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Spatial ecology and demography of eastern coyotes in western Virginia

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1 Spatial ecology and demography of eastern coyotes in western Virginia
Dana Morin Working Plan Presentation

2 Introduction Eastern range expansion of coyotes
At the time of european colonization, the coyote range was restricted to six states in the central and northern midwest. There are several theories of range expansion, but in general, it is believed coyotes were restricted by gray wolves to the north, red wolves to the south, and geographic features such as the Ohio River. Parker 1995, Animal Planet, bioweb Mike St Germain

3 Introduction Eastern range expansion of coyotes
As euro-american settlement moved westward, removal of wolves and habitat changes including logging and agriculture allowed for coyotes to move east. This happened in a northern front around the great lakes and then south into New England and Canada, and a southern front through Louisiana and Arkansas followed by the rest of the southeast. Parker 1995 Mike St Germain

4 Introduction Eastern range expansion of coyotes
As euro-american settlement moved westward, removal of wolves and habitat changes including logging and agriculture allowed for coyotes to move east. This happened in a northern front around the great lakes and then south into New England and Canada, and a southern front through Louisiana and Arkansas followed by the rest of the southeast. Parker 1995 Mike St Germain

5 Introduction Eastern range expansion of coyotes
As euro-american settlement moved westward, removal of wolves and habitat changes including logging and agriculture allowed for coyotes to move east. This happened in a northern front around the great lakes and then south into New England and Canada, and a southern front through Louisiana and Arkansas followed by the rest of the southeast. Parker 1995 Mike St Germain

6 Introduction Coyotes in Virginia

7 Human-wildlife conflicts
Livestock and agricultural losses Tomsa and Forbes 1989 Wooding and Hardisky 1990 Witmer and Hayden 1991 Witmer et al. 1993 Armstrong and Walters 1995 NASS 1999 Main et al. 2002 Gompper 2002a Houben 2004 USDA

8 Human-wildlife conflicts
Perceived threat to humans and pets Bider and Weil 1984 Chambers 1987 Billodeaux 2007 KnoxNews Joseph Hinton

9 Potential conflicts Negative impacts to prey species? Negative impact:
Cook et al. 1971, Stout 1982, Hamlin et al. 1984, Messier et al. 1986, Chambers 1987, Blanton and Hill 1989, NASS 1999, Ballard and Whitlaw 1999, Houben 2004, Staller et al. 2005, Saalfeld and Ditchkoff 2007, Kilgo et al. 2010 No impact: But see also: Ozoga and Harger 1966, Westmoreland and Woolf 1981, Decker et al. 1992, Wagner and Hill 1994, Crete and Derosier 1995, Cox 2003, Bumann and Stauffer 2002 Bullet and making differences highlighted Mark Taylor (Roanoke.com)

10 Potential conflicts Negative impacts to prey species? Negative impact:
Cook et al. 1971, Stout 1982, Hamlin et al. 1984, Messier et al. 1986, Chambers 1987, Blanton and Hill 1989, NASS 1999, Ballard and Whitlaw 1999, Houben 2004, Staller et al. 2005, Saalfeld and Ditchkoff 2007, Kilgo et al. 2010 No impact: But see also: Ozoga and Harger 1966, Westmoreland and Woolf 1981, Decker et al. 1992, Wagner and Hill 1994, Crete and Derosier 1995, Cox 2003, Bumann and Stauffer 2002 Bullet and making differences highlighted Mark Taylor (Roanoke.com)

11 Plasticity western coyote (Michael Anguiano)
Behavioral and phenotypic plasticity Hybridization Natural selection western coyote (Michael Anguiano) Coyote genes swamping red wolf/other way for gray wolf: larger font bullet sizes eastern coyote (Joseph Hinton)

12 Study Area Rockingham Bath Highland Augusta
Most complaints about coyotes, NF, low densities of deer,

13 Objectives Spatial Ecology Demography Movement patterns and home range
Habitat selection Demography Densities Population growth rates Social structure

14 Objectives Spatial Ecology Demography Movement patterns and home range
Habitat selection Demography Densities Population growth rates Social structure

15 Objectives Population responses to anthropogenic effects
Development of population simulation model

16 Objectives Population responses to anthropogenic effects
Development of population simulation model

17 General Methods Trapping and fitting with satellite collars
Scat transects with genetic identification to individual Extraction of GIS landscape/land use variables Incorporation of prey variables (deer, small mammals, vegetation)

18 Objective 1 - methods Home range and habitat selection
Trapping with padded foothold 12 satellite collars each year on trapped individuals 6 in spring 6 in fall 5 daily locations plus rotation of fine temporal scale locations Padded foothold?

19 Objective 1 – home range methods
Kernel density methods 95% fixed kernel home range (Worton 1989) 50% fixed kernel core areas Home range overlap At 95% and 50% levels (Schrecengost et al. 2009) Maximum dispersal distances

20 Objective 1 – home range expectations
Large amount in variation (1.8 km km2) Typically larger in forested areas than rural areas More seasonal variation in forested areas Substantial home range overlap between group members, little overlap between groups Post 1975, Ford 1983, Smith 1984, Sumner 1984, Harrison 1986, Priest 1986, Babb 1988, Person 1988, Morton 1989, Parker and Maxwell 1989, Crawford 1992, Holzman et al. 1992, Brundige 1993, Edwards 1996, Lovell 1996, Kendrot 1998, Eastman 2000, Way 2000, Crete et al. 2001, Bogan 2004, Atwood and Weeks 2002a, Atwood and Weeks 2002b, Gehrt 2007, Gehrt et al. 2009, Schrecengost et al. 2009

21 Objective 1 – habitat selection methods
Hierarchical Approach (Oehler and Litvaitis 1996) Landscape and home range level (Boisjoly et al. 2010) Presence only: Compare confirmed fine scale locations to available habitats Detection/nondetection: “Occupancy” models (MacKenzie 2005)in program PRESENCE at habitat scale Clarify presence-only. Presence software.

22 Objective 1 – habitat selection expectations
early successional habitat > mature forest stands > agricultural > suburban/urban Crossett 1990, Kendrot 1998, Dumond et al. 2001, Gosselink et al. 2003, Bogan 2004, Atwood and Weeks 2002a, Atwood et al. 2004, Gehrt 2007, Kays et al. 2008, Page 2010, Weckel et al. 2010

23 Objective 1 – habitat selection expectations
Vary with season, land use, and food availability Post 1975, Litvaitis and Harrison 1989, Lovell 1996, Chamberlain 1999, Priest 1986, Parker and Maxwell 1989, Person and Hirth 1991, Holzman et al. 1992, Brundige 1993, Stupakoff 1994, Oehler and Litvaitis 1996, Crete and Lariviere 2003, Thibault and Oullet 2005, Billodeaux 2007

24 Objective 2 – Demography methods
Scat transects sampling model dependent Genotyping faeces to individual Capture-Mark-Recapture Models (CMR)

25 Objective 2 – Density methods
2 large study grids 200 km2 (3-4 x home range - Maffei and Noss 2008) Bath County (forested) Rockingham County (forest-rural interface)

26 Objective 2 – Density methods
2 large study grids 200 km2 (3-4 x home range - Maffei and Noss 2008) Bath County (forested) Rockingham County (forest-rural interface) 200 km2

27 Objective 2 – Density methods
2 large study grids 200 km2 (3-4 x home range - Maffei and Noss 2008) Bath County (forested) Rockingham County (forest-rural interface) 200 km2 200 km2

28 Objective 2 – Density methods
Scat transects Standardized total transect length per grid cell Transects cleared and scat collected 1 month later Spatial replicates?

29 Objective 2 – Density methods
Spatially Explicit Capture-Recapture models (SECR) Produce density estimate and effective sampling area SPACECAP (Royle and Young 2008) DENSITY (Efford 2004) 200 km2 Density or spacecap- secr 200 km2

30 Objective 2 – Density expectations
Large amount of variation: Season Available food resources Latitude Habitat Highest post-whelping Greater to the south Median: 0.5/ km2 (Sumner 1984, Parker 1995, Clark 1972, Stoddart and Knowlton 1983, Gese et al, 1989, Parker 1995, Knowlton and Gese 1995, Rose and Polis 1998, Patterson et al. 1998, Fisher 1977, Smith 1984, Priest 1986, Chambers 1987, Blanton 1988, Babb and Kennedy 1989, Stephenson and Kennedy 1993, Samson and Crete 1997, Lloyd 1998, Patterson and Messier 2001, Richer et al. 2002, Kays et al. 2008)

31 Objective 2 – Population growth methods
Estimate changes in abundance over time Pradel reverse-time models (open populations) 200 km2 200 km2

32 Objective 2 – Population growth methods
Subsample Pradel sites 2 primary surveys/year (summer and winter) 200 km2 200 km2

33 Objective 2 – Population growth methods
Summer Winter Summer Winter Summer Winter / /

34 Objective 2 – Population growth methods
Est(1) Est(2) Est(3) Est(4) Est(5) s1 s2 s3 s4 s5 s6 Summer Winter Summer Winter Summer Winter / /

35 Objective 2 – Population growth methods
Φ Φ Φ Φ Φ s1 s2 s3 s4 s5 s6 Summer Winter Summer Winter Summer Winter / / Survival estimate Probability of being detected during a survey if present and detected in previous surveys

36 Objective 2 – Population growth methods
Fecundity estimate (reverse-time) Probability of being detected during a survey if present and detected in future surveys s1 s2 s3 s4 s5 s6 Summer Winter Summer Winter Summer Winter / / F F F F F

37 Objective 2 – Population growth methods
Summer Winter Summer Winter Summer Winter / / Population growth (λ)

38 Objective 2 – Population growth methods
Dispersal (-) Denning (+) Dispersal (-) Denning (+) Dispersal (-) Seasonal variables s1 s2 s3 s4 s5 s6 Summer Winter Summer Winter Summer Winter / / Slow down and describe more

39 Objective 2 – Population growth methods
Summer Winter Summer Winter Summer Winter / / Slow down and describe more Spatial site variables

40 Objective 2 – Population growth methods
Summer Winter Summer Winter Summer Winter / / Prey densities and diet variables

41 Objective 2 – Social structure methods
Co-occurrence models in program Presence Adapted to model co-occurrence of sexes, age groups, and/or individuals with in study sites Relatedness of individuals as covariate (from genetic analysis)

42 Objective 2 – Population growth and social structure expectations
Reproductive rates increase with mortality (Knowlton et al. 1999) Increased dispersal risk/mortality increases social cohesion and relatedness of territory transfer (Messier and Barrette 1982) Population growth positively related to prey densities

43 Objective 3 – response to anthropogenic effects methods
Models of spatial and demographic variables to incorporate human factors Habitat fragmentation (FRAGSTATS) Human population densities Road densities Harvest Control measures

44 Objective 3 – response to anthropogenic effects expectations
Home range smaller closer to human activity Within home range select for habitats away from humans Survival negatively correlated with human influences higher territory turnover near human influences

45 Objective 4 – Population simulation models
Connor et al. 2008 Created and validated for western states Variables: Pop growth Social structure Home range Density Joseph Hinton

46 Potential Problems Sufficient sample size from trapping and collars
Population growth function of age class structure. Genetic methods learning curve Misidentification of individuals from degraded DNA Spatial scale and model assumptions

47 Questions? Mike Fies (VDGIF) Carol Croy (USFS) Dr. Marcella Kelly (VT)
Chad Fox (USDA/APHIS-WS) Lauren Mastro (USDA/APHIS-WS) Warm Springs USFS District North River USFS District Dr. Marcella Kelly (VT) Dr. Jim Nichols (USGS) Dr. Lisette Waits (U. of Idaho) Dr. Dave Steffen (VDGIF) WHAPA Lab Dr. Kathy Alexander (VT) The Nature Conservancy


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