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

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.

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  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
  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
  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
  4. Study Areas

  5. kootenaynaturephotos.com Northern Forest National Geographic Image Collection

  6. Eastern Farmland Kaylee Schroeder

  7. 2,158 deer handled • 1,001 radiocollared • 436 events Capture

    Results 2011-2014
  8. Fawn Capture

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

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

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

    1979-2005
  15. Northern Forest Annual Cause-specific Mortality Summary Pooled from 2011-2014 (~mid-Sept.

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

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

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

    (~mid-Sept. to mid-Sept.)
  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???
  20. Modeling: mortality of Adult Does 58 wolf-range DMUs, 1979-2005

  21. Survival Rate Jan Feb Mar Apr May Jun Jul Aug

    Sep Annual Non-harvest Mortality Summary
  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
  23. Overall Mortality Northern Forest 0% 10% 20% 30% 40% 50%

    60% 2011 2012 2013 2014 Min. Temp. Snow Depth Southunt
  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
  25. Modeling: growth rate of DMU deer populations 58 wolf-range DMUs,

    1979-2005
  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
  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
  28. Integration of Harvest and Time-to-Event Data Used to Estimate Demographic

    Parameters for White-tailed Deer Andrew Norton April 29 2015
  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