PROGRESS the modelling
Rene Jochem
Modeling: my story line
Objective of this presentation
Focus on the methodology
Illustrated by an example of a possible pilot action
Modeling: my story line
Ecology : Habitat modeling
• Process• Who was involved• What kind of data is available• Selecting species• Combining species and data• Example
Ecology : Process
• Preparing habitat maps– Based on modeling expert knowledge
• 1st meeting: what do we have, how can we use it.– GIS maps– Distribution data– Field trip– Modeling
• New habitat maps incorporating knowledge from experts & data– Analysis on distribution data
• 2nd & 3th meeting : – habitat maps: proofing & fine tuning
Ecology : Involvement
Species Data• BTO• RSPB • English Nature
Expert input in meeting's, ornithologist from:• Forestry Commission• RSPB• English Nature• Sovon (Dutch NGO, like BTO)• Alterra
Ecology : Available GIS Data
Basic Data• Vegetation• Tree species• Felling data• Streams• Maintenance• Elevation• Tracks
• Open landscape (distance to woodland)• Slope
Ecology : Species Selection
Criteria:• Present species• Critical species
– Affected by recreation – Level of protection
• Data availability– Bird data
+ Presence/absence data
– Environmental GIS Data
Ecology : GIS Data => Habitat
Factors:• Vegetation type as mapped in 2003• Forest coniferous or broadleaved• Gorse & Bracken• Distance to Woodland (Open areas 50, 150, 300
m)• Topography
Ecology : Habitat factors & Selected species
Species Factor
Dartford warbler •Vegetation•Gorse
Woodlark •Vegetation•Woodland edge
Nightjar •Vegetation
Snipe •Vegetation•Open Landscape•Topography
Curlew & Lapwing •Vegetation•Open Landscape•Topography
Ecology : Woodlark example
Modeling: my story line
Recreation: GPS Analysis
Corrected GPS data, speed < 10km (Easter set)
0
1
2
3
4
5
6
7
0 50 100 150 200 250
Visit time (underestimated) [minutes]
aver
age
spee
d [
km/h
]
Hond uitlaters < 2xper week
Hond uitlaters > 2xper week
Wandelaars
visitors max. distance from carparkBased on GPS data (year set)
0
10
20
30
40
50
60
70
80
90
0 5000 10000 15000 20000 25000 30000 35000
distance in meters
# vi
sito
rs (
n =
155
1)
0
25
50
75
100
125
% c
um
ula
tiv
e
Recreation: GPS Statistical analysis
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
11000
12000
dist to parking [m]
Nu
mb
er o
f G
PS
Po
ints
(15
21 g
rou
ps)
0
5
10
15
20
25
30
35
40
Den
sity
[p
oin
ts/h
a]
N GPS Pointsdens of GPS Points40 * e (̂-d*0.0015)
•Recreation: GPS Analysis
Recreation: GPS Pattern Analysis
Recreation: GPS Pattern Analysis
Recreation: Pressure SCAN => compartments
Recreation: Pressure SCAN
Modeling: my story line
Modeling Effects
MASOOR => detailed recreation modeling• Input
– User profiles– GPS data– Counting data– Path network & car parks
• Result– Density flow on the path network
• Translates into– Zones of lower reproduction
Recreation: Pressure SCAN
•Wilverly
•Shatterford
Population Dynamics
0
10
20
30
40
50
60
70
80
1990 1992 1994 1996 1998 2000 2002 2004 2006
year
nu
mb
er o
f p
airs
threshold
optimal habitat
plus environmental pressure
plus recreational pressure
Extinction
Population Dynamics: Conservation
Selecting the manageable factors within the population dynamics processes
• Improving habitat• Managing
recreational pressures
• …..
Recreation & Bird behavior
•disturbance
•fleeing
•abandoning nest
•alarming•increased fouraging
•decrease nesting
•decrease reproduction
•lower densities
•fouraging neighbour patch
•decrease reproduction
•lower densities
•increase extinction
•decrease exchange
•decrease # in patch
•decrease # in surrounding patches •extinction metapopulation
– •decrease viability
•fouraging neighbour patch
•increase mortality neighbour patches•abandoning
nest•nesting in neighbour patch
Sensitivity
Species Example: 30 passages/ hour
Effect on reproduction
Low (4) 30 m
Medium (3)
Woodlark 100 m
High (2) SnipeDartford warbler Nightjar Lapwing
300 m
Very high (1)
Curlew 600 m
Sensitivity to recreation
MASOOR => density flow => disturbance zones
• Path network (including path type)• GPS & Questionnaire data
– User profiles– User data– Pattern validation
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