This document describes a participatory GIS (PGIS) project using deer habitat preference modeling to help resolve conflicts between deer management objectives on neighboring land holdings. Local knowledge from interviews was integrated with scientific data in a GIS deer preference model called DeerMAP to produce updated predictions. The updated model allocated 63.8-70.8% of independent deer location data to high preference areas, demonstrating improved predictions when local ecological knowledge was incorporated into the GIS analysis.

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. 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. 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. Deer are a common pool resource
Background

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. 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. 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. 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. 15
%
27
%
14
%
31
%
14
%
13
%21
%
Background

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. 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. 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. 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. 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. 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. 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. DeerMAP
Stalking Paths
Forage
Cover Habitat
Shelter
OS Map LCS88 DEM
Disturbance
Terrain
Shelter
Shelter Comfort
GIS construction
DeerMAP structure

18. Vegetation map
GIS construction

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. 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

23. 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

24. Wind Weighted TOPEX
Weights the contributions to the
Topex score from each cardinal
direction to take account of
prevailing wind.
No
Wind
GIS construction

25. Wind Weighted TOPEX
Weights the contributions to the
Topex score from each cardinal
direction to take account of
prevailing wind.

36. GIS construction
DeerMAP prediction

37. Validation and calibration
GIS evaluation

38. 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

39. 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

40. 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

41. 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

42. Glen Finglas:
GIS evaluation
4. Glen Finglas Count data

43. 4. Glen Finglas:
geo-referenced count data used for evaluation
GIS evaluation

44. 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

45. 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

46. Annotated
Hard Copy Map

47. 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

48. 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

49. 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

50.  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

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DeerMAP + local knowledge
Updated Habitat
63.8%
of Winter Stag
Locations
GIS evaluation

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Fences Added
65.0%
of Winter Stag
Locations
GIS evaluation
DeerMAP + local knowledge

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Paths Added
70.8%
of Winter Stag
Locations
GIS evaluation
DeerMAP + local knowledge

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Elevation Effects
73.0%
of Winter Stag
Locations
GIS evaluation
DeerMAP + local knowledge

55. 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

56. Original Deer Map prediction for Stags in Winter (Upper
25%), as used in PGIS interviews with stakeholders
GIS evaluation

57. New Deer Map prediction for Stags in
Winter (Upper 25%)
GIS evaluation

58. 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

59. DeerMap Validation
GIS evaluation

60. DeerMap Validation
All deer count locations in Glen Affric
GIS evaluation

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DeerMap Validation
Winter Stag locations in Glen Affric
GIS evaluation

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DeerMap Validation
Winter Stag locations on Winter Stag Prediction
GIS evaluation

63. 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

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Summer Autumn Winter
Hinds
Stags
DeerMap Validation
GIS evaluation

65. 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

66. 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

67. 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

68. 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)

69. chieving biodiversity objectives

70. Upland Oak
Birch, Wet or
mosaic
Birch, Pine or
mosaic
Heath
Calcareous
Grassland
Bog
Priority habitats

71. ‘ Habitat Tolerance to grazing’
Low
Moderate
Very Low
High
Low
Medium
High
Very Low
Impact
‘Tolerance’

72. ‘DeerMap Preferences’
Low
Moderate
Very Low
High
Low
Medium
High
Very Low
DeerMap
Preference

73. Tolerance
minus
Preference
-2
-3
-1
‘Hot-spots’
-1
-2
-3
Tolerance -
Preference

74. -1
-2
-3
Tolerance -
Preference

75. -1
-2
-3
Tolerance -
Preference

77. Example 2: deer movement

78. Participatory GIS for collaborative
deer management
90%
18%
20-25%
0%
?%

79. 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

80. 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

81. 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?

82. 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.

83. 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.

84. 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.

85. Figure 9. Aspirational deer densities with current woodland cover.

86. 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.

87. 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

88. 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

89. 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