Measuring the economic, environmental and ecosystem services value of herita ge gardens,

heathland and woodland in the context of determining potential impacts from the regulated plant pathogens Phytophthora

ramorum and Phytopthora kernoviae

Prepared for: Defra

Prepared by: ADAS UK Ltd in conjunction with CJC Consulting, Fera, and the London School of Economics

Date: 17th June 2011

0936648 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Acknowledgements

The project team would like to thank the time, effort and support provided by the Defra economists, Phil Cryle, Adam Bell, Meredith Ward and internal reviewers as well as the team at Fera including Alan Inman, Keith Walters, Claire Sansford, and Judith Turner.

For assistance and provision of information to:

Bruce Rothnie, Jennifer Mcvey, Pat Snowdon, Olly Stephenson, Justin Gilbert, Shona Cameron and Mark Broadmeadow (Forestry Commission), Suzanne Perry and Keith Kirby (Natural England) and Ian Wright (National Trust).

Project team:

Glyn Jones, Ben Drake (ADAS) – lead authors and contact

Nigel Boatman, John Hughes, Kate Somerwill (Fera)

Bob Crabtree (CJC Consulting)

Susana Mourato (London School of Economics)

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Executive summary

Introduction The main aim of this research is to provide estimates of the total value to society of heritage gardens, heathland and woodland which may be under threat from impacts from Phytophthora ramorum (P. ramorum) and Phytophthora kernoviae (P. kernoviae) . The estimates provide an update to the figures used in the 2008 Impact Assessment.

Background P. ramorum and P. kernoviae are exotic plant pathogens that have only recently been described in the last decade. P. ramorum was first confirmed in Great Britain in 2002, whilst P. kernoviae was first discovered here in 2003. Both species represent a threat to ornamental and wild shrubs and trees. Ornamental plants in heritage gardens can be seriously affected, as can susceptible trees in woodlands where leaf hosts (principally Rhododendron ponticum and more recently Japanese larch) occur and drive epidemics; heathland species, especially Vaccinium species are also considered at high risk. P. ramorum is present in various parts of Europe and is also reported as an introduced exotic plant pathogen in the USA; it is currently subject to EC emergency phytosanitary measures, which are under review. Following an EU-wide Pest Risk Analysis (PRA) the EC Standing Committee for Plant Health (SCPH) have provisionally agreed that P. ramorum should become listed as a harmful organism within the EC Plant Health Directive (2000/29/EC) with phytosanitary measures yet to be defined and agreed. P. kernoviae is recorded in Great Britain and in Eire, as well as in New Zealand. A UK PRA for P. kernoviae was also reviewed by the EC SCPH and there has been provisional agreement that P. kernoviae should be subject to emergency EU phytosanitary measures, again yet to be defined and agreed.

There is a Phytophthora control programme that began in April 2009 with funding of £23.5 million over five years (this included some £4 million of existing funding for work on Phytophthora). The principal aim is the combat two pathogens to reduce pathogen inoculum to epidemiologically insignificant levels by removing sporulating host plants from high risk areas. This will reduce the risk of significant tree death and significant impact on heathlands within England and Wales. In August 2009 it was established that P. ramorum had begun to cause the death of a previously unknown host, Japanese larch trees. Since infected larch produces more inoculum than R. ponticum , the programme has redirected funding from clearance of infected R. ponticum to clearance of certain infected larch and additional aerial surveillance 1.

A number of projects have been funded by Defra which have investigated the epidemiology of outbreaks caused by P. ramorum and P. kernoviae in England (PHO194, PHO195, PHO414, PHO308). These projects have established methodologies for the investigation of key aspects of the epidemiology and developed new quantitative diagnostic approaches for investigating persistence and dispersal. Studies to date have provided valuable epidemiological data, which have contributed to the development of pest risk analyses and pest eradication and containment strategies. Current work on developing disease management approaches P. ramorum and P. kernoviae on Vaccinium and other heathland species includes work on biodiversity impacts of Phytophthora, assuming different degrees of spread and host destruction, and the likely impact on ecosystem services.

1 The cost of larch clearance is met by the forest owner

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

The impacts of invasive alien species like P. ramorum and P. kernoviae are multifaceted. They can have consequences for the economy and human welfare directly (e.g. by damaging nursery stock) or indirectly (e.g. by impairing ecosystem services). Changes induced by invasive alien species can include the wholesale loss or alteration of goods (e.g. horticulture and forest products) and services (e.g. climate stabilisation and recreation).

Terms of reference The aim was to produce an estimate of the total economic value at risk (TEV at risk ) from the diseases in the absence of continued control. This is not a marginal valuation of the value at risk, but is rather a valuation of the value at risk from a cessation of disease controls. The TEV estimate is composed of use and non-use value, with the former representing the value obtained by the public from direct enjoyment of heritage gardens, heathland and woodland, while the latter represents the value obtained by the public from knowing these habitats remain unchanged simply to have the option of future use, or for altruistic or bequest motives.

This aim has given rise to a number of methodological issues. Normally the counterfactual to a scenario of no control would be the expected spread given the current controls in place. The scenarios are presented in ES Figure 1. Starting from the current period, the blue line shows a scenario whereby the diseases are contained to their current level of spread and the red line the scenario of spread with no controls. The dashed lines present potential spread with controls (these lines could be decreasing if the control is successful beyond containment).

Spread with no control

Possible spread Damage with control

Current spread: “contained” in future

Time

ES Figure 1: TEV at risk However, given the inherent uncertainty involved in modelling the spread of these diseases it was decided by the project Steering Group that using a counterfactual with current controls would present two uncertain points for the valuation and that the project should instead seek to estimate the value as represented by the difference between the blue and red lines.

Two distinct valuation exercises were undertaken to identify TEV at risk . The first undertook an assessment of the public’s willingness-to-visit (WTV) affected habitats

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

under a scenario of uncontrolled spread and their willingness to pay (WTP) to preserve habitat as described by the current spread situation. The second exercise considered the impact on the commercial forestry sector and incorporated both timber values and restocking effects (private costs) as well as carbon values (social costs).

Method In order to estimate the TEV at risk it was necessary to first estimate the total area of the habitats that were at risk from uncontrolled spread. Spread maps were created by incorporating host maps for four susceptible species into a Metapopulation Epidemic Model (MPEM) developed by Cambridge University that is being funded separately as part of the Phytophthora programme. The spread maps and underlying data were used in both aspects of the valuation exercise.

The spread maps were incorporated in a contingent valuation survey that included questions relating to WTV as well as WTP. The survey followed on from similar survey work undertaken for the Rural Economy and Land Use (RELU) project Memory and Prediction in Tree Disease Control. Unpublished results from this work suggested that changes in WTV would be minimal and that a survey should incorporate WTP questions. This is because basing estimates on changed WTV would underestimate the public values relating to the diseases and habitats by excluding the effect of the change in visit experience. To capture this, the survey included a number of photographs showing uninfected and infected plant species as well as maps showing the locations of the habitats. Respondents numbered a little below 1,000 and the survey was conducted online. This produced estimates of the publics’ WTP to prevent further spread and showed how visitation rates may or may not change at regional and national scales.

The potential area of larch at risk from the diseases was used to estimate the timber and carbon values at risk using a discounted cash flow investment appraisal model.

Results i) WTV and WTP As expected, the majority of responses were for visitation to remain unchanged. ES Figure 2 shows that there would be a net reduction in visitor rates and clearly this would be expected to have some impact on those sites with entrance fees. The net reduction is greater for national sites (those outside the respondents region). It may be that remedial changes to an infected site over time would negate even the small net reduction in visits. What is important though is that the change in visitor rates implied by the survey omits the effect of reduced enjoyment from continued visits to affected habitats.

Heritage Gardens Heathland Woodland

Change in Regional National Regional National Regional National Visitation More 105 65 60 47 65 44 The Same 620 654 681 690 692 700 Less 202 208 186 190 170 183

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

ES Figure 2: Change in willingness to visit ES Figure 3 shows a summary of the estimated WTP to prevent the spread of the diseases under conditions of no control. The WTP responses were per person and these are scaled up to the adult population of England and Wales.

Net Mean Aggregated Mean Habitat WTP (Per WTP (Per Year) Year) Regional Heritage £7.24 £313m Gardens National Heritage £6.14 £265m Gardens

Regional Heathland £4.86 £210m

National Heathland £4.08 £176m

Regional Woodland £6.12 £264m

National Woodland £5.04 £218m

ES Figure 3: Adjusted WTP and aggregated values Clearly these are large numbers due to scaling up to the whole population. The WTP questions were split between the respondents region and the rest of the country to reflect that values might be expected to fall off with distance and the potential for substitution. The wide spread of the habitats (as shown to the respondents via maps) shows that there could be plenty substitution possibilities assuming the diseases are not locally very widespread. The survey asked six WTP questions, made it clear to respondents that they were additional, and allowed respondents to change their WTP responses. Whilst this suggests that the figures should be additive it would be wise to treat such figures with caution given the potential for substitution and that such habitats might be expected to adapt/regenerate over time (naturally as well as through human assistance), thus restoring value to the public.

The information presented is the best available at this time and was presented with a degree of certainty. However, evidence suggests that if stated preference surveys illustrate levels of uncertainty, subsequent welfare estimates can be much reduced e.g. Glenk & Colombo (2011). This would argue for reduced values. Conversely though, the uncertainty with respect to these particular diseases also suggests that we are unaware of species that could become affected and are therefore not incorporated here. If this analysis had been conducted just two years ago, Japanese larch would have been excluded. ii) Timber and carbon The forestry analysis is complicated as the disease can affect stands at varying age classes leading to different economic outcomes. The economic impacts of infection also vary depending on whether stands are managed for amenity (non-rotational) or for ultimate clear-felling. The application of DECC carbon values further complicates the analysis and in particular cases gives rise to the peculiar result of the disease providing a carbon benefit to society. The timing of carbon sequestration and losses in the discounted cashflow accounting process allocates a higher carbon price to carbon emissions further into the future. However, it should be noted that a continuing

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

rotational system results in a net neutral carbon position in physical terms. ES Figure 4 includes timber, production costs and carbon values based on the average present costs of infection across age stands. The impact of the accounting method shows through clearly in the DECC central carbon price whereby the infection appears to provide benefits to society. This reflects the benefits from sequestration of higher priced carbon in crops replanted after infection.

DECC central carbon price DECC low carbon price

in 50 year in 50 year Region Non-rotational non-rotational rotation rotation North West 1.01 -3.71 -5.77 -6.83 West Midlands 0.05 -0.19 -0.29 -0.34 South East 0.54 -2.01 -3.11 -3.69 South West 2.17 -8.01 -12.43 -14.72 Wales 7.05 -26.00 -40.36 -47.79

Total England and Wales 10.83 -39.92 -61.97 -71.36

ES Figure 4: NPV of predicted infection to 2031 in regions affected by the disease (£m)

The non-rotational impacts refer to crops under a non-rotational system in which uninfected crops would not have been harvested. Losses from infection are higher under non-rotational systems because of carbon release from infected trees that, in the absence of the disease, would not have occurred.

Caveats and limitations The above alludes to a number of caveats and limitations that apply to the results:

1. Spread analysis : given the inherent uncertainty in the pathology of the diseases the estimated spread used for the valuation exercise represent just one of a large number of potential outcomes. Different outcomes would have a clear effect on the direct values affected such as timber production but the effect on WTP values is less clear.

2. Species at risk : there is uncertainty in the pathology of the diseases and the host list is continually changing. Initially Japanese larch was viewed as a low risk species but is now seen to be severely affected by the disease as well as being a major sporulating host.

3. TEV at risk with no control : as mentioned in section 4.1.1, the baseline of no further spread from the current level is somewhat unrealistic. Thus the WTP may be overstated since some spread will occur from the current level even with controls in place.

4. Commercial timber analysis : whilst the WTP exercise assumes no further controls (in the public realm), the analysis of commercial timber production assumes that when discovered, forest owners will control by clearfelling and restocking with less susceptible species.

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

5. Carbon analysis and carbon values : the accounting style analysis results in the unusual conclusion that spread can be optimal due to the assumed timing of carbon release and the step rate of increase in the DECC carbon prices. However, the net carbon change is almost zero.

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Contents Executive summary ...... 2

1. Introduction ...... 10

2. Methodology Overview ...... 11

3. Literature review ...... 11

3.1 Literature Search Protocols ...... 11 3.2 Spread and Impacts of P. ramorum and P. kernoviae ...... 12 3.3 Ecosystem Services ...... 13 3.4 Valuation Literature Discussion ...... 22 3.5 Selection of Valuation Techniques for Heritage Gardens, Heathland and Woodland ...... 24 4. TEV at Risk ...... 26

4.1 The Public’s Willingness to Pay to Prevent Further Spread ...... 26 4.2 WTV and WTP to Protect Heritage Gardens, Heathland and Woodland at Risk ...... 35 4.3 Impact on Commercial Timber Production and Carbon ...... 43 4.4 Carbon ...... 49 4.5 Net Carbon Effects of Early Harvesting of Infected Woodland ...... 50 4.6 Alternative Non-rotational Scenario ...... 52 4.7 Total Impacts of Infection on Cost, Timber and Carbon Values ...... 53 4.8 Aggregate Impacts ...... 55 4.9 TEV of Heritage Gardens, Heathland and Woodland at Risk ...... 56 5. Discussion ...... 56

6. References ...... 58

Appendices ...... 63

Figures and Tables

Figure 1: Demand curve for habitat at risk ...... 25 Figure 2: TEV at risk without control ...... 27 Figure 3: a) current spread (scenario 3) b) 20 year spread without control (scenario 2) ...... 29 Figure 4: Pilot survey WTP to protect regional habitats ...... 31 Figure 5: Pilot survey WTP to protect national habitats ...... 31 Figure 6: Distribution of ages across the sample ...... 34 Figure 7: Extra distance willing to travel if local habitat infected...... 36

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Table 1: Heathland valuation studies ...... 19 Table 2: Pilot survey results - regional habitats ...... 32 Table 3: Pilot survey results - national habitats ...... 32 Table 4: Target and actual stratification of key characteristics ...... 33 Table 5: Non-stratified characteristics ...... 35 Table 6: Change in willingness to visit ...... 36 Table 7: Mean WTP to protect habitats (with confidence intervals) ...... 37 Table 8: Mean WTP by region and habitat ...... 38 Table 9: Influence of socio-demographic characteristics on WTP ...... 40 Table 10: Mean WTP adjusted by environmental membership ...... 41 Table 11: Area of larch (all species) in England and Wales ...... 44 Table 12: Predicted areas of larch at risk under the medium risk scenario after 10 and 20 years (ha) ...... 45 Table 13: Net present cost of infection in a 30 year-old larch stand (£ per ha, timber only) ...... 48 Table 14: Net present value of infection in larch (timber and production costs) ...... 49

Table 15: Net carbon effects of infection in a 30 year old larch stand (t CO 2 per ha) ...... 50 Table 16: Net present cost of infection in a 30 year old larch stand (£ per ha, carbon only) ...... 52 Table 17: Net present value of infection in larch (carbon) ...... 53 Table 18: Net present value of infection in larch (timber, production costs and carbon) ...... 53 Table 19: Present value of predicted infection to 2031 (£m) (excluding unaffected regions) ...... 55 Table 20: TEV of heritage gardens, heathland and woodland at risk to 2031 ...... 56

Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

1. Introduction Phytophthora ramorum (P. ramorum) and Phytophthora kernoviae (P. kernoviae) are non-native diseases which have recently been introduced in the UK. Both diseases are shown to produce similar effects upon certain tree and plant species within the UK. Such effects include leaf and plant dieback, bleeding cankers and in some cases tree mortality. The UK government has a monitoring and containment programme in place to help isolate new outbreaks once identified. Despite these efforts, P. ramorum and P. kernoviae has continued to spread, especially in the south-western regions of the UK.

Nationally P. ramorum and P. kernoviae have yet to enter the exponential growth phase of the epidemic growth curve (Defra, 2008). Therefore this presents an opportunity to evaluate policy options concerning P. ramorum and P. kernoviae containment efforts while changes in policy direction could still have a significant impact on the spread of these diseases. Specifically this study aims to establish the public value in England and Wales of halting the spread of P. ramorum and P. kernoviae .

Section 2 of the report provides an overview of the methodology employed with further detail provided in the relevant sections. Section 3 provides a brief literature review of the history of P. ramorum and P. kernoviae , the tree and plant species susceptible to infection and future expectations concerning the spread of these diseases. Section 3 also provides a review of the ecosystem services generated by heritage gardens, heathland and woodland alongside the popularity of such habitats and the rationale for adopting a contingent valuation approach to measure the total economic value ( TEV ) at risk. Section 4 briefly describes the survey used to collect data regarding the public’s willingness to pay (WTP) for disease controls, the creation of P. ramorum and P. kernoviae spread maps and the total TEV at risk from P. ramorum and P. kernoviae spread. Section 5 looks at the TEV at risk from P. ramorum spread in commercial larch plantations by considering the future price of carbon alongside amongst other factors in the event of an outbreak of P. ramorum . Section 6 provides a summary of the caveats and limitations.

10 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

2. Methodology Overview The costs that P. ramorum and P. kernoviae impose on society are varied and encompass public values (environmental and recreational) and private values (commercial timber production). As such the TEV at risk from ceasing measures to control the spread of the diseases in England and Wales was estimated using two broad techniques to cover the range of impacts. The first looked at the public willingness to visit (WTV) and their WTP under the case of future spread without control. The second examined the impact of the same future spread on the commercial forestry sector in terms of reduced timber production and carbon values.

As mentioned, both analyses needed an estimate of the future spread of the diseases without the current control measures. Spread maps were created by incorporating host maps for four susceptible species into a Metapopulation Epidemic Model (MPEM) developed by Cambridge University. The MPEM produced a hazard map and a probability of spread map, which when combined illustrates to survey respondents the projected spread of P. ramorum and P. kernoviae over a 20 year period based on HM Treasury Green Book Guidance.

The spread maps were used in a contingent valuation (CV) survey that was conducted to measure the public’s WTP (and changes in WTV) for control measures aimed at stopping the spread of these diseases from their current level of spread. The selection of CV as a valuation technique is justified in Chapter 3.5 following a review in Chapter 3.4 of the types of value to be measured and the range of valuation techniques available. The data were analysed using the statistical software package Stata which revealed the TEV at risk from further spread of P. ramorum and P. kernoviae into heritage gardens and heathland. The same analysis revealed the non-use and some use values at risk from the spread of these diseases into woodland.

A discounted cash flow (DCF) investment appraisal analysis was used to identify the impact of P. ramorum spread on commercial stands of larch, which constitute use values at risk associated with woodland. This incorporated a range of facets including the future price of carbon, the cost of felling, the revenue raised from diseased and uninfected wood and the timing of carbon sequestration and release. Changes in the NPV (timber only) between infected and uninfected larch stands are indicative of the use values at risk if P. ramorum spread into English and Welsh woodland. The wider social impact of the diseases in forestry is incorporated through the inclusion of DECC carbon values. 3. Literature review

3.1 Literature Search Protocols 2 One set of search protocols were developed to investigate the direct physical impact of P. ramorum and P. kernoviae on heritage gardens, heathland and woodland. Another set of search protocols were developed to establish the various ecosystem services derived from these three habitat types. A final set

2 The search threads and criteria used for identifying appropriate literature for review are contained within Appendix 1.

11 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

of search protocols were developed to try and investigate the popularity of heritage gardens, heathland and woodland, the reasons why people visit such sites and to uncover any relevant environmental valuation literature. 3.2 Spread and Impacts of P. ramorum and P. kernoviae P. ramorum was first discovered in the mid 1990’s infecting oak forests along the Californian coastline (Rizzo and Garbelotto, 2001). In Europe P. ramorum was first discovered infecting rhododendron and viburnum in gardens and nurseries (Werres and Marwitz, 1997; Werres et al., 2001). Within the UK, P. ramorum was first discovered at a nursery in February 2002 (Lane, 2003), while P. kernoviae was first discovered in October 2003 during inspections for P. ramorum (CABI, 2008). Since these discoveries, outside of nurseries there have been 261 outbreaks of P. ramorum and 69 outbreaks of P. kernoviae up to June 2009 (Tomlinson et al., 2009). Of these outbreaks, only 85 P. ramorum and 1 P. kernoviae outbreaks have been eradicated (Tomlinson et al., 2009). Eradication of outbreaks within nurseries and garden centres has proved more successful with an eradication rate of 80-84% (Tomlinson et al., 2009). To date (December 2008) most P. ramorum and P. kernoviae infections have occurred in the south-western regions of the UK as these regions offer the most accommodative climate to these diseases in terms of warmth and moisture (Walters et al., 2009).

In broad terms (excluding nurseries) the diseases present a threat to three “habitats” – heritage gardens, heathland and woodland. While P. ramorum is commonly known as ‘sudden oak disease’ in the US, oak tree species in the UK are not as susceptible to P. ramorum , or indeed P. kernoviae . However, species such as beech, larch, rhododendron and bilberry are susceptible. With respect to heritage gardens, it is envisaged that if left unmanaged both diseases will infect all susceptible gardens within 20 years (Wright and Slawson, 2010). Despite a lack of estimates over the potential damage to heathland from P. ramorum and P. kernoviae spread, it is worth bearing in mind that Cornwall has approximately 11,000 hectares of heathland within 10km of currently infected sites (Walters et al., 2009).

The full host list for P. ramorum and P. kernoviae (Fera, 2010c; Fera 2010d) is still uncertain at this point in time and there is a great deal of uncertainty regarding the spread dynamics of these diseases. However, the following species are thought to be the most susceptible to P. ramorum and P. kernoviae in the UK (P. Jennings, pers. comm.):

• Vaccinium myrtillus (bilberry) : Present in both heathland and woodland habitats and susceptible to both Phytophthora diseases, although most commonly infected by P. kernoviae . Both diseases cause rapid defoliation and can lead to plant death. Bilberry is also seen as an important sporulating host for P. kernoviae due to its wide distribution across both heathland and woodland (CSL, 2006). • Rhododendron : Susceptibility varies among rhododendron species. The most common species, R. ponticum, is highly susceptible. Infection by P. ramorum tends to start at the bottom of the plant, causing wilting and defoliation and eventual plant death. P. kernoviae infection tends to start at the top of the plant, causing more visible symptoms of wilting and defoliation, and also leading to plant death. Rhododendron is the main sporulating host for P. ramorum and P. kernoviae , with all infected trees found to be in close proximity to infected rhododendron (Fera, 2010a).

12 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

• Larch: Susceptible only to P. ramorum , which causes rapid tree death. Larch was initially considered to be a low risk species but Japanese larch is now considered a major sporulating host and a risk to spread. The infection of large numbers of Japanese larch in the South West of England is the first time globally that P. ramorum has sporulated and reproduced on a large number of a commercial tree species (Forestry Commission, 2011c). • Magnolia : Susceptibility varies among magnolia species and varieties. Magnolia is generally more susceptible to P. kernoviae , which causes stem die-back and leaf lesions. Flowering is also affected. It is unclear whether this will lead to plant death at present. • Pieris : Susceptible to both diseases, which cause die-back of shoot tips and may affect flowering. Plants have been shown to recover if inoculum pressure is removed. • Viburnum : More susceptible to P. ramorum than P. kernoviae . Flowering is affected and plant death is likely, although plants are often removed before this occurs. • Camellia : Low susceptibility to both diseases. Infection causes stem die- back and leaf lesions. Flowering is generally unaffected and plant death is unlikely.

‘Minor’ hosts in the list above could be more susceptible to damage or more likely to spread the disease to other plants than is currently anticipated. For example, horse chestnut, sweet chestnut, beech and oak have all shown susceptibility to and damage from both Phytophthora diseases, while ash, silver birch and sycamore are known hosts of P. ramorum (Fera, 2010b). Therefore, this study examines the potential impact on ecosystem services of infection of known hosts with different levels of susceptibility. As noted above, Japanese larch was considered a species less likely to be at risk. That it is now seen to be a major sporulating host clearly demonstrates to complexity of the diseases and the level of uncertainty. This also flows into the valuation work in that this report only includes those species currently known to be at risk. 3.3 Ecosystem Services Ecosystem services can be summarized as the goods and services provided by the natural environment that bring a direct benefit to humans (POST 2007). The Millennium Ecosystem Assessment 3 (2005) describes four main categories of ecosystem services: 1. Provisioning services: Includes food, fibre, and fuel. 2. Regulating services: Includes air quality, climate, water regulation and pollination. 3. Cultural services: Includes recreational/tourism values, spiritual and religious values, education, cultural heritage and artistic inspiration. 4. Supporting services: Includes soil formation, photosynthesis and nutrient cycling.

3 There are many overlaps among the services listed in the Millennium Ecosystem Assessment and various distinct services may be provided by the same or related ecological processes.

13 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

The ecosystem services approach can be used to measure the economic value of various ecosystems, with Barbier (2001) providing the following ways of measuring the value of ecosystem services 4.

1. Goods : The value of the service can be taken as the value of the products obtained from the ecosystem. For example, a managed forest can supply timber. The value of this timber service would, therefore, be the profit that could be obtained from the sale of the timber. 2. Services: The value of the service is taken as the cost of replacing or losing the service. There are two ways of measuring this, each of which relies on hypothetical scenarios. i. Replacement cost : The value is the cost of replacing a given ecosystem service with another service of equivalent function. For example, the value of a water purification function would be the cost of running a water purification plant. However, replacing an existing “free” service with one requiring capital investment and running costs will inevitably affect demand for that service, so the cost may be overestimated. ii. Expected damage : The value is the cost of the damage that would occur if the service were removed. For example, the value of a storm protection service would be counted as the repair cost following a typical storm in the absence of that service. 3. Cultural benefits : These include aesthetic, recreational, historical, artistic, educational, and other cultural benefits. The values of these services are entirely subjective and can only be estimated on a case-by-case basis by experts and public consultation. In many cases, the service may be unique and irreplaceable.

Assessments of ecosystem services often compare distinct management options to determine which provides the greatest overall economic benefit (Defra, 2007). Quantitative analyses usually focus on a single service or a limited range of services of particular importance to the region or local population, although this form of ecosystem services assessment is beyond the scope of this report. This section aims to qualitatively identify the complete range of ecosystem services provided by heritage gardens, heathland and woodland in the UK and to describe, in broad terms, the likely impact of the loss of those services. An awareness of these services will inform future research into Phytophthora disease control options, which may in turn take a more quantitative approach to measuring the impact of disease management on such ecosystem services.

3.3.1 Heritage Garden Ecosystem Services Heritage gardens represent a wide variety of habitats, varying considerably in size and species composition. Intensive management and the central importance of

4 The methods are not mutually exclusive and a single service could provide more than one type of value under this scheme.

14 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae appearance mean that heritage gardens provide few provisioning, regulating, or supporting services. In some cases, heritage gardens could have negative effects on ecosystem services. For example, high rates of soil and vegetation turnover and the removal of litter and biomass reduce the carbon sequestration potential of gardens relative to unmanaged habitats. Furthermore, the use of fertilizers and pesticides could impede water purification. However, heritage gardens have considerable cultural value. Their management may help preserve a working knowledge of traditional and historical horticultural and agricultural techniques. They also provide an educational resource in the wide range of species represented and in the way plants are made accessible to visitors. Heritage gardens also attract visitors and so recreation and tourism are their most economically important ecosystem services. The impact of Phytophthora species on recreation and tourism in heritage gardens will depend on local conditions, the effectiveness of management, and the number of species affected. The immediate visual impact of infection will have a detrimental effect on the aesthetic appeal of gardens. However, if the range of species affected is restricted to the current host range, and management practices identify, remove, and replace infected plants, then infection may not be noticed by visitors. In this case, the cost of infection may not be much higher than the cost of additional management. However, if more species become infected or particularly important, and mature plants are infected, the impact will be less easy to overcome. Any restriction on the movement of visitors to control the disease or any large-scale removal of vegetation will also detract from the experience of tourists. Therefore, the impact on cultural services could vary widely between different gardens. The definition of a heritage garden is rather opaque, although the name does imply historical significance alongside a display of ornamental shrubs and trees. Literature relating to historic gardens and gardens is also discussed alongside notions of heritage. Gardens provide a sizable benefit to the UK economy with 24 million visitors to UK gardens every year, accounting for an estimated £300m annually (Bisgrove and Hadley, 2002). The UK is dependent upon heritage to bolster the domestic tourist industry, whereas factors such as sun, sea and sand are seen as important for tourist industries overseas (Nurick, 2002). In fact 30% of overseas tourists cite heritage above all other reasons for visiting the UK (Anholt- GMI, 2007). Indeed seven out of National Trust’s top ten visitor attractions are gardens (Ballard, 2004) with 57% of people joining the National Trust doing so because of the gardens (Bisgrove and Hadley, 2002).

The UK heritage tourism sector saw growth during 2009 despite the poor economic climate. Visitor numbers to English Heritage properties increased by 17% in the summer of 2009 compared with a year earlier, while National Trust visitor numbers increased by almost one fifth in the same year relative to 2008 (Heritage Lottery Fund, 2010). This outcome has been credited to a greater proportion of people opting for so called ‘staycations’ over holidaying abroad (Heritage Lottery fund, 2010), a plausible conclusion given the recession of 2008- 09 and the fall in the value of the British Pound. The greying of the UK population is a long term factor which is thought to underpin visitor numbers at heritage attractions. People born just after the Second World War, the so called ‘baby boomers’, are envisaged to live longer than previous generations and to have a greater propensity to travel, including to heritage attractions (Williams and Shaw, 2009).

Greater interest in gardening is hypothesized to be the result of a growing proportion of the population becoming garden owners (Connell, 2004). Indeed a

15 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

sample of 546 garden visitors revealed nearly 95% of visitors having their own garden (Connell, 2004). Approximately 75% of these same visitors indicate the quality of a garden as the most important factor in their enjoyment of a garden visit out of a list of 13 factors, while 48.7% and 44.5% of visitors listed ‘freedom to wander’ and ‘peaceful atmosphere’ respectfully as additional influencing factors.

Management of P. ramorum and P. kernoviae in historic gardens has proved to be relatively difficult compared to managing these diseases in nurseries and woodland (Tomlinson et al., 2009). One of the reasons cited is the fact that historic gardens have previously not been involved in regulation before the P. ramorum and P. kernoviae outbreaks, while there has been no similar context in which Defra and the historic garden sector have worked together (Tomlinson et al., 2009). In addition nurseries are more capable of employing successful eradication measures due to plants standing on beds or concrete which can be replaced or sterilised compares favourably with the wider environment whereby the pathogens can survive in soil sometime after the removal of infected material (Tomlinson et al., 2009).

The initial policy of eradication created difficulties for some historic gardens as the main susceptible species are the primary reason for the gardens’ existence. Indeed the National Trust, in response to a Defra consultation document, echoes these concerns by highlighting how the enforced large scale clearance of susceptible hosts may destroy the structure of gardens to such an extent that little of interest may be left for visitors (National Trust, 2008). Plant Health and Seeds Inspectors reported problems convincing garden owners of the case for plant removals, especially those infected plants displaying few symptoms (Tomlinson et al., 2009). The concerns of garden owners may be alleviated to some extent by research showing 82% 5 of National Trust visitors agreeing the statement ‘I would still be interested in visiting historic gardens if they had new designs and different types of plants, to make them more resistant to tree pests and diseases' (Tomlinson et al., 2009). Aside from removing infected plants and trees, other measures to contain P. ramorum and P. kernoviae at gardens may include keeping to footpaths, not wearing open toe shoes and disinfecting shoes. One study found little resistance to such measures from National Trust garden visitors with 93% 6 of visitors agreeing with the statement ‘I would be happy to keep to footpaths’ with 85% 7 visitors agreeing with the statement ‘I would be happy not to wear open toe shoes and to disinfect my shoes’ when visiting historic gardens (Tomlinson et al., 2009). Certainly garden visitors seem willing to make concessions in order to protect such attractions from P. ramorum and P. kernoviae , which may help overcome garden owners’ fears about the impact these measures could potentially have on visitor numbers.

3.3.2 Heathland Ecosystem Services Heathland is an artificial habitat created as a result of ancient forest clearance and extensive grazing (see Appendix table A3.7 for scale and location of heathland). The intensification of agriculture over the last 200 years has led to a decline in the economic importance of heathland as a grazing resource. Although still used for some rough grazing, increased use of improved grassland has

5 27% of all visitors strongly agreed. 6 46% of all visitors strongly agreed. 7 35% of all visitors strongly agreed.

16 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae meant that the extent of heathland in the UK has decreased dramatically over the past century and its existence is threatened by natural succession. Heathland is therefore of marginal economic importance to the livestock sector, although heathland species provide a resource for insect pollinators, including the honey bee. The value of honey production in the UK as a whole is estimated to be between £10 and £35 million (National Audit Office, 2009), although only a proportion of this can be attributed to heathland. These food provisioning services are of relatively small economic importance on a national scale and can be replicated by other habitats. However, heather provides an important source of nectar and pollen for honeybee colonies during the flowering period, and heather honey is considered to be of particularly high quality. Infection of heather could have major effects on this small scale and local industry. Heather has not been found to be affected in the UK to date, though an infected plant was found in a nursery in Holland (C. Sansford, pers. comm .). Loss of other species, such as bilberry, will likely have little impact on the overall provision of these food products. However, large scale clearance of vegetation to control the spread of P. ramorum and P. kernoviae or the loss of any or all other species not currently thought to be susceptible could eliminate these services entirely. The acidic conditions in heathland soils lead to low rates of decomposition. In wetter areas, this results in peat formation and net carbon sequestration. Burning or drying of peat for horticultural or agricultural purposes reverses this effect. Climatic warming may also lead to soil drying, resulting in increased soil respiration and, thus, carbon dioxide emissions. Management of heathland by burning limits the carbon storage potential of biomass and releases nitrous oxide into the atmosphere. Widespread plant death or increased management burning will increase the net carbon dioxide emissions, although this effect may be reversed by future vegetation growth. If any or all other heathland species not currently thought to be susceptible to Phytophthora become infected, the long- term loss of vegetation would prevent further carbon sequestration. Therefore, the impact of Phytophthora infection on the climate regulation service provided by heathland peat soils will depend on the number of species affected, the length of time the area is cleared of vegetation, and any change in management burning practices. In contrast to peat, dry heathland podzols have limited carbon storage potential. Carbon sequestration in these systems may actually be increased by afforestation (Brainard et al., 2006; Cannell, 1999; Cannell et al., 1993), which would result from natural succession if heathland management was stopped. Heathland is a characteristic habitat of northern Europe and has been a feature of the UK landscape for around 6,000 years. It currently has considerable aesthetic appeal and historical importance. The cultural services provided by heathland are most obvious in the form of tourism and recreation. Walking, cycling and other outdoor activities attract visitors and tourist revenue, while heathland is also a major habitat for game shooting, and a key quarry species, the red grouse, is dependent on heather as a food plant. Therefore, cultural services in general, and tourism in particular, can be considered the most important ecosystem service provided by heathland. As these services are heavily dependent on appearance and access, they are also the services most likely to be affected by Phytophthora infection. Loss of characteristic species may deter visitors and more widespread loss of vegetation will reduce the appeal of heathland still further. Restrictions on movement or other activities imposed to contain the disease would also reduce the desirability of tourism in heathland habitats. These effects may persist even if vegetation recovers as the reputation of heathland as a recreation venue remains damaged.

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The area covered by heathland has been in decline, with over 70% of British heathland lost since 1800 (Eglington and Horlock, 2004), with the scarcity of heathland literature testament to this decline. While there is sparse qualitative literature concerning heathland, a handful of previous heathland valuation studies have been conducted as shown in Table 1. Herein most of these studies value heathland using different measurements such as WTP per household, per person and per hectare. Unfortunately no like-for-like comparison can be made between any of these studies. However, a loose comparison is possible between one of the latest valuation studies (Christie et al., 2011) and the environmental landscape features (ELF) study (Oglethorpe, 2005), although the former looks at heathland values whereas the latter looks at both heathland and heather moorland. The former study elicits a combined WTP value of £6.46 per household for lowland and upland heath preservation, while the latter study provides regional household WTP ranges compatible with this figure.

The very latest literature to estimate the value of heathland (Sen et al., 2011) used a meta-analysis of 98 relevant studies to deduce that a recreation site with ‘mountains and heathland’ attracts a WTP premium of £1.77 per person per visit. However, the meta-analysis contained studies from North America, Western Europe, Australia and New Zealand, in addition to the UK, and therefore this estimate is not UK specific 8. Another report produced for the UK National Ecosystem Assessment (UK NEA), (Mourato, 2010), used hedonic price analysis of a million housing transactions over 1996-2008 to establish the implicit premium paid for a house located near to ‘mountains, moors and heathland’. Herein an implicit premium of £832 in house prices is observed in the government office regions of the ‘North, Northwest and Yorkshire’ for every percentage increase in the proportion of land cover classed as ‘mountains, moors and heathland’ within a 1km square containing the property. The oldest heathland valuation study in Table 1 derives an annual WTP value of £9.73 per person for protecting lowland heath from development (Hanley and Spash, 1993). This figure is larger than the WTP figure produced by a recent study to specifically value heathland (Christie et al., 2011), with the difference even greater once inflation is taken into account. This difference is likely to be as a result of the older study asking visitors to Avon Forest Park 9 their WTP to protect heathland, whereas the most recent study does not specifically survey heathland users. Therefore it should not be surprising that heathland WTP values are higher for those people directly enjoying heathland compared to surveying the general public. The other heathland valuation studies in Table 1 do not directly measure the WTP for heathland preservation per se, but attempt to value the protection of endangered heathland species (Strange et al., 2007) and bird habitat (Foster et al., 1997).

8 The proportion of revealed and stated preference studies for the 98 studies used in the meta-analysis is not reported. 9 Despite its name Avon Forest Park is actually heathland.

18 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Good Valued Case Study Authors Area Value Additional Information Economic Assessment of UK, North America, Mountains & the Recreational Value of Sen et al. Western Europe, £1.77 increase in recreational value if a recreational site

Heathland Ecosystems in Great Britain (2011) Australia & New is defined as Mountains and Heathland. (UK NEA) Zealand Price increase is a capitalised £832 increase in the price of a house for a 1% increase Mountains, value calculated using a 3% Economic Analysis of Mourato et North, Northwest & in the share of landcover covered by Mountains, Moors Moors & discount rate, which Cultural Services (UK NEA) al. (2010) Yorkshire (England) and Heathland within a 1km square containing the represents the value of future Heathland property. benefits at present values. Economic Valuation of the Christie et Lowland & Benefits of Ecosystem £0.65 per household for Lowland Heath. £5.81 per Value to maintain current BAP al. (2011) UK Upland Heath Services Delivered by the household for Upland Heath. versus no BAP. UK Biodiversity Action Plan Value for Money: Protecting Strange et Heathland Endangered Species on Denmark €3.30 per 10,000 ha increase in Heathland. al. (2007) Danish Heathland Per household values; NE England (£4.17-£10.65), NW & Merseyside (£4.26 - £10.88), Yorks & Humber (£4.06 Values based on WTP to avoid Environmental Landscape Heathland & Oglethorpe - £10.40), East Mids (£4.74 - £12.03), West Mids (£4.52 a 10% change in the Features (ELF) Model England - Regional Heather Moorland (2005) - £11.52), Eastern Region (£4.96 - £12.55), London abundance of heathland & Update (£5.19 - £13.06), SE (£5.24 - £13.23), SW (£4.31 - Heather Moorland. £11.01). Real and Hypothetical Willingness to Pay for Foster et al. Lowland Heath Environmental Southern England £1.08 per respondent to prevent loss of bird habitat. RSPB Study. (1997) Preservation: A Non- Experimental Comparison WTP to prevent development of Heathland through either a daily entrance fee to the Heathland, an Hanley and Cost-Benefit Analysis and Avon Forest Park, annual permit to the Heathland or WTP towards a Lowland Heath Spash . the Environment SW England trust fund (one off payment) for the protection of (1993) Heathlands in general. Values of £0.74, £9.73 and £25.57 per respondent respectively were obtained.

Table 1: Heathland valuation studies

19 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Heathland ownership in England is split between the Forestry Commission (30%), the Ministry of Defence (18%) and the National Trust (8%) (Spencer and Haworth, 2005). Due to the sporadic nature of P. ramorum and P. kernoviae infection, some heathland sites may remain uninfected while others become damaged. Visitor substitution away from infected to uninfected heathland may occur in this instance, although there is currently some concern over visitor levels to heathland sites and how such levels may degrade these sites (Roberts, 2007a). Therefore it is reasonable to assume the process of substitution being somewhat impeded due to environmental limitations at individual sites. Future fuel price increases may be another factor to consider in forecasting future visitation rates to British heathland sites. For instance, will higher fuel costs mean a substitution away from foreign holidays to British holidays, or will higher imported food costs, generated by fuel price rises, lead to the conversion of heathland into agricultural land (Roberts, 2007b)? 3.3.3 Woodland Ecosystem Services Wood has considerable economic importance both in the form of timber for construction and as a renewable fuel. Different tree species provide timber with different properties. Larch, which is susceptible to Phytophthora infection, is particularly durable in wet conditions and so is often used for building cladding and boat construction. Therefore the loss of larch in particular might be expected to have a disproportionate impact on these applications. However, if other species become infected or are removed as part of an effort to control the spread of the disease, other uses of timber and wood fuel might be affected with wider economic consequences. At present, a large proportion of wood and wood products, such as paper, are produced overseas and imported to the UK. Furthermore, the availability of other forms of energy means that few if any buildings are entirely dependent on wood fuel for heating. Wood fuel currently accounts for around 0.2 per cent of the UK energy supply (MacLeay et al., 2010). Consequently, the loss of large numbers of trees may affect the local price of wood but the availability of raw materials and fuel is unlikely to be greatly affected. Woodland is also a source of wild food, although this is generally a niche use of small economic or social importance. A number of biochemical and pharmaceutical products, such as taxol, have been isolated from woodland species. However, these are generally mass produced using synthetic techniques and so this ecosystem service is best characterized as a genetic resource. Woodland provides an important climate regulation service, both in the UK and internationally. This is due to the sequestration of carbon in wood and soil (Brainard et al., 2009; Cannell, 1999; Cannell et al., 1999). Loss of individual trees will cause small reductions in carbon sequestration until vegetation can be replaced. However, loss of large areas of woodland would lead to significant reductions in sequestration and reversing soil formation processes, leading to major increases in soil respiration and, thus, carbon dioxide emissions. In wet areas the removal of trees and subsequent reduction in transpiration could lead to waterlogging, anoxic soil conditions and methane production. This could also affect the local flood protection and water cycle regulation services provided by woodland. These regulating services are likely to have a much greater impact on the local environment than provisioning services, such as timber production. The cultural services provided by woodland have considerable economic and social importance. Deciduous woodland is the natural climax community of much of the UK and a characteristic habitat of northern Europe. It has also provided

20 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae raw materials for construction in Britain throughout recorded history, although little native ancient woodland now survives. Therefore, woodland has major natural and social historical value. The aesthetic appeal of woodland makes it an important environment for recreation and tourism. Indeed across England 40% of people have visited a woodland or forest in the past year (Sen et al., 2011). Use of woodland for recreation may have wider benefits, such as encouraging landowners to invest in conservation measures (Oldfield et al., 2003). The appearance of woodland may provide additional benefits, for example, by adding to the value of local housing (Garrod and Willis, 1992). The impact of Phytophthora on these cultural services depends on the visual impact of the disease. Small-scale felling of trees is a common management practice and visitors may be accustomed to seeing some deforested areas. However, wider removal of trees, conspicuous loss of particularly important species (including known minor hosts, such as oak, horse chestnut, sweet chestnut, or beech), or any restriction on the movement or behaviour of visitors will affect recreation and tourism. In terms of commercial forestry, there may be loss of biodiversity as it is expected that Douglas Fir (DF) will replace infected larch stands. In terms of pure species diversity, DF makes up a smaller proportion of the high forest in England than larch (2.6% compared with 4.8%), while the same is true in Wales where DF and larch make up 4.3% and 8.9% of the high forest area respectively. Substituting DF for larch therefore increases species diversity up to the point where the DF area equals that of larch. However, larch is a deciduous conifer and DF a dense, non-deciduous conifer. Wider biodiversity would therefore be reduced by a substitution of DF for larch. Natural England (2011) state that on non-SSSI woodland sites biodiversity interests would be best served by replacing larch with broadleaves either through natural regeneration or planting, particularly on Planted Ancient Woodland Sites (PAWS). In practice, however, our understanding is that Natural England has limited influence on species re- stocking outside SSSIs. While Garrod and Willis (1997) show the public are WTP for greater biodiversity in coniferous forests, the limited amount of species specific valuation research (Hanley et al, 2002) does not enable a useful contribution on the issue of substituting DF for larch. Natural England (2011) were unable to provide information on the areas of larch on SSSIs in potentially infected regions. Larch is not a notified feature on any woodland SSSI but Natural England state they normally have as an objective on woodland SSSIs the replacement of larch with broadleaved species, and wish, on former heathland sites, to see the open habitat restored. In such cases the clearance of infected larch can provide biodiversity gains and by extension social benefits. Natural England were unable to indicate the areas potentially affected in this way and we are therefore unable to quantify these impacts on re-stocking. Overall it seems likely that there are potential biodiversity losses through the replacement of larch with DF, although on the most valued sites (SSSIs) larch clearance appears to offer opportunities for biodiversity enhancement. There is no specific information on the public valuation of larch within woodlands, although Hanley and Ruffell (1993) undertook a CV study of forest characteristics in which both the proportion of broadleaved trees and the diversity of conifer species were included as variables in the analysis. Neither variable had a significant effect on the bid curve which suggests species composition is relatively unimportant as a forest characteristic in terms of visitor welfare. This implies a substitution of larch with DF without changing other characteristics would have minimal effect on public welfare.

21 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Even so larch is a deciduous conifer that creates colour diversity in the autumn when many people appreciate the aesthetic beauty of woodlands. Consequently there may be some loss of landscape value if larch is removed although quantifying this is not possible. Garrod (2003) attempted to value six forest landscape configurations based on shape, scale, structural variety and species variety, although no systematic links were found between the public’s WTP and the incidence of the forest design factors. However, Entec and Hanley (1997) found households are WTP to see enhancements in the appearance of British forests that moved towards an ‘ideal’ forest landscape, which indicates that shape, structure and design do indeed affect forest landscape values 10 . 3.4 Valuation Literature Discussion Before discussing the methods used to elicit public WTP values for preserving heritage gardens, heathland and woodland against P. ramorum and P. kernoviae infection, it is useful to overview the valuation literature in order to clarify exactly the different types of value associated with these types of habitat. Once it is ascertained which values we are looking to measure, it will be possible to select an appropriate valuation methodology from which to elicit public preferences to preserve heritage gardens, heathland and woodland against P. ramorum and P. kernoviae infection. 3.4.1 Total Economic Value, Use and Non-Use Values The primary purpose of this report is to measure the TEV of heritage gardens, heathland and woodland at risk from P. ramorum and P. kernoviae infection. TEV is composed of two segments; use and non-use values. The former type of value is made up of direct, indirect and option values, while the latter type of value consists of altruistic, bequest and existence value.

Direct values relate to the actual consumption of environmental goods, for instance the timber felled from a forest or the fish caught from rivers. Indirect use values as the name suggests are those benefits indirectly derived from environmental goods, such as the reduction in flood risk a forestry plantation may provide villages downstream. Option values on the other hand are not derived from the immediate benefits a person receives from an environmental good, rather people derive value in knowing the option exists for use of the good at some future point. In many ways option values are akin to insurance contracts insofar as protecting against the future loss of an asset. In summary, use values are composed of the benefits people obtain from the actual or future option to benefit from environmental goods.

10 Entec and Hanley (1997) assessed annual household WTP for forest shape, felling method and species mix in autumn, winter and spring. This produced the following WTP values for: selective felling (£12.89), organic shape (£13.90) and a diverse mix of evergreen, broadleaf and larch species (£11.36). If clearance of larch were to remove the benefits of species diversity completely there would be a loss of up to £11.36 annually per household or £15.51 at current prices (using the GDP deflator). Across England and Wales 1.4% of the high forest is predicted to be infected larch by 2021, and 2.2% by 2031. Aggregating the per household value to approximately 25.7m households in England and Wales (Department for Communities and Local Government, 2009; Welsh Assembly Government, 2010) gives a landscape diversity loss of £5.6m per year by 2021 and £8.7m per year by 2031. The PV of this loss would be around £89m to 2031. This is considered an upper limit given the loss of larch would not necessarily remove the total species diversity value of infected woodlands.

22 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Altruistic values typically arise out of a concern for other peoples’ enjoyment of environmental goods. For example, one may be concerned about the higher risk of flooding to neighbouring villages from the felling of trees in the local area. Bequest values are also born out of a concern for the wellbeing of other people, except that future generations are the object of concern. The most prominent example here is the concern expressed by the present generation regarding the impact of climate change on future generations. Existence values are generated simply from people knowing a given environmental good exists even though they never plan to visit that good. For instance, saving the polar bear from extinction is one example likely to generate existence values. In summary, non-use values do not involve the personal consumption of environmental goods. 3.4.2 Revealed Preference (RP) Valuation Techniques These valuation techniques rely on individuals revealing their WTP for non- market goods by observing their actual consumption of market goods which are related to the environmental good of interest. The most common RP methods are hedonic pricing and travel cost. The former typically involves using house prices as a proxy for a non-market environmental good, with the prices people are WTP for houses being in part reflective of the abundance of the environmental good. For instance, if there are two houses with identical attributes except for the density of trees surrounding these properties, the hedonic pricing method is envisaged to determine the value of woodland through the difference in the price of these two properties. However, in practice extremely large datasets are required to isolate the environmental good of interest from all the other factors influencing property prices. Another disadvantage of the hedonic pricing method is the value generated by this method for an environmental good does not incorporate the value non-residents and visitors may attach to the same environmental good.

The travel cost method works by gathering information on the costs incurred by individuals visiting a particular site as a proxy for a sites recreation value. Typically the cost of driving, travel time and admission costs to a recreational site is seen as representative of the value of a recreational site to a given visitor. In contrast to the hedonic pricing method, the travel cost method only ascertains the value of recreational sites to visitors. Other RP techniques involve the production function approach and the opportunity cost approach, with the former examining the value of an environmental good within a firm’s production function and the latter approach valuing the benefits foregone to provide a given environmental good. 3.4.3 Stated preference (SP) Valuation Techniques All SP techniques rely on survey data where people are asked to indicate their preferences for environmental goods with the price of the goods inferred from the preferences expressed. In contrast to RP techniques, monetary values generated from SP techniques rely on being able to realistically represent a hypothetical market to survey respondents, such that the values implicitly given by respondents are the same values they would be willing to pay in a market context. The most common methods of SP valuation are CV and choice experiments (CE).

Traditionally most SP studies have used CV whereby a hypothetical market for a good is created in which respondents are asked to state their WTP for changing to different policy scenarios concerning the provision of an environmental good. These policy scenarios are evaluated by respondents relative to a defined policy baseline scenario. CV adopts a holistic approach to valuing environmental goods

23 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

as all of the components that make up a given environmental good are included in the policy scenarios.

Increasingly CE are used to determine the value of environmental goods, specifically to value the individual attributes which make up the environmental goods in question. For instance, a CE may be used to value woodland in terms of the individual components of woodland such as the proportion of woodland classified as broadleaf, density of woodland, area of woodland, etc. The main difference between CV and CE is the former is concerned with the environmental good as a whole, whereas the latter is concerned with valuing the attributes making up environmental goods. 3.4.4 Benefit Transfer (BT) Valuation Technique This technique takes the values obtained for environmental goods in previous valuation studies, and transfers across these same values in order to value the same environmental goods in a different location and/or timeframe. This process is achieved using either a unit value or function value BT. The former assumes the original study population from which the values are extracted is identical to the policy population in terms of socio-demographics, while the latter enables BT to take into account differences in socio-demographics between the study and policy populations. 3.5 Selection of Valuation Techniques for Heritage Gardens, Heathland and Woodland One of the main issues with using RP techniques for valuing the TEV of heritage gardens, heathland and woodland at risk from P. ramorum and P. kernoviae is the fact that such techniques do not measure non-use values. Another drawback of RP techniques is they underestimate the WTP for an environmental good as the value people place on such goods is at least as great as the combined monetary and time costs people incur travelling 11 to a given recreation site. In terms of economics, the values obtained through RP techniques are likely to fall short of individuals’ actual WTP as some individuals incur costs below the maximum amount they willing to incur before a given trip to a recreational site is cancelled. The same principle applies to collecting RP data on recreational site entrance fees as such fees are not a good proxy for public WTP associated with these sites. Figure 1 (Provins et al., 2008) illustrates how the entrance fee to a heritage garden, heathland or woodland only estimates part of total WTP. For instance, the entrance fee (P m) charged by a heritage garden, heathland or woodland measures only section B of total WTP. However, nearly all q visitors are prepared to pay more than price level Pm, as seen by the demand curve lying above price level P M to the left of q. Essentially WTP is only ascertained through measuring both areas A and B of Figure 1. The WTP for environmental goods of which there exist no charges is solely represented by area A of Figure 1.

11 Alternatively if the hedonic pricing technique is used, the value of an environmental good is at least equal to the increase in property prices attributed to the environmental good.

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Price (entrance fee)

A Pm

B P0

q Number of visitors

Figure 1: Demand curve for habitat at risk Given the shortcomings of RP techniques to establish the full WTP for environmental goods, SP techniques were used to investigate the TEV at risk from P. ramorum and P. kernoviae spread. This was achieved by eliciting the public’s WTP to prevent the spread of P. ramorum and P. kernoviae to uninfected heritage gardens, heathland and woodland. While concerns exist regarding the hypothetical nature of SP techniques, such techniques are beneficial to this study insofar as being able to value changes which have not yet occurred i.e. the future spread of P. ramorum and P. kernoviae . This study therefore incorporates a CV survey to provide estimates one aspect of the TEV at risk from the spread of these diseases. While CV and CE are very similar valuation methodologies, CV was chosen over CE as the former is more suited to analysing the value of whole policy scenarios whereas the latter excels at analysing the sub-components of policy scenarios.

Another aspect of TEV at risk will be investigated using market and social prices to value the impact of P. ramorum and P. kernoviae on timber and carbon values respectively. Combining the CV exercise with theses market and social values will produce an estimate of the TEV at risk of the principle ecosystem services impacted from relaxing containment controls on P. ramorum and P. kernoviae .

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4. TEV at Risk This section considers two aspects of the economic value at risk from the spread of the diseases: • The public’s WTP to prevent further spread to heritage gardens, heathland and woodland. • The value of lost commercial timber production and related carbon sequestration. To capture the public’s WTP to prevent spread in the three habitats a CV study was undertaken with the process and results of this presented below. The impact on forestry was analysed separately (but using the same model of spread without control) by estimating the impacts of the diseases on timber production and associated carbon sequestration. 4.1 The Public’s Willingness to Pay to Prevent Further Spread 4.1.1 TEV at Risk from P. ramorum and P. kernoviae Spread Defining the TEV at risk concept is of fundamental importance prior to designing the survey. With respect to the spread of P. ramorum and P. kernoviae to heritage gardens, heathland and woodland, the majority of TEV at risk is identified as the public WTP to prevent any further spread of these diseases to such habitats over a 20 year period 12 . Another element of TEV at risk consists of impacts on commercial timber production and carbon sequestration . Figure 2 is a conceptual illustration of the value we are estimating. It is the additional damage that would occur from the current level of infection if the diseases were allowed to spread without further control.

12 TEV at risk was clarified by the project Steering Group.

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60

50

40 Total Economic Value at Risk Current 30 Spread No

Damage 20 Controls

10

0 0 10 20 Years

Figure 2: TEV at risk without control Currently there are control measures in place which limit the spread of P. ramorum and P. kernoviae and in reality the actual level of damage would be within the blue and red lines. The principal objective of the survey was to find out to what extent the public are WTP to prevent further damage to tree and plant species in heritage gardens, heathland and woodland. This presents difficulties since the status quo is a highly unlikely outcome unless the current level of control is sufficient to keep the diseases in check. A more logical baseline would be the expected future spread with control but in discussions it was decided that providing a realistic future spread would simply be adding another level of uncertainty into the analysis. 4.1.2 Modelling the Spread of P. ramorum and P. kernoviae Central to the survey is the inclusion of maps showing the spread of P. ramorum and P. kernoviae 20 years into the future without control measures in place. Such maps were created by Fera and Cambridge University to inform survey respondents about the regional variation in P. ramorum and P. kernoviae spread. This is envisaged to help respondents fully understand the future impacts they are likely to face before answering WTP and WTV questions. The calculations used in the creation of the map showing P. ramorum and P. kernoviae spread without controls are also used to determine the social value (timber and carbon values) of woodland at risk from these diseases. The following paragraphs provide an overview of the processes involved in the creation of such maps with more detail provided in Appendix 3.

MaxENT modelling was used to produce host maps for four host species considered at risk from these diseases ( Arctostaphylos uva-ursi , Vaccinium vitis-idaea , Vaccinium myrtillus , Rhododendron ponticum ) at a 1km scale. These MaxENT distributions were further refined by clipping with certain categories of the Land Cover Map 2000 (LCM2000). These categories correspond to habitats expertly judged as suitable for growth of the specific host species. Without this refinement it is assumed the host species will fill the

27 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae entirety of each 1km 2 cell it is judged to be in. By clipping with suitable habitats this incorrect assumption is removed.

The LCM2000 refined MaxENT outputs for all species were incorporated into the full host map for the Metapopulation Epidemic Model (MPEM) developed by Cambridge University 13 with the data used in a 20 year simulation. The MPEM produced two raster files: i) A hazard map showing the risk posed of P. ramorum and P. kernoviae spreading to each raster cell (assuming the disease is in all cells of the map) and ii) A map showing the probability of the disease actually spreading from its current positions to each 1km 2 cell of the map.

Multiplication of these two files produced a raster output showing the hazard posed to each cell from disease spread, weighted by the probability that the diseases would reach that cell. This is the final risk map.

The final risk map was split into high, medium and low risk scenarios. The high risk scenario (scenario 1) was defined as a situation where Phytophthora had spread to all areas of the UK that contain suitable host species. The medium risk scenario (scenario 2) showed the situation that would occur if the diseases were only able to spread as far as the MPEM model suggests 14 . The low risk scenario (scenario 3) looked at the current situation and assumed that the disease will not spread any further than it has already done. The number of heritage gardens and areas of heathland, woodland and larch at risk were calculated for each risk scenario.

The survey made use of risk maps derived from 20 year simulations of scenarios 2 and 3. These maps are presented in Figure 3, which collectively illustrate to survey respondents the spread of P. ramorum and P. kernoviae if current control measures are relaxed. The risk maps show Southern Wales, North West England and parts of Southern England as particularly at risk from P. ramorum and P. kernoviae by 2030 if controls are withdrawn. Conversely Central and Eastern regions of England are less at risk from these diseases. These results reflect the current locations of the diseases and their proximity to other locations that are likely to have susceptible hosts present.

13 The full host map consists of Fera host maps plus the National Inventory of Woodland and Trees (NIWT) and larch distribution from the Forestry Commission database containing spatial data on all Forestry Commission larch in the UK. 14 Areas with very low risk values were removed from the scenario as they were skewing the impression of risk.

28 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

a) b)

Figure 3: a) current spread (scenario 3) b) 20 year spread without control (scenario 2)

29 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

4.1.3 Survey Design and Pilot The survey was initially envisaged to focus upon the potential changes to visitor numbers to the three habitats. However, unpublished results from the Rural Economy Land Use (RELU) project Memory and Prediction in Tree Disease Control suggests that this would simply provide results suggesting that visitation levels would be minimally affected but that the satisfaction of the visit would be diminished. Thus valuing impacts based solely upon visitation rates would underestimate the value of the impact of the disease. It was therefore decided to expand the survey to include a CV element to ascertain public WTP to prevent further damage from the diseases.

The survey design was based upon that used in the aforementioned RELU project. This provided a strong base for the survey used in this study since the RELU survey had been refined by focus groups and a pilot stage. Iterations of the survey received significant input from the Steering Group and the National Trust as well as scientific input from FERA to ensure the information was presented correctly and clearly. The selection of tree and plant species is based on an identification of species most suseptable to P. ramorum and P. kernoviae in the literature review (Chapter 3.2), as well as the popularity of other at risk species. The selection of suitable photos for inclusion into the survey was determined through consultation with FERA to ensure that tree and plant damage caused by P. ramorum and P. kernoviae is accurately depicted. Close-up photos of infected species were selected for inclusion in the survey following advice from the Steering Group that landscape scale photos may hide the impact of infection upon some tree and plant species. The inclusion of photos into the survey inevitably focused attention on the aesthetic impact of these diseases, which may be thought of as the cultural ecosystem services at risk from P. ramorum and P. kernoviae infection. While other types of ecosystem services listed in the literature review may equally be at risk, such as biodiversity, the primary focus of the valuation survey is to elicit the TEV at risk from the impact of P. ramoum and P. kernoviae infection upon aesthetic values.

The survey covered the three habitats at both regional and national levels i.e. respondents were asked separately about their preferences for the habitats where they lived and in the rest of England and Wales. All survey respondents were asked about their WTP as well as their WTV (visit more, less or the same given future disease spread). All WTP and WTV questions were asked within three distinct sequential sections in the survey relating to heritage gardens, heathland and woodland. The beginning of each section contained popups enabling respondents to visualise once again the impact of P. ramorum and P. kernoviae infection upon species relevant to habitats contained in each section. This sequential approach was adopted in order to refresh respondents prior to answering WTP and WTV quesitons. This multi- factorial element meant the survey was close to 25 minutes long.

Prior to the final survey, a pilot survey was distributed online before 44 members of the public through the online research company Research Now. The pilot survey was answered by 44 people, excluding ‘speeders’, who are identified as respondents who spent insufficient time on the survey to give considered responses. This is an important stage in any environmental valuation exercise as a biased or poorly constructed survey may result in

30 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae inaccurate WTP and WTV values. The piloting of the survey took place on 5 th January 2011, which produced the following WTP values:

14

12

10

8 Heritage Gardens 6 Heathland Frequency 4 Woodland

2

0 £0 £1 £2 £3 £4 £5 £6 £7 £8 £9 £10 £15 £20 £30 £50 £0.20 £0.50 WTP

Figure 4: Pilot survey WTP to protect regional habitats

14

12

10

8 Heritage Gardens

6 Heathland Frequency 4 Woodland

2

0 £0 £1 £2 £3 £4 £5 £6 £7 £8 £9 £10 £15 £20 £30 £50 £0.20 £0.50 WTP

Figure 5: Pilot survey WTP to protect national habitats

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Immediately apparent in Figure 4 is that approximately a quarter of respondents select the highest WTP tick option of £50 for stopping P. ramorum and P. kernoviae spread to heathland and woodland in their region. In addition, a bi-modal distribution of WTP values is observed for both regional and national distributions of WTP. The average WTP values for heathland and woodland at the regional level (Table 2) are affected by the higher WTP values chosen by a number of respondents whereas the values for national habitats (Table 3) are more comparable. In addition, Tables 2 and 3 demonstrate that the sequencing of questions has no impact on WTP. The WTV responses support the findings from the RELU work mentioned above although more respondents are averse to visiting national habitats upon infection.

Table 2: Pilot survey results - regional habitats

Heritage Gardens Heathland Woodland

Average WTP £5.55 £16.91 £16.73

Visit More 5 4 6

Unchanged 33 35 32

Visit Less 6 5 6

Table 3: Pilot survey results - national habitats

Heritage Gardens Heathland Woodland

Average WTP £6.20 £4.50 £5.14

Visit More 1 4 2

Unchanged 37 34 35

Visit Less 6 6 7

However, WTP values derived from the pilot group should not be taken at face value due to the small number of respondents involved. Indeed the main purpose of the pilot survey is to ascertain whether respondents understood the survey and what was expected of them. With this in mind questions were included asking respondents whether they understood the following aspects of the survey; information on tree diseases, photographs of diseased species, disease spread maps and questions on WTP and WTV. Approximately two- thirds to three-quarters of respondents indicated they understood such aspects. In addition, we included a comments page towards the end of the survey with which respondents could suggest survey improvements. Nine respondents suggested the survey was repetitive (the same questions asked for three habitats at regional and national scales), while one respondent was frustrated with the survey not allowing respondents to enter a figure in excess of ninety-nine for the visitation rate questions. The majority of respondents chose not to leave comments concerning the survey.

32 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Very few changes of note were made to the survey as a result of minimal negative feedback from the pilot survey. While a noticeable minority of respondents said the survey was repetitive, we felt this was a small price to pay for maintaining order within the survey. Changes were made to the survey on the basis of a quarter of respondents selecting the maximum WTP tick box option of £50, together with the comment regarding limitations on the number of visits a respondent can declare. The range of WTP tick box options was extended to £75 and £100 to account for those respondents with a true WTP in excess of £50 but who did not want to expend the extra effort involved in manually entering an amount above £50. The restriction on the number of visits a respondent may enter for a WTV question was relaxed such that respondents are now able to enter a value up to 999 visits. 4.1.4 Final Survey The final survey was distributed online to 959 people, excluding speeders, during the period 28 th January to 4 th February 2011 (see appendix 4 for screen shots of the survey). Stratification of the survey was made according to a respondent’s gender, age and region of residence. However, 32 respondents were identified as providing protest bids and were removed from the sample. These respondents were identified from questions asking reasons for providing zero WTP values whereby such respondents stated that they already paid too much tax and/or do not believe income tax should be used for protecting habitats from P. ramorum and P. kernoviae spread. This left the choices made by 927 respondents available for data analysis. Table 4 shows the target and actual proportion of these 927 respondents belonging to categories of these socio-demographic characteristics.

Table 4: Target and actual stratification of key characteristics

Actual Stratification Sample Target 15

Gender Male 48.5% 47.9% Female 51.5% 52.1%

Age 18-34 Years 28.3% 28.4% 35-59 Years 43.8% 43.5% 60+ Years 25.5% 28.2%

East of England 10% 9.9% Region East Midlands 10% 9.6%

London 10% 9.8% North East 10% 10.2% North West 10% 10.4% South East 10% 10.0%

15 Office for National Statistics (http://www.statistics.gov.uk/statbase/Product.asp?vlnk=15106).

33 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

South West 10% 10.2% Wales 10% 10.1% West Midlands 10% 9.8% Yorkshire & Humber 10% 9.8%

All of the socio-demographic characteristics selected for stratification were successfully sampled to within a few tenths of a percent of the target proportions set out prior to surveying. Age was the only recorded socio- demographic characteristic with a continuous distribution, which is further represented in Figure 6. There appears to be a bi-model distribution, with peaks occurring for respondents in their late twenties and sixties.

30

25

20

15

Frequency 10

5

0 18 23 28 33 38 43 48 53 58 63 68 73 78 Age (Years)

Figure 6: Distribution of ages across the sample The remaining recorded unstratified socio-demographic characteristics are type of occupation, level of highest qualification, household income and environmental group membership. As seen from Table 5 the majority of non- stratified socio-demographic characteristics are proportionally represented by the sample. However, those respondents who look after their home full-time, and especially those respondents who hold membership of an environmental organisation, are over-represented according to the wider UK population.

34 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Table 5: Non-stratified characteristics

UK Population Actual Sample

Occupation Employed 48.1% 16 50.5% Self-Employed 7.7% 2 6.4% Unemployed 4.8% 2 3.4% Looking after home full-time 4.4% 2 8.0% Students 4.2% 2 4.5% Pensioner 26.2% 2 23.3% Unable to work 4.6% 2 3.9%

Gross Household Income £35,532 17 £32,362

Highest Held Qualification No qualification N/A 6.1% GCSE/O-Level/A-Levels Equivalents N/A 46.6% Degree or Masters N/A 33.5% PhD N/A 1.5% Professional Qualification N/A 12.2%

Environmental Membership Yes 10.0% 18 32.1% No 90.0% 67.9%

4.2 WTV and WTP to Protect Heritage Gardens, Heathland and Woodland at Risk 4.2.1 WTV to Habitats at Risk Survey respondents were asked whether P. ramorum and P. kernoviae infection of heritage gardens, heathland and woodland would impact on the frequency of their visits to such habitats. Table 6 details the impact of P. ramorum and P. kernoviae infection on respondents’ WTV heritage gardens, heathland and woodland in England and Wales.

16 Office for National Statistics (http://www.statistics.gov.uk/pdfdir/lmsuk0211.pdf). 17 Office for National Statistics (http://www.statistics.gov.uk/articles/nojournal/Taxes_Benefits_0809.pdf). 18 The number of environmental organisation memberships was calculated from the following sources: National Trust - http://www.nationaltrust.org.uk/main/w-trust/w-thecharity/w-history_trust-timeline.htm , RSPB - http://www.rspb.org.uk/news/details.aspx?id=tcm:9-251587 , English Heritage - http://www.english- heritage.org.uk/publications/EH_Annual_Report_and_Accounts_2009_10/ annual-report-and-accounts- 0910.pdf , Friends of the Earth - http://www.foe.co.uk/resource/faqs/ fund_how_many.html , Greenpeace - http://www.greenpeace.org.uk/about/faq#sup , Historic Scotland - http://thescotsman.scotsman.com/scotland/Homecoming--pushes-Historic-Scotland.5583532.jp , Royal Horticultural Society - http://www.telegraph.co.uk/gardening/6175529 /Crisis-at-the-Royal-Horticultural- Society.html , Woodland Trust - http://www.woodlandtrust.org.uk/ en/support-us/membership/Pages/join- us.aspx .

35 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Table 6: Change in willingness to visit

Heritage Gardens Heathland Woodland

Change in Regional National Regional National Regional National Visitation More 105 65 60 47 65 44 The Same 620 654 681 690 692 700 Less 202 208 186 190 170 183

These results indicate a slight net overall reduction in the number of visits to heritage gardens, heathland and woodland in the event of future P. ramorum or P. kernoviae infection. Interestingly a small minority of respondents indicate a higher WTV these habitats upon infection with P. ramorum or P. kernoviae . The most common reason given by respondents for this apparent anomaly is a willingness to show support towards these habitats if damaged by these diseases. This support is largely financial in relation to heritage gardens as respondents realise their visits generate income for this particular habitat. Overall, the WTV these habitats is unchanged for the majority of survey respondents should the diseases spread to these habitats, regardless of whether such habitats are located in a respondent’s region or elsewhere in England and Wales. Those respondents who said they would visit less were asked how much further they would be willing to travel to visit uninfected habitats. Of those who responded between a third and a half would not travel any distance, with one quarter willing to travel 10 miles and a further quarter 11-50 miles (Figure 7).

50% 45% 40% 35% 30% 25% 20% 15% 10%

% of Respondents of % 5% 0% Zero 1-10 miles 11 -20 miles 21 -50 miles 50+ miles Distance Heritage Gardens (n=111) Heathland (n=92) Woodland (n=93)

Figure 7: Extra distance willing to travel if local habitat infected The data suggests a slightly higher propensity to travel further afield to visit uninfected heritage gardens than woodland or heathland.

36 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

At first glance the unresponsiveness of respondents’ WTV to P. ramorum and P. kernoviae infection would seem to indicate a near zero TEV at risk from P. ramorum and P. kernoviae proliferation. However, these WTV figures should be seen as measuring a quantity effect associated with the whole visitation experience, which excludes potential deterioration in the quality of the visitation experience from P. ramorum and P. kernoviae infection. To measure the impact of infection upon the quality of the visitation experience, and hence TEV at risk , it is necessary to look at the public WTP to prevent infection of heritage gardens, heathland and woodland.

4.2.2 WTP to Protect Habitats at Risk The yearly WTP to protect heritage gardens, heathland and woodland from P. ramorum and P. kernoviae damage is calculated using the statistical software Stata. The WTP figures are contained within Table 7. Herein respondents are WTP the most to protect heritage gardens, while heathland is the habitat respondents are WTP the least to protect. The sequencing of questions is once again shown to have no apparent impact on WTP. In addition, there appears to be a preference for protecting local habitats over habitats in the rest of England and Wales. The confidence intervals are rather tight around the mean values, with all confidence interval ranges not exceeding the £2 mark.

Table 7: Mean WTP to protect habitats (with confidence intervals)

Lower Bound of Upper Bound of 95% Habitat Mean 95% Confidence Confidence Interval Interval

Regional Heritage Gardens £7.65 £6.66 £8.63

National Heritage Gardens £6.54 £5.60 £7.47

Regional Heathland £4.86 £4.16 £5.57

National Heathland £4.34 £3.68 £5.00

Regional Woodland £6.12 £5.27 £6.98

National Woodland £5.04 £4.28 £5.80

WTP figures were also estimated for each of the regions and are shown in Table 8. The sample sizes for each are around the 100 mark. Very broadly Wales, West Midlands and Yorkshire and Humber show lower WTP. The South East and Yorkshire and Humber both consistently show the biggest gap between regional and national WTP.

37 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Table 8: Mean WTP by region and habitat

Heritage Gardens Heathland Woodland Regional National Regional National Regional National

East England £8.00 £6.96 £5.12 £5.04 £6.52 £5.81 East Midlands £9.95 £9.12 £4.83 £4.89 £6.69 £6.17 London £8.70 £8.17 £6.99 £7.00 £7.63 £7.24 North East £8.12 £7.04 £4.86 £4.55 £5.63 £4.53 North West £6.74 £6.90 £5.23 £4.97 £7.42 £6.56 South East £7.77 £5.16 £3.76 £3.07 £5.98 £3.51 South West £7.20 £6.61 £5.63 £5.43 £6.63 £6.39 Wales £5.60 £4.57 £3.78 £3.10 £4.67 £3.45 West Midlands £6.06 £5.51 £3.00 £2.90 £4.35 £4.01 Yorks & Humber £8.48 £5.41 £5.45 £2.47 £5.69 £2.70

4.2.3 Socio-demographic Characteristics Influencing WTP Values Also of interest is the influence socio-demographic characteristics have upon the WTP to protect habitats from P. ramorum and P. kernoviae damage. Initially a selection of socio-demographic characteristics were included in an interval regression model run in Stata 19 . Such characteristics include:

• Respondents’ age, gender, occupation and qualifications. • Respondents’ knowledge of P. ramorum and P. kernoviae . • Respondents’ attitudes towards the environment. • Respondents’ understanding of the survey.

All socio-demographic characteristics were checked to see whether any significant correlation exists with other socio-demographic characteristics. Any socio-demographic characteristic found to have over 60% correlation with another characteristic was removed. The most insignificant socio-demographic characteristics were also removed until the majority of socio-demographic characteristics left in the model exhibited statistical significance. Table 9 illustrates the effect of these socio-demographic characteristics upon the WTP for protecting regional and national habitats.

The socio-demographic interactions confirm an a-priori expectation that increasing levels of gross household income raise the WTP for protecting all regional and national habitats against P. ramorum and P. kernoviae damage. Table 9 also confirms another a-priori expection of higher levels of concern over potential damage to habitats as having a positive and highly significant influence on the WTP for protecting all habitats. Both findings provide internal validity to the survey as these socio-demographic interactions demonstrate a logical influence

19 Residency in Wales was included as a socio-demographic characteristic to see if respondents living in a region with an already high occurrence of P. ramorum and P. kernoviae react differently to other respondents when asked for their WTP to protect habitats.

38 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae on WTP. Respondents with awareness of P. ramorum and P. kernoviae attach a small premium to the WTP for most habitats, while respondents who have directly observed pests and diseases on trees or plants also attach a modest premium to protect regional heritage gardens and heathland.

Interestingly respondents with membership of a conservation, environmental or gardening organisation attach a WTP premium to protect all heritage gardens and national heathland. These respondents are the most likely to visit heritage gardens and by extension are most likely of all respondents to be beneficiaries of a continuation of measures to protect heritage gardens. Finally respondents living in Wales are WTP less for protecting national heathland and woodland .

39 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Table 9: Influence of socio-demographic characteristics on WTP 20

Impact on Average WTP for: 21 Socio-demographic Characteristic Regional National Regional Regional National National HE HGs HGs HE WD WD Male £0.32 £0.83 -£0.09 £0.55 £0.25 £0.85 Gross Household Income (£1,000 increase) £0.07*** £0.05** £0.07*** £0.04*** £0.08*** £0.06*** Heard of P. ramorum (Pr) P. kernoviae (Pk) or Sudden Oak Death (SOD) £2.38** £1.22 £1.36** £1.24* £2.04** £1.36* Noticed pests/diseases on trees/plants £1.37 £1.73** £1.21** £1.20** £1.06 £1.06 Member of conservation, environmental or gardening organisation £1.86* £1.83** £0.88 £1.16* £0.54 £0.84 Number of times visited HGs in England and Wales over last 12 months. £0.22 £0.21 - - - - Number of times visited HE in England and Wales over last 12 months. - - £0.04 £0.03 - - Number of times visited WD in England and Wales over last 12 months. - - - - £0.03 £0.02 Increased concern about Pr and Pk damaging HGs (likert scale) £1.27*** £1.16*** - - - - Increased concern about Pr and Pk damaging HE (likert scale) - - £1.10*** £1.18*** - - Increased concern about Pr and Pk damaging WD (likert scale) - - - - £2.21*** £1.88*** Live in Wales -£1.58 -£1.48 -£0.87 -£1.12* -£1.03 -£1.19* ML (Cox-Snell) R2 0.058 0.048 0.065 0.061 0.082 0.073

20 Symbols *, ** and *** refer to 10%, 5% and 1% levels of statistical significance. 21 HGs = heritage gardens, HE = heathland and WD = woodland.

40 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

4.2.4 TEV of Habitats at Risk As alluded to in Table 5, there is an overrepresentation of the population holding membership of a conservation, environmental or gardening organsation. For instance, approximately 10% of the UK population hold membership of these organisations while 32.1% of survey respondents declared membership. This may be due to self selection bias where respondents with a particular interests are drawn to surveys relevant to these interests. Given these respondents are known to place a premium on protecting heritage gardens and national heathland, it is prudent to reduce the mean WTP values for these habitats accordingly prior to aggregating WTP values for the population of England and Wales .

This was accomplished by excluding the premiums for protecting heritage gardens and national heathland for 22.1% of the sample, which corrects for the oversampling of survey respondents with membership of a conservation, environmental or gardening organisation (Table 5). For example, the mean WTP for protecting regional heritage gardens, after correcting for this oversampling, is deduced by multiplying the premium attached to this habitat (£1.86) by the difference in the proportion of survey respondents and UK population with membership (22.1%). This shows the mean WTP for protecting regional heritage gardens needs to be adjusted downwards by £0.41 per person to correct mean WTP for this oversampling. This calculation is repeated for national heritage gardens and national heathland. with adjustments to mean WTP values shown in Table 10. Table 10: Mean WTP adjusted by environmental membership

Net Mean Mean WTP Mean Habitat WTP (Per (Per Year) Adjustment Year)

Regional HGs £7.65 -£0.41 £7.24

National HGs £6.54 -£0.40 £6.14

Regional HE £4.86 - £4.86

National HE £4.34 -£0.26 £4.08

Regional WD £6.12 - £6.12

National WD £5.04 - £5.04

A second source of self selection bias may be prevalent in relation to over-sampling users of these habitats 22 . Table 11 shows that 72.6%, 67% and 78.2% of respondents were users of heritage gardens, heathland and woodland respectively. The proportion of respondents visiting woodland is rather high when compared to the estimate of 40% of people who visited woodland in the last year (Sen et al, 2011). In addition, Table 11 shows that users WTP is considerably higher than that for non-users.

22 However, both sources of self selection bias may be positively correlated as users may be more likely to hold membership of an environmental organisation,

41 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Table 11: Mean WTP by users and non-users

Heritage Gardens Heathland Woodland Regional National Regional National Regional National

Users £8.77 £7.72 £6.11 £5.67 £7.04 £5.93 Non-Users £4.68 £3.39 £2.34 £1.66 £2.85 £1.84 Users’ WTP 87.4% 127.7% 161.1% 241.6% 147.0% 222.3% Premium Proportion of 72.6% 67.0% 78.2% Sample are Users

Therefore the potential exists that users of these habitats have been over-sampled relative to the true proportion of users in England and Wales, although the true proportion of users in the population is unknown across all three habitats. Given that users are WTP significantly more than non-users, it is prudent to account for this when aggregating WTP values across the population of England and Wales. Table 12 presents aggregated WTP totals based on WTP values adjusted for environmental membership, and the WTP values of habitat users and non-users.

Table 12: Aggregated mean WTP

Heritage Gardens Heathland Woodland Regional National Regional National Regional National

Mean WTP Adjusted for Environmental £313m £265m £210m £176m £264m £218m Membership

Assuming 100% of Population are Users £379m £333m £264m £245m £304m £256m (Upper Bound)

Assuming 100% of Population are Non-Users £202m £146m £101m £72m £123m £79m (Lower Bound)

The aggregated values in Table 12 are derived by mulitplying the relevant WTP values in Tables 10 and 11 by the 43 million people aged 18 years or older living in England and Wales23 . The aggregated WTP values derived from the assumption that all adults in the

23 Source: Office for National Statistics (http://www.statistics.gov.uk/statbase/Product.asp?vlnk=15106).

42 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

population are users should be treated as upper bound estimates, while the aggregated WTP values derived from assuming no habitat users should be treated as conservative estimates. Whichever assumption is used when aggregating WTP values to the population of England and Wales, it is clear there are substancial aesthetic ecosystem service values at risk from a cessation of the current control programme. In addition, the highest WTP values are associated with heritage gardens regardless of the aggregation method used. 4.3 Impact on Commercial Timber Production and Carbon This section considers the impacts on commercial timber production in terms of both lost timber value as well as the effect on carbon sequestration. Given that the forests are commercial, control options are still exercised given the effect of the disease on production i.e. control is a private good in this instance. The analysis of such impacts uses the same spread analysis as described in section 4.1.2 but is compromised somewhat by the no control in the spread scenarios i.e. overall spread would be somewhat less if the private sector continued control.

The starting point for the analysis of commercial forestry is an assessment of the area at risk. 4.3.1 Area of Larch in England and Wales Although somewhat dated, the National Inventory of Woodland and Trees (NIWT, 2001) indicates the contribution of larch to woodlands in England and Wales. The NIWT is based on surveys carried out in the late 1990s and is the only information available for private sector woodlands. It is based on a sample of woodlands and does not provide any spatial referencing. Estimates are subject to sampling error which increases as the area of interest is reduced. According to NIWT (2001) the area of larch in England and Wales is around 66,500 ha (Table 11), of which 35% is on Forestry Commission (FC) land. Larch is a relatively minor species in England (4.8% of high forest area) compared with spruce, pine and a number of broadleaved species. In Wales it is more important at 8.9% of high forest area and is the third most important species in area terms after Sitka spruce and oak. Japanese larch is the predominant larch species (53% of the total larch area).

43 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Table 11: Area of larch (all species) in England and Wales

Larch area adjusted for Larch area (ha) (NIWT, Region infection to February 2001) 2011 (ha)

East of England 2,293 2,293 East Midlands 2,166 2,166 London 4 4 North East 4,655 4,655 North West 5,458 5,458 South East 5,212 5,212 South West 9,803 8,974 Wales 22,173 21,386 West Midlands 5,698 5,698 Yorkshire & Humber 9,087 9,087 England and Wales 66,548 64,933

Given the lack of recent data we assume the 2011 regional areas of larch are the same as in column two of Table 13 with an adjustment made for areas cleared under notice for P. ramorum and P. kernoviae infection. As of February 2011 notices have been issued on 829 ha in England (all in the South West), and 787 ha in Wales, which equates to around 8.5% and 3.5% of larch in South West England and Wales being infected. In terms of England and Wales as a whole, only 2.4% of the larch area has been infected to date. 4.3.2 Predicted Spread of Infection FERA and Cambridge University have modeled the further spread of P. ramorum and P. kernoviae . Its medium risk scenario (scenario 2) gives the predicted location of infected biomass in 10 and 20 years time in the absence of intervention measures. The location of infected km squares were used to estimate the extent of infection in FC and private woodlands in 2021 and 2031 (Table 124). We assume that if a square contains infected biomass it will either be in woodland or enter woodland in the square at some point in the future. Hence all woodland in infected squares is treated as if infected. Because of the lack of spatial information on the location of private woodlands we applied the proportion infection in FC larch in each region to the estimated private sector area. Table 12 gives the areas of larch in each region predicted to be infected after 10 and 20 years. Most of the infection is located in Wales and South West England with smaller areas elsewhere. In total around 25,000 ha is predicted to be infected by 2031 in the absence of intervention measures to prevent spread of these diseases. This is around 37% of the total larch area in England and Wales, with the severest effects in the South West, North West, South East and Wales.

44 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Table 12: Predicted areas of larch at risk under the medium risk scenario after 10 and 20 years (ha) Area infected Area infected Region after 10 years after 20 years (ha) (ha) East of England 0.0 0.0 East Midlands 0.0 0.0 London 0.0 0.0 North East 0.0 0.0 North West 1,801.2 2,231.4 South East 781.5 1284.0 South West 2,503.8 5,378.4 Wales 11,407.9 16,117.1 West Midlands 21.1 140.4 Yorkshire & Humber 0.0 0.0 To tal 16,515.5 25,151.4

4.3.3 Species Infected and Re-stocking It was assumed that all susceptible woodland within an infected square would be infected within the 10 or 20 year time period. Based on experience to date the only species with non-trivial and economically significant infection levels is Japanese Larch (JL). For several reasons we treat all larch species as at risk. This is because JL is often grown in mixtures with Hybrid Larch (HL) and European Larch (EL) such that infection of JL results in notices to fell whole plantations where infection is present, including other larch species. In addition, given the susceptibility of JL there is a risk, albeit an unquantifiable one, that the other larch species will become susceptible to these diseases. Finally JL is the dominant larch species grown in England and Wales and the NIWT treats JL and HL as one category, resulting in no distinction between these species in the NIWT datasets. We assume larch is felled and replaced by a crop of Douglas Fir (DF) (as advised by the Forestry Commission) as this is the most productive alternative to larch in the west of the country (where most infection is forecast to occur). It is possible that a small percentage of the land will be planted with broadleaves but this is expected to be quite small. In addition, not all land under notice will be replanted since there is no obligation to replant. However, land previously under forestry will have little alternative use value and grant aid for re-stocking is being offered at a sufficiently attractive level to encourage replanting on at least 95% of land. For practical purposes we assume 100% replanting with DF with 15% unproductive land both in the current larch crop and its replacement. An alternative restocking assumption of, for example, 95% DF and 5% broadleaves, make very little difference to the overall social cost of infection. 4.3.4 Analysis of Value at Risk To estimate the woodland value at risk it is necessary to estimate the reduction in social benefit brought about by infection and subsequent felling. As this analysis

45 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae looks at impacts on social rather than private welfare, we use a 3.5% discount rate 24 and grant aid available for planting or restocking etc. is excluded. We commence with the impacts on timber and carbon values. Woodlands are an asset providing timber, carbon sequestration and other ecosystem services both now and in the future. They are also a potential liability when felled since carbon will at some time point be released. When woodland is felled because of infection the stream of future income and costs is disrupted. To assess the value of timber and carbon at risk it is necessary to quantify the changes in income and cost streams that result from the premature felling. This is achieved by comparing the net revenue stream from an infected crop cleared now with the stream from the crop had it not been infected. To build up the net revenue streams we need to take account of impacts on revenue from timber, impacts on carbon sequestration and release, and the costs of felling, replanting, etc. Effects on biodiversity and landscape amenity are considered later. Timber Values Both current and restocked crops were assumed to have either a rotation length of 50 years or not to be harvested (non-rotational system). A 50 year life is fairly typical of commercial confer plantations and for analytical purposes it allows an easy fit with the 5 and 10 year periods used by FC in their presentation of data relating to planting dates and carbon sequestration. A fairly conservative yield class of 10 was assumed after discussion with officials of the FC with 15% of land taken to be unplanted. To simplify the analysis we use the FC Booklet 48 JL/HL yield class 10 tables (1.7m spacing, intermediate thinning 25 ) for all timber yield and restocking estimates. It could be argued that restocking with DF may result in improved yields. However, since landowners had a preference for planting larch a conservative estimate of yield class for DF seems appropriate. Standing conifer timber prices have varied considerably over the last 20 years. We assume £18 per cu m for tree volumes in excess of 0.6 cu m and £12 per cu m for smaller tree volumes. Infected trees are assumed to have the same yield as non- infected trees but fetch a lower price of £8 per cu m for trees at over 25 years old and zero price for younger blocks. This reflects the additional costs of handling infected material, a reduced choice of outlets and a degree of oversupply in the market. The presence of excess larch timber in a locality as a result of clearing infected trees may depress market prices for all larch whether infected or not. Much will depend on the extent of infection, the ability of local markets to deal with increased supply, and whether owners change the timing of their clearfell. In addition the social cost of infection may increase should infection become widespread in a region. However, if infection is contained there may be a corresponding future increase in prices due to under-supply. This element of market disruption is difficult to quantify and has not been included in the analysis. Restocking Costs Typical conifer restocking establishment costs are £2,500 per ha (2,250 trees per ha) with £20 per ha annual management costs, which allows for protective deer fencing. However, not all plantations will require this especially in Wales, with some infected

24 In view of the considerable uncertainties associated with many aspects of the data (in particular future carbon prices and the extent of P. ramorum and P. kernoviae spread) it was considered inappropriate to add a further level of complexity to the analysis by applying a 3.0% discount rate over the years 31-75 as suggested in the Treasury Green Book. 25 Thinnings were ignored in the timber analysis since their impact on net income is small. In the carbon analysis they are accounted for within the FC lookup tables.

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younger plantations retaining suitable fencing for re-stocking. Therefore the average restocking cost are assumed to be £1,800 per ha, while annual management costs are taken as £20 per ha over 50 years. Experience indicates that crops less than 25 years old generally involve a clearance cost ranging from £300 to £2000 per ha. In a relatively small number of cases in Wales revenue has been earned from using cleared wood in energy production where forests are near an energy plant, although this is rather unusual and therefore excluded from the analysis. The average clearance cost for infected woodlands aged 25 years or younger is assumed to be £1,500 per ha. For convenience in presentation of the results the cost stream is taken as part of the timber activity, such that timber returns are treated as net of cost. In reality timber and carbon are joint outputs which have no impact on the aggregate net costs of infection. The present value (PV) of a complete rotation (timber and costs) is derived from the initial cost of £1,800 per ha plus annual costs of £20 per ha and an income from 280 cu m at £18 per cu m, less 15% (£4,284 per ha). This gives a PV of £-1,502 per ha assuming a 3.5% discount rate 26 , which equates to a PV annuity of £-64.04 per ha per year. Restocking thus makes a loss equivalent to £64 per ha per year because the discounted income fails to cover the costs with timber activity unable to deliver the 3.5% rate of return used in the social appraisal. One may wonder why such woodlands were ever planted if they produce a negative social return from timber although there are numerous possible reasons why this situation arises. Investors may have received grant aid or simply require a return lower than 3.5% per annum. In addition investors may have expected higher timber prices when planting since real prices have been substantially higher in the past. Long-term forestry investors may also be locked into investments producing low or negative returns since restocking is obligatory after a clearfell regardless of the private economics of the subsequent investment. Finally there may be private benefits from diversification within woodland such as visual amenity and from sport, all of which would add to the private return on capital. 4.3.5 Time Horizon of the Analysis - Timber The PV of infection was assessed by comparing the net revenue streams of infected and uninfected crops. For rotational trees the time horizon used for the timber appraisal in both infected and uninfected crops was the harvest date of the uninfected crop (the normal end of rotation). For trees in rotation the relevant cash flows and present values are: • Income from the sale of felled timber from infected trees less costs of felling and clearance. • Loss of future net income that would have occurred had the crop not become infected. For timber in rotation, this is the net income stream to harvest at 50 years. For a 30 year old stand this would be the net income stream over the 30 to 50 years. • Net income from restocked (infected) crop up to the time horizon (50 years for rotational timber). For an infected 30 year old crop this would be 20 years. In order to derive the net income over part of a rotation we calculate the present value of net income over a full rotation, convert to its equivalent annuity and take the PV of the period to the time horizon. In the case of a crop infected at 30 years old this would be a 20 year period. This method ensures that the time

26 Lower discount rates are used for periods beyond 30 years as suggested in the Green Book

47 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

horizons used to compare income streams from infected and uninfected crops is the same. The same time horizon was used for trees not under rotational management. 4.3.6 Calculation of Present Value of Timber Clearance following Infection (Timber) The PV from the early harvesting of infected trees represents the timber and carbon value at risk. This PV depends on the age of the woodland since planting. PVs were calculated for woodlands varying in age from 5 to 50 years, at intervals of 5 years. Infection appearing in a 50 year old stand was assumed not to affect returns. An example calculation is given in Table 135 for a 30 year old stand of larch. The present cost per ha of infection is a net £-760 per ha. This is the PV loss of clearing the current 30 year old stand and replanting, as compared with the continuation to rotation end with an uninfected crop. This analysis is based solely on timber with losses from carbon sequestration to be added to obtain a total timber and carbon impact (see below). Table 13: Net present cost of infection in a 30 year-old larch stand (£ per ha, timber only)

50 year rotation Non-rotational

Present Present Basis of Present Value Basis of Present Value Timber Value (£ per Value (£ per Calculation (£ per ha) Calculation (£ per ha) ha) ha) Income from sale of 198 cu m timber at £12, Income from sale of cleared timber +2,020 +2,020 less 15% (income now) cleared timber (infected crop) Removal of future cost £4,284 per ha timber Loss of future net stream due to income adjusted by 15%, income stream due premature clearance. less £20 per year for 20 -1,869 +284 to premature PV of £20 per year for years (discounted at clearance of crop 20 years (discounted at 3.5%). 3.5%) . Equivalent annuity of net Net income from income from the restocked crop after restocked crop for 20 clearance expressed -910 N/A years. This is £-64.04 per as an equivalent ha for 20 years annuity for 20 years. (discounted at 3.5%) . PV of restocking is Cost of restocking £1800 + £20 per year N/A -2,269 after clearance for 50 years (discounted at 3.5%). Net present value -760 +35

4.3.7 Alternative Non-rotational Scenario The above analysis assumes clearfell at 50 years. An alternative scenario assumes no felling of the current crop with established trees continuing into perpetuity without providing income. The income from the infected crop remains unchanged at a PV of £2,020 per ha and there is a small gain from removing the costs associated with managing the uninfected crop at a PV of £284 per ha (Table 135). Infection of larch

48 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

under this alternative scenario produces no loss of future income. The cost of re- stocking after infection is the initial establishment cost of £1,800 per ha and a management cost of £20 per year for 50 years, which equates to a PV of £2,269 per ha. The net gain under this alternative scenario is +£35 per ha. We therefore conclude that infection of a 30 year old crop has a PV broadly in the range £35 to +£760 per ha depending on its management. 4.3.8 Age Effects Table 146 presents the net present values (NPVs) per ha for larch infected and cleared at different ages. When in rotation, costs are substantial for crops infected at an early age because the infected crop is costly to clear. Costs also increase slightly as the infected crop matures mainly because of the lower income obtained from the infected timber compared to income obtained from uninfected timber at 50 years. Table 14: Net present value of infection in larch (timber and production costs) NPV of timber and production costs 50 year rotation Non -rotational Age at fell of (net of costs) system (net of costs) infected crop (£ per ha) (£ per ha) 5 -3,401 -3,319 10 -3,522 -3,342 15 -3,665 -3,369 20 -3,836 -3,401 25 -4,038 -3,439 30 -760 +35 35 -769 +256 40 -904 +396 45 -1,123 +504 50 -1,427 +587

Under a non-harvest system, costs are again high for younger infected crops because they are costly to clear and have to be re-stocked. As stands mature there is a small net benefit from infection as income is now generated from the felled crop, which is not the case for crops younger than 26 years old. 4.4 Carbon Carbon sequestration rates were taken from the FC lookup tables (Forestry Commission, 2011 a). These do not account for potential carbon releases when soil is disturbed at planting, although the FC indicates this would be small on mineral soils 27 . In light of the FC position we exclude possible carbon releases from soil when examining carbon sequestration rates.

27 In addition Cannell et al. (1993) find planting trees on peat soils may result in net carbon loss in the long term, depending on the depth of peat soils.

49 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Carbon release from timber or cleared material depends on the use of the product. Since larch mainly goes into fencing, packaging and some board manufacturing we assume a 20 year life after harvest before carbon release. Crops under 26 years old may be left on site to decay, used in the garden sector for short-lived products or used for combustion in the energy sector where opportunities permit. There is no precise information on the relative importance of different outlets for infected material especially in areas currently uninfected. However, the main types of outlet will tend to release carbon over a short time horizon and we therefore assume a short (5 year) period before release for crops under 26 years old. The FC tables also specify a maximum sequestration value which is the maximum amount of CO 2 sequestration per ha that can be claimed under the Woodland Carbon Code (Forestry Commission, 2011 b). This allows both for sequestration and carbon release following harvest. However, this concept cannot be applied in the current context in which harvest expectations are disrupted by infection. For example, a stand that has reached its maximum sequestration value there is still a loss of sequestration from premature harvesting and a release of CO 2 from prematurely harvested timber. 4.5 Net Carbon Effects of Early Harvesting of Infected Woodland

Two approaches were used to estimating the impacts of infection on net CO 2 emissions. The first approach calculates CO 2 effects in tonnes (t) CO 2 per ha, while the second approach applies carbon pricing to measure the effects in monetary terms. The same time horizon for sequestration was used for crops in rotation as that for timber. However, carbon is assumed to be released 5 years after harvesting for crops less than 26 years of age, and 20 years after harvest for crops over 25 years of age. This applies to both infected and uninfected crops. For crops not in rotation only the infected crop would have a carbon release following harvest since the uninfected crop is never harvested. However, unharvested JL older than 100 years does start to suffer from net carbon emission (Forestry Commission, 2011 a). Table 15 shows the net carbon effect for a crop which is 30 years old at the time of infection. The impacts are calculated both for a crop in a 50 year rotation and one under a non-rotational system. It is shown that the net carbon balance is effectively zero in each case. The small gain for non-rotational crops simply reflects the arbitrary time horizon used (year 2100), at which point the uninfected crop is losing a small amount of carbon per year. We have not accounted for any delays in replanting after infection but given the time horizons involved in forestry, the impact of infection on the net carbon balance is practically zero. This conclusion applies regardless of the age of trees at the time of infection.

Table 15: Net carbon effects of infection in a 30 year old larch stand (t CO 2 per ha) 50 year rotation Non -rotational Basis of Carbon t CO per ha Basis of Calculation t CO per ha Calculation 2 2

Loss due to release of 237.1 t CO 2e -201 Loss due t o release of -201 carbon from cleared released in 2031 carbon from cleared timber (infected crop) less 15% timber (infected crop)

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Loss of future Accumulated value -64 Loss of future -128 sequestration due to of annual sequestration due to premature clearance sequestrations to premature clearance (to 2031 less 15% 2100)

Gain from non -release Release of 312.8 t +266 Gain from non -release of N/A of carbon due to CO 2 in 2051 less carbon due to premature premature clearance 15% clearance Gain from No net gain +0 Gain from sequestration in +337 sequestration in restocked crop (to 2100) restocked crop, adjusted for subsequent release Net balance +0 +8

4.5.1 Present Value of Early Harvesting of Infected Woodland (Carbon) As social carbon prices are predicted by DECC (2010) to change over time, it does not necessarily follow that the impact of infection on the value of carbon sequestered or emitted will be unaffected by infection. To derive the effect of infection on the PV of net CO 2 we take all output to be non-traded and initially use the central DECC price estimates. If a significant proportion of the output from younger trees is used for power generation, this would substantially reduce the cost of carbon released in the period to 2030 provided DECC traded C prices were applied. However, this is only possible where suitable plants are close to infected woodlands, which is a remote possibility given that very few such plants exist. The impact of infection on carbon sequestered and released was modeled in much the same way as for timber (see above). Once again we take a 30 year old stand of larch as the exemplar. Under rotational forestry there is a release of carbon from the cleared infected crop. For a 30 year old crop felled following infection, the carbon is released 20 years after harvest (2031) with a PV cost of £7,746 per ha. There is a loss of future sequestration in cleared trees over a 20 year period which has a PV of £2,811 per ha. However, one benefit of early clearance is the carbon that would have been released 20 years after harvesting the infected crop is no longer released. This has a PV of £13,908 and reflects the much higher DECC carbon prices in 2051 compared to 2011. The rate of increase in the carbon price greatly exceeds the 3.5% discount rate used to convert future values to PVs. As with the timber analysis we bring the restocked crop to the same time horizon as the uninfected crop by calculating the carbon annuity for 20 years 28 . In total infection generates a NPV gain of £6,093 per ha from clearing a 30 year old rotational larch crop (Table 16). This perhaps surprising result mainly reflects the changes in DECC carbon prices over time which lead to carbon release taking place in 2051 (following a clear fell in 2031) being associated with much higher carbon prices than those in the more immediate future. For instance, DECC (2010) list central carbon values of £207 per t CO 2 in 2051 and £77 per t CO 2 in 2031. The social value of the sequestration between years 30 and 50 (£2,811 per ha) is greatly

28 This is an approximation as carbon prices are not fixed over time.

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outweighed by the release of 50 years of accumulated carbon at higher carbon prices (£13,908 per ha). Table 16: Net present cost of infection in a 30 year old larch stand (£ per ha, carbon only)

50 year rotation Non-rotational

Basis of PV Calculation PV(£/ha) at Basis of PV PV (£/ha) at Carbon (£/ha) 3.5% Calculation (£/ha) 3.5% Loss due to release 237.1 t CO e released in Loss due to release of of carbon from 2 2031 at £76.5 per t CO , -£7,746 carbon from cleared -£7,747 cleared timber 2 less 15% timber (infected crop) (infected crop) Loss of future Accumulated value of Loss of future sequestration due annual sequestrations to sequestration due to -£2,811 -£6,034 to premature 2031 at prices of £52.5- premature clearance clearance 76.5 per t, less 15% (to 2100) Gai n from non - Release of 312.8 t CO in Gain from non-release release of carbon 2 2051 at £207.1 per t, £13,908 of carbon due to N/A due to premature less 15% premature clearance clearance Based on a 20 year Gain from annuity for the Gain from sequestration in restocked crop of £193 sequestration in restocked crop, £2,743 £14,160 per year derived from restocked crop (to adjusted for any the equivalent 50 year 2100) subsequent release cycle annuity Net present value £6,093 £380

4.6 Alternative Non-rotational Scenario Under this scenario the existing larch woodland would not be harvested. Similarly a restocked crop after infection would not be harvested. Since DECC give no advice on carbon prices beyond 2100 we limit the analysis of carbon sequestration to a 90 year rather than an infinite horizon. However, extending the time horizon beyond 2100 by assuming the carbon price beyond 2100 is fixed at the 2100 level was found to have very little effect on the net gain or loss from infection. For the infected 30 year old crop the loss of sequestration value to 2100 is £6,034 per ha (Table 18) in PV terms. However, when a crop is restocked there is a gain in sequestration value of £14,160 because a higher rate of carbon retention takes place in years of high carbon price. The net effect is to give a social benefit from infection of £380 per ha. The complete results for crops at different stages of maturity are given in Table 17. Under a 50 year rotation there are social gains from infection at all ages up to 50 years. The pattern of benefits reflects the gain from releasing carbon now but sequestering it at higher carbon values later. The lower gains in younger plantations reflect the assumption that carbon is released after five rather than twenty years. For a 50 year plantation the impacts of infection on the carbon value is zero because the

52 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

crop would be harvested at that age under a rotational system whether infected or not. Table 17: Net present value of infection in larch (carbon) NPV of carbon

Age at fell of infected 50 year rotation Non -rotational system crop (£ per ha) (£ per ha) 5 +515 -984 10 +773 -2,291 15 +919 -3,562 20 +1,715 -3,843 25 +6,140 +165 30 +6,093 +380 35 +5,607 +643 40 +4,587 +925 45 +2,784 +1,203 50 0 +1,521

Under a non-harvest system, carbon costs decline with the age of plantations and turn into gains for plantations aged 25 years or more. This PV reflects the cost of releasing carbon that would otherwise be retained, adjusted for the gains from sequestering carbon in the re-stocked crop at higher carbon values.

4.7 Total Impacts of Infection on Cost, Timber and Carbon Values Error! Reference source not found. gives the combined social costs of infection at different growth stages of larch. The negative figures are social costs which are substantial for young plantations, in part reflecting the costs of clearance and replanting. For older plantations the crops generate timber income and benefit from higher carbon values when re-stocked. Taking the mean across age groups and using central prices, gives a small social gain from infection of £568 per ha for rotational crops and a loss of around £2,100 per ha for crops not in rotation. Table 18: Net present value of infection in larch (timber, production costs and carbon)

DECC Central carbon prices DECC Low carbon prices In 50 year Non -rotational In 50 year Non -rotational Age at fell of rotation system rotation system infected crop (£ per ha) (£ per ha) (£ per ha) (£ per ha) 5 -2,887 -4,304 -3,251 -3,849 10 -2,749 -5,633 -3,240 -4,568

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15 -2.747 -6,931 -3,233 -5,279 20 -2,122 -7,244 -2,854 -5,498 25 +2,101 -3,275 -631 -3,579 30 +5,335 +414 +2,862 -44 35 +4,838 +899 +2,557 +267 40 +3,682 +1,321 +1766 +509 45 +1,661 +1,707 +474 +720 50 -1,418 +2,108 -1,418 +1,427 Mean +568 -2,094 -3,251 -3,849

It is clear that the net gain or losses depend principally on the age of the crop when infected and cleared, whether it is in a rotational or non-rotational system and the carbon prices applied. It is also the case that earlier carbon releases than assumed here would add to the present cost of infection ( Error! Reference source not found. 20) while later release would reduce costs. The critical element in the calculations is the DECC carbon price. We used DECC’s central estimates although if DECC’s low price is used the costs of infection are substantially higher on average. Applying a constant carbon price at the 2011 level (£52.5 per t CO 2) gives mean present costs of infection of £3,251 per ha for rotational crops and £3,849 per ha for non-rotational. The conclusions from the analysis of impacts on timber and carbon is that infection of 30-35 year old stands provide social benefits but only because of the rate of increase in DECC future carbon prices, while infection of young stands provide high social costs. Since the Forestry Commission does not appear to have a policy of early harvesting or delayed re-stocking to benefit from sequestrating carbon at higher future carbon prices we might conclude that either: • There are other costs from early harvest or delayed re-stocking (e.g. biodiversity and landscape effects) which are important, or • There are other constraints imposed such as those contained in the Forestry Standard (Forestry Commission, 1998) which limit flexibility in harvesting and restocking. Certainly the Forestry Standard would require re-stocking without significant delay unless there were overriding environmental factors that indicated otherwise. DECC prices are largely uncertain and much lower estimates have been reported in the literature. For example, Pearce (2005) reviewed all available estimates of the social cost of carbon and conclude that the cost was £4-27 per t C (£1.1-£7.4 per t CO 2e). The author similarly noted that the Clarkson and Deyes (2002) carbon price estimates at the time were not being integrated into policy. Government appears to adopt a rather more cautious ‘wait and see’ approach to its application of carbon pricing in policy. One may similarly be cautious about concluding that infection of older stands leads to social benefits as this effect depends entirely on the extent to which carbon prices are predicted to increase over time. However, infection of larch may lead to impacts on biodiversity and landscape aesthetics which need to be explored. These are assessed below and elsewhere in this report.

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4.8 Aggregate Impacts In order to apply the infection costs and benefits to the areas at risk of infection, we ideally need to know the age structure of the larch and whether owners intend to clear fell or to keep under a non-rotational system. There are no satisfactory data on either element for the private estates containing a large proportion of larch in the UK. The only comprehensive dataset which has information on planting dates at regional and national levels is the NIWT, although this information is somewhat dated and therefore gives no information on recent planting and re-stocking rates. The lack of reliable recent data on larch felling, re-stocking and new planting makes it very difficult to estimate the age classes of the remaining larch stock with any precision. In addition we have no information on private sector harvesting intentions. FC informed us that infection is thought to occur randomly across all ages of trees. Given the uncertainty associated with the stock and management of private sector woods, we take the mean present costs of infection across stand ages (Table 18) and apply them to the total areas in each region at risk (Table 12) to give the results in Table 19.

Table 19: Present value of predicted infection to 2031 (£m) (excluding unaffected regions)

DECC central carbon price DECC low carbon price

in 50 year in 50 year Region non-rotational non-rotational rotation rotation North West 1.01 -3. 71 -5.77 -6.83 West Midlands 0.05 -0. 19 -0. 29 -0. 34 South East 0.54 -2. 01 -3. 11 -3.69 So uth West 2.17 -8.01 -12.43 -14.72 Wales 7.05 -26.00 -40.36 -47.79

Total England and Wales 10.83 -39.92 -61.97 -73.36

Taking Wales as an example the method of calculation is as follows: The area of infection by 2021 is predicted to be 11,408 ha. If we discount the impacts by an average of 5 years at 3.5%, the PV of infection lies between +£5.5m and -£20.1m, using DECC central prices. A further 4,708 ha is predicted to be infected by 2031. Discounting the additional costs for a further 15 years gives a PV of between £1.6m and £-5.9m, and a total PV to 2031 of between £7.1m and £-26.0m. Using DECC central carbon prices, the aggregate effect for England and Wales lies between a benefit of £10.8m and a cost of £39.9m depending on the split between rotational and non-rotational woodland management. The main cost impact in England and Wales in absolute terms is in Wales because Wales has a larger predicted area of infected larch than any English region (Table 12). If DECC low carbon prices are applied the costs of infection to 2031 are substantially higher at between £62.0m and £73.4m.

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4.9 TEV of Heritage Gardens, Heathland and Woodland at Risk The TEV at risk from P. ramorum and P. kernoviae spread in Table 20is identified by combining the WTP values produced from the online survey (Table 12) with the commercial timber and carbon sequestration values produced upon infection of larch (Table 19).These values show substantial TEV at risk when mean WTP values are adjusted for environmental membership or when assuming all the population in England and Wales are habitat users. Even when making a conservative assumption that there are no habitat users in England and Wales, the TEV at risk still totals £763m - £712m per annum. By far the largest proportion of TEV at risk relates to public WTP values for a continuation of the current programme to contain these diseases. Table 20: TEV of heritage gardens, heathland and woodland at risk to 2031

Heritage TEV at Heathland Woodland Gardens Risk WTP Aggregation Commercial Timber and Method WTP Values WTP Values WTP Values All Values Carbon Values 29

Mean WTP Adjusted for +£40m to - £1,486m - £578m £386m £482m Environmental £11m £1,435m Membership Assuming 100% of Population are +£40m to - £1,821m - £712m £509m £560m Users (Upper £11m £1,770m Bound)

Assuming 100% of Population are +£40m to - £763m - £348m £173m £202m Non-Users (Lower £11m £712m Bound)

5. Discussion

The basis for the valuation was the cause of some debate. As mentioned, the standard process would be for respondents to be presented with a counterfactual that would relate to the expected level of disease spread under the current control regime. By presenting a scenario of the current spread against future spread without control, respondents were in effect presented with a control scenario that kept the diseases in stasis. However, it seems unlikely that such an outcome is possible, particularly for P.

29 Commercial timber and carbon values are taken from Table 21 with the range of values reflective of whether one assumes a 50 year rotation or no rotation for larch at risk.

56 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae ramorum . As such the WTP figures may contribute towards an overestimate of TEV at risk .

A second potential for overestimation from the CV exercise relates to the issue of how uncertainty in outcome is presented to respondents. The analysis of P. ramorum and P. kernoviae are characterised by significant variability and uncertainty, a common trait in the analysis of invasive species. It has been shown that when such uncertainty is presented in valuation studies the WTP decreases, i.e. Glenk and Colombo (2011).

The WTP figures were grossed up to the adult population of England and Wales, which produce large estimates for each of the six elements valued. The survey was clear that their responses were additive and so combining all six seems reasonable. Respondents were also shown maps of the location of the habitats of interest as well as maps of the current and future spread of the disease which would have shown the “threat” to their use/non-use values as well as the availability of alternative sites. They were also asked their WTP for the region in which they lived and for the rest of the country. To this extent, issues of distance decay and substitution have been incorporated.

The analysis presented a number of caveats and limitations which are listed as follows:

1. Spread analysis: given the inherent uncertainty in the pathology of the diseases the estimated spread used for the valuation exercise represent just one of a large number of potential outcomes. Different outcomes would have a clear effect on the direct values affected such as timber production but the effect on WTP values is less clear.

2. Species at risk: there is uncertainty in the pathology of the diseases and the host list is continually changing. Initially Japanese larch was viewed as a low risk species but is now seen to be severely affected by the disease as well as being a major sporulating host.

3. Total value at risk with no control: as mentioned in section 4.1.1, the baseline of no further spread from the current level is somewhat unrealistic. Thus the WTP may be overstated since some spread will occur from the current level even with controls in place.

4. Commercial timber analysis: whilst the WTP exercise assumes no further controls (in the public realm), the analysis of commercial timber production assumes that when discovered, forest owners will control by clearfelling and restocking with less susceptible species.

5. Carbon analysis and carbon values: the accounting style analysis results in the unusual conclusion that spread can be optimal due to the assumed timing of carbon release and the step rate of increase in the DECC carbon prices. However, the net carbon change is almost zero.

57 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

6. References

Anholt-GMI. (2007). Visit Britain. Nation Brand Index. Ballard, P. (2004). “The Restoration and Maintenance of Historic Gardens.” In: Collins, E. (Eds.), Crafts in the English Countryside: Towards a Future, 167-186. Wetherby, UK: Countryside Agency Publications. Barbier, E. (2007). “Valuing ecosystem services as productive inputs.” Economic Policy 22 (49): 177-229. Bateman, I., Carson, R., Day, B., Hanemann, M., Hanley, N., Hett, T., Jones-Lee, M., Loomes, G., Mourato, S., Özdemiro ğlu, E., Pearce, D., Sugden, R. and Swanson, J. (2002). Economic Valuation with Stated Preference Techniques. Edward Elgar. Bisgrove, R. and Hadley. P. (2002). Gardening in the Global Greenhouse – The Impacts of Climate Change on Gardens in the UK. Technical Report. UKCIP, Oxford. Brainard, J., Lovett, A. and Bateman, I. (2006). “Sensitivity analysis in calculating the social value of carbon sequestered in British grown Sitka spruce.” Forest Economics 12 (3): 201- 228. Cannell, M. (1999). “Growing trees to sequester carbon in the UK: Answers to some common questions. Forestry 72 (3): 237-247. Cannell, M., Dewar, R. and Pyatt, D. (1993). “Conifer plantations on drained peatlands in Britain: a net gain or loss of carbon?” Forestry 66 (4): 353-369. Central Science Laboratory, (2006). Determining the Susceptibility of Key / Dominant UK Heathland Species to Phytophthora kernoviae – Final Summary Report. Central Science Laboratory, York, UK. Available from World Wide Web: [Accessed 19 October 2010]. Centre for Agriculture Bioscience International, (2008). Phytophthora kernoviae (Distribution map). Distrib. Maps Plant Dis . 1023 . Christie, M., Hyde, T., Cooper, R., Fazey, I., Dennis, P., Warren, J., Colombo, S. and Hanley, N. (2011). Economic Valuation of the Benefits of Ecosystem Services Delivered by the UK Biodiversity Action Plan . Defra, London. Available from World Wide Web: [Accessed 23 August 2011]. Clarkson, R. and Deyes, K. (2002). Estimating the Social Cost of Carbon Emissions. Government Economic Service Working Paper 140. HM Treasury, London. Available from World Wide Web:< http://www.hm-treasury.gov.uk/media/2E817/SCC.pdf > [Accessed 12 October 2010] Department for Communities and Local Government, (2009). Housing Projections to 2031, England . Department for Communities and Local Government , London. Available from World Wide Web:< http://www.communities.gov.uk/documents/statistics/pdf/1172133.pdf > [Accessed 21 January 2011] Connell, J. (2004). “The purest of human pleasures: The characteristics and motivations of garden visitors in Great Britain.” Tourism Management 25 (2) : 229-247. DECC, (2010). Updated Short Term Traded Carbon Values for UK Public Policy Appraisal – June 2010 . DECC, London. Available from World Wide Web:< http://www.decc.gov.uk/assets/decc/what%20we%20do/a%20low%20carbon%20uk/c

58 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae arbon%20valuation/1_20100610131858_e_@@_carbonvalues.pdf > [Accessed 29 January 2011] Defra, (2007). An introductory guide to valuing ecosystem services. Defra, London. Available from World Wide Web: [Accessed 24 September 2010]. Defra, (2008). Impact Assessment on Future Management of Risks from Phytophthora ramorum and Phytophthora kernoviae . Defra, London. Available from World Wide Web: [Accessed 28 September 2010] Eglington, S. and Horlock, M. (2004). East of England Opportunity Mapping Project – Final Report . Forestry Commission. Available from World Wide Web:< http://www.forestry.gov.uk/pdf/eng-ee-heathland-mapping-report.pdf/$FILE/eng-ee- heathland-mapping-report.pdf > [Accessed 5 October 2010] Entec and Hanley, N. (1997). Valuing Landscape Improvements in British Forests. Report to the Forestry Commission. Entec UK, Leamington Spa, and Environmental Economics Research Group, Stirling University. Fera, (2010 a). Phytophthora ramorum : A Threat to our Woodlands, Heathlands and Historic Gardens. Plant Disease Factsheet. Available from World Wide Web: [Accessed 3 November 2010]. Fera, (2010 b). Phytophthora ramorum and Phytophthora kernoviae . Available from World Wide Web: [accessed 2 December 2010]. Fera, (2010 c). Fera List of Natural Hosts for Phytophthora ramorum with Symptom and Location . [online] Available from World Wide Web:< http://www.fera.defra.gov.uk/plants/plantHealth/pestsDiseases/phytophthora/documen ts/pRamorumHostDec10.pdf > [Accessed 19 April 2011]. Fera, (2010 d). Fera List of Natural Hosts of Phytophthora kernoviae with Symptoms . Available from World Wide Web:< http://www.fera.defra.gov.uk/plants/plantHealth/pestsDiseases/phytophthora/documen ts/pKernoviaeHost.pdf > [Accessed 19 April 2011]. Forestry Commission, (1998). The UK Forestry Standard . Forestry Commission, Edinburgh. Forestry Commission, (2011 a). Predicting Future Woodland Carbon Sequestration . Forestry Commission. http://www.forestry.gov.uk/forestry/INFD-864g2r Forestry Commission, (2011 b). Woodland Carbon Code . Forestry Commission. Available from World Wide Web:< http://www.forestry.gov.uk/carboncode > Forestry Comimission, (2011 c). Phytophthora ramorum . Available from World Wide Web: [accessed 21 April 2011] Garbelotto, M., Svihra, P. and Rizzo, D. (2001). “Sudden oak death syndrome fells three oak species.” California Agriculture 55 (1): 9-19. Garrod, G. (2003). Landscape Values of Forests . Social & Environmental Benefits of Forestry Phase 2, Report to the Forestry Commission, Edinburgh. Centre for Research in Environmental Appraisal and Management, University of Newcastle upon Tyne. Garrod, G. and Willis, K. (1992). “The environmental economic-impact of woodland: A 2- stage hedonic price model of the amenity value of forestry in Britain.” Applied Economics 24 (7): 715-728.

59 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Garrod, G. and Willis, K. (1997). “The non-use benefits of enhancing forest biodiversity: A contingent ranking study.” Ecological Economics 21 (1): 45-61. Glenk, K. and Colombo, S. (2011). “How sure can you be? A framework for considering delivery uncertainty in benefit assessments based on stated preference methods.” Agricultural Economics 62 (1): 25-46. Hanley, N. and Ruffell, R. (1993). “The contingent valuation of forest characteristics – 2 experiments.” Agricultural Economics 44 (2): 218-229. Hanley, N. and Spash, C. (1993). Cost-Benefit Analysis and the Environment, Edward Elgar. Hanley, N., Willis, K., Powe, N. and Anderson, M. (2002). Valuing the Benefits of Biodiversity in Forests . Report to the Forestry Commission, Edinburgh. Centre for Research in Environmental Appraisal and Management, University of Newcastle. Available from World Wide Web: Heritage Lottery Fund. (2010). Investing in Success – Heritage and the UK Tourism Industry. [online] Available from World Wide Web: [Accessed 17 October 2010] Lane, C., Beales, P., Hughes, K., Griffin, R., Munro, D., Brasier, C., and Webber, J. (2003). First outbreak of Phytophthora ramorum in England on Viburnum tinus . Plant Pathology 52 (3): 414–414. MacLeay, I., Harris, K., and Annut. A. (2010). Digest of United Kingdom Energy Statistics . Available from World Wide Web: [Accessed 30 November 2010] Millennium Ecosystem Assessment, (2005). Ecosystems and Human Well-Being: Synthesis . Island Press, Washington DC. Available from World Wide Web: [Accessed 5 August 2010] Mourato, S., Atkinson, G., Collins, M., Gibbons, S., MacKerron, G. and Resende, G. (2010). Economic Analysis of Cultural Services – Executive Summary . Report to the UK National Ecosystem Assessment. London School of Economics and Political Science. Available from World Wide Web:< http://uknea.unep- wcmc.org/LinkClick.aspx?fileticket=xAcO6D5e0UI%3D&tabid=82 > [Accessed 12 June 2011] National Audit Office, (2009). The Health of Livestock and Honeybees in England . National Audit Office, London. Available from World Wide Web: [Accessed 25 August 2010] National Trust, (2008). Future Management of Risks from Phytophthora ramorum and Phytophthora kernoviae - A Response from the National Trust to Defra's Consultation October 2008 . National Trust. Available from World Wide Web: [Accessed 18 October 2010] Natural England (2011). Private communication, S. Kirby and K. Kirby. NIWT, (2001). National Inventory of Woodland and Trees . Forestry Commission. Available from World Wide Web: Nurick, J. (2002). Heritage and Tourism . Locum Destination Review, 2, 35-38. Colliers International, London. Available from World Wide Web: [Accessed 21 October 2010]

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Oldfield, T., Smith, R., Harrop, S., and Leader-Williams, N. (2003). “Field sports and conservation in the United Kingdom.” Nature 423 (6939): 531-533. Pearce, D. (2005). “The Social Cost of Carbon,” In Helm, D. (Eds.), Climate-change Policy, chapter 4, 99-133. Oxford University Press. POST, (2007). Ecosystem Services . POST, London. Available from World Wide Web: [Accessed 12 March 2010] Provins,A.,Pearce,D.,Ozdemiroglu,E.,Mourato,S.,and Morse-Jones,S. (2008). “Valuation of the historic environment: The scope for using economic valuation evidence in the appraisal of heritage-related projects.” Progress in Planning 69 (4): 131-175. Roberts, J. (2007 a) Integrated Visitor Management Plan and Review of Visitor Experiences . Cornwall Sustainable Tourism Project, Truro, UK. Available from World Wide Web:< http://www.heathproject.org.uk/content_pdf/en/Action_46_Integrated_Visitor_Management_ Plan_CoaST1217254215.pdf > [Accessed 2 October 2010] Roberts, J. (2007 b) A Strategic Plan for Sustainable Tourism and Heathlands in Cornwall . Cornwall Sustainable Tourism Project, Truro, UK. Available from World Wide Web:< http://www.heathproject.org.uk/content_pdf/en/Action_34_Strategic_Plan_for_Sustainable_T ourism_Heathland_in_Cornwall_CoaST.pdf > [Accessed 2 October 2010] Sen, A., Darnell, A., Crowe, A., Bateman, I., Munday, P. and Foden, J. (2011). Economic Assessment of the Recreational Value of Ecosystems in Great Britain . Report to the UK National Ecosystem Assessment. Available from World Wide Web: [Accessed 15 July 2011] Spencer, J. and Haworth, R. (2005). Heathland on the Forestry Commission Estate in England . Forestry Commission. Available from World Wide Web:< http://www.forestry.gov.uk/pdf/fce-heathland-july-05.pdf/$FILE/fce-heathland-july-05.pdf > [Accessed 29 September 2010] Strange, N., Jacobsen, J., Thorsen, B. and Tarp, P. (2007). “Value for Money: Protecting Endangered Species on Danish Heathland.” Environmental Management 40 (5): 761-774. Tomlinson, I., Harwood, T., Potter, C., and Knight, J. (2009). Review of Joint Inter- Departmental Emergency Programme to Contain and Eradicate Phytophthora ramorum and Phytophthora kernoviae in Great Britain . Imperial College, London. Available from World Wide Web:< http://www.relu.ac.uk/research/Animal%20and%20Plant%20Disease/Review%20of%20inter %20departmental%20Pr%20Pk%20Emergency%20Programme%20(3).pdf > [Accessed 2 October 2010] Walters, K., Sansford, C. and Slawson, D. (2009). Phytophthora ramorum and Phytophthora kernoviae in England and Wales - Public Consultation and New Programme. Gen. Tech. Rep. PSW-GTR-229. Albany, CA, USA. Available from World Wide Web:< http://www.fs.fed.us/psw/publications/documents/psw_gtr229/psw_gtr229.pdf > [Accessed 15 September 2010] Welsh Assembly Government, (2010). Household Projections for Wales (2006-Based): Local Authority Report . Welsh Assembly Government, Cardiff, UK. Available from World Wide Web:< http://wales.gov.uk/topics/statistics/headlines/housing2010/0216/?lang=en > Werres, S. and Marwitz, R. (1997). “Triebsterben an rhododendron: Unbekannte Phytophthora.“ Deutscher Gartenbau 21 : 1166-1168. Werres, S., Marwitz, R., Man In't Veld, W., De Cock, A., Bonants, P., De Weerdt, M., Themann, K., Ilieva, E. and Baayen, R. (2001). ”Phytophthora ramorum sp nov: a new pathogen on Rhododendron and Viburnum.” MycolRes 105 (10): 1155-1165.

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Williams, A. and Shaw, G. (2009). “Future play: Tourism, recreation and land use.” Land Use Policy 26S (S1) S326-S335. Wright, I. and Slawson, D. (2010). Biosecurity protocols for heritage gardens. Gen. Tech. Rep. PSW-GTR-229. Albany, CA, USA. Available from World Wide Web:< http://www.fs.fed.us/psw/publications/documents/psw_gtr229/psw_gtr229_197.pdf > [Accessed 15 September 2010]

62 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

Appendices 1. Literature Search The literature review which provided the material contained in section 3 was conducted using the following search engines; JSTOR, Science Direct, ISI Web of Science and Wiley Interscience. Search returns were ordered by relevance, with the first 20 hits from each search thread considered for the literature review. All of these hits had their abstracts reviewed for relevance to heritage gardens and heathland, with relevant abstracts retained for the full literature review. The search threads were used over the period 20th September – 24 th September 2010 and are listed as follows;

Popularity and Valuation of Heritage Gardens

• Heritage gardens AND demographics. • Heritage gardens AND expenditure. • Heritage gardens AND facilities. • Heritage gardens AND motivations. • Heritage gardens AND spending. • Heritage gardens AND tourism. • Heritage gardens AND travel distance. • Heritage gardens AND willingness to pay. • Heritage gardens AND WTP.

Heritage Gardens and P. ramorum /P. kernoviae

• Heritage gardens AND kernoviae. • Heritage gardens AND ramorum. • Heritage gardens AND sudden oak disease.

Popularity and Valuation of Historic Gardens

• Historic gardens AND demographics. • Historic gardens AND expenditure. • Historic gardens AND facilities. • Historic gardens AND motivations. • Historic gardens AND spending. • Historic gardens AND tourism. • Historic gardens AND travel distance. • Historic gardens AND willingness to pay. • Historic gardens AND WTP.

Historic Gardens and P. ramorum /P. kernoviae

• Historic gardens AND kernoviae. • Historic gardens AND ramorum. • Historic gardens AND sudden oak disease.

Popularity and Valuation of Historic Parks

63 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

• Historic parks AND demographics. • Historic parks AND expenditure. • Historic parks AND facilities. • Historic parks AND motivations. • Historic parks AND spending. • Historic parks AND tourism. • Historic parks AND travel distance. • Historic parks AND willingness to pay. • Historic parks AND WTP.

Historic Parks and P. ramorum /P. kernoviae

• Historic parks AND kernoviae. • Historic parks AND ramorum. • Historic parks AND sudden oak disease.

Popularity and Valuation of Heathland

• Heathland AND demographics. • Heathland AND expenditure. • Heathland AND facilities. • Heathland AND motivations. • Heathland AND spending. • Heathland AND tourism. • Heathland AND travel distance. • Heathland AND willingness to pay. • Heathland AND WTP.

Heathland and P. ramorum /P. kernoviae

• Heathland AND kernoviae. • Heathland AND ramorum. • Heathland AND sudden oak disease.

In addition the search engine Google was used to identify relevant grey literature using the same search threads as above. Each search thread was used twice; once to identify PDF files and once to identify DOC files. Once again the hits were sorted by relevance with the first 20 hits being shortlisted for the literature review phase. Shortlisted hits bearing relevance to heathland and heritage gardens were subsequently included in the full literature review.

The literature review which provided the material on ecosystem services was conducted using four databases: ISI Web of Knowledge, Defra Science and Research Projects, Public Library of Science and the Environmental Evidence Library of Completed Reviews. The specific ecosystem service search terms are taken from the Millennium Ecosystem Assessment (2005) and have been adapted to include plural forms and alternative spellings. The following is a list of the search terms used with each database in the project.

ISI Web of Knowledge ISI Web of Knowledge (http://wok.mimas.ac.uk/) includes the CAB Abstracts and BIOSIS databases, as well as various others. It allows the Boolean operators “AND”, “OR”, and

64 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

“NOT”; the asterisk wild card character, which can stand for any number of any characters; and the use of brackets to group search terms. The searches will not be restricted to any particular date range. Searches were conducted on 15 October 2010. Complete list of search terms: Ecosystem services and Phytophthora: • (valu OR ecosystem service OR econom OR service) AND ( phytophthora OR ramorum OR kernoviae OR sudden oak death) NOT infestans. • (valu OR ecosystem service OR econom OR service) AND ( phytophthora OR ramorum OR kernoviae OR sudden oak death) AND (uk OR united kingdom OR britain OR british). Individual services and Phytophthora: • (food) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (fiber OR fibre) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (fuel) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (biochemical OR pharmaceutical OR medic) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (ornament) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (air qualit) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (climat) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (erosion OR erod) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (waste) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (pest) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (pollinat) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (hazard) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (spirit OR religio) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (knowledge) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (educat) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (inspirat) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (aesthet) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (soci) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (heritage) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (recreation OR touris) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (photosynth) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (primary product) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (nutrient OR cycl) AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans.

Individual services, Phytophthora, and ecosystem services (used where individual services yielded too many results in previous search):

65 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

• (genetic) AND ( phytophthora OR ramorum OR kernoviae ) AND (valu OR ecosystem service OR econom OR service) NOT infestans. • (water) AND ( phytophthora OR ramorum OR kernoviae ) AND (valu OR ecosystem service OR econom OR service) NOT infestans. • (disease) AND ( phytophthora OR ramorum OR kernoviae ) AND (valu OR ecosystem service OR econom OR service) NOT infestans. • (cultur) AND ( phytophthora OR ramorum OR kernoviae ) AND (valu OR ecosystem service OR econom OR service) NOT infestans. • (soil) AND ( phytophthora OR ramorum OR kernoviae ) AND (valu OR ecosystem service OR econom OR service) NOT infestans. Ecosystem services and habitats: • (valu OR ecosystem service OR econom OR service) AND (wood OR forest heath OR garden) AND (uk OR united kingdom OR britain OR british). Individual services and habitats: • (valu OR ecosystem service OR econom OR service) AND (food) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (fiber OR fibre) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (fuel) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (genetic) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (biochemical OR pharmaceutical OR medic) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (ornament) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (water) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (air qualit) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (climat) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (erosion OR erod) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (waste) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (disease) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british).

66 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

• (valu OR ecosystem service OR econom OR service) AND (pest) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (pollinat) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (hazard) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (cultur) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (spirit OR religio) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (knowledge) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (educat) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (inspirat) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (aesthet) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (soci) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (heritage) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (recreation OR touris) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (soil) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (photosynth) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (primary product) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (valu OR ecosystem service OR econom OR service) AND (nutrient OR cycl) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). Biodiversity: • biodiversity AND (wood OR forest heath OR garden) AND (uk OR united kingdom OR britain OR british).

67 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae

• biodiversity AND ( phytophthora OR ramorum OR kernoviae ) NOT infestans. • (larch OR larix) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (bilberr OR vaccinium) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (rhododendron) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (magnolia) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (pieris) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (viburnum) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). • (camellia) AND (wood OR forest OR heath OR garden) AND (uk OR united kingdom OR britain OR british). Defra Science and Research Projects Defra Science and Research Projects (http://randd.defra.gov.uk/) allows the Boolean operators “AND”, “OR”, and “NOT”. The search facility does not permit restricting the search to a particular range of dates and the smaller range of projects in this database allows the use of more general search terms. Searches were conducted on 2 November 2010. Complete list of search terms: • Phytophthora. • Woodland. • Heathland. • Garden. Public Library of Science The Public Library of Science (http://www.plosone.org/) is a range of open access journals available on the internet. The search facility allows the Boolean operators “AND”, “OR”, and “NOT”; the use of brackets to group search terms; and the use of prefixes to restrict the search to certain fields. The “everything” prefix searches all available fields. Searches were conducted on 2 November 2010. Complete list of search terms: • ((everything: phytophthora ) OR (everything: ramorum ) OR (everything: kernoviae )) AND ((everything:uk) OR (everything:"united kingdom") OR (everything:britain) OR (everything:british)). • ((everything:woodland) OR (everything:forest) OR (everything:heathland) OR (everything:garden)) AND ((everything:uk) OR (everything:"united kingdom") OR (everything:britain) OR (everything:british)) AND ((everything:"ecosystem service")).

Environmental Evidence Library of Completed Reviews The Environmental Evidence Library of Completed Reviews (http://www.environmentalevidence.org/Reviews.htm) is a list of completed systematic review projects available under open access licences. There is no search facility and so the inclusion criteria were applied to the list of titles directly on 2 November 2010.

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2. The Contingent Valuation Model The primary objective of a contingent valuation model is to estimate what is known as a bid function 30 . Such a function seeks to explain to what extent WTP, for an environmental attribute for example, is influenced by changes in:

• The quantity and quality of non-market goods. • The price of market goods. • Household income. • Socio-demographic characteristics.

Mathematically a contingent valuation model seeks to estimate a household’s maximum WTP for a change in the level of a non-market good from its current level (Q 0) to a higher level (Q 1). As the contingent valuation model is unable to truly observe the utility a respondent derives from changes in a non-market good, the model instead estimates an indirect utility function V(.) . Such a function is expressed as:

V(Y,P,S,Q)

where indirect utility is determined by household income ( Y), the price of non-market goods (P), the socio-demographic characteristics of a household ( S) and the quantity of the non- market good ( Q). For simplicity we assumption that a higher level of the non-market good (Q 1) attains a higher utility than a lower level of the same good (Q 0) such that:

V(Y,P,S Q0) < V(Y,P,S,Q1)

Herein it is expected the respondent is prepared to pay something to obtain a higher level of the non-market good given that such a level provides the respondent with more utility relative to a lower level of the non-market good. However, the act of paying to obtain a higher level of a non-market good is in itself likely to decrease utility. Maximum WTP is therefore mathematically described within the model as the extra payment required to make the utility with the higher level of the non-market good identical to the original level prior to the increase in the level of the non-market good. This is represented as:

V(Y,P,S Q0) < V(Y - C,P,S,Q1) where C is the compensating variation measure of a change in welfare. In layman’s terms C represents a household’s maximum WTP to achieve the increase in the quantity of the non- market good. Reformulating the above equation allows C to be represented as a function of the other parameters in the model, which is known as the bid function:

C(Q 0,Q1,Y,P,S) = WTP ≤ Y

This function provides the theoretical basis for analysing CV data and allows the analyst to determine whether the WTP for a non-market good is influenced by socio-demographics, household income and of course the price and quantity of the non-market good.

30 See Bateman et al. (2002) for a more detailed discussion of the mechanics behind the contingent valuation model.

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3. Technical Description of the P. ramorum and P. kernoviae Spread Model 3.1 Methodology MaxEnt Modelling MaxEnt is a niche based modelling system that predicts a target species’ realised niche within a study area and within the environmental variables being considered based on presence only data (Phillips et al. 2006). Input variables should be in ASCII file format with known species locations supplied as point data. The output is a probability distribution that should be viewed as a relative index of environmental suitability (higher values mean better conditions for the target species).

Each cell/pixel (in this case each 1km grid square) within the study area is assigned a probability of suitability for the species being modelled. The probability is derived from the probability distribution with greatest entropy (closest to uniform). However, this probability does not relate to the ‘amount’ or area that the species is predicted to cover within each cell. In order to get some estimate of this, incorporation of the land cover map (LCM) is needed to remove areas of unsuitable habitat from the prediction (as suggested in Phillips et al. 2006).

Within each 1km cell, all areas that are deemed to have ecologically suitable habitat for the target species were summed. This gives the proportion of available habitat within each 1km 2 cell which is potentially affected by P. ramorum and P. kernoviae . Cambridge University Metapopulation Epidemic Model (MPEM) MPEM is a modelling package that outputs GIS compatible files showing the disease landscape. Such output includes hazard maps and areas likely to be infected in a given time period and disease progress curves. The model requires information on the host landscape and characteristics of the disease. Users can also include disease control options and information on detection. The model was originally based on epidemiological data collected in California, and is being reparameterised for conditions in the UK as studies provide data on the epidemiology and host responses in the UK. The user defined parameters and associated assumptions in the model are shown in Table A3 1. Control options were excluded from the model as the effectiveness of current controls is unknown. Therefore this project takes a highly conservative approach to the impacts of P. ramorum and P. kernoviae .

Final risk values for all 1km 2 grid cells are calculated by multiplying hazard values by the probability of the diseases spreading to such grid cells from their current positions. These calculations essentially weight hazard values by the probability of spread. This enables the risk map to account for situations where the hazard figure is high, but there is little to no probability of P. ramorum or P. kernoviae reaching the area in the next 20 years.

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Table A3 1: Parameters and associated assumptions in the MPEM model 31

Variable Assumptions

Simulation settings The number of hosts can be grouped into discrete units of the (including time period metapopulation level. The more bins used the closer to the and maximum number of natural state of the system and less important this assumption hosts possible per 32 becomes . location) Assumes that all hosts with the greatest sporulation hazard Landscape Data have been included.

Kernel Data (dispersal Assumes that the canopy level spread in the UK is similar to data) that observed in California.

Assumes that the infection rate is higher than in California Rate Parameters (rate of based upon manual history matching of the current state of spread)

MasterFile larch infections in the UK.

Output shows the exact state of the simulation and so assumes Data Output 100% detection and surveillance.

Detection uses a non-linear detection probability response function. This means that the more hosts are infected in a Detection and Control region the greater the chance of detection. The current control Interventions is removal of stands and in the simulation the removal is assumed to be 100% effective

Assumes host density can be approximated from large scale datasets such as the Woodland sub compartment database and Host Density the LCM clipped - MaxEnt models for the host species modelled at Fera

Assumes all hosts are initially susceptible unless they are Susceptible previously detected as having infection. Exposed Initially assumed to be zero Essential Initially only infected at sites known from inspection/ Infected surveillance Detectable Initially assumed to be zero

Removed Initially assumed to be zero

31 Assumptions supplied by Erik DeSimone of the MPEM team.

32 For example consider a 20ha cell containing 10 ha of forest. This gives a density of suitable habitat of 0.5. With a metapopulation of 10 each cell can have up to 10 "hosts" in it. So, in the 20ha cell (with 0.5 density) 5 "hosts" are allocated to that cell. In this example, each "host" unit in the model represents 2ha of forest. If more levels are used in the metapopulation a finer resolution inside the cell is simulated, i.e. a metapopulation value of 100 means each host unit = 0.2 ha.

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Assumes the canopy level spread in the UK is similar to that Kernel observed in California

Infectivity (for each host Assumes wild infectivity is similar to measured temperature and species) moisture response from lab trials

Currently assuming only proportional to host density - although Susceptibility (for each with new lab data from FERA this can be tied to the host species) environmental conditions Random initial survey with intense survey around detected Detection: Search infection sites

Optional Known actual infection data is seeded into the initial conditions Detection: Known to begin the simulation

Spray (antisporulant Such a spray exists and could be sprayed across the wild state) environment

Such a spray exists and could be sprayed across the wild Spray (protectant state) environment

The distribution of potential host species for P. ramorum and P. kernoviae will affect the spread of the disease across England and Wales. To model the predicted spread of host plant species key variables must be created and converted into ASCII format to use in MaxEnt. The following sections contain details of the data processing used to create the necessary variables. Climate Data Climate data were extracted from the UK Climate Change Programme (UKCIP) dataset on a 5km scale. It was decided to focus on average maximum and minimum temperatures and total rainfall across all the quarters of 2001-06. To minimize the total number of variables extracted across all years the total number of parameters extracted was kept low, thus ensuring the data’s compatibility with principle component analysis (PCA). The quarterly averages were used as this is a similar approach to that taken in the UK Climate Projections 2009 (UKCP09) and it was felt they could be good predictors of plant distribution. The years 2001 to 2006 were chosen as they contain the most recent set of daily weather recordings available in the UKCIP and it was felt that five years of data adequately represents the varying climate conditions in the UK. This data produced 72 climate variables to consider (6 years * 4 quarters * 3 climate variables) for 10,359 25km 2 grid cells across the UK. A PCA was performed to reduce the number of variables needed for consideration.

The aim of using PCA was to find patterns in the data such that the variation in the 72 climate variables can be described using fewer variables. The data was first standardised by subtracting the mean for each variable from the corresponding part of the data vector and dividing that same part by the standard deviation. The first six principal components (PCs) from the PCA explained 96.5% of the variance in the standardised dataset, with the first four accounting for nearly 94% of the variance. Some interpretation of PCA is given when jointly considering PCs 1 and 2. Plotting PC1 against PC2 for the 10,359 locations showed little correlation between the two components. For PC1 there is a clear divide in the influence of temperature and precipitation, and for PC2 there is a divide between the influence of maximum Q2 and Q3 temperatures and the other outputs.

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It was decided to use the first six PCs processed in ArcMAP 10 as outlined below. Table A3 2: Principal components derived from the PCA of climate variables . Table A3 2 highlights reasons for the first six PCs being relatively high or low for any particular grid cell. The method was as follows:

• The PCs and their corresponding coordinates were imported into ArcMAP10 to create a point shapefile.

• This shapefile was spatially joined to a 5km 2 grid and a 5km 2 raster created for each of the six PCs.

• The raster was resampled to a 1km 2 scale with the value of each 5km square was assigned to the 1km squares it was split into.

• These rasters were multiplied (using the raster calculator) by a second raster containing a value of ‘1’ over England, Scotland and Wales and ‘No Data’ values over areas of sea. This produced a calculation layer containing data in each raster cell over England, Scotland and Wales only.

• The six resulting calculation rasters were converted into ASCI layers to enable their use in MaxENT.

Table A3 2: Principal components derived from the PCA of climate variables 33

Principal Reasons for Positive Values Reasons for Negative Values Component

Low precipitation throughout year. High precipitation throughout year. PC1 High temperatures throughout year. Low temperatures throughout year. Low maximum daily temperature over High maximum daily temperature over Q2 and Q3. Q2 and Q3. High minimum temperatures over Q1 Low minimum temperatures over Q1 and Q4. and Q4.

PC2 High precipitation throughout year. Could be interpreted as having Could be interpreted as having less defined seasons; that is, warm/hot extreme changes in temperature over summers and cold winters. the year. Low minimum temperatures High minimum temperatures throughout the year. throughout the year. High maximum temperatures over Q2 Low maximum temperatures over Q2 and Q3. and Q3.

High precipitation throughout the year. Low precipitation throughout the year. PC3 High minima and low maxima mean More variability in temperature over less variability in temperature is summer is experienced. experienced over the Q2 and Q3 periods.

33 Note that all the highs and lows here are relative to the range of climatic conditions that were experienced over the UK in the years 2001 to 2006.

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Low values for observations from High values for observations from 2003 to 2006. 2003 to 2006. PC4 High values for observations from Low values for observations from 2001 to 2002. 2001 to 2002. High values for maximum Low values for maximum temperatures in Q1. temperatures in Q1. PC5 Low values for minimum temperatures High values for minimum and precipitation in Q3. temperatures and precipitation in Q3. Low values for Q1 and Q4 High values for Q1 and Q4 precipitation. precipitation. PC6 No other clear trends. No other clear trends.

Processing Land Cover Map 2000 (LCM2000) The distribution of potential host species for Phytophthora will affect the spread of the disease across England and Wales. To model the predicted spread of host plant species such as Vaccinium and Rhododendron, and to apply a potential affected area value for each raster cell (important in the economic valuation of Phytophthora impacts), key habitats from the LCM must be converted into ASCII format to enable use in MaxEnt.

Rasters were produced with a cell size of 1km 2 covering England, Scotland and Wales. The LCM habitat categories used are listed below.

• Acid grass • Arable • Bare ground • Bog • Bracken • Broadleaf / mixed woodland • Coniferous woodland • Dwarf shrub • Fen • Urban areas and associated Gardens • Grass – other • Grass – improved • Littoral rock and sediment • Montane • Sea • Supra littoral rock • Supra littoral sediment • Water

Creation of the rasters followed the process outlined below:

• The relevant LCM habitat category (see above) was extracted from the LCM2000 using ‘select by attribute’ and ‘export data’.

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• The extracted category was overlayed with a 1km 2 grid covering England, Scotland and Wales, using the ‘Intersect’ tool.

• This intersected layer was then ‘dissolved’, based on the 1km 2 grid id code, to create multi part features.

• The areas of these multipart features were calculated.

• The data was then opened in MS Excel 2007 and the total area in each 1km 2 cell was calculated. Some areas were greater than 100ha due to overlapping polygons in the LCM2000 dataset. This problem was overcome by calculating the relative proportion of each land class considered. For squares with areas greater than 100ha the following calculation was used: Original area (ha)/original sum (ha) * 100 • For squares with less than 100ha the formula below was used (this was used as in certain squares, very low percentages of suitable land were contained. Using the equation above would suggest that a greater proportion of the 100ha was covered by these low percentages than was true in reality. i.e. a square with 0.1ha of suitable land, using the equation above; 0.1 / 0.1*100 = 100% of the square is covered by suitable habitat, when in reality only 0.1% of the square is suitable): Original area (ha)/100 (ha) * 100) • Once areas had been calculated the dissolved layer was joined to the respective grid based on the grid ID code (FID) of the 1km 2 grid. • The shapefile created in the previous step was used to create a raster with a cell size of 1km 2 which was multiplied (using the raster calculator) by a second raster that contained ‘1’ values over England, Scotland and Wales and ‘No Data’ values over areas of sea. This resulted in a calculation layer containing the area of the extracted land class in each raster cell over England, Scotland and Wales only. • This calculated raster was converted to an ASCII file to enable its use in MaxEnt modelling. Corine Soil Data Soil type is important to the establishment of host plants as it directly affects the areas certain species grow in.

• Data for England, Scotland and Wales were extracted from the Corine Soil Database raster. • This raster was then resampled to show the major soil group code from the 1990 FAO- UNESCO soil legend. • The raster was multiplied by a second raster which contained ‘1’ values over England, Scotland and Wales and ‘No Data’ values over areas of sea. This created a calculation layer containing the soil class in each raster cell over England, Scotland and Wales only. • This calculated raster was converted to an ASCII file to enable its use in MaxEnt modelling.

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‘Panorama’ Land-form Data The Panorama land form data set shows the height of land across the UK to a 50m by 500m resolution. • The ‘Panorama’ height raster was re-processed to a 1km 2 resolution (from 50m squares) using the aggregate tool in ArcMAP. Rasters of maximum, minimum and mean height across England, Scotland and Wales were produced in this way. • The ‘slope’ and ‘aspect’ tools in ArcMAP10 were used to create slope and aspect rasters from the original height raster. • These slope and aspect rasters were then re-processed using the aggregate tools to a 1km scale. • The five resulting rasters were then multiplied (using the raster calculator) by a second raster which contained ‘1’ values over England, Scotland and Wales and ‘No Data’ values over areas of sea. This resulted in a calculation layers containing mean, maximum, minimum heights and slope and aspect values in each raster cell over England, Scotland and Wales only. • These calculated rasters were converted to an ASCII file to enable their use in MaxEnt modelling.

3.2 MaxENT Modelling

Modelling Inputs Data of known presence points for all five host species considered were obtained from the National Biodiversity Network (NBN). Only data with a resolution of 1km or better were used. Data from 1980 onwards were used in order to increase the number of presence records. All data extracted from Section 1.2 were supplied to all models (Table A3 3). The data were randomly split by the MaxENT model, with 70% of the data used for training the model and 30% used to test it. One model was created for each host species and associated response curves and model performance statistics were produced.

Area Under the Curve (AUC) scores were used as an assessment of model performance. They are usually a number between 0 and 1 however, in the case of MaxENT modelling, the maximum AUC possible is just less than 1 (see Phillips et al. 2006 for details). Higher scoring models are better at predicting the species distribution. Scores of 0.6 – 0.7 = poor, 0.7 – 0.8 = average, 0.8 – 0.9 = good and 0.9 – 1 = excellent (Araujo and Guisan 2006).

Refinements of the MaxENT Outputs The MaxEnt output assumes that everything within each 1km 2 raster cell has the same probability of containing a host species. This may not be the case as only part of the cell may contain habitat suitable for growing the host species. To overcome this issue and to give a figure for the total area at risk from Phytophthora, the LCM was used to clip out suitable areas of habitat within each raster cell.

Using the values derived from processing the land cover map we were able to produce a raster layer which contained a probability of a host species being present, weighted by the amount of habitat available for that species. To do this a random raster was generated (‘random’) and the raster calculator used to create a possible landscape from the MaxENT outputs for each species using the code ‘random’ < ‘maxEnt_output’.

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This produced a raster (‘possible_host_landscape’) with 0 or 1 as cell entries, where 0 = no host and 1 = host species is present. This has the effect of pixelating the MaxENT output. A second raster was created that contained the total area at risk from Phytophthora in each 1km 2 cell (‘available_area’). Multiplying the ‘possible_host_landscape’ by the ‘available_land’ would create a raster with each cell containing a host being scaled down to the fraction of available land. However, this assumes the entire suitable land cover within a cell is filled with a host. Therefore, we assumed that the amount of available area actually filled is proportional to the probability of that cell containing hosts. This final raster was generated using the raster calculator and the code ‘possible_host_landscape’ * ‘available_area’ * ‘maxENT_output’. Table A3 4 lists the suitable habitats from the LCM2000 for all five host species modelled. Table A3 3: Environemental variables included in the MaxENT modelling

Source Variable in Model Explanation

PC1 Value of principal component 1 from the climate PCA.

PC2 Value of principal component 2 from the climate PCA.

Climate Data PC3 Value of principal component 3 from the climate PCA.

– UKCP PC4 Value of principal component 4 from the climate PCA.

PC5 Value of principal component 5 from the climate PCA.

PC6 Value of principal component 6 from the climate PCA.

Acid Grassland Area of acid grassland within each 1km square.

Arable Area of arable land with in each 1km square .

Bare Ground Area of bare ground within each 1km square .

Bog Area of bog within each 1km square .

Bracken Area of bracken within each 1km square .

Broadleaved / Mixed Area of broadleaved/mixed woodland within each 1km Woodland Square.

Coniferous Woodland Area of coniferous woodland within each 1km square.

Dwarf Shrub Heath Area of dwarf shrub heath within each 1km square .

Fen Area of fen within each 1km square .

LCM2000 Urban areas and Area of ur ban and associated gardens within each 1km associated gardens Square.

Improved Grassland Area of improved grassland within each 1km square .

Other Grassland Area of other grassland within each 1km square .

Littoral Rock and Area of littoral rock and sediment within each 1km square. Sediment

Montane Area of montane within each 1km square.

Sea Area of sea within each 1km square.

Supra Littoral Rock Area of supra littoral rock within each 1km square .

Supra Littoral Area of supra littoral sediment within each 1km square. Sediment

Water Area of water within each 1km square.

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Corine Soil 1km summary raster from the Corine soil Database was Soil class Database used.

Maximum Elevation Maximum height within each 1km square.

Mean Elevation Mean height within each 1km square . Land-form Panorama Minimum Elevation Minimum height within each 1km square .

Data Aspect Mean Aspect within each 1km square .

Slope Mean slope within each 1km square .

Final Host Map The LCM2000 clipped refined MaxENT outputs were passed to the MPEM team. The rasters were then combined into one final host map that incorporated the MaxENT modelled species, the distribution of larch (from the sub-compartment database) and the National inventory of Woodland and Trees (NIWT).

3.3 MPEM Modelling

The MPEM model was run for a 20 year simulation with no Phytophthora control measures with results generated at the 10 year and 20 year time points. The model outputs two raster files i) a hazard map containing the number of additional cells (or ‘locations’) at risk and ii) a map showing the probability of the disease spreading from its current locations. The ‘location’ value is the number of additional cells that could be infected by a Phytophthora outbreak starting in the 'original cell' that contains the location value. At this stage the model looks at each 1 km 2 cell in turn and assumes the disease has reached that cell. It asks 'how much further could the disease spread if it reaches this point?’

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Table A3 4: Selected LCM categories and the habitats used to assess the amount of land available in each 1km 2 for each host species (Only categories listed as ‘yes’ are included in the suitable area. Any categories listed as ‘rare’ or ‘possibly’ are not included)

Arctostaphylos Vaccinium vitis- Vaccinium Rhododendron LCM category uva-ursi idaea myrtillus ponticum

Acid Grass Rare Rare Possibly Arable Bare Ground Bog Rare Yes Bracken Possibly Yes Broadleaf / Mixed Woodland Yes (acid) Yes (acid) Coniferous Woodland Rare Yes (acid) Yes (acid) Yes (acid) Dwarf Shrub Heath Rare Yes Yes Yes Fen Urban areas and Associated Gardens Grass - Other Rare Rare Rare Possibly Grass - Improved Littoral rock and sediment Montane Yes Yes Yes Sea Supra Littoral Rock Supra Littoral Sediment Water

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3.4 Assessment of Total Area at Risk Amount of Woodland and Heathland at Risk The final risk map produced from the MPEM modelling process was converted into polygons for each risk scenario. The area of woodland and heathland within each region was clipped by these polygons. The area of woodland and heathland overlaying the risk areas was calculated for both the 10 year and 20 year scenario 2 outputs. For scenario 1 all woodland and heathland was considered to be at risk, while in scenario 3 only 1km 2 cells containing a current Phytophthora outbreak and either woodland or heathland were considered at risk. Number of Heritage Gardens at Risk Spatial data of English heritage gardens were available although no such data were available for Welsh gardens. To overcome this screen shots were taken from the Parks and Gardens map search facility ( www.parksandgardens.ac.uk ), imported in to ArcMAP 10 and georeferenced. Any Welsh garden that came within 100m of a known Phytophthora outbreak or area of risk was counted. The number of English heritage gardens at risk was calculated by summing the number of sites that overlapped at risk areas in each Scenario. This was possible due to the detailed polygon data available for English heritage gardens. Assessment of Larch at Risk The amount of Forestry Commission (FC) larch at risk under scenario 2 was calculated by clipping the at risk area by the FC larch standings for each region. For scenario 3 any FC larch standing containing a known Phytophthora outbreak was considered at risk, while in scenario 1 it was assumed all larch was at risk. No spatial information were available for private larch standings and so the area of private larch at risk is estimated. An initial estimate of the amount of private larch came from the NIWT database. The proportion of FC larch at risk was applied to this estimate of private larch for each region and scenarios 2 and 3.

3.5 Results MaxENT Outputs and Final Host Maps Fera Host Maps : All MaxENT models produced good to excellent AUC scores (as defined by Araujo and Guisan 2006) with a minimum AUC test figure of 0.82 (for Vaccinium myrtillus ) to a maximum of 0.92 (for Arctostaphylos uva-ursi ). These scores show the models making good predictions of the test data set (Table A3 5).

Table A3 5: Number of presence points and associated AUC scores for the five MaxENT models

Presence Points AUC AUC Species Used in MaxENT (train) (test)

Arctostaphylos uva-ursi 201 0.98 0.92

Rhododendron ponticum 867 0.92 0.88

Vaccinium myrtillus 2468 0.85 0.82

Vaccinium vitis-idaea 591 0.95 0.90

The final model outputs are shown in Figure A3 1 to Figure A3 4. For each MaxENT model two sets of values (percentage contribution and permutation importance) were

80 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae given showing the relative importance of each variable to the overall final model. Both these measures, which are created by the MaxENT programme, need to be interpreted carefully. Firstly, the percentage contribution makes assumptions regarding independence of presence points and variables that are violated in this dataset. Secondly, the permutation importance may permute variables to values that are not possible within the confines of the data (e.g. may create permutations where the total area of LCM variables exceeds 100ha). Looking at the permutation importance figures for each model gives us an idea of the impact each variable has on the model. The main variables of interest, defined as any variable with a permutation importance of 5 or more, are listed for each species in Table A3 6.

Occasionally variables are seen that have a high percentage contribution but a low value for permutation importance. For instance, the Maximum Elevation variable for V. vitis idaea has a percentage contribution of 24% and permutation importance of 0.1 which is most likely due to correlations of variables in the model. The reverse is also seen with a low percentage contribution value matched by a high permutation figure for the broadleaved/mixed woodland variable for A. uva ursi . This is likely to be due to small numbers of presence points located in cells with values for the variable in question .

Arctostaphylos uva ursi : The host map predicts no suitable areas for A. uva ursi in England or Wales due to the limited presence of suitable habitat in the LCM2000. The amount of arable land seems to have a high degree of influence on the model (permutation importance = 23.8). However, due to the small number of presence points and the aggregated spread of these points, we do not have a high degree of confidence in this model when predicting outside of the spatial extent of the presence points. The sparse nature of the presence data (Figure A3 6) means there is little information on the effects of environmental variables across their full range of values.

Permutation Percentage Species Variable Importance Contribution

Arable 23.8 18.1

Broadleaved/mixed Woodland 22 1

Urban Areas and Associated 6.3 11.1 Gardens A. uva-ursi A. uva-ursi Improved Grassland 19.9 5.9

Acidic Grassland 5 0.6

Arable 6.3 2.2

Broadleaved/mixed Woodland 14.1 32.1

Minimum Elevation 6 7.1

R. ponticum R. ponticum PC5 21.8 14.4

PC6 7.5 7.2

Arable 17 16.8 V.

lus lus myrtil Urban areas and Associated 12.4 2.4

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Gardens

Maximum Elevation 6.2 19.5

PC1 9 18.1

PC5 9 5.6

PC6 5.5 5.1

Arable 6.8 6.3

Dwarf Shrub Heath 9.3 5.8

Urban areas and Associated 21.7 0.5 Gardens

V. vitis idaea idaea V. vitis Improved Grassland 5.5 4.1

PC1 19.1 22.5

Table A3 6: Permutation importance and percentage contribution figures for each model

Rhododendron ponticum : The predicted suitability for R. ponticum is similar to that of V. myrtillus , with areas of Wales and England predicted to be suitable, although possibly tending towards slightly lower ground than with V. myrtillus. Both PC5 and PC6 are important in the model. Looking at their respective histograms reveals the species tending to be located in areas that are not too hot during the first three months of the year, not too hot or rainy from July to September and areas with low rainfall between October and March all relative to the UK. The presence of broadleaved/mixed woodland is also expected as the species is predominantly located in this habitat type. There is a good spread of presence data across the range of all important variables leading to a high degree of confidence in this model.

Vaccinium myrtillus : Suitable areas for V. myrtillus are predicted to be in Northern England and Wales with some suitability highlighted in Southern England. Arable and urban areas may be excluding the species from areas with high values for these variables. PC5 and PC6 suggest similar preferences for R. ponticum . There is a good spread of presence data across the range of all important variables leading to a higher degree of confidence in this model.

Vaccinium vitis idaea : V. vitis-idaea is a species of upland moorland and largely pine woodland and is therefore restricted to upland areas in Wales and northern England. Again the presence of Dwarf Shrub in the model is to be expected. The histogram for the PC1 variable suggests the species is present in areas that are not low in precipitation levels or have high temperatures relative to those in the UK. There is not a good spread of presence points along the range of the arable variable and so the importance of this variable should be viewed critically.

Model Limitations Despite the good AUC scores and ecologically sensible variable selection of the models, careful consideration should be used when interpreting the results. The MaxENT model does have some limitations associated with it and these need to be remembered. Standard

82 Project FFG0921: TEV at risk from P. ramorum and P. kernoviae practice when using MaxENT is to include all environmental variables regardless of their auto-correlation (Pearson et al. 2007, Peterson et al. 2007). However, MaxENT is better able to deal with these correlated variables than other modelling techniques (Elith et al. 2011).

There is also the problem of spatial correlation in the presence points. Having gaps in the distribution of the presence points means care must be taken when interpreting the modelling output from these ‘gap’ regions as no information is available on the species responses to variables in these regions. This is the case for A. uva ursi and V. vitis idaea where most or all of the species presence points are located in Scotland, with few in England and Wales. There are differences between the input variables between the two regions (Figure 2.1.6), and despite some degree of overlap this means the modelled outputs for England and Wales should be interpreted critically. This issue affects all species modelled to a lesser extent as gaps in species presence points are seen (Figure A3 6).

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Figure A3 1: Initial MaxENT output (left), land available LCM2000 (middle), and final host map (right) for Arctostaphylos uva-ursi

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Figure A3 2: Initial MaxENT output (left), land available LCM2000 (middle) and the final host map (right) for Rhododendron ponticum

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Figure A3 3: Initial MaxENT ouptut (left), land available LCM2000 (middle) and the final host map (right) for Vaccinium myrtillus

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Figure A3 4: Initial MaxENT output (left) the land available LCM2000 (middle), and the final host map for Vaccinium vitis-idaea

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Figure A3 5: Extracted principal component values for climate variable PCs 1 and 2 (England, Wales and Scotland) 34

34 The proportion of cells at each PC value shows large difference s between the two regions.

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Figure A3 6: Presence point locations used in the MaxENT modelling for all four species

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MPEM Final Host Map The final host map was created as part of the MPEM modelling process and included all Maxent modelled species along with the National Inventory of Woodlands and Trees (NIWT) and larch distribution from the sub-compartment database ( Figure A3 7). Inclusion of these two additional datasets may lead to a double counting of larch, although the NIWT is included purely as a “low level fill for unknown possible sporulation” (i.e. it is given a very low sporulation/infectivity weighting in the model, see Table A3 1). Therefore any double counting would have very little impact on the final risk maps .

Figure A3 7: Final host map

3.6 MPEM Risk Maps Known Phytophthora Outbreaks Locations of wild P. ramorum and P. kernoviae outbreaks as of December 2010 were used to seed the MPEM model (Figure A3 8). Information regarding outbreaks on larch was sourced from the Forestry Commission, while data on outbreaks on other species were sourced from Fera.

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MPEM Outputs The two outputted raster files from the MPEM modelling are shown in Figure A3 9. High probabilities of P. ramorum and P. kernoviae spread from their current locations can be seen in Southern Wales (Breacon Beacons and Crychan Forest), Northern Wales (Dovey Forest), the Lake District (Grizedale Forest), New Forest and St. Leonards Forest (South of England) and Cornwall. All these areas show a degree of hazard indicating that should the disease reach these area, it will be capable of spreading further and infecting new 1km 2 areas.

Figure A3 8: Locations of wild outbreaks used in MPEM

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a) b) Figure A3 9: Outputs from the MPEM model a) = probability of P. ramorum and P. kernoviae spreading from current locations in the next 20 years, b) = hazard map showing the number of new locations (1km 2 cells) at risk assuming the diseases reach all cells in the map. Risk Scenarios The three risk scenarios were developed using the full host map and the MPEM outputs. These scenarios were later refined to be specific for each species by using the individual host maps for the four species considered. Risk scenario 1 shows the situation in which all possible hosts become infected, while scenario 3 shows the current situation (Figure A3 10). Scenario 2 shows the 10 and 20 year spread as predicted by the MPEM. However, most cells in these areas have a value less than 1 (0.2% of maximum risk value) and so were excluded from the scenario as they give a false impression of risk. Risk areas are seen to mirror the probability map with areas at risk in Cornwall, Wales, Southern England and the Lake District. Results for scenario 2 are given for two time point outputs of 10 and 20 years.

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Figure A3 10: Three different risk scenarios (including the 10 and 20 year outputs for scenario 2)

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3.7 Areas at Risk Results are given at the region level and are summarised in Table A3 7 and Table A3 8. Heathland and Woodland Scenario 1 shows the South East, Wales and the South West with the largest areas of woodland at risk while Wales, Yorkshire and Humberside and the North East have the largest areas of heathland (Table A3 7) at risk under this scenario. In scenario 2 the regions with the largest at risk areas are Wales, the South East and South West, while the North West has an elevated risk. Eastern and Central England have smaller areas at risk from Phytophthora. The areas with the lowest risk are the North East and East with no areas of woodland or heathland in at risk areas. South East England is regularly ranked above the South West in terms of area at risk, although the picture is mixed when considering the proportion of habitat at risk in these regions. Here the South West has a higher proportion of woodland (0.015 [10 year] and 0.04 [20 year]) but a much lower proportion of heathland (0.02 [10 year] and 0.04 [20year]) at risk compared to the South East (woodland: 0.014 [10 year] and 0.03 [20 year], heathland: 0.06 [10 year] and 0.19 [20 year]). In scenario 3 the South West and Wales have areas of heathland and woodland affected by P. ramorum and P. kernoviae , with the North West showing some areas at risk from these diseases. Heritage Gardens The South East and South West have the highest number of heritage gardens which could all be at risk in scenario 1, while the North East has the lowest number of heritage gardens at risk (Table A3 7). In scenarios 2 and 3 the South West has the highest number of heritage gardens at risk from P. ramorum and P. kernoviae with 7% of heritage gardens in the region infected under the 20 year output for Scenario 2. Estimating the number of Welsh gardens at risk was hampered due to a lack of spatial information as we did not have any information on the boundaries of the gardens. Therefore any Welsh garden point that came within 100m of a known Phytophthora outbreak or area of risk was considered to be at risk. Larch Larch in Wales has the highest area at risk out of all study regions, with 56% of larch in the region in an at risk zone under the 20 year output of scenario 2 (Table A3 8). The South West and North West are the next highest risk zones with 45% and 37% respectively of the larch in an at risk zone. Despite the high amount of larch in Yorkshire and Humberside, all of which is classed as at risk in scenario 1, no larch standing in this region is at risk in scenarios 2 or 3.

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Table A3 7: Total area of woodland, heathland (both ha) and number of heritage gardens at risk

High Risk Scenario (Scenario 1) Medium Risk Scenario (Scenario 2) Low Risk Scenario (Scenario 3)

Total Woodland at Total Heathland at Total Heritage Total Total Total Total Total Total Heritage Region Risk Risk Gardens at Risk Woodland Heathland at Woodland Heathland Gardens Gardens at Risk at Risk Risk 10 yr 20 yr 10 yr 20 yr 10 yr 20 yr

East of England 156,134 1,220.6 209 0 0 0 0 0 0 0 0

East Midlands 107,783 11,422.8 135 4.1 4.7 0 0 3 3 0 0

London 13,695 252.6 148 10.8 20.7 0 0 0 0 1 0

North East 106,241 66,694.6 53 0 0 0 0 0 0 0 0

North West 122,086 44,922.6 129 3,209.4 6,074.8 90.3 294.9 3 3 10 0

South East 333,490 14,352.3 366 4,847.8 11,558.6 880.0 2,762.9 4 6 5 0

South West 285,788 29,002.5 293 4,493.8 11,483.7 489.7 1,107.2 21 21 67 1

Wales 306,025 113,288 121 24,887.8 42,802.8 5,497.5 11,687.6 4 7 34 2

West Midlands 137,328 7,392.9 149 59.5 232.5 4.6 5.6 3 3 1 0 Yorkshire & Humber 134,239 90,253.2 116 37.4 66.4 0 0 2 2 0 0

Total 1,702,809.0 378,802.1 1,719 37,550.5 72,244.2 6,962.1 15,858.3 40 45 118 3

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Table A3 8: Total area (ha) of private and Forestry Commission larch at risk

High Risk Scenario (Scenario 1) Medium Risk Scenario (Scenario 2) Low Risk Scenario (Scenario 3) Total FC Proportion Proportion Larch from Private Private Proportion Private FC FC Larch at FC Larch at of FC Larch of FC Larch Private Larch FC Larch at Region Sub comp FC Wood Larch from Larch at of FC Larch Larch at Proportion Risk 10 yr Risk 20 yr at Risk 10 at Risk 20 at Risk 20 yr Risk Database - NIWT Risk 10 yr at Risk Risk yr yr 2010

Reference 1 2 3 4 5 6 7 8 9 10 11 12 13 Calculation =1/2 =5/1 =6/1 =4x7 =4x8 =11/1 =4x12 East of England 806 25,915 0.03 2,049 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 East Midlands 943 19,242 0.05 1,924 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 London 0 290 0.00 4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 North East 1,952 60,968 0.03 3,710 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

North West 2,499 27,980 0.09 3,593 739.23 915.80 0.30 0.37 0.00 0.00 0.00 1,062.85 1,316.72 South East 1,384 49,486 0.03 4,509 182.25 299.43 0.13 0.22 593.77 975.53 0.00 0.00 0.00

South West 4,303 36,838 0.12 7,596 906.20 0.21 0.45 47.77 0.01 84.34 1,946.62 1,599.69 3,436.34

Wales 18,816 123,559 0.15 10,154 0.39 0.56 333.10 0.02 179.76 7,417.60 10,479.62 4,002.89 5,655.30 West Midlands 2,895 19,242 0.15 1,924 8.41 56.08 0.00 0.02 5.59 37.27 0.00 0.00 0.00 Yorks & Humber 4,594 21,230 0.22 5,581 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total 38,192 384,750 41,044 9,253.69 13,697.56 7,264.79 11,421.15 380.9 264.09 - - - -

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References

Araujo, M. and Guisan, A. (2006). “Five (or so) challenges for species distribution modelling.” Journal of Biogeography 33 (10): 1677–1688.

Elith, J., Graham, C., Anderson, R., Dudik, M., Ferrier, S., Gusian, A., Hijmans, R., Huettmann, F., Leathwick, J., Lehmann, A., Li, J., Lohmann, L., Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. Overton, J., Townsend Peterson, A., Phillips, S., Richardson, K., Scachetti- Pereira, R., Schapire, R., Soberon, J., Williams, S., Wisz, M. and Zimmermann, N. (2006). “Novel methods improve prediction of species’ distributions from occurrence data.” Ecography 29 (2): 129–151.

Pearson, R., Raxworthy, C., Nakamura, M. and Townsend Peterson, A. (2007). “Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar.” Journal of Biogeography 34 (1): 102–117.

Phillips, S., Anderson, R. and Schapire, R. (2006). “Maximum entropy modelling of species geographic distributions.” Ecological Modelling 190 (3-4): 231–259.

Townsend Peterson, A., Papes, M. and Eaton, M. (2007). “Transferability and model evaluation in ecological niche modelling: A comparison of GARP and Maxent.” Ecography 30 (4): 550–560. 4. Online Survey Screenshots

This section of the appendix contains all the screenshots seen by people participating in the online survey. However, please note that actual screenshots seen by respondents were much larger and clearer than the screenshots presented below.

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If respondent says ‘Yes’ to question eQ3A, respondent is also asked question eQ4:

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Respondents clicking the links for any of the species in the previous screen are given a popup showing earlier photos of infected species. Respondents clicking the link for the disease spread maps in the previous screen are given a popup showing the earlier pair of spread maps.

Respondents clicking the link for the location of Heritage Gardens in the previous screen are subsequently presented with the following popup:

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If respondent selects ‘Probably less often’ to question eQ10 then the following question is also asked:

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Respondents clicking the link for impacts on bilberry in the previous screen are given a popup of an earlier photo of infected bilberry, while respondents clicking the link for the disease spread maps are given a popup showing the earlier pair of spread maps.

Respondents clicking the link for the location of Heathland in the previous screen are subsequently presented with the following popup:

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If respondent selects ‘Probably less often’ to question eQ17 then the following question is also asked:

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Respondents clicking the link for impacts on species in the previous screen are given a popup of earlier photos of infected species, while respondents clicking the link for the disease spread maps are given a popup showing the earlier pair of spread maps.

Respondents clicking the link for the location of Woodland in the previous screen are subsequently presented with the following popup:

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If respondent selects ‘Probably less often’ to question eQ22 then the following question is also asked:

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If respondent selects ‘Yes’ to the first question of Q25 then the following question is asked:

If respondent selects ‘Yes’ to the second question of Q25 then the following question is asked:

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If respondent selects ‘Yes’ to question eQ28 then the following question is also asked:

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