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S U B M I T T E D C O P Y I S AVA I L A B L E O N O D F W P U B L I C W E B S I T E : H T T P : / / W W W. D F W. S TAT E . O R . U S / W I L D L I F E / R E S E A R C H / I N D E X. A S P
.
IDENTIFYING MULE DEER MIGRATION CORRIDORS THREATENED BY HIGHWAY
DEVELOPMENT
Coe et al. 2015, Wildlife Society Bulletin, in press
POPULATION TRENDS
500
2500
4500
6500
8500
10500
12500
14500
2003 2004 2005 2006 2007 2008 2009 2010
Nu
mb
er
of
de
er
Silver LakeFort Rock
Upper DeschutesPaulina
PAULINA
FORT ROCK WAGONTIRE
MCKENZIEMAURY
SILVER LAKE
SPRAGUE
INDIGO
OCHOCO
INTERSTATEKENO
GRIZZLY
UPPER DESCHUTES
CRATER LAKE NP
DIXON
METOLIUS
ROGUE
0 50 10025 Kilometers±
Wildlife Management Units
La Pine
Bend
Chemult
Chiloquin
Silver Lake
MIGRATION CORRIDOR ANALYSIS
MIGRATION USE
Bend
Redmond
La Pine
Sisters
LakeviewBonanza
Chiloquin
Malin
Paisley
0 50 10025 Kilometers±
Spring/Fall Deer UseLow
Med Low
Med High
High (higher probability of use)
Mule Deer Brownian Bridge Probability of Use
HIGHWAY SURVEYS
2005-2010 deer-vehicle collisions monitored on near-daily basis by ODFW and ODOT in an attempt to collect every deer carcass during that period.
This data represented a minimum number of actual DVCs because some mortally wounded deer move off highway.
1,901 DVCs were recorded, of which 1,269 (67%) were during spring or fall migration periods.
ODOT DISPATCH COLLISION DATA
LANDSCAPE COVARIATES
• Tree canopy cover • Distance from water • Distance from development • Topographic curvature• Annual average daily traffic (AADT)
COUNTED DVC BY 500-M SEGMENT
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
EE
E
E
0 0.1 0.20.05 Kilometers.
Legend
Spring and Fall Mule deer Collisions
COUNTOFRID
0
1
2
3
4
5
6
7
8
9
10
12
13
14
CALCULATED MEANS WITHIN BUFFERED SEGMENTS
0 250 500125 Meters.
Spring and Fall Mule deer Collisions
COUNTOFRID
0
1
2
3
4
5
6
7
8
9
10
12
13
14
100 m Buffers
Curvature
ValueHigh : 3.07737
Low : -3.19658
MODEL RESULTS FOR DVCS ~ LANDSCAPE COVARIATES
MODEL COEFFICIENTS FOR TOP-RANKED MODEL
Covariatea Highway 97 Highway 31
Standardized coeff. Standardized coeff.
Canopy Cover 0.050 −0.234
Topography −0.154 0.025
Distance to Development −0.135 0.175
Migration Use 0.340 0.369
Distance to Water −0.102 −0.013
Traffic 0.152 1.668
Traffic2 −0.177 −1.557
BARRIER EFFECT
BARRIER EFFECT
CONCLUSIONS
• Mule deer migration corridors were the strongest predictor of deer-vehicle collisions on both highways
• Highway re-construction should be preceded by studies that identify migration pathways or DVCs documented.
• Roadside landscape features helped in models but were inconsistent between highways. Migration corridors are driven by larger landscape features.
• Managers attempting to maintain migratory corridors on existing highways should focus mitigation measures where DVCs are highest and, secondarily, where AADT is highest
• Migration corridor layer represents entire population of mule deer in this area so is useful for other wildlife management planning.
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