Spatial Analysis of the Impacts of Urbanisation on the Health of Ephemeral Streams in Southeast Queensland

Author Millington, Heidi Kathryn

Published 2016

Thesis Type Thesis (PhD Doctorate)

School Griffith School of Environment

DOI https://doi.org/10.25904/1912/511

Copyright Statement The author owns the copyright in this thesis, unless stated otherwise.

Downloaded from http://hdl.handle.net/10072/367358

Griffith Research Online https://research-repository.griffith.edu.au

Spatial Analysis of the Impacts of Urbanisation on the Health of Ephemeral Streams in Southeast Queensland

Heidi Kathryn Millington Master of Science Bachelor of Chemical Engineering

Griffith School of Environment Griffith Sciences Griffith University

Submitted in fulfilment of the requirements of the degree of Doctor of Philosophy April 2016

i STATEMENT OF ORIGINALITY

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

Heidi Kathryn Millington

i ACKNOWLEDGEMENTS

I would like to acknowledge the academic and financial support of the Australian Rivers Institute.

I would like to acknowledge the Australian Government for their financial support with an Australian Postgraduate Award Scholarship.

I would like to thank my supervisors for their support and guidance. They are Professor Stuart Bunn, Professor Angela Arthington, Professor Bofu Yu and Dr Doug Ward. Thank you to Professor Yu for mentoring and editorial assistance. Thank you to Dr Ward for guidance on developing the ecological connectivity metrics of Chapter 5 and editorial assistance. I would especially like to thank my Principal Supervisor, Professor Bunn, for his guidance on research questions, ecological insights and editorial clarity, as well as Professor Arthington for her ecological insights, guidance on the selection of indicator species, and generous and articulate editorial assistance.

Thank you to Brisbane City Council for provision of GIS data used in this research and the stream health data used in Chapters 5 and 6. Special thanks to Anne Simi.

Thank you to my fieldwork assistants Miriam Paul, Peter Edwards, Andrew Bentley, Richard Grantham, Tim Jardine, Kuta Rovera, Anthony Smith, Andy Tidemann and Julie Lovell. Thanks to Dominic Valdez for helping me to source equipment.

Thank you to Erin Peterson for guidance and mentoring in spatial analysis and statistics.

Thank you to Julie Lovell, Geoff Heard, Peter Edwards, James Edmonds and Erin Kenna for additional editorial guidance.

Special thanks to my colleagues and mentors for discussing ideas and concepts and providing suggestions on research directions and methods: thanks to Knox Lovell, Julie Lovell, Seth Wenger, Catherine Leigh, Fran Sheldon, Siti Amri and Peter Pollard.

Thanks to James Edmonds and Gemma Tidemann for their friendship and support.

ii DEDICATION

To my daughters, Lucinda and Stella, and my grandfather, Winston

iii ABSTRACT Aquatic ecosystems are vulnerable to threats from human activity. Numerous studies have shown that urban freshwater stream ecosystems are especially vulnerable to the intensity and complexity of stream health stressors associated with activities in the surrounding urban landscape. Scientists, government organisations and local volunteer groups are well aware of the deteriorating health of urban streams and are working towards understanding and managing the sources of stress on stream health. Improving the health of urban streams has the potential to provide local benefits such as biodiversity protection, enhanced ecosystem health, water purification, access to green space, scenic amenity and improved land values. While several important stressors have been identified in the Urban Stream Syndrome (elevated sediments, nutrients and contaminants, increased hydrologic flashiness and altered riparian and biotic assemblages) further research is required on the most important stressors and the mechanisms by which they impact stream health, especially in systems within dry climates where urban streams experience low flow conditions and flashy natural hydrology. Catchment-scale impervious surface has been identified in previous studies as a major driver of altered urban stream hydrology leading to degraded stream health. However, especially in drier climates, other aspects such as water quality and ecological processes associated with longitudinal and lateral connectivity have been identified as potentially more important stressors on urban stream health.

This study on the ephemeral urban streams of sub-tropical southeast Queensland (SEQ) was designed to detect the relative importance to stream ecosystem health of catchment- scale impervious surface as well as reach-scale riparian cover. It also investigated the role of the ubiquitous stormwater piping found in urban areas and whether it influenced ecological connectivity, especially in terms of biota dispersal. Further, the influence of the areal extent of the study on the detection of the relative importance of different stream stressor metrics was considered. Reach, local and catchment-scale spatial (inverse-distance weighted and areal buffer) and non-spatial (lumped) land-cover metrics including several novel metrics relating to effective riparian buffers and in- stream ecological connectivity were generated in a geographic information system to represent land-cover stream stressors. A lumped land-use metric (population density) and various landscape metrics such as catchment extent were also considered. The investigations were carried out in two study areas: (1) a focus study of 30 sites along two highly urbanised catchments (Bulimba Creek and Norman Creek) within the larger iv study area, and (2) the larger study area consisting of 33 sites within multiple catchments of the Lower Brisbane River and surrounding coastal catchments. For water quality and biotic diversity and abundance, land-cover and land-use stream stressor metrics and additional landscape metrics were combined into a priori models that were fitted using generalised least squares and compared using the Akaike Information Criterion statistic. Model averaging provided further evidence to differentiate the importance of relatively similar metrics. Occurrence models for several native aquatic species were developed using generalised linear modelling and tested by comparing the Root Mean Squared Predictive Errors.

The key findings of this study, which are relevant to the predominantly ephemeral urban streams in sub-tropical SEQ, were:

1) Catchment and local-scale impervious surface metrics were not strongly associated with variation in water quality and health indicators (macroinvertebrate and fish health indicators) compared with previous studies in temperate streams. This finding suggests that altered hydrology associated with impervious surface is also relatively less important in this region. 2) Reach-scale riparian buffer condition was relatively important in explaining variation in water quality and health indicators (maximum temperature and fish and macroinvertebrate diversity and abundance. 3) Metrics accounting for stormwater piping (effective riparian buffer metrics) were relatively important to explaining variation in diversity and abundance of macroinvertebrates (but not fish) and minimum dissolved oxygen, and they were relatively more important than lumped and distance-weighted catchment-scale impervious surface metrics in the study of smaller areal extent. This suggests that there is an influence of stormwater piping on stream health due to more than its association with impervious surface and altered hydrology. 4) When studied across a larger area (medium instead of small areal extent), the relative importance of catchment-scale impervious surface metrics for explaining variation in macroinvertebrate diversity and abundance in SEQ (as indicated by Stream Invertebrate Grade Number Average Level, SIGNAL2) was greater. The increased difficulty in differentiating the relative importance of various catchment-scale metrics in the medium areal extent study (due to multiple metrics having similar strengths of association with stream health), v suggests that localised effects are averaging out across larger assessment areas, as opposed to the larger-scale study better representing the scale at which stream health stressors are operating. 5) Several different ecological connectivity metrics designed to represent dispersal paths and habitat fragmentation, both in-stream and terrestrial, were relatively important in explaining variation in fish and macroinvertebrate diversity, abundance and occurrence and in several cases were relatively more important than catchment-scale impervious surface metrics. Different ecological connectivity metrics, as well as other land-cover and land-use stressor metrics, were relatively more important for explaining the occurrence of different taxa, a result that may be associated with different life history traits. Therefore such taxa may be useful indicators of the relative importance of ecological connectivity and other land-cover stressors in other studies.

The thesis concludes with recommendations for policy and planning especially relevant to ephemeral, drier climate urban streams. Reach-scale rehabilitation and the protection of ecological connectivity should be combined with traditional water sensitive urban design approaches. The intermittent occurrence of local extirpation events (due to stream drying, poor water quality, floods, etc.) in ephemeral stream ecosystems may make relatively intact riparian zones with good longitudinal ecological connectivity a vital factor in stream health protection. Further research by urban planners to prioritise their protection is recommended as an efficient means to protect urban stream health while balancing human needs.

vi TABLE OF CONTENTS

STATEMENT OF ORIGINALITY ...... i ACKNOWLEDGEMENTS ...... ii DEDICATION ...... iii ABSTRACT ...... iv TABLE OF CONTENTS ...... vii LIST OF TABLES ...... xii LIST OF FIGURES ...... xiii CHAPTER 1 INTRODUCTION ...... 1 1.1 Threats to aquatic ecosystems ...... 1 1.2 Impacts of urbanisation on stream ecosystems ...... 1 1.2.1 The urban stream syndrome ...... 1 1.2.2 Impervious cover, altered hydrology and stormwater piping ...... 1 1.2.3 Impacts of urbanisation on stream health and water quality ...... 3 1.2.4 Mechanisms of urbanisation impacts on stream health ...... 8 1.3 Riparian zones – their role in stream health and how to detect their influence ... 9 1.4 Spatial scale considerations ...... 10 1.5 Connectivity, habitat fragmentation and loss ...... 12 1.6 Urban stream concept map ...... 14 1.7 Study objectives and approach ...... 17 1.8 Structure of the thesis ...... 18 CHAPTER 2 STUDY AREA ...... 23 2.1 Southeast Queensland (SEQ) natural setting ...... 23 2.1.1 Physical setting ...... 23 2.1.2 Major catchments ...... 23 2.1.3 Climate ...... 24 2.1.4 Hydrology ...... 24 2.1.5 Geology and topology ...... 26 2.1.6 Vegetation ...... 26 2.1.7 Land use ...... 27 2.1.8 Population ...... 27 2.2 Major threats to aquatic ecosystem health in SEQ catchments ...... 28 2.2.1 Land clearing ...... 28 2.2.2 Urbanisation ...... 28 vii 2.2.3 Downstream impacts on the Moreton Bay Estuary ...... 30 2.2.4 SEQ Ecosystem Health Monitoring Program (EHMP) ...... 30 2.3 The Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) site selection ...... 31 2.3.1 Physical setting ...... 31 2.3.2 Geology and topology ...... 31 2.3.3 Vegetation ...... 32 2.3.4 Extreme weather events ...... 32 2.3.5 Site selection ...... 32 2.4 Focus catchments: Bulimba Creek and Norman Creek focus catchments ...... 33 2.4.1 Physical setting ...... 33 2.4.2 Geology and topology ...... 33 2.4.3 Hydrology ...... 34 2.4.4 Vegetation ...... 35 2.4.5 Land use ...... 35 2.4.6 Population ...... 36 2.4.7 Site Selection ...... 37 CHAPTER 3 SPATIAL ANALYSIS OF CATCHMENT-SCALE IMPERVIOUS SURFACE AND REACH-SCALE RIPARIAN COVER METRICS AND THEIR ASSOCIATIONS WITH SUB-TROPICAL URBAN STREAM HEALTH AND WATER QUALITY INDICATORS ...... 44 3.1 Introduction ...... 44 3.1.1 Multiple stressors affect urban streams ...... 44 3.1.2 Catchment-scale urban stressors ...... 45 3.1.3 Riparian zone and in-stream condition ...... 45 3.1.4 Urban streams in southeast Queensland (SEQ) ...... 46 3.2 Methods ...... 47 3.2.1 Site selection ...... 47 3.2.2 Stream health and water quality field data collection...... 47 3.2.3 GIS data processing for spatial scale metrics ...... 49 3.2.4 Generating the drainage network ...... 50 3.2.5 Spatial scale land-cover metrics ...... 51 3.2.6 Additional candidate explanatory metrics ...... 58 3.2.7 Statistical analysis ...... 58 3.3 Results ...... 62 viii 3.3.1 Preliminary investigation of candidate explanatory GIS-generated metrics and stream health and water quality indicators ...... 62 3.3.2 GLS model testing and selection ...... 62 3.3.3 Multiple scales of impact ...... 63 3.3.4 Reach-scale stressors ...... 63 3.3.5 Local-scale stressors ...... 68 3.3.6 Effective and traditional catchment-scale riparian buffer metrics...... 68 3.3.7 Population density ...... 69 3.3.8 Downstream variation ...... 70 3.4 Discussion ...... 70 3.4.1 Importance of multiple scales - reach, local and catchment ...... 70 3.4.2 Catchment-scale impervious surface and population density...... 70 3.4.3 Reach-scale riparian zone ...... 73 3.4.4 Effective riparian buffers ...... 75 3.4.5 Implications for management ...... 77 3.5 Conclusion ...... 79 CHAPTER 4 SPATIAL ANALYSIS OF THE INFLUENCE OF THE AREAL EXTENT OF A STUDY ON DETECTABLE ASSOCIATIONS BETWEEN URBANISATION AND THE ECOSYSTEM HEALTH OF SUB-TROPICAL STREAMS ...... 81 4.1 Introduction ...... 81 4.2 Methods ...... 82 4.2.1 Site selection and stream health indicators ...... 83 4.2.2 Stream biota sampling ...... 84 4.2.3 Land-cover and land-use stressors ...... 85 4.2.4 Statistical analysis ...... 86 4.3 Results ...... 87 4.3.1 GLS model testing and model averaging ...... 87 4.3.2 Overview of results ...... 87 4.3.3 Reach-scale metrics ...... 90 4.3.4 Local-scale impacts ...... 90 4.3.5 Effective riparian buffers and other catchment-scale land-cover metrics .. 90 4.3.6 Population density ...... 91 4.3.7 Catchment extent ...... 91 4.4 Discussion ...... 92 ix 4.4.1 Local and catchment-scale impervious surface metrics ...... 92 4.4.2 Reach-scale riparian zone ...... 94 4.4.3 Effective riparian buffers and stormwater piping ...... 95 4.4.4 Catchment extent ...... 96 4.4.5 Implications for management ...... 97 4.5 Conclusion ...... 98 CHAPTER 5 SPATIAL ANALYSIS OF THE LIKELY IMPACTS OF DISRUPTIONS TO ECOLOGICAL CONNECTIVITY ON THE BIOTA OF SUB-TROPICAL URBAN STREAMS ...... 100 5.1 Introduction ...... 100 5.2 Methods ...... 103 5.2.1 Site selection ...... 103 5.2.2 GIS Analysis ...... 103 5.2.3 Biological sampling ...... 107 5.2.4 Taxa selection ...... 107 5.2.5 Statistical methods ...... 109 5.3 Results ...... 113 5.3.1 Modelling of macroinvertebrate and fish diversity and abundance and occurrence ...... 113 5.3.2 In-stream connectivity and surrounding tree-cover fragmentation metrics...... 115 5.3.3 Reach-scale condition ...... 122 5.3.4 Total tributary extents and upstream catchment area ...... 123 5.4 Discussion ...... 124 5.4.1 In-stream connectivity and surrounding tree-cover fragmentation ...... 125 5.4.2 Different life history traits and different aspects of ecological connectivity ...... 126 5.4.3 Ecological connectivity and ephemeral streams ...... 132 5.4.4 Reach scale ...... 134 5.4.5 Total tributary extent ...... 135 5.4.6 Implications for management ...... 136 5.5 Conclusion ...... 142 5.5.1 Future research ...... 143

x CHAPTER 6 A PERSPECTIVE ON THE URBAN STREAM SYNDROME INCORPORATING DIFFERENT NATURAL HYDROLOGY AND ECOLOGICAL CONNECTIVITY ...... 145 6.1 Reconsidering the urban stream syndrome ...... 145 6.2 Spatial analysis of catchment-scale impervious surface and reach-scale riparian cover metrics and their associations with sub-tropical urban stream health and water quality indicators ...... 149 6.3 Spatial analysis of the influence of the areal extent of a study on detectable associations between urbanisation and the ecosystem health of sub-tropical streams ...... 152 6.4 Spatial analysis of the likely impacts of disruptions to ecological connectivity on the biota of sub-tropical urban streams...... 154 6.5 A perspective on the urban stream syndrome that incorporates different natural hydrology and ecological connectivity ...... 157 6.6 Revised concept map of urban stream health ...... 164 6.7 Recommendations for policy and planning ...... 167 6.8 Future research ...... 170 APPENDICES ...... 173 Appendix 1 Site location descriptions ...... 174 Appendix 2 Stream health and water quality indicators for studies in Chapters 3, 4 and 5 ...... 177 Appendix 3 GIS-generated land-cover, land-use and landscape metrics for studies in Chapters 3, 4 and 5 ...... 179 Appendix 4 Modelling urban drainage networks ...... 187 Appendix 5 Calculating land-cover and land-use metrics using GIS techniques ...... 189 Appendix 6 A framework for guiding the management of urban stream health ...... 190 Appendix 7 Moran’s I index – spatial autocorrelation testing for Chapter 3 study ...... 202 Appendix 8 Preliminary OLS regression for the Chapter 3 study ...... 203 Appendix 9 Spearman’s rank correlation coefficients for candidate explanatory variables in Chapters 3, 4 and 5 ...... 211 Appendix 10 Candidate explanatory metrics considered for GLS modelling for each stream health or water quality indicator in Chapter 3 ...... 225 Appendix 11 Model selection procedure for stream health and water quality indicators .. 228 Appendix 12 Chapter 3 study summary statistics ...... 231 Appendix 13 Analysis of preliminary OLS regression for the Chapter 3 study ...... 233 xi Appendix 14 Chapter 4 summary statistics ...... 235 Appendix 15 Preliminary OLS regression for the land-cover and land-use metrics in Chapters 4 and 5 ...... 238 Appendix 16 Candidate explanatory metrics considered for GLS modelling for stream health indicators in Chapter 4 ...... 240 Appendix 17 Chapter 5 summary statistics ...... 241 Appendix 18 Preliminary OLS regression for the ecological connectivity metrics in Chapter 5 ...... 245 Appendix 19 Candidate explanatory metrics considered for GLS modelling for stream health indicators in Chapter 5 ...... 247 Appendix 20 Best GLS and GLM models for Chapter 5 ...... 249 Appendix 21 Coefficient estimates, Pr(<|z|) and odds ratios for fish and macroinvertebrate occurrence data for Chapter 5...... 257 Appendix 22 Candidate explanatory metrics considered for GLM testing of taxa occurrence in Chapter 5 ...... 283 Appendix 23 Spatial autocorrelation investigation for occurrence data using GLM logistic regression for Chapter 5 ...... 289 REFERENCES ...... 292

LIST OF TABLES Table 3.1 Alternative specifications of land-cover and land-use metrics with lumped, threshold and inverse-distance weighted (IDW) weighting functions ...... 54 Table 3.2 Best GLS models for SIGNAL2_S and water quality indicators ...... 64 Table 3.3 Model averaging for SIGNAL2_S and water quality indicators ...... 66 Table 4.1 Best GLS models for SIGNAL2_L and OE2010_L ...... 88 Table 4.2 Model averaging for SIGNAL2_L and OE2010_L ...... 89 Table 5.1 Taxa selected for macroinvertebrate occurrence modelling ...... 108 Table 5.2 Species selected for fish occurrence modelling ...... 109 Table 5.3 Model averaging for SIGNAL2_C, OE2010_C and OE2011_C ...... 114 Table 5.4 Model averaging for macroinvertebrate occurrence ...... 119 Table 5.5 Model averaging for fish occurrence ...... 121 Table 5.6 Cost comparison table for Water Sensitive Urban Design (WSUD) and ecological connectivity enhancement options ...... 140

xii LIST OF FIGURES Figure 1.1 Urban stream concept maps of (a) the urban stream syndrome (revised from Walsh et al. 2005a) and (b) new considerations for the urban stream syndrome especially relevant to tropical and sub-tropical streams ...... 16 Figure 2.1 Study area and sites shown for the Lower Brisbane River catchment and surrounding coastal catchments studies (LBRCSCC) ...... 38 Figure 2.2 Lumped impervious surface area for each site/sub-catchment assessed in the Lower Brisbane River catchment and surrounding coastal catchments studies (LBRCSCC) ...... 39 Figure 2.3 Reach-scale tree cover for each site/sub-catchment assessed in the Lower Brisbane River catchment and surrounding coastal catchments studies (LBRCSCC) ... 40 Figure 2.4 Study area and sites shown for Bulimba Creek and Norman Creek catchments (BCNC) study ...... 41 Figure 2.5 Lumped impervious surface area for each site/sub-catchment assessed in Bulimba Creek and Norman Creek (BCNC)...... 42 Figure 2.6 Reach-scale tree cover for each site/sub-catchment assessed in Bulimba Creek and Norman Creek (BCNC)...... 43 Figure 3.1 Illustration of different spatial and non-spatial metrics ...... 53 Figure 5.1 Surrounding landscape tree-cover fragmentation metrics ...... 105 Figure 5.2 In-stream longitudinal ecological connectivity metrics ...... 106 Figure 6.1 Updated urban stream concept map (updated from Walsh et al. (2005a) and Chapter 1) ...... 166

xiii CHAPTER 1 INTRODUCTION

1.1 Threats to aquatic ecosystems

Aquatic ecosystems are exposed to multiple stressors from human activities impacting water quality, habitat and biological assemblages, including altered hydrology, increased sedimentation, increased nutrient concentrations, contamination with pollutants, loss of catchment vegetation, and riparian clearing and canopy opening (Allan 2004, Dudgeon et al. 2006, Vörösmarty et al. 2010). Urban freshwater ecosystems are especially vulnerable because of the complexity and scale of impacts due to the intensive use of the surrounding landscape. All of the previously mentioned impacts are typical but often exacerbated, for example via increased impervious surface, increased hydrological flashiness and rapid conveyance of pollutants to streams (Paul and Meyer 2001, Meyer et al. 2005, Wenger et al. 2009).

1.2 Impacts of urbanisation on stream ecosystems

1.2.1 The urban stream syndrome

This range of stressors has resulted in what is known as the urban stream syndrome (Meyer et al. 2005, Walsh et al. 2005a), a consistently observed suite of ecosystem health impacts on streams draining urban landscapes. Key indicators associated with this syndrome include increased hydrologic flashiness, elevated nutrients and contaminants, and altered biotic assemblages (Meyer et al. 2005).

1.2.2 Impervious cover, altered hydrology and stormwater piping

In many urban stream studies, altered run-off and channel morphology are associated with the flashier hydrology that is generated by impervious surfaces and efficient stormwater drainage. Impervious surfaces include rooftops, concrete, roads and any other surfaces in the landscape that are not permeable to water. Impervious surfaces have been shown to impact the stream hydrograph, giving urban streams more frequent, high flow events with shorter times for the flows to peak and then recede (Walsh et al. 2005a). This results in the incision of channels, scouring, alteration of habitat structure

1 and changes in water chemistry (Walsh et al. 2005a). Stream incision or ‘downcutting’ is caused by large volumes of water scouring out sediment from the stream bed (Groffman et al. 2003, Allan 2004). If accompanied by a reduction in base flow and lowering of the groundwater table, channel incision can also lead to changes in riparian vegetation (Groffman et al. 2003).

Impervious surface metrics have been specified in a variety of ways in order to measure the impact on urban stream ecosystems. They range from lumped “total impervious area” used by Leopold (1968) to “effective impervious surface” defined by Walsh et al. (2005b) and “attenuated impervious surface” (Walsh and Kunapo 2009). An effective impervious surface includes impervious surfaces directly connected to the stream via stormwater drains that are assumed to bypass riparian zones (Walsh et al. 2005b). An effective impervious surface was defined by Walsh and Kunapo (2009) as an impervious surface with less than 11.5 m between it and the connecting pipe. However, the distance of separation between an impervious surface component and a drain is often not specified in the literature for directly-connected impervious surface (Roy and Shuster 2009); or requires draining directly with no separation distance (Lee and Heaney 2003). Walsh and Kunapo (2009) used the phrase “attenuated impervious surface” for a spatially-weighted measure of impervious surface that correlated better than the effective impervious surface metric with a macroinvertebrate stream health index.

However, in tropical and sub-tropical locations where streams naturally experience flashy hydrographs, with high volume flows associated with storms and extended periods of low or no flow, studies have shown low strength of association between stream biotic diversity and abundance indicator scores and catchment-scale impervious surface area (de Jesus-Crespo and Ramirez 2011, Sheldon et al. 2012b). In a study of ephemeral streams of SEQ, catchment impervious surface was typically associated with increased flow volumes and frequencies of high flow periods, but not for all catchments studied (Chowdhury et al. 2012, McIntosh et al. 2013). Low flow hydrological responses to urbanisation are more variable than high flow responses (e.g. Lerner 2002, Brown et al. 2009, Roy et al. 2009a). While catchment impervious surface typically reduces base flow volumes and also increases the duration of low flow in perennial systems (Ferguson and Suckling 1990), in the ephemeral streams of SEQ, impervious surfaces have been shown to increase the volume and continuity of base flows

2 (Chowdhury et al. 2012, McIntosh et al. 2013). This may be due to effects of septic systems, leaky pipes, and lawn irrigation on base flow volumes, potentially offsetting losses in runoff from impervious surfaces in headwaters (and other low flow systems) (Lerner 2002), or may be an effect of other unknown factors associated with altering the hydrology of ephemeral systems (Chowdhury et al. 2012, McIntosh et al. 2013). These studies and others (Bhaskar et al. 2016, Booth et al. 2016) suggest that flashy, ephemeral systems in particular may not be affected by altered hydrology associated with catchment-scale impervious surface in the same way as perennial streams.

Stormwater piping has been considered mostly in the hydrological context of how it “directly connects” (Walsh et al. 2005b) water from impervious surfaces to streams although burying streams and converting them to stormwater piping has many additional effects such as increasing flow velocities, altering carbon and nutrient inputs, and increasing nitrogen concentrations (Kaushal et al. 2008b, Roy et al. 2009a). Stormwater piping is also a potentially important disruptor of aquatic biota dispersal paths. The extent of the “burial” of urban streams during the urbanisation process (which may results in their being paved over or converted to stormwater piping) has not been assessed in many places but can be extensive (Elmore and Kaushal 2008). While the urban stream syndrome accounts for ecological connectivity (or landscape connectivity “the degree to which the landscape facilitates or impedes movement among resource patches,” Taylor et al. 1993), research on this has generally been limited to road crossings and dams. Fish (Warren and Pardew 1998 , Mirati 1999, Norman et al. 2009, Engman and Ramírez 2012) and macroinvertebrate (e. g. Blakely et al. 2006, Watanabe et al. 2010, Hein et al. 2011) passage through road culverts and across dams can be impeded, thus affecting movement and migration.

1.2.3 Impacts of urbanisation on stream health and water quality

Urban stream biota (and therefore urban stream health) are affected by changes to water quality as well as other factors such as altered hydrology, physical habitat and the presence of alien taxa (Brown et al. 2009). Urbanisation impacts on water quality typically include reduced dissolved oxygen (DO), higher conductivity, higher turbidity, pollution with heavy metals and hydrocarbons, and other factors (Allan 2004, Dudgeon et al. 2006, Vörösmarty et al. 2010). These impacts can be due to direct alteration of water quality via pollution and release of chemicals used in the urban landscape, or by

3 vehicles or industry, or they may be related to alteration of hydrology and changes to sediment and organic matter loads from new construction and land clearing. A range of water quality and biotic indicators is typically used to assess the health of urban streams although studies vary in their use of water quality attributes and biological indicators such as taxon occurrence and abundance, measures of biotic assemblage structure (e.g. diversity and relative abundance), and metrics describing ecosystem functional attributes (Bunn et al. 2010). The selection of appropriate indicators will depend on the objectives of the study and the types and scale of stressors being assessed.

Macroinvertebrates are commonly used as indicators of the ecological health of urban streams. They show sensitivity to urbanisation, especially to impervious land cover and riparian zone quality (Paul and Meyer 2001, Booth 2005, Walsh et al. 2005a, Collier et al. 2009, Wenger et al. 2009). Benthic macroinvertebrate assemblages have been shown to respond to gradients of urbanisation more predictably than other indicators (hydrology, physical habitat, water quality, algae and fish) (Brown et al. 2009). In virtually all macroinvertebrate studies, sensitive species such as certain mayflies (Ephemeroptera) and stoneflies (Plecoptera) are absent or less abundant in areas draining urban land (Booth et al. 2004, Walsh et al. 2005a, Brown et al. 2009, Wenger et al. 2009). The species of macroinvertebrates lost can typically be grouped as either sensitive to various forms of toxic contamination, and/or to low DO concentrations (e.g. Arthington et al. 1982, Sheldon et al. 2012b). In association with the loss of sensitive species, the relative abundance of pollution tolerant species such as Chironomidae, oligochaetes, and tolerant gastropods, typically increases (Pratt et al. 1981, Medeiros et al. 1983b, Paul et al. 2006).

Although not used as often as indicators of urbanisation, reductions in the abundance and diversity of native fish and algae have also been reported in the literature for urban streams (Paul and Meyer 2001, Walsh et al. 2005a, Wenger et al. 2009). Fish have been shown to respond to in-stream connectivity disruption associated with dams, culverts and other obstructions (Warren and Pardew 1998 , Mirati 1999, Ramirez et al. 2009) as well as to impervious surface (Klein 1979) and are likely influenced by increased sediment (Wood and Armitage 1997, Paul et al. 2006), poor water quality and periods of low dissolved oxygen (Boet et al. 1999a), flow modification (Boet et al. 1999a) and the introduction of invasive species (Boet et al. 1999a). The responses of herpetofauna, riparian birds and other vertebrates to urbanisation have not been well examined but

4 limited studies have shown reduced abundance in urban stream environments (Lussier et al. 2006, Mattson and Cooper 2006, Miller et al. 2007, Wenger et al. 2009). Urban microbial communities also have not been well studied but would benefit from further investigation of their potential as indicators of pollution and other stressors (Wenger et al. 2009). Their structure and metabolism are affected by catchment-scale processes, especially hydrology, which control the supply of dissolved organic carbon to rivers and streams (Sinsabaugh and Findlay 2003). However, the fate of bacterial carbon is uncertain, with potentially very little passing up to higher trophic levels (Cole et al.

2006) and the conversion of terrestrially derived dissolved organic carbon to CO2 by bacterial communities in urban streams may provide disproportionately large contributions to the global atmospheric carbon budget in some locations such as the tropics and sub-tropics (Pollard and Ducklow 2011).

Stream functional responses to urbanisation have been studied less than structural responses (Wenger et al. 2009) but are influenced by processes operating at a range of scales, predominantly at the catchment-scale and to a lesser degree at the reach-scale. Although leaf breakdown rates are often higher in urban streams than in non-urban streams, the mechanisms are not clear. Elevated leaf breakdown rates may be due to higher rates of physical abrasion (Paul et al. 2006, Wenger et al. 2009) or to enhanced microbial activity (Imberger et al. 2008). High levels of pollution from Zn, Cu, and other metals may reduce leaf decomposition by impacting on invertebrate shredder abundance (Duarte et al. 2008). Organic matter inputs and retention have shown varying relationships with urbanisation in different studies. They have been shown to increase in an urban stream in the warm Mediterranean climate of Adelaide, South Australia (Miller and Boulton 2005) and to decrease due to scouring in temperate climate studies in North America (Meyer et al. 2005). Gross Primary Production (GPP), community respiration (CR) and net ecosystem metabolism were not found to vary with urbanisation for Piedmont or headwater streams of Georgia, USA (Gibson 2004, Meyer et al. 2005). However, partially treated wastewater discharges can lead to streams exhibiting high respiration rates due to high-quality carbon and nutrients (Ometto et al. 2004, Meyer et al. 2005, Wenger et al. 2009, Pollard and Ducklow 2011). Higher algal biomass has been seen in some urban streams (Taylor et al. 2004) and is associated with increased light (e.g. Roy et al. 2005) and increased nutrients (Catford et al. 2007). However, scouring during storms can remove algal standing crop (Murdock et al. 2004)

5 and increased toxicants can reduce algal biomass (Hill et al. 1997) so algal biomass is not necessarily a useful indicator of stream health (Wenger et al. 2009).

Reduction in DO in the water column can arise due to the decomposition of organic matter such as vegetable wastes, sewage and other effluents. Reduced DO and increased diel DO ranges are associated with higher organic loads in stormwater runoff from urban areas (Hunt and Christiansen 2000, Sheldon et al. 2012b). Reductions in DO can lead to the loss of taxa sensitive to low DO levels. In locations below effluent outfalls, a common result of reduced DO is a less diverse invertebrate assemblage dominated by bloodworms (Chironomidae) and segmented worms (Oligochaeta), with an increase in species diversity farther downstream from the outfall (Arthington et al. 1982). Low DO can also occur in urban streams due to reduced base flows or where stagnant anaerobic pools form due to increased sedimentation (Mallin 2006, Pellerin 2006, Wenger et al. 2009). Illustrating a complex aspect of urban stream health, denitrification may be enhanced under these low DO conditions and remove N from the system (Wenger et al. 2009).

Increasing dissolved nutrient concentrations (P and N) have generally been observed with increasing urbanisation of streams (Paul and Meyer 2001, Walsh et al. 2005a, Brown et al. 2009). Excessive levels are associated with fertiliser use and wastewater effluent (LaValle 1975) and leaky sewer lines (Hoare 1984, Wernick et al. 1998, Paul and Meyer 2001) and can therefore be considered as catchment-scale stressors. In some cases, preceding land-use impacts, especially associated with agriculture, have been shown to be more closely related to P and N concentrations than current land use in some cases (Sprague et al. 2007, Brown et al. 2009). Urban streams tend to exhibit elevated nitrogen loads in the water column (Groffman et al. 2004) and the biotic capacity for nitrogen attenuation in such urban streams may become diminished or saturated (Marti et al. 2004). However, intact reach-scale riparian zones have been found to reduce nitrogen loads in some regions (Newham et al. 2011).

The piping of headwaters can also lead to altered carbon and nutrient inputs and amplified nitrogen transport (Kaushal et al. 2008b). These effects can be exacerbated by increased climatic variability (Kaushal et al. 2008a, Wenger et al. 2009). During dry years, nitrogen is stored, and during wet years the nitrogen that is flushed from watersheds can contribute to eutrophication and hypoxia (e. g. Jaworski et al. 1992,

6 Jordan et al. 2003, Hagy et al. 2004, Howarth et al. 2006, Miller et al. 2006, Kaushal et al. 2008a). These differences in export rates are especially noticeable during periods of extreme drying (drought) followed by high rainfall and similar percentage increases in export rates have been found in forested, agricultural, suburban and urban catchments (Kaushal et al. 2008a). However, in general, forested lands are associated with lower nitrate-N export rates (Kaushal et al. 2008a).

Conductivity has been shown to consistently increase along gradients of urbanisation (Paul and Meyer 2001, Brown et al. 2009, Wenger et al. 2009). It is typically considered an indicator of catchment-scale stressors (Roy 2004a, Bunn et al. 2010). Stormwater runoff, non-point source pollution, de-icing salt (used predominantly in the northern hemisphere), leaking sewer lines, waste-water treatment plant effluent and poorly functioning septic systems can increase conductivity through increased concentrations of dissolved solutes (Paul and Meyer 2001, Wenger et al. 2009). Conductivity is an inexpensive indicator of impacts of urbanisation especially waste-water inputs (Wang and Yin 1997) and possibly the most consistent indicator of water quality impacts from urbanisation particularly at high flows (Brown et al. 2009).

Toxic chemical substances may have lethal or sublethal impacts on aquatic organisms; and the sensitivity levels of taxa to toxins vary greatly (Beasley and Kneale 2002). Toxic chemical substances can have detrimental impacts on ecosystem structure and function (Paul and Meyer 2001) but it is often not clear which toxicants cause the most damage (Wenger et al. 2009). The types of toxicants found in urban streams include: heavy metals such as Cd, Cr, Cu, Pb and Zn, which may be deposited from the atmosphere, sewerage overflows, road runoff, or can be a result of pollution from current or historical factories; pesticides from field applications; and polycyclic aromatic carbons from sources such as road runoff (Bannerman et al. 1993, Boxall and Maltby 1997, Paul and Meyer 2001). Bed sediments can often contain higher concentrations than the water column but toxins can become suspended at high flows (Christensen et al. 2006). Metals, pesticides, polychlorinated biphenyls (PCBs) and many other organic contaminants such as petroleum based hydrocarbons are primarily associated with particles, and in the absence of industrial point sources, it is assumed that catchment-scale stormwater runoff across non-point sources is the major route of entry to streams (Whipple and Hunter 1979, Foster et al. 2000).

7 Urban streams often have elevated base flow temperatures compared with non-urban streams, due to processes operating at reach and catchment scales. Increased solar access can occur due to loss of reach-scale riparian vegetation (Pluhowski 1970, Marsh et al. 2005b). Processes operating at the catchment scale include the heat island effect (increased air temperatures in urban cores) and reduced groundwater recharge (Pluhowski 1970), stored water releases from shallow detention ponds and waste-water treatment plant point discharges (Wenger et al. 2009). Stream temperature can influence leaf matter decomposition (Webster and Benfield 1986) and affect macroinvertebrate life history traits (Sweeney 1984). Increased insolation can result in higher algal production and increased temperatures can harm some species or favour others metabolically adapted to higher temperatures and impact whole-reach metabolism, especially respiration, thereby altering algal assemblage structure (Krause et al. 2004, Nelson 2009). In some climates, urban streams also suffer from pulses of high temperatures because runoff from heated impervious surfaces can result in highly variable temperatures over short time frames (Van Buren et al. 2000, Nelson and Palmer 2007). There is evidence that temperature can be maintained within near-natural levels and ranges by adequate riparian tree cover (Marsh et al. 2005a) and that high temperature water can be returned to near-natural levels if a stream subsequently passes through 300 m of intact riparian tree coverage (Storey and Cowley 1997).

Because sediments, pollutants and excessive nutrients can be transported downstream, urban stream health stressors can also affect estuaries (Paul and Meyer 2001, Bunn et al. 2010). Nutrients, fine sediments and toxicants such as pesticides and heavy metals can be causes of significant environmental problems in estuaries (Abal et al. 2005a). The continual resuspension of fine sediments can lead to major declines in seagrass cover (Bunn et al. 2007) and urban development is one source of high sediment loads (Fanning 1999, Olley et al. 2006, Saxton et al. 2012). Excessive nutrients in estuaries can lead to algal blooms (Dennison and Abal 1999, Udy and Dennison 2005).

1.2.4 Mechanisms of urbanisation impacts on stream health

Whilst numerous studies describe impacts on urban stream health, the exact mechanisms by which stream ecosystems become degraded are not fully understood and probably involve multiple interacting stressors, such as degradation or loss of riparian vegetation (Thompson and Parkinson 2011), loss of habitat via scouring during high

8 flows (Meade et al. 1990, Waters 1995, Burkhead et al. 1997, Sutherland et al. 2002), dislodgement of fish (eggs, larvae and young-of-the-year) and macroinvertebrates during high flows (Power et al. 1996, Poff et al. 1997, Freeman et al. 2001, Sheldon et al. 2012c), addition of excessive pollutants, nutrients and sediments (Burkhead et al. 1997, Walsh et al. 2005b), heavy metals and other toxicants dissolved in the water column or associated with particles (Medeiros et al. 1983a, Rauch and Morrison 1999, Paul and Meyer 2001), alteration of bed sediment (Roy et al. 2003), and periods of very low DO and poor water quality associated with low base flows (Sheldon et al. 2012c).

1.3 Riparian zones – their role in stream health and how to detect their influence

In rural and predominantly forested areas, riparian zones are known to play an essential role in the healthy functioning of stream ecosystems (Naiman and Décamps 1997, Pusey and Arthington 2003) and for protecting downstream estuaries from excessive sediment and nutrients (Naiman and Décamps 1997, Naiman et al. 2005, Olley et al. 2006). Riparian vegetation protects stream banks from erosion, filters nutrients and sediments, furnishes fruits, flowers and woody debris to aquatic systems, regulates the temperature of the water column, and contributes to stream habitat structure (Naiman and Décamps 1997, Werren and Arthington 2002, Pusey and Arthington 2003). Vegetated riparian zones provide shading of streams, influence plant growth and reduce invasive plant growth and algal blooms (Bunn et al. 1998, Mosisch et al. 2001, Newham et al. 2011).

In urban areas, the role of the riparian zone has been difficult to differentiate from the impact of stressors originating in the catchment, especially the influence of surrounding impervious surfaces, many of which are directly connected to streams via stormwater piping (Roy 2004b). It is also difficult to differentiate the role of the riparian zone from the altered connectivity resulting from the stormwater drainage system that bypasses riparian zones because they often co-vary (Walsh et al. 2005a). While a number of studies have emphasised the role of reach and catchment-scale vegetated riparian zones in protecting aspects of urban stream health (Stephens et al. 2002, Roy 2004a, Booth 2005), including aquatic invertebrate community composition (Collier et al. 2009, Thompson and Parkinson 2011), reductions in nutrient loads (Newham et al. 2011), and

9 control of in-stream production (Newham et al. 2011), others have found that directly- connected impervious land cover is the most significant source of urban stream degradation, in part because of its ability to bypass the riparian zone, and in part because of the stormwater runoff it causes (Hession et al. 2003, Walsh et al. 2005a, Wenger et al. 2009).

The relative influence of reach, local and catchment-scale riparian impacts on urban stream health is likely to be important when considering the recovery of restored patches of stream habitat. Smith et al. (2009) highlighted that current understanding of the movement of individual taxa between restored patches and from intact headwaters to restored patches is relatively poor. However, they suggested that this aspect of catchment dispersal and connectivity is likely to be critical to effectively harnessing the benefits from protecting and restoring riparian condition in urban streams.

1.4 Spatial scale considerations

Reach, local and catchment scales are considered in the current study, but due to differences in scale definitions used in the literature it is important to be specific about terminology. “Reach scale” has been defined at lengths ranging from a couple of hundred metres (Van Sickle and Johnson 2008) to over 1 km (Roth et al. 1996) and at widths ranging from 30 to 200 m on each side of the stream channel (Van Sickle and Johnson 2008). “Local scale” has been considered to represent single riffle-pool sequences of approximately 150 m (Roth et al. 1996) or, as in the current study, a local area of several hundred metres in all directions upslope. The term “catchment scale” may refer strictly to the entire upstream catchment area or, as in the current study, can refer to parts of the landscape that generally extend into the entire upstream catchment such as entire length and width of the upstream riparian buffer.

Reach-scale riparian zone and catchment-scale metrics represent different scales along a continuum. Processes associated with a multitude of scales are at play in urban stream systems, from the largest spatial scales, landscapes or catchments of rivers, through smaller and smaller scales of the valley segment and channel reach, to individual channel units (e.g. riffles and pools), and down to microhabitat (Frissell et al. 1986, Montgomery 1999, Fausch et al. 2002, Allan 2004). In order to determine where within

10 a catchment land cover has most impact on a given aspect of stream health, it is important to consider spatial scale from various perspectives including: (1) study area extent; (2) spatial scale of land-cover metrics (e.g. reach, local and catchment scales); and (3) the land cover configuration and whether the metrics generated to capture this are spatial or non-spatial. Spatial metrics include areal buffers and spatially-explicit inverse-distance weighted (IDW) metrics whereas non-spatial metrics are lumped metrics representing the entire catchment (King et al. 2005).

The extent of the study area investigated has been shown to affect results (Roth et al. 1996, Lammert and Allan 1999). Studies involving several sites within each catchment have exhibited greater within-catchment variation, and have attributed more impact to the reach-scale riparian zone (Lammert and Allan 1999), whereas a study done in the same general locality but across a regional extent, without multiple sites within each catchment, attributed more impact to regional land cover (Roth et al. 1996).

While the general scales of reach, local and catchment can provide a coarse approach to determining the relative importance of different land-cover components, specific representation of land-cover configuration within the landscape via different metrics can provide further evidence of the causal mechanisms by which land cover may impact stream health. Inverse-distance weighted (IDW) metrics have the beneficial property of being able to generate spatially-explicit data which captures land-cover configuration based on proximity to the stream or to the site (Peterson et al. 2011). In some cases this may provide more information than the extent of areal buffers because IDW metrics incorporate a weighting scheme which results in a gradual reduction in land use influence with increasing distance upslope and upstream from a stream location (Van Sickle and Johnson 2008). Areal buffers have more flexibility in considering roles of different kinds of land cover because they can more easily be divided into new land- cover definitions but they are limited because their use requires all land cover within an area’s boundary be attributed with equal influence and land cover outside the boundary be attributed with zero influence.

Although IDW and areal buffer approaches have been shown to outperform the non- spatial lumped approach (King et al. 2005, Van Sickle and Johnson 2008, Walsh and Kunapo 2009), an advantage of a lumped, non-spatial approach is that it allows inclusion of some metrics which cannot easily be represented as spatial metrics. For

11 example, population density data may only be provided at a level appropriate for catchment analysis, and other supporting metrics such as total catchment area are only relevant as lumped metrics.

Van Sickle and Johnson (2008) recommend that the ecological aspects of an investigation be considered when selecting model approaches. For example, when assessing a process relating to the riparian zone, a lumped approach might be most suitable but when modelling nutrients and in-stream chemistry such an approach with sharp cut-offs may be unrealistic. Alternatively, when assessing in-stream attenuation of the effects of a landscape influence on a stream ecosystem health indicator an exponential decay model (see Walsh and Kunapo 2009) may be easier to visualise than flowpath IDW metrics and thus a better option even though both these options have a continuous decaying element and could be applied.

1.5 Connectivity, habitat fragmentation and loss

While much of the recent literature on urban streams relates to enhanced hydrological connectivity due to the connections between stormwater piping and impervious surfaces, other types of connectivity have been shown to be important to stream health and may be especially important to urban stream ecosystem health. Disconnection or partial disconnection of longitudinal stream connectivity is a common effect of dams (Ramirez et al. 2009) and road crossings, especially culverts (Mirati 1999, Robison et al. 1999, Washington Department of Fish and Wildlife 1999, Oregon Department of Forestry 2002). Altered longitudinal connectivity is also associated with the piping of stream segments under developed land cover. Fragmentation of vegetation in the landscape may disrupt dispersal patterns, reduce available habitat, and disrupt nutrient processing functions and other aspects of ecosystem function (Groffman et al. 2004, Smith et al. 2009). Connection with the surrounding floodplain can be altered due to piped and concrete drains in sections of streams as well as by levee banks and alteration of the surrounding land cover. A further distinction to make is the difference between realised and potential ecological connectivity. Potential connectivity assessments are based on landscape configuration and general knowledge of expected organismal dispersal mechanisms. Realised connectivity quantifies actual organismal movement via

12 tools such as genetic or mark-and-recapture studies (Calabrese and Fagan 2004, Crooks and Sanjayan 2006, Hughes et al. 2013).

Stream ecosystem fragmentation is a natural process for many intermittent and ephemeral streams and rivers. When flows reduce or cease, this natural fragmentation is succeeded by rewetting and habitat expansion when flows increase and there are important benefits for many fish and aquatic invertebrates as well as effects on nutrient processing and leaf litter loading (Steward et al. 2012). The natural fragmentation of ecosystems in streams that intermittently cease to flow and “run dry” affects more than half of the world’s river systems (Williams 1988) and an even greater percentage of headwaters, which are often intermittent or ephemeral. Aquatic connectivity may be interrupted and altered by the presence of stormwater piping within stream channels. The fragmentation due to stormwater piping within creek channels may not afford the same reconnection opportunities during high flows that occur in natural streams, and may alter the patterns of ecological connectivity of biological communities and ecosystems that flourish during both wet and dry periods.

Rivers expand and contract longitudinally, laterally and vertically as their flow regimes change with changes in precipitation over time (Stanley et al. 1997, Doring et al. 2007). As headwater streams in temperate, sub-tropical and tropical zones cease to flow seasonally they leave perennial pools, which can act as refugia for fish and invertebrates, separated by dry sections of streams (Steward et al. 2012). Many aquatic taxa have adapted to phases of dryout of riverbeds and may benefit from the drying phase. Dry riverbeds can act as egg banks for aquatic invertebrates and seed banks for aquatic plants, algae, fungi and bacteria (Williams 2006, Lake 2011). Some aquatic crustaceans benefit from or require a desiccation phase for their eggs or cysts to hatch (Brendonck 1996). Some fish species aestivate in dry riverbeds until they are rewetted (Berra and Allen 1989). This strategy may provide competitive advantages over other fish recolonising from upstream, downstream and lateral (floodplain) refugia when flows recommence (Steward et al. 2012). The length of dry periods influences ecological successions and the interplay and energy transfer between terrestrial and aquatic ecosystem phases in these intermittently dry and wet riverbeds (Lake 2011). In addition, the lengths of dry periods affect the distribution of drought refugia for aquatic taxa (Bunn et al. 2006) and affect the rate of responses of taxa to rewetting (Larned et al 2007). Dry riverbeds can also provide important landscape connectivity for terrestrial

13 biota because the channels provide clear airspace and contain few obstacles such as trees (Coetzee 1969, Steward et al. 2012). Compared to the research focus on wetted rivers and streams dry riverbeds have been neglected and further research into their structure and function is required (Steward et al. 2012).

While many fish species are known to be impacted by in-stream connectivity issues relating to culverts and other barriers to passage (e. g. Warren and Pardew 1998 , Mirati 1999), macroinvertebrates such as crustaceans and aquatic insects may also be impacted by in-stream barriers to dispersal (e. g. Blakely et al. 2006, Watanabe et al. 2010, Hein et al. 2011). Different taxa are likely to be impacted in different ways by changes to ecological connectivity according to their life history and dispersal traits (e.g. freshwater obligate taxa vs non-obligate taxa). Stormwater piping, roads, buildings and other impervious surfaces can fragment extensive parts of the urban landscape. Fragmentation of catchment vegetation may be an important consideration for macroinvertebrates (Shandas and Alberti 2009). Connectivity related to dams blocking fish passage was observed to be more important than catchment urbanisation to the diversity and health of fish assemblages in tropical urban streams studied by Ramirez et al. (2009). Pringle (1997) noted that as well as considering upstream connectivity, fragmentation and barriers to movements downstream can transmit stressors upstream. For example, biota constrained by downstream barriers to movement, where passage is a necessary component of their life history (e.g. diadromous fish), may be extirpated in upstream tributaries. Although the importance of natural, intermittent stream fragmentation to stream health has been highlighted (Steward et al. 2012), the potentially more permanent fragmentation associated with stream burial is likely to have different detrimental effects. While stream burial and piping of stream segments can be extensive (Elmore and Kaushal 2008), the fragmentation of stream networks associated with stream burial has rarely been studied.

1.6 Urban stream concept map

The concept map in Figure 1.1 (a) illustrating current understanding of the mechanisms of key urban impacts on stream ecosystem health as per the urban stream syndrome is based on the version presented by Walsh et al. (2005a) and includes the influence of habitat alteration and movement barriers presented by Wenger et al. (2009). Directly-

14 connected impervious surface is presented as the major driver of many impacts on urban stream health. Land-cover stressors act on stream biota via reduced water quality (increased nutrient levels, toxicants, sedimentation, etc.), altered hydrology and alteration of habitat availability and quality (morphology, habitat and food). In the temperate urban streams upon which the urban stream syndrome is predominantly based, catchment-scale stressors are regarded as having the most influence on stream health while the riparian zone is generally considered to be bypassed by stormwater piping and surface water flowing at high flow volumes. This focus on the catchment scale is reflected in the concept map, which does not explicitly indicate the spatial scale of influence of catchment and riparian land cover. In this revised version, in-stream connectivity is considered in a limited way, predominantly associated with barriers to fish dispersal, as per Wenger et al. (2009).

Figure 1.1 (b) highlights (blue) several aspects identified in the literature review above that require further consideration for understanding the impacts of urbanisation and expanding the current perception of the urban stream syndrome. These aspects may be especially important for tropical and sub-tropical streams that have different hydrological behaviour compared with temperate streams. Specifically, tropical and sub-tropical streams have been shown to have naturally flashy hydrology, which may naturally mirror many of the aspects of hydrological alteration associated with urbanisation in temperate areas (Ramirez et al. 2009, Chowdhury et al. 2012, McIntosh et al. 2013). Riparian buffer condition may also be more important in drier climates, especially at the reach scale (Thompson and Parkinson 2011, Sheldon et al. 2012b). Thus this second concept map explicitly highlights the importance of catchment, local and reach-scale variation in land cover. A broader conceptualisation of impacts of ecological connectivity is also suggested from the literature. Barriers to fish passage are known to impact migratory fish dispersal (Mirati 1999) and to restrict the local movements of freshwater fish (Warren and Pardew 1998 ). However, there is some evidence that barriers to movement are also important to aquatic macroinvertebrates, and that fragmentation of vegetated terrestrial habitat may also be important for completion of adult aquatic insect life history requirements (Smith et al. 2009).

15

(a)

(b) Figure 1.1 Urban stream concept maps of (a) the urban stream syndrome (revised from Walsh et al. 2005a) and (b) new considerations for the urban stream syndrome especially relevant to tropical and sub-tropical streams Figure 1.1 (a) illustrates the mechanisms of the key urban impacts on stream ecosystem health in the established literature of urban stream health, relevant to the urban stream syndrome, predominantly focused on temperate streams. Efficient stormwater drainage from impervious surface is a major stressor driving water quality impairment and alteration of stream morphology (Walsh et al. 2005b). This version also highlights the importance of local habitat and movement barriers to fish, as does Wenger et al. (2009). The many interactions between the different components are complex and varied and are not shown here. Figure 1.1 (b) highlights several aspects requiring further consideration, especially for tropical and sub-tropical streams. In this case, temporal and regional variation in climate and hydrology may have important influences on the impacts of altered hydrology. A broader conceptualisation of impacts of ecological connectivity is also suggested from the literature, and the importance of scale is considered.

16 1.7 Study objectives and approach

Riparian zones are known to be vitally important for protecting the health of rural streams (Naiman and Décamps 1997, Allan 2004) but in urban areas, the altered hydrological connectivity and land-cover impacts at multiple spatial scales make it difficult to distinguish the role and ecological functions of the riparian zone. In several studies in temperate locations the riparian zone has been shown to be less important than catchment-scale land-cover impacts in explaining urban stream health (Roy 2004a, Walsh et al. 2005b), with most influence attributed to catchment-scale impervious surface and associated altered hydrology. In sub-tropical and tropical locations, it is likely that the influence of catchment-scale impervious surface and associated altered hydrology on urban stream health is less than in temperate climates because the natural hydrology in these more ephemeral and flashy areas reflects the characteristics associated with impervious surface influences (de Jesus-Crespo and Ramirez 2011, Sheldon et al. 2012b). Thus factors other than altered hydrology deserve further research to explain the impacts that urbanisation has on stream health where catchment- scale impervious surface may not be the dominant driver.

This study of stream health in highly urbanised freshwater streams in SEQ was designed to detect whether the role of the riparian zone in protecting stream health in urban creeks can be distinguished from the catchment land-cover impacts, and to determine the relative importance of these two land-cover scales. Another aim was to assess the relative importance of pervasive stormwater piping which reduces the effective riparian zone extent and ecological connectivity. In order to further explain why there may be differences in the relative importance of different stressors associated with different scales in different studies, the study also aimed to determine whether study area extent influenced the determination of the relative importance of land-cover stressors associated with different scales. The study then aimed to assess a broader range of ecological connectivity metrics likely to capture more aspects of its potential influence on biota with different life history traits.

The study uses a suite of ecosystem health indicators known or expected to respond to catchment and riparian condition and impacts. It applies geographical information systems (GIS) techniques to calculate land cover in the catchment and riparian zone, and incorporates stormwater piping and natural streams in the generation of flowpaths

17 and catchment boundaries. Spatial scale analysis and multivariate models allowed comparison of the relative importance of a range of land-cover metrics in explaining variations in stream health. Various GIS land-cover metrics were generated to capture riparian and catchment land cover and to represent reach, local and catchment scales of influence. These GIS metrics included both spatial (spatially-explicit IDW metrics and areal buffers) and non-spatial (lumped) measures of land cover. In addition, a lumped land-use metric (population density) was considered, as were several landscape metrics such as upstream sub-catchment size. The selection of stream-health (fish and macroinvertebrate indicators) and water quality indicators was intended to determine if there were differences in how stream health indicators respond to reach, local and catchment-scale land-cover configurations. Lumped, areal buffer and IDW metrics developed in Chapter 3 include new effective riparian buffer metrics, which are elaborated in subsequent chapters to include in-stream longitudinal connectivity and surrounding tree-cover fragmentation. Relationships between catchment and riparian cover metrics and stream health and water quality were analysed in a study area of small areal extent (Bulimba Creek and Norman Creek, BCNC) and in a study area of medium areal extent (Lower Brisbane River Catchment and surrounding coastal catchments, LBRCSCC). The findings were compared.

1.8 Structure of the thesis

Chapter 1: Introduction

Chapter 1 synthesises the current literature on urban streams and presents the broad aims and objectives of this study. Key themes in the literature review include (1) the relative importance of reach and catchment-scale riparian zone metrics compared with broader catchment land-cover and land-use metrics, especially impervious surface, (2) spatial scale modelling considerations (3) disruption to ecological connectivity, and fragmentation of urban stream ecosystem habitats, associated with urbanisation.

Chapter 2: Study Area

Chapter 2 depicts and describes the focal study areas for this research in SEQ. Information is presented relating to the ephemeral and perennial streams of the sub-

18 tropical SEQ region with a focus on the highly urbanised Lower Brisbane River catchment and surrounding coastal catchments. The chapter presents the geology, climate, hydrology, and vegetation of the region as well as discussing the anthropogenic pressures the region faces from population growth, land clearing, urbanisation and downstream impacts on estuaries.

Chapter 3: Spatial analysis of impacts of catchment-scale impervious surface and reach-scale riparian cover on water quality and health of sub-tropical urban streams

This study sought to compare the relative influences of catchment, local and reach-scale land use on urban stream health in two highly urbanised ephemeral stream catchments of the Lower Brisbane River. It also sought to account for the extensive burial and piping of stream segments in urban areas. It was hypothesised that:

1) Altered hydrology associated with catchment and local-scale impervious surface has a reduced importance in explaining urban stream health in the ephemeral sub-tropical streams of SEQ compared with temperate streams 2) Reach-scale riparian buffer condition is more important in explaining urban stream health in the ephemeral sub-tropical streams of SEQ compared with temperate streams 3) The presence of in-stream stormwater piping has a detectable influence on stream health indicators that is distinguishable from catchment-scale impervious surface impacts

To investigate the first two hypotheses, spatial and non-spatial land-cover and land-use metrics based on available DEM, land-cover and population data were generated in a GIS. These metrics were included as candidate explanatory variables in a priori hypotheses or models for stream health represented by indicators for water quality and biota (SIGNAL2). Models were fit using the generalised least squares (GLS) function and compared using an information-theoretic approach. In cases where no single candidate model was clearly the best, model averaging was undertaken to assess the relative importance of land-cover variables that appeared in the most plausible models.

19 Chapter 4: Spatial analysis of the influence of the areal extent of a study on the detectable impacts of urbanisation on the ecosystem health of sub-tropical streams

This chapter aimed to discern whether a study area of larger areal extent than the focus study in Chapter 3 alters the detectable scale (reach, local and catchment scales) at which land-cover and land-use impacts drive changes to stream health. Therefore, in this chapter, it was hypothesised that:

1) When studied across a larger area (medium areal extent), similar scales, types and configurations of land cover as those found to be relatively important to explaining stream health in Chapter 3 are associated with stream health, as measured by the diversity and abundance of macroinvertebrates and fish, in the ephemeral urban streams of southeast Queensland (SEQ)

The approach used in this chapter involved generating similar explanatory land-cover, land-use and landscape metrics to those used in Chapter 3 and applying them to models of macroinvertebrate and fish indicators of stream health. Brisbane City Council (BCC) provided the stream health indicators for this chapter and Chapter 5. The spatial analysis and statistical modelling approaches used were similar to those applied in Chapter 3. Although this second study involved a larger number of catchments in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) compared with the Chapter 3 study, it included a similar number of sites and a similar range of site upstream drainage areas.

Chapter 5: Spatial analysis of the impacts of ecological connectivity on biota in urban streams

This chapter aimed to explore ecological connectivity, specifically relating to dispersal path connectivity and habitat fragmentation, as a key factor driving ecosystem health in urban streams. It also considered whether different biota (e.g. freshwater and diadromous fish, and macroinvertebrates such as crustaceans and insects) might be impacted differently by reductions in potential dispersal path connectivity based on their life history traits and dispersal mechanisms. In this chapter, it was hypothesised that:

20 1) Ecological connectivity and habitat fragmentation are more important in explaining urban stream health in the ephemeral streams of SEQ, as indicated by fish and macroinvertebrate diversity, abundance and occurrence, than catchment-scale impervious surface area and associated altered hydrology. 2) Ecological connectivity in urban streams influences aquatic taxa differently depending on their life history requirements and dispersal mechanisms.

The approach taken in this second study of sites in the Lower Brisbane River catchment and surrounding coastal catchments was to compare the relative importance of new in- stream, terrestrial and riparian ecological connectivity metrics with the spatial and non- spatial land-cover, land-use and landscape metrics studied in Chapter 4 in explaining the variation in fish and macroinvertebrates diversity and abundance and occurrence response of selected taxa. Given that life history traits were anticipated to influence the response of specific taxa to different aspects of ecological connectivity, this chapter sought to determine whether some taxa are more sensitive than others to various aspects of in-stream and terrestrial connectivity.

The new ecological connectivity metrics were generated using network utility analysis to assess in-stream connectivity using the same base stream-health and land-cover data sets from Chapter 4. Fragmentation metrics were determined using FRAGSTATS software in a GIS. The analysis based on occurrence of selected fish and macroinvertebrate taxa was undertaken using generalised linear models (GLM) whereas the diversity and abundance models were specified as GLS functions similar to those applied in Chapters 3 and 4. Model averaging was used to assess the relative importance of explanatory metrics where no single best model could be clearly identified.

Chapter 6: A perspective on the urban stream syndrome that incorporates different measures of natural hydrology and ecological connectivity

Chapter 6 synthesises the findings of the research program and presents an updated concept map of urban stream ecosystem health highlighting the key new knowledge contributions of these studies and how they relate to the published literature. The most important scales and configurations of land-cover influence on urban stream health identified in these studies are described and discussed. The implications of local SEQ climate and hydrology and the suggested impacts of ecological connectivity on urban

21 stream health in the region are related to an expanded concept of the urban stream syndrome. Stream health restoration opportunities that are likely to be ecologically and financially effective in sub-tropical SEQ, as well as limitations, are discussed. Further areas of research into land-cover and land-use impacts on urban stream health in different climatic and hydrological settings are identified.

22 CHAPTER 2 STUDY AREA

2.1 Southeast Queensland (SEQ) natural setting

2.1.1 Physical setting

The southeast Queensland (SEQ) region is located on the east coast of Australia, with a peak elevation of 1360 m in the west along the Great Dividing Range (Abal et al. 2005a, Bunn et al. 2007). There are seven major river systems with a total catchment area of 32,000 km2 (Kennard et al. 2010). Most headwaters rise in the Great Dividing Range in the west and flow generally eastward, draining into Moreton Bay. The river systems of SEQ are predominantly perennial, intermittent and highly intermittent (Kennard et al. 2010). The SEQ region is one of 54 “natural resource management” (NRM) regions, based on catchments or bioregions, which have been delineated to cover all of Australia (Caring for our Country 2013).

Where general information on the natural setting was unavailable for all of SEQ, specific references are made to the Lower Brisbane River and surrounding coastal catchments. It is assumed that this area reflects many of the general natural characteristics of SEQ.

2.1.2 Major catchments

The SEQ region includes Moreton Bay and islands, as well as the Noosa, Maroochy/Mooloolah, Pumicestone, Pine Rivers, Redlands, Logan/Albert, Gold Coast, Bremer, Lockyer, Stanley, and the Upper, Mid and Lower Brisbane River catchments (Caring for our Country 2013). As well as the major river systems there are smaller catchments, which drain directly to the coast. Many of the major and minor catchments in SEQ, including the Brisbane River, drain to the Moreton Bay marine park, which is recognised under the Ramsar Convention as a wetland of international significance for protecting wetland habitats and migratory birds. The bay and estuaries support important commercial and recreational fisheries and areas for recreation (Abal et al. 2005b, Bunn et al. 2007). Moreton Bay is quite shallow with an average depth of 6.8 m (Dennison and Abal, 2000).

23 The Brisbane River System is the major catchment in SEQ. It has headwaters in the D’Aguilar Ranges of the Great Dividing Range and includes several urban creek systems closer to the coast.

2.1.3 Climate

The sub-tropical climate transitions between tropical and warm temperate (Bridgewater 1987, Pusey et al. 2004). Rainfall patterns are influenced to some degree by the summer cyclones and the wet season from October to March delivers approximately twice as much rain as the relatively dry season from April to September (Bureau of Meteorology 2011b). Most rainfall is in the summer period from December to March (Rolls and Arthington 2014). The northward extension of temperate weather systems can occasionally lead to significant rainfall from Autumn to mid-winter (Bridgewater 1987, Pusey et al. 2004). Average rainfall varies across SEQ in a longitudinal (east-west) direction. Average rainfall in coastal areas is 1400 mm and in the west it is 800 mm (Bridges et al. 1990, Young and Dilleward 1999). The occurrence and intensity of summer and autumn rainfall is irregular (Pusey et al. 2004) and rainfall varies greatly between years. This large time-scale variation is predominantly driven by the El Niño Southern Oscillation (ENSO), which results in rainfall variation from drought conditions to flood conditions, and rainfall in dry years is less than half that of wet years (Bureau of Meteorology 2011a). The average annual maximum daily temperature ranges from approximately 23 to 25°C (with cooler temperatures associated with higher elevations) (Bureau of Meteorology 2011a). Temperatures in SEQ rarely exceed 35°C or fall below 10°C for extended periods of time. In SEQ the average potential evapotranspiration (1450 mm) is higher than the average rainfall (1150 mm) (Bureau of Meteorology 2011a, McIntosh et al. 2013).

2.1.4 Hydrology

In accordance with the variable rainfall, stream flow in the region is seasonally variable (Pusey et al. 1993, Abal et al. 2005a, Saxton et al. 2012). The majority of stream flow in SEQ is concentrated in the summer months from January to March, with a smaller secondary peak from April to June associated with low-pressure temperate systems from the south (Pusey et al. 1993, Pusey et al. 2004). Periods of low discharge occur from August to November (Pusey et al. 2004). Because summer and autumn rainfall is

24 irregular, the incidence and magnitude of discharges in SEQ are unpredictable. Many years lack summer floods (Pusey et al. 2000, Pusey et al. 2004). Hydrology is characterised as flashy (Pusey et al. 1993) and most streams are ephemeral (Milton and Arthington 1985, Pusey et al. 1993). However, flows in the dry season from July to October are relatively stable and do not vary much (Milton and Arthington 1985, Pusey et al. 1993, Pusey et al. 2004). There is greater variability in flows in tributary streams than in the lowland river systems of SEQ.

Floodplains in SEQ are generally narrow and the main stream is often entrenched (Granger and Hayne 2000). While the streams in the upper reaches are relatively steep and often exhibit down-cutting with v-shaped transverse profiles, or consist of bedrock streams, the middle to lower reaches meander, and higher order channels widen into alluvial streams on valley floors (Hofmann 1976, Hodgkinson 2009 ).

Construction of dams and weirs as well as land use and vegetation changes have resulted in altered, flashier hydrology associated with rainfall events in SEQ (Bunn et al. 2007). SEQ contains 24 dams with crest height greater than 15 m (ANCOLD [Australian National Committee on Large Dams] Inc. 2002) and the combined storage capacity of dams and weirs is approximately 38% of the combined (natural) mean annual runoff of the major river catchments (Mackay et al. 2014). In a recent study of altered hydrology in SEQ (Mackay et al. 2014), all of the river gauges assessed indicated flow regimes that were changed from their natural state. While upstream presence of dams was generally associated with the greatest flow regime change from reference conditions, 11 of the 49 gauges assessed were not downstream of dams. Altered hydrology due to urbanisation is discussed further in section 2.2.2.

Groundwater in SEQ is associated with the clay, silt, sand and gravel of alluvium (in the more gently sloping water courses) as well as, in the coastal areas, with dune sand and the mud, silt, sand and clay of estuarine deposits. It is also associated with the basalt in the area (notably in the Lamington Group formation) and the fractures in the sandstones, greywackes, cherts, rhyolite tuff, shales, conglomerates, siltstones and mudstone of many other rock formations (Swann 1997). Most geological formations in SEQ have groundwater characteristics (Swann 1997).

25 Surface drainage in the Lower Brisbane River and surrounding coastal catchments is effectively controlled by topography with a small influence due to soil and plant cover (Beckmann et al. 1987). The mountainous areas in the northwest typically have rapid and excessive runoff due to thin and stony soils dominating the area. However, in the coastal plain and valley floors, flows are sluggish and short-term flooding is common due to flatter slopes and deep soils with high water retention capabilities (Beckmann et al. 1987).

2.1.5 Geology and topology

The SEQ region is characterised by metamorphic and acid to basic volcanic hills and ranges (including D’Aguilar Ranges) and sediment in the basins (including the Moreton Basin), extensive alluvial valleys and Quaternary coastal deposits which include high dunes on the sand islands (Queensland Wetlands Program Department of Environment and Heritage Protection 2012).

The chemical and physical properties of soils in the SEQ region may restrict plant growth through limits on nutrient levels in the naturally occurring soils (Beckmann et al. 1987). Poor drainage is also an issue in this region (Beckmann et al. 1987). The soils of the Lower Brisbane River catchments and surrounding coastal catchments include lithosols, several types of podzolic soils (red, yellow, lateric and gleyed) red earths and krasnozems, cracking clay soils (vertisols), podzols and both well-drained and poorly drained soils on transported materials (Beckmann et al. 1987). The parent rock material underlying these soils includes sedimentary and metamorphosed sedimentary rock, igneous rock (predominantly basalt and granite) as well as unconsolidated clay and sand material (Beckmann et al. 1987).

2.1.6 Vegetation

Prior to European settlement, the vegetation of SEQ was predominantly forests and woodlands (97.9%) with a small extent of grasslands (0.01%) (Bean et al. 1998). Approximately 25% of the original vegetation in SEQ remains intact (Bunn et al. 2007). Clearing and fragmentation of vegetation has most strongly impacted lowland species, such as lowland riparian rainforests, forest red gum woodlands on the alluvial plains and the paperbark forests near coastal stream estuaries. In comparison, the forests of the

26 hills and ranges have retained greater coverage of their original vegetation (The Joint Commonwealth and Queensland Regional Forest Agreement Steering Committee 1999). Forest remnants include sub-tropical and warm temperate rainforests, and moist eucalypt forests, mainly restricted to mountain ranges. Other remnant forest types include tall open forests, open eucalyptus forests and woodlands, dry eucalypt forests, Melaleuca wetlands, and Banksia woodlands and heaths (The Joint Commonwealth and Queensland Regional Forest Agreement Steering Committee 1999).

2.1.7 Land use

Predominant land use in SEQ includes production from fairly natural landscapes such as grazing (55%) and conservation and natural environments (21%) (Department of Natural Resources and Mines 2003, Saxton et al. 2012). Intensive uses (including urban land) make up 14% of the region, and dryland agriculture and plantations, and irrigated agriculture and plantations are 5% and 2% of land use respectively) (Department of Natural Resources and Mines 2003, Saxton et al. 2012).

2.1.8 Population

The population in the region is approximately 2.73 million (Australian Bureau of Statistics 2010a) and is expected to reach 3.5 million in 2021. Brisbane, the state capital and major population centre (approximately 2 million) is located on the Lower Brisbane River. Population growth in SEQ is one of the key threats to the sustainability of stream health in the region. The SEQ region includes just 1.3 per cent of Queensland's area (Caring for our Country 2013) yet two-thirds of the estimated resident population of Queensland (4.58 million) were living in SEQ (3.05 million) in June 2011 (Australian Bureau of Statistics 2010b). From June 2006 to June 2011, the average annual population growth rate for the state was 2.3%, second only to Western Australia (2.7%). In the year to June 2011, the population growth in SEQ (51,300) accounted for 69% of the total annual growth in the state (Australian Bureau of Statistics 2010b). The population in the Brisbane City Council (BCC) local government area (LGA) was approximately 2.15 million in 2011 (which equates to 1870 persons / km2) (Australian Bureau of Statistics 2012).

27 2.2 Major threats to aquatic ecosystem health in SEQ catchments

2.2.1 Land clearing

Substantial amounts of land clearing have occurred since European settlement in 1823. Approximately 60% of all native vegetation (Saxton et al. 2012) and approximately 69% of Brisbane’s original woody vegetation has been cleared (Brisbane City Council 2008a). The least disturbed remnants of vegetation within the Lower Brisbane River catchment and surrounding coastal catchments are found on steep lands, especially to the west of the city (Beckmann et al. 1987), mostly in areas of poorer soils and on wet and saline lands on the lower plains and littoral.

Significant channel erosion occurs through much of the region according to catchment modeling and tracer studies (Caitcheon and Howes, 2005). This is exacerbated by the poor riparian condition of about 50% of the 48,000 km of streams in SEQ. Poor riparian condition is associated with poor water quality and poor aquatic ecosystem health in SEQ streams (Bunn et al., 1999; Smith et al., 2005, Bunn et al. 2007).

Periodic cyclones and aboriginal fire management were the only substantial land-cover disturbances prior to European settlement (Saxton et al. 2012).

2.2.2 Urbanisation

As the population in SEQ continues to increase, urbanisation levels can be expected to increase as well, although planning decisions are likely to influence how this affects land cover, land use and urban stream health. For example, planning processes which favour high density urban form may concentrate urbanised land use, leading to further reductions in backyard vegetation and high concentrations of impervious surface in these densely populated areas (Hall 2010, McIntosh et al. 2013). However, overall, there are ecological (e.g. Gagné and Fahrig. 2010) and other environmental benefits such as reduced carbon emissions (Jones and Kammen) associated with concentrating development into areas of high housing density instead of sprawling suburbs.

Altered hydrology, one of the key urban stream stressors identified in the literature (Paul and Meyer 2001, Walsh et al. 2005a), influences the health of streams in SEQ

28 (McIntosh et al. 2013, Rolls and Arthington 2014). However, it should be noted that lumped (percent) urban land use appears to have more explanatory power for urban stream health in SEQ than lumped or distance-weighted impervious surface and altered hydrology does not appear to be an important driver of urban stream health degradation (Sheldon et al. 2012b, McIntosh et al. 2013). In general, time spent under high flow conditions increases with urbanisation in SEQ, but in some catchments high spell duration decreases, likely due to catchment characteristics such as size and topography (McIntosh et al. 2013). In SEQ, urbanisation is typically associated with increased base flows, that is a decrease in time spent under low or no flow conditions (McIntosh et al. 2013). This has been attributed as a consequence of streams being naturally ephemeral becoming more perennial (McIntosh et al. 2013) and has been shown to occur in other urbanising locations with naturally ephemeral streams (Cooper et al. 2013, Bhaskar et al. 2016). This contrasts with the reduction in base flow seen in many temperate urban streams (Paul and Meyer 2001, Walsh et al. 2005a, Bhaskar et al. 2016). However, at low levels of urbanisation (less than 5%) some larger streams in SEQ can exhibit reduced base flow, that is increased time spent under low or no flow conditions (McIntosh et al. 2013). Macroinvertebrate diversity and species richness measures in SEQ remain similar between urban and pre-development sites (McIntosh et al. 2013), however, there are fewer sensitive species in urban stream sites of SEQ compared with pre-development sites, especially in pools (Sheldon et al. 2012c, McIntosh et al. 2013). In some cases, more stable, elevated base flow volumes associated with impervious surface in some urban streams in SEQ appear to have led to increased macroinvertebrate diversity and abundance in otherwise highly ephemeral streams which naturally experience extended periods of low and no flow (Sheldon et al. 2012b, McIntosh et al. 2013). However, flow regulation in SEQ has been shown to have deleterious effects on fish assemblage composition predominantly due to intermittent streams becoming more perennial (Rolls and Arthington 2014).

The area bounded by the BCC LGA has an average impervious surface cover of 19% (Chapter 3), which is above the thresholds identified in some studies for moderate to extreme impacts on fish (Klein 1979) and macroinvertebrate (Walsh et al. 2005a) diversity and abundance.

29 2.2.3 Downstream impacts on the Moreton Bay Estuary

Nutrients (particularly nitrogen), fine sediments and, to a lesser extent, toxicants (pesticides and heavy metals) have been identified as causes of significant environmental problems affecting Moreton Bay (Abal et al. 2005a). The high catchment to bay ratio for Moreton Bay (14:1) results in residence times that range from days in the eastern bay to several months in the western embayments which can compound water quality problems in the western embayments (Abal et al. 2005b, Bunn et al. 2007).

The continual resuspension of fine sediments in the bay associated with increased turbidity has led to major declines in seagrass in the western embayments (Bunn et al. 2007). Estimated sediment loads to Moreton Bay are 30 times higher than rates pre- European settlement (NLWRA 2001). These higher sediment loads are thought to be derived from the clearing of upper catchments, cultivation of floodplains, overgrazing, clearing of riparian vegetation and development of urban areas (Fanning 1999, Olley et al. 2006, Saxton et al. 2012).

Excessive nitrogen in Moreton Bay has been associated with algal blooms (Dennison and Abal 1999, Udy and Dennison 2005). Important sources of nitrogen and other nutrients in SEQ include sewage plant discharges (although improvements to wastewater treatment processes are reducing this input), airborne emissions from industry and cars as well as increasing rates of delivery of nutrients associated with land-clearing and the use of fertilisers (Udy and Dennison 2005).

2.2.4 SEQ Ecosystem Health Monitoring Program (EHMP)

A Freshwater Ecosystem Health Monitoring Program (EHMP) was implemented in the SEQ region in 2002. Biannual measurement of 16 stream health indicators is conducted at more than 120 sites across SEQ. The indicators include measures of fish and invertebrate diversity, ecosystem processes and water quality (Kennard et al. 2005, Smith and Grice 2005, Fellows et al. 2006, Udy et al. 2006, Bunn et al. 2007). The Freshwater EHMP complements a marine and estuarine EHMP based on monthly assessment of water quality at 260 marine and estuarine sites and additional seagrass mapping. Both the freshwater and marine EHMPs are overseen by the SEQ Healthy

30 Waterways Partnership (HWP), building on preliminary investigative work in SEQ since the 1990s (Bunn et al. 2007). The results of these ongoing assessments are used to report annually on freshwater, estuarine and marine ecosystem health at a regional scale. These best-available regional results are used to guide investment in aquatic ecosystem rehabilitation at the catchment scale (Bunn et al. 2010). The data set also provides opportunities for scientific investigations, such as spatial scale analysis of land-use impacts of urbanisation on stream health (Sheldon et al. 2012b, McIntosh et al. 2013).

2.3 The Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) site selection

2.3.1 Physical setting

The study area of medium areal extent (see Chapters 4 and 5) consists of sites from the BCC ongoing Local Waterways Health Assessment (LWHA) (Figure 2.1). These sites are located in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC). These catchments drain either directly to Moreton Bay or to the bay via the Brisbane River, which, due to dredging, is tidal to approximately 80 km AMTD (Adopted Middle Thread Distance).

2.3.2 Geology and topology

The three topographic units that principally define the area included in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) are: (1) the coastal plain (areas less than 10 m above sea level); (2) undulating to low hilly lands; and (3) steep hilly and mountainous lands (Tucker and Houston 1967, Cranfield et al. 1976, Willmott et al. 1978, Beckmann et al. 1987). The greater part of the area is undulating low hilly lands. Typically the hills are less than 80 m elevation; the highest is Mt Gravatt at 255 m. The low hilly areas typically have gentle to moderate slopes. The middle to upper slopes of the higher ridges, particularly to the west, are moderately to steeply sloping. These hilly areas are dominated by the greywackes, siltstones and shales of the Neranleigh-Fernvale group. The alluvia in the valleys tend to be sands and sandy clays of medium texture.

31 2.3.3 Vegetation

The likely dominant types of original vegetation in the freshwater sections of this study area included eucalyptus open forests and woodlands, Melaleuca open forests and woodlands and closed rainforests of emergent Araucana cunninghamu (hoop pine) along stream corridors, in sheltered valleys and on southern slopes (Herbert 1951, Dowling and McDonald 1976, Elsol and Dowling 1978, Beckmann et al. 1987).

2.3.4 Extreme weather events

An important difference between the data collection periods of 2010 and 2011 was extended drought conditions prior to 2010 followed by major flooding and significantly higher than average rainfall in the Austral spring of 2010 and summer of 2010/2011 (60% and 150% respectively above the 20th century average for these seasons (South Eastern Australia Climate Initiative (SEACI) 2011)). From the 10th to 12th January 2011 there was a major rain event in Brisbane and SEQ with extreme flooding, including river and flash flooding. Heavy rain in late 2010 and early 2011 contributed to the flooding (National Climate Centre Bureau of Meteorology 2011) which saw the second highest flood-level of the Brisbane River in the vicinity of Brisbane in 100 years (Bureau of Meteorology 2014).

2.3.5 Site selection

Data for the LWHA sites used in the Lower Brisbane River catchment and surrounding coastal catchments assessments in Chapters 4 and 5 (Figure 2.1) were collected in autumn (March-April) of 2010 and 2011. Site locations range from longitude 152.7 ° E in the west to 153.2 ° E in the east and latitude 27.31 ° S in the north to 27.65 ° S in the south. There were 33 sites (Appendix 1) with complete stream health data sets and geographical information system (GIS) data for drainage and land cover required for Chapter 4 and 30 of these sites had the necessary data for the analysis of landscape fragmentation and in-stream longitudinal connectivity in Chapter 5.

These sites range from highly urbanised to relatively intact forest cover and therefore include a range of values for lumped catchment-scale impervious land cover and associated stormwater piping extents, as well as for tree, grass and impervious riparian

32 land cover at the reach and catchment scales. The mean percentage impervious land cover of the catchments draining to the 33 sites in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) study is 23%. Maps illustrating variation in lumped impervious surface area and reach-scale tree cover for the LBRCSCC studies of Chapters 4 and 5 are presented in Figures 2.2 and 2.3.

2.4 Focus catchments: Bulimba Creek and Norman Creek focus catchments

2.4.1 Physical setting

Bulimba Creek and Norman Creek (BCNC) catchments, southern tributaries of the Lower Brisbane River, are considered in a focus study in Chapter 3. The BCNC catchments have been classified as coastal streams in the EHMP regional freshwater ecosystem health assessment (Coaldrake 1961, Smith et al. 2005). Compared to the upland and lowland streams (identified in the EHMP), they have relatively lower gradients, have lower elevation and receive more rainfall. They also do not have the acidic nature of the northern tannin stained “Wallum” streams (Coaldrake 1961, Catterall and Kingston 1993, Catterall et al. 1997, Smith et al. 2005). The BCNC catchments contain ephemeral tributaries and headwater streams and relatively perennial main channel streams (Arthington et al. 1982, Brisbane City Council 1999).

Mean annual rainfall in the Bulimba Creek catchment is 1,245 mm (Arthington et al. 1982). Rainfall and natural stream flow characteristics in Norman Creek catchment are likely to be similar due to its proximity to Bulimba Creek catchment.

2.4.2 Geology and topology

Bulimba Creek catchment geology includes rock types such as quartzite, chert, slate, greywacke, conglomerates, minor shales and sandstone. Most of upper Bulimba Creek surface geology consists of consolidated sediments including fine to coarse-grained sandstones as well as siliceous conglomerates and minor shales (Cranfield et al. 1976). Most of the geology of the Norman Creek catchment is dominated by the Neranleigh- Fernvale Group, which includes greywackes, siltstones and shales (Bryan and Jones 1951, Stevens 1973).

33 The soils of the Bulimba Creek catchment are highly erodible with low nutrient value (Brisbane City Council 1999) and include red-yellow and lateritic podzolics on the low hills and hill crests of the southwest tributaries near Beenleigh, Woodridge and Coopers Plains (Beckmann et al. 1987). Limited amounts of more fertile soils can be found in the basaltic black earths on the crests and gentle slopes at Runcorn, pockets of red earths in the upper parts of the main ridges at Sunnybank as well as Kuraby and Belmont, and the alluvial soils along Bulimba Creek (Brisbane City Council 1999). The soils of Norman Creek also typically have low fertility. The red-yellow podzolics of the Beenleigh Soil Association cover most of the Norman Creek catchment (Beckmann et al. 1987), with lithosols and shallow podzolic soils of other soil associations (Pullenvale, Mount Cotton and Chermside) found in the middle sections of the catchment and on high quartzite hills.

2.4.3 Hydrology

Bulimba Creek is tidally influenced to approximately 20 km AMTD. From headwaters in the low plateaus to the south it flows in a northerly direction to the Brisbane River. In addition to the creek network the catchment contains several large wetland systems. Undulating hills with narrow valley floors are contained in the upper catchment and the central and downstream reaches are predominantly part of a wide, flat floodplain (Bulimba Creek Catchment Coordinating Committee 2016). Most of the tributaries in Bulimba Creek catchment are low gradient lateral creeks that traverse the floodplain (Brisbane City Council 1999). The mid-section consists mostly of riffles and pools. In the upper reaches riffles dominate over pools but in the headwaters the creeks are ephemeral. Flows in the upper catchment are fairly fast (over 0.2 m/s) (Arthington et al. 1982) but this slows in the deeper pools downstream. Downstream, pools are more prominent than riffles and become wider, deeper and longer (Arthington et al. 1982). The tributaries of Bulimba Creek vary in their natural character. For example, Mimosa Creek has a steeper gradient and Spring Creek is a spring-fed creek system (Brisbane City Council 1999).

Norman Creek is tidally influenced to a length of 0.9 km within the bounded catchment area with a further 3.8 km AMTD between the Norman Creek boundary and the Brisbane River Estuary. The headwaters of Norman Creek are in Toohey Forest and Mt

34 Gravatt (255 m). The tributaries of Norman Creek are mostly freshwater (Brisbane City Council 2011).

2.4.4 Vegetation

Much of Bulimba Creek has been cleared of its original vegetation, though there has been some replanting with native and introduced species (Arthington et al. 1982). The highly urbanised Norman Creek also has a little remnant vegetation in its headwaters. Remnant forested areas include tall woodland and open forests of eucalyptus and brush box (Tristania conferta). Swampy areas of Bulimba Creek are dominated by the paper- bark tree (Melaleuca quinquinerva) and swamp box (Tristania suaveolens) (Arthington et al. 1982).

Bulimba Creek and Norman Creek are impacted by introduced weeds (Brisbane City Council 2002, 2008a) including Cenchrus ciliaris (buffel grass) and Cinnamomum camphora (camphor laurel trees).

2.4.5 Land use

The following section on land use for Bulimba Creek and Norman Creek is based on information from local catchment groups and verification using available GIS data.

Land use in the Bulimba Creek catchment includes residential, rural-residential, commercial, industrial, recreational and open-space. Remnant forest remains in a ring around the catchment. About 10% of the catchment contains bushland vegetation and associated wetlands (Bulimba Creek Catchment Coordinating Committee 2016). A large area of the land around Bulimba Creek was cleared for rural development of pasture and crops 100 years ago (Brisbane City Council 1999) and has been progressively converted to residential areas.

The main channel of Bulimba Creek has been extensively modified but not as much as many other creeks in Brisbane, including Norman Creek. Bulimba Creek has a generally continuous open space corridor on its main channel and larger tributaries, in part due to the ongoing advocacy and stream rehabilitation work of the Bulimba Creek Catchment Coordinating Committee (Brisbane City Council 1999). While some headwaters (e.g. Spring Creek) are located in privately owned bushland that has been

35 protected from development, other tributaries have their headwaters in piped sections of the creek.

The Norman Creek catchment has very little original habitat remnants especially in low- lying areas, and is almost completely urbanised (Brisbane City Council 2011). Only 3% of the Norman Creek catchment area remains undeveloped (Brisbane City Council 2002). Headwaters flow through a few remnant bushland areas in the relatively pristine Toohey Forest and Mt Gravatt and from there the creek passes through residential and commercial areas. Extensive lengths of creek are piped underneath suburbs and commercial areas, fragmenting the surface creek network. There have been several stream rehabilitation and water quality rejuvenation works such as Bowies Flat in 2001, a large-scale natural water treatment system on Norman Creek (Brisbane City Council 2011). With trash racks, a sediment pond, vegetated wetland areas and a natural channel, it is designed to remove litter, sediments and nutrients (Brisbane City Council 2002). Restoration projects have also been undertaken at Moorhen Flats, Glindemann Park, Arnwood Place, and Nicholson Street (Greenslopes) (Brisbane City Council 2002). In some locations (such as the channel at Deshon Street) the stream channel has been widened for flood mitigation purposes (Brisbane City Council 2002).

The mean impervious surface area for the Norman Creek catchment (36%) is higher than for the Bulimba Creek catchment (26%).

2.4.6 Population

The population of Bulimba Creek catchment in 2006 was approximately 120,000 people in an area of 122 km2 (983 persons / km2) while the population of Norman Creek catchment was approximately 90,000 people in an area of 29.8 km2 (3020 people / km2) (Australian Bureau of Statistics 2007). The population density of Norman Creek catchment is therefore 50% higher than BCC’s LGA, and three times that of Bulimba Creek. Land cover and population density metrics were determined as part of the current study using GIS land-cover data and digital elevation model data provided by BCC.

36 2.4.7 Site Selection

The study area of small areal extent (Chapter 3) consists of 30 sites (Appendix 1) within the Bulimba Creek and Norman Creek (BCNC) catchments (Figure 2.4). While the mean impervious area of the catchments draining into these sites is 26% (minimum 5%, maximum 45%), mean reach-scale tree cover 200 m upstream for these sites is 66% (minimum16%, maximum 100%). Maps illustrating variation in lumped impervious surface area and reach-scale tree cover for Bulimba Creek and Norman Creek are presented in Figures 2.5 and 2.6.

Sites were selected to include as great a range as possible of catchment impervious land-cover percentages and associated stormwater drains and piping extents, as well as the greatest range possible in tree, grass and impervious riparian land cover at the reach and catchment scales. However, site selection was constrained by site accessibility and therefore sites that were entirely piped underground were not included for obvious access reasons. Site selection was also intended to capture nested sites and longitudinally connected sites that covered an extensive component of the catchments. That is, two sites could be located on the same streamline. The minimum distance along the stream channel between two sites is just over 500 m.

37

Figure 2.1 Study area and sites shown for the Lower Brisbane River catchment and surrounding coastal catchments studies (LBRCSCC)

38 Figure 2.2 Lumped impervious surface area for each site/sub-catchment assessed in the Lower Brisbane River catchment and surrounding coastal catchments studies (LBRCSCC)

39

Figure 2.3 Reach-scale tree cover for each site/sub-catchment assessed in the Lower Brisbane River catchment and surrounding coastal catchments studies (LBRCSCC)

40

Figure 2.4 Study area and sites shown for Bulimba Creek and Norman Creek catchments (BCNC) study

41

Figure 2.5 Lumped impervious surface area for each site/sub-catchment assessed in Bulimba Creek and Norman Creek (BCNC).

42

Figure 2.6 Reach-scale tree cover for each site/sub-catchment assessed in Bulimba Creek and Norman Creek (BCNC).

43 CHAPTER 3 SPATIAL ANALYSIS OF CATCHMENT-SCALE IMPERVIOUS SURFACE AND REACH-SCALE RIPARIAN COVER METRICS AND THEIR ASSOCIATIONS WITH SUB-TROPICAL URBAN STREAM HEALTH AND WATER QUALITY INDICATORS

3.1 Introduction

3.1.1 Multiple stressors affect urban streams

Urban streams are affected by a range of confounded stressors (Paul and Meyer 2001, Allan 2004). One important documented stressor is altered hydrology, especially reductions in base flows (Klein 1979), increases in peak flows (Dunne and Leopold 1978) and changes to channel morphology (Hammer 1972, Dunne and Leopold 1978, Morisawa and LaFlure 1979, Booth and Jackson 1997). Other important documented stressors include habitat fragmentation (Furniss et al. 1991, Mirati 1999), high nutrient levels (Omernik 1976, Meybeck 1998), and the establishment and proliferation of invasive species (Boet et al. 1999b). This commonly observed suite of stressors associated with changes in hydrology, water quality and invasive species has been identified as contributing to the urban stream syndrome (Meyer et al. 2005, Walsh et al. 2005a).

Like many processes affecting stream ecosystems, these stressors operate at different spatial scales (Lammert and Allan 1999, Black et al. 2004, Fitzgerald et al. 2012, Li et al. 2012) ranging from local shading of the streambed by the riparian canopy to regional loading of materials transported across extensive areas (Poff 1997, Strayer et al. 2003). Generally two categories of spatial scales are reported when assessing land-cover and land-use impacts on urban stream health: the reach scale (up to several hundred metres upstream and 30 to 200 metres either side of the stream channel); and the broader catchment scale (Roth et al. 1996, Allan 2004, Roy 2004a, Van Sickle and Johnson 2008). Some stressors are most strongly associated with processes in the broader catchment landscape (including terrestrial areas) and others are more strongly associated with processes in the riparian zone. Hydrological, sediment, and wastewater pollution stressors generally operate at the catchment scale, while stream temperature is generally associated with the reach or local scale. For further detail on the spatial scales

44 of different stressors influencing stream biota and water quality, see Chapter 1 and also Bunn et al. (2010).

3.1.2 Catchment-scale urban stressors

Most urban stream studies have focused on catchment-scale effects, in particular impervious surface area effects. A key focus of international research on urban streams is the configuration of impervious surface area in the catchment and how it is connected to the stream network via hydrologically-efficient stormwater piping, impacting on hydrology, geomorphology, water quality and biota.

According to some studies, a catchment impervious surface threshold may exist above which there is significant decline in stream health irrespective of other factors. Critical thresholds of urban density have been proposed by Beach (2001) (10% total impervious surface area, TIA) and Walsh et al. (2005a) (12% TIA, above which there is little variation in macroinvertebrate response to urbanisation due to a lack of sensitive taxa). Brown et al. (2009) indicated no minimum or maximum urbanisation threshold for macroinvertebrate diversity and abundance and suggested a continuing adverse response to urbanisation. Refinements to the concept of lumped TIA, such as total effective impervious surface (Walsh et al. 2005b) and distance-weighted impervious surface (Walsh and Kunapo 2009) suggest that considering where land-cover types are concentrated in a catchment deserves further consideration to better understand the mechanisms by which stressors impact urban stream health.

3.1.3 Riparian zone and in-stream condition

Riparian zones are known to protect stream banks from erosion, provide food resources and habitat structure and filter nutrients (Naiman and Décamps 1997) but their role in protecting urban stream health has not been clearly articulated in many studies because riparian influences are either difficult to differentiate from impacts of catchment-scale impervious land use (Walsh et al. 2005) or offer less explanatory power than stressors associated with catchment-scale land cover (Roy et al. 2009b, Wenger et al. 2009). However, a limited number of studies have detected reach-scale variation in urban stream health due to riparian condition, especially in naturally drier and flashier systems (Newham et al. 2011, Thompson and Parkinson 2011, Engman and Ramírez 2012) and

45 in sub-tropical urban streams where more intact riparian zones provide the benefit of increased denitrification potential (Newham et al. 2011).

Unfortunately, in many urban catchments extensive areas of the stream network are “buried”, especially in the upper catchments, further reducing the potential effectiveness of riparian vegetation (Elmore and Kaushal 2008). In addition to increases in flow velocities and alteration of water quality (alteration of carbon and nutrient inputs, and increases in nitrogen concentrationsKaushal et al. 2008b, Roy et al. 2009a), the replacement of stream length with in-stream piping has the potential to fragment and disconnect habitat patches as well as, in extreme cases, to remove significant extent of patches of stream and riparian zone and their associated chemical and physical processing of nutrients, organic particular matter and pollutants. Predominantly it is ephemeral and intermittent headwater streams that are buried (Roy et al. 2009a).

In view of previous studies and inconsistencies of findings there is clearly a need for further research on the relative importance of catchment versus reach and local-scale processes that affect urban stream health in different climatic and natural hydrological settings.

3.1.4 Urban streams in southeast Queensland (SEQ)

Bulimba Creek and Norman Creek catchments, sub-catchments of the Brisbane River in southeast Queensland (SEQ), are highly urbanised. As mentioned in Chapter 2, these sub-catchments have mean impervious surface cover of 26% and 35.5% respectively, significantly higher than the critical thresholds of urban density proposed by Beach (2001) and Walsh et al. (2005a). As presented in Chapter 1, the sub-tropical, ephemeral streams of SEQ are likely to be naturally more flashy and ephemeral than streams in the more temperate areas studied in much of the literature concerning the urban stream syndrome (e. g. Meyer et al. 2005, Walsh et al. 2005a). In a spatial study of the impacts of urbanisation on streams in SEQ (Sheldon et al. 2012b), it was found that altered hydrology and catchment-scale impervious surface were not as important to macroinvertebrate diversity and abundance as indicated in the studies of temperate streams. However, the spatial study only included a small number of urban stream sites and did not assess the influence of catchment-scale impervious surface on water quality indicators.

46 In light of the discussion in this introduction, the aim of the current study was to compare the relative influences of catchment, local and reach-scale land use on urban stream health in the highly urbanised ephemeral stream catchments of Bulimba Creek and Norman Creek. In this study, it was hypothesised that in the flashy, ephemeral streams of sub-tropical SEQ, hydrologic alteration associated with impervious surface (catchment-scale and local-scale) will have less influence on stream health as indicated by macroinvertebrate and water quality indicators than in more temperate streams. It is further hypothesised that in the streams of SEQ reach-scale tree cover in the riparian buffer will have a more detectable influence on stream health (consistent with some studies with drier climates), than in temperate streams. Finally, it is hypothesised that the presence of in-stream stormwater piping will have a detectable influence on stream health and water quality indicators that is distinguishable from catchment-scale impervious surface impacts. This influence could be associated with altered ecological connectivity or loss of stream and riparian zone extent and functions.

3.2 Methods

3.2.1 Site selection

Sites were selected from within the freshwater stream network of the Bulimba Creek and Norman Creek (BCNC) catchments (Appendix 1) to include a range of levels of total sub-catchment impervious land cover and associated stormwater drains and piping, as well as a range of tree, grass and impervious riparian land cover at the reach and catchment scales. The inaccessibility of piped or buried stream sections precluded them from selection but some sites were selected downstream or upstream of such segments. This study captured nested and un-nested sites (i.e. one site could be upstream or downstream of another site but some sites were neither) so that the site drainage areas encompassed the majority of the freshwater extent of the BCNC catchments.

3.2.2 Stream health and water quality field data collection

A primary data set of biotic stream health and water quality indicators comprising a subset of the SEQ Ecosystem Health Monitoring Program (EHMP) (Bunn et al. 2010) indicators was collected from 30 sites in one post-wet season (April 2010). The reduced

47 set of indicators were chosen to reflect reach and catchment-scale stressor impacts associated with riparian and broader catchment land use. For further details on the scales of influence of stressors on these indicators, see Chapter 1 or Bunn et al. (2010). Appendix 2 defines the selected stream health and water quality indicators and reports the EHMP guideline values for each one.

The stream health indicator data included: (1) water quality (pH, conductivity, dissolved oxygen (DO) and temperature diel readings); and (2) macroinvertebrates. The latter were analysed for (1) SIGNAL2 score (Chessman 2003) (“order-class-phylum” level), an assessment of diversity and abundance of macroinvertebrate taxa which weights pollution sensitive taxa more highly; (2) EPT (Lenat 1988), the number of families belonging to the sensitive orders Ephemeroptera, Plecoptera and Trichoptera; and (3) family richness (Resh et al. 1995), a count of the number of families found at a given site, excluding cladocerans, ostracods, copepods and spiders.

Stream temperature and DO measurements were collected using WP-82Y TPS probes. Six probes were used and rotated to obtain data for all sites over several days. The instruments were calibrated between each deployment at room temperature and for DO at zero and 100% (air calibration). Probes were left at each site in order to log data for a minimum of 24 hours. In the shallow creek systems, the probes were attached to battery operated stirrers and then attached to small baskets to ensure they remained submerged but were not resting on the bottom of the creek beds and could not become covered in leaf or other debris. pH and conductivity spot readings were obtained using a WP-81 TPS probe. pH was calibrated at pH 7 and pH 10 and conductivity was calibrated at zero and 1413uS/cm.

Benthic macroinvertebrates were collected using a 250 µm dipnet. 20 sweeps were collected across a range of edge habitats over a total length of 10 m of habitat at each site as per the AUSRIVAS Rapid Assessment Protocol (Conrick and Cockayne 2001) and according to protocols of the EHMP (Smith and Grice 2005, Bunn et al. 2010). Selected habitats (banks, pools, riffles, debris pockets, and macrophytes) were considered representative of each site and entailed at least 10% of the site area. As per the assessment protocol, selected habitats could include backwaters and undercuts but few or no macrophytes. Manually assisted dislodgement, via kicking or jarring motions with the net, was used to dislodge macroinvertebrates in non-flowing water. This was

48 required for some samples at all sites due to slow flow rates. Where rocks encountered were loose enough to be picked up they were scraped. Samples from different habitats were combined into a composite sample for each site. Macroinvertebrates were live-picked from white sorting trays by two collectors for 30 minutes at each site (each picking one half of each sample) with the aim of collecting the greatest diversity of taxa in accordance with the EHMP protocol. Samples were identified in the field to family and genus where possible and most taxa were returned to the stream. In cases where the required level of differentiation could not be done in the field, samples were stored in 70% methanol and 30% water and taken to the lab for further identification. SIGNAL2 score (“order-class- phylum” level), EPT and Family Richness were determined as per Chessman (2003).

3.2.3 GIS data processing for spatial scale metrics

A geographic information system (GIS) was used to generate land-cover metrics as well as a small number of additional explanatory metrics not classed as land cover (land-use and landscape metrics). All of these candidate explanatory metrics are defined in Appendix 3 and explanations of how they are generated are given below. The generation of the drainage network is also explained below.

The DEM data set used to create the stream network had a spatial resolution of 5 m and was based on Airborne Laser Scanning (ALS) data acquired from a fixed-wing aircraft between the 15th and 20th June 2002. The existing waterways used for burning (imprinting) into the DEM were extracted from Brisbane City Plan 2000 Waterway Corridors, a graphical representation of properties affected by the Brisbane River corridor and other waterway corridors. Data on the existing pipe and stream networks were received from Brisbane City Council (BCC) in vector format. The land-cover data used to create the metrics was based on 2005 DigitalGlobe Satellite Imagery with a spatial resolution of 2.4 m and was resampled to match the resolution of the DEM. The data sets were received and processed in Universal Transmercator projection. The land- cover data and generated stream networks were then used to generate the land-cover metrics. The software used for the GIS calculations was ESRI ArcGIS 9.3 and ArcGIS 10.0. Manual processing in ArcGIS as well as ArcGIS ModelBuilder and Python (2.5) code were used to process the metrics, predominantly using the Hydrology toolbox in Spatial Analyst, Raster Calculator and Zonal Statistics.

49 3.2.4 Generating the drainage network

In urban catchments such as those in this study, many streams are piped underground or flow under roads and are thus not connected in any simple manner on the surface. Hence, in order to apply contemporary DEM based hydrologic modelling techniques to create a connected stream network that incorporates both surface and piped flow, it was necessary to construct an artificial surface stream network by treating the piped flow as surface flow. This was achieved by burning existing stream and pipe networks into the DEM. Pipes and stormwater drains were burnt in by a distance of 1m and stream pixels and study site pixels were burnt in by a distance of 2 m. The burnt DEM was then used to generate the sub-catchment boundaries, and stream/pipe flow networks that flow to each site. Field site locations were recorded using a Garmin GPS 60 during field visits and were manually moved within the GIS environment to snap to the mapped stream network. The methods for generating the artificial stream network generally follow those of Gironás et al. (2010), in which piping, streams and other known drainage are burnt into the DEM. Further detail on the justification for the GIS methods used for generating the connected artificial stream network and contributing sub-catchment drainage pathways is given in Appendix 4.

To ensure that piped flow would drain into streams at their intersection, stream pixels representing the BCC City Plan 2000 waterway corridors had a value of 2 m subtracted from the DEM pixel (if a cell intersected a creek and a pipe it was burnt in by 2 m). Some manual deeper burning of selected pixels was required to ensure the higher order mapped creek crossed highways. Tools from the Spatial Analyst Hydrology toolbox were then applied to remove spurious sinks (Fill tool), compute flow direction using the D8 algorithm (FlowDirection tool), and accumulate flow downstream (FlowAccumulation tool). The artificial surface stream network (streams and pipes) was then delineated as any cell that had a threshold of greater than 2.5 ha contributing area (1000 x 5m x 5 m cells) flowing to it from upstream.

Each field site location for which stream health measurements were available was influenced by an upstream contributing area. To delineate the upstream contributing area, each field site was treated as a pour point (or sub-catchment outlet) and the Watershed tool in Spatial Analyst was applied to map the boundary of the field site sub- catchment (contributing area) using the previously computed flow direction raster.

50 The same process was also carried out for surface water flow by burning only the creeks and not the pipes into the DEM to generate the stream network. Land-cover metrics were created using both versions of stream networks to determine if accounting for pipes improved the ability of the metrics to explain variation in stream health and water quality indicators.

While the stream network studied may contain extensive lengths of stormwater drainage channels and piping, it is still referred to as a stream network instead of drainage network in this study because the focus is on understanding stream health in urban areas.

3.2.5 Spatial scale land-cover metrics

The generated land-cover metrics include reach, local and catchment-scale metrics and are based on vegetation and impervious surface land-cover data, existing pipe and stream networks, and the drainage network generated from the digital elevation model (DEM). The land-cover metrics are graphically illustrated in Figure 3.1. These include non-spatial (lumped) and spatial (spatially-explicit inverse-distance weighted, IDW, and areal buffer) land-cover metrics (Appendix 3) that were used as candidate explanatory variables. IDW metrics weight land-cover pixels by their proximity to stream channels (the stream network upstream of the survey site) or sampling sites, whereas lumped metrics weight all land-cover pixels equally when determining the percentage land cover in a given area (King et al. 2005, Van Sickle and Johnson 2008) and areal buffer metrics weight all land-cover pixels within an areal buffer equally. Sections 3.2.5.1 to 3.2.5.3 outline how the spatial scale metrics were created using the equations specified in Table 3.1. GIS techniques used to calculate the metrics are summarised in Appendix 5.

To distinguish the relative influence of the riparian buffers and catchment-scale impervious surface, lumped and inverse-distance weighted (IDW) catchment-scale land- cover metrics were calculated for impervious surfaces and reach-scale and catchment- scale riparian buffer metrics were calculated for tree and grass cover (Figure 3.1). Lumped, areal buffer and IDW tree and grass-cover metrics are considered in Chapters 4 and 5.

51 While different forms of inverse-distance weighted (IDW) functions were considered for distance-to-stream metrics, only inverse Euclidean distance was considered for distance-to-site metrics in this chapter. There are strong correlations between different forms of IDW metrics (Van Sickle and Johnson 2008) making them generally substitutable so statistically significant differences between forms of inverse distance- to-site metrics were not expected at the local scale (X_EucSite_S), although different forms of IDW equations were considered at the catchment scale (Figure 3.1).

3.2.5.1 Land-cover classes and processing

The raw raster land-cover data set initially comprised nine land-cover classes which were grouped into three broad land-cover types: impervious surface, standing woody vegetation (trees), and combined vegetation (grass and trees). Based on the land-cover data, impervious surface was considered to comprise layer codes 5 (roads/asphalt), 6 (buildings/structures), and 7 (concrete). Tree-covered surface included layer code 4 (tree), and vegetation was considered to include layers 3 (grass) and 4. Water and bare ground/rock were not included in the land-cover calculations in this study, as they could not easily be categorised as tree, vegetation or impervious surface. Consequently there are up to 29% discrepancies in some sub-catchments between the total area of calculated land cover and the total area, though in general they are less than 5%. To align with the extent and cell size of the DEM, the land-cover data was resampled using the Spatial Analyst nearest neighbour technique.

3.2.5.2 Lumped catchment land-cover metrics

Lumped land-cover metrics such as lumped impervious surface in the upstream sub- catchment (I_SubCatch_S, Figure 3.1) are not spatially explicit and typically represent an area-based percentage, proportion or mean in the catchment. The lumped calculation for land cover, or more generally, land use (LU) applies Equation [1]

n I(k)W i1 i %LU n 100 [1] W i1 i

with the distance-weighting function [1a] (Table 3.1) where the weight Wi is equal to 1

52

Figure 3.1 Illustration of different spatial and non-spatial metrics This figure shows different weighting types associated with the spatial and non-spatial land-cover metrics. All metrics are defined in Appendix 3. In this illustration, X in the metric codes is a wild card and, depending on land-cover type, would be I (impervious surface), T (tree cover), G (grass cover), V (vegetative cover), piped channel (P) and combined impervious surface and piped channel (IP). Land-cover weighting methods (Table 3.1) include inverse-distance weighted (IDW) to-site (X_EucSite_S) and to-stream (X_EucSteam_S, X_FlowStream_S and X_ExpStream_S) as well as lumped (X_SubCatch_S) and threshold (X_ReaRip_S and X_Rip_S). P_EffRip_S represents the extent of piped channel in the upstream tributary network.

53 Table 3.1 Alternative specifications of land-cover and land-use metrics with lumped, threshold and inverse-distance weighted (IDW) weighting functions

This table lists the equations for weighted land-cover or land-use (LU) metrics used in Chapter 3. Wi has different functional forms listed below. I(k) is an indicator function (King et al. 2005, Van Sickle and Johnson 2008, Peterson et al. 2011) and typically equals 1 if a cell, i, contains the land-cover or land-use type being assessed, and 0 if it does not. Metric definitions are given in Appendix 3 and GIS techniques are summarised in Appendix 5. Metric category Land-cover and land-use metrics and Metric their distance-weighting functions n Weighted land-cover or I(k)W land-use metrics i1 i %LU n 100 [1] W i1 i

Lumped catchment-scale �� = 1 [1a] I_SubCatch_S land-cover or land-use metrics −� Flowpath and Euclidean ��(��) = (�� + 1) [1b] I_EucSite_S inverse-distance weighted I_EucStream_S (IDW) catchment-scale where α = 1 I_FlowStream_S land-cover metrics to-site or to-stream Exponential decay IDW d I_ExpStream_S i metrics Wi e [1c] where α = 6

Areal buffer / threshold �� = �(��) = �(�� ≤ υ) [1d] I_Rip_S ���� − ����� metrics for V_Rip_S riparian buffers (reach- T_Rip_S scale buffers, and I_ReaRip_S effective and traditional V_ReaRip_S catchment-scale riparian T_ReaRip_S buffers) I_EffRip_S V_EffRip_S T_EffRip_S G_EffRip_S P_EffRip_S IP_EffRip_S for every cell in the catchment. I(k) is an indicator function (King et al. 2005, Van Sickle and Johnson 2008, Peterson et al. 2011) and typically equals 1 if a cell, i, contains the land-cover or land-use type being assessed, and 0 if it does not.

3.2.5.3 Inverse-distance weighted (IDW) catchment surface metrics

Various distance functions have been used in the literature to provide spatially-explicit information on potential land-cover and land-use impacts on aquatic ecosystem health (King et al. 2005, Van Sickle and Johnson 2008, Walsh and Kunapo 2009, Peterson et al. 2011). These include inverse Euclidean distance, exponential decay, and inverse flowpath distance, all of which are tested in the current study.

54 i) Euclidean or flowpath distance

Inverse Euclidean distance or inverse flowpath distance to-site and to-stream are calculated with equation [1] and the inverse-distance weighted (IDW) function [1b] (Table 3.1) which gives a greater weight to land closer to the site or to the stream to reflect its stronger influence on stream ecosystem health and stream habitat condition relative to land cover farther from the site or stream (Gregory et al., 1991). A value of 1 is added to the relevant distance di to prevent it from being zero at any point in the analysis, such that Wi (0) = 1.

The parameter α can be altered to change the way the function operates and although it is typically given values of 1 or 2, it can have other values (Van Sickle and Johnson 2008). In this study α is given the value of 1 and as such follows Peterson et al (2011). -1 -2 A d weighting has a smoother weight transition close to the stream than a di -1 weighting (Comelo et al. 1996). The IDW form (di +1) strongly weights cells close to the stream or close to the site, with the first cell away from the stream or site reducing its weighting to approximately 1/6 of what it is at the stream or site for the 5 m x 5 m pixel size used in the current study.

-1 In the inverse-distance weighted (IDW) function Wi = (di+1) , di is calculated as both Euclidean distances to-site and to-stream (I_EucSite_S, I_EucStream_S) and flowpath distances to-stream (I_FlowStream_S) and 0≤Wi≤1. Where Euclidean distance is the shortest distance between two points (the distance “as the crow flies”), the flowpath distance is the distance a parcel of water would flow to reach the study site or the stream if it followed the main generated surface flow direction, that is, the flowpath of steepest descent according to the FlowDirection grid (Section 3.2.3). Peterson et al. (2011) found that the Euclidean distance results were quite similar to flowpath distance results in rural and forested areas, and that in urban areas, the surface flowpath distance may be incorrect due to the presence of stormwater piping. This should not be a concern in this study because the mapped stormwater piping system was incorporated in the mapped drainage and thus in the flowpath analysis (Section 3.2.3).

55 ii) Exponential inverse-distance weighted (IDW)

The exponential decay function tested was of an inverted form similar to that used by Van Sickle and Johnson (2008) and Walsh and Kunapo (2009). Equation [1] was specified with the inverse-distance weighted (IDW) function [1c] (Table 3.1) where �� is the hydrological (flowpath) distance to the stream or to the stream-pipe network (I_ExpStream_S). This differs from the form used by Walsh and Kunapo (2009) in which the denominator of equation [1] was simply the area of the catchment. The form used here is more in keeping with Van Sickle and Johnson (2008) and the other forms from Table 3.1 used for Euclidean and flowpath distance weighting.

Results are typically expressed as half decay distance (HDD) because the exponential parameter is difficult to interpret. HDD is equal to the parameter multiplied by ln2.

This can also be understood such that the weight Wi is reduced by 50% over the HDD and is reduced by 97% over 5 HDD (Walsh and Kunapo 2009). This form of IDW function is a relatively realistic way of modelling land-cover and land-use effects on materials such as nutrients and sediment that are progressively depleted along flowpaths (Soranno et al. 1996, Smith et al. 1997, Weller et al. 1998, Johnson et al. 2007, Van Sickle and Johnson 2008). However, Van Sickle and Johnson (2008) noted that the Euclidean, flowpath and threshold forms of distance-weighted land-cover metrics produced relatively similar results. To calculate the exponential IDW metric (I_ExpStream_S) illustrated in Figure 3.1, α was set to a value of 6, which represents a HDD of 4.3 and is consistent with the values in the best models reported by Walsh and Kunapo (2009).

3.2.5.4 Threshold (areal buffer) land-cover metrics for riparian buffers

The reach and catchment-scale riparian buffer metrics are threshold land-cover metrics that were calculated with equation [1] and the distance-weighting function [1d] (Table

3.1) where υ is a distance threshold. All cells with di≤υ candidate explanatory variables are weighted equally and contribute equal influence. However, those cells with the selected land cover that have di>υ have a zero weighting (Van Sickle and Johnson 2008, King et al. 2005). HDD is not applicable to the threshold form of the distance-weighting function since distinct areas of influences are defined.

56 i) Riparian buffers in the upstream catchment

The lumped land-cover area in the mapped 30 m “buffer” (rounded end buffering) along the drainage channel upstream for the whole sub-catchment was determined for impervious surface (I_Rip_S), vegetation (V_Rip_S) and trees (T_Rip_S), as illustrated in Figure 3.1. ii) Reach-scale riparian buffers 200 m

To calculate reach-scale land-cover metrics for 200 m upstream of each site (I_ReaRip_S, V_ReaRip_S, T_ReaRip_S), a 30 m buffer either side of the drainage channel was analysed as a lumped metric (Figure 3.1). A 200 m reach length was specified as this length has been used for reaches in other urban stream studies (e.g. Roy 2004a) and roughly equates to the length (300 m) of intact forest recommended by Storey and Cowley (1997) for returning an elevated thermal regime to normal. iii) Effective riparian buffers

In urban catchments, many streams, especially headwater streams, are converted to pipes or concrete drains to carry stormwater and to allow the landscape to be developed. In these situations the surface land cover may still be grass or tree while the stream itself is piped underground. The results of an ecological performance analysis of the sites in this study (Millington et al. 2015, reprinted in Appendix 6) led to the conjecture that the effective functioning of the riparian zone is compromised by the pervasiveness of stormwater piping in highly urbanised streams. Consequently, another set of six catchment-scale riparian variables was generated to account for the loss of riparian zones via sub-surface piping (I_EffRip_S, V_EffRip_S, T_EffRip_S, G_EffRip_S, P_EffRip_S, IP_EffRip_S). This study will refer to these metrics as “effective riparian buffers”. This is an important distinction because to the author’s knowledge this is the first study in which the piped sections have been removed from riparian buffer calculations for spatial scale analysis of land-cover impacts on stream health. This is particularly relevant in highly urbanised catchments where stormwater piping is ubiquitous. Figure 3.1 illustrates an example of the extent of piping in the effective riparian buffer metrics (P_EffRip_S).

57 3.2.6 Additional candidate explanatory metrics

In addition to the land-cover metrics, a land-use metric, population density (PopDen_S), and a landscape metric, latitude (YCoord_S), were considered as explanatory variables in the stream health models. PopDen_S was calculated in a GIS based on 2006 census data (Australian Bureau of Statistics 2007) and the sub-catchment boundaries above each site. Thus effectively PopDen_S is a lumped land-use metric (Equation 1a). The Universal Transmercator northing values were used to indicate the latitudinal position of sites (YCoord_S). Bulimba Creek and Norman Creek effectively flow north towards the Brisbane River and so this metric was used to represent natural variation, with an increase in stream order and flow volume anticipated as these streams flow in a general direction from upstream to downstream.

3.2.7 Statistical analysis

The key modelling objective of this chapter was to apply a range of statistical methods to link spatial patterns of urban land use and land cover with stream health and water quality indicators in highly urbanised catchments, and to explain the factors driving the observed variation in stream health. At the outset it was important to acknowledge the possibility of spatial autocorrelation because of the tendency for entities that are closer to one another to be more related than distant entities (Tobler 1970). In contrast to standard regression models that assume independent observations, geostatistical regression models relax this assumption and allow spatial autocorrelation in the residuals. Stream networks have potential spatial autocorrelation structures that differ from terrestrial systems. This is because streams are directed networks and therefore spatial correlation is likely to occur between sites with shorter distances separating them along stream channels rather than simply shorter Euclidean distances. Valid geostatistical models require large data sets with sufficient correlated pairs of observations to provide the information necessary to estimate the additional parameters (Isaak et al. 2014). Irvine et al. (2007) recommended a minimum of 144 observations for the one parameter (range) exponential autocorrelation structure and it therefore follows that more is required for the Matern autocorrelation model (Hoeting et al. 2006) which estimates both range and smoothness. Isaak et al. (2014) suggest a minimum of 50 to 100 sites to allow estimation of parameters for stream network autocorrelation functions, with the greater number of sites needed for the more complex autocovariance

58 functions. Although the number of observations (sites) in the Bulimba Creek and Norman Creek (BCNC) study is too small to allow estimation of the spatial autocorrelation structure of the error term, if spatial autocorrelation exists between neighbouring sites it can be accommodated by specifying a correlation structure term within a generalised least squares (GLS) model. This is a useful approach when data sets are small and the aim is to explain rather than predict stream health (personal communication Erin Peterson, CSIRO, 2010).

3.2.7.1 Measuring spatial autocorrelation with Moran’s I index

To determine the extent of spatial autocorrelation, the Moran’s I index was evaluated for each stream health indicator (Appendix 7). This is a spatial autocorrelation statistic that uses inverse Euclidean distance between sites as weights. Equation 2 defines the general equation for Moran’s I.

nwij zi zz j z i j I 2 [2] wij zi z i j i

The weight, wij , measures the locational similarity of site i and site j, zi is the value of the attribute of interest at site i and n represents the total number of sites (Longley et al. 2005). The Moran I index values were calculated in R with the “ape” package (Paradis et al. 2011).

The expected value of Moran’s I, E(I), is the value for a coefficient that would indicate no spatial correlation (Wong and Lee 2005). If the observed I is greater than the E(I) then there is a clustered spatial pattern (nearby areas tend to have similar attributes). Borderline spatial autocorrelation (I > E(I)) was evident in the conductivity and minimum dissolved oxygen data, but for all other physical/chemical and macroinvertebrate diversity and abundance (SIGNAL2_S) data sets the null hypothesis that spatial autocorrelation is not statistically significant could not be rejected (Table A7.1).

59 3.2.7.2 Generalised least squares models (GLS)

GLS modelling is part of the nlme (non-linear mixed effects) parametric modelling techniques available in the R package (Pinheiro et al. 2011). The GLS function fits a linear model using generalised least squares and allows the errors to be correlated and/or have unequal variances. The data for water quality and macroinvertebrates are cross sectional (recorded in one season across a number of sites) and spatial correlation between neighbouring sites is of interest. Therefore, a Gaussian correlation structure based on Euclidean distances was specified to address spatial correlation in the model residuals. The x, y coordinates (Xcoord_S and Ycoord_S variables) at each site were used to calculate Euclidean distances.

3.2.7.3 Designing a priori model sets for each health indicator

Twenty candidate explanatory variables at the reach, local and catchment scales (Appendix 3) were calculated for the purpose of distinguishing the roles of catchment land use and riparian processes in maintaining the health of urban streams. They can be grouped as follows: (1) reach-scale riparian land-cover metrics; (2) catchment-scale stressor metrics which included IDW impervious surface land-cover metrics targeting the local and catchment scale, catchment-scale riparian buffer land-cover metrics and population density; (3) catchment extent; and (4) latitude.

Preliminary ordinary least squares (OLS) regression was used to determine the direction and strength of association between response variables (stream health and water quality indicators) and each of the candidate explanatory variables (land-cover, land-use and landscape metrics). When a stream health indicator was regressed on a candidate variable, the latter had to have a t-test p value less than 0.2 (Appendix 8, Table A8.1) to be included in the a priori model set for that stream health indicator. A variance inflation factor (VIF) over 10 and a Spearman’s correlation statistic greater than 0.75 (Appendix 9) identified which sets of covariates were collinear. The local-scale IDW impervious surface metric (I_EucSite_S) was too highly correlated with catchment- scale metrics to warrant a separate category.

Although the reach-scale stressor metrics and some of the catchment-scale stressor metrics were not strongly correlated, and further categories may have been possible, the

60 separation of reach and catchment scale variables into two groups was desirable when scale of impact was of interest and when simplicity and parsimony were important modelling objectives. While models with too few parameters (under-fitting) can suffer from bias, models with too many parameters (over-fitting) may lack precision and identify spurious effects (Burnham and Anderson 2001). Information in a data set is limited (especially in a small data set) and for each additional parameter estimated statistical inference is weakened. Therefore the initial model for each health indicator had a maximum of four candidate explanatory variables (one from each of the four groups defined above) selected on the basis of how strongly they related to the specific indicator (Appendix 10).

3.2.7.4 Model selection procedure

Stepwise regression with backward selection of variables was performed on the initial GLS model for each stream health indicator to determine if a reduced model was better. Nested models were then compared and an analysis of variance (ANOVA) on the best model determined if the correlation structure was necessary. Covariates in the best model from this last step were systematically replaced by all other covariates in their collinear set or metric category to create a set of a priori models or hypotheses (Appendix 10, Table A10.1). The best models from the a priori sets were selected according to their Akaike Information Criterion (AIC) statistic (Akaike 1981). Simple rules of thumb proposed by Burnham and Anderson (2004) were used to interpret the model results. For example, a AIC < 2 indicates that there is considerable support for a second model, 4 ≤ AIC ≤ 7 means that there is substantially less support for the second model, and AIC > 10 indicates that there is essentially no support for the second model. When no single model was clearly the best, model averaging was applied to the candidate explanatory variables in the set of top models to assess their relative importance (Johnson and Omland 2004). The model selection procedure applied in this study is covered in greater detail in Appendix 11. Other statistical analysis approaches could have been taken, such as Bayesian model selection and model averaging, which has been recommended in part because of the preferential selection of over-complicated models (Link and Barker 2006) when using the AIC approach. However the AIC based model selection approach used here, in combination with subsequent AIC based model averaging, provides several benefits due to a more simple and transparent model selection approach (Hoef and Boveng 2015). These include: (1) allowing the calculation

61 effort to be minimised, (2) helping to restrict the number of models considered, and (3) providing greater transparency to the model selection approach, permitting easier model diagnostics. Model diagnostics included investigating spurious model over-fitting (too many parameters per model leading to spurious results).

3.3 Results

3.3.1 Preliminary investigation of candidate explanatory GIS-generated metrics and stream health and water quality indicators i) Urban stream network

The artificial stream network that incorporates both surface and piped flow (Section 3.2.3) produced land-cover metrics that were better correlated with stream health and water quality indicators than metrics based on the natural stream network (Appendix 8, Table A8.2). This is consistent with the fact that stormwater piping is a major component of urban drainage (illustrated as P_EffRip_S in Figure 3.1). The set of GIS- generated land-cover, land-use and landscape metrics based on the artificial stream network was therefore used throughout this analysis. ii) OLS regression analysis

The distributions of stream health and water quality indicator variables are presented in Appendix 12, Table A12.1. Some of the water quality indicators were transformed (Appendix 12, Table A12.2) to improve their fit in preliminary regression analysis and GLS modelling. More detailed notes on the preliminary analysis are to be found in Appendix 13.

3.3.2 GLS model testing and selection

The ten best GLS models for each stream health and water quality indicator are shown in Table 3.2. The Akaike Information Criteria (AIC) statistic, change in AIC (ΔAICi), the weight of evidence statistic (ωi) and log likelihood are reported for each model. SIGNAL2 is the only macroinvertebrate indicator reported in these tables because the

62 data for EPT and family richness had too many zero values and an insufficient range of values to be useful in this analysis. It is interesting to note that only conductivity (Cond_t_S) models retained their spatial autocorrelation term (Appendix 11, Step 3).

Many of the models tested were equally plausible (AIC < 2). Consequently model averaging was necessary to determine the relative importance of the land-cover, land- use and landscape metrics. Table 3.3 reports the unconditional confidence intervals (UCIs) for metrics with moderate (80-85% UCI) to strong (90-95% UCI) evidence of support that the metric is relatively important as an explanatory variable. Key results are summarised after the tables.

3.3.3 Multiple scales of impact

The variation in the macroinvertebrate indicator SIGNAL2_S appeared to be associated with all three spatial scales (reach, local and catchment) of stressors while the variation in the water quality indicators was generally associated with either reach and/or catchment-scale stressors. Maximum temperature (Temp_Max_t_S) was associated most strongly with reach-scale stressors; dissolved oxygen range (DO_Range_t_S), conductivity (Cond_t_S) and pH (pH_S) were associated most strongly with catchment- scale stressors; and temperature range (Temp_Range_t_S) and minimum dissolved oxygen (DO_Min_t_S) were associated most strongly with reach and catchment-scale stressors.

3.3.4 Reach-scale stressors

Reach-scale stressor metrics were associated with the macroinvertebrate indicator SIGNAL2_S, the water quality indicators minimum dissolved oxygen (DO_Min_t_S), temperature (Temp_Max_t_S and Temp_Range_t_S) but not conductivity (Cond_t_S) or pH (pH_S) (Tables 3.2 and 3.3). While the reach-scale was represented in the best models for DO_Range_t_S (Models 2 and 3, Table 3.2), it was unsupported in model averaging (Table 3.3).

63 Table 3.2 Best GLS models for SIGNAL2_S and water quality indicators

Set of best models for each stream health indicator includes up to ten equally plausible models (AIC < 2) or where there is less than ten equally plausible models, models with substantially less support (4 ≤ AIC ≤ 7) have been included. Appendix 11 provides more detail on the AIC approach to model selection.

Model Log Explanatory variables AIC AIC order i i Likelihood

SIGNAL2_S 1 V_ReaRip_S, PopDen_S 78.38 0.00 0.06 -35.19 2 T_ReaRip_S, PopDen_S 78.92 0.54 0.05 -35.46 3 V_ReaRip_S, P_EffRip_S 79.04 0.66 0.05 -35.52 4 T_ReaRip_S 79.47 1.09 0.04 -36.74 5 T_ReaRip_S, G_EffRip_S 79.57 1.19 0.04 -35.79 6 T_ReaRip_S, P_EffRip_S 79.65 1.27 0.03 -35.82 7 I_ReaRip_S, PopDen_S 79.74 1.36 0.03 -35.87 8 T_ReaRip_S, I_EucSite_S 80.26 1.87 0.02 -36.13 9 V_ReaRip_S, I_EucSite_S 80.27 1.89 0.02 -36.14 10 I_ReaRip_S, P_EffRip_S 80.42 2.04 0.02 -36.21

Temp_Max_t_S 1 T_ReaRip_S -142.94 0.00 0.13 74.47 2 T_ReaRip_S, PopDen_S -141.45 1.49 0.06 74.72 3 T_ReaRip_S, IP_EffRip_S -141.34 1.60 0.06 74.67 4 T_ReaRip_S, V_EffRip_S -141.33 1.61 0.06 74.66 5 T_ReaRip_S, T_EffRip_S -141.28 1.66 0.06 74.64 6 T_ReaRip_S, P_EffRip_S -141.14 1.80 0.05 74.57 7 T_ReaRip_S, T_Rip_S -141.13 1.81 0.05 74.56 8 T_ReaRip_S, I_SubCatch_S -140.97 1.97 0.05 74.49 9 T_ReaRip_S, I_EucSite_S -140.95 1.99 0.05 74.47 10 T_ReaRip_S, I_EucStream_S -140.95 1.99 0.05 74.48

Temp_Range_t_S 1 PopDen_S -34.82 0.00 0.08 20.41 2 T_ReaRip_S, PopDen_S -34.73 0.10 0.08 21.36 3 T_ReaRip_S -34.39 0.44 0.07 20.19 4 I_ReaRip_S, PopDen_S -33.51 1.31 0.04 20.76 5 T_ReaRip_S, T_Rip_S -33.31 1.51 0.04 20.66 6 T_ReaRip_S, IP_EffRip_S -33.03 1.80 0.03 20.51 7 IP_EffRip_S -32.96 1.87 0.03 19.48 8 T_ReaRip_S, V_EffRip_S -32.94 1.89 0.03 20.47 9 T_Rip_S -32.88 1.94 0.03 19.44 10 T_ReaRip_S, T_EffRip_S -32.86 1.97 0.03 20.43

DO_Min_t_S 1 I_ReaRip_S, G_EffRip_S, Ycoord_S 29.41 0.00 0.17 -9.70 2 T_ReaRip_S, G_EffRip_S, Ycoord_S 29.45 0.04 0.16 -9.73 3 V_ReaRip_S, G_EffRip_S, Ycoord_S 29.72 0.31 0.14 -9.86

64 Model Log Explanatory variables AIC AIC order i i Likelihood 4 I_ReaRip_S, G_EffRip_S, USArea_S, Ycoord_S 30.34 0.94 0.10 -9.17 5 T_ReaRip_S, G_EffRip_S, USArea_S, Ycoord_S 30.80 1.39 0.08 -9.40 6 V_ReaRip_S, G_EffRip_S, USArea_S, Ycoord_S 31.19 1.79 0.07 -9.60 7 T_ReaRip_S, G_EffRip_S 34.40 4.99 0.01 -13.2 8 I_ReaRip_S, G_EffRip_S 35.06 5.66 0.01 -13.53 9 V_ReaRip_S, G_EffRip_S 35.29 5.88 0.01 -13.64 10 I_ReaRip_S, T_Rip_S, USArea_S, Ycoord_S 35.70 6.29 0.01 -11.85

DO_Range_t_S 1 PopDen_S -13.12 0 0.61 9.56 2 T_ReaRip_S, PopDen_S -11.44 1.67 0.27 9.72 3 T_ReaRip_S -9.9 3.22 0.12 7.95

Cond_t_S 1 PopDen_S, Ycoord_S 196.79 0.00 0.26 -93.40 2 PopDen_S, USArea_S, Ycoord_S 196.84 0.05 0.25 -92.42 3 USArea_S, Ycoord_S 196.94 0.15 0.24 -93.47 4 Ycoord_S 197.02 0.22 0.23 -94.51 5 PopDen_S, USArea_S 203.68 6.88 0.01 -96.84 6 USArea_S 203.89 7.09 0.01 -97.94 7 PopDen_S 203.97 7.18 0.01 -97.99 pH_S 1 PopDen_S 25.61 0.00 0.34 -9.80 2 T_Rip_S 26.97 1.37 0.17 -10.49 3 T_EffRip_S 27.76 2.15 0.11 -10.88 4 V_EffRip_S 28.12 2.51 0.10 -11.06 5 IP_EffRip_S 28.95 3.34 0.06 -11.48 6 V_Rip_S 29.05 3.44 0.06 -11.52 7 P_EffRip_S 29.55 3.94 0.05 -11.78 8 I_EucSite_S 29.75 4.14 0.04 -11.87 9 I_ExpStream_S 30.09 4.48 0.04 -12.04 10 I_FlowStream_S 30.11 4.50 0.04 -12.05

65 Table 3.3 Model averaging for SIGNAL2_S and water quality indicators

Model averaging results for metrics included in equally plausible models (ΔAICi < 2) in GLS model testing. Only those results with unconditional confidence intervals (UCI) between 80% -95% are reported as they are considered to have moderate (80-85%) to strong (90-95%) support in the data. Unconditional Metric scale Standard error of Impact metric Model-averaged estimate confidence level category estimate % SIGNAL2_S V_ReaRip_S Reach 2.04 1.08 90 T_ReaRip_S Reach 1.29 0.67 90 PopDen_S Catchment -0.07 0.04 90 P_EffRip_S Catchment* -1.16 0.7 90 I_ReaRip_S Reach -1.96 1.25 85 I_EucSite_S Catchment** -2.73 1.85 85 G_EffRip_S Catchment* 4.28 3.14 80

Temp_Max_t_S T_ReaRip_S Reach -0.05 0.02 95

Temp_Range_t_S T_ReaRip_S Reach -0.15 0.1 80 PopDen_S Catchment 0.011 0.006 80

DO_Min_t_S I_ReaRip_S Reach 1.31 0.55 95 T_ReaRip_S Reach -0.67 0.29 95 V_ReaRip_S Reach -1.11 0.49 95 G_EffRip_S Catchment* 3.91 1.46 95 Ycoord_S -57.51 21.91 95

DO_Range_t_S PopDen_S Catchment 0.018 0.008 95

Cond_t_S Ycoord_S 1126 347 95 PopDen_S Catchment 0.37 0.26 80 USArea_S -0.46 0.33 80

pH_S Each model tested contained a single variable, therefore model averaging was not applicable to pH_S

Note: The asterisk (*) identifies effective riparian buffer metrics at the catchment scale. The double asterisk (**) identifies local-scale metrics.

66 There was relatively strong evidence of support (90% UCI, Table 3.3) for a positive association between the macroinvertebrate indicator SIGNAL2_S and reach-scale tree cover (T_ReaRip_S) and reach-scale vegetative cover (V_ReaRip_S). The relative importance of T_ReaRip_S as an explanatory metric for SIGNAL2_S is further supported by the fact that it was the only single-variable model in the top ten models (model 4, Table 3.2).

The best model for maximum temperature (Temp_Max_t_S) was the single-variable model (Model 1, Table 3.2) with reach-scale tree cover (T_ReaRip_S). This was confirmed in the model averaging results (Table 3.3) where T_ReaRip_S has a strong negative association (95% UCI, Table 3.3) and the catchment-scale metrics from equally plausible models for Temp_Max_t_S (AIC < 2, Table 3.2) are absent. T_ReaRip_S was also the land-cover metric with the strongest association (negative, 80% UCI, Table 3.2) with variation in temperature range (Temp_Range_t_S).

A negative association between minimum dissolved oxygen (DO_Min_t_S) and reach- scale tree cover (T_ReaRip_S) had relatively strong evidence of support (95% UCI, Table 3.3). This result is consistent with the positive association found between reach- scale impervious surface cover (I_ReaRip_S) and DO_Min_t_S (95% UCI, Table 3.3). Although T_ReaRip_S does not appear in the model averaging results for dissolved oxygen range (DO_Range_t_S), preliminary OLS results (Appendix 8, Table A8.1) suggest a negative association between DO_Range_t_S and T_ReaRip_S and GLS model testing ranks T_ReaRip_S as the land-cover metric with the most influence on DO_Range_t_S (Models 2 and 3, Table 3.2). A negative association between T_ReaRip_S and DO_Range_t_S is consistent with the relatively strong evidence for a positive association between DO_Range_t_S and urbanisation as represented by population density (PopDen_S) in Table 3.3 (95% UCI).

In summary, riparian tree cover at the reach scale had a relatively strong association with variation in stream health in terms of the SIGNAL2 score based on macroinvertebrate community composition, and on temperature and dissolved oxygen levels.

67 3.3.5 Local-scale stressors

The local-scale impervious surface metric I_EucSite_S was negatively associated with variation in the macroinvertebrate indicator SIGNAL2_S according to model averaging (Table 3.3) and positively associated with maximum temperature and pH (Temp_Max_t_S and pH_S, preliminary OLS regression, Appendix 8, Table A8.1). For SIGNAL2_S, I_EucSite_S was relatively more important as an explanatory metric than all catchment-scale land-cover metrics except the effective riparian buffer metric P_EffRip_S that measures the amount of piped channel in the upstream buffer 30 m either side of the stream. For Temp_Max_t_S, the model with I_EucSite_S (Model 9, Table 3.2) was one of the equally plausible models (AIC < 2) yet I_EucSite_S was not important in model averaging (Table 3.3). In the case of pH_S, the single-variable model with I_EucSite_S (Model 8, Table 3.2) had substantially less support (AIC = 4.14) than the catchment riparian tree-cover models (Models 2 and 3, Table 3.2).

3.3.6 Effective and traditional catchment-scale riparian buffer metrics

The effective riparian buffer metrics that account for the loss of riparian buffer via stream burial (X_EffRip_S) were relatively more important in explaining the variation in the macroinvertebrate indicator SIGNAL2_S than the traditional riparian buffer metrics that disregard the extent of piping (X_Rip_S). Specifically, there was relatively strong negative association between the amount of piped channel in the effective riparian zone (P_EffRip_S) and SIGNAL2_S (90% UCI, Table 3.3) and some evidence for a positive association between the amount of grass cover in the effective riparian zone (G_EffRip_S) and SIGNAL2_S (80% UCI, Table 3.3).

The amount of grass cover in the effective riparian zone (G_EffRip_S) had a strong positive association with DO_Min_t_S (95% UCI, Table 3.3) and was the only catchment-scale metric associated with DO_Min_t_S in model averaging results. pH (pH_S) was also negatively associated (preliminary OLS regression, Appendix 8, Table A8.1) with tree-cover based catchment-scale riparian zone metrics. In the GLS modelling for pH_S the traditional riparian tree-cover metric (T_Rip_S) was the top single-variable model (Model 1, Table 3.2) although the effective riparian tree-cover metric (T_EffRip_S) model was almost equally plausible (AIC = 2.15) and other

68 traditional and effective riparian buffer metric models had considerable support (AIC < 4, Table 3.2). This support overshadowed the inverse-distance weighted (IDW) impervious surface metrics at the local or catchment scales (e.g. I_EucSite_S, I_ExpStr_S) for explaining pH_S.

Although catchment-scale riparian buffer metrics were included in the top ten GLS models for maximum temperature (Temp_Max_t_S) and temperature range (Temp_Range_t_S) (Table 3.2), they were not important in model averaging (Table 3.3). They were not important for explaining variation in dissolved oxygen range (DO_Range_t_S) or conductivity (Cond_t_S).

In summary, catchment-scale riparian buffer metrics had relatively high explanatory power for stream health in terms of macroinvertebrate community composition indicated by SIGNAL2, dissolved oxygen levels and pH. In general, the new effective riparian buffer metrics that account for the extent of piped stream channel had stronger associations with variation in stream health than did the traditional riparian buffer metrics.

3.3.7 Population density

Population density (PopDen_S) was the most important catchment-scale land-cover or land-use metric associated with temperature range (Temp_Range_t_S, 80% UCI, Table 3.3), dissolved oxygen range (DO_Range_t_S, 95% UCI, Table 3.3) and conductivity (Cond_t_S, 80% UCI, Table 3.3) as well as pH (pH_S, Table 3.2). It has a positive association with all of these indicators (see Table 3.3 and Appendix 8, Table A8.1). It was the only catchment-scale land-cover or land-use metric moderately associated with Temp_Range_t_S and Cond_t_S (Table 3.3). There was no important relationship identified between minimum dissolved oxygen (DO_Min_t_S) and PopDen_S.

Population density (PopDen_S) and reach-scale tree cover (T_ReaRip_S) were equally important for explaining variation in maximum temperature (Temp_Max_t_S) (80% UCI). Temp_Max_t_S had a positive association with PopDen_S and a negative association with T_ReaRip_S.

69 The macroinvertebrate indicator SIGNAL2_S and population density (PopDen_S) were negatively associated (90% UCI, Table 3.3). The same level of support was given to a negative association between SIGNAL2_S and the amount of piped channel in the effective riparian zone (P_EffRip_S), but not for any other catchment-scale land-cover or land-use metric (90% UCI, Table 3.3).

3.3.8 Downstream variation

The northing value or y-coordinate of each site (Ycoord_S) had a strong negative association with minimum dissolved oxygen (DO_Min_t_S) and a strong positive association with conductivity (Cond_t_S) (95% UCI, Table 3.3). Generally, sites further north were more likely to be on stream segments with greater flow volumes (i.e. further downstream) because of the generally northerly direction of flow of the main channels. There was no relatively important association detected between Ycoord_S and the macroinvertebrate indicator SIGNAL2_S or any of the other water quality indicators.

3.4 Discussion

3.4.1 Importance of multiple scales - reach, local and catchment

All three hypotheses stated in Section 3.1.3 are supported by the results. Key findings suggest that tree and vegetation cover at the reach scale benefit stream health as represented by the macroinvertebrate diversity and abundance indicator SIGNAL2_S, while the extent of stream burial in the riparian zone and the extent of impervious surface area at the local scale are negatively associated with this indicator. Key findings for catchment-scale processes suggest that population density and the extent of stream burial in the upstream effective riparian buffer have stronger associations with stream health and water quality than does catchment-scale impervious surface area.

3.4.2 Catchment-scale impervious surface and population density

The first hypothesis, regarding the relative importance of catchment-scale impervious surface is supported by the results in several ways. Firstly, compared with studies in temperate areas, the catchment-scale impervious surface metrics have low overall

70 association with all of the stream health and water quality indicators considered. For macroinvertebrate diversity and abundance (SIGNAL2_S), this finding is consistent with a recent larger-scale study in SEQ (Sheldon et al. 2012b, McIntosh et al. 2013). Additionally, catchment-scale impervious-surface metrics were outperformed by the new effective riparian metric that measures the extent of piping that has replaced the stream network in the riparian buffer (P_EffRip_S, further discussed in section 3.4.4) and population density (PopDen_S). Therefore the nature of the impact of catchment- scale land cover does not appear to be exclusively due to altered hydrology and increased pollution loads associated with directly-connected impervious surface, as has been argued in studies in more temperate locations (Walsh et al. 2005a, Walsh and Kunapo 2009). The better performance of the local-scale impervious surface metric (I_EucSite_S) than catchment-scale impervious surface metrics for explaining SIGNAL2_S further supports this argument because the local-scale impervious surface metric could be representing processes other than altered hydrology, such as a reduction in nearby terrestrial (Sheldon et al. 2012b) and aquatic habitat.

Two important factors that may be reducing the influence of impervious surface area in this study are (1) the ephemeral, flashy hydrology and low flows of streams in SEQ (Milton and Arthington 1985, Pusey et al. 1993, Mackay et al. 2014), consistent with other studies in tropical and sub-tropical ecosystems (Engman and Ramírez 2012, Ramírez et al. 2012, Sheldon et al. 2012b, McIntosh et al. 2013); and (2) the predominantly low gradient nature of the streams of Bulimba Creek and Norman Creek (Coaldrake 1961, Smith et al. 2005). Streams in Vermont, USA, with less steep hillslopes appeared to exhibit weaker relationships between macroinvertebrate diversity and abundance and total impervious area (Fitzgerald et al. 2012). It may well be that in the low-slope, slow flowing streams of Bulimba Creek and Norman Creek, the hydrological alteration associated with impervious surface area is less than in steeper, wetter stream ecosystems. The relatively flat gradients of the lowland streams in SEQ may enhance their capacity to dissipate the erosive peak flow forces generated by the efficient hydrology associated with impervious surfaces.

The relatively strong association of population density (PopDen_S) with the invertebrate indicator (SIGNAL2_S) and several of the water quality indicators (Temp_Range_t_S, DO_Range_t_S, pH_S and Cond_t_S), compared with the catchment-scale land-cover metrics, suggests that there are aspects of urbanisation

71 influencing these stream health and water quality indicators that are not captured by IDW impervious surface metrics nor indeed by any of the land-cover catchment-scale metrics considered in this study. Population density may act as a surrogate for a range of different urbanisation stressors. Factors other than altered hydrology which population density may represent include point-source pollution and wastewater plant discharge (Porcella and Sorensen 1980, Wang and Yin 1997, Litke 1999, Addy et al. 2004, United States Environmental Protection Agency 2012), sediment (Hunt and Christiansen 2000), the input of excessive nutrients (Hunt and Christiansen 2000, Pearson et al. 2003), vehicle pollution associated with greater road density and road usage (Steuer et al. 1997), lack of riparian shading (Hunt and Christiansen 2000, Addy et al. 2004), or soil compaction associated with urbanisation (Dunne and Leopold 1978, McMahon and Cuffney 2000, Morley and Karr 2002). It may also represent factors associated with initial urbanisation such as building density and habitat loss, sedimentation and pollution associated with the construction phase (McIntosh et al. 2013). In the current study, population density may be capturing similar factors as the urban land cover metrics (not impervious surface) found to be important by Sheldon et al. (2012b) who suggest that urbanisation may lead to reduced stream health because of alteration of stream morphology, habitat and biota during construction.

Urbanisation is thought to effect macroinvertebrates and fish through changes to water quality, particularly reduced DO and increased diel DO ranges associated with higher organic load in runoff that drains urban areas (Hunt and Christiansen 2000, Sheldon et al. 2012b) so it is interesting that there is no detectable important relationship between population density and the minimum DO metric (DO_Min_t_S). The positive association between DO range (DO_Range_t_S) and population density (PopDen_S) and the negative association with reach-scale tree cover are consistent with the broader urban stream literature, indicating worsening DO levels with increasing urbanisation (Sheldon et al. 2012b). The better performance of PopDen_S compared with the land- cover metrics for explaining DO range suggests that population density better represents catchment-influenced pollution.

Many streams in SEQ have naturally high conductivity associated with the underlying rocks and soils (Sylow 1995, Moss et al. 1997) and urbanisation stressors may raise these levels further as indicated by a moderate positive association between conductivity (Cond_S_t) and population density (PopDen_S). The mean Cond_t_S

72 value across all sites (640 μS cm-1) exceeds the EHMP guideline value for coastal streams (400 μS cm-1), and a few sites have extremely high conductivity that could be considered harmful to macroinvertebrates (over 1500 μS cm-1, Kay et al., 2001). The results of this study suggest that conductivity (Cond_t_S) is more strongly influenced by the natural downstream variation as stream order and flow volume increase (YCoord_S) than land-cover or land-use based stress, although this could represent a cumulative effect of catchment-scale urban land use from upstream (Wang and Yin 1997). pH was most strongly associated with variation in population density and catchment- scale riparian tree cover (T_Rip_S and T_EffRip_S). This result is consistent with other general and urban stream studies, where pH has been shown to respond more strongly to catchment land-use alteration than reach-scale riparian alteration (Roy 2004a, Bunn et al. 2010). In this study pH_S (minimum 5.49, maximum 7.05, Table 12.1, Appendix 12) remains within the bounds of the worst-case scenario levels recommended by the EHMP (minimum 4.5, maximum 10.5, EHMP 2008).

The detectable associations between stream health and water quality indicators and population density, combined with the relatively low strength of association of catchment-scale impervious surface metrics highlight that consideration of metrics targeted at detecting influences other than altered hydrology is required for better understanding the mechanisms by which urbanisation affects ephemeral streams. Reach-scale metrics and effective riparian buffer metrics are considered below.

3.4.3 Reach-scale riparian zone

Reach-scale vegetative land-cover metrics, specifically tree cover, have greater association with variation in macroinvertebrate diversity and abundance (SIGNAL2_S) and water quality (especially maximum temperature (Temp_Max_t_S) and temperature range (Temp_Range_t_S)) in these urban streams of SEQ than catchment-scale impervious or vegetative cover. This result supports the first two hypotheses of this chapter and is in contrast to many other urban stream studies which have not found the reach-scale riparian zone to be as important as catchment-scale impervious surface and other catchment-scale land-cover and land-use alteration (Roy 2004a, Walsh and Kunapo 2009). The results for SIGNAL2_S are consistent with other studies during low

73 flow periods that have found reach-scale and catchment-scale riparian condition (Collier and Clements 2011, Newham et al. 2011, Thompson and Parkinson 2011, Engman and Ramírez 2012) to be important to urban stream health.

The relative importance of reach-scale tree cover (T_ReaRip_S) to stream temperature (Temp_Max_t_S and Temp_Range_t_S) suggests that thermal impacts of urbanisation can be mitigated to some degree by more intact riparian zones, and supports the argument that reach-scale tree cover cools urban streams, consistent with the non-urban study by Storey and Cowley (1997). However, surrounding catchment land-use effects on temperature range (Bunn et al. 2011) such as the urban heat island effect and reduced groundwater recharge (Pluhowski 1970) are likely also to be important given the moderate association between population density (PopDen_S) and the temperature range metric (Temp_Range_t_S).

The results for minimum dissolved oxygen (DO_Min_t_S) that suggest that DO levels increase with urbanisation at the reach scale (e.g. DO_Min_t_S has a negative association with variation in T_ReaRip_S) are puzzling but not novel because other urban stream studies have also found that reach-scale riparian tree cover may be associated with lowering minimum DO levels (e.g. Roy 2004a, Seger et al. 2012). Part of the answer could lie in the low-flow regime of these ephemeral streams, the elevated levels of organic matter and the upstream presence of grass in the riparian zone. Riparian buffer vegetation is important for maintaining healthy DO levels (Bunn et al. 2010) as shading reduces temperatures and controls weeds and emergent plants, and buffers can extract nutrients (Hunt and Christiansen 2000). But riparian zones in urban areas can be a source of organic detritus which can lead to a reduction in minimum DO levels (Seger et al. 2012) and increased shading may reduce the photosynthesis by aquatic plants (Roy 2004a). The daily discharges in the streams of this study are characteristically low. Many of the sites were flowing quite slowly during fieldwork, and six sites persisted as isolated pools (not flowing) during the assessment. Low flows and the presence of elevated organic matter that is not flushed out during higher flows may be reducing DO levels in these streams (Mallin 2006, Pellerin 2006).

However, it should be noted that low DO does not always indicate otherwise poor water quality. In some low-flow or even non-flowing sites in the current study that were located relatively high in the catchment, minimum DO was very low but pollution

74 intolerant taxa (Decapoda: Parastacidae and Atyidae) were found. These sites otherwise appeared to be under less stress (more tree cover, less upstream impervious surface) than other sites in the study. But these locations did appear to have a different community structure than downstream sites with more continuous flow, as there was an absence of taxa intolerant of low DO. Naturally occurring low DO associated with low flows may lead to limited taxa being present (Sheldon et al. 2012c). In addition, intermittent local drying events may lead to assemblages dominated by taxa with life history mechanisms that help them survive periods of dryness (such as burrowing or aestivating, Steward et al. 2012) or returning most quickly after a dry phase (Steward et al. 2012, Lake 2011, Larned et al 2007). In cases where the less impacted sites are in the headwaters, which have lower flows than downstream sections, the negative relationship between minimum DO and reach-scale tree cover may complicate the use of DO as an indicator of urban stream health.

3.4.4 Effective riparian buffers

The fact that ‘effective riparian buffer’ metrics have outperformed catchment-scale impervious surface metrics in this study supports the third hypothesis that the presence of in-stream stormwater piping has a detectable influence on stream health and that this influence is distinguishable from catchment-scale impervious surface impacts.

Interestingly, the effective riparian buffer metrics appeared to be more important to the macroinvertebrate indicator SIGNAL2_S than they were to most water quality indicators, suggesting that urbanisation impacts not directly related to water quality are impacting invertebrate communities. Since catchment-scale impervious surface metrics would be expected to capture the impacts of pollution and altered hydrology on stream health (Roy 2004a, Walsh and Kunapo 2009), the relatively higher importance of the percentage piped channel in the upstream riparian buffer (P_EffRip_S) suggests that there are other mechanisms operating at the catchment scale for reducing macroinvertebrate diversity and abundance. These could include disruption to ecological connectivity due to underground piping and water pollution acting as barriers to the dispersal of adult aquatic insects along stream corridors (Purcell et al. 2002, Peterson et al. 2004, Blakely et al. 2006). Alternatively, the loss of riparian zone extent to piping (and impervious surface) may compromise the natural chemical and physical

75 processing capacity of the riparian zone (Groffman et al. 2003, Groffman et al. 2004, Newham et al. 2011), influencing its role in protecting stream health.

Grass cover in the effective riparian buffer (G_EffRip_S) had stronger association with minimum DO (DO_Min_t_S) than any other catchment-scale stressor metric. Higher minimum DO is usually associated with forested catchments compared with urban catchments (personal communication, Catherine Leigh, Australian Rivers Institute, 2013), so it is interesting to find that effective grass cover in the riparian buffer has more influence on DO levels than catchment-scale tree cover in the current study. The positive association between DO_Min_t_S and G_EffRip_S may be because of reduced potential for excess organic matter derived from leaves and organic debris from trees to build up. If not regularly flushed out, such organic matter can contribute to anaerobic pools (Wenger et al. 2009). Alternatively, grass in the effective riparian zone may provide an effective pervious substrate that can filter local runoff. In incised urban channels tall grasses may also provide shading and enhance stream health. While fertilisation of grass cover can be a source of nutrient pollution, which would be expected to decrease DO (Mallin 2006), these results suggests that other factors associated with grass cover in the effective riparian buffer are influencing minimum DO.

Effective riparian buffer metrics deserve further consideration as representations of urban land-cover stressors given that they outperformed not only catchment-scale impervious surface metrics, but also the impervious, tree and vegetated “traditional” riparian buffer metrics. As such, effective riparian buffer metrics hold promise as a suite of metrics for assessing impacts of ecological connectivity on urban stream health in other locations. Additional studies with a larger number of observations or refinements to these metrics may provide further insight into whether the effective riparian buffer metrics in this study are capturing impacts of (1) in-stream aquatic habitat loss; (2) fragmentation and disconnection of in-stream habitat; (3) loss of in-stream patches for processing nutrients and pollutants; (4) stormwater pipes as sources of pollutant inputs; or (5) all of these aspects. Ecological connectivity metrics are developed further in Chapter 5.

76 3.4.5 Implications for management

The results of the current study suggest that management intervention would be beneficial in the highly urbanised catchments of Bulimba Creek and Norman Creek. Relatively few studies have been done on the impacts of urbanisation on stream health for highly urbanised streams but the current study suggests that despite generally high levels of impervious surface and population density, there are detectable variations in health indicators related to the mitigating impacts of riparian zone vegetation, to local variation in impervious surface stressors and broader catchment-scale stressors such as the conversion of stream channel lengths to stormwater piping. Therefore protection or enhancement of vegetation condition in the reach-scale riparian buffer is recommended where feasible, and for macroinvertebrate diversity and abundance, the results indicate that rehabilitation or protection of reach-scale riparian buffer condition may be the most important intervention to enhance the health of these ephemeral urban streams. However, while catchment-scale impervious surface metrics were not important according to model averaging for explaining any water quality indicators nor the macroinvertebrate diversity and abundance indicator SIGNAL2_S (Table 3.3), local- scale impervious surface and certain catchment scale metrics (population density, PopDen_S, piping and grass in the effective riparian zones, P_EffRip_S and G_EffRip_S) were relatively important. Therefore, reduction of local-scale catchment impervious surface is also recommended and this may address not only potential altered hydrology but also the associated loss of surrounding local vegetation (Sheldon et al. 2012b). Catchment-scale urbanisation stressors associated with population density and effective riparian buffers deserve further consideration. The relative importance of broader catchment land-cover change related to stream burial and conversion of stream segments to stormwater piping (as indicated by the effective riparian buffer metrics) to macroinvertebrate diversity and abundance, as noted in Section 3.4.4 could be associated with disruptions to ecological connectivity or to the loss of ecosystem functions and physical and chemical processes. The potential importance of ecological connectivity will be further investigated in Chapter 5.

The influence of broader catchment processes associated with population density deserves further consideration. If the process of development and construction associated with urbanisation and increasing population density is having an important influence on stream health (McIntosh et al. 2013) then preventative actions that avoid or

77 reduce the impacts of future development on stream ecosystems (Smucker and Detenbeck 2014), such as improved sediment erosion control and changes to how land is cleared, built on and revegetated are likely to be beneficial (McIntosh et al. 2013). Water quality improvements are likely to be beneficial to stream biota (Sheldon et al. 2012b) and therefore water sensitive urban design (WSUD) should be considered.

While out-of-stream restoration can significantly improve ecosystem attributes, their values are still likely to be substantially lower than the value of attributes in reference streams (Smucker and Detenbeck, 2014). Similarly, in this study area, the improvements in stream health due to protection and rehabilitation actions might only be relatively slight, as evidenced by the weak explanatory power of the land-cover and land-use metrics in preliminary single-variable regression (low Adj. R2 values in Appendix 8, Table A8.1). One potential way to increase the effectiveness of management intervention is to target multiple scales. Where multiple stressors at multiple scales are shown to be important, as in the case of macroinvertebrate abundance and diversity, consideration of intervention at multiple scales would be warranted (such as reach-scale riparian buffer protection or rehabilitation and local- scale impervious surface minimisation as suggested above).

The mean of 26% lumped imperviousness for sites in this study is much higher than the 5-12% impervious surface thresholds reported in the literature for the onset of major impacts on stream health, especially on macroinvertebrates (Klein 1979, Schueler 1994, Booth and Jackson 1997, Walsh et al. 2005b). It could be argued that it is not worthwhile protecting the health of highly urbanised stream ecosystems because they are too highly impacted and the return on investment would be too low. However, the relationships between stream health and water quality indicators and various stressor metrics such as reach-scale riparian buffer condition, population density (PopDen_S) and effective riparian buffer metrics in the current study suggest that stressors other than catchment-scale impervious surface are important and that these stressors are having a detectable influence on stream health. This is consistent with several studies that have indicated an absence of “onset” and “exhaustion” thresholds of urban impacts on stream health (Brown et al. 2009, Cuffney et al. 2010) Additionally, the low importance of catchment-scale impervious surface for explaining macroinvertebrate diversity and abundance in less urbanised sites in SEQ (Sheldon et al. 2012b) supports the argument

78 that its low importance in the current study is not due to an exhaustion threshold being reached.

Resources for protecting urban stream health are limited. Therefore, management intervention in urban streams will likely be better served by accepting that some sites will always be impacted by urbanisation to some degree, and asking what management target is realistic given the constraints of population pressure, limited financial resources and thus potentially a limited number of sites that can be prioritised for rehabilitation or protection. Millington et al. (2015, reprinted in Appendix 6) illustrates how a performance evaluation tool can be applied to a set of stream sites to evaluate their relative ecological performance and to facilitate the estimation of the sensitivity of stream health to specific stressors at each site. Identifying sites that are relatively healthy despite a given level of stress and determining which sites are likely to be most responsive to specific rehabilitation efforts can assist in the prioritisation process such that scarce resources are allocated for maximum benefit.

3.5 Conclusion

Overall the results for this focus study of Bulimba Creek and Norman Creek suggest that mechanisms other than altered hydrology associated with impervious surface are impacting macroinvertebrate abundance and diversity and water quality in the urban streams of SEQ. This finding for highly urbanised streams supports a similar study across a wider range of urbanisation levels (Sheldon et al. 2012b) for macroinvertebrates and provides new insights into the likely influences on urban water quality. The extent of stormwater piping that has replaced the natural stream extent and population density had the most influence on macroinvertebrate abundance and diversity at the catchment scale and for most water quality indicators population density was the only catchment-scale metric with detectable influence.

More intact reach-scale riparian buffers were associated with healthier macroinvertebrate assemblages and also with lower stream temperatures and reduced ranges in stream temperature. However, the negative relationship between minimum DO and reach-scale tree cover suggests that DO range may be a more suitable indicator of urban stream water quality in SEQ.

79

The relatively strong association between macroinvertebrate SIGNAL2 score and new metrics in the effective riparian buffer category (which account for the amount of stormwater piping that has replaced stream channel length) suggests that long extents of stormwater piping may be leading to habitat fragmentation and affecting dispersal path connectivity. However, this association could also be due to loss of stream and riparian zone extent.

It is proposed that reach-scale and catchment-scale land cover are both important to water quality and biotic indicators of stream health in the urban streams of SEQ. Further, the presence of extensive lengths of stormwater piping in the stream channel is likely an important aspect of catchment-scale land cover influence, more important than catchment-scale impervious surface. These findings are relevant to the ephemeral streams of SEQ, which experience a sub-tropical climate with associated naturally flashy hydrology, but are also likely to be relevant in other areas with similar hydrological conditions.

The role of study area extent on the detection of the relative importance of reach and catchment scale land cover influences on stream health will be the focus of Chapter 4. Developing further metrics to assess ecological connectivity, and consideration of the connectivity requirements of specific taxa, will be the focus of Chapter 5.

80 CHAPTER 4 SPATIAL ANALYSIS OF THE INFLUENCE OF THE AREAL EXTENT OF A STUDY ON DETECTABLE ASSOCIATIONS BETWEEN URBANISATION AND THE ECOSYSTEM HEALTH OF SUB-TROPICAL STREAMS

4.1 Introduction

A further scale consideration is the areal extent of the study which may influence the spatial scale of the metrics that have most detectable impact on stream health (Roth et al. 1996, Lammert and Allan 1999). Urban stream health studies have been performed across various areal extents, some within single catchments and some across multiple catchments. Some of the smaller areal extents are less than 240 km2 in single (Engman and Ramírez 2012) and dual catchments (Ramirez et al. 2009). Medium areal extents (<1400 km2) (e.g. Walsh and Kunapo 2009) and larger areal extents (< 51,200 km2) are typically multi-catchment studies (Wang et al. 2000, Black et al. 2004, Sheldon et al. 2012a). However, they may consist of single catchments (Roy et al. 2003, Wenger et al. 2008, Zhou et al. 2012) and they may be divided by large impoundments.

Results from the small study area encompassing Bulimba Creek and Norman Creek (BCNC) suggested that reach, local and catchment-scale land-cover alteration influenced macroinvertebrate diversity and abundance (as identified by the SIGNAL2 score, Chessman 2003), and that reach and/or catchment-scale land cover and land use influenced water quality indicators such as dissolved oxygen (DO) minimum and range, temperature maximum and range, conductivity and pH. In this current study with a larger areal extent encompassing multiple catchments, stream health is represented by measures of the abundance and diversity of fish and macroinvertebrates.

While macroinvertebrates have often been the focus of studies of urbanisation because they typically respond to urbanisation metrics at multiple scales, the impacts of urbanisation on fish are also important and catchment-scale impervious surface is commonly reported as a major stressor for fish. Several studies have reported impact- onset thresholds for fish diversity and abundance in relation to catchment-scale impervious surface. A 10-12% impervious surface threshold at which fish species diversity was found to go from good to fair (Klein 1979), with a 30% threshold at which impacts became severe, was observed in a study of small watersheds (2.4 km2 to

81 7.3 km2) in Maryland, USA. In another study in Wisconsin, USA, Wang et al. (2000) found that the numbers of fish species per site (in a study area of approximately 7500 km2) and the associated index of biotic integrity (IBI) were consistently low when impervious surface exceeded 10%. Wenger et al. (2008) reported that 2-4% effective impervious surface was associated with zero expected presence of certain sensitive fish species.

The current study aimed to determine whether similar spatial considerations such as the scales, types and configurations of land cover studied in Chapter 3 are important for stream health in terms of diversity and abundance of stream biota (macroinvertebrates and fish) in the ephemeral urban streams of southeast Queensland (SEQ) when studied across a larger area (medium areal extent). In this chapter it was hypothesised that, similarly to Chapter 3 (and in contrast to studies of temperate streams) (a) catchment- scale impervious surface has relatively low importance, with local-scale impervious surface having marginally greater importance than catchment-scale, and (b) reach-scale riparian condition has relatively high importance for explaining stream health. In addition, as in chapter 3, it was hypothesised that (c) the presence of in-stream stormwater piping has a detectable association with stream health indicators that is distinguishable from catchment-scale impervious surface impacts.

The sites in this analysis were located in highly urbanised sub-catchments in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) of SEQ. The study area of medium areal extent (approximately 1150 km2) encompassing 16 sub- catchments in the LBRCSCC overlapped the two sub-catchments in the Bulimba Creek and Norman Creek (BCNC) study area (152 km2) described in Chapter 3. However, a similar number of sites and a similar range of site drainage areas were assessed, but with less nesting of sites within the drainage areas of other sites.

4.2 Methods

Land-cover, land-use and landscape metrics (stream health stressor metrics and other candidate explanatory metrics) were generated in a geographic information system (GIS) in a similar fashion to the Bulimba Creek and Norman Creek (BCNC) study of Chapter 3. The same statistical methods and model testing were applied as for

82 Chapter 3. The generalised least squares (GLS) function was used to fit models which were then compared using Akaike Information Criterion (Akaike 1981), and model averaging was used to identify the most important explanatory land-cover and land-use metrics from equally plausible models. Stream health indicators, digital elevation model (DEM) data and land-cover data were provided by the Brisbane City Council (BCC).

4.2.1 Site selection and stream health indicators

Stream health data for 2010 came from a long-term data set generated by BCC as part of their Local Waterways Health Assessment (LWHA) citywide stream-health monitoring project. Data has been collected for 48 sites across the BCC Local Government Area, including sites within the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) study area. All sites of the LWHA study are situated on open stream segments (i.e. not piped at the sampling location). Sampling was generally undertaken under low flow conditions.

Initially, the sites were selected by BCC based on benchmarking characterisation of the catchments into theoretically “good”, “moderate” and “poor” condition, with further consideration of geological categories (intrusives, metamorphics, tertiary and Mesozoic sediments) (Water and Environment City Design 2006). Sites that did not have complete land cover and DEM and/or stream health data were removed from analysis, leaving a total of 33 sites for the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) study (Appendix 1). The mean lumped percentage impervious land cover of the catchments draining to the 33 LBRCSCC study sites is 23% (minimum 0%, maximum 48%). Mean reach-scale tree cover 200 m upstream for these sites is 58% (minimum 8%, maximum 100%). The mean upstream sub-catchment area of the LBRCSCC study sites is 2.1 km2 (minimum 0.20 km2, maximum 13.5 km2).

The study sites which drain to the Brisbane River (Figure 2.1) are located in the catchments of Enoggera Creek and Moggill Creek to the north, Oxley Creek, Bulimba Creek and Norman Creek to the south, and in other small streams and un-named sub- catchments which drain to the western section of the Lower Brisbane River. The sites located in smaller catchments which drain directly to Moreton Bay are located in Kedron Brooke and Nundah Creek to the north of the Brisbane River and Tingalpa

83 Creek, Lota Creek and Wynnum Creek to the south of the Brisbane River. The study catchments range in their total catchment size (measured from their headwaters to the river or the bay) from the small 7.4 km2 Wynnum Creek catchment and a small unnamed catchment approximately 5 km2 draining to the Brisbane River to 260 km2 for the Oxley Creek catchment (data provided by BCC). While the sites draining to the Brisbane River form part of the larger Brisbane River catchment (approximately 13,600 km2 in size), the total extents of the catchments are defined from the point where they flow into estuarine areas of Moreton Bay or the Brisbane River. All sites that drain to the Brisbane River drain to sections that are affected by estuarine water. Estuarine extent was identified by the highest astronomical tide (HAT) and mean high water spring tide (MHWS) (data provided by BCC).

The two stream health indicators assessed are defined as follows: (1) “family” level SIGNAL2 macroinvertebrate diversity and abundance (Chessman 2003), designated as SIGNAL2_L; and (2) fish diversity and abundance, which were captured by a measure of community structure, O/E50, designated as OE2010_L (refer to Appendix 2 for definitions of stream health indicators). The O/E50 index is a comparison of the species composition of the observed fish community with the community composition predicted by a referential model fish assemblage (Kennard et al. 2006). O/E50 data was not collected for the Bulimba Creek and Norman Creek (BCNC) study described in Chapter 3 but was available for this study in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC).

4.2.2 Stream biota sampling

Fish diversity and abundance and presence-absence sampling were based on the protocols described in the “Fish and Macrophyte Methods Manual” (Ecowise Environmental 2006) in accordance with SEQ Regional Water Quality Management Strategy, Design and Implementation of Baseline Monitoring – Chapter 9 (Smith and Storey 2001) and the Australian Code of Electrofishing Practice (NSW Fisheries 1997). Sampling reach lengths were set equal to the mean channel reach width multiplied by 10 up to a maximum of 100 m, with values ranging from 50 to 100 m (Piercy 2012). The sampled reach lengths were also chosen to include as many hydraulic units (pools, riffles and runs) as possible. In the dry season when surface waters in some locations were restricted to isolated pools, all pools within the designated reaches were sampled.

84 Stop nets were placed across the borders of sampled areas to prevent movement in and out of the sampling unit. Fish were surveyed using electrofishing and, to a lesser extent, bait traps. When fish were observed but not able to be collected they were recorded as sighted only but included in the assessment (Piercy 2012).

Macroinvertebrates were collected in accordance with AUSRIVAS Rapid Assessment Protocol (Conrick and Cockayne 2001) for the edge habitat only (Piercy 2012). Edge habitat was selected with the justification that it is more frequently encountered in streams in SEQ than pool, bed or riffle habitats and that pool and edge habitat respond similarly to land use and pollution (Piercy 2012). The edge habitat includes areas of still or slow flowing water including backwaters up to 0.5 m from the bank, with microhabitats including debris and snags, undercut banks, overhanging vegetation and tree roots. At each site, 10 m of total edge habitat was sampled using 250 µm dipnets. Three replicate samples were placed into sorting trays and picked for at least 30 minutes and up to an hour by a staff member as per the protocol (Conrick and Cockayne 2001). All taxa including rare and cryptic taxa were collected while attempting not to bias towards abundant taxa. Samples were preserved in 70% ethanol and taxa were identified in the laboratory in accordance with AUSRIVAS protocols. Most taxa were identified to family level except Collembola, Nematoda, Oligochaeta, Acarina, Micro-crustacea Ostracoda, Copepoda and Cladocera, which were identified to class or order level. Chironomidae (Diptera) were identified to sub-family.

4.2.3 Land-cover and land-use stressors

The suite of stream health stressors included land-cover and land-use (population density) metrics generated in a GIS that were similar to those assessed in the Bulimba Creek and Norman Creek (BCNC) study plus metrics for catchment tree and grass cover at all the scale perspectives initially considered only for impervious surface metrics (Appendix 3). Land-cover metrics included non-spatial (lumped) metrics and spatial metrics consisting of areal buffers and spatially explicit inverse-distance weighted (IDW) metrics. Population density (Australian Bureau of Statistics 2007) was also included as a non-spatial land-use metric. The summary statistics for the land-cover metrics are presented in Appendix 14 and the Spearman’s rank correlation coefficients are presented in Appendix 9. Many of the land-cover metrics are moderately or highly correlated (Spearman’s rank correlation over 0.5).

85 4.2.4 Statistical analysis

The same approach that was used in the Bulimba Creek and Norman Creek (BCNC) study (Section 3.2.6) was applied here to formulate, fit and select the best models for the stream health indicators (SIGNAL2_L and OE2010_L). First, preliminary ordinary least squares (OLS) regression analysis was undertaken to determine the direction and strength of association between each response variable (stream health indicator) and a single candidate explanatory variable (land-cover, land-use or landscape metric). Second, an assessment of candidate explanatory variables that could be combined in the same models was made using Spearman’s correlation analysis and the variance inflation factor (VIF) statistic. Third, an initial model for each stream health indicator was specified with a Gaussian correlation structure and fit with the GLS function and stepwise regression with backward selection of variables was performed to determine if a reduced model was better. Nested models were compared and an analysis of variance (ANOVA) on the best model determined if the correlation structure was necessary. Fourth, covariates in the best model from the previous step were systematically replaced by all other covariates in their co-linear set or metric category to create a set of a priori models or hypotheses. Fifth, the Akaike Information Criterion (AIC) statistic was used to select the best models from the a priori sets, and if no single model was clearly the best, model averaging was applied to assess the relative importance of a particular variable of interest (Johnson and Omland 2004).

Simple rules of thumb proposed by Burnham and Anderson (2004) were used to interpret the model results. For example, a AIC < 2 indicates that there is considerable support (evidence) for a second model, 4 ≤ AIC ≤ 7 means that there is substantially less support for the second model, and AIC > 10 indicates that there is essentially no support for the second model. Model averaging was applied as additional evidence to separate similarly ranked models based on AIC values. For model averaging results, 90 and 95% unconditional confidence intervals (UCIs) were interpreted as strong support for a metric while 80 and 85% UCIs were considered as moderate support.

Results from the preliminary OLS regression analysis (Appendix 15) formed the basis for candidate explanatory variable selection. The a priori model set for each stream health indicator was derived from the group of candidate explanatory variables identified in Appendix 16. Candidate explanatory stream stressor variables were

86 generally grouped into three covariant sets in accordance with VIF test results and Spearman’s correlation test statistic (VIF>10 and/or correlation >0.75 identified co- linear sets), but consideration was also given to grouping reach-scale metrics and catchment-scale metrics by the coarse scales they represented even if they were not co- linear (e.g. all “EffRip” metrics were grouped together as catchment-scale metrics). Similarly to the Bulimba Creek and Norman Creek (BCNC) study of Chapter 3, it was possible for both a catchment (catchment or local-scale) and a reach-scale stressor metric to be included in the models in order to test their relative strength of association with the stream health indicator. A metric for total upstream sub-catchment area, USArea_L, was also incorporated in the initial form of each model making three the maximum number of variables in any model.

4.3 Results

4.3.1 GLS model testing and model averaging

The best GLS models (Table 4.1) are reported for the two stream health indicators, diversity and abundance of macroinvertebrates (SIGNAL2_L) and diversity and abundance of fish (OE2010_L). The Gaussian correlation term was not retained for the a priori sets of models for SIGNAL2_L or OE2010_L (p-values equal to 0.64 and 0.92 respectively) and therefore none of the best GLS models include this term. Model- averaging results for metrics appearing in the best models for SIGNAL2_L and OE2010_L are reported in Table 4.2. Definitions for the candidate explanatory variables in these tables are available in Appendix 3.

4.3.2 Overview of results

Reach-scale tree cover (T_ReaRip_L) and population density (PopDen_L) were the GIS-generated metrics that received the most support, with T_ReaRip_L relatively important for explaining both macroinvertebrate and fish diversity and abundance (SIGNAL2_L and OE2010_L) but PopDen_L only relatively important for explaining SIGNAL2_L. The total catchment area (USArea_L) was also shown to have important explanatory power for both stream health indicators. In general the same findings hold for macroinvertebrate diversity and abundance in this study (SIGNAL2_L) as in the

87 previous chapter (SIGNAL2_S) in that reach, local and catchment-scale land-cover metrics (as well as population density) have relatively important explanatory power. While effective riparian buffer metrics still received moderate support, the lumped and IDW impervious surface metrics also received moderate support. Local-scale impervious surface (I_FlowSite_L) received similar levels of support as catchment- scale impervious surface metrics.

Table 4.1 Best GLS models for SIGNAL2_L and OE2010_L Set of best models for each stream health indicator, SIGNAL2_L and OE2010_L includes up to ten equally plausible models (ΔAIC < 2). Appendix 11 provides more information on the AIC approach to model selection. Table presents: Akaike Information

Criteria (AIC), change in AIC (ΔAICi), the weight of evidence statistic (ωi), and Log Likelihood for each model. Although the sample is small, the corrected Akaike Information Criteria (AICc) cannot be used with spatial models, therefore AIC was used throughout.

Model Log Explanatory variables AIC AIC order i i Likelihood SIGNAL2_L 1 G_ReaRip_L, PopDen_L, USArea_L 58.21 0.00 0.04 -24.11 2 T_ReaRip_L, PopDen_L, USArea_L 58.23 0.02 0.04 -24.12 3 PopDen_L, USArea_L 58.35 0.13 0.04 -25.17 4 T_ReaRip_L, USArea_L 59.39 1.17 0.02 -25.69 5 I_ReaRip_L, PopDen_L, USArea_L 59.50 1.29 0.02 -24.75 6 T_ReaRip_L, IP_EffRip_L, USArea_L 59.89 1.68 0.02 -24.95 7 T_ReaRip_L, I_FlowStream_L, USArea_L 59.92 1.70 0.02 -24.96 8 T_ReaRip_L, V_EffRip_L, USArea_L 59.93 1.72 0.02 -24.97 9 G_ReaRip_L, I_FlowSite_L, USArea_L 59.97 1.75 0.02 -24.98 10 T_ReaRip_L, P_EffRip_L, USArea_L 60.02 1.81 0.02 -25.01

OE2010_L 1 T_ReaRip_L, T_Rip_L, USArea_L -33.61 0.00 0.08 21.81 2 T_ReaRip_L, G_FlowStream_L, USArea_L -33.59 0.02 0.08 21.80 3 T_ReaRip_L, G_Rip_L, USArea_L -33.59 0.02 0.08 21.80 4 T_ReaRip_L, T_FlowStream_L, USArea_L -33.37 0.25 0.07 21.68 5 USArea_L -33.25 0.36 0.07 19.63 6 T_ReaRip_L, T_EffRip_L, USArea_L -33.13 0.48 0.06 21.56 7 T_ReaRip_L, G_SubCatch_L, USArea_L -32.75 0.86 0.05 21.38 8 T_ReaRip_L, T_SubCatch_L, USArea_L -32.65 0.96 0.05 21.32 9 T_ReaRip_L, V_FlowStream_L, USArea_L -32.35 1.27 0.04 21.17 10 T_Rip_L, USArea_L -32.26 1.35 0.04 20.13

88 Table 4.2 Model averaging for SIGNAL2_L and OE2010_L

Model averaging results for metrics included in equally plausible models ( ΔAICi < 2) in GLS model testing. Only those results with unconditional confidence intervals (UCI) between 80%-95% are reported as they are considered to have moderate (80-85%) to strong (90-95%) support in the data. Standard error of Unconditional Impact metric Metric scale category Model-averaged estimate estimate confidence level %

SIGNAL2_L USArea_L 0.10 0.03 95 T_ReaRip_L Reach 0.70 0.41 90 PopDen_L Catchment -0.04 0.02 90 G_ReaRip_L Reach -0.81 0.54 85 IP_EffRip_L Catchment* -0.57 0.37 85 I_FlowStream_L Catchment -1.42 0.93 85 V_EffRip_L Catchment* 0.57 0.38 85 I_FlowSite_L Catchment** -1.42 0.90 85 P_EffRip_L Catchment* -0.55 0.38 85 I_Rip_L Catchment -1.25 0.83 85 T_EffRip_L Catchment* 0.61 0.41 85 I_SubCatch_L Catchment -1.17 0.80 85

OE2010_L USArea_L 0.05 0.01 95 T_ReaRip_L Reach 0.16 0.10 85 T_Rip_L Catchment -0.18 0.13 80 G_FlowStream_L Catchment 0.39 0.28 80 G_Rip_L Catchment 0.42 0.30 80 T_FlowStream_L Catchment -0.17 0.13 80 T_EffRip_L Catchment* -0.13 0.10 80 Note: The best candidate explanatory metric for each stream-health indicator is shown in bold. The asterisk (*) identifies effective riparian buffer metrics at the catchment scale. The double asterisk (**) identifies local-scale metrics.

In contrast, the fish diversity and abundance (OE2010_L) models showed no support for impervious surface metrics (at the reach, local nor catchment scale), nor for population density (PopDen_L). For OE2010_L, the land-cover metric with the most support in the models was reach-scale tree cover (T_ReaRip_L), which had a positive association with variation in OE_2010_L. However, the catchment-scale metrics with moderate support in the data included riparian, effective riparian and IDW to-stream grass and tree cover. Of particular interest is the moderately important negative association between the catchment-scale tree-cover metrics and OE2010_L, a reversal of the positive association in preliminary OLS regression results (Appendix 15).

89 It is interesting to note that although the areal extent of the study is greater in the current chapter, the range of total upstream sub-catchment area in both studies (USArea_S and USArea_L) was very similar (Appendices 12 and 14). The maximum upstream sub- catchment area was approximately 13 km2 for both study areas. Further detail on these results is presented below.

4.3.3 Reach-scale metrics

Reach-scale tree cover (T_ReaRip_L) had relatively strong evidence of support as an explanatory metric for the macroinvertebrate indicator SIGNAL2_L (positive association, 90% UCI, Table 4.2). Population density (PopDen_L) was the only metric to receive equal support (negative association) and the upstream sub-catchment area (USArea_L) was the only metric to receive more support (positive association). Reach- scale vegetative cover (V_ReaRip_L) did not receive the level of support as an explanatory metric for SIGNAL2_L that it did for SIGNAL2_S in the Bulimba Creek and Norman Creek (BCNC) study. Data for the fish indicator OE2010_L also showed support for T_ReaRip_L as an explanatory metric with a positive association.

4.3.4 Local-scale impacts

The macroinvertebrate indicator SIGNAL2_L data showed similar levels of support for local-scale metrics (“FlowSite”) as did SIGNAL2_S data. However, unlike SIGNAL2_S, it had similar levels of support for catchment-scale impervious surface land-cover metrics. No local-scale metrics were important for explaining variation in the fish indicator OE2010_L.

4.3.5 Effective riparian buffers and other catchment-scale land-cover metrics

More of the effective riparian buffer metrics had relatively important explanatory power for the macroinvertebrate indicator SIGNAL2_L than for SIGNAL2_S in Chapter 3, where only piping in the riparian buffer (P_EffRip_S) was relatively important. However, the level of support detected for the “EffRip” metrics (85% UCI, Table 4.2) was marginally less than was detected in Chapter 3 (90% UCI, Table 3.4). “EffRip” metrics in this chapter received marginally less support than was detected for population density (PopDen_L, 90% UCI, Table 4.2). Catchment-scale impervious surface metrics

90 received similar levels of support to “EffRip” metrics in this study, unlike Chapter 3. Model averaging indicated that the explanatory power of “EffRip” metrics was equal to the explanatory power of “SubCatch”, “FlowSite”, “FlowStream” and “Rip” metrics for SIGNAL2_L (85% UCI, a moderate level of support, Table 4.2). The inclusion of “EffRip” metrics in models with marginally lower AIC values than other metric categories provided some evidence that they were the marginally preferred catchment- scale land-cover stressor metrics (ranked after the land-use metric, population density, PopDen_L).

Data for the fish indicator OE2010_L exhibited evidence that the tree-cover effective riparian buffer metric (T_EffRip_L) was equally well supported as the traditional riparian and IDW grass and tree-cover metrics (80% UCI, a moderate level of support, Table 4.2). This relationship between OE2010_L and T_EffRip_L was negative, as were the relationships between OE2010_L and the other tree-cover catchment-scale metrics. The data did not support the grass-cover effective riparian buffer metric (G_EffRip_L) although it did support other catchment-scale grass-cover metrics including a positive relationship between the grass-cover riparian buffer metrics (G_Rip_L) and OE2010_L. It should be noted that directions of association indicated from model averaging were opposite to those in OLS regression for these catchment- scale metrics and OE2010_L.

4.3.6 Population density

Population density (PopDen_L) had more support in the data for the macroinvertebrate indicator SIGNAL2_L than any other catchment-scale metric (90% UCI, Table 4.2). As mentioned in Section 4.2.3, PopDen_L and reach-scale riparian tree-cover (T_ReaRip_L) had similar levels of support.

PopDen_L was not an important explanatory variable for the fish indicator OE2010_L.

4.3.7 Catchment extent

Upstream sub-catchment area (USArea_L) was the most important explanatory variable for both the macroinvertebrate indicator SIGNAL2_L and the fish indicator OE2010_L. Larger USArea_L values were associated with higher diversity and abundance scores.

91 This differed from the results for SIGNAL2_S (Chapter 3) for which USArea_S was not an important explanatory variable.

4.4 Discussion

The results of this chapter generally support the hypothesis that land-cover metrics of similar types and scales are associated with indicators of stream health in both this medium-scale study and the small-scale study of Chapter 3. However, some variations between the results of the two chapters and also between macroinvertebrates and fish warrant consideration. Consistent with Chapter 3, catchment-scale impervious surface was of low overall importance for explaining macroinvertebrate diversity and abundance, and variation in the macroinvertebrate indicator SIGNAL2_L was associated with land-cover metrics at multiple spatial scales. While reach-scale tree cover was the land-cover metric with the strongest association with variation in macroinvertebrate diversity and abundance, both local and catchment-scale land-cover metrics, including effective riparian buffer metrics, were also relatively important explanatory metrics for the macroinvertebrate indicator. However, a greater range of catchment-scale land-cover metrics had stronger associations with the macroinvertebrate indicator in this study than in Chapter 3, and therefore the importance of the local scale was more difficult to differentiate from catchment-scale land cover, as was the importance of the effective riparian buffer metrics. Population density was again the most important catchment-scale metric for explaining macroinvertebrate diversity and abundance. However, for fish diversity and abundance, the reach-scale tree cover metric (T_ReaRip_L) was the only land-cover metric with a moderate association that was not marginal or spurious.

4.4.1 Local and catchment-scale impervious surface metrics

The lack of differentiation between local and catchment-scale impervious surface metrics for the macroinvertebrate indicator SIGNAL2_L, in contrast to Chapter 3, supports the findings of Lammert and Allan (1999) who found that studies undertaken on several sites within each catchment exhibited greater within-catchment variation, and attributed more influence to the reach-scale riparian zone (1500 m reaches in their study). Morley and Karr (2002) also found that local-scale (200 m buffer on 1 km

92 reaches) land-cover or land-use metrics were also more strongly related to within- catchment biological condition than between catchment biological condition, which was better captured by catchment-scale land-cover or land-use metrics. The scales at which land-cover metrics are generated that best predict stream health can generally be associated with the ecological mechanisms thought to be controlling stream health (Strayer et al. 2003). So different ecologically relevant processes may be detectable in Chapter 3 compared with the current chapter. However, another explanation is that in large upstream sub-catchments separate land-cover impacts on stream health (associated with heterogeneous land cover across the catchment) may appear to average out whereas smaller sub-catchments may more clearly exhibit spatially explicit specific land-cover and land-use impacts on stream health (Strayer et al. 2003). Therefore, when studying impacts of land cover on stream health in inter-catchment studies it may be important to conduct additional smaller-scale, complementary, intra-catchment studies. This would help to identify potential cases where the larger scale studies might attribute greater explanatory importance to certain land-cover metrics because effects are averaged out, not because they represent ecologically important stressors.

In regards to fish diversity and abundance, the lack of moderate or strong support for an association with any catchment-scale metric including effective riparian buffers and population density deserves further consideration. It could suggest that catchment-scale urbanisation is not important to fish diversity and abundance or that a threshold (Klein 1979, Wang et al. 2000, Wenger et al. 2008) has been exceeded. As mentioned in Chapter 2 and earlier in the current chapter, the mean lumped impervious surface for the sub-catchments of sites in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) study was 23%, with values ranging from 0.02% to 48 %. However, the sites studied by Engman and Ramirez (2012) all had in excess of 40% urban land use upstream and still showed evidence of significant differences in fish assemblage diversity and biomass associated with catchment and reach-scale metrics. While a direct comparison cannot be made between impervious surface and urban land use, their finding suggests that the lack of importance of catchment-scale land-cover and land-use metrics for explaining fish diversity and abundance in the current study is not simply due to a threshold of impervious surface or urban land-use being exceeded. Catchment-scale decreases in tree cover and increases in grass cover (which imply increasing urbanisation) were positively associated with fish diversity and abundance (OE2010_L) according to model averaging results, although the relationships were not

93 strong and the direction of association was different to that indicated in preliminary OLS regression. Some fish in the LBRCSCC study may be responding to certain aspects of urbanisation positively. Some fish species populations have been shown to increase with urbanisation, especially those considered “cosmopolitan” (native or introduced) which are habitat tolerant and frequent pools and runs, have less dependence on riffle habitat and can tolerate fine sediment (Walters et al. 2003). Deeper pools may develop with urbanisation, especially around bridges (Paul and Meyer 2001) and culverts (Washington Department of Fish and Wildlife 1999, Millington 2004) and the loss of terrestrial and riparian catchment-scale vegetation may be associated with increased sedimentation (Saxton et al. 2012). In contrast, fish that prefer riffle habitats would be expected to decrease with urbanisation (Sheldon et al. 2012b). By exploring additional land-cover configuration metrics relating to in-stream connectivity and by targeting several specific fish species, the land-cover stressors impacting fish diversity and abundance in the urban streams of SEQ will be further investigated in Chapter 5.

4.4.2 Reach-scale riparian zone

In these highly urbanised catchments, reach-scale riparian vegetation metrics were the most important land-cover metric type for explaining variation in macroinvertebrate diversity and abundance, regardless of the areal extent of the study. For fish diversity and abundance (OE2010_L) reach-scale tree cover was also the most important explanatory land-cover metric. Larger areas of reach-scale tree cover were associated with higher OE2010_L scores.

The relative importance of reach-scale tree cover (T_ReaRip_L) as an explanatory metric for the fish indicator OE2010_L is consistent with studies of streams in tropical regions such as Puerto Rico (Engman and Ramírez 2012) where stream segments in areas of moderately high catchment urbanisation that are natural (unaltered) or ‘intermediate’ (straightened, with some bank reinforcement) had relatively high biomass, species-rich, native–dominated fish assemblages, and concrete-lined channel reaches in catchments with high levels of urbanisation had fish assemblages dominated by exotic fish species. Temperature is also an explanatory variable for fish diversity and abundance in urban streams (Engman and Ramírez 2012) which, as suggested in Chapter 3, is likely strongly influenced by reach-scale tree cover in the ephemeral streams of SEQ. In their study of forested, agricultural and urban freshwater streams in

94 SEQ, Sheldon et al. (2012a) also found reach scale to be most important scale for explaining fish diversity and abundance. However, in their study the negative association with reach-scale grass cover was most important, whereas the positive association with reach-scale tree cover is most important in the current study. Both of these results are consistent with the finding that the fish indicators in the SEQ EHMP studies are known to be positively affected by the presence of in-stream wood and also respond to local channel condition and in-stream habitat (Kennard et al. 2006, Bunn et al. 2010). The difference in the results of the current study and Sheldon et al. (2012a) may be due to the sites in the current study including greater levels of tree cover compared with grass cover in the reach-scale riparian zone. Another possible explanation is that in highly urbanised landscapes sources of woody debris may be low and the presence of trees may therefore be of increased importance.

4.4.3 Effective riparian buffers and stormwater piping

Building on the results from Chapter 3, the results of this large-scale study indicating the relative importance of effective riparian buffers provide further support for the importance of these metrics in explaining variation in the macroinvertebrate indicator SIGNAL2_L. The relative importance of these metrics in both studies suggests that fragmentation of habitat and loss of streams to burial may be equally or more important as increasing amounts of impervious surface associated with urbanisation, at least in these highly urbanised, lowland, sub-tropical streams. However, distinguishing the relative importance of effective riparian buffers compared with other catchment-scale metrics is more difficult in this study due to the similar levels of importance of catchment-scale impervious surface metrics. As with the lack of differentiation between local and catchment-scale impervious surface metrics (Section 4.3.1), this lack of differentiation in the larger-scale areal extent study of this chapter may be due to multiple catchment-scale effects on stream health averaging out or it may relate to the scale of influence of stressors indicated by these metrics (Lammert and Allan 1999, Morley and Karr 2002, Strayer et al. 2003).

There is no evidence in the data for this chapter to suggest that increasing amounts stormwater piping in the effective riparian buffers had negative influence on the fish indicator OE2010_L. However, this does not exclude the possibility that stormwater piping is associated with negative impacts on fish diversity and abundance as most of

95 the impact on fish diversity and abundance due to barriers may be associated with the first one or two barriers or partial barriers in a catchment (Cote et al. 2009). Consequently, metrics that capture accumulated connectivity impacts on stream health (such as effective riparian buffer metrics) may not be as relevant as those that consider individual or groups of barriers and their locations in a catchment. The potential importance of the first barriers encountered along a stream segment is considered in Chapter 5 by generating new ecological connectivity metrics.

4.4.4 Catchment extent

In this study, there was relatively strong evidence that upstream sub-catchment area (USArea_L) is positively associated with both fish and macroinvertebrate diversity and abundance. However, this was not apparent in the smaller-scale intra-catchment Bulimba Creek and Norman Creek (BCNC) study despite a similar range of values for sub-catchment area. The fact that the positive association between USArea_L and the macroinvertebrate diversity and abundance indicator was only evident in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) study, where a greater range of individual and potentially unique catchments were sampled, suggests that the magnitude of the entire extent of a catchment may be related to the diversity of species contained within it.

The relatively strong evidence of support for the upstream sub-catchment area (USArea_L) in explaining variation in the fish indicator OE2010_L was consistent with results from other studies (e.g. Wenger et al. 2008). Larger USArea_L is associated with greater water volumes and water depths at sites, but other factors could be important such as larger catchment areas potentially supporting greater variety of habitats and fauna, or greater water volumes providing greater resilience to low flow and drought conditions, which are a common occurrence in SEQ (Mackay et al. 2014). The presence of deeper pools and deeper stream segments associated with higher water volumes and better water quality (Sheldon et al. 2012b) could be important in a landscape potentially fragmented by stormwater piping, culverts and impervious surface as deeper pools and deeper stream segments may enhance the survival rate of fish during drought periods (Steward et al. 2012) until larger storms permit overflows of culverts (Norman et al. 2009) and short stormwater piping sections.

96 Further research would be required to confirm whether the observed association between upstream sub-catchment area and fish and macroinvertebrate diversity and abundance is purely due to the influence of water depth, habitat diversity and presence of refugial pools or whether larger catchments support more diverse fish assemblages. This could provide an insight into selection of catchments for protection or rehabilitation based on their size and faunal diversity. Total and downstream catchment size will be considered in Chapter 5 in an attempt to investigate this aspect of urban stream ecology.

4.4.5 Implications for management

There was support in this inter-catchment study for an increased range of catchment- scale land-cover metrics for explaining macroinvertebrate diversity and abundance compared with the smaller-scale intra-catchment study of Chapter 3 suggesting that that impacts of different urbanisation stressors are “averaged out” across a larger study area (Lammert and Allan 1999, Morley and Karr 2002, Strayer et al. 2003) making it harder to differentiate the relative importance of different land-cover metrics and reducing the detectable influence of metrics associated with smaller scales such as the local scale. However, reach-scale land-cover metrics were important in both chapters, that is across study area extents of both scales. The differences in results between the two chapters suggest that although maximising the effectiveness of local and reach-scale intervention may be limited by catchment-scale processes (Bernhardt and Palmer 2007), small-scale intra-catchment studies may be needed to assess potentially beneficial interventions that will have a detectable effect at the local scale. These results further indicate that there are also benefits from conducting studies across intra-catchment extents, especially as there are some factors that may be identifiable only across intra-catchment extents such as the importance of upstream catchment size.

In the streams of SEQ, reach-scale land-cover intervention is recommended to protect or enhance diversity and abundance of both fish and macroinvertebrates. Catchment-scale land-cover intervention is also likely to have benefits for macroinvertebrates. However, there are further unknowns not captured by these metrics as indicated by the fact that less than 9% of variation in the fish indicator OE2010_L was explained by them in the preliminary single-variable regressions, and less than 28% of variation in the macroinvertebrate indicator SIGNAL2_L. In contrast, upstream catchment size

97 (USArea_L) explained 62% of variation in OE2010_L and 33% of variation in SIGNAL2_L (Appendix 15).

4.5 Conclusion

Comparing this medium areal extent study with the small areal extent study in Chapter 3, the scale of relatively important land-cover stressor metrics for explaining macroinvertebrate diversity and abundance (SIGNAL2) were fairly consistent. That is, reach, local and catchment-scale land-cover metrics were relatively important. In contrast, for fish diversity and abundance (O/E50), reach-scale tree cover was the explanatory metric with the strongest association other than upstream sub-catchment area (USArea_L).

While local and reach-scale land-cover metrics were relatively strongly associated with variation in the macroinvertebrate data for both studies, a range of catchment-scale impervious surface metrics received increased support in the larger-scale inter-basin study making it difficult to differentiate the importance of local-scale metrics or effective riparian buffer metrics. This is consistent with other studies which have found that local-scale and reach-scale effects are easier to detect in smaller-scale intra-basin studies than in larger-scale inter-basin studies (Lammert and Allan 1999, Morley and Karr 2002).

In contrast with the macroinvertebrate indicator SIGNAL2, the only local or catchment- scale metrics (tree and grass-cover catchment-scale metrics) associated with fish diversity and abundance (OE2010_L) showed a possible spurious positive effect on OE2010_L with increasing urbanisation. This finding could indicate that negative effects of urban land cover on the fish assemblage are balanced out by positive effects, or that above a certain threshold of urbanisation, increasing levels are not having a detectable detrimental effect on fish diversity and abundance. Such a threshold might be very low as none was found in another study of SEQ with a greater proportion of sites with low urbanisation levels (Sheldon et al. 2012a). A possible explanation for the low importance of effective riparian buffer metrics for explaining fish diversity and abundance is that in-stream connectivity impacts on fish passage are mostly due to the first one or two barriers rather than accumulated barriers. This possibility will be

98 addressed in Chapter 5 by considering in-stream connectivity metrics that represent the first barriers on each stream segment.

A notable difference between the two studies was that in the medium areal extent study the upstream sub-catchment area (USArea_L) had a more detectable association with macroinvertebrate and fish diversity and abundance. It was the most important explanatory variable for both biotic indicators in this study. This difference suggests that in order to see the effect of upstream sub-catchment area on macroinvertebrate diversity and abundance, inter-catchment sampling is necessary.

These results further support the argument that targeting reach-scale land-cover management intervention is likely to have a positive impact on fish and macroinvertebrate assemblage health in SEQ. However, especially for macroinvertebrates, this should be considered in the context of land-cover alteration of the greater catchment. A more thorough analysis of ecological connectivity may provide insights into the limitations imposed by catchment-scale land use on local-scale management intervention. This will be further investigated through the development of alternative ecological connectivity metrics in Chapter 5.

99 CHAPTER 5 SPATIAL ANALYSIS OF THE LIKELY IMPACTS OF DISRUPTIONS TO ECOLOGICAL CONNECTIVITY ON THE BIOTA OF SUB-TROPICAL URBAN STREAMS

5.1 Introduction

Fragmentation of dispersal pathways was identified in Chapters 3 and 4 as a possible limiting factor on stream health in urban areas of southeast Queensland (SEQ), prompting further investigation of the effects of landscape connectivity, also called ecological connectivity (Taylor et al. 1993), on macroinvertebrate and fish diversity and abundance in this region. In this chapter, ecological connectivity is focused on in-stream longitudinal connectivity and fragmentation of vegetation in the catchment and surrounding landscape. These aspects of ecological connectivity are likely to affect dispersal pathways of aquatic biota (Mirati 1999, Norman et al. 2009, Smith et al. 2009), but may also potentially impact hydrological flowpaths and effective functioning of ecological processes such as nutrient and carbon cycling (Kaushal et al. 2008b, Newham et al. 2011). Hence, this chapter focuses on connectivity and its influence on ecological processes; a very different consideration to enhanced hydrological connectivity associated with impervious surfaces and efficient stormwater drainage (Walsh et al. 2005a).

The accessibility to and connectedness of terrestrial vegetation surrounding a site was identified as potentially important for explaining variation in stream health in an ecological performance analysis of urban stream sites (Millington et al. 2015, reprinted in Appendix 6). Some of the sites with better ecological performance appeared to be healthier (based on macroinvertebrate abundance and diversity and daily stream temperature range) compared to other sites with similar land-cover and land-use stressors at the local, reach and catchment scales. An explanation suggested for this observation was better connectivity to terrestrial vegetation, also identified as potentially important in recent studies of the ecological influence of upstream catchment land cover (Shandas and Alberti 2009, Sheldon et al. 2012b).

Considerable research has been undertaken on fish passage in urban streams, especially relating to the impacts of dams and culverts. Much of this research has focused on diadromous fish that need to migrate between freshwater and estuarine or marine areas.

100 A key example is the economically-important salmon stocks of North America, where improving fish passage via removal of barriers or modification of culverts has improved the condition of salmon stocks in many cases (McCullough et al. 2011, Bonneville Power Administration Bureau of Reclamation 2013). Several studies have also highlighted the detrimental effects of in-stream barriers on the genetic diversity and abundance of populations of freshwater fish (e.g. Wofford et al. 2005). Culverts can become perched and prevent fish passage at low flows. These structures can also create areas of very high flow velocities during storms, and have been found to act as barriers and partial barriers to passage of small-bodied fish for this reason (Norman et al. 2009). Although most culverts were not found to be complete barriers, they did significantly reduce dispersal rates in the Etowah River Basin (Norman et al. 2009). Likewise, Ramirez et al. (2012) found that a dam in urban Puerto Rico was a major cause of reduced fish diversity and abundance upstream, in a similar way that natural waterfalls in their study area resulted in a total absence of fish upstream. Other studies have also shown that at sites located upstream of large dams, native fish can be completely extirpated, regardless of other urban land-use stressors, and that at sites downstream from large dams, hydrological alterations affect fish populations and faunal diversity (Pringle 1997, Holmquist et al. 1998, Bunn and Arthington 2002, March et al. 2003).

There is some evidence that macroinvertebrate assemblages are also influenced by in- stream ecological connectivity and habitat and vegetation fragmentation (Vaughan 2002, Blakely et al. 2006, Norman et al. 2009, Smith et al. 2009, Ramírez et al. 2012). Roads, impervious surfaces and piping have the potential to fragment dispersal pathways directly and to reduce available habitat for adult phases of aquatic insects that feed, mate or disperse across terrestrial areas (Smith et al. 2009). Blakely et al. (2006) provided evidence of a direct effect of in-stream barriers on dispersal of the caddisfly Hydrobiosis parumbripennis, with females shown to oviposit significantly less upstream than downstream of a culvert. Impacts of dispersal barriers, especially box and pipe culverts, have also been demonstrated in other macroinvertebrates such as freshwater mussels (Voelz et al. 1998), shrimp (Resh 2005) and crayfish (Light 2003, Kerby et al. 2005).

While impacts of culverts and dams on in-stream connectivity have received significant attention, the impacts of stream burial and piping on the dispersal of aquatic organisms are almost entirely unknown. Nevertheless, where the extent of stream burial is

101 substantial, impacts on connectivity can be expected and may be considerable. Whereas the length of a single culvert typically only covers the width of a road (e.g. 10 m) or even a highway (100 m) (Millington 2004), stream burial and piping may run over several kilometres, as in the Norman Creek study area described in Chapter 3.

The aim of this chapter was to explore ecological connectivity, specifically relating to dispersal path connectivity and habitat fragmentation, as a key factor driving stream health in urban ecosystems. This chapter presents and applies a methodology for considering the relative impacts of land cover and ecological connectivity on urban freshwater ecosystems in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC). Different taxa are likely to be impacted by changes to ecological connectivity due to their different life history requirements (e.g. obligate freshwater) and/or dispersal traits (flight, downstream dispersal by drifting, crawling, swimming). Therefore, in addition to the fish and macroinvertebrate diversity and abundance indicators analysed in Chapters 3 and 4, species occurrence response modelling is used here to assess whether aspects of ecological connectivity (measured as in-stream and terrestrial dispersal pathway metrics) influence the occurrence of selected fish and macroinvertebrate taxa.

It was hypothesised that ecological connectivity and habitat fragmentation have more influence on urban stream health in the ephemeral sub-tropical streams of SEQ, as indicated by fish and macroinvertebrate diversity, abundance and occurrence, than catchment-scale impervious surface area and associated altered hydrology. To test this hypothesis, the performance of a range of newly developed in-stream ecological connectivity and habitat fragmentation metrics was compared with that of the catchment-scale land-cover, land-use and landscape metrics considered in Chapters 3 and 4. It was further hypothesised that ecological connectivity in urban streams influences aquatic taxa in different ways depending on their life history requirements and dispersal mechanisms. To test this second hypothesis a range of ecological connectivity metrics was included in occurrence models for different taxa. Finally, it was hypothesised that reach-scale condition (indicated by reach-scale land cover) has influence on stream health even when ecological connectivity metrics are considered.

102 5.2 Methods

The stream health indicators and geographical information system (GIS) data sets for land cover and stormwater piping used for this study were provided by Brisbane City Council (BCC). Stream health indicators included the macroinvertebrate diversity and abundance indicator, SIGNAL2 (Chessman 2003) and the fish diversity and abundance indicator O/E50, an indicator of community structure (Kennard et al. 2006). These indicators are referred to in this chapter as SIGNAL2_C and as OE2010_C and OE2011_C (for years 2010 and 2011 respectively). Occurrence data for species chosen for their differing biological traits included: (1) three freshwater fish, Mogurnda adspersa (Purple-spotted gudgeon); Tandanus tandanus (Freshwater catfish); and Melanotaenia duboulayi (Crimson-spotted rainbow fish), and (2) various macroinvertebrate taxa including two dragonfly families, two mayfly families, one caddisfly, a freshwater shrimp and the freshwater crayfish, Cherax destructor. Land cover and connectivity metrics were determined using a range of GIS methods.

5.2.1 Site selection

For this analysis of ecological connectivity impacts in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) study area, complete land- cover data was available for 30 of the 33 sites selected in Chapter 4 (Appendix 1). The surrounding tree-cover fragmentation metrics could not be determined for the three sites lacking complete land-cover data. Due to a lack of complete information on fish occurrence at one site, only 29 sites were considered for the fish occurrence models.

5.2.2 GIS Analysis

For this study, a reduced set of the candidate explanatory GIS-generated land-cover, land-use and landscape metrics generated in Chapter 4 was augmented with several in- stream connectivity and surrounding tree-cover fragmentation metrics defined in Appendix 3. The techniques used to generate these newly developed metrics are outlined in the sub-sections below.

103 5.2.2.1 FRAGSTATS – fragmentation of surrounding riparian and terrestrial tree cover

The surrounding tree-cover fragmentation metrics were determined as follows. Initially, circles 1 km in radius around each study site (Figure 5.1) were generated using ArcGIS 10.0. A 1 km radius was selected because insect dispersal is rarely greater than this distance (Collier and Smith 1998, Kelly et al. 2001, Briers and Gee 2004). Tree cover within these sampling circles was then estimated, as was tree cover in riparian buffer zones, defined as the 30 m distance perpendicular to each of the streams encircled. These tree-cover estimates were subsequently processed in FRAGSTATS (McGarigal et al. 2002), a program with a wide variety of inbuilt metrics that relate to different aspects of landscape fragmentation. The class Aggregation Index and the total class land-cover area metrics were selected for this assessment. These metrics measure both the total amount of a chosen land-cover type (in this case, tree cover) within a given area, as well as the amount of fragmentation or discontinuity of the given land cover (indicated by the Aggregation Index). While similar studies have focused only on upstream vegetation fragmentation (e.g., Shandas and Alberti 2009) both upstream and downstream riparian fragmentation as well as fragmentation of the surrounding terrestrial vegetation were necessary for this study because aquatic fauna move in both directions, and insect flight paths are not necessarily restricted to stream channels (Smith et al. 2009).

5.2.2.2 In-stream longitudinal connectivity metrics

In-stream longitudinal connectivity metrics assessed in this study include the effective riparian buffer metrics (introduced in Chapter 3) and the in-stream connected stream extent metrics, which measure the extent of connected stream length upstream, downstream and a total of upstream and downstream from a site. In other words, they are measures of the longitudinal distance up to the first barrier on any stream segment. In-stream connected stream extent metrics were generated in ArcGIS 10.0. Geometric networks were defined and tools for their analysis available in “Network Analyst”, specifically Utility Network Analyst, were applied.

The drainage network used in Chapter 4 was modified by masking estuarine reaches (identified by the mean high water spring tide) and the Leslie Harrison Dam. The

104 modified drainage layer was input to a geodatabase. This was combined with the study sites and a layer that combined culverts and pipes as barrier nodes to generate a layer of points that could be turned on or off. The culverts and pipes were converted from lines to points at the intersection with the stream network using a tolerance of 10 m. The network was generated in ArcCatalog using Network Analyst in ArcGIS 9.3. The Network Utility Analyst tool was used to select all of the network linework upstream, downstream or both upstream and downstream. This was done for the case where all barriers were switched off, so that the full mapped stream length could be determined (USNoBar_C etc.), and then with the barriers switched on, so that only the stream length remaining connected to the site without a mapped barrier was returned (USBar_C etc.) (Figure 5.2). Culvert locations were checked using Nearmap (http://nearmap.com/au).

Figure 5.1 Surrounding landscape tree-cover fragmentation metrics This figure provides a visual representation of the calculation of surrounding tree-cover fragmentation metrics. The threshold of influence (purple) identifies the 1 km radius area within which the amount of tree cover (C_CA_C) is calculated. For calculation of R_CA_C the area within the same 1 km radius includes only the tree cover within the riparian buffer defined as 30 m either side of the stream network. The aggregation metrics C_AI_C and R_AI_C are applied to the same extents but calculate aggregation indices to discern the amount of fragmentation or discontinuity of the tree cover (using FRAGSTATS).

105

(a) (b)

(c) (d) Figure 5.2 In-stream longitudinal ecological connectivity metrics This figure provides a visual representation of the calculation of in-stream ecological connectivity metrics. The stream network marked in blue identifies the area relevant to the respective metrics: (a) natural tributary extent metrics (assuming fully connected stream networks) TotNoBar_C, USNoBar_C, DSNoBar_C; (b) total in-stream connected stream extent (network extent stops at first barrier on a tributary) (TotBar_C); (c) upstream in-stream connected stream extent (USBar_C); (d) downstream in-stream connected stream extent (DSBar_C)

106 5.2.3 Biological sampling

Fish and macroinvertebrate sampling procedures for the data provided by BCC are outlined in Chapter 4 and by Piercy (2012). O/E50, a comparison of the species composition of the observed fish community and the community predicted by a referential fish assemblage model (Kennard et al. 2006), was determined for 2010 and 2011 (OE2010_C and OE2011_C). The two separate years of data were of interest because in January 2011 the Brisbane River flooded, with water levels peaking at 4.46 m AHD at the Brisbane City gauge (Babister and Retallick 2011), inundating vast low-lying areas. However, macroinvertebrate sampling was only undertaken in 2010. SIGNAL2 score was determined as the “family” level SIGNAL2 (Chessman 2003) score (SIGNAL2_C).

5.2.4 Taxa selection

The selection of native macroinvertebrate taxa (Table 5.1) was based on a strategy for including a variety of different life history traits. The dispersal behaviour of a range of freshwater species was compared and contrasted by Bunn and Hughes (1997) and Hughes (2007). Cherax destructor and Parataya australiens, two crustaceans (Decapoda) with potential for terrestrial dispersal as adults, were selected because they have a very different life cycle to aquatic insects with an adult flight stage, such as the Baetis mayflies (Ephemeroptera), an undescribed species studied by Bunn and Hughes (1997). The tendency for Baetis mayflies to fly upstream and oviposit has been shown to compensate for downstream drift that causes the loss of upstream individuals (Hershey et al. 1993). Caddisflies were selected because there is some evidence that adult caddisfly flight can be partially blocked by culverts (Blakely et al. 2006). Several dragonfly families (Odonata) were considered because the drift patterns and habitat preferences for still or flowing water of species in these families were known from studies in Bulimba Creek (Watson et al. 1982). For example, the family Gomphidae includes the species Austroepigomphus praeruptus which was found to breed in streams or rivers but not in still waters, and the adults stay close to their natal location (Watson et al. 1982). Conversely, there are species in the family Hemicorduliidae that frequent a range of aquatic habitats, from still waters to flowing streams and rivers, and breed in these different locations. They then typically disperse widely from their breeding sites (Watson et al. 1982). It was hypothesised in the current study that these tendencies to

107 stay close to preferred breeding habitats might lead to different impacts of barriers on local movements and dispersal. Taxa selection was also based on the findings of the fieldwork of Chapter 3, which indicated that certain taxa were not found in all locations, and in-stream ecological connectivity may be a factor in determining where they were found.

The freshwater shrimp data (Decapoda, family Atyidae) in the BCC data set were confounded with another shrimp family (Decapoda, family Palaemonidae) but were considered because of their specific life history traits. These fully aquatic crustaceans with a planktonic larval stage have exhibited surprisingly limited in-stream movement (Bunn and Hughes 1997). The two shrimp families selected from the BCC data set were identified as separate taxa or as one combined category in instances where differentiation between them was difficult. Since only the non-combined Atyidae data was used in the occurrence study it is possible that occurrences of this taxon were underestimated.

The selection of native fish (Table 5.2) was constrained by the ability of the occurrence data to produce meaningful results from separate univariate logistic regressions (equation [6], n=1) performed for each candidate explanatory variable.

Table 5.1 Taxa selected for macroinvertebrate occurrence modelling

Order Family Species name if Common name Name of occurrence applicable* data set in the current study Decapoda Parastacidae Cherax destructor Freshwater crayfish / DecPar yabby Decapoda Atyidae Likely species: Freshwater shrimp DecAty Paratya australiens Ephemeroptera Baetidae Mayfly EphBaet Ephemeroptera Leptophlebiidae Likely species: Mayfly EphLept Atalophlebia australis Odonata Gomphidae Dragonfly OdoGomp Odonata Hemicorduliidae Dragonfly OdoHemi Trichoptera Leptoceridae Likely species: Caddisfly TricLept Triplectides australis *Likely species are based on Chapter 3 fieldwork where these species were the only ones identified within each family

108 Table 5.2 Species selected for fish occurrence modelling

Species name Common Name Name of occurrence data Name of occurrence data set set in the current study in the current study (2011 (2010 data) data) Melanotaenia duboulayi Crimson-spotted rainbow fish CrimPA10 CrimPA11 Mogurnda adspersa Purple-spotted gudgeon PursPA10 PursPA11 Tandanus tandanus Freshwater catfish FrePA10 FrePA11

5.2.5 Statistical methods

The key modelling objective of this chapter was to assess the importance of in-stream ecological connectivity and fragmentation of surrounding tree-cover for explaining variation in stream health indicators and the occurrence of selected aquatic taxa, relative to land-cover metrics derived in previous chapters.

The statistical analysis is divided into two sections: (1) generalised least squares (GLS) modelling of fish and macroinvertebrate abundance and diversity (continuous data), and (2) generalised linear models (GLM) involving logistic regression for the occurrence of the selected fish and macroinvertebrate taxa (binary data).

5.2.5.1 Modelling stream health indicators

The data sets for the macroinvertebrate and fish abundance and diversity indicators (SIGNAL2_C, OE2010_C and OE2011_C) are cross sectional – recorded in one year across a number of sites – and therefore the initial GLS models were specified with a Gaussian correlation structure to allow the residuals from the fitted model to be spatially correlated. Details on the approach taken to create a set of a priori models for each stream health indicator are given in Chapter 3 (Section 3.2.6).

The literature review of the factors associated with urban stream health and the results from Chapters 3 and 4 formed the basis for creating the 44 candidate explanatory variables (Appendix 3). Summary statistics for these new variables are reported in Appendix 17. To be included in an a priori model set for a particular stream health indicator a candidate explanatory variable had to have a t-test p value less than 0.2 in preliminary univariate regression analysis (Appendix 18). The a priori model set for

109 each stream health indicator (Appendix 19) was generally divided into three covariant groups. A variance inflation factor (VIF) over 10 and a Spearman’s correlation statistic over 0.75 (Appendix 9) identified which sets of covariates were collinear, but consideration was also given to grouping reach-scale and catchment-scale stressor metrics separately because scale of impact was a major focus of this research and because other similar metrics were collinear. For example, effective riparian buffer metrics were grouped together as catchment-scale stressor metrics, and local-scale stressor metrics were also grouped with the catchment-scale stressor metrics because they were more strongly correlated with the catchment-scale stressor metrics than reach- scale stressor metrics. Constrained by the size of the data sets and the desire to group candidate explanatory variables according to scale, the initial form of each model incorporated a maximum of three candidate explanatory variables: a reach-scale riparian variable, a catchment land-cover or land-use variable and a catchment or tributary extent variable.

The Akaike Information Criterion (AIC) statistic was used to select the best models from the a priori sets and in accordance with the rules of thumb proposed by Burnham and Anderson (2004), models with AIC < 2 (Table A20.1 in Appendix 20) were considered equally plausible. Model averaging (Table 5.3) was applied to either the top 10 models or all models with AIC < 2 (whichever was the greater number of models) to assess the relative importance of a particular candidate explanatory variable of interest (Johnson and Omland 2004). Only land-cover, land-use and landscape metrics with an unconditional confidence interval (UCI) over 80% were reported in the results. UCIs of 90 to 95% were interpreted as strong support for a metric while 80 and 85% UCIs were considered as moderate support.

5.2.5.2 Logistic regression – a Generalised Linear Model (GLM) for analysing fish and macroinvertebrate occurrence

The logistic regression analysis explicitly assumed that if a species was present in a stream segment at the time of sampling it was detected during sampling (i.e., capture probability of 1). Without repeat sampling, the reliability of this assumption cannot be explicitly assessed and therefore potential biases associated with this assumption (MacKenzie et al. 2002, Kellner and Swihart 2014) must be considered when

110 interpreting results. While such an assumption is relatively common (Kellner and Swihart 2014), the results should therefore only be considered preliminary. Logistic regression belongs to the family of generalised linear models where the dependent variable is used through a “link” function that transforms the model into a linear model. When the dependent variable is binary, a logistic transformation of the odds (referred to as logit) is applied: � log(����) = �����(�) = log⁡( ) 1 − � The odds of a “presence” of a certain species is the probability of a “presence” divided � by the probability of an “absence”, ⁡.⁡ 1−�

The logistic regression model can be written as:

�����⁡(�) = �0 + �1⁡⁡⁡�1 ⁡+ ⋯ +⁡��⁡⁡⁡�� [6]

To transform the log of the odds back to a term for probability, the following formula is necessary: ���(� + � � ⁡+ ⋯ +⁡� � ) � = 0 1⁡⁡⁡ 1 �⁡⁡⁡ � 1 + ���(�0 + �1⁡⁡⁡�1 ⁡+ ⋯ +⁡��⁡⁡⁡��)

Maximum likelihood (ML) is the method most commonly used for parameter estimation in a logit model. The estimated coefficient (βi) gives the expected change in the log-odds of a “presence” for a one-unit increase in the independent variable (Xi). Exponentiating a coefficient gives the odds ratio (OR), or the expected change in the odds of a “presence” for a one-unit change in the independent variable: ���(��) >

1⁡indicates an increase in the odds; ���(��) < 1⁡indicates a decrease in the odds; and

���(��) = 1⁡ indicates a zero effect. Appendix 21 summarises the coefficients and odds ratios for occurrence response data for the selection of fish and macroinvertebrates in this study. R version 2.11.1 (20-05-31) (R Development Core Team 2010) was used to fit the logistic regression models, following the general approach of Halekoh et al. (2006).

111 5.2.5.2.1 Exploratory analysis for GLM candidate models

Once the preliminary single variable logistic regressions were performed for each taxon to determine which variables had a statistically-significant effect on its occurrence, variables with a Wald test statistic of Pr < 0.2 (Appendix 21) were selected for inclusion in the GLM models, as indicated in Appendix 22 (Tables A22.1 and A22.2). From this set of variables the appropriate land cover and connectivity variables to include in candidate models for fish and macroinvertebrate occurrence were then selected. The candidate models for fish could include: (1) a catchment-scale stressor metric (land cover, human population density, longitudinal ecological connectivity or surrounding habitat fragmentation); and (2) a metric representing catchment extent or tributary length. The candidate models for macroinvertebrates included a metric from the same two categories as for fish and, in addition, a reach-scale metric. These metric categories were determined as per the GLS analysis based on the VIF statistic (>10) and the Spearman’s correlation coefficient (>0.75).

To address the possibility of spatial autocorrelation in the occurrence data, variograms of the residuals of the initial GLMs were created in the geoR program (Ribeiro and Diggle 2001) (Appendix 23) and visually inspected. If spatial autocorrelation was apparent a spatial generalised linear mixed model (GLMM) was specified (Dormann et al. 2007). Cherax destructor (DecPar) was the only taxon for which spatial autocorrelation was evident but further testing (Dormann et al. 2007) showed that a spatial autocorrelation term was not required.

5.2.5.2.2 GLM model fitting and selection procedure

Models were fit to every linear combination of covariates and/or metrics with the constraint that metrics from each specific category of candidate explanatory variables (Appendix 22) were not included in the same model.

Following the approach taken by Peterson and Ver Hoef (2010) the root mean square prediction error (RMSPE) was used to compare the logistic regression models for each aquatic taxon. Leave-one-out cross-validation (LOOCV) prediction errors were generated at the 30 sites for every model using the cv.glm function in R (R

112 Development Core Team 2010). The output from this function was then used to calculate the RMSPE.

� 2 ����� = √∑ (��̂ − ��) ∕ �⁡ �=1 where �̂� is the prediction of the ith datum after removing it from the observed data set (Peterson and Ver Hoef 2010).

The model with the lowest RMSPE in a set is the best model. RMSPE values for models fit to the same response variable (taxa occurrence indicator) can be compared with each other. For example, if RMSPE is ¼ that of a competing model, the gain in predictive ability in using the best model is 75%.

Where RMSPE values were relatively close together, making it difficult to identify one best model, model averaging was conducted on the top 20 models for each taxon to assess the relative importance of each candidate explanatory variable (land-cover, land- use or landscape metric) (Johnson and Omland 2004).

5.3 Results

5.3.1 Modelling of macroinvertebrate and fish diversity and abundance and occurrence

The results of the GLS model testing are shown in Table A20.1 in Appendix 20 with results of subsequent model averaging in Table 5.3, which was required due to a high number of models being equally plausible. The Gaussian correlation term was not retained for the a priori sets of models for SIGNAL2_C, OE2010_C or OE2011_C (p- values all equal to 1.0) and therefore none of the best GLS models include this term.

The 20 best GLM models for each aquatic taxon, ranked by their root mean square prediction error (RMSPE), are shown in Tables A20.2 and A20.3 in Appendix 20. A spatial autocorrelation term was not required for any of the GLM models (Section 5.2.5.2.1). Model averaging results for the variables in these models are reported in Tables 5.4 and 5.5.

113 Table 5.3 Model averaging for SIGNAL2_C, OE2010_C and OE2011_C

Model averaging results for candidate explanatory metrics in all models with ΔAICi < 2 (OE2010_C, OE2011_C) in GLS model testing, or the top 10 models (SIGNAL2_C), whichever was the greater number of models. Only those results with unconditional confidence intervals (UCI) between 80%-95% are reported as they are considered to have moderate (80-85%) to strong (90-95%) support in the data.

Model-averaged Standard error of Unconditional Stressor metric estimate estimate confidence level %

SIGNAL2_C USBar_C 0.09 0.04 95 USNoBar_C 0.06 0.02 95 TotNoBar_C 0.03 0.01 95 USArea_C 0.1 0.03 95 G_ReaRip_C -0.93 0.55 90 IP_EffRip_C -0.61 0.36 90 PopDen_C -0.03 0.02 85 I_FlowStream_C -1.45 0.88 85 I_Rip_C -1.3 0.79 85 T_ReaRip_C 0.61 0.45 80

OE2010_C USNoBar_C 0.031 0.005 95 USArea_C 0.05 0.01 95 T_ReaRip_C 0.14 0.11 80 USBar_C 0.01 0.01 80 T_EffRip_C -0.13 0.1 80

OE2011_C USBar_C 0.02 0.01 80 TotNoBar_C 0.01 0 80 USNoBar_C 0.02 0.01 80 USArea_C 0.03 0.01 80

114 5.3.2 In-stream connectivity and surrounding tree-cover fragmentation metrics

5.3.2.1 In-stream connectivity metrics and fish and macroinvertebrate diversity and abundance

Two in-stream ecological connectivity metrics were strongly associated with variation in macroinvertebrate diversity and abundance (SIGNAL2_C). The extent of connected stream length upstream of each study site (USBar_C) had a positive association (95%, UCI, Table 5.3) and the amount of piping and impervious surface in the upstream stream network that has replaced natural stream length (IP_EffRip_C) had a negative association (90% UCI, Table 5.3) equal to reach-scale grass cover (G_ReaRip_C). For SIGNAL2_C, USBar_C and IP_EffRip_C were relatively more important than catchment-scale impervious surface metrics distance-weighted to the stream (I_FlowStream_C) and in the upstream riparian buffer 30 m either side of the stream (I_Rip_C) (both negatively associated with 85% UCI, Table 5.3).

In 2010, two in-stream ecological connectivity metrics, extent of connected stream length upstream (USBar_C) and tree cover in the effective riparian buffer (T_EffRip_C), had a moderate positive association with fish diversity and abundance (OE2010_C, 80% UCI, Table 5.3). In 2011, USBar_C was the only ecological connectivity metric with relative importance (80% UCI, Table 5.3) to fish diversity and abundance. No impervious surface metric at any scale (reach, local or catchment) was included in the top ten preferred models (Table A20.1 in Appendix 20) nor was important in model averaging for fish diversity and abundance in either 2010 or 2011 (OE2010_C or OE2011_C).

In summary, the in-stream connectivity metrics outperformed impervious surface metrics in models of invertebrate and fish diversity and abundance, although for the macroinvertebrate indicator SIGNAL2_C, catchment-scale impervious surface metrics were still relatively important.

5.3.2.2 In-stream connectivity metrics and fish and macroinvertebrate occurrence

In the occurrence models for several fish and macroinvertebrate taxa, both extent of connected stream length metrics and the effective riparian buffer metrics, were

115 generally relatively important. The extent of connected stream length upstream of each study site (USBar_C) had a moderate to strong association with the occurrence of several macroinvertebrate taxa (Table 5.4) and with all fish species in one or both years studied (Table 5.5). The extent of connected stream length downstream of each study site (DSBar_C) and total extent of connected stream length upstream and downstream (TotBar_C) had a moderate to strong association with the occurrence of two macroinvertebrate taxa (Table 5.4) but not with any fish species (Table 5.5). Effective riparian buffer metrics were relatively important for the occurrence of most macroinvertebrate taxa (Table 5.4) and one fish species (Mogurnda adspersa) in both years.

The extent of connected stream length upstream of each study site (USBar_C) had a strong positive association (95% UCI, Table 5.4) with the occurrence of Leptophlebiidae (EphLept) and Gomphidae (OdoGomp) and a moderate positive association (85% UCI, Table 5.4) with the occurrence of Atyidae (DecAty). Although USBar_C was selected for the a priori GLM models for Baetidae (EphBaet) (OLS analysis Appendix 22), it was outperformed by grass cover in the effective riparian zone (G_EffRip_C) and other catchment-scale grass-cover metrics (lumped and distance weighted) (Table 5.5).

The extent of connected stream length downstream of each study site (DSBar_C) and total extent of connected stream length upstream and downstream (TotBar_C) were associated with the occurrence of Atyidae (DecAty) and Cherax destructor (DecPar). DSBar_C had a strong positive association with the occurrence of both of these taxa (90% UCI in both cases, Table 5.4). TotBar_C had a moderate positive association (85% UCI, Table 5.4) with the occurrence of Cherax destructor (DecPar), and a strong positive association (90-95% UCI) with the occurrence of Atyidae (DecAty) and Gomphidae (OdoGomp).

Pre-flood (2010), the extent of connected stream length upstream (USBar_C) had a strong positive association (90% UCI, Table 5.5) with the occurrence of Mogurnda adspersa (PursPA10) and a moderate positive association (80% UCI, Table 5.5) with the occurrence of Tandanus tandanus (FrePA10). Post-flood (2011) USBar_C had a strong positive association (95% UCI, Table 5.5) with the occurrence of Melanotaenia duboulayi (CrimPA11), and a moderate positive association (80% UCI, Table 5.5) with

116 the occurrence of Mogurnda adspersa (PursPA11). USBar_C was not eligible for inclusion in the a priori set of models (Appendix 22) for Melanotaenia duboulayi in 2010 (CrimPA10), yet it was more important than any other metric for the occurrence of this species in 2011. In the case of Tandanus tandanus, USBar_C outperformed all other land-cover metrics in 2010 (FrePA10) but in 2011 (FrePA11) it was outperformed by impervious surface weighted by inverse flowpath distance to the site (I_FlowSite_C, local scale) and to the stream (I_FlowStream_C, catchment scale) (80% UCI, Table 5.5).

Effective riparian buffer metrics were strongly associated with the occurrence of Leptophlebiidae (EphLept) and Leptoceridae (TricLept), and moderately associated with the occurrence of Cherax destructor (DecPar), Atyidae (DecAty) and Gomphidae (OdoGomp) (Table 5.4). However, for these taxa, other lumped or distance-weighted land-cover metrics including those capturing impervious surface were also similarly associated with their occurrence. Grass cover in the effective riparian zone (G_EffRip_C) had a moderate positive association (85% UCI, Table 5.4) with Atyidae (DecAty) and was the best single-variable model (Table 5.5) for Baetidae (EphBaet) with a positive coefficient (Appendix 21) suggesting a positive relationship. Impervious surface and tree cover in the effective riparian buffer (I_EffRip_C, T_EffRip_C) had a strong association (90-95% UCI, Table 5.5) with the occurrence of Mogurnda adspersa in 2010 (PursPA10). Post-flood, I_EffRip_C did not have a detectable influence on the occurrence of Mogurnda adspersa (PursPA11) but other effective riparian buffer metrics including trees, impervious surface or piped channel, and piped channel (T_EffRip_C, IP_EffRip_C, P_EffRip_C) did (all 85% UCI, Table 5.5).

The number of road culverts downstream of each site (CulvRd_C) had a moderate positive association (80% UCI, Table 5.5) with the occurrence of Melanotaenia duboulayi in 2011 (CrimPA11). In other words, the probability of an occurrence increased with the number of downstream culverts. CulvRd_C did not have a detectable association with the occurrence of any other fish studied.

In-stream connectivity metrics generally outperformed impervious surface metrics for the occurrence of four macroinvertebrate taxa - Leptophlebiidae (EphLept), Atyidae (DecAty), Gomphidae (OdoGomp) (Table 5.4) and Baetidae (EphBaet) (Table 5.5), and all three fish species (Table 5.5) in one or both years (Mogurnda adspersa in 2010

117 (PursPA10), Tandanus tandanus in 2010 (FrePA10) and Melanotaenia duboulayi in 2010 and 2011 (CrimPA10 and CrimPA11)). In most of these cases the extent of connected stream length upstream of each site (USBar_C) was the most important ecological connectivity metric. In contrast, for Tandanus tandanus in 2011 (FrePA10, Table 5.5) ecological connectivity metrics were outperformed by a variety of distance- weighted and lumped land-cover metrics as well as catchment and tributary extent metrics. In the occurrence models for two macroinvertebrate taxa Leptoceridae (TricLept) and Cherax destructor (DecPar) (Table 5.4) and one fish species Mogurnda adspersa in 2011 (PursPA11, Table 5.5) the performance of the in-stream ecological connectivity metrics was equal to that of several lumped and inverse-distance weighted impervious surface metrics at the local and/or catchment scale.

5.3.2.3 Surrounding tree-cover fragmentation metrics

None of the new surrounding tree-cover fragmentation metrics (C_CA_C, R_CA_C, C_AI_C, R_AI_C) had strong or moderate associations with variation in any of the macroinvertebrate or fish diversity and abundance indicators (SIGNAL2_C, OE2010_C, OE2011_C). However, these metrics were relatively important to the occurrence of several macroinvertebrate taxa and one fish species.

The fragmentation of the riparian tree-cover within a 1 km radius and a 30 m buffer either side of the stream (R_AI_C) had a strong positive association with the occurrence of Leptoceridae (TricLept, 95% UCI, Table 5.4) and Cherax destructor (DecPar, 90% UCI, Table 5.4), and a moderate positive association with the occurrence of Leptophlebiidae (EphLept, 85% UCI, Table 5.4). Fragmentation of the surrounding terrestrial tree-cover within a 1 km radius of the site (C_AI_C) had a strong positive association with the occurrence of Cherax destructor (DecPar, 90% UCI, Table 5.4) and Gomphidae (OdoGomp, 90% UCI, Table 5.4) and a moderate positive association with the occurrence of Leptophlebiidae (EphLept, 85% UCI, Table 5.4). Tree cover in the surrounding landscape within a 1 km radius (C_CA_C) had a moderate positive association with the occurrence of Cherax destructor (DecPar, 85% UCI, Table 5.4) and Gomphidae (OdoGomp, 80% UCI, Table 5.4). Total tree cover in the 30 m riparian buffer within a 1 km radius from the site (R_CA_C) also had a moderate positive association with the occurrence of Gomphidae (OdoGomp, 85% UCI, Table 5.4.). However, there was no detectable association with a surrounding habitat fragmentation

118 Table 5.4 Model averaging for macroinvertebrate occurrence Model averaging results for candidate explanatory metrics in the top 20 preferred models for macroinvertebrate taxa occurrence.

Model-averaged Standard error of Unconditional Stressor metric estimate estimate confidence level %

Ephemeroptera, Leptophlebiidae (EphLept) USBar_C 0.86 0.41 95 I_FlowSite_C -14.92 8.98 90 I_EffRip_C -139.44 78.75 90 IP_EffRip_C -4.65 2.53 90 P_EffRip_C -4.61 2.56 90 I_Rip_C -11.02 6.45 90 I_FlowStream_C -11.17 6.77 90 USRatio_C 9.39 6.24 85 C_AI_C 0.17 0.1 85 PopDen_C -0.26 0.16 85 R_AI_C 0.25 0.15 85

Trichoptera, Leptoceridae (TricLept) PopDen_C -0.32 0.15 95 I_Rip_C -9.7 4.84 95 I_SubCatch_C -9.36 4.74 95 R_AI_C 0.16 0.08 95 IP_EffRip_C -3.78 2.27 90 I_FlowStream_C -9.63 5.07 90 I_FlowSite_C -12.95 6.83 90 T_FlowStream_C 4.25 2.58 90 P_EffRip_C -3.48 2.14 85 T_Rip_C 4.29 2.65 85 T_EffRip_C 3.54 2.27 80 USArea_C 1.73 1.21 80 USNoBar_C 0.85 0.65 80

Decapoda, Parastacidae Cherax destructor (DecPar) PopDen_C -0.27 0.13 95 I_FlowSite_C -8.49 4.67 90 C_AI_C 0.11 0.06 90 R_AI_C 0.137 0.079 90 DSBar_C 0.093 0.054 90 C_CA_C 0.02 0.01 85 TotBar_C 0.08 0.05 85 USRatio_C 6.08 4.37 80 T_FlowSite_C 4.01 3.13 80 P_EffRip_C -2.37 1.75 80 IP_EffRip_C -2.36 1.68 80

119 Model-averaged Standard error of Unconditional Stressor metric estimate estimate confidence level %

Decapoda Atyidae (DecAty) TotBar_C 0.11 0.05 95 DSBar_C 0.14 0.07 90 PopDen_C -0.1 0.06 85 USBar_C 0.21 0.13 85 G_EffRip_C 11.51 7.53 85

Odonata Gomphidae (OdoGomp) USBar_C 0.59 0.29 95 C_AI_C 0.71 0.43 90 TotBar_C 0.13 0.07 90 PopDen_C -0.9 0.6 85 G_FlowStream_C -18.2 12.06 85 R_CA_C 0.12 0.08 85 G_Rip_C -14.89 9.32 85 C_CA_C 0.1 0.07 80 T_SubCatch_C 10.73 7.93 80 T_FlowStream_C 12.08 9.16 80 T_EffRip_C 11.95 9.03 80 T_Rip_C 10.99 7.76 80

Odonata Hemicorduliidae (OdoHemi) No statistically significant model averaging estimates at 80% confidence interval for OdoHemi

Ephemeroptera Baetidae (EphBaet) No models with more than one metric were generated for EphBaet and so model averaging was not used. G_EffRip_C is the preferred model metric.

120 Table 5.5 Model averaging for fish occurrence Model averaging results for candidate metrics in the top 20 preferred models for predicting fish species occurrence.

Model-averaged Standard error of Unconditional Stressor metric estimate estimate confidence level %

2010 Melanotaenia duboulayi (Crimson spotted rainbow fish) (CrimPA10) TotNoBar_C 0.19 0.09 95 PopDen_C -0.27 0.19 90 C_CA_C 0.02 0.02 80

2011 Melanotaenia duboulayi (Crimson spotted rainbow fish) (CrimPA11) USBar_C 0.39 0.18 95 DSNoBar_C 0.12 0.06 90 TotNoBar_C 0.1 0.05 90 CulvRd_C 0.51 0.35 80 PopDen_C -0.14 0.1 80

2010 Mogurnda adspersa (Purple spotted gudgeon) (PursPA10) I_EffRip_C -93.73 47.41 95 TotNoBar_C 0.18 0.07 95 DSNoBar_C 0.22 0.09 95 USBar_C 0.37 0.21 90 T_EffRip_C 4.11 2.31 90 PopDen_C -0.17 0.11 90 I_SubCatch_C -7.52 4.42 90 T_SubCatch_C 5.13 2.89 90 I_FlowStream_C -8.43 5.26 90 G_FlowStream_C -9.17 6.22 85 T_FlowStream_C 4.68 2.81 85 IP_EffRip_C -3.24 1.97 80 P_EffRip_C -3.14 2 80 G_Rip_C -9.51 6.95 80 T_Rip_C 4.58 2.83 80 G_SubCatch_C -10.87 7.67 80

2011 Mogurnda adspersa (Purple spotted gudgeon) (PursPA11) DSNoBar_C 0.65 0.32 95 TotNoBar_C 0.21 0.08 95 T_EffRip_C 4.58 3.02 85 T_FlowStream_C 5.92 3.96 85 T_SubCatch_C 5.26 3.6 85 IP_EffRip_C -4.15 2.86 85 P_EffRip_C -4.49 3.08 85

121 Model-averaged Standard error of Unconditional Stressor metric estimate estimate confidence level % G_FlowStream_C -13.65 9.36 85 T_Rip_C 5.39 3.76 80 I_FlowStream_C -9.19 7.02 80 G_Rip_C -14.63 10.04 80 USBar_C 0.35 0.24 80

2010 Tandanus tandanus (Freshwater catfish) (FrePA10) DSNoBar_C 0.12 0.06 95 TotNoBar_C 0.26 0.15 90 USNoBar_C 0.2 0.1 90 USArea_C 0.34 0.16 90 USBar_C 0.41 0.29 80

2011 Tandanus tandanus (Freshwater catfish) (FrePA11) G_SubCatch_C -16.43 9.85 90 T_FlowSite_C 9.33 6.1 85 I_FlowSite_C -17.71 13.6 80 T_SubCatch_C 7.22 4.63 80 PopDen_C -0.31 0.24 80 G_FlowSite_C -17.07 12.75 80 I_FlowStream_C -11.1 8 80 TotNoBar_C 0.19 0.14 80 USNoBar_C 0.15 0.11 80 USArea_C 0.26 0.19 80

metric and the occurrence of Atyidae (DecAty), Baetidae (EphBaet) or Hemicorduliidae (OdoHemi) (Table 5.4).

In 2010 fragmentation of the surrounding terrestrial tree-cover within a 1 km radius of the site (C_AI_C) was positively associated with the occurrence of Melanotaenia duboulayi (CrimPA10, 80% UCI, Table 5.5).

5.3.3 Reach-scale condition

In 2010 reach-scale tree cover (T_ReaRip_C) had a moderate positive association (80% UCI, Table 5.3) with the macroinvertebrate diversity and abundance indicator (SIGNAL2_C) and the fish diversity and abundance indicator (OE2010_C). Grass cover in the reach-scale riparian buffer (G_ReaRip_C) had an even stronger, yet

122 negative, association with SIGNAL2_C (UCI 90%, Table 5.3). In 2011 (post flood) there was no detectable association between T_ReaRip_C and the fish diversity and abundance indicator (OE2011_C). Reach-scale riparian buffer condition metrics (“ReaRip”) were not important for the occurrence of any macroinvertebrate taxa or fish species studied (Tables 5.6 and 5.8).

5.3.4 Total tributary extents and upstream catchment area

Upstream drainage area above each site (USArea_C) had a strong positive association (95% UCI, Table 5.3) with macroinvertebrate diversity and abundance (SIGNAL2_C). In addition, upstream and total catchment tributary extent metrics (USNoBar_C and TotNoBar_C) had a strong positive association (95% UCI, Table 5.3) with SIGNAL2_C. USArea_C and USNoBar_C also had a strong positive association (95% UCI, Table 5.3) with fish diversity and abundance in 2010 (OE2010_C) and a moderate positive association (80% UCI, Table 5.3) in 2011 (OE2011_C). There was also some evidence (80% UCI, Table 5.3) that TotNoBar_C was positively associated with OE2011_C but not OE2010_C.

Although tributary extent and upstream sub-catchment area had relatively important explanatory power for the macroinvertebrate indicator SIGNAL2_C, they were not relatively important for modelling the occurrence of macroinvertebrate taxa, with the exception of Leptoceridae (TricLept) (Table 5.4). The upstream tributary extent and upstream sub-catchment area (USNoBar_C and USArea_C respectively) had a moderate positive association (both 80% UCI, Table 5.4) with the occurrence of Leptoceridae (TricLept).

In contrast, the metrics representing the extent of the catchment tributary network were relatively important to the occurrence of all three fish species considered (and, as noted above, for explaining the variation in fish diversity and abundance indicators in both years, OE2010_C and OE2011_C). In some cases upstream (USNoBar_C, for Tandanus tandanus in both years, Table 5.5) and in some cases downstream tributary network extent (DSNoBar_C, for all three fish studied, Melanotaenia duboulayi in 2011, Mogurnda adspersa in both years, Tandanus tandanus in 2010, Table 5.5) or total catchment freshwater tributary extent (TotNoBar_C, for all three fish studied, in both years, Table 5.5) were relatively important in occurrence models.

123 5.4 Discussion

The results of this chapter support the main hypothesis that ecological connectivity, specifically in-stream connectivity and surrounding tree-cover fragmentation, has more influence on urban stream health in the ephemeral streams of SEQ than catchment-scale impervious surface area and associated altered hydrology. All three types of ecological connectivity metrics considered (effective riparian buffer, in-stream connected stream extent and surrounding tree-cover fragmentation) were represented in the sets of best models for diversity and abundance and taxa occurrence, and in many cases outperformed catchment-scale impervious surface metrics in model averaging. All three types of ecological connectivity metrics, but particularly the metrics of in-stream connected stream extent, outperformed catchment-scale impervious surface metrics in the occurrence models of one or more taxa and/or in the models for one or more indicators of biotic diversity and abundance. The relative importance of multiple ecological connectivity aspects influencing urban stream biota, supports the argument that disruption to in-stream ecological connectivity and fragmentation of surrounding tree cover is equally or more important than altered hydrology associated with impervious surface for explaining variation in macroinvertebrate diversity and abundance (SIGNAL2) and occurrence of fish and macroinvertebrate taxa in the urban streams of SEQ. Similarly, support for a range of different ecological connectivity metrics including upstream and downstream connectivity suggests a role for factors other than in-stream nutrient processing or pollution or altered hydrology. These findings indicate the ecological relevance of the metrics used to quantify ecological connectivity, and they also suggest that various mechanistic aspects of ecological connectivity are important to the occurrence, diversity and abundance of fish and macroinvertebrates.

There is also support for the second hypothesis, that ecological connectivity in urban streams influences different biota in different ways based on their life history requirements and dispersal mechanisms. Different ecological connectivity metrics were more strongly associated with the occurrence of particular taxa. Although the new ecological connectivity metrics have some support as explanatory metrics for fish occurrence, diversity and abundance (upstream metrics only), a range of ecological connectivity metrics appears to be more important to the equivalent measures of

124 macroinvertebrate assemblages, suggesting that disruption of dispersal is a more important effect of urbanisation on macroinvertebrates than fish in this study area.

Finally, there is also partial support for the third hypothesis, concerning reach-scale condition. In the case of diversity and abundance of fish and macroinvertebrates, reach- scale metrics were found to be relatively important even when ecological connectivity metrics were included in the analysis. However, for some taxa, reach-scale metrics were not important.

By considering the likely stressors associated with, and differing spatial aspects of, the ecological connectivity metrics, as well as some of the other land-cover metrics that are associated with the occurrence of various taxa, several lines of evidence have emerged to support the importance of ecological connectivity to the health of the urban streams of SEQ.

5.4.1 In-stream connectivity and surrounding tree-cover fragmentation

As pointed out in Chapter 3 for effective riparian buffers, in order to make the argument for the importance of ecological connectivity, the new ecological connectivity metrics must be likely to represent ecological connectivity more so than other factors influencing urban stream health. Support for the extent of connected stream length upstream of a study site (USBar_C) in preference to other catchment-scale land-cover and land-use stressor metrics (such as effective riparian buffer and impervious-surface based metrics) suggests support for dispersal-based mechanisms rather than in-stream nutrient processing or pollution. The effective and traditional riparian buffer metrics are more likely to capture pollution and in-stream processing aspects given that they encompass all of the mapped intact stream segments in the entire upper catchment. Similarly, catchment-scale impervious surface metrics would also likely be capturing pollution inputs in addition to altered hydrology. Additionally, USBar_C has been included in models with catchment and tributary extent metrics and the relatively low correlation with these metrics (≤ 0.51, Appendix 9) suggests that USBar_C is not simply acting as a proxy for them.

The influence of surrounding terrestrial and riparian tree cover fragmentation on stream biota may be more important or easier to detect for specific taxa than diversity and

125 abundance. Ecological connectivity metrics representing fragmentation of the surrounding tree cover at the catchment and riparian scales (C_CA_C, C_AI_C, R_CA_C, and R_AI_C) were found to be relatively important to the occurrence of several macroinvertebrate taxa and one fish species, but not for explaining variation in fish or macroinvertebrate diversity or abundance.

The support for ecological connectivity as a key driver of urban stream ecosystem health is consistent with other studies that have shown the importance of proximity to near natural stream reaches and source populations for stream rehabilitation (Kail and Hering 2009, Sundermann et al. 2011). Disruption to ecological connectivity and disconnection from source populations could explain why some urban assemblages appear to be unresponsive to variation in the environmental conditions of their aquatic habitat (Leigh, C. Australian Rivers Institute, personal communication, 2011). The relatively strong associations between different ecological connectivity metrics and the occurrence of aquatic taxa with specific life history traits provide further support for the importance of ecological connectivity, as discussed in the next section.

5.4.2 Different life history traits and different aspects of ecological connectivity

In support of the second hypothesis, different aspects of ecological connectivity appear to influence different taxa based on their specific life history traits. For example, while the occurrence models for the macroinvertebrate taxa indicated that metrics for all three extents of connected stream length, upstream, downstream and total (USBar_C, DSBar_C and TotBar_C) could be important for the occurrence of certain taxa, models for the fish species in this study indicated only one of these metrics, USBar_C, was relatively important. Potential explanations for these and other differences are considered below.

5.4.2.1 Upstream in-stream connectivity

The extent of connected stream length upstream of each site (USBar_C) received moderate levels of support for the fish diversity and abundance indicators (OE2010_C and OE2011_C, 80% UCI, Table 5.3), and even more support in the occurrence models for all fish species considered, in one or both years. This suggests that detecting the impacts of in-stream connectivity may be more easily done for single species than for

126 fish diversity and abundance indicators that represent multiple species. There are various interpretations of the importance of USBar_C to fish diversity and abundance and occurrence given the low importance of downstream-connected stream length (DSBar_C). First, access to upstream habitat or refugia may be important for fish. Second, barriers to dispersal may be more common upstream where water may become increasingly shallow, compared with downstream reaches, which may tend to become progressively deeper. Third, upstream connectivity could be important if downstream reaches become polluted, which might happen in a flood or sewage overflow.

All three fish species considered in this study are found in freshwater and in a range of stream sizes but prefer small to medium streams (6-10 m wide) and slow flowing streams, lakes or pools (Allen 1995, Allen 1996, Pusey et al. 2004). The preference for moderately small streams may be a reason why the extent of connected stream length upstream (USBar_C) was relatively important in the occurrence models of all three fish and why the negative relationship with impervious surface in the upstream effective riparian buffer (I_EffRip_C) was relatively important for Mogurnda adspersa in 2010.

5.4.2.2 Upstream in-stream connectivity and access to habitat and refugia

In the case of Mogurnda adspersa in 2010, support for effective riparian buffer metrics relating to impervious surface and tree cover (I_EffRip_C, T_EffRip_C) and the extent of connected stream length upstream (USBar_C) (Table 5.5) suggests that upstream in- stream longitudinal connectivity influences its occurrence. The high rainfall levels and flooding in 2011 may have reconnected some stream segments making in-stream connectivity metrics less important for this species. The tolerance of this species for aspects of poor water quality (Pusey et al. 2004) (Pusey et al. 2004) suggests that access to suitable small to moderate stream habitat upstream as well as refugia drive the importance of ecological connectivity to its survival in urban streams, rather than downstream water quality.

The extent of connected stream length upstream (USBar_C) was also relatively important for explaining the occurrence of certain macroinvertebrate taxa, especially taxa with aerial adult phases, whereas downstream connectivity metrics were less important. In the occurrence models for Leptophlebiidae (EphLept) and Gomphidae (OdoGomp), relatively strong support for USBar_C (the most important land-cover or

127 land-use metric for explaining the occurrence of these taxa) suggests that access to connected upstream stream habitat is important. A possible explanation is that an upstream stream network that has good ecological connectivity (connected stream length) provides more accessible sites for adult oviposition, which would then lead to greater chance of downstream reaches containing larvae that originated from oviposition at the reach or upstream of the reach. It is likely that adult flight is more impacted by disruptions to ecological connectivity than downstream drift because the latter can occur within the water column whereas some portion of adults need to fly above the stream channel or near to the stream channel to upstream locations to oviposit and balance out downstream drift (Brittain and Eikeland 1988, Hershey et al. 1993).

Results for dragonflies also suggest that ecological connectivity is a greater issue for running water specialists than for still water generalists with wide-ranging dispersal behaviour (Watson et al. 1982). The extent of connected stream length upstream (USBar_C) was relatively important for the occurrence of Gomphidae (OdoGomp) (including the species Austroepigomphus praeruptus that breeds in running waters and likely remains close to its breeding habitat (Watson et al. 1982)), but not for Hemicorduliidae (OdoHemi) (containing species that are habitat generalists able to disperse widely and breed in running or still water (Watson et al. 1982)). Species of Odonata that show clear preferences for oviposition in running water are often found near to such habitats and therefore may be impacted by disruption of in-stream connectivity (Watson et al. 1982).

5.4.2.3 Upstream in-stream connectivity and potential interactions with water quality

Greater support for the extent of connected stream length upstream (USBar_C) in 2011 (post-flood) compared with 2010 for Melanotaenia duboulayi could indicate a requirement to access upstream stream segments to escape the downstream pollution associated with the river flood. This is consistent with the sensitivity of this species to ammonia and chlorine associated with water treatment equipment malfunctions (Gruber et al. 1989, Diamond et al. 1990, Gruber et al. 1991, Johnston et al. 1994) and the evidence that Melanotaenia duboulayi is the most sensitive of the three species studied to pollution (Harris and Gehrke 1997, Pusey et al. 2004).

128 The results for Tandanus tandanus suggest that it was not greatly affected by in-stream barriers in either year, with only moderate support for extent of connected stream length upstream (USBar_C) in 2010, and no support for any effective riparian buffer metrics. A possible explanation for the relative importance of the local-scale metrics (“FlowSite”) in 2011 is that post-flood Tandanus tandanus was responding to local- scale stressors (e.g. local pesticide and insecticide pollution (Nowak 1990, 1991, Nowak 1992, Arthington 1996), urban heat island effects (Pluhowski 1970, Marsh et al. 2005a) and local changes to nutrient processing associated with altered vegetation (Newham et al. 2011, Kaushal and Belt 2012)) once the greater quantities of water in the system during the floods allowed it to move past barriers. In 2010 it may have had restricted access to habitat and refugia provided by deeper water levels, as indicated by relatively strong support for metrics of tributary extent (DSNoBar_C, TotNoBar_C, USBar_C) and relatively moderate support for the catchment area metric (USArea_C) as explanatory metrics for the occurrence of Tandanus tandanus.

As mentioned above, Odonata species that stay close to their breeding sites are more likely to be impacted by ecological connectivity issues. However, this impact may be enhanced due to interactions between disrupted ecological connectivity and water quality because such taxa are also likely to be impacted by local pollution and water quality factors such as low DO and elevated chlorine as well as ammonia and trace metals (Arthington et al. 1982, Watson et al. 1982). Local extirpation events have been shown to occur due to incompletely treated sewerage (Watson et al. 1982). The relatively strong association between the extent of connected stream length upstream (USBar_C) and the occurrence of Gomphidae (OdoGomp) could therefore indicate a combined effect of sensitivity to water quality in addition to in-stream ecological connectivity.

5.4.2.4 Downstream in-stream connectivity

The results suggest that downstream barriers may impact macroinvertebrate taxa such as shrimps and yabbies. Of the macroinvertebrate (and fish) taxa considered, Atyidae (DecAty) and Cherax destructor (DecPar) showed the strongest associations (negative) with metrics representing downstream barriers. Cherax destructor is pollution sensitive (Arthington et al. 1982, Chessman 2003) and is often found in ephemeral streams and pools in the higher, less impacted parts of catchments with better water quality. While

129 this species can survive low DO and can burrow when waterholes dry out (Gooderham and Tsyrlin 2002), if waterholes are likely to dry out for extended periods of time, recolonisation may require ecological connectivity with downstream refugia and source populations where more persistent water is found. Upstream pollution stressors may potentially affect this species, as indicated by the relative importance of population density and local-scale impervious surface to its occurrence (Table 5.4) and its pollution sensitivity noted in Arthington et al. (1982) and Chessman (2003). However, downstream connectivity appears to be more important than upstream connectivity. This pollution sensitive species may be sensitive to local extirpation events as its preference for relatively pristine habitats may leave it vulnerable to isolation in locations where suitable downstream habitat is not available because of impaired ecological connectivity or water quality issues.

The extent of connected stream length upstream and downstream of each study site (USBar_C and DSBar_C) appeared to be important for the occurrence of Atyidae (DecAty). A species in the family Atyidae, Paratya australien, is fully aquatic and has a planktonic larval stage. In genetic studies, Paratya australiens exhibited great differences in genetic structure between populations in different sub-catchments but also indicated large differences within catchments (Bunn and Hughes 1997, Hughes 2007). This suggested little movement between streams and limited in-stream movement of these shrimps. This limited movement and fully aquatic life history make this species likely to be vulnerable to impacts on in-stream longitudinal connectivity both upstream and downstream, and other species in this family could be similarly affected. Higher values for these in-stream connectivity metrics might suggest that the location studied is higher in the catchment. However, these metrics are not likely to be simple surrogates for catchment position given that neither the best models for Cherax destructor (DecPar) nor for Atyidae (DecAty) included the tributary or catchment extent metrics.

5.4.2.5 Surrounding tree cover fragmentation

Metrics representing fragmentation of the surrounding tree cover (C_CA_C, C_AI_C, R_CA_C, and R_AI_C) were found to be relatively important to the occurrence of some macroinvertebrate taxa in this study. This finding is consistent with Smith et al. (2009) who argued that the adult phase of aquatic insects may be affected by the condition of

130 the surrounding riparian and terrestrial landscape. For Leptophlebiidae (EphLept), support for metrics which capture the fragmentation of surrounding terrestrial tree cover (C_AI_C) and both upstream and downstream riparian tree-cover (R_AI_C) is consistent with the requirement for some adult insects to access surrounding forested areas (Collier and Smith 1998, Smith et al. 2009) and fly upstream (Smith et al. 2009). Leptoceridae (TricLept) require small pieces of woody twig debris for making its shelter. Stronger support for R_AI_C than upstream tree-cover weighted by inverse- distance to the stream (T_FlowStream_C) suggests either that sources of wood for shelters can come from both downstream and upstream sources, or that Leptoceridae (TricLept) require access to intact tree cover in the adult phase. Results for Cherax destructor also suggest that fragmentation of the surrounding riparian and terrestrial vegetation (as indicated by C_AI_C, R_AI_C, C_CA_C) impacts the occurrence of this species. Cherax destructor typically moves and disperses via walking and drift (Bunn and Hughes 1997, Hughes 2007) so land-cover stressors at a local scale would be expected to interfere with its dispersal ability, and fragmented landscapes could result in its decline. The occurrence of one fish species, Melanotaenia duboulayi, in 2010, was associated with fragmentation of the surrounding tree cover. This may relate to a diet that includes a large proportion of terrestrial insects derived from the riparian zone (Pusey et al. 2004).

5.4.2.6 Other factors relating to dispersal

Another reason to consider the importance of dispersal pathways is that grass cover in the effective riparian zone (G_EffRip_C) and other catchment-scale grass-cover metrics (lumped and distance weighted) had the strongest association with occurrence of Baetidae mayflies. This is in addition to a finding from chapter 3, grass cover in the effective riparian buffer (G_EffRip_S) had more influence on the macroinvertebrate indicator SIGNAL2_S than other catchment-scale vegetation metrics. These findings may relate to dispersal patterns of adult aquatic insects that gain dispersal benefits from grasslands, such as weak flyers which may become caught in wind currents and dispersed long distances (Kelly et al. 2001, Briers and Gee 2004). Evidence of dispersal several hundred metres through dense forest (Collier and Smith 1998, Hughes et al. 2000) is more readily available than across open plain areas. However, there are mechanisms such as dispersal by wind currents which might be more prevalent across open plain areas than in forests (Smith and Collier 2006). Mating swarms of mayflies

131 often form many metres above streams, bridges and boulders. Combined with their wing design, this may enable them to utilise winds and increase their dispersal distance (Smith and Collier 2006). This could suggest a benefit of increasing urbanisation (with increasing open grass spaces instead of forested areas) to these mayflies but it could also relate to a requirement for both tree and grass covered spaces in urban areas for different purposes including dispersal of different species.

It is important to consider that some metrics designed to represent ecological connectivity may be representing other processes. The probability of an occurrence of Melanotaenia duboulayi in 2011 (CrimPA11) increased with the number of downstream culverts. A possible explanation for this is that the scour pools downstream of culverts (Washington Department of Fish and Wildlife 1999, Millington 2004) may provide refugia or additional preferred habitat for this fish, which has a general preference for slow flowing water or pools (Pusey et al 2004) (as do the other two fish studied). This fish may be an example of what was argued in Chapter 4, that it is possible that some fish in SEQ will respond positively to urbanisation, especially those that prefer pools and runs instead of riffle habitat (Walters et al. 2003, Sheldon et al. 2012b).

5.4.3 Ecological connectivity and ephemeral streams

While ecological connectivity appears to be important to stream health in SEQ, further studies would be required to establish the importance of this aspect of urban stream health in other climatological and physiographical locations. There is likely an inter- play between reduced ecological connectivity, available habitat, local extirpation events, natural hydrology and climate, and the opportunities and requirements for recovery and recolonisation. As mentioned in Chapter 1, the ephemeral nature of many sub-tropical streams in SEQ means that they may naturally experience frequent extirpation events associated with their wetting and drying cycles (Steward et al. 2012), which more consistently wet temperate streams may not experience. Stream and landscape fragmentation associated with urbanisation may exacerbate stress from such natural recurring stream fragmentation such that repopulation by biota is increasingly difficult after local extirpation events. In addition, the potential for chemical stressors in high concentrations associated with urbanisation (Sheldon et al. 2012b) means that the frequency of local extirpation events may be higher in urban streams compared with forested or natural grassland catchments. Despite the low importance of catchment-scale

132 impervious surface metrics for explaining fish and macroinvertebrate abundance and occurrence in the current study, and the low importance of altered hydrology for explaining stream health in SEQ’s ephemeral streams (Sheldon et al. 2012b), alterations to hydrology such as increased or decreased base flows or elevated peak flows (Sheldon et al. 2012b) could also lead to extirpation events.

Most of the studies of urbanisation impacts on fish with conclusive evidence of the impacts of in-stream connectivity relate to diadromous fish. The lack of a life history requirement to access estuarine or marine areas could also explain why downstream connectivity metrics were relatively unimportant explanatory variables for fish diversity and abundance or taxa occurrence in this study of freshwater fish. The moderate rather than strong support for the relative importance of the extent of connected stream length upstream (USBar_C) for explaining variation in fish diversity and abundance (OE2010_C and OE2011_C) suggests that in-stream connectivity is less important to fish diversity and abundance in SEQ than in some other study areas (Wenger et al. 2008, Ramírez et al. 2012). Another explanation is that in-stream barriers to fish dispersal may only be temporary or partial. Culvert barriers can be passed where conditions for passage are met during large storms when water overflows the road, or where water depth and velocity conditions are suitable for passage (Warren and Pardew 1998 , Queensland Department of Primary Industries and Fisheries (QDPIF) 2004, Norman et al. 2009, Anderson et al. 2012). However, partial barriers can still be cause for concern and can have cumulative effects on ease of dispersal (Cote et al. 2009). Extensive sections of piped stormwater may be more problematic than culverts for macroinvertebrate and fish passage because of the greater distances required to be travelled when water depth and velocity might be suitable, or the greater distances to be bypassed by other dispersal paths such as adult flight. Lengths of some piped stream sections up to several kilometres were identified in the data, whereas significantly shorter lengths were observed for culvert piping of 25-100 m during fieldwork for the current study. Targeted studies or new metrics may be required to effectively represent the impacts on ecological connectivity of individual cases of such extensive piped stream sections.

As discussed briefly in Chapter 4, and supported by the results of the current chapter, the first one or two barriers (total or partial) to in-stream connectivity can have the most significant effect on in-stream connectivity (Cote et al. 2009). This could explain why

133 the new in-stream connectivity metrics, that is, the extent of connected stream length metrics upstream, downstream and total (USBar_C, DSBar_C and TotBar_C), outperformed the effective riparian buffer metrics in modelling fish and macroinvertebrate diversity and abundance and the occurrence of many of the taxa studied. The effective riparian buffer metrics quantify the accumulated barriers in the upstream catchment, that is, the gross amount of piping or impervious surface within the mapped stream network that may be associated with artificial barriers to dispersal paths and disruptions to natural hydrological connectivity. In contrast, the metrics of extent of connected stream length are detecting only the first potential barrier to in- stream dispersal. In further support of this finding, there is anecdotal evidence that certain fish species are no longer found in the upper reaches of Downfall Creek (part of the current study area), including short-finned eel (Anguilla australis), long-finned eel (Anguilla reinhardtii), estuary perchlet (Ambassis marianus), empire gudgeon (Hypseleotris compressa), sea mullet (Mugil cephalus) and pacific blue eye (Pseudomugil signifer) (Davie 1990, Brisbane City Council 2008b). While the main channel in Downfall Creek is mostly open, exposed to daylight and not piped, there are sections of culverts and piping which may have had an impact on fish dispersal contributing to the loss of these species from these streams. In a study of predominantly agricultural land in the Logan-Albert River system in SEQ, Rolls et al. (2014) found that single barriers were associated with significant differences between fish assemblages (especially for diadromous and potadromous fish) above and below the barriers. However, barrier location and habitat quality influenced the detection of such differences.

5.4.4 Reach scale

Reach-scale riparian buffer condition metrics remained relatively important for explaining variation in fish (in 2010 but not 2011) and macroinvertebrate diversity and abundance even with the inclusion of the new ecological connectivity metrics in the tested candidate explanatory models. This finding supports the third hypothesis that reach-scale land cover influences stream health even when ecological connectivity metrics are considered. While reach-scale riparian buffer condition was important for explaining fish diversity and abundance in 2010, the lack of importance in 2010 could relate to the high rainfalls between the two sampling periods shifting pieces of woody

134 debris to locations with poorer riparian condition (Kennard et al. 2006, Bunn et al. 2010).

In contrast to the diversity and abundance indicators, in the occurrence models for fish and macroinvertebrate taxa, reach-scale metrics were not relatively important explanatory variables. This difference in findings suggests that reach-scale riparian condition is not as important to processes associated with occurrence of taxa, such as local extirpation or recolonisation, as it is to biotic diversity and abundance. Therefore, reach-scale tree cover may, due to its enhancement of habitat suitability (e.g. through temperature control and provision of woody debris) and provision of food sources (e.g. fruits and flowers) (Pusey and Arthington 2003, Naiman et al. 2008) act as a driver of increased biotic productivity rather than cause the absence of particular taxa. The occurrence of individual taxa appears to be driven more by catchment-scale or catchment-wide processes than reach-scale condition. In-stream connectivity (Sections 5.4.2.1 – 5.4.2.4), fragmentation of tree cover (Section 5.4.2.5), combined with hydrological, pollution-based or other extirpation events, may drive the loss of taxa. This is consistent with the concept mentioned above (Section 5.4.1) of a depauperate community of taxa across urban areas in general (Leigh et al. unpublished). It may be the interplay of multiple local extirpation events (driven by stressors at any scale) combined with in-stream and terrestrial barriers to recolonisation that results in the absence of specific taxa in some urban areas. Given the small number of taxa considered in the occurrence analysis in this study, these findings and interpretations are regarded as preliminary and require further investigation.

5.4.5 Total tributary extent

In addition to the three focal scales (reach, local and catchment) at which land-cover, land-use and connectivity stressors have been assessed in the current study, the total tributary network scale (upstream, downstream and total) also had a detectable positive association with urban stream health. The evidence of support for the total and downstream tributary network lengths (TotNoBar_C, DSNoBar_C) suggests that the entire network extent or the entire catchment size, not just the upstream component, is important to resilience of fish and macroinvertebrate communities (as measured by occurrence responses of different taxa and community diversity and abundance indicators). Larger upstream tributary lengths may correlate with higher flow rates,

135 deeper water, and more persistent pools (refugia). Support for upstream catchment size metrics (USNoBar_C and USArea_C) is consistent with the results from Chapter 4 and similar findings in other studies (such as Wenger et al. 2008). However, support for total and downstream tributary extents suggests that factors other than simply water depth are at play. Larger total catchment size and greater total tributary network extent within entire catchments may contain a greater number of different habitat types, greater habitat extent and more areas for aquatic refugia (Rolls et al. 2012).

5.4.6 Implications for management

The overall findings of this study suggest that, in addition to altered catchment hydrology and pollution identified as typical of the urban stream syndrome, several forms of ecological connectivity and access to habitat, as well as local habitat quality, should be considered when determining how best to maintain the health and resilience of urban stream ecosystems. In summary, the two kinds of in-stream connectivity metrics that had the greatest association with variation in aquatic biota indicators were: (1) the extent of connected stream length (upstream, USBar_C, downstream, DSBar_C and total, TotBar_C); and (2) effective riparian buffers representing the extent of piping and impervious surface in the upstream stream network that has replaced natural stream length (IP_EffRip_C, I_EffRip_C) and the extent of remnant “natural” stream (T_EffRip_C, G_EffRip_C). The amount and fragmentation of surrounding tree-cover also should be considered because metrics in this class were associated with the occurrence of several macroinvertebrate taxa.

Reviews of the effectiveness of stream restoration approaches (Roni et al. 2002, Roni et al. 2008) support protection or restoration of in-stream ecological connectivity as an effective stream rehabilitation approach, especially for fish. The results of the current study suggest that this approach to restoration may also be effective for macroinvertebrates. Prioritisation of in-stream connectivity as the most important stream rehabilitation option was identified by Roni et al. (2002) in an assessment of the effectiveness of stream health rehabilitation options, especially for salmon. Based on the work of Pess et al. (1998), Roni et al. (2002) additionally recommended in-stream connectivity restoration be undertaken from a catchment-wide prioritisation approach which gave higher priority to the removal of a culvert or culvert passage improvement if it would make higher quality habitat (low gradient, high pool frequency, and high wood

136 load) available, as opposed to making greater length of habitat accessible. Roni et al. (2008) also found that restoration of floodplain connectivity was one of the most effective means of increasing habitat for juvenile salmonids (Nickelson et al. 1992, Richards et al. 1992, Norman 1998, Roni et al. 2002, Henning et al. 2006, Roni et al. 2006) and providing critical rearing habitat for many other fish such as cyprinids, catostomids and other warmwater and coolwater fish (Schmutz et al. 1994, Grift et al. 2001). As a management strategy, prioritisation of areas to maintain or rehabilitate to enhance connectivity of terrestrial vegetation could be combined with prioritisation of areas to maintain or enhance urban in-stream and floodplain connectivity (a topic beyond the scope of the current study) (Hermoso et al. 2012a).

Despite the many documented studies (Nickelson et al. 1992, Richards et al. 1992, Norman 1998, Roni et al. 2002, Henning et al. 2006, Roni et al. 2006) indicating improvements in biota associated with restoring in-stream and floodplain ecological connectivity, there are several constraints that must be addressed. The effectiveness of ecological connectivity restoration on biota has been shown to be limited by catchment- scale stressors and their impacts on water quality, water quantity, erosion, and sedimentation (Moerke and Lamberti 2003, Cowx and Van Zyll de Jong 2004). Water Sensitive Urban Design (WSUD) practices such as constructed swales, wetlands and vegetated areas may be useful in mitigating some of the water quality (and hydrological) impacts on stream health (Roni et al. 2008), therefore ecological connectivity protection and rehabilitation is likely to be complementary to WSUD. Furthermore, in some cases, it may be too expensive to alter existing infrastructure in order to restore in-stream connectivity and this can be a limiting factor on the success of in-stream rehabilitation projects upstream of such locations (Bernhardt and Palmer 2007). These examples emphasise the requirement for catchment-scale prioritisation approaches to target areas that will most likely benefit from restoration efforts and to identify potentially limiting factors. Some useful approaches and promising tools for management to prioritise in-stream and catchment restoration efforts include a strategy presented by Roni et al. (2008), the stream-connectivity based systematic rehabilitation approach of Hermoso et al. (2012b) and tools such as data envelopment analysis (DEA) to generate an ecological performance index for urban stream sites presented in Millington et al. (2015, reprinted as Appendix 6).

137 Although protecting and maintaining in-stream connectivity may be the most ecologically effective approach to adopt for stream protection and rehabilitation, the costs must be tallied to enable comparison with alternative approaches. Costs to consider are: (1) the installation of bridges or culverts designed with fauna passage in mind, (2) minimisation of stream burial, (3) minimisation of road crossings through urban zoning requirements and (4) stream daylighting. Culverts designed to accommodate fish passage (and dispersal of macroinvertebrate and other fauna), and bridges, are generally more expensive than other culvert designs (Table 5.9). For a cost similar to the cost of applying WSUD per 1 km2 urban catchment area, only a couple of road crossings that would protect ecological connectivity may be affordable (Table 5.9). However, managers can choose to prioritise the main channel or prioritise sections of catchments to enhance connectivity. With first, second and third order streams (Strahler 1952) representing half, a quarter and an eighth of the total permanent stream length respectively in a New Zealand study (Wilding and Parkyn 2006), not all of the stream length may need to be protected to make a difference to ecological connectivity. Estimating the number of road crossings required to maintain sufficient ecological connectivity in a catchment requires consideration of road density. However, prioritising third order streams and main channels would, according to these figures, require an eighth of the number of crossing locations compared with protecting the entire stream length. Additional studies are recommended to further elucidate the cost of protecting urban stream ecological connectivity in appropriate areas of catchments. These could consist of assessing the association between stream health indicators and ecological connectivity metrics presented in the current study for stream segments of different stream orders.

Protecting and maintaining in-stream connectivity is likely to be less expensive than daylighting streams that have been buried. Daylighting, or deculverting, is the process of redirecting a piped or buried stream into an above-ground channel with the intention of returning the stream segment to a more natural condition (Wild et al. 2011). Zoning to protect existing above-ground (relatively natural) stream lengths would have minimal cost associated with in-stream work but may have costs associated with refusing development in certain locations or requiring alterations to road networks to accommodate prioritised ecological connectivity locations. The cost of daylighting streams varies depending on levels of existing infrastructure. Daylighting projects in the

138 U.S. have ranged from AUD 90 using volunteer labour to AUD 28,400 per linear metre in central business districts (Pinkham 2001) (also see Table 5.6).

The effectiveness of ecological restoration may also be constrained by catchment size, as suggested by the relative importance of upstream, downstream and total catchment size metrics in the current study. Management interventions targeted at catchments with larger total area (and larger total stream extent) are likely to be more successful due to greater access to the larger extents and varieties of habitat and refugia within the catchment as well as greater flow depths and flow volumes associated with greater drainage area.

The results of the current study support the value of considering species with diverse requirements (water quality tolerance, habitat preference, breeding biology, dispersal) when making recommendations for the protection and rehabilitation of urban stream ecosystems. Modelling the occurrence of a variety of individual taxa provides deeper insights than those provided by measuring changes to diversity and abundance as indicators of response to urbanisation. Different urban stressors and different mechanistic processes may impact particular taxa in different ways, as suggested in the current study. For example, different ecological connectivity metrics, other land-cover metrics and total catchment extent metrics were relatively important explanatory variables in the occurrence models for different fish and macroinvertebrate taxa. The small set of freshwater fish species with a general preference for slow flowing water or pools (Pusey et al 2004) could be expanded in future studies (using similar approaches) to include other species in SEQ with differing life history requirements and responses to altered hydrology, connectivity and water quality (Arthington et al. 2014, Rolls and Arthington 2014).

Although only two years of data were considered in the current study, detectable differences in the results pre and post-flood highlight the importance of considering multiple or series of years with different rainfall histories when developing management plans to protect urban stream biota. For example, reach-scale metrics were important for explaining fish diversity and abundance in 2010 after a long period of drought (South Eastern Australia Climate Initiative (SEACI) 2011), but this pattern was not detectable in the 2011 data. The stream and catchment extent metrics also received more support in the 2010 models of fish diversity and abundance than in the 2011 data collected after

139 the high rainfall of spring 2010 and summer 2010/2011. These interannual differences in findings support other studies indicating that extended periods of low flow can

Table 5.6 Cost comparison table for Water Sensitive Urban Design (WSUD) and ecological connectivity enhancement options Miscellaneous costs(i) per item Estimated costs(i) per 1 km2 urban drainage area, or 170 m(ii) stream length WSUD WSUD Attached dwelling AUD 200,000 AUD 5,300 (iii) (Assumes 40 dwellings per 1 km2 based on Unattached dwelling assuming 250 m2 blocks(iv)) AUD 6,300 (iii) Ecological Connectivity Ecological Connectivity Small 1 m diameter culvert (25 m long) (not AUD 200,000 for: designed for fish passage) - Two small free-span bridges AUD 800 (v) - Two box culverts, or Large culvert - One large free-span bridge AUD 16,300 - 32,600 (v) Small free-span bridge AUD 97,800 (v) Large box culvert designed to promote fish passage AUD 103,000 (v) Free-span bridge AUD 228,000 (v) Daylighting Daylighting Natural stream restoration using volunteer labour AUD 15,300 to 1,460,000 for 170 m of stream AUD 90-1,400 per linear metre (vi) length Urban park (highly structured) AUD 8,500 per linear metre (vi) Central business district AUD 28,400 per linear metre (vi) Zoning Zoning Unknown costs of refusing development or requiring road networks to avoid prioritised in- stream ecological connectivity locations (i) All costs converted to AUD 2014 (ii) The mapped stream extent in the current study resulted in a drainage density of 170 m stream length per km2 catchment area (2.5 ha was the minimum upstream drainage area mapped). (iii) Costs for SEQ, Binney and Macintyre (2012) (iv) Australian Bureau of Statistics (2005) (v) Millington (2004). Costs for culverts don’t include road grading or pavement costs. (vi) Pinkham (2001)

140 greatly affect stream ecosystem structure and function, resulting in quite different patterns to those observed in periods of relatively high and constant flow (Thompson and Parkinson 2011, Rolls et al. 2012, Burns et al. 2015).

In light of the discussion above, recommendations for managing stream health in ephemeral urban streams of SEQ can be summarised as follows:

(1) Target in-stream connectivity and reduce dispersal barriers. Use of in-stream structures which promote faunal passage, minimisation of stream burial and daylighting of streams may offer more ecologically effective and potentially less expensive options to stream managers than catchment-wide implementation of WSUD because the linear stream channel is a relatively small portion of the catchment.

(2) Address the extent of connected stream length (upstream, downstream and total) in management plans for promoting healthy biotic assemblages in the streams of SEQ.

(3) Address the fragmentation of surrounding tree cover in zoning and management plans for urban streams, recognising the dispersal requirement of adult aquatic insects in urban areas.

(4) Consider a range of single species or single taxa occurrence responses to urban land-cover metrics and connectivity metrics, in addition to fish and macroinvertebrate diversity and abundance indicators, because different taxa respond to different aspects of urbanisation in particular ways. Indicator species considered in the current study which had the strongest associations with aspects of ecological connectivity and land cover may be useful as indicator species in other studies. For example:

a. Impacts on upstream in-stream connectivity could be assessed based on the occurrence of dragonflies in the Gomphidae family as well as freshwater shrimp in the Atyidae family and fish including all three in the current study, Melanotaenia duboulayi, Mogurnda adspersa and Tandanus tandanus.

141 b. Impacts on downstream in-stream connectivity could be assessed based on the occurrence of the freshwater crayfish Cherax destructor and freshwater shrimp in the Atyidae family. The fish considered in the current study would not be recommended as indicators, however other fish may be suitable.

c. Caddisflies in the Leptoceridae family, the freshwater crayfish Cherax destructor and the fish Melanotaenia duboulayi may be useful as indicators of the effects of fragmentation of surrounding tree cover in the catchment and riparian zone.

(5) Be aware that catchments with greater total catchment size may be associated with better ecosystem health based on diversity and abundance of fish and macroinvertebrates. If this is due to more locations for refugia or to a greater number of different habitats supporting different taxa, then such catchments might be expected to respond more strongly to protection or restoration efforts due to having a more diverse assemblage and therefore potentially more recruits to sites after local or reach-scale rehabilitation efforts. Larger upstream and total catchment size was associated with better diversity and abundance of fish and macroinvertebrates as well as greater likelihood of occurrence.

5.5 Conclusion

The results of this study suggest that ecological connectivity is important to the aquatic fauna of the ephemeral urban streams of sub-tropical SEQ. Furthermore, different aspects of ecological connectivity are likely to be important in relation to the life history traits of different taxa, especially macroinvertebrate taxa. Therefore, assessing occurrence of several different indicator species with specific life history traits may be desirable when assessing impacts of ecological connectivity.

Supporting and building upon the findings of Chapters 3 and 4 relating to effective riparian buffers, the results from this chapter suggest that the disruption of ecological connectivity and fragmentation of tree cover are more important to macroinvertebrate and fish diversity and abundance in these ephemeral, sub-tropical, urban streams than

142 altered hydrology associated with catchment-scale impervious surface area. Support for the extent of connected stream length upstream in preference to effective riparian buffer metrics (which may represent multiple barriers) for fish diversity and abundance suggests that the first one or two barriers may have greater impact on fish passage than additional accumulated barriers.

The response of different taxa to different ecological connectivity and other land-cover metrics in this study highlights the importance of taking into account multiple taxa with different life history requirements when considering how to best mitigate the impacts of urbanisation on aquatic fauna. For specific macroinvertebrate taxa studied, both upstream and downstream longitudinal connectivity were found to be important as was fragmentation of the surrounding riparian and terrestrial habitat. The extent of connected stream length upstream was also associated with the occurrence of all three freshwater fish species studied in one or both years. For three macroinvertebrate taxa, these ecological connectivity metrics were preferred to the catchment-scale impervious surface, tree, and grass-cover metrics and for the occurrence of other macroinvertebrate and fish they had comparative levels of importance.

Given the ephemeral nature of these sub-tropical streams, they are likely to be affected by local extirpation events associated with their wetting and drying cycles (Steward et al. 2012) and poor water quality associated with urbanisation during periods of low flow (Sheldon et al. 2012b). To assist recovery from these recurring events, protection and rehabilitation efforts focused on ecological connectivity may provide greater benefit for less financial investment than whole-of-catchment projects such as WSUD. Poor ecological connectivity may also be a key limiting influence on the benefits from local reach-scale restoration projects. Since larger dimensions of entire catchment tributary extent and upstream sub-catchment area were associated with greater diversity and abundance of macroinvertebrate and fish (as well as fish occurrence), the success of rehabilitation actions targeting ecological connectivity is more likely for catchments with larger total area and larger total stream extent.

5.5.1 Future research

Further development and refinement of ecological connectivity metrics could include inverse-distance weighted (IDW) dispersal barrier metrics and assessment of in-stream

143 longitudinal connectivity for specific stream orders. A focused metric for only the larger order streams could give an indication of the importance of longitudinal ecological connectivity of the main stream channels as opposed to headwater streams. This would help in guiding management in targeting sections of streams for reconnection, rehabilitation and protection.

Building on the work of the current study, future urban stream studies should consider catchment and reach-scale riparian and terrestrial land cover in the light of how they impact and are impacted by ecological connectivity. Results based on small data sets need to be interpreted with care, so more extensive data sets are desirable for such future studies.

Relationships between catchment-scale urbanisation and fish assemblages remain unclear in this study of highly urbanised sub-tropical streams, although the association with urbanisation was easier to detect for specific fish species than fish diversity and abundance. While ecological connectivity appears to be important to the occurrence of the three freshwater fish species studied here there are other species with differing needs that could be suitable for inclusion in future studies in this area using similar approaches.

The potential for differences in stream health during periods of high or low annual or inter-annual rainfall should be considered when assessing macroinvertebrate assemblage health, and future research of the impacts on ecological connectivity, reach- scale riparian condition and other urban land-cover aspects should aim to account for this influence.

The extensive lengths of some piped stream sections are likely to be more problematic than culverts for macroinvertebrate and fish passage. Therefore, future research into impacts of stormwater piping on connectivity and fish passage should generate metrics in which culvert and storm-water piping contributions are considered separately. Metrics representing the influence of individual extensive sections in which streams are piped underground could also be considered further.

144 CHAPTER 6 A PERSPECTIVE ON THE URBAN STREAM SYNDROME INCORPORATING DIFFERENT NATURAL HYDROLOGY AND ECOLOGICAL CONNECTIVITY

6.1 Reconsidering the urban stream syndrome

Aquatic ecosystems worldwide are vulnerable to diverse threats from human activities on the land and in waterbodies (Allan 2004, Dudgeon et al. 2006). Urban freshwater ecosystems are especially vulnerable because of the complexity and scale of threats due to urbanisation of the surrounding landscape. Improving the health of urban streams has the potential to provide local benefits such as biodiversity protection, enhanced ecosystem health, water purification, access to green space, scenic amenity and improved land values. There are also downstream externalities (external benefits) of improved water quality and ecological health in estuaries via reduced sedimentation, reduced nutrients, and reduced contaminants, important to maintaining estuarine habitats and fisheries. The existence of both local and external benefits provides a justification for public support of activities that are concerned with the health of urban streams. Government organisations, scientists and local volunteer groups are working to protect, manage and rehabilitate urban aquatic ecosystems.

Urban stream research has been dominated by the concept of the “urban stream syndrome” since it was first posed by Meyer et al. (2005). This syndrome describes a relatively consistent suite of impacts on ecosystem health observed in streams draining urban landscapes. Key stressors and impacts associated with this syndrome include increased hydrologic flashiness, elevated sediments, nutrients and contaminants, altered riparian and biotic assemblages, and reduced health of urban stream ecosystems (Paul and Meyer 2001, Meyer et al. 2005, Walsh et al. 2005a). Management research in urban streams has focused predominantly on mitigating the hydrological alteration associated with catchment-scale “directly-connected impervious surface” which has been shown to be associated with reduced urban stream health (Walsh et al. 2005a).

Much of the subsequent literature has supported many of the aspects initially synthesised in these papers (Paul and Meyer 2001, Walsh et al. 2005a). However, an increasing number of studies has indicated that in some locations not all aspects of the urban stream syndrome hold, especially in drier locations and ephemeral streams that

145 naturally experience flashy hydrographs, with high volume flows associated with storms and extended periods of low or no flow (e.g. de Jesus-Crespo and Ramirez 2011, Newham et al. 2011, Thompson and Parkinson 2011, Sheldon et al. 2012b, McIntosh et al. 2013, Burns et al. 2015). These exceptions suggest that the urban stream syndrome needs further development and a revised paradigm may be needed to accommodate different contexts and processes. As Booth et al. (2016) articulate, while much of recent urban stream literature has focused on identifying commonalities between urban streams in different locations, it is also important to explore critical differences in how the urban stream syndrome is expressed in different climatological and physiographic settings (without presenting so many variations that stream health managers are overwhelmed by information on the individuality of urban streams). While management recommendations have expanded to consider some aspects relevant to different types of hydrological regime, including low flow regimes, management has focused primarily on mitigation of hydrologic alteration associated with catchment-scale impervious surface (Walsh et al. 2016). Further research is required to contextualise the accepted facets of the urban stream syndrome and understand the influence of a wider range of stressors (e.g. loss of connectivity and habitat fragmentation on stream biota), and in so doing enhance effectiveness of urban stream rehabilitation.

The impacts of impervious surface on hydrology are complex and variable in ephemeral stream systems that naturally experience extended periods of low-flow. Impervious surface is sometimes associated with increasing and sometimes decreasing the volumes and continuity of base flow (Sheldon et al. 2012b, McIntosh et al. 2013), while the detrimental effects of flashy peak flows associated with impervious surfaces are less obvious in ephemeral systems that have naturally flashy hydrology. While stormwater piping has been considered important because it “directly connects” water from impervious surfaces to urban streams (Walsh et al. 2005b), the potential for stormwater piping to reduce ecological connectivity and fragment habitat has not been a focus, although disruption to ecological connectivity due road crossings and dams is well studied (Warren and Pardew 1998 , Mirati 1999, Norman et al. 2009, Engman and Ramírez 2012) and other issues associated with stormwater piping have been documented, such as increasing flow velocities, altering carbon and nutrient inputs, and increasing nitrogen concentrations (Kaushal et al. 2008b, Roy et al. 2009a). Ecological connectivity, or landscape connectivity is defined as “the degree to which the landscape facilitates or impedes movement among resource patches” (Taylor et al. 1993). In

146 streams, connectivity is important in three spatial dimensions, longitudinal, lateral and vertical (Stanley et al. 1997, Groffman et al. 2003, Döring et al. 2007). The present study focused on the potential for stormwater piping to interfere with ecological connectivity in the longitudinal dimension and with the ecological roles of the riparian zone. These aspects of connectivity deserve further consideration both in how they are affected by urbanisation and how their alteration influences stream biota and ecosystem health.

This research project on ephemeral urban streams of sub-tropical southeast Queensland (SEQ) was designed to detect whether catchment-scale impervious surface is as important in explaining variation in stream health as has been reported in temperate streams, and whether reach-scale riparian cover is a more important influence. It also investigated whether the ubiquitous stormwater piping found in urban areas was associated with reduced stream health independent of the effects of impervious surface. It then considered whether this piping and other disruptions to in-stream and catchment ecological connectivity are potentially important stressors affecting urban stream health, especially in terms of potential to disrupt biotic dispersal. Furthermore, in addressing these issues, the project considered whether the areal extent of each study influenced the relative importance of land-cover stressors associated with different spatial scales (i.e. reach, local and catchment scales).

A suite of ecosystem health indicators known or expected to respond to catchment and riparian impacts was analysed. Geographical information system (GIS) techniques were applied to calculate spatial (areal buffers and spatially-explicit inverse-distance weighted (IDW)) and non-spatial (lumped) land-cover metrics at the reach, local and catchment scales for a small study area (Bulimba Creek and Norman Creek, BCNC) and a larger area (Lower Brisbane River catchment and surrounding coastal catchments, LBRCSCC). Reach scale was defined as 200 m upstream from the site (incorporating 30 m either side of the stream line to represent the reach-scale riparian buffer) and local scale was defined as a few hundred metres upstream or uphill from each site in any direction. The mapped drainage network incorporated stormwater drainage as well as natural streams. In order to consider the impact of ecological connectivity on stream health, metrics of connectivity included “effective riparian buffers” (these account for the amount of natural stream channel effectively converted to stormwater piping and

147 thus lost), in-stream longitudinal connectivity and surrounding tree-cover fragmentation in addition to the lumped and inverse-distance weighted (IDW) land-cover metrics.

The findings of this study support the need for a reconsideration of the urban stream syndrome at least for the ephemeral, sub-tropical streams of SEQ, and potentially, other streams of similar hydrological character, e.g. those that experience low flow conditions and flashy natural hydrology. Compared with studies in temperate streams, which reported high importance of catchment-scale impervious surface for explaining variation in stream health, the present study found that catchment and local-scale impervious surface were not as important for explaining variation in fish and macroinvertebrate diversity and abundance. Reach-scale tree cover was relatively more important for explaining variation in maximum temperature, and the diversity and abundance of fish and macroinvertebrates than catchment-scale impervious surface. Catchment and local-scale impervious surface metrics were not strongly associated with variation in water quality metrics.

When researching urban stream health, and assessing the most likely benefits from intervention, the scale of the study area should be considered. The study with the larger areal extent (the Lower Brisbane River catchment and surrounding coastal catchments, LBRCSCC) revealed the greater relative importance of catchment-scale impervious surface metrics for explaining variation in macroinvertebrate diversity and abundance (SIGNAL2) in SEQ. Effective riparian buffer metrics, which accounted for the extent of stream length lost to stormwater piping, were relatively important explanatory variables for macroinvertebrate (but not fish) diversity and abundance for both study area extents considered, and also for minimum dissolved oxygen levels. They were relatively more important than catchment-scale lumped and inverse-distance weighted (IDW) catchment-scale impervious surface metrics in the study of small areal extent (Bulimba Creek and Norman Creek, BCNC). These findings suggest that the presence of stormwater piping may be one of the most important land-cover stressors in urban streams and should be incorporated in a new representation of the urban stream syndrome. Metrics designed to capture disruption to ecological connectivity relating to dispersal paths and habitat fragmentation were relatively important to explaining variation in fish and macroinvertebrate diversity and abundance and occurrence. In several cases these ecological connectivity metrics were more important than catchment-scale impervious surface metrics. Further support for the importance of

148 ecological connectivity can be taken from the finding that different ecological connectivity metrics designed to capture different aspects of upstream and downstream in-stream connectivity and surrounding tree-cover fragmentation were relatively more important to particular aquatic taxa and this may be due to different life history traits. For example, Cherax destructor occurrence was associated with the extent of connected stream length downstream of each site, which might be associated with a need to access downstream habitat and refugia during prolonged periods of stream drying whereas for the dragonfly family Gomphidae (Odonata) the extent of connected stream length upstream of each site was the most important explanatory metric. Species in this family remain close to their running water breeding habitats (Watson et al. 1982) which may make them more likely than other wide-ranging dragonfly species to be impacted by upstream ecological connectivity. They are also more likely to be impacted by local water quality and toxic pollution (Arthington et al. 1982, Watson et al. 1982). This study suggests that disruption to ecological connectivity in several dimensions is potentially a key urban stream stressor in addition to altered hydrology and water quality (Sheldon et al. 2012b, McIntosh et al. 2013, Burns et al. 2015) and should be considered as another important facet of the urban stream syndrome.

6.2 Spatial analysis of catchment-scale impervious surface and reach-scale riparian cover metrics and their associations with sub-tropical urban stream health and water quality indicators

In Chapter 3, the results of a study of urban stream health within a small areal extent encompassing Bulimba Creek and Norman Creek (BCNC) suggested that the interactions between land cover and stream health are complex and factors other than impervious surface needed to be considered for sub-tropical streams of SEQ. GIS- generated lumped, threshold and inverse-distance weighted (IDW) land-cover metrics, including effective riparian buffer metrics, as well as population density and latitude, were considered as candidate explanatory variables for macroinvertebrate diversity and abundance (SIGNAL2) as well as water quality including diel dissolved oxygen (DO), diel temperature, conductivity and pH. Model testing used generalised least squares (GLS) functions with model averaging to determine which metrics from equally plausible models were the most important.

149 This study found that:

1) Catchment-scale impervious surface metrics did not have as strong an association with variation in stream health (SIGNAL2) and water quality as reported in studies of temperate streams studies (Roy 2004a, Walsh 2004, Walsh and Kunapo 2009). Local-scale impervious surface had a relatively strong association with variation in SIGNAL2 compared with other land-cover metrics considered. The importance of the local scale was also found in another study of SEQ streams (Sheldon et al. 2012a, Macintosh et al. 2013). The gentle slopes and ephemeral nature of the streams of SEQ may be part of the reason for the reduced importance of catchment-scale impervious surface metrics. The gentle slopes may reduce the potential for high flow volumes to erode stream beds (Fitzgerald et al. 2012). Biota in this study area may be naturally adapted to flashy hydrology and high peak flows, and while increases in volumes and flashiness of peak flows have been shown to occur in such systems (Chowdhury et al. 2012, McIntosh et al. 2013), they may not alter the hydrological characteristics of sub-tropical stream systems and so may not greatly affect the diversity and abundance of their biota (de Jesus-Crespo and Ramirez 2011, Sheldon et al. 2012a). Additionally, the hydrology of streams with flashy and ephemeral natural hydrology has been shown to respond to increasing levels of catchment impervious surface with higher and more constant base flow volumes (Sheldon et al. 2012b, McIntosh et al. 2013, Bhaskar et al. 2016, Booth et al. 2016) rather than the reduced base flows often seen in more temperate streams. Thus the health of biotic communities can respond positively and negatively to increases in base flow associated with urbanisation in ephemeral streams (Sheldon et al. 2012b, Cooper et al. 2013).

2) Reach-scale metrics had relatively strong associations with variation in SIGNAL2 invertebrate scores and maximum water temperature, consistent with other studies of urban streams within drier climates (Thompson and Parkinson 2011, Ramírez et al. 2012).

3) The effective riparian buffer metrics, which account for the amount of natural stream channel which has effectively been converted to stormwater piping and thus lost, were relatively important for explaining variation in SIGNAL2 scores.

150 This type of riparian metric was more important for explaining SIGNAL2 than were lumped or IDW local and catchment-scale impervious surface metrics. It is proposed that these riparian metrics are relatively more important because they either capture alteration to ecological connectivity or loss of stream and riparian zone extent. The promising support for these effective riparian buffer metrics prompted the development of additional in-stream ecological connectivity metrics and surrounding tree-cover fragmentation metrics that were subsequently investigated in Chapter 5.

Additionally, this study found that all scales of land-cover metrics considered (reach, local and catchment) were relatively important for explaining variation in at least one aspect of stream health or water quality. Reach, local and catchment-scale land-cover metrics all had relatively high explanatory power for SIGNAL2. Of the stream health and water quality indicators assessed, macroinvertebrate SIGNAL2 score appeared to respond in the most predictable way to the urban intensity gradients assessed (reach, local and catchment-scale land cover, and/or human population density). The most important scales of land-cover stressor metrics differed with different water quality indicators. For pH, catchment-scale metrics including catchment-scale riparian buffer metrics were most important. For conductivity, population density, effectively a catchment-scale land-use metric, was the most important GIS-generated candidate explanatory metric. Dissolved oxygen (minimum and range) showed strong relationships with both reach and catchment-scale metrics. The most important influences on temperature (maximum and range) were provided by reach-scale and to a lesser degree catchment-scale metrics. These results generally support the results of other studies on rural, forested and urban sites in SEQ (Bunn et al. 2010), however minimum DO responded in an unexpected way to urbanisation metrics, increasing with impervious surface and reducing with tree-cover. This may be the consequence of excess detritus not being flushed due to low flows. Interactions between slow flow rates and minimum DO may be important considerations for the health of ephemeral urban stream ecosystems. In this study, low DO was associated with some relatively pristine sites and so taxon sensitivity to pollution and DO minima can be complicated. Minimum DO therefore may not necessarily be a straightforward indicator of stream health in ephemeral urban streams that have slow flow rates. However, DO range did respond to an urbanisation metric in an expected way. It increased with increasing population density.

151

Population density (assessed as a lumped metric in the upstream sub-catchment) was also found to have a relatively strong association with variation in SIGNAL2 and some water quality metrics (transformed measures of temperature range, DO range, conductivity and pH) suggesting that population density could be used as a surrogate for a general range of urbanisation stressors acting on stream health. Population density may be similar to a general “urban land-use” metric considered by Sheldon et al. (2012b) in SEQ and Brown et al. (2009) in US streams, and its availability via census data (e.g. Australian Bureau of Statistics 2007) may be useful for assessing urbanisation levels in places where other forms of urban land-use data are not available. The relatively high importance of population density, compared with land-cover metrics, suggests that additional mechanisms of impact on stream health, such as impaired water quality or loss of ecological connectivity not represented by land-cover metrics should be explored in studies of urban stream health. The development of new ecological connectivity metrics in subsequent chapters of this thesis was an attempt to explore these additional mechanisms. Additionally, future studies could consider new ways of representing stressors associated with reduced water quality and its origins in urban areas, which this study did not address.

6.3 Spatial analysis of the influence of the areal extent of a study on detectable associations between urbanisation and the ecosystem health of sub- tropical streams

In Chapter 4 the impacts of land cover on stream health were assessed across a study comprising 16 sub-catchments in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC), a larger areal extent than the two sub-catchments of the Bulimba Creek and Norman Creek (BCNC) assessed in the Chapter 3 study. This study introduced a more comprehensive suite of land-cover metrics that considered inverse-distance weighted (IDW) vegetation (tree and grass) catchment-scale metrics in addition to impervious surface catchment-scale IDW metrics. Using data provided from Brisbane City Council’s Land and Water Health Assessment, associations between land cover at different spatial scales and macroinvertebrate and fish diversity and abundance were considered. SIGNAL2 was used as an indicator of macroinvertebrate diversity and abundance and O/E50 was used as an indicator of fish diversity and abundance. As in

152 Chapter 3, GLS model testing and model averaging techniques were used to explore the metrics with most important explanatory power. Similar metrics were relatively important for explaining SIGNAL2 scores in this study of larger areal extent and the smaller scale study of Chapter 3. However, there were a few exceptions.

Key findings from Chapter 4 follow:

1) While, as hypothesised, similarly to Chapter 3, the strength of association between catchment-scale impervious surface metrics and SIGNAL2 was relatively low, there was a higher relative strength of association than was found in Chapter 3 between catchment-scale impervious surface metrics and SIGNAL2 compared with other land-cover metrics. This also meant that the local-scale impervious surface metric was not more important for explaining variation in SIGNAL2 than catchment-scale impervious surface metrics.

2) The strength of association between catchment-scale impervious surface metrics

and fish diversity and abundance O/E50 was even weaker than for macroinvertebrates. Very low or negligible explanatory power for catchment- scale land-cover and land-use metrics was observed for fish, which is similar to findings of other studies in SEQ (Sheldon et al. 2012a), but contrasts with studies of temperate streams (Klein 1979, Wang et al. 2000). Grass cover in the catchment-scale riparian buffer and inverse-distance weighted (IDW) to-the-

stream was positively associated with marginal change in O/E50.

3) Reach-scale tree cover appeared to be important to both fish and macroinvertebrate diversity and abundance. This finding could be related to the influence of tree cover on stream water temperature and habitat structure relative to taxon preferences. The importance of reach-scale tree cover is consistent with the results for macroinvertebrates in the smaller-scale Brisbane-Norman Creek study (Chapter 3). The importance of reach-scale tree cover is consistent with the importance of in-stream wood, local channel condition and in-stream habitat identified in the SEQ ecological health monitoring program (EHMP) studies for fish indicators across urban, forested and rural catchments (Kennard et al. 2006, Bunn et al. 2010). The relative importance of reach-scale tree cover compared

153 with catchment-scale metrics is in contrast to results presented from temperate stream literature (Klein 1979, Wang et al. 2000).

4) Effective riparian buffer metrics had relatively strong associations with variation in SIGNAL2 scores. However, compared with the study in Chapter 3 of Bulimba and Norman Creeks (BCNC), the relative importance of these metrics was difficult to differentiate from the other catchment-scale metrics measured in the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC) study. The lack of differentiation in this larger-scale study between these effective riparian buffers (and also local-scale impervious surface metrics) and other catchment-scale land-cover metrics suggests that within the larger area the separate urbanisation stressor effects are difficult to differentiate. This could be because localised effects average out across the larger area (which seems likely due to the range of metrics with increased importance in this study of larger areal extent), or because the effects of stressors associated with the larger catchment scale become more apparent (Lammert and Allan 1999, Morley and Karr 2002).

Additionally:

The catchment extent metric, upstream sub-catchment area, had significantly more explanatory power for SIGNAL2 in this study than in the Bulimba Creek and Norman Creek (BCNC) study. It was also the metric with most effect on the fish diversity and abundance O/E50 data. The importance of upstream catchment size is consistent with similar findings in other studies (such as Wenger et al. 2008). This metric may relate to higher flow rates, the persistence of water in streams, mechanistic effects of low-flow hydrology, higher total longitudinal habitat extent or other factors (Rolls et al. 2012).

6.4 Spatial analysis of the likely impacts of disruptions to ecological connectivity on the biota of sub-tropical urban streams

In Chapter 5, in-stream ecological longitudinal connectivity and fragmentation of the surrounding tree cover were considered as additional land-cover stressor metrics to explain variation in fish and macroinvertebrate diversity and abundance as well as

154 occurrence. Model testing used the GLS methods of Chapters 3 and 4 as well as a generalised linear modelling (GLM) approach more suited to assessing models for the binary data for specific taxa. Model averaging was again used to determine which stressor metrics had the most explanatory power. The data set used was the same as the data set for Chapter 4, from the 16-catchment study area of the Lower Brisbane River catchment and surrounding coastal catchments (LBRCSCC).

The results in this chapter suggested that different kinds of disruptions to ecological connectivity are associated with the occurrence of different aquatic taxa.

The key findings from Chapter 5 were:

1) In-stream ecological connectivity metrics (the extent of connected stream length upstream, and an effective riparian buffer metric) were more important than lumped and inverse-distance weighted (IDW) catchment-scale impervious surface metrics for explaining variation in macroinvertebrate diversity and abundance (SIGNAL2). The extent of connected stream length upstream was also the most important catchment-scale land-cover or land-use metric for

explaining variation in fish diversity and abundance (O/E50).

2) Different ecological connectivity metrics were relatively important for explaining the occurrence of different taxa, a finding that may be related to different habitat requirements and life history traits. The metrics representing the extent of connected stream length were relatively important in explaining the variation in occurrence, abundance and diversity of fish and macroinvertebrates, and typically relatively more important than catchment-scale impervious surface metrics. Effective riparian buffer metrics were relatively important in explaining variation in macroinvertebrate diversity and abundance and macroinvertebrate and fish occurrence but these metrics were not relatively more important than catchment-scale impervious surface metrics or other catchment-scale metrics (tree, grass) in explaining variation in occurrence. Although surrounding tree- cover fragmentation metrics were relatively important to explaining the occurrence of several macroinvertebrate taxa and one fish taxon (Melanotaenia duboulayi, in one year, 2010), they were not generally more important than local-scale impervious surface metrics. In addition to metrics representing the

155 extent of downstream and upstream connected stream length, which were relatively important for explaining the occurrence of Cherax destructor and the dragonflies of the family Gomphidae (Odonata) respectively (mentioned in section 6.1 above), fragmentation of the tree cover in the surrounding riparian and terrestrial landscapes were examples of other ecological connectivity metrics that may be related to life history traits. Fragmentation of tree cover in the surrounding landscape was relative important as an explanatory metric for the occurrence of Cherax destructor, which may be related to the crawling dispersal behaviour of this species. In contrast to the Gomphidae (Odonata), there was no support in the model averaging analyses for any of the land-cover metrics assessed for explaining the occurrence of the other odonate family studied, Hemicorduliidae. It is likely that the Gomphidae included species which remain near their running-water breeding habitat whereas the Hemicorduliidae includes species which are habitat generalists and disperse widely to access breeding habitats in still or running water (Watson et al. 1982). This difference in life history traits for these odonate families may account for the finding that metrics representing the extent of connected stream length upstream from each site were only important for explaining the occurrence of the Gomphidae. In addition, Chapter 5 includes other examples of habitat and life history requirements that may account for differences in importance of ecological connectivity metrics.

3) Reach-scale riparian buffer metrics remained relatively important for explaining fish and macroinvertebrate diversity and abundance when ecological connectivity metrics were considered in model testing. However, they were not important explanatory variables in model testing for the occurrence of particular fish and macroinvertebrate taxa. That reach-scale riparian condition was more important to the abundance of macroinvertebrates and fish than to their occurrence suggests that it constitutes a habitat preference or a driver of increased biotic productivity, and not a factor in the processes of local extirpation and recolonisation. In addition, the reach scale was relatively important for explaining fish diversity and abundance in 2010 at the end of a long drought period, but not in 2011 when the preceding year’s conditions had been wetter, suggesting a refuge role more so than habitat preference.

156 Additionally:

4) In addition to the importance of upstream sub-catchment size to fish and macroinvertebrate diversity and abundance identified in Chapter 4, the total catchment tributary extent was a relatively important explanatory variable for fish and macroinvertebrate diversity and abundance and upstream, downstream and total tributary extents were important for explaining the occurrence of some taxa especially fish. For fish, metrics of upstream sub-catchment extent were important to diversity and abundance in both years, but entire catchment tributary extent was only important in 2011. A possible explanation for the relative importance of entire catchment tributary extent (e.g. for the fish Melanotaenia duboulayi and Mogurnda adspersa), as opposed to just upstream tributary extent, is that the species assemblage composition of entire catchments is related to total catchment size, perhaps because larger catchments may contain a greater number of different habitat types, greater suitable habitat extent (Rolls et al. 2012) and more areas offering refugia both upstream and downstream. Access to greater extents of downstream refugia which may therefore include locations with higher flow rates and more persistent water levels (Rolls et al. 2012) may also be associated with the importance of the downstream catchment tributary extent (which was also relatively important for explaining the occurrence of two fish, Melanotaenia duboulayi and Mogurnda adspersa).

6.5 A perspective on the urban stream syndrome that incorporates different natural hydrology and ecological connectivity

The five key hypotheses tested during this study each received support and corroboration as the findings of each chapters built upon each other. In the Chapter 3 study with small areal extent, the results suggested that catchment-scale impervious surface and, by association, altered hydrology, are less important stressors than in temperate urban streams. This chapter also suggested that reach-scale riparian condition has greater importance for explaining variation in stream ecosystem health than in temperate stream studies. In addition, new metrics were introduced to account for the amount of stormwater piping in the stream network, by reconsidering riparian buffer metrics as “effective riparian buffer” metrics. Chapter 4 suggested that similar land-

157 cover and land-use metrics were relatively important for explaining variation in stream health within a study area of medium areal extent. However, the relative importance of catchment-scale impervious surface metrics increased as the areal extent of the study increased. While this increased importance may be due to the effects of catchment-scale impervious surface (e.g. altered hydrology) operating at larger scales than the smaller- scale study accounted for, the increased difficulty in differentiating the relative importance of various catchment-scale metrics in Chapter 4 due to similar levels of importance suggests that localised effects are averaging out across larger areas (Lammert and Allan 1999, Morley and Karr 2002). Chapter 5 developed a new suite of ecological connectivity metrics that built upon the effective riparian buffer metrics of previous chapters. The new metrics represented the extent of connected stream length and fragmentation of surrounding tree cover. The new ecological connectivity metrics were all relatively important but each type was more important for some taxa than others, suggesting that different aspects of ecological connectivity influence different habitat and life history requirements. Consideration of several taxa and their requirements supported this interpretation. The five key hypotheses are revisited below and the results synthesised into an updated representation of the urban stream syndrome (Figure 1.1, Chapter 1).

Hypothesis 1: Altered hydrology associated with catchment and local-scale impervious surface area has less influence on urban steam health in the ephemeral sub-tropical streams of southeast Queensland (SEQ) than in temperate streams

Other studies have shown that impervious surface measurements alone may not capture all the important stressors associated with urbanisation, such as the loss of riparian cover (Karr and Chu 2000, Morley and Karr 2002) and important drivers of water quality impairment (Sheldon et al. 2012b). Furthermore, in different locations with different hydrology, climate and physiography, impervious surface has different effects on hydrology, especially base flow (Brown et al. 2009, Sheldon et al. 2012b, McIntosh et al. 2013, Booth et al. 2016). In the ephemeral sub-tropical streams of SEQ, the comparatively low support for catchment-scale impervious surface metrics compared with studies in temperate streams, and the relatively strong associations between effective riparian buffer metrics, ecological connectivity metrics and catchment population density with variation in SIGNAL2 scores and taxa occurrence suggests that

158 there are stressors acting on urban streams that are equally or more important than catchment-scale impervious surface and altered hydrology.

Hypothesis 2: Reach-scale riparian buffer condition has more influence on urban stream health in the ephemeral sub-tropical streams of SEQ than in temperate streams

Reach-scale riparian vegetation metrics were relatively important for explaining variation in macroinvertebrate and fish diversity and abundance (for both the small and medium scale studies reported in Chapters 3 and 4, and also in Chapter 5) but not for occurrence. This suggest that reach-scale riparian buffer condition may not be an important factor in the processes leading to local extirpation of, or recolonisation by particular taxa but that may relate to both habitat preference and to biotic productivity and the ability of a stream reach to support greater numbers of individuals.

The role of reach-scale riparian buffer condition may be more important to urban stream health in locations with drier climates and ephemeral hydrology. This may be due to the reduced importance of catchment-scale impervious surface making it easier to detect influence of reach-scale riparian condition on stream health, and there may be fewer impacts on the health of riparian zones due to altered hydrology in such locations allowing them to provide the functions they are known to support healthy streams in forested and rural locations (Naiman and Décamps 1997, Pusey and Arthington 2003), or the shading and temperature control provided by riparian zones might be more important to stream health in hotter and drier climates. The importance of the reach- scale riparian buffer condition in the current study supports Thompson and Parkinson (2011), who detected a reach-scale benefit of intact riparian tree cover on macroinvertebrate community structure. They carried out their study during a period when the creeks were drier than when they had been studied previously (Hatt et al. 2004). Burns et al. (2015) also found during a dry period in this location that natural climate variation likely had much greater influence on stream hydrology than catchment-scale impervious surface did. They found that attenuated impervious surface (similar to the inverse-distance weighted (IDW) to stream impervious surface metrics of the current study) must have been accounting for something other than altered hydrology (e.g. water quality) due to the low explanatory power of hydrological models during this period. The data for the Chapter 3 study and the 2010 data set for the Chapter 4 study were collected at the end of a long period of drought in streams that are

159 typically ephemeral with flashy natural hydrology, i.e. naturally exhibiting the characteristics typically associated with directly-connected impervious surfaces.

The role of riparian buffers in the ephemeral urban streams of SEQ is complex. Grass cover was associated with increased levels of dissolved oxygen in these slow flowing streams. This, in combination with an unexpectedly negative relationship between tree cover and minimum dissolved oxygen, suggests that excessive organic matter (e.g. detritus) in these slow-flowing streams may affect minimum dissolved oxygen levels.

Hypothesis 3: The presence of in-stream stormwater piping has a detectable influence on stream health indicators that is distinguishable from catchment-scale impervious surface impacts

Effective riparian buffer metrics, which accounted for stormwater piping mapped in or near the stream channel, were relatively important in explaining variation in macroinvertebrate diversity and abundance (SIGNAL2) in all three studies presented in this thesis (Chapters 3, 4 and 5). In the small-scale study of Chapter 3, these effective riparian buffer metrics were more important for explaining SIGNAL2 than any of the other catchment-scale metrics that accounted for impervious surface. Such stormwater piping metrics also had modest explanatory power for the occurrence of fish and macroinvertebrates in Chapter 5 although they were not necessarily more important than local and catchment-scale impervious surface metrics that did not account for stormwater piping. Because the conversion of a stream segment involves the conversion of stream length to piping and may also result in disconnection between remnant stream segments, stormwater-piping metrics may capture both loss and fragmentation of riparian and in-stream habitat.

The relative importance of effective riparian buffers quantified in this thesis suggests that the presence of stormwater piping in the stream channel may be more important for explaining variation in stream health than the impervious surface it is draining, especially in ephemeral streams such as those of SEQ. As postulated here, the mechanisms by which stormwater piping may be acting on stream biotic assemblages (loss of habitat and disconnection of habitat as well as potential water quality and hydrologic stressors) suggest that minimisation of in-stream stormwater piping could be an important management intervention. Targeting rehabilitation of stream channels and

160 riparian zones may therefore be as important as targeting catchment-scale impervious surface. Indeed, the 11.5 m threshold distance between effective impervious surface and the stream network, which Walsh and Kunapo (2009) present in their development of the concept of “attenuated impervious surface”, effectively places importance on land cover within the riparian buffer (30 m). However, the amount of intervention required could be substantial. Sheldon et al. (2012a) found that a stream could maintain “good” health (based on water quality and biotic metrics) if approximately 80% of the hydrologically-active zone of the catchment was forested. The zone they define includes smaller ephemeral tributaries that flow-to-stream. Further studies of the relevance of stormwater piping to stream health in streams of different sizes (stream orders) could be informative.

Hypothesis 4: The influence of land and riparian cover metrics on urban stream health in SEQ varies according to the spatial extent of the study area

Differences in the results of Chapters 3 and 4 suggest that catchment-scale impervious surface metrics have greater explanatory power for macroinvertebrate diversity and abundance in studies carried out across greater study area extents (a medium study area extent of 1150 km2 compared with a small study area extent of 152 km2). In comparing the findings of these two chapters, the results also indicate that the relative importance of effective riparian buffer metrics is more difficult to differentiate from other catchment-scale metrics in studies of larger areal extent. As mentioned above in Section 6.3, a likely cause for this difference is that specific and localised effects average out across larger areas (Lammert and Allan 1999, Morley and Karr 2002). In contrast, reach-scale riparian condition was found to be relatively important for explaining macroinvertebrate diversity and abundance for both scales of study area extent presented in this thesis. This suggests that the localised effects of reach-scale riparian condition on stream health are important to the streams of SEQ and the importance is detectable for studies carried out at a range of scales. That is, these local effects remain detectable even in the presence of other important stress factors. The importance of impervious surface based catchment-scale metrics in both Chapters 3 and 4 was still lower in the streams of SEQ than in other studies. Thus the lower importance of catchment-scale land cover and the higher importance of reach-scale condition in the current study compared with some others studies (Roy 2004a, Walsh

161 2004, Walsh and Kunapo 2009) is not simply due to effects of the study area extent on the detectability of influences associated with different metrics.

Hypothesis 5: Ecological connectivity relating to dispersal paths and habitat fragmentation significantly influences the health of urban streams in SEQ

In-stream ecological connectivity metrics (extent of connected stream length and effective riparian buffer metrics) were associated with fish and macroinvertebrate diversity and abundance as well as occurrence. This suggests that ecological connectivity may be an important category of stressor to consider when assessing the health of urban streams. Analysis of taxon occurrence highlighted the importance of upstream and downstream in-stream ecological connectivity as well as the possible effects of fragmentation of surrounding terrestrial tree cover.

These results for sub-tropical urban streams in SEQ support the findings of Ramirez et al. (2012) for tropical streams in Puerto Rico. They highlighted in-stream connectivity, habitat quality and pollution impacts as the key foci for mitigation of urbanisation impacts on tropical island streams, which, as discussed in Chapter 2, share naturally flashy hydrology with the streams in the SEQ study area. While Ramirez et al. (2012) focused on ecological connectivity stressors relating to large barriers such as dams, the current study provides a methodology to assess more distributed ecological connectivity associated with stormwater piping and other urban infrastructure. In addition, the statistical methods applied here allow comparison of the relative importance of land cover and in-stream ecological connectivity, and could be extended to include water quality and other stream health stressor metrics evident in these complex environments.

The fact that none of the sites within the Chapter 3 study area had a very high SIGNAL2 invertebrate score supports other studies suggesting that urban streams may have assemblages of macroinvertebrates characterised by reduced diversity and a lack of sensitive taxa compared with forested sites (e.g. Arthington et al. 1982, Watson et al. 1982, Leigh et al. unpublished). Thus taxa found at any site in an urban catchment may be a sub-sample of those in the local ”pool” of species within the less disturbed parts of the catchment. This could suggest an overall reduction in healthy levels of community structure due to lack of connectivity to source populations (Sundermann et al. 2011) once areas of impaired stream health become extensive or extirpation events occur.

162 Barriers to connectivity associated with road crossings and stream burial may reduce the resilience of a community and its capacity to recover after local acute impacts due to pollution, drying, flooding, scouring or other events that may impact stream health. The distinction between short-term (acute) and long-term (chronic) impacts on stream health and recovery times could be a fruitful area for further study.

Different catchment-scale ecological connectivity metrics and other land-cover and land-use stressor metrics were associated with the occurrence of different taxa, suggesting that species with different life history traits should be considered as indicators when prioritising management interventions and monitoring recovery in urban streams. For different invertebrate and fish taxa, examples of relatively important stressor metrics included upstream and downstream in-stream connectivity, reach-scale riparian cover, surrounding tree-cover fragmentation and impervious surface metrics, consistent with many studies (see Pusey and Arthington 2003). Life history requirements of different species influence their resilience to extirpation events and their ability to disperse (Bunn and Hughes 1997, Hughes 2007). The majority of the in- stream connectivity metrics that were shown in Chapter 5 to correlate with macroinvertebrate and fish community indicators represented upstream connectivity; although there were cases where total catchment and downstream ecological connectivity metrics were included in preferred models. Both upstream and downstream barriers may interrupt a lifecycle that involves downstream larval drift and upstream adult flight. Blakely et al. (2006) identified culverts as barriers to upstream ovipositing insects and Arthington et al. (1982) identified downstream drift as a dispersal mechanism in polluted Bulimba Creek, SEQ. Many drifting insect species are known to fly upstream to oviposit (Blakely et al. 2006, Collier and Clements 2011). Upstream connectivity is known to affect mass recruitment after local extirpation events (Jansson et al. 2007).

Ecological connectivity may be an especially important consideration in ephemeral streams. During dry periods and in drier climates, flow may be slow and waterbodies can become hydrologically disconnected as riffles dry out leaving disconnected pools (Steward et al. 2012). Aquatic refugia and the ease of access by which taxa may repopulate after local extirpation events may be affected by urbanisation (Rolls et al. 2012). By comparing upstream and downstream, accumulated (effective riparian buffer and surrounding tree-cover fragmentation metrics) and single barrier metrics (in-stream

163 ecological connectivity metrics), the analyses presented here provide a preliminary approach and a basis for further development and refinement of ecological connectivity metrics in urban stream research.

Additional considerations: temporal and regional variation

While there continue to be many aspects of the urban stream syndrome that are demonstrated in similar ways in many locations, different urbanisation stressors that act on freshwater ecosystems should be considered in the light of climate and other regional contextual differences (Bhaskar et al. 2016, Booth et al. 2016). Consideration of wetter or drier antecedent conditions and wetter or drier local climates may be important for distinguishing which aspects of urbanisation are most important at different times and over the short and long term. The most influential acute and chronic stressors may vary with natural and anthropogenic differences between regions and with inter-annual climatic variation even in non-urban locations (Arthington 2012, Rolls et al. 2012). As mentioned above, in this study area, reach-scale riparian condition was more important than in other urban stream studies, and the conversion of stream sections to stormwater piping was more important than catchment-scale impervious surface. Studies in temperate areas contrasting wetter and drier periods have also shown reduced importance of catchment-scale impervious surface, increased importance of reach-scale condition and possible increased importance of water quality during drier periods (Burns et al. 2015). Here the differences in results for 2010 and 2011 models for fish species occurrence (Chapter 5) suggest that variation in climate and thus in the magnitude, timing, frequency and duration of flow events and hydrologic variability between two consecutive years can be important when assessing which urbanisation stressors are likely to have the most important effects on stream ecosystem health in a region. While it is not recommended to assess stream health every year, as this might be costly, it highlights the need to consider different climatic conditions in studies of the influence of urbanisation on particular locations.

6.6 Revised concept map of urban stream health

The concept map first drawn in Chapter 1 is revised and updated here with new additions highlighted in blue (Figure 7.1). These additions incorporate:

164 (1) The importance of temporal and regional variations in natural hydrology (e.g. the ephemeral nature of some urban stream systems such as those in the current study) reflected in a new box near the top of the figure; (2) The importance of ecological connectivity to dispersal, access to refugia and recolonisation potential. This is reflected in a new box near the bottom of the figure, as well as by expanding habitat to include areas outside the stream itself, and by representing stormwater piping with perforated lines, to illustrate potential impacts that stormwater piping may have on ecological connectivity between habitat patches; and (3) The distinction between land-cover/land-use stressors operating at reach, local and catchment scales (by explicitly referring to these scales in the grey boxes down the left side, reach and local scales are highlighted in blue)

The potential impacts of dispersal traits on macroinvertebrate and fish occurrence are also added in bold lettering in their respective biota structure boxes. The addition of dotted lines across the edges of the pipes and sealed drains box suggests that as well as conveying water and pollutants rapidly from impervious surfaces, these drains can also act to fragment habitat. Habitat requirements, which, in addition to in-stream habitat, may include aspects of riparian zones and broader surrounding catchments, are highlighted. Ecological connectivity in this concept map represents an expansion of the barriers to fish movement of Wenger et al. (2009), incorporating evidence that connectivity in the aquatic and terrestrial environments is important for fish and macroinvertebrates (and possibly other taxa as well). While stressors associated with catchment, local and reach-scale processes impact stream health indicators, disruption to ecological connectivity may also occur across multiple scales. Hence connectivity is explicitly delineated across aquatic, riparian and catchment scales. The many interactions between the different components of the urban stream syndrome are complex and varied and not shown here. The importance of the areal extent of a study is not incorporated in the concept map explicitly, as in the present study it likely related more to the ability to detect the association between stream health and stream stressors, than to the underlying mechanisms. However, the extent of the upstream and total catchment areas is likely to be important, especially for stream health at sites in ephemeral streams, as indicated by the results in Chapters 4 and 5. Mechanistic explanations for this may include the potential for higher flow rates and more persistent aquatic habitat in streams and greater total longitudinal habitat extent (Rolls et al. 2012).

165

Figure 6.1 Updated urban stream concept map (updated from Walsh et al. (2005a) and Chapter 1) This figure illustrates the mechanisms of the key urban stressors likely to affect stream ecosystem health in the streams of SEQ. Urbanisation stressors associated with the reach, local and catchment scales are illustrated affecting in-stream, riparian and terrestrial processes. Stressors and ecosystem responses are complex and interactive. Altered hydrology due to efficient stormwater drainage is an acknowledged stressor (Walsh et al. (2005a) but associated stormwater piping also acts to disrupt ecological connectivity, indicated by the dotted partition at the end of “PIPES, SEALED DRAINS”. This version explicitly notes the spatial scale by incorporating reach and local-scale extents (in blue), as well as the importance of catchment extent (in blue), extends habitat from the stream into the riparian zone and surrounding landscape (highlighted in blue), and acknowledges the temporal and regional variations in hydrology which are likely to influence which urbanisation stressors have the most important impacts on stream health (new blue box added to left hand side of concept map). It also expands the fish movement barriers of Wenger et al. (2009) into a larger concept of ecological connectivity that spans the aquatic and terrestrial environments (also highlighted in blue). The importance of light due to shading of riparian zones is acknowledged in the water quality box. The many interactions between the different components are complex and varied and not shown here.

In conclusion, the results of this study suggest that in some regions with particular climatic and hydrological characteristics, such as SEQ, the urban stream syndrome may need to shift its focus on the influence of catchment-wide impervious surface to account for disturbances to several dimensions of ecological connectivity. These dimensions include maintenance of intact riparian vegetation and potentially other nearby surrounding vegetation as well as minimisation of the extent of stream length that is converted to piping. While the amount of catchment-scale impervious surface and its proximity to the stream channel cannot be discounted as an important stressor, likely affecting hydrology, sediment, water quality and biota (Burns et al. 2015), ecological connectivity within streams and their surrounding riparian habitats appear to be equally

166 or more important to the health of ephemeral urban streams. The interactions between disruption to ecological connectivity and local water quality conditions and other potential drivers of local extirpation also warrant investigation. The findings of this study further support the argument that the urban stream syndrome should incorporate consideration of a wider climatic and hydrological context (Sheldon et al. 2012b, Bhaskar et al. 2016, Booth et al. 2016). In this expanded vision of the urban stream syndrome, ecological connectivity and fragmentation of the surrounding tree cover are likely to have significant roles in maintaining stream ecosystem health. The likely impacts of catchment and reach-scale riparian and terrestrial land cover on stream health should be considered in light of how they interact with ecological connectivity. Protection and rehabilitation efforts in urban streams could focus on ecological connectivity when identifying sites for direct intervention. Ecological connectivity may also be a limiting factor on the benefits of reach-scale rehabilitation projects (Sundermann et al. 2011). Ecological connectivity provides a process link between spatial scales and may drive some aspects of both small (reach and local) and large (catchment) scale stressors. At the local and reach scale, impaired ecological connectivity may constrain local repopulation whereas at the catchment scale it may result in source populations becoming too distant to ever recolonise a location after an extirpation event, or populations may become fragmented and be lost over time.

6.7 Recommendations for policy and planning

To maximise stream health in SEQ urban streams, reach-scale rehabilitation and the protection of ecological connectivity should be targeted. These approaches may be applied in combination with more traditional urban stream catchment-scale management approaches such as mitigation of pollution, sedimentation, and altered hydrological stressors. Overall, the results suggest that there are benefits to urban stream health from protecting in-stream connectivity, reducing the extent of stream length that is piped, protecting riparian zones and minimising aquatic and terrestrial habitat fragmentation. In these predominantly ephemeral freshwater ecosystems, the intermittent occurrence of local extirpation events (due to stream drying, poor water quality, floods, etc.) may make the presence of relatively intact riparian zones with good longitudinal ecological connectivity a vital factor in stream health protection. Promoting maximum ecological connectivity across entire catchments would likely be logistically and financially

167 difficult (see costing presented in Chapter 5). Promotion of good ecological connectivity in strategic parts of catchments including main channels and selected smaller-order streams (that have good ecological connectivity with the main channels) may be feasible. Healthy source populations within sub-catchments or in the surrounding local area may also be necessary for successful stream rehabilitation. Stream networks and surrounding habitats with the greatest opportunity to maximise aspects of ecological connectivity should also be prioritised for greater likelihood of successful rehabilitation outcomes. These considerations could enhance the ecological outcomes of stream rehabilitation using optimisation approaches such as systematic conservation planning (Hermoso et al. 2012a).

Maintenance of good ecological connectivity through effective urban planning and use of dispersal sensitive road crossings may be an essential stand-alone or complementary approach to water sensitive urban design (WSUD), which targets water quality and hydrology. While the efforts to apply WSUD in many urban areas are commended for increasing base flow, reducing high flows of stormwater peaks, and improving water quality (Roni et al. 2008), they do not necessarily address ecological connectivity issues, and in some locations such as sub-tropical SEQ, mitigation of hydrological effects may not be important (Sheldon et al. 2012b) or may require alternative approaches designed to reduce instead of increase base flow (Sheldon et al. 2012b, Bhaskar et al. 2016, Booth et al. 2016). However, even where WSUD may not be required to mitigate hydrological alteration, or may not be effective for hydrologic alteration (Burns et al. 2012, Walsh et al. 2016), it may still have important water quality improvement benefits (Roni et al. 2008, Walsh et al. 2016) which are likely to be vital to ecosystem health of ephemeral streams in drier climates such as SEQ (Sheldon et al. 2012b) and may limit the success of reach scale intervention and ecological connectivity enhancement (Roni et al. 2008). While water quality has been shown to be relatively important for explaining variation in stream macroinvertebrate and fish assemblages in SEQ (Sheldon et al. 2012a) and potentially in other low-flow systems (Burns et al. 2015), and while urbanisation influences water quality (Paul and Meyer 2001, Hatt et al. 2004, Brown et al. 2009, Sheldon et al. 2012b) the direct relationships between urbanisation (which may be represented by population density, land use, land cover, etc.) and water quality and the subsequent impact on biota have not been clearly established and warrant further research. Ecological connectivity is likely important to the impacts of water quality on biological stream health measures,

168 especially in low-flow systems such as SEQ (Rolls et al. 2012, Steward et al. 2012). Longitudinal (Hughes et al. 2014) and lateral ecological connectivity (Hughes et al. 2014, Walsh et al. 2016) as well as vertical hydrological connectivity between the stream and groundwater (Groffman et al. 2003, Hughes et al. 2014, Walsh et al. 2016) are likely to be essential for benefits of catchment-scale hydrologic rehabilitation and or reach-scale rehabilitation projects to be effective.

When devising management recommendations, stream health impairment due to land cover and ecological connectivity issues should be considered in both wet and dry seasons in both ephemeral and perennial systems. The response to urbanisation appears to be different in ephemeral systems that experience periods of low flow and flashy spates. In these systems catchment-scale impervious surface appears to be less important as a driver of stream health, and reach-scale condition appears to be more important. The extent of stormwater piping rather than impervious surface may better reflect the effects of urbanisation on these streams where several dimensions of ecological connectivity appear to be more important than altered hydrology associated with catchment impervious surfaces.

Many stream rehabilitation and protection efforts by community groups focus on reach- scale riparian zone solutions. Detectable benefits of rehabilitation and/or protection of streams at the local scale are desirable but have not been proven in much of the literature to date, although broader catchment-scale riparian interventions have (Smucker and Detenbeck 2014). By assessing land-cover metrics at a range of different scales, and identifying the relative importance of reach-scale condition for diversity, abundance and occurrence of biota in urban streams, the current study has shown potential benefits of reach-scale rehabilitation and protection projects at least in ephemeral streams of SEQ.

Total sub-catchment size and larger total tributary catchment area are important considerations, with larger sub-catchments likely to support greater diversity of biota (e.g. fish) to provide source populations for recolonisation when local extirpation events occur. When choosing catchments for rehabilitation or protection, larger catchments may be preferred over very small catchments draining to estuarine areas.

169 This study provides a methodology to assess ecological connectivity in other study areas and to compare the relative impacts of land cover, in-stream ecological connectivity, water quality and other stream health stressor metrics on stream health. This study also establishes that stormwater piping and land-cover data collection do not need to be prohibitively expensive in order to assist local governments in studying and understanding the stressors impacting their local freshwater stream ecosystems. Impacts of urbanisation on stream health are not consistently observed in all areas, and so tools designed for local councils to help them understand the individuality of their streams in the context of general impacts of urbanisation are recommended. Councils in many metropolitan areas could collect data sets similar to those analysed in this study in a cost-effective manner.

6.8 Future research

Continued research into the stressors that have the most important impacts on urban stream health is recommended. Ecological connectivity metrics show promise as tools for prioritisation of site rehabilitation to improve urban steam health. Undertaking and monitoring reach-scale rehabilitation projects that are prioritised according to ecological connectivity considerations would be desirable to provide further insight into how ecological connectivity enhances the ability of riparian zones to mitigate urbanisation impacts on stream health. Studying rehabilitation projects across several sites connected along a stream network and comparing them across catchments could further elucidate the scale at which ecological connectivity effects are detectable and relevant.

Urban planners would be advised to research how to design cities and urban infrastructure to prioritise the protection of intact creek channels with good ecological connectivity as an efficient means to protect urban stream health while balancing human needs. This could involve prioritising main channels and other selected smaller-order streams that have good ecological connectivity with the main channels. Further investigation of stream rehabilitation projects that incorporate ecological connectivity is desirable to help establish whether the removal of barriers to dispersal (or the installation or retrofitting of dispersal sensitive design options) in single or multiple locations is optimal based on effectiveness and costs.

170 The higher explanatory power of population density than many land-cover metrics for biota and water quality indicators suggests further research into the implications of population density is needed in the sub-tropical streams of SEQ and that additional aspects of population driven pollution generation such as land-cover and land-use configuration not captured by the metrics presented should be investigated. As mentioned above in Section 6.7, further investigations of the interrelationships between urbanisation (represented by land cover or land use) and water quality and the subsequent influences on biota, especially in low-flow systems, are recommended. These should also consider ecological connectivity as a potential covariate affecting how water quality influences stream biota.

New in-stream connectivity metrics, which could build on the effective riparian buffer, the extent of connected stream length and surrounding tree-cover fragmentation metrics presented here, should consider differentiating main-stem channels from headwaters in order to better prioritise where in the catchment to plan stream protection and management intervention. As mentioned in Section 6.7, studies of stream rehabilitation projects based on ecological connectivity are desirable. These should aim to establish whether the removal of single barriers or multiple barriers is best based on effectiveness and cost, taking consideration of local hydrology, barrier locations and migratory characteristics of biota (Rolls et al. 2014).

Further research into GIS-based floodplain connectivity metrics in urban areas would also be a useful research direction. Analysing longitudinal ecological connectivity and fragmentation of the surrounding landscape within the lateral dimension of floodplain connectivity could be facilitated by considering new distance-weighted metrics, floodplain mapping data and more detailed field studies at a reduced number of sites.

Further genetic studies investigating differentiation between species populations separated by long extents of stormwater piping would provide further insights into and evidence of minimum distance requirements to maintain ecological connectivity for different species in urban streams.

The relative impacts of acute and chronic stressors on stream health warrant more research (Arthington et al. 1982, Watson et al. 1982). The relative importance of ecological connectivity metrics suggests that there is interplay between stream length

171 fragmentation and local extirpation events. Time varying studies across different seasons in this local area and across wet and dry years in this and other areas, examining the persistence of threats from low DO, nutrient and other types of pollution in the water and soils of urban streams, may help provide insights into the long-term impacts of chronic and acute stressors.

172 APPENDICES

173 Appendix 1 Site location descriptions

Table A1.1 Site location descriptions for the study in Chapter 3 of Bulimba Creek and Norman Creek (BCNC)

BCNC S_ID Location UBD Suburb Bulimba Creek at Stackpole Street parkland S_01 (description from EHMP) 201 R8 Wishart S_02 Pine Mountain Road 181 J13 Mt Gravatt East S_03 Donnington St / Selvin Creek 181 N9 Carindale S_04 Ditmas Street 201 K12 MacGregor S_05 Off Sandringham near Mansfield Primary 201 R3 Mansfield Brisbane Adventist College off Broadwater and S_06 Ham 201 N4 Mansfield S_07 Grassdale Rd nr Grassdale Country club 182 J3 Gumdale S_08 Vanderbilt 201 P16 Eight Mile Planes S_09 Roly Chapman Bush Reserve 201 B9 Upper Mt Gravatt S_10 Mimosa Creek Near Eco Centre 200 L7 Nathan S_11 Toddman St 161 J15 Seven Hills S_12 Secam St / Devlan St 181 L18 Mansfield S_13 Padstow Road Near Yimbin Park (Delafield St) 201 A17 Sunnybank S_14 Olivia Street off Pine Mountain Road 181 J11 Mt Gravatt S_15 Woodland Street 182 B12 Carindale S_16 Cribb St 182 D16 Belmont End of School Rd or possibly Millers Rd S_17 underneath Pacific Motorway 221 Q3 Eight Mile Planes Jacinda Street - Follow Malbon Street to end S_18 and keep going. 201 B19 Sunnybank S_19 Nemies Rd between Belyando and Chelsea St 220 P11 Runcorn S_20 Walley Tate Park 221 K8 Underwood S_21 Spring Creek on Scrub Rd 182 A13 Belmont S_22 Arnwood Place 180 D10 Annerley S_23 Shaftsburry 180 D14 Tarragindi S_24 Esher and Birdwood 180 J13 Tarragindi S_25 Messines Rd / Ekibin Creek 200 M1 Tarragindi Holland Park S_26 Lawn Street / Glindemann Park 180 Q15 West Holland Park S_27 Bapaume / Sterculia St 180 L17 West

174 BCNC S_ID Location UBD Suburb S_28 Cnr Turnmill / Swanfield Streets MacGregor 201 D16 MacGregor S_29 Prebble / Greenwood Streets Wishart 201 P12 Wishart S_30 Disused Tannery off Cribb Rd Mansfield 181 P14 Mansfield

175 Table A1.2 Site Location descriptions for studies in Chapters 4 and 5 of the Lower Brisbane River and surrounding coastal catchments (LBRCSCC)

LBRCSCC LBRCSCC Land Cover Connectivity LWHA Site ID Site ID Site Code UBD Location Suburb Waterway Catchment Greater Catchment Banksia Drive Upper Brisbane River S_01 S_01 KH01 174 M15 (Platypus Pk) Karana Downs Camerons Ck Kholo Creek Catchment Lake Manchestor Upper Brisbane River S_02 S_02 KH02 174 L6 Dve Mt Crosby Kholo Ck Kholo Creek Catchment Upper Brisbane River S_03 n/a PL01 176 D10 White Cedar Rd Pullenvale Pullen Pullen Ck Pullen Pullen Ck Catchment Upper Brisbane River S_04 S_03 PL02 196 D8 Kangaroo Gully Rd Bellbowrie Kangaroo Gully Pullen Pullen Ck Catchment Moggill Creek S_05 S_04 M01 136 R16 Jones Rd Brookfield Gold Creek Moggill Creek Catchment Cubberla Ck Brisbane River S_06 S_05 CB01 177 R11 Reserve (Sutling St) Chapel Hill Cubberla Ck Cubberla Ck Estuary Catchment Brisbane River S_07 S_06 SI01 179 A6 Indooroopilly Rd Indooroopilly Sandy Ck Sandy Ck Estuary Catchment Solferino Place S_08 S_07 B02 137 L17 (Bromwich St) The Gap Fish Ck Enoggera Ck Enoggera Creek S_09 S_08 B03 138 B20 Walton Bridge The Gap Enoggera Ck Enoggera Ck Enoggera Creek S_10 S_09 B04 158 H1 Greenlanes Rd Ashgrove Enoggera Ck Enoggera Ck Enoggera Creek S_11 S_10 B05 139 E19 Tennis Ave Ashgrove Enoggera Ck Enoggera Ck Enoggera Creek Kedron Brook Cabbage Tree Creek S_12 n/a K04 140 J3 Kalinga Park Clayfield Kedron Brook Kedron Brook Nundah Creek Kedron Brook Viney St (Raven St Cabbage Tree Creek S_13 S_11 N01 119 G15 Reserve) Stafford Heights Downfall Ck Nundah Ck Nundah Creek Kedron Brook Cabbage Tree Creek S_14 S_12 N02 119 Q 11 Brentwick St Chermside Downfall Ck Nundah Ck Nundah Creek Kedron Brook Parthenia St (Frank Cabbage Tree Creek S_15 S_13 N03 120 L3 Sleeman Park) Boondall Zillman Waterwholes Nundah Ck Nundah Creek Horizon Dr (Peter Upper Brisbane River S_16 S_14 MO01 197 E7 Lightfoot Oval) Middle Park Unnamed Ck Mt Ommaney Ck Catchment Upper Brisbane River S_17 S_15 MO02 197 J7 Dandenong Rd Mt Ommaney Mt Ommaney Ck Mt Ommaney Ck Catchment Brisbane River S_18 S_16 J01 197 P2 Capitol Dr Darra Jindalee Ck Jindalee Ck Estuary Catchment Brisbane River S_19 S_17 J02 198 B9 Edenbrook Dr Darra Jindalee Ck Jindalee Ck Estuary Catchment Trib Stable Swamp Oxley Creek S_20 S_18 OX01 200 A17 Gay St Acacia Ridge Ck Oxley Ck Catchment Oxley Creek S_21 S_19 OX02 240 A2 Nottingham Rd Algestor Sheep Station Gully Oxley Ck Catchment Bulimba Creek S_22 S_20 BM01 201 B19 Mothar St 8 Mile Plains Bulimba Ck Bulimba Ck Catchment Bulimba Creek S_23 S_21 BM02 201 K12 Delavan St Wishart Bulimba Ck Bulimba Ck Catchment Tributary of Bulimba Bulimba Creek S_24 S_22 BM03 182 D16 Cribb Rd Carindale Ck Bulimba Ck Catchment Brisbane Koala Bushland (Alperton Tributary of Priest Tingalpa Creek S_25 S_23 T03 202 R18 Road) Burbank Gully Tingalpa Ck Catchment Tributary of Tingalpa Tingalpa Creek S_26 n/a T04 183 N10 Sunnydene Rd Tingalpa Ck Tingalpa Ck Catchment Lota Creek S_27 S_24 LT01 163 H18 Rickertt Rd Wakerley Lota Ck Lota Ck Catchment Lota Creek S_28 S_25 LT02 163 E17 Tilley Rd Wakerley Tributary of Lota Ck Lota Ck Catchment Wynnum Creek S_29 S_26 W01 163 D9 Tantani St Manly West Wynnum Ck Wynnum Ck Catchment Wynnum Creek S_30 S_27 W02 163 E7 Wondall Rd Wynnum West Wynnum Ck Wynnum Ck Catchment Wynnum Creek S_31 n/a W03 143 G19 Tingal Road Wynnum Wynnum Ck Wynnum Ck Catchment Norman Creek S_32 S_28 NM01 180 N1 Wembley Park Camp Hill Bridgewater Ck Norman Ck Catchment Norman Creek S_33 S_29 NM02 180 D14 Shaftesbury St Tarragindi Sandy Ck Norman Ck Catchment

176 Appendix 2 Stream health and water quality indicators for studies in Chapters 3, 4 and 5 a) Indicator Category Chapter 3 Chapter 4 Chapter 5 Unit Description # families belonging to three ecologically sensitive orders: Plecoptera PET_S n/a n/a Count (stoneflies), Ephemeroptera (mayflies), and Trichoptera (caddisflies) (Lenat 1988). Macro-invertebrates MacroRich_S n/a n/a Count Number of families found at a site (Resh et al. 1995). Index that assigns a score to aquatic invertebrate families based on their Score SIGNAL2_S SIGNAL2_L SIGNAL2_C tolerance/sensitivity to pollution. Scores range between 1 (most tolerant) (1-10) to 10 (most sensitive) (Chessman 2003).

n/a OE2010_L OE2010_C Fish assemblage O/E50, (for years 2010 and 2011), which is a Fish comparison of the species composition of the observed community and n/a n/a OE2011_C % the community predicted by a referential model (Kennard et al. 2006). Temp_Range_t_S n/a n/a °C Diel water temperature range Temp_Max_t_S n/a n/a °C Maximum diel water temperature DO_Min_t_S n/a n/a % Minimum % diel dissolved oxygen saturation Physical/ DO_Range_t_S n/a n/a mg L-1 Diel dissolved oxygen range Chemical Ability of water to carry an electrical charge based on the concentration Cond_t_S n/a n/a μS cm-1 of ions present in water pH_S n/a n/a N/A Concentration of free hydrogen ions [H+] in the water

177 b) EHMP Worst Case Chapter 3 Chapter 4 Chapter 5 EHMP Guideline Values (EHMP 2008) Scenario Values (EHMP Transformed Transformation 2008) PET_S n/a n/a >=4 >=0 n/a n/a MacroRich_S n/a n/a >=22 taxa found at a site >= 0 taxa found at a site n/a n/a SIGNAL2_S SIGNAL2_L SIGNAL2_C >=4 >=2.4 N None n/a OE2010_L OE2010_C n/a n/a OE2011_C Temp_Range_t_ n/a n/a <=4 N/A Y log(x+1) S Temp_Max_t_S n/a n/a <=22 N/A Y log(x) Different units >=20% saturation Note: 5-6ppm is sufficient for most species, <3 ppm is stressful DO_Min_t_S n/a n/a Different units NA Y sqrt(x+1) to most aquatic species, <2 is fatal to most aquatic species DO_Range_t_S n/a n/a Different units <=50% saturation Different units NA Y log(x+1) Cond_t_S n/a n/a <=400 <=1870 Y sqrt(x) pH min: 6.5 pH min: 4.5 pH_S n/a n/a N None pH max: 8.5 pH max: 10.5

178 Appendix 3 GIS-generated land-cover, land-use and landscape metrics for studies in Chapters 3, 4 and 5 As a guide to understanding the metric codes, the following rules can be assumed: (1) Land-cover categories (where relevant) are indicated in the first letter of each metric name (e.g. T_ReaRip_S). Land-cover categories include vegetative cover (V, tree and grass cover combined), grass cover (G), tree cover (T) and impervious surface (I). Additional land-cover categories for the effective riparian buffer metrics include piped channel (P) and combined impervious surface and piped channel (IP). (2) Weighting methods for land-cover metrics (explained in Chapter 3) include: (a) lumped (upstream sub-catchment (SubCatch)), (b) threshold (reach-scale riparian (ReaRip) and catchment-scale riparian (Rip), and (b) inverse distance weighted (IDW) (using the Euclidean distance to the stream (EucStream) and the site (EucSite), the flow-path distance to the stream (FlowStream) and to the site (FlowSite), and the exponential distance to the stream (ExpStream). (3) A capital “S” at the end of the metric code is used for all candidate explanatory metrics in the small-scale study of Chapter 3. A capital “L” at the end indicates the larger-scale study of Chapter 4. A capital “C” at the end indicates the metric is considered in Chapter 5, which focuses on ecological connectivity. (4) Other additional metrics are also defined below, including land-use metrics (population density), landscape metrics (including catchment extent (all chapters) and tributary extent (Chapter 5)), in-stream ecological connectivity metrics (Chapter 5) and surrounding tree-cover fragmentation metrics (Chapter 5). Chapter 3 Chapter 4 Chapter 5 Metric Metric type Name Description metric code metric code metric code scale Reach scale Lumped land Reach-scale impervious surface Percentage impervious surface in riparian buffer* I_ReaRip_S I_ReaRip_L I_ReaRip_C cover 30 m either side of stream** network, for a distance 200 m upstream (lumped) Reach scale Lumped land Reach-scale tree cover Percentage tree cover in riparian buffer* 30 m T_ReaRip_S T_ReaRip_L T_ReaRip_C cover either side of stream** network, for a distance 200 m upstream (lumped) Reach scale Lumped land Reach-scale grass cover Percentage grass cover in riparian buffer* 30 m n/a G_ReaRip_L G_ReaRip_C cover either side of stream** network, for a distance 200 m upstream (lumped)

179 Chapter 3 Chapter 4 Chapter 5 Metric Metric type Name Description metric code metric code metric code scale Reach scale Lumped land Reach-scale vegetative cover Percentage vegetative cover (tree and grass cover cover combined) in riparian buffer* 30 m either side of V_ReaRip_S V_ReaRip_L V_ReaRip_C stream** network, for a distance 200 m upstream (lumped) Catchment Lumped land Lumped impervious surface in the sub- Percentage impervious land cover I_SubCatch_S I_SubCatch_L I_SubCatch_C scale cover catchment (concrete/roads/roofs/rock) in the upstream sub- catchment Catchment Lumped land Lumped tree cover in the sub-catchment Percentage tree cover in the upstream sub- n/a T_SubCatch_L T_SubCatch_C scale cover catchment Catchment Lumped land Lumped grass cover in the sub- Percentage grass cover in the upstream sub- n/a G_SubCatch_L G_SubCatch_C scale cover catchment catchment Catchment Lumped land Lumped vegetative cover in the sub- Percentage vegetative cover (grass and tree cover n/a V_SubCatch_L V_SubCatch_C scale cover catchment combined) in the upstream sub-catchment Local scale Distance- Impervious surface Euclidean inverse- Impervious surface weighted by inverse Euclidean I_EucSite_S n/a n/a weighted land distance weighted (IDW) to site distance to the site. cover Local scale Distance- Impervious surface flowpath inverse- Impervious surface weighted by inverse flowpath n/a I_FlowSite_L I_FlowSite_C weighted land distance weighted (IDW) to site distance to the site cover Local scale Distance- Tree cover flowpath inverse-distance Tree cover weighted by flowpath inverse distance n/a T_FlowSite_L T_FlowSite_C weighted land weighted (IDW) to site to the site cover Local scale Distance- Grass cover flowpath inverse-distance Grass cover weighted by flowpath inverse distance n/a G_FlowSite_L G_FlowSite_C weighted land weighted (IDW) to site to the site cover

180 Chapter 3 Chapter 4 Chapter 5 Metric Metric type Name Description metric code metric code metric code scale Local scale Distance- Vegetative cover flowpath inverse- Vegetative cover (tree and grass cover combined) n/a V_FlowSite_L V_FlowSite_C weighted land distance weighted (IDW) to site weighted by flowpath inverse distance to the site cover Catchment Distance- Impervious surface Euclidean inverse- Impervious surface weighted by inverse Euclidean I_EucStream_S n/a n/a scale weighted land distance weighted (IDW) to stream distance to the stream** cover Catchment Distance- Impervious surface exponential Impervious surface weighted by exponentially I_ExpStream_S n/a n/a scale weighted land flowpath inverse-distance weighted decaying distance along flowpaths to the stream** cover (IDW) to stream Catchment Distance- Impervious surface flowpath inverse- Impervious surface weighted by inverse flowpath I_FlowStream_S I_FlowStream_L I_FlowStream_C scale weighted land distance weighted (IDW) to stream distance to the stream** cover Catchment Distance- Tree cover flowpath inverse-distance Tree cover weighted by inverse flowpath distance n/a T_FlowStream_L T_FlowStream_C scale weighted land weighted (IDW) to stream to the stream** cover Catchment Distance- Grass cover flowpath inverse-distance Grass cover weighted by inverse flowpath distance n/a G_FlowStream_L G_FlowStream_C scale weighted land weighted (IDW) to stream to the stream** cover Catchment Distance- Vegetative cover flowpath inverse- Vegetative cover (tree and grass cover combined) n/a V_FlowStream_L V_FlowStream_C scale weighted land distance weighted (IDW) to stream weighted by inverse flowpath distance to the cover stream** Catchment Lumped land Impervious surface in the riparian buffer Percentage impervious surface in the upstream I_Rip_S I_Rip_L I_Rip_C scale cover / riparian riparian buffer* 30 m either side of stream** buffer Catchment Lumped land Vegetative cover in the riparian buffer Percentage vegetative cover (tree and grass cover V_Rip_S V_Rip_L V_Rip_C scale cover / riparian combined) in the entire mapped upstream riparian buffer buffer* 30 m either side of stream**

181 Chapter 3 Chapter 4 Chapter 5 Metric Metric type Name Description metric code metric code metric code scale Catchment Lumped land Tree cover in the riparian buffer Percentage tree cover in the entire mapped T_Rip_S T_Rip_L T_Rip_C scale cover / riparian upstream riparian buffer* 30 m either side of buffer stream** Catchment Lumped land Grass cover in the riparian buffer Percentage grass cover in the entire mapped n/a G_Rip_L G_Rip_C scale cover / riparian upstream riparian buffer* 30 m either side of buffer stream** Catchment Lumped land Impervious surface in the effective scale cover / in-stream riparian buffer Percentage impervious surface in the upstream ecological effective riparian buffer*** (which excludes piped I_EffRip_S I_EffRip_L I_EffRip_C connectivity / sections) 30 m either side of mapped stream effective riparian network buffer Catchment Lumped land Piping in the riparian buffer scale cover / in-stream Percentage piped channel**** in the upstream ecological P_EffRip_S P_EffRip_L P_EffRip_C riparian buffer* either side of mapped stream connectivity / network (an effective riparian buffer metric) effective riparian buffer Catchment Lumped land Combined piping and impervious scale cover / in-stream surface in the riparian buffer Percentage of upstream riparian buffer* 30 m either ecological side of the upstream stream** network which is IP_EffRip_S IP_EffRip_L IP_EffRip_C connectivity / designated as either impervious surface or piped effective riparian channel in the upstream mapped stream network buffer

182 Chapter 3 Chapter 4 Chapter 5 Metric Metric type Name Description metric code metric code metric code scale Catchment Lumped land Tree cover in the effective riparian scale cover / in-stream buffer Percentage tree cover in the upstream effective ecological T_EffRip_S T_EffRip_L T_EffRip_C riparian buffer*** (which excludes piped sections) connectivity / 30 m either side of mapped stream network effective riparian buffer Catchment Lumped land Grass cover in the effective riparian scale cover / in-stream buffer Percentage grass cover in the upstream effective ecological G_EffRip_S G_EffRip_L G_EffRip_C riparian buffer*** (which excludes piped sections) connectivity / 30 m either side of mapped stream network effective riparian buffer Catchment Lumped land Vegetative cover in the effective scale cover / in-stream riparian buffer Percentage vegetative cover (tree and grass cover ecological combined) in the upstream effective riparian V_EffRip_S V_EffRip_L V_EffRip_C connectivity / buffer*** (which excludes piped sections) 30 m effective riparian either side of mapped stream network buffer n/a Landscape Y-Coordinate Site Y-Coordinate in the Universal Transmercator Ycoord_S YCoord_L YCoord_C Projection (m) Catchment Catchment Upstream sub-catchment area USArea_S USArea_L USArea_C Upstream sub-catchment area (km2) scale extent Catchment Tributary extent Upstream tributary extent Upstream tributary extent determined as if there n/a n/a USNoBar_C scale were no anthropogenic barriers (m) Catchment Tributary extent Downstream tributary extent Downstream tributary extent, measured to estuary n/a n/a DSNoBar_C scale including all side streams, determined as if there were no anthropogenic barriers (m)

183 Chapter 3 Chapter 4 Chapter 5 Metric Metric type Name Description metric code metric code metric code scale Catchment Tributary extent Total tributary extent Total upstream and downstream tributary extent, n/a n/a TotNoBar_C scale determined as if there were no anthropogenic barriers (m) Catchment In-stream Extent of connected stream length Calculated as upstream mapped tributary extent n/a n/a USBar_C scale ecological upstream of site until anthropogenic barriers are reached (culverts connectivity and pipes) (m) Catchment In-stream Extent of connected stream length Calculated as downstream mapped tributary extent, scale ecological downstream of site measured to estuary and including all side streams, n/a n/a DSBar_C connectivity until anthropogenic barriers are reached (culverts and pipes) (m) Catchment In-stream Total extent of connected stream length Calculated as total (both upstream and downstream) scale ecological upstream and downstream of site mapped tributary extent from site including all n/a n/a TotBar_C connectivity tributaries and side streams until anthropogenic barriers are reached (culverts and pipes) (m) Catchment In-stream Ratio of extent of connected stream Ratio of (1) upstream mapped tributary extent to scale ecological length upstream of site to upstream n/a n/a USRatio_C anthropogenic barriers over (2) entire upstream connectivity tributary extent (USBar_C/ mapped tributary extent. USNoBar_C) Catchment In-stream Ratio of extent of connected stream Ratio of (1) downstream mapped tributary extent to scale ecological length downstream of site to n/a n/a DSRatio_C anthropogenic barriers over (2) entire downstream connectivity downstream tributary extent (DSBar_C/ mapped tributary extent. DSNoBar_C) Catchment In-stream Ratio of total connected stream extent to Ratio of (1) total (both upstream and downstream) n/a n/a TotRatio_C scale ecological total tributary extent (TotBar_C/ mapped tributary extent to anthropogenic barriers connectivity TotNoBar_C) over (2) total mapped tributary extent.

184 Chapter 3 Chapter 4 Chapter 5 Metric Metric type Name Description metric code metric code metric code scale n/a In-stream Connectivity to estuarine Connectivity to estuarine using categorical data n/a n/a EstConn_C ecological (Good = 3, Ok = 2, Bad = 1) connectivity n/a In-stream Number of downstream culverts Number of culverts at road crossings between site n/a n/a CulvRd_C ecological and main Brisbane River estuary channel connectivity Local scale Surrounding tree Total percentage surrounding tree cover cover Percentage tree cover in a 1 km radius around the n/a n/a C_CA_C fragmentation / site ecological connectivity Local scale Surrounding tree Fragmentation of surrounding tree cover Aggregation Index, a fragmentation metric cover calculated using FRAGSTATS (McGarigal et al. n/a n/a C_AI_C fragmentation / 2002), for tree cover in a 1 km radius around the ecological site connectivity Local scale Surrounding tree Total percentage surrounding tree cover cover in the riparian zone Percentage tree cover in the 30 m riparian buffer in n/a n/a R_CA_C fragmentation / a 1 km radius around the site ecological connectivity Local scale Surrounding tree Fragmentation of surrounding tree cover Aggregation Index, a fragmentation metric cover in the riparian zone calculated using FRAGSTATS (McGarigal et al. n/a n/a R_AI_C fragmentation / 2002), for tree cover in the 30 m riparian buffer in a ecological 1 km radius around the site connectivity

185 Chapter 3 Chapter 4 Chapter 5 Metric Metric type Name Description metric code metric code metric code scale Catchment Lumped land use Population density Average population density (number of people per scale hectare) from the 2006 Census (Australian Bureau PopDen_S PopDen_L PopDen_C of Statistics 2007) in the upstream catchment of the site

* Note: Although there are research questions relating to the role and function of the riparian zone, the land-cover metrics that represent riparian condition in this study are referred to as riparian buffer metrics. This terminology is necessary as the metrics relating to land cover in the riparian buffer are specifically defined in this study as the distance 30m either side of the stream channel. Another reason for this terminology is that the riparian buffer areas in this study may incorporate sections of impervious or piped land cover that would not be traditionally considered as parts of traditional riparian zones.

** Note: Stream is taken to mean the stream/piping network as stormwater pipes have replaced many streams

186 Appendix 4 Modelling urban drainage networks

The methods for generating the artificial stream network generally follow those of Gironás et al. (2010), in which piping, streams and other known drainage are burnt into the DEM, with streams burnt in at a greater distance than elements of the anthropogenic drainage. However, Gironás et al. (2010) also burnt road networks into the DEM which is not done in the current study and they burnt pipes and streams into the DEM by the same distance.

The DEM used in the current study has a resolution of 5 m. Mark et al. (2004) stated that a 5 m resolution DEM or finer offers sufficient accuracy to effectively represent the different man-made elements of the urban terrain in flood analysis. It should be noted that Gironás et al.’s (2010) 20 m resolution DEM would likely have greater inaccuracies than that used for the current study (5m DEM).

As well as considering DEM pixel size and burn distances, other issues to consider in generating artificial stream networks include: 1) availability and quality of data on actual pipe burial depths, 2) whether all anthropogenic drainage will be considered in the network, 3) whether there is an opportunity to field truth sections or an entire drainage area, or use photogrammetric processes or ortho-aerial photo interpretation to adjust mapped drainage, and 4) the purpose of the drainage network and what the study is investigating.

There is a trade-off between the accuracy of the sub-catchment boundaries and ensuring flow remains in known conduits. There is also a trade-off between time and effort to process and edit data. Gironás et al. (2010) applied and compared four methods for effectively representing urban flowpaths for stormwater modelling using a 20 m resolution DEM. These offer a selection of suitable methods for the generation of artificial drainage networks.

Important points relevant to the burn depths used in the current study include 1) selection of a suitable burn depth should force flow into the known pathways especially the altered pathways such as pipes which are not represented in surface DEMs, 2) lower burn depths are preferable for streams and pipes to avoid overestimation of watershed

187 areas and over-attribution of drainage network segments to points burnt lower at edges of watersheds.

Examples of other burn depths considered in the literature have included 50 m pipe and channel burn depths on a 25 m DEM (Lhomme et al. 2004), 0.1-0.15 m lowering of roads on a DEM (Hankin et al. 2008), and lowering of streets by 0.5 m on a 1 m DEM (Elgy et al. 1993). Burn depths of 2 m for roads and 5 m for streams and pipes resulted in overestimation of watershed areas whereas burn depths of 1 m for roads and 2 m for streams and pipes resulted in greater accuracy of watershed areas (Gironás et al. 2010). In the current study, burn depths of 2 m for streams and 1 m for pipes and stormwater drainage were used.

The DEM’s used to generate artificial drainage networks range from the 20 m resolution of Gironás et al. (2010) to much higher resolution such as that used by Kunapo et al. (2009) who used a DEM of 1m based on LiDAR data. However, while higher resolution data is desirable, data availability will depend on local resources.

188 Appendix 5 Calculating land-cover and land-use metrics using GIS techniques GIS techniques used to calculate the land-cover and land-use metrics assessed in Chapter 3 included Spatial Analyst, Raster Calculator and Zonal Statistics from ArcGIS 9.3 and 10.0. Metric category GIS methods Lumped All pixels within a specific land-cover or land-use type were counted and expressed as a ratio of the total pixels in the sub-catchment. Euclidean inverse-distance The Euclidean distance (d) of each pixel in a sub-catchment to the site or to weighted (IDW) the stream was determined using the Euclidean distance tool in Spatial

-1 Analyst. Wi = (d+1) was then calculated for each pixel in the sub- catchment (Figure 3.1). The Zonal Statistics tool in Spatial Analyst was used to sum the values for all pixels within the impervious surface and total areas respectively and the ratio was generated in Excel. Flowpath inverse-distance Similar to Euclidean IDW metrics except that d was calculated using the weighted (IDW) FlowLength function in Spatial Analyst based on the FlowDirection grid. In

addition, the cells that intersected the streams in the FlowDirection grid were changed to NoData prior to using the FlowLength tool so that the FlowLengths were only taken to the stream network (Figure 3.1). Exponential decay inverse- Similar to those for the flowpath IDW except that the value for d was distance weighted (IDW) calculated using the exponential decay distance function (α =6, HDD=4.3) along the flowpaths to the stream. Flowlength was determined as for Flowpath IDW above. Riparian (areal) buffers The 30 m buffered areas (rounded end) along the drainage channel upstream for the whole sub-catchment were generated as vectors and converted to rasters and the Zonal Statistics tool was applied to calculate the area of land-cover types within them.

The reach-scale riparian buffer extending 200 m upstream from each site was generated manually based on the flowpath distance along the generated stream network using the Editor tools of ArcGIS. Effective riparian (areal) After the riparian buffer areas were determined (as above) for land-cover buffers types, the piped sections of channel were buffered by 30 m (flat ended buffering to minimise overestimation of extent) and categorised as a separate land use called “piping”. The tree, vegetation and impervious surface land cover areas in the “effective riparian buffers” were then calculated by subtracting the “piping” area from the surface land-cover metrics. −� The value α in the inverse-distance weighted (IDW) function ��(��) = (�� + 1) is difficult to envision but it can be related to the half decay distance (HDD) as follows: in the equation HDD = [(0.5)- 1/α -1] if α takes the value of 1 or 2 then HD = 1 or 0.4 distance units respectively (Van Sickle and Johnson 2008)

189 Appendix 6 A framework for guiding the management of urban stream health

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201 Appendix 7 Moran’s I index – spatial autocorrelation testing for Chapter 3 study Table A7.1 Moran’s I values for stream health and water quality indicators included in the Bulimba Creek and Norman Creek (BCNC) focus study in Chapter 3. This table lists the Moran’s I expected and observed values. The expected value of Moran’s I, E(I), is the value for a coefficient that would indicate no spatial correlation. If the observed I is greater than the E(I) then there is a clustered spatial pattern. If I = E(I) spatial pattern is random. If I

202 Appendix 8 Preliminary OLS regression for the Chapter 3 study

Table A8.1 Preliminary OLS regression for the Chapter 3 study This table includes the results of preliminary OLS regression of stream health and water quality indicators on candidate explanatory GIS metrics (land-cover, land-use and landscape metrics) used in the Bulimba Creek and Norman Creek (BCNC) study in Chapter 3. GIS metrics were generated using the artificial stream network that incorporates both surface and piped flow (Section 3.2.3)). GIS metrics were generated in ArcGIS 9.3 and 10.0. Stream health and water quality indicators were collected during April 2010. Candidate explanatory GIS metrics with values of p < 0.2 were considered for GLS model selection for the relevant stream health and water quality indicators. Metric SIGNAL2 SIGNAL2 SIGNAL2 Temp_Max Temp_Max Temp_Max Temp_Range Temp_Range Temp_Range _S _S _S _t_S _t_S _t_S _t_S _t_S _t_S p Adj R2 coeff p Adj R2 coeff p Adj R2 coeff I_ReaRip_S 0.04 0.112 -2.534 0.031 0.125 0.07 0.189 0.027 0.239 T_ReaRip_S 0.013 0.171 1.55 0.001 0.29 -0.052 0.046 0.104 -0.184 V_ReaRip_S 0.024 0.139 2.474 0.039 0.112 -0.06 0.215 0.021 -0.203 I_SubCatch_S 0.11 0.056 -2.42 0.099 0.062 0.066 0.161 0.036 0.307 I_EucSite_S 0.022 0.145 -3.711 0.051 0.098 0.085 0.19 0.027 0.316 I_EucStream_S 0.074 0.078 -2.53 0.131 0.047 0.057 0.154 0.038 0.294 I_ExpStream_S 0.064 0.086 -2.958 0.103 0.06 0.07 0.144 0.042 0.34 I_FlowStream_S 0.07 0.081 -2.861 0.102 0.06 0.069 0.145 0.041 0.334 I_Rip_S 0.049 0.101 -2.863 0.09 0.067 0.066 0.154 0.038 0.303 T_Rip_S 0.326 -9.0e-05 0.879 0.115 0.054 -0.037 0.107 0.058 -0.205 V_Rip_S 0.202 0.024 1.954 0.12 0.051 -0.063 0.211 0.022 -0.277 I_EffRip_S 0.078 0.075 -12.98 0.188 0.027 0.26 0.78 -0.033 -0.305 P_EffRip_S 0.033 0.122 -1.499 0.101 0.061 0.031 0.208 0.022 0.131

203 Metric SIGNAL2 SIGNAL2 SIGNAL2 Temp_Max Temp_Max Temp_Max Temp_Range Temp_Range Temp_Range _S _S _S _t_S _t_S _t_S _t_S _t_S _t_S p Adj R2 coeff p Adj R2 coeff p Adj R2 coeff IP_EffRip_S 0.051 0.098 -1.343 0.045 0.106 0.037 0.102 0.06 0.164 T_EffRip_S 0.118 0.053 1.188 0.097 0.063 -0.033 0.173 0.032 -0.15 G_EffRip_S 0.089 0.068 5.662 0.732 -0.031 -0.031 0.565 -0.023 0.282 V_EffRip_S 0.087 0.069 1.273 0.052 0.097 -0.038 0.12 0.051 -0.168 PopDen_S 0.022 0.145 -0.091 0.076 0.076 0.002 0.036 0.117 0.012 Ycoord_S 0.661 -0.029 22.83 0.941 -0.036 -0.102 0.986 -0.036 0.131 USArea_S 0.389 -0.008 0.0005 0.341 -0.002 -1.4e-05 0.769 -0.032 -2.0e-05

204

Metric DO_ DO_ DO_ DO_ DO_ DO_ Cond _t_S Cond _t_S Cond _t_S Min_t_S Min_t_S Min_t_S Range Range Range _t_S _t_S _t_S p Adj R2 coeff p Adj R2 coeff p Adj R2 coeff I_ReaRip_S 0.153 0.038 0.926 0.36 -0.005 0.242 0.387 -0.008 8.824 T_ReaRip_S 0.119 0.052 -0.519 0.181 0.03 -0.181 0.828 -0.034 -1.151 V_ReaRip_S 0.158 0.037 -0.822 0.295 0.005 -0.249 0.386 -0.008 -7.955 I_SubCatch_S 0.103 0.059 1.268 0.85 -0.034 0.061 0.36 -0.005 -11.294 I_EucSite_S 0.086 0.07 1.463 0.974 -0.036 0.012 0.583 -0.024 -7.448 I_EucStream_S 0.093 0.065 1.228 0.78 -0.033 0.084 0.366 -0.005 -10.49 I_ExpStream_S 0.048 0.101 1.618 0.646 -0.028 0.157 0.379 -0.007 -11.55 I_FlowStream_S 0.057 0.092 1.54 0.689 -0.03 0.135 0.372 -0.006 -11.55 I_Rip_S 0.093 0.065 1.268 0.679 -0.029 0.13 0.497 -0.018 -8.152 T_Rip_S 0.016 0.161 -1.064 0.313 0.001 -0.188 0.477 -0.017 8.84 V_Rip_S 0.017 0.159 -1.824 0.436 -0.013 -0.251 0.534 -0.021 4.493 I_EffRip_S 0.062 0.088 7.069 0.553 -0.023 0.934 0.351 -0.003 -56.36 P_EffRip_S 0.471 -0.016 0.271 0.645 -0.028 0.07 0.814 -0.034 1.385 IP_EffRip_S 0.266 0.01 0.404 0.409 -0.01 0.122 0.864 -0.035 0.981 T_EffRip_S 0.078 0.075 -0.687 0.403 -0.01 -0.134 0.93 -0.035 0.553 G_EffRip_S 0.011 0.182 4.223 0.722 -0.031 0.253 0.281 0.007 -29.2 V_EffRip_S 0.174 0.032 -0.527 0.317 0.001 -0.158 0.862 -0.035 -1.07 PopDen_S 0.495 -0.018 0.015 0.029 0.128 0.018 0.082 0.072 0.567 Ycoord_S 0.005 0.225 -70.74 0.967 -0.036 0.452 0.001 0.318 1284.7

205 Metric DO_ DO_ DO_ DO_ DO_ DO_ Cond _t_S Cond _t_S Cond _t_S Min_t_S Min_t_S Min_t_S Range Range Range _t_S _t_S _t_S p Adj R2 coeff p Adj R2 coeff p Adj R2 coeff USArea_S 0.135 0.045 0.0004 0.261 0.011 -0.0001 0.143 0.041 -0.006

206 Metric pH_S pH_S pH_S p Adj R2 coeff I_ReaRip_S 0.664 -0.029 0.225 T_ReaRip_S 0.81 -0.034 0.064 V_ReaRip_S 0.843 -0.034 -0.092 I_SubCatch_S 0.23 0.017 0.743 I_EucSite_S 0.156 0.037 0.957 I_EucStream_S 0.223 0.019 0.709 I_ExpStream_S 0.193 0.026 0.856 I_FlowStream_S 0.195 0.026 0.84 I_Rip_S 0.254 0.012 0.686 T_Rip_S 0.103 0.06 -1.002 V_Rip_S 0.033 0.122 -0.75 I_EffRip_S 0.463 -0.016 -2.242 P_EffRip_S 0.139 0.043 0.43 IP_EffRip_S 0.098 0.062 0.467 T_EffRip_S 0.05 0.099 -0.595 G_EffRip_S 0.229 0.017 1.642 V_EffRip_S 0.061 0.088 -0.562 PopDen_S 0.016 0.161 0.039 Ycoord_S 0.651 -0.028 9.55 USArea_S 0.378 -0.007 0.0002

207 Table A8.2 Preliminary OLS regression for the Chapter 3 study – comparison of results for drainage with and without piped flow Contrasting results of OLS regression of stream health and water quality indicators (defined in Appendix 2) on land-cover metrics (defined in Appendix 3) used in Chapter 3 generated from (a) the artificial stream network that incorporates both surface and piped flow (Section 3.2.3) and (b) the stream network that incorporates only surface flow. In general, results were best (marginally higher Adj R2 values) for land-cover metrics based on drainage incorporating surface and piped flow. GIS metrics were generated in ArcGIS 9.3 and 10.0. Stream health and water quality indicators were collected during April 2010. Cases where adjusted R2 is greater than 0.1 are highlighted. DO_Min_S and Temp_Max_S were not transformed in this early analysis and therefore results differ from those for DO_Min_t_S and Temp_Max_t_S. (a) Artificial stream (b) Stream network network incorporates both incorporates only surface surface and piped flow flow Stream Candidate Coefficient Adj R2 Coefficient Adj R2 health/water explanatory quality indicator variable * Adj R2 ≥0.1 DO_Min_S I_SubCatch_S 4.6828 0.05536 4.5481 0.05214 I_EucSite_S 5.3153 0.06201 5.3419 0.06367 I_EucStream_S 4.5404 0.06109 4.1217 0.04144 I_ExpStream_S 5.8941 0.09173 4.8610 0.04867 I_FlowStream_S 5.631 0.08408 4.6399 0.04293 I_Rip_S 4.5870 0.05711 3.9834 0.03742 T_Rip_S* -3.8494 0.1448 -3.734 0.1363 V_Rip_S* -6.525 0.1394 -5.707 0.1052 I_ReaRip_S 3.3150 0.03102 2.5642 0.003567 T_ReaRip_S -1.6344 0.02508 -1.5419 0.019 V_ReaRip_S -2.857 0.02568 -2.274 0.002434

208 (a) Artificial stream (b) Stream network network incorporates both incorporates only surface surface and piped flow flow Stream Candidate Coefficient Adj R2 Coefficient Adj R2 health/water explanatory quality indicator variable * Adj R2 ≥0.1 Temp_Max_S I_SubCatch_S 3.3633 0.05554 3.2812 0.05311 I_EucSite_S 4.3292 0.09021 4.2647 0.08733 I_EucStream_S 2.875 0.03968 3.1103 0.04963 I_ExpStream_S 3.5175 0.05245 3.3880 0.04391 I_FlowStream_S 3.4762 0.05297 3.4604 0.04926 I_Rip_S 3.3231 0.05892 3.2811 0.06068 T_Rip_S -1.880 0.04789 -1.9040 0.05115 V_Rip_S -3.168 0.04447 -3.133 0.04677 I_ReaRip_S* 3.6119 0.1182 3.0074 0.06925 T_ReaRip_S* -2.6629 0.2778 -2.4624 0.2354 V_ReaRip_S* -3.102 0.1048 -2.612 0.06201 SIGNAL2_S I_SubCatch_S -2.4195 0.05633 -2.3820 0.05552 I_EucSite_S* -3.7110 0.1446 -3.675 0.1424 I_EucStream_S -2.5304 0.07811 -2.6407 0.08418 I_ExpStream_S -2.9575 0.08576 -3.1075 0.09484 I_FlowStream_S -2.861 0.08138 -3.0145 0.08996 I_Rip_S* -2.8625 0.1011 -2.8292 0.104

209 (a) Artificial stream (b) Stream network network incorporates both incorporates only surface surface and piped flow flow Stream Candidate Coefficient Adj R2 Coefficient Adj R2 health/water explanatory quality indicator variable * Adj R2 ≥0.1 T_Rip_S 0.8789 -8.956e-05 0.8649 -0.0007828 V_Rip_S 1.954 0.02376 1.980 0.02845 I_ReaRip_S* -2.5338 0.1119 -2.3374 0.08786 T_ReaRip_S* 1.5496 0.1712 1.4791 0.1549 V_ReaRip_S* 2.4744 0.1386 2.2803 0.1095

210 Appendix 9 Spearman’s rank correlation coefficients for candidate explanatory variables in Chapters 3, 4 and 5

Table A9.1 Chapter 3 land-cover and land-use candidate explanatory variables - Spearman’s Correlation statistics

T_ReaRip_S V_ReaRip_S G_ReaRip_S I_ReaRip_S I_SubCatch_S I_EucSite_S

T_ReaRip_S 1 0.87 -0.90 -0.81 -0.48 -0.59 V_ReaRip_S 0.87 1 -0.66 -0.92 -0.35 -0.47 G_ReaRip_S -0.90 -0.66 1 0.59 0.49 0.55 I_ReaRip_S -0.81 -0.92 0.59 1 0.34 0.45 I_SubCatch_S -0.48 -0.35 0.49 0.34 1 0.96 I_EucSite_S -0.59 -0.47 0.55 0.45 0.96 1 I_EucStream_S -0.50 -0.38 0.48 0.38 0.98 0.96 I_ExpStream_S -0.51 -0.38 0.49 0.38 0.96 0.95 I_FlowStream_S -0.53 -0.38 0.50 0.39 0.97 0.95 T_Rip_S 0.44 0.32 -0.41 -0.33 -0.88 -0.82 V_Rip_S 0.50 0.36 -0.45 -0.32 -0.92 -0.90 I_Rip_S -0.53 -0.40 0.50 0.40 0.97 0.96 I_EffRip_S -0.35 -0.34 0.24 0.30 0.28 0.32 T_EffRip_S 0.45 0.25 -0.47 -0.29 -0.90 -0.86 V_EffRip_S 0.50 0.37 -0.51 -0.41 -0.92 -0.89 P_EffRip_S -0.44 -0.27 0.46 0.36 0.83 0.81 G_EffRip_S 0.18 0.27 -0.11 -0.30 -0.09 -0.15

211

T_ReaRip_S V_ReaRip_S G_ReaRip_S I_ReaRip_S I_SubCatch_S I_EucSite_S

IP_EffRip_S -0.49 -0.36 0.50 0.43 0.92 0.90 PopDen_S -0.40 -0.32 0.40 0.44 0.63 0.62 USArea_S 0.00 0.07 0.08 -0.21 0.22 0.19

I_EucStream_S I_ExpStream_S I_FlowStream_S T_Rip_S V_Rip_S I_Rip_S T_ReaRip_S -0.50 -0.51 -0.53 0.44 0.50 -0.53 V_ReaRip_S -0.38 -0.38 -0.38 0.32 0.36 -0.40 G_ReaRip_S 0.48 0.49 0.50 -0.41 -0.45 0.50 I_ReaRip_S 0.38 0.38 0.39 -0.33 -0.32 0.40 I_SubCatch_S 0.98 0.96 0.97 -0.88 -0.92 0.97 I_EucSite_S 0.96 0.95 0.95 -0.82 -0.90 0.96 I_EucStream_S 1 0.99 0.99 -0.91 -0.94 0.99 I_ExpStream_S 0.99 1 1.00 -0.93 -0.95 0.99 I_FlowStream_S 0.99 1.00 1 -0.92 -0.94 0.99 T_Rip_S -0.91 -0.93 -0.92 1 0.95 -0.91 V_Rip_S -0.94 -0.95 -0.94 0.95 1 -0.94 I_Rip_S 0.99 0.99 0.99 -0.91 -0.94 1 I_EffRip_S 0.30 0.36 0.36 -0.28 -0.29 0.35 T_EffRip_S -0.90 -0.89 -0.89 0.88 0.87 -0.89 V_EffRip_S -0.89 -0.87 -0.87 0.83 0.84 -0.88

212 I_EucStream_S I_ExpStream_S I_FlowStream_S T_Rip_S V_Rip_S I_Rip_S P_EffRip_S 0.81 0.79 0.79 -0.72 -0.72 0.81 G_EffRip_S -0.05 0.00 -0.03 -0.21 -0.12 -0.05 IP_EffRip_S 0.90 0.88 0.89 -0.81 -0.81 0.90 PopDen_S 0.64 0.62 0.63 -0.57 -0.56 0.64 USArea_S 0.20 0.21 0.21 -0.22 -0.24 0.17

I_EffRip_S T_EffRip_S V_EffRip_S P_EffRip_S G_EffRip_S IP_EffRip_S T_ReaRip_S -0.35 0.45 0.50 -0.44 0.18 -0.49 V_ReaRip_S -0.34 0.25 0.37 -0.27 0.27 -0.36 G_ReaRip_S 0.24 -0.47 -0.51 0.46 -0.11 0.50 I_ReaRip_S 0.30 -0.29 -0.41 0.36 -0.30 0.43 I_SubCatch_S 0.28 -0.90 -0.92 0.83 -0.09 0.92 I_EucSite_S 0.32 -0.86 -0.89 0.81 -0.15 0.90 I_EucStream_S 0.30 -0.90 -0.89 0.81 -0.05 0.90 I_ExpStream_S 0.36 -0.89 -0.87 0.79 0.00 0.88 I_FlowStream_S 0.36 -0.89 -0.87 0.79 -0.03 0.89 T_Rip_S -0.28 0.88 0.83 -0.72 -0.21 -0.81 V_Rip_S -0.29 0.87 0.84 -0.72 -0.12 -0.81 I_Rip_S 0.35 -0.89 -0.88 0.81 -0.05 0.90 I_EffRip_S 1 -0.28 -0.04 0.10 0.15 0.09 T_EffRip_S -0.28 1 0.86 -0.91 0.02 -0.87

213 I_EffRip_S T_EffRip_S V_EffRip_S P_EffRip_S G_EffRip_S IP_EffRip_S V_EffRip_S -0.04 0.86 1 -0.90 0.17 -0.99 P_EffRip_S 0.10 -0.91 -0.90 1 -0.26 0.92 G_EffRip_S 0.15 0.02 0.17 -0.26 1 -0.21 IP_EffRip_S 0.09 -0.87 -0.99 0.92 -0.21 1 PopDen_S -0.09 -0.60 -0.77 0.65 -0.10 0.77 USArea_S 0.38 -0.16 0.00 -0.07 0.58 -0.03

PopDen_S USArea_S T_ReaRip_S -0.40 0.00 V_ReaRip_S -0.32 0.07 G_ReaRip_S 0.40 0.08 I_ReaRip_S 0.44 -0.21 I_SubCatch_S 0.63 0.22 I_EucSite_S 0.62 0.19 I_EucStream_S 0.64 0.20 I_ExpStream_S 0.62 0.21 I_FlowStream_S 0.63 0.21 T_Rip_S -0.57 -0.22 V_Rip_S -0.56 -0.24 I_Rip_S 0.64 0.17 I_EffRip_S -0.09 0.38

214 PopDen_S USArea_S T_EffRip_S -0.60 -0.16 V_EffRip_S -0.77 0.00 P_EffRip_S 0.65 -0.07 G_EffRip_S -0.10 0.58 IP_EffRip_S 0.77 -0.03 PopDen_S 1 -0.12 USArea_S -0.12 1

215 Table A9.2 Chapter 4 land-cover and land-use candidate explanatory variables - Spearman’s Correlation statistics

I_ReaRip_L T_ReaRip_L G_ReaRip_L V_ReaRip_L I_SubCatch_L T_SubCatch_L G_SubCatch_L V_SubCatch_L I_ReaRip_L 1 -0.60 0.33 -0.95 0.48 -0.48 0.30 -0.47 T_ReaRip_L -0.60 1 -0.87 0.62 -0.55 0.66 -0.61 0.54 G_ReaRip_L 0.33 -0.87 1 -0.32 0.46 -0.55 0.55 -0.42 V_ReaRip_L -0.95 0.62 -0.32 1 -0.52 0.56 -0.40 0.56 I_SubCatch_L 0.48 -0.55 0.46 -0.52 1 -0.88 0.63 -0.95 T_SubCatch_L -0.48 0.66 -0.55 0.56 -0.88 1 -0.87 0.90 G_SubCatch_L 0.30 -0.61 0.55 -0.40 0.63 -0.87 1 -0.63 V_SubCatch_L -0.47 0.54 -0.42 0.56 -0.95 0.90 -0.63 1 I_FlowSite_L 0.64 -0.67 0.53 -0.66 0.93 -0.85 0.62 -0.88 T_FlowSite_L -0.57 0.80 -0.66 0.64 -0.84 0.93 -0.82 0.84 G_FlowSite_L 0.26 -0.72 0.74 -0.32 0.54 -0.77 0.91 -0.52 V_FlowSite_L -0.62 0.69 -0.49 0.70 -0.90 0.88 -0.65 0.92 I_FlowStream_L 0.46 -0.52 0.43 -0.52 0.97 -0.91 0.66 -0.95 T_FlowStream_L -0.46 0.63 -0.55 0.55 -0.89 0.98 -0.81 0.93 G_FlowStream_L 0.38 -0.63 0.56 -0.50 0.77 -0.94 0.95 -0.79 V_FlowStream_L -0.46 0.53 -0.39 0.56 -0.93 0.92 -0.66 0.98 I_Rip_L 0.51 -0.54 0.45 -0.57 0.96 -0.91 0.67 -0.95 T_Rip_L -0.49 0.65 -0.55 0.57 -0.89 0.98 -0.82 0.92 G_Rip_L 0.32 -0.61 0.55 -0.43 0.69 -0.89 0.96 -0.70 V_Rip_L -0.49 0.55 -0.44 0.58 -0.93 0.92 -0.67 0.98

216 I_ReaRip_L T_ReaRip_L G_ReaRip_L V_ReaRip_L I_SubCatch_L T_SubCatch_L G_SubCatch_L V_SubCatch_L I_EffRip_L 0.13 -0.23 0.06 -0.29 0.61 -0.63 0.57 -0.60 T_EffRip_L -0.51 0.64 -0.57 0.57 -0.93 0.94 -0.76 0.92 G_EffRip_L -0.32 -0.06 0.03 0.19 -0.15 -0.17 0.43 0.09 V_EffRip_L -0.54 0.56 -0.48 0.59 -0.95 0.89 -0.66 0.95 IP_EffRip_L 0.55 -0.56 0.49 -0.59 0.96 -0.88 0.66 -0.93 P_EffRip_L 0.53 -0.54 0.49 -0.53 0.91 -0.84 0.65 -0.88 PopDen_L 0.54 -0.55 0.49 -0.55 0.80 -0.69 0.56 -0.73 USArea_L 0.09 0.06 -0.15 -0.07 -0.34 0.31 -0.11 0.37

I_FlowSite_L T_FlowSite_L G_FlowSite_L V_FlowSite_L I_FlowStream_L T_FlowStream_L G_FlowStream_L V_FlowStream_L I_ReaRip_L 0.64 -0.57 0.26 -0.62 0.46 -0.46 0.38 -0.46 T_ReaRip_L -0.67 0.80 -0.72 0.69 -0.52 0.63 -0.63 0.53 G_ReaRip_L 0.53 -0.66 0.74 -0.49 0.43 -0.55 0.56 -0.39 V_ReaRip_L -0.66 0.64 -0.32 0.70 -0.52 0.55 -0.50 0.56 I_SubCatch_L 0.93 -0.84 0.54 -0.90 0.97 -0.89 0.77 -0.93 T_SubCatch_L -0.85 0.93 -0.77 0.88 -0.91 0.98 -0.94 0.92 G_SubCatch_L 0.62 -0.82 0.91 -0.65 0.66 -0.81 0.95 -0.66 V_SubCatch_L -0.88 0.84 -0.52 0.92 -0.95 0.93 -0.79 0.98 I_FlowSite_L 1 -0.90 0.55 -0.96 0.91 -0.84 0.73 -0.86 T_FlowSite_L -0.90 1 -0.80 0.92 -0.84 0.91 -0.88 0.84 G_FlowSite_L 0.55 -0.80 1 -0.55 0.55 -0.73 0.86 -0.54 V_FlowSite_L -0.96 0.92 -0.55 1 -0.89 0.88 -0.76 0.91

217 I_FlowSite_L T_FlowSite_L G_FlowSite_L V_FlowSite_L I_FlowStream_L T_FlowStream_L G_FlowStream_L V_FlowStream_L I_FlowStream_L 0.91 -0.84 0.55 -0.89 1 -0.93 0.80 -0.96 T_FlowStream_L -0.84 0.91 -0.73 0.88 -0.93 1 -0.93 0.94 G_FlowStream_L 0.73 -0.88 0.86 -0.76 0.80 -0.93 1 -0.81 V_FlowStream_L -0.86 0.84 -0.54 0.91 -0.96 0.94 -0.81 1 I_Rip_L 0.90 -0.85 0.57 -0.89 0.98 -0.93 0.81 -0.96 T_Rip_L -0.85 0.92 -0.74 0.88 -0.92 0.99 -0.93 0.94 G_Rip_L 0.65 -0.84 0.89 -0.69 0.72 -0.87 0.98 -0.73 V_Rip_L -0.87 0.85 -0.56 0.90 -0.95 0.95 -0.82 0.98 I_EffRip_L 0.58 -0.61 0.39 -0.62 0.62 -0.61 0.57 -0.63 T_EffRip_L -0.87 0.90 -0.71 0.88 -0.90 0.94 -0.88 0.91 G_EffRip_L -0.17 -0.13 0.41 0.08 -0.09 -0.14 0.31 0.02 V_EffRip_L -0.89 0.84 -0.58 0.89 -0.93 0.90 -0.80 0.92 IP_EffRip_L 0.90 -0.84 0.58 -0.88 0.93 -0.89 0.80 -0.91 P_EffRip_L 0.87 -0.80 0.59 -0.83 0.88 -0.83 0.77 -0.84 PopDen_L 0.84 -0.76 0.53 -0.77 0.75 -0.68 0.65 -0.70 USArea_L -0.16 0.16 -0.17 0.18 -0.32 0.31 -0.15 0.34

I_Rip_L T_Rip_L G_Rip_L V_Rip_L I_EffRip_L T_EffRip_L G_EffRip_L V_EffRip_L I_ReaRip_L 0.51 -0.49 0.32 -0.49 0.13 -0.51 -0.32 -0.54 T_ReaRip_L -0.54 0.65 -0.61 0.55 -0.23 0.64 -0.06 0.56 G_ReaRip_L 0.45 -0.55 0.55 -0.44 0.06 -0.57 0.03 -0.48 V_ReaRip_L -0.57 0.57 -0.43 0.58 -0.29 0.57 0.19 0.59

218 I_Rip_L T_Rip_L G_Rip_L V_Rip_L I_EffRip_L T_EffRip_L G_EffRip_L V_EffRip_L I_SubCatch_L 0.96 -0.89 0.69 -0.93 0.61 -0.93 -0.15 -0.95 T_SubCatch_L -0.91 0.98 -0.89 0.92 -0.63 0.94 -0.17 0.89 G_SubCatch_L 0.67 -0.82 0.96 -0.67 0.57 -0.76 0.43 -0.66 V_SubCatch_L -0.95 0.92 -0.70 0.98 -0.60 0.92 0.09 0.95 I_FlowSite_L 0.90 -0.85 0.65 -0.87 0.58 -0.87 -0.17 -0.89 T_FlowSite_L -0.85 0.92 -0.84 0.85 -0.61 0.90 -0.13 0.84 G_FlowSite_L 0.57 -0.74 0.89 -0.56 0.39 -0.71 0.41 -0.58 V_FlowSite_L -0.89 0.88 -0.69 0.90 -0.62 0.88 0.08 0.89 I_FlowStream_L 0.98 -0.92 0.72 -0.95 0.62 -0.90 -0.09 -0.93 T_FlowStream_L -0.93 0.99 -0.87 0.95 -0.61 0.94 -0.14 0.90 G_FlowStream_L 0.81 -0.93 0.98 -0.82 0.57 -0.88 0.31 -0.80 V_FlowStream_L -0.96 0.94 -0.73 0.98 -0.63 0.91 0.02 0.92 I_Rip_L 1 -0.94 0.73 -0.98 0.58 -0.92 -0.10 -0.95 T_Rip_L -0.94 1 -0.88 0.95 -0.60 0.95 -0.14 0.90 G_Rip_L 0.73 -0.88 1 -0.74 0.52 -0.83 0.37 -0.73 V_Rip_L -0.98 0.95 -0.74 1 -0.60 0.93 0.03 0.94 I_EffRip_L 0.58 -0.60 0.52 -0.60 1 -0.54 0.40 -0.51 T_EffRip_L -0.92 0.95 -0.83 0.93 -0.54 1 0.01 0.97 G_EffRip_L -0.10 -0.14 0.37 0.03 0.40 0.01 1 0.19 V_EffRip_L -0.95 0.90 -0.73 0.94 -0.51 0.97 0.19 1 IP_EffRip_L 0.95 -0.89 0.72 -0.93 0.50 -0.96 -0.21 -1.00 P_EffRip_L 0.90 -0.84 0.72 -0.87 0.40 -0.93 -0.26 -0.97

219 I_Rip_L T_Rip_L G_Rip_L V_Rip_L I_EffRip_L T_EffRip_L G_EffRip_L V_EffRip_L PopDen_L 0.77 -0.70 0.61 -0.73 0.43 -0.79 -0.24 -0.83 USArea_L -0.33 0.27 -0.07 0.33 -0.10 0.34 0.16 0.37

IP_EffRip_L P_EffRip_L PopDen_L USArea_L I_ReaRip_L 0.55 0.53 0.54 0.09 T_ReaRip_L -0.56 -0.54 -0.55 0.06 G_ReaRip_L 0.49 0.49 0.49 -0.15 V_ReaRip_L -0.59 -0.53 -0.55 -0.07 I_SubCatch_L 0.96 0.91 0.80 -0.34 T_SubCatch_L -0.88 -0.84 -0.69 0.31 G_SubCatch_L 0.66 0.65 0.56 -0.11 V_SubCatch_L -0.93 -0.88 -0.73 0.37 I_FlowSite_L 0.90 0.87 0.84 -0.16 T_FlowSite_L -0.84 -0.80 -0.76 0.16 G_FlowSite_L 0.58 0.59 0.53 -0.17 V_FlowSite_L -0.88 -0.83 -0.77 0.18 I_FlowStream_L 0.93 0.88 0.75 -0.32 T_FlowStream_L -0.89 -0.83 -0.68 0.31 G_FlowStream_L 0.80 0.77 0.65 -0.15 V_FlowStream_L -0.91 -0.84 -0.70 0.34 I_Rip_L 0.95 0.90 0.77 -0.33

220 IP_EffRip_L P_EffRip_L PopDen_L USArea_L T_Rip_L -0.89 -0.84 -0.70 0.27 G_Rip_L 0.72 0.72 0.61 -0.07 V_Rip_L -0.93 -0.87 -0.73 0.33 I_EffRip_L 0.50 0.40 0.43 -0.10 T_EffRip_L -0.96 -0.93 -0.79 0.34 G_EffRip_L -0.21 -0.26 -0.24 0.16 V_EffRip_L -1.00 -0.97 -0.83 0.37 IP_EffRip_L 1 0.98 0.84 -0.37 P_EffRip_L 0.98 1 0.87 -0.31 PopDen_L 0.84 0.87 1 -0.04 USArea_L -0.37 -0.31 -0.04 1

221 Table A9.3 Chapter 5 land-cover and land-use candidate explanatory variables - Spearman’s Correlation statistics USNoBar_C DSNoBar_C TotNoBar_C USBar_C DSBar_C TotBar_C USNoBar_C 1 0.27 0.56 0.51 0.01 0.27 DSNoBar_C 0.27 1 0.92 0.36 0.25 0.31 TotNoBar_C 0.56 0.92 1 0.48 0.17 0.34 USBar_C 0.51 0.36 0.48 1 0.48 0.76 DSBar_C 0.01 0.25 0.17 0.48 1 0.89 TotBar_C 0.27 0.31 0.34 0.76 0.89 1 USRatio_C -0.24 0.14 0.00 0.60 0.31 0.43 DSRatio_C -0.24 -0.47 -0.51 0.09 0.70 0.51 TotRatio_C -0.30 -0.43 -0.50 0.23 0.69 0.61 EstConn_C 0.20 -0.35 -0.18 -0.03 0.22 0.28 CulvRd_C -0.15 0.30 0.12 -0.04 -0.23 -0.29 C_CA_C 0.06 0.27 0.25 0.53 0.34 0.46 C_AI_C 0.06 0.32 0.28 0.62 0.36 0.51 R_CA_C 0.14 0.30 0.31 0.52 0.29 0.44 R_AI_C 0.11 0.37 0.34 0.64 0.40 0.55 PopDen_C -0.11 -0.32 -0.30 -0.52 -0.47 -0.53 USArea_C 0.98 0.27 0.55 0.47 -0.01 0.23

USRatio_C DSRatio_C TotRatio_C EstConn_C CulvRd_C USNoBar_C -0.24 -0.24 -0.30 0.20 -0.15 DSNoBar_C 0.14 -0.47 -0.43 -0.35 0.30

222 USRatio_C DSRatio_C TotRatio_C EstConn_C CulvRd_C TotNoBar_C 0.00 -0.51 -0.50 -0.18 0.12 USBar_C 0.60 0.09 0.23 -0.03 -0.04 DSBar_C 0.31 0.70 0.69 0.22 -0.23 TotBar_C 0.43 0.51 0.61 0.28 -0.29 USRatio_C 1 0.13 0.38 -0.28 0.15 DSRatio_C 0.13 1 0.91 0.47 -0.42 TotRatio_C 0.38 0.91 1 0.43 -0.45 EstConn_C -0.28 0.47 0.43 1 -0.76 CulvRd_C 0.15 -0.42 -0.45 -0.76 1 C_CA_C 0.40 0.06 0.28 0.00 -0.10 C_AI_C 0.56 0.06 0.29 -0.08 -0.11 R_CA_C 0.31 0.00 0.19 0.01 -0.11 R_AI_C 0.53 0.06 0.26 -0.06 -0.13 PopDen_C -0.33 -0.21 -0.31 -0.02 0.05 USArea_C -0.29 -0.24 -0.31 0.20 -0.16

C_CA_C C_AI_C R_CA_C R_AI_C PopDen_C USArea_C USNoBar_C 0.06 0.06 0.14 0.11 -0.11 0.98 DSNoBar_C 0.27 0.32 0.30 0.37 -0.32 0.27 TotNoBar_C 0.25 0.28 0.31 0.34 -0.30 0.55 USBar_C 0.53 0.62 0.52 0.64 -0.52 0.47 DSBar_C 0.34 0.36 0.29 0.40 -0.47 -0.01

223 C_CA_C C_AI_C R_CA_C R_AI_C PopDen_C USArea_C TotBar_C 0.46 0.51 0.44 0.55 -0.53 0.23 USRatio_C 0.40 0.56 0.31 0.53 -0.33 -0.29 DSRatio_C 0.06 0.06 0.00 0.06 -0.21 -0.24 TotRatio_C 0.28 0.29 0.19 0.26 -0.31 -0.31 EstConn_C 0.00 -0.08 0.01 -0.06 -0.02 0.20 CulvRd_C -0.10 -0.11 -0.11 -0.13 0.05 -0.16 C_CA_C 1 0.92 0.96 0.92 -0.72 0.10 C_AI_C 0.92 1 0.90 0.98 -0.75 0.09 R_CA_C 0.96 0.90 1 0.91 -0.71 0.20 R_AI_C 0.92 0.98 0.91 1 -0.75 0.14 PopDen_C -0.72 -0.75 -0.71 -0.75 1 -0.13 USArea_C 0.10 0.09 0.20 0.14 -0.13 1

224 Appendix 10 Candidate explanatory metrics considered for GLS modelling for each stream health or water quality indicator in Chapter 3

Results from the preliminary OLS analysis (Appendix 8) formed the basis for inclusion of candidate explanatory land-cover, land-use and landscape metrics in a priori model sets. The a priori model set for each stream health indicator was derived from Tables A10.1 (a) and (b) below. The initial model used in GLS model fitting (Step 4, Appendix 11) for each of the stream health and water quality indicators was as follows:

SIGNAL2_S = T_ReaRip_S + I_EucSite_S TempRange_t_S = T_ReaRip_S + IP_EffRip_S Temp_Max_t_S = T_ReaRip_S + I_EucSite_S DOMin_t_S = T_ReaRip_S + G_EffRip_S + USArea_S + Ycoord_S DO_Range_t_S = T_ReaRip_S + PopDen_S Cond_t_S = PopDen_S + USArea_S + Ycoord_S pH = PopDen_S

In general the ANOVA p values indicated that the Gaussian correlation term was not necessary except for Cond_t_S, for which the p value was 0.068. Only the models in the a priori model set for Cond_t_S retained their correlation structure throughout model testing and selection.

Candidate explanatory variables in the initial models were systematically replaced with the eligible () covariates in their collinear sets or metric categories from Tables A10.1 (a) and (b).

225 Table A10.1 Candidate explanatory variables for each stream health or water quality indicator a) Metric Category Metric Sub- Correlated sets SIGNAL2_S Temp_ Temp_Ma Category and metrics of Range_t_S x_t_S similar scales Reach scale Reach-scale I_ReaRip_S metrics T_ReaRip_S V_ReaRip_S Catchment scale Lumped catchment-scale metric I_SubCatch_S Euclidean IDW to-site I_EucSite_S Euclidean IDW to-stream I_EucStream_S Exponentially decaying flowpath IDW to-stream I_ExpStream_S IDW flowpath to-stream I_FlowStream_S Upstream I_Rip_S riparian buffer V_Rip_S T_Rip_S Upstream I_EffRip_S effective P_EffRip_S riparian buffer IP_EffRip_S T_EffRip_S G_EffRip_S V_EffRip_S Population density PopDen_S Latitude Y-Coordinate Ycoord_S

Catchment Upstream sub- USArea_S extent catchment area

226 b)

Metric Category Metric Sub- Correlated sets DO_ DO_ Cond_t_ pH_S Category and metrics of Min_t_S Range_t_ S similar scales S Reach scale Reach-scale I_ReaRip_S metrics T_ReaRip_S V_ReaRip_S Catchment scale Lumped catchment-scale metric I_SubCatch_S Euclidean IDW to-site I_EucSite_S Euclidean IDW to-stream I_EucStream_S Exponentially decaying flowpath IDW to-stream I_ExpStream_S IDW flowpath to-stream I_FlowStream_S Upstream I_Rip_S riparian buffer V_Rip_S T_Rip_S Upstream I_EffRip_S effective P_EffRip_S riparian buffer IP_EffRip_S T_EffRip_S G_EffRip_S V_EffRip_S Population density PopDen_S Latitude Y-Coordinate Ycoord_S

Catchment Upstream sub- USArea_S extent catchment area

227 Appendix 11 Model selection procedure for stream health and water quality indicators The GLS model fitting and selection procedure was as follows:

Step 1

The distributions of the stream health and water quality indicators were analysed and transformations (Table A12.2) were considered and applied where necessary.

Step 2

Backwards stepwise regression with an initial GLS model including one covariate from each collinear set from Tables A10.1 (a) and (b) in Appendix 10 (the ones most strongly associated with the response variable (stream health and water quality indicator) according to OLS regression) was performed to determine if a reduced model was any better. Models were fit using the maximum likelihood (ML) method and included a Gaussian correlation structure. Nested models were compared using the Akaike Information Criterion (AIC) statistic since the AIC corrected for small samples (AICc) cannot be used to compare spatial models (personal communication Erin Peterson, CSIRO, 2010).

Step 3

The best model from step 2 was refitted with and without a correlation structure and restricted maximum likelihood (REML) was used for parameter estimation. An analysis of variance (ANOVA) determined whether the correlation structure was necessary.

Step 4

Once the best model from step 3 was identified, covariates were systematically replaced by all other covariates in their collinear sets (or metric categories). The models formulated in this step represented the final set of a priori hypotheses upon which inferences could be based. All models in this set were fitted using ML and compared using AIC.

228 Comparing models using AIC is an information-theoretic approach – AIC estimates the Kullback-Leibler (K-L) information loss for each model. Conceptually, K-L information is a “distance” between a model and full reality; it represents the information lost when a model is used to approximate reality (Burnham et al, 2011, Burnham and Anderson 2004). The best model in an a priori set is the one that minimises K-L information loss (smallest AIC). The first order AIC is formulated as:

AIC 2logLˆ | data 2K [A11.1] where θ is a vector of K regression parameters relating each health indicator to the candidate explanatory variables, Lˆ is the ML estimate of θ based on the candidate models and the data, K is the number of parameters to be estimated (Burnham and Anderson 2004). In GLS models K is equal to the number of parameters including the intercept plus one for the ML estimate. When individual AIC values are rescaled to

AICi AICi AIC min , the best model will have ΔAIC=0, and all other models will have positive values.

The likelihood of each model given the data is provided by the transformation exp(AICi / 2) . A weight of evidence statistic, wi, is created by normalising these values:

exp AICi / 2 wi R [A11.2] AICr / 2 r1

The weights within a set of models sum to one and the larger the weight the more evidence there is that a model is the best K-L model. If the weight is sufficiently large

(wi > 0.9) it is appropriate to make inference on this one best model (Burnham and Anderson 2002), but if it is small, model selection uncertainty exists and inference needs to be multimodel-based (Symonds and Moussalli 2011).

In this study, the best K-L model will be referred to as the “best model”. Support for the stream health models in this chapter is thus drawn from the AIC, AIC i and wi reported for each model in a set. In a small data set with only 30 sites, in a complex

229 landscape, reference can be made to comparative weights for different models in the set of equally plausible models (AIC < 2, as per the rule of thumb proposed by Burnham and Anderson (2004)). Jumps in AIC values can identify models with most support in the data (smaller AIC), and clustering of AIC values may highlight metrics that capture similar processes (personal communication Erin Peterson, CSIRO, 2010).

Step 5

Model averaging was applied to the candidate explanatory variables that appeared in the set of best models in step 4. When there is equivalent support in the observed data for multiple models, such that no single candidate model is clearly the “best”, model averaging can be applied to assess the relative importance of a particular stressor variable of interest (Johnson and Omland 2004). The weighted average of the parameter estimates across all models in the candidate stressor set and the “unconditional” standard error are used to construct confidence intervals. If the 95% confidence interval excludes zero, it can be concluded that the parameter estimate is different from zero (Mazerolle 2006), and therefore the independent variable has an effect on the dependent variable. When all models in the candidate set are considered, the averaged parameter estimate formula is:

̃̅ � ̂ � = ∑�=1 �� �� [A11.3]

where �̂� is the parameter estimate for the variable in a given model i and wi is the Akaike weight of that model, and R is the number of models in the set (Symonds and Moussalli 2011). The R package AICcmodavg (Mazerolle 2011) was used to calculate the AIC i , AIC i, wi, and log likelihood for each model in a set, and to conduct model averaging.

230 Appendix 12 Chapter 3 study summary statistics

Table A12.1 Chapter 3 summary statistics: water quality and stream health variables This table summarises the distributions of the untransformed stream health and water quality indicators (defined in Appendix 2) for the 30 sites of this study. Stream health/water 1st 3rd Minimum Median Mean Maximum quality Quartile Quartile indicator pH_S 5.49 6.35 6.545 6.487 6.75 7.05 Cond_S 161.4 340.5 505.5 640.4 927.8 1556 DO_Min_S 0 1.702 2.645 2.918 4.075 6.3 Temp_Max_S 20.6 21.22 22.05 22.23 22.8 25.8 DO_Range_S 0.42 1.165 1.985 2.405 2.645 7.67 Temp_Range_S 0.7 1.525 2.1 2.19 2.4 6.7 SIGNAL2_S 1.380 2.192 2.630 2.837 3.480 4.810

Table A12.2 Chapter 3 transformations used for water quality and stream health variables This table lists the transformations that were applied to the water quality and stream health data for the 30 sites prior to use in the GLS model testing. Stream Health/Water Quality Transformation Indicator pH_S None Cond_S sqrt(x) DO_Min_S sqrt(x+1) TempMax_S log(x) DO_Range_S log(x+1) TempRange_S log(x+1) SIGNAL2_S none

231 Table A12.3 Chapter 3 summary statistics for GIS-generated candidate explanatory metrics This table summarises the candidate explanatory land-cover, land-use and landscape variables that were generated for the 30 sites of this study. Land cover is based on 2005 DigitalGlobe Satellite Imagery. Drainage is based on 5 m DEM based on Airborne Laser Scanning (ALS) from 2002 as well as stream and stormwater drainage vector data provided by Brisbane City Council 2008. Population density data was obtained from the Australian Bureau of Statistics (Australian Bureau of Statistics 2007). Metric Metric Mini- 1st 3rd Maxi- Category mum Quartile Median Mean Quartile mum Reach I_ReaRip_S 0.000 0.002 0.043 0.097 0.144 0.642 V_ReaRip_S 0.319 0.808 0.934 0.870 0.982 1.000 T_ReaRip_S 0.157 0.474 0.685 0.659 0.899 0.998 Catchment I_SubCatch_S 0.054 0.161 0.287 0.259 0.349 0.450 I_EucSite_S 0.070 0.192 0.265 0.253 0.319 0.444 I_EucStream_S 0.026 0.156 0.268 0.244 0.328 0.462 I_ExpStream_S 0.026 0.134 0.235 0.208 0.282 0.406 I_FlowStream_S 0.029 0.138 0.245 0.218 0.295 0.412 I_Rip_S 0.028 0.169 0.267 0.241 0.318 0.453 V_Rip_S 0.504 0.650 0.678 0.709 0.750 0.929 T_Rip_S 0.193 0.337 0.384 0.456 0.547 0.887 I_EffRip_S 0.013 0.028 0.051 0.049 0.066 0.094 P_EffRip_S 0.000 0.336 0.563 0.504 0.660 0.914 IP_EffRip_S 0.041 0.352 0.587 0.535 0.676 0.942 T_EffRip_S 0.030 0.187 0.272 0.328 0.332 0.876 G_EffRip_S 0.024 0.053 0.082 0.093 0.115 0.226 V_EffRip_S 0.054 0.311 0.389 0.434 0.550 0.915 PopDen_S 2.611 14.070 15.420 15.170 17.200 21.830 USArea_S 0.130 0.512 0.775 2.192 1.724 12.918

232 Appendix 13 Analysis of preliminary OLS regression for the Chapter 3 study

SIGNAL2_S The reach-scale metrics (T_ReaRip_S (p=0.01, adj. R2=0.17), V_ReaRip_S (p=0.02), R2=0.14) and two of the effective riparian buffer metrics (P_EffRip_S (p=0.03, R2=0.12), IP_EffRip_S (p=0.05, R2=0.10)) had the most explanatory power of the riparian variables. I_EucSite_S was the most statistically significant of the catchment impervious land-cover metrics (p=0.02, R2=0.15), and equal to PopDen_S (p=0.02).

Physical / chemical water quality indicators

TempRange_t_S (transformed as log(TempRange+1)

Of all the land-cover variables, TempRange_t_S was best explained by T_ReaRip_S (p=0.05, R2 = 0.10). This result was similar to PopDen_S (p=0.04, R2 = 0.12). IP_EffRip_S, V_Rip_S and V_EffRip_S had the most explanatory power of the catchment impact metrics.

Temp_Max_t_S (transformed as log(Temp_Max)

Temp_Max_t_S did not vary much across sites (20.6° – 25.8°C). Reach-scale riparian metrics had the most explanatory power for Temp_Max_t_S, especially T_ReaRip_S (p=0.001, adj-R2 = 0.29). They were closely followed by the effective riparian buffer metrics IP_EffRip_S (p=0.05, R2=0.11) and V_EffRip_S (p=0.05, R2=0.10) and the inverse-distance weighted (IDW) catchment impervious land-cover metric I_EucSite_S (p=0.05, R2=0.10).

DO_Min_t_S (transformed as sqrt(DO_Min+1))

The effective riparian buffer metric G_EffRip_S (p=0.01, adj. R2=0.18) had greater explanatory power for DOMin_t_S than all other candidates except Ycoord_S (p=0.005, adj. R2=0.23). Generally catchment-scale land-cover metrics had more explanatory power than reach-scale metrics. However, all the coefficients of the catchment impervious measures are positive, indicating that DOMin_t_S is increasing as the levels of these urban stressors increase. The coefficient for G_EffRip_S is also positive.

233 USArea_S (p=0.14; adj. R2=0.05) was included in the a priori model set for DOMin_t_S.

DO_Range_t_S

Only two of the candidates met the criterion for inclusion in the a priori models for DO_Range_t_S; they were PopDen_S (p=0.03, adj. R2=0.13) and T_ReaRip_S (p=0.18, adj. R2=0.03). The coefficients for these metrics had the expected sign (positive for PopDen_S and negative for T_ReaRip_S).

Cond_t_S (transformed as sqrt(Cond)

Only three candidate explanatory variables could be included in the a priori model set for Cond_t_S: Ycoord_S (0.001, adj. R2=0.32); PopDen_S (p=0.10, adj. R2=0.07); and USArea_S (p=0.14, adj. R2=0.04). Cond_t_S did not have a detectable relationship with any of the land-cover metrics. pH pH did not vary much across the sites in this study (5.49 -7.05) and ranged from neutral to more acidic. The explanatory power of V_Rip_S (p=0.03, adj. R2=0.12) and T_EffRip_S (p=0.05, R2=0.12) for pH was greater than any of the other land-cover metrics and similar to PopDen_S (p=0.02, R2=0.16). Of the catchment-scale impervious surface metrics IP_EffRip_S (p=0.1) had the most explanatory power. None of the reach-scale metrics could be included in the set of a priori models for pH.

234 Appendix 14 Chapter 4 summary statistics

Table A14.1 Chapter 4 study summary statistics: macroinvertebrate and fish variables This table summarises the macroinvertebrate and fish based stream health indicators that were collected for the 33 sites of this study. 1st 3rd Indicator Minimum Quartile Median Mean Quartile Maximum SIGNAL2_L 2.14 2.80 3.32 3.38 4.03 4.76 OE2010_L 0.00 0.18 0.19 0.30 0.37 0.93

235 Table A14.2 Chapter 4 summary statistics for candidate explanatory GIS-generated metrics This table summarises the land-cover, land-use and landscape metrics that were generated in GIS for the 33 sites of this study. Land cover is based on 2005 DigitalGlobe Satellite Imagery. Drainage is based on 5 m DEM based on Airborne Laser Scanning (ALS) from 2002 as well as stream and stormwater drainage vector data provided by Brisbane City Council 2008. Population density data was obtained from the ABS (Australian Bureau of Statistics 2007). Metric Category Metric Minimum 1st Quartile Median Mean 3rd Quartile Maximum Reach I_ReaRip_L 0.00 0.00 0.03 0.11 0.19 0.64 T_ReaRip_L 0.08 0.29 0.71 0.58 0.82 1.00 G_ReaRip_L 0.00 0.10 0.23 0.26 0.39 0.91 V_ReaRip_L 0.27 0.76 0.93 0.85 0.98 1.00 I_SubCatch_L 0.00 0.07 0.27 0.23 0.38 0.48 Catchment T_SubCatch_L 0.16 0.23 0.40 0.48 0.71 0.92 G_SubCatch_L 0.07 0.19 0.26 0.24 0.32 0.43 V_SubCatch_L 0.46 0.57 0.67 0.72 0.90 1.00 I_FlowSite_L 0.00 0.10 0.23 0.22 0.35 0.50 T_FlowSite_L 0.16 0.28 0.44 0.46 0.66 0.83 G_FlowSite_L 0.13 0.17 0.25 0.26 0.32 0.49 V_FlowSite_L 0.44 0.60 0.69 0.73 0.83 1.00 I_FlowStream_L 0.00 0.05 0.20 0.19 0.30 0.46 T_FlowStream_L 0.16 0.29 0.42 0.51 0.77 0.95 G_FlowStream_L 0.04 0.12 0.26 0.25 0.33 0.48 V_FlowStream_L 0.47 0.64 0.72 0.76 0.90 1.00 I_Rip_L 0.00 0.06 0.22 0.21 0.34 0.51

236 Metric Category Metric Minimum 1st Quartile Median Mean 3rd Quartile Maximum T_Rip_L 0.16 0.26 0.43 0.50 0.75 0.96 G_Rip_L 0.04 0.14 0.25 0.23 0.33 0.47 V_Rip_L 0.43 0.60 0.69 0.74 0.91 1.00 I_EffRip_L 0.00 0.01 0.02 0.03 0.04 0.20 T_EffRip_L 0.02 0.10 0.24 0.39 0.75 0.95 G_EffRip_L 0.02 0.04 0.07 0.09 0.11 0.25 V_EffRip_L 0.06 0.21 0.33 0.47 0.82 1.00 P_EffRip_L 0.00 0.12 0.60 0.47 0.72 0.92 IP_EffRip_L 0.00 0.13 0.66 0.50 0.79 0.93 PopDen_L 0.35 5.38 10.24 10.95 15.95 24.15 USArea_L 0.20 0.44 0.72 2.12 1.54 13.57

237 Appendix 15 Preliminary OLS regression for the land-cover and land-use metrics in Chapters 4 and 5 This table includes the results of preliminary OLS regression for land-cover, land-use and landscape metrics used in both Chapters 4 and 5 (not including the newly developed ecological connectivity metrics of Chapter 5, which are presented in Appendix 18). The stream health indicators include SIGNAL2_L macroinvertebrate abundance (Chessman

2003), and OE2010_L and OE2011_L, fish assemblage O/E50, which is a comparison of the species composition of the observed community and the community predicted by a referential model (Kennard et al. 2006). Data sets are from April 2010 for SIGNAL2_L and OE2010_L. Data sets are from April 2011 for OE2011_L. GIS metrics were generated in ArcGIS 10.0. Stream health metrics were provided by Brisbane City Council. SIGNAL2_L SIGNAL2_L SIGNAL2_L OE2010_L OE2010_L OE2010_L OE2011_L OE2011_L OE2011_L Metric p Adj-R2 coeff p Adj-R2 coeff p Adj-R2 coeff I_ReaRip_L 0.04 0.10 -1.59 0.26 0.01 -0.27 0.70 -0.03 0.10 T_ReaRip_L 0.00 0.21 1.17 0.14 0.04 0.19 0.80 -0.03 0.03 G_ReaRip_L 0.02 0.15 -1.53 0.29 0.01 -0.21 0.50 -0.02 -0.13 V_ReaRip_L 0.06 0.08 1.29 0.22 0.02 0.26 0.73 -0.03 -0.08 I_SubCatch_L 0.00 0.24 -2.34 0.24 0.01 -0.29 0.23 0.02 -0.29 T_SubCatch_L 0.00 0.26 1.54 0.09 0.06 0.26 0.09 0.07 0.26 G_SubCatch_L 0.00 0.23 -3.65 0.07 0.08 -0.71 0.07 0.08 -0.70 V_SubCatch_L 0.00 0.22 2.13 0.16 0.03 0.33 0.15 0.04 0.32 I_FlowSite_L 0.01 0.19 -2.41 0.50 -0.02 -0.19 0.53 -0.02 -0.18 T_FlowSite_L 0.00 0.23 1.78 0.23 0.02 0.23 0.30 0.01 0.20 G_FlowSite_L 0.02 0.15 -3.31 0.22 0.02 -0.52 0.31 0.00 -0.43 V_FlowSite_L 0.03 0.14 1.97 0.37 0.01 0.24 0.42 -0.01 0.21 I_FlowStream_L 0.00 0.25 -2.82 0.26 0.01 -0.33 0.23 0.02 -0.34 T_FlowStream_L 0.00 0.27 1.50 0.14 0.04 0.22 0.11 0.06 0.24 G_FlowStream_L 0.00 0.26 -3.09 0.14 0.04 -0.46 0.08 0.07 -0.53

238 SIGNAL2_L SIGNAL2_L SIGNAL2_L OE2010_L OE2010_L OE2010_L OE2011_L OE2011_L OE2011_L Metric p Adj-R2 coeff p Adj-R2 coeff p Adj-R2 coeff V_FlowStream_L 0.00 0.22 2.43 0.17 0.03 0.36 0.17 0.03 0.35 I_Rip_L 0.00 0.24 -2.47 0.27 0.01 -0.28 0.27 0.01 -0.28 T_Rip_L 0.00 0.24 1.46 0.16 0.03 0.21 0.15 0.04 0.21 G_Rip_L 0.00 0.21 -3.16 0.16 0.03 -0.49 0.11 0.06 -0.55 V_Rip_L 0.00 0.21 2.16 0.21 0.02 0.30 0.23 0.02 0.28 I_EffRip_L 0.20 0.02 -4.42 0.19 0.02 -1.35 0.23 0.02 -1.33 T_EffRip_L 0.00 0.27 1.25 0.16 0.03 0.17 0.19 0.03 0.16 G_EffRip_L 0.82 -0.03 -0.50 0.46 -0.01 -0.49 0.39 -0.01 -0.59 V_EffRip_L 0.00 0.25 1.16 0.22 0.02 0.15 0.25 0.01 0.14 IP_EffRip_L 0.00 0.25 -1.14 0.25 0.01 -0.14 0.28 0.01 -0.13 P_EffRip_L 0.00 0.23 -1.13 0.31 0.00 -0.12 0.33 0.00 -0.12 PopDen_L 0.01 0.19 -0.05 0.69 -0.03 0.00 0.58 -0.02 0.00 USArea_L 0.00 0.33 0.00 0.00 0.62 0.00 0.00 0.27 0.00

239 Appendix 16 Candidate explanatory metrics considered for GLS modelling for stream health indicators in Chapter 4 Preliminary variables selected for GLS model testing for SIGNAL2_L and OE2010_L in Chapter 4. The initial model for each stream health indicator included one reach-scale metric, one catchment-scale metric and one catchment extent metric. Variables with p=0.20 in the preliminary OLS regressions () were included for testing in the GLS models.

Metric Category Metric Sub- Correlated sets and SIGNAL2_L OE2010_L Category metrics of similar scales Reach scale Reach-scale metrics I_ReaRip_L T_ReaRip_L G_ReaRip_L V_ReaRip_L Catchment scale Lumped catchment- I_SubCatch_L scale metric T_SubCatch_L G_SubCatch_L V_SubCatch_L Flowpath IDW to-site I_FlowSite_L T_FlowSite_L G_FlowSite_L V_FlowSite_L Flowpath IDW to- I_FlowStream_L stream T_FlowStream_L G_FlowStream_L V_FlowStream_L Upstream riparian I_Rip_L buffer T_Rip_L G_Rip_L V_Rip_L Upstream effective I_EffRip_L riparian buffer P_EffRip_L IP_EffRip_L T_EffRip_L G_EffRip_L V_EffRip_L Population density PopDen_L Catchment extent Upstream sub- USArea_L catchment area

240 Appendix 17 Chapter 5 summary statistics

Table A17.1 Chapter 5 summary statistics for macroinvertebrate and fish stream health indicators This table summarises the macroinvertebrate and fish abundance and diversity indicators that were collected for the 30 sites of this study. Stream Health 1st 3rd Indicator Minimum Quartile Median Mean Quartile Maximum SIGNAL2_C 2.14 2.81 3.43 3.43 4.10 4.76 OE2010_C 0.00 0.17 0.19 0.30 0.37 0.93 OE2011_C 0.00 0.17 0.22 0.29 0.40 0.68

241 Table A17.2 Chapter 5 summary statistics for candidate explanatory GIS-generated metrics This table summarises the land-cover, land-use and landscape metrics that were generated for the 30 sites of this study. Land cover is based on 2005 DigitalGlobe Satellite Imagery. Drainage is based on 5 m DEM based on Airborne Laser Scanning (ALS) from 2002 as well as stream and stormwater drainage vector data provided by Brisbane City Council 2008. Population density data was obtained from the ABS (Australian Bureau of Statistics 2007). Metric Metric category Minimum 1st Quartile Median Mean 3rd Quartile Maximum Reach I_ReaRip_C 0.00 0.00 0.04 0.11 0.17 0.64 T_ReaRip_C 0.08 0.30 0.71 0.59 0.81 1.00 G_ReaRip_C 0.00 0.10 0.22 0.26 0.40 0.91 V_ReaRip_C 0.27 0.76 0.93 0.85 0.98 1.00 I_SubCatch_C 0.00 0.07 0.30 0.24 0.38 0.48 Catchment T_SubCatch_C 0.16 0.24 0.37 0.48 0.73 0.92 G_SubCatch_C 0.07 0.15 0.27 0.24 0.32 0.43 V_SubCatch_C 0.46 0.57 0.66 0.72 0.90 1.00 I_FlowSite_C 0.00 0.11 0.24 0.23 0.35 0.50 T_FlowSite_C 0.16 0.28 0.45 0.47 0.67 0.83 G_FlowSite_C 0.13 0.17 0.25 0.26 0.32 0.49 V_FlowSite_C 0.44 0.60 0.71 0.73 0.85 1.00 I_FlowStream_C 0.00 0.05 0.22 0.19 0.30 0.46 T_FlowStream_C 0.16 0.29 0.42 0.52 0.78 0.95 G_FlowStream_C 0.04 0.11 0.27 0.24 0.33 0.48 V_FlowStream_C 0.47 0.64 0.73 0.77 0.92 1.00 I_Rip_C 0.00 0.06 0.24 0.22 0.34 0.51

242 Metric Metric category Minimum 1st Quartile Median Mean 3rd Quartile Maximum T_Rip_C 0.16 0.27 0.42 0.51 0.77 0.96 G_Rip_C 0.04 0.13 0.26 0.23 0.33 0.47 V_Rip_C 0.43 0.61 0.71 0.74 0.91 1.00 I_EffRip_C 0.00 0.01 0.02 0.03 0.04 0.20 T_EffRip_C 0.02 0.11 0.23 0.39 0.76 0.95 G_EffRip_C 0.02 0.04 0.06 0.08 0.10 0.25 V_EffRip_C 0.06 0.22 0.32 0.47 0.84 1.00 P_EffRip_C 0.00 0.12 0.62 0.48 0.71 0.92 IP_EffRip_C 0.00 0.13 0.66 0.51 0.78 0.93 PopDen_C 0.35 5.45 11.92 11.04 15.89 24.15 USArea_C 0.20 0.44 0.65 2.25 2.10 13.57 USNoBar_C 0.26 0.70 1.08 3.85 3.75 22.96 DSNoBar_C 0.15 1.53 5.29 8.55 11.11 33.24 TotNoBar_C 0.71 2.86 6.80 12.39 24.06 34.24 USBar_C 0.04 0.60 1.53 2.86 3.90 10.59 DSBar_C 0.02 0.72 2.07 6.48 6.23 39.41 TotBar_C 0.45 1.78 3.93 9.35 13.25 41.86 USRatio_C 0.01 0.04 0.08 0.19 0.17 1.00 DSRatio_C 0.00 0.02 0.08 0.26 0.17 2.81 TotRatio_C 0.00 0.03 0.07 0.14 0.15 0.82 EstConn_C 1.00 1.00 2.00 1.80 2.00 3.00

243 Metric Metric category Minimum 1st Quartile Median Mean 3rd Quartile Maximum CulvRd_C 0.00 1.00 1.00 2.00 2.75 7.00 C_CA_C 34.59 74.98 128.29 128.24 183.01 228.96 C_AI_C 59.17 68.15 76.18 76.70 88.00 93.91 R_CA_C 12.30 22.80 39.29 37.99 52.73 67.43 R_AI_C 60.65 70.97 77.84 77.92 87.30 91.27

244 Appendix 18 Preliminary OLS regression for the ecological connectivity metrics in Chapter 5 This table includes the results of preliminary OLS regression for the ecological connectivity metrics used in Chapter 5. The stream health metrics include SIGNAL2_C, macroinvertebrate abundance (Chessman 2003), and OE2010_C and OE2011_C, fish assemblage O/E50, which is a comparison of the species composition of the observed community and the community predicted by a referential model (Kennard et al. 2006). SIGNAL2_C and OE2010_C data sets are from April 2010. OE2011_C is from April 2011. GIS metrics were generated in ArcGIS 10.0. Stream health metrics were provided by Brisbane City Council. SIGNAL2_C SIGNAL2_C SIGNAL2_C OE2010_C OE2010_C OE2010_C OE2011_C OE2011_C OE2011_C Metric p R-squared coeff p R-squared coeff p R-squared coeff USNoBar_C 0.00 0.34 0.00 0.00 0.65 0.00 0.00 0.28 0.00 DSNoBar_C 0.03 0.12 0.00 0.05 0.10 0.00 0.08 0.08 0.00 TotNoBar_C 0.00 0.37 0.00 0.00 0.50 0.00 0.00 0.28 0.00 USBar_C 0.00 0.28 0.00 0.01 0.22 0.00 0.02 0.16 0.00 DSBar_C 0.24 0.01 0.00 0.43 -0.01 0.00 0.48 -0.02 0.00 TotBar_C 0.07 0.08 0.00 0.16 0.04 0.00 0.22 0.02 0.00 USRatio_C 0.95 -0.04 0.03 0.21 0.02 -0.19 0.41 -0.01 -0.12 DSRatio_C 0.06 0.09 -0.48 0.60 -0.03 -0.04 0.24 0.02 -0.09 TotRatio_C 0.23 0.02 -0.95 0.27 0.01 -0.27 0.27 0.01 -0.25 EstConn_C 0.65 -0.03 -0.09 0.29 0.01 0.06 0.86 -0.04 0.01 EstConn_C 0.84 -0.06 -0.22 0.30 -0.03 0.13 0.96 -0.07 0.01 (GOOD) EstConn_C 1.00 -0.06 0.00 0.56 -0.03 0.06 0.67 -0.07 0.04 (OK) CulvRd_C 0.64 -0.03 -0.04 0.33 0.00 -0.02 1.00 -0.04 0.00 C_CA_C 0.14 0.04 0.00 0.42 -0.01 0.00 0.71 -0.03 0.00

245 SIGNAL2_C SIGNAL2_C SIGNAL2_C OE2010_C OE2010_C OE2010_C OE2011_C OE2011_C OE2011_C Metric p R-squared coeff p R-squared coeff p R-squared coeff C_AI_C 0.05 0.10 0.02 0.31 0.00 0.00 0.71 -0.03 0.00 R_CA_C 0.04 0.11 0.02 0.12 0.05 0.00 0.50 -0.02 0.00 R_AI_C 0.02 0.16 0.03 0.14 0.04 0.01 0.02 0.16 0.03

246 Appendix 19 Candidate explanatory metrics considered for GLS modelling for stream health indicators in Chapter 5

The initial model for each stream health indicator included one reach-scale metric, one catchment-scale metric and one catchment extent metric. The metric category includes correlated sets and associated scale of processes. Selection of variables for consideration is based on p < 0.2 in preliminary OLS regression. Metric Metric Sub- SIGNAL2_C OE2010_C OE2011_C Category Category Metric Reach scale Reach-scale I_ReaRip_C metrics T_ReaRip_C G_ReaRip_C V_ReaRip_C Catchment Lumped I_SubCatch_C scale catchment-scale T_SubCatch_C metric G_SubCatch_C V_SubCatch_C Flowpath IDW to- I_FlowSite_C site T_FlowSite_C G_FlowSite_C V_FlowSite_C Flowpath IDW to- I_FlowStream_C stream T_FlowStream_C G_FlowStream_C V_FlowStream_C Upstream riparian I_Rip_C buffer T_Rip_C G_Rip_C V_Rip_C Upstream I_EffRip_C effective riparian P_EffRip_C buffer IP_EffRip_C T_EffRip_C G_EffRip_C V_EffRip_C Population density PopDen_C Catchment and C_CA_C riparian tree-cover C_AI_C fragmentation R_CA_C R_AI_C

247 Metric Metric Sub- SIGNAL2_C OE2010_C OE2011_C Category Category Metric In-stream USBar_C longitudinal DSBar_C connectivity TotBar_C USRatio_C DSRatio_C TotRatio_C Number of culverts EstConn_C downstream that go under roads CulvRd_C Catchment or Sub-catchment USArea_C tributary size or tributary extent extent USNoBar_C DSNoBar_C TotNoBar_C

248 Appendix 20 Best GLS and GLM models for Chapter 5

Table A20.1 Best GLS models for SIGNAL2_C, OE2010_C and OE2011_C Set of best models for each macroinvertebrate and fish diversity and abundance indictor (SIGNAL2_C, OE2010_C and OE2011_C) includes up to ten equally plausible models (ΔAIC < 2) or where there is less than ten equally plausible models, models with substantially less support (4 ≤ AIC ≤ 7) have been included. Appendix 11 provides more information on the AIC approach to model selection. Table presents: Akaike Information Criteria (AIC), change in AIC (ΔAICi), the weight of evidence statistic (ωi), and Log Likelihood for each model. Although the sample is small, the corrected Akaike Information Criteria (AICc) cannot be used with spatial models, therefore AIC was used throughout.

Model Log Explanatory variables AIC AIC i i order Likelihood

SIGNAL2_C 1 G_ReaRip_C, USBar_C, TotNoBar_C 49.73 0 0.13 -19.87 2 T_ReaRip_C, USBar_C, TotNoBar_C 52.65 2.92 0.03 -21.33 3 G_ReaRip_C, USBar_C, USNoBar_C 52.98 3.24 0.02 -21.49 4 G_ReaRip_C, USBar_C, USArea_C 53.17 3.44 0.02 -21.59 5 G_ReaRip_C, PopDen_C, TotNoBar_C 53.55 3.81 0.02 -21.77 6 T_ReaRip_C, USBar_C, USNoBar_C 53.59 3.86 0.02 -21.8 7 T_ReaRip_C, USBar_C, USArea_C 53.83 4.09 0.02 -21.91 8 G_ReaRip_C, IP_EffRip_C, TotNoBar_C 53.88 4.15 0.02 -21.94 9 G_ReaRip_C, I_FlowStream_C, TotNoBar_C 53.9 4.17 0.02 -21.95 10 G_ReaRip_C, I_Rip_C, TotNoBar_C 53.98 4.24 0.02 -21.99

OE2010_C 1 USBar_C, USNoBar_C -30.66 0.00 0.06 19.33 2 USNoBar_C -29.90 0.76 0.04 17.95 3 T_ReaRip_C, T_Rip_C, USNoBar_C -29.81 0.84 0.04 19.91 4 USBar_C, USArea_C -29.77 0.89 0.04 18.89 5 T_ReaRip_C, T_EffRip_C, USNoBar_C -29.76 0.90 0.04 19.88 6 T_ReaRip_C, G_Rip_C, USNoBar_C -29.45 1.21 0.03 19.72 7 T_ReaRip_C, T_FlowStream_C, USNoBar_C -29.41 1.25 0.03 19.71 8 T_ReaRip_C, G_FlowStream_C, USNoBar_C -29.40 1.26 0.03 19.70 9 T_ReaRip_C, T_SubCatch_C, USNoBar_C -29.14 1.52 0.03 19.57 10 T_ReaRip_C, USBar_C, USNoBar_C -29.08 1.58 0.03 19.54

OE2011_C 1 USBar_C, TotNoBar_C -13.99 0.00 0.10 11.00 2 USBar_C, USNoBar_C -13.26 0.74 0.07 10.63 3 TotNoBar_C -13.16 0.83 0.06 9.58 4 USBar_C, USArea_C -13.14 0.85 0.06 10.57 5 USNoBar_C -13.08 0.92 0.06 9.54 6 USArea_C -12.91 1.09 0.06 9.45 7 G_FlowStream_C, TotNoBar_C -11.62 2.37 0.03 9.81 8 G_SubCatch_C, TotNoBar_C -11.61 2.38 0.03 9.80

249 Model Log Explanatory variables AIC AIC i i order Likelihood 9 G_Rip_C, TotNoBar_C -11.50 2.49 0.03 9.75 10 G_FlowStream_C, USNoBar_C -11.45 2.54 0.03 9.72

250 Table A20.2 Best GLM models for macroinvertebrate occurrence Top 20 model results for macroinvertebrate occurrence. Models are ranked by their root mean square prediction error (RMSPE).

Model order Explanatory variables RMSPE

Ephemeroptera, Leptophlebiidae (EphLept) 103 models total 1 I_ReaRip_C, USBar_C 0.23158 2 I_ReaRip_C, USBar_C, DSNoBar_C 0.24008 3 USRatio_C 0.28172 4 USRatio_C, DSNoBar_C 0.28201 5 I_FlowSite_C 0.32470 6 C_AI_C 0.33201 7 I_ReaRip_C, I_EffRip_C 0.33607 8 PopDen_C 0.34048 9 IP_EffRip_C 0.34274 10 P_EffRip_C 0.34583 11 R_AI_C 0.34703 12 I_Rip_C 0.34816 13 I_ReaRip_C, IP_EffRip_C 0.34820 14 I_ReaRip_C, I_Rip_C 0.34918 15 I_ReaRip_C, I_FlowSite_C 0.34940 16 I_ReaRip_C, USRatio_C 0.34978 17 C_AI_C, DSNoBar_C 0.35033 18 I_ReaRip_C, I_FlowStream_C 0.35063 19 I_ReaRip_C, PopDen_C 0.35207 20 I_ReaRip_C, P_EffRip_C 0.35291

Trichoptera, Leptoceridae (TricLept) 289 models total 1 PopDen_C, TotNoBar_C 0.36686 2 I_Rip_C, TotNoBar_C 0.36767 3 IP_EffRip_C, TotNoBar_C 0.37475 4 I_SubCatch_C, TotNoBar_C 0.37552 5 I_FlowStream_C, TotNoBar_C 0.38072 6 PopDen_C, USArea_C 0.38111 7 P_EffRip_C, TotNoBar_C 0.38191 8 R_AI_C, TotNoBar_C 0.38235 9 T_EffRip_C, TotNoBar_C 0.38264 10 PopDen_C, USNoBar_C 0.38525 11 I_FlowSite_C, TotNoBar_C 0.38660 12 T_Rip_C, TotNoBar_C 0.38818 13 T_FlowStream_C, TotNoBar_C 0.38943 14 PopDen_C 0.38996 15 I_SubCatch_C, USNoBar_C 0.39225 16 T_ReaRip_C, I_Rip_C, TotNoBar_C 0.39300

251 Model order Explanatory variables RMSPE 17 I_SubCatch_C, USArea_C 0.39339 18 T_ReaRip_C, IP_EffRip_C, TotNoBar_C 0.39441 19 I_Rip_C 0.39466 20 R_AI_C, USArea_C 0.39526

Decapoda, Parastacidae Cherax destructor (DecPar) 56 models total 1 PopDen_C 0.36333 2 G_ReaRip_C, USRatio_C 0.36960 3 G_ReaRip_C, PopDen_C 0.37013 4 T_ReaRip_C, PopDen_C 0.38033 5 I_FlowSite_C 0.38917 6 T_ReaRip_C, USRatio_C 0.39113 7 G_ReaRip_C, I_FlowSite_C 0.39130 8 T_ReaRip_C, C_CA_C 0.39200 9 G_ReaRip_C, C_CA_C 0.39239 10 T_ReaRip_C, TotBar_C 0.39241 11 C_CA_C 0.39243 12 C_AI_C 0.39441 13 G_ReaRip_C, C_AI_C 0.39468 14 G_ReaRip_C, R_AI_C 0.39505 15 R_AI_C 0.39601 16 T_ReaRip_C, DSBar_C 0.40012 17 T_ReaRip_C, R_AI_C 0.40029 18 T_FlowSite_C 0.40145 19 T_ReaRip_C, P_EffRip_C 0.40172 20 T_ReaRip_C, IP_EffRip_C 0.40191

Decapoda Atyidae (DecAty) 13 models total 1 TotBar_C 0.46217 2 DSBar_C 0.46370 3 TotBar_C, DSNoBar_C 0.47585 4 DSBar_C, DSNoBar_C 0.47905 5 PopDen_C 0.49724 6 USBar_C 0.49915 7 I_FlowSite_C 0.50769 8 G_EffRip_C 0.50844 9 USBar_C, DSNoBar_C 0.51353 10 G_EffRip_C, DSNoBar_C 0.51357 11 DSNoBar_C 0.51442 12 PopDen_C, DSNoBar_C 0.51689 13 I_FlowSite_C, DSNoBar_C 0.52497

Odonata Gomphidae (OdoGomp) 41 models total 1 C_AI_C 0.23210 2 T_ReaRip_C, C_AI_C 0.24507

252 Model order Explanatory variables RMSPE 3 T_ReaRip_C, I_SubCatch_C 0.25201 4 T_ReaRip_C, TotBar_C 0.28795 5 I_Rip_C 0.29271 6 PopDen_C 0.29854 7 I_SubCatch_C 0.29909 8 C_CA_C 0.30196 9 T_SubCatch_C 0.30341 10 T_FlowStream_C 0.30502 11 T_ReaRip_C, USBar_C 0.30530 12 I_FlowStream_C 0.30562 13 T_ReaRip_C, PopDen_C 0.30821 14 T_EffRip_C 0.30894 15 G_FlowStream_C 0.31042 16 R_CA_C 0.31104 17 T_Rip_C 0.31106 18 USBar_C 0.31169 19 G_Rip_C 0.31577 20 T_ReaRip_L, I_FlowStream_C 0.31623

Odonata Hemicorduliidae (OdoHemi) 11 models total 1 USArea_C 0.32958 2 G_EffRip_C 0.33238 3 G_EffRip_C, USArea_C 0.35353 4 USRatio_C, USArea_C 0.35576 5 G_EffRip_C, USNoBar_C 0.35888 6 USNoBar_C 0.37111 7 DSRatio_C, USArea_C 0.38218 8 USRatio_C, USNoBar_C 0.39323 9 DSRatio_C, USNoBar_C 0.40346 10 DSRatio_C 0.40741 11 USRatio_C 0.40844

Ephemeroptera Baetidae (EphBaet) 1 G_EffRip_C 0.38902 2 G_SubCatch_C 0.48321 3 G_FlowSite_C 0.48410 4 G_Rip_C 0.48629 5 G_FlowStream_C 0.50316 6 USBar_C 0.51573

253 Table A20.3 Best GLM models for fish occurrence Top 20 model results for fish occurrence models. Models are ranked by their root mean square prediction error (RMSPE).

Model order Explanatory variables RMSPE

2010 Melanotaenia duboulayi (Crimson-spotted rainbow fish) (CrimPA10) 1 C_CA_C, TotNoBar_C 0.3335 2 TotNoBar_C 0.3367 3 C_AI_C, TotNoBar_C 0.3427 4 R_CA_C, TotNoBar_C 0.3433 5 T_ReaRip_C, TotNoBar_C 0.3451 6 G_FlowSite_C, TotNoBar_C 0.3492 7 R_AI_C, TotNoBar_C 0.3492 8 I_Rip_C, TotNoBar_C 0.3498 9 I_FlowStream_C, TotNoBar_C 0.3499 10 I_FlowSite_C, TotNoBar_C 0.3500 11 T_FlowSite_C, TotNoBar_C 0.3504 12 I_SubCatch_C, TotNoBar_C 0.3508 13 PopDen_C, TotNoBar_C 0.3510 14 IP_EffRip_C, TotNoBar_C 0.3511 15 P_EffRip_C, TotNoBar_C 0.3511 16 T_Rip_C, TotNoBar_C 0.3522 17 T_FlowStream_C, TotNoBar_C 0.3524 18 T_EffRip_C, TotNoBar_C 0.3529 19 T_SubCatch_C, TotNoBar_C 0.3536 20 G_FlowStream_C, TotNoBar_C 0.3542

2011 Melanotaenia duboulayi (Crimson-spotted rainbow fish) (CrimPA11) 1 USBar_C, DSNoBar_C 0.3594 2 USBar_C 0.3638 3 USBar_C, TotNoBar_C 0.3670 4 CulvRd_C, TotNoBar_C 0.3867 5 PopDen_C, TotNoBar_C 0.4000 6 TotNoBar_C 0.4053 7 R_CA_C, DSNoBar_C 0.4080 8 DSNoBar_C 0.4090 9 PopDen_C, DSNoBar_C 0.4116 10 CulvRd_C, DSNoBar_C 0.4132 11 T_FlowSite_C, DSNoBar_C 0.4141 12 R_CA_C, TotNoBar_C 0.4152 13 T_SubCatch_C 0.4165 14 T_SubCatch_C, DSNoBar_C 0.4183 15 I_SubCatch_C, DSNoBar_C 0.4184 16 PopDen_C 0.4187 17 T_FlowStream_C, DSNoBar_C 0.4189 18 T_FlowStream_C 0.4196

254 Model order Explanatory variables RMSPE 19 R_CA_C 0.4202 20 T_Rip_C, DSNoBar_C 0.4204

2010 Mogurnda adspersa (Purple-spotted gudgeon) (PursPA10) 1 I_EffRip_C, TotNoBar_C 0.3128 2 USBar_C, TotNoBar_C 0.3222 3 TotNoBar_C 0.3388 4 T_EffRip_C, TotNoBar_C 0.3569 5 TotRatio_C, TotNoBar_C 0.3574 6 PopDen_C, TotNoBar_C 0.3574 7 IP_EffRip_C, TotNoBar_C 0.3593 8 P_EffRip_C, TotNoBar_C 0.3603 9 I_EffRip_C, DSNoBar_C 0.3604 10 I_SubCatch_C, TotNoBar_C 0.3607 11 T_SubCatch_C, TotNoBar_C 0.3611 12 R_AI_C, TotNoBar_C 0.3612 13 G_FlowStream_C, TotNoBar_C 0.3622 14 G_Rip_C, TotNoBar_C 0.3625 15 T_FlowStream_C, TotNoBar_C 0.3631 16 T_Rip_C, TotNoBar_C 0.3644 17 G_SubCatch_C, TotNoBar_C 0.3645 18 C_AI_C, TotNoBar_C 0.3646 19 I_FlowStream_C, TotNoBar_C 0.3650 20 T_SubCatch_C, DSNoBar_C 0.3670

2011 Mogurnda adspersa (Purple-spotted gudgeon) (PursPA11) 1 DSNoBar_C 0.3031 2 T_EffRip_C, DSNoBar_C 0.3143 3 T_FlowStream_C, DSNoBar_C 0.3144 4 T_Rip_C, DSNoBar_C 0.3158 5 T_SubCatch_C, DSNoBar_C 0.3178 6 I_FlowStream_C, DSNoBar_C 0.3219 7 IP_EffRip_C, DSNoBar_C 0.3225 8 P_EffRip_C, DSNoBar_C 0.3226 9 G_FlowStream_C, DSNoBar_C 0.3226 10 TotRatio_C, DSNoBar_C 0.3227 11 I_SubCatch_C, DSNoBar_C 0.3253 12 G_Rip_C, DSNoBar_C 0.3259 13 I_Rip_C, DSNoBar_C 0.3274 14 PopDen_C, DSNoBar_C 0.3298 15 USBar_C, DSNoBar_C 0.3303 16 USBar_C, TotNoBar_C 0.3322 17 TotNoBar_C 0.3350 18 DSBar_C, DSNoBar_C 0.3354 19 R_AI_C, DSNoBar_C 0.3357

255 Model order Explanatory variables RMSPE 20 T_FlowSite_C, DSNoBar_C 0.3365

2010 Tandanus tandanus (Freshwater catfish) (FrePA10) 1 TotBar_C, TotNoBar_C 0.3110 2 TotNoBar_C 0.3144 3 R_AI_C, TotNoBar_C 0.3192 4 USBar_C, TotNoBar_C 0.3200 5 R_CA_C, TotNoBar_C 0.3235 6 DSBar_C, TotNoBar_C 0.3250 7 P_EffRip_C, TotNoBar_C 0.3308 8 IP_EffRip_C, TotNoBar_C 0.3313 9 T_EffRip_C, TotNoBar_C 0.3358 10 T_Rip_C, TotNoBar_C 0.3381 11 T_FlowStream_C, TotNoBar_C 0.3387 12 T_SubCatch_C, TotNoBar_C 0.3394 13 G_FlowStream_C, TotNoBar_C 0.3409 14 G_Rip_C, TotNoBar_C 0.3412 15 G_SubCatch_C, TotNoBar_C 0.3419 16 USNoBar_C 0.3437 17 USArea_C 0.3475 18 DSBar_C, USNoBar_C 0.3534 19 USBar_C, DSNoBar_C 0.3561 20 DSBar_C, USArea_C 0.3564

2011 Tandanus tandanus (Freshwater catfish) (FrePA11) 1 I_FlowSite_C, TotNoBar_C 0.3139 2 T_FlowSite_C, TotNoBar_C 0.3161 3 I_EffRip_C, TotNoBar_C 0.3192 4 T_FlowSite_C, USNoBar_C 0.3273 5 USNoBar_C 0.3275 6 T_FlowSite_C, USArea_C 0.3276 7 USArea_C 0.3279 8 T_SubCatch_C 0.3280 9 I_FlowSite_C, USNoBar_C 0.3283 10 I_FlowSite_C, USArea_C 0.3287 11 G_SubCatch_C 0.3290 12 PopDen_C, TotNoBar_C 0.3313 13 TotRatio_C, USArea_C 0.3318 14 TotRatio_C, USNoBar_C 0.3322 15 T_ReaRip_C, USNoBar_C 0.3326 16 G_FlowSite_C, USArea_C 0.3327 17 G_FlowSite_C, USNoBar_C 0.3328 18 I_FlowStream_C, USNoBar_C 0.3329 19 T_SubCatch_C, USArea_C 0.3329 20 I_FlowStream_C, USArea_C 0.3329

256 Appendix 21 Coefficient estimates, Pr(<|z|) and odds ratios for fish and macroinvertebrate occurrence data for Chapter 5 The Wald test was used to determine which variables had a statistically significant association with occurrence; variables with Pr < 0.2 were selected for inclusion in the GLM models. CrimPA10 Melanotaenia Crimson- Confidence I duboulayi spotted rainbow fish Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C -8.09 0.22 0.00 0.00 1.87 T_ReaRip_C 2.87 0.13 17.60 0.67 1.88E+03 G_ReaRip_C -2.28 0.37 0.10 0.00 8.20 V_ReaRip_C 7.34 0.15 1.54E+03 0.89 7.92E+08 I_SubCatch_C -5.54 0.07 0.00 0.00 1.04 T_SubCatch_C 4.22 0.03 68.10 1.93 5.97E+03 G_SubCatch_C -10.26 0.03 0.00 0.00 0.25 V_SubCatch_C 5.73 0.05 307.00 1.57 2.25E+05 I_FlowSite_C -5.29 0.12 0.01 0.00 2.80 T_FlowSite_C 4.51 0.06 91.00 1.14 2.10E+04 G_FlowSite_C -8.67 0.13 0.00 0.00 4.49 V_FlowSite_C 5.40 0.09 220.00 0.60 2.61E+05 I_FlowStream_C -6.39 0.08 0.00 0.00 1.33 T_FlowStream_C 3.85 0.04 47.10 1.47 3.47E+03 G_FlowStream_C -7.84 0.05 0.00 0.00 0.60 V_FlowStream_C 6.61 0.06 743.00 1.52 1.75E+06 I_Rip_C -5.35 0.10 0.00 0.00 1.72 T_Rip_C 3.71 0.05 41.00 1.27 2.83E+03 G_Rip_C -8.38 0.06 0.00 0.00 0.75 V_Rip_C 5.53 0.07 251.00 0.97 2.41E+05 I_EffRip_C -40.65 0.20 0.00 0.00 190.00 T_EffRip_C 2.97 0.05 19.40 1.23 512.00 G_EffRip_C -6.09 0.50 0.00 0.00 1.00E+04 V_EffRip_C 2.59 0.07 13.30 0.95 290.00 IP_EffRip_C -2.43 0.08 0.09 0.00 1.17 P_EffRip_C -2.44 0.09 0.09 0.00 1.29 PopDen_C -0.12 0.10 0.88 0.75 1.01 USArea_C 0.29 0.04 1.34 1.06 1.96 USNoBar_C 0.18 0.04 1.20 1.04 1.49 DSNoBar_C 0.13 0.01 1.14 1.04 1.30

257 CrimPA10 Melanotaenia Crimson- Confidence I duboulayi spotted rainbow fish Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

TotNoBar_C 0.17 0.00 1.19 1.08 1.37 USBar_C 0.36 0.02 1.44 1.09 2.05 DSBar_C 0.08 0.08 1.09 1.00 1.21 TotBar_C 0.09 0.04 1.09 1.01 1.20 USRatio_C -2.64 0.44 0.07 0.00 5.69 DSRatio_C -1.54 0.46 0.22 0.00 2.15 TotRatio_C -3.20 0.39 0.04 0.00 11.20 EstConn_C 0.08 0.90 1.08 0.31 3.74 CulvRd_C 0.01 0.96 1.01 0.58 1.68 C_CA_C 0.02 0.09 1.02 1.00 1.04 C_AI_C 0.06 0.18 1.06 0.98 1.16 R_CA_C 0.06 0.05 1.06 1.01 1.14 R_AI_C 0.09 0.11 1.09 0.99 1.24

258 CrimPA11 Melanotaenia Crimson- Confidence I duboulayi spotted rainbow fish Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C 1.07 0.68 2.92 0.01 4.12E+02 T_ReaRip_C 0.95 0.56 2.58 0.13 9.80E+01 G_ReaRip_C -2.91 0.31 0.05 0.00 6.84 V_ReaRip_C -0.50 0.84 0.60 0.01 1.63E+02 I_SubCatch_C -4.09 0.18 0.02 0.00 4.98 T_SubCatch_C 2.91 0.12 18.30 0.52 1.14E+03 G_SubCatch_C -6.04 0.19 0.00 0.00 17.60 V_SubCatch_C 4.35 0.13 77.80 0.37 4.57E+04 I_FlowSite_C -3.91 0.26 0.02 0.00 13.60 T_FlowSite_C 3.15 0.18 23.30 0.28 4.03E+03 G_FlowSite_C -5.27 0.34 0.01 0.00 111.00 V_FlowSite_C 4.04 0.21 56.70 0.13 5.89E+04 I_FlowStream_C -4.63 0.20 0.01 0.00 8.38 T_FlowStream_C 2.69 0.15 14.80 0.45 8.47E+02 G_FlowStream_C -5.11 0.19 0.01 0.00 9.05 V_FlowStream_C 4.84 0.15 1.26E+02 0.26 2.13E+05 I_Rip_C -3.79 0.24 0.02 0.00 8.90 T_Rip_C 2.52 0.17 12.40 0.37 6.79E+02 G_Rip_C -5.12 0.23 0.01 0.00 21.10 V_Rip_C 4.00 0.18 54.80 0.20 4.15E+04 I_EffRip_C -29.44 0.33 0.00 0.00 3.30E+04 T_EffRip_C 2.07 0.16 7.94 0.47 1.83E+02 G_EffRip_C -2.85 0.74 0.06 0.00 2.16E+05 V_EffRip_C 1.86 0.19 6.41 0.42 1.32E+02 IP_EffRip_C -1.72 0.22 0.18 0.01 2.64 P_EffRip_C -1.70 0.24 0.18 0.01 2.97 PopDen_C -0.12 0.12 0.88 0.74 1.02 USArea_C 0.11 0.31 1.12 0.88 1.40 USNoBar_C 0.07 0.30 1.07 0.93 1.22 DSNoBar_C 0.10 0.03 1.11 1.01 1.24 TotNoBar_C 0.10 0.02 1.10 1.02 1.21 USBar_C 0.38 0.02 1.47 1.10 2.13 DSBar_C 0.03 0.55 1.03 0.93 1.12 TotBar_C 0.04 0.22 1.05 0.97 1.13

259 CrimPA11 Melanotaenia Crimson- Confidence I duboulayi spotted rainbow fish Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

USRatio_C -1.81 0.54 0.16 0.00 9.24 DSRatio_C -1.67 0.49 0.19 0.00 2.30 TotRatio_C -3.40 0.41 0.03 0.00 13.50 EstConn_C -0.95 0.21 0.39 0.07 1.52 CulvRd_C 0.36 0.18 1.44 0.85 2.53 C_CA_C 0.01 0.30 1.01 0.99 1.03 C_AI_C 0.03 0.48 1.03 0.95 1.13 R_CA_C 0.04 0.18 1.04 0.98 1.11 R_AI_C 0.06 0.27 1.06 0.96 1.19

260 PursPA10 Mogurnda Purple- Confidence I adspersa spotted gudgeon Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C -0.40 0.86 0.67 0.00 63.80 T_ReaRip_C 1.11 0.41 3.02 0.24 51.50 G_ReaRip_C -1.52 0.45 0.22 0.00 8.90 V_ReaRip_C 1.12 0.62 3.07 0.05 4.82E+02 I_SubCatch_C -7.29 0.01 0.00 0.00 0.13 T_SubCatch_C 5.07 0.01 1.60E+02 5.37 1.09E+04 G_SubCatch_C -11.36 0.02 0.00 0.00 0.06 V_SubCatch_C 7.20 0.01 1.34E+03 9.21 6.35E+05 I_FlowSite_C -6.21 0.05 0.00 0.00 0.64 T_FlowSite_C 4.35 0.04 77.30 1.60 7.56E+03 G_FlowSite_C -4.93 0.27 0.01 0.00 27.50 V_FlowSite_C 6.46 0.03 6.37E+02 2.66 4.95E+05 I_FlowStream_C -8.08 0.02 0.00 0.00 0.15 T_FlowStream_C 4.65 0.01 1.05E+02 3.97 6.05E+03 G_FlowStream_C -9.01 0.02 0.00 0.00 0.12 V_FlowStream_C 8.26 0.01 3.87E+03 10.80 6.47E+06 I_Rip_C -6.93 0.02 0.00 0.00 0.24 T_Rip_C 4.53 0.01 92.80 3.49 5.27E+03 G_Rip_C -9.30 0.03 0.00 0.00 0.17 V_Rip_C 7.09 0.02 1.20E+03 6.23 8.58E+05 I_EffRip_C -81.71 0.04 0.00 0.00 0.00 T_EffRip_C 4.00 0.01 54.50 3.66 1.56E+03 G_EffRip_C -2.53 0.71 0.08 0.00 3.82E+04 V_EffRip_C 3.57 0.01 35.40 2.78 7.76E+02 IP_EffRip_C -3.32 0.01 0.04 0.00 0.42 P_EffRip_C -3.26 0.02 0.04 0.00 0.48 PopDen_C -0.15 0.03 0.86 0.74 0.98 USArea_C 0.42 0.09 1.52 1.09 2.99 USNoBar_C 0.24 0.08 1.27 1.05 1.84 DSNoBar_C 0.20 0.02 1.22 1.07 1.50 TotNoBar_C 0.18 0.00 1.20 1.09 1.41 USBar_C 0.39 0.02 1.48 1.11 2.18 DSBar_C 0.02 0.66 1.02 0.94 1.11 TotBar_C 0.04 0.27 1.04 0.97 1.12 USRatio_C 0.51 0.73 1.67 0.06 43.30

261 PursPA10 Mogurnda Purple- Confidence I adspersa spotted gudgeon Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

DSRatio_C -3.10 0.25 0.05 0.00 1.17 TotRatio_C -4.72 0.19 0.01 0.00 2.32 EstConn_C -0.66 0.26 0.52 0.15 1.55 CulvRd_C 0.26 0.26 1.30 0.83 2.14 C_CA_C 0.01 0.17 1.01 1.00 1.03 C_AI_C 0.06 0.09 1.07 0.99 1.15 R_CA_C 0.04 0.09 1.04 1.00 1.10 R_AI_C 0.10 0.04 1.11 1.01 1.23

262 PursPA11 Mogurnda Purple- Confidence I adspersa spotted gudgeon Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C -0.65 0.78 0.52 0.00 49.80 T_ReaRip_C 0.67 0.61 1.96 0.16 29.70 G_ReaRip_C -0.43 0.82 0.65 0.01 24.90 V_ReaRip_C 1.33 0.56 3.79 0.06 6.72E+02 I_SubCatch_C -5.11 0.05 0.01 0.00 0.80 T_SubCatch_C 3.51 0.04 33.40 1.49 1.22E+03 G_SubCatch_C -7.40 0.08 0.00 0.00 1.48 V_SubCatch_C 5.16 0.04 1.74E+02 1.73 3.68E+04 I_FlowSite_C -4.26 0.15 0.01 0.00 3.48 T_FlowSite_C 2.85 0.14 17.20 0.43 1.05E+03 G_FlowSite_C -2.24 0.59 0.11 0.00 3.35E+02 V_FlowSite_C 4.61 0.10 1.01E+02 0.57 3.93E+04 I_FlowStream_C -5.44 0.08 0.00 0.00 1.36 T_FlowStream_C 3.24 0.05 25.50 1.25 8.38E+02 G_FlowStream_C -6.17 0.07 0.00 0.00 1.17 V_FlowStream_C 5.73 0.05 3.07E+02 1.44 1.74E+05 I_Rip_C -4.70 0.09 0.01 0.00 1.49 T_Rip_C 3.13 0.06 22.80 1.09 7.54E+02 G_Rip_C -6.14 0.10 0.00 0.00 2.42 V_Rip_C 4.98 0.06 1.46E+02 1.14 4.34E+04 I_EffRip_C -38.34 0.14 0.00 0.00 3.31 T_EffRip_C 2.95 0.03 19.10 1.55 3.55E+02 G_EffRip_C -0.37 0.96 0.69 0.00 3.35E+05 V_EffRip_C 2.72 0.04 15.10 1.37 2.42E+02 IP_EffRip_C -2.51 0.05 0.08 0.01 0.85 P_EffRip_C -2.47 0.06 0.08 0.01 0.95 PopDen_C -0.12 0.07 0.89 0.77 1.00 USArea_C 0.19 0.14 1.20 0.97 1.66 USNoBar_C 0.12 0.12 1.12 0.99 1.36 DSNoBar_C 0.60 0.02 1.83 1.26 3.61 TotNoBar_C 0.21 0.00 1.24 1.10 1.52 USBar_C 0.31 0.04 1.37 1.05 1.91 DSBar_C 0.09 0.09 1.10 1.00 1.26 TotBar_C 0.09 0.05 1.10 1.01 1.23 USRatio_C 0.51 0.74 1.67 0.06 43.10

263 PursPA11 Mogurnda Purple- Confidence I adspersa spotted gudgeon Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

DSRatio_C -2.30 0.29 0.10 0.00 1.38 TotRatio_C -3.62 0.25 0.03 0.00 4.20 EstConn_C -0.66 0.26 0.52 0.15 1.55 CulvRd_C 0.26 0.26 1.30 0.83 2.14 C_CA_C 0.01 0.25 1.01 1.00 1.02 C_AI_C 0.05 0.15 1.05 0.98 1.14 R_CA_C 0.03 0.18 1.03 0.99 1.09 R_AI_C 0.08 0.08 1.08 1.00 1.20

264

FrePA10 Tandanus Freshwater Confidence I tandanus catfish Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C -8.44 0.30 0.00 0.00 4.84 T_ReaRip_C 1.90 0.33 6.66 0.22 7.36E+02 G_ReaRip_C -0.94 0.72 0.39 0.00 37.10 V_ReaRip_C 6.21 0.25 4.96E+02 0.23 1.33E+09 I_SubCatch_C -3.78 0.24 0.02 0.00 10.00 T_SubCatch_C 3.18 0.12 23.90 0.52 2.54E+03 G_SubCatch_C -8.71 0.09 0.00 0.00 2.58 V_SubCatch_C 3.97 0.19 53.10 0.18 4.74E+04 I_FlowSite_C -3.15 0.38 0.04 0.00 45.70 T_FlowSite_C 3.00 0.23 20.10 0.18 5.15E+03 G_FlowSite_C -6.48 0.29 0.00 0.00 86.40 V_FlowSite_C 3.39 0.31 29.70 0.05 4.21E+04 I_FlowStream_C -4.69 0.23 0.01 0.00 13.00 T_FlowStream_C 2.98 0.14 19.80 0.46 1.92E+03 G_FlowStream_C -6.20 0.15 0.00 0.00 5.69 V_FlowStream_C 5.00 0.17 1.48E+02 0.19 5.43E+05 I_Rip_C -4.29 0.22 0.01 0.00 8.89 T_Rip_C 3.00 0.14 20.10 0.45 1.97E+03 G_Rip_C -6.88 0.15 0.00 0.00 7.22 V_Rip_C 4.41 0.18 82.00 0.19 1.41E+05 I_EffRip_C -19.04 0.50 0.00 0.00 4.29E+06 T_EffRip_C 2.61 0.11 13.60 0.65 5.30E+02 G_EffRip_C -5.43 0.60 0.00 0.00 9.08E+04 V_EffRip_C 2.30 0.14 9.97 0.53 3.24E+02 IP_EffRip_C -2.21 0.15 0.11 0.00 2.01 P_EffRip_C -2.26 0.16 0.11 0.00 2.13 PopDen_C -0.05 0.52 0.95 0.81 1.11 USArea_C 0.35 0.02 1.42 1.10 2.09 USNoBar_C 0.21 0.02 1.24 1.06 1.56 DSNoBar_C 0.11 0.03 1.12 1.02 1.26 TotNoBar_C 0.23 0.04 1.26 1.09 1.81 USBar_C 0.31 0.05 1.36 1.02 1.93 DSBar_C 0.07 0.14 1.07 0.98 1.18 TotBar_C 0.07 0.07 1.07 0.99 1.17 USRatio_C -5.07 0.44 0.01 0.00 5.70

265 FrePA10 Tandanus Freshwater Confidence I tandanus catfish Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

DSRatio_C -4.13 0.40 0.02 0.00 1.86 TotRatio_C -7.63 0.28 0.00 0.00 4.61 EstConn_C -0.07 0.92 0.93 0.21 3.76 CulvRd_C 0.07 0.80 1.08 0.57 1.89 C_CA_C 0.01 0.18 1.01 1.00 1.04 C_AI_C 0.06 0.21 1.06 0.97 1.19 R_CA_C 0.06 0.09 1.06 1.00 1.16 R_AI_C 0.09 0.16 1.09 0.98 1.27

266 FrePA11 Tandanus Freshwater Confidence I tandanus catfish Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C -9.21 0.35 0.00 0.00 8.45 T_ReaRip_C 3.62 0.20 37.20 0.47 5.90E+04 G_ReaRip_C -3.64 0.33 0.03 0.00 9.74 V_ReaRip_C 7.76 0.27 2.34E+03 0.22 1.58E+12 I_SubCatch_C -10.91 0.08 0.00 0.00 0.15 T_SubCatch_C 8.97 0.06 7.83E+03 10.80 1.01E+10 G_SubCatch_C -20.00 0.03 0.00 0.00 0.00 V_SubCatch_C 10.39 0.07 3.25E+04 7.61 3.35E+11 I_FlowSite_C -8.63 0.09 0.00 0.00 0.94 T_FlowSite_C 8.75 0.06 6.33E+03 5.78 2.09E+09 G_FlowSite_C -22.16 0.08 0.00 0.00 0.03 V_FlowSite_C 8.38 0.07 4.35E+03 1.54 4.51E+08 I_FlowStream_C -12.14 0.08 0.00 0.00 0.18 T_FlowStream_C 7.01 0.06 1.11E+03 4.61 3.80E+07 G_FlowStream_C -14.77 0.04 0.00 0.00 0.03 V_FlowStream_C 10.99 0.06 5.94E+04 6.02 4.79E+11 I_Rip_C -8.99 0.09 0.00 0.00 0.60 T_Rip_C 6.22 0.05 503.00 3.10 3.33E+06 G_Rip_C -15.20 0.04 0.00 0.00 0.03 V_Rip_C 8.37 0.08 4.32E+03 1.91 9.35E+08 I_EffRip_C -145.83 0.06 0.00 0.00 0.00 T_EffRip_C 4.08 0.06 59.20 1.55 1.58E+04 G_EffRip_C -24.99 0.25 0.00 0.00 84.40 V_EffRip_C 3.31 0.09 27.50 0.93 3.37E+03 IP_EffRip_C -3.18 0.09 0.04 0.00 1.16 P_EffRip_C -3.14 0.11 0.04 0.00 1.34 PopDen_C -0.15 0.14 0.87 0.69 1.03 USArea_C 0.33 0.02 1.39 1.09 1.94 USNoBar_C 0.19 0.02 1.21 1.05 1.47 DSNoBar_C 0.07 0.16 1.07 0.97 1.19 TotNoBar_C 0.14 0.03 1.15 1.04 1.37 USBar_C 0.09 0.55 1.10 0.77 1.50 DSBar_C -0.13 0.43 0.88 0.51 1.05 TotBar_C -0.04 0.56 0.96 0.78 1.06 USRatio_C -9.92 0.34 0.00 0.00 4.91 DSRatio_C -31.76 0.23 0.00 0.00 0.02

267 FrePA11 Tandanus Freshwater Confidence I tandanus catfish Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

TotRatio_C -39.81 0.15 0.00 0.00 0.00 EstConn_C 0.41 0.60 1.50 0.32 7.43 CulvRd_C 0.07 0.82 1.07 0.53 1.98 C_CA_C 0.02 0.17 1.02 1.00 1.04 C_AI_C 0.05 0.30 1.06 0.96 1.19 R_CA_C 0.07 0.11 1.07 1.00 1.18 R_AI_C 0.08 0.24 1.08 0.96 1.27

268 EphLept Ephemeroptera Leptophlebiidae Confidence I Mayfly Coefficient Pr(<|z|) Odds 2.5% 97.5% Estimate Ratio I_ReaRip_C -57.21 0.15 0.00 0.00 0.00 T_ReaRip_C 0.80 0.65 2.22 0.09 1.20E+02 G_ReaRip_C 2.12 0.34 8.34 0.09 8.31E+02 V_ReaRip_C 47.60 0.12 4.72E+20 9.76E+03 3.86E+58 I_SubCatch_C -8.45 0.06 0.00 0.00 0.30 T_SubCatch_C 4.06 0.07 57.80 1.09 1.29E+04 G_SubCatch_C -4.95 0.30 0.01 0.00 84.40 V_SubCatch_C 8.20 0.05 3.65E+03 4.13 2.44E+08 I_FlowSite_C -13.33 0.03 0.00 0.00 0.03 T_FlowSite_C 5.24 0.08 1.89E+02 1.15 2.53E+05 G_FlowSite_C 0.91 0.86 2.47 0.00 6.38E+04 V_FlowSite_C 13.57 0.03 7.85E+05 55.30 1.05E+13 I_FlowStream_C -10.58 0.06 0.00 0.00 0.19 T_FlowStream_C 4.22 0.06 67.70 1.30 1.74E+04 G_FlowStream_C -6.20 0.15 0.00 0.00 4.98 V_FlowStream_C 9.68 0.05 1.60E+04 5.80 4.13E+09 I_Rip_C -10.33 0.05 0.00 0.00 0.14 T_Rip_C 4.35 0.06 77.50 1.45 2.03E+04 G_Rip_C -5.74 0.20 0.00 0.00 15.70 V_Rip_C 9.76 0.04 1.74E+04 8.44 5.36E+09 I_EffRip_C -95.99 0.08 0.00 0.00 0.00 T_EffRip_C 4.01 0.04 55.30 2.08 7.09E+03 G_EffRip_C 10.66 0.16 4.24E+04 0.01 4.53E+11 V_EffRip_C 4.69 0.03 1.09E+02 3.12 4.59E+04 IP_EffRip_C -4.60 0.04 0.01 0.00 0.34 P_EffRip_C -4.69 0.04 0.01 0.00 0.35 PopDen_C -0.25 0.04 0.78 0.58 0.95 USArea_C -0.10 0.59 0.90 0.47 1.19 USNoBar_C -0.06 0.60 0.94 0.65 1.11 DSNoBar_C 0.08 0.10 1.08 0.99 1.20 TotNoBar_C 0.05 0.26 1.05 0.97 1.14 USBar_C 0.41 0.02 1.51 1.11 2.29 DSBar_C 0.07 0.11 1.07 0.98 1.19 TotBar_C 0.08 0.05 1.09 1.00 1.19 USRatio_C 6.57 0.03 7.16E+02 11.00 7.89E+06 DSRatio_C -0.82 0.62 0.44 0.00 3.09 TotRatio_C 1.39 0.57 3.99 0.01 5.24E+02

269 EphLept Ephemeroptera Leptophlebiidae Confidence I Mayfly Coefficient Pr(<|z|) Odds 2.5% 97.5% Estimate Ratio EstConn_C -2.12 0.06 0.12 0.01 0.76 CulvRd_C 0.37 0.14 1.45 0.89 2.47 C_CA_C 0.03 0.05 1.03 1.00 1.06 C_AI_C 0.18 0.06 1.20 1.05 1.58 R_CA_C 0.07 0.07 1.07 1.01 1.18 R_AI_C 0.25 0.06 1.28 1.06 1.87

270 TricLept Trichoptera Leptoceridae Confidence I Caddisfly Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C -4.30 0.10 0.01 0.00 1.54 T_ReaRip_C 2.68 0.06 14.60 0.99 3.08E+02 G_ReaRip_C -2.11 0.28 0.12 0.00 5.42 V_ReaRip_C 4.27 0.08 71.20 0.90 1.81E+04 I_SubCatch_C -10.30 0.02 0.00 0.00 0.04 T_SubCatch_C 4.80 0.04 1.21E+02 2.59 3.86E+04 G_SubCatch_C -6.50 0.16 0.00 0.00 5.90 V_SubCatch_C 8.14 0.02 3.44E+03 9.09 2.97E+07 I_FlowSite_C -11.62 0.02 0.00 0.00 0.02 T_FlowSite_C 5.74 0.03 3.12E+02 3.40 1.52E+05 G_FlowSite_C -4.67 0.28 0.01 0.00 39.40 V_FlowSite_C 9.41 0.02 1.23E+04 13.70 2.56E+08 I_FlowStream_C -10.88 0.02 0.00 0.00 0.03 T_FlowStream_C 4.97 0.03 1.44E+02 3.30 3.73E+04 G_FlowStream_C -7.86 0.05 0.00 0.00 0.45 V_FlowStream_C 8.78 0.02 6.50E+03 10.10 7.83E+07 I_Rip_C -10.81 0.01 2.02E-05 0.00 0.02 T_Rip_C 4.97 0.03 1.44E+02 3.15 4.01E+04 G_Rip_C -6.44 0.12 0.00 0.00 2.65 V_Rip_C 8.79 0.02 6.58E+03 13.90 6.17E+07 I_EffRip_C -18.26 0.21 0.00 0.00 124.00 T_EffRip_C 4.56 0.04 95.90 3.06 3.18E+04 G_EffRip_C 12.32 0.22 2.24E+05 0.02 9.07E+15 V_EffRip_C 4.88 0.03 1.32E+02 3.92 5.49E+04 IP_EffRip_C -4.92 0.03 0.01 0.00 0.24 P_EffRip_C -4.62 0.03 0.01 0.00 0.28 PopDen_C -0.34 0.02 0.71 0.48 0.88 USArea_C 1.34 0.13 3.82 1.20 41.00 USNoBar_C 0.64 0.15 1.90 1.08 6.22 DSNoBar_C 0.08 0.21 1.08 0.98 1.26 TotNoBar_C 0.10 0.08 1.10 1.01 1.27 USBar_C 0.85 0.06 2.33 1.23 7.36 DSBar_C 0.17 0.17 1.18 1.01 1.68 TotBar_C 0.22 0.11 1.25 1.04 1.83 USRatio_C 5.07 0.28 1.59E+02 0.72 4.73E+07 DSRatio_C -2.13 0.14 0.12 0.00 0.88 TotRatio_C -2.07 0.35 0.13 0.00 10.90

271 TricLept Trichoptera Leptoceridae Confidence I Caddisfly Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

EstConn_C 0.06 0.91 1.07 0.35 3.43 CulvRd_C 0.05 0.83 1.05 0.69 1.68 C_CA_C 0.02 0.04 1.02 1.00 1.04 C_AI_C 0.10 0.03 1.11 1.02 1.23 R_CA_C 0.08 0.02 1.08 1.02 1.17 R_AI_C 0.15 0.01 1.16 1.05 1.34

272 DecPar Decapoda Parastacidae Confidence I Cherax destructor Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Freshwater crayfish / Estimate yabby I_ReaRip_C -3.64 0.40 0.03 0.00 16.00 T_ReaRip_C 3.99 0.11 54.20 1.03 3.24E+04 G_ReaRip_C -5.73 0.13 0.00 0.00 1.40 V_ReaRip_C 4.69 0.28 1.09E+02 0.18 9.59E+06 I_SubCatch_C -5.99 0.08 0.00 0.00 0.98 T_SubCatch_C 2.06 0.25 7.87 0.25 3.65E+02 G_SubCatch_C -1.41 0.75 0.25 0.00 2.22E+03 V_SubCatch_C 4.28 0.14 72.30 0.36 4.53E+04 I_FlowSite_C -8.87 0.04 0.00 0.00 0.23 T_FlowSite_C 4.76 0.07 1.16E+02 1.12 5.22E+04 G_FlowSite_C -5.53 0.32 0.00 0.00 89.90 V_FlowSite_C 7.13 0.05 1.25E+03 1.81 7.04E+06 I_FlowStream_C -5.84 0.13 0.00 0.00 2.84 T_FlowStream_C 2.11 0.23 8.22 0.29 3.65E+02 G_FlowStream_C -3.30 0.37 0.04 0.00 42.70 V_FlowStream_C 4.34 0.18 76.80 0.19 1.01E+05 I_Rip_C -6.20 0.09 0.00 0.00 1.18 T_Rip_C 2.40 0.18 11.00 0.37 5.39E+02 G_Rip_C -2.94 0.46 0.05 0.00 1.41E+02 V_Rip_C 4.98 0.11 1.46E+02 0.51 1.74E+05 I_EffRip_C -36.71 0.26 0.00 0.00 3.18E+03 T_EffRip_C 2.47 0.09 11.80 0.75 3.06E+02 G_EffRip_C 6.96 0.33 1.05E+03 0.00 2.63E+09 V_EffRip_C 2.66 0.08 14.30 0.92 4.21E+02 IP_EffRip_C -2.74 0.07 0.06 0.00 1.01 P_EffRip_C -2.79 0.08 0.06 0.00 1.07 PopDen_C -0.27 0.02 0.77 0.57 0.93 USArea_C -0.18 0.44 0.83 0.37 1.14 USNoBar_C -0.10 0.44 0.90 0.58 1.08 DSNoBar_C 0.00 0.94 1.00 0.90 1.10 TotNoBar_C -0.02 0.71 0.98 0.90 1.06 USBar_C 0.19 0.18 1.20 0.91 1.61 DSBar_C 0.06 0.14 1.07 0.98 1.17 TotBar_C 0.06 0.11 1.06 0.99 1.15 USRatio_C 3.04 0.04 20.90 1.32 7.96E+02 DSRatio_C -0.56 0.65 0.57 0.01 3.08 TotRatio_C 1.29 0.58 3.65 0.02 4.17E+02

273 DecPar Decapoda Parastacidae Confidence I Cherax destructor Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Freshwater crayfish / Estimate yabby EstConn_C -0.35 0.61 0.71 0.17 2.55 CulvRd_C 0.11 0.63 1.12 0.68 1.77 C_CA_C 0.02 0.05 1.02 1.00 1.05 C_AI_C 0.12 0.05 1.13 1.02 1.31 R_CA_C 0.06 0.07 1.06 1.00 1.15 R_AI_C 0.14 0.05 1.16 1.02 1.39

274 EphBaet Ephemeroptera Baetidae Confidence I Mayfly Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C 0.92 0.68 2.52 0.03 2.97E+02 T_ReaRip_C -1.26 0.32 0.29 0.02 3.32 G_ReaRip_C 1.42 0.44 4.13 0.11 2.13E+02 V_ReaRip_C -1.56 0.45 0.21 0.00 12.30 I_SubCatch_C -0.58 0.80 0.56 0.01 49.60 T_SubCatch_C -1.26 0.39 0.29 0.01 4.78 G_SubCatch_C 9.25 0.04 1.04E+04 3.21 3.76E+08 V_SubCatch_C -0.01 1.00 0.99 0.01 66.70 I_FlowSite_C -1.35 0.61 0.26 0.00 43.70 T_FlowSite_C -1.45 0.42 0.24 0.01 7.52 G_FlowSite_C 9.81 0.04 1.82E+04 3.50 7.67E+08 V_FlowSite_C 0.58 0.81 1.79 0.01 2.35E+02 I_FlowStream_C -0.62 0.81 0.54 0.00 99.70 T_FlowStream_C -1.14 0.42 0.32 0.02 4.81 G_FlowStream_C 5.29 0.10 1.98E+02 0.52 2.24E+05 V_FlowStream_C -0.02 0.99 0.98 0.01 1.16E+02 I_Rip_C -1.00 0.67 0.37 0.00 37.40 T_Rip_C -1.12 0.43 0.33 0.02 5.02 G_Rip_C 7.81 0.05 2.45E+03 2.13 1.80E+07 V_Rip_C 0.39 0.86 1.47 0.02 1.18E+02 I_EffRip_C -0.86 0.93 0.42 0.00 1.30E+09 T_EffRip_C -0.21 0.86 0.82 0.08 7.52 G_EffRip_C 47.31 0.01 3.51E+20 2.60E+08 1.16E+38 V_EffRip_C 0.45 0.68 1.57 0.18 14.30 IP_EffRip_C -0.55 0.61 0.58 0.06 4.94 P_EffRip_C -0.58 0.61 0.56 0.06 5.17 PopDen_C -0.05 0.40 0.95 0.85 1.06 USArea_C -0.01 0.96 0.99 0.79 1.23 USNoBar_C 0.00 0.98 1.00 0.87 1.13 DSNoBar_C -0.01 0.72 0.99 0.90 1.07 TotNoBar_C -0.01 0.76 0.99 0.93 1.05 USBar_C 0.17 0.18 1.19 0.93 1.57 DSBar_C 0.02 0.61 1.02 0.94 1.11 TotBar_C 0.03 0.42 1.03 0.96 1.11 USRatio_C 1.03 0.44 2.79 0.21 57.40 DSRatio_C -0.31 0.67 0.73 0.10 2.93 TotRatio_C -0.89 0.69 0.41 0.00 28.90

275 EphBaet Ephemeroptera Baetidae Confidence I Mayfly Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

EstConn_C -0.11 0.83 0.90 0.31 2.53 CulvRd_C -0.12 0.56 0.89 0.57 1.31 C_CA_C -0.01 0.24 0.99 0.98 1.01 C_AI_C -0.02 0.63 0.99 0.92 1.05 R_CA_C -0.02 0.36 0.98 0.94 1.02 R_AI_C -0.01 0.82 0.99 0.92 1.07

276 OdoGomp Odonata Gomphidae Confidence I Dragonfly Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C -11.70 0.41 0.00 0.00 16.60 T_ReaRip_C 5.81 0.19 3.34E+02 0.67 2.64E+07 G_ReaRip_C -5.65 0.28 0.00 0.00 7.90 V_ReaRip_C 15.10 0.29 3.69E+06 0.36 1.97E+26 I_SubCatch_C -53.00 0.13 0.00 0.00 0.00 T_SubCatch_C 10.10 0.14 2.45E+04 5.76 1.12E+14 G_SubCatch_C -12.10 0.10 0.00 0.00 1.49 V_SubCatch_C 25.50 0.14 1.20E+11 96.30 1.58E+30 I_FlowSite_C -51.70 0.21 0.00 0.00 0.00 T_FlowSite_C 1.82E+03 1.00 Inf 0.00 Inf G_FlowSite_C -28.50 0.14 0.00 0.00 0.03 V_FlowSite_C 30.30 0.10 1.38E+13 1.31E+03 2.09E+37 I_FlowStream_C -61.10 0.15 0.00 0.00 0.00 T_FlowStream_C 11.50 0.16 1.00E+05 7.88 1.78E+16 G_FlowStream_C -18.50 0.11 0.00 0.00 0.04 V_FlowStream_C 18.80 0.14 1.40E+08 25.80 4.66E+24 I_Rip_C -67.70 0.13 0.00 0.00 0.00 T_Rip_C 10.60 0.13 4.09E+04 7.49 9.91E+13 G_Rip_C -15.50 0.08 0.00 0.00 0.13 V_Rip_C 20.70 0.14 9.71E+08 45.80 6.34E+25 I_EffRip_C -134.00 0.10 0.00 0.00 0.00 T_EffRip_C 11.30 0.17 8.11E+04 8.92 5.44E+14 G_EffRip_C 2.82 0.77 16.70 0.00 6.50E+08 V_EffRip_C 19.20 0.15 2.24E+08 25.10 4.09E+24 IP_EffRip_C -49.70 0.27 0.00 0.00 0.00 P_EffRip_C -117.00 0.34 0.00 0.00 0.00 PopDen_C -0.71 0.11 0.49 0.14 0.85 USArea_C 0.01 0.96 1.01 0.60 1.32 USNoBar_C 0.00 0.96 1.00 0.74 1.18 DSNoBar_C 0.02 0.77 1.02 0.88 1.14 TotNoBar_C 0.01 0.79 1.01 0.90 1.12 USBar_C 0.56 0.03 1.75 1.16 3.56 DSBar_C 0.07 0.17 1.07 0.96 1.19 TotBar_C 0.09 0.07 1.09 1.00 1.21 USRatio_C 2.25 0.15 9.51 0.32 2.36E+02 DSRatio_C -0.06 0.96 0.94 0.01 5.10 TotRatio_C 2.63 0.32 13.90 0.03 3.67E+03

277 OdoGomp Odonata Gomphidae Confidence I Dragonfly Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

EstConn_C -1.31 0.25 0.27 0.01 1.83 CulvRd_C 0.00 1.00 1.00 0.45 1.81 C_CA_C 0.07 0.10 1.07 1.01 1.20 C_AI_C 0.69 0.09 2.00 1.14 6.55 R_CA_C 0.12 0.10 1.13 1.02 1.38 R_AI_C -11.70 0.41 0.00 0.00 16.60

278 OdoHemi Odonata Hemicorduliidae Confidence I Dragonfly Coefficient Pr(<|z|) Odds 2.5% 97.5% Estimate Ratio I_ReaRip_C 0.71 0.83 2.03 0.01 1.22E+04 T_ReaRip_C -0.51 0.77 0.60 0.01 15.10 G_ReaRip_C -0.02 0.99 0.98 0.01 2.41E+02 V_ReaRip_C -1.61 0.62 0.20 0.00 41.10 I_SubCatch_C -1.57 0.61 0.21 0.00 82.20 T_SubCatch_C 0.12 0.95 1.12 0.03 66.90 G_SubCatch_C 3.48 0.46 32.40 0.00 5.58E+05 V_SubCatch_C 1.53 0.60 4.62 0.02 2.99E+03 I_FlowSite_C -1.79 0.61 0.17 0.00 1.57E+02 T_FlowSite_C -0.14 0.95 0.87 0.01 1.08E+02 G_FlowSite_C 4.21 0.47 67.20 0.00 3.11E+07 V_FlowSite_C 1.20 0.71 3.33 0.01 3.70E+03 I_FlowStream_C -2.65 0.46 0.07 0.00 75.10 T_FlowStream_C 0.11 0.95 1.12 0.03 54.40 G_FlowStream_C 2.11 0.58 8.24 0.00 2.49E+04 V_FlowStream_C 1.84 0.57 6.27 0.01 7.12E+03 I_Rip_C -2.09 0.51 0.12 0.00 59.90 T_Rip_C -0.16 0.93 0.85 0.02 40.10 G_Rip_C 3.80 0.38 44.90 0.01 4.13E+05 V_Rip_C 1.37 0.64 3.92 0.01 2.13E+03 I_EffRip_C 38.40 0.28 4.74E+16 0.00 1.53E+54 T_EffRip_C -0.29 0.85 0.75 0.04 17.40 G_EffRip_C 1.01E+02 0.09 5.43E+43 7.55E+08 1.61E+109 V_EffRip_C 0.22 0.88 1.25 0.07 31.10 IP_EffRip_C -0.34 0.82 0.71 0.03 12.20 P_EffRip_C -0.52 0.73 0.59 0.02 11.30 PopDen_C -0.01 0.89 0.99 0.85 1.15 USArea_C 8.89 0.07 7.25E+03 4.70 1.17E+09 USNoBar_C 2.74 0.16 15.50 1.62 1.96E+03 DSNoBar_C -0.05 0.25 0.95 0.86 1.04 TotNoBar_C -0.01 0.78 0.99 0.91 1.08 USBar_C 0.21 0.36 1.23 0.86 2.29 DSBar_C 0.02 0.71 1.02 0.93 1.21 TotBar_C 0.03 0.57 1.03 0.94 1.20 USRatio_C -2.08 0.14 0.13 0.01 2.28 DSRatio_C -0.99 0.19 0.37 0.05 1.65

279 OdoHemi Odonata Hemicorduliidae Confidence I Dragonfly Coefficient Pr(<|z|) Odds 2.5% 97.5% Estimate Ratio TotRatio_C -2.68 0.26 0.07 0.00 11.10 EstConn_C 0.51 0.49 1.67 0.42 8.70 CulvRd_C -0.25 0.31 0.78 0.47 1.29 C_CA_C -0.01 0.49 0.99 0.98 1.01 C_AI_C -0.05 0.31 0.95 0.86 1.04 R_CA_C -0.02 0.48 0.98 0.92 1.04 R_AI_C -0.07 0.23 0.93 0.81 1.04

280 DecAty Decapoda Atyidae Confidence I Freshwater shrimp Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

I_ReaRip_C -1.34 0.59 0.26 0.00 25.00 T_ReaRip_C 0.20 0.87 1.22 0.10 16.20 G_ReaRip_C 0.56 0.76 1.75 0.04 72.20 V_ReaRip_C 1.35 0.54 3.86 0.06 6.01E+02 I_SubCatch_C -2.46 0.29 0.09 0.00 7.89 T_SubCatch_C 0.55 0.70 1.73 0.10 31.10 G_SubCatch_C 2.22 0.55 9.23 0.01 2.28E+04 V_SubCatch_C 1.98 0.36 7.27 0.11 6.21E+02 I_FlowSite_C -3.63 0.19 0.03 0.00 5.01 T_FlowSite_C 1.33 0.46 3.79 0.12 1.47E+02 G_FlowSite_C 1.88 0.64 6.54 0.00 2.19E+04 V_FlowSite_C 3.34 0.19 28.30 0.21 6.24E+03 I_FlowStream_C -2.52 0.36 0.08 0.00 15.80 T_FlowStream_C 0.62 0.66 1.85 0.12 30.40 G_FlowStream_C -0.19 0.95 0.83 0.00 2.84E+02 V_FlowStream_C 1.76 0.47 5.81 0.05 9.03E+02 I_Rip_C -2.46 0.32 0.09 0.00 9.35 T_Rip_C 0.62 0.66 1.86 0.12 31.30 G_Rip_C 0.81 0.80 2.26 0.00 1.80E+03 V_Rip_C 1.94 0.39 6.97 0.09 7.29E+02 I_EffRip_C -15.92 0.36 0.00 0.00 9.34E+03 T_EffRip_C 0.95 0.41 2.59 0.28 2.65E+01 G_EffRip_C 10.30 0.15 2.98E+04 0.06 3.73E+11 V_EffRip_C 1.21 0.29 3.34 0.37 33.70 IP_EffRip_C -1.23 0.27 0.29 0.03 2.56 P_EffRip_C -1.19 0.31 0.31 0.03 2.88 PopDen_C -0.10 0.10 0.90 0.79 1.01 USArea_C -0.08 0.49 0.92 0.68 1.14 USNoBar_C -0.04 0.52 0.96 0.80 1.08 DSNoBar_C 0.05 0.20 1.06 0.98 1.16 TotNoBar_C 0.02 0.46 1.02 0.96 1.09 USBar_C 0.21 0.11 1.24 0.97 1.66 DSBar_C 0.14 0.04 1.15 1.03 1.37 TotBar_C 0.11 0.03 1.12 1.03 1.26 USRatio_C 1.34 0.32 3.84 0.29 88.30 DSRatio_C -0.64 0.49 0.53 0.03 2.29 TotRatio_C 0.94 0.66 2.57 0.03 2.54E+02

281 DecAty Decapoda Atyidae Confidence I Freshwater shrimp Coefficient Pr(<|z|) Odds Ratio 2.5% 97.5% Estimate

EstConn_C -0.46 0.40 0.63 0.20 1.80 CulvRd_C 0.04 0.84 1.04 0.69 1.55 C_CA_C 0.00 0.52 1.00 0.99 1.02 C_AI_C 0.03 0.35 1.03 0.97 1.11 R_CA_C 0.01 0.51 1.02 0.97 1.06 R_AI_C 0.04 0.30 1.04 0.97 1.14

282 Appendix 22 Candidate explanatory metrics considered for GLM testing of taxa occurrence in Chapter 5

Table A22.1 Selected variables for GLM for CrimPA10, PurPA10, and FrePA10

The initial model for each fish species’ occurrence response included one reach-scale metric, one catchment-scale metric and one catchment extent metric. Preliminary single variable logistic regressions (Appendix 20) were performed to determine which variables had a statistically- significant effect on taxa occurrence; variables with a Wald test statistic of Pr < 0.2 () were selected for inclusion in the GLM models.

a) 2010 Melanotaenia duboulayi (Crimson spotted rainbow fish) (CrimPA10);2010 Mogurnda adspersa (Purple spotted gudgeon) (PursPA10); 2010 Tandanus tandanus (Freshwater catfish) (FrePA10) Metric Metric Sub- CrimPA10 PursPA10 FrePA10 Category Category Metric Reach scale Reach-scale I_ReaRip_C metrics T_ReaRip_C G_ReaRip_C Catchment Lumped I_SubCatch_C scale catchment-scale T_SubCatch_C metric G_SubCatch_C Flowpath IDW I_FlowSite_C to-site T_FlowSite_C G_FlowSite_C Flowpath IDW I_FlowStream_C to-stream T_FlowStream_C G_FlowStream_C Upstream I_Rip_C riparian buffer T_Rip_C G_Rip_C Upstream I_EffRip_C effective P_EffRip_C riparian buffer IP_EffRip_C T_EffRip_C G_EffRip_C Population density PopDen_C Catchment and C_CA_C riparian tree- C_AI_C cover R_CA_C

283 Metric Metric Sub- CrimPA10 PursPA10 FrePA10 Category Category Metric fragmentation R_AI_C In-stream USBar_C longitudinal DSBar_C connectivity TotBar_C USRatio_C DSRatio_C TotRatio_C Number of EstConn_C culverts downstream that CulvRd_C go under roads Catchment Sub-catchment USArea_C extent metric size or tributary extent USNoBar_C DSNoBar_C TotNoBar_C

b) 2011 Melanotaenia duboulayi (Crimson spotted rainbow fish) (CrimPA11); 2011 Mogurnda adspersa (Purple spotted gudgeon) (PursPA11); 2011 Tandanus tandanus (Freshwater catfish) (FrePA11)

Metric Metric Sub- CrimPA11 PursPA11 FrePA11 Category Category Metric Reach scale Reach-scale I_ReaRip_C metrics T_ReaRip_C G_ReaRip_C Catchment Lumped I_SubCatch_C scale catchment-scale T_SubCatch_C metric G_SubCatch_C Flowpath IDW I_FlowSite_C to-site T_FlowSite_C G_FlowSite_C Flowpath IDW I_FlowStream_C to-stream T_FlowStream_C G_FlowStream_C Upstream I_Rip_C riparian buffer T_Rip_C G_Rip_C

284 Metric Metric Sub- CrimPA11 PursPA11 FrePA11 Category Category Metric Upstream I_EffRip_C effective P_EffRip_C riparian buffer IP_EffRip_C T_EffRip_C G_EffRip_C Population density PopDen_C Catchment and C_CA_C Riparian C_AI_C Fragmentation R_CA_C R_AI_C In-stream USBar_C longitudinal DSBar_C connectivity TotBar_C USRatio_C DSRatio_C TotRatio_C Number of EstConn_C culverts downstream that CulvRd_C go under roads Catchment Sub-catchment USArea_C extent size or tributary extent USNoBar_C DSNoBar_C TotNoBar_C

285 Table A22.2 Selected variables for GLM for macroinvertebrate occurrence (2010 data)

The initial model for each macroinvertebrate’s presence absence response included one reach-scale metric, one catchment-scale metric and one catchment extent metric. Variables with Pr < 0.2 () were selected for inclusion in the GLM models.

a) Trichoptera, Leptoceridae (TricLept); Ephemeroptera, Leptophlebiidae (EphLept);Decapoda, Parastacidae Cherax destructor (DecPar) Metric Metric Sub- TricLept EphLept DecPar Category Category Metric Reach scale Reach-scale I_ReaRip_C metrics T_ReaRip_C G_ReaRip_C Catchment Lumped I_SubCatch_C scale catchment-scale T_SubCatch_C metric G_SubCatch_C Flowpath IDW to- I_FlowSite_C site T_FlowSite_C G_FlowSite_C Flowpath IDW to- I_FlowStream_C stream T_FlowStream_C G_FlowStream_C Upstream riparian I_Rip_C buffer T_Rip_C G_Rip_C Upstream I_EffRip_C effective riparian P_EffRip_C buffer IP_EffRip_C T_EffRip_C G_EffRip_C Population density PopDen_C Catchment and C_CA_C riparian tree-cover C_AI_C fragmentation R_CA_C R_AI_C In-stream USBar_C longitudinal DSBar_C connectivity TotBar_C USRatio_C

286 Metric Metric Sub- TricLept EphLept DecPar Category Category Metric DSRatio_C TotRatio_C Number of culverts EstConn_C downstream that go under roads CulvRd_C Catchment Sub-catchment USArea_C extent size or tributary extent USNoBar_C DSNoBar_C TotNoBar_C

b) Ephemeroptera, Baetidae (EphBaet), Odonata, Gomphidae (OdoGomp) and Odonata Hemicorduliidae (OdoHemi)

Metric Metric Sub- EphBaet OdoGomp OdoHemi Category Category Metric Reach scale Reach-scale I_ReaRip_C metrics T_ReaRip_C G_ReaRip_C Catchment Lumped I_SubCatch_C scale catchment-scale T_SubCatch_C metric G_SubCatch_C Flowpath IDW to- I_FlowSite_C site T_FlowSite_C G_FlowSite_C Flowpath IDW to- I_FlowStream_C stream T_FlowStream_C G_FlowStream_C Upstream riparian I_Rip_C buffer T_Rip_C G_Rip_C Upstream I_EffRip_C effective riparian P_EffRip_C buffer IP_EffRip_C T_EffRip_C G_EffRip_C

287 Metric Metric Sub- EphBaet OdoGomp OdoHemi Category Category Metric Population density PopDen_C Catchment and C_CA_C riparian C_AI_C fragmentation R_CA_C R_AI_C In-stream USBar_C longitudinal DSBar_C connectivity TotBar_C USRatio_C DSRatio_C TotRatio_C Number of EstConn_C culverts downstream that CulvRd_C go under roads Catchment Sub-catchment USArea_C extent size or tributary extent USNoBar_C DSNoBar_C TotNoBar_C

288 Appendix 23 Spatial autocorrelation investigation for occurrence data using GLM logistic regression for Chapter 5

Variograms of the residuals from GLM occurrence models using geoR (Ribeiro and Diggle 2001) for (a) fish and (b) macroinvertebrates.

(a) Fish

The GLM models used for the fish variograms above include: CrimPA10 = C_CA_C+ TotNoBar_C CrimPA11= USBar_C + DSNoBar_C PursPA10= I_Rip_C + TotNoBar_C PursPA11= T_Rip_C + DSNoBar_C FrePA10= USBar_C + TotNoBar_C

289 FrePA11= T_FlowSite_C + TotNoBar_C

(b) Macroinvertebrates

The GLM models used for the macroinvertebrate variograms above include: EphLept= I_ReaRip_C + USBar_C

290 TricLept= PopDen_C + TotNoBar_C DecPar= PopDen_C DecAty= TotBar_C OdoGomp=C_AI_C OdoHemi= USArea_C EphBaet= G_Rip_C

The only taxon that exhibited spatial autocorrelation was Cherax destructor (DecPar). For this taxon, a spatial generalised linear mixed model (GLMM) was specified (Dormann et al. 2007) and three correlation structures were considered. The residuals for the non-spatial model and the three spatial models were very similar, and therefore a spatial autocorrelation term was not retained.

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