EPA/Science, Technology, Research and Innovation for the ...

EPA / Science, Technology, Research and Innovation for the Environment (STRIVE) Research Programme 2007-2013 Water Quality and the Aquatic Environment

An Effective Framework For assessing aquatic ECosysTem responses to implementation of the Phosphorous Regulations (EFFECT) FINAL REPORT

EPA/STRIVE PROJECT # 2007-W-MS-3-S1

Contributing co-authors: David Taylor, Yvonne McElarney, Sheila Greene, Chris Barry, Bob Foy, Michelle Allen, Phil Jordan, Katrina Macintosh, Joerg Arnscheidt and Sarah Murnaghan

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Table of Contents

Title page Project images Table of contents Acknowledgements Project team Acronyms, abbreviations & symbols used in the text Executive summary

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Chapter 1 Introduction to the EFFECT research programme References

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Chapter 2 P-induced impairment of aquatic ecosystems: evaluating and predicting spatial differences in vulnerability and recovery (WP1) Work Package 1 Conclusion Tables and Figures References

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Chapter 3 Catchment based assessments of the long term effectiveness of Programmes of Measures at the farm level (WP2) Work Package 2 Conclusion Tables and Figures References

136 160 162 212

Chapter 4 Assessing the impact of Programmes of Measures on stream water quality particularly with respect to areas of coniferous forest (WP3) Work Package 3 Conclusion Tables and Figures References

215 245 246 288

Chapter 5 Assessment of point source (septic tank system) mitigation in a rural catchment (WP4) Work Package 4 Conclusion Tables and Figures References

298 305 307 315

Chapter 6 Synthesis of EFFECT findings, conclusions and recommendations Recommendations and conclusions Figure References

318 326 329 330

Appendix 3.1 Appendix 3.2 Appendix 3.3

337 338 341

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Acknowledgements A successful outcome to EFFECT would not have been possible without the support and help of numerous individuals and institutions. We would like to thank in particular Alice Wemaëre of the EPA, external members of the project steering committee, particularly Steve Ormerod and John Quinton, for their guidance and support throughout the project and Alex Higgins, AFBI, Rachel Cassidy, QUB, Lesley Gregg, the AFBI Freshwater Chemistry Laboratory, Forest Service Northern Ireland, Department of Agriculture and Rural Development Northern Ireland, and Colleen Ward, for help with data collection and analysis. Thanks are also due to several colleagues for their assistance with field and laboratory work; to several individuals and institutions, notably the Shannon Regional Fisheries Board (Inland Fisheries Ireland), for permitting use of their data; to the numerous landowners and estate managers who facilitated access to the field study sites and to Hugo McGrogan and Pete Devlin, University of Ulster, for technical services and Gayle McGlynn, TCD, for production of Figure 6.1.

Disclaimer Although every effort has been made to ensure the accuracy of the material contained in this publication, complete accuracy cannot be guaranteed. Neither the Environmental Protection Agency nor the authors accept any responsibility whatsoever for loss or damage occasioned or claimed to have been occasioned, in part or in full, as a consequence of any person acting, or refraining from acting, as a result of a matter contained in this publication. All or part of this publication may be reproduced without further permission, provided the source is acknowledged.

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Project team members David Taylor (Project Co-ordinator)1, Sheila Greene2 School of Natural Sciences Trinity College University of Dublin Dublin 2 Ireland Tel. +353 (0)1 896 1581 1

E-mail: [email protected]

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Bob Foy3, Yvonne McElarney4 & Chris Barry5 Agri-Environment Branch, Agri-Food & Biosciences Institute, Newforge Lane, Belfast BT59 5PX Northern Ireland Tel. +44 2890 255689 3


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Phil Jordan6 School of Environmental Sciences, University of Ulster, Cromore Road, Coleraine BT52 1SA Northern Ireland Tel. +44 (0)2870 324 401 6


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Acronyms, abbreviations & symbols used in the text Those words that are commonly replaced by acronyms or symbols or are abbreviated in scientific texts are referred to in this research report in their acronym, symbol or abbreviated form in the first instance. The comprehensive list below provides an explanation for the various acronyms, abbreviations and symbols used in text that follows. % A AFBI AIM ANOSIM ANOVA ASPT BEST BMWP BOD5 BOM BVSTEP C °C CANOCO CAP CH4 CLC CMMS Co COGAP Co Co+C COGAP CCA CSA CSO Δ D DAFF DANI DARD DCA DED DEM DHP DIN dmf DO DOC dof

Percentage Variogram range (kilometer) Agri-Food and Biosciences Institute Animal Identification and Movement Analysis Of Similarity Analysis Of Variance Average Score Per Taxon Routine available in PRIMER v6 software combining procedures found in v5 Biological Monitoring Working Party 5 day Biological Oxygen Demand Benthic Organic Matter Form of stepwise regression Carbon Degrees centigrade Canonical Correspondence Analysis programme Canonical Analysis of Principal (components) Methane Corine Landcover Cattle Movement Monitoring System County Codes of Good Agricultural Practice Variogram nugget variance Variogram sill variance Codes of Good Agricultural Practice Canonical Correspondence Analysis Critical source areas Central Statistics Office Delta (change) Simpson’s Diversity Index Department of Agriculture, Fisheries and Food Department of Agriculture Northern Ireland Department of Agriculture and Rural Development Detrended Correspondence Analysis Digital electoral division Digital elevation model Measure of organic and polymeric Phosphorus Deutsches Institut für Normung e. V. (International standard) Daily mean flow Dissolved Oxygen Dissolved Organic Carbon Degree of freedom

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Ed Ei EHS ENMS EPA EPT ES EU FE Class FFG FSNI fwMC fwMRP GFC GIS GRP GSI GSNI H ha Ho IFI IPPC K kg km-2 yr-1 km2 KW l LAM LFA M MW m3 s-1 MDA MDS mg l-1 Mm MRHS MRP N N NH4 NI NIEA NISRA NMP NO2 NO3

Community evenness measure Phosphorus export coefficient Environment and Heritage Service (Northern Ireland) Erne Nutrient Management System (Northern Ireland) Environmental Protection Agency Ephemeroptera/Plecoptera/Trichoptera taxa score Estimation subset for load apportionment model European Union Fisheries Ecosystem Class Functional Feeding Group Forest Service Northern Ireland Flow-weighted mean concentration Flow-weighted concentration of MRP (μg l-1) Grade of glass fibre filter Geographical Information Systems Glass Reinforced Plastic Geological Survey of Ireland Geological Survey of Northern Ireland Hydrogen Hectare Null hypothesis Inland Fisheries Ireland Integrated pollution prevention control Potassium Kilograms per kilometer squared per year Kilometer squared Kruskal-Wallis Litre Load apportionment model Less Favoured Area Metre Mann-Witney Cubic metres per second Multidimensional Analysis Multidimensional Scaling Milligrams per litre Millimetres Mean River Habitat Score Molybdate Reactive Phosphorus Nitrogen number of samples Ammonium Northern Ireland Northern Ireland Environmental Agency Northern Ireland Statistics and Research Agency Nutrient Management Plan Nitrogen dioxide (nitrite) Nitrogen trioxide (nitrate)

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NVZ NWIRBD OPW OSI OSNI P P PP P Regs PCA PO4 POM Q-rating R R2 RBD RBDMC RBMP RDA RIVPACS Re RK RMC RMSE RoI s SFP SI SOP SRP SUP TDN TDP TOC TON TP TRP TSP TWI VIF VS VSA WFD WP WWTP μg l-1

Nitrate Vulnerable Zones North West International River Basin District Office of Public Works Ordnance Survey of Ireland Ordnance Survey of Northern Ireland Phosphorus Probability Particulate Phosphorus Phosphorus Regulation Principle Component Analysis Phosphate Programme of measure Biological quality rating Pearson correlation coefficient Regression coefficient of determination River Basin District River Basin Management Committee River Basin Management Plan Redundancy Analysis River Invertebrate Prediction and Classification System Runoff point at which point and diffuse sources are equal Regression-kriging Riparian Management Category Root Mean Square Error Republic of Ireland Second Single Farm Payment Stable isotope Soluble Organic Phosphorus Soluble Reactive Phosphorus Soluble Unreactive Phosphorus ( Total Dissolved Nitrogen Total Dissolved Phosphorus Total Organic Carbon Total Organic Nitrogen Total Phosphorus Total Reactive Phosphorus Total Soluble Phosphorus Topographical Wetness Index Variance inflation factor Validation subset for load apportionment model Variable source area Water Framework Directive Work Package Waste water treatment plant Micrograms per litre

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Executive summary Eutrophication as a result of anthropogenic activity has become a major problem in rivers and lakes. In response, POMs have been implemented that seek to reduce and even reverse eutrophication and its effects through the mitigation of inputs of P and other nutrients. Persistent and considerable uncertainty regarding the suitablity and effectiveness of these measures and of the factors that potentially influence chemical and ecological recovery in rivers and lakes following their implementation provided the context for the EFFECT project. Specifically, and focusing on the Irish Ecoregion, EFFECT sought both to better understand the environmental and other factors that may influence the effectiveness of measures aimed at reducing P and other nutrient inputs, and to determine the surface water quality (rivers, streams and lakes) impacts of implementation of a range of these measures in different geographic settings.

WP1 determined the strength of relationships between environmental conditions and water quality at both large (individual catchment) and small (Irish Ecoregion-wide) scales. The Lough Sheelin catchment in the RoI was selected for the large scale study, which also involved development of a LAM implemented at the level of subcatchment as a means of examining the effectiveness of measures aimed at P mitigation, particularly from point sources. Data for the period 1995-2008 indicated a trend of declining P levels in all subcatchments, while the extent of poorly drained soils, cattle stocking densities and runoff levels were found to have the strongest influence on variability in P concentrations. Malfunctioning septic tank systems in the catchment may also have acted as localised point sources of P. Available data (1990-2008) for Lough Sheelin also suggested that external loadings of P fell following a peak in the early 1990s and remained relatively low to 2008, a period that includes implementation of measures aimed at mitigating P inputs. Despite this, however, P concentrations in the lake remained at early 1990s and higher levels though to 2008. Zebra mussels (Dreissena polymorpha Pallas), established in Lough Sheelin by 2004, may have been responsible for negating water quality effects of reduced external inputs of P.

Two databases were constructed for the small scale exercise contained within WP1. Both databases comprised flow weighted P concentrations for river monitoring sites

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(fwMRP) for the period 2006-2006: one of the databases contained 49 sites from the RoI only; the other was Irish Ecoregion-wide, comprising 72 sites from both the RoI and NI. Results indicated that the strongest predictors of concentrations of P in rivers in the databases were human population density, extent of artificial surfaces, run off risk, percentage of pasture, density of livestock (cattle) (all positive), mean catchment slope, drainage density, extent of forestry (all negative). Geology (in particular the susceptibility of bedrock to weathering) was also found to influence P concentrations, and was one of several variables considered that had interdependent relationships with other variables, including those relating to human activity. Geostatistical models incorporating the most important environmental predictor variables were constructed to provide a basis for predicting surface water bodies with a high likelihood of being vulnerable to impairment by P or relatively resistant to recovery following reduced inputs of P. The models based on the RoI database, divided into summer and winter periods, had greatest predictive strength. These were independently verified using data from a set of five catchments in the RoI not included in the original analysis.

WP 2 examined the biological and chemical impacts of POMs that have been in place in NI for almost two decades, providing an assessment of the effectiveness of provision of capital grants for the improvement of the management of manures and silage effluent on farms, and the ENMS, intended to reduce P applications to farmland. Specifically WP2 used measured biological and chemical water quality data determined in the 1990s for 42 low-lying streams in two catchments in NI (Colebrooke and Upper Bann) as a baseline for assessing the impacts of POMs in terms of observed water quality during the years 2008-2009. Monitoring of the streams in the 1990s revealed that both biological and chemical water quality declined with increasing farming intensity (expressed as catchment manure nutrient loading rate). By 1998 chemical water quality had improved, although little improvement in BWQ was apparent. Resurvey in 2008-9 of streams surveyed in the 1990s, using the same sample sites and methodology, revealed a continued improvement in chemical water quality. However no consistent improvement in BWQ was evident. At the catchment scale there were some indications that biological water quality may have improved in 2009 relative to the late 1990s. However the changes remained inconclusive. Sites consistently recording low BWQ tended to be those for which chemical water quality had improved least and was most

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variable. Agricultural intensity explains a significant proportion of the variation in BWQ: generally sites with stocking rates exceeding one dairy cow ha-1 were found to exhibit poor BWQ. Hydromorphological modifications, notably channelisation, may also be important, particularly in the Upper Bann where the invasive amphipod Gammarus pulex (L.) could also have restricted recolonisation and new rural housing and septic tank systems might explain a recent increase in P export.

WP3 investigated correlations between riparian measures and water quality and ecological functioning in upland streams in afforested catchments in Co. Fermanagh, NI. Results suggest low levels of correlation between riparian vegetation and macrophyte and invertebrate composition at the study sites, while water chemistry parameters were most strongly correlated with vegetation cover at the catchment scale, in particular the extent of coniferous plantations and peatland. This finding supports the view that catchment-scale characteristics, notably land use, have a greater influence on water quality than the form of relatively narrow strips of riparian vegetation. Results of SI analysis indicated that invertebrate biomass at most sample sites was predominantly derived from terrestrial matter. Moreover, macroinvertebrate community structure did not differ considerably between the sample sites, and thus appeared largely independent of riparian vegetation. Rather the most important correlates again operated largely at the catchment-scale. The degree of light reduction by riparian vegetation and the diversity of stream habitat were also correlated, however.

Based in parts of the Blackwater catchment straddling the NI-RoI border (the counties of Armagh, Monaghan and Tyrone), WP 4 evaluated initial impacts on water quality of a voluntary scheme to replace the most defective septic tank systems in an effort to mitigate P from point sources. An initial survey was carried out prior to the replacements being made in 2007, and again following the installation of the new septic tank systems. Results from the Blackwater catchment in Co. Armagh indicate some positive effects on water quality where there was little change in the density of systems. However, in parts of the catchment in counties Monaghan and Tyrone no significant improvements in water quality were evident, and this may reflect overall increases in the total number of septic tank systems due to new builds of rural housing during the survey period (land use change may also have influenced the results). This finding is of

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particular concern given that the study period also included the implementation of other efforts to mitigate P inputs, notably farm yard improvements and extensive additional fencing to restrict the access of livestock to surface water bodies.

Mixed and somewhat disappointing results following the implementation of POMs in the Irish Ecoregion indicate a need for improved understanding of the processes influencing the production and transport of pollutants and their impacts on aquatic ecosystems. There are positives, however: based on results presented here, improvements are possible. Further refinements to existing measures are recommended, however. For example, relatively narrow strips of riparian vegetation are ineffective buffers against much more extensive changes in catchment conditions. Moreover, some rivers and lakes have been so profoundly modified hydromorphologically and by the presence of invasive taxa that physical remodification may be required. Furthermore, relatively well-drained soils should be a focus of future research, given the level of attention impermeable soils here, and greater attention paid to the degree that POMs have been and are being implemented.

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Chapter 1 Introduction to the EFFECT research programme Widespread deterioration in water quality as a result of anthropogenic activity, widely recognised as a major global environmental issue (Smol, 2008), has led to the development and implementation of legislation at national and international levels. In Europe, for example, the EC Nitrates Directive (91/676/EEC) was an early attempt to mitigate eutrophication pressures from agricultural sources. Under the directive member states must produce voluntary COGAP (Anon, 1991, Anon, 1993). More recently, national legislation of EU member states has been subsumed within the WFD (2000/60/EC), which seeks to ensure the effective and sustainable management of water resources, and to achieve and maintain good water quality for all water bodies by 2015 (Anon, 2005). The WFD management unit, RBD, encourages an integrated catchment-scale approach to water quality management (Rekolainen et al., 2003; Bennion and Battarbee, 2007). Management plans drawn-up by RBDs have to include the design and implementation of POMs that are needed to ensure that the water quality objectives of the WFD are met within the stipulated timeframe. POMs devised to mitigate pollution impacts, including impacts of nutrients (Crabtree et al., 2009), are largely based on existing European regulations and policies; they may also comprise additional, or supplementary, measures. The latter are to be applied in those cases where basic measures are not enough to achieve water quality targets (Kavanagh and Bree, 2009).

Eutrophication, resulting from over-enrichment by nutrients and in particular P, is a major cause of deteriorating water quality (Smith and Schindler, 2009). Although point sources of P (for example, urban industrial or WWTPs) are important, diffuse sources of P from agriculture have been identified as the main cause of nutrient enrichment in freshwaters (Jennings et al., 2003; Sharpley et al., 2009), and continue to prove a significant challenge to water quality improvement efforts on the island of Ireland (the Irish Ecoregion), where agricultural sources account for 31% of pollution incidents (Kavanagh and Bree, 2009). Over the last two to three decades the proportion of water bodies classed as having moderate quality in Ireland has increased (McGarrigle et al., 2010a), owing to a decline from good status in some and improvements to others previously classed as having poor quality. As a result of this trend, by the middle of the last decade about 90% of water bodies in NI were thought to be at risk of not making

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the WFD objectives (EHS, 2005), while in the RoI 64% of rivers and 38% of lakes were thought at risk and probably at risk (Anon, 2005).

In the RoI the basic measure to address nutrient loading pressures from agriculture is full enforcement of the Good Agricultural Practice for Protection of Waters Regulations 2009 (S.I. 101 (2009)). These regulations in part implement Directive (91/676/EEC), commonly known as the Nitrates Directive (Howarth, 2006). Similar codes of good practice have been implemented in other EU member states, including the UK. Few studies have been carried out to test the effectiveness of POMs, or their effectiveness in conjunction with projected climate, population, land use and land management trends and policies, either in the Irish Ecoregion, or in other parts of Europe. There is considerable uncertainty regarding the suitablity and effectiveness of some measures (Kavanagh and Bree, 2009) and further research is recommended in RBD management plans (for example, Canney, 2009). Computer modelling potentially can provide a test of the likely effectiveness of particular POMs aimed at reducing pollution impacts (Irvine et al., 2005; Volk et al., 2009), thereby complementing surveillance monitoring. The WFD followed publication in the RoI of a strategy document setting out the Government’s approach to reducing P inputs (DoE, 1997). As part of the strategy, interim quality standards were identified; in order to provide a means of operationalising these, the Local Government (Water Pollution) Act, 1977 (Water Quality Standards for Phosphorus) Regulations, 1998, S.I. No. 258/1998, more commonly known as the Phosphorus Regulations, or P Regs, were introduced in RoI. As with the WFD, the P Regs require that water quality be maintained or improved according to ecological and chemical quality indices and with reference to baseline conditions. The indices used in the P Regs are the Q-rating for rivers and trophic status for lakes. On January 1st, 2007, new legislation came into operation aimed at improving the management of nutrients, including P, on farms in NI. The new legislation (the Nitrates Action Programme Regulations (Northern Ireland) 2006 and the Phosphorus (Use In Agriculture) Regulations (Northern Ireland) 2006), implemented by the NIEA, applies to all farmers in NI.

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In the RoI the EPA and relevant local authorities are required to act to secure compliance with the quality standards specified in the P Regs, with the responsibility for compliance lying principally with the local authorities (Clenaghan et al., 2005). In order to meet their responsibilities, local authorities were required to submit information on POMs aimed at reducing P inputs to be implemented, as part of a Measures Report, to the EPA by 31 July 1999. Submission of an Implementation Report to the EPA followed 12 months later, with updates submitted every two years until 2008. The EPA is similarly obliged to produce National Reports on the Implementation of the Regulations every two years until 2009. By early-2012 the EPA had made available three such reports (Clenaghan et al., 2001, 2005; Clenaghan, 2003), with no reports yet available for the period 2005-2009.

The POMs cited first in the Measures reports and subsequently, with updates, in the Implementation reports of local authorities are described in the National Implementation reports. The measures can be grouped into five categories. Planning, control and enforcement measures include the implementation of catchment and waste management plans; upgrade of existing or construction of new WWTPs; appraisal of septic tank systems and related legislation; carrying out of farm surveys, including surveys of the state of farm infrastructure; introduction of new agricultural bye-laws; and enforcement of water pollution acts. Monitoring measures include increased river, lake and groundwater monitoring, including investigations of catchment pollution hotspots, and the development of GIS for catchment management. Other measures comprise liaison between the various sectors involved in catchment management.

The most recently available National Implementation Report (Clenaghan et al., 2005) collates and summarises information contained within the implementation reports submitted to the EPA by local authorities for the two year period to 31 July 2004. According to this report, no significant improvement in national water quality was evident when compared with baseline conditions (generally 1995-1997), although some local improvements were apparent (Clenaghan et al., 2005). In the case of rivers, slightly more monitoring stations nationally reported water quality values compliant with the P Regs than was the case during the baseline period and when compared with 1998-2000 (Clenaghan et al., 2005). Notable variation exists between the local

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authorities in levels of compliance, however. Marked increases in compliance between the baseline and 2001-03 periods are apparent in some local authorities, whereas a significant decline in compliance is apparent in others, the latter in part because of the loss of high quality stations. As stations recording high quality water are primarily found in headwaters, documented declines in water quality at these will adversely impact attempts to improve the quality of polluted waters downstream. The most recent report on water quality generally in the RoI covers the period 2007-2009 (McGarrigle et al., 2010b). According to this report, and when compared with the previous reporting period (2004-2006), the most conspicuous change in chemical water quality was a slight increase in the length of river classed as slightly polluted and a concomitant decline in that characterised as unpolluted (McGarrigle et al., 2010a), and a small increase in the percentage area of lakes classed as moderately eutrophic along with a commensurate decrease in percentage area of hypertrophic conditions (Tierney et al., 2010).

EFFECT targeted important P Regs- and WFD-relevant knowledge gaps in understanding the factors influencing responses to variations in individual and combined water quality pressures on bodies of surface water in the Irish Ecoregion. The overall aim of the project was to evaluate changes in the chemical and ecological statuses of a sample of rivers and lakes in the Irish Ecoregion since the implementation in the 1990s and subsequently of POMs aimed at reducing P and other nutrient inputs to water bodies. Pollutants such as P have multiple sources within the landscape, both point and diffuse, while the processes governing their mobilisation vary spatially (e.g. as a result of differences in soil properties and land use) and temporally (e.g. due to seasonal variations in rainfall). EFFECT sought to capture this variability while examining a range of different POMs in a variety of environmental settings.

EFFECT was largely centred upon on four WPs (WPs 1-4) implemented at two different scales of analysis (Irish Ecoregion-wide and catchment/subcatchment specific). WP1 determined the strength of relationships between environmental conditions and water quality and the effectiveness of P Reg related POMs based on data from both NI and RoI. WP 2 examined the biological and chemical impacts of POMs that have been in place in NI for a number of years, providing an assessment of the effectiveness of provision of capital grants for the improvement of the management of manures and silage effluent

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on farms, and of the ENMS. By comparison, WPs 3 and 4 examined more recent and ongoing POMs. WP3 investigated correlations between riparian measures on water quality and aquatic macrophyte and macroinvertebrate community composition, functioning and recovery in headwater streams in NI following harvesting of coniferous trees. WP 4 evaluated initial impacts on water quality in selected parts of the Blackwater catchment (spanning the border between NI and the RoI) of a mitigation scheme that commenced in 2004/5 to replace the most defective septic tank systems with state of the art equipment.

The chapters that follow introduce the four WPs that underpin EFFECT and present and interpret the results (Chapters 2 to 5). Chapter 6 synthesises the collated findings of EFFECT within a context of current discussions concerning differences in the effectiveness of POMs aimed at reducing the water quality effects of P loads and provides recommendations and conclusions relating to the findings.

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References – Chapter 1 Anon (1991) Code of Good Agricultural Practice for the Protection of Water. London: MAFF. Anon (1993) Code of Good Agricultiral Practice for the Protection of Soil. London: MAFF.

Anon (2005) Article 5 The characterisation and analysis of Ireland's river basin districts. In accordance with Section 7 (2 & 3) of the European Communities (Water Policy) Regulations 2003 (S.I. 722 of 2003). National Summary Report (Ireland). Compendium of public submissions and responses. Bennion, H. & Battarbee, R.W. (2007) The European Union Water Framework Directive: opportunities for palaeolimnology. Journal of Paleolimnology, 38, 285-295. Canney, P. (2009) Final River Basin Management Plan for the Western River Basin District in Ireland (2009-2015). Galway County Council (on behalf of relevant local authorities), Galway. Clenaghan, C., Collins, C. & Crowe, M. (2001) Phosphorus Regulations National Implementation Report, 2001. EPA, Wexford. Clenaghan, C. (2003) Phosphorus Regulations National Implementation Report, 2003. EPA, Wexford. Clenaghan, C., Clinton, F. & Crowe, M. (2005) Phosphorus Regulations National Implementation Report, 2005. EPA, Wexford. Crabtree, B., Kelly, S., Green, H., Squibbs, G. & Mitchell, G. (2009) Water Framework Directive catchment planning: a case study apportioning loads and assessing environmental benefits of programme of measures. Water Science and Technology, 59, 407-416. DoE (1997) Managing Ireland Rivers & Lakes: a Catchment-Based Strategy Against Eutrophication. Department of Environment, Dublin. EHS (2005) Water Framework Directive Summary Report of the Characterisation and Impact Analyses required by Article 5 Northern Ireland, EHS, Northern Ireland. Howarth, W. (2006) The progression towards ecological quality standards Journal of Environmental Law, 18, 3-35 Irvine, K., Mills, P., Bruen, M., Walley, W., Hartnett, M., Black, A., Tynan, S., Duck, R. et al. (2005) Water framework directive-an assessment of mathematical modelling in its implementation in Ireland. Environmental Protection Agency, Wexford.

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Jennings, E., Mills, P., Jordan, P., Jensen, J.P., Søndergaard, M., Barr, A., Glasgow, G. & Irvine, K. (2003) Eutrophication from Agricultural Sources – Seasonal Patterns and Effects of Phosphorus (2000-LS-2.1.7-M2), Final Report. Environmental Protection Agency, Wexford, Ireland. Kavanagh, P. & Bree, T. (2009) Water Framework Directive programme of measures: protection of high-status sites, forest, water and on-site wastewatertreatment systems. Biology and Environment: Proceedings of the Royal Irish Academy, 109B, 345-364. McGarrigle, M., Bradley, C., Concannon, C., Cunningham, D., Kennedy, B., Lucy, J., McCreesh, P. & MacCárthaigh, M. (2010a) Water Quality of Rivers and Canals. In: M. McGarrigle, J. Lucey & M. Ó Cinnéide (eds.) Water Quality in Ireland 20072009. Wexford, Ireland: EPA, pp 41-74 McGarrigle, M., Lucey, J. & Ó Cinnéide, M. (eds.) (2010b) Water Quality in Ireland 20072009. Wexford, Ireland: EPA. OECD (1982) Eutrophication of Waters: Monitoring, Assessment and Control. Organisation for Economic Co-Operation and Development (OECD), Paris. Rekolainen, S., Kämäri, J. & Hiltunen, M. (2003) A conceptual framework for identifying the need and role of models in the implementation of the Water Framework Directive. International Journal of River Basin Management, 1, 347-352. Sharpley, A., Herron, S., West, C. & Daniel, T. (2009) Outcomes of phosphorus-based nutrient management in the Eucha-Spavinaw Watershed. In: A.J. Franzluebbers (ed.) Farming with Grass Achieving Sustainable Mixed Agricultural Landscapes. Soil and Water Conservation Society, Ankeny, Iowa, pp. 192-204. Smith, V.H. & Schindler, D.W. (2009) Eutrophication science: Where do we go from here? Trends in Ecology and Evolution, 24, 201-207. Smol, J.P. (2008) Pollution of lakes and rivers: a paleoenvironmental perspective. Blackwell, Oxford. Tierney, D., Free, G., Kennedy, B., Little, R., Plant, C., Trodd, W. and Wynne, C. (2010) Water Quality of Lakes. In: M. McGarrigle, J. Lucey & M. Ó Cinnéide (eds.) Water Quality in Ireland 2007-2009. Wexford, Ireland: EPA, pp 75-103. Volk, M., Lautenbach, S., van Delden, H., Newham, L.T.H. & Seppelt, R. (2009) How Can We Make Progress with Decision Support Systems in Landscape and River Basin

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Management? Lessons Learned from a Comparative Analysis of Four Different Decision Support Systems. Environmental Management, 46, 834-849.

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Chapter 2 P-induced impairment of aquatic ecosystems: evaluating and predicting spatial differences in vulnerability and recovery (WP1)

2.1 WP1 aims WP1 aimed to test the Ho that there is no relationship between environmental conditions and P concentrations in rivers and lakes. The test was carried out at two different scales: large (subcatchment) and small (catchments in RoI and in RoI and NI combined (the Irish Ecoregion)). Rejection of the Ho permitted the development of a geospatial model that can be used to predict stretches of rivers in the RoI that are vulnerable to P impairment. WP1 also examined the efficacy of POMs and the confounding effects of environmental factors and the activities of an invasive species.

2.2 Introduction The eutrophication of freshwater ecosystems remains the principal pressure on the quality of surface waters throughout Europe (Søndergarrd et al., 2007), where few lakes and rivers in lowland catchments in particular remain in pristine condition (Bennion and Simpson, 2011), including Ireland (Jennings et al., 2003; Lucey, 2007). Eutrophication is principally caused by inputs of nutrients from agricultural sources, industrial waste and domestic sewage, mainly P (Ulén and Kalisky, 2005) but also N (the latter particularly in coastal ecosystems) (Alexander et al., 2007).

Phosphorus exists in the environment in particulate and dissolved phases: unlike other biologically important elements, a gaseous phase of P is rare (Ashley et al., 2011). In the particulate phase P is bound in element and mineral complexes, siliceous clays, humic material and the structural cells of organisms. Particulate and dissolved fractions of P in samples of water can be separated for subsequent analysis through filtering water samples through 0.45 μm membrane filters (Jarvie et al., 2002). The P content of filtered and unfiltered water samples is termed TDP and TP, respectively. Total dissolved phosphorus includes suspended colloidal soil particles and organic matter that have high P sorption capacity (Quinton et al., 2001), and is further separated into SRP, which is equivalent to dissolved inorganic P (i.e. bioavailable P), and SUP, which is a nonbioavailable organic P compound. SRP is determined analytically, while SUP is established by subtracting SRP from TDP (Jarvie et al., 2002). The particulate P fraction,

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determined by subtracting the TDP from the TP, is the P sorbed to soil particles and organic matter with a diameter greater than 0.45 μm. The dissolved P fraction, or MRP, is equivalent to: (a) SRP, for filtered samples; and (b) SRP plus a fraction of particulate P (which is reactive to phosphomolybdenum blue method reagents) for unfiltered samples (Jarvie et al., 2002).

Levels of P in rivers and lakes are a function of the number, magnitude and location of sources of P, the mechanism and pathway through which P is transported to a waterbody, and the processes that take place within the river or lake, including sedimentation (Donohue et al., 2005, 2006; Withers and Jarvie, 2008). The P Transfer Continuum conceptualises the process through which P is transferred from a source to a river channel (Haygarth et al., 2005a; Wall et al., 2011). In this four-tiered framework, nutrient sources as inputs from land are exposed to a mobilisation mechanism and, by way of hydrological pathways, are delivered to a river channel. Depending on concentrations of P and sensitivity of the receiving waterbody, P mobilized and transferred in this way may have ecological effects. Natural sources of P include atmospheric deposition, natural weathering of soil and detritus from riparian vegetation: anthropogenic sources are often categorized into point or diffuse (Pieterse et al., 2003; Jarvie et al., 2006; Liu and Chen, 2008). Phosphorus from point sources is generally regarded as being discharged continuously from discrete points on the landscape, such as WWTPs, septic tanks with direct connection to rivers, farmyards and industrial activities, and is most commonly in soluble form (Withers and Jarvie, 2008). Conversely, P from diffuse sources is incorporated in surface sub-surface runoff and is sourced from a large proportion of entry points dispersed throughout a catchment. Diffuse sources of P tend to be active only when a hydrological pathway is available. Phosphorus from diffuse sources has a larger fraction of particulate P and is invariably linked with runoff from urban areas, septic tank soak-aways, forestry, and agriculture (Withers and Jarvie, 2008). Some sources of P, such as road and track runoff, septic tank discharges and farmyard runoff, share characteristics with both point and diffuse categories (Arnscheidt et al., 2007; Jordan et al., 2007; Edwards and Withers, 2008; Jarvie et al., 2010).

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Phosphorus from agriculture comprises the major component of diffuse-sourced P. Rainwater, passing over or through the soil at variable rates, mobilises P for potential transfer along surface and sub-surface hydrological pathways. Levels of P mobilised thus can vary greatly, both spatially and temporally (Withers and Jarvie, 2008). If fertiliser or livestock waste applications coincide with extreme rainfall, especially on impermeable soils, large amounts of P can be mobilised (Preedy et al., 2001). Furthermore, high concentrations of P can potentially be mobilised from slurry stores and farmyards and from livestock where there is a direct connection with a waterbody – such as provided by extreme rainfall events (Edwards et al., 2008).

Levels of P delivered from point sources are relatively constant throughout the hydrological year, and are therefore independent of flow, and have a high soluble P fraction (Jordan et al., 2007). By comparison, levels of P from diffuse sources are highly dependent on runoff, and have a positive linear relationship with discharge (Edwards and Withers, 2008). In Ireland and the UK, storm transfers occur intermittently and involve large quantities of particulate P. During diffuse/storm events, P in surface runoff dominates instantaneous river loads (Tunney et al., 2000; Haygarth et al., 2005b; Jordan et al., 2005a). In the intervals between high flow events with runoff pathways disconnected, P loads to rivers are low, especially when point sources are absent or a sufficient dilution factor exists. When fertiliser application overlaps with an extreme rainfall episode, critical transport pathways open, resulting in rapid transfers of P to water bodies (Withers et al., 2003; Wood et al., 2005). Any P remaining on the soil surface once rainfall levels subside and hydrological pathways close is incorporated into the soil profile.

Once present in a waterbody, P may be assimilated by biota (Withers and Jarvie, 2008). However, high levels of P can lead to the alteration of ecosystem structure and function by promoting enhanced biological growth, decreasing biodiversity and increasing fish mortality (Smith et al., 1999; Mainstone and Parr, 2002). While links between P concentrations and phytoplankton growth are well established for lakes (Vollenweider, 1968), the situation in rivers is more complex (Neal et al., 2008; Withers and Jarvie, 2008; Wall et al., 2011). For example, high P concentrations from point sources in late spring and summer when low flows tend to be most common can be more influential in

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maintaining a eutrophic status than the P load would otherwise indicate (Bowes et al., 2010; Jarvie et al., 2010; Withers et al., 2011).

Sedimentation and subsequent remobilisation processes also control the impact of P in a waterbody (Søndergaard et al., 2003; Withers and Jarvie, 2008). In lakes, for example, P can be bound in sediment to redox-sensitive iron compounds or fixed in labile forms (Selig et al., 2002). Sediment-bound P is released by mechanisms initiated by increases in organic matter sedimentation, which in turn reduces oxygen composition, encouraging low redox conditions. Under low redox conditions, Fe is chemically reduced, allowing for the release and mobilisation of P for diffusion into the water column and uptake by biota (Moore et al., 1998). This internal loading of P may have impacts on the restoration of an ecosystem by continuing to support nuisance algal growth after external sources of P have been reduced (Welch and Jacoby, 2001).

The level and rate of transfer of P to a water body depends on environmental conditions in a catchment (Foy et al., 1995; Foy and Lennox, 2000; Jordan et al., 2005a; Ulén and Jacobsson, 2005; Wood et al., 2005). Spatial and temporal variations in environmental conditions are therefore likely to impact loadings of P (Donohue et al., 2005, 2006). Moreover, impacts of P in rivers and lakes will vary according to the time of year, the nature of the water body (i.e. whether it is free-flowing or impounded, position within the drainage basin, levels of turbidity, the substrate type, amount of shade cast by riparian vegetation, depth of water etc.) and the organism(s) of interest (Brabec et al., 2004; Hilton et al., 2006; O’Driscoll et al. 2006; Schippers et al., 2006; Ibelings et al., 2007; Jeppesen et al., 2007). Alterations caused to ecosystem composition and functioning by exotic (invasive) taxa are also likely to influence the degree and longevity of impacts of P (Conroy et al., 2005). As a consequence, pollution pressures and recovery from impairment following, for example, implementation of measures aimed at mitigating P inputs are likely to vary not only according to catchment characteristics such as agriculture, forest cover and forestry operations and the degree of urbanisation, but also according to location within the drainage network. For example, sensitivities to eutrophication and oligotrophication are likely to be different in low order streams and headwater lakes– as largely heterotrophic systems with relatively low residence times and perhaps a greater influence of shoreline vegetation – compared with higher order

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rivers and lowland lakes in which residence times are higher, planktonic algae are the main primary producers and organic matter is largely autochthonous (Allan, 1996).

WP1 investigated the level of influence of a range of environmental variables on concentrations of P in surface water bodies. The context for WP1 was mixed and somewhat disappointing results following implementation in the Irish Ecoregion of POMs aimed at mitigating P impacts on rivers and lakes. Possible reasons for these disappointing results were explored through WP1. Moreover, where strong relationships between selected environmental variables and P concentrations in rivers in were found, the relationships were incorporated as environmental predictors in a geospatial model for deployment in the RoI.

2.3. Environmental influences over P concentrations in rivers and lakes in the Irish Ecoregion and implications for effectiveness of P-related POMs The Ho that there is no relationship between environmental conditions and P concentrations in rivers and lakes was tested at both large (subcatchment) and small (catchments in RoI and in RoI and NI combined (the Irish Ecoregion)) scales. The large scale/small area study focused on the catchment for Lough Sheelin in the RoI. The catchment for Lough Sheelin, which has a surface area of 18.1 km2, comprises seven subcatchments, ranging in area from 2.4 km2 to 91.6 km2, drained by ten rivers (Figure 2.1, Table 2.1). An analysis of the efficacy of POMs targeting P in rivers and Lough Sheelin was also carried out through the large scale study. A possible confounding factor regarding the impacts of attempts to mitigate P loads to the lake was the presence in the latter of the invasive zebra mussel (Dreissena polymorpha Pallas).

Zebra mussel populations were established in Lough Sheelin by 2004 (Kerins et al., 2007), with initial colonisation presumably commencing after implementation in 19981999 of a P Regs programme aimed at mitigating levels of P entering water bodies in the catchment. As a consequence, the impacts of changes in P loading and zebra mussel activity on water quality in Lough Sheelin are likely to have been combined. In an attempt to distinguish the actual impacts of implementing P Regs, monitoring data from before the onset of zebra mussel colonisation (1990-1999) were used as a control reference against which to compare P dynamics post zebra mussel establishment (2004-

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2008). The period 1990-1999 contains an earlier attempt of P mitigation in the catchment (1990-1992) that comprised the implementation of policy to limit the spreading of livestock manure over the winter months and other circumstances likely to lead to excessive runoff of the applied wastes.

By comparison, the small scale/large area study utilised river water quality and river catchment attribute data from databases for the RoI (EPA) and NI (NIEA). The available data comprised approximately 3,000 potential river study sites, out of which 72 were selected for study (49 in the RoI and 23 in NI). An additional five sites were subsequently used for validation of a geospatial model (Figure 2.2, Table 2.2a-c).

2.3.1 Large scale/small area study 2.3.1.1 Material and methods The large scale/small area component of WP1 used TP and MRP (μg l-1) concentration data from seven monitoring stations for the period 1995 to 2008 and the development of a LAM, which was then used to test the efficacy of POMs in the Lough Sheelin catchment. A lake P budget was also constructed providing a means of identifying key factors (both external and internal to the lake) influencing P concentrations in Lough Sheelin. The ability of TP loading-response models to describe the relationships between sources of P (inputs to the lake, or external P loading), response of P (P concentration in the lake), the effectiveness of measures aimed at mitigating P (represented by the temporal variability of external P loading) and environment attributes specific to the lake (morphological and hydrological variables) was tested. Variations in the relationship between chlorophyll a and TP in the lake were also examined.

2.3.1.1.1 LAM development Data requirements River TP, MRP and flow data for the study were sourced from IFI and EPA. River water samples were collected three times per week throughout the monitored period. Flow rates for the seven rivers were collected as daily mean flows (m3 s-1) from 15 minute measurements of water level and rated staff gauges (m). TP was determined by persulphate digestion (Eisenreich et al. 1975) and MRP was measured using the

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colorimetric acid molybdate method (Murphy and Riley, 1962). Data from major point sources (industrial discharges and WWTPs) in the catchment were acquired from IPCC license information held by the EPA. Digital spatial data representing rivers, lakes, geology, land cover and soils were sourced from the GSI and the EPA. These datasets were acquired in the format of ESRI raster layers and shapefiles, and were projected according to the Irish National Grid. Data on human population and cattle stocking density in the catchment were sourced from the CSO and were acquired in Excel format on the basis of DEDs.

Censored P concentrations (values recorded as below detection limit) were replaced with one half of the detection limit (e.g., < 6 μg l-1 was replaced with 3 μg l-1) (Qian et al., 2007; Reimann et al., 2008). A full annual discharge record was created by estimating values for missing or omitted flows on the basis of strong correlations (R2 > 0.8) with yearly flow values for neighbouring rivers with a continuous flow record (Mwakalila, 2003). Flow values for each river were normalised per unit area by calculating the surface runoff depth (mm day-1) (Lennox et al., 1997; Jordan et al., 2005b). The calculation of runoff depth enabled direct comparison of water drainage between the subcatchments. The P loads estimated from modelling the relationships between P and runoff by LAM application were also normalised according to subcatchment area. Data on P export per unit area were thus provided and disproportionate fluxes of P from subcatchments highlighted. Surface flow is the main delivery pathway to the rivers in the catchment (Kerins et al., 2007). Runoff between and within subcatchments was assumed to be spatially uniform and influenced by the same catchment characteristics and processes.

Flow and P data were paired according to date and organised in a database by hydrological year (1st October to 30th September). The database was divided into seven periods (e.g. 1995-1996 ranges from 1st October 1994 to 30th September 1996). Runoff and P concentration data were log-transformed to achieve a normal distribution and to minimise the affect of outliers.

GIS software and extensions (Maidment, 2002; Ormsby, 2008) were used to manipulate spatial data. Subcatchment boundaries were delineated from a DEM and river vector

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data by using each monitoring site as a pour–point. Data on flow, geology, land cover, soils, human population and cattle stocking density statistics were extracted using the subcatchment boundary outlines. An estimation of human population numbers between census years for each subcatchment was calculated from linear interpolation of census data (CSO, 2006).

LAM construction Three concentration-discharge relationships are commonly recognised for nutrients in rivers (Figure 2.3): point sources (a – inverse curve), diffuse sources (b, c – linear or exponential curve) and a third (d – u-shaped curve) describing a situation where contributions from both point and diffuse sources occur (Mohaupt, 1986; Chapman, 1996; Edwards and Withers, 2007). In the latter scenario, when both point and diffuse nutrient sources contribute to river loading, nutrient dilution is the critical factor at a low discharge range, but at higher flows dilution is suppressed and diffuse sourced P has the largest influence on loading. The Re parameter provides an estimation of the runoff depth at which point sources (runoff < Re) or diffuse sources (runoff > Re) are the predominant influence on the river, expressed as a temporal proportion of flows in any hydrological year (Bowes et al., 2008).

GLM indicated a seasonal trend in the data (Ho, 2006; Field, 2009). As a result, the database was split into S1 (November to May) and S2 (June to October) periods. Several additive models combining the relationships between P concentration as a function of runoff were tested by calibration to the study database using non-linear least square regression in PRISM 5 Graphpad software (Motulsky, 2007). The model that most accurately described the behaviour of the data and provided realistic outputs was selected and is described in equation 2.1:

Cp

a*

1 Q

b* Q

c * Q2 [2.1]

where: Cp is empirical P (μg l-1) Q is empirical runoff depth (mm day-1)

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a, b and c are estimated model coefficients.

The model components describe the relationship of P with runoff along a hydrograph. The assumptions underlying the model consist of the dilution of point sources, flow dependence of diffuse source contributions and a calibration database with a temporal resolution that captures representative samples. The ‘a’ coefficient estimates P from continuous sources. Diffuse sources represented by the ‘b’ coefficient are dependent on moderate runoff. The ‘c’ coefficient represents exponential increases in concentration owing to sheet erosion during storm events. The model coefficients were not constrained to a numerical range prior to analysis in order to permit consideration of concentration-discharge hysteresis. Uncertainties in estimations were quantified by confidence intervals (p < 0.05). Individual models were derived for each season type (S1 and S2), hydrological year combination (e.g. 1995-1996 etc) and river monitoring site. The ‘a’ coefficients (P g km-2 day-1) estimated from equation 2.1 were converted to annual (S1 plus S2) point P loading (kg y-1) using equation 2.2:

Ppoint

a * 365.25 * CA 1000

[2.2]

where: PPoint = annual point P loading (P kg y-1) a = model coefficient as a function of discharge (P g km-2day- 1) CA = subcatchment area (km2). Re values were calculated by dividing the estimated ‘a’ coefficient over the sum of the estimated ‘b’ and ‘c’ coefficients.

Measured daily runoff data were applied to each estimated seasonal model to derive the annual total P (point and diffuse) load (Lf, kg yr-1) delivered to each river. Lf was used to quantify the percentage of P derived from point and diffuse sources and was calculated through a two stage process. First, individual models for each season and time period were fitted using empirical daily runoff data (Q) and the model coefficients

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a, b and c (P load from each delivery source estimated from equation 2.1) to predict a daily P concentration when no samples were taken (Cpp,) using equation 2.3:

Cp p

1 Q

a*

b* Q

c * Q2 [2.3]

Second, estimated (Cpp) values from equation 2.3 were incorporated with the measured daily P values (Cp) to construct a complete database of daily P concentrations (Cpd). Values of (Cpd) were multiplied by daily runoff (Q) to give daily P loading, which is summed to calculate annual P loading in equation 2.4:

n 365.25

Lf

Cpd * Q j 1

[2.4]

where: Q is measured daily runoff (mm day-1) Cpd is daily P concentration Lf represents total P load per unit area (kg km-2 yr-1) for a hydrological year (n = 365.25).

LAM validation Model accuracy was tested through a 10-fold cross validation method using 28 calibrated MRP models (two summer and two winter models for each of the seven subcatchments). A randomised subset of the data (10%) was partitioned into a validation subset (VS). The remaining 90% of the data, the estimation subset (EV), were used to estimate model coefficients from equation (2.1). The predicted MRP values were compared with the empirical VS data. The success of model prediction was established by the percentage of MRP values estimated accurately in the VS and expressed as the RMSE between measured and predicted values divided by the number of data points in VS. The RMSE was divided by an average MRP value in each VS to compare results of each subcatchment (CV-RMSE).

Alternative methods for estimating P loads

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Although septic tank systems are usually considered potentially important diffuse sources of nutrients (Hanrahan et al., 2001; Drolc and Zagorc Koncan, 2002), they are also known to operate as an accumulation of small and constant point sources (Arnscheidt et al., 2007). Consequently, export coefficients (Ei) for MRP transfer from septic tank systems were used in the current research as a basis for approximating catchment point source MRP load. The MRP loadings from septic tanks in rural areas for each of the subcatchments were calculated as the product of their resident populations and an Ei of 0.26 kg MRP cap-1y-1 (LLCMMS, 2000; Carvalho et al., 2005). A 30% overall improvement of septic tank efficiency was anticipated when the regulatory controls on septic tanks were officially implemented in 2004 (Keys, 2008). This reduction factor was incorporated into the estimated export coefficient for point source MRP loads.

MRP concentrations in discharges from large point sources in the Mountnugent and Ross subcatchments for 1999 onwards were calculated from averaged values from effluent discharge records (flow m3 day-1 and MRP mg l-1). MRP loadings for WWTPs prior to 1999 were estimated as the product of level of urban population and an export coefficient of 0.66 kg P cap-1 y-1 (Azzellino et al., 2003), which included the soluble P removal rates of 7.5% at Ballyjamesduff WWTP and 30% at Oldcastle WWTP (Carvalho et al., 2005). Long-term monitoring records were not available prior to the IPPC licensing of a meat processing plant in the study area, and an average effluent volume of 10.12 m3 hr-1 and an MRP concentration of 1.85 mg l-1, as described by Cooney (2006), were therefore used.

Annual flow-weighted P loads, using the product of all empirical concentration-discharge pairs, divided by total discharge, were calculated for comparison with total LAM loads (diffuse plus point) calculated from equation 2.4 for both P fractions (Salles et al., 2008).

2.3.1.1.2 P budget for Lough Sheelin Data requirements Mid-lake water samples were taken monthly and analysed for TP (μg l-1), chlorophyll a (μg l-1) and secchi depth (m) during 1990 to 2008. Water samples from seven tributaries were tested for TP three times a week for the same period. Total P was estimated according to the method of Eisenreich et al. (1975). Chlorophyll a was determined by

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hot methanol extraction and measured spectrophotometrically using the method of Talling (1974). The TP dataset was provided by the IFI. Daily mean flows, m3 sec-1, provided by the EPA were measured for each of the seven tributaries monitored for TP. Total rainfall (mm) data for the catchment were sourced from Met Eireann. Information describing zebra mussel invasion of the lake was obtained from Millane et al. (2008). Manipulation of data was carried out using Microsoft Excel software. First, the TP, flow, chlorophyll a and secchi depth data were divided by hydrological year (1st October – 30th September). Exploratory data analysis of the full data set showed that the data for 2000 and 2001, which contained large omissions of entries owing to lack of sampling or analyses of samples, were not appropriate and these were excluded from the analysis.

Construction of TP budget for Lough Sheelin The budget was constructed using data for the hydrological years 1990-2008. The annual net retention of TP (t yr-1) in the lake (TPnet) was calculated according to the mass-balance equation adopted from Gibson et al. (2001) and Coveney et al. (2005):

TPnet = (TPend –TPstart) - (TPload– TPout)

[2.5]

where: TPnet = Net TP uptake (+) or release (-) (t yr-1) TPend = Lake water TP mass at end of year t yr-1 TPstart = Lake water TP mass at beginning of year t yr-1 TPload = External TP load (t yr-1) TPout = TP load at lake outflow (t yr-1) The annual TP load in tonnes per year (t yr-1) entering the lake from the catchment was calculated as the cumulative total TPload (t yr-1) from the catchment, comprising: (i) tributaries; (ii) ungauged tributaries; and (iii) direct runoff. These steps are explained in the following sections: (i) Annual TPload (t yr-1) entering the lake from each of seven gauged tributaries was calculated using the load estimation method in equation 2.6 (adjustments were made on the right side of the equation for unit correction to tonnes per year:

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Where: TP is the measured TP concentration (μg l-1) from each sampled river dmf is the measured daily mean flow (m3 sec-1) from each sampled river n is the number of days in the hydrological year that TP samples were taken. Annual TPload (t yr-1) entering the lake from the stretch of river before a gauged monitoring point was calculated by TP export coefficient derived from the corresponding gauged data. (ii) Annual TPload (t yr-1) for tributaries draining the three rural subcatchments that had insufficient monitoring data (Maughera, Moneybeg and Rusheen) was estimated by application of a TP export coefficient derived from rural tributaries (with similar soil types and broadly similar land uses) that were gauged (Bellsgrove, Carrick, Crover, Halfcarton and Schoolhouse). (iii) Annual TPload (t yr-1) from direct runoff to the lake was estimated from a TP export coefficient derived from a combination of the seven gauged catchments.

The TPout parameter describes the TP load leaving the lake at the Inny outlet and was calculated using the TP flow-weighted method described in equation 2.6. The result of TPend minus TPstart, otherwise known as the change in storage of TPlake (t yr-1), was calculated from the difference between the lake TP concentrations (TPlake ) from year to the year (e.g. the difference from October 1997 and October 1996). To convert to metric tonnes of TP in the entire lake, the calculated difference in TP μg l-1 was multiplied by the lake volume (m3) and divided by a conversion factor.

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The TP sedimentation rate, σ (t yr-1), was calculated as the net uptake of TP (TPnet) divided by the average annual lake TP mass (t yr-1) (i.e. the fraction of TP in the water column that enters the lakebed sediment per year). The retention coefficient for TP (Rcal) was calculated as the net uptake of TP (TPnet) divided by TP load (TPload) (i.e. the fraction of TP load that is retained in the sediment per year).

Five loading-response TP models were calibrated (annual models from 1990 to 2008) using the study dataset to predict annual average TP (μgl-1) concentrations in the lake (Table 2.3). Calibration required the calculation of model parameters and TP retention coefficients (Table 2.4).

Model parameters were classified into three generic groups: morphological parameters, TP derivatives and hydrological parameters:

Information on the hydromorphology of Lough Sheelin was known from previous studies: AL (lake surface area, 18.08 km2), VL (lake volume, 81.5 m3) and z (mean lake depth, 4.5 m).

Hydrological characteristics were estimated using lake morphological data and cumulative 12-month hydrological load (Q m3 yr-1), which was calculated using gauged flow data and export coefficients for ungauged sites. Annual lake flushing rates (ρ yr-1), a metric of the frequency of lake water renewal, were determined by dividing the cumulative hydrological load (Q m3 yr-1) by lake volume (VL). Lake water residence time (τw), the average time that water spends in a lake, was calculated as the inverse of the flushing rate, ρ. Lake areal hydraulic loading rate (qs m yr-1), the inflow water volume applied over the surface area of Lough Sheelin, was the equivalent of multiplying lake depth (z) by lake flushing rate (ρ).

Derivative of TP were calculated from data manipulations of the original sampled dataset. The annual flow-weighted TP load (TPin μg l-1) entering the lake from the catchment was calculated as the cumulative total TPload (t yr-1) (calculated from equation 2.6) divided by the cumulative annual hydrological

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flow, Q (m3 yr-1). Lake TP concentration, TPlake, was estimated from an annual average of measured lake TP concentrations. The final TP parameter, L (areal TP loading rate g m2 yr-1), was calculated using the formula:

L= (Q *TPin)/AL

[2.7]

The R coefficients, derived from the retention models in Table 2.3, were estimated using various combinations of the areal hydraulic loading rate (qs m yr-1) and TP settling velocities for the lake. The estimated R coefficients were applied to the theoretical equation of Dillon and Rigler (1974) to give predictions of TPlake.

The accuracy of predictions from models was evaluated by RMSE analysis, comparing predicted TPlake with measured TPlake, described in equation 2.8.

where: TPobs is the measured TP concentration in Lough Sheelin averaged over a year TPpred is the predicted lake TP concentration for the same year n is the number of years of data in the study.

A priori, the TP loading model that showed the highest prediction accuracy in the years prior to zebra mussel invasion (1990-1999) years was used to represent the long-term TP loading-response relationship in the lake system before establishment of the zebra mussels, and thus for predicting TPlake concentrations for the period 2004-2008 in the absence of zebra mussels. The model therefore provided a means of determining the effects of zebra mussel activity on the relationship between TPin and TPlake concentration (Hecky et al., 2004).

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Chlorophyll a - TP relationships The strengths of linear relationships between chlorophyll a and TP concentrations in Lough Sheelin, based on averaged summer (June – October) and annual monthly data, were determined for pre (1983-1999) and post (2004-2008) establishment of zebra mussels. The general regression equation used was:

where: log Chl a is the log of chlorophyll a log TP is the log of TP a and b are estimated model coefficients for the regression intercept and slope, respectively.

A test of parallelism was applied to the regression models to assess significant (p < 0.05) changes in slope and intercept between the two time periods (Kleinbaum et al., 1998; Qualls et al., 2007). The statistical analysis was implemented in PRISM 5 Graphpad software (Motulsky, 2007).

2.3.1.2 Results 2.3.1.2.1 LAM Data for 1995-2008 indicate a trend of declining MRP in all seven subcatchments (Figure 2.4). Seasonal Kendall trend tests (Helsel and Hirsch, 1991) on the hydrograph data did not reveal any significant long-term shifts in runoff values that could have influenced P trends. The largest reduction in empirical MRP was in Mountnugent; the smallest was in Halfcarton. A distinct seasonal cycle of P concentration and runoff values was evident in the dataset: higher P concentrations in the summer to autumn months, corresponding with lowest river runoff, highlighted the influence of point sources. Estimations of TP and MRP model coefficients for S1 (November to May) were significantly different (p < 0.01) from S2 (June to October) coefficients, according to GLM analysis, and both S1 and S2 components were therefore included in the LAM.

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Using the LAM methodology on the full, seasonally adjusted dataset generated 195 individual model and 585 model coefficient estimates of MRP and TP loads. The models showed a typical, intermediate concentration-runoff relationship (u-shaped, Figure 2.5) indicating input from both point and diffuse sources. Seasonal models were fitted for all cases except Crover MRP (2005-2006) in which full biennial values were used because the estimated point source winter coefficient was negative. All point source coefficients were significantly different from zero, except for Bellsgrove MRP (2005-2006, S1), Schoolhouse MRP (2003-2004, S1 and S2) and Schoolhouse TP (2003-2004, S1). A negative coefficient, evident in several models (Figure. 2.5d), was likely a result of hydrochemical hysteresis. The percentage of times that the river load was dominated by point source (runoff < Re) was also calculated for each seasonal model. Model estimates of annual TP and MRP loads (point and diffuse sources) were standardised for catchment area (km2) and the dominance in time (%) was expressed for each source, fraction and subcatchment (Table 2.5).

On the basis of the 10% subset, winter models had a higher percentage of validation and lower CV-RMSE values (Table 2.6). The highest validation (78%) and the lowest CVRMSE (41%) were for Ross winter models. However, these values decreased in summer months to 39% and 52%, respectively. The highest validation in the summer models was Halfcarton (57%). LAM loads were compared with two alternative methods of load estimation: export coefficient and flow-weighted. The relationship between point source MRP loads from LAMs and export coefficients are shown in Figure 2.6. The correlation has an overall R2 of 0.51 (p < 0.01). However individual subcatchment correlation coefficients range from R2 of 0.07 to 0.66. The relationships between annual LAM MRP and TP loads with annual flow-weighted loads have R2 values of 0.95 and 0.97 respectively, highlighting an effective validation of the overall total load estimation (Figure 2.7).

There was substantial variation between years and between subcatchments in the rates of TP and MRP exports. Normalised to catchment area, the highest annual point and diffuse exports of both TP and MRP loads were delivered to the Schoolhouse river. In 1999-2000 the TP diffuse export was 77 kg km-2 yr -1 of which 79% was in the form of MRP (61 kg km-2 yr -1). In this year the point source TP and MRP export rates were also

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higher than in any other subcatchment (TP 27 kg km-2 yr -1and MRP 13 kg km-2 yr -1). During the subsequent two year period point source TP and MRP inputs to this river remained high, but point source export rates did not remain high for the remainder of the study period, so that, for example, subsequently the point TP and MRP exports were 4 and 2 kg km-2 yr -1, respectively. The small Crover subcatchment produced low point source exports of both TP and MRP, which in 2003-2004 were only 0.7 and 0.3 kg km-2 yr -1

for TP and MRP, respectively. The large Ross subcatchment produced the lowest

diffuse export rates of both TP and MRP with notably low rates in 2005-2006 of, respectively, 1.8 and 1.2 kg km-2 yr -1. During this period diffuse sources were estimated to contribute only 8% and 18%, respectively, to the MRP and TP exports from this subcatchment.

Time series data were categorised into two distinct stages according to the timing of implementation of P-related POMs: Stage 1 (1995 to 1998, pre-implementation of P mitigation) and Stage 2 (1999 to 2008, post-implementation of P mitigation). Generally, annual loads of P from diffuse sources exceeded those from point loads in terms of magnitude and time duration, except in certain periods from Halfcarton and Ross. Point sources dominated annual P load in Halfcarton in 1997-1998 (TP and MRP), 2001-2002 (MRP) and 2003-2004 (TP): the Re value showed that P from point sources predominated in 1999-2000, 2001-2002, 2003-2004 (all TP and MRP) and 2003-2004 (TP). In Ross, levels of P from point sources were usually higher than from diffuse source, except in 1995-1996 (TP), 1999-2000 (MRP), 2003-2004 (TP) and 2007-2008 (TP and MRP).

Reductions of TP (60%, p < 0.05) and MRP (80%, p 0.05) and 2003-2004 (53% MRP load reduction, p < 0.05) decreased the length of time that P from point sources predominated throughout the year. Total phosphorus from

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point sources in Ross dominated temporally (99% TP, 52% MRP) and in terms of overall load (92% TP, 88% MRP) during 2005-2006.

Accumulated loads (point, diffuse and total) and cattle stocking densities per unit area for each subcatchment for the period following implementation of measures aimed at mitigating P impacts are listed in Table 2.7. Overall, Schoolhouse contributed the greatest levels of TP from point sources, and MRP and TP loads from diffuse sources, whereas Ross supplied the highest MRP loads from point sources. Lowest TP and MRP loads from point sources were in Carrick; Ross had the lowest MRP load from diffuse sources and Halfcarton the lowest TP load from diffuse sources. Point and diffuse sources in the Schoolhouse subcatchment contributed large loads of P during 19992000. A similar large peak in P from diffuse sources but not from point sources occurred in Crover, 2001-2002. Correlations between levels of MRP and TP from point sources and with cattle stocking density from the five rural subcatchments were statistically significant (R2 = 0.92 and R2=0.9, p < 0.05). Accumulated MRP and TP loads from point sources and accumulated human population densities were positively correlated for the five rural subcatchments (R2 = 0.97 and R2 = 0.94, p < 0.05). Furthermore, TP and MRP from diffuse sources were significantly correlated with cattle density (R2 = 0.83, R2 = 0.96, p < 0.05) after exclusion of data from the Ross sampling point.

Extreme runoff events contributed a large proportion of annual P load to rivers. In 2007-2008, runoff greater than 10 mm day-1 delivered 35% of annual loads over 20 days to Mountnugent. The sources and direction of change in P loads appear to have been altered in 2007-2008: P loads from diffuse sources in the Bellsgrove, Mountnugent, Ross and Schoolhouse subcatchments declined substantially from 1999-2000 to 2005-2006, but a high P load in 2007-2008 altered the downward trend and led to an overall increase between 1999 and 2008. The total rainfall over the two hydrological year periods of 2007-2008 and 1999-2000 were similar (2264 and 2224 mm, respectively). There were, however, more extreme flows (≥ 10 mm day-1) in each subcatchment in 2007-2008. Increased P loads from point sources were also registered for the Mountnugent (p > 0.05), Bellsgrove and Schoolhouse (p < 0.05) subcatchments in 20072008. Conversely, P loads from point sources decreased (p > 0.05) in Halfcarton and Ross (p > 0.05) during the same period.

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2.3.1.2.2 P budget for Lough Sheelin Annual flow-weighted TP concentrations (TPin) entering Lough Sheelin and TP concentrations in the lake (TPlake) for 1990-2008 are shown in Figure 2.8. The effects of restrictions on the spreading of livestock manure during winter months, introduced in the early 1990s, are evident. The relationship between TPin and TPlake shows a marked change following the 2000-2001 gap in the data, after which there was much greater variability in the response of TP lake concentrations to TP loading compared with previously. Results from the application of ANOVA showed a significant decrease (p < 0.05) in TPin from 1990-1997 (average TP 95 μg l-1) to 1998-2008 (average TP 66 μg l-1), i.e. following implementation of P Regs in the catchment. Despite this fall in TP load entering the lake, average TPlake increased between the periods 1990-1997 (25 μg l-1) and 1998-2008 (31 μg l-1), although the increase (6 μg l-1) was not statistically significant (p > 0.05). Annual averages of TPlake (μg l-1), chlorophyll a (μg l-1) and secchi depth (m) for 19902008 are summarised in Figure 2.9 and Table 2.8. The relationships between the three parameters followed a similar trend from 1990 to 1999. However, the Chl a-TP ratio altered post-2004: a decline in chlorophyll a concentrations did not correspond with similar declines in TP. As such, the situation post-2004 was different from 1991-1993: during the latter period low levels of TP were associated with low concentrations of chlorophyll a. According to the available data, the lake was eutrophic until 2005 and then varied between eutrophic and mesotrophic states until 2008. Mesotrophic characterisation was based upon a combination of low chlorophyll a and high secchi depth levels, and contrasted with characterisation based on TP concentrations alone, which remained consistently in the eutrophic range.

The TP budget for Lough Sheelin (1990-2008) comprises inflow TP (TPload), change of TP storage in the water column, outflow TP (TPout) and net TP retained/released by sediment dynamics (TPnet) (Table 2.9). Annual net TP sedimentation actions were positive (i.e. the sediments functioned as a net sink for TP) in all years except 2004, when net TP release to the water column occurred (1.76 t yr-1). This negative value was removed from the analysis because it was considered an outlier. The greatest retention

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of TP (26 t yr-1) by sedimentation took place in 1994 and the lowest (4.77 t yr-1) occurred in 1997. Values of σ and Rcal, calculated by dividing TPnet by the average annual TP mass of the lake and TPload, respectively, are also listed in Table 2.9. Variations in L, σ, and Rcal over time are shown in Figure 2.10. Total P loading showed substantial annual fluctuations owing to variations in rainfall. Although interannual variability is evident, overall the rate of TP sedimentation appears to have declined since 1990 (Figure 2.11).

TP loading - response models Initial model parameters used to calibrate the TP steady state models (1990-2008) are provided in Table 2.10. The predicted TP sediment retention (R) values, derived according to the retention models specified in Table 2.3, are shown in Table 2.11, while figures 2.12 and 2.13 illustrate predictions of TPlake based on the TP loading models, together with corresponding values of measured TPlake.

Figure 2.12 compares measured TPlake concentrations for 1990-2008 with the TPlake values predicted from the three P loading models that used τw as a scaling factor for σ (Foy, 1992; OCED 1982 - general and shallow lakes models) and the Prairie (1989) model that used a combination of estimations of σ and R. The RMSE values of predictions for the three time periods (overall - 1990-2008, pre zebra mussel establishment - 1990-1999 and post zebra mussel establishment - 2004-2008) are shown in Table 2.12. Output from the Prairie (1989) model had the lowest overall RMSE (11.4 μg l-1), i.e. the smallest deviation from observed. Foy (1992) and OECD (1982) shallow models produced very similar predictions (overall RMSE 29.8 and 29.10, respectively) and were the next most efficient models to use σ. However, both models consistently over-predicted, except in 2005. Notably, predictions from the OCED (1982) general model deviated most from measured values (overall RMSE 58.3 μgl-1) and consistently over-predicted. Figure 2.13 compares measured TPlake with levels of TPlake predicted from models of P loading using the retention coefficient, R, to describe the sedimentation of TP for the period 1990-2008. Corresponding RMSE values are listed in Table 2.12. Measured and predicted values for the same hydrological year were generally similar. However, the Larcen and Mercier (1976) model constantly predicted higher concentrations than the other models. The model derived by Ostrofsky (1978b) showed the lowest overall RMSE

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(11.9 μg l-1), whereas predictions by the Larcen and Mercier (1976) model had the highest overall RMSE (25.2 μg l-1).

Overall, the Prairie (1989), Ostrofsky (1978b) and OECD (1982) shallow models were the best predictors of TPlake for the period 1990 -2008. Results from the second test of prediction accuracy, linear regression plots and R2 values (p 2.5 indicates the presence of multicolinearity between two or more model predictors.

PCA, a means of reducing the complexity of data to enable identification of the main causes of variation, or principal components (Field, 2009), was applied to the intercorrelated predictor variables to yield standardised, independent predictors of fwMRP. Typically, the first principal component accounts for the greatest variance with each subsequent component explaining progressively less. Two sets of three principal components were estimated for each of the two databases.

The two point databases comprising MRP_winx, MRP_sumx, Irish Grid Easting and Northings coordinates, and corresponding principal components, were imported into the R computer programme v 2.12 (Hengl et al., 2007). Stepwise multiple linear regression of fwMRP as a function of a combination of the independent predictors was carried out. Optimum combinations of predictors were selected and four independent regression models calibrated. The coefficient of determination (R2) quantified the proportion of variation explained by the calibrated models (p < 0.05) and the VIFs explained the degree of multicolinearity between model predictors.

2.3.2.1.2 Development of geospatial models The procedure through which the geospatial models were developed, based on environmental predictors identified during the first part of the small scale/large areas study is summarised in Figure 2.16. The geospatial models are intended for use in identifying stretches of rivers in the RoI with a high likelihood of being vulnerable to 36

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impairment by P or resistant to recovery following implementation of measures aimed at reducing P inputs.

Geospatial models, and associated geostatistical methods, utilise the spatial structure that exists in many environmental datasets (Chilès and Delfiner, 1999; Wackernagel 2003; Webster and Oliver, 2007; Haining et al., 2010). Geostatistical analysis often comprises two stages; experimental variogram modeling, followed by prediction based on kriging. Geostatistical methods have been used to examine the spatial variability of a range of environmental parameters (McGrath et al., 2004; Zhang and McGrath, 2004; Moral et al., 2006; Romić et al., 2007; Lado et al., 2008; Yang et al., 2008; Alsamamra et al., 2009; Saby et al., 2009; Dou et al., 2010; Li 2010; Di Piazza et al., 2011; Sun et al., 2011), although the approach has only relatively infrequently been applied to river networks (Dent and Grimm 1999; Gardner et al., 2003; Kellum, 2003; Yuan, 2004; Ganio et al., 2005; Peterson and Urquhart 2006; Yang and Jin, 2010). In the current research, geostatistical modeling was used to interpolate sampling data to geographic areas that remained unmonitored. Moreover, deployment of the technique of RK potentially can provide more robust predictions than those generated by either regression or geostatistical techniques used on their own (Hengl et al., 2004, 2007).

Variogram model The variogram (also called the semivariogram) quantifies the spatial distribution of a regionalised variable (Webster and Oliver, 2007). Semivariograms express mathematically the way in which quantities of a property vary with changes in distance and direction from a point. Variogram functions relate the semivariance (half the expected squared difference between paired data values z(xi) and z(xi+h)) to the lag distance h, by which sample points are separated (Webster and Oliver, 2007):

For discrete sampling sites, the function for the variogram is:

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where: γ(h) is the experimental semivariance estimation at a lag distance h z(xi) is the value of the measured samples at sample location xi m(h) is the total number of sample points within the distance h

An experimental variogram shows the degradation of spatial correlation between two points in space when the separation distance is increased. A theoretical model (spherical, exponential, gaussian, circular) is then fitted to the variogram plot yielding information about spatial structure and providing a basis for interpolation through kriging (Cressie, 1985; Webster and Oliver, 2007). A typical variogram model has three components (Figure 2.17). The nugget variance (Co), the intercept of the variogram, occurs when a spatial process is discontinuous as the distance, h, approaches zero. This nugget effect may occur owing to measurement error or purely random variation. The range (a) is the maximum distance of spatial dependence over which two sites are related. Sites separated by a distance greater than the correlation range are assumed to have spatial independence. The sill variance (Co+ C) is the semivariance value at which the variogram levels off. Spatial correlation can be examined in two formats: anisotropic and isotropic (Webster and Oliver, 2007). Anisotropic spatial correlation is based on distance between paired points and specific direction of the correlation, whereas isotropic correlation reflects the distance between points regardless of directional relationship.

In the current research, spatial correlation, based on Euclidean distance (Peterson and Urquhart, 2006) between sample sites, was examined in the response variables (seasonal fwMRP) and residuals of regression models. Isotropy was assumed (Webster and Oliver, 2007). Spatial dependence was determined through variogram analysis and modelled using automatic variogram fitting options in the gstat package of the R programme. Sample (experimental) variograms were computed for the response fwMRP data in order to examine the initial spatial structure. A variogram model was fitted to the target variable using the automatic fitting function and initial variogram

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parameters of nugget = 0, sill parameter = the overall variance in the dataset and the range = one-quarter of the diagonal of the bounding box (maximum distance of spatial autocorrelation). To develop the regression-kriging model, a variogram was then fitted to the residuals of the four seasonal regression models from WP1b. In all cases, the most appropriate model (spherical) that best fit the variogram was selected.

RK Kriging involves the application of least-square linear regression algorithms to predict the value of a variable at unobserved locations, based on the spatial structure of available data (Webster and Oliver, 2007). Prediction is based upon application of a weighted moving average of data:

where: Ž(x0) is the predicted value at location x0 λi are the weights and meet the condition needed for unbiased estimator

and

where: z(xi) is the known value at the sampling point xi N is the number of sample points

Kriging can be used to provide reliable estimates of unobserved variables where environmental conditions are relatively homogeneous. However, where conditions vary over relatively short distances, predictions to reasonable levels of confidence can be difficult. Variations in local topography, land use, nutrient sources and transport mechanisms may not display an identifiable spatial structure. In such cases, the strength of the kriged estimates decreases, since catchment specific measurements are impacted

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by strong local variation. Adaptations of the kriging technique have been developed to surmount this weakness, with the adapted technique referred to as RK (Hengl et al., 2007).

RK incorporates regression and the spatial interpolation of regression residuals (Hengl et al., 2007). The RK procedure starts with a standard multiple regression using external variables as a function of the target variable. The regression model provides information for prediction and the unexplained model variation (residuals) is modelled using its spatial structure. In the RK procedure, instead of kriging z(xi) directly, a regression analysis is applied to z(x) as a function of a group of external attributes qk(x):

where: z(x) is the target variable; x = (x,y) represents the two-dimensional spatial coordinates z*(x) is the spatial field that can be estimated for each point x where qk(x) is known βk are fitted regression coefficients qk are the external predictor variables ε(x) is a normally distributed residual with zero-mean.

The residuals of the regression model, ε(x), retain the spatial variability of the target variable, z(x). Unexplained spatial correlation in the residuals, ε(x), is modelled by a variogram, γ(ε(x)), which is inferred from the residual spatial structure in the observed point data. The final model, a combination of the regression model, z*(x), plus the variogram model of residuals, γ (ε(x)), is kriged and interpolated to predict the target variable, Ž(x), at unobserved locations.

In the current research, the regression models developed in the first part of the small scale/large area study were combined with corresponding variogram models in R. A kriging function was then applied to these using the krige function in the gstat package. Hybrid spatial prediction models (RK models) and models of estimated predicted variance (95% confidence interval) were produced.

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Cross-validation of RK models The RK models were examined using the automated leave-one-out cross validation method in the R programme in which sample points were omitted in turn and values of fwMRP computed. This process was repeated until a complete subset of computed values was produced. The observed and predicted fwMRP values were regressed and a R2 value generated. In addition, the RMSE metric was used to examine prediction accuracy;

where: Z(Si) is the observed value for point Si Ž(Si) is the predicted value for the same point n is the number of stations used in the study.

Validation of geospatial models The summer and winter geospatial models derived from the RoI database were used to predict fwMRP concentrations at the outlet of five rivers catchments in the RoI. These catchments were external to the calibration dataset: fwMRP data from 2009-2010 were used to compare with predicted values. Prediction accuracy was assessed using RMSEs.

2.3.2.2. Results 2.3.2.2.1 Environmental predictors of P concentrations in river water Exploratory data analysis Flow weighted MRP concentrations at the 72 river monitoring sites ranged from 1 µgl-1 to 443 µgl-1 (summer) and 1 µg l-1 to 261 µgl-1 (winter). Owing to a non-normal distribution of data, a log (x+1) transformation was applied to fwMRP. All potential predictor variables except cattle density and % pasture had a non-normal distribution and these were log (x+1) transformed also. The t-tests on fwMRP data showed that the means of the two populations were statistically different (p < 0.05).

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Regression and PCA Results of simple linear regression showed 11 and 9 significant predictors of seasonal fwMRP in the RoI (EPA) database and combined RoI-NI (EPA_NIEA) database, respectively (figures 2.18 and 2.19; Table 2.17). The coefficient of determination (R2) is reported with the individual regression plots. All relationships were found to be significant (p < 0.05). The dominant predictors of fwMRP concentrations in study catchments in the RoI were catchment mean slope, TWI and human population. However, for the combined RoI and NI database, catchment mean slope was found to be a poor indicator of MRP. Moreover, although TWI was not a significant predictor, human population was. Overall, the variables % pasture, cattle density, % urban, runoff risk, % artificial, human density, % bedrock type 1 and TWI were positively related to fwMRP. Furthermore, % bedrock resistant to erosion, catchment mean slope, drainage density and % forest were inversely related to fwMRP. Drainage density was significantly related to fwMRP in the RoI database but not in the combined RoI-NI database.

Initial multiple linear regressions of fwMRP as a function of a combination of all significant predictors yielded large VIFs, ranging from 1.5 to 20.8, indicating multicolinearity between predictor variables (Table 2.18). The correlation matrices and associated Pearson Correlation coefficients (r) are provided in tables 2.19 and 2.20.

Results of PCA are displayed in figures 2.20 and 2.21 and summarised in tables 2.21 and 2.22. For both databases used in this study, the first three components have eigenvalues greater than 1. For the study catchments in RoI (EPA databse), the first three components accounted for 74% of the total variation. The main component, Principal component 1, explained 37.5% of total variation and was mainly influenced by variation in drainage density, % forest cover, mean catchment slope and % of erosion resistant bedrock, while variation in % urban, % artificial areas and human population density largely explained Principal component 2, which accounted for 25% of total variation. Variations in cattle density, % pasture, P runoff risk and TWI were reflected in Principal component 3 (11% of total variation). A slightly different combination of principal components was generated for the combined RoI-NI (EPA-NIEA) database, with

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the three main axes explaining 73% of the total variation. The first Principal component, 1, mainly influenced by cattle density, % pasture and extent of bedrock vulnerable to erosion, explained 31% of total variation. Principal component 2 accounted for 30% of total variation and was largely explained by % urban, % artificial and human population density. Principal component 3 explained 12% of total variation and was influenced mainly by P runoff risk, mean catchment slope and % forest cover.

The optimal combination of predictors of fwMRP was selected by stepwise multiple linear regression. Four regression models were produced: VIF analysis indicated that multicolinearity between predictors was not present (VIF = 1.0), while a normality test showed that the residuals did not significantly depart from a normal distribution in all cases. The intercepts, component coefficients, VIFs, R2 values and significance levels for all four regression models are listed in Table 2.23.

2.3.2.2.2 Development of geospatial models Variogram model The experimental variograms detected spatial dependence in all four target variables and regression residuals. The variogram structure and best-fitted models are shown in figures 2.22 and 2.23. All models were tested for isotropic and Euclidian distance correlation, and were fitted with a spherical model. Table 2.24 summarises the degree of fit between model parameters and variogram structures. Overall, the nugget, sill and range parameter values were smaller (with one exception) than the values estimated by direct variogram analysis on the target variable (MRPLX1). The highest decreases of nugget values between target and residual models were associated with the combined RoI-NI database. Conversely, models based on the RoI (EPA) database showed a large decline of sill from the target to residual model parameters when compared with the combined RoI-NI (EPA_NIEA) database. The distances at which SumMRPLX1 were no longer spatially correlated were 93km and 71 km for, respectively, the RoI database and combined database. However, the residuals of both summer regression models had spatial dependence up to 102 km (RoI database) and 55km (combined RoI-NI database).

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RK RK modelling of data from the RoI (EPA) database generated R2 values of 0.70 (summer) and 0.71 (winter) (Table 2.25). The equivalent R2 values for the combined RoI-NI (EPA_NIEA) database were 0.56 (summer) and 0.58 (winter). All four RK models produced higher R2 values than their corresponding regression model. Table 2.25 also summarises cross-validation diagnostics (RMSE) for each model. The lowest RMSE value relates to the winter hybrid model from the RoI database, and this model also showed the largest percentage increase in explained variability of MRP (8%). In general, the R2 values of winter models increased by a greater percentage than summer models. Crossvalidation analysis produced individual predicted values for each sample site: the strength of the relationships between predicted and observed data (with 95% confidence intervals) are illustrated in figures 2.24 and 2.25.

Validation of geospatial models Validation results show that the winter model based on the RoI database has much higher prediction accuracy than the summer model; the RMSE for winter prediction is 7μg l-1 and 18μg l-1 for summer (Table 2.26). Standardising the RMSE (RMSE divided by the SD* of the observed values) provides a means to compare between different models: a standardised RMSE of 0.4 or less indicates good prediction accuracy (Hengl et al., 2004). The winter model has a standardised RMSE of 0.3, within the limit of 0.4 recommended. The summer model, however, has poorer predictive ability (RMSE 18μg l1

) and a standardised RMSE of 1.9. Notably, the predictions from the summer model

were predominately over-estimations indicating that the model was simulating the transfer of greater amounts of MRP in the summer than observed in the rivers.

2.4 Discussion WP1 set out to test the H0 that there is no relationship between environmental conditions and P concentrations in lakes and rivers in the Irish Ecoregion. Operating at two different scales of study, WP1 has provided results that permit rejection of the H0 with some confidence. Moreover, relationships between selected environmental variables and P concentrations measured in rivers have enabled development of predictive geospatial models for deployment in the RoI. The following section discusses the results from WP1 in more detail.

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2.4.1 Large scale/small area study Point sources In addition to confirming the a priori expectation of a large point P source from the subcatchments containing urban areas, river data for the Lough Sheelin catchment also provide evidence of a point source influence in rural subcatchments. These rural point sources may be cattle access points (Sharpley and Syers, 1979; Jarvie et al., 2010), farmyard leaks (Edwards et al., 2008; Edwards and Hooda, 2008), artificial land drains (Kurz, 2000; Withers and Jarvie, 2008) and septic tank systems (Arnscheidt et al., 2007; Jarvie et al., 2010). In rural parts of the Sheelin catchment, septic tank systems are the main method of disposal of human waste. Typically, the effluent from the tanks drains to a soakaway in which the nutrients are dissipated into the soil (Harper, 1992). P transfers from septic tank systems were thought to make only a minimal contribution of P to surface waters owing to higher (than WWTP) P retention in soils. Carvalho et al. (2005) and LLCMMS (2000), however, showed that P loads from septic tank systems to water bodies may be higher than expected, owing to factors such as poor design, maintenance or position of the system and low possibilities for the dilution of cumulative loads.

Ross showed the largest accumulated point source MRP loading per unit area even following implementation of P-mitigation (at WWTP). Ross has a relatively low rural population density, a lower urban population than Mountnugent and the highest MRP:TP ratio (0.53). A high proportion of soluble P contained in TP indicates that the WWTP is a dominant contributor of MRP. The estimation of Re values also showed that the timing of dominance is important. For example in summer 2001-2002, point source TP and MRP contributed, respectively, 49% and 57% to annual P loads, but the Re value indicates that loads from point sources dominated annual flows (85% TP and 97% MRP). This suggests that the river was at a much higher risk of eutrophication in the ecologically sensitive summer period than indicated by point sources of P levels alone. Mountnugent had the second highest MRP:TP ratio (0.51), which was expected because of contributions from WWTPs. However, the overall magnitude of contributions of P from point sources in Mountnugent was relatively low. The seasonal Re values, in contrast with Ross, indicated that magnitudes of peaks of P from point sources were

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more influential than the length of time over which high loadings were sustained in both winter and summer models. The Bellsgrove and Schoolhouse subcatchments contributed some of the highest MRP and TP loads from point sources to annual loadings, indicating the presence of constant sources of P in these rural subcatchments.

The Schoolhouse subcatchment had the highest cattle stocking density of the subcatchments studied. A significant correlation of point source P load with cattle stocking density implies that there is a positive linear relationship between the two factors, and may reflect the direct delivery of livestock manure, farmyard runoff and slurry to the rivers (Jarvie et al., 2010). Rural human population densities and point sources of MRP and TP were also positively related, and this shows that malfunctioning septic tank systems may be a source of direct, localised discharge to rivers (Jarvie et al., 2010). However, the relationships were non-significant (p > 0.05) when the urban populations of Mountnugent and Ross were included. This can be attributed to the impacts of loadings of P from WWTP and industrial sources in Ross and Mountnugent, and reflects the differences in P loading dynamics between mainly rural and predominantly urban subcatchments.

Diffuse sources Soils in the Crover and Mountnugent subcatchments are predominately poorly drained gleys. Well drained brown earths cover the Ross, Halfcarton and Schoolhouse subcatchments, whereas Bellsgrove and Carrick have a mixture of both soil types. Previous studies have found that poorly drained soils have the highest Q5:Q95 ratios and can act as major diffuse sources of P (Jordan et al., 2005a), as is evident from data presented here. The overall highest accumulated P loads from diffuse sources were from the Bellsgrove, Crover, Mountnugent and Schoolhouse subcatchments. In the rural subcatchments, transfers of MRP and TP from diffuse sources were positively correlated with cattle stocking densities. However, Tunney et al. (2007) found that grazing animals significantly influence TP, and not MRP, concentration in overland flow: Douglas et al. (2007) also reported a dominance of particulate P from drumlin soils under grassland. Total Phosphorus and MRP loads from diffuse sources were positively related to rural human population densities, indicating the potential of septic tank soak-aways to act as diffuse sources. Halfcarton, which has a similar soil type to Schoolhouse, has lower

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agricultural intensity, fertiliser application rates and cattle stocking densities, and contributed the lowest loads of TP from diffuse sources. This suggests that excessive P loading is more influential than soil type.

Previous studies have shown that runoff from grasslands generally carries little sediment and that the TP load is mainly dominated by MRP (Heathwaite et al., 1997). Recently applied, inorganic fertilisers that are not incorporated into the soil, therefore remaining on the land surface, are a major source of MRP (Torbet et al., 1999, Withers et al., 2001, Kleinman et al., 2002). The rapid increase in P loads associated with high rainfall levels in 2007-2008 are likely to have led to eutrophication observed at Lough Sheelin in 2008. Increases in P transfers in association with high levels of runoff have been reported elsewhere (Webb et al., 1997; Tunney et al., 2000; Shigaki et al., 2007). Phosphorus largely in the form of MRP, i.e. the most bioavailable form of P, can have major impacts on aquatic ecology (Foy et al., 1995). Given that agriculture is usually the main contributor to diffuse sources of P (Jennings et al., 2003), implementation of measures targeting fertiliser use and slurry spreading, such as those being reviewed in the Nitrates Directive National Action Programme (Fealy et al., 2010), could reasonably be expected to facilitate recovery of Lough Sheelin.

Appraisal of P mitigation Variations in P export rates were recorded during the period following implementation of mitigation measures in all subcatchments, although levels of MRP and TP declined overall. The significant decline in levels of P from point sources in Mountnugent and Ross is likely to represent increased removal of P at WWTPs and restrictions on industrial discharges (Bowes et al., 2011). However, despite the large decreases in P from point sources in Mountnugent in 1999-2000, MRP from point sources increased the following year. This increase may reflect the continued occurrence of several point sources of P in the catchment. Nevertheless, point sources of MRP declined in 20032004, despite a rapidly growing catchment population. The decline coincided with the implementation of septic tank systems and agricultural bye-laws. P from point sources also declined in 2003-2004 in the rural subcatchments that implemented both byelaws (Bellsgrove, Carrick, Crover and Schoolhouse). However, extreme runoff events during 2007-2008 reversed these decreases. Halfcarton and Ross, the subcatchments with

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lowest Q5:95 ratios, free draining soils and lower P desorption indices, are less susceptible to high surface runoff. The Re values support this evidence; point sources in Ross dominate both load magnitude and flow duration owing to the smaller influence of diffuse sources. However, diffuse sources often predominate in subcatchments where poorly drained gley soils are extensive. A decrease in P from point sources in Halfcarton and Ross during 2007-2008, while increases occurred in the other subcatchments, is indicative of a link between runoff and the magnitude of P load from point sources where soils are frequently waterlogged.

In Ross, the Re value showed that P loads from point sources generally dominated flows during the 5 month summer season. Ross does not contribute excessive P loadings to the annual catchment P budget but high concentrations and dominance of MRP from point sources during the summer have localised eutrophication effects (e.g. Jordan et al., 2005b). The installation of temporary facilities for removal of P in 2003-2004, and the control of industrial discharges, reduced the percentage of times that summer flows were dominated by P from point sources, thereby reducing the risk of eutrophication. Increased P from point sources in 2005-2006 can be attributed to a large accidental spillage from the WWTP during August 2005 (Kerins et al., 2007) that effectively masked any improvements that might otherwise have occurred following implementation of measures aimed at mitigating P inputs.

Lough Sheelin was reclassified from mesotrophic to eutrophic in 2008. Implementation in 2003 of restrictions on the spreading of slurry on land after the end of September of each year, aimed at reducing P transfers from diffuse sources, appears to have had little positive impact on lake water quality. This may be because unusually high rainfall in 2007-2008 increased surface runoff rates and led to the mobilisation of relatively high concentrations of newly applied fertilisers and slurry.

Response of Lough Sheelin to P mitigation efforts in the catchment Data for 1990 to 2008, a period that includes implementation of two sets of measures aimed at mitigating inputs of P to water bodies in the catchment, suggest that external loadings of TP to Lough Sheelin significantly declined (p < 0.05) following a peak in the early 1990s and remained relatively low to 2008. Some inter-annual variability is

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apparent, however. Variability in annual P loads to Lough Sheelin has been attributed to unusually high levels of rainfall (EPA, 1994; Kerins et al., 2007). Moreover, as is evident from the application of LAMs in this study, the presence of impermeable soils, WWTPs and septic tanks, and occasional associated leakages and spillages, in the catchment are all factors that are likely to have contributed to inter-annual variability in external loadings of P.

Despite significant falling external P loads overall, TP concentrations in the lake remained at early 1990s and higher levels though to 2008. However, the data also show that the reaction of TP in the water column to reduced TP loadings was not consistent throughout 1990-2008. For example, from 1990 to 1998, the response of TPlake concentrations to loadings of TP showed similarities to findings in published research: decreased loadings led to reduced lake concentrations (Jeppesen et al., 2005; Köhler et al., 2005; Søndergaard et al., 2005). This loading-response relationship, apparently well established according to visual observation of the trend from 1990 to 1998, changed markedly after 2004. Despite an overall significant reduction of TP loading in the period during which P Regs have been implemented, TP concentrations in the lake showed an overall increase, although this was not statistically significant (p < 0.05). The results of implementation of the first set of measures (restrictions on the spreading of manure during winter months) 1990-1992 suggest that the change in the TP loading-response relationship after 2004 was not a secondary impact of decreased TP load to the lake and must, therefore, be a result of some other factor, one which was not present in the early 1990s.

Observations of the chlorophyll a concentration and secchi depth responses to lake TP concentration reported in other research (e.g. Jeppesen et al. (2005)) is supported by data for Lough Sheelin from 1990 to 1999 in which P is shown as a systematic positive driver of chlorophyll a, which in turn has a negative influence on level of transparency (Wetzel, 2001). The TP remediation attempt in 1990-1992 possibly led to the expected decline in TP concentration, a fall in chlorophyll a concentrations and an increase in secchi depth. These short-term reductions in P delivered to Lough Sheelin as a result of mitigation prior to the P Regs have been documented (EPA, 1994; Santillo and Pocock, 1995). The ratio of TP to chlorophyll a changed after 2004, however, with chlorophyll a

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concentrations declining rapidly without a similar fall in TP concentrations. This would suggest that conditions within the lake underwent changes in the period following implementation of the P Regs, altering the response of lake TP concentrations to TP loadings and the influence lake TP concentrations had on chlorophyll a.

Loading-response models for TP in lakes have been used in many studies to predict the value of one of the model parameters from another or to examine the impact of proposed TP loading reductions (Søndergaard et al., 1999; Conveney et al., 2005; Girvan and Foy, 2006). Here, the aim was to test a group of commonly used models with a view to identifying a particular model that would best describe the TP dynamics of the lake system, thus providing a validated predictive tool for the management of the catchment. However, despite certain models performing better than others, selection of a single TP loading-response model that was representative of the long-term relationship was not possible, owing to weak and variable regression relationships. Zebra mussels can alter TP loading-response relationships in lakes (Vanderploeg et al., 2010), however, and because of this the models were re-tested on data from before and after the establishment of zebra mussels in Lough Sheelin. The Prairie (1989) and Ostrofsky (1978b) models, with low RMSE values for the long-term data, were also the most accurate for the pre-zebra mussel establishment years, 1990-1999. Adopting these two models as the best available, predictions of TPlake concentrations for 20042008, post zebra mussel establishment, were examined for accuracy. Both models predicted lower TP concentrations than sampled concentrations, possibly because of increased release of TP from sediments to the water column, thereby leading to elevated lake TP concentrations that could not have been accounted for by the models (Søndergaard et al., 2003). Similarly, using TP mass balance studies of shallow Danish lakes, Søndergaard et al. (1999) showed reductions in TP concentrations predicted by simple equations, such as the Vollenweider model OECD (1982), were typically lower than anticipated because of the internal loading of P from sediments. The evidence provided here from the results of the loading-response models implies that the recovery of Lough Sheelin was impacted by internal lake processes that were outside the control of measures implemented as part of the P Regs. The following section will discuss the constraints in P Reg effectiveness as a result of sedimentation dynamics in the lake and the presence of zebra mussels.

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Influence of the environmental processes of a lake on reduced loadings of P Lough Sheelin was characterised as mesotrophic in 2008. Owing to high transparency and low chlorophyll a concentrations, an improvement in trophic status of the lake was assumed, despite TP remaining in the eutrophic range. Zebra mussels were established in Lough Sheelin by 2004, around the same time large improvements occurred in secchi depth and chlorophyll a concentrations. Similar characterisation problems have been observed in Lough Key, a lake in the River Shannon catchment that was colonised by zebra mussels in 1998 (Lucy et al., 2005). These initial data suggest that the effects of implementation of P Regs on TP concentration in Lough Sheelin were masked by the impacts of zebra mussel establishment in the lake. The results of the current study are supported by Millane et al. (2008) , who found that TP concentrations in the lake increased following establishment of zebra mussels, although this result was not statistically significant (p > 0.05). This previous study, however, did not consider TP concentrations in the lake in the context of variations in TP loading from the catchment or TP sedimentation dynamics.

The lake TP budget showed positive annual net TP sedimentation. Although sediment uptake and release of TP may have been simultaneously occurring in different parts of the lake, ultimately the lake sediment functioned as a net sink for TP in all years except 2004. TP incorporated in lake sediment also appeared to be greater than TP lost to the outflow, despite the estimated hydrological residence time being six months. Compared with deep lakes, where a redox dependent accumulation of P occurs in the anoxic hypolimion during stratification, shallow lakes are usually well mixed and oxidised throughout the water column, encouraging sediment retention (Søndergaard et al., 2003). Lough Sheelin is a polymictic lake; the lake is too shallow for thermal stratification to develop (O'Sullivan and Reynolds, 2005). TP retention by sediments was therefore expected. Overall, an estimated 73 % of TP load to the lake has been retained by sediments since 1990.

An overall decline in TP sedimentation rate following establishment of zebra mussels is evident from the data. The reason for this may be increased release of TP into the water column or a decreased incorporation of TP in the sediments. Results showing increased

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TP concentrations in the lake despite reduced TP loading from the catchment, however, suggest that it is the former. Zebra mussel-induced alteration of lake P sedimentation rates has been reported (Bykova et al., 2006; Turner, 2010). Adult zebra mussels filter organisms and particles containing P from the water column. The P taken us is either assimilated into their biomass or excreted. Increased accumulation of organic matter results, increasing microbial activity at the sediment-water interface, intensifying P remineralisation, decreasing oxygen penetration into sediments and releasing Fe-bound P (Søndergaard et al., 2003; Brönmark and Hansson, 2005; Bykova et al., 2006). The overall increase in average TP and decrease in average chlorophyll a evident in the data for Lough Sheelin post-implementation of the P Regs may therefore be a result of zebra mussel establishment. Contrasting impacts of zebra mussels on TP levels and chlorophyll a-TP relationships in lakes are apparent from the literature (Fishman et al., 2009; Young et al., 2011) and may be because of differences in trophic status of the lakes when invasion commenced (Qualls et al., 2007; Vanderploeg et al., 2010). The availability of soluble P tends to increase in an infested P-abundant lake, owing to P excretion by zebra mussels. However, in P-deficient systems, mussels excrete less P to sustain a constant P concentration in their biomass (Mellina et al., 1995; Vanderploeg et al., 2002). This may have occurred at the study site: Lough Sheelin was in a eutrophic state at the time of zebra mussel invasion and the TP concentration has risen since this occurrence. Nevertheless, the relationship between zebra mussel establishment and water quality effects is complex and can be confounded by, for example, the number and size of individuals comprising the established population (Idrisi et al., 2001), the degree of lake stratification (Higgins et al., 2011) and the presence of predators, parasites and ecological competitors (Molloy et al., 2001).

Overall interannual variations in net TP sedimentation in Lough Sheelin indicate changes in the net partitioning of external TP loads between the water column and the sediment. A constant sedimentation rate is essential for TP concentration of lake water to change in proportion with external TP loads (Coveney et al., 2005). Areal TP loading also shows substantial interannual variation, partially owing to fluctuations in annual rainfall. Phosphorus sedimentation rates generally reflect these variations because TP load is a primary factor in the calculation of the sedimentation rate. Nevertheless, despite this dependency, P sedimentation results are indicative of the general sediment P dynamics

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52

in the lake. The long-term TP budget shows that the key factor driving P dynamics in Lough Sheelin is TP loading from the catchment, most (~70 %) of which is usually incorporated into the sediment, a process that has recently been altered by zebra mussel activity. Ultimately, the impacts of a reduction of TP loading from the Lough Sheelin catchment, following implementation of the P Regs, were not reflected in TP concentration of the lake water column owing to increased internal TP loading from sediments induced by zebra mussel function.

A decoupling of the chlorophyll a-TP relationship following establishment of zebra mussels in Lough Sheelin is evident from the data. Departures over time from a previously established chlorophyll a-TP relationship have been reported elsewhere (Kaiser et al., 1994), including following invasion by zebra mussels (Mellina et al., 1995; Nicholls et al., 1999; Nicholls et al., 2001; Hall et al., 2003; Winter et al., 2011). A reason for this disassociation may be attributed to mussel filtration inadvertently reducing chlorophyll a concentrations but recycling TP concentrations through the sediment, thereby causing an overall decline in chlorophyll a concentration but an overall increase in TP concentration in the water column (Nicholls and Hopkins, 1993; Fahnenstiel et al., 1995; Effler and Siegfried, 1998).

2.4.2 Small scale/large area study Environmental influences over P concentrations Statistically significant relationships were found to exist between fwMRP and several catchment attributes. These relationships reflect the influence of environmental conditions on the loading, transportation and removal of P. The causal relationships and prediction strengths underlying the associations between the predictor variables are complex, owing to considerable levels of multicolinearity. Two databases were constructed and utilised in the research – one comprised only sites in the RoI (EPAdatabase), the other comprised the RoI sites in combination with sites in NI (EPA_NIEAdatabase). Differences in performance between models derived from the two databases were evident, with the combined database in general performing less well when compared with the performance of the RoI database. The DEM for NI was more coarsely resolved than the DEM for the RoI (50 m compared with 20 m). As a result, variables derived from the DEM (drainage density, TWI and mean slope) were

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also more coarsely resolved, and this is likely to have influenced their predictive ability (Shiels 2010).

The impact of urbanisation on concentrations of P was evident in both datasets from the significant positive relationships between fwMRP and human population density in particular, and also % urban cover and % artificial surfaces. Human population density was identified as a first-order predictor and urban and artificial surface land covers are related factors. The relationship between fwMRP and human population density was slightly stronger in winter than in summer, suggesting peak anthropogenic influence during what is often hydrologically the most variable period. However, summer retention of P could have caused the decreased explanation of variance (May et al., 2001). The variance of fwMRP explained by % urban and % artificial surfaces was greater in summer than winter, indicating that these land covers pose a much higher risk of nutrient enrichment in rivers during the ecologically sensitive summer period when compared with winter.

Mean catchment slope was also inversely related to fwMRP, showing the lowest P values to occur in catchments with steeper slopes, as has previously been reported for Ireland by Donohue et al. (2005). A combination of higher precipitation, thinner soils, less vegetation, resistant bedrock, groundwater contribution and a decreased influence of anthropogenic and agricultural pressures in upland, high relief areas of the catchments may explain the low P concentration (Soulsby et al., 1998; Soulsby et al., 2002; Bowes et al., 2003). Mean slope was the strongest predictor of fwMRP in the RoI database, but was one of the weaker predictors in the combined RoI-NI database. Mean slope values were derived from the DEMs, and this may be the reason for the contrasting explanation of variance between databases. Moreover, fwMRP was also inversely related to drainage density, indicating that poorly drained catchments tend to contribute greater P loads to freshwaters than their better drained equivalents (Daly et al., 2002).

The TWI, the inverse of drainage density (Sharpley and Withers, 1994), displayed the second strongest positive relationship to fwMRP for study sites in RoI. TWI is a proxy for overland flow owing to saturation-excess, which is an important transport mechanism of

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54

P from soil to freshwater. Catchments can contain relatively small but well defined areas of saturation that can be responsible for a large proportion of overland flow (Gburek and Sharpley, 1998; Pionke et al., 2000; Diamond and Sills, 2001). Where such areas overlap with high levels of soil P, substantial exports of P to freshwaters can arise (Gburek and Sharpley, 1998; Sharpley et al., 2001; Gburek et al., 2002).

Of the agricultural variables investigated, the percentage of pasture grasslands and cattle density in a catchment related directly to fwMRP in both the RoI and combined RoI and NI databases. The percentage composition of pasture in a catchment explained 5% (RoI) and 3% (RoI and NI) more variance of fwMRP in summer than winter. The opposite was the case for cattle density (6% (n = 49) and 5% (n = 72) more variation in winter). These relationships reflect complex interactions in the agricultural landscape locally and are likely to be indicative of seasonally varying agricultural practices, including the housing over-winter of livestock. Positive relationships between pasture grasslands and P in water bodies have been identified in other studies (Johnes and Heathwaite, 1997; Belsky et al., 1999; Wood et al., 2005; Withers et al., 2007; Miller et al., 2010). Ireland’s grass-based agricultural production systems are known to contribute a significant proportion of P to freshwaters (Kurz et al., 2005). High fertilisation of pastures with P based fertiliser and subsequent leaching from soils to freshwaters is a possible explanation for the elevated P concentration, as pasture cover increases (Cuttle and James, 1995; Withers et al., 2007). Levels of significance of the relationships between % pasture and the variable runoff risk, a proxy of the level of vulnerability of soils to P leaching, differ between the two databases: significant in the combined database but not in the database of RoI sites only. The strengths of the relationship between runoff risk and fwMRP in both databases are similar. The lack of relationship between soil leaching potential and pasture cover in the catchments in RoI could therefore suggest an alternative pathway to soil runoff through which P is transported to the drainage system. Cattle stocking densities were also directly related to fwMRP concentrations in the study catchments. Pasture composition and cattle density were strongly related. Cattle are responsible for both point and diffuse sources of P to rivers (McGechan et al., 2005), either through direct addition of their faeces and urine or indirectly as a result of their destruction of river banks and re-suspension of river sediments (Jarvie et al., 2010; Miller et al., 2010).

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The extent of forestry was found to be inversely related to fwMRP in the study catchments. Relative to other landscape attributes, forestry showed intermediate prediction strength but was most influential in the study sites in the RoI. The seasonal difference in explained variance of fwMRP was just 1%, implying that forestry processes that release/abstract P to or from the landscape vary little seasonally. Previous studies show that the presence of tree cover, buffer filter zones and associated natural processes leads to a decline in P export to freshwaters (Ballester et al., 2002; Anbumozhi et al., 2005; Mouri et al., 2011). Forests utilise nutrients in a largely closed cycle (Abelho, 2001), resulting in relatively low losses to the drainage network. Moreover, forested areas typically have wider and more continuous riparian buffer zones (Booth, 1991) that protect aquatic systems by controlling runoff (Rodgers et al., 2010). However, several studies have identified forest management practices, such as felling and the application of fertilizers, as diffuse sources of P (Forestry Service, 2000; Ensign and Mallin, 2001; Nisbet, 2001; Cummins and Farrell, 2003). Moreover WP3 in the current project found higher levels of nutrients in streams in catchments that had experienced harvesting. Management operations alter nutrient cycling, hydrological conditions, P availability and export pathways, increasing the delivery of P to freshwaters (Piirainen et al., 2007; Kreutzweiser et al., 2008; Rodgers et al., 2010).

Geology was also found to influence P concentrations. Two different categories of bedrock were the best predictor of fwMRP in each of the study databases. For the RoI study sites, bedrock type 3, the percentage composition of geological cover resistant to weathering, was found to be inversely related to fwMRP (summer R2 = 0.28 and winter R2 = 0.21). The percentage of bedrock type 1 (bedrock susceptible to weather erosion) was positively related to fwMRP in the combined RoI and NI database and explained a slightly higher percentage of fwMRP variance in winter (summer R2 = 0.11 and winter R2 = 0.13). Previous studies have found that the distribution of P concentrations in rivers have a strong dependence on the composition and type of bedrock geology (Dillon and Kirchner, 1975; Sliva and Williams, 2001; Rothwell et al., 2010). Interdependent relationships between bedrock types and additional predictor variables (drainage density, % urban, mean slope, % artificial area, % pasture, % forest, human density, runoff risk and TWI) were evident in the correlation matrices. Geology has a direct

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impact on environmental variables, such as hydrology, soils and relief, which are expected to influence P transfer (Skinner et al., 2004). Moreover, geology can also indirectly affect P loading, for example through its influence on human settlement and agriculture (Legg and Taylor, 2006; Jaroslaw and Hildebrandt-Radke, 2009).

Poorly drained (gley) soils are extensive across the island of Ireland (Diamond and Sills, 2001), comprising impermeable soils on drumlins and Upper Carboniferous shales and slowly permeable soils on limestone, sandstone and shale formations. Gleys are characterised by waterlogging, which increases the risk of overland flow due to saturation excess (Chesworth, 2008). Runoff risk was found to be positively related to fwMRP in the current research, with little seasonal variation in R2 values evident, thus supporting the findings of Diamond and Shanley (1998). On the basis of results presented here, waterlogging of soils appears to have a greater influence over P concentrations in rivers in NI than in RoI: in the combined RoI and NI database, runoff risk was the first (R2 = 0.29) and second (R2= 0.28) strongest predictor of fwMRP in summer and winter respectively, whereas in the database comprising only sites in the RI, runoff risk was a slightly weaker predictor (summer and winter R2 = 0.25) and was not as influential as other catchment attributes.

Development of geospatial models The geospatial models improved predictive capability when compared with the regression models produced initially in the small scale/large area study. This was particularly the case for the RoI database. The R2 value in summer RK models was 3% (RoI database) and 0.2% (combined RoI-NI database) greater than in the regression models. However, slightly larger increases occurred in the winter models (8% RoI database, 6% combined RoI-NI database), which may reflect a greater spatial correlation between sites during the hydrologically more variable months. Moreover, the final RK models based on the RoI database explained 70% (summer) and 71% (winter) of fwMRP variance, while 56% (summer) and 58% (winter) of fwMRP variance was explained by the models derived from the combined RoI-NI database. Cross-validation of all RK models showed that those based on the RoI database had the lowest RMSE values (0.18 in summer and 0.16 in winter), and can therefore be regarded as statistically superior to their corresponding regression models and all of the models derived from the combined

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RoI-NI database. Moreover, the process of validation of the RoI geospatial models, based on data from a set of five catchments in the RoI not used previously, revealed a that the winter model had a greater predictive skill than the summer model, with the latter tending to over-estimate MRP levels. Further refinement of the geospatial models, and their extension to the entire Irish Ecoregion, should be possible as greater amounts of high quality (including high spatial and temporal resolution), Ireland-wide data become available.

2.5 Conclusion WP1 determined the strength of relationships between environmental conditions and water quality at both large (individual catchment) and small (Irish Ecoregion-wide) scales. The Lough Sheelin catchment in the RoI was selected for the large scale study, which also involved development of a LAM implemented at the level of subcatchment as a means of examining the effectiveness of measures aimed at P mitigation, particularly from point sources. Data for the period 1995-2008 indicated a trend of declining P levels in all subcatchments, while the extent of poorly drained soils, cattle stocking densities and runoff levels were found to have the strongest influence on variability in P concentrations. Malfunctioning septic tank systems in the catchment may also be acting as localised point sources of P. Available data (1990-2008) for Lough Sheelin also suggested that external loadings of P fell following a peak in the early 1990s and remained relatively low to 2008, a period that includes implementation of measures aimed at mitigating P inputs. Despite this, however, P concentrations in the lake remained at early 1990s and higher levels though to 2008. Zebra mussels, established in Lough Sheelin by 2004, may have been responsible for negating any improvements to lake water quality of reduced external inputs. Two databases were constructed for the small scale exercise contained within WP1. Both databases comprised flow weighted P concentrations for river monitoring sites (fwMRP) for the period 2006-2006: one of the databases contained 49 sites from the RoI only (EPAdatabase); the other was Irish Ecoregion-wide, comprising 72 sites from both the RoI and NI (EPA_NIEAdatabase). Results indicated that the strongest predictors of concentrations of P in rivers in the databases were human population density, extent of artificial surfaces, run off risk, percentage of pasture, density of livestock (cattle) (all positive), mean catchment slope, drainage density, extent of forestry (all negative). Geology (in particular the

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susceptibility of bedrock to weathering) was also found to influence P concentrations, and was one of several variables considered that had interdependent relationships with other variables.

Taken together results from both scales of study provide a sound basis for rejecting the H0 that underpinned WP1: environmental factors clearly have a strong influence over P concentrations in water bodies in the Irish Ecoregion. The identification of environmental predictors of P concentration in rivers enabled development of geospatial models. These can be used to identify rivers with a high likelihood of being vulnerable to impairment by P or relatively resistant to recovery following reduced inputs of P.

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Chapter 2 Tables and Figures Table 2.1 Descriptive attributes of the seven study subcatchments in the Lough Sheelin catchment

Subcatchment

Area 2 (km )

Irish grid reference

Strahler stream order

Q5:Q95 ratios

Soil type

Land use

P desorption a Index

Soil type

b

Human population (1998 and 2008)

P point source

Bellsgrove

11.6

N 440 862

3

63.2

2.8

Acid brown earths, podzolics, gleys

Permanent pasture 50% farmed 51% improved

186 - 249

Septic tanks

Carrick

2.4

N 419 838

2

101.8

3.1

Acid brown earths, podzolics, gleys


39 - 42

Septic tanks

Crover

6.1

N 471 859

2

39.6

3.0

Gleys, brown earths, podzolics


105 - 125

Septic tanks

Halfcarton

14.3

N 480 797

2

28.8

1.8

Brown earths, grey brown podzolics , rezinas, lithosols


204 - 246

Septic tanks

2884 - 5143

1. WWTP for 35% population 2. Industrial P source 3. Septic tanks

1782 - 2232

1. WWTP for 50% population 2. Septic tanks

247 - 329

Septic tanks

Mountnugent

91.6

N 489 857

4

30.2

2.7

Gleys, peatland


Ross

63.1

N 487 806

3

17.2

1.8

Brown earths, grey brown podzolics


Brown earths, Permanent pasture peatland, 84% farmed podzolics 51% improved a The potential for P desorption from soil (Daly and Mills, 2006). A high value indicates an increased risk of P loss by desorption. b CORINE (CLC,2000) Schoolhouse

10.9

N 480 836

2

98.0

1.9

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Table 2.2a Catchment names & related information: small scale/large area study (RoI data (EPAdatabase) Catchment Number

Site code

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

01F010600 06B010100 34D010300 36E011400 34S050200 34C280100 26M020500 06W010500 32B030150 32C050100 07B011600 07B041600 26H010300 31R010500 30C011200 26C100300 09L020100 09T011100 10S010600 10N020600 14B011000 25B020800 25L020100 15D010400 14G040200 10A020200 25N010500

28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

River Name

EPAStation Name

Finn_Ballybofey BIG_Ballygoly DEEL_Knockadangan Erne_Kilcooney Swinford Charlestown Mountnugent White_Coneyburrow Bunowen_Louisburgh Carrowbeg_Westport Blackwater_Kells Boyne_Ballinter Hind

Bridge 2.5 km u/s Ballybofey Ballygoly Br Knockadangan Bridge Kilcooney Bridge Belturbet Swinford: Br on Foxford Road Bridge W. N. W. Of Bellahy Mountnugent Bridge Coneyburrow Br Bridge in Louisburgh Cooloughra Bridge Liscartan waterworks Ballinter Br. Bridge E. of Ballymartin Recess Canal Bridge Claregalway Claregalway Bridge Cross_Roscommon Bridge S. of Doyles Bridge Lyreen Just u/s Rye water confl Tolka Violet Hill Drive Shanganagh At Commons Road Newtownmountkennedy Br. S of Ballyphilip Barrow Pass Br (u/s Monasterevin) Ballyfinboy Ballyhooney Little brosna Br nr Mt Lucas Delour Derrynaseera Br Greese Br nr Greese Bank Aughrim_Wicklow Coats Br. Nenagh Br near Tyone Abbey Fertagh Br - d/s Arcon Mine at 15G020300 Goul Galmoy 12S022200 Slaney Scarawalsh Br 24M040900 Mahore Br at Riverville Station No. 0300 Tipperary town 16A030300 Ara Br near Railway Br Station No. 1100 Br near Anner 16A021100 Anner_c-Main Channel Hse 16B020300 Blackwater_K-macow Dangan Br 12B020500 Boro Ballynapierce Br 13M010700 Mulmontry Goff's Br 13C010100 Corock Br E of Foulksmill 13O010240 Owenduff Taylorstown Br 24D020500 Deel_Newcastlewest 0.8 km u/s Castlemahon 18B022500 Blackwater_Munster Station No. 2500 Ballyduff Br 16T010600 Tar Ford u/s Tar Br 23S020700 Smearlagh Ford u/s Feale R confl (LHS) 22S010100 Shanowen_Maine Ford (Br) u/s Maine R confl 23B040150 Big_Tralee At Dunnes Stores 21O070400 Owvane Pierson's Br (LHS) 20A020100 Argideen Jones Br. 21C040600 Dromkeare_Br Cummeragh weir 16T020080 Thonoge Br u/s Ballylooby 16S021600 Suir New Br (near Suirville House) 26I010300 Inny_Upper Ballinarink Bridge

61

Area (km2)

Easting

Northing

309.74 10.56 226.77 1491.19 17.73 25.20 91.03 55.23 70.37 36.04 698.54 1575.64 44.11 111.82 1072.39 103.27 87.62 133.21 32.52 16.19 1063.12 186.46 113.84 69.68 49.41 188.18 136.29

212475 315156 115748 236117 136632 147505 248916 305719 80678 102283 284248 289519 188040 80248 137185 201201 294316 314313 325296 329594 262245 183804 206992 229478 279834 314795 187567

395036 309883 319214 317097 300450 302542 285680 289280 280689 282745 269593 262675 261794 247482 233239 240231 238709 237425 222967 206255 210990 198048 190924 192448 195747 178944 178055

118.10

230637

170320

1030.71 44.28

298379 168953

145074 137988

44.18

189787

135151

444.70

224468

123193

109.98 173.90 56.64 62.57 103.09 261.89 2333.87 229.65 127.94 41.11 10.65 72.13 78.71 47.99 19.15 1090.80 58.30

257030 295600 287163 285387 282075 131806 196540 210816 102540 101349 84050 102397 140467 54537 200424 200215 249453

119868 136400 118531 118696 114627 130995 99120 113399 132324 109064 114850 54522 44445 68551 119604 134186 280948

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Table 2.2b Catchment names & related information: small scale/large area study (combined RoI-NI data (EPA_NIEAdatabase)

Catchment Site code Number 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

10715 10675 10728 10714 10734 10735 10747 10737 10700 10681 10688 10665 10663 10679 10330 10512 10111 10128 10380 10212 10022 10361 10438

NIEA Station Name

Area (km2)

Colebrooke r at Ballindarragh br Newtownbutler r at Newtownbutler Finn (Erne) r at Wattle br Hollybrook r at Aghalurcher Woodford r at Aghalane Swanlinbar r at Thompsons br Sillees r at Thompsons br Arney r at Drumane br Ballinamallard r at Ballycassidy br Bannagh r at Bannagh br Kesh r at Kesh br Waterfoot r at Letter br Garvary r at Larkhill Termon r at Tullyhommon Blackwater r at Benburb Lagan r at Shaws br Camowen r at Donnellys br Drumragh r at Campsie br Moyola r at Moyola new br Main r at Dunmore br Burndennet river at Burndennet bridge Ballinderry r at Ballinderry br Clady r at Glenone br

314.14 10.45 274.16 29.30 396.65 104.42 151.82 247.91 166.73 33.88 88.53 36.47 16.23 61.98 963.98 492.64 276.17 318.38 305.84 706.97 146.32 433.24 125.23

River Name Colebrooke Newtownbutler Finn_wat Hollybrooke Woodford Swanlinbar Sillees Arney Ballinamallard Bannagh Kesh Waterfoot Garvary Termon Blackwater_bun Lagan Camowen Drumragh Moyola Main_dun Burndennet Ballinderry Clady

Easting Northing 233100 241800 242500 236300 234200 225300 218100 217500 222800 216200 218000 208500 200900 211000 281900 332500 246400 245800 295600 308700 237400 292700 296300

336000 325900 320300 331100 319400 331300 344800 337500 350700 365400 363900 365200 363000 366700 352000 369000 373000 372400 390500 389600 404800 379800 403800

Table 2.2 (c) Validation study catchment names and descriptive parameters (RoI (EPA) data)

Validation catchments Catchment Number V1 V2 V3 V4 V5

Site code

River Name

EPAStation Name

Area (km2)

Easting

Northing

32N010190 25K010360 31C010100 09R020200 26S900001

Newport Kilcrow Cashla Rathmore Stream Bellsgrove

400 m u/s Newport Bridge West Br 2 km d/s Samp St 0300 Cashla Bridge Bridge S.W. of Arthurstown Bellsgrove Bridge

9.69 145.48 12.4 72.81 11.64

98815 179720 97806 294988 243704

293847 211753 226404 220883 286258

62

62

Table 2.3 Model formulae of TP loading-response models and TP retention models

63

63

Table 2.4 Definitions of the symbols used in the TP mass balance models

Symbol

Definition

AL

Lake surface area (m2)

L

Areal TP loading rate ( g m2 yr-1)

Q

Hydraulic flow rate (m3 yr-1)

qs R Remp

Areal hydraulic loading rate (m yr-1) (zρ) TP retention in lake sediment - predicted (unitless) TP retention in lake sediment - calculated from TP budget

TPin

Flow-weighted TP external annual load to lake (ug l-1)

TPlake

Lake TP annual average concentration (ug l-1)

VL z ρ σ τw

Lake volume (m3) Mean lake depth (m) Lake flushing rate (yr-1) sedimentation TP loss - calculated from TP budget Lake water residence time (yr-1)

64

64

Table 2.5 TP and MRP loads and % seasonal point source duration estimated from LAMs for the Lough Sheelin subcatchment rivers

Diffuse load kg km-2 yr-1 95-96 97-98 99-00 01-02 03-04 05-06 07-08 Point load kg km-2 yr-1

Mountnug ent T P MRP 4 9 24 3 9 23 4 5 28 5 0 25 4 0 14 3 3 14 6 6 36

20

9

8

4

12

8

10

1

8

3

2

1

35

11

Bellsgro ve T MR P P 4 0 23 3 6 24 4 8 36 5 5 24 4 4 28 3 9 17 5 7 31

Ross MR TP P

Bellsgro ve T MR P P

Halfcarto Schoolhou n se T P MRP TP MRP

3

3

6

4

3

2

5

9

7

7

5

7

9 1 0 1 5

5

27

13

4

2

6 2

7 7

3 2

30 4

10 2

9 4

2 1

Ross MR TP P

Halfcarto Schoolhou n se T P MRP TP MRP 1 1 5 32 18 9 1 5

3

60

45

12

77

61

9

2

38

33

4 1 4 1 7

2

47

30

4

29

16

7

72

33

Carrick T MR P P 3 4 13 2 1 11 1 5 9 2 9 10 2 0 11 3 5 11 5 0 23

Crover T MR P P 4 2 25 2 2 14 3 2 26 6 1 29 3 3 19 2 8 18 3 0 16

Carrick T MR P P

Crover T MR P P 9 1 1 1 0

5

3 1 1 6 1 3

2 0

97-98

Mountnug ent T P MRP 1 4 9 1 5 12

99-00

6

2

13

7

01-02 03-04

8 5

5 2

13 7

9 4

5 1 1 1 5 1 2 4

05-06

6

2

23

9

8

2

8

2

4

2

7

3

07-08

7

4

14

7

9

5

5

1

15

9

5

2

95-96

Point time (winter) % 95-96 97-98

Mountnug ent T P MRP 3 2 42 2 7 36

12

9

16

11

Ross MR TP P 37

66

47

42

7

Bellsgro ve T MR P P 1 7 16 2 9 35

Halfcarto Schoolhou n se T P MRP TP MRP 3 2 22 13 13 5 1 28 17 16 65

Carrick T MR P P 7 1 3

9 14

8 6

5 6

Crover T MR P P 1 9 15 2 0 13 65

7

32

35

01-02

8 3 3

54

52

89

3 5 4 3

03-04

5

9

36

68

0

0

05-06

4 1 7

3

99

21

0

18

55

51

0 2 8

99-00

07-08 Point time (summer )

11 37

28

%

Mountnug ent T P MRP

Ross MR TP P

Bellsgro ve T MR P P

95-96

4

6

9

8

5

1

97-98

5 2 3

16

45

78

4

17

14

23

3 2 0

15

1 1 2

28

85

97

9

37

8

55

15 38

44

43

0 6 7 1 2

0

2 2 8

31 10 0

99-00 01-02 03-04 05-06 07-08

95

16 29

8 1 8 2 9 1 9 3 3 8

67

51

25

85

87

68

91

0

0

9

24

20

21

31

27

Halfcarto Schoolhou n se T P MRP TP MRP 2 2 22 29 31 4 8 4

6 8 2 3 5 9

66

49

4

9

48

50

52

92

42

47

90

16

0

31

15

0

12

53

53

2 6 5 8 2 5 2 3 4 1

22

1 3

1

14

8

4

9

1

1

21

3 3 8

10

36

Carrick T MR P P 4 4 43 4 6 59 2 6 21 3 3 42 5 5 64 4 9 11 4 4 48

29

Crover T MR P P 2 4 38 1 0 19 3

16

0 1 0 2 5 4 7

13 8

45

66

Table 2.6 Ten-fold cross-validation results of LAM estimates (winter and summer) for the Lough Sheelin subcatchments

River

RMSE µg l-1

Validation % Winter

Summer

Winter

Summer

CV RMSE % Winter

Summer

Average µg l-1 Winter

Summer

Ross

78

39

8

16

41

52

19

30

Crover

47

36

20

45

52

68

38

66

Carrick

46

32

10

19

58

78

17

24

Halfcarton

72

57

5

20

58

110

9

18

Schoolhouse

46

46

32

34

94

69

34

49

Mountnugent

49

26

21

23

62

56

34

42

Bellsgrove

39

52

28

25

78

51

36

48

67

67

Table 2.7 Accumulated TP and MRP loads for the P mitigation stage (1998-2008) from LAMs. Cattle and human population densities are also shown.

Point MRP

Point TP

Diffuse MRP

Diffuse TP

Total MRP

Total TP

Cattle

Rural human

kg km-2

kg km-2

kg km-2

kg km-2

kg km-2

kg km-2

LUa km-2

km-2

Bellsgrove

43

95

272

486

316

581

1094

98

Carrick

21

60

127

299

148

358

1050

86

Crover

38

84

215

368

253

452

1078

94

Halfcarton

26

82

55

119

81

201

892

78

Mountnugent

32

62

234

468

266

530

1381

148

Ross

73

139

50

133

123

272

1207

66

Schoolhouse

70

161

344

528

414

688

1531

134

River

a

Livestock Unit

68

68

Table 2.8 Trophic status for Lough Sheelin during P Reg implementation (1998-2008)

Hydrological Year

Average TP (µg l-1)

Average Chlorophyll a (µg l-1)

Average Secchi depth (m)

Trophic Status

1998

26.06

24.38

2.28

Eutrophic

1999

19.47

25.02

1.94

Eutrophic

2000

No Data

No Data

No Data

No Data

2001

No Data

No Data

No Data

No Data

2002

31.79

18.82

2.08

Eutrophic

2003

46.31

27.36

1.82

Eutrophic

2004

31.13

10.11

3.14

Eutrophic

2005

36.83

5.82

3.28

Mesotrophic

2006

31.71

6.62

3.38

Mesotrophic

2007

28.00

12.53

2.83

Eutrophic

2008

24.06

6.32

2.78

Mesotrophic

Parameter

Mesotrophic

Eutrophic

Average TP (µgl-1)

>10 20 70

>60

>50

>20

9.0

< 2.5

< 4.0

< 6.0

< 8.0

< 15.0

> 15.0

Annual monitoring 10th percentile values Dissolved O2 (% sat.)

>80

Annual monitoring 90th percentile values Ammonium(mg N l-1) 5 day Biological Oxygen Demand (BOD5, mg O2 l-1)

162

Table 3.2 Livestock numbers, associated manure P and N loads, annual P and N application rates and the combined animal stocking rate expressed as dairy cow equivalents for the Colebrooke catchment in 1990, 1998 and 2008.

Colebrooke

1990

1998

2008

Sheep

15.5

10.7

8.3

Beef Cattle

5.9

6.6

5.0

Dairy Cattle

5.9

4.5

4.8

Other Cattle

13.5

15.5

12.7

Pigs

7.6

5.2

1.4

Poultry

8.6

72.8

46.2

Beef Cattle

59.8

66.3

48.8

Dairy Cattle

98.5

74.9

80.1

Other Cattle

136.5

156.6

127.8

Total Cattle

295

298

257

Sheep

15.5

10.7

8.3

Pigs

43.4

29.7

7.8

Poultry

2.3

19.5

12.4

Total Manure P (tonnes P)

356

358

285

Manure P rate (kg ha-1 yr-1)

24.5

24.6

19.7

Beef Cattle

319

354

269

Dairy Cattle

540

411

441

Other Cattle

730

837

683

Total Cattle

1589

1602

1393

Livestock Numbers (*10-3)

P Load (tonnes P)

N Load (tonnes N)

163

Sheep

139

96

75

Pigs

99

68

18

Poultry

5

44

28

Total Manure N (tonnes)

1833

1810

1516

Manure N rate (kg N ha-1 yr-1)

126

124

104

Dairy Cow Equivalents (DCE ha-1)

1.4

1.5

1.2

164

Table 3.3 Colebrooke sub-catchments with increases and decreases of manure P stocking rate between 1990 and 2008 based on all livestock, cattle sheep and pigs, and cattle only. Change in Manure P

All livestock

Cattle, Sheep and Pigs

Cattle

Increase

13,16

-

1,8,19

Decrease

6,10,20,22

5,6,16,20,21,22

3,6,16,20,21,22

Common Sites

rate

165

6,20,22

Table 3.4 Summary of livestock numbers, associated manure P and N loads, annual P and N application rates and the animal stocking rates expressed as dairy cow equivalents for the Upper Bann catchment in 1990, 1998 and 2008.

Upper Bann

1990

1998

2008

Beef Cattle

4.5

4.9

5.3

Dairy Cattle

7.8

6.9

8.5

Other Cattle

24.6

27.5

25.0

Sheep

81.5

100.3

45.0

Pigs

10.5

13.7

7.5

Poultry

186.1

202

168.9

Beef Cattle

45.8

49.7

53.8

Dairy Cattle

130.1

114.9

141.1

Other Cattle

248.5

277.8

252.2

Total Cattle

424

442

447

Sheep

81.5

100.3

45.0

Pigs

59.8

78.0

42.8

Poultry

49.7

53.8

45.2

Total Manure P (tonnes P)

615

675

580

Manure P rate (kg ha-1 yr-1)

26.3

28.8

24.8

Beef Cattle

245

266

288

Dairy Cattle

713

630

773

Other Cattle

1328

1485

1348

Total Cattle

2286

2381

2409

Sheep

733

903

405

Pigs

136

178

98

Livestock Numbers (*10-3)

P Load (tonnes P)

N Load (tonnes N)

166

Poultry

113

122

103

Total Manure N (tonnes)

3269

3584

3014

Manure N rate (kg N ha-1 yr-1)

140

153

129

Dairy Cow Equivalents (DCE ha-1)

1.6

1.7

1.5

167

Table 3.5 Upper Bann mini-catchments displaying consistent increases and decreases of manure P stocking rate based on all livestock, cattle, sheep and pigs and cattle only. Change in Manure

Cattle, Sheep

All livestock

P rate

Cattle

Common sites

and Pigs

Tabl e 3.6 Matr Moderate increase 5,6,14,15,23 9 15,23 15,23 ix of direc tion 3,8,18,19,22, Moderate decline 1,2,3,4,17,18,19 3,11,22,25 3,18,19,22,25 of 25 chan ge in SRP Strong decline 11,12,20,22,25 7,13,18,19,24 10,11,20 11,20 and TP FWMCs for Colebrooke mini-catchments between years. Significance levels based on paired t-tests. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns denotes not significant Strong increase

5,10,23

5,6

5

TP→ 1990

1996

1997

1998

1999

2009

decline***

ns

ns

decline*

decline****

increase*

increase**

ns

decline*

ns

ns

decline**

decline****

decline**

SRP↓ 1990 1996

decline**

1997

Ns

Ns

1998

Ns

increase**

ns

1999

decline*

Ns

ns

decline***

2009

decline****

decline**

decline***

decline**

168

Ns decline*

Table 3.7 Matrix of direction of change in NH4 and NO3 FWMCs for Colebrooke mini-catchments between years. Significance levels based on paired t-tests. *p < 0.05, **p < 0.01, ***p < 0.001, ns denotes not significant NO3-→ 1990

1996

1997

1998

1999

2009

decline*

ns

decline*

ns

decline***

ns

ns

ns

decline***

decline*

ns

decline***

ns

decline***

NH4+↓ 1990 1996

decline**

1997

decline**

ns

1998

decline*

ns

ns

1999

ns

ns

ns

2009

decline*** ns

ns

decline** decline**

decline*** decline***

Table 3.8 Matrix of direction of change in SRP and TP FWMCs for Upper Bann mini-catchments between years. Significance levels based on paired t-tests. *p < 0.05, **p < 0.01, ***p < 0.001, ns denotes not significant TP→ 1990

1996

1997

1998

2009

decline***

decline***

decline***

decline***

decline***

decline***

Ns

ns

increase***

SRP↓ 1990 1996

decline***

1997

decline***

decline***

1998

decline***

decline***

Ns

2009

decline**

ns

increase***

169

increase*** increase***

Table 3.9 Matrix of direction of change in NO3 and NH4 FWMCs for Upper Bann mini-catchments between years. Significance levels based on paired t-tests. *p < 0.05, **p < 0.01, ***p < 0.001, ns denotes not significant . NO3-→ 1990

1996

1997

1998

2009

decline*

decline***

decline****

decline****

decline**

decline****

decline****

decline****

decline****

NH4+↓ 1990 1996

ns

1997

decline*

decline**

1998

decline**

decline**

ns

2009

decline**

decline**

decline**

decline*** decline**

Table 3.10 Flow weighted mean concentrations for nutrients and their loadings at the most downstream main river site in Colebrooke and Upper Bann catchments. Data are shown for each annual monitoring period. 1990 1996 1997 1998 1999 2009 Upper Bann

FWMCs

TP (µg L-1)

140

81

58

60

112

SRP (µg L-1)

70

42

34

30

61

SOP (µg L-1)

30

22

15

18

20

PP (µg L-1)

40

17

9

12

31

NO3-N (mg L-1)

4.40

3.23

3.48

1.63

1.77

NH4-N (µg L-1)

161

151

109

87

74

Load (tonnes) TP

19

23

13

16

19

SRP

10

12

8

8

10

SOP

4

6

4

5

3

PP

6

5

2

3

5

611

897

800

425

299

NO3-N

170

NH4-N

22

42

25

23

13

Runoff (mm)

538

1079

891

1013

652

Colebrooke

FWMCs

TP (µg L-1)

185

113

219

207

132

69

SRP (µg L-1)

129

60

84

72

62

30

SOP (µg L-1)

7

23

30

28

18

19

PP (µg L-1)

49

29

105

106

52

20

NO3-N (mg L-1)

1.58

1.44

1.54

1.16

1.01

0.45

NH4-N (µg L-1)

139

73

99

93

136

49

Load (tonnes) TP

41

18

29

45

62

30

SRP

29

10

11

16

18

19

SOP

2

4

4

6

52

20

PP

11

5

14

23

1.01

0.45

NO3-N

354

232

204

249

136

49

NH4-N

31

12

13

20

132

69

Runoff (mm)

967

695

573

928

866

1226

171

Table 3.11 Regression equations and their significance levels for Colebrooke manure loading rates (kg P or N ha-1) from cattle and sheep versus annual nutrient flow weighted mean concentrations.

Regression

Manure P stocking rate vs. SRP ( g P L-1)

Manure P stocking rate vs. TP ( g P L-1)

Manure N stocking rate vs. NH4 ( g N L-1)

Manure N stocking rate vs. NO3 (mg N L-1)

Year

intercept

slope

R2

Significance level (p)

1990

44.6

3.05

0.38

0.0013

1998

29.0

4.08

0.52

0.0001

2008/09

22.2

1.95

0.53

0.0000

1990

77.9

6.56

0.48

0.0002

1998

59.1

8.09

0.62

0.0000

2008/09

52.4

4.08

0.58

0.0000

1990

97.7

1.48

0.17

0.0299

1998

36.2

1.76

0.39

0.0009

2008/09

15.7

1.16

0.66

0.0000

1990

0.65

0.01

0.21

0.0157

1998

0.38

0.01

0.48

0.0001

2008/09

0.17

0.01

0.69

0.0000

.

172

Table 3.12 Regression equations and their significance levels for Upper Bann manure loading rates (kg P or N ha-1) from cattle and sheep versus annual nutrient flow weighted mean concentrations. Values in bold indicate significant regressions at p20% organic C and >50cm deep, humic as soils >10% organic C topsoil, and mineral as soils with 20% organic carbon and >50 cm deep, humic as soils >10% organic carbon topsoil, and mineral as soils with 250kg

Percentage of Sites

75% 170-250kg 135-170kg 50% 100-135kg 65-100kg 25% 0-65kg

0%

1990

1998

2008/9

1990

Colebrooke

1998

2008/9

Upper Bann

Figure 3.17 Percentage of sites in the Colebrooke and Upper Bann operating within bands of manure nitrogen stocking rate (kg N ha-1 yr-1) in 1990, 1998 and 2008. Farmed land in this analysis is considered the sum total of cropland, pasture and rough grazing.

Figure 3.18 Time series of Colebrooke summer samples (May-September) achieving chemical water quality parameters indicative of salmonid (top row) and cyprinid (bottom row) favourable water. Samples are grouped into salmonid (squares), cyprinid (triangles) and no fish (circles) groups based on the chemical water quality status of streams in 1990.

189

Figure 3.19 Colebrooke chemical water quality parameters expressed as the percentage of sites meeting Fisheries Ecosystem Class criteria for summer samples only (May – September).

190

0

1

BOD5 FE-Class

2009 2006

2

1997-99 3 1994-96 1990-93

4

CB11

CB14

CB3

CB2

CB9

CB24

CB20

CB18

CB15

CB17

CB10

CB23

CB12

CB22

CB19

CB16

CB13

CB7

CB8

CB6

CB21

CB5

CB4

CB1

5

Figure 3.20 Colebrooke mini-catchment BOD5 aligned with FE Class based on summer samples for 2006, 2009 and averages of 1990-1993, 1994-1996 and 1997-1999.

191

Figure 3.21: Time series of Upper Bann summer samples (May-September) achieving chemical water quality parameters indicative of salmonid (top row) and cyprinid (bottom row) water. Samples are grouped into salmonid (squares), cyprinid (triangles) and no fish (circles) groups based on the chemical water quality status of streams in 1990.

192

Figure 3.22 Upper Bann chemical water quality parameters expressed as the percentage of sites meeting fisheries ecosystem class criteria for summer samples (May – September).

193

0

Fisheries Ecosystem Class

1

2 2009 3

1995-98 1990-94

4

5

↑↑

↑↑

↑

↑

↑

↑↓ ↓ UB10

UB1

UB19

UB5

UB8

UB21

UB18

UB17

UB6

UB22

UB11

UB2

UB25

UB13

UB3

UB24

UB20

UB4

UB7

UB15

UB14

UB16

UB12

UB9

6

Figure 3.23 Upper Bann mini-catchment fisheries ecosystem class based on summer samples for 2006, 2009 and averages of 1990-1993, 1994-1996 and 1997-1999.

Figure 3.24 Linear regressions of 1990 mini-catchment SRP and TP FWMCs against those for subsequent monitored periods for the Colebrooke.

194

Figure 3.25 Colebrooke mini-catchment SRP and TP annual FWMCs for 1990, 1996-1999 and 2009.

195

Figure 3.26 Linear regressions of 1990 mini-catchment NH4+and NO3- FWMCs against those for subsequent monitored periods for the Colebrooke.

196

Figure 3.27 Colebrooke mini-catchment annual FWMCs of NH4 and NO3 for 1990, 1996-1999 and 2009.

197

Figure 3.28 Linear regressions of 1990 mini-catchment SRP and TP FWMCs versus those for subsequent monitored periods for the Upper Bann.

198

Figure 3.29 Upper Bann mini-catchment SRP and TP annual FWMCs for 1990, 1996-1998 and 2009.

199

Figure 3.30 Linear regressions of 1990 mini-catchment NH4+ and NO3- FWMCs against those for subsequent monitored periods for the Upper Bann.

200

Figure 3.31 Upper Bann mini-catchment NH4+ and NO3- annual FWMCs for 1990, 1996-1998 and 2009.

201

Percentage of Sites

ASPT>6.5 6 < ASPT > 6.5 5 < ASPT < 6 4 < ASPT < 5 ASPT < 4

100

80

60

40

20

0

1990 1991 1992 1993 1994 1995 1996 1997 1998

2009

Figure 3.32 Percentage of sites in the Colebrooke catchment recording ASPT values within certain ranges.

8

300 2

R = 0.12 7 6 200

5

2

R = 0.07

4

150

3

100

BMWP 2

TAXA 50

2

ASPT

R = 0.17

1 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009

0

CB2

CB4

CB10

CB18

CB17

CB23

Colebrooke Main River Sites / Year

Upstream

Downstream

Figure 3.33 Annual BMWP scores, ASPT and number of taxa for Colebrooke main river sites

202

ASPT

BMWP score / No. of Taxa

250

250

8 7

200

5

150

4

ASPT

BMWP Score / No. of Taxa

6

BMWP

100

TAXA

3

ASPT 2 50 1 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009

0

CB5 Coneen Water

CB15 Many Burns

CB14 Many Burns

CB1 Cleen River

Main River Site / Year

Figure 3.34 Annual BMWP scores, ASPT and number of taxa for Colebrooke sub-catchment main river sites.

203

Figure 3.35 Annual ASPT and BMWP score for sites in the Raw mini-catchment

160

7

BMWP 120

6

TAXA ASPT

5

100 4

80 3

60 2

40

1

20 0

0 1990 1991 1992 1993 1994 1995 1996 1997 1998

Figure 3.36 Annual BMWP score, Number of taxa and ASPT for site CB9

204

2009

ASPT

BMWP Score / No. of Taxa

140

ASPT > 6.5 6.5 > ASPT > 6 5 < ASPT < 6 4 < ASPT < 5 ASPT < 4

100

Percentage of sites

80

60

40

20

0

1990 1991 1992 1993 1994 1995 1996 1997 1998 2009

Figure 3.37 Percentage of sites in the Upper Bann catchment recording ASPT values within certain ranges.

250

8

BMWP

R2 = 0.313

TAXA

7

ASPT 6 5

150

4 100

R2 = 6E-05

50

R2 = 0.0567

ASPT

BMWP score / No. of Taxa

200

3 2 1 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 2009

0

UB9

UB10

UB12

UB16

UB24

Site & Year Upstream

Downstream

Figure 3.38 Annual BMWP scores, ASPT and number of taxa for Upper Bann main river sites

205

8

7 UB9 6

ASPT

UB10 UB12 UB16

5

UB24 4

3 1990 1991

1992 1993

1994 1995

1996 1997

1998 2009

Year

Figure 3.39 ASPT at main river sites on the Upper Bann for each year assessed

Figure 3.40 Relationships between cattle and sheep manure stocking rate in Colebrooke catchment for 1990, 1998 and 2008 against annual TP, SRP, NH4 and NO3 FWMCs for 1990, 1998 and 2009 in the Colebrooke catchment.

206

Figure 3.41 Relationships between Upper Bann cattle and sheep manure stocking rate for 1990, 1998 and 2008 versus annual TP, SRP, NH4 and NO3 FWMCs for 1990, 1998 and 2009.

207

Figure 3.42 SRP FWMCs for the Colebrooke (CB- blue) and Upper Bann (UB-red) against cattle and sheep livestock manure rate for 1990, 1998 and 2008 farm census data (A), and Upper Bann and Colebrooke data combined for 1990 (blue) and 2008/09 (red).

208

Figure 3.43 Relationship between 2008 cattle and sheep manure P livestock rate and 2008/09 SRP and NO3 FWMCs for the Upper Bann and Colebrooke combined.

209

8 7

ASPT

6 5

1990

y = -0.07x + 6.32 R2 = 0.69

1998

y = -0.11x + 6.65 R2 = 0.59

4 2008/09

3

y = -0.09x + 6.89 R2 = 0.61

2 0

5

10

15

20

25

30

35

Cattle & Sheep Manure P Stocking rate (kg ha -1 yr-1)

Figure 3.44 Cattle and sheep manure P stocking rates versus ASPT for the 1990, 1998 and 2008/09 annual periods in the Colebrooke.

7

6

ASPT

1990 5

4

1998

y = -0.03x + 5.48 2

R = 0.20

y = -0.02x + 5.17 2

R = 0.05

3

2008/09

y = -0.06x + 6.06 R2 = 0.42

2 0

10 20 30 Cattle & Sheep Manure P stocking rate (kg ha -1 yr-1)

40

Figure 3.45 Cattle and sheep manure P stocking rate versus ASPT for the 1990, 1998 and 2008/09 annual periods in the Upper Bann.

210

8 7 6

1990

y = -0.06x + 6.12 R2 = 0.57

1998

y = -0.08x + 6.39 R2 = 0.53

2008/09

y = -0.09x + 6.80 R2 = 0.73

ASPT

5 4 3 2 1 0 0

10 20 30 Cattle & Sheep Manure P Stocking Rate (kg ha -1 yr-1)

40

Figure 3.46 Relationship between cattle and sheep manure P stocking rate and BWQ as assessed by the ASPT for the 1990, 1998 and 2008/09 annual periods for the Upper Bann and Colebrooke combined. 8

ASPT

7

0-9 kg P

y = -0.03x + 6.37

10-23 kg P

y = -0.10x + 6.67

24-36 kg P

y = -0.02x + 5.06

6

5

4

3 0

5

10

15

20

25

30

35

40

Manure P stocking rate (kg P ha-1 yr-1)

Figure 3.47 Cattle and sheep manure P stocking rate versus ASPT for the 1990, 1998 and 2008/09 annual periods for the Upper Bann and Colebrooke combined, with data separated based on arbitrary groupings of stocking rate.

211

7

Upper Bann

ASPT

6

y = -0.45Ln(x) + 6.75 R2 = 0.37 Colebrooke

5

y = -0.79Ln(x) + 8.90 R2 = 0.21 Catchments combined

4

y = -0.86Ln(x) + 8.80 R2 = 0.38 3 0

50

100

150

200

250

300

-1

SRP FWMC (ug L )

Figure 3.48 Relationship between SRP FWMC versus ASPT in 2009.

7

ASPT

6

5

Colebrooke 4

Upper Bann Catchments combined

y = -0.088x + 6.803 R2 = 0.726

3 0

5

10

15

20

25 -1

30 -1

Manure P stocking rate (kg ha yr )

Figure 3.49 Relationship between Manure P stocking rate and ASPT in 2009.

212

35

References - Chapter 3 Armitage, P.D., Moss, D., Wright, J.F. & Furse, M.T. (1983) The performance of a new biological water quality score based on macroinvertebrates over a wide range of unpolluted runningwater sites. Water Research, 17, 333-347. Bailey, J., Dils, R., Foy, R. & Patterson, D. (2000) The Diagnosis and Recommendation Integrated System (DRIS) for diagnosing the nutrient status of grassland swards: III Practical applications. Plant and Soil, 222, 255-262. Biggs, B.J.F. & Close, M.E. (1989) Periphyton biomass dynamics in gravel bed rivers: the relative effects of flows and nutrients. Freshwater Biology, 22, 209-231. Biological Monitoring Working Party (1981) River Quality: The 1980 survey and Future Outlook. London: National Water Council. Chesters, R.K. (1980) Biological Monitoring Working Party. The 1978 National Testing Excercise. Technical Memorandum 19. Cross, W.F., Benstead, J.P., Frost, P.C. & Thomas, S.A. (2005) Ecological stoichiometry in freshwater benthic systems: recent progress and perspectives. Freshwater Biology, 50, 1895-1912. Foy, R.H. & Kirk, M. (1995) Agriculture and Water Quality: A regional study. Journal of the Chartered Institution of Water and Environmental Management, 9, 247-256. Foy, R.H., Lennox, S.D. & Smith, R.V. (2001) Assessing the regulatory controls on farm pollution using chemical and biological indices of water quality and pollution statistics. Water Research, 35, 3004-3012. Foy, R.H. & O'Connor, W.C.K. (2002) Managing the effects of agriculture on water quality in Northern Ireland. In: Managing the effects of agriculture on water quality in Northern Gibson, C.E., Foy, R.H. & Fitzsimons, A.G. (1980) A limnological reconnaissance of the Lough Erne System, Ireland. Internation revue der gesamten Hydrobiolgie, 66, 641-644. Jordan, C. (1997) Mapping of rainfall chemistry in Ireland 1972-94. Biology and the Environment, 97B, 53-73. Kelly, D.W., Dick, J.T.A., Montgomery, W.I. & Macneil, C. (2003) Differences in composition of macroinvertebrate communities with invasive and native Gammarus spp. (Crustacea: Amphipoda). Freshwater Biology, 48, 306-315. Macneil, C., Dick, J.T.A. & Elwood, R.W. (1999) The dynamics of predation on Gammarus spp. (Crustacea: Amphipoda). Biological Reviews, 74, 375-395. McGuckin, S.O., Jordan, C. & Smith, R.V. (1999) Deriving phosphorus export coefficients for CORINE land cover types. Water Science and Technology, 39, 47-53. Roy. Comm. (1992) Royal Commision on Environmental Pollution. Freshwater Quality. HMSO, Cmnd. 1166, London.

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Royer, T.V. & Minshall, G.W. (2001) Effects of nutrient enrichment and leaf quality on the breakdown of leaves in a hardwater stream. Freshwater Biology, 46, 603-610. Walley, W.J. & Hawkes, H.A. (1997) A computer-bsed development of the biological monitoring working party score system incorporating abundance rating, site type and indicator value. Water Research, 31, 201-210. Walling, D.E. & Webb, B.W. (1985) Estimating the Discharge of Contaminants to Coastal Waters by Rivers - Some Cautionary Comments. Marine Pollution Bulletin, 16, 488-492. Webster, J.R. & Benfield, E.F. (1986) Vascular plant breakdown in freshwater ecosystems. Annual Review of Ecology and Systematics, 17, 567-594. Zhou, Q.X., Gibson, C.E. & Foy, R.H. (2000) Long-term Changes of nitrogen and phosphorus loadings to a large lake in north-west Ireland. Water Research, 34, 922-926. SNIFFER WFD72A Final Report (2007). Revision and testing of BMWP scores. Scotland and Northern Ireland forum for Environmental Research, and Environment Agency

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Chapter 4 Assessing the impact of POMs on stream water quality particularly with respect to areas of coniferous forest (WP3)

4.1 WP3 Aim The primary aim of WP3 was to assess the impact of a range of POMs, implemented by FSNI, in particular the use of different riparian vegetation types. Particular focus was placed on the ecological status of headwater streams draining peatland catchments in upland Co. Fermanagh (NWIRBD), NI.

4.2 Introduction Despite a highly depauperate native tree flora at present (Higgins et al., 2004), the natural vegetation across the majority of the Irish Ecoregion is deciduous woodland, with a relatively high percentage of peat-rich wetland habitats in the west, such as Atlantic blanket bogs, montane blanket bogs and Atlantic raised bogs (Cross, 2006). The upland areas in the west of the island, rich in bogland and peat soil, are largely unsuitable for arable agriculture and have been widely drained and planted with exotic, commercial conifers such as Picea sitchensis (Bong.) Carrière (Sitka spruce), Pseudotsuga menziesii (Mirb.) Franco (Douglas fir) and Pinus contorta Doug. (Lodgepole pine). Activities associated with forestry management such as harvesting and fertilisation are known to result in changes to water chemistry, increasing stream P and N to concentrations in excess of those that occur naturally (Gibson, 1976; Miller et al., 1996; Foy and Bailey-Watts, 1998; Cummins and Farrell, 2003a and b). This is due to the combination of N and P fertilisers, the leaching of which is augmented due to the poor P binding capacity of upland peat soils (Malcolm et al., 1977). Increased levels of suspended solids have been observed in streams draining coniferous plantations relative to undisturbed catchments as a result of drainage and harvesting (Leeks and Roberts, 1987; Quinn and Stroud, 2002). In some cases planted exotic forests can create similar conditions to native forests with respect to the volume of allochthonous inputs (terrestrially derived organic matter) and degree of shading (Quinn et al., 1997; Scarsbrook et al., 2001). However, replacing native peatland vegetation with exotic conifers can greatly change stream habitat structure and their associated biological communities. Many recent studies have focused on the response of invertebrates and fish to forestry management activities (Kelly-Quinn et al., 1996; Stone et al., 1998; Tierney et al., 1998; Giller and O’Halloran, 2004; Herlihy et al., 2005; Thompson et al., 2009). The potential ecological effects in upland streams on organic soils of forestry activities has remained uncertain with regard to the response of macrophytes (but see Hering et al., 2006 and Mykra et al., 2008).

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While low order streams draining small catchments do not require monitoring under the WFD requirements (European Parliament, 2000), first order headwater streams can contribute approximately 70% of mean annual water volume and 65% of the N flux in second order streams (Alexander et al., 2007). Up to 85% of stream length in a catchment is made up of streams < 10 m (Peterson et al., 2001), highlighting the importance of effective measures in upland agricultural areas, especially forestry plantations, as low order streams have a major effect on downstream water quality. In order to produce effective management measures, understanding the specific drivers of the key ecological elements of streams and rivers is crucial. Management measures, such as the use of buffer strips, comprised of grasses, shrubs or deciduous trees, in the riparian zone have been implemented in certain cases by FSNI. The riparian zone is the interface between a stream and the surrounding land; a transitional zone with distinct ecological and hydrological characteristics. The zone acts as filter for nutrients (Karr and Schlosser, 1978; Schlosser and Karr, 1981; Peterjohn and Correll, 1984), fine sediments and associated pollutants (Lee et al., 2000) and may vary in effectiveness depending on vegetation, age, width and structure (Doskey et al., 2007).

The role of vegetated riparian buffer strips in ameliorating the negative chemical water quality effects of agriculture has received much attention (Daniels and Gilliam, 1996; Lee et al., 2000; Jones et al., 2006; Muenz, 2006; Dosskey et al., 2010; Montreuil et al., 2010). Unvegetated stream banks are unstable and vulnerable to erosion, resulting in wider channels and inputs of suspended solids (Naiman and Decamps, 1997). Vegetation in the riparian zone influences stream water chemistry in a variety of ways; some vegetation induced processes have positive effects on water quality, such as uptake of N and P, whereas negative effects, such as anaerobic mobilisation of P to the stream, may also occur (Dosskey et al., 2010). The riparian zone can act as a net source of nutrients under certain conditions, depending on flow path, time of year, soil type and redox reactions releasing sequestered nutrients (Fiebig et al., 1990; Pinay et al., 1992; Fabre et al., 1996; Hefting et al., 2005; Ranalli and Maclady, 2010). Whether water chemistry can be successfully improved by deliberate planting of certain plant functional groups remains unclear (Dosskey et al., 2010), as riparian zone vegetation type (e.g. herbaceous vegetation or woody vegetation) is only one factor controlling the nutrient load available to streams (Pinay et al., 1992).

Large parts of upland Co. Fermanagh, NI, are devoted to forestry that was planted between the 1960s and 1980s. A significant amount of this planting has been recently harvested and the rotational cycle within forestry is being shortened due to the problem of wind-throw in upland areas. While tree harvesting represents a challenge to water quality, the potential of restructuring

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the replanted forest to minimise damage to ecological status of water bodies represents an opportunity for improving and protecting water quality. At present, the FSNI forgoes P fertilisation at replanting and plans to exclude deep peats from replanting. Fishery interests locally argue that the dense shading of streams by conifers planted to the bank sides creates a poor habitat for juvenile salmon and this is supported by previous research in Ireland and abroad (O’Driscoll et al., 2006; Giller and O’Halloran, 2004; Behmer and Hawkins, 1986). Clenaghan et al. (1998) found that shading in Irish streams affected macroinvertebrate diversity, which had an impact on salmonid density. In response, FSNI has defined supplementary POMs for replanting that include: Leaving 50m of natural vegetation between the river bank and replanted forest The instalment of sediment traps and inceptor drains The blocking of drains that would introduce sediment to the river water body

There is some grazing pressure from deer. In the same area of Co. Fermanagh a local angling group has been supported with grants to remove trees (mostly alder, birch and willows) from the narrow riparian zone of the forested streams as they move from the uplands to lowlands and flow through what is often poor quality grassland. The aim is to improve light penetration into these streams. The restructuring and measures adopted have been developed through an ad hoc process based on expert judgment. The FSNI measures listed above are more comprehensive as the sediment traps may intercept particulate P (although that seems not be a significant problem in the area) but the width of the riparian zone may offer a better potential for the absorption of SRP (Kronvang et al., 2005).

4.3 Study area WP3 focused on 25 first or second order streams in upland County Fermanagh, NI (Figure 4.1). Streams were chosen that were at least 1 km from a regional road. Five categories of stream were sampled and classified into riparian management categories (RMC) according to the managed riparian zone vegetation along the stream reach sampled. Stream categories / classes were (details shown in Table 4.1): No Buffer - preharvest sites with conifers planted to stream edge (conifer to edge) Open buffer - preharvest sites with buffers comprised of grasses, shrubs and occasional trees Broadleaved Buffer - preharvest sites with buffers of deciduous vegetation Harvested - sites with No Buffer that were harvested 12 – 24 months previously (clear felled); Control - Catchments with natural peatland vegetation

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Reaches sampled were 50 m in length. Vegetation type and soil type were calculated on three spatial scales using aerial photography (OSNI, 2005), soil maps (Jordan and Higgins, 2009) and land use maps (OSNI, 1990) in GIS. Parameters were calculated as percentage of the entire catchment area (catchment scale), percentage of a 50 m wide lateral zone each side of the entire stream length (riparian scale), and percentage of 50 m wide lateral zone either side of the sampling stretch (local riparian scale) using GIS. Vegetation characteristics in the catchment were confirmed during field visits. Sites were on predominantly organic soils (deep peat or “other” organic) as this was the most extensive soil type in the study area.

4.4 Methods WP3 investigated differences between small, low order streams in control catchments in relatively undisturbed peatland habitat and forested catchments with different riparian buffer vegetation types. The influence of riparian and catchment vegetation on stream ecology was examined, and principal drivers of invertebrate and macrophyte ecology in low order streams on organic soils in the region were defined. The structure of food webs in each stream was investigated using SI (C and N) and FFG approaches

4.4.1 Water chemistry Water chemistry samples were collected three times in each stream (summer, autumn, winter) and analysed for TP, SRP, TSP, NO3, NO2, TDN, NH4, alkalinity, conductivity, pH, chlorophyll a and DOC. Chlorophyll a was determined by extraction into hot methanol (Talling and Driver, 1961). DOC concentration was analysed using a Teledyne Techmar Apollo 9000 TOC analyser (Pt-catalysed high temperature combustion method) following filtration through a 0.45 µm durapore (polyvinylidene fluoride) filter and automated removal of the inorganic C fraction by acidification and sparging with zero-grade air. TDN was analysed using a TN module connected in series to the TOC analyser. Briefly NO was converted to NO2, which was then measured by detection of chemiluminescence. For the other parameters standard analytical methods were used (Gibson et al., 1980). To determine suspended solids, a known volume of stream water was filtered through a pre-weighed, precombusted Whatman GFC filter, dried at 100 °C overnight and weighed. Samples were then ashed for at least 4 hours at 550 °C to calculate the inorganic fraction. Organic suspended solids were determined by the difference between the values. The ratio of organic to inorganic matter was calculated.

4.4.2 Biological and organic matter sampling

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Benthic macroinvertebrates were sampled quantitatively using a Surber sampler (967 cm2, 363 µm mesh size) every 10 m along the 50 m stretch and placed in a 70% (approx.) ethanol solution in the field. Kick sampling along the entire stretch was undertaken after Surber sampling to obtain a representative invertebrate sample for SI analysis. Subsequent identification took place in the laboratory and was usually to species level using several guides and keys (Macan, 1960; Macan, 1979; Wiederholm, 1983; Eddington and Hildrew, 1995; Friday, 1998; Wallace et al., 2003). Benthic matter from the stream bed surface was also collected from the sampler at each sampling station and transported to the laboratory in a plastic bucket. The organic content of the matter was determined after drying overnight at 100 °C, weighing and igniting in a muffle furnace at 550 °C for 24 hours. Macrophyte abundance in the stream channel was recorded using the Braun-Blanquet scale (Braun-Blanquet, 1964); identification was to species level using Smith (2008), Watson (1981) and Rose (1981). Hydrophytes in the channel and bryophytes in the stream splash zone were recorded. Biofilm was removed with a toothbrush from a 10 cm quadrat from rocks on the stream bed at each sampling station (every 10 m) within each stream. The biofilm was transported to the laboratory in a plastic bucket. The biofilm was filtered onto a pre-weighed, pre-combusted 47 mm GFC (Whatman Ltd.) filter, dried at 100 °C overnight and weighed. Samples were then ashed for at least 4 hours at 550 °C to calculate the inorganic fraction. The organic fraction of the biofilm was determined by the difference between the values. The ratio of organic to inorganic matter was calculated.

4.4.3 Habitat Survey A river habitat survey based on Raven et al. (1998) was carried out at each stream site, recording channel dimensions, substrate, bank and flow characteristics at each sampling station. Flow was estimated every 5 m using an OTT C2 flow meter and an average value was calculated for the stream. Reduction in light intensity compared with open conditions was measured every 5 m using a Licor light meter; an average value was calculated for the sampling reach.

4.4.4 Statistical analyses KW and MW tests were used to investigate differences in water chemistry and biological metrics between buffer categories. The KW one-way ANOVA by ranks is a non-parametric test used to determine if several (k) independent samples are from the same population. The KW test statistic was tested against the χ2 distribution with (k-1) dof (Siegel and Castellan, 1988). The MW U test is a non-parametric test that will assess if two independent samples are drawn from the same population. When the KW one-way ANOVA by ranks indicated that at least one pair of stream

219

categories was different the MW U test was applied to identify which pair or pairs of samples differed. As n was small, the MW U test statistic was compared against the appropriate tables, otherwise the central limit theorem was invoked and the MW U test statistic was assumed to be asymptotically normally distributed with zero mean and unit variance (Siegel and Castellan, 1988). Macrophyte assemblages in the streams were explored using DCA and CCA in CANOCO (Ter Braak and Šmilauer, 2002). CCA was carried out using Hill’s scaling, which is appropriate for longer gradients (Leps and Šmilauer, 2007). Statistical significance tests of axes were carried out using Monte Carlo permutation tests. Invertebrate ecology was investigated using DCA and RDA (Van den Wollenberg, 1977); a constrained form of PCA (Hotelling, 1933) in CANOCO. Multivariate analysis of similarity (ANOSIM; Clarke, 1993) was used to measures the difference in average rank similarity between sites. The BVSTEP algorithm (BEST; Clarke, 1993) was used as a method of complementary analysis to select environmental variables that best explained invertebrate and macrophyte community patterns; the analysis maximises rank correlations between resemblance matrices. CAP (Anderson and Willis, 2003) and MDA (Tatsuoka, 1971) were used to investigate the variables discriminating between stream categories: CAP is a method of generalised canonical variates analysis. This ordination uncovers the axes in principal coordinate space that best discriminate between RMCs. MDA is a form of discriminant function analysis that uses more than one environmental variable to distinguish between groups (Field, 2001). This method was used to determine whether stream categories differed with respect to the mean of a variable and then use that variable to predict group membership. Agglomerative hierarchical classification was used to investigate clustering of stream groups based on their biological characteristics.

4.4.5 FFG analysis The FFG approach uses mouthpart morphology to group macroinvertebrates by their mode of feeding, from which their approximate diet is inferred (Merritt and Cummins, 1996). Generalist feeding behaviour is common among stream invertebrates (Mihuc, 1997) so that the FFG approach has relatively low resolution and is therefore best suited to broad studies across wide environmental gradients (Jiang et al., 2010, Vannote et al., 1980, Zilli et al., 2008, Mihuc, 1997). In the current study, macroinvertebrates were grouped into one of four FFGs: 1.

Shredders (utilising relatively coarse organic matter such as leaves and woody debris)

2.

Collectors/Gatherers (utilising fine organic matter such as algae or terrestrial matter)

3.

Grazers/Scrapers (utilising organic matter attached to surfaces such as algae and biofilms)

4.

Predators (utilising other invertebrates)

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4.4.6 Biodiversity measures Biodiversity indices such as D and the Shannon index are sensitive to the relative abundances of the different species comprising a community, such that numerical or mass dominance by one or a few reduces the biodiversity value determined. This negative weighting stems from the interpretation of uneven community structure as favouring one or more specialist species above the community, with some or many of the other constituent species being negatively impacted. This scenario, commonly observed in response to environmental disturbance, is not the rule and such weighting may not give wholly accurate comparisons of biodiversity among sites. D, yielding a metric in the range 0-1 where higher values are indicative of greater diversity, was used in WP3. The index is calculated from equation 4.1, below: D = 1 - ∑ (n/N)2

[4.1]

where: n = biomass of constituent species, N = total biomass of all species.

When applied to numerical species data the index is the probability that two randomly selected individuals will belong to different species. In this study, D is the probability that two different observations of invertebrate biomass selected randomly will belong to different species. Ed is calculated here by expressing D as a proportion of the maximum value that D could attain if the biomass of each species present was equal.

In addition to biodiversity indices, assessments of biological water quality, such as BMWP scores, were also employed since they can also provide useful information on macroinvertebrate community structure and function. Developed to assess impacts of organic pollution on invertebrates, BMWP scores are derived using low taxonomic resolution where taxa are grouped largely at the family level. Taxa are scored based on their perceived sensitivity to organic pollution so that scores for each site are the sum of the scores for each taxa present. The ASPT provides a measure of community sensitivity that is standardised according to the number of taxa present, while EPT scores are calculated as the total number of species belonging to the insect orders Ephemeroptera, Plecoptera and Trichoptera present.

4.4.7 SI analysis

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Trophic structure and feeding relationships within ecosystems are commonly conceptualised as food chains and food webs. In food chain studies, species are assigned to one of several discrete trophic levels. While such studies are useful for giving a simplified view of energy flow and trophic interactions, they inadequately represent the complexity of ecosystems; many feeding interactions cannot be observed and feeding links are not weighted according to their energetic importance. Gut contents analysis has been used to assign the trophic position of an organism, but in many cases quantitative dietary data are unattainable or biased and may not reflect actual matter assimilated by a consumer. Natural abundance C and N SI ratios can provide time-integrated information about feeding relationships and energy flow in food webs. They are potentially powerful tools for assessing food web relationships and have been extensively used across a range of freshwater systems in the last 25 years (Salonen and Hammer, 1986; Winterbourne et al., 1986; Hessen et al., 1990; Junger and Planas, 1994; Burns, 2000; Grey et al., 2001; Vander Zanden and Rasmussen, 2001; Kohzu et al., 2004). The SI C signatures (δ13C) of consumers are similar to those of their food. Given that δ13C signatures are conserved up food chains and that the potential production bases exhibit distinct δ13C signatures, the source of C consumed by an organism can be inferred from its δ13C signature. In contrast, consumers become enriched in the heavier N SI (15N) relative to their food. This results in a stepwise trophic level enrichment that can be used as an indicator of a time integrated trophic position based on pathways of energy flow.

Macroinvertebrates collected for SI analysis were sorted into individual taxa, placed into 0.45µm filtered stream water and left overnight to allow gut evacuation. After identification species were washed with Milli-Q water (Millipore) and dried at 60 °C. Several individuals of each species were pooled to give one composite sample per species, both to achieve sufficient mass for analysis and to compute statistics of central tendency. River conditioned detritus, macrophytes and riparian terrestrial litter were sorted from inorganic matter (largely from inorganic particles by density difference in suspension) and dried at 60 °C.

Replicate samples of epilithic biofilm were bulked together, mixed and subsampled for SI analysis. In those cases where water velocity and depth varied significantly over the reach, a number of replicates were sampled and analysed separately. Furthermore, if stations along the reach were less shaded, additional efforts were made to sample rocks that appeared to have a high algal component on the basis of their colour. This was done to achieve a relatively clean sample that would yield a

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predominately autochthonous isotopic end member. Macro-algae when present were also sampled and analysed separately. Biofilms were filtered onto precombusted glass fibre filters (Whatman, GFC) and dried at 60 °C. Seston was sampled by filtering sufficient stream water through precombusted glass fibre filters and dried at 60 °C. To homogenise larger samples, a Retsch MM200 ball-mill was used. Smaller samples were ground by hand using a pestle and mortar. Samples for SI analysis were analysed for δ15N and δ13C at The CHRONO Centre (Queens University Belfast) using a Thermo Scientific, Delta V Advantage IRMS. Results derived from equations 4.2 and 4.3 are given using the δ notation in per thousand units (‰).

δ (‰) = [ ( Rsample / Rreference ) -1 ] x 1000

[4.2]

R = 13C / 12C or 15N / 14N

[4.3]

and

The reference standards were secondary standards of known relation to the international standards of Pee Dee Belemnite for carbon and N2 air for N.

The SI approach relies on the fact that consumer tissues bear a consistent relationship with those of their prey, so that consumer diets can be calculated by comparing their isotopic signatures with those of potential prey items. The SI approach can yield highly specific data on macroinvertebrate diets on a site specific basis and includes the capability for calculating the importance of several resources to a consumer. A drawback of the approach is the requirement that potential dietary resources have distinctive isotopic signatures for at least one isotope. This circumstance tends to be the rule rather than the exception in unproductive upland streams (Finlay, 2001). Given sufficient distinction between the isotopic signatures (C or N) of each potential source the proportions assimilated by a consumer can be calculated using a simple two end member mass balance / mixing model. More generally, consumers will have access to several dietary sources, both allochthonous and allochthonous, making the two end member approach unsuitable. Iterative algorithms that produce a mathematically derived range of possible dietary solutions (proportions) have been developed (Phillips and Gregg, 2003; Phillips et al., 2005). Whilst such methods have definite utility, the model output is a range of feasible proportions and does not indicate which proportions are the most likely, and isotopic variability with regard to consumers, dietary sources and trophic enrichment cannot be included. More recently mixing models using Bayesian inference have been developed (Parnell et al., 2010). These models generate potential dietary proportions as true

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probability distributions, so that individual metrics of resource utilisation, such as the mode, can be acquired. Furthermore, isotopic variability of consumers, sources and fractionation can be included. Bayesian mixing models have been widely accepted by SI ecologists and employed in several studies of aquatic systems (Inger et al., 2006, 2010; Syväranta et al., 2011). The Bayesian mixing model approach is well suited to studies of resource utilisation in streams since macroinvertebrates have access to several different food types, such as biofilms, suspended organic matter, terrestrially derived leaf matter and macrophytes.

At many of the sample sites, consumer SI signatures were beyond the boundaries of those indicated by the putative sources sampled. While Bayesian mixing models will still yield an output, results will be strongly influenced by the most isotopically similar source, and may thus be misleading. In such cases, the relative degree of deviation from an entirely terrestrial diet can be calculated according to equation 4.4:

[4.4] where: δ13C consumer =

δ13C for invertebrate in question

F

fractionation factor for 13C enrichment between consumer and source

=

δ13C terrestrial =

δ13C for terrestrial matter at site

δ13C source

δ13C of putative dietary resource at site

=

This approach is analogous to a simple, single isotope two end member mixing model, and can be conceptualised as the calculation of relative isotopic distance from the terrestrial resources at a site, where the isotopic distance from a terrestrial end member is standardised by the distance between the terrestrial end member and the most isotopically distinct putative source (Figure 4.2). Many quantitative approaches suffer in their utility from their dependence on consistent signatures for basal resources across time and space (Layman et al., 2007, Hoeinghaus and Zeug, 2008). However, by calculating the degree of deviation results are effectively standardised for differences in the degree of isotopic difference between basal resources. The benefit of this approach is that it makes no assumption of the nature (allochthonous or autochthonous) of dietary components that do not resemble a terrestrial C signature.

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For sites where consumer SI signatures were beyond the bounds of the measured sources (in all such cases consumers were significantly 13C depleted), the approach employs the most isotopically different or far-removed consumer signature from the terrestrial signature as the non-terrestrial end member (equation 4.5). This consumer signature is then assumed as a proxy for the most distinct non-terrestrial dietary source and to have no reliance on terrestrial resources. Although possibly underestimating the terrestrial contribution to consumers at such sites, the approach remains valid for approximating the relative positions of consumers, in terms of what has been described as isotope niche space (Schmidt et al., 2007).

[4.5] where: δ13Cinvert-dep = δ13C of the most 13C depleted invertebrate present. At some sites more than one putative terrestrial resource was present. For these sites different δ13C for multiple potential sources prevented the application of equations 4.4 and 4.5. Furthermore, at some sites, terrestrial and non terrestrial sources had overlapping δ13C but were distinct in terms of their δ15N, for example overlapping δ13C for biofilm, bryophytes and/or river conditioned detritus. For such sites, the outputs of the SIAR Bayesian mixing model (Parnell et al., 2010) were employed to calculate % deviation from a terrestrial diet. In these cases the proportional reliance upon sources considered autochthonous were summed to yield the % deviation from a terrestrial diet. The calculated percentage deviations from a terrestrial diet for consumers are approximately equivalent to the distances or positions in isotope space relative to a wholly terrestrial diet. Thus these different positions can be considered as representative of the occupation of different dietary niches. The coefficient of variation of the consumer frequencies in 10% increments was employed as a metric describing relative niche abundance and utilisation: low coefficients are inferred to be indicative of utilisation of a greater number of niches, and high coefficients represent occupation of a similar niche by several organisms.

4.5 Results

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4.5.1 RMCs (based on buffer types) A number of water chemistry differences were associated with the management categories. Analysis of similarity using water chemistry and vegetation variables (on a catchment scale) yielded a global R value of 0.411 (p = 0.001) indicating significant differences. Pairwise tests showed relatively high R values (Table 4.2) between stream categories indicating differences between types. The least different categories with respect to catchment and water chemistry variables were streams with harvesting in the catchment and streams with open buffers. Streams in the control category had the lowest mean value for SRP, TSP, DHP, TP, DOC, NO2, NO3, TDN, organic N, C:N and conductivity. Alkalinity and TON were lowest in both control and sites with No Buffer (conifers planted to the stream edge). The harvested group of catchments had the highest mean stream water SRP, TSP, TP, DOC, TON, NO2, NH4, NO3, TDN and organic N. Relationships between vegetation characteristics and water chemistry parameters were investigated using Pearson correlation (Table 4.3). Water chemistry (Table 4.4) and biological metric (Table 4.5) differences between management categories were investigated using Kruskal-Wallis one-way AOV. Significant differences were observed for the biomass of invertebrate Collectors/Gatherers between overall site categories (Figure 4.3a; KW p = 0.05), MW pairwise tests yielded borderline significant differences (after type 1 error was accounted for) between sites with No Buffers (conifers to stream edge) and both streams with harvesting in the catchment (MW p = 0.015) and sites with open buffers (MW = 0.019). Differences were observed for SRP in harvested and Control sites (KW p = 0.028; MW p = 0.004; Figure 4.3b). The catchments that experienced harvesting had higher values for SRP than Control sites; site CF5, which had the highest proportion of catchment felling (75%) had the highest SRP value of all study streams (90 μg l-1). The KW test found borderline significant differences for TSP between Control sites and sites with an open buffer (KW p = 0.015; MW = 0.016) (Figure 4.3c). This difference is most likely driven by the pairwise difference for Control sites (lowest median and values) and sites with open buffer strips (highest median and mean values) (MW p = 0.016). TON and NO3 differed between sites with No Buffer (conifers to stream edge) and Harvested sites (Figure 4.3d and 4.3e), these parameters also differed significantly between Harvested sites and Control sites (p = 0.002 for KW and MW tests in both cases). Conductivity was significantly different between all categories with Broadleaved Buffers and Control sites being the most different (KW p = 0.044, MW p = 0.032; Figure 4.3f). Significant differences were observed for overall differences in light reduction (KW = 0.03) and river habitat survey between all groups (KW = 0.02), however; further MW pairwise tests did not yield values significant at the 5% level after type 1 error was accounted for. Borderline significant pairwise values were obtained for broadleaved and Open Buffer sites for light reduction (MW p = 0.057) and MRHS (MW p = 0.029). Broadleaved Buffer and Harvested sites were also borderline significant for

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light reduction (MW p = 0.019). Broadleaved Buffer and No Buffer sites were borderline significantly different for MRHS (MW p = 038).

4.5.2 Macrophyte ecology DCA yielded a first axis of 6.13 SD (Figure 4.4). Superimposition of management categories showed some grouping of category according the ordination of stream macrophytes. Analysis of similarity yielded a Global R value of 0.194 (p = 0.01) indicating floral differences between the stream categories. Pairwise analysis (Table 4.6) revealed relatively high R statistic values between Broadleaved Buffer and Control streams. These stream categories occurred farthest apart in multivariate space in both the DCA and CCA ordination biplots. Streams that had harvesting in the catchment and Open Buffer sites had more similarities than differences, and were located close to one another on the DCA ordination biplot.

The robustness of the buffer categories regarding macrophyte occurrence was investigated using CAP with discriminant analysis. Of the 25 sites, 64% were correctly predicted to the correct buffer category on the basis of the macrophyte flora in a cross validation exercise (Table 4.7). Permutation tests showed the buffer categories to be significantly different (p = 0.003 for the trace statistic (Anderson and Willis, 2003)). The most successfully predicted category was the Broadleaved Buffer group of streams, the least successful predictions were for the open buffer and harvested category streams.

On the basis of the DCA ordination and a complementary cluster classification of the macrophyte abundance data (Figure 4.5), stream categories were reclassified into 3 groups to reflect more accurately the macrophyte ecological continuum (Figure 4.6). Harvested site CF8 was excluded as on outlier; only three macrophyte species occurred in this stream one of which, Hygrohypnum luridum (Hedw.), only occurred at this site. Further discriminant analyses were carried out to uncover the environmental drivers of the macrophyte ecology in the study streams. A CDA (Table 4.8) based on the macrophyte stream type showed significant differences between the groups in multivariate space (p = 0.002).

Stepwise MDA, using the water chemistry, catchment, riparian and local riparian variables, showed the most influential variables discriminating between the macrophyte stream types to be the percentage of peatland vegetation in the local riparian zone, NO3, altitude (all p < 0.05), percentage of coniferous vegetation in the catchment, flow volume, organic N, percentage coniferous

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vegetation in the riparian zone (all p < 0.10), percentage deciduous vegetation in the local riparian zone, pasture vegetation in the catchment (all p < 0.20) and percentage harvesting in the riparian zone (p > 0.20) (all log (x+1) transformed) (Figure 4.7). Optimal variables were identified in the analysis as percentage of peatland vegetation in the local riparian zone and NO3 (Table 4.9). These two variables resulted in a 72% classification success rate in a validation exercise.

ANOSIM of the macrophyte ecology groups yielded a global sample statistic of 0.545 (p < 0.001). Pairwise, groups also differed significantly from each other (Table 4.10). BVSTEP analysis identified TON, mass of suspended solids, percentage of peatland vegetation in the local riparian zone and altitude as the best combination of driving variables (r = 0.415, p < 0.07).

The length of the DCA first axis of 6.13 SD implied that unimodal modelling methods were appropriate for the macrophyte dataset (Lepš and Šmilauer, 2007). A constrained unimodal modelling method, CCA, was used to determine the amount of floristic variation explained by the three significant (p < 0.05) variables identified by MDA (Figure 4.8). Eigenvalues for the first two canonical axes were 0.530 and 0.409; the first three canonical axes accounted for 16% of the explainable variation in the species data. The first and higher axes were significant (global p = 0.03). The three variables identified by MDA explained 6% of total inertia in the ordination. Decomposition of variance after forward selection showed peatland vegetation in the local riparian zone explained 2.3% of total inertia, while NO3 and altitude explained, respectively, 2.1% and 1.7%. The percentage of species variance explained by the DCA and CCA first axes were similar at 9.6% and 7%, respectively. Variance values explained by the second axes were 6.4% and 5.4% respectively, indicating that the environmental variables chosen influenced the observed variation in species composition.

4.5.3 Invertebrate ecology Results of DCA revealed a first gradient length of 3.166 SD; superimposition of the stream management categories based on buffer type showed little concordance with invertebrate ecology in multivariate space (Figure 4.9). Broadleaved Buffer sites appeared to be the only stream category to cluster in the DCA biplot based on invertebrate biomass. An ANOSIM test showed small differences in invertebrate ecology between management categories with a relatively low global R value of 0.177 (p = 0.015). Pairwise analysis yielded R values showing some differences between sites with No Buffers (conifers planted to stream edge) and sites with Broadleaved Buffers or harvesting in the catchment. The largest R statistic value was obtained for pairwise comparison of

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sites with open buffer strips and those with broad leaved vegetation in the buffer zone (Table 4.11). Control streams in peatland and streams with open buffers were the categories with least difference.

BEST analysis did not find an influential combination of variables significant at 10%. A CA of principal coordinates with DA was used to investigate the robustness of the management categories regarding invertebrate occurrence. Of the 25 sites, 20% were correctly predicted by the analysis (Table 4.12). Permutation tests showed the buffer groups were not significantly different (trace statistic p = 0.410). On the basis of the ordinations and complementary cluster classifications (Figure 4.10) of the invertebrate data, streams were reclassified to reflect the invertebrate ecological continuum. Sites in the No Buffer category (conifers planted to stream edge) – CE 148, CE185 and CE56 - were placed in a group due to their proximity in a RDA and the cluster diagram based on invertebrate biomass. A CDA based on the invertebrate stream types showed significant differences between the groups in multivariate space (p = 0.002). A correct reclassification rate of 96% was obtained (Table 4.13)

Stepwise MDA showed the most influential variables discriminating between the invertebrate stream types to be pH, NH4 (both p < 0.05) (Table 4.14), the percentage of peatland vegetation in the local riparian zone (all p < 0.10), altitude, percentage of organic soil in the catchment, alkalinity, percentage pasture in the catchment, Organic:Inorganic BOM (all p < 0.20), the percentage harvesting in the riparian zone and the percentage harvesting in the local riparian zone (p > 20 ) (all log (x+1) transformed). Optimal variables were identified as pH and NH4. These two variables resulted in an 80 % classification success rate (Figure 4.11).

ANOSIM of the invertebrate ecology groups yielded a global sample statistic of 0.844 (p < 0.001). Groups also differed significantly from each other (Table 4.15). The length of the DCA first axis (3.166 SD; Figure 4.12) implied that linear modelling methods were appropriate for the invertebrate dataset (Lepš and Šmilauer, 2007). The constrained linear modelling method RDA was used to determine the amount of faunal variation explained by the two significant (p < 0.05) variables identified by MDA (Figure 4.13). Eigenvalues for the first two canonical axes were 0.156 and 0.046; the first two axes accounted for 20% of variation in the species data. A global significance test showed that the first and higher axes were significant (p = 0.002). Ammonium accounted for 9% and pH accounted for 11% of total variance in the ordination, respectively.

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4.5.4 Structure and function of stream communities 4.5.4.1 FFG According to results of FFG analysis, Shredders comprised > 50% of the invertebrate biomass (Figure 4.14), with the amphipod, G. d. celticus, classed as a Shredder, abundant when present. The Control streams were notable for the relatively even distribution of biomass among FFGs, whereas the harvested category streams possessed a relatively high proportion of Collector/Gatherers. With the exception of the No Buffer category (conifers planted to stream edge) where invertebrate biomasses were consistently low, total invertebrate biomass was highly variable among streams (Figure 4.15).

Examining the mean proportions of biomass by FFG for each management category presents a somewhat biased view of community structure for the dataset since FFG proportions were variable for streams within individual categories. Similarities of community structure among streams, in terms of the relative biomass proportions of invertebrates belonging to each FFG, were therefore explored using MDS and cluster analysis. To eliminate the effect of differences in biomass between sites and to better reflect community structure, the biomass of each FFG was expressed as a proportion of the total invertebrate biomass. Initially the analysis was run including all FFGs (Figure 4.16a). The amount of G. d. celticus as a proportion of biomass was then removed from Shredders and included as a separate group (Figure 4.16b). Finally the biomass of G. d. celticus was entirely subtracted from the dataset to remove the influence of this abundant species (Figure 4.16c). The FFG analysis, based on the unmodified FFGs (4.16a), revealed two gradients. The first is the proportion of Shredders present (increasing left to right), and the second shows a transition from Predator to Collector/Gatherer to Grazer dominance (increasing vertically). Streams within individual management categories did not group together to any great extent, but sites with high Shredder abundance were placed close together and tended to belong to the Broadleaved Buffer and No Buffer (conifers planted to stream edge) categories. Two Harvested sites exhibited very low relative Shredder abundance. Figure 4.16b, summarising results of the analysis in which G. d. celticus was treated as a separate variable, shows a similar pattern. However, a gradient running diagonally from bottom right to top left identified the transition between non-G. d. celticus Shredder dominance to G. d. celticus dominance. On this basis, broadleaved sites grouped relatively closely due to the high biomass proportion of G. d. celticus. The top right grouping reflected sites where G. d. celticus was entirely absent and also where Shredders were at low relative abundance.

Streams could initially be divided in two by those in which Grazers and Collector/Gatherers were most abundant and those in which Shredders and Predators were most abundant when G. d. celticus

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was omitted from the dataset (Figure 4.16c). All except one Harvested site grouped together on the basis of their high Collector/Gatherer abundance. Broadleaved Buffer sites, now in the absence of G. d. celticus, still grouped together because of the high relative abundances of Grazers and Collectors. The data were further explored by RDA. Forward selection showed the MRHS, the area of catchment composed of grassland, harvesting and coniferous vegetation and the percentage of light reduction relative to unshaded conditions to be the most influential variables (Figure 4.17).

4.5.4.2 Biodiversity and other measures of stream macroinvertebrate communities D tended to decline as the proportion of the total invertebrate biomass accounted for by G. d. celticus increased (Figure 4.18). However the omission of G. d. celticus from the data set only exerted a significant effect on D for the broadleaved sites where G. d. celticus was particularly abundant (Figure 4.19). With G. d. celticus included in the calculation of D, Control sites, with no coniferous forestry influence, were found to have the highest mean biodiversity and evenness. Differences in biodiversity and evenness between sites were less when G. d. celticus was omitted.

Although classed in the Shredder FFG, G. d. celticus is widely known as a generalist feeder and has been known to predate on other invertebrates (Kelly et al., 2003). Thus it is conceivable that high G. d. celticus abundance could be associated with negative impacts on the abundance and composition of other species. This did not appear to be the case here as species richness showed a slight nonsignificant positive relationship with G. d. celticus abundance among sites, and additionally there was no relationship between species richness and the proportion of the total biomass occupied by G. d. celticus among sites.

Figure 4.20 illustrates the level of positive correlation between ASPT and both the BMWP score and species richness among sites. Thus BMWP scores and ASPT are associated with the addition or loss of higher scoring taxa in these streams, rather than the addition or loss of lower scoring taxa. No consistent difference between ASPT scores or species richness between the different RMC was found. Sites with broadleaved buffers were the least variable group and demonstrated relatively high BMWP scores and ASPTs. Four of the six streams in the No Buffer category (conifers planted to stream edge) exhibited relatively low species richness and ASPTs. Harvested sites exhibited a wide range of species richness but ASPTs were relatively high in all cases.

Chemical water quality was relatively good at all sites (Table 4.4), so that increases in biological water quality as assessed by ASPTs may largely result from a greater number of macroinvertebrate

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niches; determined by habitat and resource availability. For example, the species common to the majority of sites belonged to the following taxa (families): Baetidae, Elmidae, Gammaridae, Oligochaetae, Chironomidae, Leuctridae, Polycentropodidae and Simuliidae. The ASPT value for such a community is 5, and, with the exception of the generalist feeder Baetidae, most taxa are detritivore members of the Shredder FFG. Higher ASPT and BMWP scores generally resulted from the addition of higher scoring taxa to this list. These additional taxa tend to be more specialised with regard to their feeding habits and consist largely of members of the insect orders Ephemeroptera, Plecoptera and Trichoptera. Families within these orders are sensitive to water quality and have high BMWP scores, and include the Heptageniidae, Perlodidae, Nemouridae, Philopotamidae, Sericostomatidae, Lepidostomatidae, Leptophlebiidae, Ephemerellidae and Rhyacophilidae. A positive correlation between EPT scores and both ASPT and species richness (figures 4.21 and 4.22) supports the finding that increases in species richness are associated with the addition of more specialised, higher scoring taxa.

The number of different species recorded from within management categories was greatest for the Harvested sites, whereas 22% and 37% fewer species were recorded from No Buffer and Broadleaved Buffer categories, respectively (Table 4.16). In contrast, mean species richness showed a different pattern, with the highest value for the Broadleaved Buffer sites, although values were approximately comparable for all stream categories except the sites with no buffers where the mean was considerably lower. Thus the Broadleaved Buffer category supported communities consisting of several characteristic species.

4.5.4.3 SI analysis of food webs Six categories of stream food web components were identified on the basis of their different molar C and N content: terrestrial organic matter (largely recent terrestrial plant production), Bryophyta, river conditioned detritus (partially degraded material), biofilm, filamentous algae and macroinvertebrates (Figure 4.23).

The C and N contents of the crustacean amphipod G. d. celticus differ from those of the other macroinvertebrates, which largely belong to class Insecta, but the C:N ratio remains similar at approximately 5:1. Some members of the Shredder FFG exhibited a lower N content; these individuals were Limnephilidae larvae (caddisflies). Their different elemental stoichiometry may reflect preferential storage of lipids prior to pupation. Alternatively, gut evacuation may not have been complete and their elemental composition may have been influenced by terrestrial organic

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matter remaining in their guts. River conditioned detritus exhibited elemental compositions that were generally consistent with that of terrestrial organic matter. However, some samples exhibited greater N content, which may reflect greater processing and microbial colonisation by bacteria and fungi. C:N ratios and δ15N for terrestrial primary producers were highly variable (Figure 4.24). Consumer trophic enrichment was evident relative to potential dietary resources. Biofilm molar C:N ratios were similar to the Redfield ratio potentially reflecting both an algal component and trophic enrichment of the bacterial community. This lower C:N for biofilm relative to terrestrially derived organic matter indicates that this dietary resource is of higher nutritional quality, thus less biofilm would be required to meet the N demands of macroinvertebrates. Utilisation of biofilm relative to terrestrial organic matter may therefore depend on the relative availability of these dietary resources and the specific feeding adaptations of macroinvertebrates (Mihuc, 1997). The lower C:N of certain samples of river conditioned detritus are indicative of microbial processing, which can increase the nutritional quality of this resource (Findlay, 2010). Macroinvertebrate δ13C values ranged relatively widely among sites (-25.3 to -44.3‰) as did δ13C values for bryophytes (-26.9 to -43.5‰) (Figure 4.25). The most depleted values were not restricted to consumers within individual sites, rather certain consumers exhibited particularly depleted δ13C, while others within the same site exhibited signatures more consistent with utilisation of terrestrial organic matter (~-29‰). On this basis, many of the most δ13C depleted consumer signatures could only be consolidated by diets consisting largely of bryophytes. Nevertheless, at six sites consumers were significantly depleted relative to both the bryophytes present at the site, and all other putative dietary sources, exhibiting δ13C values below -35‰. Additionally at control site CM6 two 13C depleted taxa, while exhibiting δ13C values consistent with a bryophyte diet, were relatively depleted in 15N, indicating that bryophytes were not a significant dietary component. The macroinvertebrate taxa exhibiting the most negative δ13C were relatively consistent among the sites. Of the seven sites where depleted consumer δ13C could not be consolidated by the putative dietary sources, stoneflies nymphs (Leuctra spp.) were significantly depleted at four sites, Baetis spp. at two sites, Simulidae at two sites, Paraleptophlebia cincta (Retzius) at two sites, adult Elmidae at two sites and Oligochaetae and nymphs of Nemoura erratica Claassen and Nemurella pictet Klapálek (the latter two species of stonefly) together at one site.

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Depleted consumer δ13C values have been linked with methanotrophic bacteria suggesting that methanogenesis in riparian and hyporheic zones may be significant at these sites. Methanogenesis is proving more widespread than previously thought (Sanders et al., 2007) and subsequent production by aerobic CH4 oxidising bacteria make this form of C available to food webs (Trimmer et al., 2010). Invertebrate grazing upon methanotrophic bacteria has been shown to be a significant transfer of matter in several systems, including streams (Kohzu et al., 2004) lake sediments (Kiyashko et al., 2001), lake littoral zones and lake pelagia (Bastviken et al., 2003). Indeed methanotrophic bacteria have been found to provide up to 15% of the C for zooplankton production in some lakes (Taipale et al., 2007), up to 70% of larval chironomid C (Jones et al., 2008), and 17% of the C for certain benthic feeding fish species via benthic larval chironomidae (Ravinet et al., 2010). Thus food webs can be significantly supported by this C pathway.

As sediment burial efficiency tends to be higher where terrestrial inputs predominate and lower with greater proportions of autochthonous matter (Sobek et al., 2009), methanogenesis in lakes may be largely based on autochthonous production. By comparison, methanogenesis in streams largely involves allochthonous (terrestrial) organic matter. The waterlogged and anaerobic conditions in many soils, including peats, may be conducive to methanogenesis. Methane may then be hydrologically exported to aerobic stream interfaces where production by methanotrophic bacteria can occur. High concentrations of DOC in many of the sampled streams represent a bioavailable organic matter source, suggesting that anoxic conditions and methanogenesis may also develop in hyporheic zones. The biomass of the consumers exhibiting depleted δ13C values did not constitute a high proportion of the biomass at any site and thus cannot be interpreted as being particularly important for secondary production in a stream. However this resource may provide an additional niche for consumers with feeding adaptations that allow them to exploit such bacteria.

4.5.4.3.1 Site specific trends of consumer reliance upon terrestrially derived organic matter Consumers within each RMC were grouped into % deviation categories at 10% intervals. The proportion of consumers in each 10% category was calculated to allow comparisons between RMCs (Figure 4.26). Broadleaved Buffer sites were notable for the high frequency of consumers with signatures consistent with predominant utilisation of terrestrially derived matter. Consumers at harvested sites tended to be moderately reliant on terrestrial matter, with about 50% of consumers showing deviations from a terrestrial diet of 30-80%. Several consumers demonstrated significant deviations from terrestrial C isotopic signatures of 90-100%. In some cases these could be explained by a diet consisting of a true autochthonous end member, such as algal biofilm fractions or

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bryophytes. However, high deviations in some cases were an artefact of the methodology employed. In all such cases, the specific consumers were significantly 13C depleted relative to other food web components. Consumers that were 13C depleted relative to putative dietary resources were most frequent at No Buffer sites. Within this RMC, 28% of consumers deviated by more than 80% from the terrestrial end member, and of these 71% did so due to δ13C values depleted relative to the putative sources. Similarly invertebrates, 13C depleted relative to putative dietary sources, were present at four of the five sites in the Control category, where they accounted for nine of the 10 invertebrates with % deviations from an allochthonous diet of > 80%. At two of the four Open Buffer sites certain consumers were 13C depleted relative to measured putative sources. Within this category, 60% of consumers had deviations from a terrestrial diet of more than 80% owing to δ13C values below those of the putative sources. By contrast, the higher % deviations from a terrestrial diet among consumers at Harvested sites were, with the exception of one consumer, indicative of utilisation of a true autochthonous end member. The significant 13C depletion of consumers in the absence of a putative dietary source with which to consolidate their signatures is consistent with utilisation of methanotrophic bacteria as a C source. Oligochaetae are able to exploit methanotrophic bacteria since they tend to inhabit surficial stream sediments at the interface between aerobic and anaerobic conditions. In such locations CH4 exports from anaerobic zones may fuel production by aerobic methanotrophic bacteria that are subsequently ingested by oligochaetes. Similarly, stonefly nymphs that are members of the Shredder FFG may inhabit and feed upon detritus in which anaerobic conditions develop. The detritus may thus be favourable substrates and foci for methanotrophic bacteria. Ingestion of detritus with associated bacteria could then take place. Associated bacteria may be of significantly higher nutritional quality than the detritus which they inhabit, so that they are preferentially utilised for production by these consumers. The Ephemeropteran nymphs Baetis spp. and Paraleptophlebia cincta (Retzius) belong to the Collector/Gatherer FFG. They can therefore exercise a significant degree of feeding selectivity. Indeed the data suggest that these consumers may be actively selecting for methanotrophic bacteria or be doing so by feeding upon fine particulate organic matter that may form foci for bacteria. When consumers with δ13C below that of putative dietary sources are omitted from the analysis, the picture of resource utilisation broadly agrees with that of the FFG approach. No Buffer (conifers planted to stream edge), Broadleaved Buffers and Open Buffer categories had consumers that deviated least from a terrestrial diet in agreement with the dominant proportion of shredders

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present. At the harvested sites the mean proportion of Collector/Gatherer FFG invertebrate biomass approached 50%. SI data indicated that many consumers at these sites significantly utilised autochthonous resources, suggesting that the Collector/Gatherers were selecting for non-terrestrial dietary components. The most even distribution of invertebrate biomass among FFGs at the control sites can be interpreted as in broad agreement with SI data, since consumers showed a wide range of dietary deviations from a terrestrial diet.

Figure 4.26 illustrates the distribution in isotope space of consumer deviations from a terrestrial diet within each category. Each 10% increment from away from an entirely terrestrial diet can be conceptualised as a different niche. Highly skewed frequency distributions, such as for the Broadleaved Buffer category, are therefore indicative of several consumers utilising similar proportions of allochthonous and autochthonous matter, and thus occupying a similar niche. By contrast, even frequency distributions that are very similar among the 10% percentage deviation increments are indicative of consumers utilising several different niches, such as for the Control and Harvested RMCs. An inverse relationship is evident between total numbers of different species recorded and the coefficients of variation for each RMC, suggesting that greater species richness requires more available niches (Figure 4.27).

Table 4.16 shows that although mean species richness was relatively high at Broadleaved Buffer sites, the total number of species recorded was low. Thus Broadleaved Buffer sites had the greatest number of species common to all sites, and the high coefficient of variation suggests these consumers occupied very similar niches at all sites. Open buffer and No Buffer category sites had similar coefficients of variation which are in part due to the particularly 13C depleted signatures of several consumers at these sites, presumably because of a reliance on CH4- derived C. The same was also true to a degree for sites in the Control category, although the high number of Scraping/Grazing consumers that occupied mid range values of percentage deviations from a terrestrial diet, and lower proportions of taxa almost entirely reliant on terrestrial matter, reduced the coefficient of variation, consistent with a greater number of niches at these sites.

Harvested sites supported the greatest total species richness and exhibited the lowest coefficient of variation, suggesting that the utilisation of a relatively high number of different available niches resulted in greater biodiversity. Conditions at harvested sites are the product of the transition from highly shaded environments with relatively low allochthonous inputs (as coniferous litter) to conditions with high light availability and high allochthonous inputs, and typically increased

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inorganic nutrient availability. Energy inputs to the sampled streams from in situ primary production and from terrestrial derived matter are therefore expected to be higher than for semi-natural or established riparian buffer stream habitats. The long-term stability of the structure and function of such communities may therefore depend on the longevity of the elevated allochthonous inputs (and in stream break down rates and export) and on the time required for the establishment of riparian plant communities that restrict light penetration and subsequent aquatic primary production. Similarities among sites in terms of individual consumer reliance on terrestrial dietary sources were investigated using MDS and cluster analyses. For each site, frequency of consumers within terrestrial dietary reliance categories (defined according to % deviation from an entirely terrestrial diet with no weighting given to invertebrate biomass at each site) was expressed as a proportion of the total number of consumers. Figure 4.28 shows that sites can be initially grouped in two, based on the dietary habits of consumers, while a break-down of the dataset with sites arranged in order of the similarity groupings is evident in Figure 4.29. The first group (left side of figure) represents sites where at least half of all consumers (typically 70%) deviate from a terrestrial diet by less than 40%. In the second group, approximately 70% of consumers show deviations from a terrestrial diet of greater than 40%. Sites from individual RMCs did not show any systematic grouping. However, Broadleaved Buffer category sites all grouped with sites where consumers were predominantly exploiting terrestrial resources, four of the five sites in the Control category had a preponderance of consumers deviating significantly from a terrestrial diet, and only one of the four Open Buffer sites had consumers deviating significantly from a terrestrial diet. The environmental factors explaining most of the variation in resource utilisation among sites were investigated using DCA (Figure 4.30). The three most important factors identified were % light reduction, slope and organic biofilm mass.

4.5.4.3.2 Taxa specific trends of consumer reliance upon terrestrially derived organic matter An examination of species-specific trends in resource utilisation among invertebrate consumers revealed that members of the Shredder FFG generally showed small deviations from a diet consisting of terrestrial organic matter (Figure 4.31). The results indicated for G. d. celticus are particularly notable for the low degree of variation of dietary reliance among sites for this species. In contrast, stonefly nymphs belonging to the families Leuctridae and Nemouridae, which are also shredders, often displayed significant deviations from a terrestrial diet. In some respects these results are an artefact of the methodology employed, as Leuctra spp. were frequently depleted in 13C relative to all putative sources. However at many sites where this was not the case these macroinvertebrates also deviated more from a terrestrial diet than other members of the Shredder FFG present at the same site.

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Members of the Grazer / Scraper FFG have mouthpart morphologies suited to scraping matter from surfaces so that greater utilisation of autochthonous matter, typically epilithic and epiphytic algal matter, is expected. Limnephilid caddisfly larvae Drusus annulatus (Stephens), a scraper, is exemplary in this respect, exhibiting C and N stable isotopic signatures consistent with a diet consisting almost entirely of the algal component of the biofilm. This finding agrees with laboratory and gut content dietary studies for this species (Becker, 1994). Other members of the Grazer FFG deviated significantly from a terrestrial diet, although not to the same extent as for D. annulatus. Nevertheless, Heptageniidae mayfly nymphs and Elmidae beetles, both grazers, had mean deviations from terrestrial diets of more than 50%. In contrast, larvae of the caddisfly Silo pallipes (Fab.), classed in the Grazing / Scraping FFG, depended upon terrestrial matter for the majority of their body C, which is in agreement with Becker (1994).

Members of the Collector FFG were notable for the apparent division between the results for species that feed by some means of filtration / sit and wait predation (Simuliidae larvae and Hydropsychidae caddisfly nymphs), which were largely dependent upon terrestrial matter, and more mobile species (Baetis spp., Leptophlebiidae and Ephemerella ignita (Poda)), which largely depend upon non-terrestrial matter. Of particular note are the results for Simuliidae and Baetis spp., since members of these groups were present at the majority of sites. Simuliidae often exhibited signatures that were not consistent with a diet composed entirely of seston that generally had δ13C indicative of terrestrial vegetation; rather they incorporated substantial amounts of matter derived from both biofilms and river conditioned detritus. This suggests that benthic matter swept into suspension is an important component of their diet. Variation of dietary reliance on terrestrial matter was particularly low for Baetis spp., so this species can be said to exhibit a significant level of feeding selectivity for non-terrestrial matter at all sites. Many members of the Collector/Gatherer FFG are regarded as generalists that may display significant feeding plasticity in response to the individual resources present at different habitats. Despite broad agreement between FFG biomass proportions among RMCs and SI data, utilisation of terrestrial matter as a dietary component among individual macroinvertebrate consumers did not show consistent agreement with their FFG designation. The discrepancy can be explained on the basis that the consumers contributing to the majority of biomass at the sample sites, such as G. d. celticus, generally showed good agreement between their dietary reliance as assessed by SI analysis and their FFG.

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Species specific trends in resource utilisation among RMCs can also be examined, particularly for those species common to all management categories (Figure 4.32). With the exception of G. d. celticus all families compared across riparian management categories showed significant deviations from a terrestrial diet at the Control category sites irrespective of their FFG designation. This may reflect the relative paucity or low quality of allochthonous inputs to these sites, as well as greater abundances of autochthonous resources. Reliance on terrestrial resources was high among consumers at sites with open buffers with the exception of the stonefly families Nemouridae and Leuctridae, which although inconsistent with their designation within the Shredder FFG can be explained by members of these families often being 13C depleted relative to putative dietary sources. Consumers at No Buffer sites also showed relatively high deviations from utilisation of terrestrial matter. This suggests that allochthonous resources at such sites were of poor quality. The low relative biomass of invertebrates at most of these sites reinforces this hypothesis.

Deviations from a terrestrial diet by certain consumer groups between RMCs were apparent (Figure 4.33, Table 4.17). Heptageniidae and Elmidae were found to be predominantly reliant on terrestrial matter at Open Buffer sites despite both their Grazer/Scraper FFG designation and their high reliance on non-terrestrial material at other sites. These results imply that autochthonous material at Open Buffer sites was relatively low in abundance and accessibility. The higher DOC concentrations at Open Buffer sites relative to Control sites may reflect greater allochthonous inputs, and potentially a greater degree of light limitation of primary production. Additionally biofilms may be largely dominated by bacterial production based on DOC rather than autochthonous algal production.

Observed deviations from a terrestrial diet for consumers within Broadleaved Buffer sites and Harvested sites reflect expectations according to high allochthonous inputs of leaf litter, low to moderate shading by riparian vegetation and the FFG designations of consumers. All shredders showed small deviations from a terrestrial diet, whereas Collector/Gatherers and Grazer/Scrapers showed moderate to high deviations. Consumers at No Buffer sites, notably Simuliidae, G. d. celticus, Leuctridae and Nemouridae, showed pronounced deviations from terrestrial diets relative to Broadleaved sites and Harvested sites. G. d. celticus and Simuliidae were also less reliant on terrestrial matter at No Buffer (conifer to edge) sites compared with Open Buffer sites. The paucity of invertebrate biomass at these sites and their somewhat pronounced deviations from reliance on terrestrial matter suggest that allochthonous energy sources are particularly nutritionally poor, an observation consistent with the higher C:N ratio of terrestrial vegetation and river conditioned

239

detritus relative to macrophytes and biofilm (Figure 4.24) (Cross et al. 2005). The nutritional quality and digestibility of the leaves of many tree species has been found to be related to their lignin content (Royer & Minshall, 2001) and other intrinsic leaf qualities (Webster & Benfield, 1986). Thus coniferous needles are a poorer quality resource relative to less lignified deciduous trees.

4.5.4.3.3 Apportionment of invertebrate biomass Figure 4.35 summarises, on a site-by-site basis, relative contributions of autochthonous and allochthonous material to invertebrate biomass. Invertebrate production appears to be mainly dependent on terrestrially-derived (allochthonous) material: autochthonous matter accounted for >25% of the invertebrate biomass at 11 of the 25 sites, and >50% at four sites. Of the latter, three belonged to the Control category. Two of the four Open Buffer sites had relatively high production based on autochthonous matter, as did one individual Harvested site. G. d. celticus, like most Gammarus species, is generally considered to be a member of the Shredder FFG although significant feeding plasticity including predation on algae and other macroinvertebrates is well documented (Macneil, 2007). However this species was absent or at low relative abundance at the sites for which production based on autochthonous resources was significant.

4.6 Discussion In the study catchments, vegetation / land use at the catchment scale appeared to have more influence on water chemistry than riparian or local riparian scale factors. The percentage of coniferous and peatland vegetation at the catchment scale were both correlated with eight water chemistry parameters. Harvesting at the catchment scale was correlated to the greatest number of water chemistry parameters (9 variables; Table 4.3). Soil pH, reflecting the relationship of altitude with conductivity and alkalinity in NI (Rippey and Gibson, 1984), influences plant growth through its effect on ammonification and nitrification, with acidic conditions being associated with lower nitrification rates (Ste-Marie and Paré, 1999). The extent of peatland was negatively correlated with N fractions: the low N values in Control streams suggests higher denitrification potential in peatland soils due to low oxygen and high carbon levels (Seitzinger, 1994), resulting in a low N stream water content.

Dissolved organic carbon was positively correlated with the percentage of coniferous and rough grass vegetation cover in the catchment and negatively correlated with natural peatland vegetation. Water bodies in forested catchments in NI have experienced an increase in DOC since 1990 (McElarney et al., 2010) and this may well have implications for export of other nutrients. Loss of P

240

from forest soil can be enhanced by co-leaching with DOC (Donald et al., 1993), as P adsorbed to DOC sequesters ions (Pohlman and McColl, 1988) that would ordinarily complex with soluble P, rendering it unavailable. Any activity increasing DOC export from catchments therefore has the potential also to accelerate P loss to waters (Kreutzweiser et al., 2008). Consistent with other studies, significant negative correlations between slope of the catchment and the major nutrients were observed. A significant negative correlation between slope and DOC was also observed (r = 0.733, p

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