PRIVATE LAND DEER COOPERATIVES HARVEST AND SATISFACTION
ANALYSIS IN SOUTHERN LOWER MICHIGAN
By
Anna Marie Mitterling

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Fisheries and Wildlife – Master of Science
2013

ABSTRACT
PRIVATE LAND DEER COOPERATIVES HARVEST AND SATISFACTION
ANALYSIS IN SOUTHERN LOWER MICHIGAN
By
Anna Marie Mitterling

Private land deer cooperatives are a growing phenomenon in Southern Michigan.
In these areas, deer densities are high and focused management on reducing deer numbers
is needed. Deer cooperatives consist of landowners managing the deer herd together.
Landowners retain the rights to their properties but band together to talk about harvest
and habitat management practices. This study evaluates the influence members within a
deer cooperative have on individual doe harvest behavior, as well as considers changes in
satisfaction among deer cooperatives. Social networking analysis and general satisfaction
measurements were used to better understand deer cooperatives in southern Michigan.

ACKNOWLEDGEMENTS
I would like to give a special thanks to my patient, loving, and ever-supportive
husband, James Mitterling. I could not have done this without him.
I also want to thank all of the cooperative leaders for their assistance with data
collection, providing contact information of members, sending in pictures last minute,
and their constant support and encouragement. In particular I would like to thank to
Tony Smith, Case DeYoung, Jim Strader, Jake Ehlinger, Doug Weeks, Kirk Nartker,
Dave Curtis, and Leo Geller for the personal investment they made in our lives.
The Department of Natural Resources, Wildlife Division, has been a support since
day one. Thank you for bringing me into the Wildlife family. Thank you Kelly Siciliano
Carter for enlightening me to Human Dimensions; Doug Reeves for being such an
encouragement; and Brian Frawley for being accessible and willing to share data.
Thank you to Michigan Chapter of Quality Deer Management Association for
their financial contributions and for taking an interest in this project. Thank you Michael
Goyne for all of your support and encouragement.
Thank you to my committee who has been with me through some life challenges.
I am especially thankful for my advisor, Dr. Daniel Kramer, for giving me the
opportunity to work on such a fascinating project. I also want to thank Dr. Shawn Riley
and Dr. Kenneth Frank for serving on my committee and taking the time to help me
understand the complexities this project presented.
Finally, I thank Dr. Brent Rudolph for all of his contributions. Many times Brent
helped keep me sane in the chaos of life, school, and work – I am extremely grateful.

iii

TABLE OF CONTENTS
LIST OF TABLES ...............................................................................................................v
LIST OF FIGURES .......................................................................................................... vii
THESIS INTRODUCTION .................................................................................................1
THESIS OBJECTIVES .......................................................................................................3
UNDERSTANDING DOE HARVEST BEHAVIORS IN PRIVATE DEER
COOPERATIVES USING SOCIAL NETWORK ANALYSIS .........................................4
INTRODUCTION ............................................................................................................4
Social capital ..................................................................................................................5
Social networks and the influence of groups in individual behavior .............................7
Deer cooperatives & study area description ..................................................................8
METHODS .....................................................................................................................11
The influence model for doe harvest ...........................................................................13
RESULTS .......................................................................................................................14
Influence analysis.........................................................................................................14
DISCUSSION .................................................................................................................17
CONCLUSION ...............................................................................................................20
GREATER HUNTING SATISFACTION: A RESULT OF DEER COOPERATIVE
PARTICIPATION?............................................................................................................21
INTRODUCTION ..........................................................................................................22
Community, collective management & satisfaction ....................................................22
Satisfaction within deer cooperatives ..........................................................................23
Objectives ....................................................................................................................24
METHODS .....................................................................................................................24
Study area.....................................................................................................................24
Sampling & data collection ..........................................................................................25
Model description ........................................................................................................27
RESULTS .......................................................................................................................29
DISCUSSION .................................................................................................................34
MANAGEMENT IMPLICATIONS ..............................................................................36
APPENDICES ...................................................................................................................38
Appendix A: Network Images ........................................................................................39
Appendix B: Survey: 2010 Deer Seasons Data ..............................................................43
Appendix C: Survey: 2011 Deer Seasons Data ..............................................................47
REFERENCES ..................................................................................................................51

iv

LIST OF TABLES

Table 1. Descriptive statistics of the cooperatives studied. Table entries include
information on the approximate memberships size as defined by the cooperative leader,
the number of members sampled (n), the year the cooperative started, and approximate
cooperative acres. Southern Michigan, USA. 2010-2011...................................................9
Table 2. Doe harvest mean, median, minimum, maximum, standard deviation, and
sample size (n). Southern Michigan, USA. 2010-2011 .....................................................14
Table 3. Demographic descriptive statistics of cooperative members. Mean, median,
minimum, maximum, standard deviation, and sample size (n). Southern Michigan, USA.
2010-2011. .........................................................................................................................14
Table 4. Doe harvest influence model. Deer cooperatives, Southern Michigan, USA.
2010-2011. .........................................................................................................................15
Table 5. Mean doe harvest among the cooperatives. Southern Michigan, USA. 2011. ...16
Table 6. Influence at the cooperative level. The coefficients are centered around the
cooperative with the median value of influence. The positive and negative values are
relative to the median cooperative. Corrected coefficients are based on the actual
influence values given that cooperative 139 is .423. Deer cooperatives, Southern
Michigan, USA. 2010-2011. ..............................................................................................17
Table 7. Descriptive statistics of the cooperatives studied. Table entries include
information on the approximate membership size as defined by the cooperative leader,
the number of members sampled (n), the year the cooperative started, and approximate
cooperative acres. Southern Michigan, USA. 2010-2011.................................................20
Table 8. The average fit and satisfaction levels of cooperative members by cooperative
on a scale of 1 (high) to 5 (low). Southern Michigan, USA. 2010-2011. .........................31
Table 9. Results from questions regarding satisfaction levels of cooperative members.
Southern Michigan, USA. 2010-2011. ............................................................................32
Table 10. Descriptive Statistics for value and agreement questions of cooperative
members. Southern Michigan, USA. 2010-2011. ..............................................................33
Table 11. Satisfaction model analysis of maximum likelihood estimates. Buck harvest
agreement refers to the level of agreement members feel there is within the cooperative
regarding passing young bucks. Hunt info network considers the number of people
members talk to within their cooperative. Average buck harvest looks at the average
number of bucks harvested over 2010 and 2011. Years in cooperative, acres owned, years

v

hunt, and income are measurements considered in this model, as well as how well
members feel the fit within the group. Southern Michigan, USA. 2010-2011. ................33
Table 12. Satisfaction model odds ratio estimates for deer cooperative in Southern
Michigan, USA. 2010-2011. ............................................................................................33

vi

LIST OF FIGURES

Figure 1. Doe harvest by deer cooperative in years 2010 and 2011. Southern Michigan,
USA. ..................................................................................................................................10
Figure 2. A social network of individuals who meet socially with each other within a
single cooperative (141). The nodes represent individuals, while the lines connecting
them indicate they hunt with each other. The arrows represent the directionality of
nomination. Color is indicative of grouping that is determined by an algorithm that
maximizes ties within subgroups vs. between. ..................................................................39
Figure 3. Social interaction network of cooperative 137. The individual nodes represent
individual actors, while the arrows represent the directionality of who nominated who.
Color variation refers to grouping within the cooperative .................................................40
Figure 4. The social interaction network for cooperative 125. The individual nodes
represent individual actors, while the arrows represent the directionality of who
nominated who. Color variation refers to grouping within the cooperative .....................41
Figure 5. The social interaction network for cooperative 133. The individual nodes
represent individual actors, while the arrows represent the directionality of who
nominated who. Color variation refers to grouping within the cooperative .....................42

vii

THESIS INTRODUCTION
In Southern Michigan, deer densities often exceed population goals because food
abundance is high, winter conditions are mild, many natural predators have been nearly
eliminated, and mortality rates are low aside from hunting and deer/vehicle collisions
(Michigan Statewide Deer Management Plan). The capacity of recreational hunting to
function as a tool for deer population control is in part restricted by the limited demand
that individual hunters have to harvest deer (Brown et al., 2000). Less than half of all
Southern Michigan deer hunters are successful, and most successful hunters (64%)
harvest only a single deer, with slightly fewer antlerless deer taken relative to antlered
deer (Frawley, 2012). Wildlife managers often wish to reduce deer densities to minimize
the impacts of abundant deer populations, but this requires harvest of a sufficient number
of female deer, often exceeding hunter demand (Brown et al., 2000; Côté et al., 2004;
Giles & Findlay, 2004; Lischka et al., 2008). However, anecdotal observations suggest
hunters are beginning to join together in private cooperatives in an effort to coordinate
deer population and management efforts based on commonly held objectives. These
objectives often include harvest practices that may coincide with wildlife managers’
objectives to reduce deer densities.
Because of the close proximity of their members, local knowledge, and common
purpose, cooperatives should be able to establish effective community based natural
resources regimes (Brosius et al., 1998). What makes these groups different than hunt
clubs, or even deer camps, is that they consist of private landowners who aid in the
management process in their area yet maintain full individual rights to their land as
private landowners. While the cooperatives contain property that is “co-managed,” the

1

management goals for cooperatives are designed for the whole group but voluntarily
implemented by individuals on their own properties.
Cooperatives are able to pool observations and harvest data to gain collective
insight regarding deer at a scale more likely to approach that of a population or
subpopulation at a local level, rather than observations regarding individual deer making
use of individual properties. By joining, members gain some functional advantages of
larger landownership for the group as a whole that will extend efforts to a larger scale
than each landowner could otherwise achieve and implement specific management
guidelines (Developing Successful QDMA Deer Management Cooperatives brochure).
The problem consists of decreasing hunters while deer densities are high in many
areas of Southern Michigan. It seems that deer cooperatives may pose a possible solution
as they join together as co-managers of their local deer herds.

2

THESIS OBJECTIVES
The purpose of this project was to understand the management practices, social
networks, and structures of deer cooperatives in Southern Michigan. The main questions
included: 1) How do social networks within deer cooperatives influence individual doe
harvest behavior via the social interactions, harvest and habitat management information,
and with whom they hunt? ; and 2) Why do changes in satisfaction between current
satisfaction levels and those prior to joining a cooperative vary between cooperative
members? The specific objectives we addressed were to: 1) describe harvest behaviors of
deer cooperative members, 2) evaluate cooperative member by use of social network
analysis, 3) estimate deer harvest among deer cooperative members, 4) develop statistical
models to understand harvest outcomes and hunter satisfaction.

3

UNDERSTANDING DOE HARVEST BEHAVIORS IN PRIVATE DEER
COOPERATIVES USING SOCIAL NETWORK ANALYSIS
Anna M. Mitterling, Daniel B. Kramer, and Kenneth A. Frank1
INTRODUCTION
In Southern Michigan, deer densities often exceed population goals because food
abundance is high, winter conditions are mild, large predators have been nearly
eliminated, and mortality rates are low aside from hunting and deer/vehicle collisions
(Michigan Statewide Deer Management Plan). The capacity of recreational hunting to
function as a tool for deer population control is in part restricted by the limited demand
that individual hunters have to harvest deer (Brown et al., 2000). Less than half of all
Southern Michigan deer hunters are successful, and most successful hunters (64%)
harvest only a single deer, with slightly fewer antlerless deer taken relative to antlered
deer (Frawley, 2012). Wildlife managers often wish to reduce deer densities to minimize
the impacts of abundant deer populations, but this requires harvest of a sufficient number
of female deer, often exceeding hunter demand (Brown et al., 2000; Côté et al., 2004;
Giles & Findlay, 2004; Lischka et al., 2008). However, anecdotal observations suggest
hunters are increasingly forming private land deer cooperatives in an effort to coordinate
deer population and management efforts based on commonly held objectives. These
objectives often include harvest practices that may coincide with wildlife managers’
objectives to reduce deer densities.
Deer cooperatives are different than hunt clubs and deer camps in that they
consist of private landowners who aid in deer management in their area, yet maintain full

1

Department of Fisheries and Wildlife, Michigan State University, East Lansing,
Michigan, USA
4

individual rights to their land as private landowners. While the cooperatives contain
property that is “co-managed,” the management goals for cooperatives are designed for
the whole group but voluntarily implemented by individuals on their own properties.
Because of the close proximity of their members, local knowledge, and common
purpose, cooperatives should be able to establish effective community based natural
resources regimes (Brosius et al., 1998). Managing their land cooperatively through
shared management goals, cooperatives are able to affect the deer population and habitat
on a larger scale with greater influence through voluntarily coordinated efforts.
Cooperatives are also able to pool observations and harvest data to gain collective insight
regarding deer at a population or sub-population scale, rather than observations regarding
individual deer making use of individual properties. By joining, members gain some
functional advantages of larger landownership for the group as a whole that will extend
efforts to a larger scale than each landowner could otherwise achieve and implement
specific management guidelines (Developing Successful QDMA Deer Management
Cooperatives brochure).
The purpose of this study was to understand the sharing of information and
experiences through social networks within deer cooperatives in Southern Michigan and
how those interactions influence doe harvest behavior.
Social capital
Social capital consists of the resources accessed through social relations. The
flow of those resources is facilitated through common rules, norms, trust, and sanctions,
as well as connectedness within a group (Pretty & Smith, 2004). When social capital is
present, individuals in the group will think about their behaviors and ramifications of

5

their decisions (Pretty & Smith, 2004). Specifically, individual behaviors are embedded
in a social context. Some behaviors are condoned by the group based on shared norms
and values, while others are not. Therefore, before an individual makes a decision, the
individual weighs the social ramifications of that decision including possible social
sanctions. These sanctions also assure the individual that other members of the group
behave similarly, thus avoiding free-riding (Pretty & Smith, 2004). Therefore, social
capital, in some circumstances, becomes a key element in the successful management of
natural resources by groups.
There is evidence that high levels of social capital in groups tasked with
managing natural resources are associated with sustainable natural resource outcomes
(Pretty, 2003). Common interest within a group, such as managing a natural resource,
reduces conflict and facilitates communication (Crona & Bodin, 2006). With access to
social capital, individuals are more willing to invest in commonly held resources
knowing others are investing as well (Pretty & Smith, 2004). Social capital empowers
groups to create solutions to problems, while engaging individuals to share results and
observations, as well as discussing sanctions (Pretty & Ward, 2001). The openness of
discussions can increase trust that may increase the likelihood of successful collective
action.
Social capital is embodied in networks of relations among individuals (Anheier &
Kendall, 2002). As networks are established and connected over time, social capital is
established when influence is intentionally sought (Bourdieu, 1979). In the next section,
we discuss the importance of measuring social networks to understand the group effects
on individuals.

6

Social networks and the influence of groups on individual behavior
Social networks are important in understanding the relationships between
members and the effects those relationships have on individual behavior and attitudes.
Social networks provide a framework to understand the structure of a social group
including the strength of ties between members of the social group. Ties connecting
individuals are understood as any interaction among individuals including knowledge and
social exchanges. This combination of ties creates the social network (Appendix A,
Figure 2). Social network analysis provides insight into these interactions and is useful in
understanding the effects of social groups on individual behavior rather than relying on
assumptions about the connection between individual behavior and group influence
(Frank, 1998). Using social network analysis also allows for identifying influential
actors and patterns of communication within a group.
Social network analysis is increasingly being applied to understand natural
resource management problems (Bodin et al., 2006; Prell et al., 2007). Crona and Bodin
(2010) applied social network analysis to look at informal power structures, knowledge
sharing, and consensus building to see how these interactions affected the possibility of
transitioning a community to using collective fishery management. In the village studied,
family income was dependent on access to fishing gear, making gear exchange an
essential network for fishermen. A gear exchange network was used to explore power
relations in the community. Thus, a network analysis was used to identify powerful
individuals who could influence the community and promote change.
In a more general approach, Prell et al. (2007) used social network analysis to
conclude that many conservation initiatives fail due to lack of attention to characteristics

7

and relationships of stakeholders. They found that social networks can be used to inform
stakeholder analysis. In this project, we evaluated the role that social networks played in
influencing individual cooperative members’ doe harvest behaviors.

Based on network

analysis, we also provide some insight into how members function and relate with one
another within the cooperatives.
Deer cooperatives & study area description
Deer cooperatives have become increasingly common across the country (Ross,
2013). In Michigan, there are over 50 known deer cooperatives. This study examined 16
cooperatives ranging in membership size from 25 – 300 (preliminary descriptive findings
from this study are used to describe cooperatives in this section). Total number of acres
landed by cooperative members varied from about 2,000 acres to 9,000 acres with
individual ownership ranging from 0 - 1,800 acres (Table 1). The median acreage owned
by individual cooperative members was 66 acres. The cooperatives studied have existed
for an average of 7 years with an average membership of about 6 years.
Average individual doe harvest was 2.4, while the average number of bucks
harvested by an individual was 1.1. Buck to doe ratio of harvested deer within
cooperatives was about 1:2 (Figure 1). This compares to the buck to doe ratio of
harvested deer in southern Michigan of about 1:1.2 (Frawley, 2012).
To aid in developing and meeting shared management goals, meetings were
typically held once or twice a year. Arrangements were often made to have guest
speakers at meetings to talk about deer management, habitat management, or data
collection. Often, there were prizes and raffles, buck and doe harvest contests, shared
consumption of venison, and time set aside to chat with fellow cooperative members.

8

While the structure of these meetings varied significantly, their intent was to support
coordination of efforts to improve deer hunting in their area.
Table 1. Descriptive statistics of the cooperatives studied. Table entries include
information on the approximate memberships size as defined by the cooperative leader,
the number of member respondents (n), the year the cooperative started, and approximate
cooperative acres. Southern Michigan, USA. 2010-2011.
Approx.
Year
Approx.
Co-op
Membership
n
Started Acres
123
90
19
2008
4,900
124
35
21
2009
5,500
125
90
24
1999
9,134
126
300
18
2005
5,000
127
116
64
2006
8,000
129
50
23
2009
5,000
130
250
37
2005
7,500
131
100
36
2009
9,000
132
50
47
2006
2,500
133
55
23
2004
2,500
137
116
15
1997
7,000
139
72
18
2008
5,000
140
40
11
2008
3,500
141
50
38
2010
4,000
150
25
14
2005
5,000
151
35
6
2009
2,500
The focus of many cooperatives’ management efforts and coordination revolved
around two basic goals: improving deer habitat and deer herd quality. Managing deer
habitat typically entailed implementing various techniques including hinge cutting, food
plots, and cover planting. Cooperative members discussed these techniques at meetings
or conducted property tours of cooperative members who have initiated such habitat work
on their property. Improving the deer herd primarily meant passing young bucks to
increase the age structure of the males and harvesting more does in areas of high deer
density to decrease the overall population levels.

9

Figure 1. Doe harvest by deer cooperative in years 2010 and 2011. Southern Michigan,
USA.
100

90
2010

80

2011

70
60
50
40
30
20
10
0
123 124 125 126 127 129 130 131 132 133 137 139 140 141 150 151
According to Quality Deer Management Association (QDMA), our study area has
the highest deer cooperative densities in the state, and possibly the country. Southern
Michigan (deer management unit 486) is known for its high densities of deer and has
been a focus area for increasing doe harvest to aid in decreasing the deer population. For
the last several years the Michigan Department of Natural Resources (MDNR) has
allowed the purchase of 10 antlerless licenses per hunter and liberal overall quotas
(Michigan Department of Natural Resources, 2011). The MDNR may also provide
landowners with Deer Management Assistant Permits (to take antlerless deer in season)
and allowing out-of-season harvest of antlerless deer. As decreasing the deer population
is of particular concern in this area, and deer cooperatives are abundant, studying the
influence these cooperatives have on individual doe harvest behavior in this setting
proved ideal.

10

METHODS
The selection of cooperatives for participation in the study began by contacting
the state office of the Quality Deer Management Association (QDMA), as they were the
main organizing force behind many cooperatives in Michigan. The QDMA is a deer
hunting organization that many cooperatives consult for information on appropriate goals
and objectives for deer management. We also contacted the MDNR to identify other
cooperatives. We presented our research plan to several cooperative groups in the area in
the summer of 2010. The final list of 16 cooperatives was largely based on leader
response and pilot survey response rates.
Two surveys were distributed to cooperative members: one (2010 season survey)
in the winter of 2011, and another (2011 season survey) in the winter of 2012. The 2010
survey (i.e. time 1) consisted of 23 questions pertaining to basic demographic
information, social networks, harvest data, and harvest standards before joining the
cooperative. The 2011 survey (i.e. time 2) consisted of 35 questions. Basic demographic
information was requested again along with harvest data, habitat management activities,
and current harvest standard.
Questions regarding social networks asked (at time 1) members (i.e. nominators)
to identify other cooperative members (i.e. nominees) belonging to four networks: 1)
With whom members hunt, 2) With whom members share hunting information, 3) With
whom members share habitat management information, and 4) With whom members
socialize.
We used three methods to disseminate the surveys. A mail survey was sent out
using the member addresses supplied by cooperative leaders. Some of the surveys

11

contained cover letters from Kip Adams (QDMA Director of Education and Outreach),
while others did not based on leaders’ assessments of whether inclusion of the letter
would improve response rates. Each mailed survey was followed-up by a reminder
postcard after about two weeks. We also utilized online surveys for cooperative leaders
who had access to their members’ email addresses. Finally, we distributed paper surveys
at cooperative meetings. Approximately 55% of the data was collected at meetings, 28%
were mailed in through the Postal Service, and about 17% of surveys were collected via
email (PDF forms) or Survey Monkey.
Assessing survey response rates was difficult as cooperative membership was illdefined. Based on observations of cooperative meetings and discussion with cooperative
leader, our response rate, based on “core” or “active” members was roughly 81% versus
24% for general members. We defined an active or core member as someone who
appeared to be actively engaged with the cooperative; they attended meetings and
participate in cooperative events. A general member was defined as someone who
engaged with the cooperative at least once but not regularly, whether that included
attending a meeting, event, or corresponding with a leader regarding harvest,
observations, or questions.
2

Data analysis was done using SAS and KliqueFinder (Frank, 1996 & 1995).
KliqueFinder was used to create social network maps using Netdraw. In SAS general
linear regression was used for the statistical modeling.

2

The analysis for this paper was generated using SAS software, Version 9.2 of the SAS
System for Windows. Copyright © 2013 SAS Institute Inc. SAS and all other SAS
Institute Inc. product or service names are registered trademarks or trademarks of SAS
Institute Inc., Cary, NC, USA.
12

The influence model for doe harvest
Using information on members’ social networks, we modeled how an individual’s
behaviors were affected by those with whom they interacted (Frank, 2011). Doe harvest
behavior was chosen because it is a primary management focus in areas of high deer
density. All of the cooperatives in our study (except one) were in areas of high deer
densities and expended significant time and energy through discussions and guest
speakers at meetings emphasizing the need to increase doe harvest. Our dependent
variable (i.e. the measured behavior) was doe harvest at time two.
As stated previously, we asked members about their interactions with other
cooperative members in four types of social networks. Of the four networks considered
and after including each of the four networks in our models, we determined the social
network as most influential. We included the network term as in a standard influence
model the interaction hunter i reported with i’ between time one and time two. We then
took the average of the others with whom a hunter interacts.
The influence (i.e. exposure) variable was log transformed to ensure normality. The
model was then:
Doe harvest time 2i = β0
+ β1 exposure to previous doe harvest of network membersi
+ β2 doe harvest time 1i
+ ei
Other controls in the model included doe harvest at time one (Table 2), education,
income, years as cooperative member, and age. Education was represented as five groups
13

(high school, trade school, some college, college, graduate school) as was income ($025,000; $25,000-50,000; $50,000-75,000; $75,000-100,000; above $100,000). Acres
owned was included as it serves as a proxy for access to or opportunity for doe harvest.
Years of membership may affect harvest behavior because peer influence may be greater
on members with more years of members than on newer member. Age was included as
observations made at cooperative meetings suggested that older members may be less
likely to harvest does, possibly due to their hunting experiences when deer number were
low (Table 3).
Table 2. Doe harvest mean, median, minimum, maximum, standard deviation, and
sample size (n). Southern Michigan, USA. 2010-2011.
Variables
Mean Median Min Max
Std. Error n
Doe Harvest
1.532 1
0
17
1.839
391
(time one)
Doe Harvest
1.140 1
0
12
1.509
349
(time two)
Table 3. Demographic descriptive statistics of cooperative members. Mean, median,
minimum, maximum, standard deviation, and sample size (n). Southern Michigan, USA.
2010-2011.
Variables
Mean
Median Min
Max
Std. Error n
Acres Owned
122.432 65
0
1800
204.611
399
Years Hunt
29.586
30
1
60
13.561
413
Years in Coop 4.794
4
0
35
4.017
403
Age
46.429
47
15
78
13.605
413
Education
3.028
3
1
5
1.307
391
Income
3.508
4
1
5
1.212
372
RESULTS
Influence analysis
The influence of the network (i.e. the mean previous behavior of others in one’s
social interaction network) on doe harvest by individuals was statistically significant (t
≤.05) and positive. The effect size of influence varied by cooperative. Across all
cooperatives, a one-unit increase in exposure to others’ behaviors was associated with a
14

0.4 increase in the number of does shot (Table 4). Prior doe harvest behavior was a
statistically significant variable (t≤.001). For each increase of one doe harvested at time
one, an increase of 0.53 doe was predicted for time two. The number of acres owned was
another significant predictor (t≤.05); for every increase of 1,000 acres owned, an
approximate increase of one doe (0.80) was expected. Non-significant variables included
buck harvest, education, income, years in cooperative, and age.
Table 4. Doe harvest influence model. Deer cooperatives, Southern Michigan, USA.
2010-2011.
Parameters
Coefficients Std. Error
Intercept
1.012*
0.392
Influence (exposure)
0.423*
0.182
Doe Harvest (time one)
0.535*** 0.036
Education
-0.014
0.059
Income
0.051
0.063
Acres Owned
0.001*
0.000
Years in Coop
-0.009
0.028
Age
0.005
0.005
(* t ≤ .05, ** t ≤ .01, *** t ≤ .001)
The mean doe harvest varied among the cooperatives (Table 5), adjusting for the
other terms in the model. Cooperative 125 showed the largest mean doe harvest (1.85)
while cooperative 137 had the lowest (0.21). Cooperative 137 might be considered an
outlier, because they are located farther north than the other cooperatives where habitat
supports lower deer densities.
We looked at network influence at the cooperative level (Table 6). Since
influence was a significant variable (t ≤.05, we were able to use it as an interactive
variable, cooperative-by-cooperative. When comparing cooperative 125 and 137, we see
that the network influence effect is .7 stronger in 137 than in 125. Also note that while
cooperative 137 had the lowest mean doe harvest, their social networks were most

15

influential. Positive coefficients indicate that behavior is being reciprocated through
influence. For example, if the members nominated by the individual harvested many deer
in 2010, and the individual harvested many deer in 2011, the influence would be positive;
likewise if the nominated individuals harvested few deer in 2010, and the individual
harvested few deer in 2011. Negative coefficients indicate that the opposite behavior
occurred than the behavior of the nominators. For example, if the members nominated by
the individual harvested many deer in 2010, and the individual harvested few deer in
2011, the influence would be negative.
Table 5. Mean doe harvest among the cooperatives. Southern Michigan, USA. 2011.
Cooperative n
Mean
Std. Error Min
Max
123
14
1.214
1.311
0
4
124
18
1.056
1.162
0
4
125
20
1.850
1.694
0
5
126
17
1.647
2.849
0
12
127
53
1.132
1.225
0
6
129
21
1.143
1.352
0
5
130
23
1.609
2.190
0
8
131
32
1.313
1.378
0
4
132
43
1.186
1.500
0
6
133
19
0.368
0.684
0
2
137
14
0.214
0.579
0
2
139
14
0.643
0.842
0
2
140
10
1.500
1.780
0
6
141
35
1.229
1.457
0
5
150
10
0.600
0.843
0
2

16

Table 6. Influence at the cooperative level. The coefficients are centered around the
cooperative with the median value of influence. The positive and negative values are
relative to the median cooperative. Corrected coefficients are based on the actual
influence values given that cooperative 139 is .423. Deer cooperatives, Southern
Michigan, USA. 2010-2011.
Corrected
Parameters
Coefficients Coefficients
Std. Error
Influence*CoopID 123
0.208
0.583
0.631
Influence*CoopID 124
-0.353
0.445
0.07
Influence*CoopID 125
0.722
0.522
1.145
Influence*CoopID 126
0.259
0.472
0.682
Influence*CoopID 127
0.089
0.361
0.512
Influence*CoopID 129
-0.52
0.539
-0.097
Influence*CoopID 130
-0.159
0.266
0.264
Influence*CoopID 131
-0.315
0.292
0.108
Influence*CoopID 132
-0.284
0.338
0.139
Influence*CoopID 133
-0.704
0.994
-0.281
Influence*CoopID 137
1.372
1.382
1.795
Influence*CoopID 139
0
.
0.423
Influence*CoopID 140
0.138
0.551
0.561
Influence*CoopID 141
-0.012
0.212
0.411
Influence*CoopID 150
-0.546
1.475
-0.123

DISCUSSION
The doe harvest influence model showed that an individual’s social interaction
network affects their harvest behavior. If one’s peers are more (or less) likely to harvest
does, so is the individual, and if one’s peers were less likely to harvest does then one
decreased doe harvest.
Looking at doe harvest at the cooperative level, we saw large differences. As
noted above, cooperatives 125 and 133 had the highest and second lowest mean doe
harvests respectively. A likely explanation for the difference in mean doe harvest
between these two cooperatives is the difference in acreage between the two groups,
despite similar membership size. Cooperative 125 has 9,134 acres while cooperative 133
has 2,500. With increased acreage come greater opportunities to harvest more does.
17

The influence of social networks on individual behavior also varied among
cooperatives. Comparing cooperatives 125, 133 and 137, cooperative 137 showed the
greatest influence effect (1.4) of all the groups. While they didn’t harvest many deer (10
does over two years of those surveyed, n = 14), it was apparent that the behavior of the
nominees had influence on the harvest behaviors of the nominators. We expected
grouping within the cooperative because of the high levels of influence. It is illustrated
by increased homogeneity, or similarity in harvest behavior, among subgroups within the
cooperative. This means that cooperative members are influenced by those with whom
they interact within their subgroups.
Cooperative is the oldest of those being studied, having been organized for 35
years. Many of the members surveyed have been members for 30 plus years.
Cooperative 137’s social network (Appendix A, Figure 3) illustrates 21% connectivity
(i.e. the percentage of connections made out of all possible connections) among
individuals; arrows express directionality of which individual nominated who. While
there is some grouping, we see that 1187, 1181 and 1185 bridge the subgroups together.
Actors 1187 and 1181 are cooperative leaders.
Cooperative 125 showed modest levels of influence (0.70). The doe harvest over
two years was 33 in 2010 and 37 in 2011 (of those surveyed, n = 21). The network for
cooperative 125 (Appendix A, Figure 4) illustrates subgrouping within the cooperative,
and connectivity of 12%, roughly half that of cooperative 137 and 133. Rather than each
individual being connected to each other, there are three distinct and separate groups.
Again, separation into subgroups is consistent with high influence occurring within the
social interaction networks inside the cooperative. This is because subgroups, or social

18

interaction networks, within the cooperative are influencing individual behaviors. We do
not see any actors who bridge the subgroups. The cooperative leader is 1557 and he
receives the greatest number of social interaction nominations within this cooperative.
Finally, cooperative 133 displayed the lowest levels of influence (-0.70).
Negative influence is illustrated (Appendix A, Figure 5) by the connections made
between the two subgroups (red and blue). Instead of individuals within the subgroups
influencing each other, (as in cooperative 137 and 125), we see here that the red subgroup
influenced the harvest behavior of the blue subgroup. The doe harvest over two years was
27 in 2010, and 7 in 2011 (of those surveyed, n = 20). The red subgroup’s average
change in harvest between 2010 (15 does) and 2011 (2 does) was almost zero (-.20),
while the average change in harvest for the blue subgroup (3 does in 2010 and 2 does in
2011) was close to -1 (-1.11). This indicates that members were not influenced by their
social interaction networks, but the doe harvest of the blue subgroup did change in 2011
compared to 2010 because of influence of the red subgroup. Perhaps a cooperative level
standard is developing and bridging these two groups.
The blue subgroup consists of older (avg. age 55 years) who farmed together for
many years. The red subgroup is made up of younger (avg. age 38 years) men who seem
to be initiation the change in doe harvest, and influencing the group of older men. The
main actors, also cooperative leaders, in this network are 1058 and 1339. Individual
1302, although not in a leadership role, is the social hub of the group. All of the social
gatherings are at his home. Their network connectivity is 23%, slightly higher than
cooperative 137. While this cooperative is only 3 years younger than 125, the history of
the members hunting together, talking about deer, playing poker, and farming goes back

19

many years. The cooperative leader (1339), part of the younger red subgroup, may have a
role in developing this change and bringing together the two groups in to a new
cooperative level doe harvest behavior.
CONCLUSION
Through implementation of traditional management approaches, wildlife
managers have effectively applied harvest regulations to restrict harvest of deer, but
struggle to effectively promote adequate harvest of antlerless deer to achieve control of
impacts caused by abundant populations (Brown et al., 2000; Cote et al., 2004; Giles &
Findlay, 2004). If antlerless harvest is increased by the formation and function of private
land deer management cooperatives, wildlife managers may be able to promote the
formation of cooperatives and encourage them to operate in ways associated with
achieving harvest outcomes relevant to management objectives.

20

GREATER HUNTING SATISFACTION: A RESULT OF DEER COOPERATIVE
PARTICIPATION?
3

Anna M. Mitterling, Daniel B. Kramer, Shawn J. Riley , and Brent A. Rudolph

4

INTRODUCTION
State wildlife agencies depend substantially on revenue from hunters to fund
wildlife conservation and management programs. This revenue currently is diminishing
because of declining hunting participation (Wright et al., 2001; Jacobson & Decker,
2006). Hunter satisfaction is one attribute thought to motivate hunting participation
(Decker & Connelly, 1989). Therefore, state agencies have a vested interest in improving
hunter satisfaction to hopes to increase motivations to hunt.
There are many ways to measure hunting success. Initially “game bagged” was
used as an indication of success. It became apparent that simply bagging game was not
the only factor affecting hunter success when viewed from the perspective of the hunter
(Hendee, 1974). “Days afield” became an updated metric of success. Although a viable
measurement, it assumes that the benefits are consistent per hunter, per day, regardless of
success or quality the days presented (Hendee, 1974).
Although satisfaction is a motivation for participation, other specific indicators
may be used to gauge the benefits or successes of hunting that motivate hunters to go
afield. Potter et al. (1973) stated that more important than harvest or days afield are the
“many associated experiences and satisfactions.” At least for a short time period, the
“appeals of quality big game hunting” are independent of a “particular record of success”
3 Department of Fisheries and Wildlife, Michigan State University, East Lansing,

Michigan, USA
4 Wildlife Division, Department of Natural Resources, Lansing, Michigan, USA

21

(Stankey et al., 1973). Realizing that satisfaction is an important aspect to hunting, a
multiple satisfactions approach to explain motivations for hunting participation has been
well developed (Hendee, 1974; Vaske et al., 1986; Decker et al., 1980; Hazel et al., 1990;
Hammitt et al., 1990; Woods et al., 1996). The determinants of hunter satisfaction include
nature, skill, companionship, community, knowledge, harvest success, and game
observed (Potter et al., 1973; Kennedy, 1974; Hazel et al., 1990; Woods et al., 1996).
Community, collective management & satisfaction
Community is typically associated with a sense of belonging (Bradshaw, 2008)
and solidarity or a common identity and set of shared norms (Bhattcharyya, 2004).
Collective management within communities can build trust and encourage the
development of shared norms (Pretty & Smith, 2004), which are important components
of fit within a group or community. Fit is defined as compatibility between members,
and can lead to positive outcomes for individuals (Jansen & Kristof-Brown, 2006) and for
the group. Fit can also be predictive of commitment to the group (Jansen & KristofBrown, 2006).
In fields such as organizational behavior, management psychology, and
marketing, researchers have found a positive statistical relationship between group fit and
individual satisfaction levels (Carlson, 1969; Bohlmann et al., 2006; Wheeler et al.,
2007). The more the individual and the group share similar values, the greater the
satisfaction of the individual (Kristof, 1996).
Individual satisfaction levels can be influenced by opinions of members within a
group. A study by Bohlmann et al. (2006) looked at the effects of the opinions of others
in a group had on individual members’ satisfaction. They found that how the individual

22

identifies with group norms was not based on the reality of other group members’
opinions, but how the expressed values of other group members align with the
individuals’ expectations (Bohlmann et al., 2006). Similarly, Kennedy (1974) found that
being part of a hunting party influences information, expectations, perceptions, and
reactions. These influences had an impact on how the hunting experience was evaluated
by the individual. These findings suggest individual satisfaction with a given experience
is influenced by the group of which the individual is a part.
Satisfaction within deer cooperatives
Hunter satisfaction has been researched within quality deer management
properties. Woods et al. (1996) studied quality deer management hunting areas and
considered six determinants of a satisfying hunting experience. The satisfaction
determinants in the quality deer management areas included: buck sign and sightings,
deer herd quality, the hunters’ knowledge, management involvement, hunter conduct of
other hunters participating within the quality deer management area, and perceived image
to non-hunters. Of these factors, the conduct of other hunters had the strongest
relationship to individual satisfaction with deer herd quality and knowledge also being
important (Woods et al., 1996). Thus we included some of these factors in our study, as
they may also be of concern to members of Michigan deer cooperatives, many of which
practice quality deer management.
Being a part of decision making and understanding personal roles within a group
are also important factors in determining cooperative member satisfaction. Enck et al.
(2001) collected baseline attitudinal and behavioral data on 36 private landowners in New
York whose properties established a 12,000 acre deer cooperative in order to evaluate the

23

cooperative’s harvest strategy designed to balance concerns of landowners and hunters
regarding deer-related impacts. They found that landowners and hunters joined a
cooperative based on assumptions about how well collective management worked and
whether individual harvest standards could be changed by better information or through
the influence of peers.
Objectives
The main question this study addressed was: Why do changes in satisfaction
between current satisfaction levels and those prior to joining a cooperative vary among
cooperative members? The specific objective we addressed includes: describe
satisfaction and fit levels of deer cooperative members.
METHODS
Relying on the insights from previous work (Woods et al., 1996; Kennedy, 1974;
Enck et al., 2001; Hazel et al., 1990; Hammitt et al., 1990; Decker et al., 1980), we
assessed changing satisfaction levels of current cooperative members based on agreement
among cooperative members on buck harvest goals, fit within the cooperative, years
hunting experience, age, acres owned, income, years in cooperative, and the extent of
their hunting information network.
Study area
In Southern Michigan there has been an increase in numbers of deer cooperatives
since about 2006. Deer cooperatives commonly consist of private land that is managed
cooperatively by a group of individuals. While the group makes management decisions,
the landowners retain legal rights over their individual properties. Cooperatives are
becoming increasingly common across the country (Ross, 2013).

24

By joining a cooperative, members gain the advantages of a larger land-base
within which to coordinate management and harvest strategies based on group objectives
(Developing Successful QDMA Deer Management Cooperatives brochure). The
guidelines vary somewhat based on location, deer densities, and habitat types and
variously address antler points, deer age, and doe management needs. To aid in
developing and meeting shared management goals, meetings are typically held once or
twice a year. Arrangements often are made to have guest speakers at meetings to talk
about deer management, habitat management, or data collection. Often, there are prizes
and raffles, buck and doe harvest contests, shared consumption of venison, and time set
aside to chat with fellow cooperative members. While the structure of meetings varies
among cooperatives, their intent supports coordination of efforts to improve deer hunting
in their area. According to Quality Deer Management Association (QDMA), there are
over 50 known deer cooperatives in Michigan. This study surveyed 16 deer cooperatives.
Sampling and data collection
The selection of cooperatives for participation in the study began by contacting
the state office of QDMA, as they were the main organizing force behind deer hunting
cooperatives in Michigan. QDMA is a deer hunting organization that many cooperatives
look to for information on appropriate goals and objectives. We also contacted the
Michigan Department of Natural Resources (MDNR) to identify other cooperatives. We
presented our research plan to several cooperative groups in the area in the summer of
2010. The final list of 16 cooperatives was largely based on leader response and pilot
survey response rates.

25

Two surveys were distributed to cooperative members: one in 2010 and another in
2011. The 2010 survey (i.e. time 1) consisted of 23 questions pertaining to basic
demographic information, social networks, harvest data, and harvest standards before
joining the cooperative. The 2011 survey (i.e. time 2) consisted of 35 questions. Basic
demographic information was requested again along with harvest data, descriptions of
habitat management activities, and current harvest standard (i.e. number of antler points,
age of buck, antler score, etc.). Likert scales were used to measure satisfaction and levels
of fit (scale of 1-5, 5 being greatest).
The satisfaction questions were structured as: 1) How satisfied are you with your
overall hunting experience in your cooperative? 2) How satisfied were you with your
hunting experience before you joined your cooperative? The five point Likert scale was
defined on the scale of very important to not important. This question was compared to
MDNR’s Deer Harvest Survey Report, and was structured as: How satisfied were you
with your overall hunting experience? The five point Likert scale used by Frawley
(2012) was defined as very satisfied to strongly dissatisfied. Due to the differences with
how the scales were defined, when comparing the two results, they were grouped as
“satisfied” if survey participants selected the two highest satisfaction options.
We used three methods to disseminate the surveys. A mail survey was sent out
using the member addresses supplied by cooperative leaders. Some of the surveys
contained cover letters from Kip Adams (QDMA Director of Education and Outreach),
while others did not based on leaders’ assessments of whether inclusion of the letter
would improve response rates. Each mailed survey was followed-up by a reminder
postcard after about two weeks. We also used online surveys for cooperative leaders who

26

had access to their members’ email addresses. Finally, we distributed paper surveys at
cooperative meetings. Approximately 55% of the data was collected at meetings, 28%
were mailed in through the Postal Service, and about 17% of surveys were collected via
email (PDF forms) or Survey Monkey. Each survey method indicated not to fill out
additional surveys if previously filled out. Response duplication was not found to be an
issue.
Overall response rates were calculated in two ways. The first one took the total
number members indicated by the cooperative leaders and used it to define the
population. Total cooperative membership response rate was 24%. Secondly, we also
calculated a response rate based on “core” membership. Cooperative leaders indicated
how many members they felt were consistently at cooperative meetings. This total
number was used to define response rates as well given that these members are more
engaged with the cooperative and were sampled at a greater level than the population as a
whole. The response rate of core members was 81%.
Model description
The satisfaction model is as follows:
Δsat = intercept + bai + hii + mi + ai + yri + μ (bhi(t-1)+bhi(t)) + ii + fi
The dependent variable (Δsat) is the change in satisfaction from prior to joining a
cooperative to current levels. This variable was entered using a five point Likert scale,
however it was coded as a binary attribute for analysis: the two categories included
change in satisfaction and no change to a negative change in satisfaction. The
independent variable, bai, is based on a survey question asking members how strongly

27

they agree (five point Likert scale) that members of their cooperative pass young bucks, a
common cooperative goal.
The independent variable (hii) refers to the number of members in the cooperative
with whom the respondent regularly sought deer hunting information. This variable was
chosen to capture the importance of information flow and camaraderie. Other variables
included years as cooperative member (mi), acres owned (ai), years as a hunter (yri),
average number of bucks harvested (bhi(t-1)+bhi(t), between time one and time two), and
income (ii).
Fit (fi) is a proxy for how members believe they “fit” with their cooperative.
Respondents were asked the extent of their agreement with the following five statements:
1) I fit in well with my cooperative; 2) My cooperative has affected the way I see myself
as a hunter; 3) My options on deer management matter to other members of my
cooperative; 4) Other members of my cooperative have interests similar to mine; and 5)
Other members of my cooperative have values similar to mine. The mean of these
responses was used to calculate fit.
5

Data analysis was done using SAS . Logistic regression was used for the
modeling. Both categorical and continuous data were used. The convergence criterion
was satisfied for this method. Analysis of maximum likelihood estimates and odds ratios
were used for interpretation.
5

The analysis for this paper was generated using SAS software, Version 9.2 of the SAS
System for Windows. Copyright © 2013 SAS Institute Inc. SAS and all other SAS
Institute Inc. product or service names are registered trademarks or trademarks of SAS
Institute Inc., Cary, NC, USA.
28

RESULTS
Assessing survey response rates was difficult because cooperative membership
was ill-defined. Based on observations of cooperative meetings and discussion with
cooperative leaders, our response rate, based on “core” or “active” members was 81%
versus 24% for “general” members. We defined an active or core member as someone
who appears to be actively engaged with the cooperative; they attend meetings and
participate in cooperative events. A general member is someone who engaged with the
cooperative at least once, whether that be attending a meeting, event, or corresponding
with a leader regarding harvest, observations, or questions.
Our study cooperatives ranged in membership size from 25 to 300 members.
Acreage ranged from 2,000 to 9,000 acres. Individual ownership ranged from 0 - 1,800
acres (median = 66 acres). The cooperatives have existed for an average of 6.9 years (3 35 years) with an average membership of 6 years (Range 0 - 35 years) (Table 7).
Average individual doe harvest was 2.4 deer, while the average number of bucks
harvested by an individual was 1.07 deer. Buck to doe ratio of harvested deer within
cooperatives was about 1:2. Cooperative level fit measures ranged from 3.3 to 4.5.
Overall satisfaction averaged 4.36 (standard deviation .85). The average change in
satisfaction from before joining cooperative to after joining the cooperative was an
increase in satisfaction of .73 (Table 8).
The MDNR estimated that approximately 46% of Michigan deer hunters
responding to the 2011 Deer Harvest Survey were satisfied with their overall deer
hunting experience in 2011 (Frawley, 2012). Our study indicated 44% of hunters
retrospectively indicated they were satisfied with their overall hunting experience prior to

29

joining a cooperative. Hunters in our study indicated that their current hunting
satisfaction within their cooperative increased to 75%. This change raised questions
regarding factors to which the increased satisfaction could be attributed, especially
considering the similarity of satisfaction among cooperative members prior to joining a
cooperative to estimated overall satisfaction levels of hunters in southern Lower
Michigan. However, we understand we cannot draw many comparisons between state
satisfaction and cooperative satisfaction because we do not have data on the hunters prior
to joining the cooperative. As a result, we sought to understand why satisfaction
increased within cooperatives.
Table 7. Descriptive statistics of the cooperatives studied. Table entries include
information on the approximate membership size as defined by the cooperative leader,
the number of members sampled (n), the year the cooperative started, and approximate
cooperative acres. Southern Michigan, USA. 2010-2011.
Approx.
Year
Approx.
Co-op
Membership
n
Started
Acres
123
90
19
2008
4,900
124
35
21
2009
5,500
125
90
24
1999
9,134
126
300
18
2005
5,000
127
116
64
2006
8,000
129
50
23
2009
5,000
130
250
37
2005
7,500
131
100
36
2009
9,000
132
50
47
2006
2,500
133
55
23
2004
2,500
137
116
15
1997
7,000
139
72
18
2008
5,000
140
40
11
2008
3,500
141
50
38
2010
4,000
150
25
14
2005
5,000
151
35
6
2009
2,500

30

Table 8. The average fit and satisfaction levels of cooperative members by cooperative
on a scale of 1 (high) to 5 (low). Southern Michigan, USA. 2010-2011.
Average
Overall
Satisfaction Satisfaction
Co-op
Fit
Satisfaction Before
After
123
3.9 (0.689) 4.3 (0.704) 3 (0.845)
4.4 (0.737)
124
3.9 (0.838) 4.3 (0.767) 3.5 (1.150) 4.4 (0.702)
125
4.5 (0.508) 4.5 (0.738) 3.2 (1.220) 4.5 (0.740)
126
4.4 (0.516) 4.5 (0.632) 3.6 (1.064) 4.5 (0.624)
127
4.3 (0.832) 4.5 (0.918) 3.4 (1.036) 4.3 (1.017)
129
3.7 (0.764) 4 (0.767)
3.5 (0.759) 3.9 (0.809)
130
4.3 (0.681) 4.6 (0.662) 3.8 (1.053) 4.6 (0.662)
131
4.1 (0.852) 4.3 (0.999) 3.4 (0.871) 4.2 (1.023)
132
4 (0.843)
4.4 (0.791) 3.6 (0.914) 4.2 (0.989)
133
4 (0.775)
4.1 (0.963) 3.3 (0.749) 4.3 (0.749)
137
4 (1.009)
4.4 (0.842) 3.2 (0.699) 4.2 (0.975)
139
3.3 (0.990) 3.4 (0.938) 3.2 (0.975) 3.1 (0.997)
140
4.3 (0.755) 4.5 (0.707) 2.9 (1.524) 3.8 (1.317)
141
4.4 (0.692) 4.9 (0.284) 3.9 (0.938) 4.8 (0.453)
150
3.9 (0.622) 3.8 (0.919) 3.8 (1.135) 3.9 (0.928)
151
3.6 (0.388) 3.5 (0.837) 3.5 (1.643) 3.3 (0.816)
Standard errors are in parenthesis.

We assessed changing satisfaction levels (Table 9) of current cooperative
members based on agreement among cooperative members on buck harvest goals, fit
within the cooperative, years hunting experience, age, acres owned, income, years in
cooperative, and the extent of their hunting information network (Table 10). The analysis
of maximum likelihood estimates of buck harvest agreement within the cooperative, hunt
information networks, average number of bucks harvested, years in cooperative, number
of acres owned, years hunting, income, and fit on change in satisfaction levels were used
in our model (Table 11). Years in cooperative (chi-square ≤ 0.05) and fit (chi-square ≤
0.001) were positively significant variables. This indicates that the more years in a
cooperative and the greater the feeling of fit within the group, both played a role in
increasing satisfaction.

31

Table 9. Results from questions regarding satisfaction levels of cooperative members.
Southern Michigan, USA. 2010-2011.
Variable
Mean Median Min Max Std Err n
Overall Satisfaction
4.359 5
1
5
0.845
345
Satisfaction Before
3.474 3
1
5
1.014
348
Satisfaction After
4.271 5
1
5
0.926
347
Harvest Satisfaction
4.093 4
1
5
1.044
343
Habitat Satisfaction
4.018 4
1
5
0.992
342
Knowledge Satisfaction 4.333 5
1
5
0.864
348
Table 10. Descriptive Statistics for value and
members. Southern Michigan, USA. 2010-2011.
Variable
Mean Median
Importance of Deer Hunting 4.793 5
Habitat Agreement
3.907 4
Harvest Agreement
3.965 4
Doe Harvest Agreement
4.314 5
Buck Harvest Agreement
4.319 5
Fit
4.107 4.2

agreement questions of cooperative
Min
2
1
1
1
1
1.6

Max
5
5
5
5
5
5

Std Err
0.545
1.019
0.999
0.881
0.831
0.801

n
348
345
345
344
348
348

Since years in cooperative and fit were statistically significant and positively
associated with changes in satisfaction, we will look at them in closer detail using the
odds ratio estimates (Table 12). When years in cooperative increases, the odds of a
positive change in satisfaction versus no or negative changes is satisfaction are 1.14 times
greater; given all other variable remain constant. When fit increases, the odds of a
positive change in satisfaction versus no or negative changes in satisfaction are 2.90
times greater, given that all the other variables in the model are held constant. When
average buck harvest increases, and buck harvest agreement is 4 or greater, change in
satisfaction increases. The hunting information network, acres own, years hunt and
income cause satisfaction to decrease as they increase.

32

Table 11. Satisfaction model analysis of maximum likelihood estimates. Buck harvest
agreement refers to the level of agreement members feel there is within the cooperative
regarding passing young bucks. Hunt info network considers the number of people
members talk to within their cooperative. Average buck harvest looks at the average
number of bucks harvested over 2010 and 2011. Years in cooperative, acres owned, years
hunt, and income are measurements considered in this model, as well as how well
members feel the fit within the group. Southern Michigan, USA. 2010-2011.
Parameters
Group Estimate Std Err
Intercept
-4.386
1.167
Buck Harvest Agreement
3
-0.318
0.456
Buck Harvest Agreement
4
0.192
0.365
Hunt Info Network
0.115
0.129
Avg. Buck Harvest
0
0.450
1.058
Avg. Buck Harvest
0.5
0.568
1.054
Avg. Buck Harvest
1
0.778
1.054
Avg. Buck Harvest
1.5
1.053
1.054
Years in Cooperative
0.133*
0.067
Acres Owned
-0.001
0.001
Years Hunt
-0.009
0.024
Income
-0.151
0.133
Fit
1.065*** 0.254
(*chi-square ≤ 0.05, ** chi-square ≤ 0.01, ***chi-square ≤ 0.001)
Table 12. Satisfaction model odds ratio estimates for deer cooperative in Southern
Michigan, USA. 2010-2011.
95% Wald
Point
Effect
Estimate Confidence Limits
Buck Agreement 3 vs 5
0.728
0.298
1.778
Buck Agreement 4 vs 5
1.212
0.592
2.481
Hunt Info Network
1.122
0.872
1.443
Avg. Bucks Harvest 0 vs 2
1.569
0.197
12.473
Avg. Bucks Harvest 0.5 vs 2
1.764
0.223
13.926
Avg. Bucks Harvest 1 vs 2
2.176
0.276
17.190
Avg. Bucks Harvest 1.5 vs 2
2.866
0.310
26.509
Years in Cooperative
1.142
1.003
1.301
Acres Owned
0.999
0.997
1.001
Years Hunt
0.990
0.966
1.014
Income
0.860
0.663
1.115
Fit
2.902
1.765
4.771

33

DISCUSSION
This study is the first of its kind to address satisfaction levels within deer
cooperatives. Our results showed agreements with Potter et al. (1973), Kennedy (1974),
Hazel et al. (1990), and Woods et al. (1996) in regards to hunter satisfaction determinants
such as harvest success. While buck harvest was positively related to increased
satisfaction, it was insignificant in our model. We found that the factor related to the
greatest increase in satisfaction among southern Michigan deer cooperative members was
levels of fit. This finding substantiates Kennedy (1974) in that sense of community is an
important determinant of hunter satisfaction, but our research also suggests that in
addition to being part of community, fitting in with the community may be what
increases satisfaction.
Increased satisfaction could also be related to the involvement that cooperative
members have in management decisions and harvest goals. Woods et al. (1996) indicated
that because of the role hunter in his study had with managing the herd, they were more
likely to have reasonable expectations for harvest success than hunters who were not
directly involved with management.
The increase in satisfaction was less among those who were members for five
years or more. It is possible this is because those members may not have as clear of
memory to their satisfaction level prior to joining. It seems that memory recall will
decline over time (Tourangeau et. al, 2000). It also may be that although respondent
satisfaction changed, the newness of the cooperative experience is over and not as
exciting as it was at the beginning. It may be that the goals of the younger hunters are
different than those of the older hunters. While it is possible that increased satisfaction is

34

an instant reaction to joining a cooperative, it seems unlikely due to relative consistent
increases in satisfaction across cooperatives regardless of how long they have been a
formal group. However, if expectations are not met, satisfaction will most likely drop off
quickly.
Revenue towards wildlife management is decreasing along with the decline of
hunting participation (Wright et al., 2001; Jacobson & Decker, 2006). Agencies may be
able to address this trend in declining participation by taking steps to increase hunter
satisfaction, but most determinants of satisfaction are beyond the direct control of
wildlife managers (Woods et al., 1996). Hunter satisfaction is greater among deer
cooperative hunters in our study than that of hunters in Southern Michigan in general.
Increases in satisfaction among cooperative members are related to how well members
feel they fit in with the group, and how long they have been members.
However, fit and years of membership are just two areas identified in this study.
More research needs to be done to determine additional significant variables related to
the increase in satisfaction as a result of cooperative membership. It is evident that a
wide variety of satisfactions are derived from hunting experiences and those satisfactions
are direct products of game management (Potter et al., 1973). Efforts to promote
formation and successful operation of deer management cooperatives may provide a way
for agencies to replicate the improvement to hunter satisfaction documented in this study.
Beyond hunter satisfaction within deer cooperatives, wildlife managers may note
a management improvement that stems from these deer cooperatives. The buck to doe
ratio of harvested deer in southern Michigan was about 1:1.2 (Frawley, 2012). In deer
cooperatives, however, doe harvest is twice as great (1:2.2). It may prove fruitful for

35

local agency personnel to seek out cooperative leaders and request attending a meeting.
A healthy relationship of information sharing from both parties may aid in improved
population management as well as increased hunter satisfaction levels.
MANAGEMENT IMPLICATIONS
As wildlife managers build connections to deer cooperatives, they can make use
of established networks to distribute information regarding management priorities and
advisable management practices. Wildlife managers also have an opportunity to improve
hunter satisfaction and potentially hunter recruitment and retention by
assisting cooperative leaders in the formation and successful operation of deer
cooperatives. Finally, in areas of high deer densities, cooperatives also included a greater
proportion of does within their deer harvest in comparison to elsewhere in Michigan, and
may more effectively contribute to deer population reduction.
Cooperative leaders are relied upon by multiple members as resources for insight
on deer management. In addition to focusing effort on providing reliable information
regarding habitat and harvest strategies, cooperative leaders should create opportunities
to connect members with each other as they facilitate and organize distribution of
information within their groups. Higher fit levels within a group are positively associated
with satisfaction. Leaders may increase satisfaction among the group by creating
avenues for members to get to know each other and implementing ways for connectivity
to be built.
Cooperation between cooperatives and wildlife managers poses a strong
possibility of mutual benefit. In times of disease outbreaks, or other wildlife
circumstances, cooperatives can provide local information to wildlife managers, while in

36

return, wildlife managers can provide scientific information about the disease as well as a
wide scale effect the disease outbreak is having on the deer population. The key here is
mutual sharing of information.

37

APPENDICES

38

Appendix A
Figure 2. A social network of individuals who hunt together within a single cooperative (141). The nodes represent individuals, while
the lines connecting them indicate they hunt with each other. The arrows represent the directionality of nomination. Color is
indicative of grouping that is determined by an algorithm that maximizes ties within subgroups vs. between. For interpretation of the
references to color in this and all other figures, the reader is referred to the electronic version of the thesis.

39

Appendix A
Figure 3. Social interaction network of cooperative 137. The individual nodes represent individual actors, while the arrows represent
the directionality of who nominated who. Color variation refers to grouping within the cooperative.

40

Appendix A
Figure 4. The social interaction network for cooperative 125. The individual nodes represent individual actors, while the arrows
represent the directionality of who nominated who. Color variation refers to grouping within the cooperative.

41

Appendix A
Figure 5. The social interaction network for cooperative 133. The individual nodes represent individual actors, while the arrows
represent the directionality of who nominated who. Color variation refers to grouping within the cooperative.

42

Appendix B
2010 Deer Seasons Data – MSU Deer Cooperative Study
My name is Anna (Hamilton) Mitterling and I am working on a research project for
Michigan State University that is supported by the Michigan Department of Natural
Resources. The project started this year. As part of the project, we are talking with
members of deer cooperatives in Michigan’s Lower Peninsula in order to understand the
social networks of these organizations. We are asking members to answer some questions
about how and with whom they interact in the cooperatives. Your answers to these
questions will not be shared with anyone else. They are private. When we look at the
information you provide we will change your name to a number so that your name
is not attached to your answers. We really appreciate your time. The survey will take
roughly 10-15 minutes to complete depending on your responses.
1. Date: _______________
2. Name (First & Last): ____________________________________
3. Cooperative Name: ___________________________________
4. County & Township: __________________________________________________
5. Cooperative Acres you own: __________
6. Years Hunted Deer: _____________
7. Years with Cooperative: __________
8. Age: ________
9. Gender:

Male

Female

Knowledge Sharing & Social Interactions
10. Within your cooperative, who do you seek out deer hunting information from?
Name (First & Last):
10a.
10b.
10c.
10d.
10e.
11. Within your cooperative, who do you seek out deer habitat management information
from?
Name (First & Last):
11a.
11b.
11c.
11d.
11e.

43

Appendix B
12. Within your cooperative, who do you hunt deer with?
Name (First & Last):
12a.
12b.
12c.
12d.
12e.

Whose land?
Yours
Yours
Yours
Yours
Yours

Theirs
Theirs
Theirs
Theirs
Theirs

Other
Other
Other
Other
Other

Knowledge Sharing & Social Interactions Cont.
13. Within your cooperative, who do you meet socially (e.g. dinner, drinks, cards,
recreation) other than hunting?
Name (First & Last):
13a.
13b.
13c.
13d.
13e.
Harvest Management
In the 2010 deer seasons please provide, to the best of your memory, the following
information for each deer you individually harvested on or around your cooperative:
14. Please indicate the total number of bucks you harvested in the 2010 deer seasons:
_____________
15. Please indicate the total number of does you harvested in the 2010 deer seasons:
_____________
16a.
Deer

16b.
Sex

Deer 1

M
F

Deer 3

M
F

Deer 4

16d.
16f.
16e.
16g. Date
Antler
Dressed
Age
Harvested
Spread
Weight

M
F

Deer 2

16c.
Antler
Points

M
F

16h. Harvest
Location
Own Land
Other Co-op Land
Local Public Land
Own Land
Other Co-op Land
Local Public Land
Own Land
Other Co-op Land
Local Public Land
Own Land
Other Co-op Land
Local Public Land

44

Appendix B
17. Did you harvest the amount of does you wanted to?
More that I planned
Fewer than I planned
The number I planned
Harvest Management Cont.
18. Has your harvest changed as a result of joining the cooperative?
Yes
No (Skip to question 20)
19. Approximately, what was your harvest standard before joining the cooperative
(Check all that apply)?
Check
all that
Apply

Criteria
Antler Points

Define Standard
Ex: 6 points

Antler Score
Body Size
Age
Other
No Standard
Additional Information
20. What is your occupation?___________________________________
21. Please select the highest education you have completed:
High School
Trade School
Graduate School

Some College

College

22. Local Civic Organizations and/ Conservation Groups you are a member of (ex:
Parent/Teacher Association, Lions Club, Kiwanis Club, QDMA, Ducks Unlimited,
MUCC, National Wild Turkey Federation, etc.):
22a.
22b.
22c.
22d.
22e.
22f.

45

Appendix B
23. Annual Household Income:
0 to 25,000
25,000 to 50,000
50,000 to 75,000
75,000 to 100,000
Above 100,000
Thank you for your valuable time. This is the end of the survey.

46

Appendix C
2011 Deer Seasons Data – MSU Deer Cooperative Study
My name is Anna (Hamilton) Mitterling and I am working on a research project for
Michigan State University that is funded by the Michigan Department of Natural
Resources. The project is in its second year. As part of the project, we are talking with
members of deer cooperatives in Michigan’s Lower Peninsula in order to understand the
social networks of these organizations. We are asking members to answer some questions
about how and with whom they interact in the cooperatives. Your answers to these
questions will not be shared with anyone else. They are private. When we look at the
information you provide, we will change your name to a number so that your name
is not attached to your answers. We really appreciate your time. The survey will take
about 10-15 minutes.
1. Date: _______________
2. Name (First & Last): ____________________________________
3. Cooperative Name: ___________________________________
4. County & Township: ____________________________________________________
5. Cooperative Acres you own: __________
6. Years Hunted Deer: _____________
7. Years with Cooperative: __________
8. Age: ________
9. Gender:

Male

Female

Harvest Management
Please provide information for each deer you individually harvested during the 2011 deer
seasons on or around your cooperative. Just fill out what you remember to the best of
your ability.
10. Please indicate the total number of bucks you harvested in the 2011 deer seasons:
_____________
11. Please indicate the total number of does you harvested in the 2011 deer seasons:
_____________

47

Appendix C
12a.
Deer

12b.
Sex

12c.
Antler
Points

12d.
12e. 12f.
12g.
Antler Age Dressed Date
Spread
Weight Harvested

12h. Harvest Location

Deer 1

M
F

Own Land
Other Co-op Land
Local Public Land

Deer 2

M
F

Own Land
Other Co-op Land
Local Public Land

Deer 3

M
F

Own Land
Other Co-op Land
Local Public Land

Deer 4

M
F

Own Land
Other Co-op Land
Local Public Land

13. In the 2011 deer seasons, did you harvest the number of does you wanted on or
around your cooperative?
More that I planned
Fewer than I planned
The number I planned
Harvest Management Cont.
14. What is your minimum harvest standard (Check and define all that apply)?
Check all
that
Apply

Criteria
Antler Points
Antler Score
Body Size
Age
Other

Define Standard
Ex. 8 pts

48

Appendix C
Satisfaction and Values
For the following questions, think of your experience with your deer cooperative.
Very
Somewhat
Not
Important
Important
Important
15. How important is deer hunting to
you?
Very
Somewhat
Not
Satisfied
Satisfied
Satisfied
16. How satisfied are you with your
overall experience with your
cooperative?
17. How satisfied are you with your
hunting experience in your
cooperative?
18. How satisfied were you with
your hunting experience before you
joined your cooperative?
19. How satisfied are you with
harvest improvement on your
property as a result of joining your
cooperative?
20. How satisfied are you with the
quality of habitat as a result of
joining your cooperative?
21. How satisfied are you with the
quality of deer hunting/management
knowledge you have received as a
result of joining your cooperative?
For the following statements, think about your social interactions with members of your
deer cooperative.
Strongly
Somewhat
No
Agee
Agree
Agreement
22. I fit in well with my cooperative.
23. My cooperative has affected the
way I see myself as a deer hunter.
24. My opinions on deer management
matter to other members of my
cooperative.
25. Other members of my cooperative
have interests similar to mine.
26. Other members of my cooperative
have similar values to mine.

49

Appendix C
Satisfaction and Values Cont.
For the following questions, consider the harvest and habitat management of yourself
and members in your cooperative.
No
Strongly
Somewh
Agreeme
Agee
at Agree
nt
27 There is agreement between
members of my cooperative about
harvest management practices.
28. There is agreement between
members of my cooperative about
habitat management practices.
29. Members of my cooperative
harvest does.
30. Members of my cooperative pass
young bucks.
31. It is important to me to attend
cooperative sponsored meetings.
32. Since joining the cooperative, how many cooperative sponsored meetings have you
attended?
None of the meetings
A few of the meetings
Most of the meetings
All of the meetings
Habitat Management
33. In the last year, have you planted trees/grass/shrubs for the benefit of deer?
Yes (Please indicate approximately how many acres: _______)
No (Skip to question 35)
34. In the last year, have you planted a food plot?
Yes (Please indicate approximately how many acres: _______)
No
Contact Information
35. For the purpose of me contacting you to follow up on information you have given,
please provide the best way for me to contact you if needed.
Mailing Address, Email Address, Phone Number:
Thank you for your valuable time. This is the end of the survey.
50

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