participatory gis for collaborative deer management

Participatory GIS for collaborative deer management

Justin Irvine,

Althea Davies

http://www.macaulay.ac.uk/relu/

[email protected]

[email protected]

http://www.macaulay.ac.uk/relu/

mailto:[email protected]

mailto:[email protected]

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

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

Deer are a common pool resource

Background

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

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

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

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

15

%

27

%

14

%

31

%

14

%

13

% 21

%

Background

0

2

4

6

8

10

12

14

16

1960 1965 1970 1975 1980 1985 1990 1995 2000

Year

To

tal d

ee

r d

en

sit

y (

km

2)

Deer Commission for Scotland data redrawn from Clutton-Brock et al 2002

Increasing deer density: a source of conflict over habitat use:

Background

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

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

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

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

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

DeerMAP idea

Feeding raster

LCS88

0

1 3 4

3 0

Cover raster

Shelter raster

Output raster

1

1

1 1

0

Topex 2000 wind direction

0 1

1 1

0

x

x

=

5

GIS construction

DeerMAP

Stalking Paths

Forage

Cover Habitat

Shelter

OS Map LCS88 DEM

Disturbance

Terrain

Shelter

Shelter Comfort

GIS construction

DeerMAP structure

Vegetation map

GIS construction

0

2

4

6

8

10

12

14

16

Dry

hea

ther

moo

r

Sm

ooth

gra

ssland

Coa

rse

gras

slan

d

You

ng c

onife

reou

s woo

dlan

d

Sem

i-nat

ural

con

ifero

us w

oodl

and

Wet

hea

ther

moo

r

You

ng b

road

leaf

woo

dlan

d

Mixed

woo

dlan

d

Con

ifero

us p

lant

ation

Mat

ure

broa

dlea

f woo

dlan

d

Bra

cken

Blank

et b

og

Scr

ub

Mon

tane

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

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

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)

We

igh

tin

g .

Weighting =(cos(angle)+1)/2




prevailing wind.

No

Wind

GIS construction




prevailing wind.

GIS construction

DeerMAP prediction

Validation and calibration GIS evaluation

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

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

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

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

Glen Finglas:

GIS evaluation

4. Glen Finglas Count data

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

GIS evaluation

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

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

Annotated Hard Copy Map

West Sutherland

Estates

Roads

Footpaths

Fenced Areas

Revised Priority Habitats

Blanket Bog

Non-Priority

Native Pine Woodland

Upland Calcareous Grassland

Upland Heathland

Upland Mixed Ashwood

Upland Oakwood

Wet Woodland

I0 2 4 6 8 10 12

Kilometers

© Crown copyright. All rights reserved MLURI GD27237X 2004

Added Footpaths,

fences, habitat changes

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

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

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


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Paths Added

70.8%

of Winter Stag

Locations

GIS evaluation


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Elevation Effects

73.0%

of Winter Stag

Locations

GIS evaluation


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

Original Deer Map prediction for Stags in Winter (Upper

25%), as used in PGIS interviews with stakeholders

GIS evaluation

New Deer Map prediction for Stags in

Winter (Upper 25%)

GIS evaluation

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

DeerMap Validation

GIS evaluation

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

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

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

Base

Model

Hinds

Stags



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Glen Finglas Results

GIS evaluation

Rough

Optimal

Model

Hinds

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Glen Finglas Results


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GIS evaluation

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

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)

achieving biodiversity objectives

Upland Oak

Birch, Wet or

mosaic

Birch, Pine or

mosaic

Heath

Calcareous

Grassland

Bog

Priority habitats

‘ Habitat Tolerance to grazing’

Low

Moderate

Very Low

High Low

Medium

High

Very Low

Impact

‘Tolerance’

‘DeerMap Preferences’

Low

Moderate

Very Low

High

Low

Medium

High

Very Low

DeerMap

Preference

Tolerance

minus

Preference

-2

-3

-1

‘Hot-spots’

-1

-2

-3

Tolerance -

Preference

-1

-2

-3

Tolerance -

Preference

Example 2: deer movement

Participatory GIS for collaborative

deer management

90%

18%

20-25%

0%

?%

0

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1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

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mb

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of

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Year

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er

Ballimore Glen Finglas

Stag (crosses, black lines), hind (diamonds, grey lines)

and total (circles, dashed lines) deer numbers predicted

by the population dynamics model

Box 2

no mixing between estates

Winter Deer Densitydeer per sq km

0 - 5

6 - 10

11 - 15

16 - 20

0 1 2 3 4 5Kilometers

© Macaulay Institute 2009© Crown Copyright Ordnance Survey

Licence Number 100019294

2003 counts

with no mixing

between

estates

2003 counts

with full mixing

between

estates

Estate 2

Estate 1

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?

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.

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.

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.

Figure 9. Aspirational deer densities with current woodland cover.

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.

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

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

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

participatory gis for collaborative deer management

Education

definitions of gis

gis constructioncan

gis model3

gis predictions5

km25 deer

participatory gis platform

data input subsystem

scotland data