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

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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.