The Influence of Riparian Vegetation on Water Quality in a Mixed Land Use River Basin

Chua Evan (Orcid ID: 0000-0003-4220-7985)

The influence of riparian vegetation on water quality in a mixed land use river basin

Running Head: Riparian Damage Affects Water Quality

Evan M. ChuaA,D, Scott P. Wilson, Sue VinkC and Nicole FlintA

ASchool of Health, Medical and Applied Sciences, Central Queensland University, North Rockhampton, QLD 4702, Australia BCentre for Energy and Environmental Contaminants, Department of Environmental Sciences, Macquarie University, North Ryde, NSW 2113, Australia CCentre for Water in the Minerals Industry, Sustainable Minerals Institute, University of Queensland, St Lucia, QLD 4072, Australia DCorresponding author. Email: [email protected]

This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/rra.3410

Worldwide, agricultural activities are associated with environmental impacts including riparian degradation and increased waterway pollution. The Fitzroy Basin of Central Queensland lies within the Brigalow Belt Bioregion (BBB), which is currently experiencing the highest rates of tree- clearing in Australia driven by grazing activities, and this is likely to increase riparian degradation. Local riparian condition however, has not been consistently monitored within the Fitzroy Basin, and its relationship with water quality has not been well-established. This study assessed riparian condition of waterways in the Fitzroy Basin using two established scoring methods (the Rapid Appraisal of Riparian Condition and the Australian Rivers Assessment System Habitat Assessment), and statistically investigated the relationship between stream water quality and local riparian condition. Twelve sites in six waterways were sampled four times over two years, during ambient (non-flood) conditions. Upstream creeks with poorer riparian condition had elevated dissolved organic carbon (DOC), dissolved manganese, sulfate and total nitrogen (TN) concentrations. High concentrations of these water quality variables were associated with poor local riparian scores in principal components analysis. Dissolved manganese and sulfate were included in the multiple regression model for riparian scores (r-values: 0.83 to 0.86; p < 0.001), and DOC and TN were very significantly negatively correlated with riparian scores (r-values: -0.55 to -0.69; p < 0.001). Overall, the results suggest that protecting riparian vegetation in the Fitzroy Basin and restoring degraded riparian zones, could aid in improving water quality in waterways within this region.

Keywords: Fitzroy Basin, Brigalow Belt Bioregion, Pollution, Metals, Nutrients, Queensland, Agriculture, Mining

Degradation of riparian zones increases riverbank erosion and sediment transport into waterways, and reduces its ability to filter out pollutants from overland run-off (Carroll, Merton, & Burger, 2000; Naiman & Decamps, 1997; Talmage, Perry, & Goldstein, 2002). Worldwide, tree- clearing has been associated with large-scale ecosystem impacts including increased soil erosion (Kaiser, 2004; Lal, 1996), loss of soil fertility, and increased transport of pollutants into receiving waterways (Mainville et al., 2006). Clearing of riparian zones, including for livestock grazing activities, especially pose a threat to waterway condition (Jones, Helfman, Harper, & Bolstad, 1999). Grazing activities also result in increased riparian damage through processes such as trampling and grazing on native vegetation (Derlet, Goldman, & Connor, 2010; Malan, Flint, Jackson, Irving, & Swain, 2018).

In Australia, the Brigalow Belt Bioregion (BBB) is a major area of historical and current land- clearing for agricultural activities such as cattle grazing and cropping (Neldner et al., 2017; Yu, Joo, & Carroll, 2013). The loss of woody vegetation and conversion of land for agriculture has been associated with increased transport of pollutants into Queensland waterways (McKergow, Prosser, Hughes, & Brodie, 2005; Packett, Dougall, Rohde, & Noble, 2009). Grazing, cropping and riparian degradation in Queensland catchments are also identified as major contributors to sediment pollution in the Great Barrier Reef (McKergow, Prosser, Hughes, & Brodie, 2005; Bartley et al., 2014). Besides agricultural activities, coal mining activities also occur in the northern BBB, many of which are large open-cut mining operations which have also contributed to disturbances to natural vegetation in the region (Arnold, Audet, Doley, & Baumgartl, 2013).

The Fitzroy Basin in Central Queensland is Australia’s largest east-draining basin, and lies almost completely within the BBB (Verwey & Wearing, 2007). Beef cattle grazing is an important economic activity in the Fitzroy Basin, constituting nearly 80% of the land use (QDSITIA, 2012). Cropping is also important, constituting about 6% of the land use, while coal mining, another major economic activity in the basin, constitutes <1% of the land use (QDSITIA, 2012). Similar to other major agricultural regions, the Fitzroy Basin is impacted by extensive soil erosion (Dougall et al., 2009; Murphy, Dougall, Burger, & Carroll, 2013), along with riparian degradation from grazing and other agricultural activities (Alam, Rolfe, & Windle, 2004). Soil erosion in the Fitzroy Basin is also a

This article is protected by copyright. All rights reserved. major contributor to sediment entering the Great Barrier Reef lagoon, with five out of eight priority management units for controlling sediment export identified in the Fitzroy Basin (Wilkinson et al., 2015).

Rain events over agricultural areas are linked with increased total suspended sediment and nutrient loads (Packett, Dougall, Rohde, & Noble, 2009), and poor riparian vegetation coverage has been linked with increased electrical conductivity (EC) in Fitzroy Basin waterways (Carroll et al., 2000). Metal pollution is a problem in the basin, with dissolved Al, Cd, Co, Cu, Mn and Zn frequently exceeding local water quality guidelines. The Fitzroy Partnership for River Health (FPRH) report card has given these same metals a grade of ‘C’, ‘D’ or ‘E’ for several catchments of the Fitzroy Basin from 2010-16 (FPRH, 2018). Whilst natural sources (e.g. geological weathering) can be a contributor to metals in waterways (Förstner & Wittmann, 2012), human activities including coal mining and coal seam gas extraction (Ali, Strezov, Davies, & Wright, 2018) and agriculture-associated riparian degradation and soil erosion have been implicated in waterway metal pollution (Quinton & Catt, 2007; Zhang & Shan, 2008).

Whilst the associations between land uses and some pollutants in Fitzroy Basin waterways have been previously demonstrated (Carroll et al., 2000; Packett et al., 2009; Murphy, Dougall, Burger, & Carroll, 2013), the association between local riparian condition and adjacent water quality has not been as well studied. Furthermore, with the lack of consistent riparian monitoring in the Fitzroy Basin (Flint et al., 2017), comparisons between sites is difficult. Thus this study aims to: (i) record riparian condition in the Fitzroy Basin using two established methods; and (ii) assess the association between riparian condition and water quality during ambient flow conditions.

2.1 Sampling Sites

Only sites with permanent water were sampled. Two sampling sites were identified at each of six waterways: Comet River (CR), Isaac River (IR), Mackenzie River (MR), German Creek (GC), Scott Creek (SC) and Stockyard Creek (SY) making up 12 sites in total (Figure 1). At each waterway, the two selected sites were situated about 1km apart, with the upstream site name affixed with ’U’, and downstream site affixed with ‘D’. All sites were sampled four times over consecutive post-wet season (April) and pre-wet season (October) surveys in 2015-16. Thus surveys 1-4 were conducted in April 2015, October 2015, April 2016 and October 2016 respectively. Sites CR, IR and MR were located within the main river channels of their respective catchments, whilst sites GC, SC and SY were located on creeks upstream from the main river channels. Sites MRU and CRU were both located upstream of an impoundment. Cattle grazing occurred adjacent to the waterway at all of the creek sites, and at sites CRD, CRU and MRU.

2.2 Water Quality and Habitat Assessment

Water quality variables analysed included: dissolved metals – Al, Cd, Co, Cu, Mn, and Zn; nutrients – oxides of nitrogen (NOX), ammonia (NH3), total nitrogen (TN), and total phosphorus (TP); and water physicochemical parameters – temperature, dissolved organic carbon (DOC), electrical conductivity (EC), pH, sulfate (SO4) and turbidity. Two replicates of water samples were collected from each sampling site, with sampling conducted in accordance with the Queensland Monitoring and Sampling Manual 2009 (Queensland Department of Environment and Heritage Protection, 2009). Water was collected in sterile sampling bottles/jars provided by a commercial laboratory (ALS Environmental, https://www.alsglobal.com/au), with samples for dissolved metals and DOC field- filtered through a sterile 0.45µm cellulose-acetate filter, and a 0.9 µm pre-filter if required. Samples were refrigerated at 4°C and transported to ALS Environmental for analysis at the end of each sampling trip. Temperature, EC, pH and turbidity measurements were collected in situ using a calibrated YSI 6920 V2 water quality multimeter (YSI Inc, Yellow Springs, USA). The multimeter probe was placed in water at least 1 m away from the riverbank at the sampling site, with care taken to

This article is protected by copyright. All rights reserved. keep the probe from touching the substrate. Turbidity data were not available for two and four sampling events in April and October 2015 respectively due to probe malfunction.

Water quality results were compared to Australian water quality guideline values (WQGVs) for freshwater ecosystem protection (at the 95% level) for metals (ANZECC & ARMCANZ, 2000), and Queensland Government water quality objectives (WQOs) for nutrients and physicochemical parameters in the river catchments of the Fitzroy Basin (QDEHP, 2011a, 2011b, 2011c). WQGVs and WQOs are provided as Supporting Information (Tables S1 and S2).

Habitat and riparian condition were assessed and scored from approximately 50 m upstream to 50 m downstream of each sampling site using two established methods: The Rapid Appraisal of Riparian Condition scoring sheet (adapted from Jansen, Robertson, Thompson, & Wilson, 2005), henceforth referred to as ‘RARCSCORE’, and the United States Environmental Protection Agency (USEPA) stream habitat score sheet for low gradient streams (Barbour, Gerritsen, Snyder, & Stribling, 1999), henceforth referred to as ‘HABSCORE’. RARCSCORE is intended for assessing cattle grazing impacts to the riparian zone (Jansen et al., 2005), and has been implemented in other Australian regions (Newham, Fellows, & Sheldon, 2011; Dixon, Douglas, Dowe, Burrows, & Townsend, 2005). HABSCORE is a stream habitat score which provides ten measures of human disturbances to the river habitat, three of which measure riparian condition: Riparian Zone Score, Bank Stability and Vegetative Protection. Whilst HABSCORE does not solely assess riparian condition, it is useful for assessing overall human impacts to a waterway. HABSCORE has also been integrated into the Australian Rivers Assessment (AUSRIVAS) Physical Assessment Protocol (Parsons, Thoms, & Norris, 2002), and is included in the Queensland Australian River Assessment System (QDNRM 2001). A summary of the HABSCORE and RARCSCORE scoring criteria is provided as Supporting Information (Tables S3 and S4).

Prior to statistical analysis, all water quality data values below the laboratory’s analytical limit of reporting (

Relationships between habitat scores vs. in-stream abiotic variables were analysed using Principal Components Analysis (PCA), Spearman’s ranked correlation, and multiple regression analysis using SPSS Statistics 23 software (IBM, USA). The number of components extracted for PCA was based on inspection of the scree plot, and a rotated PCA matrix was created using the varimax method. For Spearman’s correlation analysis, two hydrology variables (Strahler stream order and recent rainfall) were also analysed to explore the possibility of an influence on water quality. Rainfall data (total rainfall four weeks prior to sampling) was sourced from the nearest monitoring station to each sampling site, available online on the Queensland Department of Natural Resources and Mines (QDNRM) Water Monitoring Information Portal (State of Queensland Government, 2018). Stream order of each sampling site was sourced from the Queensland Government’s Queensland Globe dataset (State of Queensland Government, 2017). Each site’s stream order was as follows: MRD, MRU, IRD and IRU was ‘8’; CRD and CRU was ‘7’, GCD, GCU, SYD and SYU was ‘4’; and SCD and SCU was ‘5’. Multiple regression analysis was conducted using the stepwise method, with HABSCORE and RARCSCORE as dependent variables. Tolerance values were calculated to check for collinearity between regression model variables.

3.1 Water Quality and Habitat Assessment

Full water quality results by site are provided in the supporting information section Tables S5 and S6. DOC concentration tended to be elevated at creek sites, it was highest at site GCU located at German Creek (up to 30.0 mg/L), and lowest (≤ 7.5 mg/L) at sites IRD and IRU at Isaac River and sites MRD and MRU at Mackenzie River. All sites had EC levels above the WQGV, though EC levels tended to be elevated at creek sites, and was highest at sites SYU at Stockyard Creek and GCU. Turbidity levels were elevated and above the WQO at most sites, reaching up to 356 NTU at site SCU at Scott Creek. In contrast, at sites IRD and IRU turbidity was <14 NTU. All sites had SO4 concentrations above the WQGV, though SO4 concentrations tended to be elevated at creek sites, and was highest at sites GCU and SCD

(up to 74.5 mg/L). In contrast, SO4 concentrations were <4 mg/L at sites CRD and CRU at Comet River and sites IRD and IRU. NOX concentrations tended to be elevated at the main river channel sites, and was highest at site MRU at Mackenzie River (0.69 mg/L). TN concentrations tended to be elevated at creek sites, and was highest at sites SYU (up to 2.25 mg/L) and SYD (up to 2.20 mg/L). Most sites had dissolved Al concentrations above the WQGV of 55 µg/L, reaching up to 1080 µg/L at site SCU. Dissolved Cu was slightly above the WQGV of 1.4 µg/L at sites CRU, CRU, MRD and MRU (up to 3.0 µg/L), and was below the LOR at all creek sites. Dissolved Mn concentrations were below the WQGV of 1900 µg/L at any site, but tended to be elevated at creek sites, and were highest at sites SYD, SYU and GCU (up to 1310 µg/L).

RARCSCORE and HABSCORE were generally higher (i.e. habitat condition was better) at the main river channel sites (CRD, CRU, IRD, IRU, MRD and MRU) than the creek sites (GCD, GCU, SCD, SCU, SYD and SYU), except for site MRU (Figure 2). RARCSCORE for main river channel sites ranged from 24.0 to 47.0 (out of a maximum of 50), whilst creek sites ranged from 17.0 to 25.5. HABSCORE for main river channel sites ranged from 124 to 189 (out of a maximum of 200), whilst creek sites ranged from 97 to 151. Sites IRD and IRU had the highest RARCSCORE (47/50 for both) and HABSCORE (186 and 189/200 respectively). In contrast, RARCSCORE was lowest at sites SYD and SYU (both 17/50), and HABSCORE was lowest at site GCU (97/200). Full HABSCORE and RARCSCORE by site is provided as Supporting Information (Tables S7 and S8).

The first three principle components (PCs) cumulatively explained 28.5, 47.5 and 61.0% of the variation in the data respectively. Both RARCSCORE and HABSCORE loaded onto PC1 along with DOC,

TN, dissolved Mn, and NH3 (Table 1). Whilst HABSCORE and RARCSCORE had a negative relationship with PC1, DOC, TN and dissolved Mn all had a positive relationship. Turbidity loaded onto PC2, along with TP, dissolved Al, dissolved Zn and EC. EC was negatively associated with PC2, whereas all other variables were positively associated.

The PCA plot showed a distinct separation between the creek sampling sites (GCD, GCU, SCD, SCU, SYD and SYU), and the main river channel sites (CRD, CRU, IRD, IRU, MRD and MRU) along the PC1 and PC2 axis (Figure 3). The position of creek sites relative to the eigenvectors (representing abiotic variables), indicated creek sites and site MRU were generally associated with poorer RARCSCORE and

HABSCORE, and elevated dissolved Mn, DOC, EC, NH3, and TN, along with greater variation in water quality across sampling events. Sites CRD, CRU and MRU were more strongly associated with increased levels of dissolved Al, TP and turbidity. In contrast, sites IRD and IRU were associated with lower levels of water pollutants, higher HABSCORE and RARCSCORE, and less variation in water quality across all sampling events.

RARCSCORE was significantly negatively correlated with DOC, EC and TN (r = -0.69, -0.63, -0.62 respectively; p < 0.001 for all; Table 2). HABSCORE was significantly negatively correlated with DOC, SO4, TN and turbidity (r = -0.62, -0.45. -0.48 and -0.55 respectively; p < 0.01 for all). Stream order had similar correlation trends to RARCSCORE and HABSCORE, and was also significantly negatively correlated DOC, EC and TN (r = -0.78, -0.52 and -0.59 respectively; p < 0.001 for all). Stream order was also significantly positively correlated with NOX (r = 0.35, p < 0.001).

A significant RARCSCORE multiple regression model (ANOVA F-value = 20.2, p < 0.001) included

2 dissolved Mn, NH3, SO4 and TN (r = 0.83, r = 0.69) as contributing factors. Tolerance values ranged from

0.67 for dissolved Mn, to 0.77 for SO4, indicating low level of collinearity between RARCSCORE multiple regression model variables. A significant HABSCORE multiple regression model (ANOVA F-value = 26.3, p

2 < 0.001) included DOC, dissolved Mn, SO4, and turbidity (r = 0.86, r = 0.74) as contributing variables.

This article is protected by copyright. All rights reserved. Tolerance values ranged from 0.58 for DOC, to 0.79 for SO4, indicating low levels of collinearity between HABSCORE multiple regression model variables.

In general, creek sites (GCD, GCU, SCD, SCU, SYD and SYU) tended to have poorer RARCSCORE and HABSCORE than river sites, along with elevated concentrations of DOC, TN, SO4 and dissolved Mn. The same water quality variables also had statistically significant associations with the riparian scores based on correlation, PCA and multiple regression analysis. Agricultural activities are a known source of DOC and nitrogenous nutrient pollutants entering waterways in the Fitzroy Basin (Packett et al., 2009). Elsewhere in South Australia, grazing activities and soil erosion are linked with increased waterway DOC concentrations (Nelson, Cotsaris, & Malcolm Oades, 1996), and in Western Australia, grazing damage to riparian areas is associated with increased waterway TN pollution (McKergow, Weaver, Prosser, Grayson, & Reed, 2001). Agriculture is also the largest contributor of TN to Australian waterways (Bartley, Speirs, Ellis, & Waters, 2012). Nitrogen pollution has previously been implicated in eutrophication and cyanobacterial blooms in Fitzroy Basin (Bormans, 2004) and Australian waterways (Davis & Koop, 2006). EC was negatively correlated with both riparian scores, and the present study’s results are in agreement with previous studies linking poor riparian condition with increased EC in adjacent waterways (Carroll et al., 2000; McKergow et al., 2001).

The association between dissolved metals, SO4 and riparian condition in agricultural regions has not been well studied in Australia. In the present study, SO4 concentrations were notably elevated at the creek sites. Whilst SO4 pollution has been linked to gold mining activities in the Fitzroy Basin (Holland, Duivenvoorden, & Kinnear, 2013), the lack of any mining or industrial activities upstream of the creek sites monitored in this study makes it unlikely that the sources of SO4 at these sites were industrial. High

SO4 concentrations have been recorded in Fitzroy Basin soils (up to 7000 µg/g; Carroll et al., 2000), and processes which mobilise soil minerals (e.g. soil erosion and riparian degradation) can increase the rate of soil minerals entering waterways in runoff (Carroll et al., 2000; Naiman & Decamps, 1997). It is thus hypothesised that soil erosion exacerbated by poor riparian condition may have resulted in increased transport of Mn and SO4 into waterways, though further research will be required to confirm this hypothesis. Whilst SO4 concentrations were above Queensland WQOs (5-25 mg/L) at several sites, some locally-present aquatic species are relatively tolerant to elevated SO4, with an SO4 trigger value of 545 mg/L recommended for Fitzroy Basin freshwater ecosystem protection at the 95% level (Dunlop et al., 2016). The effects of Mn pollution on Australian freshwater ecosystems has been poorly studied.

This article is protected by copyright. All rights reserved. Elsewhere, dissolved Mn at 4670 µg/L results in reduced growth and survival of brown trout (Salmo trutta; Stubblefield et al., 1997). In the present study, elevated concentrations of dissolved Mn were notably also associated with elevated DOC concentrations, especially at the creek sites. As DOC can bind to metals and reduce their toxicity to Australian freshwater fauna (Holland, Duivenvoorden, & Kinnear, 2014; Holland et al., 2013), it is possible that any impacts from elevated dissolved Mn may have been ameliorated.

Dissolved Al concentrations were notably elevated (up to 1080 µg/L) at the majority of sampling sites. Such concentrations could negatively impact algal communities, with concentrations at 3-50 µg/L pollution (at pH 5-6) inhibitory to algal growth (Lise & G.C., 1994). However, freshwater shrimp (Caradina indistincta), which are widespread in the Fitzroy Basin, may be more tolerant, with dissolved Al concentrations of approximately 3000 µg/L (at pH 7) resulting in 50% mortality (Chapman & Simpson, 2005). Soil erosion could potentially be a major source of Al entering waterways. Soil erosion is widespread in the Fitzroy Basin (Hughes et al., 2001; Silburn, Carroll, Ciesiolka, deVoil, & Burger, 2011), and Al-rich smectite-clay soils are common in the BBB (Bui, González-Orozco, & Miller, 2014). Rain events are associated with influxes of turbid water and elevated Al concentrations in Fitzroy Basin waterways, based on previous Queensland Government waterway monitoring data conducted during large flow events and coal mine water releases (BMT WBM, 2015). To better ascertain the contribution of soil erosion and riparian condition to metal pollution in the region, future studies could attempt to assess the concentrations of metals and other pollutants in run-off, comparing results from areas experiencing high rates of soil erosion and poor riparian condition, with areas in comparatively good condition.

Rainfall was not correlated with water quality variables, and did not appear to have a strong influence on site water quality during the study period. Whilst Packett et al. (2009) previously noted that intense rainfall over Fitzroy Basin agricultural lands was associated with highest pollutant concentrations in waterways, their study was mainly conducted around wet season rainfall events, whereas in the present study, sampling was conducted during ambient conditions in the post-wet season (April) and pre-wet season (October). The timing of surveys in the present study, conducted during base-flow conditions, may have lessened the influence of rainfall on water quality, allowing for the influence of riparian and habitat condition to be better discerned. Poor riparian condition in association with high

This article is protected by copyright. All rights reserved. rainfall however, can exacerbate waterway pollution. In north Queensland, narrow riparian width is associated with poor pollutant trapping by riparian vegetation due to short water retention periods caused by high rainfall, with implications for pollutant loads entering the Great Barrier Reef lagoon (Waterhouse, Brodie, Lewis, & Audas, 2016). Similar conditions may occur in the Fitzroy Basin, which despite its location in the dry tropics and subtropics, does experience episodic periods of heavy rainfall (Packett et al., 2009).

Lower-ordered streams (i.e. the creek sites) tended to have poorer habitat scores and water quality, explaining the similar correlation trends between stream order and riparian scores. Poor stream riparian condition in upstream locations has been observed in other grazing regions in New Zealand (Buck, Niyogi, & Townsend, 2004) and Brazil (Casatti, Langeani, Silva, & Castro, 2006). Smaller low- ordered streams are more vulnerable to pollution from agricultural land use and riparian degradation, and can strongly influence downstream water quality (Dodds & Oakes, 2008). In north Queensland, degraded riparian conditions in smaller low-ordered streams are considered priority areas for rehabilitation, and could play an important role in efforts to improve downstream water quality (Pert et al., 2010). Overall, the ecological importance of upstream riparian areas in maintaining healthy ecosystem function of inland creeks and rivers in Queensland, Australia is well recognised (Pert et al., 2010; W. Rassam, S. Fellows, Hayr, Hunter, & Bloesch, 2006). In the Fitzroy Basin, this implies upstream riparian and waterway conditions need to be important considerations in efforts to control waterway pollution.

Some water quality variables were elevated (and above WQGVs/WQOs) at some sites, but were not significantly negatively correlated with the riparian scores. In particular, the variables NOX, turbidity and dissolved Cu, which were especially elevated at sites CRD, CRU, MRD and MRU located on the main river channels. NOX, turbidity and dissolved Cu levels at these sites may reflect larger spatial scale land use. Turbidity and nitrogen pollution for example, have been associated with upstream agricultural intensity (particularly cropping), run-off and sediment transport into waterways in the Fitzroy Basin (Packett et al., 2009; Murphy, Dougall, Burger, & Carroll, 2013). The sources of dissolved Cu in Fitzroy Basin waterways have not yet been well studied, and the variety of land uses (cropping, grazing, urban and mining) upstream of these sites (QDSITIA, 2012), makes attribution of sources difficult. This study was not designed to identify catchment-scale land use impacts, and instead focussed on a relatively

This article is protected by copyright. All rights reserved. small number of sampling sites to assess local riparian areas and water quality. A future regional study could attempt to further analyse the relationship between water quality, upstream land use and riparian condition at different spatial scales, as previously conducted elsewhere in south-east Queensland (Sheldon et al., 2012). Careful selection of sites with different major upstream land uses, as previously conducted in a small-scale study in the Fitzroy Basin (Cowie, Thornton, & Radford, 2007), could also aid in ascertaining the contribution of various land/riparian condition factors on water quality.

Overall, both RARCSCORE and HABSCORE appear to be practical tools for assessing riparian zone and waterway condition in the Fitzroy Basin, with both methods being rapid and useful for identifying the level of degradation at multiple sites. Using the RARCSCORE method, water quality can be more directly linked to specific habitat features (e.g. riparian vegetation cover), compared with the HABSCORE method, which provides a more generalised assessment of disturbances to a waterway. The RARCSCORE however, lacks any scoring categories for streambank erosion, though it is included as one scoring category in the HABSCORE. Soil erosion is a major issue in the BBB and the adjacent Burdekin Basin, with documented impacts on water quality (Bartley et al., 2014; Dougall et al., 2009). It would thus be useful to include measures of streambank erosion and nearby gullying when assessing riparian condition in the Fitzroy Basin. With the known prevalence of riparian degradation in this region (Alam et al., 2004; Bartley et al., 2014), it is recommended that the RARCSCORE be considered for integration into waterway monitoring frameworks in future.

5 Conclusions

As per the study aims, this study has for the first time, consistently assessed riparian condition between sites and helped to identify specific water quality issues which may result from localised riparian degradation in the Fitzroy Basin, an agriculture-dominated landscape. Water quality in low- ordered streams in upper catchments may be particularly vulnerable to impacts from riparian degradation. With recent increases in tree-clearing and conversion of land for agricultural activities in the BBB in Queensland (Reside et al., 2017), it is important to protect riparian vegetation, and control soil erosion and sources of pollutants entering agricultural waterways. Overall, the findings of this study suggest that preventing riparian damage and restoring riparian vegetation could improve water quality in creeks and rivers of the Fitzroy Basin, a result that is supported by previous studies elsewhere

This article is protected by copyright. All rights reserved. (McKergow et al., 2001; Pert et al., 2010). Such information is crucial for improving land and waterway management practices, and understanding the ecological effects of various stressors on waterways.

Acknowledgements

We would like to thank D. Charlesworth (Senior Electrofisher, CQUniversity) for his assistance with fieldwork, and fieldwork volunteers K. French, M. Green, E. McGregor, L. Stitz, H. Threlkeld and L. Ukkola. We would also like to thank the two anonymous reviewers for their helpful comments in improving this manuscript. This study was supported by the CQUniversity School of Health, Medical and Applied Sciences, and a Coal Minesite Rehabilitation Trust Fund Scholarship provided by the Queensland State Government and Queensland Resources Council (to EC, supervisors NF, SW and SV), a research grant from the Australian Coal Association Research Program (project no. C24029 to NF, EC, SW and SV) and the HeART of the Basin Scholarship provided by the Fitzroy Partnership for River Health (to EC). Research was approved by the Queensland Government Department of Agriculture and Fisheries (General Fisheries Permit no. 177534) and the CQUniversity Animal Ethics Committee (approval no. A14/09-316).

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Table 1. Principal components matrix (rotated solution using varimax method) showing loadings of water quality variables, HABSCORE and RARCSCORE in each principal component (PC). Values underlined in bold had the strongest correlation to the respective PC. PC1 PC2 PC3 DOC 0.90 -0.04 0.00 HABSCORE -0.84 -0.07 -0.32 TN 0.80 0.07 -0.13 RARCSCORE -0.77 0.30 -0.19 Dissolved Mn 0.62 -0.17 -0.59

NH3 0.53 0.07 0.18 Temperature -0.36 -0.10 0.09 pH -0.21 -0.07 0.11 TP 0.17 0.83 -0.25 Dissolved Al -0.10 0.83 0.13 Turbidity 0.36 0.74 0.46 EC 0.62 -0.68 -0.13 Dissolved Zn 0.18 0.49 -0.48

SO4 0.24 -0.20 0.83

NOX -0.02 0.25 0.51

This article is protected by copyright. All rights reserved. Table 2. Spearman’s correlation matrix of habitat scores and hydrology variables vs. water quality variables, showing correlation coefficients (r-values), with p-values below in italics. r-values with significant correlations (p < 0.05) are shown in bold. Scatterplots of significantly correlated variables are provided as Supporting Information (Figure S1). Temp.: Temperature Dissolved Metals . Physicochemical Parameters . Nutrients .

Al Cu Mn Zn DOC EC pH SO4 Temp. Turbidity NH3 NOX TN TP RARCSCORE 0.26 0.39 -0.25 0.03 -0.69 -0.63 0.01 -0.24 0.23 -0.05 -0.10 0.22 -0.62 -0.05 0.07 <0.01 0.08 0.83 <0.001 <0.001 0.95 0.11 0.12 0.75 0.49 0.13 <0.001 0.74 HABSCORE -0.05 0.15 -0.02 0.06 -0.62 -0.32 -0.08 -0.45 0.14 -0.48 -0.24 0.02 -0.55 -0.13 0.76 0.30 0.92 0.67 <0.001 0.03 0.60 <0.01 0.33 <0.01 0.10 0.89 <0.001 0.40 Stream Order 0.13 0.55 -0.20 -0.04 -0.78 -0.52 0.05 -0.14 0.19 0.00 -0.11 0.35 -0.59 0.03 0.38 <0.001 0.18 0.77 <0.001 <0.001 0.76 0.34 0.19 1.00 0.44 <0.001 <0.001 0.83 Rainfall 0.47 0.09 -0.12 -0.28 -0.21 -0.18 -0.01 0.06 0.03 0.09 -0.22 0.05 -0.15 -0.02 <0.001 0.56 0.40 0.06 0.16 0.21 0.94 0.70 0.85 0.58 0.13 0.75 0.30 0.90

Figure 1. (a.) Map of the Fitzroy Basin (outlined in black), showing sampling sites and major waterways. Inset: Map of Australia showing the Fitzroy Basin in grey. (b.) Map showing major Fitzroy Basin land uses. Source: Wang, Lechner, and Baumgartl (2018)

Figure 2. Site HABSCOREs (a.) and RARCSCOREs (b.) from surveys 1-4 (Apr 2015, Oct 2015, Apr 2016 and Oct 2016 respectively). The maximum score for HABSCORE is 200, and 50 for RARCSCORE. Scores for each scoring category are provided in the Supporting Information section Tables S7 and S8

Figure 3. PCA plot showing relationship of study sites according to HABSCORE, RARCSCORE and water quality variables. Selected eigenvectors are overlaid on the PCA plot, with eigenvector lengths indicating the relative importance of the variable. DOC: dissolved organic carbon; EC: electrical conductivity; HABSCORE: habitat assessment score; RARCSCORE: rapid appraisal of riparian condition score; TN: total nitrogen; TP: total phosphorous