Identifying and mapping boundaries for Important Plant Areas: Scotland’s West Coast Important Plant Area for Atlantic Woodland

Malcolm Fraser and Sandy Winterbottom 2008. Updated 2010.

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Table of contents

1 Introduction ...... 4 1.1 Objectives ...... 4 1.2 Document scope and Outputs ...... 4 1.3 Definitions ...... 4

2 Datasets ...... 5 2.1 IPA Core Sites database ...... 5 2.2 Statutory agency datasets ...... 5 2.3 Ordnance Survey datasets ...... 5 2.4 Additional datasets ...... 5 2.5 Datasets summary ...... 6

3 Methods ...... 7 3.1 Software ...... 7 3.2 Mapping existing Atlantic woodlands ...... 7 3.3 Environmental variables related to Atlantic Oak Woodland Distribution ...... 8 3.4 Mapping zones of opportunity / potential Atlantic woodlands ...... 9 3.5 Further work ...... 12

4 Outputs ...... 13 4.1 GIS model ...... 13 4.2 West Coast IPA core sites database ...... 13

Appendices ...... 14 Appendix A: ArcGIS methods for mapping IPA Sites ...... 14 Dataset cleaning and preparation ...... 15 Figures ...... 18 4.3 References ...... 30

Table of Figures

Figure 1: base map of Kentra Bay in Ardnamurchan...... 18 Figure 2: base map overlaid with AWI polygons...... 19 Figure 3: base map overlaid with aggregated polygons ...... 19 Figure 4: base map overlaid with aggregated AWI polygons and intersecting point data ...... 19 Figure 5: base map overlaid with colour coded AWI polygons...... 20 Figure 6: map of core sites in the Ardnamurchan and Sunart area ...... 21 Figure 7: map showing course-scale data on maximum consecutive dry days in a year (1961-2000) ...... 21 Figure 8: map showing altitudinal range...... 22 Figure 9: map showing slope range...... 22 Figure 10: Proximity Suitability Map ...... 23 Figure 11: Elevation Suitability Map ...... 24 Figure 12: Slope Suitability Map ...... 24 Figure 13: areas experiencing >179 rain days per year ...... 25 Figure 14: Native Woodland Model Suitability for W11 and W17 NVC Categories ...... 25 Figure 15: Zones of opportunity derived from the Weighted Overlay Model ...... 26 Figure 16: SAC sites in example area...... 26 Figure 17: SSSI sites in example area...... 27 Figure 18: NNR sites in example area...... 27 Figure 19: Ben Averis dataset sites in example area...... 28 Figure 20: NBN Plagiochila spinulosa sites in example area...... 28 Figure 21: Plantlife Criterion A sites in example area...... 29

Virtual Landscape Centre Plantlife Scotland – West Coast Atlantic Woodland IPA

1 Introduction

1.1 Objectives The aim of this project was to identify and map the boundary of the West Coast Important Plant Area for Plantlife Scotland and map zones of opportunity for potential expansion of Atlantic Oak Woodland. There were three main objectives for this project: 1. Compile a database of all nominated IPA sites within the West Coast Atlantic Oak IPA area. 2. Identify and map boundaries for core IPAs as identified by Plantlife consultation. 3. Analyse key environmental variables in relation to identified core IPA sites to inform a predictive model identifying zones of opportunity for Atlantic Oak woodland.

1.2 Document scope and Outputs This document details the methods and resources used in completion of the above objectives. The following outputs were created in the course of the project: 1. Complete database of all sites within the West Coast IPA. 2. Maps identifying core site boundaries. 3. Predictive Model and maps identifying zones of opportunity for Atlantic woodland within the West Coast IPA. 4. An outline of methodology adopted, for future application to other IPAs across Scotland. This document describes the datasets used, their origins, and the individual tasks performed in production of the above outputs. This document follows and builds upon the report produced by exeGesIS SDM Ltd. for Plantlife Cymru1 which identified IPA boundaries within Wales and presented a test case for modelling potential zones of opportunity for Atlantic Oak IPA expansion.

1.3 Definitions The following terms require definition: West Coast Important Plant Area (IPA) is loosely defined as the extent of western mainland Scotland where Atlantic woodlands are found, which stretches from “Kinlochbervie in the north to Tarbert in the south and Glen Coe in the east”2. It specifically excludes the major islands off the west coast, but does include Bute. Atlantic woodland is defined here as oak – birch woodland in NVC W11 and W17 (Rodwell 1991). Although NVC vegetation types are widely acknowledged to describe bryophyte and lichen communities poorly, these community descriptions provide a tool for identifying broad woodland types. Atlantic woodland here, however, also includes other woodland types where they exist within a mosaic of oak – birch woodland. This follows the definition of Atlantic woodland adopted in Plantlife’s Back from the Brink management guide for bryophytes and lichens of Atlantic woodland (2010). Criterion A sites are defined as holding “significant populations of one or more species that are of global or European conservation concern”3. In the context of this project these are species listed under European Red Lists for each species group, where they exist. Criterion B sites contain “an exceptionally rich flora in a European context in relation to their biogeographic zone” 3, which in this project have been identified by species experts and includes sites with high species richness indices according to Averis (2001) and Coppins and Coppins (2002). Criterion C sites are “outstanding example[s] of a habitat type of global or European plant conservation and botanical importance” 3, which corresponds to SAC site designation. Zones of opportunity are areas adjacent or nearby to an existing IPA which have similar environmental variables, and therefore have the potential to provide likely suitable conditions for IPA expansion.

Page 4 of 31 Virtual Landscape Centre Plantlife Scotland – West Coast Atlantic Woodland IPA 2 Datasets

2.1 IPA Core Sites database Plantlife Scotland provided a partial list of Criterion A, B and C sites in Microsoft Excel format. Criterion A sites were supplied as National Grid References of varying accuracy. Criterion B and C sites were supplied as site names, in most cases corresponding to SSSI and SAC sites. This site list was created and compiled as a result of consultation with experts on the most important sites within the West Coast IPA. During the course of the project additional sites were identified by systematic analyses of other datasets. The complete database of core sites comprises the original set of expert - selected sites, plus sites identified by systematic methods and subsequently approved for inclusion by Plantlife Scotland.

2.2 Statutory agency datasets SNH provides several freely available datasets in ESRI Shapefile format via the SNHi Natural Spaces online service (http://www.snh.org.uk/snhi/). Three of these datasets were used in this project: the Ancient Woodland Inventory (AWI), SSSI database, and NNR database. Additionally a database of SSSI “features of interest” was requested from SNH and provided by Andrew Kesterton ([email protected]). A dataset of SAC boundaries and characteristics is freely available via the JNCC website (http://www.jncc.gov.uk/). This is supported by an Excel database containing an extensive range of site characteristics including “interest codes”.

2.3 Ordnance Survey datasets Elevation data was obtained from the Ordnance Survey (OS). The specific dataset used in the project was OS Land-Form PANORAMA DTM 1:50,000 terrain maps.

2.4 Additional datasets An Excel dataset of recorded locations for the indicator species Plagiochila spinulosa was obtained from the National Biodiversity Network (NBN) website (http://www.nbn.org.uk/). NBN data is freely available and this dataset is an extract from the larger dataset: “Bryophyte data for Great Britain from the British Bryological Society held by BRC”. Plantlife Scotland provided an MS Excel dataset of woodland-based bryophyte survey data compiled by Ben Averis. The dataset details bryophyte species found in 826 Scottish woodlands alongside other variables such as the presence of ravines, and total number of oceanic species. Coarse climate data is available from the Met Office for research purposes (http://www.metoffice.gov.uk/research/hadleycentre/obsdata/ukcip/index.html), for this project three datasets were assessed: . Maximum number of consecutive dry days (days with less than or equal to 2 mm of rain) in a year (1961-2000) . Number of days per month having a rainfall >= 1 mm (Rain Days) (1961-2005) . Number of days per month having a rainfall >= 10 mm (Wet Days) (1961-2005) The values in these datasets are long term averages and provided at 5km2 resolution. A paper describing the process of creating these datasets can also be found on the Met Office website4. Access to the Scottish Native Woodland Model (SNWM) was provided by SNH in December 2009. Areas identified from the SNWM as having potential for NVC categories W11 and W17 woodland were used along with other relevant variables to develop zones of opportunity in April 2010.

Page 5 of 31 Virtual Landscape Centre Plantlife Scotland – West Coast Atlantic Woodland IPA

2.5 Datasets summary Table 1: Datasets acquired for the study Source Dataset Extent Resolution Date of Notes publication Plantlife Core Sites West n/a 10/12/07 MS Excel database containing Scotland database Coast all Criterion A, B, and C sites IPA initially identified by Plantlife Scotland. SNHi Ancient Scotland 12.5m 27/03/06 ESRI Shapefile dataset Natural Woodland detailing Ancient Woodland Spaces Inventory (AWI) Inventory boundaries and dataset some characteristics. SNHi SSSI dataset Scotland 5m 27/02/07 ESRI Shapefile dataset Natural detailing SSSI boundaries and Spaces some characteristics. SNH SSSI Features of Site-by- n/a 18/12/07 MS Excel database detailing all Interest site features of interest for all SSSI sites in Scotland. SNHi NNR dataset Scotland 5m 17/01/08 ESRI Shapefile dataset Natural detailing NNR boundaries and Spaces some characteristics. JNCC SAC dataset UK Unknown 15/01/07 ESRI Shapefile dataset detailing SAC boundaries. JNCC SAC Features of UK n/a 15/01/07 MS Excel database containing interest an extensive range of SAC site characteristics. OS Land-Form West 1:50,000 Frozen NTF format map tiles Panorama DTM Coast dataset (no constituting the approximate IPA longer extent of the West Coast IPA. updated) NBN Plagiochila UK Variable: 25/04/07 MS Excel database containing Gateway spinulosa 100m to a data extract from the freely- locations 10km available dataset: “Bryophyte data for Great Britain from the British Bryological Society held by BRC” Plantlife Ben Averis’ Scotland 100m 2006 MS Excel database detailing Scotland survey of 826 bryophyte species found in highland woodland surveys. woodlands Met Maximum UK 5km2 27/06/02 ASCII grid file describing the Office number of long-term average (1961-2000) consecutive dry maximum number of days in a year consecutive dry days in a year. Met Number of days UK 5km2 27/06/02 ASCII grid file describing the Office per month long-term average (1961-2005) having a rainfall number of days per month >= 1mm (Rain having a rainfall >= 1mm (Rain Days) Days) Met Number of days UK 5km2 27/06/02 ASCII grid file describing the Office per month long-term average (1961-2005) having a rainfall number of days per month >= 10mm (Wet having a rainfall >= 10mm Days) (Wet Days) SNH Native Woodland Scotland 1:50,000 2004 Compiled from the 1:250,000 Model scale national soil maps (MISR 1982) and the 1:25000 scale Land Cover of Scotland 1988 (MLURI 1993).

Page 6 of 31 Virtual Landscape Centre Plantlife Scotland – West Coast Atlantic Woodland IPA 3 Methods

3.1 Software ESRI ArcGIS version 9.3 was used to: collate data, generate maps, and model environmental variables and potential areas of IPA expansion. Microsoft Excel 2003 was used for data preparation and manipulation. Minitab version 15 was used for statistical analysis.

3.2 Mapping existing Atlantic woodlands

3.2.1 Basis of mapping process In order to locate existing Atlantic woodlands within the West Coast IPA the following six datasets were identified and used: . Criterion A/B/C: sites provided by Plantlife which were selected by experts as outstanding sites in their categories. . Criterion B: SSSI sites containing one or more of the features of interest: Upland oak woodland, Bryophyte assemblage, Lichen assemblage. . Criterion B: NNR sites flagged as important for bryophyte or lichen assemblages. . Criterion B: Sites from Ben Averis’ dataset containing 18 or more oceanic bryophyte species. . Criterion B: Sites from the NBN Plagiochila spinulosa dataset that are precise to 1km2 or 100m2 (i.e. listed with National Grid references of 6-figure or more accuracy). . Criterion C: SAC sites containing the feature of interest H91A0 - ‘Old sessile oak woods with Ilex and Blechnum in the British Isles’. Using a range of source datasets ensures a large number of potential core sites are captured. For each source dataset a list of potential core sites was generated and passed to Plantlife Scotland for approval in order to avoid inclusion of unsuitable sites. The mapping method described in the exeGesIS report represented Criterion A sites as points, and Criterion B and C sites by SSSI or SAC boundaries, however the report contained recommendations for improvements to this process: Firstly there was acknowledgment that a statutory site boundary may not be equivalent to the core site boundary. For example, the Sunart SAC extends over 10,000Ha and contains the H91AO feature of interest, but if the SAC boundary is examined we find that most of the area within is made up of Loch Sunart and only a small percentage is made up of fringing woodlands. Therefore if we simply include the Sunart SAC on the list of core sites then we greatly overestimate the extent of Atlantic woodland present and also map its location incorrectly. Secondly exeGesIS suggested creating boundaries for point data. By mapping Criterion A sites and other data as points within the GIS model then we underestimate the extent of Atlantic woodland present, and if the points are not accurate then incorrect locations may be mapped. Clearly a process was required to improve mapping accuracy and to act upon the above recommendations, the SNH AWI dataset was used as the basis for this process. The AWI dataset maps and describes woodlands that were recorded as semi-natural on either the 1750 Roy maps or the 1860 1st edition OS maps, the woodlands are divided into compartments so what may appear on a map as a single contiguous woodland may be represented in this dataset by multiple polygons. The process of determining which woodland blocks may contain Atlantic woodland is described in Section 3.2.2 below. It is important to note an assumption, made at this stage in the project. The AWI dataset is being used as the basis for mapping Atlantic woodland and we are assuming that all woodlands relevant to this project are described in the AWI dataset. This assumption was tested by calculating the number of data points from the Ben Averis dataset which were found within AWI blocks. 113 of 137 points were found to be within AWI blocks which equals 82.5% site capture. To improve the number of captured sites a buffer of 100m was applied to each data point resulting in 129 of 137, or 94.1% site capture. This buffering is justified by the fact that the majority of point data were accurate to 6 figures or 100 square metres. In light of this a 100m buffer was applied to all point datasets when calculating their intersections with AWI blocks.

Page 7 of 31 Virtual Landscape Centre Plantlife Scotland – West Coast Atlantic Woodland IPA

3.2.2 Spatial intersection of datasets with AWI The first step in improving mapping accuracy was to aggregate the AWI woodland blocks into larger compartments more appropriate for the scope of this project. This process can be seen in Figures 1 to 3 and is described in full in Appendix A. By carrying out this process of aggregation we make a second assumption: that the relevant bryophyte and lichen species may be found in all parts of contiguous woodlands and are not limited to specific compartments. The second step was to intersect spatially the aggregated woodland blocks with data from source datasets. Figure 4 shows two points from the Ben Averis dataset (represented by orange dots) intersecting aggregated woodland blocks. The intersection process is described in Appendix A. Each of the six source datasets required a certain amount of data cleaning and preparation prior to being intersected with the AWI features. The data cleaning process for each dataset is described in Appendix A. The third stage in this process was to relate the intersections back to the original AWI dataset, ensuring all the information present in the original AWI dataset is retained alongside intersection data. Figure 5 shows the original AWI polygons which now contain information regarding their intersection with the Ben Averis data points. This process is described in full in Appendix A. After carrying out this process for each source dataset the output is an updated version of the AWI database which retains all of the original information as well as identifying which woodland blocks are core sites, and why they have been nominated as core sites. In other words we now have a map of the core sites within the West Coast IPA, and a dataset describing the origin of each core site. An example map showing core sites originating from all six of the source datasets can be seen in Figure 6. It is important to note that sites may qualify for inclusion under multiple criteria, e.g. a block of woodland may be within the boundary of both an SSSI and SAC as well as being present in the Ben Averis dataset.

3.3 Environmental variables related to Atlantic Oak Woodland Distribution A variety of environmental variables were selected by Plantlife Scotland as key constraints which determine the likely presence or absence of Atlantic woodland. These variables were mapped individually and formed the basis for the development of the predictive model.

3.3.1 Number of wet days per year / Oceanicity. Number of wet days per year is a simple method of measuring oceanicity, which in turn is a factor that defines the presence of Atlantic woodlands5. Another method of measuring oceanicity, which is particularly relevant to oceanic bryophyte species, is to measure the maximum consecutive number of dry days. Mapping both of these measures was possible via Met Office datasets, however the data is coarse (at 5km2 accuracy) as can be seen in Figure 7, an example map of the dry days data.

3.3.2 Soils data / Native Woodland Model Soil type is a constraint on presence or absence of both tree and bryophyte species. SNH and The Macaulay Land Use Research institute (MLURI) hold a variety of datasets on soil characteristics which have been used to derive the “Native Woodland Model” (NWM) dataset, developed to assist in the planning of native woodland expansion. The base datasets used are the 1:250,000 scale National Soils Map and the 1:25,000 Land Cover of Scotland (LCS88) dataset .The soil and land cover data have been related to the requirements of different types of native woodland in order to predict the woodland NVC types that would be expected to grow under current soil and vegetation conditions with no or minimal ground intervention. The documentation for the Native Woodland Model suggest that is has been found to be ~60% accurate in predicting areas with the potential for supporting Oakwoods (SNH and MLURI, 2004), as such it is a key addition to the modelling process for this project. The NWM dataset was provided to Plantlife Scotland in ArcGIS Shapefile format.

3.3.3 Elevation Atlantic woodland (W11) extends up to 450m, although it generally occurs below 180m. Palaeoecological evidence (eg Birks 1988) have suggested that in western Scotland, this is a natural range for these woodland types and does not appear to have been significantly limited by human activity since prehistory. Elevation was therefore identified as a potential key variable in the distribution of Atlantic woodland.

Page 8 of 31 Virtual Landscape Centre Plantlife Scotland – West Coast Atlantic Woodland IPA

Data on elevation at a resolution of 50 metres are available from the Ordnance Survey through the Landform Panorama dataset and this was imported directly into ArcGIS. Although more detailed data are available at a resolution of 10m (Landform Profiles dataset), the extent of the area of analysis is large and so a more detailed dataset was not appropriate. Figure 8 is an elevation map for a sample area.

3.3.4 Presence of steep slopes and ravines Steep slopes may correlate with oceanic bryophyte presence via two mechanisms: firstly steep slopes, with limited viewsheds, may indicate the presence of ravines which typically have higher atmospheric moisture content than flat land; secondly steep slopes may indicate the presence of bare rock which many oceanic bryophytes require as their substrate. Slope was therefore identified as another key variable likely to influence the distribution of Atlantic woodland. An example of slope mapping can be seen in Figure 9.

3.4 Mapping zones of opportunity / potential Atlantic woodlands

3.4.1 Approach The environmental variables identified as being important in determining the distribution of Atlantic woodland were analysed or assessed to identify the values of each variable which were most suitable. Suitability maps for each variable were created using a scale of 1 to 9 (with 9 being the most suitable areas and 1 being the least suitable). These were then incorporated into a weighted overlay model in order to map zones of opportunity.

3.4.2 Generation of proximity suitability maps A series of 1km buffer rings were created around the core sites network spanning 0km to 5km in distance. These buffers were then restricted to the coastline of the mainland and small islands, ensuring zones of opportunity were not inappropriately mapped in the sea. This buffering process enables visualisation of the distance between sites and indicates which areas may be the most suitable zones of opportunity for habitat expansion based on existing Atlantic woodland proximity. A suitability scale was applied to the buffer map as shown in table 2: Table 2: Suitability scale derived from buffer distances from existing areas of Atlantic woodland Proximity to existing Atlantic woodland Suitability for new Atlantic woodland Within 1km of existing woodland 9 (most suitable) Within 2km of existing woodland 7 Within 3km of existing woodland 5 Within 4km of existing woodland 3 Within 5km of existing woodland 1 Greater than 5km from existing woodland Not Suitable

A sample of the proximity suitability map can be seen in Figure 10.

3.4.3 Calculating suitability for significant topographic variables. The two key topographical variables identified as being important in terms of the distribution of Atlantic woodland were elevation and slope. Elevation and slope values were extracted for all areas identified as existing Atlantic woodland and simple summary statistics for these data-sets were derived as shown in table 3. Table 3: Summary statistics for Elevation and Slope within existing IPA core sites Statistic Elevation Slope Minimum Value 0m 0 degrees Maximum Value 626m 55 degrees

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Mean Value 90m 16 degrees Standard Deviation (SD) 71m 9 degrees

In order to use this information to derive a suitability scale for elevation and slope, it was decided to use the mean value +/- 1 SD to map areas where slope and elevation were most suitable, the mean value +/- 2 SDs to identify areas that were moderately suitable and the mean values +/- 3 SDs to map areas that are least suitable. All other values were considered as not suitable. However, an issue with this approach is that the data were not normally distributed. Graph 1 below shows the distribution of elevation values for existing areas of Atlantic woodland:

Graph 1: Elevation values for existing Atlantic woodland

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Count800

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0 0 28 56 84 112 140 168 196 224 252 280 308 336 364 392 420 450 490 600

Elevation

In order to normalise the data, the elevation values for Atlantic woodland areas were transformed using a Natural Log transformation and the summary statistics derived for the logged data set and exported to Excel. The mean and +/- SD values were calculated in Excel from the logged data set and the results transformed back using Exponential function (Table 4). This provided suitability ranges that matched the distribution of the non-normal data set. Table 5 summarises the elevation and slope values used in calculating the suitability maps. Table 4: Summary statistics for logged elevation and slope maps Anti-log summary Anti-log Summary Stats stats for us in Summary summary stats logged model (in metres stats for for us in model Elevation elevation a.s.l.) Slope logged slope (in degrees) Mean 4.162721012 64.24609938 Mean 2.558221 12.91282495 STDev 0.9446783 2.571985838 STDev 0.745166 2.106791133 Mean +1SD 5.107399312 165.2400577 Mean +1SD 3.303387 27.20462512 Mean +2SD 6.052077612 424.9950883 Mean +2SD 4.048553 57.31446299 Mean +3SD 6.996755912 1093.081348 Mean +3SD 4.793719 120.7496024 Mean -1SD 3.218042712 24.97918085 Mean -1SD 1.813055 6.129143393 Mean -2SD 2.273364412 9.712021147 Mean -2SD 1.067889 2.909231625 Mean -3SD 1.328686112 3.776078781 Mean -3SD 0.322723 1.380882793

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Table 5: Elevation and slope values used in calculating the suitability maps: Elevation Values Suitability Value Slope Value Suitability Values 25m to 165m 9 (Most Suitable) 6 degrees to 27 degrees 9 (Most Suitable) 10m to 25m and 6 (Moderately Suitable) 3 degrees to 6 degrees 6 (Moderately Suitable) 165m to 425m and 27 degrees to 57 degrees 4m to 10m and 3 (Least Suitable) 1 degree to 3 degrees 3 (Least Suitable) 425m to 1093m and 57 degrees to 90 degrees Below 4m and above Not Suitable N/A Not Suitable 1093m

Figure 11 and 12 show the suitability maps for elevation and slope.

3.4.4 Mapping climatic variables In attempting to map the influence of rainy days on the presence of Atlantic woodland the data were first examined in Minitab. A boxplot found the minimum number of rainy days in the existing core sites network to be 179. However when this was mapped as a constraining variable, the entire West Coast IPA (and the rest of western Scotland) was found to be above this value. This can be seen in Figure 13. In initial discussions of the importance of rainy days a value of >220 rain days was suggested as a possible lower constraint. This value was mapped but found to be very restrictive and eliminating significant parts of the existing core sites network, as seen in Figure 13. On further consideration, it was decided to exclude climatic data from the analysis. If more detailed climatic data becomes available for the project, then this could be included as a variable at a later date.

3.4.5 Incorporation of the Native Woodland Model The key NVC types for Atlantic woodland are W11 (Upland Oak – birch woodland with Bluebell / wild hyacinth) and W17 (Upland Oak – birch woodland with blueberry). All records in the attribute table for this dataset containing the codes W11 or W17 were selected. The NWM predicts single woodland types where possible. Where two or more woodland types are considered equally well suited to an area, polygons are classified as “interchangeable” for both types of NVC type (e.g. W17/W18). Single and interchangeable areas were assessed as having the potential for 80 – 100% woodland cover by the NWM. In some areas, the soil and land cover pattern vary on a scale below that of the NWM and in these cases “Mosaics” have been defined where different woodland types have been matched to different components of a landscape. In these cases, the most dominant class is listed first (e.g. W11 + W7 Mosaic). In many areas, the potential Native Woodland cover would be restricted to particular topography within very complex landscapes rather than densely covering whole areas and these have been identified as having potentially more complex mosaic woodland compositions. On the basis of the NWM classifications for woodland potential, suitability maps were derived from the selected records, containing the W11 and W17 categories. All areas listed as single W11 or W17 NVC or as interchangeable categories were assigned a suitability value of 9 (most suitable). Those classified as NVC mosaic areas with either W11 or W17 listed first were assigned a suitability value of 6 (moderate to highly suitable), with mosaic areas with W11 or W17 listed second assigned a value of 3 (low to moderate suitability) and all complex mosaics assigned a value of 1 (low suitability). All areas which did not include the W11 or W17 categories in the NWM were considered to be not suitable. Table 6 summarises the suitability values assigned to the data and Figure 14 shows the output suitability map. Table 6: Suitability values for Atlantic Oak Woodland derived from the Native Woodland Model. NVC Text in NWM attribute Table Suitability Basin Bog woodland/scrub + W11 1 Peatland with scattered trees/scrub + W4 Birch with purple\moor grass and 1 open ground + W17/W18 Mosaic W11 + W4 Mosaic 6 W11 + W7 Mosaic 6 W11 + basin bog woodland/scrub 6

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W11 Upland Oak-Birch with bluebell/wild hyacinth 9 W11/W17 9 W11/W7 9 W11/W9 9 W17 + W4 Mosaic 6 W17 Upland Oak-Birch with bilberry/blaeberry 9 W17/W11 9 W17/W18 9 W17/W18 & W4 Birch (with open ground) Mosaic 6 W18 + W11 Mosaic 3 W18/W17 9 W4 + W17 Mosaic 3 W4 Birch (with open ground) + Peatland with\scattered trees/scrub + 1 W17/W18 Mosaic W4 Birch (with open ground) + W17/W18 Mosaic 1 W6 + W11 Mosaic 3 W7 + W11 Mosaic 3 W7 + W17 Mosaic 3 W7/W11 9 W9/W11 9

3.4.6 Mapping zones of opportunity The four suitability maps (proximity, elevation, slope and NWM) were combined in a weighted overlay model in ArcGIS. Each variable was assigned a weight of 25% so that all the key variables were considered equally important. The output from the weighted overlay model is a scaled map showing areas that are most to least suitable based on the combined input of the four variables. Figure 15 shows the output from the weighted overlay model for the Ardnamurchan and Sunart area.

3.5 Further work This project has built upon the process carried out by exeGesIS SDM and Plantlife Cymru, the recommendations made in the initial project have been incorporated into this process and it benefits from more accurate mapping and modelling. However there are still areas where improvements can be made.

3.5.1 Mapping The initial assumption that the AWI dataset contains all relevant woodlands must be borne in mind when assessing these maps. This assumption would benefit from targeted field validation. In addition, the second assumption made, that relevant bryophyte and lichen species may be found in all parts of contiguous woodland and are not limited to specific compartments, has not been validated. There would be value in ground-truthing these assumptions and surveying to see whether all compartments of contiguous woodlands are equally suitable for the bryophyte and lichen assemblages found within, or whether woodland not present in the AWI dataset is of interest in terms of this IPA.

3.5.2 Modelling One of the key improvements to the model would be to include climate data. At present, the coarse resolution of the freely available data is insufficient to gain an insight into the constraining climatic variables for Atlantic woodland. More detailed data are available from the Met Office, but the cost of these data sets is beyond the means of this project. A further option would be to incorporate the “proximity rating” process described by exeGesIS. This positively weights zones of opportunity which are located between two (or more) core sites, and negatively weights those one the outer edges of the core sites network. However, this may not be a significant improvement over the current mapping process which is relatively robust and self-explanatory.

3.5.3 Application on the ground The next stage of this project is to begin to provide maps to land owners and managers to enable them to prioritise sites for Atlantic woodland in terms of identifying appropriate management. These maps are an important component of Plantlife’s conservation framework for Atlantic woodland (Worrell 2010) and

Page 12 of 31 Virtual Landscape Centre Plantlife Scotland – West Coast Atlantic Woodland IPA provide the spatial element of conservation advice that enables land managers to identify important sites and to target appropriate management at priority sites. Detailed guidance on appropriate management is available in Plantlife’s Back from the Brink management series, available at www.plantlife.org.uk

4 Outputs

4.1 GIS model Figure 6 is an example map that shows existing Atlantic woodland sites in the Ardnamurchan and Sunart area. On this map it is possible to see sites that are drawn from all of the source datasets. Figure 15 is an example map showing the core sites and zones of opportunity as predicted by weighted overlay model. These data sets are the key outputs from the GIS analysis and modelling.

4.2 West Coast IPA core sites database The core sites database originally supplied by Plantlife Scotland at the start of this project has been expanded via the systematic identification of appropriate core sites. The database was agreed as complete for the purposes of mapping and modelling on 06/02/08. The methods used to determine which sites were to be included in the database are described in Appendix A.

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Appendices

Appendix A: ArcGIS methods for mapping IPA Sites

Ancient Woodland Inventory aggregation The AWI dataset is a Shapefile and so added directly into ArcMap. Within ArcMap the “Aggregate” tool was used to join polygons located within a specified aggregation distance of 50m into single features, this generated the new Shapefile: AWI_aggregate_50m.shp. The new shapefile does not retain any of the fields/information found in the original dataset, however this new dataset can be joined to the original one after intersection with source datasets has been performed. A new DBF table was automatically created by ArcGIS during the aggregate process. Output AWI_aggregate_50m.shp AWI_aggregate_50mTbl.dbf

Intersecting the aggregated AWI Shapefile with source datasets (using SAC data as an example) The Shapefiles Source_SAC_H91A0_Only.shp and AWI_aggregate_50m.shp were opened in ArcMap. In the SAC Shapefile, a select by attribute query was run to select the sites whose “Include” field equalled “y”. A select by location query was then run to select features from the AWI Shapefile which intersected the selected features from the SAC shapefile. A text field named “INT_SAC” was added to the AWI Shapefile attribute table. This field was then populated with “y” for polygons which intersect the “y” SACs, and “n” for all other polygons. This process was repeated for the remaining four source datasets, adding a new text field to the AWI Shapefile, and populating this field with “y” for polygons which intersect source data. For point feature datasets (i.e. Ben Averis, P. spinulosa, and Criterion A datasets) an alteration was made to the select by location query to add a 100m buffer zone around each point. Output Updated version of AWI_aggregate_50m.shp

Joining the aggregated AWI Shapefile to the original AWI Shapefile After intersecting the aggregated AWI Shapefile with each of the source datasets and populating the Shapefile with intersect information it was joined back to the original Shapefile. The original AWI_V3.shp Shapefile was joined to the AWI_aggregate_50m_Tbl.dbf by the fields FID and INPUT_FID respectively. The AWI_aggregate_50m.shp file is now joined to AWI_V3.shp by the fields FID and AWI_aggregate_50m_Tbl.OUTPUT_FID respectively. This composite dataset was exported to a new Shapefile: Intersect_AWI_all_sites.shp Output Intersect_AWI_all_sites.shp

Creating layer files for each data source The composite AWI Shapefile Intersect_AWI_all_sites.shp formed the basis of a series of select by attribute queries. For example, the select by attribute query was run to select woodland blocks which intersect “y” SAC sites. A new layer was created from this selection and added to the map then saved as a layer file. This process was carried for each of the source datasets, generating 5 layers from the main AWI_plus_data layer.

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A sixth layer was generated containing all sites from all categories (to simplify the buffering process). Output Intersect_AWI_SAC.lyr Intersect_AWI_SSSI.lyr Intersect_AWI_BA.lyr Intersect_AWI_Pspin.lyr Intersect_AWI_CritA.lyr Intersect_AWI_All_sites.lyr

Dataset cleaning and preparation

SAC dataset Data from Excel database’s ‘SAC feature data’ tab was custom filtered to select sites in “Highland” and “Argyll & Bute”. The Excel database was filtered a second time to select sites listed with the relevant Interest Code H91A0 - ‘Old sessile oak woods with Ilex and Blechnum in the British Isles’. These two filtering process generated a list of 30 SACs. The SAC codes were noted and the SAC Shapefile was opened within ArcMap. The SAC codes formed the basis of a select by attribute query and the resulting selection was exported to a new Shapefile named Source_SAC_H91A0_Only.shp The site list of 30 SACs was passed to Plantlife Scotland for final site selection, from which 19 SAC sites were confirmed to be included in the model. To maintain the integrity of the original Shapefile a text field named “Include” was added to its attribute table. This field was then populated with “y” for included sites, and “n” for non-included sites. Similarly a second text field was added to provide rationale for non-included sites. Finally the Shapefile symbology was modified for ease of interpretation, displaying “y” sites green, and “n” sites transparent. Figure 16 shows the SAC sites in the example area. Output Source_SAC_H91A0_Only.shp

SSSI dataset Before cleaning the data the relevant ‘features of interest’ were identified as: Upland oak woodland, Lichen assemblage, and Bryophyte assemblage. The Excel database of SSSI features of interest (SSSI_Features_master.xls) was opened and filtered to select one feature of interest e.g. “Upland Oak Woodland”. The filtered results were copied to a new file and saved as a DBF table: SSSI_Feature1_Oak.dbf The new DBF table was amended so its column headings matched those in the SSSI Shapefile SSSI_SCOTLAND.SHP. This meant renaming two columns to “PA_CODE” and “SITE_NAME”. Within ArcMap the DBF table was added to the SSSI Shapefile by right-clicking on the Shapefile > Joins and Relates > Join. The above steps were repeated for the remaining two features of interest resulting in the SSSI_SCOTLAND.SHP being joined to three DBF tables. The SSSI Shapefile was opened within ArcMap and a select by attribute query was run to select all sites containing one or more of the features of interest. This selection was exported to a new Shapefile: SSSI_SCOTLAND_3_features.shp Manual site selection was performed to select all sites within the IPA boundary (as discussed with Plantlife).

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An “Include” text field was added to the shapefile and populated with “y” for sites within the IPA, and “n” for sites outside. Finally the Shapefile symbology was modified for ease of interpretation, showing “y” sites red, and “n” sites transparent. Figure 17 shows the SSSI sites in the example area. Output SSSI_Feature1_Oak.dbf SSSI_Feature2_Bryophyte.dbf SSSI_Feature3_Lichen.dbf Source_SSSI_SCOTLAND_3_features.shp

NNR dataset NNRs to be included in the IPA model were identified by Plantlife Scotland. These NNR sites were manually selected within the NNR_SCOTLAND Shapefile. To maintain the integrity of the original Shapefile a text field named “Include” was added to its attribute table. This field was then populated with “y” for included sites, and “n” for non-included sites. Finally the Shapefile symbology was modified for ease of interpretation, displaying “y” sites blue, and “n” sites transparent. Figure 18 shows the NNR sites in the example area. Output Updated version of NNR_SCOTLAND.shp

Ben Averis dataset The Excel database was opened and the ‘Summary’ tab was copied to a new tab. The data in the new tab was cleaned by removing unwanted rows beneath the site list, and unwanted columns containing data on individual species. Easting and Northing values were calculated from existing National Grid References and added as new columns. The tab was then exported as a DBF table. Within ArcMap the DBF table was converted to a point feature shapefile via Tools > Add X-Y Data. Manual site selection was performed to select all sites within the IPA boundary (as discussed with Plantlife). An “Include” text field was added to the shapefile and populated with “y” for sites within the IPA, and “n” for sites outside. A second text field named “Above Threshold” was added to the attribute table and populated with “y” for sites with 18 or more oceanic species, and “n” for the remaining sites. Finally the symbology was modified for ease of interpretation: sites outwith the IPA are transparent, sites inside but below the threshold are brown, sites within and above the threshold (i.e. relevant sites) are orange. Figure 19 shows the Ben Averis datasets sites it the sample area. Output Averis_all_sites.dbf Source_BAveris_all_sites.shp

NBN Plagiochila spinulosa dataset The NBN data download was in tab delimited format which had to be converted to format readable by ArcGIS and cleaned. Additionally the data was at varying levels of accuracy e.g. some records are accurate to 100m, others only to 10km. The downloaded file was opened within Excel and saved in XLS format. A series of Excel functions were used to clean up non-standard grid references and standardise all records to 100m accuracy and to the bottom-left point of the relevant square. For example: NB20 was amended to NB 200 000.

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Easting and Northing references were calculated from the standardised grid references and the data was exported as a DBF table. Within ArcMap the DBF table was converted to a point feature shapefile via Tools > Add X-Y Data. Manual site selection was performed to select all sites within the IPA boundary (as discussed with Plantlife). An “Include” text field was added to the shapefile and populated with “y” for sites within the IPA, and “n” for sites outside. Finally the symbology was modified for ease of interpretation with “n” sites transparent and “y” sites divided into 4 bands according to their precision. The least accurate data (to 10km2) are coloured red, those of intermediate accuracy (to 2km) are orange, and the most accurate (to either 1km or 100m) are green. Figure 20 shows the NBN Plagiochila spinulosa sites in the example area. Output Source_P_spinulosa_BL_all.shp

Plantlife core sites database Criterion A grid references were exported to a DBF table and Easting and Northing references were calculated and added as new columns. Within ArcMap the DBF table was converted to a point feature shapefile via Tools > Add X-Y Data. Figure 21 shows the Plantlife Criterion A sites in the example area. Output Source_Criteria_A_sites.shp

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Figures Figure 1: base map of Kentra Bay in Ardnamurchan.

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Figure 2: base map overlaid with AWI polygons.

Figure 3: base map overlaid with aggregated polygons

Figure 4: base map overlaid with aggregated AWI polygons and intersecting point data

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Figure 5: base map overlaid with colour coded AWI polygons.

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Figure 6: map of core sites in the Ardnamurchan and Sunart area

Figure 7: map showing course-scale data on maximum consecutive dry days in a year (1961-2000)

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Figure 8: map showing altitudinal range.

Figure 9: map showing slope range.

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Figure 10: Proximity Suitability Map

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Figure 11: Elevation Suitability Map

Figure 12: Slope Suitability Map

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Figure 13: areas experiencing >179 rain days per year

Figure 14: Native Woodland Model Suitability for W11 and W17 NVC Categories

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Figure 15: Zones of opportunity derived from the Weighted Overlay Model

Figure 16: SAC sites in example area.

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Figure 17: SSSI sites in example area.

Figure 18: NNR sites in example area.

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Figure 19: Ben Averis dataset sites in example area.

Figure 20: NBN Plagiochila spinulosa sites in example area.

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Figure 21: Plantlife Criterion A sites in example area.

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4.3 References 1 exeGesIS, 2007. “IPA Boundaries Contract Report”. exeGesIS SDM Ltd. 2 Plantlife, “IPAs in detail”. Plantlife website. http://www.plantlife.org.uk/uk/plantlife-saving-species- plant-areas-boundarymaps.html [accessed 08/02/08]. 3 Anderson, S. 2002. “Identifying Important Plant Areas”. Plantlife International. 4 Perry M and Hollis D. 2005. “Generation of monthly gridded data sets for a range of climatic variables over the UK”. International Journal of Climatology, 25:1041-1054. 5 Baarda P. 2005. Atlantic “Oakwoods in Great Britain: Factors Influencing their Definition, Distribution and Occurrence”. Botanical Journal of Scotland. 57(1+2);1-20. 6 Averis, B. 1991. A survey of the bryophytes of 448 woods in the Scottish Highlands. SFSU report S54. Nature Conservancy Council, Edinburgh. Coppins, A.M. and Coppins, B.J. 2002 Indices of Ecological Continuity for Woodland Epiphytic Lichen Habitats in the British Isles British Lichen Society 37pp Averis, B. 2001. The effects of woodland management on bryophytes and lichens in the Western Highlands. Scottish Natural Heritage Contract no: RASD/133/96AWEB Rodwell, J. S (Ed.) 1991. British Plant Communities Volume 1: woodlands and scrub. Cambridge University Press, Cambridge. Plantlife (2010) Lichens and bryophytes of Atlantic woodland in Scotland: an introduction to their ecology and management. www.plantlife.org.uk Birks, H.J.B. 1988. Long term ecological change in the British uplands. In Usher, MB, Thompson, D.B A (Eds). Ecological change in the uplands. Oxford. 7 The potential for native woodland in Scotland: the native woodland model - SNH and MLURI (2004). ISBN 1 85397 390 4 Acknowledgements Plantlife Scotland would like to thank Sandy Winterbottom and Malcolm Fraser at the Virtual Landscapes Centre, University of Stirling, for all their work in developing the method for identifying IPA boundaries in complex landscapes. Their work enables Plantlife to make significant progress in conserving IPAs on the ground. We would also like to thank Gordon Rothero, who first identified the west coast IPA, and Sandy Coppins for their expert guidance and help, which has been invaluable. This project was supported by Scottish Natural Heritage.

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www.plantlife.org.uk

Plantlife International – The Wild Plant Conservation Charity Plantlife Scotland Balallan House, Allan Park, Stirling FK8 2QG Tel. 01786 478509 [email protected]

© May 2010

Plantlife’s work in Scotland is supported by

Plantlife International – The Wild Plant Conservation Charity is a charitable company limited by guarantee. Registered Charity Number: 1059559. Registered Company Number: 3166339. Registered in England. Charity registered in Scotland no. SC038951

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