. leucas in its habitat through testing of theories that are proposed by this and previous studies on juvenile bull shark behaviour. In the following chapters, the methodology and results of each component of this study will be presented and discussed in turn, while the overall outcomes as well possible future research directions will be discussed in Chapter VI.

1.4 HYPOTHESIS

Based on the results of previous studies (Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010) on the movement patterns of juvenile C. leucas, it is hypothesised that short- term movement behaviour during non-foraging periods can be explained by the shark‟s instinctive goal to maintain optimal homeostasis and metabolism based on active selection of (and movement between) energetically beneficial physical habitats and environmental conditions.

CHAPTER II

WATER QUALITY ASSESSMENT OF TALLEBUDGERA CREEK

6 2.1 INTRODUCTION

Assessment of water quality is a fleeting term, as “water quality” covers a wide range of physicochemical as well as biological parameters, ranging from simple measurements of temperature and salinity to nutrient concentrations and suspended sediments to bacterial counts and algal biomass. However, for the purposes of this study, assessment of water quality will only encompass traditional measurements of temperature, salinity, dissolved oxygen, pH, turbidity and chlorophyll a. As mentioned in Chapter I, all the above mentioned parameters with the exception of chlorophyll a, have been reported to affect movement of juvenile C. leucas (Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010). It follows that an investigation into the spatio-temporal dynamics of these parameters in Tallebudgera Creek is essential for understanding how these dynamics might translate into potential movement responses of C. leucas. Furthermore, the water quality campaign of Tallebudgera Creek allows the study to gain important empirical insight into the drivers of spatial and seasonal variations of the system, which in turn will serve the purpose of establishing and validating a hydrodynamic model for the system (Chapter III).

The Tallebudgera Creek estuary (153.45°E, -28.10°S), is situated next to the suburb of Burleigh Heads, Gold Coast, Australia, and drains a small coastal catchment of ~110 km2 by discharging its runoff directly into the Pacific Ocean (Figure 2.1.1). The relatively small estuary (~10.2 km in length) is classified as a tide-dominated and highly modified system, with two artificial canals adjoining the natural system in its lower reaches (EHMP 2007). While the estuary is in a modified state and under pressure from continuous urban development, the general water quality level is classified as "B" or "good" by the Environmental Ecosystem Health Program (EHMP) of Southeast Queensland (EHMP 2007). A total of 78 fish species and 17 species of crustaceans has been recorded inside the Tallebudgera Creek estuary in 1973 (Shine et al. 1973). However, there is to the author‟s best knowledge no recent surveys documenting the continued presence of all of these, or the occurrence of additional, species.

The lower reaches of the estuary are characterized by high urbanisation and modification while the middle and upper reaches still have 47% of area as unmodified riparian habitat (EHMP 2007), with the remaining areas being used for rural residential purposes. Tallebudgera Creek is popular for recreational fishing and boating/swimming activities, and is known to be utilised by C. leucas as a nursery ground (Werry 2010). Due to the relatively small size of the tide-dominated estuary and its semi-enclosed nature, Tallebudgera Creek is a suitable site for studying the movement of resident juvenile C. leucas, as this species has a tendency to stay inside estuaries at early life-stages (Werry 2010). Furthermore, only a relatively small number of measuring stations for water quality sampling is needed to cover the full spatial extent of the estuary.

2.1.1 AIMS & PURPOSE As an integral part of this project, the main aim and purpose of the water quality campaign is to collect temperature and salinity validation data for the hydrodynamic model (Chapter 3), and secondarily to directly assess the general status as well as the seasonal variations of the tide-dominated system of the Tallebudgera Creek estuary.

2.2 METHODOLOGY

2.2.1 SAMPLING DESIGN In order to capture a representative picture of the spatiotemporal variation in water quality in the tidal-influenced zone of Tallebudgera Creek, 13 measuring stations (Figure 2.2.2) were sampled throughout the system on a weekly to biweekly basis over one year (Jan 2010 - Jan 2011). Sample sites were selected on the basis of accessibility from land, while keeping stations no further than a maximum of ~1.5 km apart from the nearest neighbour. Using a handheld YSI 6820V2-1 multi-probe sonde, salinity, temperature, dissolved oxygen, pH, and turbidity and were recorded 20 cm below the water surface at each station as well as at 2 m depth if the sample site allowed for it. While some sample stations were deeper than 2 m, this depth was sampled due to the interpolated bathymetry scheme (explained in Chapter 3) on average being 2 m below mean sea level. This allowed measurements to give an indication of the spatiotemporal change of parameters at the average bottom depth of the system compared to surface values. Furthermore, chlorophyll a content was measured at four select stations (Figure 2.2.2) in order to get an indication of the primary productivity and biological condition of the system.

Figure 2.2.2: The location of water quality stations throughout Tallebudgera Creek; see colour legend for station type

Five additional stations corresponding to the Ecosystem Health Monitoring Program (EHMP) monitoring stations are present in the system (Figure 2.2.2). These stations are measured from boat at several depths once every month as part of an ongoing water quality monitoring program for Southeast Queensland. The water quality parameters mentioned above, with the exception of chlorophyll a, were measured by the EHMP using an YSI 6920 sonde, which is identical to the YSI 6820 Sonde in terms of the sensors utilised. The EHMP data was therefore deemed to have a sufficient level of compatibility with the data from the other sample stations established for this study, and was assimilated into the measured dataset for final analysis. Chlorophyll a was, however, determined by the EHMP using a different methodology than that used in this study (explained in detail in section 2.2.6) and data were therefore not assimilated into the chlorophyll dataset but treated separately. Furthermore the EHMP stations provide the study with monthly measurements of ammonia, organic nitrogen,

10 oxidised nitrogen, total nitrogen, filterable reactive phosphorous and total phosphorous, thus allowing the measured chlorophyll content to be related to nutrient availability.

2.2.2 TEMPERATURE Temperature and conductivity were measured using an YSI 6560 conductivity/temperature probe installed in a handheld YSI 6820V2-1 multi-probe sonde. The temperature probe produces a temperature reading in degree Celsius with a resolution of 0.001 °C and an accuracy of + 0.15 °C in the range of -5 to 50 °C (YSI 2006).

2.2.3 SALINITY Salinity was calculated by measuring conductivity using the same conductivity/temperature probe as mentioned above. Since conductivity is highly dependent on temperature, the conversion to salinity was done by the built-in software in the YSI 6820V2 sonde, which accounts for the temperature effect in accordance with standard algorithms (APHA 1971). The measurements produced have a resolution of 0.01 ppt and an accuracy of + 1.0% of the salinity reading, in the range of 0 to 70 ppt and -5 to 50°C range (YSI 2006).

2.2.4 PH pH was measured by an YSI 6561 pH probe installed in the 6820V2 multi-probe sonde and utilises a combination electrode consisting of a proton selective glass reservoir filled with buffer at ~ pH 7 and an Ag/AgCl reference electrode. The pH probe reports values with a resolution of 0.01 with a accuracy of + 0.2 units in the -5 to 50 °C range (YSI 2006).

2.2.5 TURBIDITY Turbidity was measured by an YSI 6136 Turbidity probe installed in the YSI 6820V2 unit. The probe consists of a light emitting diode which emits radiation near the infrared region of the spectrum (830 to 890 nm) to illuminate the sample, while a highly sensitive photodiode detects the scattered light at a 90° angle from the light source in accordance with the International Standards Organisation (ISO) protocol. The output from the turbidity probe is then processed by the 6820V2 software and is recorded in Nephlometric Turbidity Units (NTU). The values are recorded by the sonde with a 0.1 resolution and has an accuracy of + 2% of the reading in the 0 to 1000 NTU and -5 to 50 °C range (YSI 2006).

11 2.2.6 DISSOLVED OXYGEN Dissolved oxygen (DO) was measured by an YSI 6562 Rapid Pulse polarographic probe installed in the YSI 6820V2 unit. The probe utilizes a Clark-type sensor that measures the electrical current that is associated with the reduction of oxygen as it diffuses through the Teflon-membrane, which is proportional to the partial pressure of oxygen in the sample. Dissolved oxygen is measured in mg/L and is converted into percentage saturation (%) using the sonde's temperature and conductivity reading. DO is measured with a resolution of 0.1% with an accuracy of + 2% of the reading in the range of 0 to 200% air saturation and -5 to 50°C (YSI 2006).

2.2.7 CHLOROPHYLL Chlorophyll content was measured by collecting triplicate 500 mL water samples at ~20 cm beneath the water surface at four different locations throughout the study site (see Figure 2.2.2), and kept on ice until filtration of the sample was possible. Millipore nitrocellulose (0.45 μm pore size) filters were used for the filtration of the samples, and 500 mL was filtered under mild suction unless a high turbidity in the sample prohibited full filtration of the sample. All samples were filtered under dark conditions to prevent undue chlorophyll denaturation. After filtration, 3 to 5 drops of a saturated MgCO3 solution were added to the filters to prevent acidity in the filter. The filters were then placed in 50 mL centrifuge tubes with 10 mL of 90% aqueous acetone and stored in the dark at +5 °C. After at least 20 hours, the samples are centrifuged for 5 min at 4200 rpm and the resulting supernatant decanted into 1-cm path length glass cuvettes, before the absorbance of each sample was measured at the wavelengths of 750, 664, 647 and 630 nm using a Novaspec II spectrophotometer. The measured absorbance values were used to calculate the amounts of chlorophyll a, b and c in (μg/l) using the empirical relationships described in (Parsons 1984):

12 Where Ex is the absorbance at wavelength x and corrected by subtracting the absorbance of the 750 nm (turbidity) reading. The total amount of chlorophyll in the sample was then calculated using the equation:

Where Chla, Chlb, and Chlc are the previously calculated amounts of Chlorophyll a, b and c, respectively, while is the volume of acetone in mL (10 mL), and is the volume of filtered seawater in litres. The final value of was then determined by calculating the mean value of the triplicate samples for each site.

2.2.8 SONDE CALIBRATION & MEASUREMENT TECHNIQUES Before each sampling event, each sensor of the YSI 6820V2-1 sonde was checked for any discrepancies when measuring known standard solutions for pH, turbidity and conductivity. If any significant drift was noticed the sensor was calibrated according to the instructions of the manufacturer (YSI 2006). The DO sensor was, however, always calibrated to the current pressure level at mean sea level using the recorded pressure data supplied from the Bureau of Meteorology (BOM) at Burleigh Heads. During transport to and from the study site the sonde would be kept secure while fitted with a protected cup filled with tap water in order to prevent the DO membrane from drying out. Before any samples were recorded the sonde would be kept submerged at the given depth for a minimum of 5 minutes to allow sensors to stabilise with the ambient water conditions.

2.2.9 DATA ANALYSIS All processing of water quality data was performed using the commercially available software package, MATLAB R2007b and R2010b. Prior to any statistical analysis each measured water quality parameter at each station was tested for normality and homoscedasticity using the Lilliefors test (Lilliefors 1967) and the Levene‟s test, respectively (Levene 1960). Data transformation using log and square root transformations were undertaken where necessary and alpha, was adjusted for multiple tests using the Bonferroni correction. Due to the spatio-temporal connectivity between measurements It is unlikely that the measured concentrations of a given water quality parameter are truly independent of each other, however for the purpose of this study they will be assumed independent.

13 In order to test if there was a difference between surface vs. bottom values for each station, the non-parametric Kruskal-Wallis test was applied to data sets that met the requirement of homoscedasticity but was found to be non-normal, while one-way ANOVA‟s were applied to data sets meeting the assumptions of normality and homoscedasticity. Data sets failing to meet the requirement of homoscedasticity but fitting a normal distribution was alternatively tested with a Welch‟s t-test (Welch 1951). The parametric Pearson correlation test was used to determine the relationship between each annually averaged parameter and distance to the river mouth.

In accordance with the report guidelines of the EHMP (EHMP 2007), temporal plots showing the seasonal variation of each measured parameter were created by calculating the median as well as the 20th and 80th percentile values using the measurements from all stations on each sampling day. Likewise for the generation of spatial plots, the annual median together with the 20th and 80th percentile values were calculated for all sample days at each station located in the course of the natural river system. Finally, a series of 3D contour plots was created for visual representation of the spatio-temporal dynamics of each measured parameter.

2.3 RESULTS

2.3.1 TEMPERATURE Temperature ranged from 17.38 (July) to 33.14 oC (January) over the course of 2010, meaning a potential increase in the rate of physiological reactions in elasmobranch fishes by a factor of 2-3 between winter and summer conditions (Brett & Groves 1979). Drops in temperatures were registered following heavy rainfalls and increased freshwater discharge throughout the year, with the upper reach station being the most sensitive, dropping as much as 5.2 degrees on one occasion, while the river mouth only decreased by 0.44 oC. The median annual temperature for each measuring station was not significantly correlated with increased distance to the river mouth (P = 0.066, R = -0.47), although there was a slight trend for water temperature to decrease when moving upstream. However, during prolonged periods with no significant rainfall in spring and summer, there were several registered cases of an inverse relationship, where the shallower and slower moving water of the upper reaches were warmer than the river mouth.

Figure 2.3.1: A) Seasonal variation in temperature throughout the course of 2010. B) Spatial variation in temperature throughout the course of 2010. The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study

There was no significant difference between temperature in the surface layer (~20cm depth) and at 2 m depth for any stations throughout the year, indicating the system being generally well-mixed by a conjunction of tidal movements and surface waves due to wind action as well as regular freshwater discharge events that prevent any noticeable thermocline to be established in the shallow water system. Only on five sampling events out of a total of 180 was there a difference equal or greater than 2 oC between top and bottom temperatures, while the mean of the differences was only 0.09 oC.

Figure 2.3.2: A contour plot of the spatiotemporal variation of temperature throughout the sampling period. The data set depicted contains only the measurements conducted by this study

2.3.2 SALINITY Salinity levels were found to be highly variable throughout the year and across all measuring stations in the estuary, with the river mouth ranging from 5.39 to 39.0 PSU, and the end of the tidal dominated area, from 0.03 to 25.92 PSU. The seasonal pattern in salinity was characterized by wide fluctuations between individual sampling dates (Figure 2.3.3) in particular during summer and autumn months. The observed pattern can be explained by the increased frequency of heavy rainfalls and the subsequent freshwater discharges during the wet summer season which has a clear impact on the salinity levels throughout the system, while salinity levels remain generally higher and more stable during the dry winter months. There was a strong negative correlation between the annual medians of each station and distance to the river mouth (P < 0.001, R = -0.97), with an average difference in salinity between the river mouth and uppermost station of 26.63 PSU.

Figure 2.3.3: A) Seasonal variations in salinity throughout the course of 2010. Note the abrupt fluctuations between sample days. B) Spatial variation in salinity throughout the course of 2010, showing a general decline of salinity proportional to distance from river mouth. Note the higher level of variation, which is indicated by the increased width between the 20th and 80th percentile, as the distance from the river mouth increases. The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study.

Only two stations had significantly different salinity levels between surface and 2 m depth measurements (P < 0.01). However, each of these stations was located at the entrance to the adjoining man-made canals, which are both relatively deep (> 4m) and may be assumed to

16 have an altered flow regime compared to the rest of the natural river system. There was an average difference of 3.51 between top and bottom measurements for those stations that were non-significantly different, which could indicate the occurrence of a halocline in the system, though the lack of any apparent thermocline (as mentioned above) suggests any stratification to be relatively weak. Furthermore, there was no single sample day where all stations displayed a difference greater than 2.0 between top and bottom, indicating that any halocline present was local rather than covering the entire estuary.

Figure 3.3.4: A contour plot of the spatiotemporal variations of salinity throughout the sampling period. The data set depicted contains only the measurements conducted by this study.

2.3.3 DISSOLVED OXYGEN Dissolved oxygen (DO) levels across stations and throughout the year of 2010 were generally relatively high, with an overall mean of 7.19 ±1.44 mg/l (S.D., n=665). The annual median DO was positively correlated with distance from the river mouth (P < 0.01, R = 0.78). No seasonal variation in DO levels seemed to be evident, aside from slightly elevated DO concentrations in the spring of 2010. Out of 665 measurements over 43 sampling days, only 4.6% of all measurements registered a DO level that was below 4.0 mg/l. Of these, 71% were bottom measurements, while 45% were registered on the same day (9th of August).

Figure 2.3.5: A) Seasonal variations in dissolved oxygen throughout the sampling period. Note the relative small difference between the 20th and 80th percentile levels across sampling days. B) Spatial variations in dissolved oxygen across the tidal-dominated part of the system. Note the slight increase of dissolved oxygen concentrations with increased distance to the river mouth. The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study.

There was no registration of DO levels below 2.0 mg/l over the sampling period of 2010. Only one station (EHMP stations excluded) had a significant decrease in DO between its surface and bottom values over the course of 2010 (P < 0.01, n = 60) with a mean difference between surface and bottom of 1.18 ±0.82 mg/l (S.D., n=60). The lack of hypoxia events (DO < 2.0 mg/l) coupled with a relative small number of low DO concentrations (DO < 4 mg/l), suggests that the tide-dominated part of the Tallebudgera Creek estuary is well mixed on a spatiotemporal scale and not prone to hypoxic events. The shallow nature of the system is a major contributor to this pattern.

Figure 2.3.6: A contour plot of the spatiotemporal variations of dissolved oxygen throughout the sampling period. The data set depicted contains only the measurements conducted by this study.

2.3.4 PH pH levels fluctuated around a mean of 7.66 ±0.47 (S.D., n=665), with an overall range spanning from 6.37 to 8.66 over the course of 2010. pH was, as expected, negatively correlated with distance to the river mouth (P < 0.01, R = -0.96) due to the more alkaline nature of seawater dominating the river mouth while freshwater discharge and rainfall generated run-off causing a drop in pH in the upper reaches of the system. No station displayed a significant difference between surface and bottom pH levels, with the highest observed difference between surface and bottom being 0.7, which was registered at the uppermost station during the presence of a weak halocline (4.58 difference between surface and bottom salinities). The seasonal variation of pH was expected to be strongly influenced by freshwater discharge/rainfall events, with the pH levels dropping down to as little as 6.7 at the river mouth the week after a ~96.0 m3/s discharge event.

Figure 2.3.7: A) The seasonal variations of pH throughout the sampling period. Note how the fluctuations in pH tend to follow the same general trend as salinity (Figure 2.3.3A). B) Spatial variations in pH throughout the course of 2010, showing a general decline of pH levels proportional to distance from the river mouth. The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study.

Figure 2.3.6: A contour plot of the spatiotemporal variation of dissolved oxygen throughout the sampling period. The data set depicted contains only the measurements conducted by this study.

20 2.3.5 TURBIDITY Turbidity levels were generally low across stations and throughout the year, with a median value of 2.00 NTU (n = 665), but with spikes up to 252.80 NTU following a discharge event of ~103.5 m3/s. Turbidity was positively correlated (P < 0.01, R = 0.96) with distance to the river mouth, indicating a general increase of suspended solids upstream. There was no significant difference between surface and bottom layers throughout the year, and any seasonal variation of turbidity was most likely the result of a change in frequency and magnitude of freshwater input between summer and winter.

Figure 2.3.7: A) The seasonal variations of turbidity throughout the sampling period. B) Spatial variations in turbidity throughout the course of 2010, showing a general increase of turbidity levels proportional to distance from the river mouth. Note that the scale of the y-axis differs between A) and B). The data set depicted is the assimilated data set of EHMP measurements along with measurements made by this study.

Figure 2.3.8: A contour plot of the spatiotemporal variations of turbidity throughout the sampling period. The data set depicted contains only the measurements conducted by this study.

2.3.6 CHLOROPHYLL A Measured levels of chlorophyll a was low for all stations throughout the year, with a mean value of 0.31 ± 0.28 μg/l (S.D., n = 120) and a maximum value of 1.42 μg/l registered the 3rd November 2011 at the uppermost station. There was no correlation (P = 0.11, R = 0.88) between chlorophyll a and distance to the river mouth. However, data suggests that there is a general tendency of an increase of chlorophyll a concentrations upstream. The values measured by this study were in contrast to the chl a concentrations reported by the EHMP, where the mean value across stations and throughout the year was 2.71 ± 5.32 μg/l (S.D., n = 65) and a maximum value of 42.25 μg/l reported the 16th April 2011 for the middle reach, and will be discussed further in Section 2.4.

Figure 2.3.8: A) Seasonal variations in chlorophyll a throughout the sampling period of 2010. Note that chlorophyll a concentration only exceeded 20 μg/l on one occasion. B) Spatial variations in chlorophyll a throughout the course of 2010, showing a weak trend of increased chlorophyll a concentrations upstream. The data set depicted is the assimilated product of EHMP measurements along with measurements made by this study.

2.4 DISCUSSION

As stated in Section 2.1 the two primary objectives of the water quality measuring campaign was: 1) acquisition of validation data for comparison with hydraulic model outputs (detailed in Chapter III); and 2) assessment of general water quality conditions and the spatio-temporal water quality dynamics of the Tallebudgera Creek system. While the data sets collected by this study, because of the limited temporal and spatial extents, do not allow an in-depth analysis of ecosystem health, several key aspects that appear to influence ecosystem health can be identified.

Importantly for the purposes of this study, vertical stratification of the water column was found to be insignificant throughout the natural parts of the system, although prolonged stratification was evident in the adjoining artificial canals. These findings are unsurprising due to the shallow nature of the unmodified part of the estuary compared to the canals (> 5 m). Due to their placement perpendicular to tidal currents and their lack of major freshwater sources, the artificial canals are expected to be more prone to stratification. In turn, the lack of any significant spatio-temporal stratification patterns within the natural part of the system further justifies this study's application of a two-dimensional depth-averaged hydrodynamic

23 model for simulating the transport and dispersion of salinity as well as heat exchange (Chapter I and Chapter III).

Due to the limited temporal resolution of the data sets, it was not possible to assess the diel variation of dissolved oxygen (See Chapter III for a discussion on diel variation in salinity and temperature). While oxygen levels are known to fluctuate as high as >15 mg/l over a 24 hr period, especially in organically enriched waters (Lamb 1985), the results presented in Section 2.3.3 suggest that DO levels throughout the system and over the course of the year are in compliance with the EHMP Water Quality Guidelines (EHMP 2007). It is interesting to note that nearly all readings of DO that were > 4 mg/l occurred on the same sample day and at all stations (even those located in the well-mixed waters of the river mouth). While this result cannot be dismissed due to faulty equipment, it is possible that this "outlier" in the data set was caused by the electrolyte solution of the DO sensor being partly dried during storage before the sampling event. Furthermore, the persistent high levels of DO, coupled with relatively low background concentrations of chlorophyll a, suggests that the system is predominantly aerated by physical means, i.e. diffusion from the atmosphere, and was little affected by eutrophication. Only on one occasion was the chl a level high enough to classify as a bloom (42.25 μg/l, EHMP measurement), which occurred after a prolonged period of increased freshwater discharge (~7x above base levels during dry periods). It is thus plausible that this bloom was triggered by an increase of organic material and nutrients being brought into the system with the freshwater discharge. However, three days later there was already no sign of an increased chl a concentration in the system (< 1.5 μg/l), which suggests that the bloom to either have dissipated or become localised in small patches not covered by sampling stations.

As mentioned in Section 2.3.6, there was a notable difference in the chl a measurements obtained through this study's field campaign as opposed to the EHMP measurements. Since this study sampled on different days and at different sample sites than the EHMP, it is difficult to determine whether this discrepancy between the data sets is caused by natural variation originating from the spatio-temporal dynamics of the system, or the result of an inadequate chl a extraction technique (Section 2.1.6).

Aside from the tide-induced mixing of the system, the main driver of spatio-temporal changes in measured water quality parameters was without equal heavy rainfall and the subsequent run-off and discharge from the catchment. Freshwater discharge events had, not surprisingly,

24 the greatest effect on salinity and turbidity, but were also found to cause a notable change in temperature and pH levels. While salinity obviously is the most sensitive to increased freshwater inputs, the system as a whole displayed a remarkable resilience to these abrupt system-wide shifts by returning to a pre-discharge state in a matter of days; e.g. registered turbidity levels of 252.8 NTU dropped to 4.8 NTU just 6 days after a ~100 m3/s discharge event had taken place on the 4th of May, while salinity also increased from 0.3 PSU to 9.8 PSU at the same location. The system's natural ability to return to a steady-state can likely be contributed to its small volume, meaning that relatively few tidal cycles are required for flushing the entirety of the system. However, the small size of Tallebudgera Creek also means that less freshwater discharge is needed before the system is being pushed into a phase-shift, which in turn ultimately can/will result in a higher frequency of sudden system- wide shifts between oligohaline and meso/polyhaline water conditions compared to larger and more voluminous estuaries. It is highly plausible that these sudden changes in salinity and turbidity will impose a range of physiological challenges for species residing within the estuary, and the potential implications of these abrupt phase-shifts will be discussed in detail in the following chapters.

In conclusion, the results gathered from the water quality campaign in conjunction with the EHMP data are in good general agreement in supporting the past EHMP assessments of Tallebudgera Creek, which in the past 4 years have been given a rating of "Good" to "Excellent" water quality (EHMP 2009).

CHAPTER III

HYDRODYNAMIC MODELLING OF TALLEBUDGERA CREEK

26 3.1 INTRODUCTION

Numerical modelling of shallow water systems has in the past few decades experienced rapid development, thanks to the availability of increasingly powerful computers capable of resolving the complex numerical models necessary for adequate representation of the relevant physical processes (Dias & Lopes 2006b). While the complexity of those physical processes involved still prompts for future research and development of the mathematical formulation of these processes, depth-averaged 2D-models have proved to be reliable in capturing the hydraulic properties of shallow water systems (Cheng et al. 1993, Inoue & Wiseman Jr 2000, Umgiesser et al. 2003, Dias & Lopes 2006b, Zacharias & Gianni 2008).

Furthermore, advanced hydrodynamic modelling tools, such as MIKE by DHI (Warren & Bach 1992, DHI 2009), have consistently been used by researchers and engineers to simulate the hydraulic properties of a wide range of systems, e.g. estuaries (Szylkarski et al. 2004, Mirfenderesk & Tomlinson 2007, Paliwal & Patra 2011), coastal lagoons (Dias & Lopes 2006, Zacharias & Gianni 2008), large offshore straits (McClimans et al. 2000) and entire oceans (Almroth & Skogen 2010).

In previous field studies on C. leucas movement (Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010), a recurrent issue is the difficulty in obtaining measurements of the physical as well as the biological parameters at the actual position of the animal. While recent developments in acoustic transmitters now allow in situ measurements of parameters such as temperature and salinity, these transmitters remain relatively expensive while also incapable of measuring the magnitude and direction of flow. However, the rapid advances in computer technology and development of adaptable grid discretisation schemes have recently increased model spatial resolution to a level that allows governing 2D physical processes in narrow estuaries and adjacent near-shore areas to be resolved. This has opened up new possibilities for comparing short- and long-term movement of large fish species, such as C. leucas, with model predictions of a wide range of hydrodynamic entities (e.g. Werry (2010)).

While the prospect of direct comparison between fish movement and fine-scale hydraulic model outputs poses a unique opportunity to investigate fish responses relative to dynamic shifts in the hydraulic regime, the implementation of a hydrodynamic model also provides the foundation for any further model development, e.g. agent-based models. In recent years,

27 ecological studies, e.g. Humston (2004) and Goodwin (2006), have coupled the Eulerian framework of hydrodynamic models with the Lagrangian framework of agent-based models, in order to simulate fish movement in relation to hydraulic cues in various aquatic environments. While agent-based models will be discussed in detail in Chapter V, the remainder of this Chapter will deal with implementation and validation of a hydrodynamic model for Tallebudgera Creek.

3.1.1 AIMS & PURPOSE As a fundamental part of this study, the aim of this chapter is to implement a MIKE 21FM hydrodynamic model of the Tallebudgera Creek estuary. The two main objectives are: 1) to validate the model to a satisfactory degree, so that hydraulic model outputs may be used to analyse short-term tracks of C. leucas (Chapter IV); and 2) use the validated hydrodynamic model as a foundation for developing an agent-based model capable of replicating observed short-term movement of C. leucas (Chapter V).

3.2 METHODOLOGY

The commercially available MIKE21 modelling software package was used to create a 2-D hydrodynamic model of the tidal part of Tallebudgera Creek. This model is capable of simulating the effect of freshwater discharge vs. tide-driven water flow while accounting for the transport processes of salinity and temperature (Warren & Bach 1992, DHI 2009). In the following sections, a short description encompassing the most fundamental properties of the model framework and setup is given.

3.2.1 EQUATIONAL FRAMEWORK The MIKE 21 FM Hydrodynamic Module is based on an unstructured flexible mesh approach to resolving the two-dimensional flow simulation of the Tallebudgera Creek estuary and the nearby offshore environment surrounding it (Warren & Bach 1992). The numerical scheme adopts a finite-volume approach to solving the depth-integrated incompressible Reynolds averaged shallow water equations, while using 2nd order transport equations to resolve the advection/dispersion of salinity and temperature. Furthermore, a range of separate turbulence closure schemes can be applied to resolve sub-grid scale mixing processes (DHI 2009).

28 The local continuity equation is:

Eq. 3.1

While the two horizontal momentum equations for the x- and y-components are, respectively:

Eq. 3.2

Eq. 3.3

Where t is time, while x, y, z are the Cartesian co-ordinates, is the surface elevation, d is the still water depth and is the total water depth. The depth-averaged velocity components are given by and in the x and y directions, respectively. f is the Coriolis parameter and g is gravitational acceleration. is the density of water and is the reference density of water. , , and are the constituents of the radiation stress tensor, while is the vertical turbulent viscosity. is atmospheric pressure and is the magnitude of a discharge from point sources, while and is the corresponding velocity by which the water mass concerned is discharged into the ambient water. are the lateral stresses, which include viscous friction, turbulent friction and differential advection, while are the surface and bottom stresses (DHI 2009).

3.2.2 TRANSPORT EQUATIONS The two-dimensional transport of temperature, , and salinity, , follows the general transport-diffusion equations:

Eq. 3.4

Eq. 3.5

In the above equations, and are the depth averaged temperature and salinity, respectively, is the total water depth while and are the depth averaged velocities in the and directions, respectively. is a source term due to heat exchange with the atmosphere and is

29 the horizontal diffusion term. Finally and are respectively the temperature and salinity of a point source (DHI 2009).

3.2.3 HEAT EXCHANGE Heat exchange with the atmosphere is calculated on the basis of four major physical processes: latent heat flux, sensible heat flux, net short-wave radiation and net long-wave radiation. In the heat exchange calculation, the model assumes that all processes, except net short-wave radiation, occur at the water surface (DHI 2009). In order to define an absorption profile for the short-wave heat flux, a modified Lambert-Beer equation is used to approximate light attenuation throughout the water column:

Eq. 3.6

Where is the light intensity at depth below the surface, while is the light intensity just below the surface. is a user-specified constant that accounts for the fraction of light energy that is absorbed near the surface and is the user-specified light extinction coefficient (DHI 2009). For two-dimensional calculations, the source term is given by:

Eq. 3.7

In equation 3.7 is the evaporative heat loss (or latent heat flux), is the sensible heat flux due to convection, is the short-wave radiation and is the long-wave radiation.

is the reference density of water and is the specific heat capacity of water (DHI 2009).

The latent heat flux is given by:

Eq. 3.8

Where = 4370 and is the latent heat constant encompassing the latent heat vaporization constant L and the Dalton number . is the wind speed 2 m above the water surface while and is the temperature at 0°C. is the relative humidity in percent. is the temperature of the water surface and is air temperature. and are user specified constants with default values of 0.5 and 0.9, respectively (DHI 2009).

The heat flux due to convection, ( , depends on the boundary layer between the water surface and the atmosphere. The model assumes that this layer is predominantly turbulent and thus implies the following relationship:

Eq. 3.9

Where is the air density , is the specific heat capacity of air while and is the sensible transfer coefficients for heating and cooling, respectively. is the wind speed 10 m above the water surface (DHI 2009).

The intensity of the short-wave radiation, which mainly consists of light with wavelengths between 4000 to 9000 Ǻ, depends on the distance to the sun, declination angle and latitude, extraterrestrial radiation, cloud cover and the amount of water vapour in the atmosphere (DHI 2009). The net short-wave radiation is:

Eq. 3.10

Where is reflection coefficient and is the average hourly short wave radiation.

Long-wave radiation consists of wavelengths between 9000 and 25000 Å and is dependent on cloud cover, air temperature, the vapour pressure in the air and the relative humidity (DHI 2009). The net outgoing long-wave radiation is given by Brunt's equation:

Eq. 3.11

Where is the vapour pressure at dew point temperature measured in mb. is the Stefan Boltzman constant and is . is the number of hours of sunshine and is the maximum number of hours of sunshine. The coefficients and are defined by the values of:

For a more detailed overview of the equational framework of the hydrodynamic model the reader is referred to the MIKE 21 scientific documentation (DHI 2009).

31 3.2.4 MODEL DOMAIN The bathymetry measurements used for the model's grid generation were supplied by DHI, and originally surveyed by Gold Coast City Council in 2001. As mentioned in the section above, the model applies a flexible mesh to spatially represent the physical bathymetry of the system. The mesh created for the Tallebudgera model consists of a mixture of triangular and quadrangular grid cells. The resolution of the flexible mesh, or the actual size of the individual grid cell, varies from relatively small quadrangular cells each with an area of 50 m2 to larger offshore triangular cells each with an area of 12000 m2. In total the entire mesh consists of 34253 individual calculation cells that are solved and updated for each model time step. A variable time step interval ranging from 0.01 to 30 seconds is used in the calculation of both the shallow water equations, and the transport equations are determined so that the Courant-Friedrich-Lévy (CFL) number is less than a critical CFL number in all computational nodes. Figures 3.2.2 and 3.2.3 below provide an overview of the entire model area, and an example of the structure of flexible mesh grid, respectively.

Figure 3.2.1: Illustration showing the bathymetry of the Model domain, plotted in a MGA-56 projection. The area inside the black box is the part of the non-tidal dominated freshwater zone that is included in the model setup (see main text for further explanation). The red box marks the area shown in Figure 3.2.3 below.

Figure 3.2.2: An example of the flexible mesh of the model. The green markers each indicate a smaller sub-area within the mesh. The figure is plotted using MGA-56 projection coordinates (unit: meters).

In order to capture the effects of freshwater discharge on the temperature, salinity and hydrodynamics of the system, the model domain was created to include a connection between the point source of freshwater discharge data (See section 3.2.5.1) and the tide-dominated area of the system (See black box in Figure 3.2.2).

3.2.5 INPUT PARAMETERS In order to accurately simulate the hydrodynamics as well as the heat exchange between the water and the atmosphere, a range of input data was required. In the following sub-sections, each input parameter in the model design will be described.

34 3.2.5.1 OFFSHORE BOUNDARY DATA The tidal data utilised by the offshore boundary of the Tallebudgera model domain are extracted from DHI's Global Tide model, which represents the major diurnal (K1, O1, P1 and Q1) and semi-diurnal tidal constituents (M2, S2, N2 and K2) with a spatial resolution of 0.25° × 0.25° and is based on altimetry data gathered from the NASA/CNES TOPEX- /POSEIDON satellite. Boundary values for salinity and temperature are extracted as daily values from the Global HYCOM model, which is a data-assimilative hybrid isopycnal- sigma-pressure (generalized) coordinate ocean circulation model (the Hybrid Coordinate Ocean Model or HYCOM). Due to the large spatial resolution of the HYCOM model, the entire offshore area of the Tallebudgera Creek lies within a single HYCOM calculation cell, and thus it is assumed that salinity and temperature vary in time, but are constant along the boundary. For further documentation on the HYCOM model the reader is referred to Wallcraft (2009).

3.2.5.2 FRESHWATER DISCHARGE Freshwater discharge1 is handled as a point source in the model, with the source located in the very top of the river in the model domain (see Figure 3.2.2). The freshwater discharge data are measured and supplied by the Queensland Department of Environment & Resource Management and provides a point measurement every six hours of freshwater discharge in cubic meters along with corresponding salinity and temperature.

3.2.5.3 METEOROLOGICAL DATA In order to calculate the heat exchange between the atmosphere and water surface (as described in section 3.2.3), meteorological data from the Coolangatta Weather Station, supplied by the Australian Bureau of Meteorology (BOM), was incorporated into the model with the assumption that it is representative of the Tallebudgera Creek area, which is located ca. 9 km away from the weather station. The time series of data used in the Heat exchange module includes: air temperature, relative humidity, wind speed and wind direction - all with a temporal resolution of 30 min. The heat exchange module assumes a default constant value of 70% for the clearness coefficient, which is a term for the given cloud cover conditions, but due to the lack of readily available cloud cover data for the area, this parameter was treated as a calibration parameter throughout the model development.

1 Based on or contains data provided by the State of Queensland (Department of Natural Resources and Water) [2011], on the condition that the State gives no warranty in relation to the data (including accuracy, reliability, completeness, currency or suitability) and accepts no liability (including without limitation, liability in negligence) for any loss, damage or costs (including consequential damage) relating to any use of the data. Data must not be used for direct marketing or be used in breach of the privacy laws.

35 3.2.5.5 PRECIPITATION Rainfall was incorporated into the HD model as a time series of measured daily rainfall for the Tallebudgera Creek area supplied by the Queensland Bureau of Meteorology. The model does not take any run-off into account from the adjacent downstream catchments, meaning that rainfall will only "fall" within calculation cells and not in the surrounding land values that is not covered by the model mesh. It is however assumed that the majority of the Tallebudgera systems total run-off originates from the much larger upper catchment, and is thus captured in the freshwater discharge measurements by the DERM flow gauge station located ~15 km from the river mouth (Uppermost point in figure 3.2.2).

3.2.5.6 WAVE RADIATION STRESS In order to account for wave-induced longshore currents immediately offshore to the Tallebudgera Creek river mouth, the second order stresses due to breaking of short period waves was included in the hydrodynamic model. The wave radiation stresses Sxx, Syy and Sxy (m3/s2), were calculated over the course of 2010 by utilizing the MIKE21 Spectral Wave model (SW) to calculate the wave transformation and breaking processes occurring from offshore to the Tallebudgera Creek river mouth (figure 3.2.4). The mesh of the model domain was generated using bathymetry data obtained from the DHI, while offshore wave boundary given by significant wave height, peak wave period and wave direction was obtained from the Gold Coast Seaway wave buoy located approximately 10 km north of Tallebudgera Creek river mouth. The setup of this wave model assumes that the wave conditions at the Gold Coast Seaway are representative of the conditions at Tallebudgera, and constant along the offshore boundary of the model domain.

Figure 3.2.3: Model domain of the created offshore Spectral Wave model. The red box marks the Tallebudgera Creek entrance. The figure is plotted using MGA-56 projection coordinates (unit: meters).

3.2.5.7 INITIAL CONDITIONS The initial conditions of temperature and salinity throughout the entire model domain were generated by using historical EHMP measurements to produce an area map with spatially varying temperature/salinity regimes according to the historical data. The generated initial fields were then applied to a model setup simulating one month prior to the actual start date of the full 2010-2011 model setup. This allowed the initial fields of temperature and salinity to move into a quasi-stationary equilibrium through tidal current induced mixing and heat exchange with the atmosphere. The resulting spatial maps containing data of surface elevation, temperature, salinity, total water depth as well as u- and v-velocities was then extracted and used as initial values for the full 2010-2011 model run.

3.2.6 CALIBRATION PARAMETERS Certain variables and constants needed for the model setup was beyond the scope of this study to obtain measurements of, and were thus treated as calibration parameters in order to

37 obtain satisfactory simulation results. Table 3.2.1 presents the list of the various calibration constants and solution settings specified in the model setup, while spatially varying parameters are explained in more detail below.

Table 3.2.1: The values and user-specified settings of the enabled modules in the final model setup.

Shallow water equations Value Unit Time integration Low order, fast algorithm - Space discretisation Low order, fast algorithm - Flooding and drying Value Unit Drying depth 0.005 meter Flooding depth 0.05 meter Wetting depth 0.1 meter Eddy Viscosity Value Unit Horizontal eddy viscosity 0.02 Heat Exchange Value Unit Time integration Low order, fast algorithm - Space discretisation Higher order - Constant in Dalton‟s law 0.5 dimensionless Wind coefficient in Dalton‟s law 0.9 dimensionless Sun constant, a, in Ångstrøm's law 0.295 dimensionless Sun constant, b, in Ångstrøm's law 0.371 dimensionless Standard meridian for time 153.45 Degrees, East Beta in Beer's law 0.3 dimensionless Light extinction coefficient 1 Clearness Coefficient 70 Percentage

The dispersion coefficient (m2/s) that accounts for the sub-grid mixing processes was through a trial and error process found to be highly related to cell size, and thus a spatial dispersion coefficient map was created by defining a unique dispersion coefficient for each calculation cell through the following standardized equation:

(Eq.3.12)

Where is the dispersion coefficient while is the area of element cell i. Any instability found in individual cells caused by the above formulation was manually decreased to a value where they no longer produced any instability in the transport equations.

Bed resistance was described in the model setup by defining a spatial map of the model domain with a value of the Manning‟s number, for each cell. The default value

38 for the entire domain was set to 50, and later decreased to lower values (thus increasing bed resistance) for local areas where high current velocities caused numerical instability during model simulation.

3.2.7 VALIDATION DATA For the purpose of validating the outputs of the hydrodynamic model, a wide range of measurements were obtained throughout 2010. The following sub-sections give a brief explanation to the origins and/or methodology used to obtain the validation data.

3.2.7.1 HISTORICAL DATA The initial calibration of the HD model relied on historical data obtained from two data points located in the middle and upper reaches of the estuary (Figure 3.2.4). The data covers a period from 29/03/2007 to 01/05/2007 and was provided by the Griffith Centre for Coastal Management (GCCM). The data consisted of surface elevation (meters), temperature (°C) and salinity (ppt) with a temporal resolution of 15 min, and logged using Greenspan CTD's. Due to the lack of supplementary information of this data, e.g. deployment depth, specific Greenspan CTD type, calibration method and accuracy levels, it only served as a secondary validation period compared to the data obtained through the efforts of this study.

3.2.7.2 ADCP DATA As a means to obtain validation data for the hydrodynamic model outputs, two Nortek Aquadopp Current Profilers (ADCP) were deployed in the middle and upper reaches of the Tallebudgera system (figure 3.2.4) in the period 23/9-2010 to 20/10-2010. Both ADCP‟s were deployed in the middle of the river stream at ca. 4 m depth. The Aquadopp Profiler uses three acoustic beams slanted at 25° to accurately measure the current profile in a user selectable number of cells. The internal tilt and compass sensors tell the current direction and the high-resolution pressure sensor gives the depth and the tidal elevation, with an accuracy of ±1% of the measured value (Nortek 2008).

Figure 3.2.4: The deployment locations of ADCP's and CTD recorders in the middle and upper reaches of the system (MR & UR). Blue stations mark the 2007 GCCM stations. The figure is plotted using MGA-56 projection coordinates (unit: meters).

Both ADCP‟s were set to an acoustic frequency of 2.0 MHz, with a bin size of 0.2 m and a total of 20 bins. Sampling frequency was set to 600 seconds. Two custom-made wooden frames were constructed for the purpose of keeping the ACDP‟s in place on the riverbed and preventing tilting of the instrument (Figure 3.2.5).

Figure 3.2.5: Picture of the constructed wooden frame with the ADCP mounted on it. All metal used was either lead or stainless steel in order to reduce any effect on the internal compass.

Each frame was constructed using marine grade stainless screws to prevent drift of the internal compass, and weighted down by 10 kg of lead dive weights. Furthermore, a 15 kg cinder block was attached as an anchor to each frame by the end of an 8 m rope. Prior to deployment, sensors and compass headings were tested and calibrated in accordance to the Nortek user manual and a small westward drift of ~3.0 degrees were registered in the compass bearings. Furthermore, the head of each ADCP was covered in zinc oxide cream to prevent any biofouling to occur. Data extraction and processing was done using the Nortek Storm software (Nortek 2008), and Matlab 2007b.

3.2.7.3 TEMPERATURE/SALINITY DATA Two Odyssey Salinity/Conductivity recorders were used for long-term deployment at locations in the upper and middle reaches of the system in the periods of 19/1-2010 to 6/2- 2010 and 11/2-2010 to 30/3-2010. Reported accuracy levels lied within ±3% of the measured value in 0-65 °C and 0-80 mS/cm conductivity range, respectively.

41 Prior to deployment both recorders were calibrated according to the Odyssey user manual, and mounted inside an open PVC pipe housing that allowed ample water to flow through while also acting as a protective casing for the recorder. Recorders were deployed ca. 0.3m below the lowest predicted tide levels for the planned deployment period and set to continuously log temperature and conductivity every 10 min throughout their deployment period. A weekly measurement of temperature/conductivity using the YSI 6920 Sonde (described in Chapter 1) at each deployment location was made in order to check for any potential drift in the Odyssey recorders during deployment.

Data extraction was done using the supplied Odyssey software, and conductivity measurements were then converted into salinity for comparison with model outputs using the Unesco standard algorithms for computation of fundamental properties of seawater (Fofonoff 1983). YSI temperature/salinity data (described in detail in Chapter II) were furthermore utilised to validate spatial and seasonal variations in the model output of temperature and salinity.

3.2.8 CALIBRATION & DATA ANALYSIS Calibration of surface elevation and current velocities was achieved by varying the Manning number throughout the domain, while also continuously optimizing the mesh resolution of the domain, until a satisfactory fit between simulated and observed values was achieved. Salinity and temperature were calibrated by varying the dispersion coefficient throughout the domain (section 3.2.6) until the best possible fit was obtained under the limitations of applying a 2D model to simulate transport/diffusion processes that are inherently three- dimensional in nature. Temperature was furthermore calibrated by varying the user-specified constants (table 3.2.1) in the heat exchange module of the model setup.

Model outputs in terms of surface elevation, current speed, current direction, temperature and salinity were extracted from the simulation result files at the corresponding locations and time periods of the measured validation data. In accordance with DHI standards, all individual datasets were analysed by calculating a model Quality Index consisting of the mean, bias, standard deviation, root mean square error (RMS) and the correlation coefficient between simulated and measured variables. Bias was defined and calculated as the mean of the difference between simulated values and corresponding measured values. RMS error was defined as the square root of the mean of the squared differences between modelled and measured values. Time series as well as spatial plots for each type of dataset were generated

42 using Matlab R2007b and the built-in MIKE Plot Composer functionality for visual comparison between simulated and measured values.

3.3 RESULTS

3.3.1 SURFACE ELEVATION VALIDATION Modelled surface elevation for the initial calibration period of 2007 was in good agreement with measured values at the MR-2007 Calibration point (MR-2007), with a correlation coefficient of 0.962 along with bias and root mean square (RMS) values of 0.069 an 0.121, respectively (Figure 7). The bias indicates that there is only a small difference on average between measured and simulated data. The relatively high RMS value (compared to the bias value), however, suggests that there is a high variation between differences. The positive bias is because the tidal wave trough is slightly underestimated in the model as seen from below figure. This is most commonly due to limitations in the accuracy of the measured bathymetry of the system. Limitations in grid resolution through narrow gaps in the river could also affect this.

Figure 3.3.1: Modelled surface elevation (red line) plotted against measured values (blue line) during the period of 29/3-2007 to 8/4-2007 at the MR-2007 calibration point.

43 For the UR-2007 calibration point (UR-2007), the correlation coefficient was slightly lower (R = 0.918), with bias and RMS values of 0.068 and 0.166, respectively. The same general pattern therefore seems to be present at both the UR-2007 and the MR-2007 calibration point.

Surface elevation for the 2010 middle reach calibration point (MR-ADCP) was similar to 2007 in demonstrating good agreement with measured values, with a correlation coefficient of 0.963 and a bias of 0.036. The RMS value of 0.102 does, however, indicate relatively high variability in the differences between simulated and measured values. The simulated vs. measured values for the Upper reach calibration point (UR-ADCP, Figure 3.3.3) displays the same trend as MR-ADCP, with a correlation coefficient 0.969 together with bias and RMS values of 0.183 and 0.203, respectively. The increased bias and RMS values of the UR- ADCP calibration point suggest that model accuracy in terms of tidal prediction decreases with increased distance from the offshore boundaries of the system.

Figure 3.3.2: Modelled surface elevation (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the MR-ADCP calibration point.

As evident from Figures 3.3.2 and 3.3.3, a small flood event occurred in the system between the 12th and 13th of October due to a heavy rainfall event, leading to freshwater discharge levels at the upper reach of > 110 cumecs. As from the above-mentioned figures, the model performs well in capturing the elevated water level at both calibration points, confirming the

44 model‟s ability to handle sudden flooding events, with only minor discrepancies from measured levels.

Figure 3.3.3: Modelled surface elevation (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the UR-ADCP calibration point.

3.3.2 FLOW VELOCITY VALIDATION Simulated current speeds for the UR-ADCP calibration point (Figure 3.3.4) for the entire measuring period display a weak correlation of 0.28 with measured values, together with bias and RMS values of 0.011 and 0.020, respectively. However, there is a strong correlation (R = 0.980) in the period 10/10 to 14/10, where increased discharge levels caused an increase in current speed. The results shown in Figure 3.3.5 suggest that during dry periods the model predicts a current speed that fluctuates between 0.4 cm/s and 7 cm/s, corresponding to a very weak tidal signal. The abrupt-fluctuating measured values suggest an almost non-existent tidal influence in this part of the system in the model, ultimately causing the low correlation coefficient between data sets. It is also important to note that the bias of 0.011 corresponds to a mere 1.1 cm/s difference in mean current speeds. The model‟s ability to accurately simulate current speeds in this end of the system is, however, confirmed during high discharge events (the 10th to 14th of October).

Figure 3.3.4: Simulated current speed (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the UR-ADCP calibration point.

Figure 3.3.5: Simulated current speed (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 27/9-2010 at the UR-ADCP calibration point.

Simulated current directions for the UR-ADCP calibration point (Figure 3.3.7) were found to be affected by the same issue as described above with a correlation coefficient of 0.56 together with bias and RMS values of -10.89 and 65.23, respectively. As it is evident from Figure 3.3.7, the predicted current directions followed a clear tidal pattern during dry periods, whereas measured values fluctuated abruptly with only a weak tidal signal present. The abrupt fluctuations are likely an indication of small local eddy vortexes, which are below the mesh resolution of the model grid, and thus not possible to capture directly. During the discharge event, the correlation between data sets increases to 0.75 while the relative values of the bias and RMS drop to 4.77 and 36.74, respectively. Taking the drift of ~3.0 degrees of

46 the ADCP compass into account (Section 3.2.7.2), the bias is reduced even further; however, the high RMS value still indicates a high variability in the differences.

Figure 3.3.6: Simulated current directions (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the UR-ADCP calibration point.

While the reason remains unclear, the ADCP deployed in the Middle reach failed measuring reliable data, possibly due to the instrument sinking into the fine sediment and blocking the beams responsible for measuring the u- and v-components. The measured values of current speed and direction are displayed against simulated values in Figure 3.3.8 and 3.3.9, respectively.

Figure 3.3.7: Simulated current speed (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the MR-ADCP calibration point.

47 As it is evident from Figure 3.3.8, measured values of current speeds are extremely high, topping at ~1.6 m/s during dry periods, which is nearly 15x higher than the predicted levels, and peculiarly dropping below 0.5 m/s during the flooding event. Furthermore, neither measured values for current speed nor direction display any persistent tidal signal, adding to the growing evidence of equipment failure.

Figure 3.3.8: Simulated current directions (red line) plotted against measured values (blue line) during the period of 24/9-2010 to 19/10-2010 at the UR-ADCP calibration point.

The lack of reliable validation data for the Middle reach unfortunately prevents assessment of model performance and accuracy in terms of flow velocities for this part of the system. However, due to the good agreement between predicted and measured values in the Upper reach, it is plausible that model performance for the Middle reach is in good agreement as well, because of its closer proximity to the tidal forcing from the offshore model boundary.

3.3.3 SALINITY VALIDATION Due to a large freshwater discharge event taking place in between the two sampling periods for salinity and temperature validation data, the two periods can be regarded as a temporal snapshot of the system in a dry-period state (First period) and a wet-period state (Second period), respectively. The different nature of the two sampling periods allowed for an opportunity to assess model performance in terms of salinity during these contrasting system states.

Simulated levels of salinity for the first period at the MR-CTD calibration point was found to be in good agreement with measured data, with a correlation coefficient of 0.84 together with

48 bias and RMS values of 1.96 and 2.22, respectively. As indicated by the bias value, the model tends to simulate slightly elevated levels of salinity compared to measured values, and it follows from Figure 3.3.9 that a constant discrepancy between the measured and simulated values occurs around the low tide mark.

Figure 3.3.9: Simulated salinity (red line) plotted against measured values (blue line) during the period of 20/1-2010 to 1/2-2010 at the UR-CTD calibration point.

Simulated salinity for the calibration point located in the Upper reach was in less good agreement with measured values, with a correlation coefficient of 0.49 together with bias and RMS values of -10.21 and 10.34, respectively. Tidally caused fluctuations in salinity were hardly present in the measured data set, whereas the model predicted salinity level considerably lower, but with a clear tide-induced fluctuation. While not a definitive answer, this result suggests that the Upper reach CTD was placed too deep in the water column where salinity levels were high, while average background levels of freshwater discharge during that period (> 0.05 cumecs) only affected salinity in the surface layer. Due to the depth-averaged solution technique of the model, this kind of variation caused by stratification was therefore not captured.

Figure 3.3.10: Simulated salinity (red line) plotted against measured values (blue line) during the period of 11/2-2010 to 29/3-2010 at the MR-CTD calibration point.

The performance of the model dropped in the second sampling period for the Middle reach, with a correlation coefficient of 0.78 together with bias and RMS values of -1.90 and 4.63, respectively. As evident from Figure 3.3.10, both data sets display a clear tide-dominated pattern in fluctuations, but with a relatively big difference in the magnitude. While these results indicate that the simulated saltwater intrusion into the system was small compared to observed salinity levels, the good agreement between simulated and measured surface elevation (Section 3.3.1) dictates otherwise.

Figure 3.3.11: Simulated salinity (red line) plotted against measured values (blue line) during the period of 11/2-2010 to 29/3-2010 at the UR-CTD calibration point.

50 The UR-CTD calibration point had a correlation coefficient of 0.57, together with bias and RMS values of 0.059 and 1.784, respectively. While the mean value between measured and simulated was in good agreement with a very small bias, it is evident from Figure 3.3.11 that the model failed to capture the two observed saltwater intrusions accurately. Taking the results from the Middle reach as well the first sampling period into account, it is plausible that the sub-grid variation of salinity levels increases when the system leaves its steady-state (dry periods), thus making the comparison between point measurements and depth-averaged model cells more difficult.

Figure 3.3.12: (Left) Seasonal variation in station-averaged salinity (blue line) plotted against corresponding simulated values (red line). (Right) Annually averaged salinity for each measuring station in relation to river mouth distance, plotted against corresponding simulated values. EHMP data is omitted from the two measured data sets.

On a larger temporal scale, the model performed remarkably well with an average correlation coefficient of 0.95, and the results for each measuring station (described in Chapter 2) are summed up in Table 3.3.1. With an average correlation coefficient of 0.98, the model performed just as well for the comparison between simulated and predicted salinity levels on each sampling day in relation to each station‟s location in the system. The results for the spatial comparison are summarised in Table 3.3.2 and figures for both the temporal and spatial analysis are available in Appendix I.

51 Table 3.3.1: Calculated results of the Quality index for each measuring station over the course of 2010 vs. corresponding simulated values. See Appendix I for the plotted data sets.

TABLE 3.3.1: TEMPORAL QUALITY INDEX - SALINITY Station Correlation Simulated Mean Measured Mean Bias RMS 1 0.87 33.11 31.93 1.18 0.68 2 0.94 32.52 31.32 1.20 0.67 3 0.96 30.33 29.41 0.92 0.52 4 0.95 29.36 27.51 1.85 0.65 5 0.99 23.26 23.48 -0.21 0.70 6 0.97 19.33 19.89 -0.56 0.19 7 0.97 12.99 14.15 -1.16 1.22 8 0.95 11.98 12.07 -0.09 2.46 9 0.96 8.60 9.51 -0.92 1.85 10 0.95 7.32 8.07 -0.75 1.15 11 0.92 4.74 5.29 -0.55 3.25

As evident from the results presented in Tables 3.3.1 and 3.3.2, the model performs quite well on a seasonal scale while also capturing the spatial variation between the river mouth station and the end of the tidal dominated zone on most occasions. Despite the discrepancies between the simulated and measured values on a fine temporal scale, these results suggest that the issues witnessed on a short temporal scale are not severe enough to significantly affect model performance on a larger scale.

Table 3.3.2: Calculated results of the Quality index for each sampling day in relation to distance from the river mouth vs. corresponding simulated values. See Appendix I for the plotted datasets.

TABLE 3.3.2: SPATIAL QUALITY INDEX - SALINITY Sampling day Correlation Simulated mean Measured mean Bias RMS 1 0.99 26.18 27.39 -1.21 0.68 2 0.99 26.72 28.60 -1.89 0.67 3 0.83 8.67 2.38 6.28 0.52 4 0.99 20.04 19.36 0.68 0.65 5 0.99 14.02 11.08 2.95 0.70 6 0.99 12.21 10.96 1.25 0.19 7 0.99 18.98 17.40 1.58 1.22 8 0.99 20.04 21.13 -1.09 2.46 9 0.99 23.70 26.36 -2.67 1.85

52 TABLE 3.3.2: SPATIAL QUALITY INDEX - SALINITY (Continued) Sampling day Correlation Simulated mean Measured mean Bias RMS 10 0.91 8.03 1.67 6.36 1.15 11 0.98 13.88 12.90 0.98 3.25 12 0.99 22.31 22.97 -0.66 2.52 13 0.99 21.67 21.21 0.46 2.29 14 0.99 20.09 23.47 -3.37 1.91 15 0.98 27.12 28.50 -1.39 1.17 16 0.98 24.81 25.45 -0.65 2.45 17 0.99 22.88 23.73 -0.85 1.32 18 0.99 27.35 28.33 -0.98 0.55 19 0.98 31.52 31.90 -0.39 0.13 20 0.96 32.21 31.88 0.33 2.62 21 0.99 26.69 27.30 -0.61 2.21 22 0.99 16.55 16.98 -0.43 3.59 23 0.99 14.74 16.40 -1.66 20.23 24 0.99 22.37 23.79 -1.42 15.90 25 0.99 18.89 20.54 -1.65 6.64 26 0.99 17.33 18.19 -0.86 12.81 27 0.99 4.91 3.39 1.51 2.68 28 0.99 16.14 15.90 0.24 2.26 29 0.99 8.57 7.21 1.36 2.03 30 0.98 13.72 13.50 0.21 5.81

3.3.4 TEMPERATURE VALIDATION The simulated vs. measured fluctuations in temperature for the first period at the MR-CTD calibration point were in poor agreement with a correlation coefficient of only 0.39. Upon inspection of Figure 3.3.13, it is evident that the cause of the weak correlation is due to the fact that the model predicted temperature fluctuations to be purely diurnal, while measured values at times fluctuated semi-diurnal, resulting from the tidal cycle. The presence of a tidal- influenced semi-diurnal temperature fluctuation in the measured data set suggests that there was a noteworthy difference between water temperatures of the upper and lower reaches of the system during this time period, which in turn was not captured by the model. Despite a low correlation coefficient, a small bias value of 0.11 indicates that the simulated mean temperature over the sampling period is in good agreement with the measured mean. However, a high RMS value (relative to the bias) of 1.29 suggests high variability in between individual differences, which is confirmed by Figure 3.3.13.

Figure 3.3.13: Simulated temperature (red line) plotted against measured values (blue line) during the period of 20/1-2010 to 1/2-2010 at the MR-CTD calibration point.

A correlation coefficient of 0.41 for the UR-CTD calibration point in the first sampling period indicates poor agreement with observed temperature levels, and unlike the MR-CTD calibration point, a relatively high bias of -1.78 together with a RMS value of 2.27 were registered. Upon inspection of the data sets in Figure 3.3.15, it becomes evident that the measured temperature values did not fluctuate in a set diurnal or semi-diurnal pattern, which explains the low correlation coefficient with the simulated diurnal fluctuations of the model. The lack of a diurnal fluctuation pattern adds to the evidence that the CTD of the Upper reach calibration point was deployed too deep (as mentioned in Section 3.3.3) and thus failing to capture the immediate heat exchange between the atmosphere and surface layer.

Figure 3.3.14: Simulated temperature (red line) plotted against measured values (blue line) during the period of 20/1-2010 to 1/2-2010 at the UR-CTD calibration point.

54 Simulated fluctuations in temperature for the second period at the MR-CTD calibration point was in good agreement with measured values, with a correlation coefficient of 0.88 together with bias and RMS values of 0.37 and 1.04, respectively. The agreement between data sets similarly increased for the UR-CTD calibration point, with a correlation coefficient of 0.73 together with bias and RMS values of 0.69 and 1.93, respectively. As evident from Figure 3.3.16, the simulated temperature levels between day and night varies as much as nearly 4 °C, while measured differences between day and night conditions typically only varies ~1.5 oC. Considering that the simulated model values were predicted for a roughly 200 m2 large depth- averaged cell, it is unlikely that a fluctuation of 4 oC over a single diurnal cycle is representative of the actual conditions. Possible reasons for these model predictions will be discussed in further detail in Section 3.4.

Figure 3.3.16: Simulated temperature (red line) plotted against measured values (blue line) during the period of 11/2-2010 to 29/3-2010 at the MR-CTD calibration point.

Similar to the predicted levels of salinity, the model performed quite well at a larger temporal scale, with an average correlation coefficient of 0.93.The results for each measuring station are summarised in Table 3.3.3. Spatially, the model performed less accurately, with an average correlation coefficient of 0.73. The results for the spatial comparison are summarised in table 3.3.4 while graphed figures for both the temporal and spatial analysis are available in Appendix I. Since there were only 11 comparison points and little to no variation in temperature between the river mouth and the uppermost sampling station on most occasions, the spatial correlation analysis for temperature was prone to be adversely affected by even small outliers deviating from the mean difference (>2 oC). This is in contrast to the spatial

55 correlation analysis for salinity, which displayed a high level of spatial variation throughout the year.

Figure 3.3.17: (Left) Seasonal variation in station-averaged temperature (blue line) plotted against corresponding simulated values (red line). (Right) Annually averaged temperature for each measuring station in relation to river mouth distance, plotted against corresponding simulated values. EHMP data is omitted from the two measured data sets

As evident from the results presented in Tables 3.3.3 and 3.3.4, the model performs quite well on a seasonal scale while also capturing the spatial variation between the river mouth station and the end of the tidal dominated zone on most occasions. Similar to the case with salinity in Section 3.3.3, the discrepancies between simulated and measured temperatures on a fine temporal scale further suggest that the observed model discrepancies on a short temporal scale are not severe enough to significantly affect model performance on a larger scale.

Table 3.3.3: Calculated results of the Quality index for each measuring station over the course of 2010 vs. corresponding simulated values. See Appendix I for the plotted data sets.

TEMPORAL QUALITY INDEX - TEMPERATURE Station Correlation Simulated Mean Measured Mean Bias RMS 1 0.91 24.48 23.77 0.71 2.23 2 0.94 24.47 23.60 0.86 2.21 3 0.92 24.13 23.75 0.38 1.74 4 0.93 24.15 23.68 0.47 1.49 5 0.94 24.13 23.87 0.26 0.96 6 0.94 23.77 23.60 0.18 0.10 7 0.96 23.42 23.64 -0.22 1.28 8 0.95 23.21 23.55 -0.34 0.95 9 0.96 23.16 23.38 -0.21 1.50 10 0.94 23.12 23.40 -0.27 1.94 11 0.89 23.00 23.14 -0.13 2.54

Table 3.3.4: Calculated results of the Quality index for each sampling day in relation to distance from the river mouth vs. corresponding simulated values. See Appendix I for the plotted data sets.

SPATIAL QUALITY INDEX - TEMPERATURE Sampling day Correlation Simulated Mean Measured Mean Bias RMS 1 0.95 29.44 29.41 0.03 2.23 2 0.74 27.79 26.44 1.35 2.21 3 0.91 25.05 25.36 -0.31 1.74 4 0.64 27.83 28.88 -1.05 1.49 5 0.91 26.36 25.43 0.93 0.96 6 0.82 24.05 23.57 0.49 0.10 7 0.84 26.06 26.20 -0.14 1.28 8 0.77 25.65 26.51 -0.86 0.95 9 0.55 24.41 24.33 0.08 1.50 10 0.88 20.92 19.97 0.95 1.94 11 0.93 21.44 21.99 -0.55 2.54 12 0.90 21.79 21.03 0.76 1.33 13 0.67 19.80 20.37 -0.57 1.28 14 0.41 18.51 20.02 -1.51 1.35 15 0.81 19.84 19.89 -0.05 1.47 16 0.93 20.09 19.53 0.57 1.15

57 SPATIAL QUALITY INDEX – TEMPERATURE (Continued) Sampling day Correlation Simulated Mean Measured Mean Bias RMS 17 0.83 20.80 20.04 0.76 1.35 18 0.58 23.05 22.65 0.41 2.10 19 -0.33 22.26 21.15 1.11 1.77 20 0.27 22.20 21.28 0.92 1.78 21 0.86 23.06 22.69 0.37 1.31 22 0.77 21.55 21.92 -0.37 0.04 23 0.71 24.58 25.08 -0.50 0.13 24 0.81 23.93 23.03 0.90 0.19 25 0.95 26.54 25.87 0.67 0.00 26 0.85 26.75 26.09 0.65 0.16 27 0.70 21.91 23.30 -1.39 0.55 28 0.86 24.56 23.52 1.04 0.78 29 0.64 24.06 24.03 0.03 1.03 30 0.88 27.67 27.85 -0.18 0.50

3.4 DISCUSSION

The HD model captured measured surface elevation to a satisfactory level (Figures 3.3.1 to 3.3.3). However, there was a tendency for the model to overestimate the trough of the ebb tide, which in turn resulted in a positive bias between simulated and measured surface elevation. Furthermore, there was a slight drop in model accuracy from middle reach to upper reach calibration points. Since the initial model calibration by the application of a bed roughness sensitivity analysis of the model (Section 3.2.9) did not reveal a notable change in the model prediction of trough elevation (ebb tide), it is unlikely that insufficient model representation of bed roughness is the cause of the discrepancies.

It is more plausible that the main reason for the overestimation of the ebb tide elevations and observed decline of accuracy with increased distance to the offshore boundaries is due to limitations in accuracy of the measured bathymetry of the creek. Current and accurate bathymetry is one of the most fundamental pre-requisites for successful modelling (Cheng et al. 1991). With regards to the bathymetry made available for this study, there are three primary limitations. 1) The model bathymetry data dates back to 2001, which means that nearly a decade of sediment transport, in particular around the river mouth, could have altered several characteristics of the bottom contour, while flooding events during the wet season are

58 likely to have eroded parts of the upper reach; 2) due to relative high sediment disposition rates at the river mouth, the Gold Coast City Council annually dredge the main channel to keep the river mouth open (with an annual average of 38000 m3 sand removed, GCCM, unpubl. data). While the bathymetry adopted resembles the immediate post-dredging conditions, it is likely that model accuracy will be affected as sediment is redeposited over time in the natural system, resulting in changed flow dynamics; 3) bathymetry data was measured using LIDAR remote sensing (Light Detection And Ranging), which means the accuracy of measured bathymetry data is likely to decline for the upper reach compared to the wider downriver sections, due to increased tree and mangrove foliage obstructing measurements along the river edges.

While the aforementioned issues with the bathymetry offer a plausible explanation for the observed discrepancies in surface elevation, limitations in model grid resolution through narrow and shallow gaps in the river could also affect model performance in terms of simulating surface elevation. As mentioned in section 3.2.4, the smallest grid cells were 5x10 m in size (width*length), which can be considered quite small in comparison to the 30x30 m cells utilised in a similar HD model of the nearby Gold Coast, Broadwater estuary (Mirfenderesk & Tomlinson 2007). However, due to flooding and drying of model grid cells over the course of tidal cycles, in particular neap tides, the narrow and shallow parts of the system are on occasion represented by relatively few inundated grid cells, which can ultimately result in a decrease of model accuracy in areas upriver from these bottlenecks. Inaccurate bathymetry definition and narrow sections within a model domain was likewise reported as a cause for model discrepancies in Dias and Lopes (2006).

While a decrease in cell size can help eradicate the potential problems associated with model bottlenecks, it is important to note that a decrease in cell size and the subsequent increase of total number of cells has a significant impact on required simulation times. Therefore, there is ultimately a compromise between cell grid resolution, model performance and the associated simulation times. In summary, since problems with inaccurate bathymetry data along the river often has a cumulative negative effect on model performance, it is common that the correlation between modelled and measured surface elevations decreases upstream, while the severity of the decrease is likely to be enhanced by the presence of bottlenecks. Finally, meteorological events such as large low-pressure systems, which are not represented in the model formulation, could potentially cause some of the observed discrepancies between the simulated and measured water levels. For example, in the immediate period after the

59 significant flooding event that took place between the 12th and 13th of October 2010, the model underestimated the actual water level compared to measured levels.

Figure 3.4.1: Simulated current speeds at a model bottleneck during low tide (top), incoming tide (middle) and high tide (bottom). The bottleneck is located ~3 km upriver from the river mouth, while the first ADCP calibration point is located ~3 km further upriver from the bottleneck.

60 The failure of the middle reach ADCP unfortunately prevented a direct comparison between measured and simulated flow velocities for this part of the estuary. However, since Tallebudgera Creek is a tide-dominated estuary, the predominant flow velocities throughout the system are undoubtedly closely linked to the time and magnitude of the tide. Also, the good agreement between measured and simulated surface elevations for the middle reach area suggests that simulated current velocities are in good agreement as well. The model overestimated the current speeds for the upper reach area during the low-discharge period prior to the flooding event. Because the currents are so small in the upper reach during dry- period conditions, it is almost impossible to model these accurately with the aforementioned model constraints in regards to bathymetry accuracy and model grid resolution. However, the good agreement between measured and simulated current velocities during the flooding event shows how model performance in terms of flow velocities increases when the number of "flooding and drying" cells (cells that are continuously flooded during high tide and subsequently dried out during low tide) decreases as an effect of increased freshwater discharge.

As evident from the results, the model showed a delayed response time and to some degree an underestimated amplitude in oscillations of salinity levels immediately after a discharge event had pushed the system away from its dry-period quasi-stationary equilibrium. Despite having a good representation of surface elevation and flow velocities, the slow response time of the model before returning to "normal" salinity levels likely reflects the following two issues: 1) insufficient model resolution of subscale turbulence dispersion processes that drive the mixing of fresh and saline water masses, and thus determine how quickly the system is restored to "normal" conditions after a rainfall event. Due to the limitation of the spatial resolution of Tallebudgera Creek, it is not possible to resolve these subscale processes by utilising the MIKE21FM Smagorinsky eddy-viscosity turbulence model formulation to a satisfactory degree, and thus subscale dispersion processes had to be resolved by assuming a temporally-constant, but spatially-varying dispersion coefficient formulation (Section 3.2.6). In a MIKE21 water quality study on a shallow lagoon, Lopes et al. (2008) notes that using a spatially-varying dispersion coefficient is more accurate; however, the results of this study indicates that a dispersion coefficient that varies spatio-temporally is needed to improve model performance. 2) While it was established in Chapter 2 that there was no significant difference between top and bottom salinities at individual measuring stations on an annual scale, one cannot rule out the possibility that stratification occurred in the deeper parts of the

61 system (> 3 m) over shorter temporal scales (especially during local events of small to medium rainfall events), which in turn could bias the comparison between modelled and measured values due to the depth-averaged nature of the HD model. With the relatively large difference in density between oligohaline creek water and offshore euryhaline water, it is plausible that the first few saltwater intrusions following a major freshwater discharge event could cause brief haloclines to form in parts of the system. The depth-averaged MIKE21 model has in the past been successfully implemented for simulation of salinity and temperature in a shallow lagoon (Zacharias & Gianni 2008). However, with an average and maximum depth of 0.4 and 2 m respectively, it is likely that any vertical differences in salinity are miniscule compared to potential differences in Tallebudgera Creek. While an implementation of a 3D HD model would allow a test of this hypothesis, and possibly result in an improvement of the aforementioned dispersion processes, it was outside the timeframe and scope of this study to conduct this investigation. Furthermore, it is possible that the computed evaporation scheme utilised in the model setup is insufficient to capture the actual evaporation taking place, which in turn could affect modelled salinity levels. Zacharias & Gianni (2008) utilised calculated time series of evaporation in their study of a shallow hypersaline lagoon in southern Greece, and it is possible that an adaptation of their method could improve model performance.

Despite the abovementioned problems, the model performed well in resolving oscillations of salinity once a quasi-stationary condition was present (Figure 3.3.6), and captured observed salinity gradients on most sampling days satisfactorily (R > 0.90). In summary, the model limitations with the simulation of salinity seem to be linked to a fast response to increased freshwater inputs while the subsequent response to saltwater intrusion remains slow, with an underestimation of the amplitude in salinity oscillations. However, the model‟s capability to resolve mean values on a larger spatio-temporal remains good.

Diel oscillations in temperature were often overestimated by the model. However, on larger temporal scales, the model performed well in capturing mean temperature levels. Since the model utilises the same equations for solving the advection and dispersion of both temperature and salinity, the observed discrepancies between modelled and measured temperature fluctuations can likely be explained, at least in part, by the abovementioned issues in terms of the model‟s dispersion formulation. However, in the case of temperature, other important limitations in the current model setup are evident, which in turn will affect model performance: 1) it was not possible to obtain cloud cover data for the Tallebudgera

62 Creek area, and thus the clearness coefficient was defined as the default value in the model setup. Due to this obvious limitation, it is likely that the model will overestimate diurnal fluctuations in temperature when cloudy conditions prevail in the natural system. In a similar study, Zacharias & Gianni (2008) estimated the clearness coefficient through measurements of solar radiation and rainfall, and it is possible that an adaptation of their method in future development of the model could improve performance of the heat exchange module. 2) Time series of wind speed/direction, air temperature and relative humidity are assumed to be constant throughout the domain, and are furthermore measured ~10 km south from the system domain. While there is no better meteorological data available for the Tallebudgera area, it is plausible that differences between utilised data and actual conditions differ enough to contribute to the apparent discrepancies in the simulation of temperature. Wind speeds can in particular be expected to decrease when moving from the coastal river mouth to the more sheltered and vegetated areas of the upper reaches. However, the effect of spatially-varying wind speeds is unknown due to lack of data. 3) Offshore boundary values of temperature rely on the performance of the HYCOM model, from which daily values are extracted. With a grid resolution of 0.25° × 0.25° it follows that any discrepancies between the sea surface temperatures predicted by the HYCOM model and actual offshore conditions will be transferred to the Tallebudgera Creek model.

It follows from the above that there is a variable degree of uncertainty attached to the use of modelled temperature and salinity. In the case of salinity, this uncertainty increases in the immediate time after larger discharge events. This impact naturally has implications for the functioning of any future implementation of an agent-based model that relies on salinity as a driver for movement. While this issue cannot be disregarded (Dias & Lopes 2006), it is important to note that due to the narrow nature of Tallebudgera Creek, the salinity gradient is primarily longitudinal in nature, with little to no variation in salinity at any given cross- section of the river. This means that any favourable habitat in terms of salinity would during post-discharge events be situated slightly further downriver in the model compared to its true position in the system.

In conclusion, despite the aforementioned assumptions and limitations of the model setup in regards to the accuracy of simulated parameters, the model still met an overall satisfactory level of accuracy, and was sufficiently representative of actual conditions in the system. The model was found to have a delayed response in salinity after large freshwater discharge, but predicted mean levels were in good agreement with measured data. Diel oscillations of

63 temperature were at periods overestimated by the model, but predicted mean temperature over time was in good agreement with measured data sets. While improvement of model performance is possible through an implementation of higher quality data, in particular more refined bathymetry data, one has to appreciate the fact that this work was solely supported by publically available data, making the model in its current setup quite cost-effective. Furthermore, the good agreement in surface elevation between both 2007 and 2010 data suggests that the model in its current form can be readily reapplied for future studies of a similar nature in Tallebudgera Creek by using only current datasets for the period of interest.

CHAPTER IV

SHORT-TERM MOVEMENT OF A JUVENILE BULL SHARK IN TALLEBUDGERA CREEK

65 4.1 INTRODUCTION

In the past decades, elasmobranch species have faced increased fishing pressures causing a substantial decrease in populations (Graham et al. 2001), while rapid increases in urbanisation of coastal and estuarine regions over the same period (Lee et al. 2006, Martínez et al. 2007) have increased the pressure on the natural nursery grounds of estuary-dependent species, such as C. leucas. Once a common cosmopolitan species in the tropical and subtropical regions, the bull shark is now listed as „near-threatened‟ by the International Union for Conservation of Nature (IUCN), and as a species with slow growth rates (Branstetter & Stiles 1987, Schmid & Murru 1994) and late sexual maturity (Voight & Dietmar 2011), it is less able to compensate for increased mortality and is therefore likely to be more vulnerable to extinction (García et al. 2008). The need for conservation strategies for C. leucas is imminent, and further investigation in how anthropogenic disturbances and environmental change affect the pattern of habitat use in their riverine nurseries is needed in order to formulate effective management strategies for this species.

Heupel & Simpfendorfer (2011) found that mortality rates of young C. leucas in estuaries were low compared to other juvenile sharks in neighbouring marine nursery grounds, and hypothesised that this was due to a lower predation risk from other sharks, coupled with a decreased competition for resources as other juvenile shark species were absent from estuaries. It is, however, unclear how juvenile bull sharks cope with the increased impacts of urbanisation on their natural nursery grounds, and if this reduces the low-mortality value of these systems.

The physical characteristics of riverine environments include a significantly smaller size and volume relative to the open coastal environment. Riverine ecosystems are therefore also often more prone to environmental fluctuations in both time and space. Thus, it is likely that neonate and juvenile C. leucas inside riverine habitats are faced with more frequent changes in flow and water quality, which in turn might translate into a response in movement and changes in their distribution pattern (Heupel & Simpfendorfer 2008, Ortega et al. 2009).

With changes in hydrology as a common effect of urbanisation, the transport and advection- dispersion of salinity is likely to be affected as well (Lee et al. 2006). Salinity in particular has been correlated with the movement and distribution of young C. leucas (Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010). While the

66 physiology behind the osmoregulatory capabilities of the bull shark at different life stages has been investigated extensively in previous studies (Thorson et al. 1973, Pillans & Franklin 2004, Pillans et al. 2005, Pillans et al. 2006), the metabolic costs associated with osmoregulating are yet to be established. Pillans et al. (2006), however, reported that juvenile bull sharks face a greater osmoregulatory challenge when moving from 75% to 100% seawater than their larger counterparts (> 2 m TL), which suggests that juvenile bull sharks might avoid euryhaline waters as a means to reduce energetic costs. This notion is further backed by the results of field-studies, which have reported a 7-20 salinity preference for juvenile C. leucas, and avoidance of salinities < 7 PSU (Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Ortega et al. 2009).

While field-studies of C. leucas have identified several other potential drivers of movement in estuaries (Chapter I), these short-term tracking studies have normally relied on sampling from a boat 50-100+ m away from the animal, or by inferring correlation with measured parameters from one or more fixed points in the study site (Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010). This limitation introduces a potential bias in the analysis due to the non-uniform advection/dispersion and transformation of these parameters across the system. Further, it is often very difficult to assess spatio-temporal gradients of the various parameters over the course of a tracking event, and how they might affect animal movement and distribution. Furthermore, most short-term tracking studies only track an individual animal once, which prevents an assessment of how significant changes in hydrology over time and space affect the fine-scale movement and habitat selection of each individual.

By combining replicated short-term acoustic tracking of individual C. leucas with the outputs of a hydrodynamic model (Chapter III), it is possible to investigate the spatio-temporal variation of the hydrology of a system on a relatively fine scale, and allows for an examination of how the tracked animal responds to changes in hydrology through time and space. The assessment of individual variations in movement patterns relative to changes in hydrology will provide more detailed knowledge of C. leucas movement responses to environmental change, and may prove to be a key to predicting movement and distribution through the application of an agent-based model.

67 4.1.1 AIMS & PURPOSE As a means to better understand individual variation in movement of juvenile C. leucas, replicated short-term acoustic tracking of individual C. leucas was conducted and correlated with the outputs of a hydrodynamic model of Tallebudgera Creek as well as traditional water quality sampling. However, the main purpose of this study is to obtain a detailed track record of juvenile C. leucas inside the Tallebudgera Creek, which in turn can be used as validation data for the development of an agent-based model that is capable of replicating and predicting short-term movement of this species (Chapter V).

4.2 METHODOLOGY

4.2.1 CATCHING AND TAGGING Capture of sharks occurred in the period of 13/1 to 17/1-2010, and was approved by the Animal Ethics Committee of Griffith University (ENV/1709/AEC 2010). Protocol for capturing and tagging followed Werry (2010). Ten baited drum lines with a breaking tension of 1000 kg were set in the middle reach of the creek and sea mullet, Mugil cephalus, a local and common resident in the system was used as bait on non-barbed 8/0 tuna hooks. The lines had lengths varying from 5 to 15 m and were secured in position by a large float at the top and a 7 kg brick as an anchor. After initial deployment, all lines were checked every two hours and re-baited if necessary. In addition, a gill net with a mesh size of 8x8 cm and length of 20 m was stretched across the creek at selected locations. The gill net was continuously deployed and relocated in the middle and upper reaches of the system, with an average deployment time of 1 hour at each location (Figure 4.2.1).

Figure 4.2.1: Picture of the deployed gill net. Due to the narrow nature of the system, the gill net was often capable of stretching almost across the full width of the estuary.

Captured sharks were immediately removed from the gill net and restrained in a harness specially designed for shark capture and handling by Werry (2010) (see Figure 4.2.2). There was no by-catch of other species except a single stingray that was released immediately after capture. Upon a capture event, the surface salinity and temperature were measured using an YSI 6820V2-1 sonde (see section 3.3), and the captured shark kept in a 3 m long harness in order to control and restrain movement for the duration of the tagging. Each shark was marked externally with a numbered roto-tag as well as a VEMCO VR16-4H-R64K coded acoustic transmitter with a set frequency for future tracking purposes. The roto-tag and VR16 pinger were secured to each other using 2 mm thick stainless steel wire, thus allowing the two tags to be applied to the shark simultaneously. A hole was drilled through the approximate centre of the dorsal fin using an electrical power drill (drill diameter 0.5 cm), through which the combined roto-VR16 tag was attached by securing the standard locking mechanism of the roto-tag.

Figure 4.2.2: Juvenile bull shark suspended in the specially designed harness, while water flow across the gills was maintained by directing the boat against the current. Note the combined acoustic/roto-tag attached to the shark’s dorsal fin (red arrow).

After applying the tag, various morphological dimensions of the shark were measured using a soft plastic measuring tape, including: pre-caudal length (PCL), fork length (FL), total length (TL) and clasper length for males. The weight of each shark was measured using a handheld scale. Effort was made to perform tagging and measurement of morphological dimensions as fast as possible and took on average less than 10 minutes per individual. After all data had been recorded, the shark was kept in the harness for a few additional minutes while the boat moved at a slow cruising speed to keep a steady flow of water through the gills of the shark, thus allowing it to regain strength before release in the immediate area where it was caught.

4.2.2 ACOUSTIC TELEMETRY AND TRACKING METHODS All tracking events took place on a boat of the Stacer Seahorse type equipped with a Yamaha 30 HMH, 22 kW, 2-stroke engine. For short-term tracking of the tagged animals, a VEMCO VR60 Ultrasonic Receiver was used together with a VH65 Omni-directional hydrophone and a V10 directional hydrophone. Together these allowed for initial detection of the tagged animals using the VH65, followed by directional detection of the animals using the VH10. The VH10 unit was mounted on a 3 m long aluminium pole, reaching ca. 30 cm beneath the keel of the boat. The top piece of the pole was so constructed as to allow for a 360° rotation of the hydrophone. A Suunto Commander lockable compass was mounted to the rotating top piece in order to make accurate recordings of heading of the directional hydrophone. Once an

70 animal was located in the system, a recording of hydrophone heading and signal strength of the hydrophone as well as hydrophone gain was made every 5-10 min for the following six hours.

For each hydrophone recording, the position of the boat was marked using a Garmin 60 handheld GPS navigator with an estimated accuracy of ca.10 m. While tracking, the boat engine was kept off as much as possible while maintaining a distance of at least 30 m from the animal. However, maintaining said distance was sometimes made impossible when the animal turned around and started heading towards the boat. Whenever it became necessary to follow the shark with the engine on, it was done so with the lowest speed possible. Any visible boating and/or fishing activity during the tracking effort were recorded for possible inclusion in future statistical analyses. Throughout the tracking, salinity, temperature, dissolved oxygen, pH, turbidity and chlorophyll concentration were recorded at the location of the research vessel at each fix-point, using one or two YSI 6820V2-1 sondes (For more information, see Water Quality measurements). Since stratification is believed to play a minor role in the generally well-mixed and shallow waters of Tallebudgera Creek, only the surface water (ca. 20-30 cm depth) was sampled during tracking.

4.2.3 HYDROPHONE CALIBRATION AND SHARK POSITION The calibration of the V10 directional hydrophone and the VEMCO VR60 Ultrasonic Receiver was done by recording the range of signal strengths at various gain with the distance known from the signal source (V-16 tag deployed ca. 50 cm under water). The calibration process was done for three separate sections of the Tallebudgera estuary that differed in the type of bottom substrate (sand, mud and rocky) in order to establish potential differences in the performance of the hydrophone. As a means to establish and quantify the relationship between a given signal strength and hydrophone receiver gain to an unknown distance, a biharmonic spline interpolation was done on each recorded dataset (Sandwell 1987).The interpolated values were plotted in a 3D surface plot in order to visualize the relations, and are shown in Figure 4.2.3.

Figure 4.2.3: The resulting surface plots from each calibration. Note the clear differences in distances at maximum signal strength and maximum gain (16 and 36 respectively), between the three calibrations. See main text for further information on the associated accuracy levels of each calibration.

The individual calibrations revealed a clear difference in hydrophone performance in relation to bottom substrate type. The accuracy of the sand bottom calibration was found to be ±5 m up to a total distance of ca. 70 m before accuracy abruptly dropped to ±15 m, whereas the mud bottom calibration decreased from ±5 to ±15 m after exceeding a total distance of ca. 40 m to the transmitter. The rocky bottom had an accuracy of ±5 m within ca. 20 meters of the transmitter, but then dropped to ±15 m and as much as ±25 m after ca. 40 m.

The position of the shark relative to the boat was calculated post-tracking by converting the recorded signal strength at a given hydrophone gain to a corresponding distance between the signal source originating from the V-16 transmitter and the hydrophone. Due to the registered differences in the acoustic properties of the system, each distance from the boat to animal fix- point was calculated using the calibration scheme appropriate to the animal‟s location; e.g. distances to fix-points located in areas known to have a muddy substrate would be calculated utilizing the signal/gain relationship determined by the calibration over muddy substrates.

The actual position of the animal was estimated using the GPS coordinates of the boat, the compass heading of the hydrophone and the estimated distance between the signal source and receiver as described above. The V-16 transmitter does not give any estimation on the depth of the shark, and thus it was only possible to estimate the animal‟s horizontal position in the system. Videos of the movement path of each track was created using MATLAB R2007b,

72 and is available for viewing on the supplied DVD along with other video outputs of this study (Chapter V).

4.2.4 MIKE21FM DATA Model outputs from the validated hydrodynamic model (Chapter III), such as temperature, salinity, current speed and direction together with total water depth were extracted at the corresponding points in time and space for each animal fix-point. The average values of the above-mentioned parameters were calculated for the entire model domain over the duration of each tracking event in order to create spatial time-averaged maps, which allowed assessment of the general hydrological conditions of the system. By dividing each model output parameter into specific value ranges, the total water volume for each range was calculated in order to assess the amount of available habitat in the specific water quality ranges, over the duration of each tracking period. The offshore area of the model domain was not included in the volume calculations, since it was not considered to be part of utilised habitat by the neonate and juvenile animals living inside the system.

4.2.5 DATA ANALYSIS The rate of movement (ROM) was estimated between fix-points assuming the animal swam in a linear direction between consecutive fix-points. Similarly, the total distance covered over the course of a track was calculated by the same assumption of linearity. This assumption undoubtedly leads to an underestimation of the ROM and consequently also biasing the total distance covered by the animal. The estimated ROM was further investigated using the non- parametric Spearman rank correlation test as a means to detect possible effects of measured water quality parameters and simulated model outputs on ROM. Due to the underlying bias in the estimation of ROM and the associated problems with comparing water quality measurements sampled ca. 30-40 m away from the actual location, the results of the statistical tests were only intended as a guide to potential movement pattern.

By dividing both measured and simulated parameters into specific value ranges (as mentioned in section 4.2.4), the proportion of time the animal spent in a given value range was calculated for each track. Using the calculated volume proportions of temperature, salinity, water depth and current speed (section 4.2.4), the predicted proportion of time spent in a given parameter range was calculated by assuming that the size of the volume of each parameter range was proportional to time spent. The observed vs. predicted proportions of time spent was then tested using a -test, as a means to establish if there are any significant

73 differences between the two. Since it was not possible to calculate definite water volumes for dissolved oxygen, pH and turbidity, these parameters were not included in the -test.

Furthermore, an adapted version of the Manly‟s alpha preference index (Manly 1972), was applied to datasets where volume values were available, in order to determine if the animal displayed preferential behaviour relative to available water quality regimes. The adaptation of Manly‟s equation used was:

(Eq. 4.1)

Where and are the proportion of time spent in the and water quality regime, while . Similarly, and are the volume proportions of the and water quality regime relative to the total volume of the system. The value of ranges from 0 to 1, where 0 indicates no preference while values close to 1 indicates a high level of preference, while .

Animal position in relation to the width of the river was determined by defining and calculating a river width ratio for each fix-point, in order to establish if the animal had a latitudinal preference in relation to the riverbank. The ratio was defined as:

(Eq. 4.2)

Where is the shortest distance from a fix-point to either adjacent riverbank, while is the total width of the river at the given location. ranges from 0 to 1, where values approaching 1 indicates that the animal is positioned near the middle of the river.

A local nearest-neighbour convex-hull construction of home ranges was calculated on the basis of the total amount of recorded animal fix-points (Getz et al. 2007). However, due to the elongated and narrow nature of the system combined with the widespread range of fix- points throughout the system, the above-mentioned method produced unrealistic results by including land areas as part of the home range. Instead, the home range of the animal was estimated using more traditional and simplistic means by calculating the percentages of fix- points in relation to the distance to the river mouth.

74 4.3 RESULTS

A total of three sharks was caught and tagged over the course of this study. All sharks were caught by gill net (as described in section 4.2) in the upper reach of the river where the mean water depth is > 2 m. All three sharks were caught in the net from the shallow banks of the river. Two sharks were caught during the daylight hours with an outgoing tide, while the third was caught on an incoming tide shortly after sunset. All sharks were caught in a salinity range of 12 to 15 PSU and at 30°C (Table 2.). Two of the sharks were neonates (a male and female) and most likely young-of-the-year with fresh umbilical scars evident. The female neonate (FM1) had a total length of 74.0 cm and a weight of 3.9 kg, while the male neonate (MN1) had a total length of 78.2 cm and a weight of 2.9 kg. The third shark was a young juvenile male (JM1) with a total length of 108.0 cm and a weight of 9.0 kg. Clasper length was 6.2 cm and not calcified. Despite an intensive fishing effort, no sharks were caught using baited lines.

Table 4.3.1: Details of the tagged sharks. Note that MN1 (male neonate), despite being of greater length than FN1 (female neonate), is a whole kilogram lighter than his female counterpart.

Shark name: FN1 JM1 MN1 Tag number 1074582 1074580 1074581 Roto-tag number 151 122 120 Tag frequency 84 78 81 Time of capture 17-01-10 12:35 17-01-10 13:20 17-01-10 19:04 Catch Method Gill net Gill net Gill net Catch Location (CL) Upper reach Upper reach Upper reach Salinity at CL (PSU) 12.70 14.60 14.65 Life stage Neonate Juvenile Neonate Sex Female Male Male Pre-caudal length (cm) 57.00 84.00 59.00 Fork length (cm) 64.50 95.00 65.30 Total length (cm) 74.00 108.00 78.20 Weight (kg) 3.90 9.00 2.90 Clasper length NA 6.20 4.10

Of the three animals only JM1 proved possible to track properly in the following months due to the following reasons: 1) the tag of the male neonate was found dislodged from the animal upon the first tracking event; 2) in most instances the female neonate was found to move in extremely shallow water in the Upper reaches and often within mangrove coverage making the tracking effort near impossible from boat as performance of the hydrophone decreased significantly in these conditions (See section 4.2.3). A total of six movement tracks based on

75 the juvenile male (JM1) were recorded throughout the study, and each track will be described and analysed in turn, and finally followed by an assessment of the collective results.

4.3.1 TRACK I The track on the 28th of January took place from 14:45 to 20:35 with the majority of the track occurring on an incoming tide. As the tide was coming in, the shark spent roughly 3 hr moving around in the same section of the middle reach, which can be characterized as a relatively deep and wide section compared to the rest of the system. As dusk approached and roughly an hour before the high tide peaked, the shark started moving upriver until it reached the approximate border of the tide-influenced zone, where it lingered for about an hour. When the tide started moving out, the shark returned to the same area it had been utilising earlier. During the tracking period the shark was estimated to have travelled a total of 6.22 km, assuming unidirectional travel between recordings. Estimated swimming velocities were relatively low, with the animal spending 80% of the track travelling with a speed of 0 to 0.5 m/s, while predominantly having a heading along with the current (>50%)

As evident from Figure 4.3.2, the animal spent all of its time in the 30-32 °C temperature range, despite that this regime only encompassed 35.3% of the total water volume, with the remaining volume of the system being of colder temperatures. This translated into a significant difference (P < 0.01) between observed and expected proportions of time spent. Furthermore, the calculated value Manly‟s α in relation to the 30-32 °C regime was 1.0, indicating an affinity for high temperatures.

The animal spent > 50% of its time in the salinity regime of 9-18 PSU. Only 5.3% percent of the total water volume was within this regime. In contrast, 82.0% of the total water volume was within the 27-36 PSU range, but with no animal registrations within this zone. This translated into a significant difference (P < 0.01) between observed and expected proportions of time spent in the four defined salinity regimes (Figure 4.3.2). Furthermore the calculated value of Manly‟s α in relation to the 9-18 salinity regime was 0.73, indicating an affinity for this salinity range.

Despite spending more than 50% of its time in current speeds in the 0.1-0.2 m/s regime, which corresponded to 21.9% of the total water volume, there was no significant difference between observed and expected proportions of time spent. Manly‟s α of 0.8 did, however,

76 indicate a preference for this current speed regime. Nearly 90% of time spent was at locations of 1-3 m water depth, corresponding to 52.2% of available habitat in terms of depth. This translated into a significant difference in observed vs. expected proportion of time spent at a given depth, and a Manly α value of 0.76 suggests that the animal had an affinity for this depth range. This result is backed by the animal spending close to 60% of its time close to the middle of the river channel (0.6-1.0 Riverbank ratio).

As mentioned in section 4.2.5, it was not possible to determine a value for Manly‟s α and/or any statistical difference in observed vs. expected proportion of time spent relative to dissolved oxygen, pH and turbidity, due to the lack of modelled volume data for these parameters. However, as shown in Figure 4.3.3, the animal spent all its time in areas with oxygen levels >5 mg/l, and the majority of its time in turbidity of 0-2 NTU, while remaining in the 7.5-7.75 pH range >90% of the time.

TRACK I: 28/01 – 2010 (14:45 to 20:35)

Figure 4.3.1: Recorded animal locations during the track undertaken on the 28/01-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_1_28012010" in the Track_Videos subfolder.

Figure 4.3.2: Proportion of time spent at various temperature (top left), salinity (top right), current speed regimes (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 28/01-2010.

Figure 4.3.3: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 28/01-2010.

4.3.2 TRACK II The track on the 3rd of February took place from 9:20 to 15:11, with a turning tide occurring at around 12:00. The shark lingered around the same area as in the first track, moving both with and against the tide, with no apparent sign of being affected significantly by tidal current. While not leaving the middle reach area (Figure 4.3.4), its level of activity was relatively high, with an estimated distance of 5.56 km travelled during the six hours of tracking (Figure 10). The apparent affinity for this general middle reach area suggests that this particular animal has a specific habitat preference for this section of the system, and might be a display of territorial behaviour. Estimated swimming velocities were elevated compared to the previous track, with the animal spending nearly 50% of the track travelling with a speed of 0.25-0.75 m/s, while displaying no clear preference of swimming direction relative to the current (Figure 4.3.6).

79 As evident from Figure 4.3.5, the animal spent all of its time in the 28-30 °C temperature range, despite that this regime only encompassed 14.0% of the total water volume. The remaining volume of the system was of colder temperatures (84.9% being 26-28 °C). This did not translate into a significant difference (P > 0.05) between observed and expected proportions of time spent. However, the calculated value of Manly‟s α in relation to the 28-30 °C regime was 1.0, which indicates an affinity for higher temperatures.

The animal spent all of its time in the salinity regime of 18-27 PSU, which corresponded to only 8.2% percent of the total water volume. In contrast, 86.0% of the total water volume was within the 27-36 PSU range, but with no animal registrations within this zone. This translated into a significant difference (P < 0.01) between observed and expected proportions of time spent (Figure 4.3.5). Furthermore the calculated value of Manly‟s α in relation to the 18-27 salinity regime was 1.0, which indicates an affinity for this salinity range throughout this particular track.

The animal was found to spend more than 80% of its time in current speeds in the 0-0.2 m/s regime, which corresponded to the prevailing current speeds in 81.8% of the total water volume, which led to no significant difference between observed and expected proportions of time spent. Manly‟s α at 0.69 did, however, indicate a weak preference for this current speed regime, compared to 0.31 for higher flow regimes (0.2-0.5 m/s).

Well above 60% of time spent was at locations with 3-4 m water depth, corresponding to 21.7% of available habitat in terms of depth (only 11.8% of the total area). This translated to a significant difference in observed vs. expected proportions of time spent at a given depth, and a Manly α value of 0.66 suggests that the animal had an affinity for this depth range. This result is further backed by the animal spending close to 60% of its time close to the middle of the river channel (0.6-1.0 Riverbank ratio). As evident from Figure 4.3.6, the animal spent all of its time in oxygen levels >5 mg/l, while remaining in turbidity between 0-2 NTU and in the 7.5-7.75 pH range.

TRACK II: 03/02 – 2010 (9:20 to 15:11)

Figure 4.3.4: Recorded animal locations during the track undertaken on the 03/02-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_2_03022010" in the Track_Videos subfolder.

Figure 4.3.5: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 03/02-2010.

Figure 4.3.6: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 03/02-2010.

4.3.3 TRACK III The track on the 27th of February took place from 13:45 to 19:34, with a turning tide at around 14:20 and thus the majority of the track took place during an incoming tide. The juvenile was again located in the same area as illustrated in Figure 10, and spent the first three hours patrolling this area before dusk (~ 17:10), then started heading downriver against an incoming tide. The shark evidently kept close to the shallow banks instead of the deeper parts in the middle section of the river while travelling downriver (Figure 4.3.7). Sticking close to the shallow mangrove-covered river bank might help to shelter the animal from other sharks. Since flow usually decreases at shallow river banks compared to deeper parts of the river, this behaviour could also be a means of minimising energetic costs when swimming against the current. Within the same track, the shark travelled back with the tide to its preferred area, and after lingering there for an additional hour, it went downriver once more, again following a path close to the river bank. The estimated total distance travelled during

83 this track was 5.14 km. The estimated swimming velocities for this track were relatively low, with the animal spending 60% of the track travelling with a speed of 0-0.25 m/s, while spending equal amounts of time moving along and against the current.

Salinity levels in the middle reach of the system had dropped due to increased freshwater discharge in the preceding weeks, which was reflected by the animal spending > 80% of its time in the salinity of 0-9 PSU, corresponding to 12.4% percent of the total water volume (compared to 0.6% and 2.4% for Track I and II, respectively). The observed behaviour translated into a significant difference (P < 0.01) between observed and expected proportions of time spent in the four defined salinity regimes (Figure 4.3.8). Furthermore, the calculated value of Manly‟s α in relation to the 0-9 salinity regime was 0.78, suggesting an affinity for this salinity range during this track.

As evident from Figure 4.3.8, the animal spent > 50% of its time in the 28-30 °C temperature regime, despite that this regime only encompassed 12.0% of the total volume, with the remaining volume of the system being of colder temperatures. However, there was no significant difference (P > 0.05) between observed and expected proportions of time spent. The calculated value of Manly‟s α in relation to the 28-30 °C regime was on the other hand 0.96, once again indicating an affinity for high temperatures.

Unlike previous tracks, the animal spent more than 40% of its time in current speeds in the 0.2-0.3 m/s regime, which corresponded to only 8.6% of the total water volume, but did not prove to be significantly different in terms of observed and expected proportions of time spent. Manly‟s α of 0.56 did, however, indicate a slight preference for this current speed regime. As shown in Figure 4.3.9, the animal spent all its time in oxygen levels >6 mg/l, and in 0-2 NTU turbidity, while primarily staying in the 7.25-7.75 pH range (>70% of the time.)

Once again, the animal spent nearly 50% of its time at locations where the water depth was 2- 3 m, corresponding to 22.9% of available habitat in terms of depth. This translated into a significant difference (P < 0.01) in observed vs. expected proportions of time spent at a given depth, while a Manly α value of 0.48 suggests a slight affinity for this depth range compared to others. Once again this result is backed by the animal spending almost 40% of its time close to the middle of the river channel (0.8-1.0 Riverbank ratio).

TRACK III: 27/02 – 2010 (13:45 to 19:34)

Figure 4.3.7: Recorded animal locations during the track undertaken on the 27/02-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_3_27022010" in the Track_Videos subfolder.

Figure 4.3.8: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 27/02-2010.

Figure 4.3.9: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 27/02-2010.

4.3.4 TRACK IV The track on the 6th of March took place from 12:03 to 14:58 with the tide turning to an outgoing tide as the track commenced. The week prior to the 6th of March had heavy rains, and a large runoff from the catchment and hinterland had caused a substantial change in the salinity gradient of the system, while increased flow rates caused relatively high levels of turbidity (up to 30.2 NTU in the middle reach). For the first time during the tracking program, the juvenile shark was located outside the area where it was previously found (Figure 4.3.10). The shark was initially located ~3.6 km from the river mouth, where salinities were between 10-14 PSU. The shark continued to move further downriver on the outgoing tide, lingering around in salinities of 26-28 PSU, before turning around and swimming against the outgoing tide for ~ 1 km. However, the shark was later recorded moving further downriver again, and was finally recorded very close to the marine

87 recreational boating area near the river mouth, where more than seven boats were engaged in high-speed water sport activities at the time.

Unfortunately, due to equipment breakdown during the tracking event, it was not possible to complete a full 6-hour track. The animal travelled a total distance of 3.33 km, while spending >50% of the track travelling with a speed of 0.25-0.50 m/s, and predominantly having a heading along with the current (>70%).

As evident from Figure 4.3.11, the animal spent 65% of its time in the 28-30 °C temperature range, while this regime encompassed 39.4% of the total volume, with the remaining volume of the system being of colder temperatures. This did not, however, translate into a significant difference (P > 0.05) between observed and expected proportions of time spent. The calculated value Manly‟s α in relation to the 28-30 temperature regime was 0.74, indicating an affinity for this temperature regime.

The animal spent 65% of its time in the salinity regime of 27-36 PSU, corresponding to 49.1% percent of the total water volume. In contrast, the animal spent 30% of its time in the 9-18 PSU range, which only corresponded to 2.7% of the total water volume. This translated into a significant difference (P < 0.05) between the observed and expected proportions of time spent in the four defined salinity regimes (Figure 4.3.11). Furthermore, the calculated value of Manly‟s α in relation to the 9-18 salinity regime was 0.89, indicating an affinity for this salinity range.

Despite spending nearly 70% of its time in current speeds in the 0.1-0.2 m/s range, which corresponded to 28.4% of the total water volume, there was no significant difference between the observed and expected proportions of time spent. Manly‟s α of 0.54 did, however, suggest that the animal had a slight preference for this current speed regime during this track.

Forty-five percent of time spent was at locations with 0-1 m water depth, corresponding to only 8.0% of the total water volume (however, 0-1 m deep areas encompass 28.4% of the total area). This translated into a significant difference (P < 0.01) in the observed vs. expected proportions of time spent at a given depth, and a Manly α of 0.74 further suggests that the animal had an affinity for the 0-1 m depth range. This result is backed by the animal spending close to 70% of its time in relative close proximity to the riverbank (0-0.6 Riverbank ratio).

Like in previous tracking events, the animal spent all its time in oxygen levels >5 mg/l. While the animal spent the majority of its time in turbidity of 2-6 NTU, the turbidity levels downriver were considerably less than those at the animal‟s normal location in the middle reach during this track (5.4 vs. 30.2 NTU). The animal spent most of its time in the 8.0-8.25 pH range, which is likely to be an effect of elevated pH levels throughout the system caused by high increases in freshwater discharge.

TRACK IV: 06/03 – 2010 (12:03 to 14:58)

Figure 4.3.10: Recorded animal locations during the track undertaken on the 06/03-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_4_06032010" in the Track_Videos subfolder.

Figure 4.3.11: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 06/03-2010.

Figure 4.3.12: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading (bottom right) for the track conducted on the 06/02-2010.

4.3.5 TRACK V The track on the 20th of March took place from 9:30 to 16:00, with most of the tracking occurring during an outgoing tide. The juvenile shark was again registered at the same middle reach area as previous tracks. Measured turbidity levels were still elevated compared to the normal range of 0-2 NTU and ranged from 2-9 NTU, while salinity in the immediate area ranged from 0-9 PSU during the tracking period. With the change to an outgoing tide at approximately 10:40, the shark started moving downriver while sticking close to the shallow banks, as had been observed in previous tracks. The salinity in the downstream section ranged from 6-20 ppt. After travelling an estimated 1.2 km downriver, the signal to the animal was lost and not regained before an hour later. At this point it had moved back to the same area as it had been in the first place, where it remained for the remainder of the track. The total distance travelled by the juvenile shark during the track was estimated to be 7.73 km.

The animal spent >50% of the track travelling with a speed of 0.25-0.50 m/s, and predominantly moving perpendicular to the current (nearly 60%). Once again the animal spent all its time in oxygen levels >5 mg/l, and spent the majority of its time in turbidity between 4-8 NTU, while remaining in the 7.0-7.5 pH range >80% of the time.

As evident from Figure 4.3.14, the animal spent >50% of its time in the 22-24 °C temperature range, despite that this regime only encompassed 15.6% of the total volume, with the remaining volume of the system being of warmer temperatures. While this observed behaviour translated into a Manly's α value of 0.82 in relation to the 22-24 temperature regime, there was no significant difference between the observed and expected proportions of time spent in the five defined temperature regimes.

The animal spent all of its time in the salinity regime of 0-9 PSU, corresponding to only 16.0% percent of the total water volume being within this regime. This translated into a significant difference (P < 0.01) between the observed and expected proportions of time spent in the four defined salinity regimes (Figure 4.3.14). Furthermore, the calculated value of Manly‟s α in relation to the 0-9 salinity regime was 1, which suggests the animal had a strong preference for this salinity range during this track.

The animal spent more than 90% of its time in current speeds in the 0-0.2 m/s regimes, which also corresponded to 90.7 % of the total water volume. There was no significant difference between the observed and expected proportions of time spent. Manly‟s α of 0.8 did, however, indicate a preference for this current speed regime, compared to higher flow velocities.

While there was a significant difference (P < 0.1) in the observed vs. expected proportions of time spent at a given depth regime, Manly‟s α for each regime did not exceed 0.31, which suggests that the animal had no particular affinity for any depth range. Similarly, the calculated riverbank ratios for this track did not suggest that the animal had any preference for particular positions relative to the middle of the river.

TRACK V: 20/03 – 2010 (9:30 to 16:00)

Figure 4.3.13: Recorded animal locations during the track undertaken on the 20/03-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_5_20032010" in the Track_Videos subfolder.

Figure 4.3.14: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 20/03-2010.

Figure 4.3.15: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 20/03-2010.

4.3.6 TRACK VI This track took place on the night between the 20th and 21st March during the timeframe of 21:13 to 03:00. The tide peaked and shifted to an outgoing tide at ~ 23.30. The animal was located in the area downstream of the middle reach area where it had been found on four other occasions. The shark stayed in this general area for almost three hours, but as the tide turned the shark moved into the adjoining upstream area where it normally lingered, and stayed there for two hours before moving to the same downstream section as before. At the end of the six-hour track the animal was estimated to have covered a total distance of 7.16 km. Estimated swimming velocities were almost identical to the previous track (although slightly decreased), with the animal spending 50% of the time travelling with a speed of 0.25- 0.50 m/s. The animal showed no clear preference for swimming along or against the current, but predominantly swam perpendicular to the current (Figure 4.3.18). The animal spent all its time in oxygen levels >7 mg/l, and spent the majority of its time (> 70%) in turbidity between 2-4 NTU, while remaining in the 7.25-7.75 pH range >80% of the time.

Not surprisingly, the animal spent approximately the same amount of time (>60%) in the 22- 24 °C temperature range, as it did in the track earlier the same day. Due to the fact that the volume of this temperature regime had decreased to 12.8%, this translated into an increased Manly‟s α value of 0.90 in relation to the 22-24 temperature regime (0.82 in the previous track). As in the previous tracks, there was no significant difference between the observed and expected proportions of time spent in the five defined temperature regimes.

The animal spent 85% of its time in the salinity regime of 0-9 PSU, corresponding to 11.5% percent of the total water volume. This translated into a significant difference (P < 0.01) between the observed and expected proportions of time spent (Figure 4.3.17). However, the calculated value of Manly‟s α in relation to the 0-9 salinity regime was only 0.52. There was a significant difference (P < 0.01) between the observed and expected proportions of time spent in various current speed regimes; however, the values of Manly‟s α did not suggest any strong affinity for a particular current speed regime. Similarly, for the depth regimes there was no particularly affinity for a specific regime, and there was no significant difference between the observed and expected proportions of time spent at a given depth.

96 TRACK VI: 21/03 – 2010 (21:13 to 03:00)

Figure 4.3.16: Recorded animal locations during the track undertaken on the 21/03-2011, plotted using an MGA-56 coordinate projection. See main text for track details. An animated movement path is available for illustration in the DVD appendix, filed as "Track_6_21032010" in the Track_Videos subfolder.

Figure 4.3.17: Proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted on the 21/03-2010.

Figure 4.3.18: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading regimes (bottom right) for the track conducted on the 21/03-2010.

4.3.7 COLLECTIVE RESULTS As mentioned in section 4.2.5, it was not possible to calculate reliable estimates of the animal‟s home range using the local nearest-neighbour convex-hull construction of home ranges as proposed by Getz et. al (2007). However, from above mentioned results, and further emphasised by Figure 4.3.19, the animal seemed to have a clear affinity for the middle reach of the system, with ~90% of all registrations made within this area, while occasionally utilising both up- and downriver sections of the system. At no point was the animal registered beyond the tide-dominated zone, or in the adjoining artificial canals.

Throughout the six recorded tracks, the animal's estimated rate of movement (ROM) was quite low, with nearly 80% of the time spent swimming between 0.0-0.5 m/s, with an average swimming speed of 0.32 ± 0.25 m/s (S.D., n=188). As evident from the collective results presented in Figure 4.3.20 and 4.3.21, the animal spent the majority of its time in current speeds of 0-0.2 m/s, while spending near-equal amounts of time swimming along and

99 against the current. However, when current speeds exceeded the mean ambient current speed (Figure 4.3.22), the animal decreased its frequency of movement perpendicular to the current, whereas in low current speeds the animal spent more time moving perpendicular to the current. This suggests a directional response of the animal in respect to flow velocities, possibly as a means to stay in the same general location under changing flow conditions. ROM was significantly negatively correlated with current speed (P < 0.05); however, the low correlation coefficient of -0.16 suggests only a weak relationship.

The collective results for depth (Figure 4.3.20) suggest that the animal had an overall affinity for intermediate to deep depths within the 1-4 m interval, while only spending modest amounts of time (< 10%) in shallow water. It is likely that the lack of time spent at locations that exceed a depth 4 m is due to the fact that the majority of these areas are located near the river mouth and adjoining artificial canals, rather than an actual avoidance of deep holes. Taking the collective riverbank ratios into account (Figure 4.3.20), the evidence of the animal having a higher affinity for the middle, and thus deeper, sections of the system is further supported. ROM was negatively correlated with both depth (P<0.01) and the riverbank ratio (P<0.01) with correlation coefficients of -0.29 and -0.41, respectively. While the correlation coefficients are relatively low, this result still suggests that the animal lingers in the deeper parts of the system by decreasing its movement speed and generally moving faster closer to the riverbanks, which in turn could indicate that the animal forages for prey close to the mangrove sheltered riverbanks.

Throughout the six tracks, the animal spent the majority of its time (>50%) in the 0-9 PSU regime, while spending only 6% of its time in the 27-36 PSU regime, despite the latter being the salinity regime that encompassed the largest volume of habitat in the system on average (~55%). The lack of a significant correlation between ROM and salinity (P > 0.05) further suggests that the animal had an affinity for a broad range of salinities and that it did not utilise a change in rate of movement as a strategy to avoid areas with too high or too low salinity. However, a directional response to change in salinity remains a possibility, and serves as a possible explanation for the animal being found downriver on the 6th of March (Section 4.3.4).

The collective results for temperature reveals an almost uniform distribution of proportion of time spent at the various temperature regimes (Figure 4.3.20), which is likely the result of the observed system-wide temperature fluctuations in between tracks, thus clouding any potential

100 short-term temperature affinity. ROM was negatively correlated with temperature (P < 0.05, R= -0.18), and while the correlation coefficient is quite low, this could indicate that the animal displayed a slight behavioural response to temperature by decreasing its swimming velocity in higher temperatures (>30 °C).

The animal spent the majority of its time (57%) in turbidity levels of 0-2 NTU, with decreasing amount of time spent with increasing turbidity level, suggesting an overall affinity for low turbidity levels (Figure 4.3.21). With almost 50% of time spent in pH levels less than 7.5-7.75, the overall proportion of time spent in the various pH regimes reflected the animal‟s primary choice of habitat in the middle reach mixing zone between freshwater discharge and tidal movements. This indicates that pH could be a factor in habitat selection, among other abiotic factors. While dissolved oxygen levels were relatively high throughout all tracks (> 5 mg O2/l), the animal spent the most time in the 7-8 mg O2/l regime (Figure 4.3.21), indicating an affinity for higher oxygen levels. There was no significant correlation between ROM and turbidity, pH or dissolved oxygen, which could be due to the lack of any strong spatial gradients of these parameters on each given tracking event. However, the scarcity of these data sets could also likely be a factor in the calculated correlation; e.g. at no point was the animal registered in conditions below 4 mg O2/l, above 10 NTU or in potential harmful pH levels, making it very difficult to relate each parameter‟s effect on ROM.

101

ALL TRACKS: 28/01/2010 to 21/03/ 2010

Figure 4.3.19: Total amount of recorded animal locations during all six track undertaken from the 28/01-2010 to the 21/03-2011, plotted using an MGA-56 coordinate projection. Note the concentration of registrations in the middle reach. See main text for explanatory details.

102

Figure 4.3.20: Total proportion of time spent at various temperature (top left), salinity (top right), current speed (middle left), swimming speed (middle right), depth (bottom left) and riverbank ratio regimes (bottom right) for the track conducted from the 28/01/2010 to the 21/03/2010.

103

Figure 4.3.21: Proportion of time spent at various dissolved oxygen (top left), pH (top right), turbidity (bottom left) and animal heading (bottom right) for the track conducted on the 20/03-2010.

Figure 4.3.22: Frequency of animal heading relative to current direction during below-mean flow velocities (red), and above-mean flow velocities (blue).

104 4.4 DISCUSSION

A significant limitation of this study is the fact that only a single individual was studied, and thus it lacks the ability to confidently generalise the observed movement behaviour for C. leucas. While a larger number of tracked individuals could have increased the general validity of the observed movement patterns, and allowed an assessment of intraspecific variation in terms of habitat choices and utilisation, this study still provides interesting insight into the potential variability of an individual‟s behaviour in relation to temporal changes in water quality regimes. It is nevertheless important to emphasise that this study does not attempt to validate the results‟ general applicability, and that any conclusions based on the results presented in section 4.3 are valid for the tracked individual only.

In contrast to the reported preferred salinity range of 7-20 PSU for juvenile bull sharks in the Caloosahatchee River (Florida, USA), and avoidance of salinity ranges < 7 and > 20 PSU (Simpfendorfer et al. 2005) (Ortega et al. 2009), the tracked juvenile in this study was found to prefer a wider salinity range of 0-27 PSU. Considering the large amount of time (> 50%) spent in the 0-9 PSU regime when more saline conditions were available downriver, the range of tolerable salinity levels is wider for this juvenile, with no evidence of immediate avoidance of lower salinities. However, the lack of registrations above 28 PSU relative to the large volume of water within this range still suggests an avoidance of high salinities. While purely speculative, it could be hypothesised that due to Tallebudgera Creek being more sensitive to freshwater discharges compared to larger estuaries, the high frequency of drastic shifts in salinity in this system could require resident young C. leucas to have a wide salinity tolerance range. This early life-stage adaptation could in turn aid young C. leucas to better utilise the full extent of the space-limited estuary, rather than congregate in the same limited salinity range with larger conspecifics. As cannibalism has been reported within this species (Snelson et al. 1984), it is possible that avoidance of especially larger conspecifics is a strong driver for movement than salinity in size-limited habitats.

As evident from Figure 4.3.19, the tracked animal had a high affinity for the middle reach area, despite salinities in this area being highly variable over time, and at times was < 1 PSU. Only on two occasions did the shark leave this area, with one being a rather quick upriver migration on an incoming tide, before returning to its usual home range with the turning of the tide. This upriver excursion might be a foraging trip for prey, while utilising the tide to

105 minimize energetic costs associated with this type of directed movement. As mentioned in section 4.3.4, the other occasion when the animal was found outside of its usual home-range was shortly after a freshwater discharge event had shifted the system into a state of elevated flow, higher turbidity and decreased salinity. Since this was the only track conducted in the time immediately after a system-wide shift in water quality, it is difficult to assess if the unusual downriver position of the animal was the result of avoidance of unfavourable conditions, e.g. elevated turbidity levels, or simply being driven downriver by elevated flow instead of spending energy to maintain position at the middle reach. Likewise, it is difficult to assess the contribution of decreased salinity levels to the downriver migration. However, two weeks later when flow velocities and turbidity levels had decreased to pre-discharge levels in the middle reach, but salinity levels remained low at 0-3 PSU, the animal spent the following 12 hours at its usual middle reach location. This suggests that while salinity might contribute to movement and habitat selection in a more passive manner, elevated flow and/or turbidity levels could possibly have a higher impact on movement associated with avoidance strategies in the event of a system-wide shift.

As evident from the collective results in section 4.3.7, the animal did not show any clear preference in terms of swimming direction relative to the current, although it did spend a slightly larger amount of time travelling with, rather than against, the current. When taking the affinity for a specific home-range into account, the near-equal amount of time spent travelling with and against the current suggests that the animal predominately attempts to maintain its position in the middle reach. This may be due to the relatively low flow velocities throughout the middle reach area. This behaviour likely shifts during elevated flow regimes, as might have been the case on the track conducted the 6th of March (Section 4.3.4).

The apparent site-affinity could partly be due to the fact that this section is relatively deeper and wider compared to the surrounding sections. Werry (2010) reported an association of C. leucas with deep holes in the Gold Coast canals, and suggested the animal might use deep holes as a means to maintain their position against the current and conserve energy.

106

Figure 4.4.1: Mean current speeds as predicted by the HD model, during the tracking campaign period, spanning from 28/01/2010 to 21/03/2010. Note the patches of reduced flow velocities within the middle reach area (marked by red arrows).

As evident from Figure 4.4.1, the physical characteristics of the middle reach do indeed provide patches of reduced mean flow velocities compared to the adjoining areas. This suggests that part of the animal‟s site affinity could be linked to the presence of a more heterogeneous bathymetry that provides patches of preferred reduced flow conditions.

While salinity did not seem to be a strong short-term driver for directed movement, it is possible that it is a major driver for habitat selection on a larger temporal scale. When considering the range of mean predicted salinity levels over the entire tracking campaign (28/01 to 21/03 - 2010) throughout the system (Figure 4.4.2), it is evident that the mean salinity levels (7-14 PSU) for the middle reach area lies within the preferred salinity range reported by Simpfendorfer et al. (2005) and Ortega et al. (2009).

107

Figure 4.4.2: Mean salinity as predicted by the HD model, during the tracking campaign period, spanning from 28/01/2010 to 21/03/2010. Note how the area normally utilised by the animal (marked by the black box) lies within the 7-14 PSU range.

It may be hypothesised that the reason why the animal stayed within the middle reach when salinity levels were below 7 PSU is that the energetic costs associated with moving downriver in search for more saline habitats outweighs the costs of maintaining its current position until preferred salinity levels return with the incoming tide. However, when other variables comes into play, such as increased turbidity and flow in conjunction with a drop in salinity, the energetic costs of staying put in the same location are likely to overweigh the costs of downriver migration, as was witnessed in Track IV.

In terms of predicted mean temperatures for the middle reach during the period of 28/01 to 21/03-2010, there was only a 0.3°C difference in predicted mean temperatures between the middle and uppermost reaches, while the area at the river mouth was ~1.0°C lower than at the middle reach. Due to the lack of any notable spatial variation throughout the system, the impact of temperature on movement and habitat selection is not easily assessed. It seems unlikely that temperature plays a major role in habitat selection on a short-term scale, although it is difficult to rule out entirely as a long-term driver, as ROM was negatively correlated with temperature.

As mentioned in section 4.3.7, the lack of spatio-temporal data for dissolved oxygen, pH and turbidity makes it very difficult to assess how these parameters affected movement. While

108 there is evidence that suggests turbidity has been a driver in the downriver migration witnessed on the 6th of March, the contribution of DO and pH remains elusive. Due to DO levels being generally high throughout the system over the course of 2010 (Chapter 2, Section 2.3), it is plausible that DO have a limited effect on movement as long as levels remain high. However, as the bull shark is a ram-obligate shark species (Weihs et al. 1981), it is plausible that hypoxic conditions could result in an increase of swimming velocities, since this behavioural response has been reported for other ram-obligate shark species under hypoxic conditions (Parsons & Carlson 1998, Carlson & Parsons 2001).

Although a lack of proper data resolution in respect to the abovementioned parameters restricts the ROM analysis, it is important to note that the bias associated with the calculation of ROM will undoubtedly lead to a biased result in the correlation analysis. It follows logically that movement with a high level of linearity in open unobstructed areas will have a relative small bias associated with the calculation of ROM using the Euclidian distance between points as a proxy for true distance travelled. However, more tortuous movement in confined areas (such as Tallebudgera Creek) will in turn potentially grossly underestimate the true ROM unless registration of animal position is made in quick succession. As mentioned in section 4.2, this study utilised a variable 5-10 minute interval between registrations. However, on multiple occasions while the animal was in its middle reach home range, it was noted that it would swim in one direction before turning and returning down the same path within a 5-10 minute interval. While effort was made to capture this back and forth behaviour, it was near impossible to predict when or if the animal would backtrack at a given point in time, and thus a great deal of detail could have been lost, which in turn would result in an underestimation of ROM.

While the obvious limitations in the data set warrant further investigations into the movement of C. leucas in Tallebudgera Creek, one has to bear in mind that the main objective of the tracking campaign - the acquisition of movement data for validation of an agent-based model (ABM) - was fulfilled to a satisfactory degree. Logically, a more detailed data set encompassing repetitive tracks of several animals over a range of different water quality conditions would improve confidence in the observed pattern on a more general level. However, for the purpose of testing the ABM approach as a means to study and replicate the movement patterns of C. leucas, a large and comprehensive sample size is not necessarily mandatory.

109

CHAPTER V

AGENT-BASED MODELLING OF A JUVENILE BULL SHARK ON A SHORT SPATIO-TEMPORAL SCALE

110 5.1 INTRODUCTION

As an apex predator potentially up to 3.5 m in total length (Voight & Dietmar 2011), the bull shark fulfils an important ecological role in the top-down regulation of food chains in its natural environment (Martin 2005, Heithaus et al. 2008). Due to the bull shark‟s ability to enter estuarine and freshwater systems, this species has always been living in close proximity to human settlements. It is a potential dangerous species to humans and has been implicated in many reported shark attacks (Werry 2010). As human populations continue to grow in coastal areas, so does the pressure on habitats frequented by C. leucas, and there is an urgent need for effective management of this keystone species (Werry 2010).

Many mobile aquatic species move extensively as they use different habitats for different purposes, e.g. foraging, spawning and refuge. These species may thus be affected if any of these habitats are degraded as a result of anthropogenic impact (Goodwin et al. 2001). While habitat degradation in terms of water quality and hydrology can be modelled on a fine spatio- temporal scale through the implementation of Eulerian models (Szylkarski et al. 2004, Dias & Lopes 2006b, Zacharias & Gianni 2008), the resulting impact on fish movement and behaviour remains elusive.

Movement of terrestrial and aquatic organisms has often been modelled by biologists using a Lagrangian object-oriented model framework, which unlike the Eulerian framework, allows for the individual representation of organisms (Nestler et al. 2005). However, many of these early agent-based models do not account for spatial variations in habitat and fish movement (Railsback et al. 1999). The inability of the Eulerian framework to simulate individual entities and the lack of a spatial heterogeneous habitat representation in Lagrangian models can, however, be overcome by coupling the two model frameworks.

The coupled Eulerian-Lagrangian framework allows for an accurate representation of hydrology and water quality within a spatially complex system over time, while also capable of simulating higher trophic levels such as fish on an individual level, which moves independently of the Eulerian model grid (Nestler et al. 2005). As simulated agents within the model domain can be made capable of reacting to Eulerian gradients, it is thus possible to investigate potential effects of hydraulic cues on fish movement on a complex spatial scale over time (Nestler et al. 2005).

111 From an ontological point of view, one can never truly know how a wide range of complex environmental stimuli is perceived neurologically and translated into a movement response by a fish. Thus, simplifying assumptions on how the fish perceives the outside environment is a necessity in order to simulate possible movement responses to environmental cues without first describing the complex nature of the neurological translation of perceived stimuli. Although many agent-based models are relatively complex in nature (Jopp et al. 2011), many of these models limit the number of possible environmental stimuli to only a few parameters that are hypothesised to be the main drivers of movement, e.g. Humston et al. (2000) simulated the migration of bluefin tuna solely through the use of temperature stimuli, while Goodwin et al. (2006) simulated the downstream migration of juvenile salmon using flow velocities, dissolved oxygen and temperature as primary drivers for movement.

While long-term agent-based models are required to simulate some measure of food-uptake and growth in order to remain biological plausible, e.g. Humston et al. (2004), it is a reasonable assumption that short-term movement are not necessarily driven by foraging, and that growth of the individual is negligible given the short time-frame. Juvenile C. leucas have been reported to move and distribute in relation to hydrology and physical cues by several studies (Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010), which has also been backed by the findings of this study (Chapter IV). It is therefore possible that short-term movement and distribution of this species can be modelled to a plausible degree as has been demonstrated in some teleosts, e.g.: (Van Winkle et al. 1998, Railsback et al. 1999, Humston et al. 2000, Goodwin et al. 2001, Humston et al. 2004, Goodwin et al. 2006).

5.1.2 AIMS & PURPOSE As the final component of this study, this chapter sought to develop an Eulerian-Lagrangian agent-based model capable of replicating and predicting the short-term movement of juvenile C. leucas using hydraulic and physical cues simulated by the hydrodynamic model as drivers for movement. It was predicted that the outcome of this agent-based modelling approach would help to better understand the drivers of C. leucas movement and with time, provide the first brick of a more comprehensive ABM that accounts for the ontogenetic habitat use by C. leucas in the estuarine-marine continuum.

112 5.2. METHODOLOGY

The MIKE ECO Lab integrated agent-based modelling module was utilised for developing an individual-based model relating to the virtual habitat defined by the hydrodynamic model described in Chapter III. The MIKE ECO Lab ABM module is still in a developmental stage and not part of the commercially available MIKE software package provided by the DHI. However, it has been tested reliable though application of in-house DHI projects, e.g. (Andersen et al. in prep, Canal-Vergés et al. in prep, Hansen & Potthoff in prep). In the following section a short description of the fundamental properties of the MIKE ABM module will be given, before presenting each movement model in turn.

5.2.1 THE COUPLED EULERIAN-LAGRANGIAN MODEL FRAMEWORK The MIKE ABM-module consists of a Lagrangian framework integrated into the Eulerian framework of the hydrodynamic model (Chapter III) that simulates the physical dynamics of the environment of the agents. The integrated Eulerian-Lagrangian framework makes it possible for a simulated agent (or individual) to move independently of the Eulerian model grid and potentially affect Eulerian variables such as dissolved oxygen or phytoplankton/zooplankton concentrations through consumption, if such variables were simulated in the Eulerian framework. In addition to manipulation of the Eulerian environment, each agent can through user-specified equations react to other nearby agents to simulate specific types of interaction between individuals, such as flocking/avoidance- behaviour, predation, and mating.

Through the use of model built-in spatial functions, it is possible to define a sensory sphere that can potential stretch across Eulerian grid cells (Figure 5.2.1). The implementation of a sensory sphere enables the agent to detect the gradient Eulerian variables and/or the presence of other Lagrangian agents within the radius of its sensory sphere. The size of the sphere can be defined through a user-specified radius together with the angle of the agent‟s field of view, meaning that if needed an agent can be specified to only sense variables ahead of its direction of orientation.

113

Figure 5.2.1: Two-dimensional view (longitudinal and latitudinal) of a shark’s sensory range. The sensory range is independent of the Eulerian model grid, and is often estimated from the body length of the animal and model timestep. Adapted from Goodwin et al. (2006)

Movement of agents in the horizontal plane in the model setup are expressed by assigning values (constant or variable over time) as a horizontal vector, consisting of a speed (m/s) and a direction (degrees). Movement in the vertical plane is defined through a positive (swimming upwards) or negative (swimming downwards) speed, although in this study the agents are limited to moving in the horizontal plane with a constant Z-value. In the current version of the ABM module, it is possible to define a maximum of five movement vectors, where the final movement is found by internally combining all vector sub-sections for each timestep. The implementation of two or more movement vectors allows the agents to have their final movement vector affected by several factors, e.g. the flow direction and velocity of the water body. The length of the timestep determines the time spent travelling with a given speed and direction before a new trajectory is calculated for each agent. The size of the timestep is therefore an essential parameter for the performance of the movement models tested, and will be discussed in more detail later in this chapter.

5.2.2 THE RANDOM WALK MODEL Although it is unlikely that animal movement is truly random in nature, this assumption is often made when modelling animal movement in the biological sciences (Codling et al. 2008), as it provides a reasonable „null‟ model for hypothesis testing . The Random walk

114 model represents the simplest of assumptions by assuming that the animal shows no behavioural sensitivity to stimuli from its external environment, and thus provides a suitable control for comparison against movement models of increased sophistication (Humston et al. 2004), such as the kinesis model that is described in a later section (5.2.3).

5.2.2.1 RANDOM WALK MOVEMENT EQUATIONS

Direction (degrees) in the random walk model is defined as 360 multiplied with a random number drawn from a uniform distribution ranging from 0 to 1.

Eq. 5.1

Equation 5.1 ensures that the frequency of chosen swimming directions will be uniformly distributed around the compass rose, and thus no directional preference will be evident in the agent‟s movement.

Swimming velocity is defined as:

Eq. 5.2

Where and are the maximum and minimum swimming speeds respectively, and is a random number drawn from a uniform distribution ranging from 0 to 1. The above equation allows the agent to swim at a uniformly distributed range of swimming velocities varying from to . The term is included in the formulation of since bull sharks are obligate ram-ventilators and are required to swim continuously in order to maintain adequate water flow over its gills. represents this basal level of swimming activity. See section 5.6.0 for a further discussion of and .

In the random walk model, simulated agents will not be affected by any external cues besides water flow velocity. No other movement rules apart from mechanical forcings that prevent the agents from crashing into model boundaries will be implemented. See section 5.2.5 for a description of these movement forcings.

5.2.3 THE KINESIS MODEL The following model description builds upon the one-variable kinesis model first proposed by Humston et al. (2000) as a way for blue-fin tuna to locate habitat that provided optimal conditions in terms of temperature. Later this model was developed further to include two variables; prey abundance and optimal salinity conditions for bonefish Albula vulpes

115 (Humston et al. 2004). For the purpose of this study, two types of kinesis search models will be tested: 1) A one-variable kinesis search for optimal salinity and 2) a three-variable kinesis search where it will be assumed that the animal responds to salinity, temperature and current velocities, while naturally also being passively affected by flow velocities as would any object in the water column.

Kinesis describes an agent‟s reactive, yet non-directional, response to external or internal stimuli relative to a preferred state, where the intensity of stimulation compared to the preferred state will affect the change of speed (orthokinesis) and/or the frequency of directional changes (klinokinesis) (Humston et al. 2000).

The mechanisms behind kinesis correspond to a minimalist assumption of cognitive capabilities in the animal, where no spatial or temporal evaluation of sensory inputs is taken into account: the agent solely reacts on the concurrent stimuli in its point of presence with no "knowledge" of the surrounding environment, and thus not able to move towards a specific direction based on external cues (Humston et al. 2004).

5.2.3.1 KINESIS MOVEMENT EQUATIONS

The agent‟s swimming velocity components, and , are respectively defined as:

Eq. 5.3

Eq. 5.4

Where is a function describing the deterministic response to an external stimulus based on the swimming velocities of the previous timestep, while is a stochastic function (Eq.

5.8) that determines the random component of the agent‟s reactive response to a stimulus. and are the flow velocities predicted by the hydrodynamic model at the agent‟s location.

The equation of is identical to that of except is substituted for , and is defined as follows:

Eq. 5.5

is the swimming velocity in the previous timestep , and describes the influence of inertia on movement, meaning that an agent moving at higher speeds is more likely to continue in the same general direction in the next timestep. , and are Gaussian equations describing the reaction to salinity, current speed and temperature in the

116 current timestep relative to a theoretical optimum. , and are, respectively, variable weighting factors for salinity, current speed and temperature, which determine the relative contribution of , and and is dependent on the difference between the ambient and preferred levels of salinity, current speed and temperature. The functions ,

and are defined as follows:

Eq. 5.6

Eq. 5.7

Eq. 5.8

Where , C and are the given temperature, current speed and salinity, respectively, in the present timestep, while , and denotes the respective optimal/preferred value by the agent. , , and are shape parameters determining the Gaussian variance and height, which in turn dictate the sensitivity of the kinesis response to external stimuli

(Humston et al. 2004). The stochastic component, , in Eq. 5.3 and 5.4, is defined as:

Eq. 5.9

Similar to , the stochastic component consists of three Gaussian equations,

, and , that describe the respective reactions to salinity, temperature and current speed in a given timestep relative to a theoretical optimum, and weighted by , and , respectively. The stochastic variable in this study is defined as a random number,

, drawn from the standard normal distribution with a mean of 0 and variance of

(resulting in 99.7% of all numbers drawn being in between ±1), and subsequently scaled according to to prevent any agent reaching swimming velocities that exceed the specified maximum:

Eq. 5.10

The Gaussian equations , and appearing in Eq. 5.9 are defined as:

117

Eq. 5.11

Eq. 5.12

Eq. 5.13

In the above equations, determines the height of Gaussian curve while the variance parameters , and are identical to the ones specified in Eq. 5.5, 5.6 and 5.7. Finally the weighting parameters used in Eq. 5.5 and 5.9 are defined as:

Eq. 5.14

Eq. 5.15

Eq. 5.16

The value of , and ranges from 1 to , depending on the difference experienced between the current external stimulus and preferred optimum of salinity, current speed and temperature, respectively. The value of determines the range of possible weightings, and is assigned the value of 5 following Humston et al. (2004), due to the inability of parameterisation of from biological data.

It is evident from Eq. 5.11, 5.12 and 5.13 that (the variable X is used for the purpose of illustration) will move towards its global minimum defined as which is reached only when , meaning that the stochastic contribution to movement is minimal when the agent is located in an optimal environment (Figure 5.2.).

118

Figure 5.2.2: Plot A: and plotted against the variable X with optimum , and

and . Plot B: and plotted against the variable X with optimum ,

and and .

(Eq. 5.6, 5.7 and 5.8), on the other hand, has its maximum value, , when , which will cause an increased contribution of inertia from and results in a decrease of the likelihood of the agent turning to another direction when close to its optimal state. When the agent is moving away from its optimal state, the relative contribution of will decrease while increases (Figure 5.2.2), thus resulting in a more random movement. This functional response is intended to mimic the behaviour of an animal in search for optimal conditions without spatial "knowledge" of its surroundings, and only able to sense the environmental conditions in its own point of presence. As Plots A and B in Figure 5.2.2 illustrate, the shape of the Gaussian functions is highly dependent on the specified optimum as well as the shape parameters , and . Furthermore, Figure 5.2.2 illustrates that if (Plot A) it will result in decreased swimming velocities when located close to the optimal state, whereas when (Plot B) a relatively higher swimming velocity will be maintained in optimal conditions.

Similarly, the functional response of and when reacting to more than one variable (X and Y for illustration), results in a three-dimensional matrix (Figure 5.2.3). As evident from

Figure 5.2, the value of peaks when the optimum values for both X and Y are met

( , in this demonstration), while the stochastic contribution of is

119 reduced to its minimum value, and vice versa for values located away from the optimum values.

Figure 5.2.3: The weighted functional response of (left) and (middle) plotted separately and combined (right). The optimum values depicted ( , and ) are for illustration purposes only.

5.2.4 MODEL STRUCTURE The random walk model (RW), the kinesis search for optimal salinity model (KS) and kinesis search for optimal salinity, temperature and current speed (KSTC) were applied to each of the six registered animal tracking events (Chapter 4) with 100 agents released at the registered start location of each track. The simulation period covered the full duration of the recorded tracks in order to perform a direct comparison between observed and simulated movement tracks.

In order to test the predictive abilities of the above-mentioned movement models, 100 agents were randomly distributed within the model domain and had their movement simulated for 12 hours prior to the timestamp of the initial animal location for each track. The start location versus end location of simulated agents allows for the assessment of the agents ability to react to the prevailing environmental conditions throughout the system and locate the habitat where the tracked animal was registered upon the start of a tracking event.

Table 5.1 provides a list of the final values of each calibration parameter in the three movement models, and their values explained in turn in the following section.

120 5.2.5 MODEL SETUP & CALIBRATION EFFORT The timestep of the ABM model setups were initially set to 30 seconds, but due to relative high agent velocities that caused agents to crash into land boundaries in a single timestep, the value was reduced to 10 seconds to reduce the amount of time travelled before making a new movement decision, but at the cost of increased simulation times. was originally set to a value of 3.0 m/s, which is well above the reported cruising speed of 0.56-0.80 m/s for C. leucas (Weihs et al. 1981), while was set to 0.1 m/s. Initial model runs revealed that the average speeds of agents were around 0.6-0.7 m/s using these values; however, due to the very narrow nature of the model domain, the rare event of agents reaching the maximum velocity often resulted in agents crashing into model boundaries. As a necessary means to avoid the loss of agents, was consequently scaled down to 1.5 m/s.

Table 5.2.1: The model calibration parameters and their final values adopted in the three types of movement models.

Parameter RW KS KSTC Unit Reference Timestep 10 10 10 seconds -

1.5 1.5 1.5 m/s - 0.1 0.1 0.1 m/s - - 13.5 13.5 ppt - - - 20 °C - - - 0.1 m/s -

- 20 20 - - - - 10 - - - - 0.5 - - - - 5 - - - - 0.7 - Humston et al. (2000) - - 0.9 - Humston et al. (2000)

While exact optimum values for salinity, temperature and flow velocities have not been established in the literature for juvenile C. leucas, the optimum value of salinity , was selected in order to reflect the range of salinities in which the tracked individual was recorded in 0-27 ppt as well as the salinity range of 7-20 recorded by the literature (Simpfendorfer et al. 2005, Heupel & Simpfendorfer 2008, Ortega et al. 2009). The selected temperature optimum of 20 °C was selected as a means to have the agents look for the “coldest” possible water in the prevailing temperature conditions in a given simulation. The “cold-water”

121 optimum was chosen to reflect one of the model assumptions that the agents are not looking for prey but habitat (in terms of water quality) where metabolic costs is minimum, e.g. it has been reported that the euryhaline bat ray, Myliobatis californica, displays behavioural thermoregulation by feeding in warmer waters and resting in cooler waters (Matern et al. 2000). The optimum value for current flow was set to 0.1 m/s due the observations made in Chapter 4, where the majority of time spent was in the flow regime of 0.0-0.1 m/s while the animal on one occasion was observed downriver after a relative high freshwater discharge event, indicating a possible avoidance of elevated flow velocities (Chapter 4).

In order to assess the contribution of the above-mentioned optimums on the resultant movement behaviour of the agents, each track and pre-track simulation (section 5.2.4) was run with salinity optimums set to zero and compared with the model results where the optimum was set (Table 5.2.1). In a similar fashion, the contribution of flow velocities on the simulated movement was assessed by running each model scenario with the effects of current flows removed from the equations, so that agents would move around in the system unaffected by the movement of the water body.

Following Humston et al. (2000), the value of the shape parameters , , and was chosen to reflect the variability of salinity, temperature and current flow through the system on a spatio-temporal scale. The values of 0.7 and 0.9 for and , respectively, were adapted from Humston et al. (2004) in order to have agents decrease their movement speed in conditions that are close or equal to the specified optimum values, thus decreasing the probability of moving out of the area and increase the time spent in optimal conditions.

As mentioned in section 5.2.2.1, all three types of models were subject to movement-forcing rules, with the intention of preventing agents from crashing into model boundaries or land values. The forcing rules consist of two general criteria formulated into an IF statement, described as follows:

Eq. 5.17

Where is the ambient total water depth at the agent‟s location, while is the gradient magnitude of an artificial channel placed in the middle of the estuary within a 10 m radius (Figure 5.4). is the direction to the highest gradient magnitude of the channel forcing within a 60 m radius, and is the resultant direction of the and components calculated from the movement equations specified in the above sections.

122

Figure 5.2.4: The artificial channel forcing shown for the middle reach of the model domain. Should an agent move more than 10 metres away from the channel forcing, it will automatically shift its heading towards the channel forcing in the next timestep.

The artificial channel consists of a one to two Eulerian-cell wide line that spans from the river mouth to the limit of the tide-dominated zone in the upper reaches. It follows from the movement rule in Eq. 5.17 that should an agent happen to move more than 10 metres away from the channel, it will no longer be able to register its magnitude, and will shift its direction towards the channel in the following timestep and continue this direction until it again is within 10 metres of the channel. The logic behind this movement rule is to first and foremost prevent agents from crashing into model boundaries and thus become stranded, while also preventing them from entering water that is too shallow for them to inhabit. The obvious downside to this movement rule is that it restricts freedom of movement and biases the agent‟s frequency of directions toward the middle of the river. However initial calibration runs of the models revealed that without this movement rule in place, the vast majority (97- 100%) of released agents would crash and get stuck onto land values before the simulation

123 came to an end, and thus it was deemed necessary to keep the rule in place, despite its obvious downsides.

5.2.6 DATA ANALYSIS The specified outputs for each movement model consisted of ambient values of salinity, temperature, current speed, total water depth, horizontal speed and direction of agents as well as their X- and Y-coordinates for each timestep. Prior to data analysis, agents that stranded on land values despite the enforced movement rules specified by Eq. 5.17, were excluded from the dataset. Thus, the „agent success rate‟ is defined as the number of agents that make it through the entire simulation without getting stuck on cells containing dry land values.

Data for the corresponding times between simulated tracks and the observed animal locations were extracted in order to calculate the distance from each agent to the registered location of the animal. Due to the curved and bending nature of the system, the distance was calculated by generating a mid-channel longitudinal transect line with an equidistant distance between coordinate points (0.973 m), which spans from the river mouth to the end of the tide- dominated zone. At model timesteps corresponding to recorded track timestamps, the closest point on the transect point for each agent (and corresponding animal locations) was then determined by calculating the Euclidian distance from agent locations to each point on the transect line and finding the respective minimum distances for each agent. The final estimated distances between simulated agents and animal locations were then defined as:

Eq. 5.18

Where is the number of the coordinate set corresponding to the transect point closest to the ith agent at the jth timestep, while is the number of the coordinate set corresponding to the transect point closest to the observed animal location at the jth timestep. is the distance between transect coordinate points (0.973 m).

124

Figure 5.2.5: Simplified schematic of the middle reach area, highlighting the essence of the proposed distance estimation methodology. Note the potential for a huge bias in the Euclidian distance estimation (as illustrated), compared to distance estimation using the transect line as a reference.

A relatively minor bias is attached to the use of this method, because it "projects" all agents and animal locations onto the middle of the river, but due to the narrow nature of the system this method is deemed more accurate than calculating the Euclidian distance between points, since it takes the shape of the system into account (Figure 5.2.5). The calculated distances for all simulated agents were further processed by calculating the median distance as well the 90th and 10th percentile and plotted against the duration of the simulated track period in order to assess the general performance of the model.

Similarly the median together with the 90th and 10th percentile was calculated for ambient values of salinity, temperature, current speed, total water depth and horizontal swimming velocity and plotted against the values extracted from the hydrodynamic model at the observed location of the animal over the duration of each tracking event. For further assessment of model performance, the Quality index (explained in Chapter 3) was calculated for each of above mentioned parameters. The frequencies of simulated and observed horizontal swimming directions between corresponding points in time were divided into four

125 bins corresponding to North (>315-45°), West (>45-135°), South (>135-225°) and East (>225-315°) and compared using the χ2-test. Furthermore, using the test for Circular-Circular correlation (Berens 2009), the sequential headings of the best-fitted track (meaning the agent who proved to have the least distance to observed locations) was analysed against the sequential headings of observed tracks.

The performance of the pre-track models were analysed by calculating the start distance of each of the 100 randomly distributed agents to the registered start location of the given track using the same calculation method as described above, and compare them to the end distance of the agents after a 12-hour simulation. Agents that became stranded during the simulation were, however, excluded from analysis. Since the agents‟ end location is considered heavily dependent on their start location, only descriptive statistics will be used to analyse these results. In a similar fashion, start levels of salinity, temperature and current speed will be compared to the levels at the end of the simulation in order to evaluate the agents‟ ability to locate habitat that is closer to the optimum conditions specified by the model equations.

5.3 RESULTS

Due to the substantial amount of data produced by the applied model templates for every track and associated pre-tracks, only the most essential results of each model will be presented in the following sections. Based on the collective model results summed up in Tables 5.3.1 to 5.3.12 the best-fitting model template for a particular track/pre-track will be defined and presented in turn, while remaining model results are available in the “Appendixes” subfolder on the DVD-Appendix. Selected video outputs of model tracks are likewise available for inspection in the attached DVD appendix in the “ABM_Videos” folder. Specific ABM output videos are available upon request of the author.

5.3.1 TRACK I Compared to the track conducted on the 28/01-2010, none of the applied model templates were able to replicate the animal‟s migration to the upper reach, resulting in a high mean and minimum distance to actual animal locations for all model simulations (Table 5.3.1). The No- Current control Kinesis search model for optimal salinity, temperature and flow velocities (KSTC–NC) provided the best fit with measured data, with a minimum mean distance of 892.8 m to observed track locations (S.D. = 1282.5 m, n = 73). Inter-model performance

126 differed mostly in agent success rate, with the Random Walk (RW) model having the highest agent success rate of 90 agents.

Table 5.3.1: Primary model results from the five applied model templates for the simulation covering the track that was conducted on the 28/01 - 2010. RW = Random Walk Model, KS = Kinesis Search for Optimal Salinity, KSTC = Kinesis Search for optimal salinity, temperature and current flow, KSTC-NC = KSTC with the contribution of current flow on agent movement removed, KSTC-ZS = KSTC with an optimum salinity of 0 PSU.

TRACK: 28/01 -2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 90 78 37 73 11 Mean Distance (m) 1324.2 1304.8 1299.0 1005.6 1287.3 Min. Distance (m) 1303.9 1266.3 1249.8 892.8 1239.4 S.D. (Mean) 1400.7 1401.7 1401.4 1282.5 1392.8

As mentioned in section 5.2.5, the KSTC-NC model allows agents to move around without being affected by the u- and v-components of the current flow, and while this model template is meant as a control to assess the contribution of current flow on simulated agent movement, the better fit of the KSTC-NC model suggests that the simulated movement of current- affected models are too sensitive in regards to current for flow. However, the overall inability across all models to capture the observed upriver migration (Figure 5.3.1) suggests that this behaviour cannot be explained sufficiently by a random walk or a kinesis search for optimal conditions in terms of salinity, temperature and flow velocities alone.

127

Figure 5.3.1: The best-fitted simulated agent locations as predicted by the KSTC-NC model vs. observed animal locations throughout the track conducted on 28/01 – 2010. Displayed agent locations correspond to the time of each animal registration, while agent locations in the time between animal registrations are not displayed.

As tested by the Pre-track model scenario (described in section 5.2.4), the predictive abilities of each model template were quite poor, as none of the models were able to achieve a decrease in distance from the agents‟ start-to-end location relative to the observed animal location. The clear difference of mean start distance in between model types (Table 5.3.2) is due to the fact that while the 100 randomly-selected release locations remain the same throughout each model type, only the agents that make it through the entire simulation are used for the calculation of the start and end mean distances. Thus the calculated distances are highly dependent on the agent success rate. The Kinesis search for optimal salinity (KS) model had the highest agent success rate of 83, while the KSTC-NC control model had the lowest mean end distance to the observed animal location (Table 5.3.2). Upon inspection of Figure 5.3.2, it becomes apparent that the agents of the KSTC-NC model linger in the same general area of release, thus neither increasing nor decreasing their end distance to the observed animal location.

128 Table 5.3.2: Primary model results from the five applied model templates for the simulation covering the 12- hr period prior to the track conducted on 28/01 - 2010.

PRE-TRACK: 28/01 -2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 49 83 11 42 2 Mean Start Distance (m) 3173.4 2465.3 3318.0 3603.5 6077.2 S.D. (Start) 1918.3 1806.6 2217.2 2004.3 53.6 Mean End Distance (m) 5796.9 4055.1 5546.1 3614.0 7059.1 S.D. (End) 553.6 1785.7 996.4 1971.5 69.5

Figure 5.3.2: The start (point of release) and end locations of simulated agents predicted by the KSTC-NC model vs. the first registered animal location on the track conducted on 28/01 – 2010. Displayed start/end agent locations correspond to the specific agents that make it through the simulation without getting stuck on a land value. The lack of release points in the upper reach is due to the fact that agents released in this section did not make it through the entire simulation.

5.3.2 TRACK II Compared to the previous track simulations, there was a noteworthy increase in model performance for the track conducted on 03/02 -2010. The KSTC-NC control model once again provided the best fit to the observed data set, with an agent success rate of 96 and a minimum mean distance of 110.9 ± 146.4 m. The good fit between the control model and

129 measured data further supports the notion that the u- and v-components of the current flow exert an overwhelming effect on the other model templates. It is, however, also plausible that the good fit is an artefact of registered animal locations being concentrated in the same general area, and that the lack of current influence on the KSTC-NC template causes simulated agents to linger in the same general area as it was the case for the 28/01-2010 KSTC-NC Pre-track simulation.

Table 5.3.3: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 03/02 - 2010.

TRACK: 03/02 -2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 91 51 55 96 5 Mean Distance (m) 1140.7 1007.6 1001.1 179.9 1051.3 Min. Distance (m) 948.9 812.8 753.8 110.9 933.4 S.D. (Mean) 955.6 843.4 852.7 146.4 876.0

The contribution of current flow to the movement of simulated agents is visualised in Figure 5.3.3 (KSTC) and Figure 5.3.4 (KSTC-NC), from which it becomes evident that animal movement in the current-affected KSTC-model is clearly affected by an outgoing tide, whereas KSTC-NC agents hardly have any longitudinal travel in comparison. While this apparent issue is discussed in detail in Section 5.4, an immediate explanation for this obvious sensitivity to current flow is likely that the non-corrected u- and v-components of simulated agents were too weak in magnitude compared to the relative contribution of current flow, ultimately resulting in a flow-corrected agent u- and v-component that is dominated by the speed and direction of the current. While the KSTC-NC model provided the best fit in terms of distance to observed locations, it is important to note that this model template was intended as a control through the removal of the contribution of current flow on the agents‟ resultant u- and v-component. This is in essence against the laws of physics, as any object suspended in a water body will be affected by the movement of the water. Thus, the KSTC-NC results will not be treated as a primary source for model validation, but rather function as a stress test of the four other model templates.

130

Figure 5.3.3: The best-fitted simulated agent locations as predicted by the KSTC model vs. observed animal locations throughout the track conducted on 03/02 – 2010. Note how the simulated agent follows the tide downriver.

Figure 5.3.4: The best-fitted simulated agent locations as predicted by the KSTC-NC model vs. observed animal locations throughout the track conducted on 03/02 – 2010. Note how the agent has very limited longitudinal travel.

131

Figure 5.3.4: KSTC-model results (median, 10th and 90th percentile values) of distance (top left), depth (top right), salinity (middle left), temperature (middle right), swimming velocity (bottom left) and current speed (bottom right) versus corresponding track values (black line) for the 3rd of February 2010 plotted on a temporal axis.

132 As evident from Figure 5.3.4, the calculated distances for the KSTC model were in relative good agreement with registered animal locations until approximately 12:00 pm, when the current speeds started picking up along with the outgoing tide, resulting in a downriver movement and increased distance to observed animal locations, as well as a slight increase in agent swimming velocities. As an effect of the modelled downriver movement, ambient levels of salinity slightly increased from 20 to 25 PSU while observed values remained relatively constant around 20 PSU. Observed swimming velocities mostly remained within the 10th and 90th percentile values of modelled velocities, while the mean total distance travelled was 5.22 km compared to 5.66 km estimated from the recorded track, and there was a significant circular-correlation (P < 0.05, R = 0.37) between sequential animal and best- fitted agent headings. Not surprisingly, ambient depth levels for simulated vs. recorded animal locations were in poor agreement, which primarily is due to the movement constraints imposed by equation 5.17 (section 5.2.6), effectively preventing agents from getting too close to the riverbank.

The pre-track results for each applied model template (Table 5.3.4) displayed a poor predictive ability, with no model being able to simulate an overall agent migration toward the registered location of the animal. However, the movement predicted by the RW and, particularly the KS model, caused a congregation of agents in the lower part of the middle reach as well as in the upper reach (Figure 5.3.5), which is also evident from a notable decrease in the standard deviation of the mean end distance (Table 5.3.4).

Table 5.3.4: Primary model results from the five applied model templates for the simulation covering the 12- hr period prior to the track that was conducted on 03/02 - 2010.

PRE-TRACK: 03/02 - 2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 40 74 9 50 0 Mean Start Distance (m) 2656.2 2032.1 2214.0 3242.8 Nil S.D. (Start) 1770.2 1426.3 1762.9 1937.4 Nil Mean End Distance (m) 2674.5 2192.7 2401.1 3243.1 Nil S.D. (End) 820.4 865.9 1309.0 1913.1 Nil

133

Figure 5.3.5: The start (point of release) and end locations of simulated agents predicted by the KS model vs. the first registered animal location on the track conducted on 03/02 – 2010. Displayed start/end agent locations correspond to the specific agents that made it through the simulation without getting stuck on a land value.

5.3.3 TRACK III For the model simulations covering the track conducted on the 27th of February, the KSTC- NC model once again provided the best fit to observed animal locations, with an agent success rate of 94 and a minimum mean distance of 295.2 ± 439.3 m, while the KS model provided the next best fit in terms of both agent success rate and minimum mean distance (Table 5.3.5).

Table 5.3.5: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 27/02 - 2010.

TRACK: 27/02 - 2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 87 97 50 94 47 Mean Distance (m) 595.0 553.1 533.5 381.0 541.6 Min. Distance (m) 532.7 465.3 443.6 295.2 416.6 S.D. (Mean) 340.9 317.3 314.0 439.3 321.7

134 As it is evident from Table 5.3.5 there are only relatively small differences between models in terms of mean distances to observed values (KSTC-NC excepted), which suggests that the Kinesis search models (KS, KSTC and KSTC-ZS) in terms of model formulation does not explain the observed movement better than the random walk (RW) control model for this particular track simulation. This notion is further backed by the similarities between the best- fit results from the KS model and RW model depicted in Figure 5.3.5 and 5.3.6, respectively.

Figure 5.3.5: The best-fitted simulated agent locations predicted by the KS model vs. observed animal locations throughout the track conducted on 03/02 – 2010. Note the similarity between the best-fitted movement locations predicted by the KS model versus the RW model depicted in Figure 5.3.6.

135

Figure 5.3.6: The best-fitted simulated agent locations predicted by the KS model vs. observed animal locations throughout the track conducted on 03/02 – 2010. Note the similarity between the best-fitted movement locations predicted by the RW model versus the KS model depicted in Figure 5.3.5.

Salinity and temperature levels for simulated agents were in good agreement with observed levels (Figure 5.3.7), with a mean salinity and temperature of 7.9 PSU and 27.9 °C, respectively. With a mean of 0.17 m/s, simulated swimming velocities were decreased compared to the previous track simulation on the 3rd of February. This result is most likely due to the relatively close proximity of the ambient salinity levels to the specified KS model optimum of 13.5 PSU, causing agents to slow down in accordance with the Kinesis model formulation as described in section 5.2.

The total mean distance travelled by simulated agents was 5.71 km as opposed to 5.14 km estimated from registered animal locations, and there was a significant circular-correlation (P < 0.05, R = 0.37) between sequential animal and best-fitted agent headings.

136

Figure 5.3.7: KS-model results (median, 10th and 90th percentile values) of distance (top left), depth (top right), salinity (middle left), temperature (middle right), swimming velocity (bottom left) and current speed (bottom right) versus corresponding track values (black line) for the 27th of February 2010 plotted on a temporal axis.

137 Pre-track simulations for the 27th of February displayed the same behaviour as the abovementioned pre-track simulations, by having a poor predictive ability. Simulated agents across model templates (KSTC-NC excluded) had a tendency to congregate at upstream and downstream areas, with the predominant current flow and direction being the main driver for longitudinal migration.

Table 5.3.6: Primary model results from the five applied model templates for the simulation covering the 12- hr period prior to the track that was conducted on 27/02 - 2010.

PRE-TRACK: 27/02 - 2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 32 77 25 52 9 Mean Start Distance (m) 2669.7 2173.4 2231.2 2950.5 1393.0 S.D. (Start) 1991.0 1550.0 1755.3 1900.6 928.5 Mean End Distance (m) 5218.7 3712.5 4725.6 2955.9 4484.5 S.D. (End) 664.0 1498.7 710.9 1909.5 320.9

Figure 5.3.8: The start (point of release) and end locations of simulated agents predicted by the KS model vs. the first registered animal location on the track conducted on 27/02 – 2010. Displayed start/end agent locations correspond to the specific agents that made it through the simulation without getting stuck on a land value.

5.3.4 TRACK IV The KSTC-ZS model (Salinity optimum: 0 PSU) performed the best for the track simulation for the 6th of March in terms of the minimum mean distance. However, the random walk control model had the best overall mean distance of 558.3 ± 229.2 m (n = 100). As evident

138 from Table 5.3.5, there was little difference in performance between model templates, suggesting that the contribution of the Kinesis search equations (Section 5.2.1) for this particular model scenario is approaching that of a random walk. Simulated swimming velocities were, however, notably different between the RW and KSTC-ZC models, with the mean and 90th percentile values being 0.52 and 0.73 m/s, respectively. The increased swimming velocities predicted by the KSTC-ZC model were primarily caused by the ambient salinity levels of 20-24 PSU, which in turn caused agents to move faster due to being far from the salinity optimum of 0 PSU.

Table 5.3.7: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 06/03 - 2010.

TRACK: 06/03 - 2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 100 100 100 100 99 Mean Distance (m) 558.3 582.7 596.8 815.3 582.2 Min. Distance (m) 494.0 450.8 480.0 706.5 436.0 S.D. (Mean) 229.2 253.4 259.0 497.9 254.1

Figure 5.3.9: The best-fitted simulated agent locations predicted by the KSTC-ZS model vs. observed animal locations throughout the track conducted on 06/03 – 2010.

139 Mean total distance travelled was 4.51 km, as opposed to the recorded 3.36 km. The best- fitted sequential agent headings compared to sequential animal headings had a circular- correlation coefficient of 0.75, although it was not significant (P = 0.06).

The performance of the pre-track simulations for the 6th of March increased significantly compared to previous pre-track simulations, with all model templates (KSTC-NC excluded) predicting a notable decrease in both their mean end distance and corresponding standard deviation (Table 5.3.8). The highest decrease between start and end distances (-1529.0 m) was achieved by the KS-model, while also having the highest agent success rate (n = 90) and largest reduction in standard deviation (-906).

Table 5.3.8: Primary model results from the five applied model templates for the simulation covering the 12- hr period prior to the track that was conducted on 06/03 - 2010.

PRE-TRACK: 06/03 - 2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 64 90 58 69 48 Mean Start Distance (m) 2501.3 3238.9 2494.0 2281.1 3122.3 S.D (Start) 1701.8 2078.4 1748.7 1518.4 1772.1 Mean End Distance (m) 1164.9 1709.9 1349.3 2250.6 1587.6 S.D. (End) 891.3 1172.1 1001.2 1477.9 1096.9

As evident from the KSTC-NC model results listed in Table 5.3.8, there was no notable decrease in neither mean end distance or its corresponding standard deviation, which once again indicates that the predicted downriver migration of agents by the other model templates was an effect of increased outgoing currents due to higher levels of freshwater discharge during the pre-track period (See Section 4.3.4). The notion that the downriver migration is an effect of current flows rather than a search for optimal salinity and temperature is further backed by the fact that the RW model performs almost identical to the KS and KSTC model, while the KSTC-ZS model departs significantly from its zero salinity optimum.

140

Figure 5.3.10: The start (point of release) and end locations of simulated agents predicted by the KS model vs. the first registered animal location on the track conducted on 06/03 – 2010. Displayed start/end agent locations correspond to the specific agents that made it through the simulation without getting stuck on a land value.

5.3.5 TRACK V In terms of agent distances to observed locations, the best-fitted model (KSTC-NC excluded) for the track conducted on the 20th of March 2010, was the KSTC-ZS model, with an agent success rate of 100, and minimum mean distance of 380.2 ± 312.5 m (Table 5.3.9). The KSTC model performed almost identically to the KSTC-ZS model, while simulated agents from both the RW and KS model were driven downriver by the outgoing tide. Since KSTC and KSTC-NC performed similarly despite having different salinity optimums (13.5 and 0 PSU respectively), these results suggest that the difference in model performance between the RW/KS and KSTC/KSTC-ZS models are due to the addition of a kinesis search for optimal temperature and current flow in the KSTC/KSTC-NC models.

141 Table 5.3.9: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 20/03 - 2010.

TRACK: 20/03 -2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 95 46 98 100 100 Mean Distance (m) 1580.6 1440.2 434.7 335.3 406.9 Min. Distance (m) 1466.1 1220.0 383.4 287.4 380.2 S.D. (Mean) 1177.9 1058.5 326.5 359.4 312.5

Figure 5.3.11: The best-fitted simulated agent locations predicted by the KSTC-ZS model vs. observed animal locations throughout the track conducted on 20/03 – 2010.

The most notable difference between the KSTC-ZS and KSTC model was in terms of predicted swimming velocities, where the mean and 90th percentile values were 0.26 (KSTC- ZS) and 0.39 m/s, respectively. Again, this difference in predicted swimming velocities was due to ambient salinity levels being practically 0 PSU, meaning the simulated agents of the KSTC-ZS model were in their defined salinity optimum and thus would slow down in accordance with the model formulation described in section 5.2. The difference in swimming velocities between the two models resulted in a notable difference in the predicted mean total distance travelled, with the KSTC-ZS model predicting a mean total distance of 3.63 km, while the KSTC model predicted 5.57 km. Both models did, however, underestimate distance travelled compared to the recorded value of 7.73km. The best-fitted sequential agent heading

142 for both models were significantly correlated with recorded animal headings (P < 0.01), with correlation coefficients of 0.46 (KSTC-ZS) and 0.53 (KSTC).

Model performance dropped drastically for the pre-track simulations (Table 5.3.10), and results resembled the previously described pre-track patterns for the 28/01, 03/02 and 27/02, with simulated agents predominantly following the direction of the current flow. This further supports the notion that the applied model templates are inadequate in their respective formulations in regards to directing agents to move more independently relative to current flow, as witnessed by the track data.

Table 5.3.10: Primary model results from the five applied model templates for the simulation covering the 12-hr period prior to the track that was conducted on 20/03 - 2010.

PRE-TRACK: 20/03 -2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 53 83 37 58 39 Mean Start Distance (m) 2953.3 2546.6 2891.5 2863.6 2803.7 S.D. (Start) 2061.5 1852.7 2020.2 1904.9 2245.9 Mean End Distance (m) 3726.0 2949.5 3689.5 2872.9 3303.0 S.D. (End) 1365.2 1696.0 1360.4 1889.6 1703.3

5.3.6 TRACK VI All model templates (KSTC-NC excluded) performed similarly for the simulation covering the 21st of March, and were reasonably fitted to observed animal locations. The best-fitted simulation was achieved by the KSTC model, with a minimum mean distance of 577.1 ± 518.0 m. However, the similarity in performance between the RW and Kinesis models once again suggests that simulated movement was primarily governed by prevailing flow directions, rather than the kinesis movement equations.

Table 5.3.11: Primary model results from the five applied model templates for the simulation covering the track that was conducted on 21/03 - 2010.

TRACK: 21/03 - 2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 94 88.00 91 100 93 Mean Distance (m) 737.8 701.5 697.5 618.2 696.7 Min. Distance (m) 689.7 592.8 577.1 456.6 612.1 S.D. (Mean) 561.0 518.0 518.0 397.5 515.7

143

Figure 5.3.12: The best-fitted simulated agent locations predicted by the KSTC-ZS model vs. observed animal locations throughout the track conducted on 20/03 – 2010.

The mean total distance travelled in the KSTC simulation was 4.85 km with a mean swimming velocity of 0.25 m/s as opposed to the 7.16 km travelled and mean velocity of 0.38 m/s estimated from recorded animal locations. With a correlation coefficient of 0.47., the best-fitted sequential agent headings predicted by the KSTC model were significantly correlated (P < 0.01) with registered animal headings. As evident from Table 5.3.12, pre- track model performance remained poor, with no notable improvement from the start location of simulated agents across model types to the location of agents at the end of simulation, and thus again reflecting the applied models' inability to predict and locate the utilised habitat of the juvenile bull shark, under the given model assumptions.

Table 5.3.12: Primary model results from the five applied model templates for the simulation covering the 12-hr period prior to the track that was conducted on 21/03 - 2010.

PRE-TRACK: 21/03 - 2010 Model Template: RW KS KSTC KSTC - NC KSTC - ZS Agent Success Rate (n) 48 73 46 62 35 Mean Start Distance (m) 2321.9 1937.8 2133.6 2146.0 1878.2 S.D (Start) 1648.6 1372.2 1628.7 1554.7 1604.7 Mean End Distance (m) 2536.3 1794.6 2169.7 2135.2 1708.8 S.D. (End) 1245.5 1020.7 1193.1 1562.6 1101.3

144 5.4 DISCUSSION

While neither of the proposed movement models was able to replicate observed movement patterns of the track individual to a satisfactory degree, it should be noted that the proposed model formulations to this date still are in development. The time-constraints imposed on this project also did not allow for a full investigation of the potential performance of the proposed models (section 5.2). Due to the developmental nature of the models, the results presented in the previous section are affected by both technical restrictions and theoretical limitations, and the nature of these two limitations will be discussed in turn.

5.4.1 TECHNICAL RESTRICTIONS The technical difficulties that the project had mainly revolved around preventing agents from stranding onto dry cells of the model. As mentioned in section 5.2.5, it was necessary to impose a very restrictive movement rule (Eq. 5.17) as well as decreasing the maximum swimming velocity, , to 1.5 m/s in order to keep agents from crashing onto dry cells over a 10-second timestep. While this formulation was successful (in most simulations) in preventing agents from stranding, it also produced very unfortunate side-effects on the models, which can be summarised as: 1) severely limiting latitudinal movement, effectively rendering the agents incapable of utilising the system to its full extent, while making a comparison between observed latitudinal migration (riverbank to riverbank movement) and simulated movement impossible; 2) by decreasing from 3.0 to 1.5 m/s the agents‟ average swimming speed was as a result also decreased. This made agents more susceptible to the effects of current speed and direction, which is evident by the results presented in section 5.3. This undesirable effect was found to be enhanced by the equational formulation of the Kinesis model, as agents located close to their defined optimums would slow down their movement speed even further, which in turn resulted in an almost passive drift of agents; 3) by forcing particles to move towards the channel-gradient (Figure 5.2.4) whenever they were > 15 m away from it, this resulted in a reduction of the relative contribution of model equations on movement, e.g. if an agent was located 16 m away from the channel gradient, the movement rule of Eq. 5.17 would supersede the Kinesis-search equations and make it swim toward the channel gradient with its current swimming velocity. Since agents simulated by the Kinesis-search formulation has a decreased probability of turning in the following timestep (depending on their swimming velocity in the previous timestep), agents

145 would on occasion continue in the same direction in the following timestep. This resulted in agents swimming past the channel gradient and ending up > 15 m away from it on the other side, which ultimately resulted in a nonsensical zigzagging of the agents; 4) very narrow parts of the system (discussed in section 3.4), which were < 20 m in total width prevented a decrease of the distance-response value to the channel gradient (15 m), since the likelihood of agents stranding in these sections of the system would approach 100% at higher distance- response values; 5) while a decrease in the model timestep (10 s) results in agents travelling shorter distances in between time steps, and thus decreases the likelihood of hitting dry cells, a reduction of the timestep to 2 s meant an extreme increase in simulation times (from 20 minutes to 36 hours). Furthermore, a reduction in the timestep would result in agents adjusting their speed and direction more often, resulting in a more tortuous movement. While any animal capable of movement inherently has the ability to adjust its speed and direction at any given point in time, it seems unlikely that a large slow-moving animal such as C. leucas would randomly change its speed and direction (following the definitions of the random walk and kinesis search equations) every 2 seconds. Even with a 10-second timestep it can be argued that the model are likely to overestimate the frequency and intensity of changes in animal heading and velocity, which is in sharp contrast with the 25-min timestep used in the original kinesis formulations by Humston et al. (2000) and Humston et al. (2004). However, due to the abovementioned reasons, a minimal timestep was required to prevent animals from crashing into land values. As a concluding remark on the technical restrictions, it is important to note that most if not all of these issues arose from the flooding & drying tidal characteristics of the system as well as the very narrow (relative to length) nature of Tallebudgera Creek.

5.4.2 THEORETICAL LIMITATIONS As mentioned in section 5.1 and 5.2, the models that have been tested in this study were applied by making a range of simplifying assumptions about the behavior and biology of the tracked animal. First and foremost the model does not include any predator-prey interactions due to the assumption that the tracked individual was not foraging during tracking, thus rendering any effects on movement behavior negligible. Young sandbar sharks, C. plumbeus, which is considered a closely related species to the bull shark, have been reported to take up to 80 hrs for full gastric evacuation while having a feeding duration of 8-9 hrs (Medved et al.

146 1985, 1988). In a review of feeding habits of sharks, Wetherbee et al. (1990) suggest that sharks that are fasting after a large feeding event, are likely to encounter potential prey in this period, but may not be attracted to it because they are satiated. While no detailed data exists on the feeding durations and gastric evacuation rates of the bull shark (to my best knowledge), it is plausible that this species follows a similar digestive pattern, with prolonged non-foraging periods between feeding excursions. However, as there was no direct way to determine whether the tracked animal was in a state of foraging, the validity of abovementioned assumption relies solely on probability, e.g. if the tracked animal‟s feeding/non-feeding time allocations are identical to those reported for young sandbar sharks, there would be a ~90% chance that the animal would be in a state of non-foraging at the start of a given track, but only a 53% chance of the shark being in a non-foraging state at the start of all six tracking events (assuming that consecutive tracks are independent). In reality the chance of the animal being in non-foraging state throughout the entire tracking period would be even lower, as the shark might shift from non-foraging to foraging at any point during a tracking event. Furthermore there is the chance that the animal will perform asynchronous opportunistic feeding events at any given point during its „non-foraging‟ period.

Aside from assuming foraging behavior to be neglible in short-term movement, the model also assumed that movement originating from interaction with conspecifics is negligble. In a long-term study (18 months), Heupel & Simpfendorfer (2011) suggest that estuaries provide a low-mortality nursery ground for neonate bull sharks due to lower predation risk and decreased competition for food compared to coastal nurseries utilised by neonates of other shark species. While the tracked animal was a juvenile, and potentially large enough to prey on neonate conspecifics, Werry (2010) reported that sub-adult C. leucas make regular excursions into Gold Coast estuaries from nearby coastal areas and linger in the system before heading offshore again. The plausible presence of sub-adults inside the Tallebudgera system is therefore likely to affect the movement and habitat selection of neonate and juvenile C. leucas, although the actual contribution of predator-avoidance movement is difficult to assess given the obvious limitions of the tracking record.

In addition to the potential effects on movement of foraging and species interaction, the Kinesis model assumes that only salinity, temperature and flow velocities affect movement, while disregarding other potential water quality influences such as dissolved oxygen, pH, and turbidity. The results of Ortega (2009) reported a correlation between C. leucas ROM and

147 DO, pH and turbidity, and while it remains unclear what effect these parameters have on the short-term movement of C. leucas, the track record of this study indicates that turbidity could play a part in the displacement of bull sharks after a large freshwater influx (Section 4.4). While the HD model (Chapter III) in its current state does not simulate any of the abovementioned parameters, an implementation of the MIKE ECOLab module could allow a future spatio-temporal representation of these parameters (Szylkarski et al. 2004), which in turn could be related to a new and more detailed track record of C. leucas and eventually support a more detailed agent-based model.

5.4.3 CONCLUDING REMARKS Due to the technical problems described above, the ability of the applied movement models to replicate the movement patterns is difficult to assess. It remains clear that the efforts made to prevent agents from crashing onto land in turn had a detrimental effect on the actual movement equations (Section 5.3), and the resultant decreased swimming velocities led to an almost passive drift of agents with the current. The dominant effect of current velocities on all models (KSTC-NC excluded) also clouded potential differences in inter-model performance, which is reflected by the little to no difference between simulated movements of the RW- KS- KSTC- and KSTC-ZS models between tracks. However, the results of the no-flow KSTC-NC model revealed that agents only spread out across on a 200-300 m stretch from their point of origin (See DVD, Appendix V for position plots). While this could indicate that the kinesis search formulation performs poorly in replicating observed movement patterns, it is likely that the movement rule of Eq. 5.17 was the main cause of the simulated KSTC-NC pattern. Although inconclusive in nature, the results for all Pre-track simulations (KSTC-NC excluded) for the 6th of March (Section 5.3.4) showed a stronger displacement downriver of all agents, which is accordance with the observed results. While this is an effect of increased outgoing flow due to greater freshwater influx during this simulation period, it suggests that the theory of flow-induced movement of juvenile C. leucas (Heupel & Simpfendorfer 2008) might be correct.

Although the current developmental stage of the applied models does not provide the ability to conclude on model performance and thus, contribute significantly to the current knowledge-base of C. leucas, this study has nevertheless established the framework needed for future study of the bull shark in Tallebudgera Creek or similar water bodies, e.g. residential canals. The Achilles heel of the ABM-development of this project was without

148 doubt the flooding and drying characteristics as well as the narrow and shallow nature of Tallebudgera Creek, which necessitated the development of the channel-gradient movement rule (Eq. 5.17). In the current stage of development, the overly-restrictive nature of Eq. 5.17 is essentially the only thing that stands in the way of a full assessment of the kinesis models and the implementation of more advanced and sophisticated models that allow greater incorporation of more biological information on the bull shark. Reformulation of Eq. 5.17 to a less restrictive yet still effective means of keeping agents from crashing onto land has proven difficult due to current model software limitations, as it is currently not possible to assign dry cells with a value that the agents can detect and respond to. However, once this problem has been overcome it is predicted that the MIKE ABM Module will provide an excellent tool for testing and assessing theories of bull shark movement in Tallebudgera Creek and similar water courses. This prospect will be will discussed in more detail in Chapter VI.

149

CHAPTER VI

GENERAL DISCUSSION AND FUTURE DIRECTIONS

150 6.1 SUMMARY

Over the course of this study a range of interdisciplinary techniques was applied as a means to test an alternative approach to studying C. leucas movement. From a year-long water quality campaign to hydrodynamic modelling and acoustic telemetry of live C. leucas, this study has gone through all the essential stages of the construction, application and assessment of a dynamic agent-based model. Although the final results of this study remains elusive, and thus preventing a full assessment of proposed movement models at the present stage, there is still immediate academic value to be gained from this project. The purpose of this Chapter is to highlight and discuss the value of the outputs generated by this study, and discuss possible directions for future research.

6.2 EVALUATION OF THE HYDRODYNAMIC MODEL

While the hydraulic properties of Tallebudgera Creek proved much more difficult to capture through a 2D model framework, the end product met satisfactory standards, especially in terms of flow and tide-dynamics (Section 3.4). Simulation of salinity and temperature dynamics on a short-term scale did reveal room for improvement in the current model formulation in terms of a finer resolution of model bathymetry and application of a 3D rather than 2D model. However, the immediate value of the currently established HD model remains clear, and the possibilities of utilising the model for future study are many. Since the model has demonstrated an ability to simulate representative tide-dynamics for periods in both 2007 and 2010 by using a 2001 bathymetry, one can in theory (and with caution) back- calculate the spatio-temporal flow dynamics of Tallebudgera Creek for as long as the freshwater discharge record allows. This in turn opens up for the possibility of relating previous ecological studies in the Tallebudgera estuary with model outputs, and gain an increased understanding of how hydraulics affect specific ecological properties of the system. Similarly, any future ecological studies independent of this project could potentially be strengthened by incorporating the existing and validated hydrodynamic model. Although the shallow and narrow nature of Tallebudgera Creek originally imposed difficulties for the calibration and validation of the HD model, the same nature of the system makes it close to being ideal for in-depth study of local fish population dynamics in relation to system hydraulics. The results of such studies could in turn be utilised as a data source for including movement and distribution of prey in future formulations of agent-based models in this

151 system (See section 6.2.1). On an urban management level, the model also holds great potential in terms of assessing potential flood levels under different discharge scenarios, since it was demonstrated that the model captures such events to a satisfactory degree (Section 3.3.1).

6.2.1 ECOLOGICAL MODELLING The existing HD model provides a solid base for further model expansion in terms of developing a dynamic ecological Eulerian model through the MIKE ECOLab module, capable of simulating water quality and primary productivity. The ECOLab module can be fully integrated into the MIKE 21 HD model, coupled with an Advection-Dispersion model that handles the transportation of the various ECOLab state-variables throughout the model domain. The ECOLab module is in essence designed as a model-independent equation solver for ecological modelling that allows for tailor-made process descriptions to be incorporated directly into the model formulation.

In a study of the neighbouring Pimpama estuary, an ECOLab model template was developed and validated in order to account for eutrophication effects on the benthic vegetation (Szylkarski et al. 2004). Given the relative short distance to Tallebudgera Creek (36 km), it is plausible that the custom-made Pimpama model template can be adapted to the Tallebudgera Creek with only relative minor modifications needed. While it was the original intent of this study to include ecological modelling of Tallebudgera Creek as a means to relate model outputs of water quality to movement of C. leucas, the time-constraints of the project did not allow the development of such a model. Notwithstanding being an extensive project in itself, the implementation and validation of an ecological model coupled with the HD model would allow a further investigation into how water quality affects fish distributions, as well as C. leucas, on a fine spatio-temporal scale. Finally, any Eulerian variables simulated by an ecological model could also be utilised as potential drivers for movement in an agent-based model along with the outputs of HD model, which in turn could help investigate the effects of DO, turbidity and pH on C. leucas movement.

6.3 AGENT-BASED MODELLING

Compared to other model approaches, agent-based models have the advantage of representing observation and current knowledge in a form that is highly congruent with how we understand interaction (Jopp et al. 2011), and have in the past proved useful for the study of

152 predator-prey interactions (Charnell 2008), schooling behaviour (Reuter & Breckling 1994) and behavioural shifts under varying conditions (Railsback et al. 1999, Goodwin et al. 2001, Goodwin et al. 2006, Peacor et al. 2007), as well as the formation of colonies and the description of structural-functional development of modular organisms (Eschenbach 2005). The nature of agent-based modelling does, however, demand a high level of understanding of the species in question in order to create theoretically sensible model formulations. While significant recent advances are evident in the knowledge base of C. leucas (Anderson et al. 2005, Pillans et al. 2005, Simpfendorfer et al. 2005, Pillans et al. 2006, Heupel & Simpfendorfer 2008, Pillans et al. 2008, Ortega et al. 2009, Carlson et al. 2010, Werry 2010, Heupel & Simpfendorfer 2011, Werry et al. 2011), vital pieces of information, especially regarding bioenergetics of the bull shark in various water quality regimes, are still missing.

The kinesis search model formulation in this study was applied as a means to indirectly simulate the proposed energetic benefits of staying within certain water quality ranges, but without actually modelling associated metabolic costs. While this represents a minimalist assumption of animal behaviour (Section 5.3), the apparent weakness of this formulation is that it quickly loses its theoretical plausibility over longer time scales, since the model in its current stage does not account for food uptake and growth of agents. Humston et al. (2004) coupled a kinesis search model of fish cohorts with a bioenergetic growth model by including spatio-temporal data on prey distribution as well as salinity in the collective model formulation. It is certain that a further adaptation of Humston et al. (2004) would increase the theoretical validity of the kinesis model proposed in this project on a longer temporal scale. However, no spatio-temporal data currently exists for the distribution of common C. leucas prey species (e.g. mullet) in Tallebudgera Creek, and it cannot be emphasised enough how important such data will be for any future ABM-development of C. leucas.

Furthermore, as of this date and the author‟s best knowledge, no laboratory-controlled experiments conducted on the effects of salinity, temperature, flow velocities, pH and dissolved oxygen on C. leucas metabolism, despite all of these parameters having been reported to affect bull shark movement (Heupel & Simpfendorfer 2008, Ortega et al. 2009, Werry 2010). The associated costs and immediate difficulties of such experiments are likely causes for the apparent lack of such data, however, they could provide researchers with a vital key to understanding observed C. leucas movement patterns. Furthermore, these studies would aid in establishing the much-needed empirical relationships between varying water quality regimes and energetic costs for future ABM development.

153 6.3.1 FUTURE MODELLING DIRECTIONS As mentioned above, future ABM-development for C. leucas could significantly benefit from directed empirical research of their bioenergetics over a range of water quality regimes, as well as the distribution patterns of common prey species e.g. the sea mullet, Mugil cephalus. However, in terms of actual development of the future bull shark ABM model structure, researchers need to consider other options than a kinesis search formulation for capturing the dynamics of bull shark movement. Sharks have been reported to utilise a variety of movement strategies both between and within species, e.g. tiger sharks Galeocerdo cuvier, have been shown to perform directed „walks‟ on large spatial scales, which suggests that the animal has a spatial knowledge of its home range (Papastamatiou et al. 2011). Furthermore, through their sensory system sharks have been shown to use a range of signals to navigate , which includes but are not limited to: water currents, temperature and geomagnetic fields (Klimley 1993, Montgomery & Walker 2001, Meyer et al. 2005), while it has also been proposed they might utilise olfactory cues to navigate over long distances (Papastamatiou et al. 2011).

In its current formulation, the kinesis search model assumes that the simulated agents have no sensory abilities besides the ambient conditions of their current location, e.g. agents cannot sense spatial-gradients in water quality, nor do they have information about different habitat characteristics on a system-wide scale. Given the growing evidence of the sensory abilities of sharks as a means to navigate, the current minimalist assumption formulation of the model might well be an under-representation of the animal‟s actual ability to utilise its sensory system for locating favourable habitats. Therefore, any future ABM-development for C. leucas should aim to explore the possibility of implementing a sensory range of simulated agents, and maybe even “cognitive maps” of hotspots in the system that indicate favourable foraging grounds.

However, as mentioned in section 6.2, in order for the agent-based model to be applicable on a larger temporal scale, the emphasis in future ABM-development must lie in developing a meaningful C. leucas-specific theoretical growth model. This first and foremost implies a need for prey representation in the model formulation, but also long-term tracking records of both C. leucas and common prey species, as a means to validate the model. While C. leucas is considered a generalist feeder (Werry 2010), stomach contents of C. leucas suggests that the species primarily feed on teleosts fishes (Snelson et al. 1984, Wetherbee et al. 1990). Teleost prey species inside the Tallebudgera Creek system could in theory be modelled as

154 three anonymous functional groups: freshwater teleosts, estuarine teleosts and marine teleosts. Each group will be described by a simple random walk model, but with movement rules that would prevent them from entering water bodies outside of their given salinity range, while utilising avoidance behaviour in the presence of simulated C. leucas agents. It could then be hypothesised that the relative abundance and distribution of prey species in the system would follow the spatial extent of the salinity gradient at any given time, which in turn might provide adequate representation of natural prey migration dynamics. In terms of gathering long-term movement records, the relatively small size and narrow nature of Tallebudgera Creek makes the site ideal for strategic placement of remote listening stations, with only a relatively small number of stations needed to cover the full extent of the system. However, the number of long-term acoustic tags needed for both C. leucas and associated prey species, coupled with Tallebudgera Creek being a popular destination for recreational fishing (pers. obs.) imply that the immediate costs associated with such a project will be substantial.

While any model must be validated by empirical evidence, one of the benefits of agent-based modelling is that one can start testing theory and develop model formulation prior to collecting data for validation. Thus, future ABM-development of Tallebudgera Creek could essentially continue before additional field data become available. Such data would of course be required at a later point for model validation.

6.3.2 POSSIBILITIES FOR CONSERVATION MANAGEMENT Grimm et al. (2006) succinctly summarised the benefits of agent-based models as being “important both for theory and management because they allow researchers to consider aspects usually ignored in analytical models: variability among individuals, local interactions, complete life cycles, and in particular individual behaviour adapting to the individual‟s changing internal and external environment” (Grimm et al. 2006). However, due the often complex nature of many agent-based models, they are also often challenging to analyse, document and communicate compared to analytical models (Grimm 1999, Jopp et al. 2011). While this can raise potential issues for the use of ABM‟s as a tool for environmental managers and policy-makers in terms of formulating the results of an ABM into conservation strategies, the versatility of ABM‟s as tools for conservation management remains. The ability to representatively simulate a dynamic virtual environment and the resulting movement response of virtual fishes to different environmental stimuli

155 effectively allows the modeller to forecast fish movement to a range of different scenarios. Goodwin et al. (2006) utilised a coupled Eulerian-Langrangian model to forecast downstream migration behaviour of juvenile salmon through fish bypasses in three hydropower dams, with a total of 20 structural and operational configurations. In a similar approach, the impact of naturally occurring flood events as well as anthropogenic changes to the environment on the movement and distribution of C. leucas in Tallebudgera Creek could potentially be forecast through the use of an agent-based model coupled with an Eulerian framework. Although the ABM for C. leucas in its current stage is far from being useful as a tool to formulate conservation strategies for this IUCN „near-threatened‟ species, it is predicted that once the model has been properly validated, it could become a powerful tool in future conservation management of this species.

156 REFERENCES

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161 APPENDIX I

TEMPORAL VALIDATION OF SALINITY AND TEMPERATURE ACROSS ALL STATIONS

Appendix I, Table 1: Calculated results of the Salinity Quality index for each measuring station over the course of 2010 vs. corresponding simulated values. See following pages for plotted data sets.

TEMPORAL QUALITY INDEX - SALINITY Station Correlation P-value Simulated Mean Measured Mean Bias RMS 1 0.867 0.000 33.108 31.925 1.184 0.681 2 0.936 0.000 32.516 31.320 1.196 0.670 3 0.960 0.000 30.327 29.412 0.915 0.515 4 0.948 0.000 29.355 27.509 1.846 0.654 5 0.987 0.000 23.264 23.477 -0.213 0.698 6 0.973 0.000 19.330 19.888 -0.558 0.188 7 0.974 0.000 12.988 14.150 -1.162 1.216 8 0.950 0.000 11.975 12.066 -0.091 2.455 9 0.963 0.000 8.595 9.513 -0.918 1.853 10 0.945 0.000 7.318 8.070 -0.753 1.147 11 0.919 0.000 4.737 5.289 -0.552 3.249

Appendix I, Table 2: Calculated results of the Temperature Quality index for each measuring station over the course of 2010 vs. corresponding simulated values. See following pages for plotted data sets.

TEMPORAL QUALITY INDEX - TEMPERATURE Station Correlation P-value Simulated Mean Measured Mean Bias RMS 1 0.910 0.000 24.480 23.767 0.713 2.228 2 0.935 0.000 24.465 23.604 0.861 2.208 3 0.918 0.000 24.129 23.751 0.378 1.742 4 0.933 0.000 24.153 23.681 0.472 1.494 5 0.936 0.000 24.127 23.868 0.259 0.959 6 0.943 0.000 23.773 23.597 0.176 0.098 7 0.962 0.000 23.420 23.641 -0.221 1.280 8 0.953 0.000 23.207 23.548 -0.340 0.952 9 0.959 0.000 23.161 23.375 -0.214 1.498 10 0.940 0.000 23.124 23.398 -0.273 1.940 11 0.885 0.000 23.004 23.138 -0.134 2.540

162

Appendix I, Figure 1: Temporal validation plots for Temperature (left) and Salinity (right) throughout the course of 2010 for measuring stations 1 to 3. RM-Dist is station distance to the river mouth in metres.

163

Appendix I, Figure 2: Temporal validation plots for Temperature (left) and Salinity (right) throughout the course of 2010 for measuring stations 4 to 6. RM-Dist is station distance to the river mouth in metres.

164

Appendix I, Figure 3: Temporal validation plots for Temperature (left) and Salinity (right) throughout the course of 2010 for measuring stations 7 to 9. RM-Dist is station distance to the river mouth in metres.

165

Appendix I, Figure 4: Temporal validation plots for Temperature (left) and Salinity (right) throughout the course of 2010 for measuring stations 10 to 11. RM-Dist is station distance to the river mouth in metres.

166 SPATIAL VALIDATION OF SALINITY AND TEMPERATURE ACROSS ALL SAMPLING DAYS

Appendix I, Table 3: Calculated results of the Salinity Quality index for each sampling day in relation to distance from the river mouth vs. corresponding simulated values. See following pages for plotted data sets.

SPATIAL QUALITY INDEX - SALINITY Sample Day Correlation P-value Simulated mean Measured mean Bias RMS 1 0.999 0.000 26.182 27.394 -1.211 0.681 2 0.996 0.000 26.715 28.601 -1.886 0.670 3 0.827 0.002 8.666 2.383 6.283 0.515 4 0.990 0.000 20.043 19.363 0.680 0.654 5 0.994 0.000 14.022 11.077 2.945 0.698 6 0.988 0.000 12.209 10.960 1.248 0.188 7 0.993 0.000 18.982 17.402 1.580 1.216 8 0.993 0.000 20.041 21.127 -1.086 2.455 9 0.990 0.000 23.698 26.364 -2.666 1.853 10 0.914 0.000 8.026 1.666 6.360 1.147 11 0.984 0.000 13.878 12.895 0.982 3.249 12 0.992 0.000 22.314 22.969 -0.655 2.515 13 0.992 0.000 21.671 21.208 0.462 2.294 14 0.996 0.000 20.092 23.465 -3.373 1.910 15 0.977 0.000 27.116 28.503 -1.387 1.170 16 0.983 0.000 24.805 25.450 -0.645 2.445 17 0.990 0.000 22.883 23.732 -0.849 1.321 18 0.992 0.000 27.352 28.334 -0.982 0.554 19 0.979 0.000 31.516 31.902 -0.386 0.131 20 0.956 0.000 32.212 31.881 0.331 2.616 21 0.997 0.000 26.689 27.299 -0.610 2.208 22 0.996 0.000 16.546 16.980 -0.434 3.585 23 0.993 0.000 14.738 16.401 -1.663 20.225 24 0.992 0.000 22.369 23.788 -1.418 15.899 25 0.995 0.000 18.887 20.539 -1.651 6.641 26 0.996 0.000 17.333 18.190 -0.858 12.809 27 0.997 0.000 4.906 3.392 1.513 2.677 28 0.989 0.000 16.137 15.900 0.237 2.259 29 0.991 0.000 8.569 7.205 1.364 2.025 30 0.980 0.000 13.717 13.503 0.214 5.808

167 Appendix I, Table 4: Calculated results of the Temperature Quality index for each sampling day in relation to distance from the river mouth vs. corresponding simulated values. See following pages for plotted data sets.

SPATIAL QUALITY INDEX - TEMPERATURE Sample Day Correlation P-value Simulated Mean Measured Mean Bias RMS 1 0.950 0.000 29.439 29.409 0.029 2.228 2 0.743 0.009 27.790 26.439 1.351 2.208 3 0.908 0.000 25.046 25.355 -0.309 1.742 4 0.644 0.032 27.833 28.880 -1.048 1.494 5 0.910 0.000 26.360 25.430 0.930 0.959 6 0.824 0.002 24.054 23.567 0.487 0.098 7 0.841 0.001 26.056 26.196 -0.141 1.280 8 0.765 0.006 25.648 26.509 -0.861 0.952 9 0.550 0.080 24.411 24.328 0.083 1.498 10 0.881 0.000 20.920 19.966 0.954 1.940 11 0.927 0.000 21.441 21.991 -0.550 2.540 12 0.897 0.000 21.787 21.028 0.758 1.334 13 0.671 0.024 19.802 20.367 -0.565 1.280 14 0.409 0.212 18.507 20.018 -1.511 1.347 15 0.807 0.003 19.839 19.885 -0.045 1.470 16 0.932 0.000 20.091 19.525 0.567 1.154 17 0.833 0.001 20.803 20.039 0.763 1.352 18 0.579 0.062 23.053 22.645 0.408 2.104 19 -0.333 0.317 22.261 21.146 1.114 1.773 20 0.272 0.419 22.197 21.275 0.922 1.776 21 0.862 0.001 23.056 22.685 0.371 1.309 22 0.768 0.006 21.551 21.918 -0.367 0.038 23 0.705 0.015 24.582 25.079 -0.497 0.128 24 0.811 0.002 23.928 23.025 0.903 0.190 25 0.952 0.000 26.539 25.867 0.672 0.002 26 0.850 0.001 26.745 26.092 0.653 0.162 27 0.700 0.017 21.906 23.298 -1.392 0.554 28 0.855 0.001 24.563 23.522 1.041 0.780 29 0.636 0.035 24.059 24.030 0.029 1.031 30 0.875 0.000 27.671 27.850 -0.179 0.502

168

Appendix I, Figure 5: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 1 to 3 with each Station plotted in relation to river mouth distance

169

Appendix I, Figure 6: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 4 to 6 with each Station plotted in relation to river mouth distance.

170

Appendix I, Figure 7: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 7 to 9 with each Station plotted in relation to river mouth distance.

171

Appendix I, Figure 8: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 10 to 12 with each Station plotted in relation to river mouth distance.

172

Appendix I, Figure 9: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 13 to 15 with each Station plotted in relation to river mouth distance.

173

Appendix I, Figure 10: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 16 to 18 with each Station plotted in relation to river mouth distance.

174

Appendix I, Figure 11: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 19 to 21 with each Station plotted in relation to river mouth distance.

175

Appendix I, Figure 12: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 22 to 24 with each Station plotted in relation to river mouth distance.

176

Appendix I, Figure 13: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 25 to 27 with each Station plotted in relation to river mouth distance.

177

Appendix I, Figure 14: Spatial validation plots for Temperature (left) and Salinity (right) on sampling days 28 to 30 with each Station plotted in relation to river mouth distance.

178 DVD APPENDIX

Please find additional result appendices (II-VI) on the attached DVD, along with videos of observed C. leucas movement and selected ABM and HD-model videos. Additional videos of model outputs are available upon request.

179