Survival and Cause-Specific Mortality of White-tailed Deer in Wisconsin

Authors: Tim Van Deelen, Karl Martin, Dan Storm, Andrew Norton, Camille Warbington, Brittany Peterson, Ryan Walrath and Chris Jacques. Presentation given at the 2015 Midwest Wolf Stewards Conference at Northland College. April 2015.

Transcript

1. Survival and Cause-Specific Mortality of
   White-tailed Deer in Wisconsin
   Photo credit: Dean Dekarske
   Tim Van Deelen, Karl Martin, Dan Storm, Andrew Norton, Camille
   Warbington, Brittany Peterson,Ryan Walrath, Chris Jacques
   

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2. Figure 1. Principal components analysis revealed 12 concerns loaded significantly (≥0.45) on three factors (KMO
   = 0.821, Barlett's X2 = 662.394, df = 66, p<0.01), explaining 50.7% of total variance.
   Lute ML, Bump A, Gore ML (2014) Identity-Driven Differences in Stakeholder Concerns about Hunting Wolves. PLoS ONE 9(12): e114460.
   doi:10.1371/journal.pone.0114460
   http://127.0.0.1:8081/plosone/article?id=info:doi/10.1371/journal.pone.0114460
   

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3. Adult Survival
   2011-2015
   Fawn Recruitment
   2011-2013
   Two Telemetry Projects
   (with comparisons to modeling from harvest data)
   Collect data to improve
   population estimates
   Understand factors that
   are limiting recruitment
   

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4. Study Areas
   

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5. kootenaynaturephotos.com
   Northern Forest
   National Geographic Image Collection
   

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6. Eastern Farmland
   Kaylee Schroeder
   

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7. 2,158 deer handled
   • 1,001 radiocollared
   • 436 events
   Capture
   Results
   2011-2014
   

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8. Fawn Capture
   

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9. Adult Winter Capture
   Box Traps
   Darting
   Helicopter
   Netted Cage Traps
   Drop Nets
   

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10. Telemetry
    • Weekly bi-angulations
    • Occasional aerial telemetry (~5-6 times/year)
    

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11. Mortality Investigations
    •Field Necropsy and Site Investigation
    • Ante-mortem wounds, bruising
    • Portion consumed
    • Predator sign (cache, beds, scat, trails, etc.)
    •Veterinarian Necropsy
    •Proportional Assignment to cause
    

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12. 2011, n 48
    2012, n 46
    2013, n 45
    2011, n 30
    2012, n 30
    2013, n 29
    0.0 0.2 0.4 0.6 0.8 1.0
    2011, n 48
    2012, n 46
    2013, n 45
    2011, n 30
    2012, n 30
    2013, n 29
    Survival Rate
    Northern Forest
    Month
    Jun Jul Aug Sep
    Fawn Survival Summary
    2011, 2012, and 2013
    Mostly Predation
    (Bear, Canid, Bobcat)
    0.0 0.2 0.4 0.6 0.8 1.0
    2011, n 48
    2012, n 46
    2013, n 45
    2011, n 3
    2012, n 3
    2013, n 2
    

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13. 0%
    5%
    10%
    15%
    20%
    0%
    5%
    10%
    15%
    20%
    Annual Fawn (0-4 mo. old) Mortality Summary
    2011-2013
    Radiocollared deer that died from each cause
    Eastern Farmland
    29-33%
    n = 139 radiocollared
    Northern Forest
    48-78%
    n = 89 radiocollared
    

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14. Modeling: Percent fawns in fall deer
    harvest
    58 wolf-range DMUs, 1979-2005
    

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15. Northern Forest Annual Cause-specific Mortality Summary
    Pooled from 2011-2014
    (~mid-Sept. to mid-Sept.)
    

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16. Northern Forest Annual Cause-specific Mortality Summary
    Pooled from 2011-2014
    (~mid-Sept. to mid-Sept.)
    

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17. 11%
    Northern Forest Annual Cause-specific Mortality Summary
    Pooled from 2011-2014
    (~mid-Sept. to mid-Sept.)
    

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18. 21%
    Northern Forest Annual Cause-specific Mortality Summary
    Pooled from 2011-2014
    (~mid-Sept. to mid-Sept.)
    

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19. Annual Non-harvest Mortality Summary
    2011-2014
    21%
    …but this is variable depending
    on the winter. Likely below 5% in
    very mild winter and >30% is
    severe winter…
    Compensatory mortality???
    

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20. Modeling: mortality of Adult Does
    58 wolf-range DMUs, 1979-2005
    

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21. Survival Rate
    Jan Feb Mar Apr May Jun Jul Aug Sep
    Annual Non-harvest Mortality Summary
    

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22. 0%
    10%
    20%
    30%
    40%
    50%
    60%
    2011 2012 2013 2014
    Juvenile North Adult North Juvenile East Adult East
    Overall Hazard Variation
    Model Δ DIC
    Yearint
    + Ageint
    0
    NULL 52.7
    Model Δ DIC
    Yearint
    + Ageadd
    0
    NULL 6.8
    n = 546; events = 54
    Northern Forest Eastern Farmland
    n = 574; events = 125
    Mortality Rate (~Jan. – Sep.)
    Southunt
    

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23. Overall Mortality Northern Forest
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    2011 2012 2013 2014
    Min. Temp.
    Snow Depth
    Southunt
    

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24. 2011 2012 2013 2014
    Human
    Predation
    Starvation
    Cause-specific Mortality
    Northern Forest Eastern Farmland
    Juveniles
    Adults
    0%
    10%
    20%
    30%
    40%
    50%
    2011 2012 2013 2014
    0%
    10%
    20%
    30%
    40%
    50%
    Southunt
    

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25. Modeling: growth rate of DMU deer
    populations
    58 wolf-range DMUs, 1979-2005
    

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26. In Wisconsin’s wolf range…
    1. Deer survival from birth through first year of life is variable
    • likely reflects phenology of spring green-up and winter
    severity
    • High bear predation mortality before 4 months of age
    2. Adult (>1 yr old) survival is less variable and primarily depends
    on harvest regulations
    • For antlered deer, hunter harvest is by far the leading cause
    • For does, depends on antlerless tag allocation, but human-
    related mortality plus starvation generally exceed predation
    rates, especially if predation is compensatory
    3. Hunting and overwinter weather probably have the greatest
    impacts on deer population dynamics
    

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27. Summary
    Northern Forest
    • Overwinter mortality related to
    snow >12” and temperature >32F
    • Predation was leading cause
    • Scavenging or compensatory mortality?
    • Coyote (28), Wolf (17), Bobcat (10),
    Bear (3), Unknown Predator (12)
    Eastern Farmland
    • Overwinter mortality related to temperature >32F
    • Human related mortality was leading cause
    • Starvation for juveniles in 2013 and 2014
    • Only coyote predation (3)
    Southunt
    

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28. Integration of Harvest and Time-to-Event Data Used to
    Estimate Demographic Parameters for White-tailed Deer
    Andrew Norton
    April 29 2015
    

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29. Data collection and analysis would not be possible without
    WIDNR: Science Services, Wildlife Management, Law Enforcement, Wildlife Health
    Individuals: Dan Storm, Scott Hull, Robert Rolley, Mike Watt, Ryan Walrath, Brittany
    Peterson, Brian Dhuey, Camille Warbington, Marcus Mueller, Christine Priest, Aaron
    Johnson, Mike Preisler, Chris Jacques, >40 Field Technicians
    >1,000 volunteers and landowners
    Funding and Support
    Dajun Wang
    

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