Independent Review of Status of the Bobcat Population in the
Northern Lower Peninsula of Michigan
Prepared by: Thomas M. Gehring, Timothy S. Preuss, and Bradly A. Potter
Department of Biology, Central Michigan University
Mount Pleasant, MI 48859
Summary:
The number of bobcats harvested by hunters in the northern Lower Peninsula of
Michigan (NLP) increased from approximately 140 to 291 during 1985-2002. However, given
the current data, it is difficult to determine if this increase was a function of the bobcat
population increasing or an increase in the number of hunters or other variables. Statewide, an
increase in the number of bobcats harvested by hunters was correlated with the increase in the
number of hunters. During the same period, average pelt price for bobcats appeared to decrease.
These results suggest that bobcats may now be more valued by hunters as a trophy species rather
than an alternate income source and/or bobcat hunting is driven more by the recreational
compared to economic incentives. Overall, when the change due to the number of hunters was
removed, the bobcat population in Michigan appeared to be relatively stable. However, we
could not adequately address the status of the NLP population separately. The proportion of
juveniles and yearlings remained relatively high throughout the time period. Based on estimates
from opinion surveys, if trapping and hunting occurred annually within in the NLP, an estimated
620 to 3,000 bobcats would be harvested each year assuming 100% filling of bag limits. An
estimated 420 to 1,500 bobcats would be harvested annually assuming a 50% success rate.
Without accurate population index or other independent data for the entire NLP, it is difficult to
evaluate the impact that this harvest figure would have on the NLP bobcat population.
Distribution of bobcat harvests in the NLP suggests there are 1 or 2 relatively disjunct regions of
Zone 2 that maintain relatively high harvest intensity, whereas the areas between exhibit lower
harvest rates. There also appears to be a correlation between harvest intensity and the density of
streams and rivers in the NLP. Additionally, we found greater than expected harvest rates on
public land compared to private land in the NLP. Investigation into the possible source-sink
dynamics and spatial patterning of bobcat harvest in the NLP should be pursued further. These
investigations could provide insight into possible new zoning recommendations based on
landscape-level features (e.g., hydrologic patterns) and/or spatial patterning of high-quality
bobcat habitat (e.g., contiguous patches of lowland forest and non-forested wetland habitat).
Introduction:
In 1975, bobcats (Lynx rufus) were listed in Appendix II of the Convention on
International Trade in Endangered Species (CITES) because of concern for the viability of their
populations. This listing required state agencies to provide the U. S. Fish & Wildlife Service
with data on the status and viability of bobcat populations. The current geographic range of the
bobcat includes all of Michigan although bobcat densities are likely greater in the northern 2/3 of
the Lower Peninsula of Michigan. Currently, bobcats are harvested by hunting only in Zone 2 of
the northern Lower Peninsula (NLP), with a bag limit of 1 bobcat per hunter. Zone 2 is
1
subdivided into North and South zones with season lengths of 60 days (1 January to 1 March)
and 33 days (15 January to 16 February), respectively (Figure 1).
Figure 1. Northern Lower Peninsula of Michigan bobcat harvest units depicting Zone 2
North and Zone 2 South units (source: MDNR)
Annually, since 1985 the Michigan Department of Natural Resources (MDNR) has
collected age, sex, and location data for bobcats harvested in the state. During 1991-1996,
MDNR researchers conducted a mark-recapture study and scent-station survey in Crawford,
Missaukee, and Roscommon counties in the NLP to evaluate bobcat survival and relative
abundance (Earle et al. 2003a). Recently, Central Michigan University (CMU) began a radiotelemetry study of bobcats predominantly in Missaukee and Roscommon counties (Houghton
Lake Study Area; Preuss and Gehring, unpublished data). Additionally, in 2003 we began
assessing the relative abundance of bobcats using a scent-station survey in Roscommon and
Missaukee counties, as well as parts of Clare, Crawford, Gladwin, Kalkaska, and Osceola
counties. Collectively, these data allow an examination of bobcat population characteristics in
the NLP over an approximately 20-year period.
2
Objectives:
1)
2)
3)
Evaluate changes in the relative abundance and population structure of bobcats
in the NLP;
Evaluate the designation of Zone 2 into a North and South unit; and
Provide recommendations for future bobcat management in the NLP.
Methods:
Relative Abundance
We graphically examined the change in the number of registered bobcats over time.
Further, we used furbearer harvest survey results (Frawley 2001, 2003, unpublished data) to
examine the fur taker population during 1985 to 2002. Because pelt price might influence
trapping effort (Royama 1992), we examined this possible influence in the number of bobcats
harvested via hunting. We used average bobcat pelt price data from Wisconsin (Dhuey et al.
2003) and regressed (PROC REG, SAS Institute 1994, α = 0.05) this with the number of bobcat
hunters. Reliable data were not available for an estimate of the number of bobcat hunters in the
NLP only due to high variance. Thus, we pooled Zone 1 and Zone 2 bobcats harvested by
hunters and used estimates of the number of bobcat hunters in the State of Michigan (Frawley,
unpublished data).
We conducted linear regression analysis on the number of registered bobcats hunted
(dependent variable) and the number of bobcat hunters. We transformed data using the natural
logarithm to gain stationary variance (Royama 1992). We examined the residuals from this
regression by plotting residuals in time (i.e., sequence plot) to determine if there was a change in
the number of bobcats hunted over time (i.e., a time-related effect, Neter et al. 1996).
Additionally, we plotted differenced values [N difference = ln (N t) – ln (N t+1)] for number of
bobcats harvested by hunters and the estimated number of hunters. Differencing provided a
stationary mean in the values and allowed estimates to be examined as rates of change over time
(Royama 1992; Swanson 1998).
We conducted a scent-station survey during October – November 2003 in Roscommon
and Missaukee counties, as well as parts of Clare, Crawford, Gladwin, Kalkaska, and Osceola
counties. The survey was patterned after Linhart and Knowlton (1975), with modifications by
Roughton and Sweeny (1982). Recommendations by Sargeant et al. (1998) and Sargeant et al.
(2003) also were incorporated. Scent-stations consisted of a 0.9-m diameter circle of sand with a
fatty-acid scent tablet placed in the center as an attractant. Seventy transects with 10 stations
along each transect were checked for 2 nights. Stations were placed 480-m apart along each
transect and transects were located ≥5 km from the nearest transect. The presence of all tracks
was recorded to species whenever possible, and visitation rates were calculated for the
proportion of stations and the proportion of transects visited by each species.
We qualitatively compared various population indices to assess changes in the relative
abundance of bobcats over time in Crawford, Missaukee, and Roscommon counties (i.e., the
1991-1996 MDNR study area and our current study site). We only used a qualitative comparison
3
because objectives and methodology for studies were not consistent, and the data record for the
total time series (1985-2002) was not complete. These data sources included: 1) number of
bobcats harvested during 1985-2002; 2) MDNR scent-station survey data from 1992-1996 (Earle
et al. 2003a); 3) CMU scent-station survey data from 2003 (Preuss and Gehring, unpublished
data); 4) number of bobcats captured per 1,000 trap nights by the MDNR, 1991-1996; and 5)
number of bobcats captured per 1,000 trap nights during 2003 (Preuss and Gehring, unpublished
data).
We also attempted to estimate the range in the number of bobcats that would be trapped
in the NLP if a trapping season was offered. To estimate this number, we used fur harvester
opinion survey report data for 2003 (Bull and Peyton 2004), and the number of respondents that
indicated that they were ‘very likely’ to trap bobcats in the NLP if it were legal. We estimated a
total NLP bobcat harvest (hunting and trapping combined) by adding the mean of the number of
bobcats hunted during 1998-2002 in the NLP to our estimated trapper harvest of bobcats in the
NLP (i.e., trapping includes additive mortality above the average hunt-related mortality). We
used 2 estimates of a trap harvest of bobcats: 1) based on the total proportion of respondents in
the Bull and Peyton (2004) survey that indicated they were ‘very likely’ to trap in the NLP (i.e.,
estimated at 18% of furtaker license holders); and 2) based on the proportion of respondents in
the Bull and Peyton (2004) survey that had previously trapped or hunted bobcats and indicated
they were ‘very likely’ to trap in the NLP (i.e., 19.5%). We used the mean number of bobcat
hunters and trappers during 1998-2002 in our latter estimate (Frawley 2001, 2003). We assumed
a bag limit of 1 bobcat per hunter or trapper and a trapping success rate of 1 bobcat per trapper
(Frawley 2001, 2003). That is, we assumed that all trappers would fill their bag limit. We also
calculated estimates assuming that 50% of trappers would fill their bag limit.
We initially attempted to parameterize the Minnesota Furbearer Population Model (e.g.,
Rolley et al. 2001) with new harvest estimates and existing estimates of other population
parameters; however, our simulations of population trends were very sensitive to the value of the
initial population size. Furthermore, small changes in the initial population size resulted in
vastly different population trends. Rolley et al. (2001) suggested that this model should be
calibrated to either an independent source of population trend data or an estimate of absolute
population size. We chose not to include these analyses because of concerns about the lack of
accurate population indices or absolute counts of the number of bobcats in the NLP over the time
period 1985-2002.
Population Structure
We sorted MDNR databases to isolate the age and sex of registered hunted bobcats. We
used the sex identified based on the MDNR’s assessment using lower canine tooth maximal
thickness and width of the root (Friedrich et al. 1983) when given. If this lab-determined sex
was not provided, we used the field-determined sex. The MDNR estimates age of registered
bobcats by counting cementum annuli in cross sections of the lower canine root (Crowe 1975).
We classified age as juvenile (0.5 year), yearling (1.5 year) and adult (2.5+ year). Animals that
did not have the sex or age identified were excluded from further analyses. Initially, we
graphically examined the change in estimated age and sex ratios based on harvested bobcats in
4
Zone 2. Significant changes in ratios over time were determined by linear regression (PROC
REG, SAS Institute 1994) with an α-value of 0.05.
Preliminary Modeling of Potential Bobcat Population Size and Distribution
We obtained a broad estimate of bobcat abundance by determining the number of male
and female bobcat home ranges that could theoretically fit in the NLP using ArcView GIS
(ESRI, Redlands, California). Average minimum convex polygon (MCP) home ranges were
calculated for male and female bobcats radio collared in the Houghton Lake Study Area (HLSA).
We captured bobcats during March-June 2003 using No. 3 Victor Soft-Catch padded foot-hold
traps (Earle et al. 2003b). We immobilized bobcats via an intramuscular injection of 10 mg/kg
ketamine hydrochloride and 1.5 mg/kg xylazine hydrochloride (Kreeger 1999) in order to
determine sex, age, reproductive condition, ear tag and radio collar animals. Radio-collared
bobcats were located a minimum of 2-3 times weekly from May – December 2003 using a
vehicle-mounted 4-element Yagi directional antenna and electronic compass (Lovallo et al.
1994). Triangulations were made from telemetry stations with 3-4 bearings obtained as quickly
as possible to reduce telemetry error (White and Garrott 1990). Bobcat locations were estimated
using the microcomputer program LOCATE II (Nams 1990). We estimated home-range size
using the MCP method (Mohr 1947) using the Animal Movement Analysis Extension to
ArcView GIS (Hooge and Eichenlaub 1997).
Estimates of home-range size (Table 1) were consistent with those of bobcats in
Wisconsin (Lovallo and Anderson 1996) and Minnesota (Fuller et al. 1985). We divided the
total area of the NLP by the average home-range size of male ( x = 46.5 km2 = 18 mi2, sd = 27.3
km2 = 10.5 mi2) and female ( x = 10.4 km2 = 4.0 mi2, sd = 5.9 km2 = 2.3 mi2) bobcats to obtain an
estimate of the number of male and female bobcats that could potentially reside in the NLP. This
model assumed that there was no intrasexual home-range overlap, all bobcats were resident (i.e.,
bobcats without established home ranges were not accounted for), and bobcats occurred in all
landcover types (i.e., bobcats exhibited no selection of habitat).
Table 1. Preliminary data on summer home-range size for bobcats monitored during 2003 in the
Houghton Lake Study Area as part of a Central Michigan University Study (Preuss and Gehring,
unpublished data).
Bobcat ID
Sex
M01
M02
M03
F01
F02
F03
F04
Male
Male
Male
female
female
female
female
Minimum Convex
Polygon (km2)
26.15
77.44
35.86
9.25
7.01
6.24
19.08
5
Minimum Convex
Polygon (mi2)
10.10
29.90
13.85
3.57
2.71
2.41
7.37
We also developed sex-specific models of bobcat abundance relative to preferred
landcover types since bobcats exhibit habitat selection (e.g., Lovallo and Anderson 1996; Preuss
and Gehring, unpublished data). We used the 2001 landcover GIS theme available from the
MDNR. Within ArcView GIS, 2 hexagonal grids were constructed and overlaid on the NLP
(Figure 2). The area of each hexagon in the first grid was equivalent to the average home range
of male bobcats. The area of each hexagon in the second grid was equivalent to the average
home range of female bobcats. We calculated the percentage of streams, lowland conifer forest,
and lowland conifer forest/non-forested wetlands within each hexagon of the sex-specific grids.
Lowland conifer forest and non-forested wetland cover types were chosen because radio-collared
bobcats in the HLSA used these cover types in greater proportion to their availability based on
chi-square analysis (χ2 > 29.4, P < 0.001; Neu et al. 1974). We modeled percentage of streams
because data from radio-collared bobcats, scent-station surveys, and harvest data suggested a
possible relationship between bobcats and streams. We then modeled abundance using 2
approaches.
For the first approach, we calculated the mean and minimum percentages of each
landcover class occurring within the home ranges of male and female radio-collared bobcats.
For each landcover class, we then modeled abundance by assigning 1 bobcat to each hexagon
with a percentage greater than or equal to the mean and minimum percentages of that landcover
class occurring within a home range. Lovallo and Anderson (1996) found that some home
ranges of bobcats in Wisconsin were composed of <5% lowland conifer; however we used 5% as
a cut-off and modeled bobcat abundance by assigning 1 bobcat to each hexagon with greater than
or equal to 5% lowland conifer composition. For our second approach, we used known bobcat
locations obtained from radio telemetry and scent-station surveys to define a sample of hexagons
known to be occupied within each grid (n = 42 and 69 hexagons for males and females,
respectively). We then calculated the mean proportion of each landcover class within this set of
occupied hexagons. For each landcover class, we then modeled abundance by assigning 1
bobcat to each hexagon with a proportion greater than or equal to the mean proportion of that
landcover class occurring within known occupied hexagons. This allowed us to obtain potential
estimates of the number of male and female bobcats in the NLP. These approaches assume that
there is no intrasexual home-range overlap and all bobcats are resident (i.e., bobcats without
established home-ranges are not accounted for).
6
a)
b)
Figure 2. a) Male and b) female hexagonal grids were used to model potential bobcat
population size in the northern Lower Peninsula.
7
Distribution of Harvest
We qualitatively examined changes in the distribution of harvested bobcats in the NLP
using a GIS database and ArcView GIS. We used hydrology, transportation, landcover, land
ownership, and township data themes available from the MDNR for analyses. Because of time
constraints and the fact that not all harvest locations were mapped to Section, we chose to map
locations to the scale of township. We mapped the locations of harvested bobcats as harvestintensity maps with townships mapped into equal intervals of number of bobcats harvested. We
mapped the distribution of harvested bobcats in 3-year time periods from 1985-2002. We also
created harvest-intensity maps for bobcats harvested in Zone 1 (Upper Peninsula). We overlaid
separately the harvest-intensity maps for Zone 2 onto a hydrology and a transportation theme to
qualitatively assess any link between harvest and landscape features.
Additionally, we examined the distribution of harvested bobcats on public vs. private
land to determine any disparity in harvest intensity. We intersected the GAP landownership
theme with townships and reclassified townships as public or private land based on the dominant
landownership type (i.e., 50% or more of township). We identified commercial forest land as
privately owned land. We then overlaid harvest locations for bobcats during the time period
1998-2002 to gain a count for each year. We used contingency analysis (Zar 1996) to determine
if bobcats were harvested differentially by year and landownership type.
Results:
Relative Abundance
The number of bobcats harvested in Zone 2 (North and South hunting units combined)
increased during 1985-2002; however the linear trend was only slightly positive (Figure 3).
When Zone 2-North and Zone 2-South were viewed separately, a similar trend was exhibited.
However, more bobcats were generally harvested in the North Unit compared to the South Unit,
most likely due to the longer hunting season in the North Unit (Figure 4).
8
Figure 3. Number of bobcats harvested in Zone 2 (hunting units combined) during 1985-2002.
Trend line is shown as the dashed line.
Figure 4. Number of bobcats harvested in Zone 2 (hunting units separated) during 1985-2002.
9
During 1985-2002, the estimated number of bobcat hunters in Michigan (Zone 1 and
Zone 2 combined) appeared to increase (Frawley 2003; Figure 5). This increase corresponded
with a decrease in the average pelt price of bobcats, based on Wisconsin pelt prices (Dhuey et al.
2003; Figure 6). We found a significant inverse relationship between the estimated number of
bobcat hunters and the average pelt price (F = 20.83; 11,12 df; P = 0.001). Thus, as bobcat fur
prices appear to have become reduced and stabilized at approximately $40-50, the estimated
number of bobcat hunters in Michigan has increased to nearly 1,900 (Figure 7). This trend
suggests that hunter effort
may
be functioning
independent
fur prices and bobcats may be
Est
Number
of MI
BobcatofHunters
valued more as a trophy animal or the recreational aspects of the hunt are more important.
Number of Hunters
2500
2000
1500
1000
500
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
0
Year
Figure 5. Estimated number of bobcat hunters in Michigan (Zone 1 and Zone 2) based on
WI Avg Bobcat Pelt Price
MDNR mail harvest survey (Frawley 2003).
100
Average Pelt Price ($)
90
80
70
60
50
40
30
20
10
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
0
Year
Figure 6. Average pelt prices for bobcats in Wisconsin during 1980-2002 (Dhuey et al. 2003).
10
Average WI Bobcat Pelt
Price, $
Relation of Pelt Price to Number MI
Bobcat Hunters
100
80
60
40
20
0
0
500
1000
1500
2000
2500
Estimated Number MI Bobcat Hunters
Figure 7. Relationship between estimated number of bobcat hunters in Michigan and average
price for bobcat pelts. The correlation coefficient was r = -0.81.
We found a significant positive linear relationship between the estimated number of
bobcat hunters and the number of bobcats harvested in Michigan (F = 14.88; 10,11 df; P = 0.003;
Figure 8). The number of bobcat hunters estimated 60% of the variation in the number of
bobcats registered (r2 = 0.598). Therefore, most of the changes in the number of bobcats
harvested in the entire State of Michigan appear to be due to increases in the estimated number
of bobcat hunters in the state.
Figure 8. Relationship between estimated number of bobcat hunters and the number of bobcats
harvested in Michigan (Zone 1 and Zone 2 combined).
11
We plotted the residuals from the regression model of bobcat hunters and number of
bobcats harvested relative to year (i.e., sequence plot of residuals) to assess any time-related
patterns. There appears to be no trend in the sequence plot of residuals with relatively random
variation around the mean of zero over the time period 1985-2002 (Figure 9). Thus, with the
influence of the number of hunters removed, the number of bobcats harvested during 1985-2002
appears to be relatively stable. There does appear to be some annual variation in the number of
bobcats registered in Michigan. This variation might be due to changes in the abundance of
bobcats although it could reflect annual variation in weather conditions. Variation might also be
explained in part by significant declines in pelt price. For example, the apparent decrease in the
number of bobcats harvested during 1993 coincided with a bottoming out of fur prices that
stabilized at relatively low prices over the next decade (Figure 6 and 9). Determination that
similar dynamics are occurring specifically in the NLP bobcat harvest and population are not
possible given the lack of goodStd
estimates
of the number
of bobcat
Residuals
- point
plothunters in the NLP.
2
1.5
0.5
2001
1999
1997
1995
1993
1991
1989
-0.5
1987
0
1985
residual
1
-1
-1.5
-2
Year
Figure 9. Sequence plot of residuals from the regression model in Figure 8.
Plots of differenced values for the number of bobcats harvested and the estimated number
of bobcat hunters in Michigan provided similar results. The rate of change in both quantities was
closely related, with the change in the number of hunters closely tracking the change in the
number of bobcats harvested during 1996-2002 (Figure 10). Prior to 1996, data on the number
of bobcat hunters was less complete, making inferences about the rates of change difficult. A
regression model of the difference values provided a similar positive linear relationship as
outlined above (Figure 11).
12
Figure 10. Time-series plot of difference values (natural-log transformed) for estimated number
of hunters and number of bobcats harvested in Michigan (Zone 1 and Zone 2 combined), 19852002.
Figure 11. Regression relationship between difference values (natural-log transformed) for
estimated number of hunters and number of bobcats harvested in Michigan (Zone 1 and Zone 2
combined), 1985-2002.
Additional data on the hunter effort in Zone 2 of the NLP (e.g., estimated number of
bobcat hunters and/or number of field-days of hunting effort) would be useful in estimating
catch-per-unit-effort (CPUE). These estimates should provide an indication of the current
population trend for bobcats in the NLP. With the current analyses, we used estimates of the
number of bobcat hunters for Zone 1 and Zone 2, and this may not provide an accurate indication
of the trends in the NLP bobcat population in part because of the much greater number of
13
bobcats harvested in Zone 1 compared to Zone 2. There also may be a difference in hunting
effort that exists between Zone 1 and Zone 2.
We examined several indices of the bobcat population in portions of Crawford,
Missaukee, and Roscommon counties of the NLP to assess population fluctuations. We captured
and radio-collared 5 bobcats during 2003 with 1,353 trap-nights of effort with foot-hold traps
(catch per effort = 3.7 cats per 1,000 trapnights). We monitored a total of 7 bobcats (3 males, 4
females), 1 of which was captured in a box trap and 1 was an incidental capture by federal
biologists. Average home-range size (MCP) was 46.5 km2 and 10.4 km2 for males and females,
respectively (Preuss and Gehring, unpublished data, Figure 12). Additionally, we recorded 49
bobcat visits across 700 scent stations (7% visitation of stations, 43% of transects with visits)
conducted during fall 2003 (Preuss and Gehring, unpublished data, Figure 13).
30 km
Figure 12. Location of Central Michigan University bobcat study with home-range polygons of
bobcats monitored during 2003-2004.
14
Figure 13. Location of Central Michigan University bobcat study with scent stations and
transects identified by rows of circles. Stations with bobcat visits during fall 2003 are identified
in red.
During 1991-1996, the MDNR bobcat study in approximately the same area we are
currently working, reported catch per trapping effort ranging from 3.1 to 6.2 bobcats per 1,000
trapnights ( x = 5.2, sd = 1.2 bobcats per 1,000 trapnights; Earle et al. 2003a). A scent-station
survey conducted by the MDNR during 1992-1996 provided estimates of the number of bobcat
visits ranging from 27 to 49 bobcats (Earle et al. 2003a). Although the time series are not
extensive, nor are they complete, in general the fluctuations in the number of bobcats harvested
in the Houghton Lake Study Area appears concordant with the indices used to assess population
change during 1992-1996. Scent-station data, but not catch-per-effort data for 2003 also appear
to reflect a possible increase in the numbers of bobcats harvested. However, our catch-per-effort
estimate was within the confidence intervals for the mean of the MDNR estimate.
15
We suggest caution in interpreting increases in the bobcat population in the north-central
LP using harvest data, however, due to the likely strong positive relationship between the
number of bobcat hunters and the number of bobcat harvested in Michigan. This caution
assumes that this same trend was evident in the NLP. Further, we suggest that comparisons
between our study and the 1991-1996 MDNR study are tentative because study methods differed
with respect to scent-station protocols and study objectives (e.g., the MDNR study attempted to
assess different attractants at stations using a series of experimental treatments that varied over
time, Earle et al. 2003a). Furthermore, our study results are all preliminary and based on a
relatively small sample of bobcats and 1 scent-station survey to date. Our continued efforts to
radio collar bobcats during spring-summer 2004 and our continuation of a scent-station survey
during fall 2004 should provide additional insight into the relative abundance and changes in
abundance of this local population.
In their fur harvester opinion survey report, Bull and Peyton (2004) reported that 19.5%
of respondents who had hunted or trapped bobcats before would be ‘very likely’ to trap bobcats
in the NLP if it were legal. During 1998-2002, we estimated a mean of 2,324 ± 271 bobcat
hunters and trappers in Michigan based on furbearer survey reports (Frawley 2001, 2003). Thus,
an estimated 400 to 506 bobcat hunters and trappers in the state would likely trap bobcats in the
NLP if it were legal. Additionally, the kill success rate for bobcat trappers averages 1 bobcat per
trapper per year. During 1998-2002, an average of 220 bobcats per year was hunted in the NLP.
Assuming a bag limit of 1 bobcat per person per year in the NLP, if bobcat trapping and hunting
occurred, the annual harvest would be an estimated 620 to 726 bobcats. Assuming a bag limit of
0.5 bobcats per person per year in the NLP (i.e., 50% success rate among trappers), if bobcat
trapping and hunting occurred, the annual harvest would be an estimated 420 to 473 bobcats.
Additionally, Bull and Peyton (2004) found that 18% of furtakers (approximately 3,000
individuals) indicated they would be ‘very likely’ to trap bobcats in the NLP if it were legal.
Thus, an estimated 3,000 and 1,500 bobcats might be harvested annually in the NLP assuming
bag limits of 1 bobcat per person per year and 0.5 bobcats per person per year, respectively.
Population Structure
The proportion of females in the Zone 2 North harvest was approximately 0.40 during
1985-2002 with no significant linear trend (F = 0.21; 16,17 df; P = 0.66; Figure 14a).
Conversely, the proportion of females in the Zone 2 South harvest decreased from over 0.50 to
near 0.40 during the same time period and this trend was nearly significant (F = 4.00; 16,17 df;
P = 0.06; Figure 14b). When harvest data for the Zone 2 units were pooled, the proportion of
females remained at nearly 0.40 during 1985-2002 (Figure 14c). The apparent decline in the
proportion of females in Zone 2 South may simply be an artifact of the relatively low number of
bobcats harvested in this unit prior to 1994 compared to Zone 2 North. Possible fluctuations in
the sex ratio also may be related to differential vulnerabilities of animals, differential mortality
rates between sexes and/or differential selectivity by hunters. The harvest data do not provide
explanations for possible fluctuations in Zone 2 South.
16
North
a)
South
b)
Combined
c)
Figure 14. Proportion of females in the bobcat harvest during 1985-2002 for: a) Zone 2 North;
b) Zone 2 South; and c) Zone 2 (North and South units combined).
17
Assuming that the proportion of different age classes in the harvest are representative of
age ratios in the bobcat population, it appears that the proportion of adult bobcats is increasing
and the proportion of juvenile bobcats is decreasing. This suggests that the population may be
aging and recruitment of kittens into the yearling and adult age classes may be decreasing, thus
the rate of increase of the NLP bobcat population may be decreasing. During 1985-2002, mean
proportions of juveniles, yearlings and adults in the NLP harvest were 23%, 30%, and 47%,
respectively (Figure 15).
Figure 15. Age ratios for juvenile (0.5 year), yearling (1.5 year) and adult (2.5+ year) bobcats
harvested in Zone 2 (North and South units combined) during 1985-2002.
Age pyramids suggested that the proportion of juveniles and yearlings remained
relatively high throughout the time period. The 2.5 and 3.5-year classes appeared to increase in
the harvest during the early and late 1990’s as well as most recently. MDNR analysis of the
bobcat population in the north-central NLP suggested that more young bobcats (juveniles and
yearlings) were harvested by hunters compared to MDNR trapping efforts (Earle et al. 2003a).
Wildlife populations that are exploited by harvest typically have greater numbers of juveniles
and yearlings in the harvest record compared to older animals. This may explain the
preponderance of younger animals in the NLP harvest (Fritts and Sealander 1978; Rolley 1985)
or it may simply be a function of hunter selectivity for younger animals, whereby any bobcat that
is treed is harvested. Younger bobcats are presumably more naïve and easier to tree compared to
older individuals. Interpretation of these population structure data should be cautious, however.
Caughley (1974) cautioned about using age ratio data to infer dynamics of populations. Instead,
information on the rate of increase for populations would more accurately reflect population
dynamics compared to age ratios (Caughley 1974).
18
GIS Methods for Modeling Potential Bobcat Population Size and Distribution
Our GIS modeling efforts produced theoretical estimates of bobcat population size in the
NLP with assumptions outlined above. Estimates, however, varied widely from a total of 843 to
3,543 bobcats in the NLP (Table 2).
Table 2. Population size estimates for resident bobcats in the northern Lower Peninsula of
Michigan assuming habitat preference relationships and that intra-sexually there are nonoverlapping home ranges.
Estimated Estimated Estimated
Model
# Males
# Females
Total
Figure
No selection for habitat (complete saturation)
1,044
4,665
5,709
--Mean Lowland Forest
255
908
1,163
16
Mean Lowland Forest & Non-Forest Wetland
185
658
843
17
Minimum Lowland Forest
94
922
1,016
18
>5% Lowland Forest (Lovallo)
699
2,844
3,543
19
Models depicted in figures 16, 17, and 18 highlight similar regions of the NLP that may
be core habitat areas for bobcats. These same general areas also correspond with areas that
typically have the highest intensity of bobcat harvest (see Figure 20). Figures 16-18 also might
offer some insight into the disjunct nature of the North and South hunting units in Zone 2. That
is, 2 relatively disjunct habitat patches are distributed in the northeastern and southwestern
quarters of Zone 2. A possible 3rd habitat patch might exist in the east-central region of Zone 2.
Figure 19 probably provides a maximum population size since most of the NLP is
depicted as potential bobcat habitat using a lower proportion of lowland forest in bobcat home
ranges. We caution, however, that these estimates are based on preliminary data and small
sample size of the number of bobcats monitored via radio telemetry as well as only 1 location
where scent stations have been conducted in the NLP to provide landcover type associations in
these models. For example, our sample sizes for figures 16-18 were n = 42 and 69 hexagons for
males and females, respectively. These models will need to be refined and validated before they
might offer greater insight into potential bobcat habitat and better approximations of the size of
the bobcat population in the NLP.
19
a)
b)
Figure 16. a) Male and b) female hexagonal grids predicted to potentially contain male (blue)
or female (red) resident, adult bobcats in the NLP. Predictions are based on the mean proportion
of lowland forest in bobcat home ranges as determined by a sample of radio-collared bobcats and
bobcats detected using a scent-station survey.
a)
b)
Figure 17. a) Male and b) female hexagonal grids predicted to potentially contain male (blue)
or female (red) resident, adult bobcats in the NLP. Predictions are based on the mean proportion
of lowland forest and non-forested wetland habitat in bobcat home ranges as determined by a
sample of radio-collared bobcats and bobcats detected using a scent-station survey.
20
a)
b)
Figure 18. a) Male and b) female hexagonal grids predicted to potentially contain male (blue)
or female (red) resident, adult bobcats in the NLP. Predictions are based on the minimum
proportion of lowland forested habitat found in bobcat home ranges (22% for males, 18% for
females) as determined by radio-collared bobcats in the CMU bobcat study.
a)
b)
Figure 19. a) Male and b) female hexagonal grids predicted to potentially contain male (blue)
or female (red) resident, adult bobcats in the NLP. Predictions are based on the proportion of
lowland forest in bobcat home ranges (>5%) as determined by Lovallo and Anderson (1996) in
northwestern Wisconsin.
21
Distribution of Harvest
The distribution of bobcats harvested during 1985-2002 is relatively widespread
throughout Zone 2; however, harvest locations do appear to concentrate into 1 large patch in the
northeastern portion of the NLP with a secondary linear patch in the central NLP (Figure 20). A
portion of this distribution pattern may be related to the different season lengths between the
North and South hunting units in Zone 2, although these same regions are identified in our
habitat analysis above.
Figure 20. Distribution of harvested bobcats across the NLP during 1985-2002. Harvest
intensity of bobcats is mapped to the scale of township. Cooler colors indicate lower harvest
intensity, whereas warmer colors represent greater numbers of bobcats harvested.
We mapped the distribution of harvested bobcats across 3-year intervals to examine
changes in the pattern of harvest intensity. We maintained the same scale of harvest intensity in
Figure 21. In general, harvest intensity remained relatively constant and localized in townships
of the northeastern portion of the NLP, with 8-18 bobcats harvested per township during 19852002. The central portion of the NLP exhibited periods of greater harvest intensity during 19851990, reduced harvest pressure during 1991-1996, and an apparent increasing harvest intensity
22
since 1996 (Figure 21). Conversely, the distribution of harvest intensity for hunted bobcats in
Zone 1 appeared relatively stable despite the increases in bag limit in 1994 and 1996 (Figure 22).
These patterns may suggest that there are 1 or 2 relatively disjunct regions of Zone 2 that
maintain relatively high harvest intensity, whereas the areas between exhibit lower harvest rates.
As such, these patterns provide some support to the idea that the NLP bobcat population may
exhibit source-sink dynamics and may need to be managed as 2 separate units. However
additional demographic data on rate of population increase and dispersal would be needed before
characterizing it as a true source-sink population (e.g., Pulliam 1988).
We also mapped harvest intensity of bobcats during 1985-2002 with respect to landscape
features (hydrology and road systems) to identify potential patterns. In the NLP, bobcat harvest
pressure appears to be concentrated predominantly along streams and rivers, including headwater
tributaries (Figure 23). This pattern is likely linked to the lowland forest and non-forested
wetland habitats (including beaver sloughs) located along water courses. These habitats appear
to be preferred habitat for bobcats in the NLP (Preuss and Gehring, unpublished data). A more
ambiguous pattern was noted for the relation between bobcat harvest intensity and road density
in the NLP, perhaps a function of the extensive road network that exists throughout much of the
NLP (Figure 23).
23
a) 1985-1987
b) 1988-1990
Figure 21. Distribution of harvest intensity of bobcats, mapped to the scale of township, over
the NLP during 1985-2002. Lighter colors indicate lower harvest intensity, whereas darker red
colors represent greater numbers of bobcats harvested.
24
c) 1991-1993
d) 1994-1996
Figure 21. Distribution of harvest intensity of bobcats, mapped to the scale of township, over
the NLP during 1985-2002. Lighter colors indicate lower harvest intensity, whereas darker red
colors represent greater numbers of bobcats harvested.
25
e) 1997-1999
f) 2000-2002
Figure 21. Distribution of harvest intensity of bobcats, mapped to the scale of township, over
the NLP during 1985-2002. Lighter colors indicate lower harvest intensity, whereas darker red
colors represent greater numbers of bobcats harvested.
26
a) 1985-1987
b) 1988-1990
Figure 22. Distribution of harvest intensity of bobcats, mapped to the scale of township, over
the UP during 1985-2002. Lighter colors indicate lower harvest intensity, whereas darker red
colors represent greater numbers of bobcats harvested. Bag limit was increased to 2 bobcats per
hunter and 3 bobcats per hunter in 1994 and 1996, respectively.
27
c) 1991-1993
d) 1994-1996
Figure 22. Distribution of harvest intensity of bobcats, mapped to the scale of township, over
the UP during 1985-2002. Lighter colors indicate lower harvest intensity, whereas darker red
colors represent greater numbers of bobcats harvested. Bag limit was increased to 2 bobcats per
hunter and 3 bobcats per hunter in 1994 and 1996, respectively.
28
e) 1997-1999
f) 2000-2002
Figure 22. Distribution of harvest intensity of bobcats, mapped to the scale of township, over
the UP during 1985-2002. Lighter colors indicate lower harvest intensity, whereas darker red
colors represent greater numbers of bobcats harvested. Bag limit was increased to 2 bobcats per
hunter and 3 bobcats per hunter in 1994 and 1996, respective
29
a)
b)
Figure 23. Distribution of harvested intensity of bobcats across the NLP during 1985-2002
mapped to the scale of township. Lighter colors indicate lower harvest intensity, whereas darker
red colors represent greater numbers of bobcats harvested. a) Overlay of harvest intensity and
hydrology illustrating the general pattern of greater number of bobcats harvested along streams
and rivers and associated tributaries; b) Overlay of harvest intensity and primary and secondary
roads illustrating no clear pattern of higher harvest in more or less roaded areas.
30
During 1998-2002, a greater number of bobcats was harvested on private land compared
to public land in both the NLP ( x = 64 bobcats on public and x = 155 bobcats on private land,
P<0.001) and UP ( x = 278 bobcats on public and x = 416 bobcats on private land, P = 0.02).
However, harvest was not allocated proportional to the amount of public or private land area in
either area. Contingency analysis indicated that a greater than expected proportion of bobcats
was harvested on public land and a lower than expected proportion was harvested on private land
in the NLP during 1998-2002 (χ2 = 78.21, 4df, P < 0.001). Similarly, a greater than expected
proportion of bobcats was harvested on public land and a lower than expected proportion was
harvested on private land in the UP during 1998-2002 (χ2 = 69.28, 4df, P < 0.001). These are
likely underestimates because we classified public vs. private land to the scale of township and
harvest locations were mapped to this same scale (Figure 24).
Figure 24. Distribution of harvested bobcats across public (red) and private (white) townships
in the NLP during 1998-2002.
31
Summary:
Effective management of wildlife populations requires ecological data pertaining, in part,
to abundance, distribution, and habitat use of species. Furthermore, effective management of a
harvested species requires additional scientific information. Rolley et al. (2001) identified
information needs for harvested bobcat populations to include: mandatory harvest registration,
population indices, population models, user statistics, market-value surveys, and periodic habitat
inventories. In part due to the difficulty in adequately surveying for low-density carnivores such
as bobcats, Wisconsin has used a relatively conservative approach using restrictive harvest
strategies (Rolley et al. 2001). Rolley (1987) recommended that at least 2 population-assessment
methods be incorporated into a monitoring program in order to provide independent data on
population trends. For example, scent-station surveys or winter-track surveys and sighting data
(e.g., Archer’s index or sighting reports) are currently used by some states (Bluett et al. 2001).
The Archer’s index or other sighting data from hunters could provide long-term trend data, but
may not adequately assess annual population fluctuations (Bluett et al. 2001).
Recently, the increased use of GIS modeling to identify and predict areas of quality
habitat has allowed wildlife managers to focus their management and conservation efforts more
efficiently. Models developed by Lovallo et al. (2001) resulted in the first harvest of bobcats in
Pennsylvania in 30 years. Nielson and Woolf (2002) developed models that linked habitat and
relative abundance to evaluate distribution and abundance of bobcats in Illinois. These models
were used to assess bobcat status and contributed to the delisting of bobcats as a threatened
species in Illinois (Woolf et al. 2002). Integrating demographic data (e.g., data determined via
telemetry methods) and GIS spatial models can provide a powerful tool to focus management
efforts. These tools are rare for solitary carnivores and region-specific models should be
developed to more effectively direct bobcat management (Lovallo et al. 2001). Additional
modeling efforts based on our CMU study of bobcats in Michigan should aid in making
management recommendations.
The number of bobcats harvested in the NLP increased from approximately 140 to 291
during 1985-2002. However, given the current data, it is difficult to determine if this increase
was a function of the bobcat population increasing or an increase in the number of hunters.
Statewide (Zone 1 and Zone 2), the increase in the number of bobcats harvested by hunters was
correlated with the increase in the number of hunters. As such, the use of bobcat harvest
numbers without an assessment of hunter effort does not appear to provide great insight into
bobcat population trends. In particular, good estimates of the number of hunters and/or hunter
effort for the NLP are needed to fully assess changes in that bobcat population. Estimates of the
number of bobcat hunters in the NLP might be obtained via an increased sampling of this
stratum.
Additionally, a more extensive (and independent) assessment of the relative abundance of
bobcats across the NLP is needed. Based on more localized data for the north-central counties of
Crawford, Missaukee, and Roscommon counties, the bobcat population there appeared to be
fairly stable during 1992-1996 (Earle et al. 2003a). However, harvest intensity has fluctuated in
this region with relatively high harvest rates during 1988-1990 and then again during 1997-1999.
Presumably, the number of hunters did not decrease in between these time periods and this
32
decrease in harvest may reflect a decline in the population. Additional data from other sites
throughout the NLP would be needed to more fully assess the dynamics of the Zone 2 bobcat
population. Additional population indices such as winter track counts, an Archer’s index, an
estimate of the number of bobcats sighted by MDNR personnel, and/or the number of bobcats
treed per day by hunters would provide valuable information on population trends at least over a
longer time period.
Based on estimates from surveys, if trapping and hunting occurred annually in the NLP,
an estimated 620 to 3,000 bobcats would be harvested each year assuming a current bag limit of
1 bobcat per individual. An estimated 420 to 1,500 bobcats would be harvested each year
assuming a current bag limit of 0.5 bobcats per individual. Without an accurate population index
or other independent data for the entire NLP, it is difficult to evaluate the impact that this harvest
figure would have on the NLP bobcat population. However, the high estimates likely exceed the
total bobcat population in the NLP. The lower estimates might approximate 50% of the total
NLP bobcat population assuming that our preliminary GIS modeling of potential size of the NLP
bobcat population. Investigation into the possible source-sink dynamics and spatial patterning of
bobcat harvest in the NLP should be pursued further because harvest intensity appears to be
clustered into 2 distinct regions of the NLP and bobcat habitat distribution appears to
demonstrate a similar patchiness. These investigations could provide insight into possible new
zoning recommendations based on landscape-level features (e.g., hydrologic patterns) and/or
spatial patterning of high-quality bobcat habitat (e.g., contiguous patches of lowland forest and
non-forested wetland habitat).
A possible opening of a trapping season in the NLP as a whole would not be warranted
until additional population indices and hunter effort measures are developed and implemented to
assess dynamics of the population before a change in harvest regulations. In particular, there
remains a need to assess the impacts of predicted higher harvest levels if a trapping season would
be added. A possible adaptive resource management model could be applied to bobcat
management in a portion of the NLP, whereby trapping would be allowed for a 3 or 4-year
period. However, at the end of such a trial period, an assessment would need to be conducted to
determine the effects of a trapping season. In order to conduct an adequate assessment, a sound
monitoring program should include: 1) harvest effort data; 2) bobcat population demographic
data such as abundance, fecundity, survivorship, and dispersal; 3) multiple population indices to
detect changes in bobcat abundance; and 4) habitat assessments. Ideally, the monitoring
program would be established for several years: 1) before a change in harvest regulations; and 2)
during the period of changes to the regulations. These monitoring efforts would be conducted at
a site (or sites) where trapping was allowed and control sites where no trapping was allowed.
Further, if warranted, these data could be useful in setting bobcat harvest quotas within the NLP.
The current CMU bobcat study site might serve as a useful control site, assuming that trapping
pressure elsewhere does not influence the study area (e.g., bobcat dispersal rates are not
impacted and/or hunting pressure is not displaced away from a trapping zone and into the control
study area).
33
Acknowledgements:
We are grateful to Central Michigan University (CMU) and the Department of Biology
for providing time and access to facilities to complete this review. We also thank CMU,
Defenders of Wildlife, Wildlife Forever, and CITGO Petroleum Corporation for funding of our
bobcat research. We thank the MDNR for providing trapping equipment, housing facilities and
assistance with logistics for our research. We discussed aspects of our review with Eric M.
Anderson and Bradley J. Swanson and are grateful for their assistance.
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