Mapping hotspots of multiple landscape functions: a case study on farmland afforestation in Scotland

Abstract

Many conservation and restoration efforts in developed countries are increasingly based on the premise of recognising and stimulating more ‘multi-functionality’ in agricultural landscapes. Public policy making is often a pragmatic process that involves efforts to negotiate trade-offs between the potentially conflicting demands of various stakeholders. Conservationists’ efforts to influence policy making, can therefore benefit from any tool that will help them to identify other socio-economic functions or values that coincide with good ecological conservation options. Various types of socio-economic objectives have in recent years been mapped across landscapes and so there are now important opportunities to explore the spatial heterogeneity of these diverse functions across the wider landscape in search of potential spatial synergies, i.e. ‘multiple win locations’ or multifunctional ‘hotspots’.

This paper explores the potential occurrence of such synergies within the agricultural landscape of northeast Scotland and evaluates an existing woodland planting policy using and combining three different policy objectives. Our results show that there are indeed broad areas of the studied landscape where multiple objectives (biodiversity, visual amenity and on-site recreation potential) could be achieved simultaneously (hotspots), and that the case study which we evaluate (the Farm Woodland Premium Scheme) could be much better spatially targeted with regards to each individual objective as well as with regards to these hotspots of multifunctionality.

Appendix 1

Modelling day visits to forests

In this work we used the number of potential visitors as an index of provision of recreation to create the “recreation” criterion map. We treated each 500 m cell in our map as an area of interest for which the number of potential visitors were predicted using the methods outlined below.

We derived the recreation map following Hill et al. (2003), who based their approach on Brainard et al. (2001). These authors used site-specific characteristics, human population availability, and the location of existing woodlands to predict the number of visitors to forest sites in England.

Hill et al. managed to explain 50% of the variation in number of visitors using:

• 1. The population within 90 minutes of travel time to a given location (Pop90)

• 2. An index of substitute availability i.e. the number for woodlands larger than 10 ha within 2 h of travel time for a given location (SLRG120).

• 3. An index summarising availability of facilities for visitors, such as walking paths, toilets etc.

The Hill et al. (2003) equation used in this work is:

$$ {\text{Log(No}}{\text{. of Visitors)}}{\text{ = }}{\text{0}}{\text{.247}} \times {\text{log(Pop90)}}{\text{+}}{\text{1}}{\text{.539}}\times {\text{log(SLRG120)}}{\text{+}}{\text{0}}{\text{.735}} \times {\text{log(Facilities)}} $$

The travel times are those giving the best-fit model among a number of different values they investigated.

We assumed that the locations would have no facilities. For the other two variables we used population census data (Census Output Areas for 2001) and existing road maps (with different average speed of travel attributed to different road types) to create maps of population availability and woodland maps to create maps of substitute woodland availability, using each 500 m cell as the location of interest.