Ecological Archives A021-067-A1

Atte Moilanen, Barbara J. Anderson, Felix Eigenbrod, Andreas Heinemeyer, David B. Roy, Simon Gillings, Paul R. Armsworth, Kevin J. Gaston, and Chris D. Thomas. 2011. Balancing alternative land uses in conservation prioritization. Ecological Applications 21:1419–1426.

Appendix A. Creation of the primary data.

This appendix closely follows methods first described in Eigenbrod et al. (2009) and Anderson et al. (2009).

(i) Biodiversity of taxa of conservation concern

For this layer we included all non-marine feeding birds, terrestrial mammals, herptiles (amphibians and reptiles), vascular plants, bryophytes and butterflies which are considered to be “species’ of conservation concern” for the UK. That is we used the UK Biodiversity Action Plan (UK BAP) list species ( as these species have statutory protection and as such, they represent the element of biodiversity for which conservation targets are in place; and because of their priority status, distribution data are likely to be more complete. We are concerned with associations between biodiversity and terrestrial ecosystem services, therefore marine species and fish were excluded, as were birds whose main feeding resources are marine. Limited distribution data prevented us from including fungi, and other non-vascular plants and terrestrial invertebrates.

For bryophytes, butterflies, vascular plants, mammals and herptiles, we used all distribution records held by the Biological Records Centre, Centre for Ecology & Hydrology as of Jan 2008. Where records were at finer resolution than the 2 km × 2 km tetrads used throughout this study the record was centred on the tetrad. All duplicate (same species and tetrad) records were then removed. Records only available at coarser resolution were not used.

For the bird data we used the tetrad data on which the 1981–1991 New Atlas of breeding birds in Britain and Ireland is based (Gibbons et al. 1993). Where possible at least 8 tetrads were visited in each 10 km × 10 km BNG square. Each tetrad was visited for one hour, in April-May and again in June-July, although in some remote upland areas a single two hour visit was made. These data were then used to model the probability of occurrence for all tetrads, including those not surveyed. Generalized Additive Models were built (see Franco et al. 2009 for more detail) using the 1990 Land Cover Map data (Total amount of each of 13 classes in each tetrad; original resolution 25 m; Fuller, Groom & Jones 1994) plus altitude and geographical location (x and y coordinates, Ordnance Survey grid reference).

(ii) Carbon storage

The carbon storage layer is an estimate of combined organic soil and above ground vegetation carbon (in kg C) calculated at the 1 km × 1 km grid resolution. We obtained vegetation carbon data at the 1 km × 1 km grid resolution from the Centre for Ecology & Hydrology (Milne and Brown 1997). Soil parameter, land use and soil series data was obtained from the National Soil Resources Institute (NSRI) for the top 1 m of soil (to bedrock or 1 m depth, whichever was less) which enabled us to calculate soil carbon density at the 1 km × 1 km grid resolution in two steps. First, we calculated the soil organic carbon density values for each of the 977 soil series in Britain based on their percent soil organic carbon, bulk density and stoniness. Secondly, we calculated the average soil organic carbon density per 1 km × 1 km grid cell based on this soil series and land use data. The latter calculation was done as a weighted average based on the five dominant land uses (Wood, Semi natural, Grassland, Arable, and Garden). Estimates for areas with no specified soil carbon content (e.g. towns, roads etc. or soil series with unknown carbon content) were obtained from the area weighted average of specified carbon densities of land use and soil series combinations within each grid cell. This may lead to a slight overestimation of soil carbon within built up areas and roads. However, as urban areas already have the lowest carbon levels in England in this layer, this potential bias will have very little effect on the results.

We then calculated the total carbon per 1 km × 1 km grid cell by first converting soil carbon density per m2 to total carbon per 1 km × 1 km grid cell, and then adding the soil organic carbon and vegetation carbon grids together. This grid was then spatially delineated using GIS to include only the land area of England as described earlier. Finally, we calculated the amount of carbon in each of the 2 × 2 km grid cells for which we had biodiversity data (above).

(iii) Agricultural production

We obtained detailed information on the land area covered by major crops and number of livestock for England from the June Agricultural Survey for England (DEFRA 2004), Wales (Welsh Assembly Government 2006), and Scotland (SEERAD 2006). The June Agricultural Survey is a randomly stratified survey (30% of farms in England) that is spatially explicit at the ward/local authority level. We obtained boundary layers for these areas from UKBorders ( We then calculated the agricultural land area of each ward (cropland plus pastures and any grassland, including rough grazing and calcareous grassland) based on the Land Cover Map 2000 (Fuller et al. 2002). We converted the area of a crop/number of livestock in the agricultural land of each ward into gross margins by multiplying them by gross margin per unit area (or per unit of livestock) as obtained from the Farm Management Handbook (FMH) 2007/2008 (Beaton et al. 2007) (Table A1). If more than one estimate of gross margin per unit area was given, we used the intermediate value or the average of the high and low value. The gross margin accounts for variable costs of production. We excluded subsidies from the gross margin per unit area by removing the decoupled single payment subsidy (‘all other output’ in the FMH) from the output based on whole farm data for either cereal, horticulture, dairy, lowland cattle and sheep or ‘less favoured areas’ (LFA) cattle and sheep farms.

We calculated separate gross margins for the lowlands and LFA areas for cows and sheep to account for the two estimates of gross margins per livestock unit present in the FMH. We clipped the agricultural census layer by a layer delineating least favoured areas obtained from If a ward contained both less favoured areas and lowlands, we divided the number of cattle and sheep between the less favoured areas and lowlands based on the percentage of the ward that was located in each area. We did not calculate gross margins for hay and other crops raised to feed livestock as we assumed these would be included as variable costs for livestock. We also did not include poultry or pigs in our estimates as both are largely produced in factory farms which are largely disconnected from inputs from the land on which they occur.

We then calculated the value of agriculture for of all 2 × 2 km grid cells for which we had recreation data based on the area weighted mean value (pounds/ha) of agricultural production of all the wards with which each 2 × 2 km cell overlapped.

(iv) Urban Area

This layer is the summed area of urban and suburban classes in each tetrad from the CEH landcover (LC) 2000 land cover map converted to a proportion of the total area of each cell, as some coastal cells are less than 4 km2.

TABLE A1. Summary of values used in calculating the gross margin of agriculture production (Eigenbrod et al. 2009).

Output Gross margin per unit Notes
Horticulture 8627 Includes fruit, vegetables, hardy stock, nursery, vineyards, flowers
Barley 205 Assume 50/50 split between spring and winter barley
Wheat 354 Assuming all winter wheat
Potatoes 1053 Maincrop ware
Oilseed rape 20.76 Assuming all winter rape. Biofuel subsidy removed.
Field beans -19.31 Protein crop subsidy removed
Peas for dry harvesting -125.31 Protein crop subsidy removed
Sugar beet 255  
Dairy 805.30 (£/cow)  
Ewe equivalent (LFA) 21.9 (£/ewe equivalent) 1 breeding cow = 7.5 ewe equivalent; 1 ewe = 1 ewe equivalent27
Ewe equivalent (lowland) 34.2 (£/ewe equivalent)  
Gross margins per unit area in £/ha unless otherwise indicated.


Anderson, B. J., P. R. Armsworth, F. Eigenbrod, C. D. Thomas, S. Gillings, A. Heinemeyer, D. B. Roy, and K. J. Gaston. 2009. Spatial covariance between biodiversity and other ecosystem service priorities. Journal of Applied Ecology 46:888-896.Beaton, C., Catto, J., Kerr, G. eds., 2007. The farm management handbook 2007/2008. Scottish Agricultural College, Edinburgh, UK.

DEFRA (2004) June Agricultural Survey. Department for Environment Food and Rural Affairs. Available: Accessed: March 2008.

Eigenbrod, F., B. J. Anderson, P. R. Armsworth, A. Heinemeyer, S. F. Jackson, M. Parnell, C. D. Thomas, and K.J. Gaston. 2009. Ecosystem service benefits of contrasting conservation strategies in a human-dominated region. Proceedings of the Royal Society, Series B 276:2903–2911.

Franco, A. M. A., Anderson, B. J., Roy, D. B., Gillings, S., Fox, R., Moilanen, A., Thomas, C. D. (2009) Surrogacy and persistence in reseve selection: landscape prioritization for multiple taxa in Britain Journal of Applied Ecology 46:82–91

Fuller, R. M., Groom, G. B. & Jones, A. R. (1994) The Land Cover Map of Great Britain: an automated classification of Landsat Thermatic Mapper data. Engineering & Remote Sensing, 60:553–562.

Fuller, R. M., Smith, G. M., Sanderson, J. M., Hill, R. A., Thomson, A. G., 2002. The UK Land Cover Map 2000: Construction of a parcel-based vector map from satellite images. Cartographic Journal 39:15–25.

Gibbons, D. W., Reid, J. B., Chapman, R. A., 1993. The New Atlas of Breeding Birds in Britain and Ireland: 1988–1991. Poyser, London.

Milne, R., Brown, T. A., 1997. Carbon in the vegetation and soils of Great Britain. Journal of Environmental Management 49:413–433.

SEERAD (2006) June Agricultural Survey. Scottish Executive Environment and Rural Affairs Department (SEERAD).

Welsh Assembly Government (2006) Agricultural Small Area Statistics. Available: Accessed: January 2008.

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