081215 Thesis Merge SS

Usefulness of Macroinvertebrates for In Situ Testing of Water Quality

Louisa Jane Oswald B. Sc. Hons. (Monash)

Institute for Applied Ecology Faculty of Applied Science University of Canberra, ACT 2601 AUSTRALIA

The thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy at the University of Canberra.

October 2008 “One thing to my mind was certain, that to make any laboratory experiments that would compare in any measure to what goes on in a river, it was absolutely necessary that the water experimented upon should be running, and not merely exposed to light and air in a bottle.”

Tidy (1880)

ABSTRACT

For various reasons, existing methods for the assessment of aquatic pollution do not always adequately address the way in which contaminants affect receiving environments and their component ecosystems. The main advantage of biological assessment over the measurements of physical and chemical aspects of water quality is that biota provide an integrated response to all prevailing influences in their environment. Biological assessment protocols have been developed for a range of test organisms, from bacteria to mammals using measurement from molecular biomarkers to indicators at the population or community level of organisation. Macroinvertebrates in particular have been popular for ecological assessment of habitat and water quality because they are small and straight forward to sample and identify using relatively simple and inexpensive equipment and readily available taxonomic keys.

However, various biological assessment techniques also have their limitations. Field-based assessment of biological communities does not provide direct evidence to determine underlying causal relationships, while laboratory or mesocosm toxicity tests are criticised for their limited ability to extrapolate to natural field conditions. To help bridge the gap, this thesis aims to investigate the efficacy of using caged macroinvertebrates in situ to assess the ecological condition of aquatic environments, and whether a causal relationship can be established when macroinvertebrates are deployed in situ at sites known to have impaired water quality. Endpoints employed in this thesis include survival, measurements of morphology (as a surrogate for growth) and condition and, for trials assessing sites that receive mine drainage, the tissue concentration of certain trace metals.

Development of an in situ approach to water quality monitoring and assessment will potentially provide methods for use by resource managers, community groups and aquatic researchers that are less expensive and faster to run than existing methods and will complement other approaches employed in the assessment of water quality.

In situ testing of water quality using macroinvertebrates requires the collection, handling, caging, deployment and retrieval of test organisms at sites of suspected pollutant impact. As such procedural factors may affect test organisms and potentially confound their responses, it is important to consider and understand as many of these factors as possible. Aquatic macroinvertebrates held in finer mesh cages had larger heads than in coarser mesh cages. This was likely due to increased substrate available for growth of epilithon and periphyton on ii which the caged organisms could graze. Caging density had no effect on amphipod mortality over the trial period, however, individuals held at higher densities increased in size (as indicated by longer dorsal lengths) more than those held at lower or intermediate densities. Temporary storage of test organisms in laboratory aquaria may facilitate the collection of abundances required for in situ trials, however, tanked individuals were smaller and had lower biomasses than individuals collected and deployed immediately. While this is likely to result from differences in feeding during the storage period, it is also possible that tank storage and the “double handling” deleteriously affected them, or reduced their tolerance.

The effects of transplanting macroinvertebrates between sites varied considerably depending on the characteristics of “source” and “transplant” sites. Certain taxa suffered marked mortality within 24 hours even at their source site, indicating an adverse effect of the caging itself, or perhaps via the change in food, shelter or microclimate which could potentially render them unsuitable as test organisms in caging studies. Other taxa did not differ in survival or body size when relocated between sites, with some evidence of increased growth at sites dissimilar from their source site. In general, organisms relocated to sites that are “similar” to their source environment performed less well at the transplant site. However, organisms transplanted to “dissimilar” sites were found to be bigger than those caged and deployed back to the source site.

When employed to assess known pollution scenarios in and around Canberra, macroinvertebrate responses were, in some instances, able to be linked to specific environmental parameters or combinations thereof. In Case Study 1, findings varied in relation to the response endpoint being examined, and between test species, although concentrations of metals were significantly higher in tissue of macroinvertebrates deployed at the impact site downstream of the abandoned Captains Flat mine and increased with time exposed. In Case Study 2, freshwater shrimp suffered significant mortality within 24 hours of deployment at the impact sites, with larger individuals more susceptible at sites receiving urban stormwater runoff. While various biological effects were most closely correlated with ammonia concentrations at the site, different body size endpoints were affected in opposite ways. In Case Study 3, body size endpoints for one test organism varied consistently with respect to site and time factors, but none of the changes could be linked to any of the environmental data collected. Response variables for a different test species also indicated significant effects arising from both deployment site and time, however, each endpoint iii responded in a different way to the treatment factors, and aligned with different combinations of environmental data.

In general, linking of macroinvertebrate responses with environmental data was difficult because of the high variability in the environmental data. However, it was further complicated by the mismatch in the level of replication between the two datasets. As a consequence of this, the macroinvertebrate data had to be collapsed to a lower level for comparison with the environmental data, resulting in a loss of natural variability and analytical power. Since only the strongest treatment effects, which could be detected above the background “noise”, were detected and modelled against the environmental data, it is possible that other “cause” and “effect” relationships may have been overlooked.

From these results, it is clear that many macroinvertebrate taxa are suitable for use as bioindicators in in situ trials, but that criteria used for selection of test species should definitely include more than just impact-sensitivity and abundance. However, there are several aspects associated with the experimental set up of field-based protocols involving caged macroinvertebrates that may limit their usefulness as a rapid and reliable bioassessment tool, and need to be considered when designing and undertaking these kinds of trials. It is also apparent that choice of endpoint can greatly influence conclusions, with detection of treatment effects reported in this thesis varying greatly depending on which morphological endpoint was examined.

This study clearly demonstrated that there may be significant difficulties in establishing causal relationships between environmental data and biotic responses of macroinvertebrates deployed under field conditions. However, it has also shown that deployment of caged macroinvertebrates in situ may assist in the determination of biological effects arising from impaired water quality, which can then serve as the basis for more focussed laboratory or mesocosm studies in which environmental conditions can be more readily controlled or monitored. v

Under Section 35 of the Copyright Act of 1968, the author of this thesis is the owner of any copyright subsisting in the work, even though it is unpublished.

Under Section 31(I)(a)(i), copyright includes the exclusive right to “reproduce the work in a material form”. Thus, copyright is protected by a person who, not being the owner of the copyright, reproduces or authorises the reproduction of the work, or of more than a reasonable part of the work, in material form, unless the reproduction is a “fair dealing” with the work “for the purpose of research or study” as further defined under Section 40 and 41 of the Act.

This thesis must therefore be copied or used only under the normal conditions of scholarly fair dealing for the purposes of research, criticism or review, as outlined in the provisions of the Copyright Act 1968. In particular, no results or conclusions should be extracted from it, nor should it be copies of closely paraphrased in whole or in part without the written consent of the author. Proper written acknowledgement should be made for any assistance obtained from this thesis.

Copies of this thesis may be made by a library on behalf of another person provided the officer in charge of the library is satisfied that the copy is being made for the purposes of research or study. vi

ACKNOWLEDGEMENTS

Firstly I would like to challenge those that believe postgraduate research is a solo pursuit…that this document now appears in this form is a testament to “Team PhD” and all those that have participated along the way.

In particular I would like to thank the following:

 My supervisors Professor Richard Norris, Professor Bill Maher and Associate Professor Martin Thoms for their commitment to this project, me personally, and their contributions to the field of aquatic ecology. They have helped me broaden my strengths, but also acknowledge my limitations, and afforded me with much strength to overcome numerous hurdles. YMCA holds special memories for me !  Land and Water Australia (formerly Land and Water Resources Research and Development Corporation) for research funding, and University of Canberra (UC) and the Cooperative Research Centre (CRC) for Freshwater Ecology who supported the research and my candidature in many ways. Special thanks to Divisional Higher Degree Committee members who have been particularly helpful.  All those that braved Canberra’s weather extremes, in particular Angie and Dan, and to Amina and Bron for endless hours spent capturing digital images of over 10,000 mostly uncooperative macroinvertebrates. Thanks also to technical staff from the UC and the CRC for Freshwater Ecology, in particular the analysts associated with the Ecochemistry laboratory.  Ross Cunningham from Centre for Resource and Environmental Studies, Australian National University (ANU), and Christine Donnelly at the ANU’s Statistical Consulting Unit for assistance with statistical analyses.  Paul Sheahan for arranging access to the ever-elusive hydrological records and Jim Longworth for assistance with GIS interfaces.  Australian Government Natural Resource Management Team for their investment in me while completing this thesis, in particular the Monitoring and Evaluation section. vii

There are also numerous people “behind the scenes” who have expended considerable energy coaxing me through the most difficult times, and whose efforts have greatly lightened the load.

I would particularly like to mention:

 Carol and Danielle with whom I share the most incredible camaraderie. I miss you both and look forward to sharing way too many beverages after we all make it up “the hill”.  My family for their dent-less enthusiasm, encouragement and the occasional “home truth” especially in the final throes.  The amazing Pam who helped me work my way through my mysterious inner workings and just let me vent irrationally on occasion !  Linda, David and Mike for your attempts at being interested in bugs, and for not pushing your views on me too hard !  Those friends who have politely avoided asking the “death question”, and also those rude blighters who made it their personal mission to badger me constantly !  Rupert and Abigail for light relief, and for companionship when the rest of the world was asleep.  Peter for his generosity and endless enthusiasm, and for not questioning my irrational mood swings and idiosyncrasies at critical times during my candidature. “I’ve finished now …… do you want to use your computer ?”

Addendum And now that, despite some significant hurdles, the thesis revisions are finally complete, I would like to extend my acknowledgements to the following:

 Two anonymous examiners whose comments and suggestions greatly improved this thesis.  My treatment team whose expertise has helped me on the road to recovery over the last 18 months, especially Debbie Douglas, Anne Davies and Tom Sutton.  My partner Peter for his endurance, and darling baby girl Charlotte Jane for arriving at exactly the right time ! Thankyou for reminding me of the things that matter most ! viii

TABLE OF CONTENTS

Abstract...... i Certificate of Authorship of Thesis...... iv Copyright ...... v Acknowledgements ...... vi Table of Contents ...... viii List of Tables ...... xi List of Figures ...... xv Chapter 1: PREAMBLE...... 1 1.1 Aquatic Pollution ...... 1 1.1.1 Chemical and Physical Monitoring of Aquatic Pollution ...... 1 1.1.2 Use of Biota for Assessing Pollution ...... 2 1.1.3 Rationale for this Thesis...... 12 1.2 Objective...... 13 1.2.1 Overall Aim ...... 13 1.2.2 Structure of the Thesis...... 13 Chapter 2: METHODS AND MATERIALS ...... 15 2.1 Introduction...... 15 2.2 Taxon Selection Criteria...... 15 2.3 Cage Development...... 16 2.4 Experimental Set-up...... 16 2.4.1 Harvesting of Test Organisms ...... 17 2.4.2 Caging ...... 17 2.4.3 Deployment...... 18 2.5 Overall Experimental Design ...... 18 2.6 Site Selection Process...... 20 2.6.1 Laboratory Trials...... 20 2.6.2 Field Trials...... 20 2.7 In Situ Macroinvertebrate Trials ...... 24 2.7.1 Preparation of Sampling Containers ...... 24 2.7.2 Retrieval of Macroinvertebrates on Collection Days...... 25 2.7.3 Assessment of Survival ...... 25 2.8 Sub-Lethal Endpoint Responses ...... 26 2.9 Environmental Site Data...... 28 ix

2.9.1 Field Measurements ...... 28 2.9.2 Laboratory Analysis ...... 29 2.10 Data Analysis...... 31 Chapter 3: STUDY REGION AND TRIAL SITES...... 33 3.1 Upper Murrumbidgee Catchment ...... 33 3.2 Study Sites ...... 33 3.3 Climate ...... 33 Chapter 4: IN THE ABSENCE OF IMPAIRED WATER QUALITY, DOES EXPERIMENTAL SET-UP AFFECT MACROINVERTEBRATE RESPONSES? ...... 39 4.1 Introduction...... 39 4.1.1 Rationale...... 39 4.1.2 Background...... 39 4.1.3 Objective of this Chapter...... 40 4.2 Additional Methods...... 41 4.3 Results ...... 41 4.3.1 Experiment 1: Effect of Caged Deployment on Macroinvertebrates...... 43 4.3.2 Experiment 2: Effect of Temporary Tank Storage on Macroinvertebrates...... 45 4.3.3 Experiment 3: Effect of Stocking Density on Macroinvertebrates...... 48 4.4 Discussion...... 50 4.4.1 Experimental Results...... 50 4.4.2 Conclusion...... 57 Chapter 5: HOW DO MACROINVERTEBRATES RESPOND TO SITE RELOCATION? 59 5.1 Introduction...... 59 5.1.1 Rationale...... 59 5.1.2 Background...... 59 5.1.3 Objective of this Chapter...... 60 5.2 Additional Methods...... 61 5.3 Results ...... 64 5.3.1 Deployment to Sites of a Similar Nature...... 64 5.3.2 Deployment to Sites of a Different Nature...... 68 5.4 Discussion...... 81 5.4.1 Experimental Results...... 81 5.4.2 Conclusion...... 92 x

Chapter 6: HOW DO MACROINVERTEBRATES RATE AS BIOMONITORS WHEN CAGED AND DEPLOYED IN SITU TO ASSESS SITES WITH IMPAIRED WATER QUALITY ? ...... 96 6.1 Introduction...... 96 6.1.1 Rationale...... 96 6.1.2 Background...... 96 6.1.3 Description of Pollution Scenarios ...... 97 6.1.4 Objective of this Chapter...... 100 6.2 Additional Methods...... 101 6.2.1 Methods Common for all Case Study Trials ...... 101 6.2.2 Trace Metal Analysis for Case Study 1...... 102 6.2.3 Comparison of Macroinvertebrate Responses with Environmental Variables108 6.3 Results ...... 109 6.3.1 Case Study 1: Acid Mine Drainage...... 109 6.3.2 Case Study 2: Urban Stormwater Runoff...... 143 6.3.3 Case Study 3: Persistently High Turbidity ...... 151 6.4 Discussion...... 162 6.4.1 Case Study 1 – Mine Drainage ...... 162 6.4.2 Case Study 2 – Urban Stormwater Runoff...... 171 6.4.3 Case Study 3 – Persistently High Turbidity ...... 178 6.4.4 Conclusion...... 182 Chapter 7: SYNTHESIS ...... 184 7.1 Conclusions...... 194 REFERENCES ...... 196 Appendix 3 ...... 239 Appendix 4 ...... 245 Appendix 5 ...... 253 Appendix 6 ...... 275 xi

LIST OF TABLES

Table 2.1: Predictor variables used by the AUSRIVAS ACT spring riffle predictive model to classify sites used in this study...... 22

Table 2.2: Predictor variables used by the AUSRIVAS ACT spring edge predictive model to classify sites used in this study...... 22

Table 2.3: Predictor variables used the AUSRIVAS ACT autumn edge predictive model to classify sites used in this study...... 23

Table 2.4: Operating conditions for the Perkin-Elmer Elan 6000 ICP-MS...... 31

Table 3.1: Details for sites employed in this study...... 35

Table 4.1: Experimental detail of the three separate trials examining macroinvertebrate response to aspects of experimental set-up...... 42

Table 4.2: Paired t-test comparing body size at deployment on trial day 0 of Paratya australiensis for wild and “tanked” treatments in Experiment 2...... 46

Table 4.3: Analysis of Variance table for regression between average wet weight and stocking density within deployment cages during Experiment 3...... 50

Table 4.4: Results of probability tests for fixed factors affecting macroinvertebrate morphology in experimental logistics trials...... 51

Table 5.1: Experimental detail of the trials examining macroinvertebrate response to relocation. Part A: Relocation of test organisms between sites of a similar nature...... 62

Table 5.2: Experimental detail of the trials examining macroinvertebrate response to relocation. Part B: Relocation of test organisms between sites that are dissimilar in nature...... 63

Table 5.3: Results of probability tests for fixed factors affecting macroinvertebrate morphology in relocation trials...... 82

Table 5.4: Morphological measurements for Paratya australiensis retrieved from source and transplant sites in Experiment 5...... 86

Table 5.5: Mean values for macroinvertebrate response variables found to differ between sites and/or site types when deployed between source and dissimilar transplant sites...... 93

Table 6.1: Experimental detail of the trials used to test the usefulness of macroinvertebrates for in situ testing of water quality – Case Study 1 – Mine Drainage...... 103 xii

Table 6.2: Experimental detail of the trials used to test the usefulness of macroinvertebrates for in situ testing of water quality – Case Study 2 – Urban Stormwater Runoff...... 104

Table 6.3: Experimental detail of the trials used to test the usefulness of macroinvertebrates for in situ testing of water quality – Case Study 3 – Persistently High Turbidity...... 105

Table 6.4: Operating conditions for the Perkin-Elmer Elan 6000 ICP-MS...... 106

Table 6.5: Metal concentrations in the tissue of Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 115

Table 6.6: Analysis of Variance table for regression between trial day and log10-transformed metal concentrations in the tissue of Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 116

Table 6.7: Metal concentrations in the tissue of Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 120

Table 6.8: Analysis of Variance table for regression between trial day and log10-transformed metal concentrations in the tissue of Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 121

Table 6.9: Mixed model significance tests for body size endpoints measured for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 128

Table 6.10: Mixed model significance tests for mean head width and log10- transformed freeze dried weight for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 128

Table 6.11: Metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 132

Table 6.12: Analysis of Variance table for regression between trial day, season and log10-transformed metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 133

Table 6.13: Analysis of Variance table for simplified regression between trial day and log10-transformed metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 137 xiii

Table 6.14: Details of regression equations for log10-transformed metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 138

Table 6.15: Correlation co-efficients for linear regressions between aqueous metal concentrations and bioaccumulated metal concentrations in the tissue of Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 142

Table 6.16: Correlation co-efficients for linear regressions between aqueous metal concentrations and bioaccumulated metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 143

Table 6.17: Results of probability tests for interactions between fixed factors affecting Notalina sp. wet weight for Case Study 3 trials...... 157

Table 6.18: Restricted Maximum Likelihood table outlining tests estimating the significance of the variation between environmental measurements collected, and survival of Notalina sp. retrieved after deployment at control and impact sites during in situ assessment trials for Case Study 3...... 159

Table 6.19: Analysis of Variance for regression between survival of Notalina sp. retrieved after deployment, and the strongest environmental covariates measured at control and impact sites during in situ assessment trials for Case Study 3...... 160

Table 6.20: Restricted Maximum Likelihood table outlining tests estimating the significance of the variation between environmental measurements collected, and average head width of Notalina sp. retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 3...... 161

Table 6.21: Analysis of Variance for regression between average head width of Notalina sp. retrieved alive after deployment at control and impact sites during during in situ assessment trials for Case Study 1, and the strongest environmental covariates...... 161

Table 6.22: Results of probability tests for fixed factors assessing the effects of mine drainage on various biological endpoints of the macroinvertebrate test species Paratya australiensis, Austrolestes cingulatum and Atalophlebia australis retrieved alive after deployment at field sites during in situ assessment trials for Case Study 1...... 163 xiv

Table 6.23: Relationships between dissolved aqueous metal concentrations and bioaccumulated metal concentrations in the tissue of Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 169

Table 6.24: Results of probability tests for fixed factors assessing the effects of urban stormwater runoff on various biological endpoints for Paratya australiensis retrieved alive after deployment at field sites during in situ assessment trials for Case Study 2...... 173

Table 6.25: Average carapace dimensions and wet weight for Paratya australiensis retrieved alive and dead after deployment at urban impact sites only on each of the first three trial days during in situ assessment trials for Case Study 2...... 176

Table 6.26: Results of probability tests for fixed factors assessing the effects of persistent turbidity on various biological endpoints for Paratya australiensis and Notalina sp. retrieved alive after deployment at field sites during in situ assessment trials for Case Study 3...... 179

Table 6.27: Relationships between Notalina sp. response variables, measured after deployment at control and impact sites during in situ assessment trials for Case Study 3, and the water quality parameters (measured on retrieval day) with which they had the strongest covariance...... 182 xv

LIST OF FIGURES

Figure 2.1: Design of cages used to hold macroinvertebrates for all in situ deployment experiments of this study...... 17

Figure 2.2: Random factors associated with set-up of deployment experiments...... 19

Figure 2.3: Measurements of body size for some of the test organisms deployed in this study...... 27

Figure 3.1: Study area in and around the Australian Capital Territory, Australia...... 34

Figure 3.2: Long term annual rainfall at Canberra (Holt) from 1887 to 2003...... 37

Figure 3.3: Monthly rainfall throughout the study area during A) 1997 and B) 1998...... 38

Figure 4.1: Percent survival of test organisms retrieved after deployment in holding cages during Experiment 1...... 43

Figure 4.2: Mean head width for Austrolestes cingulatum and Atalophlebia australis deployed in cages at their source site for 24 days during Experiment 1...... 44

Figure 4.3: Mean head width for Atalophlebia australis collected on trial day 0 and retrieved alive after deployment at their source site for up to 24 days in holding cages with 500 m or 800 m mesh panels during Experiment 1...... 45

Figure 4.4: (A) Mean carapace length and (B) mean logn-transformed wet weight for Paratya australiensis retrieved alive after deployment at their source site for 10 days during Experiment 2...... 47

Figure 4.5: Mean dorsal length for Austrochiltonia sp. retrieved alive after deployment in cages for 13 days at different densities during Experiment 3...... 49

Figure 5.1: Percent survival of Cheumatopsyche sp.6 retrieved after deployment at source and transplant sites from the same AUSRIVAS site group during Experiment 4...... 65

Figure 5.2: Percent survival of Cheumatopsyche sp.6 retrieved over time after deployment at sites from the same AUSRIVAS site group during Experiment 4...... 65

Figure 5.3: Mean logn-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites from the same AUSRIVAS site group during Experiment 5...... 68 xvi

Figure 5.4: Percent survival of Cherax destructor retrieved after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 6...... 70

Figure 5.5: Percent survival of Cherax destructor retrieved over time after deployment at sites from different AUSRIVAS site groups during Experiment 6...... 70

Figure 5.6: (A) Mean head width and (B) mean logn-transformed wet weight for Austrolestes cingulatum retrieved alive after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 6...... 72

Figure 5.7: Mean head width for Austrolestes cingulatum retrieved alive over time after deployment at sites from different AUSRIVAS site groups during Experiment 6...... 72

Figure 5.8: Mean logn-transformed wet weights for Rhadinosticta simplex retrieved alive over time after deployment at sites from different AUSRIVAS site groups during Experiment 6...... 73

Figure 5.9: Mean head width for Triplectides australicus ciuskus retrieved alive after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 6...... 73

Figure 5.10: Mean carapace length for Cherax destructor retrieved alive after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 6...... 74

Figure 5.11: Percent survival of Notalina sp. retrieved after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7...... 75

Figure 5.12: Percent survival of Cloeon sp. retrieved over time after deployment at sites from different AUSRIVAS site groups during Experiment 7...... 76

Figure 5.13: Percent survival of Paratya australiensis retrieved after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7...... 76

Figure 5.14: Percentage of Paratya australiensis retrieved with broken frontal rostra after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7...... 77

Figure 5.15: Percentage of Cloeon sp. that escaped from holding cages over time after deployment at sites from different AUSRIVAS site groups during Experiment 7...... 78

Figure 5.16: Mean head width for Notalina sp. retrieved alive after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7...... 79 xvii

Figure 5.17: Mean head width for Cloeon sp. retrieved after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7. Error bars indicate one standard deviation...... 80

Figure 5.18: Mean logn-transformed orbital carapace length for Paratya australiensis retrieved alive over time after deployment at sites from different AUSRIVAS site groups during Experiment 7...... 80

Figure 5.19: Percent survival of test organisms deployed for a period of 12 days at their source site and transplant sites of a “similar” nature...... 84

Figure 5.20: Body size endpoints for (A) Archicaulioides sp., (B) Cheumatopsyche sp.6 and (C ) Notalina sp. retrieved alive after deployment at source and transplant sites from the same AUSRIVAS site group...... 85

Figure 5.21: Percent survival of test organisms deployed for a period of 12 days at their source site and transplant sites with “dissimilar” characteristics...... 87

Figure 5.22: Mean mortality, carapace length and wet weight for Cherax destructor retrieved alive after deployment at (A) source and (B) transplant sites from different AUSRIVAS site groups during Experiment 6...... 89

Figure 5.23: Mean head widths, log10-transformed wet weights and survival for Cheumatopsyche sp.6 retrieved alive after deployment at source and transplant sites from the same AUSRIVAS site group in Experiment 4...... 90

Figure 5.24: Mean (A) head width and (B) logn-transformed wet weight for Rhadinosticta simplex retrieved alive after deployment at source and “dissimilar” transplant sites during Experiment 6...... 94

Figure 6.1: Percent survival of Paratya australiensis retrieved after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 109

Figure 6.2: Percentage of Paratya australiensis with dark patches on carapaces when retrieved after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 110

Figure 6.3: Percentage of Paratya australiensis with dark patches on carapaces when retrieved over time after deployment at sites sites during in situ assessment trials for Case Study 1...... 111

Figure 6.4: Mean log10-transformed carapace length for Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 112 xviii

Figure 6.5: Mean log10-transformed orbital carapace length for Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 112

Figure 6.6: Mean log10-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 1...... 113

Figure 6.7: Mean log10-transformed freeze dried weight for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 1...... 114

Figure 6.8: Mean weight measurements for Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 114

Figure 6.9: Average concentration of iron in Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 117

Figure 6.10: Average concentration of iron in Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 1...... 117

Figure 6.11: Average concentration of copper in Paratya australiensis retrieved alive from the source site on trial day 0, and after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 118

Figure 6.12: Average concentration of zinc in Paratya australiensis retrieved alive from the source site on trial day 0, and after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 118

Figure 6.13: Average concentration of zinc in Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 122

Figure 6.14: Average concentration of iron in Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 122

Figure 6.15: Average concentration of iron in Austrolestes cingulatum retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 1...... 123

Figure 6.16: Percent survival of Atalophlebia australis retrieved after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 124 xix

Figure 6.17: Percent survival of Atalophlebia australis retrieved over time after deployment at sites during in situ assessment trials for Case Study 1...... 124

Figure 6.18: Percentage of Atalophlebia australis that escaped from holding cages after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 126

Figure 6.19: Percentage of Atalophlebia australis that escaped from holding cages after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 126

Figure 6.20: Percentage of Atalophlebia australis that molted after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 127

Figure 6.21: Mean head width for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 129

Figure 6.23: Mean head width for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 130

Figure 6.24: Mean log10-transformed freeze dried weight for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 130

Figure 6.25: Average concentration of copper in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 134

Figure 6.26: Average concentration of copper in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 135

Figure 6.27: Average concentration of zinc in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 135

Figure 6.28: Average concentration of zinc in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 136

Figure 6.29: Average concentration of cadmium in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 136 xx

Figure 6.30: Relationship between tissue concentration of zinc and dissolved zinc concentrations from Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 140

Figure 6.31: Relationship between tissue concentration of cadmium and dissolved cadmium concentrations from Paratya australiensis retrieved alive after deployment at the impact site only during in situ assessment trials for Case Study 1...... 140

Figure 6.32: Relationship between tissue concentration of zinc and dissolved zinc concentrations from Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 141

Figure 6.33: Relationship between tissue concentration of lead and dissolved lead concentrations from Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 141

Figure 6.34: Relationship between tissue concentration of iron and dissolved iron concentrations from Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1...... 142

Figure 6.35: Percent survival of Paratya australiensis after deployment at control and urban impact sites during in situ assessment trials for Case Study 2...... 144

Figure 6.36: Percent survival of Paratya australiensis retrieved over time after deployment at sites during in situ assessment trials for Case Study 2...... 144

Figure 6.37: Mean carapace length and mean orbital carapace length for Paratya australiensis retrieved alive after deployment at control and urban impact sites during in situ assessment trials for Case Study 2...... 145

Figure 6.38: Mean carapace length for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 2...... 146

Figure 6.39: Mean logn-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 2...... 147

Figure 6.40: Mean logn-transformed wet weight for Paratya australiensis retrieved alive after deployment at control and urban impact sites from different AUSRIVAS site groups during in situ assessment trials for Case Study 2...... 147 xxi

Figure 6.41: Mean logn-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites for three days during in situ assessment trials for Case Study 2...... 148

Figure 6.42: Mean logn-transformed wet weight for Paratya australiensis retrieved alive after deployment at control and urban impact sites from different AUSRIVAS site groups for three days during in situ assessment trials for Case Study 2...... 149

Figure 6.43: Mean orbital carapace length of Paratya australiensis retrieved alive after deployment at control and urban impact sites with levels of ammmonia above and below the method analytical detection -1 limit (0.5 gL NH3) during in situ assessment trials for Case Study 2...... 150

Figure 6.44: Mean carapace length and mean orbital carapace length for Paratya australiensis retrieved alive after deployment for three days at control and impact sites with levels of ammonia above and below -1 the method detection limit (0.5 gL NH3) during in situ assessment trials for Case Study 2...... 151

Figure 6.45: Mean log10-transformed carapace length for Paratya australiensis retrieved alive after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3...... 152

Figure 6.46: Mean log10-transformed carapace length for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 3...... 153

Figure 6.47: Mean log10-transformed orbital carapace length for Paratya australiensis retrieved alive after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3...... 153

Figure 6.48: Mean log10-transformed orbital carapace length for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 3...... 154

Figure 6.49: Mean log10-transformed wet weight for Paratya australiensis retrieved alive after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3...... 155

Figure 6.50: Mean log10-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 3...... 155

Figure 6.51: Percent survival of Notalina sp. retrieved after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3...... 156 xxii

Figure 6.52: Percentage of Notalina sp. individuals that escaped after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3...... 157

Figure 6.53: Average wet weight of Notalina sp. retrieved alive after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3...... 158

Figure 6.54: Mean head width of Notalina sp. retrieved alive after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3...... 159

Figure 6.56: Relationship between tissue concentration of copper and dissolved copper concentrations from Atalophlebia australis retrieved alive after deployment at the downstream impact site during in situ assessment trials for Case Study 1...... 170

Figure 6.57: Relationship between tissue concentration of zinc and dissolved copper concentrations from Atalophlebia australis retrieved alive after deployment at the downstream impact site during in situ assessment trials for Case Study 1...... 171

Figure 6.58: Percent survival of Paratya australiensis retrieved over time after deployment at sites from different AUSRIVAS site groupings during in situ assessment trials for Case Study 2...... 175

Figure 6.59: Average body size measurements for Paratya australiensis retrieved alive and dead after deployment at urban impact sites during in situ assessment trials for Case Study 2...... 175

Figure 7.1: Mean body size measurements for Paratya australiensis retrieved alive after deployment at unimpacted test sites in two component experiments of this study...... 191 1

Chapter 1: PREAMBLE

Few of the world’s ecosystems remain untouched by human disturbance, and aquatic environments in particular are becoming increasingly subject to a wide range of impacts including pollution. Both direct and indirect effects of contaminant stress adversely affect the structure and function of these ecosystems, and the welfare of those who depend upon them. Existing methods do not adequately address the way in which contaminants affect receiving environments, and have not always proven successful in detecting an impact, its extent or severity. This has lead to an increasing need for the development of techniques that specifically investigate how, and to what degree, ecosystem health is impacted, and investigate the causal linkage between aquatic pollutants and their environmental consequences.

1.1 AQUATIC POLLUTION

Aquatic pollution can be defined as the introduction into the environment of substances or energy liable to cause hazards to human health, harm to living resources and ecological systems, damage to structure or amenity, or interference with legitimate uses of the environment (Holdgate 1979). Such pollutants can arise from point sources, such as discharge from factories or wastewater treatment facilities, or may enter waterways diffusely via catchment drainage and runoff processes (Mason 2002). Examples of diffuse or non-point source pollutants include agricultural chemicals, urban runoff and deposition from the atmosphere (Crathorne et al. 1996).

1.1.1 CHEMICAL AND PHYSICAL MONITORING OF AQUATIC POLLUTION

According to Mason (1996), there are in excess of 1500 potential pollutants discharged to freshwaters, many of which can be quantified, with some level of accuracy, by appropriately skilled and equipped laboratories. Direct chemical analysis of environmental samples allows precise scientific reporting of specified compounds, and facilitates the comparison of environmental concentrations over time and space, and with levels of allowable concentrations (Mason 1996). However, for largely practical reasons, resource managers cannot hope to measure all contaminants potentially present in the aquatic environment (Jeffries and Mills 1994, Mason 1996). 2

While chemical samples analysed as part of a routine surveillance program will indicate the concentration of a given compound at a particular point in time and space, a chemical “snapshot” is unlikely to represent the natural environmental variation and will undoubtedly fail to detect intermittent pollution some of the time (Rosenberg and Resh 1993). Furthermore, quantification of various contaminant concentrations does not take into account bioavailability of the pollutant or any interaction (additive, synergistic or antagonistic) between specific compounds that may comprise a particular source of pollution (Thorp and Lake 1974, Cairns 1984a, Jeffries and Mills 1994, Mason 1996, Lowell et al. 2000). Nor does chemical monitoring provide any information on likely ecosystem responses, or the inherent variability therein, or provide information linking those responses to a given pollutant concentration.

1.1.2 USE OF BIOTA FOR ASSESSING POLLUTION

Biota, on the other hand, may respond to episodic or intermittent pollution events as well as unsuspected or new environmental pollutants (Pascoe and Shazili 1986, Calow 1993). Organisms may even respond to pollutants at concentrations too low to be detected using routine chemical methods (Calow 1993), and where pollutants accumulate in plant or animal tissue, the biological concentrations can give an indication of environmental pollution levels over time (Wang 1987, Mason 1996).

Biological assessment of pollutants includes both laboratory and field techniques (Hellawell 1986, Mason 2002) which provide an evaluation of water quality over time using ecosystem values rather than chemical concentrations or lethal dose statistics. This is achieved by correlating change in any of a range of biologically-relevant endpoints to the severity of the pollution (Jeffries and Mills 1994, Mason 1996). Data produced by biological assessment techniques can also provide information on both structural and functional aspects of ecosystems, encompassing indices across various levels of biological organisation from community indicators to sub-organismal or molecular level measurements (Cairns 1983b, Hellawell 1986, Calow 1989, Calow 1993, Mayfield 1993, Reynoldson and Day 1993, Brock and Budde 1994, Kersting 1994, Urban 1994, Mason 2002, Slijkerman et al. 2004, Camargo and Martinez 2006, Sturm et al. 2007). 3

Conventional Toxicity Tests Considerable data has been collected on the effects of various pollutants on biota, and the nature and mechanism of their toxic activities (Jeffries and Mills 1994). This has largely been achieved through standardised single species toxicity tests conducted in controlled laboratory environments (Rand and Petrochelli 1985, Graney 1994a). Using standardised laboratory protocols designed to assess contaminant effects on ecosystems, ecotoxicologists have produced a range of accurate and reproducible relationships between contaminant concentration and biological effects for various test organisms, with most data collected on responses of fish and invertebrate species to acute toxicity of chemicals (Hellawell 1986, Mance 1987, Solbé 1993, Mason 1996).

Measurements of toxicity generally rely on a standardised scheme that compares various degrees of damage to a test organism by a known concentration of a particular chemical in a given timeframe (Jeffries and Mills 1994). The overall aim of such tests is to identify “safe” pollutant levels at which no discernible ecosystem effects occur in the long term, including indirect effects that may be manifested in subsequent generations (Calow 1993, Jeffries and Mills 1994). Data produced has then been used to establish environmental guideline values and pollutant discharge standards necessary to protect water quality (Calow 1993, Jeffries and Mills 1994, Mason 1996). After extrapolation to natural conditions, these are deemed as the levels which (if not exceeded) will not cause biotic impairment and will adequately protect the aquatic environment (Lloyd 1981, Hart 1982, Maher et al. 1992). Field sites can then be monitored for their adherence to these guideline levels by more routine water quality assessment programs.

Conventional toxicity tests are widely used in the northern hemisphere, and are largely performed using standardised test organisms that can be easily cultured under controlled conditions (e.g. Cairns et al. 1976, Cairns et al. 1986, Baudo 1987, Reynoldson and Day 1993, Rippon and Chapman 1993, Ingersoll et al. 1995, Mason 1996, Burton et al. 2000). Laboratory bioassays have commonly employed freshwater organisms such as midges, amphipods and cladocerans (Lenat 1993b, Persoone and Janssen 1993, Reynoldson and Day 1993, Naylor and Rodrigues 1995, Burton et al. 1996, Kemble et al. 1998, Burton et al. 2000, Kater et al. 2001, Milani et al. 2003, de Lange et al. 2005, Ingersoll et al. 2005, Stanley et al. 2005, Leppanen et al. 2006, Norberg-King et al. 2006, Tatarazako and Oda 2007), with bioassays also conducted in various species of aquatic plants (Taraldsen and Norberg-King 4

1990, Winner et al. 1990, Lewis 1993). Multiple species tests are also becoming more common, with two or more species more likely to respond to a wider range of pollutants than a single test species (Cairns 1983a, Cairns et al. 1983b, Cairns 1984b, Phipps and Holcombe 1985, Cairns et al. 1986, Dutka and Kwan 1988, Munawar et al. 1989, Pontasch at al. 1989, Cairns and Cherry 1993, Emans et al. 1993, Okkerman et al. 1993, Reynoldson and Day 1993, Gruber et al. 1994, Versteeg et al. 1999, Sanchez et al. 2005).

While such tests provide an efficient and cost-effective means for assessing potentially adverse effects of chemicals (Rand and Petrochelli 1985) and regulating their release into the environment (Graney 1994a), the success of conventional toxicity tests lies in their reproducibility (Calow 1993, Graney 1994a) and ability to identify and manipulate factors that influence pollutant effects (Cairns 1984a), such as temperature, pH, water hardness and dissolved oxygen concentration (Jeffries and Mills 1994).

However, there are problems in applying the results of conventional toxicity tests beyond the laboratory setting (Persoone et al. 1989, Reynoldson and Day 1993). Nor do conventional toxicity tests account for antagonistic or synergistic effects of multiple pollutants, indirect pollutant effects such as loss of food, habitat or changes to inter-species interactions such as predation and competition. Phenomena such as pollutant transformation and transfer between water, sediments and biotic phases (Levy et al. 1985, Reynoldson and Day 1993) are also beyond the realms of laboratory extrapolation. Furthermore, conventional toxicity tests are commonly conducted in the laboratory under continuous exposure conditions, which rarely occur in natural field environments (Graney 1994a).

Generally speaking, the efficacy of laboratory-based toxicity tests to adequately protect the natural environment relies heavily on the reliability of the dose-response relationships produced, and how well they represent complex natural communities (Graney 1994a). However, the responses of single species under laboratory conditions is unlikely to be representative of field populations (Cairns 1983a, Blanck 1984, Cairns 1984b, Odum 1984, Kimball and Levin 1985, Kelly and Harwell 1989, Reynoldson and Day 1993, Forbes and Forbes 1994, Graney 1994a, Hall and Giddings 2000, Culp et al. 2005), with evidence that even closely related species may show different responses to particular chemicals (Mason 1996). As such, the ability to tightly govern pollutant activity in the laboratory does not easily facilitate extrapolation from the controlled environment to natural field conditions (Cairns et al. 1982, Cairns et al. 1988, Emans et al. 1993, Graney 1994a, Jeffries and Mills 1994, 5

Chappie and Burton 1997, Pereira et al. 1999, Pereira et al. 2000), leading to criticism that laboratory-based toxicity tests generally lack environmental realism (Cairns 1983a, Cairns and Cherry 1993, Graney 1994a).

Species Sensitivity Distributions Despite difficulties extrapolating laboratory toxicity results to the field, ecotoxicologists over the last two decades have been developing empirical toxicity models based on existing laboratory toxicity data. One such example is species sensitivity distributions, which are based on the premise that better environmental protection can be afforded by combining all known data for a given compound (Posthuma et al. 2002b). Species sensitivity distributions (SSDs) can be defined as probabilistic models that represent the variation in the sensitivity of biological species to a particular contaminant or set of contaminants (Aldenberg et al. 2002), and are estimated from a sample of existing toxicity data (Posthuma et al. 2002b). SSDs can be constructed using either acute and chronic toxicological endpoints, although acute toxicity data is most commonly used because of its availability and ease of interpretation (Posthuma et al. 2002b). And while SSDs derived from larger datasets are likely to be more robust, models can be constructed from datasets comprised of results from only a few test species (Aldenberg et al. 2002, Posthuma et al. 2002b).

The first step in constructing a species sensitivity distribution is the selection and collation of toxicity data for all relevant species, using a consistent test endpoint such as lethal concentration (LC) or no effects concentration (NOEC), to a particular contaminant or mixture of concern (Posthuma et al. 2002b). After collation of existing toxicity results, the dataset is transformed into a cumulative distribution function (Posthuma et al. 2002b) and statistically analysed using parametric or nonparametric techniques (Wagner and Lokke 1991, Aldenberg and Slob 1993, Jagoe and Newman 1997, Newman et al. 2000, van der Hoeven 2001). Once derived, model outputs can be interpreted in either direction, to derive environmental quality criteria (Aldenberg et al. 2002, Solomon and Takacs 2002, Warren- Hicks et al. 2002) for ecological risk assessment (Van Straalen and Denneman 1989, Klepper et al. 1998, Steen et al. 1999, van Straalen 2002).

The main advantages of the species sensitivity distribution concept are that they use common toxicological data, are easily understood and are widely accepted by practitioners and regulators (Posthuma et al. 2002a). However, criticisms of this approach relate to some of its 6 underlying principles, construction of the models themselves and possible over- interpretation of SSD outputs.

In order for the SSD to be reliable and robust, the dataset should be statistically and ecologically relevant to the community or species of interest, however, in practise it is more common for availability of toxicity data to be the main criteria for its inclusion in the model (Wagner and Lokke 1991, Posthuma et al. 2002b). Concern has also been raised about whether it is possible to accurately describe SSD outputs with any kind of statistical distribution, parametric or otherwise (Wagner and Lokke 1991, Aldenberg and Slob 1993, Jagoe and Newman 1997), and while incorporation of safety factors has satisfied SSD advocates (Posthuma et al. 2002a), critics suggest such factors or confidence limits are arbitrary (Forbes and Forbes 1993), especially when the relationship is based on limited laboratory data (Calow 1996, Calow and Forbes 1997, Calow et al. 1997, Baird and Van den Brink 2007).

And though it seems likely that an ecosystem will be better protected by selecting a conservative toxicant concentration (Van Straalen and Denneman 1989, van Leeuwen 1990), the SSD approach does not provide any ecological evidence for this (Posthuma et al. 2002b). In fact, existing toxicology results used to construct SSDs may be biased towards organisms that are either tolerant or sensitive (Posthuma et al. 2002b), or even those that are easily cultured and maintained under laboratory conditions. While in the field the ecosystems of interest may be comprised of entirely different species many of which have not been, and may not ever be, subject to conventional toxicity testing because of experimental, ethical or resource restrictions (Van Straalen and Denneman 1989, Power and McCarty 1997, Chapman et al. 1998, Posthuma et al. 2002b).

Toxicity Identification Evaluations in Complex Mixtures Laboratory toxicity tests are also used in the assessment of complex effluents through laboratory fractionation processes (Pankhurst et al. 1979, Walsh and Garnas 1983, Hall 1996). Widely known as toxicity identification evaluations (TIE), these protocols have been used to evaluate a range of effluents including pesticides (Fernandez et al. 2004), organics (Stronkhorst et al. 2003), surfactants (Ankley et al. 1990), heavy metal contamination (Deanovic et al. 1999, Fjallborg et al. 2006), municipal sewage (Burkhard and Jenson 1993, Hongxia et al. 2004) and various pulp mill effluents (Bailey and Young 1997, Dube and MacLatchy 2001, Jenkins et al. 2003, Wiklund and Broman 2005). 7

Collectively TIE protocols provide methodologies on the characterisation, identification and confirmation of the toxic constituents in freshwater and marine effluents (USEPA 1992, USEPA 1993a, USEPA 1993b, USEPA 1994) as well as sediments which can be an important long-term “sink” for toxic compounds (Reynoldson and Day 1993, Hall 1996, Ho et al. 2002, Kwok et al. 2005, Araujo et al. 2006, Kay et al. 2008).

The first step in these protocols is to manipulate whole effluent in various ways to separate its components into different chemical groups such as organics, volatiles or sorbable compounds, oxidants and heavy metals (Hall 1996). Physical and chemical treatment processes may include aeration, filtration, solid-phase extraction, adjustment of pH, oxidation and addition of specific compounds, as well as adsorption of various compounds (USEPA 1992, Hall 1996, Tobinaga and Shoji 2004). Toxicity of the untreated effluent and the variously modified versions are then compared by means of conventional laboratory-based toxicity tests that employ standardised test organisms such as cladocerans, fish, algae or bacteria to obtain acute or chronic endpoints (Jeffries and Mills 1994). The latter stages of TIE protocols require the analysis of the effluent using a range of analytical instrumentation (i.e. Gas Chromatography and High Performance Liquid Chromatography, and Inductively Coupled Plasma Atomic Emission Spectrophotometry) in an attempt to identify the toxic compound (USEPA 1993a, Hall 1996), followed by the final testing stage to confirm the correct contaminants have been identified (USEPA 1993b).

TIE methods have the advantage of being able to identify the toxic components where multiple contaminants may be present, however, there is some concern that “rigorous” laboratory extraction methods may not provide ecologically-relevant data on bioavailability of certain contaminants, which could lead to an overestimation of toxicity, particularly in sediment tests (Wiklund and Broman 2005). And while they may be one of the most cost- effective ways to identify the causal toxic agent in a complex effluent (Hall 1996), toxicity identification evaluation protocols do require considerable analytical equipment that may not be available in all laboratories or on all budgets.

Field Assessment of Biological Communities Since the 1970s, advances in the assessment of aquatic biological communities in the field have facilitated the detection of poor water quality even when f levels have been exceeded intermittently, if at all (Plafkin et al. 1989, Rosenberg and Resh 1993, Lenat and Barbour 1994). While biological assessment techniques cannot replace laboratory toxicity testing or 8 chemical and physical monitoring (Buikema et al. 1982, Maher and Norris 1990), protocols have been developed for use across biotic groups from bacteria to mammals that have been successfully employed in many countries (James and Evison 1979, Hellawell 1986, Mersch and Johansson 1993, Reynoldson and Day 1993, Rosenberg and Resh 1993, Jeffries and Mills 1994, Gruber et al. 1994, Polman and de Zwart 1994, Ostrander et al. 1995, Chappie and Burton 2000, Burki et al. 2006).

To be suitable for use in biological assessment field studies, biota must relatively easy to sample and identify (Jeffries and Mills 1994), and their occurrence should be a function of water quality rather than other factors (Voshell et al. 1989, Hellawell 1996). While most organisms are differentially sensitive to a range of pollutants (Lenat 1993a, Chessman and McEvoy 1998), the most useful bioindicators are thought to be those that are sensitive to the particular pollutant under investigation. For these and other reasons, benthic macroinvertebrates have long been favoured for routine biological monitoring and surveillance protocols carried out in the field (Slooff 1983, Voshell et al. 1989, Persoone and Janssen 1993, Resh and McElravy 1993, Resh and Rosenberg 1993, Reynoldson and Day 1993, Lenat and Barbour 1994). Benthic macroinvertebrates are an important component in aquatic food chains (Persoone and Janssen 1993, Irving et al. 2003), have intimate association with benthos through burrowing or feeding activities (Reynoldson and Day 1993), and there is extensive autecological knowledge related to their sensitivities, tolerances and responses to pollution (Hellawell 1986, Voshell et al. 1989, Hart et al. 1991, Jeffries and Mills 1994).

There are several approaches to field testing of biological communities in situ (Calow 1989, Maltby and Calow 1989, Metcalfe 1989, Reynoldson and Day 1993, Lenat and Barbour 1994). These range from indicator groups or species, general measurements of community structure such as relatively simple descriptors representing species richness and diversity (Woodiwiss 1964, Chandler 1970, Metcalfe 1989, Pontasch et al. 1989), to biotic indices that take into account the relative sensitivities of the component species such as measurements of saprobity (Sladacek 1979, Cairns and Pratt 1993), richness of sensitive groups such as Ephemeroptera, Plecoptera and Trichoptera (Lenat 1988), and indices such as the Biological Monitoring Working Party (BMWP) score and its derivatives (Chesters 1980, Extence et al. 1987, Extence and Ferguson 1989, Hawkes 1998).

The main advantages of biological monitoring over the measurements of physical and chemical characteristics of water quality is that the biota integrate the combined effects of 9 multiple influences, including both natural and anthropogenic factors (Hellawell 1986, Rosenberg and Resh 1993, Jeffries and Mills 1994, Southerland and Stribling 1995). However, it too has its limitations. One of the main problems with biological sampling in natural field studies is the variability among aquatic habitats and thus difficulties related to replication of treatments and obtaining statistically meaningful data (Brassard et al. 1994, De Noyelles et al. 1994, Urban 1994). Secondly, data produced does not provide evidence sufficient to confidently attribute causality (Rosenberg and Resh 1993, Jeffries and Mills 1994, Norris et al. 1995, Kraufvelin et al. 2002) and it may be difficult to definitively link changes in biotic community structure and function to particular variables (Day et al. 1995). While there may be evidence of a relationship between particular pollution scenarios and biological community changes, other factors such as habitat quality or natural variability may actually underlie the observed biotic responses (Sheldon and Haick 1981, Jeffries and Mills 1994, Brewin et al. 1995, Death and Winterbourn 1995, Parsons and Norris 1996). For instance, ecological effects have been found to vary with food type (Winner et al. 1977), life stage (Green et al. 1986, Williams et al. 1986, McCahon et al. 1989, Metcalfe-Smith and Green 1992, Calow 1993, Solbé 1993, Donkin and Williams 1995, Mason 1996), or season (Cossa et al. 1980, Ongley 1982, Furse et al. 1984, Dahlgaard 1986, Dermott and Munawar 1993, Jeffries and Mills 1994, Zamora-Munoz et al. 1995).

Biological monitoring of water quality using macroinvertebrates is a rapidly growing field all over the world. The last fifteen years or so have seen the development of more complex multivariate techniques (Wright et al. 2000) based on the prediction of community assemblages from few physical variables (Wright et al. 1984, Moss et al. 1987, Johnson and Wiederholm 1989). Such techniques include Great Britain’s River Invertebrate Prediction and Classification System (RIVPACS; Wright et al. 1984, Wright et al. 1989, Wright et al. 1993, Wright 2000) and the Benthic Assessment of Sediment (the BEAST) approach employed in North America (Reynoldson et al. 1995, Reynoldson et al. 2000) and the Australian River Assessment Scheme (AUSRIVAS; Davies 2000, Simpson and Norris 2000). Based largely on the principles of RIVPACS, AUSRIVAS has been developed to cluster riverine sites in ordination space on the basis of macroinvertebrate composition and abundance information, as well as both site and habitat characteristics collected at a particular point in time and space (Wright et al. 1984, Wright et al. 1989, Wright et al. 1993, Wright 1995, Davies 2000, Simpson and Norris 2000). Having been employed for river health assessment across the continent, AUSRIVAS predictive models (Simpson and Norris 2000) have become nationally 10 recognised and accessible through the internet (Davies 2000), with the incorporation of ecosystem “trigger values” into national water quality guidelines for specified levels of ecosystem protection (ANZECC 2000).

However, these kinds of rapid bioassessment techniques are initially data intensive to establish (Reynoldson and Day 1993) and depend entirely on binomial or abundance data of component organisms (Norris 1994, Chessman 1995, Resh et al. 1995, Reynoldson et al. 1995, Davies 2000, Wright 2000), and so would not routinely consider lethal and sub-lethal effects that may indirectly influence survival such as breeding success or physiological changes (Day and Scott 1990, Urban 1994). Nor does such numerical information easily link cause and effect, or convey why an ecosystem is healthy or unhealthy (Culp et al. 2005).

Artificial Aquatic Ecosystems Use of artificial aquatic ecosystems such as mesocosms can potentially provide a conduit between controlled laboratory trials and the more complex field studies (Boyle 1985, Giddings and Franco 1985, Gearing 1989, Graney 1994a, Culp et al. 2005). In this context, mesocosms can be defined as intermediate-sized semi-controlled ecosystems which are subject to the inherent variability of regional weather and ecological interactions, for example natural recolonisation processes, and whose physical dimensions and basic water chemistry is known and controlled (Boyle and Fairchild 1997, Harris et al. 2007). They may include artificial streams as well as experimental ponds and tanks, or lakes divided into segments by walls of some description (Bowling et al. 1980, Rogers et al. 1980, Stout and Cooper 1983, Kaushik et al. 1985, Gearing 1989, Mayfield 1993, Hruska and Dube 2004, Clark and Clements 2006), and should be able to be replicated and manipulated (Graney 1994a, Harris et al. 2007) to enable potentially causal relationships between environmental characteristics and responses at various levels of biological organisation to be investigated in an environment resembling a natural ecosystem (Kosinksi 1989, Touart and Slimak 1989, Crossland and La Point 1992, Belanger et al. 1993, Lamberti and Steinman 1993, De Noyelles et al. 1994, Belanger 1997, O’Connor and Judge 1997, Kampichler et al. 1999, Glova and Jellyman 2000).

Generally comprising a number of similar experimental units that are similar in their biological structure and function, such artificial ecosystems have been used to overcome the difficulties in determining cause and effect in field situations, including of replication of treatments that often affects field trials (Odum 1984, Crossland and La Point 1992, Brassard 11 et al. 1994, Graney 1994b, Urban 1994, Culp et al. 2000). For this reason, mesocosms have been favoured for validating between laboratory and field research approaches (Shriner and Gregory 1984, Boyle 1985, Lundgren 1985, Gearing 1989, Kosinski 1989, Graney 1994a, Lawton 1995, Brooks et al. 1996, Cairns et al. 1996, Boyle and Fairchild 1997, Caquet et al. 2000, Culp et al. 2000, Kraufvelin et al. 2002).

The main advantages of mesocosms lie in having greater control than in field surveillance studies (Touart 1988, Brassard et al. 1994, Urban 1994, Kraufvelin et al. 2002), and more natural exposure regimes than can be achieved by laboratory trials, with ecosystem responses taking into account prevailing climatic conditions or mitigating water quality conditions that may alter exposure dynamics (Graney 1994a). Mesocosms also allow measurement of direct and indirect effects on single or multiple species, as well as both structural and functional parameters at community, population or ecosystem level (Touart 1988, Clark and Cripe 1993, Urban 1994)

However, despite attempts to emulate the natural environment, artificial ecosystems still lack natural complexity and suffer from their intermediate position between environmental realism and laboratory control (Kemp et al. 1980, Lamberti and Steinman 1993, Lawton 1996, Kraufvelin 1998, Kraufvelin 1999, Kraufvelin et al. 2002). There may also be additional artefacts associated with the mesocosm structure itself such as effects of walls or mesocosm depth (Levy et al. 1985, Chen et al. 1997, Kraufvelin 1999, Petersen et al. 1999).

Further, results from mesocosm studies must be interpreted with care as they cannot predict ecological changes arising indirectly from contaminant stress such as habitat change or genetic shift in populations (Lamberti and Steinman 1993, Culp et al. 2000, Barata et al. 2002, Lopes et al. 2005, Lopes et al. 2006). Biological responses to experimental treatments or manipulations may also differ with respect to the reduced scale inherent in mesocosms compared with natural habitats (Kemp et al. 1980, Frost et al. 1988, O’Neill et al. 1989, Petersen et al. 2003), potentially limiting their applicability to different environments or pollution scenarios, and their usefulness in the assessment of ecosystem level responses (Carpenter 1999, Kraufvelin et al. 2002).

Deployment Studies In addition to sampling organisms collected from the field to measure pollutant levels, test species can be placed in cages and deployed under natural field conditions so that effects or 12 uptake rates can be measured over defined periods of time in known exposure scenarios (Buikema et al. 1982, Phillips and Rainbow 1994, Mason 1996).

Among the first to utilise “sentinel” organisms for the in situ assessment of potentially impaired water quality were marine bioaccumulation studies where test organisms are held in cages, or attached to artificial substrates such as rope and other introduced surfaces (Reynoldson and Day 1993, Phillips and Rainbow 1994, Harris 1995). With concern about the impacts of anthropogenic activities on fisheries and aquaculture industries, it is not surprising that fish and bivalves are most frequently employed for these kinds of bioassessment studies (e.g. Buikema et al. 1982, Roesijadi et al. 1984, Solbé 1993, Uhler et al. 1993, Colombo et al. 1995, Dezwart et al. 1995, Haynes et al. 1995, Soto et al. 1995, Cairns et al. 1996, Julshamn and GrahlNielsen 1996, Morcillo et al. 1997, Baumard et al. 1998b, Stien et al. 1998, Albalat et al. 2002, Ringwood and Keppler 2002, Caselli and Fabbri 2005, Mayon et al. 2006, Nakhle et al. 2007).

Although comparatively fewer deployment studies have been undertaken in freshwater systems, publication of research in more recent years has highlighted the potential for in situ methods to be more widely adopted (Hickey et al. 1995, Brooks et al. 1996, Chappie and Burton 1997, Hatch and Burton 1999, Pereira et al. 1999, Sibley et al. 1999, Graca et al. 2002, Greenberg et al. 2002, Grapentine et al. 2004, Jergentz et al. 2004, Phillips et al. 2004, Burton et al. 2005). And while the efficacy of using benthic macroinvertebrates as sentinel organisms may be limited by their mobility, small size and benthic habit, various studies have employed macroinvertebrates in cages or test chambers of some kind (Colborn 1982, Hall et al. 1988, Matthiessen et al. 1995, Schulz and Liess 1999, Meregalli et al. 2000, Smith and Beauchamp 2000, Castro et al. 2003, Schulz 2003, Bervoets et al. 2004, Soares et al. 2005, Custer et al. 2006, Barata et al. 2007, Robertson and Liber 2007).

1.1.3 RATIONALE FOR THIS THESIS

Each of the various approaches described above have assisted with the advance in water quality assessment over the last few decades. However, there is still an urgent need to improve the linkage between pollutant “cause” and biological “effect” under natural field conditions that may be used to assess the effect of pollutants in an aquatic ecosystem.

An approach involving in situ assessment of caged macroinvertebrates for pollutant evaluation has several advantages over chemical analyses, biological monitoring or 13 ecotoxicological protocols. Assessment can be undertaken in natural field conditions, using locally relevant test organisms, and both lethal and sub-lethal endpoints can be used to assess the impact of ecological condition on test organisms. Overall effects on the test organisms can be quantified, even where effects may be affected by interaction between individual pollutants or environmental conditions, or where the nature of the contamination is unknown. In addition, Munawar and Munawar (1987) and Graney (1994b) also suggest that these methods are likely to be less expensive and faster to run than conventional toxicity tests or other biomonitoring techniques, largely because at least some endpoints can be measured in the field.

1.2 OBJECTIVE

1.2.1 OVERALL AIM

This thesis aims to investigate the efficacy of using caged macroinvertebrates to assess ecological condition of aquatic environments, and to provide information for in situ water quality testing protocols for use by water agencies, community groups and aquatic researchers. While bioaccumulation studies have assessed sentinel organisms, much less work has employed organisms from uncontaminated environments deployed to assess the effects of a suspected pollution source.

Fundamentally, this thesis aims to investigate whether an in situ approach to water quality assessment is informative and sensitive to water quality impacts. However, the robustness of the approach, reliability and reproducibility of the data, as well as practical issues such as resource requirements, will be important. This will be evaluated to assess the potential and suitability for such in situ assessment protocols to enhance existing monitoring programs.

1.2.2 STRUCTURE OF THE THESIS

The thesis aims were addressed in component studies comprising Chapters 4, 5 and 6 of this thesis; investigations of the effects of experimental manipulation and transplantation of test organisms followed by validation of testing protocols using specific case studies.

In situ testing of water quality using macroinvertebrates requires the collection, handling, caging, deployment and retrieval of test organisms at various sites. If such techniques are to be useful in the assessment of aquatic impact, it is first necessary to determine any effects 14

(deleterious or stimulatory) of these experimental procedures on the organisms themselves. The first experimental chapter (Chapter 4) aims to assess whether logistical aspects of experimental set-up effect macroinvertebrate growth and survival and specifically investigated the caging itself, use of different mesh sizes, density of caged individuals and the efficacy of temporarily storing test taxa in laboratory aquaria prior to field deployment.

In some instances, a test site may not support macroinvertebrate fauna suitable as test organisms for in situ testing, or there may be too few to conduct experiments at sufficient power to obtain reliable results. Consequently, it may be necessary to relocate appropriate taxa to test sites from their source site. The second experimental chapter (Chapter 5) aims to assess whether the transplantation of macroinvertebrates between field sites (both similar and dissimilar to the source site) that are considered to be in “reference” condition (i.e. free from any known physical or chemical disturbance or degradation) has any effect on their body size or survival.

Chapter 6 aims to preliminarily test the methods developed in the previous two chapters using local point and diffuse source impacts in and around Canberra, Australia. These chapters are followed by a general discussion of the component studies and how they relate to the overall thesis aims, and address the difficulties faced in assessing ecological condition. It then provides a short conclusion and perspectives on this research and includes broad guidance for improving current water quality assessment protocols.

The general methods used throughout this thesis and all study sites are detailed in Chapters 2 and 3 respectively, and will be referred to throughout the experimental chapters.

2 15

Chapter 2: METHODS AND MATERIALS

2.1 INTRODUCTION

As discussed in Section 1.2.2, the research presented here comprised of three interrelated studies examining the usefulness of placing caged macroinvertebrates in situ to test water quality. To avoid repetition in the experimental chapters (Chapter 4, 5 and 6), the methods employed in this study that were common to all component experiments are detailed in this chapter. Details related to specific experiments, and any refinements to these general methods, are detailed in Chapters 4, 5 and 6.

2.2 TAXON SELECTION CRITERIA

Test organisms were sourced from accessible reference sites (Reynoldson et al. 1997, Reynoldson and Norris 2000, Stoddard et al. 2006) in the ACT and surrounding NSW. The source site for a given experiment was usually the experimental control site, for a given experiment was an independent, and uncontaminated, source site (Section 2.6.2). Further information on sites is provided in Chapter 3.

During field reconnaissance, instream habitats and macroinvertebrate assemblages from potential experimental sites were assessed and biota was returned to the laboratory for taxonomic verification. As riffle habitats were markedly contracted by low flows during the drought conditions of the experimental period (Appendix 3), test organisms for this study were mainly harvested from (and later deployed in) edge habitats.

Taxa were selected for in situ trials on primarily the basis of their sensitivity to the suspected pollutant to be assessed using the SIGNAL biotic index (Chessman 1995, Chessman et al. 1997, Chessman and McEvoy 1998, Chessman 2003) and abundance at accessible reference sites. As the experimental design (see Section 2.5) required large number of conspecifics, the likely abundance of potential test organisms at reference sites was derived from the Australian River Assessment System (AUSRIVAS) predictive models (Davies 2000, Simpson and Norris 2000). AUSRIVAS models were also used to ensure that selected test organisms were highly likely to occur at the various experimental sites in the absence of impact. 16

Other important criteria considered in selecting test organisms included ease of handling, body size, caging feasibility (Phillips 1980, Hellawell 1986, Giesy and Hoke 1989), nature of feeding and other life history characteristics such as inter-molt period (Weeks and Moore 1991) as well as the ease and reliability of measuring the various morphological endpoints (Giesy 1988).

Similarly-sized conspecifics were selected from natural communities at the chosen harvest sites (Section 2.6.2) to improve the ease of identification (Hellawell 1986, Weeks and Moore 1991, Doledec et al. 2000, Basset et al. 2004, Nijboer and Verdonschot 2004) and reduce the variability in individual sensitivity that can occur across instars (Collyard et al. 1994, Kiffney and Clements 1996, Harris et al. 2000, Medina et al. 2002, Hyder et al. 2004).

2.3 CAGE DEVELOPMENT

Holding cages (Figure 2.1) were developed from 70 ml screw-capped leak-proof sample containers comprised of clear polypropylene tube bases and polyethylene caps. Using a hole saw and drill press, holes of 32 mm and 38 mm diameter were drilled into the base of each container and the centre of each cap respectively, and covered by nylon polyfilament mesh circles.

Solvent-based contact adhesive (Selley’sTM Kwik Grip) was applied sparingly to join the nylon mesh to the vial cap and base initially, as it was found to have excellent adhesion with both the polystyrene and polyethylene plastics of the container, was water resistant and non- toxic. When the plastics were firmly joined, additional contact adhesive was applied to strengthen the bond and fill any small gaps between the mesh and the rim of the vial into which the test taxa and debris may hide or get trapped.

2.4 EXPERIMENTAL SET-UP

In order to reduce the potential stresses on the test organisms, only a few hours lapsed between harvesting, caging and deployment of the test organisms as described below. 17

57 mm

44 mm

Figure 2.1: Design of cages used to hold macroinvertebrates for all in situ deployment experiments of this study.

2.4.1 HARVESTING OF TEST ORGANISMS

Test taxa were collected from in-stream edge habitats in slow flowing or still water within 1 m of the river bank. A 250 m kick net with a 35 cm opening was vigorously swept through overhanging, emergent or surficial streamside vegetation, root mats, logs, undercut banks and backwater areas. Debris, including dislodged macroinvertebrates, was collected in the net and placed in large plastic trays. Net samples were flooded with river water through which air was bubbled with a portable pump and airstone. Selected test organisms were carefully live-picked from the tray using stainless steel feather-tip forceps to avoid physical damage until sufficient individuals for the trial had been obtained.

2.4.2 CAGING

Test organisms were then carefully placed in the deployment cages (Figure 2.1), which were then placed in a large vessel of containing river water through which air was also bubbled. In some instances, sufficient individuals of more than one test species were available at the harvest site for deployment and multiple deployment trials were conducted simultaneously. Where multiple test species were deployed concurrently, the live-picked sample populations of each test species were collected and caged separately to minimise interactions. In allocating 18 individuals to each cage, care was taken to avoid stresses on test organisms and specific attention was paid to ensure each cage, and the sub-sample returned to the laboratory, was comprised of a set of individuals representative of the population size range. The baseline samples were preserved and returned to the laboratory as detailed for macroinvertebrate retrieval in Section 2.7.2.

While the number of individuals placed in each cage was determined by number harvested and biomass required for laboratory analysis, this was sometimes limited by availability of the source population. Caged individuals were not provided substrate or vegetation within the cage as it would be impossible to control the quality or quantity provided between cages, or monitor how this was shared between individuals within a cage for comparison with macroinvertebrate responses.

2.4.3 DEPLOYMENT

Stocked cages were randomly allocated to plastic mesh bags for deployment at all experimental (i.e. control, transplant or test) sites (Figure 2.2). Cages were deployed in weighted mesh bags in habitats comparable to their source habitats, attached to the bank and submerged in the river.

Where multiple test species were deployed concurrently, mesh bags contained cages for all test species (caged separately as previously described) to remove any effect of bagging the different taxa separately (Figure 2.2).

2.5 OVERALL EXPERIMENTAL DESIGN

Experiments compared the responses of caged macroinvertebrates (e.g. survival, contaminant body burden, physiological damage and body size) across various treatments. All in situ trials generally followed the same basic nested design comprised of control and treatment blocks sampled across the experimental period. Treatments (i.e. fixed factors under investigation for a given experiment) varied throughout the study, with various experiments designed to test the impact of experimental set-up factors (Chapter 4), site characteristics (Chapter 5) and sites potentially impacted by pollution (Chapter 6). These treatments were assessed by retrieving samples on given collection “days” (e.g. trial day 1, trial day 3, trial day 18 etc.) throughout 19

Individual(s) of each tests species (not to scale)

Species A Species B Species C

Deployment cages containing n individuals of a single test species

Mesh bags containing a mixture of species’ cages

NB: Several bags were deployed for each treatment.

Figure 2.2: Random factors associated with set-up of deployment experiments (not to scale). 20 each experiment. Individual macroinvertebrates served as the experimental units, for which various responses (e.g. survival, size, condition) were recorded. There were also random factors associated with experimental set-up (Figure 2.2).

Details of experimental design for particular trials is presented in Chapters 4, 5 and 6.

2.6 SITE SELECTION PROCESS

The site selection process differed depending on the specific question addressed by each experiment (see earlier Section 1.2).

2.6.1 LABORATORY TRIALS

Laboratory aquaria were used to conduct some of the experiments detailed in Chapter 4 that examined experimental logistics (i.e. cage mesh size, density of caged individuals, efficacy of temporarily storing test organisms). Aquaria were established in an air-conditioned light- controlled room set to match the environmental conditions of the source site. Identical tanks (standard measurements 460 mm x 255 mm x 305 mm; volume 35 L), each with independent capabilities for temperature control, water filtering and circulation, were all covered with glass lids to minimise evaporation and contamination. Each tank was fitted with a Thermomix, which was calibrated to circulate aquarium water at rates comparable to flow conditions at the source site and maintain the water at approximately 18oC with a submerged heating element.

2.6.2 FIELD TRIALS

This section details the process used to select the field sites for this study and its component trials. Further information of each of the sites selected is provided in Chapter 3.

Reference Sites Logistics and transplant experiments (Chapters 4 and 5) were all conducted at sites free from aquatic impact, where faunal communities could be considered to be at reference condition (Norris 1994, Reynoldson et al. 1997, Reynoldson and Norris 2000, Wright et al. 2000, Stoddard et al. 2006) as were all control sites for impact experiments (Chapter 6). In all instances, sites potentially impacted, by thermal, hydrological or other related disturbance, 21 from release of impounded waters or main channel habitats immediately downstream of tributary inflows were avoided.

Reference sites used in this study (Figure 3.1) were selected from reference sites used to construct and validate AUSRIVAS (Australian River Assessment System) predictive models (Davies 2000, Simpson and Norris 2000), and field reconnaissance was undertaken to ensure that there was no evidence of post-hoc or local disturbance. Site selection was based on the nature of the site, using AUSRIVAS group classification, and the suitability of flow and instream habitat conditions prevailing at the site for the deployment trials and the macroinvertebrate communities expected to occur at a site.

As control sites would ideally be the source of appropriate test organisms, probability of occurrence for families with high sensitivity grades, derived from AUSRIVAS models, was also considered (Armitage et al. 1983, Chessman 1995, Chessman et al. 1997, Chessman and McEvoy 1998). However, other considerations included site location and travelling time associated, where use of remote sites may be precluded because of the time needed to undertake regular site visits. More importantly however, a protracted time delay between collection and caging at the source site and deployment may have deleterious effects on the test organisms.

Impact Sites Impact sites used in the trials detailed in Chapter 6 (Figure 3.1 and Table 3.1) were selected from sites known to have persistent water quality contamination (e.g. DUS 1996). It was important to establish that this degradation was related to water quality rather than habitat quality because the test organisms would be caged and prevented from direct contact with the physical environment.

Acceptable impact test sites were classified into the AUSRIVAS site group to which it would have the highest probability of belonging in the absence of impact (Simpson and Norris 2000). This was established using the predictor variables responsible for defining the relevant AUSRIVAS model (Tables 2.1, 2.2 and 2.3).

Methods for physical, chemical and habitat assessment and measurement for these predictor variables were adopted from AUSRIVAS protocols (Norris 1994, Davies 1994, Williams and Norris 1997, Simpson and Norris 2000). Site characteristics such as altitude, latitude, 22

Table 2.1: Predictor variables used by the AUSRIVAS ACT spring riffle predictive model to classify sites used in this study.

AUSRIVAS ACT spring riffle model

Altitude (m ASL) Latitude and longitude -1 Alkalinity (mgL CaCO3) Bank width (m) – an estimated average for the reach, measured from the top of one bank to the top of the other bank Percentage riffle area within the reach, i.e. flowing broken water over gravel, pebble, cobble or boulder, with a depth of greater than 10 cm Riffle depth (cm) – calculated as mean of 3 measurements taken at ¼, ½ and ¾ across the width of the riffle sampling area Percentage of the stream shaded (at noon) by riparian vegetation (category 1-5) Percentage of the riffle habitat (within the reach) covered with emergent or submergent rooted macrophtyes (category 0-4) Embeddedness – amount of fine sediment in which gravel, cobble and boulders are embedded (category 0-20) Number of velocity/depth habitats present within the reach e.g. slow deep (<0.3 ms-1 & >0.5 m), slow shallow, fast deep, fast shallow; category 0-20)  indicates that reach is defined as 5 times the mean stream width either side of the edge habitat sampled.  indicates that habitat characteristics with higher categorical scores represented better habitat condition for macroinvertebrates.

Table 2.2: Predictor variables used by the AUSRIVAS ACT spring edge predictive model to classify sites used in this study.

AUSRIVAS ACT spring edge model

Altitude (m ASL) Catchment area upstream (km2) Latitude and longitude -1 Alkalinity (mgL CaCO3) Percentage of the edge habitat within the reach covered with emergent or submergent rooted macrophtyes (category 0-4) Trailing bank vegetation along the reach – amount of bank vegetation hanging over or in the water (category 1-4) Bottom substrate and available cover within the reach – amount of bottom substrate that is available as macroinvertebrate habitat e.g. pebble, cobble, gravel, boulder, logs or undercut banks (category 0-20) Streamside vegetation cover along the reach – dominant vegetation type e.g. trees, shrub, grass, sedge, ferns (category 0-10)  indicates that reach is defined as 5 times the mean stream width either side of the edge habitat sampled.  indicates that habitat characteristics with higher categorical scores represented better habitat condition for macroinvertebrates. 23

Table 2.3: Predictor variables used the AUSRIVAS ACT autumn edge predictive model to classify sites used in this study.

AUSRIVAS ACT autumn edge model

Altitude (m ASL) Longitude Distance from source (km) Stream order (Strahler) Percentage of the grasses, ferns and sedges comprising the riparian zone along the reach Edge depth (cm) – calculated as mean of 3 measurements taken at ¼, ½ and ¾ width of the edge sampling area -1 4 1 Edge water velocity (ms ) – calculated from mean of upper ( /5 depth) and lower ( /5 depth) measurements (or ½ depth if edge habitat is less than 30cm deep) at ¼, ½ and ¾ across the width of the edge sampling area Percentage of substrate within the reachcomprised of bedrock Percentage of substrate within the reachcomprised of boulder (>256 mm diameter) Percentage of substrate within the reachcomprised of pebbles (16-64 mm diameter) Percentage of the edge habitat (within the reach) covered with emergent or submergent rooted macrophtyes (category 0-4) Percentage of substrate within the edge habitat comprised of pebbles (16-64 mm diameter) Number of velocity/depth habitats present within the reach e.g. slow deep (<0.3 ms-1 & >0.5 m), slow shallow, fast deep, fast shallow; category 0-20) Streamside vegetation cover along the reach – dominant vegetation type e.g. trees, shrub, grass, sedge, ferns (category 0-10)  indicates that reach is defined as 5 times the mean stream width either side of the edge habitat sampled.  indicates that habitat characteristics with higher categorical scores represented better habitat condition for macroinvertebrates. longitude and stream order (Strahler 1952) were measured from 1:100 000 topographical maps. Using topographic highs to locate catchment boundaries, a digital planimeter (Placom N-series) was used to calculate distance from source, upstream catchment area. Methods for field determination of alkalinity are detailed in Section 2.9.1.

AUSRIVAS models also produced taxon probabilities for the test site, from which “short- listed” test organisms were identified. As test organisms were sourced from unimpacted sites, it was important to ensure that they had a high probability of occurrence at the test site naturally in the absence of impact.

It was then necessary to identify a reference site with similar characteristics at which the same “short-listed” test organisms would be highly likely to occur. By using a stream classification system such as AUSRIVAS, this avoided the need for paired sites up and downstream of a suspected impact which has previously been a common design problem (Hurlbert 1984, 24

Norris 1986), especially when upstream sites are also impacted by aquatic degradation, inappropriate for comparison, or unavailable at all. Ideally, this reference site would have served as an experimental control site as well as the source of the test organisms, however, it was sometimes necessary to identify an independent site from which to source test organisms, particularly where test organisms were not sufficiently abundant.

Independent Source Site As described above, AUSRIVAS models were used to match control sites for the test sites examined in Chapter 6 using their respective taxon probabilities for “short-listed” test organisms. In the event that there were too few individuals of these taxa available at the control site despite high probability of occurrence according to AUSRIVAS, test organisms were harvested from an independent source site for caging and deployment at both the test and control sites. Having identified potential source sites (i.e. reference sites with high probability of occurrence for the “short-listed” test organisms), field reconnaissance was undertaken to make sure sufficient test organisms could be harvested for the deployment experiment.

Field Reconnaissance For all experimental sites, field reconnaissance was undertaken to ensure that site and instream conditions were appropriate, suitable collection and deployment habitats were available, and to verify safety and access routes. Instream macroinvertebrate communities, if present, were assessed for evidence of recent or localised aquatic impact or other information that may affect its selection as an experimental site. In some instances, a site selected on the basis of information derived from AUSRIVAS models had to be rejected on the basis of local disturbance since development of predictive models (e.g. bankslumps, riparian clearance or roadworks).

2.7 IN SITU MACROINVERTEBRATE TRIALS

2.7.1 PREPARATION OF SAMPLING CONTAINERS

As macroinvertebrate samples were retrieved during each trial for laboratory analysis of various chronic endpoints (see Section 2.8), it was necessary to prepare sampling containers to avoid contamination of the retrieved organisms, and facilitate storage until all samples were processed. The polyethylene snap-cap containers were soaked for 24 hours in a Neutracon 25 phosphate free detergent (DeconTM, East Sussex, England) bath and then rinsed thoroughly with ultra-pure MilliQ deionised water (MilliporeTM, Massachusetts, USA). Vials were then soaked for a further 24 hours in a 2% nitric acid bath, rinsed thoroughly with ultra-pure MilliQ water, dried in an enclosed laminar flow drying cabinet and sealed to prevent contamination.

2.7.2 RETRIEVAL OF MACROINVERTEBRATES ON COLLECTION DAYS

On specified collection days over the duration of each trial, a number of replicate cages for each test species was retrieved from each of the trial sites. The number of cages retrieved per experimental day varied for each experiment, and specific details for particular trials is presented in Chapters 4, 5 and 6. For each test taxon deployed, cages were retrieved from the total number of cages deployed at each site, and were randomly taken from different mesh bags. Care was taken not just to select those cages near the opening of the mesh bag but to select cages from different positions within the mesh bags for retrieval. Again, this ensured randomisation of any “bag” effects across cages and ensured that individual deployment cages that were retrieved were true replicates and not pseudoreplicates of the bag itself.

While retrieving the set of replicate cages on a particular collection day, disturbance to the remaining deployment cages was kept as even as possible. To ensure all cages remaining in mesh bags were evenly disturbed during retrieval, all bags were shaken gently while submerged. This also cleared any debris or particulate build-up from around the cages, and maintained the flow of water to the cages and the individuals held within.

2.7.3 ASSESSMENT OF SURVIVAL

Immediately following the retrieval of each cage, stationary individuals were gently nudged with feather-tipped forceps to establish whether or not they were dead. Alive and dead individuals and discarded exuviae from each cage were placed in separate acid-washed snap- cap vials and covered with Parafilm™ laboratory film to prevent contamination and contact with the lids. As the total number of individuals retrieved from each deployment cage was on occasion less than the number of individuals that had been deployed on trial day 0 of the relevant trial, results are expressed in terms of the number retrieved. 26

2.8 SUB-LETHAL ENDPOINT RESPONSES

Macroinvertebrate samples returned to the laboratory were assessed for acute and chronic endpoint related to experimental stress including environmental contaminants (Monk 1983). Body weight of all macroinvertebrate test organisms deployed in this study was measured using a digital balance to 100 g. Wet weight was taken for all species, and freeze dried weight was also obtained for samples to be analysed for trace metals (see Section 6.2.2).

Morphological dimensions, which were class and taxon specific, were determined from digital images captured by a Leica MicrosystemsTM digital colour video camera mounted to a Leica MZ100 binocular microscope. A camera mount (x 0.5) was incorporated onto the microscope phototube to match the microscopic field of view to the image captured by the camera. Magnification ratios were calculated for each of the magnification stop-points of the microscope using a calibrated graticule and these ratios were corrected for any vertical or horizontal distortion in the digital image. Distances and shapes were analysed using the public domain image analysis software available from Scion CorporationTM.

Linear and freehand distance measurements were based on cuticular body parts as these were deemed to be readily identifiable and to vary less with organism condition (Figure 2.3). Head widths were taken for all insect taxa as the linear distance between the widest part of each individual’s head (Figure 2.3). This was usually the distance between the outer margins of each eye, however in Austrolestes cingulatum it was a measurement between the outer margins of the posterior projections of the head capsule behind the eyes. Carapace dimensions were measured for decapod crustaceans (Figure 2.3), and for Paratya australiensis, orbital carapace length was also recorded because the anterior rostrum was often found to be damaged. As detailed in Figure 2.3, orbital carapace length was taken as the linear distance from the posterior margin of the carapace to the base of the eyestalk, which correlates strongly with carapace length in undamaged shrimp (n = 2625, r2 = 0.8351).

Morphological endpoints for the amphipods (dorsal length and surface area) were obtained using the freehand drawing tool. Dorsal length was the distance between the most and anterior and posterior apices of the individual, while surface area was considered to be the lateral body area covered by integument. This was achieved by outlining all body segments from head to 27 a) Ephemeroptera: Leptophlebidae b) Odonata: Lestidae (e.g. Atalophlebia australis) (e.g. Austrolestes cingulatum)

c) Trichoptera: Leptoceridae (e.g. Triplectides australicus ciuskus, Notalina sp.)

d) Amphipoda: Ceinidae (e.g. Austrochiltonia sp.) f) Decapoda: Parastacidae (e.g. Cherax destructor)

OCL

e) Amphipoda: Ceinidae (e.g. Austrochiltonia sp.)

g) Decapoda: Atyidae (e.g. Paratya australiensis)

Figure 2.3: Measurements of body size for some of the test organisms deployed in this study. Morphological endpoints included insect head width (a, b, c), amphipod dorsal length (d) and lateral surface area (e), decapod carapace length (f, g) and orbital carapace length indicated by OCL (g). 28 telson and quantification of the enclosed area (including coxae but excluding antennae, mouthparts, distal limb segments and gills).

Microscopic examination of macroinvertebrates demonstrated evidence of physiological damage, particularly for individuals deployed downstream of Captains Flat mine (Chapter 6). Presence/absence data were collected for physiological damage or defects (including burns, lesions, exoskeletal infections etc.) revealed during microscopic examination.

2.9 ENVIRONMENTAL SITE DATA

Rainfall and discharge information held by ACTEW Corporation was extracted using the industry-standard HydsysTM software, with other climatic information obtained from the Australian Government Bureau of Meteorology and Rainman (Clewitt et al. 2003).

2.9.1 FIELD MEASUREMENTS

Basic Water Measurements A core set of environmental variables were recorded including standard biological monitoring variables. Water temperature, dissolved oxygen, pH, electrical conductivity and turbidity were measured in the field on each collection day using a multiprobe HydrolabTM meter (model SCOUT 2).

Alkalinity was determined on site at all trial sites using colourimetric titration to pH 4.5 using a mixed bromcresol green-methyl red indicator solution (Cooper 1941). Field titrations were conducted in duplicate using 0.02 N standard hydrochloric acid (Analar, BDH) as the titrant. Aliquots of titrant were incrementally added using a disposable 10 ml syringe (0.2 ml gradations; polypropylene cylinder, polyethylene and naturalised rubber plunger) until equilibrium at pH 4.5 was achieved and titrant volume was recorded (APHA 1992). Bicarbonate alkalinity was calculated from determination of total alkalinity (Dye 1958, APHA 1992) to facilitate comparison of major ions across trial days and sites.

Water Samples All water samples were collected in 250 ml high density polyethylene bottles that had been soaked for 24 hours in a 2% Decon™ bath and rinsed with ultra-pure MilliQTM water, dried in an enclosed laminar flow drying cabinet and sealed to prevent contamination. In addition to 29 the DeconTM soak, bottles storing trace metal samples were also for 24 hours in a 2% nitric acid bath and then rinsed, dried and sealed as described above.

On each sampling occasion, river water was used three times to thoroughly rinse bottles and caps before collecting water samples from 15 cm below the surface as close as possible to the deployed invertebrates. Sufficient space was left in each bottle for addition of preservative (if required), and to allow expansion of the sample with freezing. Water samples were transported on ice back to the laboratory and preserved awaiting laboratory analysis.

Suspended Solids

Glass fibre filter papers (47 mm diameter; 1.5 m nominal pore size; Whatman grade 934AH, Millipore type AP40) were washed thoroughly with three successive 20 ml aliquots of MilliQTM ultra-pure water. Papers were dried at 103-105oC, cooled to balance temperature in a dessicator and weighed to within 0.1 mg.

Suspended solids samples were processed in duplicate immediately on return from each field visit. Known volumes of water were passed through the pre-weighed filter papers using hand- operated filtering apparatus (Chanin et al. 1958). For water samples comprising excessive suspended material, reduced volumes of sample water were filtered. After sample filtration, three successive 20 ml aliquots were washed through the filtered sample to collect all particulate residue and suction was continued for several minutes. Filter paper and residue were dried at 103-105oC overnight, cooled to balance temperature and weighed (APHA 1992).

2.9.2 LABORATORY ANALYSIS

Standard methods were followed for all laboratory analysis (APHA 1992).

Nutrient Concentrations Total nitrogen and phosphorus concentration samples were digested in DeconTM-washed 30 ml Kimex™ tubes with teflon polytetrafluoroethylene (PTFE) lined rubber disc crew caps. Using Decon™-washed tips and calibrated autopipetter, 10 ml of sample and a 2 ml aliquot of digest reagent (K2SO4) was added to the Kimex™ tubes, screw caps replaced and hand- tightened immediately prior to heating. 30

An Atherton Steam Steriliser autoclave operated at 15 psi was used to digest nutrient samples. Samples were heated to 120oC for an hour, after which the autoclave was cooled to 80oC slowly to maintain the pressure differential. Pressure and temperature within the autoclave were monitored continuously using the fitted pressure gauge and a thermocouple. All digest runs incorporated standards of known concentration and digest blanks to monitor and assess analytical drift and enable correction factors to be applied. Digested samples were then cooled to room temperature and analysed immediately or refrigerated if there was any delay in analysis.

Nutrients were analysed using an automated ion flow injection analyser (FIA) (Lachat Instruments 1992a, 1992b, 1992d, 1992e, Lachat 1994, Lachat 1998). FIA programs quantified total nitrogen in the form of ammonia (Lachat Instruments 1992d, 1992e) and total phosphorus was quantified in its orthophosphate form (Lachat Instruments 1992b, Lachat 1998). A series of intermediate working standards was used to calibrate the FIA output, and all analytical runs incorporated both aqueous standards and blanks as well as digest standards and blanks.

Samples for ammonia analysis were not digested, but otherwise followed the above methods.

Major Anions and Cation Concentrations Concentrations of chloride and sulphate were obtained using the Lachat flow injection analyser (Lachat Instruments 1991, Lachat Instruments 1992c). Calibration standards and blanks were routinely included with each analytical run as standard operating procedure, with a subset of samples duplicated for further quality assurance.

Cation concentrations were quantified using a Perkin Elmer Inductive Coupled Plasma-Mass Spectrometer (Elan 6000 ICP-MS) fitted with a Scott Double Pass spray chamber, a Cross- flow nebuliser and an AS-90 autosampler. All measurements by ICP-MS were calibrated using a series of intermediate working standards, and all analytical runs incorporated both aqueous standards and blanks as a check against contamination levels and to monitor analytical drift and recovery between samples. These standards were prepared on the day of analysis for quality assurance and to avoid any degradation of the standard solutions. Analytical runs followed the operating conditions for multi element analysis outlined in Table 2.4 and were corrected for spectrophotometric background absorptions. 31

Table 2.4: Operating conditions for the Perkin-Elmer Elan 6000 ICP-MS.

RF power 1100W Argon flow rates: Auto Plasma/coolant Auto Auxiliary Auto Nebuliser 0.8 min-1 Dwell time 50 ms Sweeps per reading 16 Readings per replicate 1 Number of replicates 3

2.10 DATA ANALYSIS

A multi-level naturally unbalanced experimental design was used for experiments in this study. Mixed model statistical analysis estimated using restricted maximum likelihood statistic (REML; Searle 1987) was used to test for significant differences in macroinvertebrate responses between treatments or fixed factors using the Genstat statistical package (Genstat5 1993). The dependant variables were macroinvertebrates responses of survival, growth, contaminant uptake and any other physiological effect of the fixed or treatment factors. REML also partitions variation attributable to the “random” factors, which are those factors necessary to conduct the experiment but which were not in themselves of ecological interest (e.g. cage, bag, individuals) (Figure 2.2), and interactions among experimental factors. Unless individuals displayed interdependence within a random factor grouping (e.g. individuals within a holding cage, or cages within a mesh bag), experimental variation could be averaged across the random factors of experimental set-up. Using REML, the significance of variation found between fixed factors for quantitative data was tested against the Chi-square statistic (using both the Wald statistic, and the more accurate but conservative Change-in-Deviance statistic) by computing the change in deviance between the two models; one including the factor, and one excluding it. For response endpoints measured binomially (i.e. survival, escape, molting), the significance test for binomial variables was also estimated using the Chi- square statistic (Zar 1984).

Thus the analysis takes into account any added variance contributed by, for example, day when testing for treatment, and vice versa (Searle 1987). The full statistical model includes all fixed factors and their interactions as additive terms. Where fixed factor comparisons do not 32 produce significant changes in deviance, as described above, that particular term is completely removed from the full model and variation is attributed to a "causal" fixed factor and averaged across other fixed factors. If there is a significant difference in variance by removing a term, it is retained by the model. In so doing, the full model is simplified until it represents the experimental factors which influence the variance of a particular macroinvertebrate response endpoint. This process is repeated with successive iterations, with one term tested for significance with each iteration and removed if the change in variance is found to be non-significant.

In this way, it is possible to determine which fixed factors (e.g. treatment, site, day, season), or any interactions between these fixed factors, are responsible for the variation in the dependant variable or macroinvertebrate response endpoint. Because differences cannot be accurately partitioned across the various fixed factors that were not found to significantly affect the response endpoint, errors are represented by the least squared differences (LSD) value accompanied by the significance value (tested at  = 0.05 unless otherwise stated). The output informs when significant differences exist, but it does not specify where those differences lie. To do this, it is necessary to examine a plot of means for each factor, taking into account the standard error of those means. Where multiple comparisons of treatment pairs were required, the level of significance for each comparison was adjusted according to the Bonferroni method (Day and Quinn 1989), in order to achieve an experiment-wise error rate of 0.05.

This procedure was selected for several reasons. Firstly, these in situ experiments incorporated both fixed and random factors in their design. Secondly, REML is able to partition the variation across the fixed factors, without complication with variation contributed by random factors (Searle 1987). This technique is also well suited to datasets which are naturally unbalanced or which have many missing values (Searle 1987), for example, use of morphological data from individuals retrieved alive only.

3 33

Chapter 3: STUDY REGION AND TRIAL SITES

3.1 UPPER MURRUMBIDGEE CATCHMENT

This project was conducted in the upper Murrumbidgee River catchment, an upland area of approximately 13,000 km2 with all rivers and streams flowing directly or indirectly into the Murrumbidgee River, and eventually the Murray River (Figure 3.1).

Like many upland streams, dominant flow conditions in the study area have produced reaches characterised by riffle pool sequences comprised of heterogenous mixtures of sand through to cobble materials. However, major rivers in the study area are regulated by Tantangara Reservoir at 40 km from the source of the Murrumbidgee River, Corin, Bendora and Cotter dams along the Cotter River at 20 km, 40 km and 70 km from its source respectively and by Googong Reservoir at 80 km from the source of the Queanbeyan River.

Landuse impacts across the study area are mainly urbanization and agriculture, dominated by sheep and cattle grazing, forestry and sizable government holdings by way of national parks and potable water supply areas. Since fieldwork was undertaken, large areas of the catchment were decimated by the summer bushfires of 2002 and 2003.

3.2 STUDY SITES

Details of all project sites employed in this study (Figure 3.1) are presented in Table 3.1.

3.3 CLIMATE

The climate of the Upper Murrumbidgee catchment is characterised as cool temperate, with cold winters and warm summers, with periods of severe drought and floods in response to the rainfall and temperature patterns in the region. The long term total average rainfall (TAR) in Canberra is approximately 660 mm (Figure 3.2), and like most of Australia’s Great Dividing Range, is classified with low to moderate inter-annual variability of 0.658 (calculated from the recorded TAR range as a proportion of the median TAR; Appendix 3, Table A3.4). 34

Figure 3.1: Study area in and around the Australian Capital Territory, Australia. Further information on reference (▲) and impact (□) sites employed in this study is provided in Table 3.1. ACT border is indicated by dashed line and shading represents urban areas of Canberra and Queanbeyan. 35 ) 2 192 366 13.7 5675 5679 6589 231.4 363.3 475.7 721.2 5724.1 Catchment Catchment Area (km 36 18 31 15 66 75 77 190 191 199 222 (km) Distance Distance from source E E E E E E E E E E E o o o o o o o o o o o 7 8 7 148.9530 149.0002 149.051 149.0738 149.0709 149.072 149.0764 148.889 148.9415 148.9497 148.9506 S S S S S S S S S S S o o o o o o o o o o o 6 2 9 7 9 2 1 3 Longitude 20 50 Latitude and Latitude (decimal degrees) 35.45 35.452 35.346 35.324 35.32 35.322 35.363 35.688 35.600 35.513 35.5083 1. 45 560 560 580 500 500 480 550 860 6 580 578 Figure 3. Altitude Altitude (m ASL) n i 7 1 4 4 5 7 4 4 4 6 6 provided Order Stream

er ocations are ocations h Cott upstream te l Si . ng”, 1 km Hut Crossing int yed in this study yed in this ’s Corner s Crossing y’ dge Details for sites emplo Details for Murrumbidgee River Murrumbidgee th Tharwa Bri River Murrumbidgee River at Tharwa Bridge Murrumbidgee River at Tharwa Po Murrumbidgee River upstream of with Murrumbidgee Point Hut Pond upstream of confluence Gudgenby River downstream of Glendale Crossing Gudgenby River downstream of Gudgenby River at “Naas Station” Murrumbidgee River at “Cuppacumbalo of Murrumbidgee River downstream of confluence wit Murrumbidgee River downstream River at pump station Paddy’s River at Murray Cotter River at Vanit m downstream of Cotter Dam Cotter River 200 confluence 100 m upstream of Cotter River at campground, wi Site location 7 8 9 5 6 2 3 4 1 11 10 Table 3.1: 36 ) 2 66 22 51 41 3.1 116 18.6 69.3 18.1 127.7 100.5 106.3 114.8 Catchment Area (km 4 1 21 30 14 18 13 15 6.8 19.5 22.5 23.5 12.5 (km) Distance Distance from source E E E E E E E E E E E E E E o o o o o o o o o o o o o o 4 3 1 2 4 7 3 149.447 149.0773 149.159 149.1194 149.0748 149.0559 149.0443 149.020 149.060 149.226 149.456 149.441 S S S S S S S S S 149.0147 S S S S 149.4567 S o o o o o o o o o o o o o o 4 9 6 2 2 5 1 Longitude 60 Latitude and Latitude (decimal degrees) 35.2151 35.2100 35.407 35.543 35.6194 35.6194 35.585 35.531 35.3150 35.328 35.172 35.2294 35.21 35.216 560 560 540 740 880 880 840 765 560 570 610 570 560 560 Altitude (m ASL) 4 4 3 4 4 1 4 5 2 4 3 2 4 4 Order Stream . Falls ed in this study ed in this Cemetery oy of pl h Drive se upstream Captains Flat township, 50 m oad of Captains Flat township Drive h Murrumbidgee River h Murrumbidgee of orne Drive , 100 m Details for sites em Details for ra reek at Canberra Avenue : o River upstream o River tributary o River downstream o River at “Silver Hills” mberra C umla Creek at Cotter R inderra Creek at Copland Drive ongl ongl ongl ongl (continued) Ginn Smit Ginninderra Creek at Kingsford Ginninderra Creek at Florey Ginninderra Creek at Osb Jerrabo upstream of Gungahlin Ginninderra Creek at Mirrebei Drive, and Yerrabi Ponds Clo Lake Ginninderra off Diddam’s Mol Mol below confluence with Copper Creek Mol Yarral Tuggeranong Creek downstream of Lake Tuggeranong, downstream of Lake Tuggeranong, Tuggeranong Creek wit upstream of confluence Burra Creek at Bur Mol Site location 24 20 21 22 23 17 18 19 14 15 16 12 13 14a Table 3.1 37

Total annual rainfall for 1997 was well below the long term average (Appendix 3, Table A3.6), while above average monthly rainfalls in the latter part of 1998 produced a total annual rainfall in Canberra of 672 mm, slightly higher than the long term average rainfall (Figure 3.2).

1200

1000

800

600

400 Total annual rainfall (mm) 200

0 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year

Figure 3.2: Long term annual rainfall at Canberra (Holt) from 1887 to 2003, showing long term median rainfall of 646 mm (Clewitt et al. 2003).

However, rainfall recorded across the region throughout the study period (Appendix 3, Table A3.6) was below long term monthly means (Appendix 3, Table A3.5), particularly during 1997 and early 1998 (Figure 3.3). Rainfall and stream flow conditions for each field trial are presented in Chapters 4, 5 and 6.

A more detailed discussion of the region’s climatic patterns is provided in Appendix 3. 38

200 Tuggeranong Gunghalin 180 Upper Naas 160 Belconnen Long Term Mean at Holt (1887-2003) 140

120

100

Monthly rainfall (mm) 60

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec B)

200 Tuggeranong Gunghalin 180 Upper Naas 160 Belconnen Long Term Mean at Holt (1887-2003) 140

120

100

Monthly rainfall (mm) 60

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3.3: Monthly rainfall throughout the study area during A) 1997 and B) 1998. Rainfall data for Tuggeranong, Gungahlin and Upper Naas was provided by Hydsys, while Belconnen and long term mean data was obtained from Clewitt et al. (2003).

4 39

Chapter 4: IN THE ABSENCE OF IMPAIRED WATER QUALITY, DOES EXPERIMENTAL SET-UP AFFECT MACROINVERTEBRATE RESPONSES?

4.1 INTRODUCTION

4.1.1 RATIONALE

Ecological experiments require decisions to be made regarding test organisms, equipment and the procedures employed during trial preparation, execution and analysis. For in situ toxicity testing protocols to be reliable and informative for the assessment of water quality, it is fundamental that we understand the potential influence of experimental set-up on macroinvertebrate responses. Failure to recognise the influence of non-treatment effects can potentially confound, or even invalidate, research outcomes, and affect scientific understanding of ecological processes and management decisions.

4.1.2 BACKGROUND

Establishing definitive “cause and effect” linkages is a focus of ecological research, and has long been a problem for environmental researchers. However, identifying causal factors can be further complicated by failure to recognise non-treatment effects, or “artefacts” of the sampling regime or experimental set-up. Practically, manipulation of test biota and introduction of experimental interventions are requisite to both laboratory and field research. However, where treatment effects cannot be properly distinguished from co-variates or random effects of the trial itself, interpretation of research findings are further complicated. Consequently, it is important to elucidate aspects of experimental set-up that may affect test organisms or populations, or the quality of experimental or survey data.

Aspects of experimental set-up such as choice of test population, collection, handling and caging are among the artefacts demonstrated to significantly affect experimental populations, thus affecting research quality and its interpretation (Peterson and Black 1994, Kellison et al. 2003). Ecological studies have demonstrated experimental artefacts associated with sampling effort (Hickford and Schiel 1996, Griffiths et al. 2000, Weinberg et al. 2002), test methods 40 used (Auperin et al. 1997, Cervin and Aberg 1997, Arnaud et al. 2000), sample preservation (Baier and Purcell 1997), test substrates (Arnaud et al. 2000, Clarke et al. 2003), and equipment choice (Benedetti-Cecchi and Cinelli 1997, Gallienne and Robins 2001, Kellison et al. 2003) and aspects of gear or equipment design (Mitchell 1984, Ostrowski 1989, Quinn and Keough 1993, Finch 1995, Hickford and Schiel 1996, Duft et al. 2002).

While many studies report significant experimental artefacts, few have explicitly assessed their influence on research findings (Stewart and Davies 1989, Quinn and Keough 1993, DeMontaudouin 1996, Micheli 1996, McGuinness 1997, Kampichler et al. 1999, Arnaud et al. 2000, Kellison et al. 2003). Handling has been found to affect reproductive endpoints in white suckers irrespective of treatment group (Jardine et al. 1996). Conversely, techniques employed in trials to assess intertidal herbivory were found to be consistent across treatment types (McGuinness 1997).

Despite suggestions that experimental artefacts could lead to inaccurate quantification of ecological processes (Peterson and Black 1994, Hickford et al. 1997, McGuinness 1997, Tanaka and Leite 1998, Gallienne and Robins 2001), if experimental artefacts are shown to be consistent, treatment comparisons may well be valid. Where researchers have considered the potential for practical aspects to confound, some have concluded their chosen methodologies are free from experimental bias (McGuinness 1997, Nieminen and Setala 1997, Kampichler et al. 1999, Eckman et al. 2001). Other researchers endorse calibration or development of compensatory methods for overcoming complicating influences (Candy and McQuillan 1998, Kellison et al. 2003).

4.1.3 OBJECTIVE OF THIS CHAPTER

If in situ toxicity testing techniques are to be useful in the assessment of aquatic impact, it is first necessary to identify any experimental effects (deleterious or stimulatory) of these experimental procedures on the organisms themselves. In this way, effects of experimental logistics (artefacts) may be differentiated from the effects of the pollutant(s) and appropriate assessments made. In this study, experiments were designed to assess the effects of various experimental set-up factors on the growth and survival of caged macroinvertebrates without fear of confusion with other experimental factors as in toxicity tests. These include collection, handling, caging, deployment and retrieval of test organisms. 41

4.2 ADDITIONAL METHODS

Three separate experiments comprise this chapter, each of which examines procedural aspects of the in situ deployment trials as outlined in Table 4.1. Apart from methods detailed here, these experiments follow the general project methods and materials as presented in Chapter 2.

Each of the experiments detailed in this chapter were planned at times and locations where climatic and hydrologic conditions would be relatively stable, and were conducted in environments free from known water quality or habitat impacts. Other experiments detailed in this chapter were conducted in laboratory aquaria filled with water collected from the test organisms’ source site.

Harvesting and Deployment of Test Organisms Test organisms employed for these trials (Table 4.1) were selected from those abundant at source sites as described in Section 2.2). The different caging densities employed in Experiment 3 were based on the reported seasonal abundances of this species (Austrochiltonia sp.) at the source site. Where multiple test species were deployed concurrently, each taxon was caged separately as described in Section 2.4.3. In all instances, only a few hours lapsed between harvesting, caging and deployment of the test organisms as described in Section 2.4.

Endpoint Measurements Pooled across Treatment Replicates As it was impossible to obtain accurate wet weights for individual amphipods in Experiment 3, average wet weights were obtained by dividing the total wet weight of all individuals retrieved alive from a particular deployment cage and dividing it by the number of individuals comprising this sample. As there was no internal replication, REML analysis could not be performed as described in Section 2.10. In this instance, a linear regression (and the associated Analysis of Variance) was performed to compare the response variable (average wet weight) with the fixed treatment factors under consideration (i.e. density and trial day).

4.3 RESULTS

Prevailing environmental conditions, field measurements and descriptions of macroinvertebrate body size data are presented in Appendix 4. 42

 y 2 km aquaria up. - ately o River , Universit ogy ongl te 14 te 20 approxim upstream of dam wall Laboratory CRC for Freshwater Ecol Canberra Experimental Site Experimental Si Mol upstream of Captains Flat township Si Lake Ginninderra ages (3 replicates) individuals (5 per cage) individuals (5 per c cage) individuals (5 per cages (3 replicates) individuals (5 per cage) cages (5 replicates) individuals (5, 15 or 25 per cage) cages (3 replicates) Random Experimental Factors Experimental Random invertebrate response to aspects of experimental set aspects of experimental response to invertebrate 3) test f y (n = gin o ori sms (n = 2) al tri day (n = 7) day (n = 7) day (n = 6) day (n = 4) - eatment Factors eatment al al al al re mesh size (n = 2) tri tri p organi tri caging densit tri Tr e three separate trials examining macro trials examining e three separate th f ebiidae) sp. Experimental detail o Experimental nal site information for the sites employed in these trials is provided in Table 3.1. nal site information for the sites employed eroptera: Leptophl Additio  Austrochiltonia (Amphipoda: Ceinidae) (Odonata: Lestidae) Paratya australiensis (Decapoda: Atyidae) Atalophlebia australis (Ephem Austrolestes cingulatum Experiment 3 Experiment 2 Experiment 1 Table 4.1: Test Species (Order: Family) 43

4.3.1 EXPERIMENT 1: EFFECT OF CAGED DEPLOYMENT ON

MACROINVERTEBRATES

Aim As it is necessary to be able to retrieve test organisms during in situ field trials, it is important to identify whether macroinvertebrate responses may be confounded by the cages in which they are deployed. Experiment 1 also examined the effect of different cage mesh size on test organisms.

Macroinvertebrate Responses – Atalophlebia australis and Austrolestes cingulatum Experimental Effects on Survival Of the 317 individuals deployed in cages at their source site, only 23 mortalities were recorded over the 24 day trial period, however, only two of these individuals were Atalophlebia australis (Figure 4.1). With survival for Austrolestes cingulatum decreasing substantially after trial day 6, only 53% of individuals were retrieved alive at the end of the experiment. None of the variation in survival was attributed to interaction between the treatment factors, and survival was not significantly affected by cage mesh (χ2 statistic = 3.026, df = 1, p = 0.085).

100

40 Survival (%) 30 Austrolestes cingulatum 20 Atalophlebia australis 10

0 0 5 10 15 20 25 Trial day Figure 4.1: Percent survival of test organisms retrieved after deployment in holding cages during Experiment 1. 44

However, survival was significantly different between test species (χ2 statistic = 26.819, df = 1, p < 0.0001) and trial day (χ2 statistic = 28.532, df = 7, p < 0.0001).

Experimental Effects on Body Size There was weak evidence that experimental day had an effect on the increase in head widths for the two test species (Δ-in-deviance statistic = 11.64, df = 6, p = 0.0705), however, it was relatively small in comparison with differences between the head widths of the two species from trial day 0 (Δ-in-deviance statistic = 22.14, df = 1, p = 0.000003; Figure 4.2).

3.5 Atalophlebia australis LSD = 0.2788 Austrolestes cingulatum 3 p < 0.0001

2.5

1.5 Head width (mm) 1

0.5

0 Day 0 Later Trial day Figure 4.2: Mean head width for Austrolestes cingulatum and Atalophlebia australis deployed in cages at their source site for 24 days during Experiment 1. LSD = Least squared differences (2 x standard error of differences).

There were no significant effects of the interaction between trial day and mesh (χ2 statistic = 2.5, df = 6, p = 0.87), on the head widths of Atalophlebia australis, however, their heads were significantly different (-in-deviance statistic = 5.554, df = 1, p = 0.018) between the mesh sizes (Figure 4.3). While there was weak evidence that heads of the caged A. australis nymphs increased in width with experimental day (χ2 statistic = 12.1, df = 6, p = 0.06), individuals retrieved alive from cages made with finer mesh panels had significantly wider heads (mean = 1.41  0.45 mm, n = 104,) than those retrieved alive from in cages with coarser mesh (mean = 1.26  0.36 mm, n = 108,) as depicted in Figure 4.3. 45

1.6 LSD = 0.1980 1.5 p = 0.018

1.4

1.3

1.2

1.1 Head width (mm)

0.9

0.8 Day 0 Retrieval day (800μm) Retrieval day (500μm) Trial day Figure 4.3: Mean head width for Atalophlebia australis collected on trial day 0 and retrieved alive after deployment at their source site for up to 24 days in holding cages with 500 m or 800 m mesh panels during Experiment 1. LSD = Least squared differences (2 x standard error of differences).

4.3.2 EXPERIMENT 2: EFFECT OF TEMPORARY TANK STORAGE ON

MACROINVERTEBRATES

Aim If such in situ field trials are to be useful in assessing aquatic conditions, an adequate sample population of a suitable test species (see Section 2.2) must be available for field deployment and times and locations that suit experimental objectives. In circumstances where such sample populations may not be readily available, it may be useful to be able to store test organisms under controlled conditions prior to field deployment. However it is important to identify whether macroinvertebrate responses after their deployment in the field may be affected by a prior period of temporary storage in laboratory aquaria.

Macroinvertebrate Responses – Paratya australiensis Experimental Effects on Survival Of the 270 individuals deployed in cages at their source site, only five mortalities were recorded over the 12 day deployment trial period; four of the individuals that had been stored 46 temporarily in laboratory aquaria were retrieved dead after trial day 9, and the other was retrieved dead within 24 hours of having been collected and deployed immediately as part of the “wild” treatment. Survival was not significantly affected by the interaction between treatment and trial day (χ2 statistic = 4.793, df = 4, p = 0.325), treatment (χ2 statistic = 1.817, df = 1, p = 0.2), or trial day (χ2 statistic = 7.215, df = 6, p = 0.32).

Experimental Effects on Body Size Size at Deployment At the commencement of the deployment trial, wild and “tank” treatment individuals were not significantly different in size despite the latter having been temporarily stored in laboratory aquaria for 13 days prior to deployment (Table 4.2).

Table 4.2: Paired t-test comparing body size at deployment on trial day 0 of Paratya australiensis for wild and “tanked” treatments in Experiment 2.

Endpoint F test Student’s t test Carapace length F = 1.15, df = 22, 10, p = 0.859 t = 0.27, df = 32, p = 0.79 Orbital carapace length F = 1.09, df = 20, 10, p = 0.823 t = 0.29, df = 30, p = 0.78

Logn-transformed F = 1.09, df = 20, 10, p = 0.924 t = -0.16, df = 30, p = 0.87 wet weight

Orbital Carapace Length There were no significant effects on orbital carapace length of the interaction between trial day and treatment (χ2 statistic = 3.0, df = 6, p = 0.8), trial day (χ2 statistic = 4.9, df = 6, p = 0.55) or treatment (χ2 statistic = 0.0, df = 1, p  1) with average orbital carapace length of wild individuals (mean = 3.814  0.56 mm, n = 149) only marginally smaller but less variable than individuals that were temporarily stored in tanks (mean = 3.820  0.80 mm, n = 106). 47

Carapace Length There were no significant effects on carapace length of the interaction between trial day and treatment (χ2 statistic = 5.8, df = 6, p = 0.46) or trial day (χ2 statistic = 1.3, df = 6, p = 0.97). However, there was strong evidence of a significant effect of treatment (Δ-in-deviance statistic = 13.91, df = 1, p = 0.0002) with the carapaces of “wild” treatment individuals retrieved alive measuring 10% longer (mean = 7.19  1.11 mm, n = 148) than those individuals that were retrieved alive having been temporarily stored in laboratory aquaria (mean = 6.49  1.46 mm, n = 103) for 13 days prior to deployment (Figure 4.4A).

(A) (B) 7.4 3.9 Carapace length (mm) Logn--transformedn wet weight (mg) lnWWx2

7.2 LSD = 0.35 LSD = 0.138 3.8 p = 0.0002 p = 0.0002 7

3.7 6.8

6.6 3.6

Carapace length (mm) 6.4 -transformed wet weight (mg) n 3.5

6.2 Log

6 3.4 Tanked Wild Tanked Wild Experimental treatment

Figure 4.4: (A) Mean carapace length and (B) mean logn-transformed wet weight for Paratya australiensis retrieved alive after deployment at their source site for 10 days during Experiment 2. Tanked individuals were held in laboratory aquaria for 10 days before deployment; wild individuals were collected and immediately deployed. LSD = Least squared differences (2 x standard error of differences).

Wet Weight

There were no significant effects on logn-transformed wet weight of the interaction between trial day and treatment (χ2 statistic = 6.3, df = 6, p = 0.41) or trial day (χ2 statistic = 9.4, df = 6, p = 0.17). However, there was strong evidence of a significant effect of treatment (Δ-in- 48 deviance statistic = 14.65, df = 1, p = 0.00013). Paratya australiensis individuals that were harvested, caged and deployed immediately to the source site on trial day 0 had significantly higher logn-transformed wet weights (mean = 7.31  1.96 mg, n = 103) than individuals deployed after being held for 13 days in laboratory aquaria before caging and deployment at the same source site (mean = 6.52  2.39 mg, n = 103; Figure 4.4B).

4.3.3 EXPERIMENT 3: EFFECT OF STOCKING DENSITY ON MACROINVERTEBRATES

Aim As in situ field trials require the periodic retrieval of deployed test organisms, it is important to identify whether macroinvertebrate responses may be influenced by the density under which they are caged. Given that macroinvertebrate communities vary considerably with numerous habitat and climatic factors, and it is therefore unlikely that source populations will boast the same number of individuals of a selected test species at all times, there may be instances where otherwise comparable trials employ different caging densities of test organisms.

Macroinvertebrate Responses – Austrochiltonia sp. Experimental Effects on Survival Of the 541 field-collected individuals deployed in cages of varying densities in laboratory aquaria, only 10 mortalities were recorded during the 13 day trial period, six of which were retrieved on the final day from the high density treatment. Single dead individuals were also retrieved from the high density treatments on trial days 1 and 3 and from the medium density treatment on trial day 8. Survival was not significantly affected by the interaction between density and trial day (χ2 statistic = 10.93, df = 6, p = 0.095), density (χ2 statistic = 3.773, df = 2, p = 0.07), or trial day (χ2 statistic = 7.255, df = 3, p = 0.14).

Experimental Effects on Body Size Dorsal Length There were no significant effects on dorsal length of interaction between trial day and treatment (χ2 statistic = 3.7, df = 6, p = 0.77), trial day (χ2 statistic = 0.6, df = 3, p = 0.9). However, there was a significant effect of caging density (Δ statistic = 7.63, df = 2, 49 p = 0.0211; Figure 4.5) with dorsal lengths of individuals deployed in high density cages (mean = 4.591 mm  0.88 mm, n = 255) significantly longer than those deployed in medium (mean = 4.076  0.82 mm, n= 170) or low density (mean = 4.165  0.78 mm, n = 55) cages respectively.

4.8

4.7 LSD = 0.622 p = 0.0211 4.6

4.5

4.4

4.3

4.2

Dorsal length (mm) 4.1

3.9

3.8 Low Medium High Stocking density Figure 4.5: Mean dorsal length for Austrochiltonia sp. retrieved alive after deployment in cages for 13 days at different densities during Experiment 3. Low medium and high densities refer to 5, 15 and 25 individuals per cage respectively. LSD = Least squared differences (2 x standard error of differences).

Surface Area

There were no significant effects on logn-transformed surface area of the interaction between trial day and density (χ2 statistic = 3.4, df = 6, p = 0.75), trial day (χ2 statistic = 1.8, df = 3, p = 0.63) or density (χ2 statistic = 3.3, df = 2, p = 0.21).

Wet Weight There were no significant effects on average wet weight of treatment density, experimental day or the interaction between trial day and density (Table 4.3). 50

Table 4.3: Analysis of Variance (ANOVA) table for regression between average wet weight and stocking density within deployment cages during Experiment 3.

Source df Sum of Mean Square F ratio p Squares

Density 2 0.3688 0.1844 1.516 0.239 Trial day 3 0.0389 0.1301 0.107 0.955 Density x day 6 0.1586 0.0264 0.217 0.968 Residual 25 3.0391 0.1216 Total 37 3.6055 0.0974

4.4 DISCUSSION

4.4.1 EXPERIMENTAL RESULTS

Of the species deployed in these experiments, only Austrolestes cingulatum suffered significant mortality when multiple individuals were caged together, where within-cage cannibalism, even when individuals were similar in size, reduced the overall survival to 53% by trial day 24 (Figure 4.1) within the holding cages. While there was minimal mortality in most of the trials, Table 4.4 summarises the effects of aspects of experimental set-up on macroinvertebrate body size.

Effects of Caging on Body Size of Macroinvertebrate Test Organisms Few of the test organisms employed in this study showed adverse effects of caging itself, in fact there is some argument that organisms thrived in caged conditions, possibly attributable to the protection from predators afforded by the holding cages themselves (Persson and Eklov 1995, Franken et al. 2006) or the availability of food on the cage surfaces (Broekhuizen et al. 2002, Eggert and Wallace 2003, Holomuzki and Biggs 2006). Generally, there was only minimal increase in body size during the trial when organisms were caged and returned back to their source sites. The exception being Austrolestes cingulatum, where a 51% increase in head widths across the trial (Figure 4.2) can be clearly linked to cannibalism (Hopper et al. 1996) within the holding cages where only slightly more than half the deployed individuals survived. 51 - n 

th related to gure 4.5) gns wi only (Figure 4.2) ali m. s probably als. Unless specifically als. Unless s tri ic ngulatum ndividuals (Fi i ty ensi (Figure 4.3) . Endpoints tested were head width (HW), . Endpoints tested yment cage. 6 (Figure 4.1) and i ow) d Austrolestes ci Austrolestes cingulatum > l > 800 μm Brief description of effect Brief description n experimental logist n experimental in deplo i th edium gy at α = 0.05 lo wi samples (avWW). samples (avWW). tests > (m y ty head width for  dth at 500 μm ** survival after trial day carnivory gh densi head width over time for head width over  Head wi 4.4) Wild > tanked individuals (Figure Hi

tained from pooled NS NS NS NS NS macroinvertebrate morpho macroinvertebrate ing Interaction (p < 0.0001) species x day  NS NS NS NS NS Day r fixed factors affect r fixed factors fo transformed individual wet weight (lnWW), orbital carapace length (OCL), dorsal length (DL) and log carapace length (OCL), dorsal wet weight (lnWW), orbital transformed individual Experimental Fixed Factors Experimental Fixed  NS NS - n p = 0.018 p = 0.0211 p = 0.0002 p = 0.0002 Treatment y tests = 0.05.  ‡ surface area (lnSA) and average wet weight ob surface area (lnSA) CL DL HW OCL lnSA HW** lnWW avWW Response Endpoint s of probabilit m) s) vs  Result (REML) probabilit from Restricted Maximum Likelihood stated, results are (CL), log carapace length transformed y feeding ies) vs 800 indicates probability tests derived from Analysis of Variance regression. indicates probability tests derived from indicates that there was a significant interaction term involving this factor, partitioning of experimental variation must consider this interaction ter term involving this factor, partitioning of experimental variation must indicates that there was a significant interaction nal m NS refers to “not significant” at  ‡ io  Caging Densit (3 densit Mesh Size (500 Tanked (13 day Wild Individuals Taxa of different Taxa of different funct groups Treatment Table 4.4: 52

Effect of Mesh Gauge on Body Size of Macroinvertebrate Test Organisms Head widths of Atalophlebia australis deployed in the finer mesh (500 μm) for this study were found to be significantly larger than those deployed in the coarse mesh (800 μm) cages (Figure 4.3). Over the trial duration, head widths in finer mesh cages increased by 35% compared with a 21% increase in head width for those in 800 μm mesh cages). This is likely to correspond with the finer mesh providing greater surface area, and thus epilithon and periphyton growth, on which the confined nymphs can graze (Broekhuizen et al. 2002, Eggert and Wallace 2003, Holomuzki and Biggs 2006). Although use of an even finer cage mesh could have supported greater food source for caged organisms, it is likely that the cage microclimate would be affected by processes related to increased surface tension or clogging (Zelt and Clifford 1972, Slack et al. 1991).

Various studies report that choice of net mesh size affects community composition and abundance of macroinvertebrate samples (Zelt and Clifford 1972, Resh 1979, Mitchell 1984, Storey and Pinder 1985, Williams 1985, Slack et al. 1991, Reynoldson and Rosenberg 1996, Tanaka and Leite 1998, Sobrino et al. 2000, Crewe et al. 2001, Callaway et al. 2002). Macroinvertebrate sampling nets made from finer mesh have been found to capture more animals and species richness through a wider size range of individuals (Zelt and Clifford 1972, Slack et al. 1991) as well as greater sediment and debris (Downing 1984). However, such samples have been deemed inaccurate representations of the benthic community as larger animals are lost over the net rim (Resh 1979) as a result of net clogging (Zelt and Clifford 1972, Slack et al. 1991).

While meshed cages or enclosures have been found to protect larger organisms from predation, certain studies have reported that molluscs suffer reduced growth, increased mortality and stress when flow rates are reduced by mesh or netting (Dutra et al. 1996, Beal and Krause 2002). Depending on the environment, denser mesh may clog with sediment (Dutra et al. 1996) or biofoul (Mitchell 1984, Widman and Cooper 1996, Hodson et al. 1997, Eckman et al. 2001) more readily, thereby potentially preventing waste or water exchange across the mesh. This may deleteriously affect enclosed organisms, and all but the most tolerant organisms may perish.

Conversely, coarser mesh would avoid these problems, however there is a greater chance that predators may gain access, or that test organisms may escape (Fenske 1997, Crewe et al. 53

2001). Individuals enclosed in mesh with larger apertures may be expected to favour organism condition (Penney and Mills 1996) as the coarser mesh would interfere less with flow dynamics (Dutra et al. 1996, Obeirn et al. 1996, Winterbottom et al. 1997), enable bigger particulate matter to pass through the mesh and facilitate better exchange with stream water (Cole et al. 1996, Beal and Krause 2002). However, larger mesh would also affect taxa selection, limiting the choice to late developmental stage individuals or large organisms. There is also the possibility that test organisms and their response endpoints could be influenced by other biota passing uncontrolled across the mesh (Stewart and Davies 1989).

Nonetheless, it would interesting to further investigate the relationship between growth of caged organisms across a range of mesh sizes and functional feeding groups such as predators, suspension feeders as well as grazers.

Effect of Stocking Density on Body Size of Macroinvertebrate Test Organisms In this study, dorsal length was the only endpoint to be significantly affected by the density of caged amphipods. However, given the density-dependant nature of cohort and resource competition (Tonn et al. 1994, Bohlin et al. 2002, Heins et al. 2002), it is somewhat surprising that the caged amphipods in these experiments grew more at the higher densities (Figure 4.5). While amphipods from the high density treatment recorded greater body dimensions than other treatment densities (Figure 4.5), only dorsal length was significantly different despite the high correlation between individual lengths and lateral surface area (r2 = 0.818, n = 503).

The morphological endpoints used to assess body size, however, did not take into account the tightness of the dorsal arc, where the carapace margin of the more tightly curved individuals was harder to differentiate, potentially reducing the precision of digital image analysis techniques used to measure the lateral surface area of each amphipod. Given that amphipod gills are located on their ventral surfaces, the tightness of this curve may influence respiration and gas exchange.

It is also important to consider that the relationship between density and organism responses may differ according to demography of the test population, and that density is not solely related to the number of organisms in a given space, but is a function also of organism size (Lazo and Chapman 1998). This was demonstrated by Barry et al. (1995) where organisms employed in static toxicity tests grew during the test, making it difficult to establish which 54 treatment factor induced particular stress responses; the toxicant or the increase in density as a function of increased size of the test organisms. Unless all individuals are relatively similar in size and age, both the low and high density groups should have comprised a similar size range of individuals.

Studies involving highly stocked populations have confirmed stresses associated with increased proximity including physical damage (Fenske 1997, Lenihan and Micheli 2000), morphological abnormalities (Chen and Chen 2001) and increased susceptibility to other stressors (Linke-Gamenick et al. 1999, Moe et al. 2002) or infection (Plumb et al. 1994, Tonn et al. 1994, Shoemaker et al. 2000). While Austrochiltonia sp. survival was not significantly affected during this trial, many have reported correlations between higher density and reduced survival (Brunkow and Collins 1996, Tucic et al. 1997, Flecker et al. 1999, Linke-Gamenick et al. 1999, King et al. 2000, Walker 2001). Increased density has also been linked to adverse effects on organism fecundity (Tucic et al. 1997), growth (Emmett 1985, Simkiss et al. 1993, Jess and Marks 1995, Southgate and Beer 1997, Taylor and Scott 1997, Deming and Russell 1999, Grice and Bell 1999, Agnew et al. 2002, Bohlin et al. 2002), development (Iba et al. 1995, Jess and Marks 1995, Taylor and Scott 1997, Tucic et al. 1997, Walls 1998, Agnew et al. 2002), morphology, reproductive output (Egonmwan 1992, Taylor and Scott 1997, Tucic et al. 1997, Wahle and Peckham 1999), physiology (Iba et al. 1995, Heins et al. 2002) as well as aggression (Adams and Tschinkel 1995), feeding behaviours (Jess and Marks 1995) and habitat preferences (Glova 2002).

Perhaps the treatment densities employed in this study were not sufficiently high to challenge the amphipods’ resource needs, or that resource pressure on amphipod body size would have become more evident over a longer time period. It is also possible that the high density treatment may have indirectly favoured amphipod growth, through greater within-cage circulation, or that their stresses were mitigated through adaptive behaviour (e.g. Gomez LaPlaza and Morgan 1993). As relationships between density and organism condition are unlikely to be linear, further research is warranted to examine organism survival and growth over a greater range of treatment densities and trial durations.

Effect of Temporary Storage on Body Size of Macroinvertebrate Test Organisms While there was minimal mortality during either the period of tank storage or during field deployment, shrimp (Paratya australiensis) collected and deployed immediately were 55 significantly larger and heavier than individuals stored in laboratory aquaria before caging and deployment (Figure 4.4).

In these trials, “wild” individuals remained in their natural environment prior to collection and immediate deployment, and could therefore have increased in size during this 13 day pre-trial period. These individuals were able to graze in their natural environment, and could feed on attached biofilms plus suspended material and any allochthonous material being introduced or re-suspended by biotic factors or abiotic conditions in their natural environment. In contrast, individuals stored in laboratory tanks for the 13 days were not provided physical habitat or food, and their only nutritional resources were those suspended in the source site water used to fill the aquaria, which may over time have been degraded or consumed by the shrimp or aquatic micro-organisms. Size and condition of captive individuals may also have been affected by reduction in body reserves from continual activity, lack of gut contents and tissue atrophy in the low food environment as well as stress from disruption and greater physical damage from double handling than those that were collected, caged and deployed without transfer to the laboratory and back to the site for deployment.

Ideally, it would be possible to somehow confine individuals in their source environment, thus not denying them food and habitat resources available to the unconfined individuals. While all shrimp were originally collected from the same population, condition and size of confined individuals would have been adversely affected by storage in laboratory tanks prior to deployment. Thus, it is surprising that the two trial day 0 groups were not significantly different in body size, particularly biomass (Table 4.4). Despite having comparable sizes at the time they were deployed in the field, significant differences in body size between the treatments suggest that the adverse effects of tank storage affected their performance after deployment at their source environment. It may be reasonable to expect that such trial effects, or their magnitude, may correlate with the duration of the storage period. It would be interesting to compare effects between wild and confined organisms over a range of confinement durations, and for aquaria where food and/or refugial habitat are available. However, efficacy of temporary storage on test organisms cannot be properly assessed from trials on one species, and further examination employing different test species is clearly required.

Provided storage conditions are appropriate to the organisms and their life history characteristics, scientific literature demonstrates few adverse effects of confining aquatic 56 organisms in holding tanks. Considerable research has examined the effects of confinement on a range of biota, however many studies involve fish, crustacean and mollusc taxa, and have been undertaken in relation to various aquacultural industries.

Various aquatic organisms have been successfully held in storage ponds or tanks with relatively low mortality include molluscs, crustaceans, fish, cephalopods and various insect taxa (e.g. Robinson et al. 1988, Munzinger and Monicelli 1991, Dejean and Lachaud 1992, Hanks et al. 1993, Baker 1994, Jones 1995, Smith and Wilson 1995, Hart et al. 1999). Studies comparing wild and captive organisms have reported comparable growth rates (Bohlin et al. 2002, Kellison et al. 2003), response to caging (Fenet et al. 1996), oocyte size (Morehead 1998), genetic variability (Arnaud-Haond et al. 2003) and feeding behaviours (Cohen 2000, Clarke et al. 2003, Kellison et al. 2003). Confinement was not found to induce morphological abnormalities in cultured amphipods (Robinson et al. 1988) or trochus (Clarke et al. 2003), and physiologies were not found to differ between confined and unconfined hand-caught lobsters (Paterson et al. 1997) or fish (Carragher and Rees 1994, Croke and McDonald 2002). In fact, laboratory conditions have been found to enhance sexual reproduction and growth of captive corals (Borneman and Lowrie 2001) and shell weight in cultured trochus (Clarke et al. 2003). Various authors also demonstate that organism response to toxicant exposure is not affected by confinement (Dahl and Blanck 1996, Fenet et al. 1996, Benhra et al. 1997, Fenet et al. 1998).

Various studies have demonstrated different growth rates between wild and confined individuals of various taxa (Walz 1977, Knights 1982, Hart et al. 1999, Bohlin et al. 2002, Clarke et al. 2003) and a higher incidence of morphological abnormalities in hatchery-bred fish larvae, leading to significantly reduced growth and development rates (Hilomen-Garcia 1997). Confinement has also been found to adversely affect physiology (Pickering et al. 1991, Pankhurst and Sharples 1992, Dahl and Blanck 1996, Hart et al. 1999, Pecl and Moltschaniwskyj 1999, Davis et al. 2001) and organism response to water or habitat contamination (Robinson et al. 1988). Not surprisingly, certain species are particularly sensitive to hydrostatic stress (Charmantier-Daures and Charmantier 1991) often linked with design of tanks and circulatory systems, pathogens (MacMillan et al. 1994), or prevailing conditions in the holding environment (Walz 1977, Munzinger and Monicelli 1991, Karney et al. 1996, Foyle et al. 1997). 57

As culturing or breeding macroinvertebrates has proven difficult (Persoone and Janssen 1993), it may be valuable to have the option of storing test organisms under laboratory or controlled conditions prior to field deployment. A ready source of test organisms would then be available for deployment to undertake field assessment of changes to flow or water quality with minimal delay. It would also allow experimental work to be adjusted for field conditions, resource difficulties or where field safety concerns arise or where it is impractical to source test organisms and deploy them without considerable time delays. Alternatively, such a ready source could be useful in instances where it may take several collections over time to amass sufficient individuals necessitated by experimental design requirements, especially where source environments and communities are contracted because of prevailing conditions such as drought. In addition to these practical issues, having the flexibility of storing test organisms temporarily between collection and experimental set-up may facilitate differentiation between the effects of collection and transport from those of any effects of handling and caging during experimental deployment.

4.4.2 CONCLUSION

Artefacts of experimental design and procedures are virtually inevitable. And while adequate experimental controls and corrective measures would ideally be available to facilitate differentiation between treatment and non-treatment effects, this is clearly not always possible. Aspects of experimental set-up have confounded observational studies in a range of ecosystems (Burger and Louda 1994, Mumm and Sell 1995, Richardson et al. 1999, Connell 2002, Shima and Osenberg 2003) and have long complicated the interpretation of toxicity tests (McCann and Hitch 1980, Martin et al. 1993, Pottinger and Calder 1995, Midtlyng 1997).

Results presented in this chapter highlight the need to consider the test organisms and testing environment when selecting experimental set-up methods and procedures. However, it is important to note that aspects of experimental set-up may affect various test organisms differently (e.g. Figure 4.1), and that not all response endpoints for a given species will concur as to the effect of particular treatment factors or their magnitude (e.g. Austrochiltonia sp. in Experiment 3) as summarised in Table 4.4.

Given that the in situ protocols proposed by this thesis intend to employ endemic macroinvertebrate populations as the source for test organisms, it is likely these populations 58 will be somewhat variable in age, size, and condition. As a consequence, it is likely their response to treatments, and of course aspects of experimental set-up, may also vary. Despite experimental artefacts, employment of caged macroinvertebrates for in situ biological assessment of instream condition is promising, and has the potential to make biomonitoring techniques more ecologically-relevant to endemic organisms and natural field conditions. Even when treatment effects seem clear, it is important to consider potential biases and interpret results accordingly as determining with confidence that experimental artefacts can be effectively differentiated from treatment responses is not simple.

These results do not provide clear advice for the set-up of such types of experiments in the future, however, they do highlight the need for researchers to rationalise choices made in relation to experimental logistics. While there are no easy solutions for avoiding experimental artefacts, or even for ensuring their effects are consistent, ignoring their potential impact on test organisms will clearly not improve the quality of research or its interpretation.

5 59

Chapter 5: HOW DO MACROINVERTEBRATES RESPOND TO SITE RELOCATION?

5.1 INTRODUCTION

5.1.1 RATIONALE

Biological monitoring commonly involves the assessment of endemic biota at field sites, particularly those suspected to have impaired water or habitat quality. Where suitable test organisms cannot be sourced at the test site, studies have involved the relocation of test organisms to study sites. However, few empirical studies have specifically investigated the relocation on the transplanted organisms themselves.

5.1.2 BACKGROUND

Biota have been intentionally relocated for many reasons, including conservation of valuable ecosystems (Gydemo 1994, Hansten et al. 1997, Villella et al. 1997), remediation or stabilisation of degraded habitats (West et al. 1990, Pant and Singh 1995, Rinkevich 1995, Borneman and Lowrie 2001, Raymundo 2001, Rinkevich 2005), biological control (Miller 1985, Van Thielen et al. 1994, Calumpang et al. 1995, Schooler et al. 2003), and of course to expand the aquaculture industry (Carriker 1992). Such transplantation exercises have involved the relocation of “chunks” of entire ecosystems, including substrate material (e.g. West et al. 1990, Rinkevich 2005), specific taxa (e.g. Dobbinson et al. 1989), or deployment of bioindicator or “sentinel” organisms used to assess specific interactions or causal relationships (Phillips and Rainbow 1984, Thirb and Benson-Evans 1985, Yap and Gomez 1985, Lin et al. 2002).

Such trials have also greatly helped ecologists untangle complex ecological processes governing distribution (Hodgson 1980, Dobbinson et al. 1989, Marsden 1991, Carlton 1992, Streever et al. 1996, Dick et al. 1997, Lohrer and Whitlatch 2002), patchiness (Stiling and Rossi 1994) and performance (Razek 1992, Crivelli 1995, Healey and Prince 1995, Neale et al. 2001). In particular, reciprocal transplant studies have investigated drivers behind habitat preference (Thirb and Benson-Evans 1985, Dobbinson et al. 1989, Lesser et al. 1994, DeMontaudouin 1996, Gough and Grace 1998, Stokes et al. 1999) and tolerance limits 60

(Berrill et al. 1985, Dyrynda 1992, Rosemund et al. 1992, Titus and Hoover 1993, Coles and Ruddy 1995, Moore et al. 1996, Gough and Grace 1998, MacNeil et al. 2000). In relation to bioassessment, organisms have been transplanted as a way of improving ecotoxicological methods (Shindo and Otsuki 1999), validating or “ground-truthing” laboratory-derived toxicity data (Borthwick et al. 1985) or improving the relevance of abiotic monitoring equipment such as semi-permeable membrane devices (Prest et al. 1995, Hofelt and Shea 1997). However, little has been documented on the effect of the relocation on the organisms themselves, and most studies that do investigate relocation between sites or environments involve aquaculture species.

As organisms found naturally in contaminated environments are often highly tolerant or have developed acclimation or physiological strategies to cope (e.g. Booij et al. 2002), considerable benefits can be achieved by development of reliable in situ protocols using appropriate biota as sentinels (Phillips and Rainbow 1984, Maher and Norris 1990, Marsden 1991, Haynes et al. 1995, Cairns et al. 1996). While relocated organisms will generally respond to their new environment rather than their source site (Harris 1984, Hinch and Green 1989, Brock and Casanova 1991, Lowe et al. 1994, Sacco et al. 1994, Idestam-Almquist and Kautsky 1995, Chapman 2000, Carnegie and Barber 2001, Lambrinos 2002), too many studies assume that deployment of test organisms removes site-based variation in organism responses.

5.1.3 OBJECTIVE OF THIS CHAPTER

If in situ biomonitoring techniques are to be useful in the assessment of aquatic impact, it is first necessary to determine the effect of relocation on the organisms themselves. Failure to distinguish any transplant effect from organism’s response at the transplant site may confound our scientific understanding, and potentially mislead management decision-making processes. By relocating endemic macroinvertebrate taxa to transplant sites that are considered to be “similar” or “dissimilar” to their source site, this chapter aims to investigate the effects of relocation on the organisms, and whether the nature of the transplant site affects these responses. 61

5.2 ADDITIONAL METHODS

Four separate experiments comprise this chapter, with two experiments each examining the responses of test organism relocated to sites that are either “similar” and “dissimilar” in nature. In all experiments, organisms harvested for the trial were caged and deployed at their source site as well as the deployment sites selected for the specific trial. Details of these four component experiments are provided in Tables 5.1 and 5.2. Apart from methods detailed here, these experiments follow the general project methods and materials as presented in Chapter 2.

Site Selection All sites employed for these experiments conformed to “reference” condition (Reynoldson et al. 1997, Reynoldson and Norris 2000, Stoddard et al. 2006) and had previously been used to construct and validate AUSRIVAS (Australian River Assessment System) predictive models (Davies 2000, Simpson and Norris 2000). Similarity or dissimilarity in the nature of source and transplant sites was based on the site group classifications derived from relevant AUSRIVAS predictive models whose predictor variables are detailed in Section 2.6. The probabilities of a particular transplant sites belonging to a given AUSRIVAS site group is presented in Appendix 5, Table A5.1. Each of the experiments detailed in this chapter were planned at times where climatic and hydrologic conditions would be relatively stable, and pre- trial reconnaissance visits were conducted to ensure that sites were free of known water quality or habitat impacts.

Harvesting and Deployment of Test Organisms Only macroinvertebrate families known to be relatively sensitive to aquatic disturbance (Lenat 1993a, Chessman 1995, Chessman et al. 1997, Chessman and McEvoy 1998, Chessman 2003) were considered for deployment in these transplant trials. Potential test organisms were then short-listed for use in these trials based on the likelihood of occurrence derived from relevant AUSRIVAS predictive models for both source and transplant selected (Appendix 5, Table A5.2). However, the test organisms ultimately used in the transplant trials were those that were found to be sufficiently abundant at source sites to satisfy the statistical power requirements of the experimental design (Section 2.5). Where multiple test species were deployed concurrently, each taxon was caged separately as described in Section 2.4. In all instances, only a few hours lapsed between harvesting, caging and deployment of the test organisms as described in Section 2.4. 62 Cotter Dam f rner on y’s Crossing  River at Naas Stati iver 200m downstream o iver 200m downstream ’s River at Murray’s Co Part A: Relocation of test organisms between sites organisms between of test Part A: Relocation . te 2: Cotter R River at Tharwa Bridge te 9: Murrumbidgee te 7: Gudgenby te 1: Cotter River at Vanit te 5: Paddy Bridge te 9: Murrumbidgee River at Tharwa relocation Experimental Sites Source Site Si Transplant Site Si Source Site Si Transplant Sites Si Si Si invertebrate response to invertebrate Random Experimental Random Experimental Factors cage) individuals (1 per cages (5 replicates) cage) individuals (1 per cages (4 replicates) individuals (5 per cage) cages (5 replicates) . pe (n = 2) pe (n = 2) pe (n = 2) als examining macro als examining day (n = 6) day (n = 5) day (n = 6) al al al te ty te ty te ty e tri tri si tri si tri Treatment Factors si th f (see Section 2.6) (see Section chidae daliidae sp.6 dropsy sp. yidae a similar nature a similar traliensis Experimental detail o Experimental of nal site information for the sites employed in these trials is provided in Table 3.1. nal site information for the sites employed .1: choptera: Hy Additio  Decapoda:At Cheumatopsyche Tri Paratya aus Archicauliodes Megaloptera: Cory able 5 Experiment 5 (Order: Family) Experiment 4 T Test Species 63 f ng”, Glendale Crossing on of nstream of confluence with ation of test organisms between sites test organisms between ation of Tharwa Bridge of h Murrumbidgee River  am River at Naas Stati River downstream ’s River at Murray’s Corner ’s River at Murray’s upstre Part B: Reloc . confluence wit Cotter River at pump station 1 km te 10: Murrumbidgee River upstream of Pt Hut Crossing te 10: Murrumbidgee River upstream te 5: Paddy o at campground, 100 m upstream te 3: Cotter River te 4: Murrumbidgee River dow te 7: Gudgenby te 6: Gudgenby te 8: Murrumbidgee River at “Cuppacumbalo Si Experimental Sites Source Site Si Transplant Sites Si Si Source Site Si Transplant Sites Si Si invertebrate response to relocation response to invertebrate cages (3 replicates) Random Experimental Random Experimental Factors cage) individuals (1 per cages (3 replicates) cage) individuals (5 per cages (3 replicates) individuals (3 per cage) cages (3 replicates) individuals (1 per cage) cages (5 replicates) individuals (3 per cage) cages (3 replicates) individuals (5 per cage) cages (5 replicates) individuals (4 per cage) . ) 2) 2) (n = 2) pe (n = 2) pe (n = pe (n = 2) pe pe (n = 2) pe (n = 2 pe (n = als examining macro als examining day (n = 4) day (n = 4) day (n = 4) day (n = 4) day (n = 4) day (n = 4) day (n = 4) al al al al al al al te ty te ty te ty te ty te ty te ty te ty e tri si tri si tri si tri si tri si tri si tri Treatment Factors si tri th f yidae at are dissimilar in nature (see Section 2.6) in nature at are dissimilar th Experimental detail o Experimental nal site information for the sites employed in these trials is provided in Table 3.1. nal site information for the sites employed sp. sp. : choptera: Leptoceridae choptera: Leptoceridae Additio  Paratya australiensis Decapoda:At Notalina Tri Odonata:Lestidae Cloeon Ephemeroptera: Baetidae Triplectides australicus ciuskus Triplectides australicus Tri Cherax destructor Decapoda: Parastacidae Rhadinosticta simplex Austrolestes cingulatum Odonata:Lestidae able 5.2 Experiment 7 Experiment 6 T Test Species (Order: Family) 64

5.3 RESULTS

Prevailing environmental conditions, field measurements and descriptions of macroinvertebrate body size data are presented in Appendix 5.

5.3.1 DEPLOYMENT TO SITES OF A SIMILAR NATURE

Macroinvertebrate Responses – Experiment 4 Experimental Effects on Survival Archicaulioides sp. Of the Archicaulioides sp. individuals deployed and retrieved before trial day 9, only 1 individual perished at the source site. Archicaulioides sp. survival was not significantly affected by the interaction between site and trial day (χ2 statistic = 0, df = 3, p = 1), site differences (χ2 statistic = 1.498, df = 1, p = 0.235) or trial day (χ2 statistic = 2.937, df = 3, p = 0.42).

Cheumatopsyche sp.6 Of the individuals deployed and retrieved before trial day 9, seven individuals perished, most of which were retrieved dead on trial day 6 (Figure 5.1). Survival was not significantly affected by the interaction between deployment site and trial day (χ2 statistic = 1.5642, df = 3, p = 0.325) or deployment site (χ2 statistic = 1.946, df = 1, p = 0.18) however, individuals began to perish after trial day 2 at the transplant site (Figure 5.1). While comparison of survival beyond trial day 6 was not possible as the experimental set-up at the source site was vandalised, potentially influencing even the organisms that were retrieved, survival was significant affected by trial day (χ2 statistic = 12.676, df = 3, p = 0.006) with a notable decrease in survival across the trial duration and only 28.6% retrieved alive on trial day 6 (Figure 5.2).

Experimental Effects on Escape of Caged Cheumatopsyche Individuals Only two Cheumatopsyche sp.6 larvae escaped from the deployment cages before trial day 9, and escape was not significantly affected by the interaction between site and trial day (χ2 statistic = 3.0592, df = 3, p = 0.45), site differences (χ2 statistic = 0, df = 1, p = 1) or trial day (χ2 statistic = 2.9061, df = 3, p = 0.42). 65

100

90 Source Transplant 80

Survival (%) 40

0 0 2 4 6 8 10 12 Trial day Figure 5.1: Percent survival of Cheumatopsyche sp.6 retrieved after deployment at source and transplant sites from the same AUSRIVAS site group during Experiment 4.

Day 1 = Day 2 >> Day 3 > Day 6

100 LSD = 34.125 90 p = 0.006

40 Survival (%)

0 Day 1 Day 2 Day 3 Day 6 Trial day Figure 5.2: Percent survival of Cheumatopsyche sp.6 retrieved over time after deployment at sites from the same AUSRIVAS site group during Experiment 4. LSD = Least squared differences (2 x standard error of differences). 66

Experimental Effects on Body Size – Archicaulioides sp. There were no significant effects on untransformed head width arising from the interaction between trial day and site (F statistic = 0.09, df = 3, 31, p = 0.966), retrieval day (F statistic = 0.12, df = 3, p = 0.947) or deployment site (F statistic = 0.64, df = 1, p = 0.428) although individuals retrieved alive from the source site were larger (n = 20, mean = 3.89 mm) than those retrieved alive from the transplant site (n = 30, mean = 3.58 mm). Similarly, there were no significant effects on log10-transformed wet weight arising from the interaction between trial day and site (F statistic = 0.03, df = 3, 31, p = 0.994), retrieval day (F statistic = 0.39, df = 3, p = 0.758) or deployment site (F statistic = 1.22, df = 1, p = 0.278). Individuals retrieved alive from the source site were also heavier (n = 20, mean = 52.8 mg) than those retrieved alive from the transplant site (n = 30, mean = 39.1 mg).

Experimental Effects on Body Size – Cheumatopsyche sp.6 There were no significant effects on untransformed Cheumatopsyche sp.6. head width arising from the interaction between trial day and deployment site (F statistic = 0.04, df = 3, 15, p = 0.987), deployment site (F statistic = 0.14, df = 1, p = 0.715) or retrieval day (F statistic = 0.57, df = 3, p = 0.643). Whilst not significantly different, larval head widths increased throughout the trial particularly at the source site where those individuals retrieved alive were wider overall (n = 13, mean = 0.942 mm) than those retrieved alive from the transplant site

(n = 19, mean = 0.908 mm). There were no significant effects on log10-transformed wet weight arising from the interaction between trial day and deployment site (F statistic = 0.32, df = 3, 18, p = 0.813), retrieval day (F statistic = 0.68, df = 3, 18, p = 0.576) or deployment site (F statistic = 0.600, df = 1, 18, p = 0.449), although individuals retrieved alive from the source site were heavier (n = 13, mean = 0.795 mg) than those retrieved alive from the transplant site (n = 19, mean = 0.700 mg).

Macroinvertebrate Responses – Experiment 5 Experimental Effects on Survival Paratya australiensis Of the 600 individuals deployed only 10 died during the trial period, apart from five individuals retrieved from a single deployment cage at one of the transplant sites on trial day 12, all other mortalities were single individuals in five separate cages. Three dead individuals were retrieved on trial day 1; two from separate cages at one of the transplant sites and another from a transplant site, and two individuals were retrieved dead on trial day 2 from 67 different transplant sites. The cage that contained all dead individuals when retrieved was excluded from analysis of survival as the tissue was badly decomposed when retrieved from the deployment site, indicating that the mortality was probably related to some factor other than the experimental factors examined in this study.

Shrimp survival was not significantly affected by the interaction of trial day and site factors (χ2 statistic = 5.712, df = 10, p = 0.838) or deployment site (χ2 statistic = 0.475, df = 3, p = 0.922). While there was some evidence of a difference in survival across trial day (adjusted for site type; χ2 statistic = 11.10, df = 5, p = 0.049), this can be related to retrieval of the only five dead individuals over the first two trial days.

Experimental Effects on Incidence of Physical Damage Of the 100 individuals retrieved for each trial day, between 10 and 23 individuals were found to have damaged frontal rostra (i.e. 17.1% of the test organisms). Broken or damaged rostra were not significantly affected by the interaction of trial day and site factors (χ2 statistic = 12.054, df = 10, p = 0.288) or deployment site (χ2 statistic = 2.311, df = 2, p = 0.533). And while there was weak evidence that the number of broken rostra was significantly different between retrieval days (χ2 statistic = 10.049, df = 5, p = 0.078), there was no apparent trend, with fewer damaged individuals retrieved on trial days 1 and 12 than on the intervening trial days.

Experimental Effects on Body Size – Paratya australiensis

There were no significant effects on logn-transformed carapace length arising from the interaction between trial day and deployment site (χ2 statistic = 1.0, df = 5, p = 0.95), day adjusted for site type (χ2 statistic = 7.7, df = 5, p = 0.19), or site type adjusted for trial day 2 (χ statistic = 1, df = 1, p = 0.34). Neither was logn-transformed orbital carapace length significantly affected by the interaction between trial day and deployment site (χ2 statistic = 1.5, df = 5, p = 0.915), day adjusted for site type (-in-deviance statistic = 9.228, df = 5, p = 0.1091) or site type adjusted for trial day (χ2 statistic = 0.2 df = 1, p = 0.68).

While there were no significant effects on logn-transformed wet weight arising from the interaction between trial day and site (χ2 statistic = 3.10, df = 5, p = 0.685) or site type 2 adjusted for trial day (χ statistic = 0.4 df = 1, p = 0.54), logn-transformed wet weight was significantly different between retrieval days (-in-deviance statistic = 12.21, df = 5, p = 0.0322). Individual shrimp massdecreased throughout the deployment trial (Figure 5.3) 68 following deployment on trial day 0 (n= 87, mean = 45.9 mg), with those retrieved alive on trial days 3 and 6 (n = 98, mean = 47.5 mg and n = 99, mean = 46.8 mg respectively) than those retrieved alive on trial day 12 (n = 93, mean = 39.3 mg).

Day 0 > Day 3 > Day 6 > Day 9 > Day 12

3.85 LSD = 0.116 p = 0.032 3.8

3.75

3.7

3.65

3.6 -transformed wet weight (mg) n

Log 3.55

3.5 Day 0 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 5.3: Mean logn-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites from the same AUSRIVAS site group during Experiment 5. LSD = Least squared differences (2 x standard error of differences).

5.3.2 DEPLOYMENT TO SITES OF A DIFFERENT NATURE

Macroinvertebrate Responses – Experiment 6 Experimental Effects on Survival Austrolestes cingulatum Of the 36 individuals deployed, the only two mortalities during the trial period occurred at one of the transplant sites on trial days 8 and 12 respectively. While survival was not significantly affected by interaction of trial day and site treatment factors (2 statistic = 0, df = 3, p = 1), there is weak evidence that survival decreased across the trial days (2 statistic = 3.175, df = 3, p = 0.08) and was lower at transplant sites from different AUSRIVAS groups (2 statistic = 4.920, df = 2, p = 0.09). 69

Rhadinosticta simplex Of the 60 individuals deployed only four mortalities were recorded during the trial period, with two dead individuals retrieved from the source site (one each on trial days 8 and 12), and one from each of the transplant sites, on trial days 1 and 12 respectively. Survival of Rhadinosticta simplex did not significantly differ with the interaction between trial day and deployment site (2 statistic = 3.00, df = 3, p = 0.41), site (2 statistic = 0, df = 2, p = 1) or across trial day (2 statistic = 2.934, df = 3, p = 0.425).

Triplectides australicus ciuskus Of the 175 individuals deployed only four mortalities were recorded during the trial period, with two dead individuals retrieved from the source site (one each on trial days 1 and 3), and two dead individuals retrieved from one of the transplant sites trial day 12. Survival of Triplectides australicus ciuskus did not significantly differ with the interaction between trial day and deployment site (χ2 statistic = 0, df = 3, p = 1), site type (χ2 statistic = 0, df = 1, p = 1), AUSRIVAS site group (χ2 statistic = 3.025, df = 2, p = 0.235) or across trial day (2 statistic = 2.95, df = 3, p = 0.42).

Cherax destructor As Cherax destructor is predatory even in its early instars, only 64% of the individuals deployed were retrieved alive in this experiment. The interaction between site and trial day treatment factors did not significantly affect this organism’s survival (χ2 statistic = 4.915, df = 3, p = 0.195), however, the findings showed a different trend over time between site types (Figure 5.4). While survival did not significantly differ with deployment site (χ2 statistic = 0.853, df = 1, p = 0.386), C. destructor survival decreased significantly across trial day when adjusted for site type (χ2 statistic = 26.50, df = 3, p < 0.0001), with survival on trial day 12 (37.5 ± 5.55%) significantly different from the first few retrieval days (Figure 5.5).

Experimental Effect on Body Size – Austrolestes cingulatum Mean head width was not significantly affected by the interaction between trial day and site treatment factors (-in-deviance statistic = 6.2, df = 3, p = 0.104). However, there is weak evidence that head widths were larger at transplant sites (-in-deviance statistic = 2.855, df = 1, p = 0.091; Figure 5.6A), and increased from 2.37 mm to 2.86 mm during the trial

(-in-deviance statistic = 7.308, df = 3, p = 0.063; Figure 5.7). Logn-transformed wet weight, 70

100

90 Source Transplant 80

Survival (%) 40

0 0 2 4 6 8 10 12 Trial day Figure 5.4: Percent survival of Cherax destructor retrieved after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 6.

Day 0 > Day 1 > Day 3 > Day 8 >> Day 12 100

90 LSD = 24.6 80 p < 0.0001

40 Survival (%)

0 Day 0 Day 1 Day 3 Day 8 Day 12 Trial day Figure 5.5: Percent survival of Cherax destructor retrieved over time after deployment at sites from different AUSRIVAS site groups during Experiment 6. LSD = Least squared differences (2 x standard error of differences). 71 however, was not significantly different across trial days adjusted for site type (-in-deviance statistic = 6.023, df = 3, p = 0.111), while there was weak evidence of an interaction between trial day and site type (-in-deviance statistic = 7.3, df = 3, p = 0.075) and individuals retrieved alive at the transplant sites were larger than those at the source site (2 statistic = 3.1, df = 1, p = 0.085; Figure 5.6B).

Experimental Effect on Body Size – Rhadinosticta simplex There was weak evidence of an interaction between trial day and site type on head width (2 statistic = 6.5, df = 3, p = 0.095), with R. simplex head widths increasing over time at transplant sites. However, there were no significant differences in mean head widths between site type adjusted for trial day (-in-deviance statistic = 3.318, df = 3, p = 0.36) and trial day adjusted for site type (-in-deviance statistic = 0.132, df = 3, p = 0.99). A similar interaction between trial day and site was evident for logn-transformed wet weight of R. simplex (-in- deviance statistic = 6.8, df = 3, p = 0.08), with an increase across time at the transplant site.

And while there was no effect of site type on logn-transformed wet weight (-in-deviance statistic = 0.1622, df = 1, p = 0.69), when adjusted for site type, there was weak evidence of a day effect on logn-transformed wet weights (-in-deviance statistic = 6.518, df = 3, p = 0.09). This was reflected by a marked difference between mean biomass of individuals retrieved on trial day 12 in comparison with earlier trial days (Figure 5.8).

Experimental Effect on Body Size – Triplectides australicus ciuskus There were no significant effects on head width of an interaction between trial day and site type (χ2 = 1.6, df = 3, p = 0.67) or experimental day adjusted for site type (χ2 = 4.6, df = 3, p = 0.22), however there was some evidence that head widths were larger at transplant sites (-in-deviance statistic = 2.913, df = 1, p = 0.09; Figure 5.9).

Experimental Effects on Body Size – Cherax destructor Wet weight did not significantly differ in relation to an interaction between trial day and deployment site (χ2 statistic = 3.1, df = 3, p = 0.395), site type adjusted for trial day (-in- deviance statistic = 1.1, df = 1, p = 0.32) or trial day adjusted for site type (-in-deviance statistic = 2.737, df = 3, p = 0.445). While Cherax destructor carapaces were not significantly affected by the interaction between trial day and site treatment factors (-in-deviance 72

(A) (B) 2.8 3.8 Head width (mm) Logn--transformedn wet weight (mg) lnWWx2

2.7 3.7 LSD = 0.180 LSD = 0.252 p = 0.091 p = 0.085 2.6 3.6

2.5 3.5

Head width (mm) 2.4 3.4 -transformed wet weight (mg) n 2.3 3.3 Log

2.2 3.2 Source Transplant Source Transplant Site type

2.8

2.7 LSD = 0.258 P = 0.063 2.6

2.5

2.4

2.3 Head width (mm)

2.2

2.1

2 Day 0 Day 1 Day 3 Day 8 Day 12 Trial day Figure 5.7: Mean head width for Austrolestes cingulatum retrieved alive over time after deployment at sites from different AUSRIVAS site groups during Experiment 6. LSD = Least squared differences (2 x standard error of differences). 73

2.6 LSD = 0.327 2.5 p = 0.09

2.4

2.3

2.2

2.1

1.9 -transformed wet weight (mg) n 1.8 Log 1.7

1.6 Day 0 Day 1 Day 3 Day 8 Day 12 Trial day

Figure 5.8: Mean logn-transformed wet weights for Rhadinosticta simplex retrieved alive over time after deployment at sites from different AUSRIVAS site groups during Experiment 6. LSD = Least squared differences (2 x standard error of differences).

LSD = 0.0417 0.95 p = 0.09

0.9

0.85

Head width (mm) 0.8

0.75

0.7 Source Transplant Site type Figure 5.9: Mean head width for Triplectides australicus ciuskus retrieved alive after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 6. LSD = Least squared differences (2 x standard error of differences). 74

Statistic = 5.5, df = 3, p = 0.15) or trial day adjusted for site type (-in-deviance statistic = 2.988, df = 3, p = 0.3935), there is weak evidence that carapace length was significantly greater at the source site when adjusted for trial day (-in-deviance statistic = 2.883, df = 1, p = 0.090; Figure 5.10).

8.7 LSD = 0.190 8.6 p = 0.090

8.5

8.4

8.3

8.2

8.1 Carapace length (mm) 8

7.9

7.8 Source Transplant Site type Figure 5.10: Mean carapace length for Cherax destructor retrieved alive after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 6. LSD = Least squared differences (2 x standard error of differences).

Macroinvertebrate Responses – Experiment 7 Experimental Effects on Survival Notalina sp. Only 7.2% of the individuals deployed perished overall during the trial, however, apart from two dead individuals retrieved from the source site on trial day 12, all other Notalina sp. mortalities were recorded from cages retrieved from transplant sites after trial day 3. Survival of Notalina sp. was significantly affected by the interaction between trial day and site factors (χ2 statistic = 22.190, df = 6, p = 0.0013; Figure 5.11). While there was no mortality on trial days 1 to 8 inclusive at either the source site or the transplant site from the same AUSRIVAS site group (site 8), fewer individuals had perished by trial day 12 at the source site (Figure 5.11). 75

100

Survival (%) 40 Site 7 (source)

30 Site 6 (transplant)

20 Site 8 (transplant) LSD = 25.1 p = 0.0013 10 Site 10 (transplant)

0 0 2 4 6 8 10 12 Trial day Figure 5.11: Percent survival of Notalina sp. retrieved after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7. LSD = Least squared differences (2 x standard error of differences).

Cloeon sp. Cloeon sp. survival was not significantly affected by the interaction of trial day and site factors (χ2 statistic = 9.074, df = 6, p = 0.195) or deployment site (χ2 statistic = 2.1831, df = 3, p = 0.54) despite the collection of more alive individuals on each retrieval day from transplant sites. However, differences in survival across trial day when adjusted for site type were highly significant (χ2 statistic = 16.3315, df = 3, p < 0.0001), with survival on trial days 1 and 3 significantly higher than on trial day 12 (Figure 5.12).

Paratya australiensis Shrimp survival in this transplant experiment was significantly affected by the interaction of trial day and site factors (χ2 statistic = 12.917, df = 6, p = 0.048), however, 60 of the 63 individuals retrieved dead from all sites during the trial were from one of the transplant sites (Figure 5.13). After exclusion of deployment site 8, survival was not significantly affected by site type (χ2 statistic = 0, df = 1, p = 1), site (χ2 statistic = 0.0003, df = 1, p = 0.988), AUSRIVAS site group (χ2 statistic = 0.0003, df = 2, p > 0.999) or trial day (χ2 statistic = 4.339, df = 3, p = 0.235), with 99% overall survival for the trial. 76

Day 1 > Day 3 > Day 8 > Day 12

100 LSD = 40.89 90 p < 0.0001 80

40 Survival (%)

0 Day 1 Day 3 Day 8 Day 12 Trial day

Figure 5.12: Percent survival of Cloeon sp. retrieved over time after deployment at sites from different AUSRIVAS site groups during Experiment 7. LSD = Least squared differences (2 x standard error of differences).

100

60 LSD = 11.55 p = 0.048 50

Survival (%) 40 Site 7 (source) 30 Site 6 (transplant)

20 Site 8 (transplant) 10 Site 10 (transplant)

0 0 2 4 6 8 10 12 Trial day Figure 5.13: Percent survival of Paratya australiensis retrieved after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7. LSD = Least squared differences (2 x standard error of differences). 77

Experimental Effects on Physical Damage to Deployed Individuals Physical damage to the frontal rostra of Paratya australiensis deployed in this experiment was found to be significantly affected by the interaction of trial day and site factors (χ2 statistic = 11.468, df = 3, p = 0.0095). However, after exclusion of individuals retrieved from site 8 due to the high site-specific mortality described above, there was no apparent trend in the number of damaged individuals retrieved over time at the remaining trial sites (Figure 5.14) and rostral damage was not significantly affected by site type (χ2 statistic = 1.3091, df = 1, p = 0.255), AUSRIVAS site group (χ2 statistic = 0.6809, df = 2, p = 0.722) or trial day (χ2 statistic = 4.339, df = 3, p = 0.235). As over 80% of the Paratya australiensis individuals retrieved dead during this experiment were at least partially decomposed and other morphological measurements could not be obtained, all site 8 individuals were excluded from the statistical analysis of physical condition.

40 Site 7 (source) 35 Site 6 (transplant) Site 10 (transplant) 30

5 Individuals retrieved with broken rostra (%) 0 0 2 4 6 8 10 12 Trial day Figure 5.14: Percentage of Paratya australiensis retrieved with broken frontal rostra after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7.

Experimental Effects on Escape The escape of Cloeon sp. from deployment cages over time was not significantly different with the interaction between site and retrieval day (χ2 statistic = 4.564, df = 6, p = 0.6) or site (χ2 statistic = 0, df = 2, p = 1) despite more individuals escaping from deployment cages at the 78 source sites. However, escape over trial day when adjusted for site type was significantly different (χ2 statistic = 39.915, df = 3, p < 0.0001) with significantly more escapees by retrieval days 8 and 12 than early in the trial (Figure 5.15).

Experimental Effect on Body Size – Notalina sp. Notalina sp. head widths were not significantly affected by interaction of trial day and site treatment factors (2 statistic = 7.1, df = 6, p = 0.33), day adjusted for site type (2 statistic = 2.8, df = 3, p = 0.44) or site type adjusted for trial day (2 statistic = 0, df = 1, p = 1). Despite comparable mean head widths between source (mean = 0.543 mm; n = 44) and transplant sites (mean = 0.542 mm; n = 122) there was a significant difference in head width between transplant sites from different AUSRIVAS site groups (2 statistic = 8.0, df = 2, p = 0.022). Individuals transplanted to a site from the same AUSRIVAS site group as the source site (mean = 0.495 mm, n = 45) were significantly smaller when retrieved than individuals deployed to sites from different AUSRIVAS site groups (mean = 0.568, n = 77) (Figure 5.16).

Day 1 < Day 3 << Day 8 < Day 12

100 LSD = 32.02 90 p < 0.0001

40 Escapees (%) 30

0 Day 1 Day 3 Day 8 Day 12 Trial day Figure 5.15: Percentage of Cloeon sp. that escaped from holding cages over time after deployment at sites from different AUSRIVAS site groups during Experiment 7. LSD = Least squared differences (2 x standard error of differences). 79

0.6 Source 0.58 LSD = 0.064 Transplant p = 0.022 0.56

0.54

0.52

0.5

0.48 Head width (mm) 0.46

0.44

0.42

0.4 Group 1 Group 1 Group 3 Group 4 AUSRIVAS site group Figure 5.16: Mean head width for Notalina sp. retrieved alive after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7. LSD = Least squared differences (2 x standard error of differences).

Experimental Effect on Body Size – Cloeon sp. Cloeon sp. head width did not significantly differ with the interaction of trial day and site treatment factors (2 statistic = 1.9, df = 6, p = 0.93), day adjusted for site type (2 statistic = 0.6, df = 3, p = 0.895), site type adjusted for trial day (2 statistic = 0.1, df = 1, p = 0.75) or AUSRIVAS site group (2 statistic = 2.8, df = 2, p = 0.445). However, when compared within trial day and site type treatment groups, Cloeon sp. individuals that were dead when retrieved from the deployment cages had significantly larger head widths than individuals that were retrieved alive (F statistic = 4.84, df = 1, 24, p = 0.038; Figure 5.17).

Experimental Effect on Body Size – Paratya australiensis

Logn-transformed orbital carapace lengths were not significantly affected by the interaction of trial day and site treatment factors (2 statistic = 4.6, df = 4, p = 0.35), site type adjusted for trial day (2 statistic = 0.5, df = 1, p = 0.48) or AUSRIVAS site group (2 statistic = 1.1, df = 2, p = 0.585). There was, however, a weak evidence of a day effect (2 statistic = 7.2, df = 3, p = 0.07) with a decrease in logn-transformed orbital carapace lengths between trial day 1 (mean = 1.171, n = 91) and trial day 12 (mean = 1.083, n = 67) (Figure 5.18). 80

1.3 Retrieved Alive Retrieved Dead LSD = 0.087 1.2 p = 0.038

1.1

0.9 Head width (mm) 0.8

0.7

0.6 Day 1 Day 3 Day 8 Day 12 Day 1 Day 3 Day 8 Day 12 Source Transplant Figure 5.17: Mean head width for Cloeon sp. retrieved after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7. Error bars indicate one standard deviation. LSD = Least squared differences (2 x standard error of differences).

1.2 LSD = 0.091 p = 0.07 1.18

1.16

1.14

1.12

1.1

1.08 -transformed orbital carapace length (mm) n Log 1.06 Day 0 Day 1 Day 3 Day 8 Day 12 Trial day

Figure 5.18: Mean logn-transformed orbital carapace length for Paratya australiensis retrieved alive after deployment at source and transplant sites from different AUSRIVAS site groups during Experiment 7. LSD = Least squared differences (2 x standard error of differences). 81

Similarly, logn-transformed carapace lengths were not significantly affected by the interaction of trial day and site treatment factors (2 statistic = 5.0, df = 4, p = 0.3), day adjusted for site type (2 statistic = 3.0, df = 3, p = 0.22), AUSRIVAS site group (2 statistic = 2.0, df = 2, p = 0.39) or site type adjusted for trial day (2 statistic = 0.6, df = 1, p = 0.46). However, individuals retrieved from the source site had shorter carapaces (mean = 5.931 mm, n = 87) than those retrieved from the transplant sites (mean = 6.064 mm, n = 205).

Logn–transformed wet weights were not significantly affected by the interaction of trial day and site treatment factors (2 statistic = 4.0, df = 4, p = 0.42) or site type adjusted for trial day 2 ( statistic = 1.3, df = 1, p = 0.26). And while logn–transformed wet weights for P. australiensis were not significantly affected by trial day (adjusted for site type) either (2 statistic = 3.3, df = 3, p = 0.37), shrimp body mass increased between trial day 1 (mean = 32.355 mg, n = 100) and trial day 12 (mean = 38.352 mg, n = 67).

5.4 DISCUSSION

5.4.1 EXPERIMENTAL RESULTS

Findings of these relocation experiments varied with some species endpoints indicating adverse effects of relocation, while others suggested organisms thrived at unfamiliar sites. When relocated to similar sites, macroinvertebrate responses only varied significantly with trial day. In contrast, when organisms were relocated to sites from different AUSRIVAS site groups, some macroinvertebrate responses were influenced by site factors. Table 5.3 summarises the effects of relocating organisms between sites that are similar and dissimilar in nature.

Relocation of Macroinvertebrates to Sites of a Similar Nature to the Source Site Taxon survival was generally high when relocated to sites with similar habitat characteristics (Figure 5.19), with the exception of Cheumatopsyche sp.6 and Cloeon sp. whose survival decreased with increasing trial duration when deployed back to the source site (Figures 5.1 and 5.12 respectively). Apart from these effects, which could be attributable to caging or to trial duration, relocation of test organisms between similar sites also resulted in the earlier demise of Cheumatopsyche sp.6 individuals at the transplant site than the source site (Figure 5.1) and the higher mortality of Notalina sp. during the final few days of Experiment 7 at the 82 dth the wi ns detail the ns detail t of io gure 5.2) (Figure 5.3)  (Fi  Unless specifically stated, stated, Unless specifically yment sites dissimilar from on o ively). dead individuals on or before trial dead individuals on or before trial size is adjusted, abbreviat size is adjusted, on trials. on trials. at depl ment sites dissimilar from source site ment sites dissimilar from source  yment duration

oy dy size endpoints tested were head tested dy size endpoints ocati oyment durati Bo the only al day . n rel of i as deplo at depl effect on survival found to be an artefac effect on survival found to be an Brief description of effect of relocation Brief description   gy as depl as tri eval lo 2   dth ght ** Day retri day at α = 0.05 gure 5.16) h base 10 and base n respect h base 10 and base Head wi (Fi No effects detected Survival Wet wei Survival source site (Figure 5.11) tests y ns wit io ning of experimental variation must consider this interaction term. ning of experimental variation must consider NS NS NS NS NS NS NS NS NS NS NS NS Site x Day p = 0.0013 artitio interaction macroinvertebrate morpho macroinvertebrate ing  NS NS NS NS NS NS NS NS NS Day p = 0.006 p = 0.032 p = 0.05**  NS NS NS NS NS NS NS NS NS NS NS site p = 0.022 Relocation or fixed factors affect or fixed f and ln indicate logarithmic transformat and ln indicate logarithmic 10 cted Maximum Likelihood (REML) probabilit Likelihood cted Maximum y tests = 0.05.  W Restri WW W 10 10 Endpoint g g probabilit lnWW Survival HW Survival HW lo Survival HW lo Survival Rostra damage lnCL lnOCL 6 ts of ts are from sp. esul sp. R resul body Where raw wet weight (WW). length (OCL), orbital carapace length (CL) (HW), carapace log transformation (i.e. Relocation to similar sites Relocation to similar Relocation to dissimilar sites : sp. indicates that there was a significant interaction term involving this factor, p indicates that there was a significant interaction s  NS refers to “not significant” at Notalina Paratya australiensis Cheumatopsyche Specie Archicauliodes Table 5.3 83 . te; tes source si m tes (Figure 5.5) ment si gure 5.12) in relocation trials in relocation gure 5.15) oy s (Fi ment si (Fi  int oy  ssimilar fro on tes di at depl across depl day (Figure 5.7) day at transplant sites day (Figure 5.8) al al al tri tri tri al day al day ment si    ment duration response endpo response

oyment durati oy tri tri th th th oy  

wi wi wi th th    wi as depl at depl wi Brief description of effect of relocation of effect Brief description as depl     dth at transplant > source (Figure 5.6A); dth at transplant > source (Figure dth 5.6B) ght at transplant > source (Figure dth ght 5.9); dth at transplant > source (Figure  invertebrate invertebrate macro Carapace length at source > transplant (Figure 5.10) Carapace length at source > transplant Survival Escape Survival Survival Head wi Head wi Wet wei Head wi Wet wei Head wi Survival ous vari NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS ing p = 0.08 p = 0.095 Site x Day Site x Day interaction NS NS NS NS NS NS NS NS NS NS NS NS Day p = 0.08 p = 0.09 p = 0.063 p = 0.085 p < 0.0001 p < 0.0001 p < 0.0001 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS site y tests for fixed factors affect y tests for p = 0.09 p = 0.09 p = 0.09 p = 0.091 Relocation Relocation = 0.05. WW  10 g at Results of probabilit Results of ” lnWW Survival HW Survival CL WW lnCL lnOCL lnWW Survival HW lo Survival HW Endpoint Survival Escape HW Survival Rostra damage : not significant “ raliensis (continued) sp. NS refers to ax destructor Triplectides australicus ciuskus Cher Rhadinosticta simplex Austrolestes cingulatum Paratya aust Table 5.3 Species Cloeon 84

“similar” transplant site than at the site from which the test organisms were sourced (Figure 5.11).

While there were no significant differences between organism size attributed to deployment site type when deployed to source and transplant sites of a similar nature (Table 5.4), there was some evidence that at least some test species were larger at the source site than similar transplant sites (Figure 5.20), suggesting that transplantation away from familiar conditions may have an adverse effect on organism condition and performance. In contrast, relocation of Paratya australiensis to similar sites resulted in larger individuals than those retrieved at the source site (Table 5.4) although nor were these differences statistically significant. However, as only 40% of the P. australiensis individuals deployed at site 8 were retrieved alive, this finding may be related to the demise of the smallest individuals (Nylin and Gotthard 1998, Rodriguez-Saona and Miller 1999, Phoofolo et al. 2001) due to prevailing conditions at that site (Appendix 5, Table A5.10). As morphological measurements could only be obtained from 12.5% of the P. australiensis individuals that were retrieved dead from site 8, it is impossible to confirm or refute this suggestion.

Cheumatopsyche sp.6sp.6 Source site

Similar sites Cloeon sp.

Notalina sp.

Archicaulioides sp.

Paratya australiensis

0 20 40 60 80 100 Survival (%)

Figure 5.19: Percent survival of test organisms deployed for a period of 12 days at their source site and transplant sites of a “similar” nature. 85

(A)

3.9 1.8 Head width (mm) Log 10 -transformed wet weight (mg)

3.8

1.7 3.7

3.6

Head width (mm) 1.6 -transformed wet weight (mg)

3.5 10 Log

3.4 1.5 Source Transplant Source Transplant Site type

(B)

0.95 0.85 Head width (mm) Log 10 -transformed wet weight (mg) 0.94

0.93 0.8 0.92

0.91

0.9 0.75

0.89

Head width (mm) 0.88

0.7 -transformed wet weight (mg)

0.87 10

0.86 Log

0.85 0.65 Source Transplant Source Transplant Site type

(C)

0.58 Head width (mm) Log -transformed wet weight (mg) 0.85

0.56

0.8 0.54

0.52 0.75 0.5 Head width (mm) 0.48 0.7

0.46

0.44 0.65 Source Transplant Site type

Figure 5.20 (continued): Body size endpoints for (A) Archicaulioides sp., (B) Cheumatopsyche sp.6 and (C) Notalina sp. retrieved alive after deployment at source and transplant sites from the same AUSRIVAS site group.

Table 5.4: Morphological measurements for Paratya australiensis retrieved from source and transplant sites in Experiment 5. Data presented is mean measurements with the number of individuals in parentheses.

Morphological Endpoint Day 0 Source Transplant

Carapace length (mm) 5.862 5.931 6.326 (n = 28) (n = 87) (n = 36) Orbital carapace length (mm) 3.178 3.252 3.333 (n = 36) (n = 94) (n = 40)

Relocation of Macroinvertebrates to Sites Dissimilar in Nature to the Source Site When relocated to from a source site to “dissimilar” transplant sites, organism survival was variable, with some species exhibiting increased mortality, while others maintained virtually 100% survival throughout the trial even at dissimilar sites (Figure 5.21). Austrolestes cingulatum and Cloeon sp. both exhibited reduced survival at sites dissimilar from their 87 source site over the whole trial, and survival of both Cherax destructor and Notalina sp. was affected differentially across trial day for each site type, with earlier mortality at transplant sites than either source or “similar” transplant sites (Figures 5.4 and 5.11 respectively).

Differences in macroinvertebrate body size when relocated to dissimilar sites were detected, although the relationships varied with test species. Longer carapaces observed for Cherax destructor individuals deployed back to their source site (Figure 5.10) supports the notion that relocating the test organisms may adverse affect their performance. However, significantly greater measurements were reported for individuals retrieved from dissimilar transplant sites (Triplectides australicus ciuskus head width; Figure 5.9, Austrolestes cingulatum head width and wet weight; Figure 5.6 and Notalina sp. head width; Figure 5.16). Further, Rhadinosticta simplex wet weights were found to increase more over trial duration at dissimilar transplant sites (Figure 5.8).

Source site Cherax destructor Dissimilar sites Cloeon sp.sp.

Notalina sp.sp.

Rhadinosticta simplex

Austrolestes cingulatum

Triplectides australicus ciuskus

Paratya australiensis

0 20 40 60 80 100 Survival (%) Figure 5.21: Percent survival of test organisms deployed for a period of 12 days at their source site and transplant sites with “dissimilar” characteristics.

As body size measurements were not generally attainable for Cherax destructor individuals that were retrieved dead during the deployment trial, it is not possible to contest the notion of size-specific mortality. In fact, mean C. destructor carapace length and wet weights across the 88 trial period differed considerably between deployment site type (Figure 5.22) and could be argued to correlate with C. destructor mortality.

While both mean carapace length and wet weight increased markedly in the first 24 hours at the source site, there was virtually no change in body size at transplant sites until trial day 8 (Figure 5.22B). Survival, however, began to reduce at the transplant sites less than one day after caging and deployment, while there was no mortality at the source site until after trial day 3 (Figure 5.22A). However, the slower decline in survival of C. destructor over time at the transplant site could indicate retarded growth or compromised predatory ability at this site (Hopper et al. 1996), or may reflect that at their source site, yabbies are acclimated and continue to feed and grow until their expiry (Table 5.4). Both scenarios could also explain the larger body size exhibited, albeit weakly, at the source site (Figure 5.10).

Experimental Effects Not Related to Relocation between Deployment Sites In addition to the transplant effects discussed above, trial duration significantly affected a number of the test organism endpoints examined. Survival of several tests organisms employed decreased over time regardless of site type (Figures 5.2, 5.5 and 5.12) and increasingly more Cloeon sp. individuals escaped from the deployment cages with an increase in trial day (Figure 5.15).

Even at its source site, Cheumatopsyche sp.6 survival decreased significantly across time, particularly after trial day 8 (Figure 5.2). As filter-feeding larvae (Mackay and Wiggins 1978), hydropsychids tend to feed almost continually, preferentially selecting larger particulate material trapped in their silk nets (Edler and Georgian 2004). Given that the cage mesh may intercept such material, it is possible that mortality of Cheumatopsyche sp.6 was directly linked to its feeding, or lack thereof, whilst confined to the deployment cages. However, this does not explain the significant increase in head width, with which the increased mortality corresponds (Figure 5.23).

Although size-specific mortality could explain such an inverse relationship between survival and body size, there was no significant difference between Cheumatopsyche sp.6 individuals retrieved alive and dead when compared within site and trial day treatment groups for either head width (F statistic = 0.26, df = 1, 17, p = 0.619) or log10-transformed wet weight (F statistic = 0.15, df = 1, 17, p = 0.703). As such, it is perhaps more likely that the noted 89

(A)

250 11 Wet weight Carapace length Mortality 10

200 9 8 7 150 6 5 100 4 Wet weight (mg)

3 Carapace length (mm) 50 2

1 Number of individuals retrieved dead 0 0 Day 1 Day 3 Day 8 Day 12 Trial day

(B)

250 11 Wet weight Carapace length Mortality 10

200 9 8 7 150 6 5 100 4 Wet weight (mg)

3 Carapace length (mm) 50 2

1 Number of individuals retrieved dead 0 0 Day 1 Day 3 Day 8 Day 12 Trial day

1.1 8

Head width 7 1 Wet weight Survival 6

5 0.9

0.8 3 Head width (mm)

-transformed wet weight (mg) 2 10 0.7 Log 1 Number of individuals retrieved alive

0.6 0 Day 1 Day 2 Day 3 Day 6 Trial day

increase in head width (Figure 5.23) is a function of a change in molt frequency known to correlate with other invertebrate stress responses (Allan and Maguire 1992, Beyers et al. 1994, Anger 2003, Brown et al. 2003, Paschke et al. 2004).

In terms of body size, Paratya australiensis wet weight varied significantly with retrieval day when relocated to sites similar in nature to the source site (Figure 5.3), with test organisms beginning to decrease in body mass immediately following deployment. As less than 1% of deployed shrimp were retrieved dead during the deployment trial, this difference in biomass is not an artefact of size-specific mortality. The decrease in the latter part of the deployment trial probably relates to gut clearance and lack of food within the cages, however, it is also feasible that organisms convert body tissue to satisfy metabolic requirements. The initial decrease in biomass of relocated shrimp, however, is more likely to be caused by physiological or metabolic changes arising from the various disturbances associated with the experimental set-up (Pankhurst and Sharples 1992, Davey et al. 1993, Dahl and Blanck 1996, Auperin et al. 1997, Harak et al. 1998, Lavallee et al. 2000, Croke and McDonald 2002). 91

In addition to the significant day effects discussed above, there was weak evidence of an increase in body size over trial duration for Austrolestes cingulatum (Figure 5.7), Rhadinosticta simplex (Figure 5.9). While both these Odonatan test species were caged and deployed individually and were not provided with prey, it is possible they were able to feed on small organism or other material that passed through the cage mesh.

Selection of Test Organisms In preparing these transplantation experiments, AUSRIVAS taxa probabilities were used to ensure that test organisms were not deployed at sites where they would be unlikely to occur (Section 5.2). Ideally, it would have been possible to employ taxa whose likelihood of occurrence was high at both source and transplant sites (Appendix 5, Table A5.2). However, many such suitable taxa did not occur at the selected source sites in sufficient number to satisfy experimental design requirements and substitute test species had to be used. In reality, despite low taxa probabilities at source sites, several test organisms were selected largely because they were sufficiently abundant (e.g. Paratya australiensis, Cherax destructor; Appendix 5, Table A5.2). As such, there were instances where test organisms were relocated to transplant sites with, at least some, higher taxa probabilities.

Relationship between Taxa Probabilities and Performance If the likelihood of taxa occurring at a given site could be expected to correlate with organism performance, it could be argued that there is a greater chance of detecting a site effect with increasing differences between taxa probabilities at deployment sites. Further, if scores such as SIGNAL (Chessman 1995, Chessman et al. 1997, Chessman and McEvoy 1998, Chessman 2003) were a reliable measure of tolerance to disturbance, such site effects may be more pronounced for more sensitive organisms. Perhaps then it is not surprising that no site effects were detected in Experiments 4 and 5 (Table 5.3) where taxa probabilities were comparable between deployment sites and test organisms had relative low SIGNAL scores (Table 5.3; Appendix 5, Table A5.2).

Following on, it would not have been surprising if organisms thrived in those new environments where they were “more” likely to occur. However, where differences in body sizes between sites or site types were reported, larger body sizes were detected at sites with much lower taxa probabilities (Table 5.5). For example, Notalina sp. head widths were highest at site 10, where the AUSRIVAS-derived taxa probability for Leptoceridae was 0.4304, while at site 6, where the taxa probability for the same family was over 0.95, 92 leptocerid head widths were significantly smaller than at other transplant sites (Figure 5.16, Table 5.4). Similarly, at site 5 where the taxa probability for Parastacidae was below 0.1, individuals were much larger than at sites where their likelihood of occurrence was greater than 0.5 (Table 5.5).

Where Rhadinosticta simplex body size increased differentially with trial day at the deployment sites (Table 5.3), there was an inverse relationship between growth and taxa probability at the transplant sites. Head widths and wet weights increased over the trial period by 47% and 76% respectively at the transplant site where the taxa probability of Isosticidae occurrence was 0.5747, while at the site where there was nearly a 90% probability that isostictids would be found individuals only increased by 38% and 56% for head width and wet weights respectively (Figure 5.24).

As such, it would seem that taxa probability is not a particularly useful indicator of biotic performance, at least in the case of sites, species and body size endpoints employed in these experiments. Future targeted experiments could examine such relationships in more detail, although it would be wise to avoid drought conditions when ecosystems and their inhabitants are likely to be stressed.

5.4.2 CONCLUSION

With the exception of Cherax destructor, all site differences in macroinvertebrate endpoints demonstrated that individuals redeployed back to their source environment were smaller than those relocated to different sites (Figures 5.6, 5.9 and 5.16). Thus, it seems that test organism performance is not deleteriously affected by relocation between sites, even those that are dissimilar in nature to the source site. In fact, there is some argument that organism growth is greater in unfamiliar environments, particularly those where given taxa are deemed less likely to occur.

From these studies, it is apparent that macroinvertebrates respond to site relocation somewhat unpredictably. Clearly, there are certain taxa or individuals that perish at both transplant and source sites, which in some cases (e.g. Cloeon sp.) is unrelated to SIGNAL sensitivity (Appendix 5, Table A5.2). It is likely that the success of caging and deployment will improve if organisms were not resource limited, however, the practicalities associated with the provision of food are complicated and could potentially introduce additional confounding factors. 93 y ive 8 8 7 7 7 Taxa Sensitivity Taxa probabilit ed between source and ed between ae oy dae dae ively. id ct depl idae Lest Leptoceri Parastac Leptoceri Family Isostictidae pes when pes when 100 100 92.7 0.941 8.316 1.984 2.482 2.758 1.654 Site 4 0.4291 0.5104 0.5987 0.5737 0.4304 0.5747 Site 10 e AUSRIVAS ACT Spring Edge predict ACT Spring e AUSRIVAS th m fro Transplant Sites 91.7 83.3 2.58 96.3 8.41 1.827 2.237 1.661 0.965 Site 3 Site 6 0.5606 0.9652 0.8996 0.9486 0.9432 0.6748 as derived as derived 5 100 95.5 1.92 96.8 2.248 2.392 1.588 0.863 8.551 Site Site 7 0.5433 0.9996 0.9122 0.9483 0.9591 0.0677 yment sites yment sites Source Site und to differ between sites and/or site ty between sites und to differ fo at deplo variables ndicate logarithmic transformations with base 10 and base n respe base 10 and base with transformations ndicate logarithmic i occurring occurring n ) ) ) ) response response and log 10 dth (mm dth (mm dth (mm dth (mm transformed wet weight (mg) Log a given family a given family - transformed wet weight (mg) transformed wet . - n 10 ad wi Carapace length (mm) Survival (%) Head wi Log Survival (%) Head wi Log Survival (%) He Response variable Head wi acroinvertebrate acroinvertebrate ikelihood of ikelihood . AUSRIVAS Likelihood of taxon occurrence at site AUSRIVAS Likelihood of taxon occurrence at site AUSRIVAS Likelihood of taxon AUSRIVAS Likelihood of taxon occurrence at site of taxon occurrence at site AUSRIVAS Likelihood occurrence at site AUSRIVAS Likelihood of taxon occurrence at site AUSRIVAS Likelihood of taxon del sp. ssimilar transplant sites ssimilar di the l refers to mo Mean values for m Mean values : 5 Notalina ciuskus Cherax destructor Austrolestes cingulatum Triplectides australicus Rhadinosticta simplex Test organism able 5. T 94

(A)

Day 1 Day 3 Day 8 Day 12 Taxa probability 2.1 1 0.9 2 0.8 0.7 1.9 0.6 1.8 0.5 0.4

1.7 Taxa probability Head width (mm) 0.3 0.2 1.6 0.1 1.5 0 Source Transplant Transplant (Site 5) (Site 3) (Site 4) Site type (B)

Day 1 Day 3 Day 8 Day 12 Taxa probability 2.8 1 0.9 2.7 0.8 2.6 0.7 2.5 0.6 2.4 0.5 0.4 2.3

0.3 Taxa probability 2.2 -transformed wet weight (mg)

n 0.2 2.1 Log 0.1 2 0 Source Transplant Transplant (Site 5) (Site 3) (Site 4) Site type Figure 5.24: Mean (A) head width and (B) logn-transformed wet weight for Rhadinosticta simplex retrieved alive after deployment at source and “dissimilar” transplant sites during Experiment 6. Taxa probability refers to the likelihood of a given family occurring at deployment sites as derived from the AUSRIVAS ACT Spring Edge predictive model. 95

Further research is clearly needed to elucidate the effect of relocation on a wider range of macroinvertebrate species. Future experiments could examine the responses of a broader range of test organisms, including both sensitive and tolerant taxa, and select deployment sites that represent a range of taxa probabilities for selected test species. While the experiments reported here have demonstrated that there are many practicalities that need to be considered when planning and executing this kind of in situ trial, the concept of relocating macroinvertebrates as sentinels has potential, particularly in light of the shift towards more ecologically-relevant ways to assess habitat and water quality.

6 96

Chapter 6: HOW DO MACROINVERTEBRATES RATE AS BIOMONITORS WHEN CAGED AND DEPLOYED IN SITU TO ASSESS SITES WITH IMPAIRED WATER QUALITY ?

6.1 INTRODUCTION

6.1.1 RATIONALE

For biological monitoring techniques to be useful in assessing aquatic impact, test organisms need to be able to detect an impact, and to do so in a reproducible and quantifiable manner. It is even more useful if the way in which a test organism responds can be linked to one or more causal factors, such as a change in their physical surroundings, ecological interactions or environmental contamination. While considerable work over many years has produced a raft of laboratory bioassays that successfully produce known dose-response relationships for many contaminants and standardised test organisms, establishing such relationships under natural field conditions and with locally endemic organisms has yet to be achieved.

6.1.2 BACKGROUND

Ecotoxicological advances in recent years have seen the application of laboratory bioassay protocols to natural field environments, and macroinvertebrates are becoming increasingly more popular in the identification and evaluation of the biological effects of environmental contaminants in the field (Colborn 1982, Medeiros et al. 1983, Hall et al. 1988, Schulz and Liess 1999, Scott and Kaushik 2000, Choi et al. 2002, Schulz 2003, Hruska and Dube 2004, Jergentz et al. 2004, Faria et al. 2006, Barata et al. 2007, Faria et al. 2007b).

However, because of their mobility, small size and benthic habit, there are obvious limitations for their usefulness as aquatic sentinels in deployment and retrieval studies. For this reason, macroinvertebrate test organisms are usually deployed in cages or test chambers of some kind to assess impacts that are both localised (Greenberg et al. 2002, Bervoets et al. 2004, Robertson and Liber 2007) and more diffuse in nature (Matthiessen et al. 1995, Hatch and Burton 1999, Graca et al. 2002, Grapentine et al. 2004, Phillips et al. 2004, Custer et al. 2006, Lopes et al. 2007). 97

Deployment of endemic macroinvertebrate taxa at sites known to have persistently poor water quality will examine whether such in situ bioassessment techniques can help to establish a relationship between environmental causes and macroinvertebrate responses. This chapter is comprised of three case studies, each representing a differently degraded aquatic systems:

Case Study 1: Mine Drainage Case Study 2: Urban Stormwater Runoff Case Study 3: Persistently High Turbidity

6.1.3 DESCRIPTION OF POLLUTION SCENARIOS

Point Source Pollution at Captains Flat The first case study examines the point source impact on the Molonglo River arising from the discontinued mining operations at Captains Flat approximately 60 km south-east of Canberra in New South Wales (Figure 3.1). The ore mined at Captains Flat was a lead, zinc, copper sulphide deposit with subsiding amounts of silver and gold laid down in the Silurian period (Erskine 1980). Following the discovery of gold in the area in 1864, mining enterprises were established in 1886, and Lake George Mines commenced operation in 1939 using underground methods to produce lead, copper, zinc, pyrite and gold extracts before its closure in 1962 (Pryke 1995).

A number of studies have examined the effect of the Captains Flat mine discharge on downstream aquatic ecosystems. Benthic macroinvertebrate communities have been assessed at various stages since the mine’s closure, reporting a reduction in taxa richness and total abundance in comparison with Molonglo River sites upstream of the mine (Weatherley et al. 1967, Nicholas and Thomas 1978, Norris 1986). Weatherley et al. (1967) suggested that elevated zinc load was having a direct toxic effect on the biota, with certain taxa completely absent from downstream benthic communities. Zinc toxicity has also been blamed for rapid mortality rates of rainbow trout fingerlings (Weatherley et al. 1967, Graham et al. 1986) and mussels (Millington and Walker 1983). In addition to direct effects of metal toxicity, changes in benthic fauna have also been attributed to indirect mechanisms including loss of periphyton resulting in substrate instability and reduction in food availability (Weatherley et al. 1967), and smothering by the floc precipitate of metal oxides (Nicholas and Thomas 1978). 98

Since cessation of mining operations, restoration activities have been undertaken including reshaping of the hills, filling of sludge pits with clay and rocks, and major revegetation works (Craze 1977c, Norris 1986, Jacobson and Sparksman 1988). Despite these works, the problem of chemicals and heavy metals leaching into the Molonglo River from springs and contaminated bed sediments continues to affect downstream benthic communities (Norris 1986). This is supported by a longitudinal study of benthic communities which reported that macroinvertebrate assemblages are significantly different from upstream reference sites until approximately 60 km downstream of the former mine site (Sloane 1999).

Given the ongoing negative effects of these activities on the benthic macroinvertebrate communities downstream of the Captains Flat mine and tailings dump, this site offers the perfect opportunity to test the efficacy of using caged macroinvertebrates for in situ biomonitoring of a point source pollution scenario that is both familiar and well understood.

Diffuse Source Pollution at Various Urban Sites around Canberra The other two case studies examine more diffuse pollution scenarios associated with Canberra’s urban development. Prior to European settlement, many of Canberra’s waterways would have been little more than a chain of ponds consisting of marshy channels between deeper scour ponds (Eyles 1977). Since then, the region’s landscape has changed considerably, with efforts by early settlers to increase the grazing value of the region through extensive clearing of trees, construction of dams and draining of swamp areas by bullock- drawn ploughs (Robinson 1977). With the expansion of Canberra in the latter part of the twentieth century, drainage lines were further incised (Lyne 1978), essentially to convey stormwater away as quickly as possible, to avoid danger and damage to Canberra’s residents and property (O’Loughlin and Robinson 1987, Ormsby 1992).

Effects of urbanisation on local waterways has been well documented, with a decrease in stream quality corresponding with increases in population (De Silva and De Silva 1994, Levy and Winsby 1995, Edyvane 1999, Weber and Bannerman 2004, Couceiro et al. 2007) and catchment imperviousness (Klein 1979, Brown 1988, Stepenuck et al. 2002, Morse et al. 2003). Urban channels characteristically suffer from short but intense flooding (Eagleson 1972, Arthington et al. 1982) with increased peak discharges (Stone et al. 1982, Hollis 1988, Ng and Marsalek 1989), and urban runoff is a major contributor to pollution in downstream ecosystems (Heaney and Huber 1984, Lenat and Crawford 1994) and in sediments (Liston and Maher 1986, Nightingale 1987, Beasley and Kneale 2002). 99

Nutrient-rich inflows are also typical of waterways draining urbanised areas (Glandon et al. 1981, Watson et al. 1981, Braat and Van der Ploeg 1982, Jones and Redfield 1984, Loeb et al. 1984, Valiela et al. 1992, Walker 1997, Graves et al. 1998, Safran et al. 1998,). And when pH levels, turbidity and water temperatures rise (Arfi et al. 1981, Whiting and Clifford 1983, Fitzgerald et al. 1998, Graves et al. 1998, Thorpe and Lloyd 1999, Couceiro et al. 2007), high nutrient loads can often lead to eutrophication (Chakraborty and Kushari 1985, Lapointe and Matzie 1996, Paerl 1999), especially following storm events (Long and Cooke 1978, House and Warwick 1998, Nebelkova et al. 2005). Not surprisingly such streams often have low oxygen levels (Arthington et al. 1982, Chakraborty and Kushari 1985, Valiela et al. 1992, Graves et al. 1998, Paerl 1999, Couceiro et al. 2007) resulting in part from the high chemical and biochemical oxygen demands (Whipple and Hunter 1981, Petruck et al. 1999, Marani et al. 2004) and bacterial concentrations (Hynes 1960, Eisen and Anderson 1979, Scott et al. 1996, Murray et al. 2001, Schiff and Kinney 2001).

Physical changes to such receiving environments include decreased bed stability (Borchardt and Statzner 1990, Onorato et al. 1998), arising from scouring (Sloane-Richey et al. 1981, Arthington et al. 1982, Jones and Redfield 1984) as well as imported sediment (Whiting and Clifford 1983, Lenat and Crawford 1994, Hammer and Stoermer 1997). This can lead to reduced macroinvertebrate habitat quality (Pedersen and Perkins 1986, Lenat and Crawford 1994, Collier 1995, Paerl 1999), particularly through substrate homogeneity (Fitzgerald et al. 1998), loss of instream refuges (Borchadt and Stazner 1990, Onorato et al. 1998, Pearl 1999) and unpredictable food sources (Pedersen and Perkins 1986, Sloane-Richey et al. 1981, Jones and Redfield 1984). Higher solids loads associated with urbanisation are also accompanied by increased turbidity (Arfi et al. 1981, Stone et al. 1982, Thorpe and Lloyd 1999), as well as contamination by a range of organic and inorganic compounds (Whiting and Clifford 1983, Spies et al. 1985, Liston and Maher 1986, Davis and George 1987, Nightingale 1987, Prowse 1987, Madigosky et al. 1991, Beasley and Kneale 2002).

Although the pollution dynamics at urban sites are not well understood (Beasley and Kneale 2002), changes to benthic macroinvertebrate communities are likely to be influenced by small to medium scale habitat factors related to water temperature and electrical conductivity, as well as sediment-bound pollution (Beasley and Kneale 2002, Beasley and Kneale 2003, Pettigrove and Hoffman 2005). As a result, benthic communities in urban receiving environments are quite different in composition from those found at unimpacted sites 100

(Arthington et al. 1982, Medeiros et al. 1983, Jones and Clark 1987, Hachmoller et al. 1991, Lenat and Crawford 1994, Winter and Duthie 1998, Thorpe and Lloyd 1999, Milner and Oswood 2000, Voelz et al. 2005). They typically have lower taxa diversity (Garie and McIntosh 1986, Pedersen and Perkins 1986, Brown 1988, Mangun 1988, Milner and Oswood 2000, Beasley and Kneale 2002, Stepenuck et al. 2002, Morse et al. 2003) while total abundances are not comparably reduced (Whiting and Clifford 1983, Couceiro et al. 2007). In fact, total macroinvertebrate abundance and biomass may be higher than at non-polluted sites (Couceiro et al. 2007), however this is usually directly related to an increase in pollution-tolerant groups (Graves et al. 1998, Pettigrove and Hoffman 2005, Couceiro et al. 2007).

Dipterans, oligochaetes and other species tolerant of organically enriched rivers are usually well represented in benthic communities receiving urban runoff (Polls et al. 1980, Brown 1988, Oswood 1989, Suren and McMurtrie 2005, Couceiro et al. 2007), while more sensitive taxa (such as epheropeterans, trichopterans and plecopterans) may be rare (Milner and Oswood 2000, Stepenuck et al. 2002, Morse et al. 2003). In terms of functional feeding groups, urban communities boast more collectors and gatherers, while a decrease in the proportional abundance of filterers, scrapers and shredders (Stepenuck et al. 2002) corresponds with an increase in shade resulting from enhanced algal productivity (Glandon et al. 1981, Walker and Hillman 1982, Collier 1995, Shieh et al. 1999).

While no peer-reviewed research has been published on the urban sites examined in Case Studies 2 and 3, research clearly demonstrates that streams draining urban areas are harsh environments for benthic macroinvertebrates. The complex and dynamic nature of urban sites (Arthington et al. 1982), which would be virtually impossible to accurately emulate under laboratory conditions, makes the sites employed for these two case studies ideal for testing the usefulness of caged macroinvertebrates to assess non-point source pollution.

6.1.4 OBJECTIVE OF THIS CHAPTER

Having examined experimental factors associated with these sorts of in situ trials (Chapter 4) and the potential for using relocated organisms to monitor impacts (Chapter 5), this chapter aims to examine the relationship between macroinvertebrate responses and the environmental factors which drive them under natural field conditions at local sites known to have degraded water quality in and around Canberra. It is not intended to provide a thorough biological 101 assessment of the waterways in and around Canberra, nor to compare in situ experimental findings with the tomes of ecotoxicological literature pertaining to highly controlled exposure studies. The objective is, however, to examine whether a relationship between environmental “causes” and biotic “effects” is evident when endemic test organisms are deployed in mesh- covered holding cages at sites around Canberra that have impaired water quality.

6.2 ADDITIONAL METHODS

The experimental design was based on that outlined in Section 2.4, where internal replication was dependant on the availability of test organisms at the source site.

6.2.1 METHODS COMMON FOR ALL CASE STUDY TRIALS

In all experiments, organisms harvested for the trial were deployed in cages at control and impact test sites to examine their responses to different scenarios of degraded water quality in and around Canberra ACT (Figure 3.3). Details of the component experiments are provided in Table 6.1, 6.2 and 6.3. Apart from methods detailed here, these experiments follow the general project methods and materials as presented in Chapter 2.

Harvesting and Deployment of Test Organisms As described in the previous chapter, low macroinvertebrate abundances at source sites necessitated the relocation of appropriate test organisms found to be sufficiently abundant from other locations in order to satisfy the statistical power requirements of the experimental design (Section 2.5). Where multiple test species were deployed concurrently, each taxon was caged separately as described in Section 2.4. In all instances, only a few hours lapsed between harvesting, caging and deployment of the test organisms as described in Section 2.4.

All control sites employed in these case studies conformed to “reference” condition (Reynoldson et al. 1997, Reynoldson and Norris 2000, Stoddard et al. 2006) and had previously been used to construct and validate AUSRIVAS (Australian River Assessment System) predictive models (Davies 2000, Simpson and Norris 2000). Each of the experiments detailed in this chapter were planned at times where climatic and hydrologic conditions would be relatively stable, and pre-trial reconnaissance visits were undertaken to confirm their suitability for the case study experiments. 102

Endpoint Measurements Pooled across Treatment Replicates As it was impossible to obtain accurate wet weights for Notalina sp. individuals in Case Study 3 (Table 6.3), average wet weights were obtained by dividing the total wet weight of all individuals retrieved alive from a particular deployment cage and dividing it by the number of individuals comprising this sample. As there was no internal replication, REML analysis could not be performed as described in Section 2.10. In this instance, a linear regression (and the associated Analysis of Variance) was performed to compare the response variable (average wet weight) with the fixed treatment factors under consideration (i.e. deployment site and trial day).

6.2.2 TRACE METAL ANALYSIS FOR CASE STUDY 1

In this case study, macroinvertebrate response variables included endpoints representing contaminant uptake as well as binomial (e.g. survival) and morphological endpoints. Macroinvertebrate samples were analysed for trace metal body burden after retrieval from the deployment sites. In addition to the water samples collected for all trials (Section 2.8), samples were collected for analysis of trace metals in the water. Low density polyethylene sample bottles were were soaked in a 2% DeconTM bath for 24 hours, and then rinsed thoroughly with ultra-pure MilliQTM water. Bottles were then soaked for a further 24 hours in a 2% acid bath (nitric acid, HNO3, Aristar BDH). After soaking, bottles were rinsed thoroughly with ultra-pure MilliQTM water, dried in an enclosed laminar flow drying cabinet, sealed with their lids to prevent contamination and stored in acid-washed bags awaiting use.

Field samples were collected on each retrieval day at each deployment site, and approximately 30 ml of concentrated nitric acid (Aristar, BDH) was added to fix the metals in each preserved by 250 ml water sample. Fixed samples were refrigerated at 4oC awaiting laboratory analysis.

ICP-MS Analysis Trace metal concentrations were analysed using an Inductively Coupled Plasma – Mass Spectrometer (Perkin-Elmer Elan 6000 ICP-MS) fitted with a Scott Double Pass spray chamber, a Cross-flow nebuliser and an AS-90 autosampler.

All measurements by ICP-MS were calibrated using a series of intermediate working standards, and all analytical runs incorporated both aqueous standards and blanks as a check 103

th th confluence wi confluence wi y of of Captains Flat township Captains Flat township of of tream  testing of water qualit testing of River at Naas o River upstream o River downstream o River upstream o River downs ongl ongl ongl ongl in situ te te te Si Creek pact Si pact Si te 7: Gudgenby te 14: Mol te 15: Mol te 14: Mol te 15: Mol Experimental Sites Source Site Si Control Si Im Si Copper Source/Control Site Si Im Si Copper Creek usefulness of macroinvertebrates for of macroinvertebrates usefulness Random Experimental Random Experimental Factors cage) Individuals (5 per Cages (5 replicates) Cages (5 replicates) Individuals (1 per cage) Individuals (5 per cage) Cages (5 replicates) trials is provided in Table 3.1. comprised single cages (each containing 5 individuals) deployed on trial day 0 and retrieved from each site on trial comprised single cages (each containing 2) 2) 2) (n = 5) (n = 6) (n = 5) als used to test the als used pe (n = pe (n = pe (n = est organism was excluded from mixed model analyses using REML. est organism was excluded from mixed al day al day al day e tri te ty te ty te ty th Season (n = 4)† Si Tri Si Tri Treatment Factors Si Tri f Atalophlebia australis Mine Drainage. – 1 ebiidae) winter trial for this t he he winter trial for Case Study – Experimental detail o Experimental nal site information for the sites employed in these nal site information for the sites employed australis As such, t 12. eroptera: Leptophl indicates that t Additio  † day Austrolestes cingulatum (Odonata: Lestidae) Atalophlebia (Ephem Paratya australiensis (Decapoda: Atyidae) Table 6.1: Test Species (Order: Family) 104 h Drive y Drive  River at Naas River at Naas testing of water qualit testing of inderra Creek at Kingsford Smit genby in situ tes tes Si pact Si te 7: Gud te 7: Gudgenby at Burra te 13: Burra Creek Road te 17: Yarralumla Creek at Cotter Avenue te 18: Jerrabomberra Creek at Canberra Drive te 21: Ginninderra Creek at Copland te 22: Ginn te 23: Ginninderra Creek at Florey Drive te 24: Ginninderra Creek at Osborne Experimental Sites Source Site Si Control Si Si Im Si Si Si Si Si Si Random Experimental Random Experimental Factors 5) individuals (n = cages (n = 5) od (n = 2) als used to test the usefulness of macroinvertebrates for of macroinvertebrates to test the usefulness als used pe (n = 2) peri day (n = 6) e tri al al te group (n = 2) te ty th tri si Treatment Factors si tri f Urban Stormwater Runoff. Runoff. Urban Stormwater – 2 Case Study Experimental detail o Experimental – Additional site information for the sites employed in these trials is provided in Table 3.1. Additional site information for the sites  rder: Family) (O Paratya australiensis (Decapoda: Atyidae) Table 6.2: Test Species 105 y confluence with of  testing of water qualit testing of o River at “Silver Hills” ongl in situ te te tes Si pact Si pact Si te 13: Burra Creek at Burra te 13: Burra Creek Creek at Mirrebei Drive te 19: Ginninderra te 16: Mol te 13: Burra Creek at Burra te 11: Point Hut Pond upstream of Lake te 12: Tuggeranong Creek downstream Drive te 19: Ginninderra Creek at Mirrebei Experimental Sites Source/Control Site Si Im Si Source Site Si Control Si Im Si Crossing Murrumbidgee River at Point Hut Si Tuggeranong Si e 3.1. Random Experimental Random Experimental Factors cage) individuals (5 per cages (5 replicates) individuals (6 per cage) cages (5 replicates) y od (n = 2) als used to test the usefulness of macroinvertebrates for of macroinvertebrates to test the usefulness als used pe (n = 3) pe (n = 2) ly High Turbidit ly High day (n = 6) peri day (n = 6) e tri al al al te ty te ty th si tri tri Treatment Factors si tri f Persistent – 3 ceridae) Case Study Experimental detail o Experimental – aliensis Additional site information for the sites employed in these trials is provided in Tabl Additional site information for the sites  Notalina sp. (Trichoptera: Lepto (Order: Family) Paratya austr (Decapoda: Atyidae) Table 6.3: Test Species 106 against contamination levels, and to monitor analytical drift and recovery between samples. -1 A fullscan standard was produced from 0.25 mL of Merck standard (20 μgmL in 5% HNO3) -1 and 0.50 mL Perkin Elmer standard (10 μgmL in 5% HNO3) and was diluted to a volume of 50 mL. This provided a 200 μgL-1 stock standard of most elements, in 1% HNO3 (Aristar, BDH), from which stock sub-standards of 100 μgL-1, 10 μgL-1 and 1 μgL-1, serially diluted, and a reagent blank (1% HNO3, Aristar, BDH) were prepared. These standards were prepared on the day of analysis for quality assurance and to avoid any degradation of the standard solutions. Analytical runs followed the operating conditions for multi-element analysis outlined in Table 6.4 and were corrected for spectrophotometric background absorptions.

Table 6.4: Operating conditions for the Perkin-Elmer Elan 6000 ICP-MS.

RF power 1100W Argon flow rates: Auto Plasma/coolant Auto Auxiliary Auto Nebuliser 0.8 min-1 Dwell time 50 ms Sweeps per reading 16 Readings per replicate 1 Number of replicates 3

Preparation of Biological Tissue ICP-MS analysis follows the methods detailed above, however, the online autodilutor was programmed to perform a second tenfold dilution of the nitric acid digest, producing a 100-fold dilution of the original sample digest for analysis. All collection vials, teflon digestion vessels and centrifuge tubes were prepared as previously described for bottles used to collect samples for trace metal analysis.

Trace metal concentrations were only measured in samples comprising individuals that were retrieved alive from their deployment cages. This wasbecause it was not possible to ascertain when and why an individual died, or to be able to positively attribute its death to water quality. It was also observed that dead individuals were often associated with particulate debris (including ferric flocculant) and were often in the process of decomposition by aquatic fungus. Because fungal hyphae could not be separated from the decomposing remains of the dead individual (particularly for individuals retrieved late in the trial), metal body burden in the test organisms could not be distinguished from the metals bound by the fungus. Even if a 107 reliable measure of trace metal body burden were to be quantified for dead individuals, there is some question about metal depuration from senescent tissues.

Following retrieval from the field sites, samples were placed on ice, transported to the laboratory where they were frozen until processing. Frozen macroinvertebrate samples were rinsed in ultra-pure MilliQTM water to remove residual debris and particulate material from the exoskeleton. Digital images of all macroinvertebrates were then captured according to Section 2.8, after which samples were rinsed thoroughly in MilliQTM ultra-pure water placed in acid-washed plastic containers freeze dried for four days at the CSIRO, Division of Wildlife and Ecology.

Because freeze dried masses of replicate samples were too low to provide adequate mass for digestion and analytical detection, live individuals were pooled across trial day, deployment site, test species and season to obtain sufficient sample masses. While it is possible to perform successful digestion procedures on tissue masses less than the prescribed 70 mg (Baldwin et al. 1994) by adjusting acid to tissue to diluent ratios, sample masses in this study were too low for this to be viable.

The entire freeze dried mass of the pooled macroinvertebrate samples was weighed to 0.1 mg, processed into fine particles using stainless steel scissors which were thoroughly rinsed with ethanol (30% w/w; PRONLYS, AR Grade, Selby-Biolab) before processing each sample, and between samples, and placed in acid-washed teflon digestion vessels. Large samples were ground using an acid-washed mortar and pestle to homogenate the sample for sub-sampling.

Using an automated dispenser, 1 ml analytical grade nitric acid (Aristar, BDH) was added to each digestion vessel or “bomb”, two of which were then placed into a 120 ml teflon polytetrafluoroacetate (PFA) pressure relief digestion vessels with 10 ml of ultra-pure MilliQTM water. Twelve of these larger pressure relief digestion vessels were placed within a digestion rack, the centre of which held a pressure release container filled with at least 10 ml of ultra-pure MilliQTM water connected by tubing to the centre of each of the caps of all the larger digestion vessels. If the sample run was not full, spare digestion vessels were treated as nitric acid blanks to ensure all digestions were conducted at the same pressures.

Digest blanks and Standard Reference Materials (SRM) were routinely included to assess effect of acid in digestion, detect and monitor any contamination, and examine percentage recovery of metals. As no freshwater invertebrate standard reference material was available, 108 the copepod homogenate SRM (International Atomic Energy Agency; MA-A-1) was used and trace metal recoveries were greater than 80% across the entire sample set for metals analysed.

Samples, digest blanks and SRMs were digested in a 600 W microwave oven (MDS 81D, CEM, Indian Trail, NC, USA) using a low volume microwave digestion technique (Baldwin et al. 1994, Deaker and Maher 1997). Samples were digested for two minutes on full (100%) power, two minutes on no (0%) power and then 45 minutes on 75% power.

After digestion, the samples were cooled, allowed to vent in a fume cupboard, decanted to acid-washed 10 ml centrifuge tubes and diluted to 5 ml using ultra-pure MilliQTM water. Diluted digests were then stored at 4oC until analysis.

Statistical Analysis of Freeze Dried Weight Data As all live individuals had to be pooled across trial day and site combination to obtain sufficient sample masses for analysis of metal concentrations as described above, this response variable was not replicated at the cage level, and analysis using the restricted maximum likelihood (REML; Searle 1987) statistic could not be employed as described in Section 2.10. Analysis of the relationship between tissue metal concentrations and the fixed treatment factors under consideration (Table 6.1) was performed using linear regressions and associated Analyses of Variance.

6.2.3 COMPARISON OF MACROINVERTEBRATE RESPONSES WITH ENVIRONMENTAL

VARIABLES

As outlined in Section 2.10, the restricted maximum likelihood statistic (REML; Searle 1987) was used to test for significant differences between treatment or fixed factors associated with the experimental set-up (Tables 6.1, 6.2 and 6.3).

If treatment factors were found to produce significant variation in any of the response variables, the macroinvertebrate endpoint data was coupled with environmental measurements (including water quality covariates) to determine which covariates, if any, were responsible for any significant effects identified. As environmental information was not replicated beyond the combination of trial day and deployment site, individual endpoint measurements (for example head width) were collapsed to provide a weighted mean to compare with the environmental data for the same combination of all treatment factors (e.g. trial day, deployment site and trial). Regression analyses were then employed to make comparisons 109 between the macroinvertebrate responses and water quality covariates, and relationships were compared using Analysis of Variance.

6.3 RESULTS

Prevailing environmental conditions, field and water quality measurements and descriptions of macroinvertebrate body size and response variables for all three case studies are presented in Appendix 6.

6.3.1 CASE STUDY 1: ACID MINE DRAINAGE

Macroinvertebrate Responses Case Study 1 Test organism – Paratya australiensis Experimental Effects on Survival and Escape of Paratya australiensis Of the 300 individuals deployed, almost 75% were retrieved alive. Survival was significantly affected by the interaction of trial day and site factors (2 statistic = 11.600, df = 5, 12, p = 0.042) with more than 90% of the individuals retrieved dead during the trial collected from the impact site (Figure 6.1). While survival was slightly lower at the impact site over the first three days of the trial, it decreased rapidly after trial day 3 to 44.4% on trial day 6, and

100

80 LSD = 13.86 p = 0.042 70

Survival (%) 40

20 Control 10 Impact

0 0 2 4 6 8 10 12 Trial day Figure 6.1: Percent survival of Paratya australiensis retrieved after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences). 110 only two of the 25 individuals (8%) collected from the impact site on trial day 12 were retrieved alive (Figure 6.1).

Only seven individuals escaped from the deployment cages during this trial. Escape was not significantly affected by the interaction of trial day and site factors (2 statistic = 2.818, df = 5, p = 0.73), deployment site (2 statistic = 1.402, df = 1, p = 0.24) or trial day (2 statistic = 9.294, df = 5, p = 0.098).

Experimental Effects on Physical Appearance of Paratya australiensis Dark patches resembling chitinous burns were identified on the carapace of almost 40% of the Paratya australiensis individuals employed in this trial, however only one of these individuals was retrieved from the control site. Appearance of these patches was not significantly affected by the interaction of trial day and site factors (2 statistic = 0, df = 4, p = 0) while there was a significant effect of both deployment site (2 statistic = 183.4595, df = 1, p < 0.0001; Figure 6.2) and trial day (2 statistic = 160.512, df = 4, p < 0.0001; Figure 6.3). A significantly greater number of individuals with dark patches on their carapace were retrieved from the impact site (Figure 6.2) and after the first three trial days (Figure 6.3).

70 LSD = 8.188 60 p < 0.0001

10 Individuals with dark carapace patches (%) 0 Control Impact Site type

Figure 6.2: Percentage of Paratya australiensis with dark patches on carapaces when retrieved after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences). 111

50 LSD = 3.552 p < 0.0001 45

25 Individuals with dark carapace patches (%) 20 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day Figure 6.3: Percentage of Paratya australiensis with dark patches on carapaces when retrieved over time after deployment at sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences).

Experimental Effects on Body Size of Paratya australiensis

There was a significant effect of the interaction between trial day and deployment site on log10-transformed carapace length of Paratya australiensis (F statistic = 2.66, df = 5, 47, p = 0.0377). As shown in Figure 6.4, carapace length of individuals retrieved alive from the control site was comparable, there was a notable decrease in carapace length for individuals retrieved alive from the downstream impact site as the trial progressed.

While there were no significant effect on log10-transformed Paratya australiensis orbital carapace length (i.e. measurement from posterior margin of the carapace to the base of the eyestalk; Figure 2.3) arising from the interaction between trial day and site (F statistic = 0.98, df = 5, 48, p = 0.4399) or trial day adjusted for deployment site (F statistic = 1.59, df = 5, p = 0.1800), there was strong evidence that P. australiensis individuals retrieved alive from the impact site had significantly shorter log10-transformed orbital carapace lengths than those retrieved from the upstream control site (F statistic = 6.59, df = 1, p = 0.0134; Figure 6.5). 112

0.95 Control Impact LSD = 0.292 p = 0.0377

0.9

0.85

0.8

0.75 -transformed carapace length (mm) 10 Log

0.7 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

0.65

LSD = 0.0155 p = 0.0134

0.645

0.64

0.635 -transformed orbital carapace length (mm) 10

Log 0.63 Control Impact Site type

There were no significant effects of the interaction between trial day and site type on either log10-transformed wet weight (F statistic = 1.29, df = 5, 48, p = 0.2847) or log10-transformed freeze dried weight (F statistic = 0.29, df = 5, 48, p = 0.9171). However, trial day significantly affected both log10-transformed wet weight (F statistic = 5.30, df = 5, p = 0.0006; Figure 6.6) and log10-transformed freeze dried weight (F statistic = 3.14, df = 5, p = 0.0157; Figure 6.7).

And while individuals were found to be heavier at control than impact sites (Figure 6.8), differences in Paratya australiensis weight between site types were significantly different for log10-transformed freeze dried weight only (F statistic = 17.78, df = 1, p = 0.0001; Figure 6.8).

1.7

LSD = 0.1428 1.6 p = 0.0006

1.5

1.4

1.3

1.2 -transformed wet weight (mg) 10

Log 1.1

1 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 6.6: Mean log10-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences). 114

0.95

LSD = 0.0850 p = 0.0157 0.9

0.85

0.8 -transformed freeze dried weight (mg) 10 Log 0.75 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 6.7: Mean log10-transformed freeze dried weight for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences).

1.5 Log 10 -transformed wet weight Log 10 -transformed freeze dried weight 0.9

LSD = 0.0465 LSD = 0.0797 0.88 p = 0.0730 p = 0.0001

1.45 0.86 (mg) 0.84 1.4 -transformed wet weight (mg) -transformed freeze dried weight 10

0.82 10 Log Log

1.35 0.8 Control Impact Control Impact Site type

Figure 6.8: Mean weight measurements for Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences). 115

Experimental Effects Tissue Concentrations of Trace Metals in Paratya australiensis Trace metal tissue concentrations in Paratya australiensis are presented in Table 6.5. ANOVA results for the linear regressions between trace metal concentrations in Paratya australiensis tissue and trial day at the deployment sites employed in Case Study 1 are presented in Table 6.6.

Tissue concentrations of iron (Table 6.5) were significantly higher at the downstream impact site (Figure 6.9) and increased significantly across trial day (Figure 6.10). However, as the effect of the interaction between trial day and site was not statistically significant at α = 0.05 (Table 6.6), the rate of increase in iron concentrations in P. australiensis tissue was statistically comparable at both deployment sites.

Table 6.5: Metal concentrations (μgg-1 FDW) in the tissue of Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Trial Fe 56 Cu 65 Zn 68 Cd 114 Pb 207 Day (μgg-1 FDW) (μgg-1 FDW) (μgg-1 FDW) (μgg-1 FDW) (μgg-1 FDW)

Site 7 (source) – Gudgenby River at Naas 0 89.88 49.43 59.64

Tissue body burdens of copper and zinc were significantly affected by the interaction of site and trial day (Table 6.6), with significant increases in the concentrations of tissue metals in organisms retrieved from the impact site on trial day 12 (Table 6.5, Figures 6.11 and 6.12).

Table 6.6: Analysis of Variance (ANOVA) table for regression between trial day and log10-transformed metal concentrations in the tissue of Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Source df Sum of Squares Mean Square F ratio p

Log10Fe Site type 1 0.3473 0.3473 30.46 0.0009 Trial day 1 0.1908 0.1908 16.74 0.0046 Site type x trial day 1 0.0328 0.0328 2.88 0.1337 Residual 6 0.0798 0.0133 Total 9 1.7182 0.1909

Log10Cu Site type 1 0.0475 0.0475 8.01 0.0254 Trial day 1 0.0555 0.0555 9.35 0.0184 Site type x trial day 1 0.1176 0.1176 19.83 0.0030 Residual 6 0.0415 0.0069 Total 9 0.1795 0.0199

Log10Zn Site type 1 1.240 1.240 633.46 <0.0001 Trial day 1 0.0208 0.0208 10.65 0.0138 Site type x trial day 1 0.0953 0.0953 48.68 0.0002 Residual 6 0.0137 0.0023 Total 9 1.3698 0.1522

Log10Cd Site type 1 0.0025 0.0025 19.32 0.0032 Trial day 1 0.0181 0.0181 142.56 <0.0001 Site type x trial day 1 0.0187 0.0187 146.78 <0.0001 Residual 6 0.0009 0.0002 Total 9 0.0959 0.0107

Log10Pb Site type 1 0.4847 0.4847 19.68 0.0030 Trial day 1 0.2280 0.2280 12.34 <0.0001 Site type x trial day 1 0.5042 0.5042 20.47 0.0027 Residual 6 0.1724 0.0287 Total 9 4.3810 0.4868 117

800

700 FDW) -1

μ 600

500

400

300

200

100 Average tissue concentration of Fe ( gg 0 Control Impact Site type

Figure 6.9: Average concentration of iron in Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

700

FDW) 600 -1 μ 500

400

300

200

100 Average tissue concentration of Fe ( gg 0 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 6.10: Average concentration of iron in Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 1. 118

120

Source

FDW) 110

-1 Control

μ Impact 100

50 Average tissue concentration of Cu ( gg 40 Day 0 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

1000

Source FDW)

-1 Control 800

μ Impact

600

400

200 Average tissue concentration of Zn ( gg 0 Day 0 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

While the effect of the interaction between trial day and site on log10-transformed tissue concentrations of cadmium and lead were strongly significant (F ratio = 146.78, df = 1, p < 0.0001 and F ratio = 20.47, df = 1, p = 0.0027 respectively) this was because concentrations of these metals in the majority of P. australiensis tissue samples collected from the upstream control site were below method detection limits (Table 6.5).

Test organism – Austrolestes cingulatum Experimental Effects on Survival and Molting of Austrolestes cingulatum Of the 50 individuals deployed, only a single individual was retrieved dead on trial day 1 from the control site. Three molt exuviae were also retrieved, one from the upstream control site on trial day 1 and two from the downstream impact site on trial day 12. Neither treatment factor significantly affected survival (site type: 2 statistic = 1.498, df = 1, p = 0.17; trial day: 2 statistic = 3.393, df = 4, p = 0.48) or molting (site type: 2 statistic = 0.407, df = 1, p = 0.534; trial day: 2 statistic = 6.233, df = 4, p = 0.174) of Austrolestes cingulatum deployed in these experiments.

Experimental Effects on Body Size of Austrolestes cingulatum There were no significant effects on head width of A. cingulatum of the interaction between trial day and site type (F statistic = 1.35, df = 4, 38, p = 0.270), deployment site (F statistic = 0.79, df = 1, p = 0.898) or trial day adjusted for site type (F statistic = 0.02, df = 4, p = 0.541).

There were also no significant effects on log10-transformed freeze dried weight of A. cingulatum of the interaction between trial day and site type (F statistic = 1.42, df = 4, 38, p = 0.246), deployment site (F statistic = 0.47, df = 1, 38, p = 0.496) or trial day adjusted for site type (F statistic = 0.02, df = 4, 38, p = 0.703).

Experimental Effects Tissue Concentrations of Trace Metals in Austrolestes cingulatum Trace metal tissue concentrations in Austrolestes cingulatum are presented in Table 6.7. ANOVA results for the linear regressions between trace metal concentrations in Austrolestes cingulatum tissue and trial day at the deployment sites employed in Case Study 1 are presented in Table 6.8.

Only the body burden of zinc was significantly affected by the interaction of site and trial day, with a marked increased in zinc concentration in organisms retrieved from the impact site on trial day 12 (Table 6.8 and Figure 6.13). While a similar pattern was exhibited by the tissue 120 concentrations of iron, and despite strong evidence of an interaction between trial day and site on the iron concentration in A. cingulatum tissue, this interaction was not statistically significant at α = 0.05 (F statistic = 5.75, df = 1, p = 0.0534; Table 6.8). There was, however, a significant effect of both site type and trial day on tissue concentration of iron in A. cingulatum (Table 6.7), concentrations were significantly higher at the downstream impact site (Figure 6.14) and increased significantly across trial day (Figure 6.15).

Table 6.7: Metal concentrations (μgg-1 FDW) in the tissue of Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Trial Fe 56 Cu 65 Zn 68 Cd 114 Pb 207 Day (μgg-1 FDW) (μgg-1 FDW) (μgg-1 FDW) (μgg-1 FDW) (μgg-1 FDW)

Site 14 – Source/Control Site 0 45.59 13.86 66.27 0.2087

While the effect of the interaction between trial day and site on log10-transformed tissue lead concentrations was strongly significant (F ratio = 1844.62, df = 1, p < 0.0001; Table 6.8) this was because all lead concentrations in A. cingulatum tissue samples collected from the control site were below method detection limits (Table 6.7). Tissue concentrations of cadmium and copper on the other hand did not differ significantly between sites or across retrieval days, although there was some evidence that the latter increased as the trial progressed (F ratio = 5.66, df = 4, p = 0.0549; Table 6.8). 121

Table 6.8: Analysis of Variance (ANOVA) table for regression between trial day and log10-transformed metal concentrations in the tissue of Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Source df Sum of Squares Mean Square F ratio p

Log10Fe Site type 1 1.0711 1.0711 58.46 0.0003 Trial day 1 0.0312 0.0312 17.02 0.0062 Site type x trial day 1 0.1054 0.1054 5.75 0.0534 Residual 6 0.1099 0.0183 Total 9 1.5982 0.1776

Log10Cu Site type 1 0.0047 0.0047 1.57 0.6405 Trial day 1 0.0169 0.0169 5.66 0.0549 Site type x trial day 1 0.0083 0.0083 2.80 0.1454 Residual 6 0.0179 0.0030 Total 9 0.0477 0.0053

Log10Zn Site type 1 0.0995 0.0995 107.66 <0.0001 Trial day 1 0.0672 0.0672 72.70 0.0001 Site type x trial day 1 0.0578 0.0578 62.59 0.0002 Residual 6 0.0055 0.0009 Total 9 0.2300 0.0255

Log10Cd Site type 1 0.0059 0.0059 1.00 0.3549 Trial day 1 0.0027 0.0027 0.45 0.5277 Site type x trial day 1 0.0003 0.0003 0.05 0.8371 Residual 6 0.0355 0.0059 Total 9 0.0443 0.0005

Log10Pb Site type 1 0.6158 0.6158 4981.36 <0.0001 Trial day 1 0.2280 0.2280 1844.62 <0.0001 Site type x trial day 1 0.2280 0.2280 1844.62 <0.0001 Residual 6 0.0007 0.0001 Total 9 1.0725 0.1192 122

250 Source FDW)

-1 Control 200 μ Impact

150

100

50 Average tissue concentration of Zn ( gg 0 Day 0 Day 1 Day 2 Day 3 Day 6 Day 12 Trial day

Figure 6.13: Average concentration of zinc in Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

500

450 FDW) -1 400 μ

350

300

250

200

150

100

Average tissue concentration of Fe ( gg 0 Control Impact Site type Figure 6.14: Average concentration of iron in Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. 123

600 FDW) -1 500 μ

400

300

200

100

Average tissue concentration of Fe ( gg 0 Day 1 Day 2 Day 3 Day 6 Day 12 Trial day Figure 6.15: Average concentration of iron in Austrolestes cingulatum retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 1.

Test organism – Atalophlebia australis Experimental Effects on Survival, Escape and Molting of Atalophlebia australis Of the 407 individuals retrieved, only 16 individuals died, with mortality recorded in all experimental seasons and both control and impact sites but not until trial day 3. Survival was not significantly affected by interactions of trial day, site and season (2 statistic = 3.219, df = 8, p = 0.918), trial day and site (2 statistic = 0.426, df = 4, p = 0.979) or trial day and season (2 statistic = 3.764, df = 8, p = 0.985). However, seasonal patterns were not consistent for both site types (2 statistic = 8.726, df = 2, p = 0.014), with average survival far more different between sites types in autumn than between site types in spring and summer trials (Figure 6.16). There was also a significant decrease in average survival across trial day (2 statistic = 23.26, df = 4, p < 0.0001), with an average of only 82% of individuals retrieved alive after being deployed at the study sites for 12 days (Figure 6.17). 124

Spring Summer Autumn 100 LSD = 11.61 p = 0.014

Average survival (%) 85

80 Control Impact Site type Figure 6.16: Percent survival of Atalophlebia australis retrieved after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences).

100 LSD = 6.67 p < 0.0001

90 Average survival (%) 85

80 Day 1 Day 2 Day 3 Day 6 Day 12 Trial day Figure 6.17: Percent survival of Atalophlebia australis retrieved over time after deployment at sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences). 125

Across all the seasonal experiments, only 38 Atalophlebia australis individuals escaped, with half of these occurring after trial day 6 and over 70% from cages deployed at the impact site. Escape of caged nymphs was not significantly affected by interactions of trial day, site and season (2 statistic = 8.292, df = 8, p = 0.417), trial day and season (2 statistic = 10.931, df = 8, p = 0.216) or site type and season (2 statistic = 4.56, df = 2, p = 0.104). However, escape patterns were significantly different between site types across the trial duration (2 statistic = 19.318, df = 4, p < 0.0001) with an increasing number of escapees as the trials progressed at the impact sites (Figure 6.18). Significantly more individuals escaped from deployment cages in summer than in the other seasons (2 statistic = 6.667, df = 2, p = 0.038; Figure 6.19).

Approximately 5% of the deployed individuals molted during the 12 day deployment trials, however there was no apparent pattern across season, site type or trial day. Exoskeletal sloughing by caged Atalophlebia australis was not significantly affected by interactions of trial day, site and season (2 statistic = 4.779, df = 8, p = 0.775), site type and season (2 statistic = 3.147, df = 2, p = 0.219), trial day and site (2 statistic = 3.849, df = 4, p = 0.441) or site type (2 statistic = 0.005, df = 1, p = 0.942). While number of nymphal molts recovered from the deployment cages was significantly different across the trial period in the various seasonal trials (2 statistic = 19.091, df = 8, p = 0.016), no patterns were apparent (Figure 6.20).

Experimental Effects on Body Size of Atalophlebia australis Significant differences in size of caged Atalophlebia australis nymphs were only related to season, with no effect of trial day, site or any interaction of fixed factors (Table 6.9). Both head width and log10-transformed freeze dried weight were greatest for nymphs in the spring trial, with the smallest nymphs in the autumn trial (Figures 6.21 and 6.22). Nymphal head widths for spring and summer trials were significantly greater than head widths for

A. australis retrieved in the autumn trial (Figure 6.21), while in terms of log10-transformed freeze dried weight, spring individuals were heavier than autumn individuals, whilst neither group differed significantly from nymphs retrieved in summer (Figure 6.22). 126

30 Control Impact

LSD = 10.30 20 p < 0.0001

Escapees (%) 10

0 Day 1 Day 2 Day 3 Day 6 Day 12 Day 1 Day 2 Day 3 Day 6 Day 12 Trial day Figure 6.18: Percentage of Atalophlebia australis that escaped from holding cages after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences).

LSD = 6.78 p = 0.038 20

10 Escapees (%)

0 Winter Spring Summer Autumn Season Figure 6.19: Percentage of Atalophlebia australis that escaped from holding cages after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences). 127

20 Day 1 Day 2 Day 3 Day 6 Day 12

LSD = 15.31 p = 0.016 15

Molted individuals (%) 5

0 Winter Spring Summer Autumn Season

Figure 6.20: Percentage of Atalophlebia australis that molted after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences).

When body size between the seasonal trials was examined separately for the different site types, season was found to have a stronger effect at the impact sites than at control sites (Figure 6.23). Head widths were significantly shorter in autumn at both control and impact sites (Figure 6.23). And while the seasonal pattern was similar for log10-transformed freeze dried weight (Figure 6.24), retrieved nymphs were only significantly different between seasons at the impact site (Table 6.10).

Fixed factor df F statistic p

Head width Season 2, 58 12.51 <0.0001 Site type 1, 58 0.45 0.5046 Trial day 4, 58 0.56 0.6947 Season x site type 2, 58 0.37 0.6903 Site type x trial day 4, 58 0.18 0.9502 Season x trial day 8, 58 0.62 0.7567 Season x site type x trial day 8, 58 0.47 0.8740

Log10-transformed freeze dried weight Season 2, 58 7.68 0.0011 Site type 1, 58 0.43 0.5141 Trial day 4, 58 0.36 0.8373 Season x site type 2, 58 0.08 0.9205 Site type x trial day 4, 58 0.28 0.8869 Season x trial day 8, 58 0.47 0.8702 Season x site type x trial day 8, 58 0.46 0.8763

Table 6.10: Mixed model significance tests for mean head width and log10-transformed freeze dried weight for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Head width (mm) Log10-transformed freeze dried weight (mg)

Fixed factor df F statistic p F statistic p

Control site Season 2, 29 4.76 0.0163 2.92 0.0701 Trial day 4, 29 0.34 0.8513 0.41 0.7971 Season x trial day 8, 29 0.53 0.8270 0.47 0.8682 Impact site Season 2, 29 7.61 0.0022 0.20 0.0157 Trial day 4, 29 0.42 0.7928 0.20 0.9379 Season x trial day 8, 29 0.49 0.8532 0.43 0.8914 129

Spring Summer Autumn 1.8

1.75 LSD = 0.122 p < 0.0001 1.7

1.65

1.6

1.55

Head width (mm) 1.5

1.45

1.4

1.35 Winter Spring Summer Autumn Season Figure 6.21: Mean head width for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences). Note that statistical comparison excluded winter sample.

Spring Summer Autumn 0.5

0.4 LSD = 0.074 p = 0.0011

0.3

0.2

0.1 -transformed freeze dried weight (mg) 10 Log 0 Winter Spring Summer Autumn Season

Figure 6.22: Mean log10-transformed freeze dried weight for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences). Note that statistical comparison excluded winter sample. 130

1.85 LSD = 0.178 LSD = 0.188 p = 0.0163 p = 0.0022 1.8

1.75

1.7 Spring 1.65 Summer Autumn 1.6 Head width (mm)

1.55

1.5

1.45 Control Impact Site type Figure 6.23: Mean head width for Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. LSD = Least squared differences (2 x standard error of differences).

0.5 LSD = 0.111 p = 0.0157

0.45

0.4 Spring Summer Autumn 0.35

0.3 -transformed freeze dried weight (mg) 10 Log 0.25 Control Impact Site type

Experimental Effects Tissue Concentrations of Trace Metals in Atalophlebia australis Trace metal tissue concentrations in Atalophlebia australis are presented in Table 6.11. ANOVA results for the linear regressions between trace metal tissue concentrations and season and trial day at the deployment sites employed in Case Study 1 are presented in Table 6.12.

Tissue concentrations of all five metals measured were significantly affected by trial day and site type (Table 6.12) with metal concentrations increasing as trials progressed and tissue concentrations generally higher at the impact sites (Table 6.11). Body burden data for copper, zinc and cadmium also exhibited significantly different tissue concentration patterns across trial periods, seasons and between site types (Table 6.12 and Figures 6.25 to 6.29).

The concentration of copper in Atalophlebia australis tissue was significantly greater at impact site than the control site, especially in spring trial (Figure 6.25) and on trial day 12 (Figure 6.26). While zinc concentrations were relatively stable in nymph tissue obtained from control sites, there was an increase in zinc with trial day at the impact site (Figure 6.27) and this was particularly marked in summer, less so in spring, and appeared to decrease slightly with trial duration in the autumn trial (Figure 6.28). Tissue concentration of cadmium in the tissue of Atalophlebia australis was shown to behave significantly differently across trials at the different sites with respect to season (Figure 6.29).

Given that tissue concentrations of iron and lead in Atalophlebia australis were not significantly affected by any interaction between any of the fixed treatment factors (Table 6.12), it is reasonable to simplify the mixed model for these body burden endpoints (see Section 2.10) in an attempt to elucidate the causal mechanism underlying this variation. As season was among the non-significant factors that could be removed from the mixed model analysis, variation in tissue iron and lead concentration could be partitioned across trial day and site type separately for each component season. 132 * 11.39 95.58 98.13 237.68 119.41 2 498.43 157.17 180.74 147.97 226.35 406.85 119.11 192.54 213.04 289.06 Pb 207  * .011

Table 6.12: Analysis of Variance (ANOVA) table for regression between trial day, season and log10-transformed metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Source df Sum of Mean F ratio p Squares Square

Log10Fe Season 2 0.0501 0.0250 0.81 0.4632 Site type 1 4.4231 4.4231 142.34 <0.0001 Trial day 1 0.5714 0.5714 18.39 0.0005 Season x site type 2 0.0405 0.0202 0.65 0.5337 Site type x trial day 1 0.0002 0.0002 0.01 0.9365 Season x trial day 2 0.1045 0.0523 1.68 0.2155 Season x site type x trial day 1 0.0206 0.0103 0.33 0.7222 Residual 17 0.5282 0.0311 Total 28 5.7386 0.2050 Log10Cu Season 2 0.0910 0.0455 6.08 0.0102 Site type 1 1.1080 1.1080 148.04 <0.0001 Trial day 1 0.0981 0.0981 13.10 0.0021 Season x site type 2 0.1314 0.0657 8.78 0.0024 Site type x trial day 1 0.0391 0.0391 5.23 0.0354 Season x trial day 2 0.0475 0.0238 3.18 0.0673 Season x site type x trial day 1 0.0467 0.0467 3.12 0.0701 Residual 17 0.1272 0.0075 Total 28 4.311 0.1539 Log10Zn Season 2 0.0254 0.0127 1.46 0.2609 Site type 1 0.9135 0.9135 104.77 <0.0001 Trial day 1 0.1005 0.1005 11.53 0.0034 Season x site type 2 0.0434 0.0217 2.49 0.1126 Site type x trial day 1 0.0406 0.0406 4.66 0.0456 Season x trial day 2 0.0729 0.0364 4.18 0.0334 Season x site type x trial day 1 0.0440 0.0440 2.52 0.1099 Residual 17 0.1482 0.0087 Total 28 3.6151 0.1291 Log10Cd Season 2 0.0176 0.0088 2.66 0.0989 Site type 1 0.0442 0.0442 13.37 0.0020 Trial day 1 0.0202 0.0202 6.13 0.0242 Season x site type 2 0.0267 0.0134 4.05 0.0365 Site type x trial day 1 0.0092 0.0092 2.78 0.1138 Season x trial day 2 0.0017 0.0008 0.25 0.7832 Season x site type x trial day 1 0.0445 0.0445 6.74 0.0070 134

Table 6.12 (continued): ANOVA table for regression between trial day, season and log10-transformed metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Source df Sum of Mean F ratio p Squares Square

Log10Pb Season 2 0.0179 0.0090 0.27 0.7699 Site type 1 7.2090 7.2090 213.81 <0.0001 Trial day 1 0.3035 0.3035 9.00 0.0081 Season x site type 2 0.0254 0.0254 0.38 0.6914 Site type x trial day 1 0.0022 0.0022 0.07 0.8017 Season x trial day 2 0.0280 0.0140 0.41 0.6670 Season x site type x trial day 1 0.0211 0.0106 0.31 0.7351 Residual 17 0.5732 0.0337 Total 28 20.3634 0.7273

160

FDW) 140 -1

μ 120

100 Spring 80 Summer Autumn 60

20 Average tissue concentration of Cu ( gg 0 Control Impact Site type Figure 6.25: Average concentration of copper in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. 135

180 Control

FDW) 160 -1 Impact

μ 140

120

100

Average tissue concentration of Cu ( gg 0 Day 0 Day 1 Day 2 Day 3 Day 6 Day 12 Trial day

Figure 6.26: Average concentration of copper in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

2000 Control 1800 FDW) -1 Impact 1600 μ

1400

1200

1000

800

600

400

200

Average tissue concentration of Zn ( gg 0 Day 0 Day 1 Day 2 Day 3 Day 6 Day 12 Trial day

Figure 6.27: Average concentration of zinc in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. 136

3500

FDW) 3000 -1 μ 2500

2000

1500

1000

500 Average tissue concentration of Zn ( gg 0 Spring Summer Autumn Spring Summer Autumn Control Impact Figure 6.28: Average concentration of zinc in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

4.5 FDW) -1 4 μ 3.5

2.5

1.5

0.5 Average tissue concentration of Cd ( gg 0 Spring Summer Autumn Spring Summer Autumn Control Impact Figure 6.29: Average concentration of cadmium in Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. 137

Results of ANOVA for the linear regressions between tissue concentrations of iron and lead and trial day at the deployment sites employed in Case Study 1 are presented in Table 6.13.

There was a significant difference in log10-transformed body burden of iron between control and impact sites in all seasons, with the greatest differences between sites in autumn (Table

6.13). Similarly, there was a significant difference in log10-transformed body burden of lead between control and impact sites in all seasons, with the greatest differences between sites in spring (Table 6.13).

Significant day effects in tissue iron were also evident in all seasons, with the greatest increase over the trial duration evident in summer (Table 6.13), as indicated by a significant positive slope (0.0572) in linear regression between iron uptake and trial day (Table 6.14).

However, with respect to log10-transformed body burden of lead, there was only weak evidence of a day effect in summer (Table 6.13) with none of the relationships between lead uptake and trial day having slopes that were significantly different from zero (Table 6.14).

Table 6.13: Analysis of Variance (ANOVA) table for simplified regression between trial day and log10-transformed metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Source df Sum of Mean F ratio p Squares Square

Log10Fe Experiment 9 – Spring Site type 1 0.376 0.376 7.65 0.0326 Trial day 1 0.228 0.228 4.63 0.0750 Site type x trial day 1 0.014 0.014 0.28 0.6132 Residual 6 0.295 0.049 Total 9 1.759 0.195 Experiment 9 – Summer Site type 1 0.614 0.614 17.99 0.0054 Trial day 1 0.515 0.515 15.11 0.0081 Site type x trial day 1 0.000 0.000 0.01 0.9213 Residual 6 0.205 0.034 Total 9 2.175 0.242 Experiment 10 – Autumn Site type 1 0.712 0.712 125.93 < 0.0001 Trial day 1 0.068 0.068 11.97 0.0180 Site type x trial day 1 0.006 0.006 1.15 0.3333 Residual 5 0.028 0.006 Total 8 1.754 0.219 138

Table 6.13 (continued): Analysis of Variance (ANOVA) table for simplified regression between trial day and log10-transformed metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Source df Sum of Mean F ratio p Squares Square

Log10Pb Experiment 9 – Spring Site type 1 2.968 2.968 131.70 < 0.0001 Trial day 1 0.061 0.061 2.70 0.1516 Site type x trial day 1 0.000 0.000 0.01 0.9372 Residual 6 0.135 0.135 Total 9 7.584 0.843 Experiment 9 – Summer Site type 1 2.257 2.257 40.75 0.0007 Trial day 1 0.214 0.214 3.87 0.0967 Site type x trial day 1 0.020 0.020 0.36 0.5725 Residual 6 0.332 0.055 Total 9 6.951 0.772 Experiment 10 – Autumn Site type 1 2.102 2.102 99.44 0.0002 Trial day 1 0.063 0.063 2.96 0.1460 Site type x trial day 1 0.004 0.004 0.20 0.6736 Residual 5 0.106 0.021 Total 8 5.617 0.702

Sum of Mean slope df F value p Squares Square

Log10-transformed tissue concentration of iron Spring 0.0380 1 0.2278 0.2278 5.15 0.0574 Summer 0.0572 1 0.5152 0.5152 17.59 0.0041 Autumn 0.0213 1 0.0634 0.0634 10.95 0.0162

Log10-transformed tissue concentration of lead Spring 0.0196 1 0.061 0.061 3.14 0.1195 Summer 0.0369 1 0.214 0.214 4.26 0.0779 Autumn 0.0206 1 0.059 0.059 3.24 0.1218 139

Linking Biological Responses with Environmental Covariates Paratya australiensis The relationship between aqueous metal concentrations (Appendix 6, Table A6.3) and tissue concentrations in Paratya australiensis (Table 6.5) was variable for the different metals analysed. There was a positive correlation between the P. australiensis tissue concentration and the dissolved concentrations in the water samples for zinc (r2 = 0.785; Figure 6.30) and cadmium (r2 = 0.786; Figure 6.31), however the latter relationship was derived solely from samples collected at the downstream impact site, as cadmium concentrations in the P. australiensis tissue from the control site were all below method detection limit (Table 6.5). Relationships between aqueous and tissue concentrations for the other metals examined were virtually non-existent with a line of best fit explaining less than 10% of the data spread (r2 = 0.015, r2 = 0.048, and r2 = 0.072 for iron, copper and lead respectively). Austrolestes cingulatum In all trace elements analysed, high concentrations of dissolved metals (Appendix 6, Table A6.3) corresponded with high tissue metal concentrations in Austrolestes cingulatum (Table 6.7). There was a particularly strong correlation between the Austrolestes cingulatum tissue concentration and the dissolved concentrations in the water samples for zinc (r2 = 0.976; Figure 6.32), lead (r2 = 0.962; Figure 6.33) and iron (r2 = 0.877; Figure 6.34).

It should be noted that these relationships may be influenced by the sample collected on the final retrieval day at the impact site where macroinvertebrate sample mass was very low, possibly affecting the accuracy of the metal concentrations in the sample tissues (Figures 6.32, 6.33 and 6.34). However, as demonstrated in Table 6.15, even after exclusion of this potentially spurious data positive relationships between aqueous and bioaccumulated metal concentrations are still evident, especially for zinc. 140

1000

900 FDW) -1

μ 800 r2 = 0.7853 700

600

500

400

300

200

100

Average tissue concentration of Zn ( gg 0 0 2000 4000 6000 8000 10000 12000 14000 Average dissolved concentration of Zn (μ gL-1) Figure 6.30: Relationship between tissue concentration of zinc and dissolved zinc concentrations from Paratya australiensis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

1.4 FDW)

-1 1.2 μ r2 = 0.7863 1

0.8

0.6

0.4

0.2

Average tissue concentration of Cd ( gg 0 0 1 2 3 4 5 6 7 8 9 10 Average dissolved concentration of Cd (μ gL-1)

250 FDW) -1

200 2 μ r = 0.9760

150

100

Average tissue concentration of Zn ( gg 0 0 10000 20000 30000 40000 50000 60000

Average dissolved concentration of Zn (μ gL-1)

12 FDW) -1 10 gg μ u r2 = 0.9622 8

Average tissue concentration of Pb ( 0 0 50 100 150 200 250 300 350 400 450 Average dissolved concentration of Pb (uμgL-1)

1200 FDW) -1 1000 μ

800 r2 = 0.8769

600

400

200

Average tissue concentration of Fe ( gg 0 0 2000 4000 6000 8000 10000 12000 14000 Average dissolved concentration of Fe (μ gL-1) Figure 6.34: Relationship between tissue concentration of iron and dissolved iron concentrations from Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Table 6.15: Correlation co-efficients (r2) for linear regressions between aqueous metal concentrations and bioaccumulated metal concentrations in the tissue of Austrolestes cingulatum retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1. Data is presented both with and without the sample collected on trial day 12 at the downstream impact site.

Element Full dataset Trimmed dataset

Fe 0.8769 0.5392 Pb 0.9622 0.5250 Zn 0.9760 0.7494  indicates that the correlation co-efficient was calculated after exclusion of potentially spurious data.

Atalophlebia australis Although increasing concentrations of dissolved metals were generally related to higher body burdens in Atalophlebia australis these relationships were relatively weak, with no more than 55% of the spread of the data explained by the correlation coefficients even after exclusion of potentially spurious data arising from very low sample masses (Table 6.16). 143

Table 6.16: Correlation co-efficients (r2) for linear regressions between aqueous metal concentrations and bioaccumulated metal concentrations in the tissue of Atalophlebia australis retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 1.

Element Full dataset Trimmed dataset

Fe 0.2677 0.3029 Cu 0.0412 0.5400 Zn 0.1039 0.5012 Cd 0.0008 0.0376 Pb 0.2484 0.4281  indicates that the correlation co-efficient was calculated after exclusion of tissue concentrations arising from very low sample mass.

6.3.2 CASE STUDY 2: URBAN STORMWATER RUNOFF

Macroinvertebrate Responses Case Study 2 Test Organism – Paratya australiensis Experimental Effects on Survival and Escape of Paratya australiensis Of the 1503 Paratya australiensis individuals retrieved during the Case Study 2 trials survival of those deployed at urban impact sites was heavily reduced, while only 2% of individuals died at control sites (Figure 6.35). Survival dropped to 42% within 24 hours of deployment at urban impact sites and was virtually 0% after trial day 3. Survival was found to be strongly influenced by the interaction between trial day and site type (2 statistic = 11.965, df = 5, 48, p = 0.0375) while the mean number of individuals retrieved alive across the trial duration was comparable for the two component trials (Figure 6.36).

Since Paratya australiensis survival at the urban impact sites beyond trial day 3 was only 0.2%, macroinvertebrate body size results are presented for analyses of both the full 12 day trial, and when the trial was restricted to first three trial days only. 144

100

80 LSD = 13.654 p = 0.079

Survival (%) 40

Control 20 Impact

0 0 2 4 6 8 10 12 Trial day Figure 6.35: Percent survival of Paratya australiensis after deployment at control and urban impact sites during in situ assessment trials for Case Study 2. LSD = Least squared differences (2 x standard error of differences).

100 Trial A Trial B

Survival (%) 40

0 0 2 4 6 8 10 12 Trial day Figure 6.36: Percent survival of Paratya australiensis retrieved over time after deployment at sites during in situ assessment trials for Case Study 2. 145

Experimental Effects on Body Size of Paratya australiensis – full 12 day trial While there were no significant effects of the interaction between trial day and site type on Paratya australiensis carapace length (Δ-in-Deviance statistic = 1.58, df = 5, 96), there was a significant effect of site type on carapace length (Δ-in-deviance statistic = 6.449, df = 1, p = 0.0110; Figure 6.37). Carapace lengths were significantly shorter at urban impact sites (n = 147, mean = 5.54 ± 0.96 mm) than at control sites (n = 385, mean = 6.10 ± 1.09 mm).

There were no significant effects of the interaction between trial day and site type on Paratya australiensis orbital carapace length (Δ-in-deviance statistic = 1.79, df = 5, 96) or trial day (Δ-in-deviance statistic = 9.24, df = 5, p = 0.1). There was however, a significant effect of site

6.4 Carapace length Orbital carapace length 3.4

6.2 LSD = 0.3534 LSD = 0.1557 3.3 p = 0.0111 p = 0.0148 6

3.2 5.8

5.6 3.1

Carapace length (mm) 5.4

3 Orbital carapace length (mm) 5.2

5 2.9 Control Impact Control Impact Site type Figure 6.37: Mean carapace length and mean orbital carapace length for Paratya australiensis retrieved alive after deployment at control and urban impact sites during in situ assessment trials for Case Study 2. LSD = Least squared differences (2 x standard error of differences).

type on orbital carapace length (Δ-in-deviance statistic = 5.966, df = 1, p = 0.0148; Figure 6.37), with orbital carapace lengths again significantly shorter, and more variable, at urban impact sites (n = 158, mean = 2.97 ± 0.63 mm) than at control sites (n = 434, mean = 3.30 ± 0.58 mm). There was also strong evidence of an effect of trial day on Paratya australiensis 146 carapace length (Δ-in-deviance statistic = 11.1, df = 1, p = 0.051) with a decrease in carapace length evident as the trial progressed, particularly after trial day 3 (Figure 6.38).

There were no significant effects of the interaction between trial day and site type on logn- transformed wet weight of Paratya australiensis (Δ-in-deviance statistic = 1.43, df = 5, 96) or site type (Δ-in-deviance statistic = 1.37, df = 1, p = 0.240). There was, however, a significant effect of trial day on logn-transformed wet weights of P. australiensis (Δ-in-deviance statistic = 23.03, df = 5, p = 0.0003), with individuals retrieved from the deployment sites on trial days 1 and 2 significantly heavier than those retrieved later in the trial (Figure 6.39). Other than the effect of trial day on the logn-transformed wet weights of Paratya australiensis (Figure 6.39), this response variable was also strongly affected by the AUSRIVAS reference group to which the site would belong (regardless of whether it was a control or urban impact site) in the absence of aquatic impact (Δ-in-deviance statistic = 4.212, df = 1, p = 0.0421; Figure 6.40).

6.1

6 LSD = 0.3684 p = 0.051

5.9

5.8

5.7 Carapace length (mm)

5.6

5.5 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day Figure 6.38: Mean carapace length for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 2. LSD = Least squared differences (2 x standard error of differences). 147

3.5 LSD = 0.1524 p = 0.0003

3.4

3.3

3.2 -transformed wet weight (mg) n 3.1 Log

3 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 6.39: Mean logn-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 2. LSD = Least squared differences (2 x standard error of differences).

3.5 LSD = 0.2004 p = 0.0421 3.4

3.3

3.2 -transformed wet weight (mg) n 3.1 Log

3 Group 1 Group 2 AUSRIVAS site group

Experimental Effects on Body Size on Paratya australiensis – first three trial days only As previously described, shrimp suffered high mortality at some of the deployment sites (Figure 6.35 and 6.36), thus macroinvertebrate body size comparisons presented here relate to retrieval on or before trial day 3 only.

In terms of logn-transformed wet weight of Paratya australiensis, there was no significant effect of the interaction between trial day and site type (Δ-in-deviance statistic = 0.58, df = 2, 48) or site type (Δ-in-deviance statistic = 3.1, df = 1, p = 0.22). However, individuals retrieved on trial days 1 and 2 were significantly heavier than individuals retrieved on trial day 3 (Δ-in-deviance statistic = 8.622, df = 2, p = 0.013; Figure 6.41). And, as determined for the full 12 day trial, variation in Paratya australiensis logn-transformed wet weight was strongly affected by the AUSRIVAS reference group to which the site would belong (regardless of whether it was control or urban sites) in the absence of aquatic impact (Δ-in- deviance statistic = 4.131, df = 1, p = 0.0493; Figure 6.42).

3.5 LSD = 0.1524 p = 0.0134

3.4

3.3 -transformed wet weight (mg) n Log

3.2 Day 1 Day 2 Day 3 Trial day

Figure 6.41: Mean logn-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites for three days during in situ assessment trials for Case Study 2. LSD = Least squared differences (2 x standard error of differences). 149

3.6 LSD = 0.2228 p = 0.0493 3.5

3.4

3.3

3.2 -transformed wet weight (mg) n 3.1 Log

3 Group 1 Group 2 AUSRIVAS site group

There were no significant effects on Paratya australiensis carapace length of the interaction between trial day and site type (Δ-in-deviance statistic = 0.43, df = 2, 48) or experimental day (Δ-in-deviance statistic = 1.15, df = 2, p = 0.3). Nor was there a significant effect on orbital carapace length of the interaction between trial day and site type (Δ-in-deviance statistic = 0.211, df = 2, 48) or experimental day (Δ-in-deviance statistic = 4.1, df = 2, p = 0.14).

And while site type had a strong effect on both carapace length and orbital carapace length of Paratya australiensis over the restricted trial period, these effects need to be considered in conjunction with the effects of environmental data collected. Linking Biological Responses with Environmental Covariates When correlated with the water quality measurements collected across all 12 trial days (Appendix 6, Tables A6.7 and A6.8), longer Paratya australiensis orbital carapace lengths were found to be significantly related to higher ammonia levels (Δ-in-deviance statistic = 8.221, df = 1, p = 0.0041; Figure 6.43), while longer carapace lengths were weakly linked 150 with both higher pH (Δ-in-deviance statistic = 3.2, df = 1, p = 0.078) and ammonia below detection limits of 0.5 gL-1 (Δ-in-deviance statistic = 3.2, df = 1, p = 0.078).

When correlated with the water quality measurements collected in the first three days of the trial only (Appendix 6, Table A6.7 and A6.8), the effect of site type was found to interact significantly with the level of ammonia in solution (Figure 6.44) on both Paratya australiensis carapace length (Δ-in-deviance statistic = 5.339, df = 1, p = 0.021) and orbital carapace length (Δ-in-deviance statistic = 7.315, df = 1, p = 0.007). However, the results were not consistent for the two site types. At control sites, longer carapaces were reported where ammonia was detected, while at urban impact sites, presence of detectable ammonia concentrations resulted in shorter carapace lengths (Figure 6.44).

3.4

LSD = 0.1469 p = 0.0041 3.3

3.2

3.1

Orbital carapace length (mm) 3

2.9 Yes No Ammonia detected

Figure 6.43: Mean orbital carapace length of Paratya australiensis retrieved alive after deployment at control and urban impact sites with levels of ammmonia above -1 and below the method analytical detection limit (0.5 gL NH3) during in situ assessment trials for Case Study 2. LSD = Least squared differences (2 x standard error differences). 151

7 3.8 Control Impact 3.7 6.8 LSD = 0.4569 LSD = 0.2486 p = 0.0209 p = 0.0069 3.6 6.6 3.5

3.4 6.4 3.3 6.2 3.2

3.1 Carapace length (mm) 6

3 Orbital carapace length (mm) 5.8 2.9

5.6 2.8 Yes No Yes No Ammonia detected Figure 6.44: Mean carapace length and mean orbital carapace length for Paratya australiensis retrieved alive after deployment for three days at control and impact sites with levels of ammonia above and below the method detection -1 limit (0.5 gL NH3) during in situ assessment trials for Case Study 2. LSD = Least squared differences (2 x standard error of differences).

6.3.3 CASE STUDY 3: PERSISTENTLY HIGH TURBIDITY

Macroinvertebrate Responses Case Study 3 Test organism – Paratya australiensis Experimental Effects on Survival of Paratya australiensis Of the 455 Paratya australiensis individuals deployed, only 15 were retrieved dead with no significant effect of the interaction between site type and trial day (2 statistic = 7.22, df = 5, 18, p = 0.216), site type (2 statistic = 0.412, df = 1, p = 0.412) or trial day (2 statistic = 5.08, df = 5, p = 0.42).

Experimental Effects on Body Size of Paratya australiensis

There were no significant effects of the interaction between trial day and site type on log10- transformed carapace length (F statistic = 1.38, df = 5, 48, p = 0.2488). There was, however, there was a significant effect of site type on log10-transformed carapace length (F statistic = 4.33, df = 1, p = 0.0429; Figure 6.45), with carapace lengths significantly longer at sites with 152 persistently high turbidity (n = 259, mean = 6.21 ± 1.07 mm) than at control sites (n = 131, mean = 6.00 ± 0.92 mm). There was also a strong day effect (F statistic = 3.57, df = 5, p = 0.0080) with a decrease in size over the trial duration, with carapaces of P. australiensis individuals retrieved on trial days 6, 9 and 12 considerably shorter than those retrieved on trial days 1 and 2 (Figure 6.46).

There were no significant effects of the interaction between trial day and site type on log10- transformed orbital carapace length (F statistic = 1.28, df = 5, 48, p = 0.2867). There was, however, a significant effect of site type on log10-transformed orbital carapace length (F statistic = 4.4, df = 1, p = 0.0412; Figure 6.47), with orbital carapace lengths significantly longer at sites with persistently high turbidity (n = 280, mean = 3.31 ± 0.58 mm) in comparison with individuals retrieved from the control site (n = 143, mean = 3.18 ±

0.50 mm). There was also a highly significant effect of retrieval day on log10-transformed orbital carapace length (F statistic = 6.63, df = 5, p < 0.0001), with individuals collected on trial days 1 and 2 having significantly longer orbital carapace lengths than those retrieved on or after trial day 3 (Figure 6.48).

0.86

LSD = 0.0127 0.855 p = 0.0429

0.85

0.845

0.84

-transformed carapace length (mm) 0.835 10 Log 0.83 Control Impact Site type

0.87

LSD = 0.0231 0.86 p = 0.0080

0.85

0.84

0.83 -transformed carapace length (mm) 10 Log 0.82 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 6.46: Mean log10-transformed carapace length for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 3. LSD = Least squared differences (2 x standard error of differences).

0.64

LSD = 0.0110 p = 0.0412

0.63

0.62

0.61 -transformed orbital carapace length (mm) 10

Log 0.6 Control Impact Site type

0.65

LSD = 0.0195 p < 0.0001

0.63

0.61 -transformed orbital carapace length (mm) 10

Log 0.59 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 6.48: Mean log10-transformed orbital carapace length for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 3. LSD = Least squared differences (2 x standard error of differences).

There were no significant effects of the interaction between trial day and site type on log10-transformed wet weight (F statistic = 1.90, df = 5, 48, p = 0.1119). There was however, a significant effect of site type on log10-transformed wet weight (F statistic = 4.6, df = 1, p = 0.0371; Figure 6.49) with individuals retrieved from sites with persistently high turbidity (n = 280, mean = 34.91 ± 18.05 mg) heavier than those retrieved from the control site (n = 143, mean = 30.89 ± 14.03 mg). There was also a significant effect of retrieval day on log10-transformed wet weight (F statistic = 3.24, df = 5, p = 0.0134) with an overall trend for decreased weight as the trial progressed (Figure 6.50). 155

1.65

LSD = 0.0357 p = 0.0371 1.6

1.55

1.5 -transformed wet weight (mg)

10 1.45 Log

1.4 Control Impact Site type

1.56

1.54 LSD = 0.0641 p = 0.0134 1.52

1.5

1.48

1.46

-transformed wet weight (mg) 1.44 10

Log 1.42

1.4 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 6.50: Mean log10-transformed wet weight for Paratya australiensis retrieved alive over time after deployment at sites during in situ assessment trials for Case Study 3. LSD = Least squared differences (2 x standard error of differences). 156

Test organism – Notalina sp. Experimental Effects on Survival and Escape of Notalina sp. Survival of Notalina sp. at the deployment sites was not significantly different in relation to the interaction between the component trial and trial day (χ2 statistic = 2.514, df = 5, p = 0.12). There was, however, a significant difference in survival between trial A (mean survival = 85.3%) and trial B (mean survival = 93.4%) (χ2 statistic = 26.01, df = 1, p < 0.0001) and a significant effect due to the interaction between trial day and deployment site (χ2 statistic = 47.954, df = 10, p < 0.0001; Figure 6.51). While the mean number of individuals retrieved dead was significantly higher in trial A than in trial B (Figure 6.51), this was largely due to survival of only 44% of the Notalina sp. individuals deployed at Site 13 (control) on trial day 12 in trial A (Figure 6.51).

100

70 Site 13 (control) - trial A Site 13 (control) - trial B Survival (%) 60 Site 11 (impact) - trial A

Site 12 (impact) - trial A LSD = 6.087 50 Site 19 (impact) - trial A p < 0.0001 Site 19 (impact) - trial B 40 0 2 4 6 8 10 12 Trial day Figure 6.51: Percent survival of Notalina sp. retrieved after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3. LSD = Least squared differences (2 x standard error of differences).

Escape of caged Notalina sp. individuals at the deployment sites followed a similar pattern (Figure 6.52). Mean number of escapees was found to vary significantly with interaction between trial day and site (χ2 statistic = 22.48, df = 10, p = 0.014) with an overall increase in escapees over time at both control and impact sites, but a dramatic increase in escape at site 13 after trial day 9 in trial A (Figure 6.52). There was also a significant difference in the number of escapees between component trials (χ2 statistic = 5.33, df = 1, p = 0.022) where an 157 average of 11.1% of deployed individuals escaped during trial A compared to only 6.1% during trial B.

50 Site 13 (control) - trial A LSD = 9.333 45 p < 0.0001 Site 13 (control) - trial B 40 Site 11 (impact) - trial A 35 Site 12 (impact) - trial A 30 Site 19 (impact) - trial A 25 Site 19 (impact) - trial B

20 Escapees (%) 15

0 0 2 4 6 8 10 12 Trial day Figure 6.52: Percentage of Notalina sp. individuals that escaped after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3. LSD = Least squared differences (2 x standard error of differences).

Experimental Effects on Body Size of Notalina sp. Average wet weight was not significantly affected by the interaction of any of experimental treatment factors (Table 6.17), deployment site (χ2 statistic = 8.50, df = 5, p = 0.7142) or retrieval day (χ2 statistic = 2.50, df = 5, p = 0.777). However, Notalina sp. average wet weights differed significantly between the two trials (χ2 statistic = 12.73, df = 1, p < 0.0001), with heavier organisms observed in trial B (Figure 6.53).

Table 6.17: Results of probability tests for interactions between fixed factors affecting Notalina sp. wet weight for Case Study 3 trials. Unless specifically stated, results are from Restricted Maximum Likelihood (REML) probability tests at α = 0.05.

Interaction between Treatment Factors Significance Tests χ2 statistic df p

Trial day and deployment sites within a particular trial 20.94 25 0.696 Sites within a particular trial 8.55 5 0.128 Trial day within a particular trial 4.02 5 0.547 158

1.3 LSD = 0.167 p < 0.0001 1.2

1.1

0.9 Average wet weight (mg)

0.8

0.7 Trial A Trial B Trial

There was no significant difference in mean head width between deployment site (χ2 statistic = 1.323, df = 5, p = 0.25), however this measurement was significantly affected by the interaction between trial and retrieval day (Δ statistic = 19.75, df = 5, p = 0.0018), with greater increases in head width over time during trial B, especially after trial day 3 (Figure 6.54).

Linking Biological Responses with Environmental Covariates Of all the environmental parameters examined throughout this experiment, none were found to covary in relation to Paratya australiensis survival or the changes in body size reported above.

Results of REML significance tests and ANOVA for the linear regression between Notalina sp. survival and trial day at the deployment sites employed in Case Study 3 are presented in Tables 6.18 and 6.19 respectively. Various environmental variables, in conjunction with 159

1 Control 1 2 3 6 9 Impact

0.9

0.8 LSD = 0.097 p = 0.002 0.7

0.6 Head width (mm) 0.5

0.4

0.3 Site 13 Site 19 Site 13 Site 11 Site 12 Site 19 Trial A Trial B Figure 6.54: Mean head width of Notalina sp. retrieved alive after deployment at control and impact sites known to have high suspended solids during in situ assessment trials for Case Study 3. LSD = Least squared differences (2 x standard error of differences).

Table 6.18: Restricted Maximum Likelihood (REML) table outlining tests estimating the significance of the variation (using Wald and Chi-square statistic) between environmental measurements collected, and survival of Notalina sp. retrieved after deployment at control and impact sites during in situ assessment trials for Case Study 3.

Source Wald Statistic χ2 statistic p pH 12.27 9.23 <0.001 Water Temperature 7.12 5.35 <0.001 Turbidity 4.49 3.38 0.010 Dissolved Oxygen 4.04 3.04 0.016

Logn-transformed Total Phosphorus 3.12 2.35 0.039

Logn-transformed Total Nitrogen 2.66 2.00 0.063 Ammonia 2.64 1.98 0.065

Logn-transformed Suspended Solids 1.47 1.11 0.211 Electrical Conductivity 0.40 0.30 0.649 160

Table 6.19: Analysis of Variance (ANOVA) for regression between survival of Notalina sp. retrieved after deployment, and the strongest environmental covariates measured at control and impact sites during in situ assessment trials for Case Study 3.

Mean Deviance Source df Deviance p deviance ratio pH Covariate 1 20.489 20.489 4.94 0.029 Trial day 2 31.514 15.757 3.80 0.026 Trial day x covariate 2 108.890 54.490 13.13 <0.001 Residual 99 410.844 4.150 Total 104 571.827 5.498 Water Temperature Covariate 1 101.276 101.376 26.39 <0.001 Trial day 2 31.514 15.757 4.11 0.019 Trial day x covariate 2 59.091 29.545 7.70 <0.001 Residual 99 379.947 3.838 Total 104 571.827 5.498

retrieval day, were found to have an influence on survival of larval Notalina sp. in these trials (Table 6.18). Variation in Notalina sp. survival over the trial duration (Figure 6.51) was found to correlate most strongly with daily measurements of pH (F statistic = 13.13, df = 2, p < 0.001; Table 6.19) and water temperature (F statistic = 7.70, df = 2, p < 0.001; Table 6.19), with lower survival of Notalina sp. aligning with higher pH (slope = -5.13) and water temperature (slope = -0.16) measurements on the retrieval day.

Variation in average wet weights for Notalina sp. was not found to covary in relation to any of the environmental parameters examined (Appendix 6, Tables A6.9 and A6.10). Results of REML significance tests and ANOVA for the linear regression between Notalina sp. head width and environmental covariates at the deployment sites employed in Case Study 3 are presented in Tables 6.20 and 6.21 respectively. As with survival, a number of water quality variables, in combination with trial day, were found to have an influence on average head width for Notalina sp. in these trials (Table 6.20). There was a statistically significant relationship between the mean head width of Notalina sp. and the measurements of both water temperature (F statistic = 21.74, df = 1, p < 0.001; Table 6.21) and total phosphorus concentration (F statistic = 12.96, df = 1, p = 0.002; Table 6.21) taken on the retrieval day, 161 where an increase in mean Notalina sp., head width was correlated with higher water temperatures (slope = 0.023) and total phosphorus concentrations (slope = 0.040).

Table 6.20: Restricted Maximum Likelihood (REML) table outlining tests estimating the significance of the variation (using Wald and Chi-square statistic) between environmental measurements collected, and average head width of Notalina sp. retrieved alive after deployment at control and impact sites during in situ assessment trials for Case Study 3.

Source Wald Statistic χ2 statistic p

Water Temperature 20.33 15.27 <0.001

Logn-transformed Total Phosphorus 9.42 10.1 0.002 pH 6.02 6.2 0.014

Logn-transformed Total Nitrogen 3.62 3.58 0.057 Electrical Conductivity 2.41 2.55 0.121 Sodium 1.67 1.82 0.196 Sulphate 1.34 1.35 0.247

Logn-transformed Suspended Solids 1.25 1.27 0.263 Turbidity 0.85 0.9 0.356 Magnesium 0.40 0.42 0.527 Dissolved Oxygen 0.01 0.002 0.967

Table 6.21: Analysis of Variance (ANOVA) for regression between average head width of Notalina sp. retrieved alive after deployment at control and impact sites during during in situ assessment trials for Case Study 1, and the strongest environmental covariates (measured on retrieval day).

Source df Sum of Mean F ratio p Squares Square

Water Temperature Covariate 1 0.2684 0.2684 21.74 <0.001 Residual 40 0.4938 0.01235 Total 41 0.7622 0.01859 Total Phosphorus Covariate 1 0.1424 0.1424 12.96 0.002 Residual 40 0.6198 0.01550 Total 41 0.7622 0.01859 162

6.4 DISCUSSION

6.4.1 CASE STUDY 1 – MINE DRAINAGE

Experimental Results of this Case Study In this study, findings varied greatly in relation to the response variable being examined, and between test species (Table 6.22). In fact, only one of the 11 biological endpoints examined responded similarly to the treatment factors in all three species examined (Table 6.22).

Given that the downstream impact site had consistently higher aqueous concentrations of the metals examined, it is not really that surprisingly that macroinvertebrate bioconcentrations were higher at this site and increased with time exposed (Figures 6.9 to 6.15, Figures 6.25 to 6.27, and Tables 6.5, 6.7 and 6.11). But despite significant site-related differences in metal bioconcentrations in the insect taxa (Table 6.22), these differences were not reflected by other biological effects, at least in the timeframe of these trials. In short, there were no effects of mine drainage at Captains Flat on body size of either Austrolestes cingulatum or Atalophlebia australis arising from deployment site and trial day, while treatment effects on the latter were complicated by other factors in particular season (Table 6.22). In contrast, however, both deployment site and trial day significantly affected the majority of the Paratya australiensis endpoints assessed (Table 6.22) despite having the lowest SIGNAL sensitivity score (Chessman 1995) of the three species employed in this case study. Not only were there significant differences in tissue bioconcentrations (Figures 6.9 to Figure 6.12) but survival, body size and the appearance of dark carapace patches in Paratya australiensis (Figure 6.1 to Figure 6.8) were also significantly affected by site and trial day, with decreased condition as the trial progressed and at the impact site (Table 6.22).

In this latter test species, the decrease in survival over time at the downstream impact site appears to closely mirror the relationship between carapace length and trial day (Figure 6.55). While it is conceivable for the health of an individual or population (e.g. body condition, fecundity, sensitivity) to deteriorate in a degraded environment, exoskeletal endpoints such as carapace length cannot reduce in size. As such, it is most likely that the decrease in Paratya australiensis body size over time actually reflects size-specific mortality, with the smaller individuals surviving longer at the downstream impact site. Unfortunately, as many of the 163 gure 6.5) cted dy burden) of stri te (Fi te (Figure 6.4) the gure 6.1) eved alive after eved alive te (Fi te > impact si retri at impact si si 12 (Figure 6.7) endpoints of endpoints trol 3 (Figure 6.3) al day gical lo tri 12 (Figure 6.6) 

day at impact si th day al al wi tri  ight at control site > impact site (Figure 6.8) ight at control site > impact site  h ed weight on trial day Atalophlebia australis Atalophlebia ght on tri tal variation must consider this interaction term. tal variation must consider this interaction and en Unless specifically stated, results are from Re results are from stated, Unless specifically reeze dri survival wit 6.2) dark patches at impact site (Figure dark patches after trial day wet wei f 1. Brief description of effect Brief description    Carapace length Orbital carapace length at con   Freeze dried we Endpoints tested were survival, head width (HW), carapace length (CL), (HW), carapace head width tested were survival, Endpoints NS NS NS NS NS . r Case Study te x day te x day si p = 0.042 si fo p = 0.0377 Interaction Austrolestes cingulatum Austrolestes , at α = 0.05 † † † † † † † tests Season y assessment trials assessment FDW. 1 - in situ Paratya australiensis Paratya r fixed factors assessing the effects of mine drainage on various bio drainage on various the effects of mine assessing r fixed factors   NS NS Day fo during p = 0.0006 p = 0.0157 p < 0.0001 Experimental Fixed Factors Experimental Fixed y tests ength (OCL), wet weight (WW) and freeze dried weight and the tissue concentrations (referred to as bo weight and the tissue concentrations weight (WW) and freeze dried ength (OCL), wet at field sites at field sites   NS NS Site 0.0134 probabilit p = p = 0.0001 p < 0.0001 ment carapace l invertebrate test species invertebrate oy tal macro depl probabilit Likelihood (REML) Maximum orbi various metals. Results of Results of rs to “not significant” at α = 0.05. indicates that as there was a significant interaction term involving this factor, partitioning of experim indicates that as there was a significant indicates that the significance tests for this species did not consider the effects of this treatment factor. indicates that the significance tests for Method detection limit for Cd and Pb = 0.05 μgg Method detection limit for Cd and Pb NS refe †   CL OCL WW FDW 10 10 10 10 Log Log Log Log Paratya australiensis Survival Escape Carapace patches Endpoint Table 6.22: 164  . 1 te < si l endpoints of endpoints gical . ound to be an artefact ound to be an artefact lo 12 (Figure 6.11) on f on f at α = 0.05 te interacti te interacti assessment trials for Case Study trials for Case assessment y tests x si x si al day (Figure 6.10) in situ tri 

ssue [Pb] in samples from the contro th ti during gnificant day gnificant day  all tissue [Cd] in samples from the control site < DL all tissue [Cd] in samples from the several **si of **si of DL ssue [Fe] wi site on trial day ssue [Cu] at impact site on trial day 12 (Figure 6.12) ssue [Zn] at impact ti ti ti Brief description of effect Brief description site > control site (Figure 6.9) Tissue [Fe] at impact    experimental variation must consider this interaction term. experimental variation must consider this NS NS NS NS NS te x day te x day te x day te x day si si si si p = 0.0030 p = 0.0002 Interaction p < 0.0001** p = 0.0027** cted Maximum Likelihood (REML) probabilit Likelihood cted Maximum † † † † † † † † † Restri eved alive after deployment at field sites at field sites after deployment eved alive Season m retri y tests for fixed factors assessing the effects of mine drainage on various bio on various of mine drainage the effects fixed factors assessing y tests for FDW. 1 -     NS NS NS NS Day p = 0.0046 Experimental Fixed Factors Experimental Fixed Results of probabilit Results of invertebrate test species invertebrate     NS NS NS NS Site acro : p = 0.0009 m several results are fro stated, Unless specifically urden urden urden burden burden b b b indicates that as there was a significant interaction term involving this factor, partitioning of indicates that as there was a significant indicates that the significance tests for this species did not consider the effects of this treatment factor. indicates that the significance tests for Method detection limit for Cd and Pb = 0.05 μgg Method detection limit for Cd and Pb   NS refers to “not significant” at α = 0.05. † oint ing t Fe body Pb body Zn body Cd body Cu body Survival Mol HW FDW Austrolestes cingulatum Endp Table 6.22 (continued) Table 6.22 165 .  1 s of int endpo gical gure 6.18) ound to be an artefact e control site < DL lo 5. th te (Fi on f m te interacti assessment trials for Case Study trials for Case assessment y tests at α = 0.0 y tests at x si al day (Figure 6.15) 12 (Figure 6.17) in situ tri al day at impact si 

l day tri th  h during variation must consider this interaction term. variation must consider this interaction gnificant day all tissue [Pb] in samples fro **si of parable for other seasons (Figure 6.16) parable for other seasons (Figure ssue [Fe] wi site on trial day 12 (Figure 6.13) ssue [Zn] at impact ti ti survival on tria escapes wit Brief description of effect Brief description site > control site (Figure 6.14) Tissue [Fe] at impact   site in autumn trial, but Survival at control site > impact com   (Figure 6.19) Escape in summer > spring or autumn No pattern evident (Figure 6.20) lihood (REML) probabilit lihood (REML) season NS NS NS x te x day te x day te x day te x season p = 0.016 si si p = 0.014 si p = 0.0002 p = 0.0001 Interaction si day p < 0.0001 ** cted Maximum Like cted Maximum † † † † †   Restri eved alive after deployment at field sites at field sites after deployment eved alive Season p = 0.038 m retri y tests for fixed factors assessing the effects of mine drainage on various bio on various of mine drainage the effects fixed factors assessing y tests for FDW. 1 -    NS NS NS Day p < 0.0001 p = 0.0062 Experimental Fixed Factors Experimental Fixed Results of probabilit Results of invertebrate test species invertebrate    NS NS NS NS Site acro : p = 0.0003 m “not significant” at α = 0.05. several results are fro stated, Unless specifically en urden urden urden burd burden b b b indicates that as there was a significant interaction term involving this factor, partitioning of experimental indicates that as there was a significant indicates that the significance tests for this species did not consider the effects of this treatment factor. indicates that the significance tests for Method detection limit for Cd and Pb = 0.05 μgg Method detection limit for Cd and Pb   NS refers to † t Fe body Pb body Zn body Cu body Cd body Escape Mol Atalophlebia australis Survival Endpoint Table 6.22 (continued) Table 6.22 166 at . 1 day gure al tri th wi endpoints of endpoints  gical . lo te, but or each season (Fi si f te (Table 6.11) rol si nt trol co at α = 0.05 assessment trials for Case Study trials for Case assessment y tests day (Table 6.11) al day (Table 6.11) al in situ tri tri  

ively stable at th th gure 6.27) during te (Fi gure 6.26) ssue [Fe] wi ssue [Pb]wi gure 6.25) e seasonal trials at control and impact sites (Figure 6.34) e seasonal trials at control and impact ti ti Tissue [Pb] at impact site > con Brief description of effect Brief description ) >>> Autumn (Figure 6.21) (Spring > Summer > Autumn (Figure 6.22) Spring > Summer  site > control site (Table 6.11) Tissue [Fe] at impact site, especially in spring Tissue [Cu] at impact site > control (Fi site, especially on trial day Tissue [Cu] at impact site > control 12 (Fi day Different pattern of tissue [Zn] by 6.28) Tissue [Zn] relat impact si tissue [Cd] across trial days in No pattern evident with respect to th 

NS NS NS NS x site te x day te x day si si p = 0.0024 p = 0.0354 p = 0.0024 p = 0.0456 p = 0.0070 Interaction season x site season x day season x day ctor, partitioning of experimental variation must consider this interaction term. ctor, partitioning of experimental variation cted Maximum Likelihood (REML) probabilit Likelihood cted Maximum    NS NS Restri eved alive after deployment at field sites at field sites after deployment eved alive Season m p < 0.0001 p = 0.0011 retri y tests for fixed factors assessing the effects of mine drainage on various bio on various of mine drainage the effects fixed factors assessing y tests for 0005    NS NS Day p = 0.0081 p = 0. Experimental Fixed Factors Experimental Fixed Results of probabilit Results of invertebrate test species invertebrate    NS NS Site acro : p < 0.0001 p < 0.0001 m ral seve results are fro stated, Unless specifically urden urden burden burden burden b

b indicates that as there was a significant interaction term involving this fa indicates that as there was a significant NS refers to “not significant” at α = 0.05.  FDW 10 Cd body Pb body Zn body Fe body Cu body Endpoint HW Log Table 6.22 (continued) Table 6.22 167

0.9 100 Carapace length Survival 90

80 0.85 70

0.8 50

40 Survival (%)

30 0.75 -transformed carapace length (mm) 20 10

Log 10

0.7 0 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day

Figure 6.55: Percent survival and log10-transformed carapace length for Paratya australiensis retrieved alive after deployment at the downstream impact site during in situ assessment trials for Case Study 1. individuals that were retrieved dead during the trial were badly decomposed, a number of the morphological endpoints could not be measured and no comparison of body size between individuals retrieved dead and alive was possible.

Reported Effects of Metal Pollution at Captains Flat The effect of trace metal pollution on aquatic biota in the Molonglo River downstream of Captains Flat has previously been examined in a number of studies (i.e. Weatherley et al. 1967, Nicholas and Thomas 1986, Norris 1986). These studies agreed that there was a link between mine contamination and reduced species richness and abundance, however the mechanism of these effects could not be determined (Weatherley et al. 1967, Nicholas and Thomas 1978, Millington and Walker 1983). In other experiments, however, high mortality rates for rainbow trout fingerlings (Graham et al. 1986) and freshwater mussels (Millington and Walker 1983) in the Molonglo River at Captains Flat have been attributed to zinc toxicity.

Linking Test Organism Responses with Ambient Metals in the Current Study The relationships between aqueous metal concentrations in the Molonglo River sites and the concentrations of particular metals bioaccumulated in the macroinvertebrate tissue presented 168 in this study varied considerably between the test species, the metals examined and, for Atalophlebia australis, the experimental season.

As presented in Section 6.3.1, the strongest correlations between ambient and tissue concentrations of various metals were observed for Austrolestes cingulatum in relation to bioaccumulated iron, lead and especially zinc (Figures 6.32 to 6.34). In contrast, correlations between metal concentrations in the water and Paratya australiensis tissue were generally weak (Figures 6.30 and 6.31), though these relationships were derived from few unreplicated samples. The correlations involving Atalophlebia australis tissue were also generally weak (Table 6.16), despite being derived from significantly more, albeit unreplicated, data.

Overall, this study found that experimental differences between deployment site and across trial days were most marked when using bioaccumulated zinc concentration as the response endpoint (Table 6.22), thus supporting published claims that zinc toxicity is responsible for biological effects downstream of the Captains Flat mine (Millington and Walker 1983, Graham et al. 1986). However, there are a number of aspects of these experimental findings that need to be considered before drawing any conclusions and attempting to attribute causality.

Given the ambient metal concentrations in the Molonglo River downstream of the former King Georges Mine, it comes as no surprise that higher tissue concentrations were derived from the test organisms that were deployed at this site for the longest time. However, the trace element concentrations were found to be an order of magnitude higher in the samples (both Austrolestes cingulatum tissue and ambient metals) retrieved from the downstream impact site on trial day 12 of the spring trial (Figures 6.32 to 6.34 and Table 6.7; Appendix 6, Table A6.3). This is probably related to active leaching of metals from the mine and its tailings dump (Jacobson and Sparksman 1988) in response to local rainfall and increased streamflow in the later part of the trial (Figure A6.3). However, it could potentially be the result of the very low sample mass for this field site by trial day combination, or contamination during field collection or laboratory analysis. At any rate this measurement could arguably be influencing the relationships presented in Table 6.7 and Figure 6.13 to Figure 6.15 and thus should be treated with caution.

In this case study, given the number of individuals deployed and the biomasses involved, it was necessary to pool all individuals retrieved alive from the replicate cages comprising a 169 particular site and retrieval day combination to obtain sufficient tissue for reliable element analysis (Section 6.2). Therefore, metal body burden data is unreplicated, and potentially unreliable. In order to improve internal replication for the tissue bioconcentration endpoints greater sample masses are required, meaning experiments would need to employ larger organisms, more individuals, or both to obtain replicate samples that satisfy the tissue mass requirements for trace metal analysis. This would greatly increase the sampling and analytical effort required, which would in turn increase the cost and timeframe needed to obtain results.

In examining the relationships between metals in the water and macroinvertebrate tissue, the metal concentrations (in both water and tissue) at the control and impact sites are so disparate, that a single regression line may be somewhat akin to a straight line between two groups of closely clustered points (e.g. Figures 6.32 to 6.34). Using the regressions obtained for bioaccumulated metals in Austrolestes cingulatum tissue and aqueous metal concentrations as an example, it becomes apparent that the relationships between dissolved and accumulated metals are less strong when the two clusters are examined separately (Table 6.23). As such, deducing a causal link between the ambient and bioaccumulated metals in a particular test species is premature.

Element Regression Coefficients All Data Points Control Site Impact Site

Fe 0.8769 0.1732 0.2190 Pb 0.9622 * 0.8185 Zn 0.9760 0.3816 0.2175 * indicates there was no variation in tissue [Pb] at control sites as all tissue concentrations were below the detectable limit of 0.05 μgg-1 FDW.

Results of the deployment trials involving Atalophlebia australis showed that the relationships between aqueous and bioaccumulated metal concentrations varied greatly between seasons when different metals were examined (Table 6.11, Table 6.16 and Appendix 6, Table A6.3). This is perhaps best demonstrated by the distinctly different patterns of copper and zinc uptake by this test species at the impact site (Figures 6.56 and 6.57). 170

Bioconcentrations of copper and zinc in A. australis tissue were remarkably uniform in samples collected in the autumn trial. In contrast, and despite relatively similar ambient concentrations of dissolved copper and zinc in the water, concentrations of metals bioaccumulated by the nymphs were much more variable in summer (Figures 6.56 and 6.57). While further experiments would need to be undertaken to elucidate such differences, it is likely that timing of field trials will need to consider the dynamics of the impact itself, as well as the likely effects of the developmental stage of the test organisms, on their exposure responses.

Even with these limitations in mind, and after the exclusion of potentially spurious data, correlations between metal bioconcentrations in Austrolestes cingulatum and dissolved metal concentrations in the environment for lead (r2 = 0.5250), iron (r2 = 0.5392) and zinc (r2 = 0.7494) are still evident (Table 6.15). That the relationship between ambient and bioaccumulated zinc boasts a considerably higher correlation coefficient than for the other metals, infers a causal link between zinc pollution and the biological effects, at least for this test species.

250 Spring FDW)

-1 Summer 200 μ Autumn

150

100

Average tissue concentration of Cu ( gg 0 0 10 20 30 40 50 60 70 80 90 100 Average dissolved concentration of Cu (μ gL-1) Figure 6.56: Relationship between tissue concentration of copper and dissolved copper concentrations from Atalophlebia australis retrieved alive after deployment at the downstream impact site during in situ assessment trials for Case Study 1. 171

3500

Spring

FDW) 3000

-1 Summer

μ Autumn 2500

2000

1500

1000

500 Average tissue concentration of Zn ( gg 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Average dissolved concentration of Zn (μ gL-1) Figure 6.57: Relationship between tissue concentration of zinc and dissolved copper concentrations from Atalophlebia australis retrieved alive after deployment at the downstream impact site during in situ assessment trials for Case Study 1.

6.4.2 CASE STUDY 2 – URBAN STORMWATER RUNOFF

Experimental Results of this Case Study Tables 6.24 summarises REML-based inferences for responses of Paratya australiensis when exposed to urban stormwater runoff in Case Study 2. In this study, the most noteworthy finding was that Paratya australiensis immediately suffered significant mortality when deployed at the impact sites, with very few individuals surviving longer than three days at the sites receiving urban stormwater runoff (Figure 6.35). While there was no corresponding effect of deployment site on organism biomass (Table 6.24), logn-transformed wet weight results did show that individuals deployed at sites similar to their source site (regardless of whether it was a control or impact site) were larger than those at sites from a different AUSRIVAS site grouping (Figures 6.40 and 6.42), consistent across both sites treatments (Table 6.24). This finding was contrary to that reported earlier (Table 5.2), where none of the body size endpoints examined for Paratya australiensis were found to be significantly different in between groups relocated to transplant sites that were similar or dissimilar in nature to their source site (Experiments 5 and 7 respectively). 172

As detailed in Appendix 6, there was some evidence of intracage correlation between individuals held within a particular deployment cage during this trial. On closer examination, it seems that individuals survived less long if dead individuals within a replicate cage group were retained within the cage, suggesting some toxic degradation of the carcass. Because holding cages at Group 2 sites were regularly cleared of dead individuals, it was expected that, if any effect of AUSRIVAS site group were evident, individuals from Group 2 would have been more persistent, surviving longer and thriving in the absence of degrading organic matter. However, there was some evidence that survival was higher at Group 1 sites (i.e. sites with similar characteristics to the organisms’ source site; Figure 6.58) and Group 1 individuals were significantly heavier (Figure 6.42).

In addition to the dramatic effect of urban impacts on Paratya australiensis survival, body size endpoints also showed a corresponding decline over time, especially after trial day 2 (Figures 6.36, 6.38, 6.39 and 6.41 and Table 6.24). In terms of biomass, the decrease in body weight as the trial progressed could result from gut clearance, a lack of food in the deployment cages or the conversion of body tissue to satisfy physiological requirements. However, carapace dimensions do not shrink over time. As such, the decrease in carapace length over the trial duration implies some size-specific mortality, with smaller individuals persisting longer.

On closer examination, individuals retrieved alive were indeed consistently smaller than those retrieved dead from the urban impact sites (Figure 6.59 and Table 6.25). However, there was only weak evidence that orbital carapace length was affected by retrieval state of the test organisms (F statistic = 3.03, df = 1, 648, p = 0.0824), while differences in carapace length

(F statistic = 0.48, df = 1, 563, p = 0.4902) and logn-transformed wet weight (F statistic = 0.96, df = 1, 660, p = 0.3279) were not statistically different between individuals that were dead and individuals that were alive when retrieved from their deployment cages in these experiments. 173 gure Paratya gure tes (Fi gure 6.43) dpoints for dpoints for gure 6.35) en days 3 to 12 (Fi te (Fi al gure 6.38) gical > DL (Fi Unless specifically stated, stated, Unless specifically 3 tes > impact si lo . (Fi si 2 trol day < DL al if any, environmental measurements measurements if any, environmental 3 USRIVAS Group 1 > Group 2 tri pH A  

NH day at impact si th th th rom al al days 1 and 2 > tri tri tes f  Endpoints tested were survival, carapace length carapace tested were survival, Endpoints . h ength wi ength wi ength wi carapace length where NH ght on tri ght at si ace l tal at α = 0.05 gure 6.40) survival wit carapace l carap carapace l orbi assessment trials for Case Study trials for Case assessment Brief description of effect Brief description  impact sites (Figure 6.37) Carapace length at control sites >    Orbital carapace length at con 6.37)  Wet wei 6.39) Wet wei (Fi tests y in situ 3 3 S tests. pH NS N y NH NH p = 0.078 p = 0.078 p = 0.0041 Covariate field sites during field sites NS NS NS at e x day it s p = 0.0375 Interaction yment . 1 - ion term involving this factor and an environmental covariate, partitioning of experimental variation must consider this covariate, partitioning of experimental variation must consider this ion term involving this factor and an environmental NS NS NS p = 0.0421 AUSRIVAS = 0.5 μgL 3 cted Maximum Likelihood (REML) probabilit Likelihood cted Maximum y tests for fixed factors assessing the effects of urban stormwater runoff on various bio runoff on of urban stormwater the effects fixed factors assessing y tests for  Restri NS Day retrieved alive after deplo retrieved carapace length (OCL) and wet weight (WW). The covariate column indicates which, column indicates The covariate wet weight (WW). length (OCL) and carapace p = 0.051 Experimental Fixed Factors Experimental Fixed p = 0.0003 tal ts are from  NS Site australiensis resul (CL), orbi probabilit with the results of these REML were found to align Results of probabilit Results of p = 0.0148 p = 0.0110 : indicates that as there was a significant interact indicates that as there was a significant indicates that as there was a significant interaction term involving this factor, partitioning of experimental variation must consider this interaction term. interaction term involving this factor, partitioning of experimental variation indicates that as there was a significant Method detection limit (DL) for NH  NS refers to “not significant” at α = 0.05.   interaction term. WW n OCL Log Full 12 day trial Full 12 day trial CL Endpoint Survival Table 6.24 174

2. gical lo > DL, but at 3 tests. y The covariate column The covariate > DL (Figure 6.44) 3 . day 3 (Figure 6.41) al α = 0.05 at ength where NH USRIVAS Group 1 > Group 2 ength where NH A y tests assessment trials for Case Study trials for Case assessment rom carapace l al days 1 and 2 > tri  in situ tes f carapace l  during tes ght on tri ght at si gure 6.42) control sites, Brief description of effect Brief description At impact si Wet wei Wet wei (Fi 3 3 NS yment at field sites yment at e x NH e x NH it it p = 0.021 p = 0.007 s s Covariate n factors assessing the effects of urban stormwater runoff on various bio runoff on of urban stormwater the effects factors assessing cted Maximum Likelihood (REML) probabilit Likelihood cted Maximum NS NS NS Restri Interactio eved alive after deplo eved alive om retri y tests for fixed y tests for . 1 - ion term involving this factor and an environmental covariate, partitioning of experimental variation must consider this covariate, partitioning of experimental variation must consider this ion term involving this factor and an environmental 0493 NS NS p = 0. AUSRIVAS = 0.5 μgL 3 NS NS Results of probabilit Results of Day Paratya australiensis Paratya p = 0.013 Experimental Fixed Factors Experimental Fixed or s f int .   NS Site endpo results are fr stated, Unless specifically results of these REML probabilit were found to align with the if any environmental measurements indicates which indicates that as there was a significant interact indicates that as there was a significant indicates that as there was a significant interaction term involving this factor, partitioning of experimental variation must consider this interaction term. interaction term involving this factor, partitioning of experimental variation indicates that as there was a significant Method detection limit (DL) for NH interaction term  NS refers to “not significant” at α = 0.05.   WW n CL OCL Log Endpoint days only First three trial Table 6.24 (continued): Table 6.24 175

100 Group 1 sites LSD = 3.4134 90 Group 2 sites p = 0.079 80

40 Survival (%)

0 0 2 4 6 8 10 12 Trial day Figure 6.58: Percent survival of Paratya australiensis retrieved after deployment at over time after deployment at sites from different AUSRIVAS site groupings during in situ assessment trials for Case Study 2. LSD = Least squared differences (2 x standard error of differences).

Carapace length Log n -transformed wet weight Orbital carapace length 6.4 3.6

6.2 3.5

6 3.4

5.8 3.3

5.6 3.2

5.4 3.1 Carapace length (mm) -transformed wet weight (mg) n 5.2 3 Orbital carapace length (mm) Log

5 2.9 Alive Dead Alive Dead Alive Dead Status of retrieved organisms

Figure 6.59: Average body size measurements (pooled over trial days 1, 2 and 3) for Paratya australiensis retrieved alive and dead after deployment at urban impact sites during in situ assessment trials for Case Study 2. 176

Body Size Endpoint Trial day 1 Trial day 2 Trial day 3

Individuals retrieved alive Carapace length (mm) 5.78 5.59 5.89 Orbital Carapace length (mm) 3.01 2.93 3.11 Wet weight (mg) 39.14 32.01 29.35 Individuals retrieved dead Carapace length (mm) 6.26 6.16 5.97 Orbital Carapace length (mm) 3.38 3.45 3.43 Wet weight (mg) 40.10 34.71 33.32

Implications of Mortality Patterns In comparing the results of significance tests on various Paratya australiensis endpoints between the two trial time frames analysed, the effect of the size-specific mortality at the urban impact site becomes even more apparent (Table 6.22). As the larger individuals deployed at the urban impact site perished, body size measurements obtained from the individuals deployed at the control site, where greater than 90% of deployed individuals survived (Figure 6.35), made a greater relative contribution towards the trial averages (pooled over all trial days) as the trial progressed. This in turn produced significant differences in carapace length and body weight across trial day (Table 6.22, Figures 6.38 and 6.39). Similarly, the effect of site type was only found to significantly affect carapace dimensions when the full dataset was employed (Table 6.22, Figure 6.37). Effectively, using data from the full 12 day trial biases the findings towards the response of the individuals deployed at the control site rather than detecting physiological differences in the body sizes of organisms deployed. Therefore, it is important that where datasets are imbalanced because measurements for certain endpoints cannot be obtained (i.e. body size of dead individuals not included in REML modelling as detailed in Section 2.8), statistical hypothesis testing the data compared should be adjusted accordingly to avoid inferences that are ecologically invalid.

Linkage of Macroinvertebrate Responses with Water Quality Parameters As summarised in Table 6.24, response endpoints for Paratya australiensis body size were significantly affected by different treatment factors, and certain endpoints linked these effects 177 with urban contaminants, attributing significant differences in survival and body size to a combination of site type, ammonia and AUSRIVAS reference group. However, not all body size measurements were similarly affected. For example, wet weights were not found to significantly differ with deployment site in either the full 12 day trial or the restricted period (Table 6.22), while the carapace length endpoints showed consistency in their site-type differences (Figure 6.37).

Of all the environmental covariate data modelled (Section 6.2.3), it was the concentration of ammonia that best approximated the significant differences in carapace dimensions (Table 6.22). However, despite a strong correlation between carapace length and its orbital surrogate (n = 2625, r2= 0.835), opposite trends were produced by the two carapace measurements employed, with longer orbital carapace lengths and shorter “entire” carapaces with detectable levels of ammonia (Table 6.24, Figure 6.43).

From these preliminary results, it seems possible that an in situ testing approach may be more sensitive than other existing techniques. In summary, this case study demonstrated that at control sites it seems presence of detectable ammonia stimulated growth of Paratya australiensis, either directly or via increased primary productivity. However, at urban impact sites, higher ammonia, even though below toxic levels, resulted in smaller individuals, mostly probably through size-specific mortality as discussed above. In comparison with guideline values for ammonia, 58 of the 60 concentrations recorded in this case study were below the th lowest EC10 (Effect Concentration at the 10 percentile) values for ammonia toxicity to -1 endemic New Zealand invertebrates (0.05 mgL NH3; Hickey and Vickers 1994) or fish -1 (EC50 = 1.80 mgL NH3; Richardson 1991), and were well below the US EPA final acute -1 value (FAV) of 0.52 mgL NH3; USEPA 1985). This is probably related to interactive and highly variable nature of urban stormwater pollution compared with controlled toxicity testing.

Interestingly, while both pH and ammonia are commonly linked with urban pollution (Polls et al. 1980, Glandon et al. 1981, Oliver and Grigoropoulos 1981, Chakraborty and Kushari 1985, Wafar and le Corre 1989, Lenat and Crawford 1994, Graves et al. 1998), measurements taken during these trials (Appendix 6, Tables A6.7 and A6.8) were not outside “acceptable” ranges (ANZECC 2000). Dissolved oxygen concentration, on the other hand, was well below the recommended levels for streams (4 mgL-1), but was not found to contribute to the variation in Paratya australiensis body size (Table 6.22). 178

6.4.3 CASE STUDY 3 – PERSISTENTLY HIGH TURBIDITY

Experimental Results of this Case Study In this case study, the two test organisms responded quite differently to sites with persistently high turbidity (Table 6.26). Despite no significant difference in Paratya australiensis survival in relation to site type or trial day, all measurements of body size varied significantly, and consistently, across both these treatment factors (Table 6.26). However, Experiment 15, in which Notalina sp. was deployed at the same impact sites, demonstrates that variation in different endpoints in a single test species can respond differently to the same fixed, or treatment factors.

Interestingly, variations in all four Notalina sp. endpoints were found to differ significantly with respect to their component trial, either singly or in conjunction with retrieval day (Table 6.26). While the difference in survival (Figure 6.51) and escape (Figure 6.52) could conceivably be an artefact of some kind of local disturbance at site 13 in trial A, this would not explain the trial differences in Notalina sp. body size (Table 6.24 and Figures 6.53 and 6.54). Rather, it is more likely that prevailing conditions in trial B were more conducive to larval growth (i.e. greater availability of food), or that the three day delay in starting time for trial B corresponded with a cohort growth phase or developmental milestone of some type.

Linkage of Macroinvertebrate Responses to Water Quality Parameters Despite significant differences in the various body size endpoints of P. australiensis, none of this variation was found to align with any of the environmental parameters measured (Appendix 6, Tables A6.9 and A6.10). So while deployment site and deployment time are clearly affecting body condition of P. australiensis, the underlying mechanism causing these effects could not be determined in these trials.

The relationship between the response endpoints of Notalina sp. and the prevailing environmental conditions is not at all clear, with different macroinvertebrate endpoints correlating with different combinations of water quality covariates (Table 6.26). Common though, is that water temperature on the retrieval day covaries significantly with both survival and head width (Tables 6.18 to 6.21 and Table 6.26), where warmer surface temperatures correlate with higher larval survival and larger body sizes. 179 s Unless Paratya 3.

days 6 to 12 al Endpoints tested were Endpoints . ogical endpoints for ogical endpoints ays 1 to 3 > tri ol d al at α = 0.05 ous bi ength at impact sites (Figure 6.45) ength at impact sites gure 6.48) ght at impact sites (Figure 6.49) assessment trials for Case Study trials for Case assessment carapace length at impact sites (Figure 6.47) carapace length at impact sites (Figure y tests ghts on tri on vari tal ty in situ di gure 6.46) gure 6.50) to 12 (Fi carapace l orbi wet wei Brief description of effect Brief description  2 > trial days 6 to 12 Carapace length on trial days 1 and (Fi  1 and 2 > trial day Orbital carapace length on trial days 3  Wet wei (Fi turbi during od (REML) probabilit od (REML) NS NS NS NS Covariate NS NS NS NS Interaction eved alive after deployment at field sites at field sites after deployment eved alive † † † † Trial retri sp. 0.0001 NS Day y tests for fixed factors assessing the effects of persistent of persistent the effects fixed factors assessing y tests for Notalina p = 0.0080 p < p = 0.0134 Experimental Fixed Factors Experimental Fixed and NS Site p = 0.0429 p = 0.0412 p = 0.0371 australiensis Likeliho Maximum are from Restricted stated, results specifically wet weight (WW). length (OCL) and orbital carapace length (CL), (HW) carapace escape, head width survival, Results of probabilit Results of indicates that the significance tests for this species did not consider the effects of this treatment factor. indicates that the significance tests for NS refers to “not significant” at α = 0.05. † WW OCL CL 10 10 10 Log Log Log Endpoint Paratya australiensis Survival Table 6.26: 180 9 Unless .

day 3 al A al ter tri gical endpoints of gical endpoints af lo 12 in tri perature al day . water tem TP concentration   y on various bio y on various h h te 13 on tri α = 0.05 at at si assessment trials for Case Study trials for Case assessment  stently high survival in trial B while stently high survival n escape at site 13 in trial A i y tests perature in situ in survival correlated most strongly with pH and in survival correlated most strongly  survival wit survival wit over time at both control and impact sites, with over time at both control and impact gure 6.51) n    ic ** consi survival (Fi io at r tem gure 6.52) during Brief description of effect Brief description A < trial B** Survival in trial variat wate Escape in trial A > trial B escape dram (Fi

ty TP di n pH DO og water x l x NS turbi x = 0.01 perature x x yment at field sites yment at p day p < 0.001 p < 0.001 p = 0.016 p = 0.039 day Covariate day tem day day Likelihood (REML) probabilit Likelihood or fixed factors assessing the effects of persistent turbidit the effects of persistent factors assessing or fixed 0.014 te x day te x day cted Maximum ts f si si p < 0.0001 eved alive after deplo eved alive Interaction y tes retri Restri Trial p = 0.022 p < 0.0001 ts are from    Day Results of probabilit Results of Experimental Fixed Factors Experimental Fixed stated, resul :   there was a significant interaction term involving this factor and an environmental covariate, partitioning of experimental variation must consider this involving this factor and an environmental covariate, partitioning of experimental there was a significant interaction term Site macroinvertebrate test species test species macroinvertebrate o tw specifically indicates that as indicates that as there was a significant interaction term involving this factor, partitioning of experimental variation must consider this interaction term. interaction term involving this factor, partitioning of experimental variation indicates that as there was a significant interaction term. NS refers to “not significant” at α = 0.05.   Escape Endpoint Notalina sp. Survival Table 6.26 (continued) Table 6.26 181 Unless . 3 gical endpoints of gical endpoints lo perature . y on various bio y on various water tem TP concentration ht were obtained using linear regression (and ht were obtained using linear regression  

th th at α = 0.05 assessment trials for Case Study trials for Case assessment al B (Figure 6.54) head width correlated most strongly with head width correlated n tri y tests i phosphorus perature and total in situ in HW wi HW wi n   io during Brief description of effect Brief description days 1 to 3 < later in the trial, head width on trial especially variat water tem trial B (Figure 6.53) Average wet weight in trial A < he effects of persistent turbidit he effects 14 perature TP n pH g NS lo yment at field sites yment at p < 0.001 p = 0.002 p = 0.0 Covariate Likelihood (REML) probabilit Likelihood water tem

day x NS ted Maximum al tri p = 0.0018 Interaction eved alive after deplo eved alive y tests for fixed factors assessing t fixed factors assessing y tests for retri  Trial p < 0.0001  NS Day Results of probabilit Results of Experimental Fixed Factors Experimental Fixed : NS NS Site as there was a significant interaction term involving this factor and an environmental covariate, partitioning of experimental variation must consider this involving this factor and an environmental covariate, partitioning of experimental as there was a significant interaction term macroinvertebrate test species test species macroinvertebrate o tw are from Restric stated, results specifically ▲ WW indicates that indicates that as there was a significant interaction term involving this factor, partitioning of experimental variation must consider this interaction term. interaction term involving this factor, partitioning of experimental variation indicates that as there was a significant HW NS refers to “not significant” at α = 0.05. wet weig replication at the trial x day x site combination, significance tests for average ▲ indicates that as there was no internal associated Analyses of Variance.   interaction term. Average Endpoint Table 6.26 (continued) Table 6.26 182

On closer examination, however, the correlations between Notalina sp. responses and the environmental measurements with which they covary are not very informative. While the REML analyses indicate that survival and head width do indeed covary strongly with certain water quality parameters, the correlation coefficients fail to account for all but the smallest proportion of the spread in the data (Table 6.27).

Slope Probability of Regression Correlation Covariance Co-efficient

Survival versus water temperature -0.162 p < 0.001 0.0232 pH -5.139 p < 0.001 0.0010 Head width versus water temperature 0.023 p < 0.001 0.3521

logn transformed total phosphorus -0.003 p = 0.002 0.0493 pH 0.131 p = 0.014 0.0575

6.4.4 CONCLUSION

As stated at the outset of this chapter, the overall objective was to examine whether a relationship between environmental “causes” and biotic “effects” could be determined when endemic test organisms were deployed in mesh-covered cages at sites around Canberra known to have impaired water quality. The case studies presented here demonstrate that where test organisms’ responses to treatment factors such as deployment site are detected, these can, in some instances, be linked back to environmental measurements. However, there was little agreement between test organisms, let alone between multiple response endpoints employed for particular species, as to which water quality parameters underlie the macroinvertebrate responses.

The ability to identify which, if any, environmental parameters were likely to be “causing” the biotic “effects” was further complicated by high variability in the environmental data collected (Figures A6.1 to A6.6, Tables A6.1 to A6.3 and Tables A6.7 to A6.10). That 183 potentially causal relationships were identified despite this suggests that these relationships may indeed be quite strong indeed.

From the results presented here, it becomes apparent that the level of replication at which data is collected is paramount to the reliable detection of any causal link. In these case studies, there was a mismatch between the experimental design used to collect the macroinvertebrate data (Figure 2.2 and Tables 6.1 to 6.3), and the single measurements taken to represent the environmental conditions for a given deployment site and trial day combination (Appendix 6, Figures A6.1 to A6.6, Tables A6.1 to A6.3 and Tables A6.7 to A6.10). As such, the macroinvertebrate data had to be collapsed to align with the level of replication present in the environmental data. Consequently, there is a loss of both the natural variability present in macroinvertebrate response data, and the analytical power to detect any linkage between the datasets. However, even with the reduced analytical power resulting from the required data collapse previously described, relationships linking water quality parameters and macroinvertebrate endpoints were identified.

While there is value is in being able to link response endpoints with environmental covariates, it is perhaps even more important to consider the nature of the apparent relationship, and not just whether its linear representation varies significantly from zero. For example, there were instances in these case studies where, despite evidence of a causal relationship between environmental conditions and macroinvertebrate responses these relationships only explained a small amount of the variation (Table 6.27).

In attempting to link macroinvertebrate responses with pollutant concentrations, the relationship would ideally follow some kind of concentration gradient. However, in these case studies, test organisms were deployed at either sites considered to be in “reference” condition (Norris 1994, Reynoldson et al. 1997, Reynoldson and Norris 2000, Wright et al. 2000, Stoddard et al. 2006), or sites with persistently impaired water quality (see Section 2.6.2), and none representing an intermediate condition. While this situation could be improved by including sites of intermediate water quality in these kinds of field trials, there is still no way to control environmental conditions, and hence, environmental variability may still be high. Rather, it may be better to assess biotic responses under a gradient of environmental parameters, or combination thereof, under laboratory or mesocosm conditions.

7 184

Chapter 7: SYNTHESIS

There are many approaches used to assess water quality and the impact of pollutants on ecosystem structure and function, each of which has its own benefits and limitations. However, ecotoxicologists have yet to definitively link biological “effects” with environmental “causes” in an ecologically-relevant way under field conditions. In theory, the in situ testing approach applied in this study could help bridge this gap by employing methods that are locally-relevant, can be applied to lethal and sub-lethal response endpoints and will take into account antagonistic or synergistic effects between components of complex effluents.

However, adoption of in situ testing protocols is somewhat limited by adverse effects identified within experimental design and set-up, and relocation of test organisms under certain conditions. Until these inherent problems can be further investigated a direct comparison with existing techniques is difficult, and implications would be potentially misleading.

Linking Environmental “Causes” with Macroinvertebrate “Effects” This project identified that water quality characteristics can in some instances be aligned with both acute and chronic endpoints, such as growth and physiology, in macroinvertebrates. However, attributing definitive links between environmental “causes” and macroinvertebrate “effects” in this study was difficult for various reasons. Firstly, there was a mismatch in the level of replication between the two datasets (as discussed in Section 6.4), meaning only the strongest treatment effects could be detected. Secondly, as has typically been reported for field studies (Brassard et al. 1994), there was there high variability in the environmental data (Appendix 6).

There is also the issue of how well the environmental datasets reflect the water quality at trial sites over time. In attempting to link biological responses with environmental conditions in this study (Chapter 6), macroinvertebrate endpoint data was modelled against the environmental “snapshot” as detailed in Section 6.2.3. Water quality for a given deployment site and trial day combination was characterised by in situ measurements and analysis of samples collected once daily (and even then only on days when some test organisms were due 185 to be retrieved). With no reference to conditions at that deployment site over the preceding days during which the retrieved organisms were caged in situ, the environmental datasets may not accurately reflect the water quality over a particular diurnal period, let alone the entire deployment period. As such, it could conceivably be argued that to align biological responses with environmental information based solely on measurements collected only when test organisms were retrieved, and ignores all but the last moments of the deployment period, is misleading at best.

While replicates of environmental data could be obtained through more regular monitoring, by using data loggers or telemetry for example, this would increase the experimental effort and resources required, which effectively goes against the basic rationale of the in situ testing approach.

Attributing Causality In the same way that traditional chemical monitoring has been criticised, it would still be impossible to attribute “cause” to a site or water quality attribute for which no information was collected. Thus, if the actual “cause” of the variation was not measured, then “effects” may be wrongly attributed to another covariate or not at all.

However, it is always possible that a treatment effect is attributed to a particular aquatic contaminant, or combination thereof, while the effect is actually indirectly linked to that contaminant (e.g. increased body size linked to increased nutrients through stimulation of primary productivity and thus increased food availability). Alternatively, the contaminant could be affecting the biological effects through a different mechanism (e.g. increased body size linked to increased molt frequency under contaminant stress; Dahl and Breitholtz 2008) or the apparent linkage may be incorrect altogether, with the actual “cause” being completely unrelated.

Attempting to employ these in situ techniques and statistical procedures to attribute causality also involves practical considerations. If assessing an unknown pollutant and its effect on aquatic biota, one scenario would be to collect and measure as many water quality attributes as possible and model the chronic endpoint data using REML. Apart from the specialist knowledge required to analyse the array of water samples and perform the statistical analyses, this scenario would be time, cost and labour intensive and potentially financially prohibitive. And it could be that the deployed macroinvertebrates are not significantly affected at all. 186

Use of Restricted Maximum Likelihood (REML) Techniques. Although not the only statistical approach to retain a level of robustness when dealing with uneven datasets, REML statistical techniques have the advantage of being able to account for imbalance of the experimental design and missing data (Searle 1987). In these experiments, missing data resulted from vandalism, individuals escaping from holding cages, or were damaged or lost, and thus unmeasurable, as well as experiments that were comprised of different numbers of sites from each of the treatment groups (e.g. control, transplant, impact). Perhaps the greatest imbalance in these datasets was caused by the high mortality of certain test species. This resulted in different numbers of morphological measurements for control and treatment blocks. While mortality in itself does not constitute a missing value, body size endpoints for senescent organisms can decay over time and were not included in the analysis of treatment effects by REML.

One argument is that when an individual dies, and ergo ceases to grow, growth could be recorded as zero, however this would require some kind of reliable baseline measurement from which to gauge the extend of the change in the body size of test organisms. This in itself would necessitate a substantial baseline sample which, if collected at the source site on trial day 0 (see Section 2.4), would potentially reduce the analytical power of the experiment by reducing the number of individuals available for inclusion in the deployment trial itself. The other alternative would be to obtain a preliminary measurement of body size prior to caging and deployment, although as discussed in Chapter 4, doubling handling and the increased likelihood of physical damage to the organisms also renders this option less than ideal.

One of the implications of such imbalance in experimental datasets was explored in Section 6.4.2. While recording a zero measurement for “growth” of all individuals that were dead when retrieved from the deployment cages would produce a balanced dataset, averages for the experimental treatments (i.e. retrieval day, deployment site) would still be biased towards the surviving individuals. Having considered the practicality of these options, it was decided that to include measurements of body size from dead individuals when the cause or time of death was not known was inappropriate.

In retrospect, it has become apparent that high variability in macroinvertebrate responses probably decreased the power of the test, and increased the detectable effect size (Underwood 1989, Brassard et al. 1994, Calow 1996). It is possible that some reorganisation of the random experimental design factors depicted in Figure 2.2 (e.g. mesh bags, cages within bags, 187 individuals within cages) may assist in reversing these inherent limitations. As designed, much of the internal replication did not significantly increase the power of the findings, but rather decreased its sensitivity. Improvements in experimental design could be achieved relatively easily, and would result in a more robust model requiring fewer individuals, and yielding a more powerful and sensitive test with the ability to detect more subtle change in macroinvertebrate responses to aquatic impacts.

Where multiple test organisms are deployed in an attempt to increase the robustness of the findings, species differences become relevant. While species from the same or closely related families may respond comparably, it would not be unexpected for test organisms to respond quite differently, especially when organisms differ in taxonomic guilds, functional feeding groups, life history characteristics or behaviour (e.g. Blanck et al. 1984).

Where REML is employed to partition the variation within in a particular response endpoint data (e.g. survival, morphology or pollutant concentration), the inclusion of species as a fixed factor would likely overshadow the inferences that could be made about the more ecologically relevant factors. In such a case, it would probably not be unexpected if a large proportion of the variation in a given response endpoint were to be attributed to this interspecies comparison, potentially rendering any other effects statistically, let alone ecologically, insignificant by comparison. As such, if multiple species are to be examined it would be more sensible to conduct several single species trials (as done in this study) unless the differentiation of species is of particular interest.

Factors Affecting Selection of Test Organisms Caging macroinvertebrates seems to have great potential for in situ water quality testing for various reasons. Smaller organisms offer certain practical advantages for biomonitoring studies employing in situ holding cages. Despite relatively high metabolic requirements, they will consume less food, require less space and may suffer reduced stress of higher caging densities than larger organisms. However, smaller organisms (such as Cloeon sp. and Notalina sp.) have a greater ability to escape from the deployment cages, which may preclude them as in situ sentinels. While finer mesh cages could potentially overcome escape issues for smaller instars or species, mesh clogging and surface tension could potential introduce additional experimental artefacts, including a different intracage microclimate compared to ambient riverine conditions, as has been discussed in Section 4.4. 188

While few adverse effects of caging were apparent from this study, no comparison between caged organisms and individuals that were not handled and confined was undertaken. This could be achieved by some simple experiments specifically designed to test such an aim. Provided a sufficiently large baseline sample was collected on trial day 0, field samples of the same taxa could be harvested from the field site for comparison with those that had been harvested on trial day 0 and caged for a period of time.

Condition of test organisms prior to deployment trials can also influence their response to treatment factors, pollutants or other stressors. Better performance by organisms that were harvested and deployed immediately at their source site compared to individuals that were double handled and held in laboratory tanks prior to deployment (Section 4.3.2), suggests that it would be inappropriate to gradually amass sufficient organisms from repeated collections or different populations. Rather, to avoid the situation where only the largest treatment effects can be detected when background variation is very high, test organisms should be as similar as possible to reduce the background “noise” against which “effects” are compared (Underwood 1989, Calow 1996).

Family-based indices have been widely used for rapid biomonitoring, because species information gives little additional information (Furse et al. 1984, Corkum 1989, Ferraro and Cole 1992, Growns et al. 1995, Marchant et al. 1995) and there is known to be a high level of redundancy in community level data (Guzman-Alvis and Carrasco 2005, Hirst 2006, Heino and Soininen 2007). However, research has also indicated that species level identification is required to achieve accurate conclusions based on macroinvertebrate ecology (Resh and Unzicker 1975, Cranston 1990, Resh 1994).

While conventional toxicity tests have the advantage of standardised test organisms, often derived from genetically similar stock and from the same cohort (and often instar), which have been exposed to identical culture conditions (Anderson-Carnahan et al. 1995), the same is not true for field-harvested organisms. In this study, a certain level of replication was required by the experimental design (Table 4.1, Tables 5.1 to 5.2 and Tables 6.1 to 6.3). However, abundance of similarly-sized test organisms at the chosen source site on trial day 0 influenced other decisions regarding the level of experimental replication of random factors (Figure 2.2). Therefore, it is likely that variability in responses was greater because groups of test organisms allocated to each deployment cage were comprised of a set of individuals from a size range representative of the source population (see Section 2.4.2). 189

Trial duration may also preclude use of certain taxa as sentinels in these kinds of trials. Cheumatopsyche sp.6, for example, would only be employed for short-term trials, as mortality increased markedly after trial day 3 at most deployment sites (Figures 5.1 and 5.2), even at the source site where survival dropped to 25% by trial day 6. Methods of feeding will also affect the aptitude of taxa to caging, and adverse caging-related effects need to be distinguished from any water quality effects exhibited by deployed organisms. Filter feeding species such as hydropsychid caddis flies are likely to be inappropriate because quality and quantity of their food intake would be difficult to control (and quantify). And further, interpretation of bioavailability, or any relationship between consumption, contamination and responses affected would be virtually impossible in the field.

While predators have been successfully used as test organisms in mesocosm studies (Culp et al. 1996, Culp et al. 2000), use of predators as test organisms has implications for experimental design and resources needed when employing holding cages (Hopper et al. 1996). Collection of sufficient numbers of individuals (of a comparable size) from a suitable predatory species may be difficult, especially when environmental conditions are strained even at reference sites (e.g. from drought). However, various studies advocated the use of biomonitors from a range of trophic levels (Morgan et al. 1984, Castillo et al. 2000). Predators also may reflect different levels of sensitivity to pollutants, with arguments for both higher and lower sensitivities in comparison with lower order taxa. While caging several individuals within a cage at the scale used in these experiments is inappropriate (Figure 4.1), one individual per cage will affect resources and space needed to undertake the experiment as well as have an impact on the appropriate experimental design to use. Unless an appropriate prey species were provided (and quality or quality controlled across all trial individuals at different deployment sites), lack of feeding may affect reliability of predators as test organisms.

Because of the difficulties locating riffle sites due to the prevailing drought conditions in Canberra during the experimental period (Figure 3.3), this research was largely conducted using individuals collected from, and deployed in, lower flow environments along banks and edges, and among macrophyte stands in areas away from the main stream channel. However, edge-dwelling macroinvertebrates are potentially more resilient to variable water quality conditions than riffle taxa (Barton and Metcalfe-Smith 1992, Turak et al. 1999, Brooks et al. 2002), possibly because their environments are subject to greater diurnal fluctuation, higher 190 temperatures and organic matter loads and reduced oxygen saturations. While edge organisms may be more tolerant to environmental stress, their use strengthens the applicability for in situ assessment of water quality, in that significant effects may be more conservative than those expected for the more sensitive organisms such as those accustomed to a more stable riffle environment.

Paratya australiensis, in particular, remained abundant in local waterways during the trial period despite the drought conditions, and was consequently employed as a test organism for many of the project trials (Table 4.1, Table 5.2 and Tables 6.1 to 6.3). This test organism also seemed largely unaffected by handling, caging and relocation procedures (Table 4.4 and Table 5.2). However, it could be reasoned that an increased tolerance to handling pressures may lead to an increased tolerance to more variable aquatic conditions, and thus reduced sensitivity to water quality impairment. However, Paratya australiensis demonstrated significant site-related treatment effects to each of the pollution scenarios to which it was exposed (Tables 6.22, 6.24 and 6.26). And in Case Study 1, where three test organisms were deployed in holding cages, it produced the greatest site-related effects on survival and growth despite having the lowest sensitivity score (Chessman 1995).

Factors Affecting Choice of Endpoints Significant effects related to trial day or deployment site were detected for at least some of the macroinvertebrate response endpoints in all experiments (Table 4.4, Table 5.2 and Tables 6.22, 6.24 and 6.26). However, the finding that different endpoints for a given test organism reacted differently to experimental treatments is not altogether surprising and lends support to the need for multiple organisms and endpoints (Dutka and Kwan 1988, Munawar et al. 1989). While consistent inferences drawn from various response endpoints and test organisms would lend strength to the findings themselves, substantial disagreement between the findings of multiple endpoint tests would warrant further research.

Interestingly, variation in body size of Paratya australiensis over the duration of Experiment 5 was almost mirrored by different another body size endpoint for the same species in Experiment 2 (Figure 7.1). While neither differed significantly with respect to deployment time in the respective trials, it is difficult to overlook the similarity between the responses of Paratya australiensis to deployment in cages at unimpacted sites over a 12 day period. 191

Log -transformed wet weight (Expt 5) 3.9 n 3.7 Orbital carapace length (Expt 2) 3.85 3.6 3.8

3.75 3.5

3.7 3.4 3.65 3.3 3.6

3.55 3.2 -transformed wet weight (mg) n

3.5 Orbital carapace length (mm)

Log 3.1 3.45

3.4 3 Day 0 Day 1 Day 2 Day 3 Day 6 Day 9 Day 12 Trial day Figure 7.1: Mean body size measurements for Paratya australiensis retrieved alive after deployment at unimpacted test sites in two component experiments of this study.

Not only did different measures of macroinvertebrate morphology disagree, but statistical analyses linked different macroinvertebrate responses with different water quality covariates, or combinations thereof (Tables 6.24 and 6.26), as discussed more fully in Section 6. This is particularly evident for Case Study 2, where pH and ammonia seemingly produced opposite responses from different morphological endpoints (Table 6.24). While urban runoff is clearly the factor “causing” the differences in carapace dimensions of Paratya australiensis, it is possible that the covariates specified by REML (see Section 2.10) were not actually responsible for these macroinvertebrate “effects”. Rather, is it quite likely that the environmental dataset did not encompass all possible contaminants associated with urban runoff. There is also the argument, as described previously, that the environmental “snapshots” collected from the impact sites in Case Study 2 did not provide an accurate representation of water quality at the sites during the trial period, especially at the impact sites, where high diurnal variability and pollutant pulses, as are characteristic for urban runoff (Oliver and Grigoropoulos 1981, Arthington et al. 1982, Chakraborty and Kushari 1985, Graves et al. 1998), could be expected to occur. 192

When selecting which biological endpoints should be assessed, ecologists need to consider several factors including the ecological relevance and sensitivity of the given endpoint to organism stress, as well as accuracy and ease of measurement. Where treatment effects were assessed for both biomass and other body size measurements, a decrease in biomass often preceded a change in carapace dimensions or head width (Table 5.2 and Tables 6.22, 6.24 and 6.26). Wet weight also appeared to exhibit greater differences and less variability when compared between experimental treatments than morphological dimensions for stored and wild Paratya australiensis individuals (Table 4.4 and Figure 4.4), and individuals that were deployed at source and transplant sites (Table 5.2, Figures 5.6B and 5.8). Given this variability, it may be prudent to favour a measurement of dried biomass rather than a fresh or wet weight measurement. Despite the need for precision instrumentation that may be required for obtaining weights of small individuals, dried biomass would be a record of tissue mass, without potential artefacts related to changes in the aqueous fraction.

Of all the experiments that comprised this study, only the amphipods held in mesh cages in Experiment 3 (see Section 4.3.3) increased significantly in dorsal lengths without a corresponding change in biomass when held at a higher caging density (Table 4.4 and Figure 4.5). Rather, biomass was generally more sensitive to differences in site characteristics and trial duration, while exoskeletal dimensions were influenced more by the water quality parameters modeled in the pollution scenarios (Chapter 6).

Differences in biomass (either increasing or decreasing) can relate to growth, increased or decreased feeding (Nalepa et al. 2000, Shulman et al. 2005, Strayer and Malcom 2006), organism health (Pascual et al. 2004, Ward et al. 2004) or, alternatively, reduction in body condition by metabolism of tissue (Canavoso and Rubiolo 1998, Balch et al. 2000, Locke and Sprules 2000, Dutra et al. 2007), loss of water or an empty digestive tract (Brooke et al. 1996, Sibley et al. 1997). Scleratised body parts, on the other hand, grow discontinuously, with increases related to successive molts or nymph instars (Nijhout 1981, Ayres and MacLean 1987, Hutchinson et al. 1997, Tammaru 1998, Esperk and Tammaru 2004). Therefore, if, such increases are slow and irregular, any biological responses (whether stimulated or restricted) would be expected to appear earlier for biomass, and by greater differences than for exoskeletal endpoints. Similarly, morphological endpoints such as head width and carapace length would not be expected to decrease over time, but may well decrease in their “rates” of increase with environmental or other stressors. Other response endpoints (e.g. molting 193 frequency, intermolt period) may also be more accurate reflectors of sub-lethal stress than linear dimension endpoints such as carapace length and head width.

Nature of the Deployment Site As demonstrated in Chapter 5, not all organisms will be equally suited to a range of unimpacted sites, with habitat, flow conditions, water quality and available diet all likely to affect their capability to survive and thrive. While some test organisms performed better at sites similar to those from which they were harvested, others thrived at sites with different habitat and water quality characteristics (Table 5.2).

Of all the species trialled in Chapter 5, only Paratya australiensis was relocated between AUSRIVAS site groupings in Chapter 6 (see Section 2.6.2). While relocation of macroinvertebrates between AUSRIVAS site groups at reference condition was not found to significantly affect P. australiensis acutely or sublethally (Table 5.2), both types of endpoints were adversely affected when the deployment sites were impacted or stressed in some way (Table 6.24). Whether this is because of their relocation between groups, increased disturbance from checking and clearing cages, or some other confounding factor, or whether it relates indirectly to factors not examined (such as lack of preferred or suitable food or refugia) remains unknown. What has become evident though, is that if already affected in some way by caging and deployment, test organisms could potentially react more sensitively to poor water quality. If this were the case, it may be that using relocated macroinvertebrates as test organisms in in situ assessment of a particular pollutant could identify lower effect concentrations than laboratory-based trials. While the interaction between stresses associated with experimental logistics and water quality would need to be carefully differentiated, increased sensitivity of the test organism tested in situ in the natural environment could be considered advantageous for a more conservative, or earlier, warning system. However, specifically designed experiments that investigate the performance of test species in relation to site groupings and taxa probabilities would be necessary to properly elucidate this prior to employing them for impact assessment. 194

7.1 CONCLUSIONS

Being able to establish some kind of linkage between macroinvertebrate response and environmental covariates under field conditions would be a considerable step forward in the field of aquatic ecotoxicology. The findings of this project are optimistic for the long-term applicability of in situ methods for the detection of differences between macroinvertebrate responses between different sites. And while some alignment has been achieved using in situ testing techniques to assess three pollution scenarios, this approach is fraught with the likelihood of confounding design factors and should definitely be used with caution.

In situ test results presented here indicate certain advantages over laboratory toxicity studies and more conventional chemical and biological monitoring techniques. They satisfy many of the criticisms of these well-known methods, and have the potential to help elucidate cause and effect in a more ecologically-relevant way. Furthermore, the in situ testing methods employed here can be conducted with simple, inexpensive field equipment rather than controlled laboratory toxicology facilities, can be left to run over the trial duration with constant or close supervision, and can be undertaken effectively without extensive experience in macroinvertebrate taxonomy. In addition, these techniques appear to be comparatively inexpensive to run and easy to maintain compared with other techniques such as mesocosms (Levy et al. 1985, Mayfield 1993, Graney 1994a).

Another benefit of in situ testing methods is that the results of field trials can be gauged within a short timeframe, thus providing an early warning system so more focussed monitoring or testing can be implemented without delay. Timely identification of water quality impacts on aquatic biota would also be useful for “ground truthing” existing or proposed water quality guidelines, triggering focussed research and remediating aquatic ecosystems under threat more proactively, rather than post-hoc rehabilitation.

Perhaps the way forward is to couple in situ approach trialled here with laboratory-based dosing experiments or toxicity identification evaluation procedures, both of which have the benefit of being able to manipulate conditions and definitively link the biological effect with its cause (Cairns 1980, Kemp et al. 1980, Calow 1993, USEPA 1993a, Graney 1994a, Jeffries and Mills 1994, Cairns et al. 1996, Hall 1996, Lawton 1996). Field deployment could be used initially to determine if something about a particular waterway is impacting relevant test organisms, endemic or relocated. If there is no discernible effect on biota (over a suitable 195 range of species and endpoints) there may be no need to invest additional resources. Conversely, if biota are affected, either adversely or by means of stimulated production, then more intensive investigation can be undertaken.

Despite all this, it should be clear that application of in situ methods for the assessment of water quality using macroinvertebrates will not provide information of which factors affect contaminant toxicity (Skidmore 1974, Cairns 1984a, Wang 1987, Muncaster et al. 1990, Ankley et al. 1994), or the mechanisms of these effects (Bjerregaard 1991). They do, however, have the benefit of ecological realism, and could quite conceivably be incorporated into the battery-type assessments more frequently being proposed in recent years (Slabbert and Venter 1999, Arkhipchuk et al. 2000, Castillo et al. 2000). While studies implementing this approach are fraught with danger of confounding from experimental artefacts, their value lies in helping to elucidate the knowledge gap between laboratory toxicity testing and conventional physical, chemical and biological monitoring. 196

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Appendix 3

CLIMATIC CONDITIONS OF THE STUDY AREA

The climate of the Upper Murrumbidgee catchment is characterised as cool temperate, with cold winters and warm summers, and periods of severe drought and flooding in response to the rainfall and temperature patterns across the region. Mean daily temperatures are consistently several degrees near the source of the Murrumbidgee system in the Snowy Mountains than further downstream (Table A3.1), with mean daily minimum temperatures varying approximately 6-7oC across the region. Mean daily maxima, however, vary considerably less across the region, with only 2.1oC separating the mean daily maximum temperatures at Cooma and Yass in June and July (Table A3.1).

Table A3.1: Long term mean daily temperature (oC) range recorded throughout study area (Bureau of Meteorology 2005).

Burrinjuck Yass Canberra Gudgenby Cooma (1908-2004) (1898-2004) (1887-2004) (1886-1988) (1858-2004)

Min Max Min Max Min Max Min Max Min Max

Jan 15.3 29.2 13.8 29.3 12.9 28.2 9 25.7 10.9 27.4 Feb 15.6 28.8 13.9 29 13.1 27.8 9.3 25.4 10.8 26.6 March 13.3 25.8 11 25.7 10.8 24.8 6.5 22 8.6 24.3 April 9.5 20.8 7 21.1 6.8 20.1 2.5 17.9 4.8 19.5 May 6.4 16 4.1 16.3 3.3 15.5 0.1 13.1 1.2 15 June 4.1 12.4 2.1 12.5 1.1 12.3 -2.4 10.1 -0.6 11.5 July 2.9 11.5 1 11.5 0 11.5 -3.1 9.4 -2 10.9 August 3.6 13.3 1.9 13.3 1.1 13.2 -1.7 10.8 -1 13.2 Sept 5.6 16.6 4 16.5 3.4 16.5 0 13.8 1.6 16.7 Oct 8.2 20 6.2 20.3 6.1 19.8 3.1 17.2 4.3 20 Nov 10.9 23.7 9.1 24.1 8.9 23.4 5.2 19.9 7.2 23.1 Dec 13.6 27.4 11.9 27.7 11.5 26.7 7 23.6 9.3 26

While temperatures over 37oC have been recorded across the entire study area, and temperatures greater than 35oC are recorded an average of 5.1 days across the year in Canberra (Figure A3.1), long term mean daily maximum temperatures for each month across the region do not exceed 30oC (Table A3.2). Sub-zero temperatures are considerably more frequent in the study area, with Gudgenby recording an average of approximately 115 days per year where the minimum temperature is 0oC or below, 0.4 days of which occur in January (Table A3.2). However, long term mean daily minimum temperatures only reach 0oC or below during winter months at Gudgenby and Cooma (Table A3.2). 240

Table A3.2: Mean number of days per month where stations throughout study area experience extreme temperatures (less than 0oC and greater than 35oC) (Bureau of Meteorology 2005).

Burrinjuck Yass Canberra Gudgenby Cooma (1908-2004) (1898-2004) (1887-2004) (1886-1988) (1858-2004)

<0oC >35oC <0oC >35oC <0oC >35oC <0oC >35oC <0oC >35oC

Jan 4.4 4.5 2.6 0.4 1.1 1.4 Feb 3.1 2.9 1.3 0.5 0.5 0.6 March 0.6 0.3 0.2 2.4 0.5 0.1 April 1 1.3 10.3 5.5 May 0.3 5.6 7.6 15.1 13.1 June 1.6 11.1 13.1 19 18.8 July 4 13.9 16.9 21.6 23.5 August 2.2 10.6 13.3 19 21.5 Sept 0.5 4.7 6.1 15.4 12.9 Oct 0.9 1 7.5 5.8 Nov 0.5 0.4 0.3 0.2 3.5 1.5 Dec 1.9 1.7 0.8 1.2 0.1 0.4

During the summer months the region receives approximately nine hours of daily sunshine and the evaporation rates as high as 8.1 mmd-1 (Figure A3.2), which is equivalent to approximately 45% of the long term average annual evaporation for Canberra (1200 to 1400 mm; Bureau of Meteorology 2005). Between May and August, however, daily evaporation rates are less than 2.6 mmd-1 and the relative humidity is above 80% (Figure A3.2). Monthly wind speed also follows a sinoidal pattern, with lowest mean winds measured in March and wind speeds over 10 kmh-1 through the spring months (Figure A3.2). Like much of southern Australia, the region receives relatively little thunder and electrical storm activity, with Canberra reported to receive an average 15-20 thunder days per year and an average of only one lightning ground flash per square kilometre each year (Bureau of Meteorology 2005).

Long term annual rainfall in Canberra is 646.5 mm (Clewitt et al. 2003) and is relatively evenly spread throughout the year (Table A3.3). In contrast, long term annual precipitation in Burrinjuck is clearly dominated by winter rainfalls (Table A3.4). While the highest daily rainfall on record for the region is 188.7 mm (recorded at Queanbeyan), daily rainfalls greater than 50 mm are recorded in Canberra on only two or three days per year (Bureau of Meteorology 2005). Despite uniformity between long term monthly rainfall statistics for Canberra throughout the year (Table A3.3), the nature of the monthly rainfall varies considerably. Not only is summer rainfall in Canberra more variable, it is recorded over fewer raindays than winter rainfall, with an average of 5.5 and 9.5 raindays for February and August respectively (Figure A3.3). While Gudgenby and Yass also experience more raindays in winter (Figure A3.3), the number of monthly raindays at Cooma is relatively constant throughout the year (ranging between 6.9 and 9.1 raindays per month; Figure A3.3), correlating with lower rainfall totals in winter months and snowfalls above altitudes of 1200 m (Table A3.5 and Figure A3.3). 241

Mean number of days with daily minimum < 0 ooCC Mean number of days with daily maximum > 35o oCC Mean daily minimum temperature Mean daily maximum temperature 30 20

18 25 16

20 14 C) o 12 15 10 10 8 Temperature ( 5 6

4 0 2 Days per month with extreme temperatures

-5 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure A3.1: Mean temperature at Canberra airport (1939-2004; Bureau of Meteorology 2005).

12 100

90 10 80 )

-1 70 8 60

6 50

40 4

Mean wind speed (kmh 30 Mean relative humidity (%) Mean daily evaporation (mm) Mean daily hours of sunshine 20 2

Mean wind speed at 9am Mean daily evaporation 10 Mean daily hours of sunshine Mean relative humidity at 9am 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure A3.2: Mean monthly evaporation, daily humidity, hours of daily sunshine and wind speed (Bureau of Meteorology 2005). 242

Table A3.3: Summary of monthly rainfall (mm) recorded at Holt (Canberra) between 1887 and 2003 (Clewitt et al. 2003).

median mean min max 90th 10th St dev CoV LT ptile ptile variability

Jan 54.5 64 0 315 119 8 57.81 90.32 2.04 Feb 38 47.85 0 196 106.5 4 42.79 89.43 2.70 March 42 55.85 0 327 116.5 7.5 53.06 95.01 2.60 April 41 49.19 0 214 99.5 8 40.22 81.76 2.23 May 39.5 51.66 0 316 108.5 6.5 46.45 89.93 2.58 June 45 55.97 5 197 110.5 14.5 38.56 68.91 2.13 July 41.5 51.35 4 227 102 13 36.56 71.2 2.15 August 50.5 52.43 0 142 90 14 30.49 58.15 1.51 Sept 56.5 56.76 1 185 96 15 35.15 61.92 1.43 Oct 54.5 66.47 1 196 131.5 21 43.01 64.72 2.03 Nov 49 56.17 1 169 110.5 8.5 39.45 70.23 2.08 Dec 43 52.72 0 226 109.5 8.5 42.86 81.29 2.35

Annual 646.5 660.73 287 1168 871.5 428.5 181.78 27.51 0.69

 LT (long term) variability is calculated as the difference between the 90th and 10th percentiles as a proportion of the median. Rainfall variability of 0.5 is considered to be low, while moderate and high rainfall variabilities are classified by values greater than 1.0 and 1.25 respectively.

15 14 13 12 11 10 9 8 7 6 5 4 3 Cooma (1858-2004) Yass (1898-2004) Mean number of days per month with rain 2 Gudgenby (1886-1988) Burrinjuck (1908-2004) 1 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure A3.3: Long term mean number of monthly raindays across the region (Bureau of Meteorology 2005). 243

Much of Australia was experiencing drought conditions between 1996 and 1998 (Table A3.5). While the total annual rainfall received in 1996 and 1998 was above the long term annual mean of 660 mm (Table A3.3), below average monthly rainfalls were recorded for approximately half the months in this three year period (Table A3.5). Of the 15 experimental trials presented in this thesis, 14 were conducted in the period between November 1996 and April 1998, during which time above average monthly rainfalls were only recorded in June and September 1997 (Figure A3.4).

Table A3.4: Long term mean monthly rainfall (mm) throughout study area (Bureau of Meteorology 2005).

Burrinjuck Yass Canberra Gudgenby Cooma (1908-2004) (1898-2004) (1887-2004) (1886-1988) (1858-2004)

Jan 63.4 50.9 58.6 76.6 57.2 Feb 54.1 44.4 48.7 57.9 53.0 March 58.4 47.5 52.0 65.4 52.1 April 66.8 49.6 46.6 55.7 36.9 May 83.4 51.3 46.6 55.6 34.2 June 96.7 57.3 47.3 69.3 35.7 July 102.8 58.8 44.6 58.2 27.1 August 100.4 59.5 47.7 58.2 25.9 Sept 84.8 56.7 51.6 69.7 37.1 Oct 88.1 66.6 62.1 74.6 47.3 Nov 69.6 54.8 56.7 63.7 53.9 Dec 60.3 51.2 51.9 63.9 51.1

Rainfall Long term mean monthly rainfall 140

120

100

Monthly rainfall (mm) 40

0 Jul Jul Apr Oct Apr Oct Apr Jun Jan Jun Jan Jun Feb Mar Feb Mar Aug Sep Nov Dec Aug Sep Nov Dec May May May 1996 1997 1998

Figure A3.4: Rainfall (mm) recorded at Belconnen during the study period and the long term average rainfall (recorded between 1887 and 2003) at Holt (Clewitt et al. 2003). 244

Table A3.5: Rainfall (mm) recorded at various stations between 1996 and 1998. Shading represents measurements below the long term mean monthly rainfall recorded for Holt between 1887 and 2003 (Clewitt et al. 2003).

Gungahlin  Belconnen  Tuggeranong  Upper Naas 

1996 Jan 115.7 98 81.2 88.3 Feb 46.2 40 34.6 20.2 March 54.3 35 61.5 79.4 April 19.0 18 8.1 24.6 May 62.9 78 91.2 62.7 June 51.1 60 39.4 37.6 July 85.4 69 51.4 32.7 August 46.6 38 41.1 74.4 Sept 95.3 100 95.0 115.4 Oct 49.6 44 55.6 45.4 Nov 58.2 59 73.4 87.4 Dec 68.6 49 64.9 84.7 Annual Total 753 688 697.5 752.7 1997 Jan 20.0 34 48.8 59.1 Feb 46.8 17 33.2 20.4 March 29.6 35 30.8 51.1 April 0.2 0 0.8 1.7 May 28.6 31 29.1 37.6 June 101.8 97 92.1 153.8 July 18.9 14 12.6 21.4 August 36.3 32 31.2 27.3 Sept 114.0 109 79.5 65.6 Oct 39.2 20 27.5 52.1 Nov 16.8 24 13.1 44.4 Dec 17.9 10 4.7 31.4 Annual Total 470.1 423 403.3 565.9 1998 Jan 22.1 31 25.8 73.5 Feb 38.3 24 18.2 32.7 March 0.7 1 0.8 1.9 April 54.1 57 41.6 45.8 May 32.5 25 22.0 27.0 June 142.6 124 91.4 135.4 July 97.3 92 70.6 82.1 August 118.3 112 120.2 164.5 Sept 61.6 69 60.8 39.7 Oct 68.0 62 42.9 46.6 Nov 107.6 65 98.3 77.5 Dec 8.3 10 8.3 59.9 Annual Total 751.3 672 600.9 786.6  Monthly rainfall records from Clewitt et al. (2003).  Monthly rainfall records from ACTEW Corporation. 245

Appendix 4

EXPERIMENT 1 – EFFECT OF CAGING AND MESH SIZE ON

MACROINVERTEBRATES

EXPERIMENTAL FIELD CONDITIONS

Prevailing Conditions Daily rainfall records were not available for the Upper Molonglo Valley. However rainfall during June and July 1996 in the Upper Naas Valley to its west (Table A3.5), was not markedly lower than the long term monthly averages for the region (Table A3.4). However, 21.9 mm of the 37.6 mm recorded at Upper Naas for the whole of June 1996 was recorded on trial day 6 (Figure A4.1). Antecedent rainfall was also relatively low, with total daily rainfalls of more than 5 mm recorded on only two days in the preceding six week period (Figure A4.1). This rainfall pattern was reflected at other rainfall stations across the study region. 40 1.8 Rainfall Di scharge

35 1.6

1.4 )

30 -1 s 3 1.2 25 1 20 0.8 15 0.6 Daily rainfall recorded (mm)

10 Daily stream discharge (m 0.4

5 0.2

0 0 1 5 9 13 17 21 25 29 2 6 10 14 18 22 26 30 4 8 12 16 20 24 28 May 1996 June 1996 July 1996 Figure A4.1: Daily rainfall recorded at Upper Naas Valley and stream discharge recorded on the Molonglo River at Copper Creek during mid 1996. Horizontal bar represents the trial period for Experiment 1.

Field Site Measurements Streamflow in this reach of the Molonglo River was relatively constant throughout the trial (Figure A4.1), and water quality characteristics at the deployment site were also relatively stable (Table A4.1). Higher turbidity measured on trial day 0 is probably related to the stream disturbance during deployment of the experiment, and elevated turbidity and electrical conductivity (EC) on trial day 6 corresponded with heavy rainfall recorded for the area (Figure A4.1, Table A4.1). 246

Table A4.1: Water quality characteristics during Experiment 1.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 14a (source) – tributary 100m upstream of confluence at Cemetery Falls

0 7.1 424 8.41 12.71 2.3 1 6.48 428 7.66 11.59 1.8 Site 14 (deployment) – Molonglo River upstream of Captains Flat township

0 7.37 55.5 8.37 12.28 44.2 1 6.92 55.5 7.46 12.81 7 2 5.58 54 8.25 13.15 8.5 3 4.9 53.2 7.65 12.91 6.4 6 5.34 109.3 7.62 12.61 59.9 12 5 63.2 7.35 13.26 8.8 18 3.71 62 7.4 13.71 10.9 24 1.68 72.3 7.27 13.17 7.3

DESCRIPTION OF BODY SIZE DATA

Austrolestes cingulatum Descriptive statistics show Austrolestes cingulatum head widths ranged from 0.79 to 4.35 mm (mean 2.76  0.81 mm, n = 171) during the trial period. Distribution of Austrolestes cingulatum head widths was not normally distributed (Shapiro Wilks statistic = 0.969, p = 0.028), however normality was not improved by either logarithmic or square root transformation. Measurements were evenly spread in a bell shape around the median value (kurtosis = -0.552, skewness = -0.322) and none of the head widths were considered to be statistical outliers.

Atalophlebia australis Descriptive statistics show Atalophlebia australis head widths ranged from 0.56 to 2.79 mm (mean 1.49  0.42 mm, n = 337) during the trial period. There was some evidence that head width data was not normally distributed (Shapiro Wilks statistic = 0.927, p = 0.032), with 80% of heads widths falling within 0.7 mm of the mean head width. While the frequency distribution of the raw data was not improved by logarithmic or square root transformation, the histogram was only slightly skewed to the right (kurtosis = 0.385, skewness = 0.926), with seven head width measurements wider than 2.5 mm.

Intradependance There was little evidence of intracage correlation between individuals caged together in terms of (ρ= 0.199). 247

EXPERIMENT 2 – EFFECT OF TEMPORARY STORAGE ON

MACROINVERTEBRATES

EXPERIMENTAL FIELD CONDITIONS

Prevailing Conditions The study area was extreme dry in early 1998 (Table A3.5) with daily rainfall records in excess of 20 mm recorded only twice in the preceding six months. Daily rainfall records are similar for different stations in the region, with only 0.7 mm of rainfall recorded just north of Lake Ginninderra in the six week period leading up to the commencement of this trial (Figure A4.2).

40 0.0 8 Rainfall Discharge

35 0.0 7 )

30 0.0 6 -1 s 3

25 0.0 5

20 0.0 4

15 0.0 3 Daily rainfall recorded (mm)

10 0.0 2 Daily stream discharge (m

5 0.0 1

0 0 1 5 9 13 17 21 25 1 5 9 13 17 21 25 29 2 6 10 14 18 22 26 30 Febr uary 1 998 March 1 998 April 1998 Figure A4.2: Daily rainfall and stream discharge record at Ginninderra Creek immediately upstream of Barton Highway in early 1998. Horizontal bar represents the trial period for Experiment 2, with holding period indicated by the unshaded portion immediately beforehand.

Field Site Measurements There was no stream discharge recorded for Ginninderra Creek upstream of the study site during either the holding or deployment periods of this trial. Water quality characteristics (Table A4.2), as well as concentrations of major anions and cations (Table A4.3) and nutrients (Table A4.4), were also relatively stable throughout the deployment period at the field site. 248

Table A4.2: Water quality characteristics in laboratory aquaria at the start and end of the 13 day holding period and at the field trial site before and during Experiment 2.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 20 (source) – Lake Ginninderra off Diddams Close - water transferred to laboratory aquaria collected for holding “tanked” individuals

-13 22.76 320 8.11 7.48 2.8 Laboratory aquaria – end of holding period

0 22.7 324 8.03 5.34 7.0 Site 20 (deployment) – Lake Ginninderra off Diddams Close

0 22.95 325 7.90 6.83 6.2 1 22.45 328 7.63 7.6 6.7 2 22.53 324 8.39 6.69 10 3 22.65 326 8.36 7.13 4.5 6 21.46 323 8.08 7.16 9.7 12 24.06 322 8.54 8.72 6.3

However, windy conditions on trial day 6 appeared to produce elevated turbidity that corresponded with lower surface water temperature and depressed oxygen saturation conditions (Table A4.2). While wind conditions stabilised for the latter part of the deployment trial, the concentration of dissolved bicarbonates (Table A4.3), pH and electrical conductivity were all elevated for trial days 9 and 12 (Table A4.2).

Water collected from the deployment site and used in the laboratory aquaria for the 13 day holding period prior to the deployment trial, had lower concentrations of both ions (Table A4.3) and nutrients (Table A4.4) than measured throughout the deployment trial. The ion composition was relatively proportionate to samples collected during the deployment trial and did not change markedly during the 13 day holding period (Table A4.3). 249

Table A4.3: Ionic composition of Lake Ginninderra water collected for use in the laboratory for “tanked” treatment and at the field trial site during Experiment 2.

-1 Trial Na Mg K Ca SO4 Cl HCO3 (mgL -1 -1 -1 -1 -1 -1 Day (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) as CaCO3)

Site 20 (source) – Lake Ginninderra off Diddams Close - water transferred to laboratory aquaria collected for holding “tanked” individuals -13 18.21 10.44 2.23 12.70 25.5 14.25 67.88 Site 20 (deployment period) – Lake Ginninderra off Diddams Close 0 20.97 11.62 2.71 13.56 29 16.8 73.95 1 21.06 11.81 2.73 13.45 29 17 73.98 2 20.95 11.74 2.70 13.31 30 17 71.88 3 20.91 11.51 2.78 13.31 28 16 73.89 6 21.39 11.64 2.95 13.40 29 17 75.94 9 21.30 11.80 2.89 13.24 30 16 81.94 12 20.62 11.57 2.77 13.22 29 16 89.56

Table A4.4: Total nutrient concentrations of Lake Ginninderra water collected for use in the laboratory for “tanked” treatment experiments and at the field trial site during Experiment 2.

Trial Day Total Nitrogen (mgL-1) Total Phosphorus (mgL-1)

Site 20 (source) – Lake Ginninderra off Diddams Close - water transferred to laboratory aquaria collected for holding “tanked” individuals -13 0.49 0.015 Site 20 (deployment period) – Lake Ginninderra off Diddams Close 0 0.56 0.018 1 0.72 0.02 2 0.54 0.02 3 0.61 0.02 6 0.64

In contrast, characteristics of the aquaria water changed over the 13 day holding period despite constant circulation within the tanks themselves. Both nutrient concentrations (Table A4.4) and turbidity (Table A4.2) increased and aquaria water became less well oxygenated (Table A4.2), probably as a result of metabolic products of the “tanked” shrimp or transformation of dissolved organic material. 250

DESCRIPTION OF BODY SIZE DATA

Paratya australiensis Orbital Carapace Length Descriptive statistics show orbital carapace lengths of Paratya australiensis ranged from 2.2 to 6.7 mm (mean 3.82  0.66 mm, n = 291) during the trial period. There was some evidence that the distribution was not normally distributed (Shapiro Wilks statistic = 0.985, p = 0.005, kurtosis = 0.933, skewness = 0.018), which is partly attributable to the five individuals whose orbital carapace length exceeded 5.2 mm.

Carapace Length Descriptive statistics show Paratya australiensis carapace lengths ranged from 3.05 to 10.82 mm (mean 6.91  1.34 mm, n = 291) during the trial period. The data was normally distributed (Shapiro Wilks statistic = 0.996, p = 0.608) with a model bell shape (kurtosis = 0.005, skewness = -0.017).

Wet Weight Descriptive statistics show Paratya australiensis wet weights ranged from 9 to 158.2 mg (mean 49.20  26.67 mg, n = 288) during the trial period. The data was transformed by the natural logarithm to achieve a normal distribution (Shapiro Wilks statistic = 0.995, p = 0.432, kurtosis = -0.158, skewness = -0.308) with no statistical outliers.

Intradependance There was some evidence of intracage correlation between individuals for growth in terms of carapace length ( = 0.09), orbital carapace length ( = 0.029) and logn-transformed wet weight ( = 0.045).

EXPERIMENT 3 – EFFECT OF STOCKING DENSITY ON MACROINVERTEBRATES

EXPERIMENTAL FIELD CONDITIONS

Prevailing Conditions Trials to assess the effect of density were conducted under laboratory conditions. Prior to field collection of test organisms for this trial, rainfall across the study region had been significantly lower than the long term monthly averages for the region (Table A3.5). Approximately five weeks prior to the start of this trial 30.6 mm of rain had been recorded in the Upper Naas Valley over two consecutive days with only 2.7 mm recorded in the intervening time (Figure A4.3). This rainfall pattern was reflected at other rainfall stations across the study region. 251

40 Rainfall Discharge 0.08

35 0.07 )

30 0.06 -1 s 3

25 0.05

20 0.04

15 0.03 Daily rainfall recorded (mm)

10 0.02 Daily stream discharge (m

5 0.01

0 0 1 5 9 13 17 21 25 1 5 9 13 17 21 25 29 2 6 10 14 18 22 26 30 February 1998 March 1998 April 1998 Figure A4.3: Daily rainfall recorded in Tuggeranong and stream discharge recorded at Tuggeranong Creek immediately upstream of the Monaro Highway in early 1998. Arrow indicates field collection of test organisms for Experiment 3 which was conducted under laboratory conditions at the University of Canberra.

Field Site Measurements Prior to the field collection of Austrochiltonia sp., this reach of the Molonglo River was relatively constant in terms of flow (Figure A4.3) and ionic constitution (Table A4.5). While the domination of anions (2.9%) suggest the water comprised dissolved cations (between 0.084 and 0.463 meqL-1) other than those analysed, the water was strongly dominated by magnesium, sulphates and bicarbonates (Table A4.5). Water conditions in the laboratory aquaria also remained relatively constant throughout the trial (Table A4.6). The water temperature remained at approximately 21oC under laboratory conditions while both electrical conductivity and pH increased as the trial progressed (Table A4.6), possibly as a result of amphipod metabolic products or transformation of dissolved organic material

Table A4.5: Ionic composition of Molonglo River water collected and transferred to the laboratory aquaria for Experiment 3. Measurements are mean with standard deviation italicised in parentheses.

-1 Trial Na Mg K Ca SO4 Cl HCO3 (mgL -1 -1 -1 -1 -1 -1 Day (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) as CaCO3)

0 26.50 22.43 2.11 27.71 101.0 21.5 100.99 (0.828) (0.343) (0.023) (0.155) (1.414) (0.577) 252

Table A4.6: Water quality characteristics in aquaria during Experiment 3.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 14 (source) – Molonglo River upstream of Captains Flat township 0 15.14 528 7.24 5.26 2.3 Laboratory aquaria 0 21.55 531 7.67 5.61 0 1 21.56 539 7.7 5.78 2.8 3 20.6 553 7.8 6.07 0 8 20.81 583 7.95 5.44 0.2 13 20.44 618 8.04 6.37 0

DESCRIPTION OF BODY SIZE DATA

Austrochiltonia sp. Dorsal Length Descriptive statistics show dorsal lengths of Austrochiltonia sp. ranged from 2.05 to 6.95 mm (mean 4.38  0.87 mm, n = 504) during the trial period. While there was some evidence that the distribution of dorsal lengths was not normally distributed (Shapiro Wilks statistic = 0.987, p = 0.0002), normality was not improved by either logarithmic or square root transformation. As removal of statistical outliers (six individuals with dorsal lengths greater less 6.6 mm) did not improve the shape of the histogram (kurtosis = -0.018, skewness = 0.395), they were not trimmed from the dataset.

Wet Weight As individual biomasses were extremely low, average wet weights (total amphipod weight divided by the number of individuals held in the cage) were used. Descriptive statistics show average wet weight of Austrochiltonia sp. individuals ranged from 0.48 to 3.1 mg (mean 1.16  0.31 mg, n = 39 pooled weights) during the trial period. Average wet weights were normally distributed (Shapiro Wilks statistic = 0.978, p = 0.645, kurtosis = -0.515, skewness = -1.180) with no extreme measurements.

Surface Area Descriptive statistics show surface area of Austrochiltonia sp. ranged from 0.51 to 5.89 mm2 (mean 2.36  0.93 mm2, n = 505) during the trial period. The data was transformed by the natural logarithm to achieve normality (Shapiro Wilks statistic = 0.996, p = 0.243, kurtosis = 0.005, skewness = -0.063) with only six individuals having surface areas greater than 5.3 mm2.

Intradependance There was little evidence of an intracage correlation between individuals for growth in terms of dorsal length ( = 0.207) and logn-transformed surface area ( = 0.226). No intracage correlation could be calculated for biomass as low individual weights were pooled to obtain the wet weight endpoint for each replicate cage. 253

Appendix 5

AUSRIVAS PREDICTIVE MODEL DATA USED TO SELECT EXPERIMENTAL SITES

AND TEST ORGANISMS

As detailed in Section 5.2, sites to which test organisms were transplanted in the experiments detailed in Chapter 5 were selected on the basis of the likelihood of their belonging to a particular AUSRIVAS (Australian River Assessment System) site group (Table A5.1).

Table A5.1: Experimental sites used to assess macroinvertebrate response to relocation, including probability of belonging to particular AUSRIVAS site groups.

AUSRIVAS model Site location Experimental Site Group Site Group Probability

Experiment 4 ACT Spring Riffle Cotter River 200 m downstream of Cotter Site 2 4 0.671 Dam Site 9 Murrumbidgee River at Tharwa Bridge 4 0.920 Experiment 5 ACT Autumn Edge Site 7 Gudgenby River at Naas Station 1 0.989 Site 1 Cotter River at Vanity’s Crossing 1 0.945 Site 5 Paddy’s River at Murray’s Corner 1 0.974 Site 9 Murrumbidgee River at Tharwa Bridge 1 0.999 Experiment 6 ACT Spring Edge Site 5 Paddy’s River at Murray’s Corner 4 0.972 Cotter River at campground, 100 m upstream Site 3 1 0.958 of confluence with Murrumbidgee River Murrumbidgee River downstream of Site 4 3 0.999 confluence with Cotter River at pump station Experiment 7 ACT Spring Edge Site 7 Gudgenby River at Naas Station 1 0.989 Gudgenby River downstream of Glendale Site 6 4 0.654 Crossing Murrumbidgee River at “Cuppacumbalong”, 1 Site 8 1 0.701 km upstream of Tharwa Bridge Murrumbidgee River upstream of Pt Hut Site 10 3 0.997 Crossing 254

Table A5.2: Potential test organisms (and their sensitivity to aquatic disturbance) considered for relocation trials, including probability of occurrence at particular AUSRIVAS sites.

Trial AUSRIVAS taxa probability Sensitivity Family Source Site Transplant Sites

Experiment 4 (ACT Spring Riffle) Site 2 Site 9 Caenidae 7 0.8812 0.9285 *Corydaliidae 4 0.3247 0.2345 Elmidae 7 0.9949 0.8939 Gripopterygidae 7 0.9857 0.7811 *Hydropsychidae 5 0.5734 0.5039 Leptophlebiidae 10 0.9403 0.7387 Experiment 5 (ACT Autumn Edge) Site 7 Site 1 Site 5 Site 9 *Atyidae 4 0.3110 0.3053 0.3092 0.3124 Baetidae 7 0.8754 0.8769 0.8759 0.8750 Caenidae 7 0.9999 1 1 0.9999 Leptoceridae 8 0.9990 0.9950 0.9977 0.9999 Leptophlebiidae 10 0.9382 0.9410 0.9391 0.9376 Experiment 6 (ACT Spring Edge) Site 5 Site 3 Site 4 Caenidae 7 0.9411 0.9340 0.9999 Gripopterygidae 7 0.9893 0.9850 0.9999 *Isostictidae 7 0.9122 0.8996 0.5747 *Lestidae 8 0.9483 0.9486 0.4291 *Leptoceridae 8 0.9591 0.9432 0.5104 *Parastacidae 7 0.0677 0.6748 0.5987 Experiment 7 (ACT Spring Edge) Site 7 Site 6 Site 8 Site 10 *Atyidae 4 0.5319 0.5080 0.4555 0.4289 *Baetidae 5 0.5341 0.7731 0.5624 0.5731 Caenidae 7 0.9302 0.9951 0.9719 0.9996 Gripopterygidae 7 0.8673 0.7652 0.9614 0.9998 *Leptoceridae 7 0.9996 0.9652 0.5991 0.4304 * indicates families employed for relocation trials. 255

RELOCATION OF MACROINVERTEBRATE TEST ORGANISMS BETWEEN

“SIMILAR” SITES

EXPERIMENT 4

EXPERIMENTAL FIELD CONDITIONS

Prevailing Conditions A total of 45 mm rain was recorded at Tuggeranong between October and December 1997, with the last substantial fall of 19.6 mm recorded nearly nine weeks prior to the commencement of this deployment trial (Figure A5.1). Both the Cotter and Murrumbidgee Rivers responded to localised rainfall with increased stream discharge, and prior to increased discharge in the Murrumbidgee River recorded between trial days 6 and 7, stream flows were very stable (Figure A5.1).

25 16 Rainfall Discharge in Cotter River below Cotter Dam 14 Discharge in Murrumbidgee River at Lobbs Hole 20 )

12 -1 s 3

10 15

10 6 Daily rainfall recorded (mm)

4 Daily stream discharge (m 5 2

0 0 1 6 11 16 21 26 31 5 10 15 20 25 30 5 10 15 20 25 30 October 1997 November 1997 December 1997 Figure A5.1: Daily rainfall recorded at Tuggeranong and stream discharge at the transplant sites employed for Experiment 4. Horizontal bar represents the trial period.

Field Site Measurements With the exception of electrical conductivity, which was substantially higher in the Murrumbidgee River (Table A5.3), source and deployment sites had very similar water quality characteristics including nutrient concentrations (Table A5.5). Early in the trial, pH and water temperature at both sites were unsettled, possibly in response to recent rains (Figure A5.1). 256

Table A5.3: Water quality characteristics measured at source and transplant sites employed for Experiment 4.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 2 (source) – Cotter River downstream of Cotter Dam 0 24.28 59.5 7.20 7.18 0 1 24.16 7.3 7.75 6.95 0 2 20.51 58.8 7.18 8.08 0 3 20.91 58.9 7.05 7.50 0 6 20.73 61.3 7.15 7.42 0 9 21.77 61.6 7.33 7.34 0 11 * * * * * Site 9 (transplant) – Murrumbidgee River at Tharwa 0 21.50 135 8.04 7.59 0 1 21.30 135 8.02 8.98 0 2 19.21 134 7.59 8.23 0 3 21.58 134 7.80 8.25 0 6 21.88 132 7.90 8.00 0 9 21.62 134 7.97 8.05 0.03 11 26.37 135 8.14 8.46 4.89 * indicates data was unreliable because of equipment failure.

Ion composition at both sites was dominated by bicarbonates, calcium and magnesium, with greater concentrations of sulphate detected in the Murrumbidgee River samples (Table A5.4). Concentrations were higher at the Murrumbidgee River site for all ions except sulphate, which was lower but more variable across the trial period downstream of the Cotter Dam (Table A5.4). Bicarbonate concentration was found to decrease with the increasing flow in the Murrumbidgee River after trial day 6, while the Cotter River concentrations during the trial may be a function of the low concentrations themselves (Table A5.4). Concentrations of all other major ions were relatively stable throughout the trial at the respective sites (Table A5.4). Total nitrogen concentrations were relatively stable across the trial duration and similar between deployment sites, while total phosphorus concentrations were below detectable limits in all samples collected (Table A5.5). 257

Table A5.4: Ion concentrations for source and transplant sites employed for Experiment 4.

 -1 Trial Na Mg K Ca SO4 Cl HCO3 (mgL -1 -1 -1 -1 -1 -1 Day (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) as CaCO3)

Site 2 (source) – Cotter River downstream of Cotter Dam 0 4.61 2.63 0.89 2.92 4.57 2.12 23.99 1 4.70 2.69 0.88 2.63 4.02 1.90 9.97 2 4.79 2.71 0.80 3.06 5.37 2.24 21.62 3 4.75 2.65 0.77 3.29 5.30 2.00 11.99 6 4.78 2.84 0.74 3.66 5.80 2.08 21.99 9 4.86 2.81 0.80 3.54 5.91 2.22 19.99 11 * * * * * * * Site 9 (transplant) – Murrumbidgee River at Tharwa 0 9.69 4.84 1.53 9.32 4.65 4.93 51.95 1 9.83 4.78 1.56 6.29 5.32 4.81 50.95 2 10.25 4.76 1.32 9.73 4.75 4.90 56.26 3 10.48 4.55 1.50 6.32 5.75 4.56 55.97 6 8.99 4.74 1.39 5.26 4.70 5.01 48.96 9 9.29 5.08 1.26 9.90 6.78 5.31 37.95 11 9.24 5.15 1.29 9.97

Table A5.5: Total nutrient concentrations for source and transplant sites employed for Experiment 4.

Trial Day Total Nitrogen (mgL-1) Total Phosphorus (mgL-1)

Site 2 (source) – Cotter River downstream of Cotter Dam 0 0.227

DESCRIPTION OF BODY SIZE DATA

Archicaulioides sp. Head Width Descriptive statistics show Archicaulioides sp. head widths ranged from 2.14 to 6.31 mm (mean 3.81  1.08 mm, n = 50) during the trial period. Although there was some evidence that data was non-normal (Shapiro Wilks statistic = 0.936, p = 0.027), normality was not improved by transformation. Distribution of head widths were relatively even across the size range (kurtosis = -1.35, skewness = 0.056) with eight statistical outliers whose head widths were less than 2.6 mm wide.

Wet Weight Descriptive statistics show Archicaulioides sp. wet weights ranged from 11.2 to 236.6 mg (mean 65.05  52.9 mg, n = 50) during the trial period. Data was transformed by a logarithmic (base 10) transformation to achieve normality (Shapiro Wilks statistic = 0.956, p = 0.126) although measurements were distributed more flatly (kurtosis = -1.090, skewness = -0.045) with 10 statistical outliers whose wet weights were less than 20 mg.

Intradependance There was a small correlation between individuals retrieved on the same trial day for growth in terms of head width ( = 0.136) and log10-transformed wet weight ( = 0.137).

Cheumatopsyche sp.6 Head Width Descriptive statistics show Cheumatopsyche sp.6 head widths ranged from 0.66 to 1.15 mm (mean 0.93  0.15 mm, n = 33) during the trial period. Although there was some evidence that data was non-normal (Shapiro Wilks statistic = 0.935, p = 0.04), normality was not improved by transformation. Data was bell-shaped (kurtosis = -1.511, skewness = -0.138) with no statistical outliers.

Wet Weight Descriptive statistics show Cheumatopsyche sp.6 wet weights ranged from 1.1 to 15.80 mg (mean 6.54  4.35 mg, n = 32) during the trial period. Data was transformed by a logarithmic (base 10) transformation to achieve normality (Shapiro Wilks statistic = 0.919, p = 0.057, kurtosis = -1.437, skewness = 0.056) with no statistical outliers.

Intradependance There was some correlation between individuals retrieved on the same trial day for growth in terms of head width ( = 0.0307) and log10-transpformed wet weight ( = 0.0987). 259

EXPERIMENT 5

EXPERIMENTAL FIELD CONDITIONS

Prevailing Conditions Rainfall at various stations in the Upper Murrumbidgee catchment indicate that the study area had received lower than the long term mean monthly rainfall for six consecutive months since September 1997 (Table A3.4). As a total of only 2.5 mm of rain was recorded in the Upper Naas valley in the six weeks prior to the trial (Figure A5.2), the Gudgenby River was found to exist only as a series of pools under these conditions. Streamflow at all sites during the trial was very low with no discharge recorded in the Gudgenby River until after the rainfall recorded on trial day 10 when 15.6 mm rain was recorded Figure A5.2). Following these local rainfalls, the Gudgenby River and the other larger streams employed for this trial reacted quickly (Figure A5.2).

18 Rainfall 2.5 Discharge in Gudgenby River at Naas 16 Discharge in Murrumbidgee River at Lobbs Hole Discharge in Cotter River at Vanity's Crossing 2

14 ) -1 s 3 12 1.5 10

8 1 6 Daily rainfall recorded (mm) 4 Daily stream discharge (m 0.5

0 0 1 6 11 16 21 26 3 8 13 18 23 28 2 7 12 17 22 27 February 1998 March 1998 April 1998 Figure A5.2: Daily rainfall recorded at Tuggeranong and the Upper Naas Valley and stream discharge at the transplant sites employed for Experiment 5. No stream discharge data was available for the deployment site on Paddy’s River. Horizontal bar represents the trial period. 260

Field Site Measurements As a consequence of low stream discharge in the region, water quality characteristics at the trial sites were relatively stable throughout the trial period, although the local rainfall during the trial resulted in a sharp increase in turbidity at all sites after trial day 9 (Table A5.6). The Paddy’s River site recorded the lowest water temperature, pH and oxygen saturation during the trial, while electrical conductivity in the Cotter River was considerably lower than at other deployment sites (Table A5.6).

Cation concentrations were remarkably similar at each site over the trial duration, with concentrations of all major cations markedly lower in the Cotter River at Vanity’s Crossing (Table A5.7). Sodium was the most dominant cation overall, however, its relative percentage at Naas was much higher where potassium accounted for less than 1% of the ion composition (Table A5.7). Relative composition of major anions at all sites was dominated by bicarbonate, although the relative proportion was greatest at Naas where chloride and sulphate ions contributed less than 3% to the total anion complement (Table A5.7). Sulphate concentrations tended to increase over the trial at each of the sites, while chloride concentrations were relatively stable at each site through most of the 12 day trial period (Table A5.7). In comparison, bicarbonate concentrations were variable between trial days and across the different sites, with bicarbonate concentrations highest in the Gudgenby River at Naas, and lowest in the Cotter River at Vanity’s Crossing (Table A5.7). The decrease in concentrations of the major ions, most obviously at the Naas site, on trial day 12 could be related to the considerable rain recorded within the preceding three days (Figure A5.2).

The water quality characteristics were not comparable between different habitats at the Naas site (Tables A5.6 and A5.7). Bicarbonates and major cations, in particular sodium, were considerably higher at the deployment site (Table A5.7), which was also found to have higher water temperatures, pH and dissolved oxygen levels and lower turbidity than the source site further downstream (Table A5.6).

Of the trial sites employed for this experiment, total nutrient concentrations (Table A5.8) were consistently lowest at Vanity’s Crossing, while the highest concentrations, in the Gudgenby River at Naas, may have been related to the extremely low flow conditions. Generally, total nitrogen concentrations tended to decrease with trial duration (Table A5.8), however the elevated nitrogen concentration recorded in the Murrumbidgee at Tharwa for trial day 12 was probably related to the increase in stream discharge in response to catchment rainfall (Figure A5.2). While total phosphorus concentration was considerably higher in the area downstream of the deployment site from where the shrimp were harvested, nitrogen concentration was approximately 50% higher at the more upstream deployment site (Table A5.8). 261

Table A5.6: Water quality characteristics measured at source and transplant sites employed for Experiment 5.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 7 (source) – Gudgenby River at Naas 0 14.75 186 7.48 4.4 5.2 Site 7 (transplant) – Gudgenby River at Naas 0 17.21 245 8.13 7.12 1.9 1 21.35 255 8.28 6.20 1.9 2 15.24 250 8.05 7.66 4.3 3 21.81 253 8.49 9.10 3.8 6 22.34 267 8.30 8.40 4.4 9 20.15 248 8.02 8.29 10.7 12 17.2 222 8.39 7.14 25.2 Site 1 (transplant) – Cotter River at Vanity’s Crossing 0 15.71 55.5 7.78 7.78 2.3 1 19.56 51.4 8.13 9.36 0.7 2 14.58 55.8 7.65 7.48 0.7 3 20.79 50.5 8.12 7.71 0 6 21.46 49.4 8.27 7.93 0 9 17.24 50.7 8.04 7.53 4.1 12 16.86 45.5 8.26 7.00 6 Site 5 (transplant) – Paddy’s River at Murray’s Corner 0 14.82 238 7.63 5.08 2.9 1 13.64 236 7.53 5.57 10.8 2 14.35 244 7.62 5.83 1.2 3 16.21 244 7.65 5.70 0.4 6 16.58 251 7.60 5.15 0.4 9 15.01 239 7.91 4.77 5.1 12 14.58 240 7.80 3.85 5.8 Site 9 (transplant) –Murrumbidgee River at Tharwa 0 18.48 190 8.34 7.81 0.7 1 19.25 188 7.97 8.05 3 2 16.59 191 8.28 8.71 1.3 3 19.78 193 8.21 8.42 1.2 6 20.87 195 8.15 6.54 1.3 9 20.16 188 8.24 7.13 7.6 12 16.88 183 7.83 6.38 5.3 262

Table A5.7: Ion concentrations for source and transplant sites employed for Experiment 5.

-1 Trial Na Mg K Ca SO4 Cl HCO3 (mgL -1 -1 -1 -1 -1 -1 Day (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) as CaCO3)

Site 7 (source) – Gudgenby River at Naas 0 14.276 4.778 1.763 9.193 1.76 11.47 79.98 Site 7 (transplant) – Gudgenby River at Naas 0 22.958 6.077 1.543 10.745 1.57 2.81 95.93 1 23.672 6.257 1.558 11.068 1.87 4.95 111.91 2 24.090 6.346 1.597 11.236 2.05 4.71 105.95 3 24.532 6.243 1.802 11.137 2.26 4.85 100.30 6 25.243 6.460 1.685 11.184 2.46 4.97 103.90 9 25.244 6.542 1.634 11.581 2.48 5.04 101.90 12 22.224 5.818 1.700 10.357 2.16 4.30 97.88 Site 1 (transplant) – Cotter River at Vanity’s Crossing 0 4.214 2.015 0.635 1.643 1.00 4.20 13.97 1 4.225 1.995 0.672 1.637 1.39 2.25 19.93 2 4.436 2.287 0.980 1.937 1.65 2.49 15.98 3 4.173 1.972 0.654 1.628 1.52 2.93 16.49 6 3.991 1.873 0.631 1.555 1.60 3.11 21.91 9 4.001 1.931 0.795 1.629 1.60 3.09 23.95 12 3.669 1.784 0.617 1.493 1.38 4.27 17.91 Site 5 (transplant) – Paddy’s River at Murray’s Corner 0 20.333 7.496 2.511 9.477 4.97 7.84 75.98 1 20.535 7.513 2.518 9.417 5.32 7.08 97.98 2 20.460 7.697 2.746 9.700 6.07 7.22 79.98 3 20.789 7.722 2.609 9.566 5.91 6.98 88.65 6 20.779 7.858 2.644 9.916 6.32 7.14 89.98 9 20.900 8.075 2.738 10.179 6.88 7.20 93.96 12 20.216 8.195 3.231 10.691 7.26 8.54 93.97 Site 9 (transplant)– Murrumbidgee River at Tharwa 0 12.354 6.939 1.791 8.283 2.31 3.20 75.89 1 12.525 7.063 1.797 8.319 5.93 6.03 65.95 2 12.545 7.050 1.751 8.393 6.67 5.62 77.91 3 12.521 7.010 1.787 8.225 6.79 5.09 65.08 6 12.710 7.054 1.794 8.428 6.98 4.92 69.93 9 13.139 7.245 1.790 8.578 7.56 5.05 67.91 12 13.281 6.907 2.028 8.400 7.70 5.02 60.97 263

Table A5.8: Total nutrient concentrations for source and transplant sites employed for Experiment 5.

Trial Day Total Nitrogen (mgL-1) Total Phosphorus (mgL-1) 

Site 7 (source) – Gudgenby River at Naas 0 0.604 0.082 Site 7 (transplant) – Gudgenby River at Naas 0 0.913 0.046 1 0.860 0.031 2 0.917 0.040 3 1.051 0.055 6 0.805 0.037 9 0.617 0.042 12 0.642 0.036 Site 1 (transplant) – Cotter River at Vanity’s Crossing 0 0.346 0.0228 1 0.296

DESCRIPTION OF BODY SIZE DATA

Paratya australiensis Carapace Length Descriptive statistics show Paratya australiensis carapace lengths ranged from 1.24 to 11.80 mm (mean 8.30  1.09 mm, n = 481) during the trial period. After logarithmic (base n) transformation, data was still not normal (Shapiro Wilks statistic = 0.985, p < 0.0001), however, transformed data was no longer right-skewed (skewness = 0.349) or as highly peaked (kurtosis = 0.173) and residuals were evenly distributed. Despite transformation, there were still 15 extreme measurements, 13 of which corresponded to carapaces longer than 11 mm.

Orbital Carapace Length Descriptive statistics show orbital carapace lengths of Paratya australiensis ranged from 2.4 to 6.7 mm (mean 4.39  0.67 mm, n = 580) during the trial period. After logarithmic (base n) transformation, data was still not normal (Shapiro Wilks statistic = 0.985, p < 0.0001), however, transformed data was now slightly skewed to the left (skewness = -0.168, kurtosis = 0.517) and residuals were evenly distributed. Despite transformation, there were still 12 extreme measurements; two of which corresponded to measurements greater than 6.3 mm and 10 individuals whose orbital carapace lengths were 3 mm or less.

Wet Weight Descriptive statistics show Paratya australiensis wet weights ranged from 11.5 to 120.3 mg (mean 43.94  18.12 mg, n = 578) during the trial period. After logarithmic (base n) transformation, data was still not normal (Shapiro Wilks statistic = 0.985, p < 0.0001), however, transformed data was no longer strongly right-skewed (skewness = 0.632) and was considerably less peaked (kurtosis = 0.375). Despite transformation, there were still 12 extreme measurements; 11 of which corresponded to wet weights in excess of 100 mg.

Intradependance There was some evidence of intracage correlation between individuals for growth in terms of logn-transformed carapace length ( = 0.0592), logn-transformed orbital carapace length (=0.0622) or logn-transformed wet weight ( = 0.0689). 265

RELOCATION OF MACROINVERTEBRATE TEST ORGANISMS BETWEEN

“DISSIMILAR” SITES

EXPERIMENT 6

EXPERIMENTAL FIELD CONDITIONS

Prevailing Conditions Daily rainfall records were not available for the Lower Cotter Valley, however rainfall at Tuggeranong, Gungahlin and Belconnen indicate that the region had received lower than the long term mean monthly rainfall since September 1997 (Table A3.4). However, approximately one third of the 25.8 mm recorded at Tuggeranong for January 1998 was recorded on trial day 12 of this experiment (Figure A5.3). Antecedent rainfall was also relatively low, with total daily rainfall greater than 5 mm of rain recorded on only two days in the preceding six week period, both in the fortnight prior to the commencement of the trial (Figure A5.3). Streamflow in the deployment sites was relatively constant until increased stream discharge along the Murrumbidgee River site several days after the cages had been deployed (Figure A5.3).

10 4 Rainfall 9 Discharge in Cotter River below Cotter Dam 3.5 Discharge in Murrumbidgee River at Lobbs Hole 8 )

3 -1 s 7 3

2.5 6

5 2

4 1.5 3 Daily rainfall recorded (mm)

1 Daily stream discharge (m 2

0.5 1

0 0 1 6 11 16 21 26 1 6 11 16 21 26 31 5 10 15 20 25 30 November 1997 December 1997 January 1998 Figure A5.3: Daily rainfall recorded at Tuggeranong and stream discharge at the transplant sites employed for Experiment 6. No stream discharge data was available for the source site on Paddy’s River. Horizontal bar represents the trial period. 266

Field Site Measurements Water quality was consistent throughout the deployment period and largely comparable between the trial sites (Table A5.9), and elevated turbidity recorded on trial day 0 at the source site on Paddy’s River (Table A5.9) was probably related to the stream disturbance during deployment of the experiment. Of the trial sites, the deployment site on the Murrumbidgee River below its confluence with the Cotter River recorded the highest pH values and oxygen saturation (Table A5.9).

Table A5.9: Water quality characteristics measured at source and transplant sites employed for Experiment 6.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 5 (source) – Paddy’s River at Murrays Corner 0 25.16 144 7.36 6.01 11.3 1 23.07 129 7.22 4.5 3.1 3 21.84 140 7.22 3.92 3.8 8 22.15 145 6.95 3.66 4.4 12 21.5 155 7.23 4.92 3.7 Site 3 (transplant) – Cotter River upstream of confluence with Murrumbidgee River 0 26.11 127 7.21 4.89 4.5 1 25.35 149 7.35 5.36 3.9 3 23.41 168 7.23 4.48 3.8 8 25.42 173 7.14 5.14 2.5 12 22.52 160 7.22 6.02 3.5 Site 4 (transplant) – Murrumbidgee River downstream of confluence with Cotter River 0 26.9 134 7.67 5.47 2.6 1 26.29 138 7.77 5.27 3.4 3 25.24 162 7.96 6.55 4.1 8 26.17 173 7.93 5.75 3.6 12 24.02 177 8 5.48 3

Relative composition of major ions at all sites was dominated by bicarbonate and calcium ions (Table A5.10), although sodium concentrations in the Paddy’s River samples were markedly higher that at the other two sites (Table A5.10). Major ion concentrations downstream of the Cotter River confluence increased gradually as the trial progressed, as did the proportion of unidentified minor ions (Table A5.10), which was probably attributable to the increased flows in the latter part of the trial in the Murrumbidgee River (Figure A5.3). While ion composition at Murray’s Corner was more consistent across the trial (Table A5.10), the Cotter River site was found to have lower concentrations of all major ions as well as both phosphorus and nitrogen (Table A5.11) after trial day 8. 267

Table A5.10: Ion concentrations for source and transplant sites employed for Experiment 6.

-1 Trial Na Mg K Ca SO4 Cl HCO3 (mgL -1 -1 -1 -1 -1 -1 Day (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) as CaCO3)

Site 5 (source) – Paddy’s River at Murray’s Corner 0 15.56 4.10 1.70 7.67 8.95 6.41 52.00 1 14.81 3.39 1.74 7.06 4.45 4.57 50.00 3 15.07 3.91 1.65 8.03 12.1 5.31 54.00 8 15.16 4.30 1.98 8.79 5.20 4.44 48.00 12 16.10 4.68 1.73 8.82 7.30 6.10 64.00 Site 3 (transplant) – Cotter River upstream of confluence with Murrumbidgee River 0 12.14 4.32 1.63 6.96 6.35 3.85 46.0 1 14.33 4.79 1.98 8.36 6.14 5.79 46.0 3 8.40 3.21 1.10 4.30 6.02 3.15 32.0 8 6.42 2.82 0.90 3.49 6.47 2.36 30.0 12 5.91 2.98 0.87 3.18 4.6 2.30 34.0 Site 4 (transplant) – Murrumbidgee River downstream of confluence with Cotter River 0 10.59 4.95 1.59 8.00 6.11 4.89 43.98 1 11.19 5.22 1.52 8.59 3.40 5.19 43.98 3 12.72 6.18 1.73 9.76 6.28 7.71 49.96 8 12.78 6.54 1.81 11.71 5.00 8.76 61.97 12 13.10 6.74 1.77 12.09 3.90 8.30 73.96

Table A5.11: Total nutrient concentrations for source and transplant sites employed for Experiment 6.

Trial Day Total Nitrogen (mgL-1) Total Phosphorus (mgL-1)

Site 5 (source) – Paddy’s River at Murray’s Corner 0 0.44 0.02 1 0.49 0.03 3 0.40 0.02 8 0.48 0.02 12 0.40

Table A5.11 (continued): Total nutrient concentrations for source and transplant sites employed for Experiment 6.

Trial Day Total Nitrogen (mgL-1) Total Phosphorus (mgL-1)

Site 4 (transplant) – Murrumbidgee River downstream of confluence with Cotter River 0 0.37

DESCRIPTION OF BODY SIZE DATA

Austrolestes cingulatum Head Width Descriptive statistics show Austrolestes cingulatum head widths ranged from 1 to 4.4 mm (mean 2.47  0.61 mm, n = 51) during the trial period. Although there was some evidence that data was non-normal (Shapiro Wilks statistic = 0.911, p = 0.009, skewness = 0.503, kurtosis = -0.427), normality was not improved by transformation. After removal of the highest measurement (4.4 mm), which was nearly 1 mm wider than the remaining head widths, treatment effects were examined on untransformed data.

Wet Weight Descriptive statistics show Austrolestes cingulatum wet weights ranged from 7.1 to 105.3 mg (mean 45.75  28.3 mg, n = 51) during the trial period. After logarithmic (base n) transformation, data was normally distributed (Shapiro Wilks statistic = 0.973, p = 0.531) with no statistical outliers, and was only slightly skewed to the right (skewness = 0.328, kurtosis = -0.087).

Intradependance Despite being caged individually, there was high intradependance between head widths ( = 0.00014) and logn-transformed wet weight ( < 0.00001) of Austrolestes cingulatum individuals retrieved from the same site and trial day combination.

Rhadinosticta simplex Head Width Descriptive statistics show Rhadinosticta simplex head widths ranged from 0.98 to 2.38 mm (mean 1.75  0.35 mm, n = 79) during the trial period. While there was some evidence that measurements were not normally distributed (Shapiro Wilks statistic = 0.918, p = 0.0013), residuals were normally distributed (Shapiro Wilks statistic = 0.975, p = 0.313) and the shape of the distribution (skewness = 0.004, kurtosis = -0.738) was not improved with logarithmic or square root transformation. 269

Wet Weight

Descriptive statistics show Rhadinosticta simplex wet weights ranged from 2 to 25.8 mg (mean 9.25  4.95 mg, n = 79) during the trial period. After logarithmic (base n) transformation, measurements were normally distributed (Shapiro Wilks statistic = 0.984, p = 0.701) with a slightly flattened distribution (skewness = 0.124, kurtosis = -0.613) and no statistical outliers.

Intradependance There was a small inter-cage correlation between individuals from the same site and trial day combination for growth in terms of head width ( = 0.12), while logn-transformed wet weights were more highly correlated ( = 0.009) despite being caged individually.

Triplectides australicus ciuskus Head Width Descriptive statistics show Triplectides australicus ciuskus head widths ranged from 0.29 to 1.46 mm (mean 0.92  0.26 mm, n = 166) during the trial period. This measurement of growth was not normally distributed (Shapiro Wilks statistic = 0.946, p < 0.0001), but because standard transformations resulted in highly heterogenous residual variability, untransformed data was used. The data was slightly flattened (kurtosis = -1.126, skewness = -0.21), however, there were no statistical outliers.

Intradependance There was a high intracage correlation between individuals for growth in terms of head width ( = 0.00093).

Cherax destructor Carapace Length Descriptive statistics show Cherax destructor carapaces ranged from 4.51 to 10.78 mm (mean 8.54  1.58 mm, n = 69) in length during the trial period. This measurement of growth was not normally distributed (Shapiro Wilks statistic = 0.941, p = 0.0028), but because standard transformations resulted in highly heterogenous residual variability, untransformed data was used. The data was slightly skewed to the left (skewness = -0.632, kurtosis = -0.165), however, there were no statistical outliers and the residual variability was normally distributed (Shapiro Wilks statistic = 0.978, p = 0.274).

Wet Weight Descriptive statistics show Cherax destructor wet weights ranged 12.9 to 268.5 mg (mean 132.29  67.5 mg, n = 69) during the trial period. This measurement of growth was normally distributed (Shapiro Wilks statistic = 0.9666, p = 0.072), and data distribution was slightly flat but symmetrically distributed (skewness = 0.141, kurtosis = -1.016) and there were no statistical outliers.

Intradependance There was some evidence of an intracage correlation between individuals for growth in terms of carapace length ( = 0.013) and wet weight ( = 0.014). 270

EXPERIMENT 7

EXPERIMENTAL FIELD CONDITIONS

Prevailing Conditions The study area was extremely dry during the experimental period with total monthly rainfall at Upper Naas Valley only exceeding long term average rainfall in June 1997 (Table A3.4). Elevated stream discharge in both the Gudgenby and the Murrumbidgee Rivers mid-trial corresponded with considerable daily rainfalls on trial days 3 and 4 (Figure A5.4). Prior to these rains, the antecedent conditions were dry with only two daily rainfall records in the preceding six weeks greater than 5 mm even in the Naas Valley (Figure A5.4). As previously mentioned, stream discharge increased in both the Gudgenby and Murrumbidgee Rivers mid- way through the deployment trial (Figure A5.4).

40 Rainfall in Upper Naas Valley 3.5 Rainfall in Tuggeranong

35 Discharge in Murrumbidgee River at Lobbs Hole 3 Discharge in Gudgenby River at Naas )

30 -1 s

2.5 3

25 2 20 1.5 15

1 Daily rainfall recorded (mm)

10 Daily stream discharge (m

5 0.5

0 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 December 1997 January 1998 February 1998 Figure A5.4: Daily rainfall recorded at Tuggeranong and in the Upper Naas Valley and stream discharge at the transplant sites employed for Experiment 7. Horizontal bar represents the trial period. 271

Field Site Measurements Generally, the sites employed in this trial had comparable water quality, with only electrical conductivity consistently lower in the smaller Gudgenby River (Table A5.12). Similarly, lower pH and levels of dissolved oxygen were recorded for trial day 3 at all trial sites (Table A5.12). The less well oxygenated water in the Murrumbidgee River at Cuppacumbalong throughout the trial (Table A5.12) can probably be attributed to a persistent sand slug from upstream extraction activities resulting in the braided nature of the river channel in this reach.

Table A5.12: Water quality characteristics measured at source and transplant sites employed for Experiment 7.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 7 (source) – Gudgenby River at Naas 0 26.15 132 8.4 6.54 2.1 1 26.17 135 7.99 6.05 4.3 3 20.73 142 7.52 4.71 5.9 8 22.93 114 8.02 6.99 4.3 12 24.71 121 7.79 5.73 4.7 Site 6 (transplant) – Gudgenby River at Glendale Crossing 0 22.39 111 7.94 7.81 4.4 1 21.63 110 7.73 5.96 4.18 3 19.4 115 7.4 6.14 2.4 8 18.67 96 7.77 7.39 4.5 12 21.33 105 7.69 5.98 3.3 Site 8 (transplant) – Murrumbidgee River at Cuppacumbalong 0 21.82 * 7.53 * * 1 22.95 154 7.37 5.03 5.7 3 19.9 174 7.16 2.93 3.8 8 21.69 152 7.55 4.72 5.6 12 23.4 151 7.02 3.4 5.4 Site 10 (transplant) – Murrumbidgee River upstream of Point Hut Crossing 0 * * * * * 1 27.36 151 7.92 5.71 4.7 3 25.03 150 7.75 5.64 4.2 8 25.26 152 8.09 6.7 5.7 12 28.01 151 8.02 6.63 5.7 * indicates data was unreliable because of equipment failure. 272

Ion concentrations were consistently lower at the most upstream site on the Gudgenby River, however the relative ion composition at each field site was relatively stable throughout the trial period (Table A5.13). There was, however, a notable decrease in ion concentrations, particularly sodium, calcium and bicarbonate, between trial days 3 and 8 in the Gudgenby River (Table A5.13) possibly in response to local rainfall and increased stream discharge (Figure A5.4). While all samples were dominated by bicarbonate ions, Murrumbidgee River sites had notably more dissolved magnesium and sulphate. Nutrient concentrations were consistent across trial days at each of the field sites (Table A5.14), with slightly higher total nitrogen concentrations at the source site.

Table A5.13: Ion concentrations for source and transplant sites employed for Experiment 7.

-1 Trial Na Mg K Ca SO4 Cl HCO3 (mgL -1 -1 -1 -1 -1 -1 Day (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) as CaCO3)

Site 7 (source) – Gudgenby River at Naas 0 12.29 3.54 1.12 6.36 0.99 1.93 65.88 1 12.04 3.60 1.18 6.92 0.86 1.66 59.95 3 12.76 3.95 1.23 7.55 0.17 3.89 69.98 8 10.69 3.07 1.26 5.66 1.10 4.20 55.95 12 10.46 3.28 1.20 6.15 1.40 1.80 59.97 Site 6 (transplant) – Gudgenby River at Glendale Crossing 0 9.71 2.77 0.98 6.24 0.27 5.09 43.96 1 9.62 2.81 1.14 6.42 0.59 2.05 43.97 3 9.83 2.94 1.09 6.50 0.12 10.63 55.99 8 8.42 2.28 1.22 5.38 0.47 4.45 39.97 12 8.97 2.57 1.10 5.84 0.75 2.15 48.98 Site 8 (transplant) – Murrumbidgee River at Cuppacumbalong 0 9.38 5.85 1.52 7.07 3.51 2.38 61.98 1 9.59 6.07 1.59 7.15 3.35 1.70 59.99 3 10.29 7.31 1.91 8.20 2.00 5.21 59.99 8 9.79 5.43 1.72 5.65 3.61 1.80 57.98 12 10.01 5.78 1.86 6.79 3.85 1.46 61.99 Site 10 (transplant) – Murrumbidgee River upstream of Point Hut Crossing 0 10.13 5.88 1.54 7.04 3.87 5.42 54.37 1 10.05 5.81 1.58 7.03 1.11 1.74 49.96 3 10.09 5.80 1.50 6.10 3.83 7.69 51.97 8 10.13 5.84 1.47 5.95 4.27 2.80 59.94 12 10.35 5.68 1.48 6.59 4.27 1.71 61.95 273

Table A5.14: Total nutrient concentrations for source and transplant sites employed for Experiment 7.

Trial Day Total Nitrogen (mgL-1) Total Phosphorus (mgL-1)

Site 7 (source) – Gudgenby River at Naas 0 0.44 0.02 1 0.47 0.03 3 0.40 0.02 8 0.49

DESCRIPTION OF BODY SIZE DATA

Notalina sp. Head Width Descriptive statistics show Notalina sp. head widths ranged from 0.32 to 0.9 mm (mean 0.54  0.13 mm, n = 147) during the trial period. While data was not normally distributed (Shapiro Wilks statistic = 0.953, p < 0.0001, skewness = 0.714, kurtosis = 0.252), standard transformations did not markedly improve normality or residual variability, and treatment effects were examined on untransformed data.

Intradependance There was high intracage correlation of head widths between individuals within a deployment cage ( = < 0.00001). 274

Cloeon sp. Head Width Descriptive statistics show Cloeon sp. head widths ranged from 0.68 to 1.24 mm (mean 1.01  0.11 mm, n = 87) during the trial period, with more than 65% of measurements at or above the median head width of 1 mm. There was some evidence that data was non-normal (Shapiro Wilks statistic = 0.962, p = 0.012) and the data frequency was skewed to the left (skewness = -0.66, kurtosis = 0.406). Standard transformation did not improve normality and resulted in highly heterogeneous residual variability, thus treatment effects were examined on untransformed data.

Intradependance There was virtually no intracage correlation between individual head widths for individuals caged together ( = 0.3405).

Paratya australiensis Carapace Length Descriptive statistics show Paratya australiensis carapace lengths ranged from 2.67 to 10 mm (mean 6.01  1.28 mm, n = 303) during the trial period. After logarithmic (base n) transformation, there was still some evidence of non-normality (Shapiro Wilks statistic = 0.980, p = 0.0003), however, transformed data was no longer strongly right-skewed (skewness = 0.110, kurtosis = 0.327), residuals distribution was homogenous and there were no statistical outliers.

Orbital Carapace Length Descriptive statistics show orbital carapace lengths for Paratya australiensis ranged from 0.83 to 6.67 mm (mean 3.22  0.75 mm, n = 334) during the trial period. After logarithmic (base n) transformation, there was still some evidence of non-normality (Shapiro Wilks statistic = 0.984, p = 0.0009), however, transformed data was less peaked (kurtosis = 1.486) and slightly skewed to the left (skewness = -0.213).

Wet Weight Descriptive statistics show Paratya australiensis wet weights ranged from 10.4 to 134.8 mg (mean 35.68  22.9 mg, n = 334) during the trial period. After logarithmic (base n) transformation, there was still some evidence of non-normality (Shapiro Wilks statistic = 0.944, p < 0.0001), however, transformed data was less peaked (kurtosis = 0.627) and slightly skewed to the left (skewness = -0.418) with only four individuals weighing in excess of 120 mg.

Intradependance There was virtually no intracage correlation between growth of individuals caged together in terms of logn-transformed measurements of carapace length ( = 0.3404), orbital carapace length ( = 0.3316) or wet weight ( = 0.3317). 275

Appendix 6

CASE STUDY 1 – ACID MINE DRAINAGE

EXPERIMENTAL FIELD CONDITIONS

Prevailing Rainfall Conditions Daily rainfall records were not available for the Upper Molonglo Valley. Local rainfall conditions were approximated using data available for Upper Naas to the west of Captains Flat.

Experiment 8 – Spring Total rainfall between October and December 1996 in the Upper Naas Valley (Table A3.4) was not markedly lower than the long term monthly averages for the region (Table A3.4). A total of 13.2 mm of rain was recorded at Upper Naas during the trial, with no daily rainfalls greater than 5 mm during this time (Figure A6.1). However, stream discharge in the Molonglo River at the field site increased quickly in response to total daily rainfall of 12 mm immediately prior to the commencement of the trial and remained elevated above baseflow level until trial day 9 (Figure A6.1).

60 Rainfall in Upper Naas Valley 1.8 Discharge in Molonglo River downstream of Copper Creek 1.6 50

1.4 ) -1 s 3 40 1.2

1 30 0.8

20 0.6 Daily rainfall recorded (mm) 0.4 Daily stream discharge (m 10 0.2

0 0 1 6 11 16 21 26 31 5 10 15 20 25 30 5 10 15 20 25 30 October 1996 November 1996 December 1996 Figure A6.1: Daily rainfall recorded in the Upper Naas Valley and stream discharge recorded in the Molonglo River at the impact site employed for Case Study 1 (Experiment 8 – Spring). Horizontal bar represents the trial period. 276

Experiment 9 – Summer Antecedent rainfall at Upper Naas was highly variable in December 1996 with more than 60% of the total monthly rainfall recorded on one particular day (Figure A6.2). Despite this, over 140 mm of rain was recorded in the nine weeks preceding Experiment 9, during which the region experienced 17 raindays, with rainfall in excess of 5 mm recorded on six of those days (Figure A6.2). As a result, streamflow in the Molonglo River was variable, including a rapid elevation in discharge immediately before the commencement of Experiment 9 in response to four consecutive raindays (Figure A6.2). During the experiment however, stream discharge remained fairly constant at around 0.03 MLd-1, and only rose slightly in response to approximately 15 mm of rain on trial day 8 (Figure A6.2).

60 1.6 Rainfall in Upper Naas Valley

Discharge in Molonglo River downstream of Copper Creek 1.4 50 )

1.2 -1 s 3 40 1

30 0.8

0.6 20 Daily rainfall recorded (mm)

0.4 Daily stream discharge (m

10 0.2

0 0 1 6 11 16 21 26 31 5 10 15 20 25 30 4 9 14 19 24 December 1996 January 1997 February 1997 Figure A6.2: Daily rainfall recorded in the Upper Naas Valley and stream discharge recorded in the Molonglo River at the impact site employed for Case Study 1 (Experiment 9 – Summer). Horizontal bar represents the trial period. 277

Experiment 10 – Autumn Virtually no rain was recorded at Upper Naas in the nine weeks preceding Experiment 10 (Figure A6.3), with less than 2 mm of rain recorded anywhere in the study region during April 1997 (Table A3.5). Despite some local rain on trial day 5, there was no increase in stream discharge at the Molonglo River field site until immediately after Experiment 10 ended when a few days of welcome rain were received (Figure A6.3).

60 0.12 Rainfall in Upper Naas Valley Discharge in Molonglo River downstream of Copper Creek 50 0.1 ) -1 s 3 40 0.08

30 0.06

20 0.04 Daily rainfall recorded (mm) Daily stream discharge (m 10 0.02

0 0 1 6 11 16 21 26 31 5 10 15 20 25 30 5 10 15 20 25 30 March 1997 April 1997 May 1997

Figure A6.3: Daily rainfall recorded in the Upper Naas Valley and stream discharge recorded in the Molonglo River at the impact site employed for Case Study 1 (Experiment 10 – Autumn). Horizontal bar represents the trial period. 278

Experiment 11 – Winter Like much of the study region, rainfall at Upper Naas during July and August 1997 was considerably less than long term mean monthly rainfall statistics (Table A3.4 and A3.5). In the two weeks preceding Experiment 11, a total of 18.9 mm was recorded at Upper Naas (Figure A6.4), during which time streamflow in the Molonglo River was relatively stable at around 0.1 MLd-1 (Figure A6.4). Unfortunately ACTEW ceased stream discharge monitoring at this Molonglo River site a week before Experiment 11.

80 Rainfall in Upper Naas Valley 10

Discharge in Molonglo River downstream of Copper Creek 9 70 8 )

60 -1 s 7 3 50 6

40 5

4 30 3 Daily rainfall recorded (mm)

20 Daily stream discharge (m 2 10 1

0 0 1 6 11 16 21 26 1 6 11 16 21 26 31 5 10 15 20 25 30 June 1997 July 1997 August 1997 Figure A6.4: Daily rainfall recorded in the Upper Naas Valley and stream discharge recorded in the Molonglo River at the impact site employed for Case Study 1 (Experiment 11 – Winter). Horizontal bar represents the trial period.

Field Site Measurements Within a given season, differences in water quality measurements were relatively uniform at sites and across trial days (Table A6.1). Across all seasons, water temperature was consistently a few degrees lower at the upstream control site while both pH and dissolved oxygen were slightly higher, and both turbidity and electrical conductivity were much more variable across trial days and were considerably higher, than at the upstream control site (Table A6.1). At the control site, electrical conductivity was lowest in autumn, and the concentrations of dissolved oxygen highest in the winter trial. 279

Table A6.1: Water quality characteristics measured at control and impact sites in the Molonglo River employed for Case Study 1.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Experiment 9 – Spring Trial

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 14.84 78.4 7.65 7.65 5 1 12.77 76.8 7.21 8.47 13.8 2 11.96 66.1 7.15 8.68 14.3 3 14.53 60.2 7.42 8.8 7.6 6 8.88 63.8 7.43 10.12 12.9 12 14.21 51.1 7.15 8.89 14.9 Site 15 (impact) – Molonglo River downstream of confluence with Copper Creek 0 16.78 542 5.11 7.72 25 1 12.95 328 6.59 8.81 46 2 14.2 244 6.56 8.85 39 3 16.2 152 6.68 8.66 46.2 6 13.33 134.8 7.07 9.38 62.2 12 15.5 530 6.05 8.58 21.7 Experiment 10 – Summer Trial

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 18.87 62.1 7.14 6.44 14.2 1 17.7 61.4 7.05 7.1 16.2 2 16.07 63.1 7.16 7.16 4.67 3 16.51 63.3 6.84 7.4 14.2 6 18.65 63.9 6.87 6.86 14 12 16.62 69.2 7.15 6.66 15.6 Site 15 (impact) – Molonglo River downstream of confluence with Copper Creek 0 22.93 482 6.79 6.27 22.9 1 20.66 482 6.51 6.7 11.4 2 18.44 483 6.45 7.55 14.4 3 18.3 477 6.56 7.9 14.6 6 19.75 479 6.48 7.2 16.5 12 20.36 457 6.44 7.2 26 280

Table A6.1 (continued): Water quality characteristics measured at control and impact sites in the Molonglo River employed for Case Study 1.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Experiment 11 – Autumn Trial

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 11.17 48.6 7.34 7.02 9.2 1 * * * * * 2 11.67 48.5 7.32 8.12 79.7 3 6.81 48.2 7.3 9.82 85.1 6 8.6 49 7.19 8.91 80.8 12 8.19 54.3 6.32 8.75 79.4 Site 15 (impact) – Molonglo River downstream of confluence with Copper Creek 0 14 344 6.64 7.16 16.7 1 * * * * * 2 13.46 360 6.75 7.55 77.7 3 7.4 346 6.75 8.62 78.5 6 11.37 343 6.73 8.1 79.4 12 10.68 758 3.64 7.81 74.6 Experiment 12 – Winter Trial

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 6.05 65.1 7.61 11.83 9.3 1 5.86 63.3 7.37 11.91 10.2 2 5.15 59.8 7.43 11.6 8.5 3 2.18 61.1 6.91 12.82 2.2 6 4.5 66.6 6.23 12.71 3.5 12 6.65 60.8 7.14 11.91 66.6 Site 15 (impact) – Molonglo River downstream of confluence with Copper Creek 0 8.92 690 6.24 9.72 21.9 1 8.84 706 6.33 9.95 6.1 2 8.48 719 6.3 10.08 17.3 3 4.58 728 6.49 11.23 11.5 6 9.15 746 6.22 10.62 21.4 12 11.23 777 6.03 9.86 22.5 * indicates data was unreliable because of equipment failure.

Concentrations of major ions were obtained from water samples collected in autumn and winter trials only (Table A6.2). In all samples, the concentrations of most ions were considerably higher at the downstream impact site, especially the concentration of sulphates. The exception was bicarbonate concentrations which were slightly higher the upstream control site (Table A6.2). Ion concentrations at the control site were mostly lower in autumn 281 than winter, especially sulphates which were mostly below detectable limits in the former trial. Again the exception was bicarbonates, which were between three and five times higher in the autumn trial depending on the site (Table A6.2).

Table A6.2: Ion concentrations for control and impact sites in the Molonglo River employed for Case Study 1.

 -1 Trial Na Mg K Ca SO4 Cl HCO3 (mgL -1 -1 -1 -1 -1 -1 Day (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) (mgL ) as CaCO3)

Experiment 11 – Autumn Trial

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 3.962 2.291 0.304 1.039 2.022 3.497 19.99 1 4.026 2.292 0.313 1.075

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 5.45 3.048 0.392 2.294 5.98 4.57 5.45 1 5.346 2.938 0.374 2.176 6.82 4.33 5.35 2 5.054 2.806 0.359 1.942 4.37 4.34 5.05 3 5.104 3.012 0.348 1.994 4.07 4.44 5.10 6 5.275 3.188 0.353 2.306 4.92 4.47 5.28 12 5.351 2.886 0.509 1.953 4.58 4.71 5.35 Site 15 (impact) – Molonglo River downstream of confluence with Copper Creek 0 14.777 33.18 1.723 35.942 3253.7 87.79 4 1 14.59 33.119 1.538 37.038 3389.8 86.86 4 2 14.74 34.43 1.605 38.289 3474.2 87.59 4 3 15.17 34.96 1.733 38.68 3452.2 92.14 4 6 15.385 35.88 1.7259 40.139 3669.0 90.04 4 12 15.25 36.469 1.668 40.756 3713.1 88.35 2

 2- -1 Method detection limit (DL) for SO4 = 1.56mgL . 282

Trace metal concentrations varied considerably in these trials, even between trials days collected from the same site in a given season (Table A6.3). Lead, arsenic, cadmium and copper concentrations were consistently low at upstream sites in all seasons, with the latter two metals below detectable limits at this site in the spring and summer trials (Table A6.3). Iron was the most dominant of the trace elements analysed at the upstream control site, while at the downstream impact site, zinc was the most concentrated metal (Table A6.3).

Table A6.3: Dissolved metal concentrations in water samples collected at control and impact sites in the Molonglo River employed for Case Study 1.

Trial Fe 54 Cu 63  Zn 64 As 75 Cd 114  Pb 208 Day (μgL-1) (μgL-1) (μgL-1) (μgL-1) (μgL-1) (μgL-1)

Experiment 8 – Spring Trial

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 1146.57

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 1370.46

Table A6.3 (continued): Dissolved metal concentrations in water samples collected at control and impact sites in the Molonglo River employed for Case Study 1.

Trial Fe 54 Cu 63  Zn 64 As 75 Cd 114  Pb 208 Day (μgL-1) (μgL-1) (μgL-1) (μgL-1) (μgL-1) (μgL-1)

Experiment 10 – Autumn Trial

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 695.67 1.618 11.678 0.307

Site 14 (control) – Molonglo River upstream of Captains Flat reservoir 0 336.37 1.618 6.823 0.266

DESCRIPTION OF BODY SIZE AND BODY BURDEN DATA

Paratya australiensis Carapace Length Descriptive statistics show Paratya australiensis carapace lengths ranged from 3.88 to 9.42 mm (mean 6.15  0.90 mm, n = 211) during the trial period. Data was transformed by a logarithmic (base 10) transformation to more closely approximate normality (Shapiro Wilks statistic = 0.980, p = 0.0042, kurtosis = 0.659, skewness = 0.411). None of the statistical outliers (i.e. a single individual whose carapace was less than 4 mm in length plus two individuals with carapaces greater than 9 mm in length) were removed from the dataset as this did not improve the normality of the dataset at all.

Orbital Carapace Length Descriptive statistics show Paratya australiensis orbital carapace lengths ranged from 1.33 to 5.83 mm (mean 3.38  0.58 mm, n = 268) during the trial period. Data was transformed by a logarithmic (base 10) transformation to more closely approximate normality (Shapiro Wilks statistic = 0.955, p < 0.0001) although the data was still highly peaked (kurtosis = 3.056, skewness = -0.128). There were 10 statistically outlying orbital carapace measurements (i.e. four measuring less than 2.2 mm and six measuring in excess of 4.8 mm), which were not removed from the dataset as such trimming did not improve its normality.

Wet Weight Descriptive statistics show Paratya australiensis wet weights ranged from 2.2 to 143 mg (mean 31.33  19.91 mg, n = 262) during the trial period. Data was transformed by a logarithmic (base 10) transformation to improve normality (Shapiro Wilks statistic = 0.957, p <0.0001) and while this significantly reduced the right skew (skewness = -0.666), the transformed data was still quite peaked (kurtosis = 1.394). There were 16 individuals whose wet weights were less than 7.5 mg plus five individuals whose wet weights were greater than 100 mg, however, they were not removed from the dataset as this did not improve its normality.

Freeze Dried Weight Descriptive statistics show Paratya australiensis freeze dried weights ranged from 1.5 to 25.9 mg (mean 6.59  3.70 mg, n = 267) during the trial period. Data was transformed by a logarithmic (base 10) transformation to improve normality (Shapiro Wilks statistic = 0.970, p < 0.0001, skewness = 0.674, kurtosis = 0.888) There were 10 statistical outliers (i.e. individuals whose freeze dried weights were less than 1.6 mg or greater than 17 mg), however data normality was not improved by their removal and they remained part of the dataset.

Tissue Metal Concentrations Descriptive statistics showed that tissue concentrations of iron, copper, zinc, cadmium and lead in Paratya australiensis were all strongly right skewed (Table A6.4). As the maximum value was obtained from an unreplicated sample, and could possibly have arisen from contamination during collection or analysis, these values were excluded from statistical 285 analysis. Normality of tissue concentration data was improved by a logarithmic (base 10) transformation (Table A6.4).

Intradependance There was some evidence of an intracage correlation between individuals retrieved on the same trial day for growth in terms of log10-transformed carapace length ( = 0.052), log10-transformed orbital carapace length ( = 0.071) and log10-transformed freeze dried weight ( = 0.078), while there was less evidence that individuals caged together were correlated in terms of log10-transformed wet weight ( = 0.187). No intracage correlation could be calculated for biomass as low individual weights were pooled to obtain the necessary freeze dried weight required for trace metal analysis (Section 6.2.2).

Table A6.4: Summary of datasets for metal concentrations in Paratya australiensis and descriptive statistics for data before and after transformation.

Fe 54 Cu 63 Zn 64 Cd 114  Pb 208 

Minimum Concentration 81.45 37.39 37.06

Log10-transformed data Shapiro Wilks statistic 0.884 0.874 0.742 0.669 0.535 Pr

Austrolestes cingulatum Head Width Descriptive statistics show Austrolestes cingulatum head widths ranged from 2.42 to 6.98 mm (mean 3.61  0.60 mm, n = 50) during the trial period. Normality of the distribution was affected by a single large individual. After its removal, variation in the spread of data was almost halved (mean 3.55  0.357 mm, n = 49) and data was more normally distributed (Shapiro Wilks statistic = 0.928, p = 0.0051), with the slight positive skew measured (skewness = 0.0486, kurtosis = 1.335) attributable to the seven individuals whose heads widths between 4.06 and 4.36 mm, thereby qualifying as statistical outliers.

Freeze Dried Weight Descriptive statistics show Austrolestes cingulatum freeze dried weights ranged from 3.4 to 12.2 mg (mean 7.13  2.41 mg, n = 50) during the trial period. Data was transformed by a logarithmic (base 10) transformation to achieve normality (Shapiro Wilks statistic = 0.965, p = 0.1496) with a relatively even but flat distribution (skewness = 0.119, kurtosis = -0.980) with no statistical outliers.

Tissue Metal Concentrations Descriptive statistics showed that tissue concentrations of iron, copper, zinc, cadmium and lead in Austrolestes cingulatum were all strongly right skewed and required logarithmic (base 10) transformation to improve the overall shape of the distributions (Table 6.5). As the maximum value was obtained from an unreplicated sample and could possibly have arisen from contamination during collection or analysis, these values was excluded from statistical analysis. Normality of tissue concentration data was improved by a logarithmic (base 10) transformation (Table A6.5)

Intradependance There was some evidence of correlation between individuals retrieved on the same deployment site and trial day for growth in terms of log10-transformed freeze dried weight ( = 0.017) but very little intracage correlation in terms of head widths ( = 0.131). No intracage correlation could be calculated for tissue metal concentrations as low individual masses were pooled to obtain the necessary freeze dried weight required for trace metal analysis (Section 6.2.2). 287

Table A6.5: Summary of datasets for metal concentrations in Austrolestes cingulatum and descriptive statistics for data before and after transformation.

Fe 54 Cu 63 Zn 64 Cd 114 Pb 208 

Minimum Concentration 32.75 11.89 66.27 0.158

Atalophlebia australis Head Width Descriptive statistics show head width of Atalophlebia australis ranged from 0.68 to 2.79 mm (mean 1.67  0.45 mm, n = 399) across the component trials. Although there was some evidence that the data was non-normal (Shapiro Wilks statistic = 0.982, p < 0.0001) normality was not improved by transformation. Data was bell-shaped (kurtosis = -0.713, skewness = 0.247) and there were no statistical outliers.

Freeze Dried Weight Descriptive statistics show freeze dried weight of Atalophlebia australis ranged from 0.1 to 16.6 mg (mean 1.89  2.32 mg, n = 399) across the component trials. After logarithmic (base 10) transformation, data was still not normal (Shapiro Wilks statistic = 0.933, p < 0.0001), however, transformation reduced the skew (skewness = 0.860) and the data was considerably less peaked (kurtosis = 0.312). Despite transformation, there were still four extreme values which corresponded to freeze dried weights above 11 mg, however they were retained in the dataset as their removal did not improve normality tests.

Tissue Metal Concentrations Descriptive statistics showed that tissue concentrations of iron, copper, zinc, cadmium and lead in Atalophlebia australis were all strongly right skewed (Table A6.6) and required logarithmic (base 10) transformation to improve the overall shape of the distributions (Table 6.6). As the maximum value was obtained from an unreplicated sample and could possibly have arisen from contamination during collection or analysis, these values was excluded from statistical analysis. Normality of tissue concentration data was improved by a logarithmic (base 10) transformation (Table A6.6).

Only the exclusion of the sample collected on trial day 12 at the test site in the summer trial improved normality of the log10-transformed tissue cadmium concentration data noticeably (Shapiro Wilks statistic = 0.964, p < 0.0001, kurtosis = -0.863, skewness = -0.012). As its removal would have reduced the statistical power of the dataset, thereby reducing the value of any comparison across fixed experimental factors, it was not excluded from the dataset. Only the metal concentrations for the sample collected on trial day 1 at the control site of the autumn trial were removed because the spurious nature of the results suggested the sample had been contaminated.

Intradependance There was some evidence of a correlation between individuals retrieved from the same deployment cage in terms of log10-transformed freeze dried weight ( = 0.0359) and high intradependance between head width ( = 0.0050) of Atalophlebia australis individuals retrieved from the same site and trial day combination. As tissue samples were pooled for all individuals retrieved from the same site on the same trial day (Section 6.2.2), no correlation between individuals comprising the pooled sample could be obtained. 289

Table A6.6: Summary of datasets for metal concentrations in Atalophlebia australis and descriptive statistics for data before and after transformation.

Fe 54 Cu 63 Zn 64 Cd 114 Pb 208

Minimum Concentration 973.67 14.925 208.00 0.828 1.350 (μgg-1FDW) Maximum Concentration 40672.9 211.66 3155.17 4.294 498.43 (μgg-1FDW) No of observations 387 387 387 386 387 Mean (μgg-1FDW) 8067.79 60.13 692.48 1.913 90.95 (± standard deviation) (7636.0) (52.92) (545.55) (0.707) (105.50) Untransformed data Shapiro Wilks statistic 0.823 0.800 0.795 0.950 0.7973 Pr

Log10-transformed data Shapiro Wilks statistic 0.950 0.843 0.835 0.973 0.814 Pr

CASE STUDY 2 – URBAN STORMWATER RUNOFF

EXPERIMENTAL FIELD CONDITIONS

Prevailing Rainfall Conditions Precipitation recorded at Upper Naas and Gungahlin had been below long term mean monthly rainfall levels for much of 1997 (Tables A3.4 and A3.5), resulting in low discharges across the study region. Despite localised rainfall during both Experiments 12 and 13 (Figure A6.5), flows remained below 2 MLd-1 except immediately after rainfall events (Figure A6.5).

45 1.4 Rainfall in Upper Naas Valley

40 Rainfall in Gungahlin Discharge in Gudgenby River at Naas 1.2

35 Discharge in Ginninderra Creek at Charnwood )

Discharge in Yarralumla Creek at Curtin -1 1 s 3 30

0.8 25 Experiment 12

20 0.6

Experiment 13 15 0.4 Daily rainfall recorded (mm) Daily stream discharge (m 10

0.2 5

0 0 1 6 11 16 21 26 31 5 10 15 20 25 30 4 9 14 19 24 December 1997 January 1998 February 1998 Figure A6.5: Daily rainfall recorded at Gungahlin and in the Upper Naas Valley and stream discharge at control and impact sites in various local waterways employed for Case Study 2. Horizontal bars represent the trial periods.

Field Site Measurements Water quality characteristics were variable throughout the trials with respect to field site and trial day (Table A6.7). Electrical conductivity and turbidity were higher and much more variable at the impact sites, especially those on Ginninderra Creek (Table A6.7). Concentrations of dissolved oxygen were much lower at impact sites, with approximately two thirds of the measurements taken at Ginninderra Creek sites across the component trials reading less than 1mgL-1 (Table A6.7). Ginninderra Creek sites also had slightly depressed water temperatures compared with other sites, possibly due localised groundwater recharge and/or shading by riparian willows. 291

Table A6.7: Water quality characteristics measured at control and impact sites in various local waterways employed for Case Study 2.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Experiment 12

Site 7 (control) – Gudgenby River at Naas 1 20.79 143.3 7.59 7.61 2.1 2 22.15 124.8 7.35 4.38 4.8 3 24.91 130.1 7.53 4.61 5.4 6 21.28 124.0 7.46 5.48 3.2 9 26.83 132.4 8.19 7.86 0.7 12 24.08 124.4 7.64 8.16 6.1 Site 18 (impact) – Jerrabomberra Creek at Canberra Avenue 1 24.48 240 7.94 5.58 19.5 2 25.38 242 7.66 4.51 24.1 3 25.55 241 8.25 6.31 20.6 6 21.43 327 6.76 1.32 43.8 9 26.96 428 7.06 3.86 7.5 12 23.57 410 7.11 2.46 18.9 Site 21 (impact) – Ginninderra Creek at Copland Drive 1 20.19 954 7.45 1.17 6.1 2 20.67 100 7.48 0.94 8.3 3 20.93 1007 7.54 0.99 8.8 6 19.13 339 6.42 0.29 33.9 9 19.02 416 6.55 0.31 6.3 12 18.80 971 6.58 0.43 13.3 Site 23 (impact) – Ginninderra Creek at Florey Drive 1 20.96 935 7.56 1.76 8.5 2 20.93 101 7.48 0.95 7.1 3 20.99 1113 7.53 1.62 9.0 6 19.85 170 6.48 1.43 10.1 9 20.05 401 6.79 0.87 5.6 12 19.42 841 7.21 0.84 43.8 Experiment 13

Site 7 (control) – Gudgenby River at Naas 1 25.83 141.7 8.12 6.74 1.5 2 26.84 132.4 8.19 7.86 0.7 3 24.41 122.1 8.04 6.71 7.3 6 24.61 123.2 8.49 8.32 6.6 9 22.45 130.2 7.82 6.74 7.4 12 25.87 136.3 8.16 5.86 13.4 292

Table A6.7 (continued): Water quality characteristics measured at control and impact sites in various local waterways employed for Case Study 2.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 13 (control) – Burra Creek at Burra 1 24.97 606 7.76 4.29 3.0 2 25.44 612 7.77 4.41 2.4 3 23.31 568 7.77 3.91 9.5 6 23.55 572 7.91 5.31 8.4 9 19.34 572 7.74 3.73 16.2 12 21.74 574 7.85 4.76 17.4 Site 17 (impact) – Yarralumla Creek at Cotter Road 1 22.55 648 7.39 1.83 11.6 2 22.41 680 7.47 3.64 4.8 3 19.67 645 7.52 2.71 9.1 6 20.35 704 7.46 4.87 9.7 9 16.32 582 7.68 4.08 8.9 12 17.12 604 7.73 3.74 15.1 Site 21 (impact) – Ginninderra Creek at Copland Drive 1 18.81 673 7.09 0.38 6.8 2 19.69 749 7.21 0.95 5.9 3 19.97 802 7.47 0.71 9.2 6 19.66 1127 7.77 3.00 9.3 9 16.04 1167 7.79 2.54 13.4 12 16.89 1150 7.99 3.93 15.1 Site 22 (impact) – Ginninderra Creek at Kingsford Smith Drive 1 19.07 417 6.81 0.48 4.4 2 20.42 441 6.85 0.77 4.4 3 19.33 425 7.02 0.47 9.3 6 19.18 426 7.27 1.65 12.9 9 15.73 489 7.25 1.15 18.7 12 16.94 542 7.34 2.27 17.9 Site 24 (impact) – Ginninderra Creek at Osborne Drive 1 19.67 215 6.52 0.83 5.2 2 19.82 222 6.53 0.37 4.3 3 18.75 224 6.73 0.37 15.2 6 18.57 294 6.83 0.35 19.1 9 14.99 290 6.87 0.57 23.3 12 15.08 289 7.12 0.72 19.4 293

Nutrients and suspended solids were more stable throughout the trials at control sites (Table A6.8). In contrast, impact sites were found to have total nitrogen and phosphorus measurements at least twice as concentrated than at control sites (Table A6.8) and the suspended solids load was considerably higher and more variable, especially at the field sites on Ginninderra Creek (Table A6.8).

Table A6.8: Concentrations of nutrients and suspended solids measured at control and impact sites in various local waterways employed for Case Study 2.

Trial Suspended Solids Total Nitrogen Total Phosphorus Ammonia  Day (mgL-1) (mgL-1) (mgL-1) (mgL-1)

Experiment 12

Site 7 (control) – Gudgenby River at Naas 1 2.0 0.437 0.004 0.004 2 2.4 0.535 0.013 0.003 3 1.3 0.537 0.055

Table A6.8 (continued): Concentrations of nutrients and suspended solids measured at control and impact sites in various local waterways employed for Case Study 2.

Trial Suspended Solids Total Nitrogen Total Phosphorus Ammonia  Day (mgL-1) (mgL-1) (mgL-1) (mgL-1)

Experiment 13

Site 7 (control) – Gudgenby River at Naas 1 1.2 0.445 0.01

Table A6.8 (continued): Concentrations of nutrients and suspended solids measured at control and impact sites in various local waterways employed for Case Study 2.

Trial Suspended Solids Total Nitrogen Total Phosphorus Ammonia  Day (mgL-1) (mgL-1) (mgL-1) (mgL-1)

Site 24 (impact) – Ginninderra Creek at Osborne Drive 1 11.429 0.894 0.125 0.053 2 15.233 0.829 0.163 0.008 3 18.222 0.675 0.175

DESCRIPTION OF BODY SIZE DATA

Paratya australiensis Carapace Length Descriptive statistics show carapace length of Paratya australiensis ranged from 3.36 to 10.33 mm (mean 6.05  1.04 mm, n = 991) during the trial period. While there was some evidence that the distribution of carapace lengths was not normally distributed (Shapiro Wilks statistic = 0.9410, p < 0.0001), normality was not markedly improved by either logarithmic or square root transformation. As removal of statistical outliers (three very small individuals and 47 individuals whose carapaces were greater than 8.16 mm in length) did not improve the shape of the histogram (kurtosis = 1.428, skewness = 1.003), they were not removed from the dataset.

Orbital Carapace Length Descriptive statistics show carapace length of Paratya australiensis ranged from 1.04 to 5.75 mm (mean 3.28  0.62 mm, n = 1124) during the trial period. While there was some evidence that the distribution of orbital carapace lengths was not normally distributed (Shapiro Wilks statistic = 0.9626, p < 0.0001), normality was not markedly improved by either logarithmic or square root transformation. As removal of statistical outliers (eight individuals with orbital carapace measurements were less than 2 mm and 37 individuals whose orbital carapace measurements were greater than 4.67 mm in length) did not improve the shape of the histogram (kurtosis = 1.457, skewness = 0.695), they were not removed from the dataset. 296

Wet Weight Descriptive statistics show wet weight of Paratya australiensis ranged from 5.9 to 161.80 mg (mean 34.42  19.98 mg, n = 1136) during the trial period. After logarithmic (base 10) transformation, data was still non-normal (Shapiro Wilks statistic = 0.9619, p < 0.0001) although measurements were distributed more flatly (kurtosis = 2.438) and transformed data was no longer right-skewed (skewness = 0.846). Despite transformation, there were still 104 individuals whose wet weights were in excess of 62 mg. As removal of these measurements did not improve the shape of the histogram they were not removed from the dataset.

Intradependance There was some evidence of intracage correlation between individuals for carapace length ( = 0.0302), orbital carapace length ( = 0.0236) and log10-transformed wet weight ( = 0.0559). 297

CASE STUDY 3 – PERSISTENTLY HIGH TURBIDITY LOAD

EXPERIMENTAL FIELD CONDITIONS

Prevailing Rainfall Conditions Precipitation recorded at Upper Naas and Gungahlin had been below long term mean monthly rainfall levels for much of 1997 (Tables A3.4 and A3.5), resulting in low discharges across the study region. While considerable rainfall was recorded across the region approximately two weeks before Experiment 14 (Figure A6.6), stream discharge had returned to baseflow levels by the time before the start of the trials (Figure A6.6).

45 Rainfall in Upper Naas Valley 0.14 Rainfall in Gungahlin

40 Discharge in Gudgenby River at Naas 0.12 Discharge in Ginninderra Creek at Barton Highway 35 ) -1

0.1 s 3 30

0.08 25

20 0.06

15 Experiment 15 0.04 Daily rainfall recorded (mm) 10 Daily stream discharge (m Experiment 14 0.02 5

0 0 1 6 11 16 21 26 31 5 10 15 20 25 2 7 12 17 22 27 January 1998 February 1998 March 1998 Figure A6.6: Daily rainfall recorded at Gungahlin and in the Upper Naas Valley and stream discharge at control and impact sites in various local waterways employed for Case Study 3. Horizontal bars represents the trial periods.

Field Site Measurements Most of the water quality characteristics examined (Table A6.9) were relatively stable across trial days. Not surprisingly, impact sites for these experiments exhibited higher turbidity and hence warmer water temperatures, however, dissolved oxygen concentrations at impact sites were comparable if not higher than measurements for control sites (Table A6.9).

Concentration of suspended solids was much higher at impact sites (Table A6.10). In contrast, concentrations of total nitrogen and phosphorus was not markedly different between field sites, with concentrations below method detection limits recorded for both site types sites (Table A6.10). 298

Table A6.9: Water quality characteristics measured at control and impact sites in various local waterways employed for Case Study 3.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Experiment 14

Site 13 (control) – Burra Creek at Burra 1 19.51 587 7.78 5.13 6.4 2 19.14 588 7.86 4.25 3.5 3 19.35 590 7.79 3.85 8.1 6 21.21 594 7.90 4.68 10.5 9 25.33 599 7.82 4.25 15.8 12 21.76 571 7.56 3.74 10.8 Site 11 (impact) – Point Hut Point upstream of confluence with Murrumbidgee River 1 23.56 439 7.72 3.93 31.8 2 26.59 438 7.89 5.71 27.9 3 22.37 437 7.86 4.43 33.4 6 26.77 437 8.01 5.88 27.0 9 28.36 444 7.92 4.48 18.4 12 24.93 406 7.77 5.48 25.1 Site 12 (impact) – Tuggeranong Creek below Lake Tuggeranong 1 24.31 259 7.91 6.87 19.8 2 25.55 264 8.25 6.89 18.8 3 22.48 279 7.77 7.05 14.6 6 27.96 354 7.88 9.62 12.2 9 29.84 444 7.65 6.93 11.7 12 24.26 284 7.69 7.53 12.4 Site 19 (impact) – Ginninderra Creek upstream of Yerrabi Pond 1 23.18 496 8.28 9.32 67.7 2 23.18 496 8.33 9.32 67.7 3 21.56 604 8.64 12.84 36.3 6 27.19 806 8.27 9.36 33.3 9 28.88 941 8.12 8.97 40.5 12 23.37 364 7.11 4.09 16.1 Experiment 15

Site 13 (control) – Burra Creek at Burra 1 19.34 572 7.74 3.73 16.2 2 19.58 577 7.81 4.33 11.4 3 19.28 575 7.79 4.62 9.8 6 21.85 568 7.68 3.58 15.5 9 22.08 630 7.72 4.46 4.5 12 19.01 624 7.74 3.61 6.3 299

Table A6.9 (continued): Water quality characteristics measured at control and impact sites in various local waterways employed for Case Study 3.

Trial Temperature EC pH DO Turbidity Day (oC) (mScm-1) (units) (mgL-1) (NTU)

Site 19 (impact) – Ginninderra Creek upstream of Yerrabi Pond 1 16.25 925 7.8 5.69 32.5 2 17.75 934 7.91 5.44 26.8 3 20.69 959 7.94 7.57 53.3 6 20.63 960 7.91 5.48 25.2 9 19.87 1028 7.89 6.02 27.1 12 19.16 1058 7.68 4.81 25.6

Table A6.10: Concentrations of nutrients and suspended solids measured at control and impact sites in various local waterways employed for Case Study 3.

Trial Suspended solids Total Nitrogen Total Phosphorus  Day (mgL-1) (mgL-1) (mgL-1)

Experiment 14

Site 13 (control) – Burra Creek at Burra 1 4.8 1.106

Table A6.10 (continued): Concentrations of nutrients and suspended solids measured at control and impact sites in various local waterways employed for Case Study 3.

Trial Suspended solids Total Nitrogen Total Phosphorus  Day (mgL-1) (mgL-1) (mgL-1)

Site 19 (impact) – Ginninderra Creek upstream of Yerrabi Pond 1 43.10 1.118 0.025 2 27.76 0.889 0.029 3 49.09 0.942 0.065 6 28.45 0.944 0.029 9 38.26 0.681 0.047 12 185.46 * * Experiment 15

Site 13 (control) site – Burra Creek at Burra 1 5.2 0.731 0.012 2 12.8 * * 3 4.8 0.686 0.025 6 22.6 0.549 0.023 9 5.6 0.613 0.024 12 5.2 0.866 0.042 Site 19 (impact) – Ginninderra Creek upstream of Yerrabi Pond 1 54.8 0.898

DESCRIPTION OF BODY SIZE DATA

Notalina sp. Head Width Descriptive statistics show head widths of Notalina sp. ranged from 0.24 to 1.20 mm (mean 0.58  0.18 mm, n = 1046) across both component trials. While there was some evidence that the distribution of Notalina sp. head widths was not normally distributed (Shapiro Wilks statistic = 0.9259, p < 0.0001), normality was not markedly improved by either logarithmic or square root transformation. As removal of statistical outliers (38 individuals whose heads were greater than 1 mm wide) did not improve the shape of the histogram (kurtosis = 0.790, skewness = 0.987), they were not removed from the dataset. 301

Wet Weight

Descriptive statistics show the average wet weight of Notalina sp. ranged from 0.25 to 2.45 mg (mean 1.17  0.41 mg, n = 1027) across both component trials. While there was some evidence that the distribution of average Notalina sp. wet weights was not normally distributed (Shapiro Wilks statistic = 0.9688, p < 0.0001), normality was not markedly improved by either logarithmic or square root transformation. As removal of statistical outliers (19 individuals that weighed more than 2.34 mg) did not improve the shape of the histogram (kurtosis = 0.333, skewness = 0.650), they were not removed from the dataset.

Intradependance There was virtually no intracage correlation between Notalina sp. individuals for head width ( = 0.5421) or average wet weight ( = 0.1678).

Paratya australiensis Carapace Length Descriptive statistics show carapace length of Paratya australiensis ranged from 4.17 to 10.25 mm (mean 6.14  1.02 mm, n = 390) during the trial period. While there was some evidence that the distribution of carapace lengths was not normally distributed, normality was improved by logarithmic (base 10) transformation (Shapiro Wilks statistic = 0.973, p < 0.0001, skewness = 0.562, kurtosis = 0.808). As removal of statistical outliers (11 individuals whose carapaces were greater than 8.5 mm in length) did not improve the shape of the histogram, they were not removed from the dataset.

Orbital Carapace Length Descriptive statistics show orbital carapace length of Paratya australiensis ranged from 2.08 to 6.08 mm (mean 3.26  0.58 mm, n = 423) during the trial period. After logarithmic (base 10) transformation, data normality was improved (Shapiro Wilks statistic = 0.974, p < 0.0001, skewness = 0.104) despite the data remaining quite peaked (kurtosis = 1.897). There were no statistical outliers.

Wet Weight Descriptive statistics show wet weight of Paratya australiensis ranged from 12.2 to 144.2 mg (mean 33.55  18.05 mg, n = 423) during the trial period. After logarithmic (base 10) transformation, data was still non-normal (Shapiro Wilks statistic = 0.970, p < 0.0001, skewness = 0.674, kurtosis = 0.888) with no statistical outliers.