Appendix A – Description of the Conwy Research Catchment.

The river Conwy is the third largest river discharging into the Irish Sea along the north Wales coast. It drains a catchment of 678 km2, the main drainage channel covering a distance of 55 km. The upper reaches of the river drain the Migneint - one of the largest blanket bogs in Wales and a major store of carbon. Further downstream the Conwy is joined by tributaries (Machno, Lledr and Llugwy) draining the eastern side of the Snowdonia mountain range, before reaching the tidal limit, 20 km inland from the estuary mouth. For much of its length the tidal Conwy flows through a vulnerable floodplain, and is generally no more than 200m in width, receiving inflows from a number of minor tributaries.

There is a strong climatic gradient across the catchment with annual rainfall varying between 500mm in the north-east to more than 3500mm falling in the Snowdonia mountains to the west. The underlying geology is a mix of Silurian mudstones to the east and harder mixed igneous and sedimentary Cambrian formations to the west. The proportions of major soils are close to the Welsh national average, and typical of UK upland areas.

Land use varies from mountain and moorland with extensive sheep grazing and areas of coniferous forestry plantation in the headwaters and on poorer ground, to intensive sheep, beef and dairy farming with some arable production in lower elevation and more productive land. Semi-natural woodland can be found in many of the Conwy’s tributary catchments, including important areas of wet woodland. There are drinking water supply reservoirs (Llyn Conwy and Llyn Cowlyd) along with hydropower generation (e.g. at Dolgarrog). Recreation and tourism are important to the local economy and the catchment is a significant fishery for sea trout and salmon. The Conwy estuary also contains commercial shellfish beds. Tables A1 to A4 contain further details of the Conwy Catchment.

Table A-1. Geographic, demographic, physical and chemical characteristics of the Conwy catchment and Conwy River, North Wales, UK (3o5 ’ W, 5300̒ o00’ N).

Conwy CatchmentArea (km2) Total 580

Above tidal limit 380Elevation (m) Minimum 0

River headwaters 460Maximum 1064

Geology Age Mixed PaleozoicType Igneous, Sedimentary

Landscape Dominant soils Brown podzols,peats, gleys

Dominant land use Upland Farming, Arable, Mixed Livestock, Woodland

Rainfall (mm) Mean annual, sea level 500Mean annual, high elevation 3500

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Air temperature (deg C)(216 m ASL)

Mean annual 10February 5July 15

Population (number) Urban/suburban 10,000Rural 10,800

Economic activity(% working population)

Wholesale and retail 16Health-related 14Education 12Tourism 10Agriculture, forestry, fishing 4

Conwy RiverRiver channel length (km) Main channel 55

Above tidal Limit 35River discharge (m3 s-1)(at tidal limit)

Mean annual 18.7Q95 1.35Q10 45.3Median annual flood max 376

Chemical water quality, concentration ranges (mg l-1) (at tidal limit)

Nitrate-N 0.2 – 2.8Ammonium-N < 0.03 – 0.04Phosphate-P < 0.02 – 0.05Dissolved organic C 1.5 – 10pH 5.7 – 7.2

Biological water quality, indicator ranges(scores) (in headwaters)

Biological Monitoring Working Party (BMWP) 40 – 160Acid Water Indicator Community (AWIC) 20 – 110

Table A-2. Percent of land cover in different LCM 2007 classes in the Conwy catchment,

Wales, and England & Wales

Percent land cover

Conwy WalesEngland & Wales

Broadleaved woodland 5.07 5.96 6.96Coniferous woodland 9.89 6.92 2.96Arable and horticulture 1.58 8.40 36.38Improved grassland 24.67 40.43 29.10Rough grassland 10.91 10.40 4.69Neutral grassland 0.36 0.36 0.79Acid grassland 20.56 13.80 3.99Fen, marsh and swamp 0.01 0.03 0.05Heather 4.09 2.10 1.24Heather grassland 11.27 3.29 1.89Bog 7.46 2.01 1.58

Table A-3. Percent of land area in different soil classifications in the Conwy catchment, Wales,

and England plus Wales

Percent land area

Conwy WalesEngland & Wales

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Humic rankers 9.87 1.56 0.72Typical sand-pararendzinas 0.02 0.54 0.27Typical brown earths 9.35 26.18 13.53Typical brown alluvial soils 1.02 1.83 0.75Stagnogleyic argillic brown earths 0.17 0.63 5.74Typical brown podzolic soils 29.00 22.15 4.85Humic brown podzolic soils 1.00 1.29 0.35Ferric stagnopodzols 14.02 8.78 1.40Cambic stagnogley soils 7.33 15.07 6.08Cambic stagnohumic gley soils 12.12 8.04 3.59Typical alluvial gley soils 1.72 1.92 0.97Raw oligo-amorphous peat soils 14.15 3.30 0.54

Appendix B – Derivation of an empirical model of annual above-ground Net Primary Production

Methods

NPP and plant species compositional data

During summer 2008, plant species composition was recorded in varying numbers of random 1m2

quadrats on each of seven experimental sites (Figure B1 and Table B1). NPP was also measured for these quadrat locations by cutting, drying and weighing aboveground biomass in 50cm2 sub-plots. In grazed systems, exclosures (gabion cages) were established over the biomass plots in late winter and grazers excluded until harvesting at the end of the growing season. While this method does not overcome possible issues with negative and positive compensatory growth as a result of grazing, uncertainty over the importance and direction of these effects is great and no clearly superior method appears to exist that accounts for these effects while also excluding grazers (McNaughton, Milchunas & Frank 1996). In the upland broadleaved woodland site NPP was measured as the sum of the herbaceous understorey production, estimated from grazing exclosures as above, coupled with annual tree leaf production.

To ensure coverage of the terrestrial ecosystem productivity gradient we extracted additional data for six ecosystems from published sources. NPP values and paired plant species cover data for montane calcareous grassland were taken from (Perkins et al. 1978) and for ombrogenous blanket mire from Tables 2 and 13 in (Forrest & Smith 1975). In all cases NPP and plant species composition were measured in the same locations at the same time. Two methods were used to measure NPP in the upland calcareous grassland; (1) hand clipping of vegetation above the plant bases; (2) separation of all living above-ground material from turf cores (6cm in diameter and 8.5cm in depth) collected to determine root and above-ground production. We averaged the values of above-ground biomass from both methods. Independent test data for measured NPP under natural conditions for two key herbaceous dominants characterising wet heath in north west Europe (Erica tetralix L. and Molinia caerulea (L.) (Moench) were extracted from (Aerts & Berendse 1989). Paired NPP and

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species compositional data were also extracted from (Smart et al. 2002) for two unimproved, species rich hay meadows and two intensively managed lowland grasslands (Table B1).

Abundance-weighted Specific Leaf AreaWe extracted species-specific SLA values from (Grime et al. 1995) with additional values from (Kleyer et al. 2008) and (Wright et al. 2004). All vascular plant species had SLA values. Having attached these to the plant species abundance data, mean abundance-weighted SLA for each sample plot was calculated as,

mSLAj = sum (SLAij x (covij))/sum(covij),

where (covij) was the percentage cover value for species i in each sample plot j. SLA values are not widely available for bryophytes and so they did not contribute to calculations. All calculations of mSLA used a single published value per species.

Species richness and diversitySpecies richness was calculated as the count of all vascular plant species in each plot. Species diversity was based on the Shannon-Wiener index (SW) where richness is weighted by the proportional contribution of each species to total abundance in each plot as follows;

SW = -1*Σ(pi*ln(pi))

where pi= the proportion of the total cover of higher plants in each plot contributed by species i.

Statistical analysis

Because measurements of above-ground NPP and mSLA were clustered by ecosystem type, a hierarchical modelling approach was adopted to ensure that the non-independence of plots within sites was fully accounted for and that variances were correctly modelled given the nested structure of the data. We applied a Bayesian random effects model with fixed effects of mSLA, species diversity and species richness estimated as slope parameters with intercepts varyingly randomly between sites. Hence, we fitted models of the form:

yi = αj(i) + Σ(βk * Xi) + εi

where α specifies a random residual intercept in each study system j and βk are regression slopes for each fixed effect k. We adopted a Bayesian modelling approach implemented in the OpenBUGS software version 3.2.1. (http://www.openbugs.info/w/). Following Gelman (2006) the between-site residual variance across the random intercepts and the overall residual variance, after having accounted for between-site variation due to fixed effects and random intercepts, were both modelled by specifying standard deviations drawn from a uniform distribution. Non-informative normal priors were specified for the individual random intercepts, the mean of the random intercepts and the regression slope parameters. Bayes p values for the slope parameters were specified as a stochastic node to monitor during the model run. This simply records the number of

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times the estimated parameter is greater or less than zero. The mean of these values across the Monte Carlo Markov Chains (MCMC) provides the probability value (Ntzoufras 2009).

Models were generated for all combinations of untransformed NPP and the natural log of NPP predicted by mSLA, species richness and species diversity based on square-root transformed and untransformed percentage species cover data. In addition, models were applied to explanatory and response variables with and without centring and standardising to zero mean and unit standard deviation. Quadratic terms for species richness and diversity were also tested given the possibility that NPP could increase or decrease as species diversity changed (Cardinale et al. 2011). Outliers were identified by examining standardised residuals and testing their likely membership of the posterior predictive distribution using Bayes p values for each observation (Ntzoufras 2009). We calculated a probability of observing a higher value than the observation under the posterior density as follows,

Pr(i) = P (r_rep(i) > r(i) | y),

where r_rep is the residual calculated for a predicted value drawn at random from the posterior predictive distribution and r(i) is the residual of the observed value y(i). Model fitting was based on three MCMC chains initialised with different parameter starting values. Convergence was assessed by inspecting trace plots (Ntzoufras 2009). Posterior estimates for all parameters of interest were based on a sample of 20000 iterations after discarding a 5000 burn-in period. The model was run for each dataset of transformed or untransformed data and the best model selected was that which minimised the overall residual variance. An overall measure of the variability explained by the covariates was based on calculation and comparison of Bayes r2 values from a hierarchical model with covariates and random intercepts versus a linear regression model with no random intercepts. A comparison of the two values indicates the extent to which the random residual intercepts absorb additional between-site variation having already included the explanatory power of the fixed effects.

Model testing We used the measured NPP data from the experimental study systems as a reference dataset representing each ecosystem type. The best fitting regression model was then applied to independent plant species compositional data from a national ecological surveillance program for Great Britain (Smart et al. 2003). The aim was to predict NPP values for the independent data that were comparable to the ecosystems used to build the regression model. Equivalence, in terms of ecosystem types, was established by matching the study systems against the habitat classification for the national surveillance data (Smart et al. 2003). Having extracted vegetation plots in these equivalent types, we predicted NPP and then compared these predictions against the measured experimental site data.

Results

Model fitting

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Two outliers were identified (standardised residuals less than -3 and probabilities <0.01 of encountering lower values from the posterior predictive distribution). Both were located in the unimproved acid grassland site and coincided with high cover of bare rock and bryophytes. The model was re-run excluding these outliers. After testing all combinations of covariates, the best fitting model comprised the natural log of NPP predicted by mSLA and the Shannon-Wiener index with both calculated on square-root transformed plant cover data but no centring or standardising of explanatory or response variable (Table B2). All parameter estimates and statistics were judged robust based on the rule-of-thumb that the Bayesian MCMC error should be less than 5% of the standard deviation of the posterior density (Table B2).

There was a significant positive relationship between ln(ANPP) and mSLA and also a significant residual relationship with SW that was best expressed by a quadratic function where only the squared term attained significance by Bayes p value (Table B2). The r2 values for the non-hierarchical and hierarchical regression models were 0.51 and 0.89 respectively. Therefore approximately 38% of the variation in the data was explained by the random intercepts for each site after fitting the final best set of predictors.

Model testing

Predictions for the independent test data corresponded well with the ordering of ANPP values measured in equivalent ecosystem types in the training data although with substantial residual variation unaccounted for (Figure B2). In particular, the low productivity unimproved acidic grassland site appeared to be especially different in the model training data from the sample of the same habitat type in the national dataset (Figure B2).

References

Aerts R., Berendse F. (1989) Above-ground nutrient turnover and net primary production of an evergreen and a deciduous species in a heathland ecosystem. Journal of Ecology 77, 343-356.

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Cardinale B.J., Matulich, K.L., Hooper, D.U., Byrnes, J.E., Duffy, E., Gamfeldt, L., Balvanera, P., O’Connor, M.I.. (2011) The functional role of producer diversity in ecosystems. American Journal of Botany 98, 572-592.

Forrest, G.I., Smith, R.A.H. (1975) The productivity of a range of blanket bog vegetation types in the northern Pennines. Journal of Ecology 63, 173-202.

Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1, 515-533.

Grime, J.P., Hodgson, J.G., Hunt, R., Thompson, K. (1995) The Electronic Comparative Plant Ecology. London. Chapman & Hall.

Kleyer, M., Bekker, R.M., Knevel, I.C., Bakker, J.P., Thompson, K., Sonnenschein, M., Poschlod, P., van Groenandael, J.M., Klimes, L., Klimesova, J., Klotz, S., Rusch, G.M., Hermy, M., Adriaens, D., Boedeltje, G., Bossuyt, B., Danneman, A., Edndels, P., Gotzenberger, L., Hodgsno, J.G., Jackel, A.K., Kuhn, I., Kunzmann, D., Ozinga, W.A., Romermann, C., Stadler, M., Schlegelmilch, J., Steendham, H.J., Tackenberg, O., Wilmann, B., Cornelissen, J.H.C., Eriksson, O., Granier, E., Peco, B. (2008) The LEDA traitbase: a database of life-history traits of the NW European flora. Journal of Ecology 96, 1266-1274.

McNaughton, S.J., Milchunas, D.G., Frank, D.A. (1996) How can net primary productivity be measured in grazing ecosystems? Ecology 77, 974-977.

Ntzoufras, I. (2009) Bayesian Modelling Using WinBUGS. Wiley Series in Computational Statistics. John Wiley & Sons, Inc. New Jersey.

Perkins, D.F., Jones, V., Millar, R.O., Neep, P. (1978) Primary production, mineral nutrients and litter decomposition in the grassland ecosystem. In: Production Ecology of British Moors and Montane Grasslands (eds Heal, O.W., Perkins, D.F.), pp. 304- 332. Springer-Verlag, Berlin.

Smart, S.M., Clarke, R.T., van de Poll, H.M., Robinson, E.J., Shield, E.R., Bunce, R.G.H., Maskell, L.C. (2003) National-scale vegetation change across Britain; an analysis of sample-based surveillance data from the Countryside Surveys of 1990 and 1998. Journal of Environmental Management 67, 239-254.

Wright, I.J., Reich, P.B., Westoby, M., Ackerly, D.D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J.H.C., Diemer, M., Flexas, J., Garnier, E., Groom, P.K., Gulias, J., Hikosaka, K., Lamont, B.B., Lee, T., Lee, W., Lusk, C., Midgely, J.J., Navas, M.L., Niinements, U., Oleksyn, J., Osada, N., Poorter, H., Poot, P., Prior, L., Pyankov, V.I., Roumet, C., Thomas, S.C, Tjoelker, M.G., Veneklaas, E.J., Villar, R. (2004) The world-wide leaf economics spectrum. Nature 428, 821-827.

Figure B1. Map showing the location of the experimental sites within which paired above-ground ANPP and plant species compositional measurements were made.

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Table B1. Parameters and descriptive statistics for the posterior predictive distribution of the best model for estimating ln(NPP), (n=95 plots across 14 experimental study systems). Significance by Bayes p values indicated for each model term.

Explanatory variable Mean SD

Monte Carlo error 2.5%tile median 97.5%tile

mSLA (<0.0001) 0.0352 0.007846 2.88E-04 0.02004 0.03508 0.05047

Shannon-Weiner (ns) -0.4079 0.2589 0.01193 -0.9304 -0.4073 0.08218

(Shannon-Weiner) ^ 2 (<0.05) 0.1194 0.07169 0.003243 -0.01869 0.1192 0.2652

Mean of random intercepts 5.633 0.2777 0.009511 5.107 5.626 6.193

Figure B2. An independent test of observed versus predicted lnNPP (gm m-2 yr-1) using subsets of vegetation plots from the GB Countryside Survey in 1998. Observed lnNPP in the model training data was compared with lnNPP predicted from the empirical model and applied to all plots from the

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survey that were referable to the same ecosystem types as those represented by the experimental study systems. Each point represents the mean +/- 1 se.

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Table B2. Datasets used to model ANPP in terms of plant species composition. UK Biodiversity Action Plan habitat names were taken from the online list (UK_list_of_Priority_Habitats_and_Species_for_download_v1.4 2010 2505.xls) at http://jncc.defra.gov.uk/page-5436. Dominant plant species are either bryophytes (b), trees (t), grasses (g), sedges (s), dwarf shrub (ds) or forbs (f).

Experimental site

Number of paired ANPP and SLA measurements

UK Biodiversity Action Plan Broad (B) or Priority (P) Habitat type

FAO soil group

Mean soil pH (+/se) Dominant plant species Management regime

Newborough 9 Neutral grassland (B) Cambisol 7.67 (0.05)

Agrostis capillaris L. (g)Arrhenatherum elatius (g)Hylocomium splendens (Hedw.) Schimp. (b)

Low intensity rabbit and cattle grazing.

Pontbren 8 Improved grassland (B)

Gleysol & Cambisol

5.46(0.09)

Holcus lanatus L. (g)Agrostis stolonifera L. (g)Lolium perenne L. (g)

High intensity sheep grazing.

Pwllpeiran 7 Acid grassland (B) Podzol 4.06(0.02)

Nardus stricta L. (g)Carex binervis Sm. (s)Vaccinium myrtillus L. (ds)

Moderate intensity sheep grazing.

Ribble 6 Coastal and Floodplain Grazing Marsh (P)

Fluvisol 6.35 (0.17)

Festuca rubra L. (g)Elytrigia repens (L.) Desv. (g)Agrostis stolonifera (g)

Ungrazed or very low intensity cattle grazing.

Moorhouse 5 Blanket bog (P) Histosol 4.17 (0.01)

Calluna vulgaris (L.) Hull (ds)Empetrum nigrum L. (ds)Eriophorum vaginatum L. (s)

Very low intensity sheep grazing.

Snowdonia 1 Upland Calcareous grassland (P)

Leptosol 6.80 (0.78)

Agrostis capillaris (g)Festuca ovina L. (g)Rhytidiadelphus

Low intensity sheep grazing

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Experimental site

Number of paired ANPP and SLA measurements

UK Biodiversity Action Plan Broad (B) or Priority (P) Habitat type

FAO soil group

Mean soil pH (+/se) Dominant plant species Management regime

squarrosus (Hedw.) Warnst. (b)

Sharp’s A 6 Improved grassland (B)

Gleysol 6.00(0.07)

Lolium perenne (g) Intensive grass silage production and cattle grazing

Sharp’s B 6 Improved grassland (B)

Gleysol 6.30(0.25)

Lolium perenne (g)Poa trivialis (g)

Intensive grass silage production and cattle grazing

Eades Meadow 5 Lowland Hay Meadow (P)

Cambisol 6.23 (0.60)

Agrostis capillaris (g)Cynosurus cristatus L. (g)Rhinanthus minor L. (f)

Low intensity Spring and Autumn cattle grazing and Summer hay cut

Piper’s Hole 6 Upland Hay Meadow (P)

Cambisol 5.87 (0.15)

Lolium perenne (g)Geranium sylvaticum L. (f)Sanguisorba officinalis L. (f)

Low intensity Spring and Autumn sheep and cattle grazing and Summer hay cut

Deeside 19 Upland Birchwoods (P)

Podzol 4.21 (0.98)

Betula pendula Roth (t)Deschampsia flexuosa (L.) Trin. (g)Rhytidiadelphus triquetrus (Hedw.) Warnst. (b)

Ten sites ungrazed. The remainder lightly to moderately cattle and sheep grazed.

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