1

Interim Progress Report

Research Title:

Population Status and Foraging Ecology of Eastern Coyotes in New York State

Principle Investigators:

Dr. Jacqueline Frair and Dr. James Gibbs

SUNY College of Environmental Science and Forestry

Gordon Batcheller and Paul Jensen

New York State Department of Environmental Conservation

Graduate Student Investigators:

Robin Holevinski, ESF Ph.D. Candidate

Sara Hansen, ESF M.S. Candidate

Project Initiation: 1 January 2007 Coverage of Report: May 2009 – April 2010

Summary: The goals of this research are to estimate coyote abundance and evaluate

the impacts of coyotes on deer populations in New York State. Since project initiation

in January 2007, our research has focused on intensive monitoring of radio- and GPS-

collared coyotes in Steuben and Otsego Counties to estimate deer kill rates and evaluate

methods for estimating coyote population size. Christina Boser successfully defended

her thesis on coyote diet selection and movements in our two focal areas in December

2009. In summer 2009, R. Holevinski completed a second season of tracking GPS-

collared coyotes (n = 7) and detected 21 fawn kills predominantly in un-mowed hay and

fallow fields. Kills of alternate prey species (turkey and woodchuck) were also detected.

Surveys of coyote populations using non-invasive genetic approaches continue, and lab

analyses of the 800+ scat samples collected to date are currently underway. This year,

we expanded our population monitoring design to include both site-occupancy

approaches and vocalization surveys. A pilot study of vocalization surveys undertaken

by S. Hansen indicated coyote call response rates between 24-34%. Given two

observers that triangulate the calling coyote, we propose to link vocalization surveys to

distance sampling to provide robust populations estimates. These road-based surveys

may prove advantageous in the long-term over scat or DNA-based approaches, which

may be limited by access to private lands and involve time-intensive surveys. In summer

2010, we will undertake a multi-scale and multi-survey approach to assessing the status

of coyote populations statewide.

2

Progress

Christina Boser successfully defended her thesis on coyote seasonal diets, movements,

and habitat selection in December 2009. The bulk of her research has been reported in

previous progress reports, and a digital copy of her thesis is available upon request.

Sara Hansen (M.S. student) joined the coyote project in September 2009 to investigate

the use of vocalization surveys combined with distance sampling as a method of estimating

coyote densities over broad spatial scales. Sara will be coordinating the statewide surveys this

summer.

In the summer of 2009 we deployed collars on coyotes in our two focal areas and

backtracked GPS collared coyotes to locate kill sites of white-tailed deer fawns. This was the

last planned deployment of collars for this study as we transition to statewide surveys. During

this last year we continued to collect scat for non-invasive, genetic-based population estimates.

Robin Holevinski identified an alternative state variable – site occupancy – that might prove

advantageous for monitoring coyote populations because it involves less investment than a

formal population estimate. We report herein on a pilot study of site occupancy that Robin

presented at The Wildlife Society annual meeting this past September. This past winter, Sara

refined a vocalization survey design using the remaining radio-marked coyotes in Steuben and

Otsego Counties. To follow is a summary of the field and

laboratory work that was completed from May 2009

through April 2010, as well as an outline of our plans for

summer 2010.

Coyote Trapping, Monitoring, and Mortalities

Coyote Trapping: To date, 31 coyotes have been collared

in Otsego County (Table 1) and 19 in Steuben County

(Table 2). During the time covered by this report, 5

coyotes were trapped and collared (2 GPS, 3 VHF) in

Steuben County.

Coyote Monitoring: Of the 50 animals collared since the

start of this study, we continue to monitor 7 animals with

active collars that still reside in our study areas. These

include 3 coyotes (1 GPS, 2 VHF) in Otsego County and 4 (1

GPS, 3 VHF) in Steuben County. We have lost track of a

Dave Wilckens of Paul Smith's

College assists with capture of

female coyote in Steuben

County.

3

total of 10 animals since the start of the study – 6 females and 1 male from Otsego County, and

2 females and 1 male from Steuben County. The breakaway devices on 5 GPS collars (3 Otsego,

2 Steuben) have malfunctioned, and the collars remain on the animals past their recovery

dates. We are cooperating with hunters, houndsmen, and trappers to help recover those

collars. One remaining deployed GPS collar is scheduled to drop off in June 2010.

Coyote Mortalities: Crude mortality rates can be calculated from our telemetry study using

animals of known fate (dead or currently being monitored) or that were monitored for at least

1 full year prior to censoring (GPS collars dropped off). Since the start of our study, crude

mortality rates have been 75% (19 deaths/25 known fate) in Otsego County and 43% (6

deaths/14 known fate) in Steuben County. In the past year alone, crude mortality rates were

50% in Otsego County (4 deaths/8 known fate), including 1 vehicle strike and 3 hunter harvests

(Table 1). In Steuben County, the past years crude mortality rate was 42.8% (3 deaths/7 known

fate), with 1 coyote shot by a landowner near his house when the coyote confronted his dog,

and the other 2 killed by houndsmen (Table 2).

Quantifying Deer Killed by Coyotes – field

summary (R. Holevinski)

The objective of this work is to provide a

robust estimate of the per capita kill rate of deer by

coyotes, with separate estimates for adults and

fawns (the latter restricted to a summer estimate

only). The field methods involve monitoring coyote

movements with high frequency GPS locations,

identifying “clusters” of GPS locations where coyotes

spent 40+ minutes that might indicate a kill site, and

field reconnaissance of GPS clusters within

approximately 3 days of the coyote having been

there to find the deer carcass along with sufficient

evidence to determine whether the coyote killed the

deer or scavenged an already dead animal.

In summer 2009, 7 GPS-collared coyotes (2

males, 5 females) were monitored using 20-minute

GPS fix intervals set to collect locations for 3-5

consecutive weeks, which were staggered

throughout a 9 week period (24 May – 26 July). A

Justin Stevenson and Joe Gonzalez collect

fawn remnants and record habitat data at a

GPS cluster site.

4

total of 9 field crew members investigated 562 GPS clusters for these animals (defined as two

or more points within 50 m). The searchable area was limited by access to private property,

but within a total area of 245 km2 (94.4 mi2) we found carcasses of 21 fawns, 6 adult deer

(scavenged), 10 turkeys, and 13 woodchucks. Combining the two years of back-tracking data

we have a total of 34 documented fawn kills. Fawn carcasses were found in un-mowed hay and

fallow fields (61.9%), wetlands (14.3%), forest (14.3%) and shrub (9.5%) habitat types. Fawn

kills peaked in the second week in June and timing of GPS clusters at fawn carcasses indicated

that fawns were killed between 1800 -1200 hours, with most killed between 2100 and 2200

hours. Coyotes remained at fawn carcasses between 20 and 100 minutes. Due to timing of GPS

clusters and the corresponding condition of fawn carcasses, we assumed all fawns found during

GPS backtracking efforts were killed by coyotes rather than by farm machinery. Individual

coyotes exhibited variation in diet choices between fawns, turkeys, and woodchuck. However,

these patterns may reflect the changing vulnerability of fawns to predation rather than dietary

preferences given the staggering of our GPS monitoring intervals. Ongoing analyses will

estimate an empirical fawn kill rate (# fawns killed/coyote/summer) for use in modeling the

effects of predation on deer populations.

After 2 winter and 2 summer field seasons of backtracking GPS-collared coyotes to

locate prey items, we have gained useful insights into coyote predation on deer. As reported

previously (see earlier progress reports), we detected few kills of adult deer (3 of 39 known fate

carcasses). Although 3 kills may preclude a robust statistical estimate of deer kill rates, we

know that kill rates on adult deer are low. Further, these three animals each had injuries that

made them more vulnerable to predation or death by other causes, indicating that predation

on adult deer by coyotes in our study area may be a largely compensatory form of mortality.

The kill rates for fawns are higher than for adult deer, and may vary markedly among individual

coyotes. These observations lead to the hypotheses that predation by coyotes may not limit

deer populations to a lower carrying capacity (through predation on adults) but may slow

population recovery following a decline (through predation on fawns), and further that the

structure of a coyote population may dictate total predation levels. However, deeper snow or

more severe winters than we observed may alter deer vulnerability to predation and,

consequently, alter the effects of coyote predation on deer populations. These hypotheses will

be explored more formally using a stochastic population projection model, which will allow us

to test how sensitive deer population growth rates are to plausible predation scenarios based

on our empirical studies.

5

Table 1. Capture details1 and status of GPS- and VHF-collared coyotes in Otsego County from June 2007 – April 2010.

Animal Capture Age / Collar Trap Cause

ID # date Sex status type type Injuries/comments Current status of death

M1 6/10/07 M Adult VHF Foothold Dead 1/23/09 Trapped

M2 6/10/07 M Adult GPS Foothold Cut on foot Dead 11/5/07 Shot by landowner

M3 6/11/07 M Adult VHF Cable restraint Swollen neck Dead 2/15/08 Shot by landowner

F4 6/18/07 F Adult GPS Cable restraint Dead 10/20/07 Shot by landowner

F5 6/21/07 F Adult GPS Foothold Cut on foot Dead 1/24/09 Shot by houndsman

M6 6/22/07 M Adult GPS Foothold Dead 11/18/07 Found dead, broadhead

M7 6/30/07 M Yearling VHF Cable restraint Dead 11/2/09 Shot by hunter

F8 7/15/07 F Sub-Adult VHF Cable restraint Swollen neck Dead 5/12/08 Found dead, unknown

F9 7/25/07 F Adult VHF Foothold Cut on foot Dead

F10 7/2707 F Adult VHF Foothold Tracking

M11 8/15/07 M Adult VHF Foothold Dead 6/01/08 Shot by landowner

F12 11/8/07 F Adult VHF Foothold Missing 2 toes Dead 11/14/07 Shot by landowner

M13 11/18/07 M Sub-Adult VHF Foothold Cut on foot Dead 11/20/08 Shot by landowner

F14 1/13/08 F Adult VHF Foothold Missing

F15 1/29/08 F Yearling VHF Foothold Dead Shot by houndsman

M16 4/10/08 M Adult GPS Foothold Dead 1/8/09 Trapped in Pennsylvania

F17 4/17/08 F Sub-Adult VHF Foothold Missing

F18 4/17/08 F Sub-Adult VHF Foothold Dead 6/18/09 Road kill

F19 4/18/08 F Adult GPS Foothold Tracking, drop-off failed

F20 4/19/08 F Adult GPS Foothold Dead 1/14/09 Shot by houndsman

M21 4/22/08 M Adult GPS Foothold Toe bleeding Dead 10/31/09 Shot by hunter

F22 4/25/08 F Sub-Adult VHF Foothold Cut on foot Dead 1/13/09 Found dead, gunshot

F23 6/13/08 F Sub-Adult VHF Foothold Cut on foot Missing

M24 11/04/08 M Sub-Adult VHF Foothold Cut on toe Missing

F25 11/05/09 F Sub-Adult VHF Foothold Missing

F26 11/15/08 F Adult GPS Foothold GPS dropped off

F27 4/16/08 F Yearling VHF Foothold Cut on foot Dead 2/8/10 Shot by hunter

F28 4/16/09 F Sub-Adult GPS Foothold Cut on foot Dead 1/19/10 Shot by houndsman

F29 4/18/09 F Sub-Adult VHF Foothold Missing

M30 4/18/09 M Adult GPS Foothold Tracking

M31 4/20/09 M Adult GPS Foothold Tracking

1 To protect the integrity of the study, capture locations and radio-collar frequencies will be kept confidential until the close of the study.

6

Table 2. Capture details1 and status of GPS- and VHF-collared coyotes in Steuben County from June 2007 – April 2010.

Animal Capture Age / Collar Trap Cause

ID # date Sex status type type Injuries/comments Current status of death

SF1 11/02/07 F Adult VHF Foothold Cut on foot Missing

SF2 11/05/07 F Adult VHF Foothold Cut on foot VHF battery expired

SF3 11/06/07 F Sub-Adult GPS Foothold Cut on back GPS dropped off

SF4 11/17/07 F Adult GPS Foothold GPS dropped off

SM5 12/30/07 M Adult GPS Cable restraint Swollen neck Drop-off failed, no contact

SM6 1/14/08 M Sub-Adult GPS Foothold GPS dropped off

SF7 1/18/08 F Sub-Adult GPS Cable restraint Dead 2/23/08 Found dead, gunshot

SF8 4/09/08 F Adult VHF Foothold Cut on foot Dead 11/15/08 Shot by hunter

SF9 4/12/08 F Adult GPS Foothold Cut on foot Drop-off failed, no contact

SF10 4/12/08 F Adult GPS Foothold Cut on foot Dead 12/10/09 Shot by hunter

SM11 4/24/08 M Adult VHF Foothold Abrasion on foot Dead 11/6/08 Trapped

SM12 4/26/08 M Adult VHF Cable restraint Swollen neck Missing

SM13 6/12/08 M Adult VHF Cable restraint Swollen neck Tracking

SF14 4/29/08 F Adult VHF Foothold Cut on foot Missing

SM15 5/07/09 M Adult VHF Foothold Tracking SF16 5/11/09 F Adult VHF Foothold Foot amputated Dead 10/28/09 Shot by landowner SF17 6/04/09 F Sub-Adult GPS Cable restraint Swollen neck Dead 2/13/10 Shot by houndsman SF18 6/28/09 F Adult GPS Cable restraint Swollen neck Tracking SF19 12/19/09 F Adult VHF Foothold Tracking

1 To protect the integrity of the study, capture locations and radio-collar frequencies will be kept confidential until the close of the study.

7

Assessing the Status of Coyote Populations

Non-invasive Genetic Mark-Recapture Study (R. Holevinski)

For DNA-based estimates of the

size of the coyote populations in our

focal areas, we collected 212-466 scat

samples each winter from 2007-08,

2008-09, and 2009-10. We are

conducting DNA analyses in the lab of

Chris Whipps at SUNY ESF, and

replicating the protocol identified by our

2008 pilot study through Wildlife

Genetics International. To date, we

have extracted DNA from 113 scat

samples collected in Steuben County

and 105 from Otsego County using

QiagenTM QIAamp DNA Stool Mini Kits.

Due to physical similarities in scats of

several species (coyote, red fox, grey

fox, and bobcat), we are extracting DNA from those most likely to be coyotes based on the size,

consistency, and/or presence of coyote tracks at the collection site, and will conduct

mitochondrial DNA (mtDNA) species tests to eliminate non-coyote samples.

From the samples processed so far, DNA concentrations measured on a NanoDropTM

spectrophotometer ranged from 1.7 – 133.5 ng/µl, indicating that some samples have very low

quantities of DNA and may not amplify in subsequent steps. The amplification and

microsatellite work are time-intensive and expensive steps. Thus, after identifying coyote

samples we will continue processing only the high DNA concentration samples for microsatellite

work, holding onto the lower quality samples should we have the time, money and need to

work with them later. This summer, DNA will be extracted from the remaining samples,

samples will be screened to confirm they are from coyotes, and coyote samples having high

quality DNA will be genotyped (using 10 loci). This fall capture-mark-recapture models will be

fit to estimate the coyote population size in each of our two focal areas for the three winters

under study. DNA samples (scats) will be collected at an additional three sites this summer (see

Statewide Population Monitoring Plans). Those samples will be analyzed in the coming year

(and may be sent to an outside lab to expedite the process).

Nick Deuel and Amanda Fischedick extract DNA from

scat samples at SUNY ESF research lab.

8

Site Occupancy Approach (R. Holevinski)

Non-invasive genetic approaches are a powerful way to estimate population size, but

require a substantial investment of field, lab, and analytical time. An alternative population

state variable, such as the proportion of area occupied by coyotes rather than population size

per se, may prove more advantageous for long-term or broad-scale monitoring of coyote

populations. Site “occupancy” (i.e., coyote presence) may be determined using a variety of

methods (scats, hair, tracks, photos, vocalizations), and repeat surveys for coyote sign in an

area are used to estimate both the probability of detecting coyotes and the probability of site

occupancy (MacKenzie et al. 2006). Ecological theory and empirical studies on various species

indicate that as population density increases more of the available habitat becomes occupied

by individuals. Likewise, given a population at equilibrium, the proportion of area occupied

reflects the inherent quality of the habitat available. Thus, the proportion of area occupied, or

probability of occupancy (which is synonymous in certain study designs) is assumed to reflect

actual population size. Occupancy based monitoring works quite well with rare or “patchily”

distributed species, and there are challenges when applying these approaches to common and

widespread species. One main problem is that occupancy metrics are scale-dependent, and

determining the appropriate scale of study may not be straight-forward.

By simultaneously estimating in multiple areas actual coyote abundance (using genetic

mark-recapture approaches) and the proportion of area occupied (within nested spatial scales),

we can:

1) test the assumption that occupancy reflects abundance for coyotes,

2) Identify the appropriate scale for monitoring changes in occupancy metrics for

coyotes, and

3) design a robust tool for

rapidly monitoring changes

in coyote populations over

time and space.

In summer 2009, we conducted

a pilot study in our focal areas where we

expected coyote densities to vary by

about a two-fold difference according to

the average size of GPS collared coyotes

home ranges in Otsego (20.3 km2, SD =

6.5) and Steuben County (48.8 km2, SD =

Figure 1. Occupancy sampling design showing locations of

scat transects (lines) and hair snares (points) within 16 km2

grid cells covering 400 km2 area where coyotes were

collared in Steuben County.

9

24.1). We established a nested sampling scheme by first dividing each 400 km2 study area into

16 km2 sampling grids (n=25; Figure 1). Within each sampling frame we established 2 km of

transects for scat collection and placed double-stranded barbed wire hair snares within grid

cells. This provided three levels of sample resolution – the individual grid cell (16 km2), quarter

cells (4 km2) and individual sections of the scat transects or individual hair snare stations.

Between January and April 2009, we collected 212 scats but only 19 hair samples. Due to the

difficulty collecting hair and the time required to set up hair snares, hair snares were not

deemed to be an efficient method for detecting coyotes.

Preliminary analysis with program PRESENCE indicated a high detection probability for

coyotes (0.40 and 0.61 for Steuben and Otsego County, respectively) at the 16 km2 scale. The

proportion of area occupied was estimated to be 0.81 (± 0.13 SE) and 0.78 (± 0.10 SE) in

Steuben and Otsego County, respectively. Given no difference in the estimates between our

two study areas, we conclude that sampling occupancy at the 16-km2 resolution may be

insensitive to even fairly large differences in coyote population size. However, the actual

differences in population size may not be as great as that indicated by home range sizes.

Because the probability of site occupancy predictably increases with increasing size of the

sampling unit, we expect that a smaller sampling unit (like the individual scat transect) will be

required to effectively detect differences between populations. The three additional sites

surveyed for the DNA mark-recapture study this

summer will also be sampled in a design that will

inform an occupancy model. Ultimately, we will

compare a multi-scale occupancy metric and

population estimate in 5 different areas around the

state to evaluate whether occupancy provides a

useful population monitoring tool for coyotes.

Population Estimates from Vocalization Surveys

(S. Hansen)

To get around property access limitations (for

scat-based coyote surveys), we are working on road-

based coyote call-response surveys paired with

distance sampling to estimate coyote abundance.

Distance sampling allows for the estimation of the

probability of detecting a given species based on the

assumption that detectability, but not density,

Audra Valaitis and Jennifer Kurilovitch

conduct coyote vocalization surveys in

Otsego County.

10

degrades with distance from the surveyor (Buckland et al. 2001). In most cases, distance

sampling techniques rely on passive observance of an animal or its sign, however, recent

research has shown applications utilizing a call-response method to elicit a response from an

otherwise seldom detected species (Conway and Gibbs 2005). Because coyotes are social

animals, it is possible to play a recorded call and elicit a response call from an animal within

hearing distance (Alcorn 1946).

Pilot study (2009-10): To test the utility of this technique, vocalization surveys using radio-

marked coyotes in Steuben County (2 males, 4 females) and Otsego County (3 males, 3 females)

were conducted during two prime response periods: mid-June through August 2009 and

January through April 2010. During the summer trials, 2 different devices (Code 3 coyote

locator siren and an electronic coyote locator call) were used to elicit a coyote vocalization

response between dusk and midnight. Prior to each survey, the target animal was triangulated

to acquire an accurate location, the observed moved to a specified distance from the animal,

the call was broadcast to elicit a response. Aiming first in the direction of the collared coyote,

we played a 20-second siren or coyote call, followed by a 2 minute listening period. If a coyote

response was heard, we recorded a general distance/quality category (close and loud,

intermediate and clear, far and indistinct), compass

bearing to the call, response duration, approximate

number of coyotes responding (1, 2, or 3 +), and age

class of respondents (adults, pups, both). If a

response was not detected, we repeated the cycle up

to 3 times, broadcasting the call in each cardinal

direction. We recorded weather conditions, cloud

cover, wind speed, wind direction, and a description

of ambient noise. During these summer trials,

collared coyotes responded to the siren in 13 of 41

(31.7%) sessions and to the coyote locator call in 16

of 43 (37.2%) sessions yielding an average response

rate of 35% overall.

From January through April 2010 the survey

process was refined, and a pilot survey extended to

random points where the presence of coyotes was

not known. We conducted point-count surveys with

two observers, each stationed 500 meters apart, and

broadcast a series of coyote group-yip howls through

a pair of loudspeakers pointed in opposite directions.

SUNY ESF grad student Sara Hansen

and technician Nick Deuel conduct

howling simulations to determine

distance estimation accuracy.

11

Each random point was spaced 6 km away from the next closest point to ensure sampling

independence. When responses were heard, the number of coyotes responding and their

distance from the person observing was estimated. Each observer took a bearing so as to

triangulate the position of the responding coyote(s).

Our winter effort produced 32 call-response sessions in which an accurate location was

attained and conditions were right for howling. Of the 32 sessions, 13 responses were detected

resulting in a total response rate of 34% when coyotes were known to be present (radioed

animals) showing consistency in responses across the expected prime howling seasons. A total

of 87 random points were also surveyed between both study areas (Steuben and Otsego

County) yielding 18 detected responses and resulting in a total response rate of 24% when

presence of coyotes was not known, which has been adequate for accurate density estimates in

songbird surveys (Nelson and Fancy 1999). Our comparison of response rates given known vs.

unknown coyote presence confirms that we can expect adequate response rates during the

summer sampling to deliver a statewide abundance estimate using this approach.

Statewide Population Monitoring Plans – Summer 2010

To estimate coyote densities statewide, we will employ a combination of broad-scale

vocalization surveys and focal area non-invasive genetic surveys. Three primary needs underlie

our sampling design:

1. Capture broad-scale geographic variation in habitat conditions and potential coyote

densities for both vocalization and genetic-based surveys.

2. Obtain > 80 coyote detections statewide (using vocalization surveys) so as to estimate

the probability of detection with a high degree of confidence.

3. Maximize independent spatial replicates of vocalization surveys, and ensure spatial

independence among survey sites.

To meet the first need (geographic coverage), we stratified the state according to major ecoregions and will sample each in proportion to the size of the region (Figure 2). Given the intensive nature of scat searches, and our ability to support a maximum of three field crews, we are limited to three additional focal areas in which we will produce a noninvasive genetic population estimate, bringing the statewide total to 5 sites (Figure 2). The location of those three sites was selected to capture ecoregions that were different from the two already surveyed, and located adjacent to suitable housing for the field crew. In these three areas we will conduct both the DNA mark-recapture and full site occupancy surveys. An additional two

12

DNA estimates to fill in spatial gaps (near Syracuse and at the Huntington Wildlife Forest in the central Adirondacks) may also be possible via opportunistic sampling of scats.

To meet the second need (80+ coyote detections for vocalization surveys), we require a minimum of 300 sampling stations (assuming the probability of detection to range between 0.24-0.34 as we observed in our pilot studies). Because these are point counts, each point is the unit of replicate, and for a given survey night between 5-10 points can be reasonably surveyed. The map shows crudely how 30 routes of 10 points each might be laid out to effectively capture geographic variation.

Figure 2. Locations of scat (squares) and vocalization surveys (points) to be conducted May –

August 2010.

To meet the third need (maximize spatial replication for vocalization surveys), we plan to conduct a single survey (with no revisitation) at any given survey station, and expect that we can conduct a total of 600 surveys over the course of the summer (which would be double the amount of stations shown in the map).

13

Our survey crews consist of two paid technicians and 2-3 volunteers (chosen from among 61 applications following a nationwide search). If you wish to contribute to the scat sampling or vocalization surveys this summer, please contact Sara Hansen (contact information on last page).

Literature Cited

Alcorn, J. R. 1946. On the decoying of coyotes. Journal of Mammalogy 27:122-126. Buckland, S. T. 1987. On the variable circular plot method of estimating animal density.

Biometrics 43:363-384. Conway, C., and J. Gibbs. 2005. Effectiveness of call-broadcast surveys for monitoring marsh

birds. The Auk:26-35. MacKenzie, D. I. J, D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006.

Occupancy estimation and modeling: Inferring patterns and dynamics of species

occurrence. Academic Press, Burlington, MA.

Nelson, J., and S. Fancy. 1999. A test of the variable circular-plot method where exact density of

a bird population was known. Pacific Conservation Biology 5:139-143.

Outreach

The following outreach efforts were made since May 2009.

Public Presentations

Huntington Lecture Series, Adirondack Ecological Center (August 2009)

Finger Lakes Institute at Hobart and William Smith Colleges (October 2009)

Richmondville Historical Society (October 2009)

Tioga County Trappers Association (October 2009)

Finger Lakes Community College Wildlife Society (December 2009)

National Wild Turkey Federation, New York Chapter (January 2010)

Paul Smith’s College Fish and Wildlife Seminar Series (February 2010)

SUNY Cobleskill Wildlife Techniques Course (February 2010)

SUNY Ulster and the Catskill Institute for the Environment (March 2010) Posters

“Foraging ecology of coyotes in New York State” - Holevinski, R., Frair, J. L., Batcheller, G., Jensen, P. Displayed at public meetings on deer management, New York State Department of Environmental Conservation, 2009.

14

“Foraging ecology of coyotes in New York State” - Holevinski, R., Frair, J. L., Batcheller, G., Jensen, P. Displayed at the New York State Trappers Convention, Herkimer, New York, 2009.

Interviews

Outside Magazine (Elizabeth Royte), March 2010

Professional Presentations

66th Annual Northeast Fish and Wildlife Conference – 2010, Boston, Massachussetts

“The dividing line: differentiating two stocks of coyotes in New York State” (oral presentation) - Frair, J.L., Whipps, C., Kretzer, A., and Holevinski, R.

The NY Section of The Wildlife Society Annual Conference – 2010, Alexandria Bay, NY

“Foraging ecology of coyotes in New York State” (poster presentation) - Holevinski, R., Frair, J. L., Batcheller, G., Jensen, P. *** Won award for best graduate student presentation ***

The Wildlife Society Annual Conference – 2009, Monterey, California

“Coyote foraging ecology and selection for white-tailed deer fawns” (oral presentation) - Boser, C. L., Frair, J.L., Holevinski, R., Batcheller, G.

“Using site occupancy and detection probabilities to estimate eastern coyote populations” (oral presentation) - Holevinski, R., Frair, J.L., Gibbs, J.P. , Boser, C. L., Batcheller, G.

“The dividing line: differentiating two stocks of coyotes in New York State” (oral presentation) - Frair, J.L., Whipps, C., Kretzer, A., and Holevinski, R.

Ecological Society of America Annual Conference – Santa Fe, New Mexico

“Coyote foraging ecology and selection for white-tailed deer fawns” (oral presentation) - Boser, C. L., Frair, J.L., Holevinski, R., Batcheller, G.

Additional Funding Received

$5,900 in salary and research support to Sara Hansen, summer 2010. Source: Edna Bailey

Sussman Foundation.

$2,000 of travel support to Sara Hansen, summer 2010. Source: American Wildlife

Conservation Foundation.

15

Related Developments

Christina Boser successfully defended her thesis entitled “Diet and hunting behavior of coyotes

in agricultural-forest landscapes of New York State” in December 2010. She has taken a full-

time position with The Nature Conservancy in California and is working on revising her chapters

for publication.

A related research project is being initiated within the Frair lab at SUNY-ESF. Scott Warsen, MS

Candidate, is investigating the evolving diets of coyotes in the central Adirondacks (following up

on the dissertation work by G. Brundige) and their niche overlap with native predators in the

region (specifically red fox and bobcat). Scott will be studying diets both via scat analysis and

using stable isotope techniques. The latter requires tissue samples that Scott will be collecting

from trappers in the region. Scott’s research is funded by SUNY-ESF and a Grober Graduate

Research Fellowship. Additional funding is being sought that would allow Scott to deploy GPS

collars on coyotes at Huntington Widlife Forest so he could back-track to determine adult and

fawn deer kill rates following the approach of R. Holevinski. Scott began his graduate studies in

January 2010 and can be contacted for further information about his research at

sawarsen@syr.edu.

Robin Holevinski successfully completed her Ph.D. candidacy exam in April 2010.

Acknowledgements

Funding sources: NY State Department of Environmental Conservation, SUNY College of

Environmental Science and Forestry, USDA McIntire-Stennis program, Edna Bailey Sussman

Foundation, American Wildlife Conservation Foundation, NY State Trappers Association.

In addition to the approximately 300 landowners in the Otsego and Steuben County study areas that granted access to their land, and the cadre of students, professionals, and organizations that have provided expertise, equipment, and technical support to the project (see lists in previous progress reports), we wish to acknowledge the following for their dedication and contributions to the project this past year: SUNY ESF Amanda Fischedick (intern) Justin Stevenson (intern) Ian Harding (volunteer) Joe Gonzalez (intern) Nick Deuel (intern) Sandy Walczyk (paid technician) William Dunker (volunteer) (Acknowledgements continued on next page)

16

SUNY Cobleskill Amanda Novko (volunteer) Amanda Stott (volunteer) Amber Boruck (volunteer) Bettina Scherer (volunteer) Dana Harenda (volunteer) Eric Pettit (volunteer) Jennifer Kurilovitch (intern) Jeremy Cook (volunteer) Kristen Wokanick (volunteer) Lindsey Williams (volunteer) Phil West (volunteer) Stephanie Parsons (volunteer) Tina Scharf (volunteer)

Contact Information

Dr. Jacqueline Frair Dr. James Gibbs SUNY ESF SUNY ESF 1 Forestry Drive 1 Forestry Drive 405 Illick Hall 250 Illick Hall Syracuse, NY 13210 Syracuse, NY 13210 Office Phone: 315-470-4905 Office Phone: 315-470-6764 Email: jfrair@esf.eduUTH Email: jpgibbs@esf.eduUTH

Gordon Batcheller Paul Jensen NYS DEC NYS DEC 625 Broadway, 5P

thP Floor 232 Hudson Street Extension, PO Box 220

Albany, NY 12233-4754 Warrensburg, NY 12885-0220 Office Phone: 508-402-8885 Office Phone: 518-623-1242 Email: grbatche@gw.dec.state.ny.usUT Email: pgjensen@gw.dec.state.ny.usUTH

Robin Holevinski (Ph.D. Student) Sara Hansen (M.S. Student) SUNY ESF, 254 Illick Hall SUNY ESF, 254 Illick Hall 1 Forestry Drive 1 Forestry Drive Syracuse, NY 13210 Syracuse, NY 13210 Office Phone: 315-470-6762 Office Phone: 315-470-6762 Email: raholevi@syr.edu Email: sjhansen@syr.edu

Finger Lakes Community College Nick Vermuelen (volunteer) Duke University Audra Valaitis (intern) Paul Smith’s College David Wilckens (intern) Trent University Dan Andres (intern) Private Sector Kris Hoard (volunteer) Sheri Baity (volunteer)