Participatory GIS for collaborative deer management

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Participatory GIS for collaborative deer management

  1. 1. Participatory GIS for collaborative deer management Justin Irvine, Althea Davies http://www.macaulay.ac.uk/relu/ justin.irvine@hutton.ac.uk Althea.davies@hutton.ac.uk
  2. 2. 1. Background to the issue 2. Constructing a GIS model 3. Participation in GIS 4. Validating GIS predictions 5. Using GIS to address local NRM conflicts Structure
  3. 3. Income for landowners - Venison - Jobs for stalkers - Enjoyment for hunters/tourists BUT also a source of conflict: Damage to forest/farm crops Road traffic accidents Overgrazing priority habitats Deer are an important rural resource: Deer as a case study in conflict and collaboration: Background
  4. 4. Deer are a common pool resource Background
  5. 5. Sporting estates • Red deer regarded partly as an economic resource and partly as a cultural service • Revenue derived from paying clients stalking trophy stags and from venison revenues (only stags generate trophy revenues stags generate twice the venison revenues of hinds) • Costs incurred in stalkers’ wages • management objective: – Sufficient stag densities to ensure sporting success translate to 15- 20/km2 – maximise profit derived from stalking Background
  6. 6. Conservation woodland • managed by conservation authorities SNH, NTS, RSPB • enhance biodiversity • regenerate native woodland • high deer densities prevent regeneration of native tree species management objective – reduce population density to c. 5 deer per km2 to initiate regeneration of native trees – typically reduce population to this level over 5 year timescale – hold population at reduced level for further 20 years to allow regenerating woodland to establish – minimise cost, given these constraints time deer density 5 25 5 20 Background
  7. 7. Neighbouring businesses: different objectives Sporting Estate Woodland Restoration 20 deer per km2 Hinds:stags 1.3:1 5 deer per km2 Hinds:stags 0.6:1 Background
  8. 8. Neighbouring businesses: conflicting objectives Conservation Woodland Conservation Woodland £5070 km -2 £5070 km -2 Sporting Estate Sporting Estate £3537 km -2 £3537 km -2 Sporting Estate Conservation Woodland £3004 km -2 £6397 km -2 lower profits higher costs Background
  9. 9. 15 % 27 % 14 % 31 % 14 % 13 %21 % Background
  10. 10. 0 2 4 6 8 10 12 14 16 1960 1965 1970 1975 1980 1985 1990 1995 2000 Year Totaldeerdensity(km2) Deer Commission for Scotland data redrawn from Clutton-Brock et al 2002 Increasing deer density: a source of conflict over habitat use: Background
  11. 11. Definitions of GIS • A data input subsystem that collects and processes spatial data from various sources. This subsystem is also largely responsible for the transformation of different types of spatial data (i.e. from isoline symbols on a topographic map to point elevations inside the GIS). • A data storage and retrieval subsystem that organizes the spatial data in a manner that allows retrieval, updating, and editing. • A data manipulation and analysis subsystem that performs tasks on the data, aggregates and disaggregates, estimates parameters and constraints, and performs modeling functions. • A reporting subsystem that displays all or part of the database in a tabular, graphic, or map form.” Michael N. DeMers, 2000 Can GIS help? GIS construction
  12. 12. Participatory GIS for collaborative deer management Why use a participatory GIS platform for natural resource management? PGIS can facilitate the integration of stakeholders’ and scientific knowledge Collection and integration of knowledge It can facilitate improved understanding and stimulate discussions over the use of resources Analysis and assessment, Modelling/planning Will affect collaboration Facilitate communication of preferences & knowledge exchange Definition of participation:- To take part; to have or possess GIS construction
  13. 13. Participatory GIS for collaborative deer management Can local & scientific knowledge be integrated to create shared knowledge to underpin sustainable management? • Consensus building, negotiation of compromises & development of management innovations Capture local practitioner knowledge. Collate scientific knowledge e.g.–habitat maps, topography Collaboration tool Integrate to inform conflict – e.g. DeerMap prediction of deer distribution GIS construction
  14. 14. DeerMAP combines spatial data to produce a preference map. It does this by combining raster maps of: Forage •Shelter •Comfort •Disturbance Developing the p-GIS….. GIS construction
  15. 15. Each of the input layers is a continuous value map between 0 and 1, and so the output map is also a continuous value map between 0 and 1 Scientific knowledge: Produce a baseline preference map by combining: • Forage - Land Cover of Scotland 1988 with habitats ranked by grazing ecologists • Shelter - Topographic Exposure maps (TOPEX) • Comfort - OS Digital Elevation Model (DEM) • Disturbance – paths and stalking The input maps are ‘multiplied’ together, Preference = Forage x Shelter x Comfort x (1 - Disturbance) DeerMAP: - A spatial model of deer habitat preference. GIS construction
  16. 16. DeerMAP idea Feeding raster LCS88 0 134 3 0 Cover raster Shelter raster Output raster 1 1 11 0 Topex 2000 wind direction 01 11 0 x x = 5 GIS construction
  17. 17. DeerMAP Stalking Paths Forage Cover Habitat Shelter OS Map LCS88 DEM Disturbance Terrain Shelter Shelter Comfort GIS construction DeerMAP structure
  18. 18. Vegetation map GIS construction
  19. 19. 0 2 4 6 8 10 12 14 16 D ry heatherm oor Sm ooth grasslandC oarse grassland Young conifereous woodland Sem i-naturalconiferous w oodland W etheatherm oor Young broadleafwoodlandM ixed w oodland C oniferous plantation M ature broadleafw oodland Bracken Blanketbog Scrub M ontane Relative forage, shelter and cover scores for each vegetation type (values set by a group of grazing ecologists then scaled to 0-1) Stags in Winter GIS construction
  20. 20. GIS Shelter and cover: using DEM & TOPEX TOPEX uses GIS Digital Elevation Maps (DEM) • A measure of Topographic Exposure • It is the sum of angle to skyline in the eight cardinal directions • with the negative angles recorded as zero (Wilson, 1984). • High Topex scores indicate well sheltered locations GIS construction
  21. 21. Wind Weighted TOPEX Weights the contributions to the Topex score from each cardinal direction to take account of prevailing wind. TOPEX Wind Direction Weighting 0 0.2 0.4 0.6 0.8 1 1.2 0 45 90 135 180 225 270 315 360 Angle (degrees) Weighting. Weighting = (cos(angle)+1)/2
  22. 22. Wind Weighted TOPEX Weights the contributions to the Topex score from each cardinal direction to take account of prevailing wind. No Wind GIS construction
  23. 23. Wind Weighted TOPEX Weights the contributions to the Topex score from each cardinal direction to take account of prevailing wind.
  24. 24. GIS construction DeerMAP prediction
  25. 25. Validation and calibration GIS evaluation
  26. 26. We have access to 4 datasets which have records of actual deer numbers and locations: 1. Mar Lodge / Invercauld – 9 deer with GPS collars between Apr 1998 and Feb 2000 2. Rum – c.1700 counts (1-2 per month per block) between Mar 1981 and Nov 1999 3. Glen Affric – 25 (monthly) counts between Jun 2003 and Jun 2005 4. Glen Finglas – 46 (bi-monthly) counts between Jul 2004 and May 2007 Plus DCS Deer Census records from 1961 - 2006 Plus the results reported by a paper summarising habitat use by sheep, hinds and stags on Ardtornish estate between Dec 1976 and Oct 1977 Habitat Use By Red Deer (Cervus elaphus L.) and Hill Sheep in the West Highlands B. C. Osborne, The Journal of Applied Ecology, Vol. 21, No. 2 (Aug., 1984), pp. 497-506 DeerMap Validation GIS evaluation
  27. 27. 3. Glen Affric - 464 locations from 25 (monthly) counts between Jun 2003 and Jun 2005 1. Mar Lodge - 33,018 locations from 9 deer with GPS collars between Apr 1998 and Feb 2000 4. Glen Finglas - 553 locations from 46 (bi-monthly) counts between Jul 2004 and May 2007 Ardtornish - summary of observations of habitat use by sheep, hinds and stags between Dec 1976 and Oct 1977 2. Rum - 76,763 locations (5022 distinct) from c.1700 counts (1-2 per month per block) between Mar 1981 and Nov 1999 DeerMap evaluation: How good is it? GIS evaluation
  28. 28. Comparison of preferred areas with data derived from deer with GPS collars - split the data and use one estate for calibration, the other for validation Filter the location events to remove spatial and temporal auto-correlation (e.g. minimum of 1 day and/or 1 km distance between events) The method is VERY time consuming (need to generate a DeerMap map for nearly every filtered event as there is not much overlap in location/season/weather/sex combinations) for each filtered event: 1. lookup the weather conditions (i.e. wind direction) 2. generate an appropriate deermap prediction 3. determine the preference score at the event location 4. determine where in the preference scale this value occurred calculate stats on the preference scores 1. Validating using Invercauld & Mar Lodge GPS data GIS evaluation
  29. 29. 1. Split location events into sex and season combinations (Hind/Stag + Summer/Winter, ignore the Rut) 2. Determine average wind direction in each season (from wind database) 3. Generate single DeerMap predictions for each sex/season combination 4. Split the prediction into two equal area quantiles (low scores and high scores) determine the proportion of events in the ‘high’ score zone Should be > 50% (!) - and the higher the better (!?) 4. Glen Finglas count data Irvine et al, 2009, J.App.Ecol GIS evaluation
  30. 30. Glen Finglas: GIS evaluation 4. Glen Finglas Count data
  31. 31. 4. Glen Finglas: geo-referenced count data used for evaluation GIS evaluation
  32. 32. Stags in Winter with original ‘top half’ prediction 51.1% of winter stag locations in top half of the preference scale GIS evaluation 4. Glen Finglas Count data
  33. 33. Summer Hinds 48.1% Summer Stags 25.3% Winter Hinds 55.7% Winter Stags 51.1% Mean 45.1% For each season/sex combination: Percentage of locations in the top half of DeerMap predicted preference areas Evaluation before using local knowledge: GIS evaluation
  34. 34. Annotated Hard Copy Map
  35. 35. West Sutherland Estates Roads Footpaths Fenced Areas Revised Priority Habitats Blanket Bog Non-Priority Native Pine Woodland Upland Calcareous Grassla Upland Heathland Upland Mixed Ashwood Upland Oakwood Wet Woodland I 0 2 4 6 8 10 12 Kilomet Added Footpaths, fences, habitat changes
  36. 36. Adding in the local knowledge: 1 Factor Shelter Forage Comfort Disturbance Terrain Habitat Slope Elevation Walkers Stalking + - + - + - + - + - + - + - Hinds BDMG (n=10) 6 0 31 0 20 0 3 0 12 0 2 4 0 0 WSDMG (n=8) 12 0 39 0 24 0 1 2 22 6 0 2 0 5 Column total 18 0 70 0 44 0 4 2 34 6 2 6 0 5 Factor total 88 44 46 13 Stags in winter BDMGA (n=10) 15 0 57 1 24 1 2 0 23 8 0 3 1 7 WSDMG (n=8) 4 0 36 0 33 0 6 0 29 1 0 9 0 3 Column total 19 0 93 1 57 1 8 0 52 9 0 12 1 10 Factor total 113 58 69 23 Overall factor total 201 102 115 36 4:2:2:1 GIS evaluation
  37. 37. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x^2 sqrt(x) one:one Re-scaling: simple way to deal with non-linearities
  38. 38.  Habitat updates  Fenced areas  Paths and tracks  Importance of shelter to deer distribution  Preference for higher ground in summer  Forage, shelter, comfort and disturbance importance rescaled Adding in the local knowledge: Then forage, shelter, comfort and disturbance is scaled to reflect emphasis given in interviews GIS evaluation
  39. 39. !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( DeerMAP + local knowledge Updated Habitat 63.8% of Winter Stag Locations GIS evaluation
  40. 40. !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( Fences Added 65.0% of Winter Stag Locations GIS evaluation DeerMAP + local knowledge
  41. 41. !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( Paths Added 70.8% of Winter Stag Locations GIS evaluation DeerMAP + local knowledge
  42. 42. !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( Elevation Effects 73.0% of Winter Stag Locations GIS evaluation DeerMAP + local knowledge
  43. 43. Stags in Winter with: shelter rescaled 76.6% of winter stag locations in top half of the preference scale Evaluation after using local knowledge: GIS evaluation
  44. 44. Original Deer Map prediction for Stags in Winter (Upper 25%), as used in PGIS interviews with stakeholders GIS evaluation
  45. 45. New Deer Map prediction for Stags in Winter (Upper 25%) GIS evaluation
  46. 46. 3. DeerMAP Validation using Glen Affric data Just looking at high prefernce areas in relation to counts is a bit simple: there ought to be some animals in the ‘low’ half as well – but how many ? As before, split location events into sex and season combinations (Hind/Stag + Summer/Winter and include the Rut) Determine average wind direction in each season (from wind database) Generate single DeerMap predictions for each sex/season combination split the prediction into several equal area quantiles (from low to high scores) determine the proportion of events in each quantile Compare these with the proportion of location events observed in each zone GIS evaluation
  47. 47. DeerMap Validation GIS evaluation
  48. 48. DeerMap Validation All deer count locations in Glen Affric GIS evaluation
  49. 49. !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( DeerMap Validation Winter Stag locations in Glen Affric GIS evaluation
  50. 50. !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( DeerMap Validation Winter Stag locations on Winter Stag Prediction GIS evaluation
  51. 51. 0 100 200 20 40 60 80 100 % Band 0 200 20 40 60 80 100 % Band RUTSTAG r2=0.68 rmsd=61 0 50 100 150 200 250 300 350 20 40 60 80 100 % Band WINSTAG r2=0.61 rmsd=129 0 100 200 300 400 500 600 700 20 40 60 80 100 % Band Count Modelled count in each 20% band Observed count in each 20% band DeerMap Validation GIS evaluation
  52. 52. !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( Summer Autumn Winter Hinds Stags DeerMap Validation GIS evaluation
  53. 53. Base Model Hinds Stags Summer Autumn Winter SUMHIND r2=0.69 rmsd=90 0 100 200 300 400 500 600 700 800 20 40 60 80 100 % Band Count RUTHIND r2=0.80 rmsd=60 0 100 200 300 400 500 600 700 800 20 40 60 80 100 % Band Count WINHIND r2=0.71 rmsd=118 0 200 400 600 800 1000 1200 20 40 60 80 100 % Band Count SUMSTAG r2=0.58 rmsd=133 0 100 200 300 400 500 600 700 20 40 60 80 100 % Band Count RUTSTAG r2=0.68 rmsd=61 0 50 100 150 200 250 300 350 20 40 60 80 100 % Band Count WINSTAG r2=0.61 rmsd=129 0 100 200 300 400 500 600 700 20 40 60 80 100 % Band Count Glen Finglas Results GIS evaluation
  54. 54. Rough Optimal Model Hinds Stags Summer Autumn Winter Glen Finglas Results SUMHIND r2=0.82 rmsd=62 0 100 200 300 400 500 600 700 800 20 40 60 80 100 % Band Count RUTHIND r2=0.91 rmsd=43 0 100 200 300 400 500 600 700 800 20 40 60 80 100 % Band Count WINHIND r2=0.89 rmsd=78 0 200 400 600 800 1000 1200 20 40 60 80 100 % Band Count SUMSTAG r2=0.88 rmsd=54 0 100 200 300 400 500 600 20 40 60 80 100 % Band Count RUTSTAG r2=0.80 rmsd=52 0 50 100 150 200 250 300 350 20 40 60 80 100 % Band Count WINSTAG r2=0.80 rmsd=100 0 100 200 300 400 500 600 700 20 40 60 80 100 % Band Count GIS evaluation
  55. 55. Managing wild deer in Scotland: linking science and practice to resolve grazing conflicts Conflicts between:- •Between neighbours •Between livestock and wildlife •Between policy and practice Common thread is that the conflict involves local resource managers Yet these people are not involved in setting policy, regulations or incentives Need inclusive approaches for setting priorities P-GIS in use
  56. 56. DeerMAP as a conflict resolution tool: • To inform conflict between neighbours over deer movement and culling strategies • To communicate and negotiate public and private objectives (local solutions to global issues) Example 1: Developing a new approach to involving local land managers in achieving biodiversity objectives P-GIS in use • To explore future policy objectives (e.g. woodland expansion)
  57. 57. chieving biodiversity objectives
  58. 58. Upland Oak Birch, Wet or mosaic Birch, Pine or mosaic Heath Calcareous Grassland Bog Priority habitats
  59. 59. ‘ Habitat Tolerance to grazing’ Low Moderate Very Low High Low Medium High Very Low Impact ‘Tolerance’
  60. 60. ‘DeerMap Preferences’ Low Moderate Very Low High Low Medium High Very Low DeerMap Preference
  61. 61. Tolerance minus Preference -2 -3 -1 ‘Hot-spots’ -1 -2 -3 Tolerance - Preference
  62. 62. -1 -2 -3 Tolerance - Preference
  63. 63. -1 -2 -3 Tolerance - Preference
  64. 64. Example 2: deer movement
  65. 65. Participatory GIS for collaborative deer management 90% 18% 20-25% 0% ?%
  66. 66. 0 50 100 150 200 250 300 350 400 450 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year Numberofdeer 0 100 200 300 400 500 600 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year Numberofdeer Ballimore Glen Finglas Stag (crosses, black lines), hind (diamonds, grey lines) and total (circles, dashed lines) deer numbers predicted by the population dynamics model
  67. 67. Box 2 no mixing between estates Winter Deer Density deer per sq km 0 - 5 6 - 10 11 - 15 16 - 20 0 1 2 3 4 5 Kilometers 2003 counts with no mixing between estates 2003 counts with full mixing between estates Estate 2 Estate 1
  68. 68. Example 3. future scenarios: Woodland expansion 1. Land managers have aspirations for deer densities 2. These aspirations might not match actual densities 3. Aspirations and actual densities might not be consistent with woodland expansion 4. Where should trees be planted?
  69. 69. Figure 3. Winter 2010 deer density across CSDMG, represented in the three classes used in the CSDMG aspirational deer density map. Data source as for Figure 2.
  70. 70. Figure 2. Current deer density across CSDMG, based on the winter 2010 deer count. Data shown in five deer density groups, including zero, to illustrate potential for incorporating more than three classes. Data from Fraser, D. (2010) Red deer counts. East and West Grampians, DCS.
  71. 71. Figure 5. Difference between aspirational deer densities and 2010 count levels, using three density classes (lower/moderate/higher). Symbols ≤ and ≥ are used where an estate has multiple aspiration zones, since 2010 count data relate to the whole of each estate. Comparisons of aspiration and count per zone may be possible where estates have more detailed records. Annotations are shown as an example of how text can be added to clarify mapping.
  72. 72. Figure 9. Aspirational deer densities with current woodland cover.
  73. 73. Suitable for woodland but high deer density aspiration Suitable for woodland and low/ moderate deer density aspiration Not suitable for woodland plus high deer density aspiration Figure 12. Current woodland cover and suitability of surrounding ground for woodland growth, based mainly on biophysical criteria, overlaid with aspirational red deer densities. Woodland suitability data provided by Forest Research.
  74. 74. Benefits of pGIS Spatial focus allows for broad understanding and detailed discussion Combines local and scientific knowledge Better informs management decisions Greater trust of managers in DeerMap as a management tool Respect, trust and understanding built up during workshops Increases willingness to work towards other solutions
  75. 75. Recognise that this is a dynamic system – challenges will vary over time in response to changes in climate, land use and governance. pGIS supports a novel approach to adapt to change: • That integrates across disciplines and • Involves participation of managers and policy makers at the outset Adaptive management
  76. 76. Identify conflict Dialogue [among Policy, Research & Practitioner communities] Identify alternative solutions [eg technical– incentive–policies–subsidies–markets] Test solutions & monitor success Manage conflict pGIS in adaptive management PGIS Evaluate progress

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