The Effects of Oceanographic Drivers on the Catchability of Spanner Crabs
Author Spencer, David
Published 2018-08
Thesis Type Thesis (PhD Doctorate)
School School of Eng & Built Env
DOI https://doi.org/10.25904/1912/2999
Copyright Statement The author owns the copyright in this thesis, unless stated otherwise.
Downloaded from http://hdl.handle.net/10072/380993
Griffith Research Online https://research-repository.griffith.edu.au
The Effects of
Physical Oceanographic Drivers
on the Catchability of
David Mark Spencer
Bars
BSc (Hons)
Submitted in fulfilment for the degree of Doctor of Philosophy
August 2018
Griffith School of Engineering & Built Environment
Griffith Sciences
Griffith University, Gold Coast Campus, Queensland, Australia
SUMMARY
The world’s largest commercial spanner crab (Ranina ranina) fishery, situated on the east coast of Australia, is showing signs of a fishery-wide population decline. This trend has become ubiquitous in many global fishery stocks and is most commonly attributed to anthropogenic pressure and the rapidly changing climate. Thus, the importance of better understanding how commercially-important fishery species thrive in their immediate and surrounding environment is imperative.
Catchability is an important parameter used to describe the relationship between catch rates and the abundance of the target species. Variations in catchability are often attributed to fluctuations in environmental and oceanographic conditions in the species’ preferred habitat. Earlier research had suggested several hydrodynamic and hydrographic parameters affect the catchability of spanner crabs. However, a review of the literature (thesis chapter 2) indicated the need for further investigations, particularly on the specific oceanographic processes responsible for these effects.
This thesis examines the effects of several oceanographic parameters on spanner crab catch rates at a range of spatiotemporal scales. At large spatial scales (100:500 km) over several years, higher surface chlorophyll-a concentrations, relative to the range of chlorophyll-a observed in each management region, were correlated with lower catch rates in fishing grounds close to the coast and bays. Additionally, region-specific processes responsible for bringing oceanic waters into spanner crab fishing grounds were associated with an increase in catch rates. The link between oceanic water and increased catch rates was further supported by a more localised study (~100 km) that
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showed cooler ambient temperatures, occasionally attributed to wind-driven upwelling, increased catch rates of spanner crabs at both seasonal and shorter (days-months) temporal scales.
At various fishing locations off the Gold Coast, Australia, the effects of current speed, direction and turbidity were examined to determine how specific conditions (day to day) and changes in conditions during soak times (hours) affected catch rates. The most significant finding from this work suggests that catch rates of spanner crabs benefit from a specific range of current speeds (~0.07-0.12 m.s-1), and current speeds exhibiting a large amount of variability may trigger a spike in catch rates over a period of hours.
Results from this work show current speed can help explain short-term catch rate anomalies that are currently deemed “random” in stock assessment models.
Incorporating environmental and oceanographic parameters into stock assessment models has been an ever-evolving challenge for fishery management. Depending on the region-specific oceanographic and coastal processes, various remotely-sensed oceanographic parameters are also useful in explaining catch rates. The most significant outcomes from this thesis indicate bottom temperature, alongshore wind stress, and bottom current speed are suitable in explaining variability in spanner crab catch rates. A coupled hydrodynamic and biogeochemical model capable of resolving and predicting fluctuations in these oceanographic parameters will help support studies in other areas of the Australian fishery and smaller fisheries in the Indo-Pacific, help improve the accuracy of stock assessment models, and greatly benefit the economic efficiency of commercial crabbing operations.
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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 or written by another person except where due reference is made in the thesis itself.
Date: 1 / August / 2018 David Spencer
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TABLE OF CONTENTS
SUMMARY ...... i STATEMENT OF ORIGINALITY ...... iii TABLE OF CONTENTS ...... iv LIST OF FIGURES AND TABLES ...... vii ACKNOWLEDGEMENTS ...... ix RESEARCH CONTRIBUTIONS ...... x ACKNOWLEDGEMENT OF PUBLICATIONS ...... xii
CHAPTER 1 – General Introduction ...... 1
1.1 Introduction ...... 2 1.2 Thesis aims and structure ...... 5
CHAPTER 2 – A critical review of oceanographic processes affecting the catchability of spanner crabs ...... 8
2.0 Abstract ...... 10 2.1 Introduction ...... 11 2.2 The Australian Fishery ...... 12 2.3 Habitats, lifecycle and catchability ...... 16 2.4 Physical oceanographic processes affecting catchability of spanner crab ...... 17 2.5 Shelf dynamics in the Australian spanner crab fishery ...... 23 2.6 Discussion ...... 30 2.7 Conclusion ...... 33 2.8 Acknowledgements ...... 34
CHAPTER 3 – Regional oceanographic effects on the catchability of spanner crabs in the Queensland fishery, Australia ...... 35
3.0 Abstract ...... 37 3.1 Introduction ...... 38 3.2 Materials and methods ...... 41
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3.2.1 Study region ...... 41 3.2.2 Spanner crab survey data ...... 43 3.2.3 Environmental and oceanographic data ...... 44 3.2.4 Statistical analyses ...... 45 3.3 Results ...... 46 3.3.1 Oceanography and CPUE ...... 46 3.3.2 GAM results ...... 47 3.4 Discussion ...... 54 3.5 Conclusion ...... 57 3.6 Acknowledgements ...... 57 Appendix 3A ...... 58
CHAPTER 4 – Upwelling enhances the catchability of spanner crabs in far South-East Queensland, Australia ...... 70
4.0 Abstract ...... 72 4.1 Introduction ...... 73 4.2 Materials and methods ...... 76 4.2.1 Study region ...... 76 4.2.2 Data collection and processing ...... 77 4.2.2.1 Field study ...... 77 4.2.2.2 SEQ study ...... 79 4.2.3 Statistical analyses ...... 81 4.3 Results ...... 83 4.3.1 Field study ...... 83 4.3.2 SEQ study ...... 86 4.4 Discussion ...... 91 4.5 Conclusion ...... 96 4.6 Acknowledgements ...... 97
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CHAPTER 5 – Short-term effects of bottom current speeds on spanner crab catch rates in shelf waters of southern Queensland, Australia ...... 98
5.0 Abstract ...... 100 5.1 Introduction ...... 101 5.2 Materials and methods ...... 103 5.2.1 Study region ...... 103 5.2.2 Mooring materials and design ...... 105 5.2.3 Data collection and processing ...... 105 5.2.3.1 Crab catch and effort data ...... 105 5.2.3.2 Oceanographic data ...... 108 5.2.4 Statistical analyses ...... 109 5.3 Results ...... 110 5.4 Discussion ...... 117 5.4.1 Recommendations for future work ...... 119 5.5 Acknowledgements ...... 120 Appendix 5A ...... 121
CHAPTER 6 – General Discussion and Conclusion ...... 124
6.1 Overview ...... 125 6.2 Synthesis of key findings ...... 125 6.2.1 Relating oceanographic drivers to catchability of spanner crabs ...... 125 6.2.2 How the spatiotemporal variability of oceanographic drivers can affect spanner crab ecology and their catchability ...... 128 6.3 Implications for management, smarter fishing practices and future directions ...... 131
REFERENCES ...... 133
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LIST OF FIGURES AND TABLES
CHAPTER 2
FIGURES
2.1 The Australian spanner crab fishery ...... 14 2.2 Topics covered in studies specifically relating to spanner crab ...... 15 2.3 Water column profiles of temperature using a conductivity, temperature, and depth (CTD) profiler ...... 20 2.4 Spatial complexity of the East Australian Current (EAC) ...... 25 2.5 CTD profiles illustrating the effects of the EAC and eddies ...... 26 2.6 Near-bottom current speeds at the National Reference Station mooring off the coast of North Stradbroke Island (NRSNSI) ...... 27 2.7 Wave-induced velocities near seabed ...... 29
CHAPTER 3
FIGURES 3.1 May climatologies of chlorophyll-a, sea surface temperature (SST), and geostrophic currents throughout the Queensland fishery ...... 42 3.2 Catch rate variability in May between 2003 and 2016 ...... 48 3.3 Effects of environmental parameters on catch rates in the Generalised Additive Model (GAM) incorporating data from all Management Regions (MR) ...... 50 3.4 Effects of oceanographic parameters on catch rates in separate GAMs for each individual MR ...... 52
3A May monthly means of chlorophyll-a, sea surface temperatures (SST), and geostrophic currents throughout the Queensland fishery for each year during 2003-2016 (excluding 2004 and 2012) ...... 58
TABLES 3.1 GAM results from both statistical methods (Method 1 and Method 2) ...... 49
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CHAPTER 4
FIGURES 4.1 Map of study region ...... 78 4.2 Bottom boundary layer temperature (BBLT) data from small temperature loggers versus data from the NRSNSI mooring ...... 84 4.3 General linear correlation models showing the relationship between BBLT and SST, and BBLT and 7-day alongshore wind stress (τ7) ...... 87
4.4 BBLT, SST, τ7, and catch rate data between 2011 and 2014 ...... 88
TABLES 4.1 BBLT data from small temperature loggers and the NRSNSI mooring and their associated effects on catch rates ...... 85 4.2 Generalised Linear Models (GLM) used to assess the effects of seasonal spanner crab behaviour, BBLT, and τ7 on catch rates ...... 90 4.3 GLMs assessing the effects of BBLT and alongshore wind stress on catch rates within each season ...... 92
CHAPTER 5
FIGURES 5.1 Map of study region ...... 104 5.2 Oceanographic mooring design ...... 106 5.3 Effects of current speed and month on catch rates ...... 115 5.4 Timeseries of current profiles, near-bottom current speeds, and total catch per unit of effort (CPUE) in each fishing period on 31 August 2015 ...... 116
5A Current profile data on six of the ten fishing days ...... 121
TABLES 5.1 CPUE and oceanographic information for twenty-six fishing periods carried out across the ten fishing days ...... 111 5.2 The effects of current speed and month on CPUE in the final GAM ...... 113
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ACKNOWLEDGEMENTS
This thesis is dedicated to Mum who always inspired me to make her proud. I also dedicate this to Dad and Elke who provided amazing moral and financial support to keep me on track, always reiterating the importance of “getting a piece of paper
(degree) behind me”. My girlfriend, Jess, was also incredibly supportive, especially during these last few stressful months of writing.
Thank you to my supervisors: Ian Brown for providing invaluable technical advice and going through all my written work with a fine-tooth comb; Mark Doubell for being a great mentor, welcoming me at SARDI, and providing amazing support with analyses and writing; Charles Lemckert and Joe Lee for the great advice on experimental design, data collection and mentorship; and finally, Hong Zhang for technical and administrative support. Sincerest gratitude for project support is also extended to commercial fishermen Richard Hamilton and Mark “Mondo” Cheeseman – a lot of this work would not be possible without their help. Jason McGilvray for providing and assisting with commercial logbook and survey data. Chris Brown for exceptional help regarding generalised linear and additive modelling in R.
Finally, thanks also go out to Fernando Andutta, Johan Gustafson, Lorry Hughes, Ana
Redondo-Rodriguez, Nicole Patten, John Middleton, Richard McGarvey, Geoff Turner,
Chuen Lo, Yvonne Yu, George Cresswell and Tony Courtney for providing advice and/or support with this project. To all my other family and friends, thanks for the companionship – especially the beers after a long week!
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RESEARCH CONTRIBUTIONS
Peer reviewed papers published and under review
Spencer D. M., Brown I. W., Lee S. Y., and Lemckert C. J. (2017). Physical oceanographic processes affecting catchability of spanner crab (Ranina ranina) – a review. Fisheries Research. 186, 248-257.
Spencer D. M., Brown I. W., Doubell M. J., Brown C. J., Redono-Rodriguez A., Lee S.
Y., Zhang H., and Lemckert C. J. Bottom boundary layer cooling and wind-driven upwelling enhances the catchability of spanner crabs (Ranina ranina) in South-East
Queensland, Australia. Submitted to Fisheries Oceanography (In Press)
Presentations:
Spencer D. M. Physical oceanographic influences on spanner crabs. (2014, 2015).
Project observer at the FRDC 2013/020 [Physical oceanographic influences on
Queensland reef fish and scallops] steering committee meetings. Brisbane, QLD,
Australia 2014/2015.
Spencer D. M., Brown I. W, Lee S. Y., and Lemckert C. J. (2015). Physical oceanographic processes affecting the catchability of spanner crabs (Ranina ranina) in eastern Australian waters. 12th AOGS Annual Meeting, Singapore. 2 – 7 August 2015
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Spencer D. M., Brown I. W., Lee S. Y., and Lemckert C. J. (2016). Could upwelling be enhancing short-term spanner crab (Ranina ranina) catchability? NZMSS-AMSA Joint
Conference, Wellington, New Zealand. 4 – 7 July 2016
Spencer D. M., Lemckert C. J., Lee S. Y., and Brown I. W. (2017). In situ observations of shelf Bottom Boundary Layer currents and implications for the catchability of spanner crabs (Ranina ranina). 14th AOGS Annual Meeting, Singapore. 6 – 11 August
2017
Spencer D. M., Brown I. W., Doubell M. J., Brown C. J., Redono-Rodriguez A., Lee S.
Y., Zhang H., and Lemckert C. J. (2018). The effects of wind stress and temperature on the catchability of spanner crab (Ranina ranina) in South-East Queensland, Australia.
Ocean Sciences Meeting, Portland, OR, USA. 11 – 16 February 2018
Awards:
“Australian Postgraduate Award (APA)”. The APA was funded by the Australian
Federal Government to support postgraduate research training and was awarded to students of "exceptional research potential". 2014-2017
“Peter Holloway Oceanography Award”. Recipient of the Australian Marine Science
Association (AMSA) award for the best full-length student oral presentation related to
Oceanography. 2016
“Nortek Day Award”. Recipient of the 2016 Nortek Day Signature Series equipment prize. Awarded to a member(s) of the host institution’s research group. 2016
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ACKNOWLEDGEMENT OF PUBLICATIONS
Included in this thesis are papers in Chapters 2, 3, 4 and 5 which are co-authored with other researchers. The bibliographic details/status, co-authors, and my contribution to each paper are included at the beginning of each chapter. Appropriate acknowledgements of those who contributed to the research but did not qualify as authors are included in at the end of each paper.
(Signed) (Date) 1 / August / 2018
David Spencer
(Countersigned) (Date) 1 / August / 2018
Supervisor: Hong Zhang
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CHAPTER 1
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General Introduction
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1.1 Introduction
Over the next 30 years, the global demand for seafood is projected to increase by 70 percent, yet more than one third of global wild fisheries stocks continue to decline or are already depleted (Gutierrez et al., 2011; Moruf and Adekoya, 2018). The status of these stocks is largely attributed to anthropogenic pressure, specifically overexploitation; however, several well managed fisheries are beginning to show positive signs of rebuilding and sustainability (Olsen et al., 2018). Effective management of these rebuilding and sustainable fisheries has been bolstered by better understanding factors that affect the ecology and catchability of the target species.
Spanner crabs (Ranina ranina) are true marine brachyurans, with populations occurring on sandy substrates of the continental shelf throughout the Indo-Pacific (Moussac et al.,
1987; Boulle, 1995; Brown et al., 2008; Baylon and Tito, 2012; Thomas et al., 2013).
Based on laboratory observations, they are believed to spend most of the time buried in the seafloor to avoid predators, only emerging to feed and mate (Skinner and Hill, 1986,
1987; Brown et al., 1999, 2001; Kirkwood et al., 2005; Faulkes, 2006). Commercial fishermen target spanner crabs using size- and species-selective industry-standard tangle nets, which minimises the bycatch of undersize crabs and other biota (Sumpton et al.,
1995; Brown et al., 2001). By area and total catch, the Queensland fishery is home to the largest commercial spanner crab fishery in the world (Dichmont and Brown, 2010;
Thomas et al., 2013), with gross annual landings contributing approximately $5M to the
Queensland economy (Campbell et al., 2016). Catch and effort information derived from fishery-independent (survey) and fishery-dependent (commercial and recreational logbook) data (Maunder and Punt, 2004), are used in setting Queensland’s annual
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commercial spanner crab quota and has allowed for better understanding factors that affect their catchability.
Catchability is principally defined as the measure between total catch and the abundance of the target species (Arreguin-Sanchez, 1996). From information gathered over several years, fisheries managers can use a broad range of input and output controls to manage fishery stocks (Cochrane, 2002; Dichmont et al., 2006; Liu et al., 2014). In the
Australian spanner crab fishery, input controls have involved managing fishing gear
(e.g. quantity and type of gear) as well as annual fishery closures (O’Neill et al., 2010).
It was the first commercial fishery in Queensland to implement strict quota-based output controls by setting both total allowable catch (TAC) and individual quota allocation systems (Dichmont and Brown, 2010; O’Neill et al., 2010). While the
Queensland spanner crab fishery has been exemplary in fishery management, recent trends suggest populations are still declining at a rapid rate (J. McGilvray, pers comms).
Consequently, other factors that affect their catchability, including environmental (e.g. oceanographic) fluctuations (Arreguin-Sanchez, 1996), necessitate further investigation.
Oceanographic processes can have both indirect and direct effects on catchability.
Factors that alter their availability to the fishery can indirectly affect catchability, including currents that facilitate larval transport (Hjort, 1914; Hardy et al., 1994; Hare,
2014). In addition, specific oceanographic conditions (i.e. temperature) can determine the presence of predatory species, which in turn can alter food web dynamics or deter the target species from foraging (Bax, 1998; Lilly et al., 2000; Cooley et al., 2009;
Brown et al., 2010). Specific oceanographic conditions can also directly affect catchability by altering the phenology (timing of life cycle events, i.e. mating/spawning)
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of target species; their ability to detect bait; their emergence and/or migration patterns; or, in extreme cases, causing mass mortality when tolerances of (e.g) temperature, oxygen, and pH are exceeded (Madeira et al., 2014, Scott et al., 2015; Faulkes, 2017;
Barth et al., 2018). While inevitably complex, determining how oceanographic process affect catch rates over multiple spatiotemporal scales is fundamental if specific parameters are to be used as a fisheries management tool (Hobday and Hartog, 2014;
Scales et al., 2017).
A common approach used to analyse oceanographic effects is to match known positions of the target species to a range of physical data (e.g. temperature), adjust for effort and spatiotemporal variability, and examine the associated effects using statistical modelling
(Bigelow et al., 1999; Howell and Kobayashi, 2006; Zuur et al., 2009; Hobday and
Hartog, 2014; Mourato et al., 2014; Bacha et al., 2017). While these methods are proven effective, it is often unclear what specific physical oceanographic processes are responsible for these relationships. Thus, it is important to identify and understand how these processes vary over space and time.
Over a large enough spatial domain, there may be several different oceanographic processes (e.g. shelf boundary currents and mesoscale eddies) responsible for localised oceanographic conditions (e.g. ambient temperature and current speeds) that affect catch rates. For example, Grant and Madsen (1986) provide an excellent review on the various oceanographic processes responsible for the complex nature of the continental shelf bottom boundary layer (where spanner crabs reside), which is discussed in more depth in chapter 2 of this thesis.
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This thesis examines the direct effects of physical oceanographic processes on the catchability of spanner crabs (Ranina ranina) in Australia. The large spatial domain of the fishery was used to examine how different oceanographic parameters and processes affect catch rates over large (100:500 km and years), medium (~100 km, seasons and weeks-months) and small (1:100 km and hours-days) spatiotemporal scales.
Implications and recommendations for future management strategies based on these findings are also discussed.
1.2 Thesis aims and structure
Each thesis data chapter addresses specific aims that contribute to the whole thesis story:
Conduct a critical review of the literature to examine the effects of various physical oceanographic parameters on catch rates of spanner crabs (Spencer et al., 2017).
CHAPTER 2 builds on the findings from previous studies in order to determine useful environmental and oceanographic parameters that could be incorporated into stock assessment models in the Australian spanner crab fishery. These studies were reviewed and discussed in relation to physical oceanographic processes in the Australian fishery to better understand which oceanographic processes may be responsible for these relationships. Additionally, the methodologies these studies used to obtain their results were also reviewed to understand why there are some inconsistencies between various literature. We aimed to use the information from these studies, and conclusions drawn from the critical literature review as a platform for further research addressed in later chapters of this thesis.
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Determine whether regional oceanographic processes affect catch rates between years and different management regions
CHAPTER 3 investigates the regional effects of oceanographic processes on the catchability of spanner crabs (Ranina ranina). A holistic approach was used to assess the effects of different large and mesoscale oceanographic processes across the whole
Queensland fishery over several years. The main objective was to determine whether surface oceanographic parameters, observable from satellite remote sensing, explain changes in catch rates and whether these effects were attributed to localised oceanographic features within different regions of the fishery.
Investigate whether localised oceanographic processes influence the relationship between bottom temperature and catch rates
CHAPTER 4 builds on the findings from chapter 3 to determine how localised oceanographic processes in a smaller region of the fishery affect catch rates at seasonal and shorter-terms (days-months). Previous research had found warmer ambient temperatures improve commercial catch rates over a fairly broad spatial domain characterised by different oceanographic processes (Brown et al., 2008). Consequently, this current work was carried out to get a better understanding of how more localised oceanographic processes affected BBL temperature fluctuations and, consequently, catch rates of spanner crabs. Sea surface temperature (SST) and alongshore wind stress were also incorporated in the study as proxies for BBL temperature fluctuations in relation to localised upwelling and downwelling.
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Determine whether specific benthic conditions associated with current and turbidity patterns affect catchability during and between various fishing days
CHAPTER 5 explores the effects of currents and turbidity at various fishing locations in the same study region as chapter 4. With the aid of commercial fishermen operating off the coast of Gold Coast, Australia, the aim was to determine whether changes in catch rates were due to specific current speeds, turbidity concentrations, tidal phases, current directions, or oceanographic processes more difficult to quantify. Additionally, changes in current speed, direction and turbidity during fishing soak times (over a period of 2-3 hours) were also assessed to quantify whether sporadic changes in oceanic conditions trigger an ecological response that may impact their foraging behaviour and, in turn, affect catch rates.
Each data chapter (CHAPTER 2-5) is structured in manuscript form to comply with the general standards of peer-reviewed journals. These chapters are either published, submitted and currently under review, or in preparation to be submitted for publication.
A general discussion is included at the end of the thesis to provide an overview and synthesis of all the results, implications for management of the fishery, and emphasise the main areas for future research (CHAPTER 6). All references cited in each chapter are included at the end of the thesis (REFERENCES).
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CHAPTER 2
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A critical review of oceanographic processes affecting the catchability of spanner crabs
Article published as:
Spencer, D. M., Brown, I. W., Lee, S. Y. and Lemckert, C. J. (2017).
Physical oceanographic processes affecting catchability of spanner crab
(Ranina ranina)—A review. Fisheries Research, 186, 248-257. https://doi.org/10.1016/j.fishres.2016.09.005
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STATEMENT OF CONTRIBUTION TO CO-AUTHORED PAPER
This chapter is written in manuscript form and includes a co-authored paper. My contribution to the paper involved collation and collection of all relevant data, data processing and analyses, and writing.
(Signed) (Date) 31 / July / 2018
David Spencer (Corresponding author)
(Countersigned) (Date) 1 / August / 2018
Supervisor: Hong Zhang
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2.0 Abstract
This review examines the influence of physical oceanographic processes on catchability of spanner crab (Ranina ranina) in northeast Australia. Physical oceanographic processes may affect crab catchability by influencing their activity levels and ability to detect bait. Bottom temperature, current velocity, and swell intensity appear to influence catches of spanner crab. At this stage, it appears warmer temperatures enhance catchability of spanner crab. Spanner crabs were more catchable in stronger currents, and crabs were observed to arrive from down-current of baited traps. However, a decline in catch was observed following periods of intense swell. Data derived from
Waverider buoys suggest that occasionally these periods may create strong wave- induced seabed current velocities, lead to at depths of 70 m. The oscillatory motion of wave-induced seabed velocities may cause higher suspended sediment concentrations.
These observations corroborate the views of local fishermen that spanner crabs avoid
‘murky’ water. The effect of turbidity on catchability requires further research. Overall, we advocate that studies employ robust methodologies to measure physical oceanographic processes to accurately predict catchability. Moreover, large-scale physical oceanographic processes may also play an important role in catchability of spanner crab; including upwelling, eddies, and the East Australian Current. Integrating physical oceanography and fisheries interactions will considerably benefit commercial fishermen as well as provide valuable information for evidence-based management of these valuable resources.
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2.1 Introduction
Fisheries are highly valuable resources worldwide but are under continuous pressure to meet increasing demand. As these industries expand, they should be closely monitored to ensure harvests do not exceed sustainable levels (Hill and Wassenberg, 1999). Many fishery management regimes require well developed performance indicators, analytical methodologies, and decision rules for resource sustainability (O’Neill et al., 2010).
Changes to current and historical approaches have typically focussed on tightening management controls on effort and total allowable catch (TAC) (Dichmont and Brown,
2010). Although tight management controls may help ensure the sustainability of fisheries, it is important to understand the optimum ecological conditions in which fishery species thrive.
Physical oceanographic processes may affect fisheries by modifying the species’ abundance and/or catchability (Green et al., 2014) - a particularly important, but poorly understood, factor frustrating ecologically-based stock assessments. A better understanding of catchability is important, as it is a useful indicator of changes in stock size while accounting for fishing effort and allowing for improved management of the fishery (Arreguín-Sánchez, 1996). Abundance and catchability are factors that ultimately determine how large or small each harvest will be. Species abundance is affected by abiotic factors during their larval stages, as the transport of larvae can be explained by the physical forcing of wind and associated currents (Goodrich et al.,
1989; Olaguer-Feliú et al., 2010; Wing et al., 1995), whereas their adult stage has mainly been governed by biotic factors (Green et al., 2014). Catchability depends on the fishing strategy for the target species (Arreguín-Sánchez, 1996), using a passive fishing method (relying on attraction to bait) or an active method such as trawling or haul
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netting. Catchability is also associated with varying equipment, crew, and environmental conditions (Pennington and Godѳ, 1995). Poor weather frequently prevents the operation of smaller vessels and consequently accounts for annual catches being consistently less than the potential Total Allowable Commercial Catch (TACC)
(Dichmont and Brown, 2010). A range of environmental conditions may also explain crab abundance via their effect on the species’ growth and survival (Zheng and Kruse,
2006). Such effects would ultimately impact catchability in the long-term. However, physical oceanographic fluctuations may also impact short-term catchability.
Coastal and shelf water fishery habitats experience very complex physical oceanographic fluctuations. These waters interact with tides, shelf-break fronts, upwelling, estuarine fronts, and the coastal topography (Acha et al., 2004). Shelf-break fronts are often associated with large-scale processes, including major boundary currents and eddies (Jilan, 2004). With such complex physical oceanographic processes, biophysical interactions in shelf water habitats are even more complex. This paper explores literature and methodologies used to determine whether physical oceanographic processes are influencing the catchability of spanner crabs. Due to high variability of shelf waters in the coastal ocean, the Australian fishery is of particular interest in this study.
2.2 The Australian fishery
Commercial fisheries for the spanner crab have been established across the Indo-Pacific, and Australia accounts for the greatest annual landings (Kennelly and Scandol, 2002;
Thomas et al., 2013). The Australian fishery is centred on shelf waters along the coast of southern Queensland and northern New South Wales (NSW) (Brown et al., 1999;
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Skinner and Hill, 1986) mostly in depths between 30 and 80 m (Dichmont and Brown,
2010). The crab fishery developed in the 1970s as a result of substantial catches being taken in waters adjacent to Moreton Island (Brown et al., 1999; R. Freeman, pers. comm.). After the development of a profitable live export market in the early 1990s, the fishery grew, and annual catches increased to over 3,000 t, at which point stringent output-based management arrangements were developed (Brown et al., 1999). This fishery is divided into six management regions subject to individual TACCs (Figure
2.1).
The TACC has fluctuated since the live-export market was established, currently set at
947 t (1631 t in: Spencer et al., 2017 [McGilvray and Johnson, 2014]). In recent years, the annual catch in Queensland has averaged about 900-1000 t with approximately 100 t from NSW (McGilvray and Johnson, 2014). The reduction in catch since the early
2000s is largely due to the introduction of the TACC-based management plan and an associated reduction in fleet size (Brown et al., 2013). TACC operates with individual transferable quota and was first introduced in the mid 1990’s as a sustainability measure after a substantial increase in fishing effort, where the total number of boat days increased from 2,733 in 1990 to 17,765 in 1994 (up 550% in 4 years) (Brown et al.,
1999). A particularly informative illustration of the reduction of fishing effort and improved catch rate since the mid 1990’s can be found in Dichmont and Brown (2010).
Since reduced effort accounts for the substantial reduction in catch, the influence of physical oceanographic processes affecting total annual catch are considered to be minimal over the long-term. Other important considerations include the lifecycle of the spanner crab, known behaviour,
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Figure 2.1 – Australian spanner crab fishery, Regions 2-7, where the TACC is enforced and most spanner crabs are caught. Inset map displays location of Waverider buoys, IMOS NRSNSI mooring, and CTD cast region.
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Frequency 0 5 10 15 20
Feeding/diet
Population dynamics/ecology
Growth/size
Stock management
Mortality/predation
Larval stage
Reproduction
PO processes
PO processes vs. catchability
Figure 2.2 – Topics covered in studies specifically relating to spanner crab (published and unpublished). Literature often includes more than one category. ‘PO processes’ includes the effects physical oceanographic processes have on spanner crabs in any way, and ‘PO processes vs. catchability’ shows how many of those studies were related only to catchability.
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and preferred habitats. Only by understanding both fishery management controls and ecological factors that may affect catchability of spanner crab is it possible to determine additional physical oceanographic effects.
2.3 Habitats, lifecycle and catchability
Spanner crab (Ranina ranina) populations occur from the east coast of Africa across the
Indian Ocean to as far as the Hawaiian Islands (Baylon and Tito, 2012; Kasinathan et al., 2007). They are known locally under various names, including Kona crab (Hawaii), red frog crab (Japan), curacha (Philippines), krab ziraf (Seychelles), and spanner crab is the local name in Australia (Baylon and Tito, 2012; Dichmont and Brown, 2010). Their habitats occur mainly in shelf waters, sometimes adjacent to coral reefs (Thomas et al.,
2013). The crabs’ distribution patterns typically occur on sandy substrates with little vertical structure (i.e. rocky or coral reef) (Boulle, 1995; Brown et al., 2008; Moussac et al., 1987). The crabs are hypothesised to spend most of the time buried in the seafloor with only their eyes protruding to avoid predators (Faulkes, 2006; Kirkwood et al.,
2005). They spend longer periods buried during spawning summer months, only emerging to feed and mate (Brown et al., 2001, 1999; Kennelly and Watkins, 1994;
Krajangdara and Watanabe, 2005; Skinner and Hill, 1986; Thomas et al., 2013).
Minagawa (1990b) observed complete larval development, from first instar zoea to megalopa, occurs over 36-62 days and, once fully developed, megalopae and juvenile crabs buried into the sand. From juveniles to adults, Kirkwood et al. (2005) found they grow slowly and the age of recruitment to the fishery was predicted to be 4.5 years and
6.5 years for males and females, respectively. Males and females are known to reach
150 mm and 120 mm carapace length, respectively (Dichmont and Brown, 2010), which, based on model results from Kirkwood et al. (2005), may take up to 15 years.
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Catchability of spanner crab is affected by moulting and their reproductive cycle, and thus catchability may ultimately depend on the time of year. Skinner and Hill (1987) conducted a laboratory experiment on spanner crabs and observed that males and females did not feed for 52 and 22 days, respectively, during moulting. In the Australian fishery, newly moulted spanner crabs frequently appear ‘lighter’ in colour and weight across autumn months (R. Hamilton, pers. comm.). Due to these crabs being lighter, and therefore having less meat, they are discarded along with the undersize crabs (< 100 mm carapace length). While spanner crabs spend longer buried during spawning summer months, they were found to be more active in September-November (Skinner and Hill,
1987), feeding and mating in large aggregation areas known to local commercial fishermen. Consequently, this results in higher catch per unit of effort (CPUE: total catch [kg]/number of net lifts) in spring months leading up to the annual fishery closure
(20 November - 20 December).
Both fishery managers and commercial fishermen believe that a greater understanding of physical oceanographic processes influencing the catchability of crabs could greatly benefit fishing strategies (Brown et al., 2008; Thomas et al., 2013; R. Freeman and R.
Hamilton, pers. comm.). Commercial spanner crab fishermen operating in Southeast
Queensland (SEQ) (R. Hamilton and R. Freeman, pers. comm.) state that knowing when ideal and unfavourable physical oceanographic conditions for catchability of spanner crab occur will improve catch rates and economics of their fishing operations.
2.4 Physical oceanographic processes affecting catchability of spanner crab
A total of 42 papers were reviewed related to spanner crabs (Figure 2.2). Only papers relating to the influence of physical oceanographic processes on spanner crabs were
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categorised as (a) those affecting the species in any way (e.g., larvae tolerance to temperature and salinity) and (b) those specifically related to catchability (e.g., daily spanner crab catch rates vs. temperature). Five studies had particular focus on physical oceanographic processes and catchability: the influence of current velocity (Craig and
Kennelly, 1991; Hill and Wassenberg, 1999), temperature (Brown et al., 2008; Skinner and Hill, 1986), the effects of El Niño Southern Oscillation events, salinity, and swell
(Brown et al., 2008), and swell exposure (Thomas et al., 2013).
Using a custom designed, inexpensive current meter, Craig and Kennelly (1991) reported increasing spanner crab catches with increasing current strength. The lowest catch rates occurred during periods of current velocity below 1.5 m.s-1 and increased with strengthening currents, which surprisingly reached in excess of 4 m.s-1. This may be related to the spanner crab’s sensory system that allows it to detect bait from considerable distances – at least up to 70 m depending on current strength and direction
(Hill and Wassenberg, 1999; Dichmont and Brown, 2010). Using a video camera on a frame looking down at a net to observe the direction in which spanner crabs would arrive at the bait, Hill and Wassenberg (1999) found a highly significant relationship between current direction and catch. A piece of string was used to determine the current direction, which found crabs always approached the net while moving against the current. Notably, the current speeds measured by Craig and Kennelly (1991) are unusually high. Recent work using an Acoustic Doppler Current Profiler (ADCP) yielded near-bed velocity readings less than 0.2 m.s-1 and 0.1 m.s-1 from Coffs Harbour and Sydney, respectively (Schaeffer et al., 2013). These velocities agree much better with current readings Hill and Wassenberg (1999) observed offshore Moreton Island, which ranged between 3.3 cm.s-1 and 15.6 cm.s-1. Changes in current speed and
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direction may also be responsible for bottom temperature fluctuations within the spanner crab fishery (Brown et al., 2008).
The published evidence for a significant temperature effect on spanner crab catches is conflicting, as field and aquarium studies by Skinner and Hill (1986) found no significant correlation between temperature and response to food in the lab nor catch rates in the field; while Brown et al. (2008) found a significant positive relationship.
These conflicting observations may be related to differences in methodology, as Skinner and Hill (1986) measured temperature at 25 m (with a normal water depth >55 m), which is not typically an accurate representation of benthic ambient temperature (Figure
2.3). Additionally, the Skinner and Hill (1986) study was conducted once a month for four years. They reported catches were higher in August and September when temperature was 20-21oC, than in February when the temperature was 25-26oC.
However, these differences in catch may be primarily driven by biological effects (i.e., reproduction and/or moulting) and natural food availability. Brown et al. (2008) conducted the study by attaching a small temperature logger to nets owned by commercial fishermen spread out across the fishery, relying on them to download the data on a weekly basis for one year. While the published results are conflicting, the temperature loggers attached to the nets were collecting bottom temperature data and many fishermen also believe spanner crab catch rates are adversely affected by cooler temperatures (R. Freeman and R. Hamilton, pers. comm.). Laboratory studies have shown temperature to be a key determinant of the survival of spanner crab larvae, with the optimum temperature range being 25–29 oC for growth and morphogenesis during the zoeal instars (Minagawa, 1990a). Mortality amongst all instars was high at the lowest experimental temperatures (17-21 oC), while at the higher temperatures (33 oC)
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Figure 2.3 – Water column temperature profiles recorded using an RBR CTD on three separate days (28.13oS, 153.61o E June; 28.09o S, 153.66o E August; and 27.81o S, 153.63o E October) offshore the Gold Coast, Queensland. Profiles illustrate the variability of temperature throughout the water column. Red dashed line indicates 25 m in the water column, showing a temperature difference of 1-5oC when compared with the BBL.
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only the later instars were adversely affected (Minagawa, 1990a). In another laboratory study, Ichikawa et al. (2004), found the lowest temperature range at which spanner crab eggs would develop was between 14 and 18oC. These results suggest cooler temperatures not only affect catchability of adult spanner crab but may also affect the early life history stages of spanner crabs and, thus, affect future spanner crab abundance.
Swell exposure appears to influence spanner crab behaviour, with reports of a noticeable decline in catch rates immediately following periods of heavy swell activity in the Queensland spanner crab fishery (Brown et al., 2008). Thomas et al. (2013) found a similar trend in Hawaii, with lower catch rates in areas exposed to stronger swell intensity. The observation that catch rates could be affected several days after periods of intense swell poses an interesting question as to how much wave-induced motion reaches the sea floor in 30-80 m depths, as wave action is attenuated in deeper waters
(Denny et al., 1985) and effects are expected to be negligible near the seabed. This has been further explored later in section 5 (2.5) of this paper.
Climate change is receiving much attention and has prompted numerous studies on its likely impact on marine species, including those supporting fisheries in Australian waters (Hobday et al., 2008; Vinagre et al., 2015). Global warming is expected to cause changes in large-scale ocean circulation patterns (Johnson et al., 2011) as well as increasing sea level, sea surface temperature, shelf bottom temperature, and stratification (Chilton et al., 2010). Correlation analyses assessing the effects of climate variability on spanner crab catch per unit of effort (CPUE) demonstrate a significant relationship with El Niño years (Brown et al., 2008). Considering that climate models
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show El Niño is enhanced by global warming (Latif et al., 2015), future climate change may benefit the spanner crab fishery. This assessment also investigated the effects of salinity and found a negative correlation with catchability for years 1988-2001, but the relationship lost significance when the study was extended using additional and more recent data (Brown et al., 2008). Minagawa (1992) found that spanner crab larvae could survive under various salinity concentrations in a laboratory environment, with the optimum survival range being 27-34 ppt. It was only when salinity was below 20 ppt or above 40 ppt spanner crab larvae exhibited acute effects on metamorphosis and survival.
Brown (1986) also investigated various parameters that may be influencing catchability of spanner crab; including water depth, season, and lunar phase. Although these factors are not physical oceanographic processes, swell intensity weakens, and temperature often cools as water depth increases; lunar phase relates to tidal currents; and weather patterns change with respect to season. The results published in 1986 suggested spanner crabs were more catchable in 30-40 m, May-June, and during the “dark” phase of the moon (new moon). Since these results were published, many fishermen believe that spanner crabs have migrated to deeper waters (50-60 m) over the past decade in the southern parts of the Queensland fishery (R. Hamilton, pers. comm.). However, this may be a result of localised depletion of the stock in shallower regions. Thomas et al.
(2013) also used depth as one of the explanatory variables for spanner crab catches and distribution around the Hawaiian Islands, but found this was the least important, possibly due to the coarseness of the data available. However, the island platforms which have the largest area in the suitable depth range (< 200 m), particularly Penguin
Bank, had the highest catch rates of spanner crabs.
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From the literature it is apparent that bottom current velocity, temperature, and swell have been linked to influencing catchability of spanner crab. In the context of this review, it is important to understand the shelf physical oceanography in SEQ and northern NSW, which encompass the Australian spanner crab fishing grounds. This will help provide a sound understanding of how these parameters are affected by processes such as upwelling, eddies, and major boundary currents which impinge upon the spanner crabs’ habitat.
2.5 Shelf dynamics in the Australian spanner crab fishery
Spanner crabs reside on the seabed in sandy shelf waters, inhabiting the portion of the water column known as the Bottom Boundary Layer (BBL). The BBL is a homogenous mixed layer largely controlled by the sediment-water interface (Nayak et al., 2015;
Villacieros-Robineau et al., 2013). Physical oceanographic processes in the BBL are driven by ocean currents (Salon et al., 2008) and play an important role in sediment transport, shelf circulation, tidal energy, and nutrient mixing (Grant and Madsen, 1986;
Nayak et al., 2015). Current velocities on the continental shelf are also influenced by major boundary currents, which transport large volumes of water on to the shelf during eddy encroachment and isotherm uplift (Simpson and Sharples, 2012). The East
Australian Current (EAC) forms the major western boundary current along the shelf edge of the eastern Australian seaboard (Suthers et al., 2011).
The EAC develops in the Coral Sea, expanding to > 30 km wide and 200 m deep, and transporting on average 22 Sv (1 Sv = 106 m3.s-1) along the Queensland coast into NSW
(O’Kane et al., 2011; Suthers et al., 2011). The circulation of this western boundary current has vast spatial complexity (Roughan et al., 2011), including eddy field
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circulation (Figure 2.4a) and encroachment of the jet (Figure 2.4b). As the strongly- flowing EAC encroaches onto the shelf it also drives coastal upwelling; carrying cold, salty, nutrient-rich and oxygen poor water onto the continental shelf (Brassington et al.,
2011; Rossi et al., 2014; Roughan and Middleton, 2004). Due to predominant south- easterly winds (which inhibit upwelling), other ‘not-well-understood’ processes are presumed to be the main cause of upwelling in eastern Australia; including current- driven Ekman pumping, topography, EAC spreading or intrusions, and cold-core eddy activity (Brieva et al., 2015; Oke and Middleton, 2000; Rossi et al., 2014; Roughan and
Middleton, 2002, 2004). Numerous eddies form along the east Australian coastline as a result of the EAC, and the effects from eddies usually extend down to the seabed
(Amores et al., 2014). Recent CTD observations illustrate the extent of the effect of mesoscale eddies in Gold Coast shelf waters (Figure 2.5).
Tidal flows have been known to dominate some continental shelf regions (Grant and
Madsen, 1986). Current velocities in the deeper parts of the fishery are likely to be dominated by both tidal forcing and the EAC. Sloyan et al. (2016) compiled data from the EAC mooring array at 27o N (offshore Brisbane) and reported the mean along-slope currents reached a maximum of 0.6 m.s-1 at 50 m below the surface, just beyond the shelf break. ADCP data collected from the IMOS mooring National Reference Station offshore North Stradbroke Island (NRSNSI) (Figure 2.6) show currents typically average around 0.15 m.s-1 to 0.2 m.s-1 in the BBL at 65 m depth. The wave-induced pressure only reaches the seabed in water depths less than about half the wavelength
(Grant and Madsen, 1986). Hemer et al. (2007) conducted a five-year mean significant wave height (Hs) study for Australia and found that SEQ typically experiences 1-2 m.
This is consistent with Mooloolaba and Brisbane (SEQ) waverider buoy data for 2011,
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a) b)
Figure 2.4 – Spatial complexity of the EAC with a mesoscale eddy (a) and encroachment of the jet (b) passing through the southern part of the fishery. Observations made via the IMOS OceanCurrent portal (oceancurrent.imos.org.au – site accessed on 18/11/2015). Colour scales describe sea surface temperature and arrows describe sea surface current velocity
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Figure 2.5 – Three water column temperature profiles were recorded on two separate days offshore the Gold Coast, Queensland (28.12o S, 153.58o E May; and 28.12o S, 153.62o E October). Observation dates coincide with Figure 2.4, where the presence of an eddy in May mixes the entire water column and the absence of the eddy in October resulted in water column stratification.
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Figure 2.6 – Average monthly near bottom current speed at approximately 65 m between 2011 and 2014. ADCP data collected from the IMOS NRSNSI mooring offshore North Stradbroke Island, Queensland. Monthly median current speed is indicated by horizontal red line inside each boxplot and the upper and lower boundaries of the box represent the 75th and 25th percentiles, respectively. The whiskers of the boxplot illustrate the most extreme values which deviate from the mean current speed. Individual outliers were removed via despiking.
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which are sited in different depths (32 m and 70 m, respectively). The mean Hs at the
Mooloolaba and Brisbane buoys were 1.2 m and 1.7 m, respectively. Using the zero- crossing wave period (Tz) from the waverider buoys, and assuming the wavelength (λ) is the minimum length for waves to be felt near the bed, the maximum horizontal wave- induced velocity (u) was computed using Airy’s wave theory:
퐾푔휂 u = 0 (2.1) 휔 cosh(퐾푑)
where K is the wave number (2π/λ); 푔 the gravitational acceleration; 휂0 the wave amplitude (Hs/2); 휔 the angular frequency (2π/Tz); and d the water depth (Pedlosky,
2013). Figure 2.7 exhibits the wave-induced velocity at the seabed for the Mooloolaba and Brisbane waverider buoy locations. At both waverider buoy locations, EAC encroachment, eddies, upwelling, and intense swell events all contribute to the BBL being highly dynamic in SEQ. A greater understanding of how long-term and short-term physical oceanographic conditions fluctuate necessitates further research and the collection of meteorological and oceanographic data (Rossi et al., 2014). This information, when combined with a sound knowledge of crab responses to environmental conditions, will help promote smarter commercial fishing and contribute to increasing efficiency and sustainability of the fishery (Brown et al., 2008; Nakada et al., 2014).
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Figure 2.7 – Wave-induced horizontal velocity near the seabed between January 2011 and January 2012. Blue circles represent the velocities at the Mooloolaba waverider buoy in 32 m (26.57o S, 153.18o E) and the red circles represent the velocities calculated at the Brisbane Waverider buoy in 70 m (27.49o S, 153.63o E). The spikes in these data coincide with long period waves and large significant wave heights.
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2.6 Discussion
Of the literature that considers physical oceanographic processes that affect spanner crabs, some studies are manipulative laboratory experiments (e.g., Ichikawa et al., 2004;
Minagawa, 1990a, 1990b, 1992; Skinner and Hill, 1986). Although such manipulative experiments are ideal for establishing empirical evidence for controlled factors (i.e., temperature), laboratory experiments typically do not consider multiple physical and ecological interactions and thus, may not accurately reflect the crabs’ natural environment. For shelf water environments adjacent to major boundary currents, large- scale physical oceanographic processes are believed to be responsible for the physical state of the BBL (Salon et al., 2008). Different shoreward transport mechanisms that form from these large-scale processes may be the primary currents passing through parts of the spanner crab fishery. While there is some evidence suggesting that cold water
(e.g. from upwelling) may reduce the catchability of spanner crabs, there may be compensating effects relating to current strength and food transport into the fishing grounds. The effects of eddies on catchability should be considered, as the entrainment of warm shelf waters reach the seabed and many mesoscale eddies have been observed to form off the east Australian coast (Amores et al., 2014; Roughan et al., 2011). Recent
CTD observations show that the effects of eddies extend all the way to the seabed in depths to 50 m. Further quantification of on-shelf transport intruding onto the fishing grounds, driven by large and mesoscale processes, merits further investigation and could provide the means to predict localised changes within the fishery.
The EAC and mesoscale eddies are expected to warm and strengthen, based on experiments averaged over the period 2055-2085 (Cai et al., 2005); creating diverse weather patterns and altering species distributions (Suthers et al., 2011), which could
30
potentially impact catchability of spanner crab. Various measures of climate variability affecting catchability of spanner crab were studied by Brown et al. (2008) for the period
1988-2001 and then extended to 2007. Increasing wind and current velocities, resulting from altered weather patterns associated with the ENSO, were significantly correlated with catchability. On the other hand, salinity was negatively correlated with spanner crab CPUE in 1988-2001, but the relationship lost significance with the addition of more recent data. Minagawa’s (1992) laboratory study indicated that spanner crab zoeae were able to survive over a wide salinity range (27-34 ppt). Considering seawater salinity is typically around 35 ppt (at the higher end of spanner crab zoeal optimum survival range), any climate-related decrease due to ocean freshening (Munk, 2003) would likely still fall within the crabs’ optimum survival range with no significant impact on long-term catchability.
Temperature may be the most important physical oceanographic driver of catchability due to the effect it has on animal behaviour and metabolic rates, ultimately influencing their activity levels (Briffa et al., 2013). Ambient temperature has also been found to contribute to changes in long-term catchability by driving a shelf-wide population shift of tanner crab, snow crab, and red king crab communities in the eastern Bering Sea
(Kotwicki and Lauth, 2013). Cooler temperatures may impact long-term spanner crab catches by increasing the mortality rate of spanner crab larvae, based on results from
Minagawa (1990a). Findings from Brown et al. (2008) are more reliable than the field study conducted by Skinner and Hill (1986), as spanner crab catch was compared to
BBL temperatures (their actual habitat) instead of temperature recorded at 25 m
(midwater – not their natural habitat). The 2008 study found catchability of spanner crab is positively influenced by warmer temperatures, which suggests ocean warming, up to
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a certain threshold, may positively affect the spanner crab fishery. However, other climate change stressors such as ocean acidification, in combination with ocean warming should also be considered. Dungeness crab (Metacarcinus magister) landings in central California were found to be influenced by ocean temperature, where the fishery decline in 1960-61 and subsequent declines thereafter corresponded with rising ocean temperature (Wild, 1980). Recent 2015 fishery closures in multiple states on the
West Coast of USA resulted from the crabs becoming toxic, which, according to
NOAA, is believed to be due to unusually warm water leading to a more toxic and widespread harmful algal bloom (NOAA, 2015, 2016). Consequently, these sorts of climatic effects require careful consideration.
Changes in the environment can also impact crabs’ awareness of their surroundings, impacting their ability to look for food, a mate, or a suitable habitat (de la Haye, 2011).
Spanner crabs are highly dependent on their sense of smell to detect food (and bait)
(Hill and Wassenberg, 1999) and, consequently, dispersion of the food odour plume.
Stronger current velocities may enhance catchability of spanner crab by carrying the odour considerable distances, allowing it to reach crabs further from the food source as it undergoes advection and dispersion downstream (Taylor et al., 2013; Westerberg and
Westerberg, 2011). The magnitude of currents measured off the north coast of NSW by
Craig and Kennelly (1991) are unusually high (> 4 m.s-1 or 14.4 km.h-1). However, readings from the instrument may still show the variation in relative current strength and not negate the relationship between catchability of spanner crab and current strength. More recent near-bed ADCP data from the IMOS NRSNSI mooring agree more closely with data collected by Hill and Wassenberg (1999) (< 15.6 cm.s-1 or 0.56 km.h-1). This difference may conceivably be due to measurement inaccuracy with the
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earlier instrument. However, both studies found a positive relationship between current strength and spanner crab catches. Considering spanner crabs appear to prefer warmer temperatures and stronger currents, to understand which processes are creating this ideal condition would be highly beneficial for fishermen.
The magnitude of wave-induced current velocities felt near the seabed derived from two
Waverider buoys suggests that there are periods when large swells disrupt near-bottom water movement, even at depths of 70 m. Current velocities are proportional to catches, yet periods following intense swell have been observed to be detrimental to catches. If intense swell causes stronger wave-induced velocities near the seabed, spanner crabs may associate the oscillatory motion with danger or catchability could be adversely affected by higher turbidity from wave-induced sediment resuspension. Local spanner crab fishermen responded to these suggestions and agree that spanner crabs may avoid highly turbid water, observing less catch when the waters appeared ‘murky’ (R.
Hamilton, pers. comm.); prompting further research into the effect turbidity has on catchability of spanner crab.
2.7 Conclusion
Physical oceanographic processes affecting the catchability of spanner crabs have sparked interest in the past and several studies. Furthermore, local fishermen have stated that these processes appear to affect spanner crab catches. Overall, an important consideration is that to confidently relate physical oceanographic processes with fluctuations in catches, the methodology used to measure these parameters is crucial.
Further research into the processes linked to catchability of spanner crab, particularly currents, bottom temperature, and swell is required. How these processes change over
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short-term and long-term periods, as a result of upwelling, eddies, the EAC and atmospheric forcing, is also required.
Further research and oceanographic monitoring will assist with understanding how these complex physical oceanographic processes affect catchability of spanner crab. The
Australian National Marine Science Plan 2015-2025 stresses the need for more sustained in situ ocean variability observations in SEQ in order to better understand how the EAC influences marine biodiversity in the region. Rossi et al. (2014) found that satellite remote sensing and in situ measurements of physical oceanographic data helped to improve their understanding of biophysical interactions in SE Australia. Suthers et al.
(2011) emphasise that, with the strengthening of the EAC, these processes require further attention; notably in relation to their effects on fisheries. A high resolution numerical model could be of considerable economic benefit to the fishing industry, as well as providing valuable information for evidence-based management of nearshore fisheries.
2.8 Acknowledgements
We would like to acknowledge Gold Coast local spanner crab fishermen Richard
Hamilton, Mark Cheeseman and crew for advice and allowing access to the
‘Dubrovnik’ vessel for CTD data collection. Sunshine Coast fisherman Richard
Freeman also provided valuable insight for this work. Acknowledgement is also extended to the Queensland Department of Science, Information Technology and
Innovation for the availability of Waverider buoy data and IMOS for physical oceanographic data availability through the IMOS Ocean Current portal and the
Australian Ocean Data Network (AODN) portal.
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CHAPTER 3
______
Regional oceanographic effects on the catchability of spanner crabs in the
Queensland fishery, Australia
Article under review:
Spencer, D. M., Doubell, M. J., Brown, I. W., Redondo Rodriguez A., Lee
S. Y., Zhang H., and Lemckert, C. J. (2018). Regional oceanographic effects on the catchability of spanner crab (Ranina ranina) in Queensland,
Australia. Estuarine Coastal and Shelf Science (under review)
35
STATEMENT OF CONTRIBUTION TO CO-AUTHORED PAPER
This chapter is written in manuscript form and includes a co-authored paper. My contribution to the paper involved data collation and processing, analyses, and writing.
(Signed) (Date) 31 / July / 2018
David Spencer (Corresponding author)
(Countersigned) (Date) 1 / August / 2018
Supervisor: Hong Zhang
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3.0 Abstract
In the Australian spanner crab (Ranina ranina) fishery management and industry are looking for improvements to the existing indicators of stock abundance, namely factors responsible for variations in catchability. Prior research linked several oceanographic parameters to the catchability of spanner crabs; however, it remains unclear whether nearshore (e.g. river-runoff) or region-specific oceanographic features (e.g. eddies and the East Australian Current) are responsible for these effects on catch rates. Using satellite remote sensing and fishery-independent survey data, oceanographic variables influencing spanner crab catch rates were analysed using Generalised Additive
Modelling (GAM). GAM outputs suggest that catch rates exhibit a large amount of spatial variability across the shelf and between different management regions. Lower catch rates were associated with higher concentrations of surface chlorophyll-a, but only in survey blocks closer to the coast and bays. Additionally, offshore oceanic waters transported into various management regions by different oceanographic processes appear to benefit spanner crab catch rates. These results highlight that the effects of oceanographic parameters on catch rates were not homogenous across the South East
Queensland fishery. Rather, significant relationships between oceanographic parameters and catch rates were linked to localised (~100 km), region-specific, highly dynamic coastal and oceanographic features that dominate different management regions.
Outcomes from this work show that the spatial variability of oceanographic processes should be taken into consideration before incorporating oceanographic parameters in spanner crab stock assessment models.
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3.1 Introduction
Spanner crabs (Ranina ranina) are large marine brachyurans found throughout the Indo-
Pacific, from East Africa and Seychelles, to shelf waters around Southeast Asia, and as far east as the Hawaiian Islands (Thomas et al., 2013). The Queensland fishery on the east coast of Australia comprises two Managed Areas: Area A that is subject to total allowable commercial catch (TACC) restrictions, and Area B where very few crabs are caught (Brown et al., 2001). Area A, located in South East Queensland (Figure 3.1), supports the world’s largest commercial spanner crab fishery by both size and annual landings (Kennelly and Scandol, 2002; Brown et al., 2008; O’Neill et al., 2010).
Several input (e.g. quantity of gear) and output (e.g. TACC) controls were introduced to ensure the sustainability of the fishery (Dichmont and Brown, 2010). However, in recent years, signs of a fishery-wide spanner crab population decline have been detected from both the Queensland fishery-independent Long-Term Monitoring Program
(LTMP) and fishery-dependent commercial logbook data. This resulted in a significant
(more than 40%) reduction in the TACC, from 1631 t to 947 t for the May 2018 – June
2019 cycle (Campbell et al., 2016; J. McGilvray, pers. comm.). Fishery management and industry are examining possible reasons for this decline and looking for improvements to the existing indicators of stock abundance. For instance, several studies have stressed the importance of environmental conditions, including a number of oceanographic drivers, on the abundance, distribution and catchability of target species; recommending that they should be included in stock assessment models where possible (Bigelow et al., 1999; Damalas et al., 2007; Hobday and Hartog, 2014; Bacha et al., 2017; Scales et al., 2017).
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Catchability of spanner crabs has been linked to several oceanographic variables.
Previous studies have shown that bottom temperature (Brown et al., 2008), bottom currents (Craig and Kennelly, 1991; Hill and Wassenberg, 1999) and wave exposure
(Brown et al., 2008, Thomas et al., 2013) affect spanner crab catch rates (Spencer et al.,
2017). Spanning the entire Managed Area A, a climatological study for the period 1988-
2007 also found catch rates increased with El Niño years, stronger eastward zonal surface currents, and weaker southerly winds (Brown et al., 2008). However, given the limited spatial resolution of the previous climatological study (Brown et al., 2008) and the dynamic nature of shelf waters throughout the Queensland fishery (Spencer et al.,
2017); it is unclear whether nearshore (e.g. estuarine runoff) or offshore oceanographic features, such as eddies (Weeks et al., 2010; Ismail et al., 2017; Roughan et al., 2017;
Ribbe et al., 2018) and encroachment of East Australian Current (EAC) (Roughan et al.,
2004; Suthers et al., 2011), are related to these fluctuations in catch rates.
The exchange between coastal and oceanic waters are complex and, depending on the regional topography, the underlying oceanographic processes driving these exchanges can be vastly different over space and time (Oke and Middleton, 2000; Ridgeway and
Dunn, 2003; Brink, 2016). Various mesoscale and frontal eddies, including the
Capricorn Eddy and Fraser Gyre, occur throughout the area of the Australian spanner crab fishery (Weeks et al., 2010; Mao and Luick, 2014; Ribbe and Brieva, 2016; Ismail et al., 2017; Ribbe et al., 2018). Eddies have been linked to cross-shelf exchange through the Capricorn Channel (Middleton et al., 1994; Weeks et al., 2010) and south of
Fraser Island (Brieva et al., 2015; Ismail et al., 2017; Ribbe et al., 2018). These eddies occur more frequently during austral autumn and winter, while current- and wind-driven processes (e.g. upwelling) dominate during spring and summer (Roughan and
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Middleton, 2002; 2004; Brieva et al., 2015; Ismail et al., 2017). At various times of the year, meandering of the EAC from the shelf break toward the coast has also been observed to drive shelf water exchanges on the east coast of Australia (Rochford, 1972;
Roughan and Middleton, 2002; Cresswell et al., 2017). Importantly, the various oceanographic processes that drive cross-shelf exchange may be responsible for ecological and fishery “hotspots” within the Australian fishery (e.g. Brieva et al., 2015).
Productive fisheries are commonly associated with specific oceanographic conditions
(or “hotspots”) that create an ideal environment for a range of species to thrive. Ocean colour (e.g. chlorophyll-a) and sea surface temperature (SST) are especially useful for detecting hotspots in the ocean (Bacha et al., 2017). Altimetrically-derived surface geostrophic currents (Picaut et al., 1990), eddy kinetic energy (Jia et al., 2011), and sea level anomalies (SLA) (Larnicol et al., 2002) can also be used to monitor various oceanographic processes and circulation patterns. These hotspot areas can change spatially and in intensity from year to year, which may affect the survival and population density of some fishery species. Oceanographic drivers responsible for these ecological hotspots (e.g. eddies) can be responsible for changes in oceanographic conditions in the BBL (Amores et al., 2014) and, therefore, may impact the catchability of spanner crabs between years and different areas of the fishery (Spencer et al., 2017).
Using several oceanographic parameters derived from satellite remote sensing we aimed to 1) to improve our understanding of the effects of large-to-mesoscale oceanographic features on the catchability of spanner crabs in the Queensland fishery, and 2) to determine whether the effects of these features are consistent across different regions of the fishery. We discuss the results in terms of surface oceanographic features – how
40
they may indicate oceanographic processes that can influence the BBL (i.e. the crabs’ habitat) and consequently how they may affect spanner crab catch rates.
3.2 Materials and methods
3.2.1 Study region
Annual spanner crab surveys are conducted in May throughout Managed Area A to provide fishery-independent estimates of stock size (O’Neill et al., 2010). Shelf waters in Area A are divided into five Management Regions (numbered 2 to 6; Figure 3.1), which extend from the southern Great Barrier Reef off the coast of Rockhampton
(23.38o S) to shelf waters off the Gold Coast (28.10o S). The bulk of the commercial catch is taken from depths between about 30 and 80 m (Dichmont and Brown, 2010;
Spencer et al., 2017). The Management Regions are characterised by different coastal and topographic surroundings (Figure 3.1), which can influence the behaviour and presence of different oceanographic features (e.g. Oke and Middleton, 2000; Brink,
2016). Spatial variability in spanner crab populations, and the effects of different coastal and topographic characteristics were accounted for by analysing 1) LTMP data from the whole Managed Area A and; 2) separating the analyses for each Management Region
(2-6). Regions 4 and 5 were further sub-divided into 4a, 4b, 5a, and 5b (Figure 3.1) due to their proximity to the shelf break (e.g. influences of nearshore or oceanic waters) and topographical surroundings (e.g. islands). Hereafter, each Management Region and sub- region are all referred to as Management Regions (MR).
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Figure 3.1 – Maps showing the distribution of survey blocks (small black squares) grouped by Management Regions (large black boxes) and sub-regions (white ellipses, left) across the Queensland spanner crab fishery. The colourmaps show the distribution of SST (left) and chlorophyll-a (right) climatologies for May 2003-2016. Geostrophic currents are superimposed on each map, with arrows illustrating the absolute current direction (U and V combined). Size of the arrows indicate the magnitude of the current speed. The colour legend for each map represents SST in oC (left) and chlorophyll-a in mg.m3 (right).
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3.2.2 Spanner crab survey data
Catch and effort data from the fishery-independent spanner crab surveys were provided by the Queensland Department of Agriculture and Fisheries (DAF). This dataset covered the period 2003-2016 except for 2004 and 2012 when no surveys took place
(Brown et al., 2008; Campbell et al., 2016). Data were collated from 22 6’ × 6’ (6 x 6 n mi.) survey blocks throughout MRs 2-6 (Figure 3.1). Within each MR, we analysed data from up to four survey blocks. During annual surveys, fifteen groundlines containing industry standard tangle nets were set in each block for an average soak time of ~50 minutes (O’Neill et al., 2010). The LTMP database contains site details (i.e. grid ID, latitude, longitude, and year), number of nets per groundline, groundline time in and time out of the water (soak time), and numbers of spanner crabs of both sexes and all sizes caught. Catch per unit effort (CPUE) for each of the 22 survey blocks was estimated using total catch (number of crabs) divided by the total number of net hours:
∑ 퐶푎푡푐ℎ 퐶푃푈퐸 = (3.1) ∑ 푁푒푡_푙𝑖푓푡푠 × ∑ 푆표푎푘_푡𝑖푚푒
where ƩCatch, ƩNet_lifts, and ƩSoak_time are the total number of crabs caught, total number of nets lifted, and total soak time (hours) from all groundlines used at each survey block, respectively. Normalisation of CPUE was best achieved via natural logarithm transformation (Brown et al., 2008).
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3.2.3 Environmental and oceanographic data
Satellite oceanographic data were sourced from the Integrated Marine Observing
System (IMOS, oceancurrent.imos.org.au) Australian Ocean Data Network (AODN) archive portal (portal.aodn.org.au). Gridded (0.25o resolution) sea level anomalies (SLA
[m]) and the derived geostrophic zonal (east-west, U [m.s-1]), and meridional (north- south, V [m.s-1]) currents measured by the TOPEX Poseidon altimeter. Eddy kinetic energy (EKE) was calculated using (Jia et al., 2011):
1 EKE = (푈2 + 푉2) (3.2) 2
where EKE (indicative of eddies) is a function of both U and V geostrophic currents
(cm2.s-2). Additionally, gridded (1 km resolution) daily sea surface temperature (SST) and chlorophyll-a concentration (OC3M algorithm) derived from the Moderate
Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite.
Chlorophyll-a data were quality controlled based on the standard NASA level 3 product flags (oceancolor.gsfc.nasa.gov/atbd/ocl2flags). Normalisation of EKE and chlorophyll- a data products were achieved via log transformation. May monthly means of all oceanographic parameters were then calculated for each LTMP survey grid.
Climatologies for SST, chlorophyll-a, U and V were derived by calculating the average of all May monthly means from years 2003-2016. Climatological maps were generated to visualise the spatial variability of oceanographic processes across the Queensland fishery domain.
Distance to the shelf break, approximating the influence of nearshore or offshore oceanographic processes, was derived from Google Earth (Beaman, 2010; Wang et al.,
44
2017) as the distance between the centre of the survey block and the shelf break. The latter was defined as the 200 m bathymetric contour or -200 m elevation at the ocean surface (Glaeser, 1978; Holligan and Groom, 1986; Williams and Bax, 2001; Hanson et al., 2005).
3.2.4 Statistical analyses
The effects of various oceanographic parameters on catch rates (CPUE) were analysed using Generalised Additive Models (GAM: Wood, 2017). Two methods were used to relate the oceanographic variables to variations in catch rates. Method 1 (M1) used a single GAM incorporating data from all survey blocks to investigate how each oceanographic parameter affected catch rates across the whole study region (e.g.
Bigelow et al., 1999; Bonanno et al., 2016). “MR” and “distance to shelf break” were used to account for the various spatial effects within Managed Area A. All environmental and spatial parameters were modelled using four-knot (k = 4) cubic regression splines and “MR” was incorporated as a parametric (categorical) term (Zuur et al., 2009).
Method 2 (M2) comprised several GAMs to test the effects of oceanographic parameter within the individual MRs. These separate analyses were carried out to account for different oceanographic effects associated with spatiotemporal variability in both catch rates (i.e. due to differences in population sizes) and oceanographic features (similar to
Mourato et al., 2014). Oceanographic parameters were modelled using four-knot cubic regression splines; however, due to an imbalance in the number of survey sites between
MRs, spatial effects (distance to shelf) were not incorporated in the separate GAMs.
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Models providing the best fit to the observed data were chosen using the Information
Theoretic Akaike’s Information Criterion (IT-AIC) model selection method (Burnham and Anderson, 2002; Whittingham et al., 2006; Richards et al., 2011). The IT-AIC method was carried out using the multi-model inference algorithm MuMIn (R package,
Barton, 2009), which iteratively tests all combinations of explanatory variables until the best possible combination is produced with the lowest corrected AIC (AICc) value and highest AICc weight (Burnham and Anderson, 2002; Scholoerke et al., 2016).
Explained deviances were used to determine how much catch rate variability was explained by the best model for the combined GAM (all MRs) and the GAMs for each individual MR. CPUE was modelled with Gaussian errors and analyses were carried out in R Statistical Environment using the “mgcv” package (Zuur et al., 2009; R
Development Core Team, 2018; Wood, 2012).
3.3 Results
3.3.1 Oceanography and CPUE
May climatologies of SST, chlorophyll-a, and geostrophic currents illustrate variability in the distribution of oceanographic features across different parts of the fishery.
Onshore transport of surface water prevailed in MRs 2, 3, 4a and 5a; while there was a dominant presence of the EAC passing through MRs 4b, 5b, and 6 near the shelf break
(Figure 3.1). Closer to the coastline, surface waters were cooler (lower SST) and chlorophyll-a concentrations were higher (Figure 3.1). Visual inspections of May monthly means for each survey year show oceanographic processes, such as eddies and strengthening and meandering of the EAC, varied between years and MRs (see
Appendix 3A). As observed in previous studies undertaken during austral autumn, there
46
was a strong presence of the Capricorn Eddy and Fraser Gyre (Appendix 3A; e.g.
Weeks et al., 2010; Ismail et al., 2017; Ribbe et al., 2018). Eddies generally formed in wider shelf areas of the fishery, while the EAC was dominant in narrow shelf areas
(Appendix 3A). Meandering of the strong EAC (as indicated by the size of the geostrophic current vectors) onto the shelf sometimes caused these eddies to weaken or disappear (e.g. 2006, 2007, and 2010; Appendix 3A), resulting in weaker onshore transport and more dominant alongshore transport.
CPUE varied across the fishery, with the highest catch rates taken from MRs 2, 4a and
4b and lowest taken from MR 3 and 5b (Figure 3.2). As similarly illustrated in the June
2016 – May 2018 cycle report (Campbell and O’Neill, 2016), some MRs appeared to exhibit cyclical patterns (4a and 4b); others showed a general increasing trend (2 and
5b) or decreasing trend (3 and 5a); and MR 6 was relatively neutral (Figure 3.2).
3.3.2 GAM results
GAM results from the model using data from all MRs (Method 1) show a number of explanatory variables were significant, with the best model having an explained deviance of 36% (Table 3.1). Spatial effects across the fishery were evident, as both
“MR” and distance to the shelf break (dist_shelf) exhibited a strong statistically significant relationship with CPUE (Table 3.1). These spatial effects are further illustrated in Figure 3.3, where higher CPUE were correlated with survey blocks within
40 km of the shelf break. While the GAM model using data from all MRs highlights strong spatial effects on CPUE, it also shows stronger zonal (U) currents and chlorophyll-a concentrations < ~0.4 mg.m3 were correlated with higher CPUE (Figure
3.3). These results suggest that, after accounting for both cross-shelf (distance to shelf
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Figure 3.2 – Catch rate variability between 2003 and 2016 for each Management Region and sub-region (MR) in Queensland. Each data point represents the mean CPUE from all survey sites within each MR. Gaps in the timeseries indicate the absence of data for years 2004 and 2012.
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Table 3.1 – GAM results from Method 1 (first row, grey) and Method 2 (corresponding rows, white) using data from all survey grids (All) and each MR, respectively. Number of observations from each model are denoted by n. The corrected Akaike’s Information (AICc), AICc weights (weight), and explained deviances (ED) statistics are from the model-of-best-fit (δAICc = 0). Significance of each explanatory covariate in the best models are illustrated using p-values. Blank cells signify variables were not included in best model.
Explanatory covariates (p-values)
n SST Chl. Ucur Vcur EKE SLA dist_shelf MR AICc weight ED (%)
All MRs 237 ** ** *** ** 633.7 .24 36
MR 2 48 * ** *** .09 N/A N/A 141.2 .24 47
MR 3 35 *** * ** N/A N/A 100.6 .40 72
MR 4a 36 ** .06 .10 N/A N/A 123.6 .13 32
MR 4b 24 .08 N/A N/A 30.9 .21 29
MR 5a 12† ** N/A N/A 27.5 .80 81
MR 5b 36 * *** N/A N/A 72.9 .24 48
MR 6 46 ** * .13 N/A N/A 72.4 .14 41 Significance levels: *** <0.001, ** <0 .01, * <0 .05 † low n restricted number of covariates. Best model was based on three covariates (h_chl, Ucur, & SLA) that produced the highest ED
49
Figure 3.3 – Estimated smooth function effects for all significant covariates from best model across (all) MRs. Results are reported on the scale of the linear predictor. Numbers in brackets in y-axis label are the estimated degrees of freedom of the smooth curves. The vertical lines along x-axis (inside) are the values of the covariate. Shaded areas represent the 95% confidence intervals (Wood, 2006). Due to identifiability constraints, estimated curves are centred to pass through zero on the y-axis (Marra and Wood, 2011).
50
break) and meridional spatial effects (“MR”), processes responsible for lower chlorophyll-a and stronger zonal currents may be useful indicators of CPUE.
When MRs were analysed separately (Method 2), explained deviances (ED) ranged from
29% to 81% (Table 3.1). At lower latitudes the estimated effects, given by the scale of the linear predictor (Figure 3.4), were stronger than those at higher latitudes and are most likely because CPUE fluctuations were noticeably smaller for MRs 5a, 5b and 6 (Figure 3.2). Zonal geostrophic current frequently appeared in the models-of-best-fit and were excluded from only two MRs (4b and 5a; Table 3.1). CPUE was higher in MR 2 when currents travelling west (negative U) were stronger; when currents were travelling offshore (east, positive U) in
MRs 3 and 6; and when currents were travelling east or west (both positive and negative U) in MR 5b (Figure 3.4). Meridional geostrophic currents travelling south (negative V) were correlated with higher CPUE in MR 2 (Table 3.1 and Figure 3.4). In contrast, a significant correlation was found between low CPUE and a strong southerly flow in MR 3 (Figure 3.4), but this effect was greatly skewed by a single event and removal of this data point resulted in
V being excluded from the best model. Lower concentrations of chlorophyll-a (relative to the observed range of concentrations in each MR) correlated with higher CPUE in MRs 2, 3, and
5a (Figure 3.4). For MRs 4a and 5b, higher CPUE were correlated with warmer SST. EKE was only significant for MR 6 where it showed an increase in CPUE when EKE was low
(~0.5 cm2.s-2). Although SLA was included in the best models for several MRs (Table 3.1), it was not statistically significant (p>.05) for explaining variations in spanner crab catch rates.
51
o -3 -1 -1 2 -2
SST ( C) High Chl. (mg.m ) U current speed (m.s ) V current speed (m.s ) EKE (cm .s )
2
MR
3
MR
4a
MR
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5a
MR
5b
MR
6
MR
Figure 3.4 – Estimate of smooth function effects of all covariates from best model GAM for each MRs. Results are reported on the scale of the linear predictor. . Lower latitude regions (MR 2, 3, 4a) and higher latitude regions (MR 5a, 5b, 6) have a different y-axis scale to allow for easier comparison of the covariate effects. The vertical lines along x-axis (inside) are the values of the covariate. Estimate curves are centred to pass through zero on the y-axis and shaded with 95% confidence intervals (Wood, 2006; Marra and Wood, 2011).
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3.4 Discussion
During the month of May, our results show that spanner crab catch rates are influenced by various oceanographic features that vary between years and the different MRs in the
Queensland spanner crab fishery. Noticeable oceanographic features include the southern end of the Capricorn Eddy (e.g. Weeks et al., 2010); indistinct circulation patterns in Hervey Bay; onshore transport south of Fraser Island, occasionally generating smaller gyres (e.g. Ismail et al., 2017); and the EAC predominantly travelling along the shelf break (e.g. Suthers et al., 2011) (Appendix 3A). Consistent with the May climatology and monthly means, lower (higher) concentrations of chlorophyll-a and warmer (cooler) SST appeared to be linked to offshore oceanic
(nearshore coastal) surface waters.
Results from the GAM analyses using all MRs (Method 1) demonstrate higher CPUE occurs closer to the shelf break (< 40 km). This may be due to differences in habitat type (e.g. seabed roughness and hardness) or an indication of stock depletion in more accessible waters closer to the coast (Brown et al., 2008; Spencer et al., 2017). After accounting for cross-shelf spatial effects (distance to shelf), lower concentrations of chlorophyll-a and stronger zonal surface currents were also associated with higher
CPUE. However, considering the vast spatiotemporal complexity of oceanographic features throughout the Queensland fishery, it was impossible to infer which features were responsible for fluctuations in specific oceanographic parameters.
By separating the analyses for each MR (Method 2), the effects of oceanographic parameters were easier to relate to region-specific oceanographic features that dominated each MR. For example, the Capricorn Eddy can be seen to bring oceanic 54
waters into the survey grounds of MR 2 via westward-travelling surface currents that were correlated with higher CPUE; but, when the eddy was weak or shifted spatially in some years (Appendix 3A), oceanic waters are brought into the same area from the north, which was also correlated with higher CPUE. Chlorophyll-a was significant in three MRs, which were located between small islands and the coastline (MR 2) or at the mouth of coastal estuaries and bays (MR 3 and 5a). Chlorophyll-a had no effect on
CPUE at MRs closer to the shelf break, which may be an indication that, at the affected
MRs, the adverse effect from high chlorophyll-a is associated with coastal processes causing surface nutrient enrichment and increased primary productivity. Such processes may include tidal flushing and advection of high-nutrient mangrove water (Middleton et al., 1994), density-driven outflows of Hervey Bay water (Ribbe, 2006), or river-runoff
(Brieva et al., 2015).
For MRs 4a and 5b, the increase in CPUE as a result of warmer SST may be related to offshore oceanic (warmer) waters passing through survey blocks, which was often typical of EAC encroachment (Roughan and Middleton, 2002; 2004; Suthers et al.,
2011; Schaeffer et al., 2014). Sea level anomaly (SLA) was not significantly correlated with CPUE, while eddy kinetic energy (EKE) was only significant in shelf waters off the Gold Coast (MR 6). The effect associated with low EKE(~0.5 cm2.s-2) was difficult to interpret, but this may be related to periods of low geostrophic energy as a result of weaker southerly flow passing through the survey blocks. This region generally experiences a strong southerly meridional flow driven by the EAC (Figure 3.1) and, therefore, a weaker EAC may help facilitate offshore (eastward zonal) transport of surface waters, which was significantly correlated with higher CPUE.
55
For the majority of MRs that returned a significant relationship between zonal current velocities and CPUE, higher CPUE correlated with offshore (eastward) travelling surface currents. This finding supports the previous study (Brown et al., 2008) that found a correlation between higher commercial catch rates and stronger eastward zonal currents across the whole Managed Area A. This may also be indicative of oceanic waters having increasing catch rates via upwelling; as deep, cooler oceanic waters can be transported onto the shelf through the BBL, which replaces the surface waters moving offshore (Roughan and Middleton, 2004). The positive effect of weaker southerly winds on commercial catch rates (Brown et al., 2008) may be related to downwelling, as strong southerly winds have been linked to inner shelf current reversals that prevent both current- and wind-driven upwelling (Cresswell et al., 2017).
These effects across the different MRs could be related to both population density- independent factors (e.g. feeding) and density-dependent factors (e.g. predation) that are likely to affect species’ foraging behaviour (Kotwicki and Lauth, 2013). For example, there may be a lower abundance of top predators in oceanic waters, such as bull sharks that prefer nearshore coastal waters as hunting grounds (Froeschke et al., 2010), and, in turn, generate more desirable conditions for spanner crabs to forage. Additionally, high chlorophyll-a concentrations attributed to (e.g.) excessive estuarine/river outflow, causing an increase in localised primary productivity and trophic transfer (Brown et al.,
2010), may periodically attract some other large marine species and reduce catch potential (Chassot et al., 2010). For instance, an unusually high number of manta rays were attracted to Lady Elliot Island (24.1o S, 152.7o E) in early 2013, which was linked to anomalously high chlorophyll-a concentrations (Weeks et al., 2015). These sorts of marine species aggregations could potentially deter spanner crabs from foraging. 56
3.5 Conclusion
Analyses of catch rate data from the fishery-independent Queensland spanner crab surveys, in conjunction with satellite remote-sensed oceanographic data, indicated that the effects of oceanographic parameters on CPUE are not homogenous throughout the study region, at least during the month of May. Rather, variability in CPUE is attributed to region-specific nearshore and oceanographic features. While our results provide valuable insight into the effects of large-mesoscale oceanographic features on spanner crab catch rates, these features were detected at the surface and not in the bottom boundary layer (i.e. where spanner crabs reside). This was an important ecological consideration raised by Spencer et al. (2017) when determining the effects of temperature on catch rates. Consequently, more localised studies would benefit from in situ observations of hydrodynamic and hydrological parameters measured in the BBL to gain a better understanding of how spanner crabs respond to these larger-scale oceanographic features. For example, the effects of upwelling should be considered at smaller spatiotemporal scales (i.e. localised short-term and seasonal upwelling), as there may be compensating effects associated with stronger currents transporting cool oceanic waters into spanner crab fishing grounds (Spencer et al., 2017). The effect of localised upwelling has been further explored in the next chapter of this thesis (Chapter 4).
3.6 Acknowledgements
We thank IMOS for the accessibility of various remote sensing data, and the
Queensland Department of Agriculture and Fisheries for providing the fishery- independent LTMP data. In particular, Jason McGilvray was very helpful by assisting with use of the LTMP database. Sincere thanks are also extended to Dr. Christopher
Brown for advice on statistical modelling using GAM analyses. 57
Appendix 3A – May monthly mean geostrophic currents, SST (left) and chlorophyll-a (right) for each year from 2003-2016 (excluding 2004 and 2012 when no LTMP surveys took place) across the Queensland spanner crab fishery
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CHAPTER 4
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Upwelling enhances the catchability of spanner crabs in far South-East
Queensland, Australia
Article accepted (pending 2nd review):
Spencer, D. M., Brown, I. W., Doubell, M. J., Brown C. J., Redono-
Rodriguez A., Lee, S.Y., Zhang, H., and Lemckert, C.J. Bottom boundary layer cooling and wind-driven upwelling enhances the catchability of spanner crab (Ranina ranina) in South-East Queensland, Australia.
Fisheries Oceanography
70
STATEMENT OF CONTRIBUTION TO CO-AUTHORED PAPER
This chapter is written in manuscript form and includes a co-authored paper that has been accepted in Fisheries Oceanography but pending a second review. Suggested changes and edits by the Editor and two anonymous reviewers have been incorporated into the version presented in this thesis chapter. My contribution to the paper involved data collection, collation and processing as well as data analyses and writing.
(Signed) (Date) 31 / July / 2018
David Spencer (Corresponding author)
(Countersigned) (Date) 1 / August / 2018
Supervisor: Hong Zhang
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4.0 Abstract
Species catchability is an important parameter used to help optimise stock assessment modelling and the economic efficiency of commercial fishing operations. Previous studies have shown several physical oceanographic parameters, including ambient temperature, waves, and currents affect the catchability of spanner crabs (Ranina ranina) throughout the Indo-Pacific. Most notably in the Australian fishery, where oceanographic processes vary over space and time, a positive relationship between bottom boundary layer temperature (BBLT) and catch rates was observed. Here, we aimed to better understand how localised oceanographic processes affected this relationship in the southernmost South-East Queensland (SEQ) sector of the Australian fishery at seasonal and short temporal scales. Our results show cooler BBLT, upwelling-favourable alongshore wind stress and increased catch rates occurred during mating season in austral spring. At the end of austral summer, BBLT began warming, downwelling-favourable winds were dominant, and catch rates declined around the post-moult period. Outputs from the generalised linear models (GLMs) that separated these effects in each season show that, at shorter temporal scales, daily catch rates also increased with episodic BBLT cooling and upwelling-favourable alongshore wind stress, but only during austral autumn and winter. These new findings suggest that region-specific, short-term and seasonal variability of oceanographic processes responsible from changes in BBLT play an important role in influencing the catchability of spanner crabs. We suggest that the effects of region-specific physical oceanographic processes must be considered in future work when investigating the catchability of commercially important fisheries species fished over large spatial domains.
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4.1 Introduction
Fisheries sustainability is a global issue requiring formalised controls for successful resource management (O’Neill et al., 2010). The Australian spanner crab fishery is the largest of the commercial fisheries for Ranina ranina across the Indo-Pacific region, which include Japan, Hawaii, Philippines and the Seychelles (Kennelly & Scandol,
2002; Thomas et al., 2013). It was the first fishery in Queensland to implement stringent quota-regulated output controls using an empirical management procedure (O’Neill et al., 2010); resulting in an annual total allowable catch (TAC) with an individual transferable quota (ITQ) system (Dichmont & Brown, 2010).
Catchability is an important parameter for fisheries management and can be described as the interaction between species abundance and the efficiency of fishing gear
(Arreguín-Sánchez, 1996). Commercial spanner crab fishers use highly size- and species-selective gear (tangle nets) which minimises the bycatch of undersize crabs and other biota (Sumpton et al., 1995; Brown et al., 2001). The adopted management controls for the Australian spanner crab fishery demonstrate promising progress towards a sustainable fishery, but our understanding of the environmental conditions influencing their catchability remains poorly resolved (Spencer et al., 2017), limiting the predictive capacity of stock assessment models and the overall economics of commercial fishing operations.
Globally, various hydrographic (e.g. temperature) and hydrodynamic (e.g. currents and waves) processes have been linked to the catchability of spanner crabs (Craig &
Kennelly, 1991; Hill & Wassenberg, 1999; Brown et al., 2008; Thomas et al., 2013). In the Australian fishery, a positive correlation between bottom temperature and 73
commercial catch rates (CPUE) was found for the area between Noosa, Queensland
(26.36o S, 152.97o E), and Ballina, New South Wales (28.84o S, 153.57o E) (Brown et al., 2008). The positive effect temperature had on CPUE was assumed to be due to a temperature-mediated increase in metabolic activity, resulting in more frequent foraging and an increased likelihood of capture (Brown et al., 2008). Additionally, time of year
(month) was found to have a strong statistically significant effect on catch rates (Brown et al., 2008). Spanner crabs are known to be more active in spring and summer, feeding and mating prior to their annual spawning and less active during their moult stage in autumn (Skinner and Hill, 1986; 1987) and, therefore, the effect of month may be related to their reproductive behaviour and moulting cycles.
Similarly, a positive correlation was found between temperature and surveyed catch rates for the portunid crab Scylla serrata in estuarine waters of Queensland at both short- (day-to-day) and longer-term (month-to-month) temporal scales (Williams and
Hill, 1982). The study concluded this may be related to the feeding activity of S. serrata, which markedly decreased at temperatures below 20oC (Hill, 1980). For other crab species, cooler ambient temperatures have been found to negatively affect their growth and survival (Green et al., 2014), osmoregulation and energy budget (Madeira et al., 2014), emergence response time (Briffa et al., 2013), and drive shelf-wide shifts in populations (Kotwicki & Lauth, 2013). Considering ambient temperature is an important physical parameter affecting many crab species, a knowledge of the physical processes that drive temperature fluctuations in the bottom boundary layer (BBL) (i.e. the crabs’ habitat) over space and time is important for understanding changes in the species’ behaviour.
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While the large-scale gradients in bottom boundary layer temperature (BBLT) on the continental shelf are often related to latitudinal changes (Richaud et al., 2016), spatiotemporal variations such as upwelling and downwelling are commonly associated with physical oceanographic processes that influence cross-shelf transport. Upwelling drives cool, nutrient rich water onto the shelf from deep waters past the shelf break, which can result in short-term (< 10 days) decreases in BBLT of up to 5oC (Roughan &
Middleton, 2004) and, at times, cooler sea surface temperatures (SST) (Suthers et al.,
2011). Conversely, downwelling causes depression of the water column isotherms, which suppresses cool water from reaching the shelf (Roughan & Middleton, 2002).
Across most of the Australian spanner crab fishery (25-29.5o S), downwelling is predominant during summer to mid-winter (January-August), while upwelling is more frequent during spring (September-November) (Rossi et al., 2014). This is mostly governed by alongshore wind stress, which also changes seasonally (Brieva et al., 2015;
Cresswell et al., 2017), but various oceanographic processes also contribute to sporadic upwelling and downwelling. In the coastal area where the Australian spanner crab fishery operates, upwelling and downwelling is facilitated by alongshore wind stress as well as complex coastal topography, tides, eddies, and the East Australian Current
(EAC) (Middleton et al., 1994; Oke & Middleton, 2000; Suthers et al., 2011).
The Australian spanner crab fishery extends from the southern Great Barrier Reef region in Queensland to northern New South Wales (23-29.43o S) on the east Australian continental shelf. In the wider shelf region of the southern Great Barrier Reef (23-25o S) there is evidence that the Capricorn Eddy drives upwelling and downwelling (Weeks et al., 2010) and upwelled waters can be transported towards the coast via flood tides through the Capricorn Channel (Middleton et al., 1994). At mid-latitudes of the fishery, 75
south of Fraser Island (25-27o S), the shelf region is characterised by the downwelling- favourable cyclonic Fraser Gyre during autumn and winter (Ismail et al., 2017) and the
“Southeast Fraser Island Upwelling System” in spring and summer (Brieva et al., 2015).
Further south, the EAC has been found to intensify where the continental shelf narrows off the coast of northern New South Wales, resulting in current- and wind-driven upwelling (Cresswell et al., 2017; Oke & Middleton, 2000).
This suggests that localised and seasonal oceanographic processes may be important for variations in both BBLT and spanner crab catch rates in the Australian fishery. Our main objective for this study was to determine the seasonal and shorter-term effects of
BBLT on spanner crab catch rates within a smaller region of South-East Queensland
(SEQ) sector of the Australian fishery. We also investigated whether SST and alongshore wind stress; parameters that have been linked to upwelling and downwelling, explain fluctuations in both BBLT and spanner crab catch rates.
4.2 Materials and Methods
4.2.1 Study region
Oceanographic, catch, and fishing effort data used in this study were obtained from the shelf waters of SEQ, Australia, from 27.35oS to 28.25oS (Figure 4.1). The region is bounded by Moreton Island and North Stradbroke Island to the north and the
Queensland and New South Wales border to the south. Spanner crabs in this region are typically taken from depths of 30-80 m (Dichmont & Brown, 2010) on a shelf width of approximately 40 km. Here the benthic habitat consists primarily of uniform, medium-
76
fine sandy substrata (Marshall & Oldham, 1980) with scattered, low-profile rocky reef
(Richmond & Stevens, 2014). SEQ typically experiences cool, dry winters and warm, wet summers. Prevailing winds are from the south-east year-round with northerlies more frequent during spring (Brieva et al., 2015). To the east, the fishery is exposed to effects of the EAC (Spencer et al., 2017), a major western boundary current flowing southward along the shelf break of Australia’s eastern seaboard.
4.2.2 Data collection and processing
4.2.2.1 Field study
A preliminary study was conducted to i) assess the effects of BBLT on catch rates and ii) ensure the Integrated Marine Observing System (IMOS; imos.org.au) oceanographic mooring reference station located within the study region (Figure 4.1) was suitable for the more extensive SEQ study. This involved the collection of in situ measures of
BBLT between May 2015 and April 2016 from various locations within our study region. Field measures of BBLT were obtained with the assistance of an individual commercial spanner crab fisher operating out of the Gold Coast Seaway, Queensland
(Figure 4.1). Small temperature loggers (TidbiT™ or Sensus Ultra™) sampling at 1 Hz were attached to a single net on a setline of 15 nets. Three setlines, each containing one temperature logger, were deployed on the seabed for approximately two hours up to three times a day for a total of 64 days. Mean daily BBLTs were derived by averaging the temperature records from each setline on a given day. Corresponding catch and effort information was recorded on each day and included date, catch (total retained wet weight of legal-sized crabs in kilograms), total number of net lifts, and midday latitude
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Figure 4.1 – Map of study region, including an inset map illustrating the study region relative to the whole Australian spanner crab (SC) fishery and its locality within Australia. Orange ‘Crabbing zone’ illustrates the 30 to 80 m depth range where most spanner crabs are caught. Markers, as identified in the legend, exhibit the locations of the metocean data sources for the SEQ study.
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and longitude. Catch and net lift data were skewed and therefore subjected to cube-root transformation (Campbell et al., 2016).
BBLT recorded with the data loggers were compared with coincident data from the
IMOS North Stradbroke Island National Reference Station (NRSNSI; Figure 4.1). Here,
BBLT data were obtained from a Sea-birdTM CTD moored approximately 2 m above the seafloor in a total water depth of 65 m. Data were downloaded from the Australian
Oceanographic Data Network portal (portal.aodn.org.au) and mean BBLT were derived from 15 min burst records between fishing hours conducted by the individual commercial fisher (5am to 5pm).
4.2.2.2 SEQ study
Building on the fieldwork results, we expanded the extent of our study region to include data from 2011-2014, using metocean measures of BBLT, SST, and alongshore wind stress. Daily mean BBLT were obtained from the NRSNSI. Daily mean SST data were obtained by averaging 15 min records from the Brisbane Waverider buoy
(https://data.qld.gov.au; Figure 4.1). Wind speed and direction data recorded at 3-hourly intervals were obtained from the Australian Bureau of Meteorology (BOM: http://www.bom.gov.au) Cape Moreton Lighthouse weather station (Figure 4.1). Wind data were converted into north-south (v) and east-west (u) components and vector averaged to provide estimates of the daily average alongshore wind stress (τ) using the formula (Smith, 1988):
2 τ = 퐶퐷 휌 푣10 (4.1)
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-3 where ρ is density of air, 1.22 kg m , v10 is the alongshore (north-south) wind speed
adjusted for the reference height at 10 m above sea level (Large & Pond, 1981), and CD is the drag coefficient calculated using the formula (Large & Pond, 1981):
−1 3 1.2, 4 ≤ 푣10 < 11 푚. 푠 10 퐶퐷 = { −1 (4.2) 0.49 + 0.065푣10, 11 ≤ 푣10 < 25 푚. 푠
Positive and negative values of τ correspond with southerly (downwelling-favourable) and northerly (upwelling-favourable) wind stress, respectively. Considering a possible lag in upwelling and downwelling in response to the wind stress, daily average values of
τ were low-pass filtered by applying a lagged running mean of the previous seven days
(including the fishing day) to provide a weekly index of wind-driven upwelling or downwelling (7).
Corresponding spanner crab logbook data were provided by the Queensland Department of Agriculture and Fisheries (DAF). Daily catch and effort information is compulsorily supplied by all spanner crab quota holders and includes: fishing date, estimated total retained catch (kg), number of net lifts, quantity of gear (number of nets) permitted on- board, boat ID, and fishing location (latitude and longitude). In total, the dataset included 1750 fishing trips for a total of 747 days. Boat ID and quantity of gear were considered as measures to account for the effect of different commercial fishermen
(Campbell et al., 2016) but could not be included in our analyses due to an unbalanced number of fishing days across various license holders. Catches were aggregated across the fleet within the study region, whereby the total sum of estimated retained catch and total net lifts on a given fishing day were derived. Daily aggregated catch and net lifts 80
were normalised using a cube-root transformation for further statistical analyses
(Campbell et al., 2016).
4.2.3 Statistical Analyses
The difference between the two BBLT datasets (loggers and NRSNSI) was analysed using a linear correlation model, and goodness-of-fit indicated by the coefficient of determination r2. Multiple linear regression models were used determine whether variations in BBLT could be attributed to wind stress favouring upwelling or downwelling, and whether they can be identified from coastal SST signatures, after accounting for any similarities in seasonal variability. This was performed by modelling
BBLT against 7 and month, and BBLT against SST and month, where month was modelled as a second order polynomial to account for seasonal cycles. Multiple r2 (R2) and p statistics were used to determine the strength of the models and significance of each variable, respectively. General linear correlation plots and r2 values were then used to illustrate the strength of the relationships between BBLT and 7, and between BBLT and SST. Prior to further statistical analyses, visual inspections of catch rates (kg/net lifts), BBLT, SST, and 7 were made to identify specific seasonal trends in the broader
SEQ study.
Generalised Linear Models (GLM) were used to model the effect of BBLT on catch and effort data from both field and SEQ studies. For the SEQ study, SST and 7 were considered as explanatory variables in separate GLMs depending on their relationship with BBLT. Catch was modelled with Gaussian errors and an offset for the number of net lifts (Zuur et al., 2009). The offset allowed us to adjust for fishing effort and
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estimate catch rates. Considering time of year (month) had a strong statistically significant effect on catch rates (Brown et al., 2008), month was modelled as a numerical term using a second order polynomial to account for seasonal (cyclical) reproductive and moulting patterns (Skinner and Hill, 1986; 1987). For the extended
SEQ study, year was included to account for interannual variability. We used an a priori model selection process, which allowed for comparison between the two BBLT datasets in the field study, the use of SST or 7 in lieu of BBLT in the SEQ study, and the effect seasonal reproductive and moulting cycles had on catch rates in both studies.
Goodness-of-fit of each model was compared by the difference in Akaike’s Information
Criterion (AIC) values (∆AIC), where the best model was assigned a value of zero.
Further investigation was carried out by assessing short-term effects of BBLT, SST and
7 within separate seasons (i.e. summer, autumn, winter, and spring), whereby season was modelled as a parametric (factor) term (Zuur et al., 2009).
Explained deviance for each model was calculated using the following equation (Zuur et al., 2009):
푛푢푙푙 푑푒푣𝑖푎푛푐푒−푟푒푠𝑖푑푢푎푙 푑푒푣𝑖푎푛푐푒 퐸퐷 = 100 × (4.3) 푛푢푙푙 푑푒푣𝑖푎푛푐푒 where the null and residual deviances are equivalent to the total sum of squares and the residual sum of squares, respectively (Zuur et al., 2009). The effect (i.e. positive or negative correlation) and significance of each variable was assessed by 95% confidence intervals (CI) (du Prel et al., 2009). All statistical analyses were performed using the statistical environment package, R (R Development Core Team, 2018).
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4.3 Results
4.3.1 Field study
BBLT data from the temperature loggers and mooring ranged from approximately 16 to
22.5oC (Figure 4.2), with warmest and coolest periods occurring around May-June and
September-November, respectively. The two BBLT datasets showed good agreement (r2
= 0.65) except for five days (7% of the records) on which differences were greater than
2oC. Exclusion of the data from those days resulted in r2 increasing to 0.79. The most variability across consecutive days occurred in November 2015, while the greatest difference between the two datasets on a given day occurred in March 2016 (3.2oC), when a warm event (24.2oC) was detected by the temperature loggers but not at the
NRSNSI.
Results from the GLM analysing the effects of BBLT on catch rates showed temperature logger data had a lower AIC value compared to the model using NRSNSI data when seasonal effects (using month) were not modelled (Table 4.1). Both the small temperature loggers and NRSNSI BBLT data exhibited significant, inversely proportional relationships with catch rates (95% CI from -0.5 to -0.1 and -0.47 to -0.09, respectively). However, when seasonal effects were modelled (i.e. by including month),
BBLT was no longer significant (95% CI from -0.29 to 0.05 for loggers and -0.33 to
0.02 for NRSNSI) but AIC decreased for each model. The GLM using month without either BBLT dataset explained a similar amount of catch rate variability (ED = 31.1%;
∆AIC = 1), which indicated the effect of BBLT could not be distinguished from the seasonal changes in BBLT and catch rates. For the GLMs modelled with month and
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Figure 4.2 – BBLT data collected from the small temperature loggers and the NRSNSI mooring within the study region on 64 days for the period of May 2015 to April 2016 inclusive. Date labels on the x-axis represent the middle of each month (day 15). Lines connecting each data point were used to better illustrate how each dataset agreed over the timeseries. Each point represents daily average temperature during fishing hours.
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Table 4.1 – The effects NRSNSI BBLT data, small temperature logger (Loggers) BBLT data, and month had on catch rates in the field study (May 2015 – April 2016).
GLM AIC ∆AIC ED(%)
NRSNSI + Month2 + offset(Net 174.4 0 34.3 lifts)
Loggers + Month2 + offset(Net 175.4 1 33.2 lifts)
Month2 + offset(Net lifts) 175.4 1 31.1
Loggers + offset(Net lifts) 188.1 12.7 15.7
NRSNSI + offset(Net lifts) 191 15.6 11.8
offset(Net lifts) 196.9 21.5 0
2: Fitted with 2nd order polynomial to model cyclical (seasonal) effects. Net lifts were modelled using an offset to estimate catch rates. AIC: Akaike’s Information Criterion; ∆AIC: difference in AIC value from best model; ED(%): percentage of explained deviance.
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BBLT, there was little difference when using either the NRSNSI or small temperature logger BBLT data (AIC = 174.4 and 175.4, respectively).
4.3.2 SEQ study
The multiple linear regression model assessing the collective contribution of SST and month in explaining BBLT variability had an R2 value of 0.14; whereas the model using
2 τ7 and month had a higher R value of 0.31. BBLT and SST were not significantly correlated (p > 0.05); while significant relationships were observed between BBLT and
τ7, and between BBLT and month (p < 0.001). Consistent with the multiple linear regression model results, the general linear correlation plots illustrated a weak
2 correlation between SST and BBLT (r = 0.03, Figure 4.3a), whereas the 7 and BBLT model exhibited a better fit (r2 = 0.26, Figure 4.3b).
Timeseries plots of SST, BBLT, 7, and catch rates illustrate clear seasonal cycles
(Figure 4.4). SST exhibited less short-term variability compared to the variations observed in BBLT, 7, and catch rates. During spring (September-November), catch rates were consistently much higher, northerly 7 (upwelling-favourable) were more frequent, and BBLT was generally cooler than other seasons (Figure 4.4, blue shaded area). Catch rates then decrease around the end of summer and start of autumn
(February-March) and, at the same time, southerly 7 (downwelling-favourable) became dominant and BBLT typically started to increase (Figure 4.4, orange shaded area). The
Australian spanner crab fishery closure occurs annually from 20 November to 20
December, and immediately following this period catch rates were consistently high
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Figure 4.3 – General linear models showing the relationship between BBLT and SST (a) and BBLT and τ7 (b) for the 747 fishing days during 2011 and 2014. Goodness-of-fit is described using the coefficient of determination (r2).
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Figure 4.4 – Daily SST, 7, BBLT, and catch rate data from the 747 days from 2011 to 2014. Daily mean catch rate represents standardised catch (total retained weight [kg]) by the measure of effort (total net lifts) across all spanner crab fishermen operating on each day. Positive and negative alongshore wind stress represents southerlies and northerlies, respectively. Date ticks correspond with the middle of the labelled month (day 15). Blue shaded areas highlight the period spanner crabs become more active which corresponds with seasonal upwelling and the orange shaded areas highlight the period when spanner crabs start becoming less active during post-moult.
across all years. SST was excluded from further analyses with catch rates due to the weak correlation with BBLT.
The GLM analyses for the SEQ study (Table 4.2) illustrates 7 and BBLT had a similar effect on daily catch rates but the model using BBLT had a lower AIC value and higher explained deviance. Without modelling seasonal effects, both 7 and BBLT had a significant, inversely proportional effect on catch rates (95% CI from -9.8 to -4.6 and -
0.28 to -0.18, respectively). ∆AIC values indicate that month was the most important parameter, explaining 14.9% of catch rate variability alone, which highlights a strong seasonal (cyclical) effect as observed in the field study (Table 4.1). Even when seasonal variability (month) was incorporated in the GLM, 7 and BBLT returned a significant inversely proportional relationship with catch rates (95% CI from -5.3 to -0.03 and -
0.19 to -0.08, respectively), though their effects were weaker. The weaker, but still significant, effects suggest both BBLT and 7 explain some short-term variation in catch rates beyond that explained by the confounding seasonal variability of BBLT, 7 and catch rates. The BBLT and τ7 GLMs modelled with month and year had explained deviances of 18.4% and 16%, respectively (Table 4.2). The model of best fit was the
GLM that included BBLT, month and year (∆AIC = 0).
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Table 4.2 – GLM models used to analyse the various effects of BBLT, τ7, month and year on catch rates in the SEQ study (2011-2014).
GLM AIC ∆AIC ED(%)
BBLT + Month2 + Year + offset (Net lifts) 2229.4 0 18.4
BBLT + Month2 + offset (Net lifts) 2233.6 4.2 17.7
2 τ7 + Month + Year + offset (Net lifts) 2250.6 21.2 16
2 τ7 + Month + offset (Net lifts) 2255.2 25.8 15.3
Month2 + offset (Net lifts) 2257.1 27.7 14.9
BBLT + offset (Net lifts) 2305.6 76.2 9.1
τ7 + offset (Net lifts) 2348.9 119.5 3.7 offset (Net lifts) 2375.3 145.9 0
2 Fitted with 2nd order polynomial to model cyclical (seasonal) effects. Net lifts were modelled using an offset to estimate catch rates. AIC: Akaike’s Information Criterion; ∆AIC: difference in AIC value from best model; ED(%): percentage of explained deviance.
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By separating each season, further analyses of the short-term effects BBLT and τ7 had on catch rates showed significance only during autumn and winter months (Table 4.3).
Explained deviances indicate that far more of the variability in catch rates was accounted for by BBLT (28%) than by τ7 (10%). This suggests that episodic BBLT cooling and less frequent upwelling-favourable τ7 improves catch rates but only during seasons that generally experience warmer BBLT and downwelling-favourable τ7 (Figure
4.4).
4.4 Discussion
Our main objective of this work was to investigate the effect of BBLT on spanner crab catch rates in the southern part of SEQ, Australia. The field study showed that BBLT had a significant effect on catch rates, however due to limited data, the GLMs using month highlighted a spurious relationship between BBLT, season and catch rates for the field study. This caused both BBLT data (loggers and NRSNSI) to lose significance.
This may be due to the field study being limited by data from a single fisherman and an unbalanced number of observations across various months.
Subsequent investigation of the effect of BBLT on catch rates over larger spatiotemporal scale for the SEQ study region involved the use of catch and effort data from a number of vessels in the fishing fleet. Firstly, we demonstrated that the NRSNSI oceanographic mooring was a suitable source of long-term BBLT data for our study region. With the exception of 5 out of 64 days, when the difference between the loggers and the NRSNSI exceeded 2oC, the two datasets exhibited a strong correlation. Given the complex nature of the BBL on the continental shelf (Grant & Madsen, 1986), these
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Table 4.3 – Short-term effects BBLT and τ7 have on catch rates within each season.
GLM Season CI (2.5%) CI (97.5%) ED(%)
summer -.10 .01
autumn* -.15 -.03 BBLT:Season + offset(Net lifts)
28 winter* -.15 -.05
spring -.09 .03
summer -3.37 5.04
autumn* -18.57 -9.78 τ Season + offset(Net lifts) 7: 10 winter* -21.69 -12.6
spring -4.37 6.53
* negatively correlated and significant within 95% confidence intervals. Net lifts were modelled using an offset to estimate catch rates. CI (2.5%) and CI (97.5%): lower and upper confidence limits, respectively. ED(%): percentage of explained deviance.
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anomalies observed on the five days may be associated with a lag between oceanographic processes such as eddies, internal waves, and internal tides passing through the region (e.g. Halliwell & Mooers, 1979; Griffin & Middleton, 1992; Pineda,
1994; Huthnance, 1995; Oke & Middleton, 2000; Ribbe & Brieva, 2016; Schaeffer et al., 2017). The GLMs using month, however, showed that both BBLT datasets yielded similar results.
Findings from the SEQ study were consistent with the field study, though BBLT remained statistically significant even after modelling seasonal effects, which suggested there was a relationship between BBLT and catch rates at both seasonal and short-term temporal scales. This difference between the field and SEQ studies may be related to the
SEQ study having been based on a much larger dataset and therefore able to include several short-term fluctuations in both catch rates and BBLT. GLM results also demonstrated τ7 and BBLT share a similar, significant effect on daily catch rates. Even though τ7 only explains a small amount of BBLT variability, our results highlight there is still a significant relationship with catch rates; suggesting that wind-driven upwelling and downwelling play a role for the catchability of spanner crab. BBLT, however, consistently performed better than τ7 in all models tested, which suggests processes other than wind driven upwelling responsible for short-term decreases in BBLT also increase their catchability.
Our results differ from the positive relationship between BBLT and CPUE found in the previous study (Brown et al., 2008). Similar to our field study, the study of Brown et al.
(2008) also had large data gaps between days, seasons, and even years, which may partly explain the difference in findings. However, these differences may also be related
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to higher catch rates taken from a region that generally experiences warmer BBLT. For example, off the Gold Coast the BBLT range was approximately 3-4oC cooler and the mean CPUE was approximately 30% less than that measured off the Sunshine Coast.
Latitude was not incorporated into the original GLM analyses (Brown et al., 2008) to account for such meridional spatial variability. Thus, the difference in temperature ranges and catch rates may be due to latitudinal differences in spanner crab populations and/or oceanographic processes, such as the seasonally-reoccurring Fraser Gyre (Ismail et al., 2017) which influences the Sunshine Coast but not the Gold Coast.
The effect of seasonal reproductive and moulting behaviour on catch rates (e.g. Skinner and Hill, 1986; 1987) appears to be reflected in our data (Figure 4.4 and Table 4.2).
Coincidently, similar seasonal trends were apparent in τ7 and BBLT. Most notably, catch rates were highest during the period of seasonal upwelling in austral spring and declined as BBLT warmed and downwelling-favourable winds began to dominate. The exception to these trends occurred in December (immediately following the fishery closure) and may be related to an increased abundance (and availability) of spanner crab after an intermittent period of no fishing effort. The increase in catch rates following the onset of seasonal, upwelling-favourable alongshore winds may be a useful guide for commercial fishermen and assist with the transition to more ecologically-based stock assessments. Additionally, off-season (autumn-winter) episodic BBLT cooling, occasionally attributed to wind-driven upwelling, can be useful in helping explain short- term fluctuations in catch rates.
These results raise questions as to why spanner crabs are responding favourably to cooler BBLT and upwelling favourable winds. A possibility may be that there is a
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spurious relationship between seasonal changes in BBLT (as seen in the field study), due to wind-driven upwelling and downwelling, and seasonal changes in catch rates as a result of increased feeding during mating season (spring) and decreased feeding during their post-moult period (starting at the end of summer). However, this possibility lacks explanation as to why episodic BBLT and wind-driven upwelling increases catch rates during austral autumn and winter. Alternatively, there may be a phenological relationship between spanner crab movements and changes in BBLT, whereby seasonal upwelling and downwelling is used as a biological or ecological “cue”.
Crustaceans have been observed to use multiple environmental cues for foraging
(Zimmer-Faust, 1987), migration (Rebach, 1981) and reproduction (Skov et al., 2005).
For example, hermit crabs have been observed to migrate when water temperatures fall below 10oC (Rebach, 1981). Temperature can also affect the timing of seasonal reproductive behaviour for some crab species, as seen with fiddler crabs (Uca terpsichores) where courtship occurred earlier at locations where the sediment temperature declines seasonally than at locations where temperatures remained elevated throughout the year (Kerr et al., 2014). Spanner crabs may exhibit similar behavioural traits by using cooler BBLT and upwelling as a cue for seasonal mating. While upwelling and catch rates were consistently higher during austral spring, spanner crab mating activity may be stimulated by off-season episodic BBLT cooling, making them more active in the fishery during autumn and winter. Warming BBLT at the end of summer may encourage spanner crabs to moult, which could benefit both larvae
(Minagawa, 1990) and adult growth and survival (e.g. Green et al., 2014) during these particularly vulnerable periods of their lifecycle. During their post-moult stage, spanner
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crabs can spend up to 52 days buried (Skinner & Hill, 1987), reducing their availability
(and catchability) to the fishery.
4.5 Conclusion
Using two independent datasets, we have shown a significant inversely proportional relationship between BBLT and spanner crab catch rates in the southernmost part of the
SEQ sector of the Australian fishery. This finding appears to be related to seasonal fluctuations in catch rates for both studies and short-term changes in catch rates during autumn and winter months for the SEQ study, where cooler BBLT and upwelling- favourable wind stress significantly improves daily spanner crab catch rates.
Consequently, BBLT should be considered in future stock assessment models. In lieu of
BBLT, τ7 may also be a useful indicator of upwelling, which could help anticipate seasonal and short-term variations in catch rates and improve the overall economics of commercial fishing operations. Future field studies would benefit from understanding the specific mechanisms driving BBLT fluctuations, including a more precise quantification of wind-driven upwelling and downwelling. Our results also emphasize that any study attempting to examine the relationship between catchability and oceanographic parameters must consider the spatiotemporal variability of physical oceanographic processes. Future laboratory studies would benefit from investigations into the spanner crabs’ response to changes in temperature at different times of the year, which could help determine whether the crabs use temperature as a cue for mating and moulting. The Australian spanner crab fishery could benefit greatly from the development of numerical hydrodynamic and biogeochemical models capable of predicting BBLT fluctuations.
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4.6 Acknowledgements
We sincerely thank collaborating fishermen Richard Hamilton and Mark Cheeseman for attaching our small temperature loggers to their nets and assisting with data collection during the 12-month field study. BOM, IMOS, and the Queensland Department of
Environment and Heritage Protection provided all additional oceanographic and meteorological data, and we extend special thanks to the Queensland Department of
Agriculture and Fisheries, particularly Jason McGilvray, for providing and helping to interpret the commercial spanner crab logbook dataset. Christopher J. Brown was also supported by a Discovery Early Career Researcher Award (DE160101207) from the
Australian Research Council.
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CHAPTER 5
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Short-term effects of bottom current speeds on spanner crab catch rates in shelf waters of southern Queensland, Australia
Research Note submitted:
Spencer, D. M., Brown, I. W., Doubell, M. J., Lee, S. Y., and Lemckert, C.
J. Bottom currents affect spanner crab catch rates in southern Queensland,
Australia. Submitted to Marine and Coastal Fisheries
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STATEMENT OF CONTRIBUTION TO CO-AUTHORED PAPER
This chapter is written in manuscript form and includes a co-authored paper. My contribution to the paper involved collation and collection of all relevant data, data processing and analyses, and writing.
(Signed) (Date) 31 / July / 2018
David Spencer (Corresponding author)
(Countersigned) (Date) 1 / August / 2018
Supervisor: Hong Zhang
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5.0 Abstract
During daily fishing operations spanner crab (Ranina ranina) catch rates can fluctuate substantially and the environmental drivers responsible for these fluctuations largely remains unresolved. Earlier research suggests that spanner crab catchability increases with strengthening currents, but uncertainties surround the magnitude of the measured current speeds and consequently their relationship with catch rates. More recent studies have also suggested catch rates may be adversely affected by highly turbid waters. Here we explore the effects of bottom currents and turbidity on spanner crab catch rates in
South East Queensland, Australia using Generalised Additive Models (GAM). Our results indicate that catch rates are highest at mid-range current speeds between ~0.07 and 0.12 m.s-1. Short-term variability in current speeds during soak times did not have a significant effect on catch rates, but after an oceanographic event resulted in stronger current speeds migrating into the bottom boundary layer, catch rate increased substantially between consecutive fishing periods on one of the fishing days. Turbidity concentrations were consistently low throughout the study and close to the upper detection limits of the instrument, and therefore the effects on catch rates were inconclusive. Outcomes from this study suggest current speed could be a useful indicator of catch rates and may be considered in future stock assessment models, enabling a better understanding of spanner crab population densities throughout the
Australian fishery.
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5.1 Introduction
Spanner crabs (Ranina ranina) are large marine brachyurans that inhabit coastal waters throughout the Indo-Pacific (Baylon and Tito, 2012; Thomas et al., 2013). They are of significant economic importance in Australia, with gross landing contributing approximately $5M to the Queensland economy (Campbell et al., 2016). In the
Australian fishery, spanner crabs are mostly caught in coastal waters between depths 30 to 80 m (Dichmont and Brown, 2010) using size- and species selective tangle nets
(Sumpton et al., 1995). Catches are generally taken in daylight hours and, although catch rates can fluctuate substantially during the day, there appears to be no significant difference in catch rates at various times during the diurnal fishing period (e.g. morning versus afternoon; Brown, 1986). It is however widely known that successful fishing using a static fishing apparatus (i.e. baited tangle nets) depends on bait odour plumes to attract target species downstream of the traps (Finelli et al., 2000; Taylor et al., 2013;
Westerberg and Westerberg, 2011). Consequently, bottom currents that help facilitate or disrupt the transport of bait scent plumes could explain a significant amount of variability in spanner crab catch rates.
In a study of the spanner crab fishery in northern New South Wales, Craig and Kennelly
(1991) found a positive linear relationship between bottom current speed and catch rates, suggesting the effective fishing area of the traps is proportional to the local current speeds. In contrast, foraging efficiency of green crabs (Carcinus maenas) decreased in stronger currents ranging from ~0.23 to 0.58 m.s-1 (Robinson et al., 2011).
Additionally, current speeds measure by Craig and Kennelly (1991) were in excess of
1.5 ms-1, with maximum speeds reaching up to 6 m.s-1, which are usually strong for this area. More recent studies have shown that similar shelf habitats in South East
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Queensland are typically characterised by maximum bottom and near-bottom (~ 3 m above the seabed) current speeds ranging between 0.15 m.s-1 (Hill and Wassenberg,
1999) and 0.27 m.s-1 (Spencer et al., 2017), which is merely ~3% of the maxima reported earlier by Craig and Kennelly (1991). Consequently, the effect of current speed on spanner crab catch rates deserve further attention, as there may be a point at which stronger current speeds start to reduce the foraging activity and, therefore, the catchability of spanner crabs.
In more recent studies, turbidity has also been found to influence spanner crab catch rates. Brown (2012) reported much lower catch rates of spanner crabs in highly turbid waters off Jumpinpin Bar (Figure 5.1) than in clearer waters off South Passage Bar
(27.37o S, 153.43o E) in South East Queensland. These findings corroborates the view of some commercial crab fishermen who believe that spanner crab catch rates are adversely affected by highly turbid waters (Spencer et al., 2017). Additionally, previous studies have linked wave exposure to a decrease in spanner crab catchability (Brown et al., 2008; Thomas et al., 2013). This may be a result of wave-induced sediment resuspension causing highly turbid environments (Spencer et al., 2017) or a disruption of the bait plume through near-seabed turbulence (Brown et al., 2008).
The effects of changes in current and turbidity patterns over shorter temporal scales
(minutes to hours) on the catchability of spanner crabs also remains unknown.
Commercial fishermen allow a minimum soak time (time nets are in the water) of ~30 minutes for crabs to arrive at the baited tangle nets and a maximum time of 2-3 hours to avoid trap saturation and minimise the risk of predation (Brown et al., 2001; Dichmont and Brown, 2010; Thomas et al., 2013). When they detect the bait odour plume crabs
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emerge from the substrate and move upstream towards the baited traps (Skinner and
Hill, 1987; Hill and Wassenberg, 1999). Hence, fluctuations in current speeds, direction and turbidity may impact the crabs’ ability to track the bait and ultimately affect catch rates.
In this paper we investigate the effects of mean current speed and turbidity on spanner crab catch rates and determine whether catch rates are influenced by specific (mean) current directions and/or tidal phases. We also examine the variability (standard deviation) in near-bottom measures of current speed, direction and turbidity during soak times and the associated effects on catch rates.
5.2 Materials and Methods
5.2.1 Study region
Crab catch, effort and oceanographic data were collected from a 10 m commercial spanner crab vessel operating off the Gold Coast, Queensland, Australia. Fishing locations were chosen by the vessel’s skipper, which extended from South Stradbroke
Island south to the Queensland-New South Wales border. Fishing took place approximately 12-20 km offshore at depths between 50 and 80 m (Figure 5.1). Within this area, the shelf seafloor consists mainly of sandy sediment of medium-fine grain size with patches of hard substrate including rocky reefs (Marshall, 1980, Richmond and
Stevens, 2014). Fishing was conducted at least 1-2 km from reef to avoid snagging the fishing gear and because spanner crabs are typically found on flat sandy substrates
(Brown et al., 2008).
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Figure 5.1 – The locations of the oceanographic mooring (flag symbols) and fishing locations (dotted circles) off the Gold Coast during the 10 fishing days between June 2015 and September 2016. The orange area encloses the 50-80 depth range where spanner crabbing is typically carried out in this region.
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5.2.2 Mooring materials and design
A light-weight, compact mooring (Figure 5.2) was designed for short-term deployment and recovery from the crabbing vessel. A polyform teardrop “A Series” buoy with an attached pole-mounted flag was used as the surface marker. This was attached by a length of 12 mm polypropylene mooring line, approximately 1.5x the depth of the water, to a marine grade stainless steel frame with a 20 kg weight bolted to its base.
Oceanographic data were collected using a Nortek VectorTM Acoustic Doppler
Velocimeter (ADV) and a Nortek AquadoppTM Acoustic Doppler Current Profiler
(ADCP) attached near the base of the mooring line. The upward-facing ADCP was attached to the mooring line about 1.5 m above the seafloor and suspended in the water column from a 0.3 m polystyrene float placed 10 m above the sensor head. The ADV was placed inside the steel frame anchored to the seabed, with the flexible sensor head facing upward and fixed above the frame to collect unobstructed, near-bottom current velocity data.
5.2.3 Data collection and processing
5.2.3.1 Crab Catch and Effort Data
Field studies were carried out opportunistically when the fishing days, locations and times (chosen by the vessel’s skipper) were appropriate. Catch and effort data were derived from ten fishing days during the periods June-October 2015 and August-
September 2016. On arrival at the chosen fishing site, the instrument mooring was deployed, and fishing operations were conducted within a 2 km radius of the mooring.
Spanner crabs were caught using industry-standard tangle nets comprising a single layer of
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Figure 5.2 – Oceanographic mooring showing arrangement of instrumentation: marine grade stainless steel frame (1) with a 20 kg weight (2); Nortek ADV (3); Nortek ADCP (4); 12” polystyrene float (5); and “A series” polyform teardrop buoy (6).
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~50 mm mesh net made of 2 mm diameter monofilament nylon tightly strung across flat rectangular frames (e.g. Hill and Wassenberg, 1999). Clipped to the centre of each net were mesh bait holders filled with pilchards before each deployment. Due to the small aperture of the net mesh, the crabs’ limbs become entangled as they move towards the bait. Fifteen nets spaced approximately 60 m apart were attached to each of three groundlines and were deployed on the seafloor across the shelf in an east-west direction.
After 2-3 hours the first groundline was winched back onboard, legal size crabs were retained and undersized crabs (<10 cm rostral carapace length) immediately discarded.
The groundline was then redeployed at a second location within 2 km of the mooring.
This process was repeated until all groundlines had been retrieved and reset. The fishing period is defined as the time elapsed between setting the first and retrieving the last of the groundlines.
Catch per unit effort (CPUE) was calculated as the total retained weight of legal-sized crabs divided by the total number of net hours:
푡표푡푎푙 푟푒푡푎𝑖푛푒푑 푤푒𝑖푔ℎ푡 퐶푃푈퐸 = (5.1) 푡표푡푎푙 푛푒푡 ℎ표푢푟푠 where total retained weight was estimated from the number of bins filled (or partly filled) after each fishing period (1 full bin weighing approximately 22 kg), and total net hours were the product of the total number of net lifts and the sum of soak times (hr) in each fishing period (Brown et al., 2008).
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5.2.3.2 Oceanographic Data
The ADV and ADCP instruments each collected 1 Hz current velocity data, sampling 1 min averages at 5 min intervals. Raw data reported on the Cartesian coordinate system
(XYZ) were cleaned by removing observations with a signal-to-noise ratio less than 5 dB or signal correlation values less than 60% (Chanson, 2008). Cleaned ADCP data were then converted to earth referenced units (ENU) using Nortek StormTM software
(www.nortek.no). The ADCP measured current velocity in 5 m bins of the 40 m water column above the sensor head.
From the ADV data, single point horizontal speed (ms-1) and direction (degrees) were derived near the seabed as follows:
퐶푢푟푟푒푛푡 푠푝푒푒푑 = √푥2 + 푦2 (5.2) where x and y are the east-west and north-south components, respectively. Current direction (direction of travel) was then calculated using:
퐶푢푟푟푒푛푡 푑푖푟푒푐푡푖표푛 = 푀푂퐷(푎푟푐푡푎푛2(푥, 푦) × 180⁄휋 , 360°) (5.3)
The modulo inverse tangent was used to avoid negative degrees and correct for true current direction (www.nortek.no). The ADV’s pressure sensor provided depth data, allowing changes in tidal phase to be monitored throughout the observation period. As neither instrument was equipped with a turbidity meter, a proxy for turbidity was provided by the ADV signal amplitude which measured the intensity of the received reflections (backscatter) of suspended particulate matter across the three orthogonal beams (Lohrmann, 2001). The ADV data was used to analyse the effects on CPUE by
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calculating the mean current speed and direction, tidal phase, and turbidity for the duration of each fishing period. Standard deviation was used as an additional statistical metric to describe the fluctuating behaviour of the logged (5-minute intervals) current speed, direction and turbidity data.
5.2.4 Statistical analyses
The effects of each environmental parameter on CPUE was analysed using Generalised
Additive Models (GAM; Wood, 2017). Thin plate regression splines with a predefined number of knots (k = 4) were used to model the effects of mean and standard deviation current speed, direction and turbidity on catch rates. Tidal phase (ebb, flood, or slack tide) was modelled as a parametric (categorical) term (Wood, 2003; Zuur et al., 2009;
Wood, 2017). Seasonal effects on catch rates (e.g. Brown, 1986; Skinner and Hill, 1986;
1987) were also modelled using cubic regression but limited to one segment of regression (knots = 2) to avoid overfitting between only two seasons (winter-spring).
Several family and link functions were tested but the best method to ensure GAM assumptions were met was to normalise CPUE via natural logarithm and model the transformed CPUE with Gaussian errors (Brown et al., 2008; Zuur et al., 2009).
An initial GAM incorporated all statistical metrics (i.e. mean and standard deviation) used to measure the effect of current speed, direction, tidal phase and turbidity on catch rates. P-value, F-value and estimated degrees of freedom (edf) statistics were used to assess the effect of each explanatory variable. Redundant explanatory variables were removed by stepwise elimination of the least significant variable (highest p-value) until all were significant in the final GAM (Feekings et al., 2015). Explained deviance provided an indication of how much variability was explained by the final GAM. All
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GAMs were generated in R statistical environment using the package “mgcv” (R
Development Core Team, 2018; Wood, 2012).
5.3 Results
Twenty-six fishing periods were recorded over ten fishing days between June 2015 and
September 2016 (Table 5.1). CPUE ranged from 0.13 to 1.83 kg/net hour. Mean near- bottom current speeds measured by the ADV ranged from 0.03 to 0.21 ms-1 (0.06 – 0.41 kt). Average turbidity (backscatter) from the ADV ranged between 50-80 counts, which was in the very low range of the instrument (e.g. Ozturk et al., 2016; Chanson et al.,
2008; Schulz et al., 2016) and close to the noise floor (40-50 counts). Mean current direction and tidal phase recorded during each fishing period varied across all ten fishing days. In only two fishing periods was there a high amount of variability (as estimated by the standard deviation) in current direction (84 and 91o) and turbidity (7 and 8 counts) compared to the other 24 fishing periods (Table 5.1). Current speed was more variable than the other environmental parameters, with one particular fishing period fluctuating by 0.16 m.s-1. Due to the very low counts of suspended particles (as our proxy for turbidity) during all fishing periods, turbidity was assumed to be an unreliable indicator of catch rates in this study and, therefore, excluded from further analyses.
In the initial GAM, month, tidal phase and the average and standard deviation of current speed and direction were all considered as possible significant indicators of catch rates.
However, stepwise elimination resulted in all environmental variables being excluded from the final model except for average current speed (Table 5.2).
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Table 5.1 – CPUE and oceanographic data for all fishing periods carried out over the 10 fishing days.
Mean Standard deviation Fishing Fishing Current Current Current Current Tidal Depth Turbidity Turbidity CPUE date period speed direction speed direction phase
06:25-08:45 0.11 54 215 0.025 1 9 Slack 0.54 3 JUN 15 72 08:30-10:35 0.08 54 225 0.006 0 24 Ebb 0.27
07:00-09:20 0.17 70 120 0.008 0 4 Flood 0.28
24 JUN 15 09:05-11:30 83 0.21 71 120 0.023 1 7 Flood 0.49
11:15-13:30 0.21 70 110 0.015 1 3 Flood 0.36
07:45-09:45 0.07 50 220 0.012 2 23 Slack 0.18 31 AUG 15 08:00-11:00 67 0.06 50 245 Ebb 0.13 0.018 4 32 11:15-14:45 0.08 52 170 0.041 2 91 Ebb 0.72
06:00-8:05 0.15 59 55 0.014 1 2 Ebb 0.88
7 OCT 15 07:50-10:20 52 0.15 62 55 0.017 7 1 Slack 0.7
10:00-11:50 0.14 57 60 0.019 1 2 Flood 1.02
06:45-09:00 0.14 58 50 0.013 1 2 Flood 0.96 15 OCT 15 58 08:45-10:50 0.13 58 60 0.015 2 3 Ebb 0.56
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06:40-09:00 0.07 72 50 0.024 2 16 Flood 1.29 30 OCT 15 52 09:30-11:30 0.08 74 60 0.014 2 19 Ebb 1.83
06:45-09:00 0.04 61 280 0.008 1 11 Slack 0.43
26 AUG 16 08:50-11:00 69 0.09 64 250 0.029 2 7 Flood 0.76
10:45-13:55 0.10 61 275 0.020 1 21 Flood 0.75
05:45-08:30 0.09 65 95 0.006 0 4 Ebb 0.8 30 AUG 16 76 08:15-11:30 0.09 65 105 0.009 0 7 Ebb 0.83
06:15-08:45 0.11 64 315 0.020 1 5 Ebb 0.59
13 SEP 16 08:30-11:30 76 0.09 66 320 0.012 1 6 Ebb 0.57
11:00-12:30 0.10 68 310 0.011 1 3 Flood 0.98
05:30-08:00 0.04 70 235 0.013 8 17 Flood 0.2
20 SEP 16 07:45-11:00 50 0.04 75 115 0.010 3 84 Flood 0.26
10:45-13:30 0.06 73 85 0.017 3 39 Ebb 0.49
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Table 5.2 – Effects of current speed and month on spanner crab catch rates in the final GAM
Smooth term edf+ F ED (%)
Current speed (mean) 2.8 5.0**
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Month 1 11.8**
Significance levels: *** < 0.001, ** < 0.01, * < 0.05 + Estimated degrees of freedom
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The estimated effects of mean current speed and month on catch rates (CPUE) are illustrated by the smoothing splines shown in Figure 5.3 (Mara and Wood, 2011; Wood,
2017). When CPUE was low, mean current speeds were also low (negative linear predictor values). As current speed increased, catch rates increased until reaching approximately 0.1 m.s-1 (positive linear predictor values). As current speeds exceeded
~0.12 m.s-1, CPUE showed a slight decrease before reaching a semi-plateaus trend. At current speeds > 0.15 m.s-1, the effect on CPUE was relatively neutral (as indicated by the 95% confidence bands centred around 0), with a higher amount of uncertainty when current speeds exceeded 0.2 m.s-1. Consequently, highest catch rates occurred when current speeds ranged between approximately 0.07 and 0.12 m.s-1 (Figure 5.3). As expected from previous studies (e.g. Chapter 4), month also had a significant effect on catch rates (Table 5.2), with catch rates increasing from winter to spring (Figure 5.3).
The final model containing current speed and month had a sizeable explained deviance of 58% (Table 5.2).
While changes in current speed and direction (i.e. standard deviation) during soak times were not significantly correlated with catch rates, a substantial increase in catch rate (~
+400%) was observed on one particular fishing day (31 August 2015). This anomalous result may have been due to the stronger current travelling 30 m above the seafloor
(Figure 5.4). This current was observed to migrate into the bottom boundary layer
(BBL), whereupon it began slowing down over a 1.5 hour period. This resulted in the most substantial change in CPUE between any of the pairs of fishing periods. The vertical distribution of current speeds on the other fishing days (when the ADCP was equipped to the mooring) showed no other sudden migration of currents travelling above the seabed (top 35 m) into the BBL (bottom 5 m, Appendix 5A).
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Figure 5.3 – The effects current speed and month have on catch rates of spanner crabs across all fishing days. Results are reported on the scale of the linear predictor. Data points are the residuals estimated by the GAM, curved line indicates the fit of the regression spline (centred for model identifiability), shaded areas are the 95% confidence bands of the model, and vertical lines along the x-axis inside each graph are the values of the explanatory variables.
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0.2
) 0.16
1 - 0.12 0.08
ADV (m.s ADV 0.04 0 7:45 12:00 Speed (m/s) 40 0.30 CPUE (kg/net lift) Period 3 0.7 0.25
30 0.6 0.20 0.5 20 0.15 0.4
Distance (m) Distance 0.3 0.10 10 Period 1 0.2 0.05
Period 2 0.1
9:00 12:00 15:00 Time
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Figure 5.4 – Timeseries of ADV (top) and ADCP (bottom) data on 31 August 2015. Gaps in ADV timeseries are from removal of erroneous data. ADCP profile is shown as a function of current speed above the sensor head (~ 2.5 m above seabed). The placement of Period (#) denotes the mean CPUE (2nd y-axis) and soak time (length of line relative to x-axis) from each fishing period. Each row of rectangles represents the current speed averaged over 5 m bins of the 40 m profile every 5 minutes.
5.4 Discussion
Analyses from the present study showed that neither current direction nor tidal phase significantly affected catch rates. Compared to other studies where turbidity counts were much higher – e.g. 140 and 230 counts in the Wadden Sea (Schulz et al., 2016) – the very low range of turbidity concentrations observed in this study make it difficult to draw any conclusions regarding the effects of high turbidity concentrations on spanner crab CPUE. However, current speed was a significant environmental driver of catch rates in our study.
We observed a non-linear relationship between mean current speed and catch rates, which highlighted a possible optimal current speed range (~0.07–0.12 m.s-1) where the catchability of spanner crabs is maximised. The observed range of current speeds have been described in other studies as having different plume structures (Zimmer-Faust et al., 1995), which may affect the efficiency of the attracting bait plume. The study conducted by Zimmer-Faust et. al. (1995) found that weak (0.04 ms-1), intermediate (0.1 ms-1), and strong currents (0.25 ms-1) currents generated plumes that had different advective and dispersion properties. Plumes generated by the intermediate current speeds appeared to be a satisfactory balance between advection and dispersion in the immediate surroundings and at a reasonable distance from the baited net (Zimmer-Faust et al., 1995). The optimal current speed range observed in our study fell around 0.1 m.s-
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1, and therefore could indicate the optimal current speeds create an “optimal plume structure” to attract spanner crabs downstream of the nets.
Furthermore, the observed range of current speeds in this study agreed well with more recent studies (e.g. Hill and Wassenberg, 1999; Spencer et al., 2017) in the South East
Queensland region, but were one to two orders of magnitude lower than those reported by Craig and Kennelly (1991). This difference in current speed measurements is possibly attributable to the current meter used in the earlier study and, consequently, their observed relationship between current speed and catch rates remains open to question (Spencer et al., 2017).
The effect of current speed variability (i.e. standard deviation) during soak times was not significant in the final GAM. However, there was a substantial increase in catch rates on 31 August 2015 (Figure 5.4) following a sudden increase in current speed. The effect on catch rates may be associated with a periodic amplification of the current, transporting the bait plume further distances and attracting more crabs further from the nets. It was, however, difficult to confidently relate this event to a change in catch rate.
For example, our collaborating commercial fishermen typically target fishing grounds where they believe spanner crabs are numerous, and if limited numbers were hauled up, we would move some distance (up to 2 km) before the next fishing period. Thus, the enhanced CPUE on this day (Figure 5.4) may not be due to the sudden change in current speed but simply to the fact we moved to a location that (possibly) had a larger population of spanner crabs.
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5.4.1 Recommendations for future work
Spanner crab surveys conducted in May each year as part of Queensland’s fishery- independent surveys (O’Neill et al., 2010; Campbell et al., 2016) employ similar fishing gear to that used in this work. However, survey locations are chosen using a stratified random approach to help remove bias associated with fishing hotspots that commercial fishers regularly target. This sort of sampling design should be considered in future experiments. Due to financial constraints, the experimental design of our work was limited as a result of relying on a commercial spanner crab vessel and lacked spatial replication. Had the sampling design been more flexible, the number of net lifts and soak time would have been consistent throughout the study and fishing locations would have remained within 200 m of the oceanographic mooring.
Nonetheless, some interesting findings were produced in relation to the effects of current speed on catch rates of spanner crabs, where an optimal range of current speeds were correlated with higher catch. Further investigations using underwater video apparatus attached to baited tangle nets (e.g. Hill and Wassenberg, 1999; Brown 2012), in conjunction with oceanographic mooring data, would allow these oceanographic influences to be more accurately related to spanner crab movements. Given a more complete understanding of these effects, current speed (and potentially turbidity) might be a useful parameter to help improve future stock assessment models. In particular, these data may help identify possible reasons for inexplicable anomalies in apparent population densities – a recurring issue with stock assessment model outputs.
Additionally, commercial fishermen may be able to use this information for strategic fishing to improve the economic efficiency of their daily operations.
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5.5 Acknowledgements
The work in the chapter is dedicated to Richard Hamilton and Mark “Mondo”
Cheeseman for the invaluable assistance with data collection and mooring deployments, including fishing days devoted to “trial and error” during the design stages of the mooring. Chuen Lo and Geoff Turner at the Griffith School of Engineering were also incredibly helpful in assisting with mooring design, particularly advising on the various materials used.
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Appendix 5A – ADCP profiles on six of the other nine fishing days (ADCP was not equiped in June [days 1 and 2] and the instrument failed to record on 15 October 2015 [day 5]). Note the scale of the colourbar (in m.s-1) is different for 30 Oct 2015 to better illustrate stronger current patterns in the 40 m water column. Speed (m/s)
40 0.30 Day 4 – 7 Oct 2015 0.25
30 0.20
20 0.15
0.10 Distance (m) Distance 10 0.05
7:00 9:00 11:00 Time Speed (m/s)
40 0.40 Day 6 – 30 Oct 2015
30 0.30
20 0.20
Distance (m) Distance 0.10 10
0.05
7:00 9:00 11:00
Time
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Speed (m/s) Speed (m/s) 40 40 0.300.40 Day 7 – 26 Aug 2016 30 0.250.30 30 0.20 20 0.20 20 0.15
Distance (m) Distance 0.100.10 Distance (m) Distance 10 10 0.050.05
6:00 9:00 12:00
TimeTime
Speed (m/s)
40 0.30 Day 8 – 30 Aug 2016 0.25
30 0.20
20 0.15
0.10 Distance (m) Distance 10 0.05
7:00 9:00 11:00 Time
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Speed (m/s)
40 0.30 Day 9 –13 Sep 2016 0.25
30 0.20
20 0.15
0.10 Distance (m) Distance 10 0.05
9:00 12:00 Time Speed (m/s)
40 0.30 Day 10 –20 Sep 2016 0.25
30 0.20
20 0.15
0.10 Distance (m) Distance 10 0.05
6:00 9:00 12:00 Time
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CHAPTER 6
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General Discussion and Conclusion
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6.1 Overview
Understanding the effects of environmental and oceanographic drivers on species’ catchability is a challenging but necessary task in order to maximise the productivity and sustainability of global fishery stocks (Hofmann and Powell, 1998; Punt et al.,
2013; Green et al., 2014). Work presented in this thesis includes a literature review
(CHAPTER 2) published in Fisheries Research that highlighted conflicting results across various studies and the need to better understand how large-mesoscale oceanographic processes may affect these results over space and time (Spencer et al.,
2017). Furthermore, this thesis demonstrates the effects of oceanographic drivers across large (several years and distances of 100-500 km, CHAPTER 3) moderate (inter- and intra-seasonally at ~100 km, CHAPTER 4) and small (hours to days and distances <
100 km, CHAPTER 5) spatiotemporal scales. Overall, my results highlight important interactions between oceanographic parameters associated with various oceanographic features and processes across a range of spatiotemporal scales, the behaviour of spanner crabs, and how these interactions influence their catchability.
6.2 Synthesis of key findings
6.2.1 Relating oceanographic drivers to catchability of spanner crabs
In Chapter 2, past studies investigating the effects of oceanographic parameters on catchability were reviewed which revealed several inconsistencies within the literature
(Spencer et al., 2017). In particular, two studies that employed discrete sampling methodologies found different effects of temperature on catch rates (Skinner and Hill,
1986; Brown et al., 2008), and catch rates were found to increase with unusually large current speeds that were measured using a custom designed current meter (Craig and
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Kennelly, 1991). To help elucidate the uncertainties surrounding these findings, additional exploratory studies were carried out in Chapter 2. This involved collating and analysing a wide range of in situ oceanographic data from a variety of measurement platforms; including a Waverider buoy, ocean mooring, CTD profiles, and remote sensing information. These results helped reveal two major considerations: 1) the underlying need for robust methodologies when measuring the effects of different hydrological and hydrodynamic parameters on spanner crab catch rates and 2) the impact various oceanographic processes, which exhibit vast spatiotemporal variability in the Australian fishing areas, could have on these relationships with catch rates.
Subsequent investigations, which are reported in the following chapters of this thesis, began with determining the effects of large-mesoscale oceanographic features on spanner crab catch rates (Chapter 3).
Chapter 3 was aimed at determining how region-specific oceanographic processes associated with mesoscale oceanographic features affected the catchability of spanner crabs across various management regions of the Queensland fishery. Using remote sensing and fishery-independent catch rate information, my results indicate a clear distinction between the effects of nearshore and offshore oceanic processes on spanner crab catch rates. These effects were largely associated with region-specific oceanographic features, which occur as a result of the complex oceanography and topography throughout the East Coast continental shelf region (Suthers et al. 2011;
Weeks et al., 2015; Brieva et al., 2015; Ribbe and Brieva, 2016; Ismail et al., 2017). At fishing grounds closer to the coast and bays, a reduction in catch was associated with higher surface chlorophyll-a concentrations (relative to the observed range in each management region) and, at numerous locations across the fishery, higher catch rates
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were correlated with processes responsible the presence of offshore oceanic waters.
Overall, remotely-sensed oceanographic parameters used to describe variability in catch rates were highly dependent on region-specific oceanographic features. However, a reoccurring feature across various parts of the fishery was the correlation between higher catch rates and the offshore transport of surface waters, suggesting upwelling may be an important mechanism enhancing spanner crab catch rates.
Given the limitations of remote sensing, which detect oceanographic features at the sea surface, it was difficult to quantify oceanic intrusions near the seabed. In Chapter 4, in situ data acquired within a smaller region of the Queensland fishery (~100 km) found cooler bottom boundary layer temperature (BBLT) and upwelling-favourable alongshore wind stress can increase catch rates, at least in the southernmost part of the
Queensland fishery. This finding supported the possible effect that upwelling may have on catch rates (as reported in Chapter 3) but were contrary to the previously reported finding that warmer BBLT improved commercial catch rates (Brown et al., 2008). The contrast in findings may be explained by large-scale latitudinal gradients in BBLT on the continental shelf (Richaud et al., 2016), as the previous study (Brown et al., 2008) recorded higher catch rates in a more northerly region that experiences warmer BBLT.
Moreover, the study did not use any statistical metrics to account for latitudinal differences in catch rates (as seen in Chapter 3) or BBLT.
Chapter 5 presents new findings that show catch rates increased as a result of an optimal current speed range, rather than the proportional (linear) relationship reported by Craig and Kennelly (1991). Additionally, information gathered from time-series measures of currents throughout the water column and near the seabed show that oceanographic
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features capable of amplifying current speeds in the bottom boundary layer may considerably improve catch rates. Near-bottom current speeds of 0.03-0.21 m.s-1 measured by the Acoustic Doppler Velocimeter (ADV) agree with more recent studies of bottom current speeds (e.g. Hill and Wassenberg, 1999; Spencer et al., 2017), which further substantiated my findings. Attempts to determine the effect of turbidity on catch rates were inconclusive due to the very low range of observed suspended particle concentration, suggesting the need for an alternate sampling design (i.e. additional fishing locations or different equipment used to measure turbidity).
6.2.2 How the spatiotemporal variability of oceanographic drivers can affect spanner crab ecology and their catchability
One of the main thesis objectives was to investigate how region-specific oceanographic features affected the use of oceanographic parameters as indicators of spanner crab catch rates across a range of spatiotemporal scales. In doing so, I demonstrate that the associated effects on catchability may be related to the behaviour of spanner crabs, which is dependent on the scales at which oceanographic parameters/features fluctuate over space and time.
In Chapter 3, my results show that various oceanographic parameters are associated with changes in spanner crab catch rates, but the importance of these parameters is relative to the oceanographic features of each region. For example, in the northernmost part of the fishery the Capricorn Eddy facilitates the transport of oceanic waters into spanner crab survey grounds – a feature that consistently correlated with higher catch rates. As the effects of eddies are known to reach the seabed on the continental shelf
(Amores et al., 2014; Spencer et al., 2017), the Capricorn Eddy also facilitates the
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transport of upwelled waters through the Capricorn Channel (Middleton et al., 1994;
Weeks et al., 2010). Changes in surface chlorophyll-a provide another example of how region-specific coastal and oceanographic features affect the use of oceanographic parameters in different regions of the fishery. For instance, catch rates were lower in some years when chlorophyll-a concentrations were higher, but this effect was only found in areas closer to the coast and bays. Consequently, this may be a result of nearshore processes causing nutrient enrichment (Brieva et al., 2015), boosting primary production and trophic transfers (Brown et al., 2010). Such events nearer the coast may attract sharks, which are known to prey on spanner crabs (Brown et al., 2001), deterring the crabs from foraging or even reducing the numbers of crabs available to the fishery through predation.
Supporting the findings in the previous chapter, results described in Chapter 4 indicate upwelling may enhance the catchability of spanner crabs in the southernmost part of the
Queensland fishery. Here, I show cooler BBLT (indicative of upwelling) and northerly alongshore wind stress (upwelling-favourable) increase catch rates at both seasonal and shorter timescales in a more localised region of the fishery (~ 100 km). Considering these findings are opposite to those reported in Brown et al. (2008), which suggested that warmer ambient temperatures increase the spanner crabs’ metabolic activity, it was important to understand how and why they respond favourably to cooler temperatures instead. Accordingly, I attributed the strong seasonal effect to a phenological relationship between seasonal upwelling and the spanner crabs’ pre-spawning mating period; a behaviour that was similarly observed in fiddler crabs (Uca terpischores) in response to seasonal changes in temperature (Kerr et al., 2014). The pre-spawning mating behaviour triggers spanner crabs to form large aggregations, often in areas
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known to commercial fishermen (Spencer et al., 2017), resulting in higher catch rates around spring (September-November) when bottom temperatures are cooler. Upon further investigation into the effects of BBLT and wind-stress within seasons, spanner crabs were found to be more catchable as a result of episodic BBLT cooling and upwelling-favourable wind stress during austral autumn and winter (when upwelling is less frequent). This suggests that spanner crabs may be stimulated by episodic (off- season) upwelling to search for a mate due to the link between seasonal BBLT cooling and the crabs’ seasonal ecological response to emerge from the sediment (Skinner and
Hill, 1986) and begin mating (Skinner and Hill, 1987), ultimately resulting in an increased likelihood of capture at seasonal and shorter timescales.
In Chapter 5, I found spanner crab catch rates were highest when bottom current speeds were between 0.07-0.12 m.s-1 during fishing periods conducted over 2-3 hours. In accordance with Zimmer-Faust et al. (1995), this may indicate that this range of current speeds generate an optimal plume structure for attracting spanner crabs from downstream (e.g. Hill and Wassenberg, 1999). Additionally, during the 2-3 hour fishing period, my findings suggest oceanographic features responsible for intermittent increases in bottom current speeds over a period of minutes to hours can increase catch rates. This may be related to stronger current speeds periodically enhancing the extent of bait scent plume and therefore stimulating spanner crab foraging further distances from the traps. Development of a high-resolution hydrodynamic model capable of resolving current patterns over minutes to hours at spatial scales less than 100 km should help identify the cause of fluctuations in catch rates and the reasons for anomalies in apparent population densities.
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6.3 Implications for management, smarter fishing practices and future directions
This thesis reviews previously reported effects of oceanographic drivers on the catchability of spanner crabs and explores some of these effects at various spatiotemporal scales. Outcomes from this thesis, which include papers in Fisheries
Research (published) and Fisheries Oceanography (accepted pending second review), highlight that spatiotemporal variability of oceanographic features must be considered when attempting to relate oceanographic parameters to catch rates. Furthermore, my results show that a broad range of environmental variables could be used as an indicator of catch rates across the various management regions of the Queensland fishery.
Specifically, changes in BBLT (e.g. via upwelling) may be a reliable indicator of fluctuations in catch rates both seasonally and at shorter temporal scales within autumn and winter. Chapter 3 further suggests that some parts of the fishery may be more influenced by oceanic waters closer to the shelf break, but other areas may be adversely affected by riverine/estuarine outflow closer to shore as indicated by elevated chlorophyll-a concentrations.
Future studies should consider the collection of in situ bottom boundary layer data when analysing oceanographic effects in crustacean (and other bottom dwelling species) fisheries. Bottom boundary layer data should be collected in other regions of the
Queensland fishery to verify the findings from Chapter 3 and 4 that suggest upwelling is a key driver of spanner crab catchability at both seasonal and shorter temporal scales. A well-designed fishery-independent experiment using an underwater video apparatus attached to the baited tangle nets, coupled with data from a nearby oceanographic mooring (as described in Chapter 5), could also provide a resolution to some of the
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uncertainties surrounding the effects of currents and turbidity on spanner crab catchability.
At this stage, commercial fishermen might benefit from using alongshore wind patterns
(i.e. consistent northerlies over a seven day period) to help determine when the onset of seasonal upwelling (and increased catchability) might occur as well as short bursts of increased catchability during autumn and winter. Provided they can get access to ocean forecast models, they may also benefit from models that can accurately predict near- coast surface chlorophyll-a concentrations and offshore oceanic intrusions. In light of my findings, fishery managers and scientists at the Queensland Department of
Agriculture and Fisheries are discussing ways in which they incorporate the various oceanographic parameters in catch standardisation models (J. McGilrvay and S.
Williams, pers. comm.). By incorporating this information into standardisation and stock assessment models, my findings may also help explain the recent fishery-wide decline in catch rates in Australia as well as support future management strategies directed towards maintaining the productivity and sustainability of the fishery.
132
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