https://cpw.cvlcollections.org/files/original/e32fdf500d0bafad7e369597d1bda0b0.pdf e6a80a534e60c8b1c0b14fae94e60ff3 PDF Text Text The research in this publication was partially or fully funded by Colorado Parks and Wildlife. Dan Prenzlow, Director, Colorado Parks and Wildlife • Parks and Wildlife Commission: Marvin McDaniel, Chair • Carrie Besnette Hauser, Vice-Chair Marie Haskett, Secretary • Taishya Adams • Betsy Blecha • Charles Garcia • Dallas May • Duke Phillips, IV • Luke B. Schafer • James Jay Tutchton • Eden Vardy �The Journal of Wildlife Management 85(5):1017–1030; 2021; DOI: 10.1002/jwmg.22055 Research Article Diverse University Students Across the United States Reveal Promising Pathways to Hunter Recruitment and Retention VICTORIA R. VAYER,1 Department of Parks, Recreation and Tourism Management, North Carolina State University, Raleigh, NC 27695, USA LINCOLN R. LARSON , Department of Parks, Recreation and Tourism Management, North Carolina State University, Raleigh, NC 27695, USA M. NILS PETERSON, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA KANGJAE JERRY LEE, Department of Parks, Recreation and Tourism Management, North Carolina State University, Raleigh, NC 27695, USA RICHARD VON FURSTENBERG, Department of Parks, Recreation and Tourism Management, North Carolina State University, Raleigh, NC 27695, USA DANIEL Y. CHOI , Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA KATHRYN STEVENSON Raleigh, NC 27695, USA , Department of Parks, Recreation and Tourism Management, North Carolina State University, ADAM A. AHLERS, Department of Horticulture and Natural Resources, Kansas State University, Manhattan, KS 66506, USA CHRISTINE ANHALT‐DEPIES, Wisconsin Department of Natural Resources, Madison, WI 53716, USA TANIYA BETHKE, South Dakota Game, Fish, and Parks, Ft. Pierre, SD 57532, USA JEREMY BRUSKOTTER, School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA CHRISTOPHER J. CHIZINSKI , School of Natural Resources, University of Nebraska, Lincoln, NE 68583, USA BRIAN CLARK, Kentucky Department of Fish and Wildlife Resources, Frankfort, KY 40601, USA ASHLEY A. DAYER , Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA 24061, USA BENJAMIN GHASEMI, Department of Rangeland, Wildlife and Fisheries Management, Texas A&M University, College Station, TX 77843, USA LARRY GIGLIOTTI, Department of Natural Resource Management, South Dakota State University, Brookings, SD 57007, USA ALAN GRAEFE, Department of Recreation, Park and Tourism Management, The Pennsylvania State University, University Park, PA 16802, USA KRIS IRWIN, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA SAMUEL J. KEITH, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA MATT KELLY, College of Forest Resources and Environmental Science, Michigan Tech University, Houghton, MI 49931, USA GERARD KYLE, Department of Rangeland, Wildlife and Fisheries Management, Texas A&M University, College Station, TX 77843, USA ELIZABETH METCALF, W. A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA WAYDE MORSE, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA MARK D. NEEDHAM, Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA NEELAM POUDYAL, Department of Forestry, Wildlife and Fisheries, University of Tennessee, Knoxville, TN 37966, USA MICHAEL QUARTUCH, Colorado Parks and Wildlife, Denver, CO 80203, USA SHARI RODRIGUEZ, Forestry and Environmental Conservation Department, Clemson University, Clemson, SC 29631, USA CHELSIE ROMULO, Department of Geography, GIS, and Sustainability, University of Northern Colorado, Greeley, CO 80639, USA RYAN L. SHARP, Department of Horticulture and Natural Resources, Kansas State University, Manhattan, KS 66506, USA WILLIAM SIEMER, Department of Natural Resources and Environment, Cornell University, Ithaca, NY 14853, USA MATT SPRINGER, Department of Forestry and Natural Resources, University of Kentucky, Lexington, KY 40546, USA RICHARD STEDMAN, Department of Natural Resources and Environment, Cornell University, Ithaca, NY 14853, USA TAYLOR STEIN, Department of Forest Resources and Conservation, Gainesville, FL 32611, USA TIM VAN DEELEN , Department of Forestry and Wildlife Ecology, University of Wisconsin, Madison, WI 53705, USA JASON WHITING, Department of Recreation Administration, California State University, Fresno, CA 93740, USA Received: 16 November 2020; Accepted: 13 March 2021 1 E‐mail: vrvayer@ncsu.edu Vayer et al. • Hunting and College Students 1017 �RICHELLE L. WINKLER, Department of Social Sciences, Michigan Technological University, Houghton, MI 49931, USA KYLE MAURICE WOOSNAM, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA ABSTRACT Declining participation in hunting, especially among young adult hunters, affects the ability of state and federal agencies to achieve goals for wildlife management and decreases revenue for conservation. For wildlife agencies hoping to engage diverse audiences in hunter recruitment, retention, and reactivation (R3) efforts, university settings provide unique advantages: they contain millions of young adults who are developmentally primed to explore new activities, and they cultivate a social atmosphere where new identities can flourish. From 2018 to 2020, we surveyed 17,203 undergraduate students at public universities across 22 states in the United States to explore R3 potential on college campuses and assess key demographic, social, and cognitive correlates of past and intended future hunting behavior. After weighting to account for demographic differences between our sample and the larger student population, 29% of students across all states had hunted in the past. Students with previous hunting experience were likely to be white, male, from rural areas or hunting families, and pursuing degrees related to natural resources. When we grouped students into 1 of 4 categories with respect to hunting (i.e., non‐hunters [50%], potential hunters [22%], active hunters [26%], and lapsed hunters [3%]), comparisons revealed differences based on demographic attributes, beliefs, attitudes, and behaviors. Compared to active hunters, potential hunters were more likely to be females or racial and ethnic minorities, and less likely to experience social support for hunting. Potential hunters valued game meat and altruistic reasons for hunting, but they faced unique constraints due to lack of hunting knowledge and skills. Findings provide insights for marketing and programming designed to achieve R3 objectives with a focus on university students. © 2021 The Wildlife Society. KEY WORDS college students, constraints, hunting, motivations, R3, segmentation, wildlife values. Hunting is a key aspect of North American culture (Reiger 2001, Mahoney and Jackson 2013) that provides economic benefits to rural communities (Frew et al. 2018), helps wildlife agencies achieve ecological management goals (Heffelfinger et al. 2013), and forms the backbone of the wildlife conservation funding system in North America (Loveridge et al. 2006, Serfass et al. 2018). Despite these benefits, since the 1980s the number of annual license holders in the United States has decreased by approximately 2 million (U.S. Fish and Wildlife Service [USFWS] 2020) and the number of active hunters has declined by approximately 30% (USFWS 2018). The decline has been greater among generations of young adults born since 1980 (Enck et al. 2000, Winkler and Warnke 2013). Today, <5% of the population in the United States hunts in any given year (USFWS 2020). Peterson et al. (2011) attributed this decline to shifts in social structures and priorities resulting in diminishing social support for hunting. Specific factors affecting hunter recruitment include competing demands for time and money, lack of accessible mentors, urbanization, land ownership changes that affect hunting access, negative media coverage, and a growing disconnect between humans and nature (Winkler and Warnke 2013, Larson et al. 2014, Kellert et al. 2017). Regardless of the causal factors, declining hunter numbers affect the capacity of wildlife management agencies to achieve their missions and goals (Mockrin et al. 2012, Larson et al. 2014). To slow declining participation in hunting, state wildlife agencies and many conservation organizations focused on game species are increasingly emphasizing hunter recruitment, retention, and reactivation (R3) efforts (Responsive 1018 Management and National Shooting Sports Foundation [NSSF] 2017, Ringelman et al. 2020). Despite a growing emphasis on R3, however, its efficacy remains questionable (Seng et al. 2007, Larson et al. 2013). Misunderstanding of unique subpopulations of potential hunters and overreliance on conventional marketing tactics have limited recruitment from outside existing hunting communities (Ryan and Shaw 2011, Responsive Management and NSSF 2017). Although traditional hunters—typically white men from rural backgrounds (Decker et al. 1984, Stedman and Heberlein 2001, Larson et al. 2014)—comprise the majority of the hunting community, hunters initiated from these backgrounds are no longer sufficient to offset declines in hunting participation (Winkler and Warnke 2013, Price Tack et al. 2018). Countering declines in hunting participation requires wildlife management agencies to move beyond the white, masculine conceptualization of hunting and identify R3 strategies that work for a more diverse population of potential hunters (Lee et al. 2014). Non‐traditional path hunters (NTPHs) are individuals who enter the hunting community as adults, have limited hunting experience, have little or no familial or social support for hunting, and are part of an underrepresented group within the hunting community (Quartuch et al. 2017). Thus NTPHs tend to be women, individuals who are black, indigenous, or people of color (BIPOC), residents of urban areas, or people from non‐agricultural backgrounds (Quartuch et al. 2017). Some NTPHs may be locavores interested in consuming food (i.e., game meat) they consider ethically grown or locally harvested (Tidball et al. 2013, Stedman et al. 2017). Others may be motivated The Journal of Wildlife Management • 85(5) �to hunt for conservation or civic‐oriented reasons, such as improving ecosystem health or controlling wildlife damage (Decker et al. 2015). In many cases, the motivations and constraints of NTPHs mirror those of traditional hunters (Peterson et al. 2009, Decker et al. 2015). Social support and relationships are key to recruiting and retaining hunters (Byrne and Dunfee 2018), and may be particularly important for NTPHs. But finding and fostering social support for hunting among diverse and geographically dispersed NTPHs remains a major R3 challenge (Larson et al. 2014). Undergraduate students at universities across the United States are potential NTPHs who are relatively easy to locate and access. About 40% of young adults in the United States aged 18–24 years currently attend some type of college or university, and that number has increased steadily since 1980 (National Center for Education Statistics 2019). Of about 20 million undergraduate students, 55% identify as female, 47% identify as BIPOC, and most are from urban areas (U.S. Census Bureau 2018). Most college and university students are in an age group prone to adopting new activities (i.e., emerging adulthood). Emerging adulthood is distinguished by relative independence from traditional social roles and expectations, with an emphasis on role exploration, boundary testing, risk‐taking, and self‐ identification (Arnett 2000, Hartmann and Swartz 2006). Although emerging adults may lack financial resources that limit adoption of expensive activities, they have freedom from the supervisions that constrain adolescents and are not fully burdened by the responsibilities associated with adulthood (Johnson and Goldman 2011). Colleges and universities present students with a unique social setting that facilitates exploration of new ideas and behaviors without perceived consequences or commitment (Arnett 2007, Ravert 2009). As emerging adults, students are primed to experiment with new leisure activities they may adopt long‐term (Luyckx et al. 2006, Larson et al. 2017). This period also affords opportunities for retaining or reactivating individuals whose hunting participation may wane or lapse during the college years. In short, college students may be naturally inclined to explore new activities such as hunting, and the social atmosphere on university campuses can help nurture non‐traditional pathways into hunting. College‐focused R3 programs are therefore increasing in popularity (Stayton et al. 2017, Ringelman et al. 2020). Hunter recruitment, retention, and reactivation efforts will not resonate with every student on a diverse campus. Market segmentation, an approach widely used in other disciplines (Dolnicar 2002) that is gaining traction in the conservation field (Metcalf et al. 2019), could help R3 program managers assess which groups of students have the highest potential of being recruited and retained and through what mechanisms. Studies have used market segmentation to place hunters into particular subgroups based on hunting experience preferences (Miller 2003, Needham and Vaske 2013), harvest preferences (Floyd and Gramann 1994, Ward et al. 2008), hunting motivations (Gigliotti 2000), and license purchasing behavior (Hinrichs et al. 2020). Limited empirical research has compared groups of hunters and non‐hunters to identify strategies for recruiting new types of hunters. Our descriptive study used data collected from students at 22 public universities across the United States to investigate Figure 1. States (in red) containing the 22 large public universities across the United States that participated in the student survey effort from 2018–2020. Vayer et al. • Hunting and College Students 1019 �hunting participation rates among college students, factors associated with past hunting participation, likelihood of hunting in the future, and factors associated with future hunting participation. To better understand future hunting participation and provide R3 insights, we investigated differences in socio‐demographic attributes, social support, and hunting‐related beliefs, motivations, and constraints among 4 groups of college students: non‐hunters, potential hunters, active hunters, and lapsed hunters. STUDY AREAS From 2018–2020, we worked with university researchers at 22 public universities in 22 states (Table S1, available online in Supporting Information) to conduct a web‐based survey of undergraduate students in all USFWS regions across the United States (Fig. 1). Most schools were land‐grant universities, which often feature majors and courses related to wildlife and natural resources that might attract traditional and non‐traditional path hunters. METHODS Data Collection At 20 of these universities we sampled, researchers sent a web survey link (Qualtrics, Provo, UT, USA) to a random sample of undergraduate students (typically 5,000 in the sample frame but ranging from 3,000 to 16,000) provided by university administrators (Table S2, available online in Supporting Information). In the 2 cases where a university‐ wide random sample was not possible, we worked with colleges within the university to obtain a random sample of participants across a variety of majors. We used an adapted version of the Dillman et al. (2014) approach to administer the questionnaire. This method included 2 email contacts at approximately weekly intervals, followed by a shorter survey of non‐respondents (featuring a subset of identical items) to check for potential response bias. The survey process involving human subjects was approved by the North Carolina State University Institutional Review Board (Protocol 12676) prior to implementation. Survey Instrument Our questionnaire was developed by researchers at North Carolina State University with written and verbal input from collaborators across participating institutions and R3 staff from state agency partners (Table S1). The instrument was designed to describe and assess university students' beliefs, attitudes, and behaviors with respect to hunters and hunting. Most constructs were based on theoretical frameworks commonly employed in outdoor recreation and leisure research. We measured past hunting experience by asking participants “have you ever hunted before?” with response options of “yes” (1), “I have accompanied someone hunting but did not personally hunt” (0.5), or “no” (0). If students answered “yes” or that they have accompanied someone, we asked additional questions about how old they were for their first hunting experience and how many times they hunted in the past year. 1020 We measured future hunting participation by asking participants “how likely are you to hunt in the future?” with response options of “I will definitely not hunt” (1), “I will probably not hunt” (2), “Not sure” (3), “I will probably hunt” (4), or “I will definitely hunt” (5). If a participant answered 3 or higher, we asked a question regarding how often they predicted they would hunt in the future, with the response options of “Might try it once” (1), “Rarely (once every few years)” (2), or “Regularly (at least once per year)” (3). We measured social support for hunting by asking participants to indicate who in their lives hunts (e.g., parent, sibling, other relative). We then grouped responses into 3 categories: immediate family (parents and siblings), extended family and friends (all other hunting connections), and no social support. Patterns of socialization into hunting help to create social norms, or unwritten rules about how to think and behave, that ultimately influence hunting participation (Hrubes et al. 2003, Stedman and Heberlein 2009, Larson et al. 2014). We measured beliefs about hunters and hunting in several ways. First, we asked participants if they approved of “legal, regulated hunting” on a scale from “strongly disapprove” (1) to “strongly approve” (5) following the approach used in previous studies (Responsive Management and NSSF 2017). We also asked participants whether they “disapproved” (1), were “neutral” (2), or “approved” (3) of hunting for 9 different reasons such as engaging in sport or recreation, being close to nature, or obtaining local, free‐ range meat. We adapted potential reasons for hunting from previous studies (Decker et al. 2015, Quartuch et al. 2017, Responsive Management and NSSF 2017). We also asked participants to rank their level of agreement with 9 statements about hunters and hunting on a scale from “strongly disagree” (1) to “strongly agree” (5), including items such as “hunting is a safe activity,” “hunters behave responsibly and follow hunting laws,” and “hunters financially contribute to wildlife conservation.” We assessed motivations to hunt using items from previous studies that matched the approval items referenced above and covered a wide range of possible motivations (Decker et al. 2015, Responsive Management and NSSF 2017). These included hunting for meat, hunting to obtain a trophy (i.e., animal body parts that can later be displayed), and hunting for egoistic (i.e., hunting for personal benefit) and altruistic reasons (i.e., hunting to contribute to conservation and society) described in the broader motivations literature (Batson et al. 2002). Participants indicated if they, personally, would consider hunting for each purpose with response options of “no” (1), “maybe” (2), or “yes” (3). We investigated constraints to hunting using items from previous studies to identify a range of potential hunting constraints (Metcalf et al. 2015, Responsive Management and NSSF 2017). We listed 20 potential constraints designed to cover a range of intra‐personal, interpersonal, and structural (or contextual) constraints frequently identified in the recreation literature (Stodolska et al. 2019). All items The Journal of Wildlife Management • 85(5) �were rated on a scale from “not at all” a barrier (1) to “very much” a barrier (4). We assessed wildlife value orientations, or basic beliefs about wildlife, in 2 ways. Using items from existing scales, we used 4 items to measure wildlife‐specific value orientations across the dominionistic to mutualistic spectrum (Teel and Manfredo 2010, Manfredo et al. 2020). We assessed broader conservation caring using 4 items that focused on personal perceived importance of wildlife conservation (Skibins and Powell 2013). Each of these items was rated on a scale from “strongly disagree” (1) to “strongly agree” (5) with a midpoint of “neither” (3). In addition to these predictors of behavior, we explored key demographic correlates of hunting participation that help to define NTPHs (Quartuch et al. 2017). These attributes included information about participants' gender identity (choices included male, female, or not listed), race and ethnicity (choices included White, Hispanic or Latino, Black or African American, Asian, American Indian, Middle Eastern or North African, Native Hawaiian or Pacific Islander, other), college major (grouped into 6 categories, later coded as agriculture or natural resource majors vs. other majors), and the population size of the area where a participant grew up (e.g., urban vs. rural based on population density). We measured respondents' participation in other non‐consumptive outdoor recreation activities during the past year with a checklist including adventure sports, bird watching, camping, canoeing or kayaking, hiking, and wildlife viewing or photography. We created an outdoor recreation index by summing these activities, with scores ranging from 0 (no participation) to 6 (very high levels of participation). On the shorter survey checking for non‐response bias, we used only 1 item to measure each of the key themes (8 items total). Vayer (2020) and supplemental tables (Tables S3–S8, available online in Supporting Information) provide more details about the survey instrument. Data Analysis Prior to analysis, we filtered out survey responses that were <33% complete (the key questions about past and future hunting participation appeared a third of the way through the survey) and removed respondents who were not undergraduate students within the 18–34‐year age range. This resulted in the removal of 13% of all surveys that were started. We used Stata version 16.1 (StataCorp, College Station, TX, USA) and SPSS Statistics version 25 (IBM, Armonk, NY, USA) for all analyses. We first used principal component factor analysis (PCF) with an orthogonal rotation to reduce multiple items into larger thematic constructs (Acock 2016). We calculated mean composite scores for each core construct (e.g., motivations, constraints, value orientations) or sub‐dimensions that appeared in subsequent analyses. We used Cronbach's alpha to assess the reliability of these scales (Vaske 2019). Prior to interpreting frequencies, we conducted post‐stratification weighting based on enrollment and student demographic data provided by the National Center of Education Statistics (2019). Vayer et al. • Hunting and College Students Following suggestions outlined by Vaske (2019), we developed normalized multiplicative weights for each case (i.e., respondent) based on their school enrollment, their gender identity (male vs. female), and their race and ethnicity (white vs. BIPOC; Table S2). These weights helped us account for potential sampling bias and develop more precise predictions during analyzes using the Stata fweight procedure. Sample sizes for each analysis varied because of missing data on approximately 10% of surveys. To assess which factors influence hunting participation by university students, we examined the weighted estimate of past hunting participation. We then fit a blocked logistic regression model to examine the relative influence of various factors on past hunting participation. The dependent variable represented membership in 1 of 2 clusters: no previous hunting participation, including respondents who had accompanied someone on a hunt (0), and previous hunting participation (1). We added independent variables sequentially to the model in blocks, beginning with demographic variables followed by value orientations, beliefs about hunters and hunting, and social support for hunting. We assessed the contributions of each block to the overall predictive power of the model using change in Akaike's Information Criterion (AIC), block χ2, model classification accuracy, and change in Nagelkerke R2. After comparing the effects of each block, we assessed the significance of specific predictor variables in the full model using parameter estimates and odds ratios (OR). To examine the sensitivity of our analysis, we tested both weighted and unweighted models and found no significant differences. We therefore reported unweighted model results. To assess predictors of future hunting behavior, we developed 4 future hunting clusters of respondents based on a combination of past hunting experience and likelihood of future hunting. Non‐hunters were individuals who had not hunted in the past and expressed no interest in future hunting (responses of 1 or 2 on the future hunting scale). Potential hunters were individuals who had not hunted in the past but expressed possible interest in future hunting (responses of 3 to 5). Active hunters were individuals who hunted in the past and expressed strong interest in future hunting (responses of 4 or 5), plus those who indicated they were not sure about future hunting (3) but said they might still hunt rarely or regularly. Lapsed hunters were individuals who hunted in the past but indicated they had no interest in hunting in the future (responses of 1 or 2), plus those who were not sure (3) but said they might only try hunting once. We used chi‐square tests (for categorical variables) and analysis of variance (ANOVA) tests (for continuous variables) with weighted data to compare each groups' socio‐demographic attributes and beliefs about wildlife and hunting. When the assumption of unequal variances was violated, we used Welch's ANOVA with Games‐Howell post hoc tests to determine differences between future hunting subgroups. We assessed effect size using Cramer's V (for chi‐square tests) and eta (for ANOVA), applying cutoff criteria for small, medium, and large effect sizes outlined by Vaske (2019). To further 1021 �Table 1. Variables used in data analysis, with unweighted means (x̄ ) and standard deviations (SD) for single items and aggregated scales based on entire sample of university students across 22 universities in the United States, 2018–2020 (n = 17,203). Variable Race Gender Major Hometown Outdoor recreation score Wildlife value orientation: mutualistic Wildlife value orientation: dominionistic Conservation caring score Overall approval Approval: altruistic Approval: egoistic Approval: meat Approval: trophy Beliefs about hunters and hunting Motivation: altruistic Motivation: meat Motivation: egoistic Motivation: trophy Constraints: other activities Constraints: morals and comfort Constraints: skills and knowledge Constraints: logistical Constraints: judgment and experiential Social support: immediate Social support: extended a Cronbach's α x̄ SD Items Dummy variable: 1 if white, 0 if BIPOCa or mixed race Dummy variable: 1 if male‐identifying, 0 if female‐identifying or gender non‐conforming Dummy variable: 1 if majoring in field related to agriculture (Ag) or natural resources (NR), 0 if not Ag/NR field Dummy variable: 0 if urban (>50,000), 1 if rural (<50,000) Index: sum of 6 items, higher score means more participation Scale: 1 = strongly disagree to 5 = strongly agree 0.75 0.43 0.43 0.49 1 1 0.20 0.40 1 0.51 2.85 3.68 0.50 1.74 0.88 1 1 2 0.651 Scale: 1 = strongly disagree to 5 = strongly agree 2.96 0.95 2 0.596 Scale: 1 = strongly disagree to 5 = strongly agree Scale: 1 = strongly disapprove to 5 = strongly approve Scale: 1 = disapprove to 3 = approve Scale: 1 = disapprove to 3 = approve Scale: 1 = disapprove to 3 = approve Scale: 1 = disapprove to 3 = approve Scale: 1 = strongly disagree to 5 = strongly agree Scale: 1 = no to 3 = yes Scale: 1 = no to 3 = yes Scale: 1 = no to 3 = yes Scale: 1 = no to 3 = yes Scale: 1 = not at all to 4 = very much Scale: 1 = not at all to 4 = very much Scale: 1 = not at all to 4 = very much Scale: 1 = not at all to 4 = very much Scale: 1 = not at all to 4 = very much Dummy variable: 1 if ≥1 immediate family member hunts, 0 if they do not Dummy variable: 1 if ≥1 extended family member or friend hunts, 0 if they do not 4.07 3.72 2.62 2.21 2.55 1.58 3.42 2.02 2.01 1.84 1.39 3.11 2.22 2.22 1.93 1.29 0.39 0.67 1.23 0.59 0.73 0.70 0.78 0.91 0.86 0.90 0.80 0.71 1.09 1.09 1.08 0.78 0.56 0.48 4 1 2 5 1 1 9 2 1 5 1 1 4 6 6 3 1 0.799 0.27 0.44 1 Definition 0.823 0.938 0.938 0.940 0.930 0.908 0.935 0.805 0.735 BIPOC refers to individuals who are black, indigenous, or people of color. explore differences for key variables among the future hunting groups, we tested a multinomial logistic regression model comparing the 4 groups with non‐hunters as the reference category. Results of this multivariate analysis supported patterns observed in the bivariate analysis. To facilitate interpretation, we described differences among future hunting groups based on bivariate comparisons. RESULTS The overall survey response rate was 14.2% (ranging from 6.1% to 31.5% among universities), yielding a total effective sample size of 17,203 across all institutions (Table S2 provides a breakdown by university). After data weighting, the sample included 65% of respondents identifying as white, 47% identifying as male, 47% from rural hometowns or cities smaller than 50,000 residents, and 17% majoring in subjects related to agriculture or natural resources (Table 1). These ratios roughly align with the national averages of students at public universities across the United States (U.S. Census Bureau 2018). We also collected 6,585 questionnaires from students who did not respond to the initial survey invitations. Our χ2‐based non‐ response check revealed relatively minor differences between full survey respondents and these non‐respondents. Based on weighted averages across all schools, a smaller percentage of non‐respondents had hunted in the past (23% vs. 29%). A smaller percentage of non‐respondents indicated they would definitely hunt in the future (15% vs. 29%), though more said 1022 they might hunt in the future (32% vs. 27%). A larger percentage of non‐respondents were male (47% vs. 41%). The effect sizes for all of these differences were small (Cramer's V < 0.05). We observed the biggest difference for college major, with non‐respondents less likely to report agriculture or natural resource majors (12% vs. 17%, Cramer's V = 0.09). All other variables, including conservation caring and approval of hunting, were nearly identical across both groups. Survey Scales and Constructs The PCF analysis for hunting approval items identified 4 categories (Table S3): egoistic motivations focused on personal reasons for hunting such as spending time with friends and family and connecting with nature (5 items, Cronbach's α = 0.938), altruistic motivations focused on community benefits of hunting such as controlling wildlife damaging ecosystems or causing problems for people (2 items, α = 0.823), hunting to obtain meat (1 item), and hunting to obtain a trophy (1 item). The PCF analysis for beliefs about hunters and hunting identified 1 overarching factor (9 items, Cronbach's α = 0.936; Table S4). The PCF analysis for motivations to hunt yielded 4 categories identical to the approval items (Table S5): egoistic motivations (5 items, Cronbach's α = 0.939), altruistic motivations (2 items, α = 0.946), hunting to obtain meat (1 item), and hunting to obtain a trophy (1 item). The PCF analysis for hunting constraints revealed 5 categories The Journal of Wildlife Management • 85(5) �(Table S6): individual constraints focused on morality and comfort such as a reluctance to kill an animal and a personal discomfort around firearms (4 items, Cronbach's α = 0.908); skills and knowledge constraints such as lacking the knowledge and skills to prepare game meat and properly store equipment and firearms (6 items, α = 0.935); logistical constraints such as uncertainty about where to hunt and not having anyone to hunt with (6 items, α = 0.805); judgment and experience constraints such as feeling discouraged by negative experiences in the outdoors and feeling uncomfortable because of a lack of diversity in hunting (3 items, α = 0.735); and an alternative activities constraint of “I would rather do other activities” (1 item). The PCF analysis for the 4 wildlife value orientation items identified 2 factors (Table S7) that aligned with previous research (Teel and Manfredo 2010): mutualistic wildlife value orientations (2 items, Cronbach's α = 0.647) and dominionistic wildlife value orientations (2 items, α = 0.592). The PCF analysis for the 4 conservation caring items identified 1 overarching factor including statements about the importance of wildlife conservation and willingness to voluntarily spend money on conservation (Cronbach's α = 0.799; Table S8). Past Hunting Experience The weighted estimates revealed 29% of respondents (±0.7% for 95% CI) reported previous hunting experience and an additional 11% (±0.5%) had accompanied a hunter in the field. But 33% (±0.7%) of respondents who had hunted in the past had not been hunting in the last 12 months. About 59% (±0.7%) of respondents approved or strongly approved of legal, regulated hunting. Results of the full blocked logistic regression supported a strong relationship between predictors and past hunting participation ( χ182 = 9,543.7, P < 0.001, Nagelkerke pseudo R2 = 0.659). The overall rate of correct classification in the model was 87%, surpassing the proportional by chance accuracy rate cutoff criterion of 59%. Iterative incorporation of blocks in the model suggested that past participation in hunting was most strongly associated with demographic variables and beliefs about hunters or hunting, followed by social support and value orientations (Table 2). Social support for hunting among immediate family members was the single strongest predictor of past hunting participation (OR = 12.47), and support from an extended network of family and friends (OR = 1.44) was also Table 3. Parameter estimation (β) and odds ratios (OR) from a full hierarchical logistic regression model predicting past hunting participation of university students across 22 universities in the United States, 2018–2020 (n = 15,109). The unweighted percentage of students responding “Yes, I've hunted in the past” was 31%. Cragg‐Uhler (Nagelkerke) R2 = 0.659, classification accuracy = 86.8%, χ182 = 9,543.7, P < 0.001. β SE −9.709 0.306 −0.268 0.063 0.069 0.091 0.067 0.073 0.765** 1.065 1.071 0.75 0.57 0.337 0.077 1.400*** 0.43 0.81 1.402 0.060 4.064*** 0.19 0.390 0.069 1.476*** 0.49 3.68 0.340 −0.080 0.055 0.036 1.404*** 0.923* 2.95 −0.051 0.033 0.950 4.08 3.73 2.22 2.62 2.56 1.58 3.42 0.262 0.245 0.220 −0.108 −0.079 0.563 1.087 0.048 0.034 0.065 0.070 0.066 0.040 0.059 1.300*** 1.278*** 1.245** 0.897 0.924 1.756*** 2.960*** 0.363 2.523 0.084 0.075 1.438*** 12.465*** x̄ Variables Constant Region (reference = Midwest) Northeast Southeast West Race or ethnicity (reference = BIPOCa or mixed race) White Gender (reference = female or non‐conforming) Male College major (reference = not Ag/NR) Agriculture (Ag) or natural resources (NR) Childhood location (reference = urban) Rural Wildlife value orientation: mutualisticb Wildlife value orientation: dominionisticb Conservation caringb Overall approvalc Approval: egoismd Approval: altruismd Approval: meatd Approval: trophyd Beliefs and attitudes about hunters and huntingb Social support (reference = no support) Extended support Immediate family support 0.34 0.13 0.30 0.23 0.25 OR 0.51 0.34 0.27 0.39 *, **, *** denote statistically significant odds ratios (OR) at α = 0.05, 0.01, and 0.001, respectively. a BIPOC refers to individuals who are black, indigenous, or people of color. b Scale: 1 = strongly disagree to 5 = strongly agree. c Scale: 1 = strongly disapprove to 5 = strongly approve. d Scale: 1 = disapprove to 3 = approve. important (Table 3). Among the variables in the demographic block, all but region were statistically significant. Students who were male (OR = 4.06), white (OR = 1.40), agriculture or natural resource majors (OR = 1.48), and Table 2. Relative predictive power of distinct variable blocks in a hierarchal logistic regression model predicting past hunting participation among university students across 22 universities in the United States, 2018–2020 (n = 15,109). Logistic regression variable block Δ R2 a Accuracyb AICc χ2 df P Demographics Value orientations Beliefs about hunting Social support Full model 0.292 +0.047 +0.222 +0.098 0.659 74.8% 76.8% 83.6% 86.8% 86.8% 15,227.8 14,582.7 11,089.8 9,225.0 9,225.0 3,518.8 651.1 3,504.9 1,868.8 9,543.7 7 3 6 2 18 <0.001 <0.001 <0.001 <0.001 <0.001 a b c Refers to Nagelkere pseudo‐R2; + denotes change in R2. Refers to model classification accuracy rate. Akaike's Information Criterion. Vayer et al. • Hunting and College Students 1023 �from rural areas (OR = 1.40) were more likely to report previous hunting participation (Table 3). Among variables in the beliefs block, positive beliefs about hunters and hunting (OR = 2.96), overall approval of hunting (OR = 1.28), and approval for egoistic (OR = 1.25) and trophy‐seeking reasons (OR = 1.76) were all positively associated with past hunting participation. Approval of hunting for altruistic reasons (civic or conservation purposes) and to obtain local, ethically sourced meat did not significantly predict past hunting participation. Of the variables in the value orientation block, conservation caring scores (OR = 1.30) were positively associated with, and mutualistic value orientations were negatively associated with (OR = 0.92), past hunting participation (Table 3). Future Hunting Participation Our weighted estimates revealed that 19% (±0.6% for 95% CI) of respondents in our sample reported they would definitely hunt in the future and 27% (±0.7%) reported they might hunt in the future. Integrating responses from the past hunting question, we placed students into 4 different groups: 50% (±0.7%) of all students were non‐ hunters, 22% (±0.6%) were potential hunters, 26% (±0.7%) were active hunters, and 3% (±0.2%) were lapsed hunters. Among potential hunters, 36% of respondents indicated they might try it once, 49% reported they might hunt rarely, and 15% indicated they intended to hunt regularly. About 76% of active hunters intended to hunt regularly in the future. Membership in the 4 groups varied based on socio‐demographic attributes and social support (Fig. 2) and hunting‐related beliefs, motivations, and constraints (Fig. 3). Most BIPOC (64%) and female (66%) respondents were non‐hunters, though both groups were well represented in the potential hunter group (23% and 19%, respectively; Table S9, available online in Supporting Information). Most students from urban hometowns (56%) and majors other than agriculture or natural resources (53%) were non‐ hunters, although some were potential hunters (20% and 21%, respectively). Whereas 74% of students who lacked social support were in the non‐hunter category, only 5% were in the active hunter category. Nearly 20% of students without any social support were in the potential hunting group (Table S9). When we examined distributions of students within each future hunting subgroup, active hunters primarily were white (84%), male (74%), and from rural hometowns (62%; Fig. 2; Table S9). About 81% of active hunters had immediate family who hunted, and only 7% reported no social support for hunting. Potential hunters were more diverse than current hunters: 38% of potential hunters were BIPOC or mixed race, 47% were female, 79% were not agriculture or natural resource majors, 43% were from urban hometowns, and 74% did not have immediate family members who hunt. Lapsed hunters were mostly from rural hometowns, white, male, and enrolled in disciplines outside the natural resources. Lapsed hunters were similar to active hunters with respect to these characteristics, but similarities ended with social support; 53% of lapsed hunters reported having immediate familial support compared to 81% of active hunters. Non‐hunters, the largest group of students, were 55% white, 72% female, and mostly majoring in disciplines outside of agriculture or natural resources. These students were more frequently from urban areas and lacked Figure 2. Demographic attributes of college students across 22 universities in the United States, 2018–2020, assigned to 4 future hunting groups based on survey responses: non‐hunters (n = 7,820, 50% of sample), potential hunters (n = 3,572, 22% of sample), active hunters (n = 4,421, 26% of sample), and lapsed hunters (n = 718, 3% of sample). Distribution represents weighted percentage of students within each hunting group (with 95% CI) defined by race or ethnicity (% white), gender (% male), major (% agriculture [ag] or natural resource [nat res] major), childhood location (% rural), and social support for hunting (% immediate family and % extended family and friends). Weights accounted for enrollment, gender, and race ratios across schools and were rounded to nearest integers in chi‐square analysis. All chi‐square tests are significant at P < 0.001. Effect size denoted as * = small (0.1), ** = medium (0.3), and *** = large (0.5). 1024 The Journal of Wildlife Management • 85(5) �Figure 3. Comparison of mean ratings among future hunting groups of college students across 22 universities in the United States, 2018–2020, based on A) wildlife value orientations (WVO) and beliefs about conservation and hunting, B) constraints to hunting, C) reasons to approve of hunting, and D) motivations to hunt. All variables represent aggregate scales. Value and belief variables were rated on a scale from 1 = strongly disagree to 5 = strongly agree. Constraints were rated on a scale from 1 = not at all to 4 = very much a barrier. Approval items were rated on a scale from 1 = disapprove to 3 = approve. Motivations were rated on a scale from 1 = no, I would not hunt for this purpose to 3 = yes, I would hunt for this purpose. Effect size denoted as * = small (0.1), ** = medium (0.3), and *** = large (0.5). social support for hunting (Fig. 2; Table S9). We also observed differences between future hunting groups with respect to other outdoor recreation activities, with non‐ hunters participating in fewer non‐consumptive outdoor recreation activities (x̄ = 2.39) than potential hunters (x̄ = 2.76) and active hunters (x̄ = 3.23; Table S10, available online in Supporting Information). We found differences with large effect sizes between the 4 respondent groups based on their hunting‐related beliefs, motivations, and constraints (Fig. 3; Table S10). Active hunters had the most positive beliefs about hunters and hunting, followed by potential hunters, lapsed hunters, and non‐hunters (Fig. 3A). Conservation caring and wildlife value orientation scores were similar across all groups, although dominionistic value orientations were slightly higher among active and potential hunters (Fig. 3A). The constraint most frequently cited among non‐hunters, potential hunters, and lapsed hunters was “I would rather do other activities,” but non‐hunters and lapsed hunters ranked this constraint as more important than other groups (Fig. 3B). Non‐hunters ranked moral constraints higher than the other groups, and active hunters ranked logistical constraints higher than the other groups. Knowledge‐related constraints were prominent for potential hunters. Approval of hunting for different purposes varied among the 4 groups, with potential and active hunters ranking altruistic, egoistic, Vayer et al. • Hunting and College Students and harvest‐oriented reasons for hunting as more acceptable than non‐hunters and lapsed hunters. All 4 groups generally viewed altruistic and harvest‐oriented reasons for hunting positively (Fig. 3C). Potential and active hunters ranked altruistic, egoistic, and harvest‐oriented reasons for hunting as more important than non‐hunters and lapsed hunters, though altruistic motivations were rated as most important within the 2 non‐hunting groups (Fig. 3D). The multinomial logistic regression analysis of future hunting correlates highlighted similar patterns, supporting key group attributes described above (Table S11, available online in Supporting Information). DISCUSSION This study suggests university students represent a promising target for R3 efforts. The percentage of students who engaged in hunting in the past (29%) is higher than national, self‐reported estimates of past hunting participation among all adults in the United States (23%; Manfredo et al. 2018). The proportion of university students who say they are active hunters (26%) is higher than the general population's annual purchase rate for hunting licenses (5%; USFWS 2020). Additionally, a substantial percentage of university students without previous hunting experience (22%) would consider hunting in the future, higher than national estimates of future interest in hunting among the 1025 �general public (16%; Manfredo et al. 2018). These numbers show there are many active—and perhaps even more prospective—hunters on diverse college campuses around the United States. A better understanding of university students and the factors influencing their relationship with hunting could inform future R3 research and programming. Our results confirmed the persistence of traditional pathways into hunting and the important role of social support in the outdoor recreation adoption model (Byrne and Dunfee 2018). We found that traditional hunter characteristics (e.g., rural hometown, male, white, social support from immediate family) were strongly associated with past and future hunting participation, a pattern that has been observed in other studies (Brown et al. 2000, Stedman and Heberlein 2009, Larson et al. 2014). Although social support from extended family (i.e., grandparents, aunts, uncles, other relatives) and friends was important for university students, social support from immediate family (i.e., mother, father, siblings) was among the strongest correlates of past and future hunting participation. Family relationships that focus on hunting across generations cultivate positive connections and access to the activity from an early age (O'Leary et al. 1987), influencing identity adoption and recreation participation later in life (Heberlein and Thomson 1996, Stedman and Heberlein 2009). The cultural contexts and social habitats that support hunting behaviors have always been key to R3 (Larson et al. 2014), and they may be especially important on university campuses where access to prototypical rural hunting settings is limited. About half of potential hunters were in non‐traditional hunter demographic categories (i.e., female, racial minority, ethnic minority, urban), and they reported different pathways into hunting than more traditional participants. Potential hunters rarely enjoyed the social support from immediate family members that was familiar to active hunters; however, many potential hunters did acknowledge support from friends and extended family. These indirect connections to hunting may be a fruitful avenue for NTPH‐ focused R3 efforts, providing a unique pool of mentors and social support for hunting that is largely absent among students and young adults drawn to hunting later in life (Quartuch et al. 2017, Ringelman et al. 2020). As hunting participation among NTPHs, especially women (Heberlein et al. 2008, Metcalf et al. 2015), continues to increase, understanding and nurturing their unique pathways into hunting will be critical (Quartuch et al. 2017). A desire to engage in other activities instead of hunting was the largest constraint to hunting among all groups except active hunters, perhaps not surprising because university students are exposed to a wide range of activity choices across campus (Ravert 2009). Potential hunters identified lack of skills and knowledge as the second largest constraint to participation. This is promising for managers and agencies who can directly address skill and knowledge deficiencies through strategic programming (Ringelman et al. 2020, Vayer 2020). Patterns in reported constraints also highlight the influence of growing public 1026 discourse about the morality of hunting (Fischer et al. 2013). Like non‐hunters, lapsed hunters reported preferences for other activities and moral and comfort barriers as major constraints to participation. State wildlife agencies might struggle to address constraints faced by non‐hunters and lapsed hunters because these tend to be intrapersonal constraints that students navigate on their own (Kocak 2017). Unlike other groups, active hunters indicated logistical constraints (e.g., losing access to hunting land, lacking free time to hunt) were their primary reasons for not hunting. Moving away from familiar areas to attend college was a common issue for active hunters. Other studies report similar results, with active hunters likely to indicate structural constraints (Wright et al. 2001, Barro and Manfredo 1996, Metcalf et al. 2015). Our findings support the assertion that constraints are hierarchical (Crawford et al. 1991, Wright et al. 2001), with new constraints emerging and growing in importance as engagement with an activity increases. For example, logistical constraints to hunting may be irrelevant to students who lack interest and motivation and are unable to negotiate moral and comfort barriers. Similarly, students who lack the financial resources to hunt may not cite cost as a constraint because they only learn about costs after negotiating moral and comfort barriers. These results suggest that an R3 initiative will not effectively recruit or retain every student; a variety of approaches are needed to help diverse subgroups of students negotiate specific types of constraints (Raymore 2002). Procurement of ethically and locally sourced meat was the most important hunting motivation for all groups of respondents. Game meat harvest has been recognized as a prominent reason for hunting (Duda et al. 2010), and may be particularly important for NTPHs hoping to access local, free range meat (Tidball et al. 2013, Stedman et al. 2017). For potential hunters in our sample, the 2 strongest motivations to hunt were to obtain game meat and to support conservation (e.g., controlling overabundant wildlife populations for the benefit of ecosystems). Results suggest R3 efforts that capitalize on altruistic reasons for hunting could be popular among urban dwellers and young adults (Decker et al. 2015, Byrd et al. 2017). Egoistic motivations for hunting such as being closer to nature and relaxing or escaping from everyday life were popular among active hunters, slightly less important among potential hunters, and minimally important to lapsed hunters and non‐ hunters. Hunting for trophies, on the other hand, was strongly opposed by every group except active hunters, reflecting ethical concerns documented in the general population (Gunn 2001). Overall, our results indicate that all groups of students, including non‐hunters, might be willing to support or perhaps even engage in hunting focused on game meat harvest or altruistic goals. Other studies have revealed similar trends regarding hunting motivations (Larson et al. 2014) and public support for hunting (Decker et al. 2015, Byrd et al. 2017), which might influence the way managers communicate about hunting and attempt to recruit NTPHs. The Journal of Wildlife Management • 85(5) �Efforts to create and expand R3 efforts at universities could have conservation benefits that extend beyond increased hunting participation. University students reported less support for hunting (59%) than adults in the American public (70–80%; Duda et al. 2010, Responsive Management 2017), providing room to bolster support for hunting via strategic, university‐focused messaging and programming. Because positive beliefs about hunting lead to more consistent participation and presumably more political and social support for hunting, it is critical to frame hunting in a way that resonates with a diverse public (Larson et al. 2014, Byrd et al. 2017, Manfredo et al. 2018). Positive beliefs are often associated with familial role models who reinforce the value of hunting, yet this familial support is absent for many university students. The campus environment provides alternative support mechanisms (e.g., student organizations) that influence identity development during the period of emerging adulthood (Arnett 2000, Nelson and Barry 2005). Positive social interactions with peers who are active and potential hunters could affect the way students think and act with respect to hunting (Johnson and Goldman 2011). These interactions might persuade some non‐hunters to become hunting advocates, leading to more support for hunting and conservation‐related policies (Stedman and Decker 1996). This potential is underscored by the fact that our respondents, whether or not they hunted, generally reported pro‐conservation attitudes and mutualistic wildlife value orientations. Such patterns may reflect a broader shift in wildlife value orientations among young adults, mirroring trends reported in the larger population across the United States (Manfredo et al. 2016, 2020). Trends revealed in our study also present opportunities for wildlife management agencies (Manfredo et al. 2016). Stronger emphasis on the conservation implications of hunting might attract new groups inspired by pro‐ conservation motivations (Larson et al. 2014, Stayton et al. 2017). Emphasis on connections between conservation and hunting might also help to alleviate perceived conflicts among hunters, environmental advocates, and the general population (Knezevic 2009). These beliefs and values suggest strong interest in conservation among diverse university students that might translate into future support for innovative conservation funding strategies (Serfass et al. 2018). Leveraging common ground could help to create a more cohesive and sustainable base of support among hunters and non‐hunters, ultimately advancing wildlife agency missions, policies, and conservation goals (Blascovich and Metcalf 2019). Limitations Although many of our binary demographic predictor variables were strong correlates of hunting participation, future research could investigate nuanced differences within demographic subgroups to assist wildlife agencies with marketing and recruitment methods. This might include examination of potential variation among BIPOC subpopulations (Shinew et al. 2006), in addition to interactions Vayer et al. • Hunting and College Students among different demographic groups (e.g., women from urban areas, Latinx students who are not natural resource majors). Such interactions may be particularly important when considering constraints to hunting participation (Shores et al. 2007, Rushing et al. 2019). Our quantitative approach enabled us to cover a wide geographic area and study a range of possible hunting behaviors and correlates, but a qualitative approach would deepen understanding of students' broader engagement with hunting and potentially reveal mechanisms behind some of the observed patterns. The self‐reported nature of past hunting and intended future hunting behaviors is another limitation of our study. Although self‐reported behavior and behavioral intent are widely viewed as effective measures of overt behavior (Ajzen and Driver 1992), particularly in hunting studies (Hrubes et al. 2003, Larson et al. 2014), there is potential for social desirability bias in responses. Our sample was large and geographically diverse, but the study included only large public universities and most were land grant institutions with a longstanding emphasis on agriculture and natural resource management. Our sampling frame may be biased in unknown ways because it did not represent students at all types of institutions (e.g., private schools, smaller public schools, community colleges). Furthermore, our decision to focus on 18–34‐year‐old students at large institutions excluded non‐traditional undergraduates (e.g., older adults pursuing college degrees), and our low response rate to the online survey across most schools raises questions about potential response bias. But our non‐response check suggested the survey was representative of the student population at the 22 universities we surveyed, both demographically and behaviorally (with respect to hunting participation). Our use of post‐ stratification weighting based on student enrollment and demographic data allowed us to account for potential sampling bias and develop more precise estimates. But non‐ response checks and population proportion‐based post hoc weighting do not fully eliminate response bias in online surveys (Vaske et al. 2011), and it is not clear how our sample of university students compares to other populations of young adults. MANAGEMENT IMPLICATIONS Our study demonstrated interest in hunting among diverse university students, highlighting the growing importance of non‐traditional pathways into hunting and revealing unique subgroups (i.e., market segments) of hunters and non‐ hunters that could assist with R3 programming on university campuses. For R3 program managers interested in recruitment at universities, the potential hunter subgroup of students is an ideal target. This group was large, amenable to hunting, and far more diverse than other subgroups (with the exception of non‐hunters). To effectively connect with NTPHs in the potential hunter group and foster a more inclusive hunting community, agencies need messages and communication strategies that resonate with diverse populations. This might include development of peer support networks to fill existing gaps in social support for hunting 1027 �and creation of R3 spaces where non‐traditional (e.g., female, BIPOC) voices are welcomed and amplified. An enhanced emphasis on game meat harvest and conservation connections are motivating factors for many students and offer ways to attract and retain potential NTPHs. These strategies could help new hunters from non‐traditional backgrounds overcome skill and knowledge deficits and find the support needed for sustained hunting participation. To enhance retention and reactivation, more information and resources are needed to help university students who hunt (or would like to hunt) overcome structural and logistical constraint. Possible solutions include offering information about local hunting opportunities, providing transportation to improve access to game lands, facilitating hunting equipment storage for students, and fostering peer networks of active hunters (possibly using digital platforms that are frequently used by students) to reinforce social support. Using these approaches, wildlife agencies can collaborate with university partners to develop more effective tools and strategies as they seek to reverse declines in hunting participation and change the contemporary face of hunting in the United States. ACKNOWLEDGMENTS We thank all of the collaborators at universities and state wildlife agencies around the United States who assisted with questionnaire design, survey implementation, and data management and analysis (see Table S1 for full list of collaborators). The authors would also like to thank the anonymous reviewers for their constructive feedback on earlier versions of this manuscript. This research was funded by the Association of Fish and Wildlife Agencies' Multistate Conservation Grant Program (U.S. Fish and Wildlife Service Awards F18AP00171 and F19AP00094). All data used in this study may be accessed at https://doi. org/10.5061/dryad.dz08kprx8. LITERATURE CITED Acock, A. C. 2016. A gentle introduction to Stata (fifth edition). Stata Press, College Station, Texas, USA. Ajzen, I., and B. L. Driver. 1992. Application of the Theory of Planned Behavior to leisure choice. Journal of Leisure Research 24:207–224. Arnett, J. J. 2000. 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Wright, B. A., E. B. D. Rodgers, and K. F. Backman. 2001. Assessing the temporal stability of hunting participation and the structure and intensity of constraints: a panel study. Journal of Leisure Research 33:450–469. Associate Editor: Terry Messmer. SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article at the publisher's website. The Journal of Wildlife Management • 85(5) � https://cpw.cvlcollections.org/files/original/069ef5a67b6b74bd6b3cab91c36417ad.pdf 546280e8905082d7bf704415324e4334 PDF Text Text Table S1. Full list of project collaborators at (a) universities and (b) state wildlife agencies by United State Fish and Wildlife Service region and state. a) Region SEAFWA WAFWA WAFWA SEAFWA SEAFWA SEAFWA SEAFWA MAFWA WAFWA WAFWA SEAFWA MAFWA MAFWA WAFWA SEAFWA SEAFWA SEAFWA SEAFWA SEAFWA SEAFWA MAFWA NEAFWA NEAFWA MAFWA WAFWA NEAFWA SEAFWA MAFWA SEAFWA SEAFWA WAFWA WAFWA WAFWA NEAFWA MAFWA MAFWA State AL CA CO FL GA GA GA IN KS KS KY MI MI MT NC NC NC NC NC NC NE NY NY OH OR PA SC SD TN TN TX TX TX VA WI WI Role University University University University University University University University University University University University University University University University University University University University University University University University University University University University University University University University University University University University Organization Auburn University California State University, Fresno University of Northern Colorado University of Florida University of Georgia University of Georgia University of Georgia Indiana University Kansas State University Kansas State University University of Kentucky Michigan Tech University Michigan Tech University University of Montana North Carolina State University North Carolina State University North Carolina State University North Carolina State University North Carolina State University North Carolina State University University of Nebraska, Lincoln Cornell University Cornell University Ohio State University Oregon State University Pennsylvania State University Clemson University South Dakota State University University of Tennessee University of Tennessee Texas A&M University Texas A&M University Texas A&M University Virginia Tech University University of Wisconsin University of Wisconsin Name Wayde Morse Jason Whiting Chelsie Romulo Taylor Stein Kris Irwin Kyle Woosnam Sam Keith James Farmer Adam Ahlers Ryan Sharp Matt Springer Matt Kelly Richelle Winkler Elizabeth Metcalf Lincoln Larson Jerry Lee Nils Peterson Daniel Choi Torey Vayer Rich von Furstenberg Chris Chizinski William Siemer Rich Stedman Jeremy Bruskotter Mark Needham Alan Graefe Shari Rodriguez Larry Gigliotti Neelam Poudyal Kiley Davan Larry Hysmith Gerard Kyle Ben Ghasemi Ashley Dayer Tim Van Deelen Christine Anhalt-Depies �b) Region SEAFWA SEAFWA WAFWA WAFWA WAFWA WAFWA WAFWA SEAFWA SEAFWA SEAFWA MAFWA WAFWA SEAFWA SEAFWA MAFWA WAFWA SEAFWA SEAFWA MAFWA MAFWA NEAFWA NEAFWA MAFWA MAFWA WAFWA WAFWA NEAFWA SEAFWA MAFWA SEAFWA SEAFWA WAFWA NEAFWA MAFWA State AL AL CA CA CO CO CO FL GA GA IN KS KY KY MI MT NC NC NE NE NY NY OH OH OR OR PA SC SD TN TN TX VA WI Role Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Agency Organization Alabma Dept. of Conservation & Natural Resources Alabma Dept. of Conservation & Natural Resources California Dept. of Fish & Wildlife California Dept. of Fish & Wildlife Colorado Parks & Wildlife (CPW) Colorado Parks & Wildlife (CPW) Colorado Parks & Wildlife (CPW) Florida Fish & Wildlife Conservation Commission Georgia Wildlife Federation (GWF) Georgia Dept. of Natural Resources (GADNR) Indiana Dept. of Natural Resources Kansas Dept. of Wildlife, Parks & Tourism Kentucky Dept. of Fish & Wildlife Kentucky Dept. of Fish & Wildlife Michigan Dept. of Natural Resources Montana Fish, Wildlife & Parks North Carolina Wildlife Resources Commission (NCWRC) North Carolina Wildlife Resources Commission (NCWRC) Nebraska Game & Parks Commission Nebraska Game & Parks Commission New York Dept. of Environmental Conservation (NYDEC) New York Dept. of Environmental Conservation (NYDEC) Ohio Dept. of Natural Resources Ohio Dept. of Natural Resources (and NWTF) Oregon Dept. of Fish & Wildlife Oregon Dept. of Fish & Wildlife Pennsylvania Game Commission South Carolina Dept. of Natural Resources (SCDNR) South Dakota Game, Fish & Parks Tennessee Wildife Resources Agency Tennessee Wildife Resources Agency Texas Parks & Wildlife Dept. Virginia Dept. of Game & Inland Fisheries Wisconsin Dept. of Natural Resources Name Marisa Futral Amy Silvano Robert Pelzman Clark Blanchard Mike Quartuch Bryan Posthumus Travis Long Tyler Allen Charles Evans Walter Lane Jack Basiger Aaron Austin Becky Wallen Brian Clark Steve Beyer Greg Lemon Deet James Chet Clark Micaela Rahe Jeff Rawlinson Mike Schiavone Kelly Stang Eric Postell Johanna Dart Allen Molina Brandon Dyches Coren Jagnow Billy Downer Taniya Bethke Randy Huskey Michael May Steve Hall Brian Moyer Keith Warnke �Table S2. (a) Response rate data, (b) enrollment data, and (c) weighting calculations by university campus for surveys of university students in the United States, 2018–2020. The total normalized weight applied to each individual response was multiplicative: school × gender × race or ethnicity. Excel files available upon request. a) School Auburn University California State University, Fresno University of Northern Colorado University of Florida University of Georgia Ball State University Kansas State University University of Kentucky Michigan Tech University University of Montana NC State University University of Nebraska Cornell University Ohio State University Oregon State University Penn State University Clemson University South Dakota State University University of Tennessee Texas A&M University Virginia Tech University of Wisconsin Totals and averages State AL CA CO FL GA IN KS KY MI MT NC NE NY OH OR PA SC SD TN TX VA WI Implementation 2020: Mar-April 2019: Feb-March 2018: Aug-Sept 2019: Sept-Oct 2019: March-April 2018: March-Oct 2018: Feb-March 2018: Nov-Dec 2018: Nov-Dec 2018: Apr-May 2018: Feb-March 2019: Sept-Oct 2018: April 2018: April-May 2019: Nov 2019: Feb-March 2018: Nov-Dec 2019: April 2019: Sept 2019: Sept-Oct 2019: Oct 2018: April-May Sample Completed surveys 5,000 645 5,000 918 3,000 482 10,000 594 7,500 1,111 7,000 423 5,000 845 1,758 370 3,000 946 3,000 478 5,000 893 5,000 1,089 3,701 745 5,000 738 5,000 796 5,000 530 5,000 740 8,552 928 5,000 1,088 16,000 1,122 5,000 1,017 5,000 705 123,511 17,203 Response rate 12.90% 18.36% 16.07% 5.94% 14.81% 6.04% 16.90% 21.05% 31.53% 15.93% 17.86% 21.78% 20.13% 14.76% 15.92% 10.60% 14.80% 10.85% 21.76% 7.01% 20.34% 14.10% 13.93% �b) School Auburn University California State University, Fresno University of Northern Colorado University of Florida University of Georgia Ball State University Kansas State University University of Kentucky Michigan Tech University University of Montana NC State University University of Nebraska Cornell University Ohio State University Oregon State University Penn State University Clemson University South Dakota State University University of Tennessee Texas A&M University Virginia Tech University of Wisconsin Totals and averages State AL CA CO FL GA IN KS KY MI MT NC NE NY OH OR PA SC SD TN TX VA WI Implementation 2020: Mar-April 2019: Feb-March 2018: Aug-Sept 2019: Sept-Oct 2019: March-April 2018: March-Oct 2018: Feb-March 2018: Nov-Dec 2018: Nov-Dec 2018: Apr-May 2018: Feb-March 2019: Sept-Oct 2018: April 2018: April-May 2019: Nov 2019: Feb-March 2018: Nov-Dec 2019: April 2019: Sept 2019: Sept-Oct 2019: Oct 2018: April-May Enrollment Ratio of total enrollment Female proportion Female students White ratio White students 24,628 0.046 0.484 11,920 0.792 19,505 22,125 0.042 0.588 13,010 0.178 3,938 10,232 0.019 0.650 6,651 0.579 5,924 35,491 0.067 0.563 19,981 0.530 18,810 29,611 0.056 0.568 16,819 0.689 20,402 16,160 0.030 0.596 9,631 0.783 12,653 17,869 0.034 0.471 8,416 0.777 13,884 22,136 0.042 0.555 12,285 0.753 16,668 5,797 0.011 0.275 1,594 0.880 5,101 8,306 0.016 0.554 4,602 0.762 6,329 25,199 0.047 0.466 11,743 0.671 16,909 20,830 0.039 0.472 9,832 0.745 15,518 15,182 0.028 0.530 8,046 0.364 5,526 46,820 0.088 0.487 22,801 0.666 31,182 25,699 0.048 0.465 11,950 0.627 16,113 40,363 0.076 0.468 18,890 0.655 26,438 19,669 0.037 0.490 9,638 0.814 16,011 10,544 0.020 0.532 5,609 0.864 9,110 22,815 0.043 0.508 11,590 0.782 17,841 53,743 0.101 0.473 25,420 0.603 32,407 27,811 0.052 0.430 11,959 0.652 18,133 31,705 0.060 0.513 16,265 0.699 22,162 532,735 1.000 0.504 268,653 0.658 350,566 �c) School Auburn University California State University, Fresno University of Northern Colorado University of Florida University of Georgia Ball State University Kansas State University University of Kentucky Michigan Tech University University of Montana NC State University University of Nebraska Cornell University Ohio State University Oregon State University Penn State University Clemson University South Dakota State University University of Tennessee Texas A&M University Virginia Tech University of Wisconsin Totals and averages State AL CA CO FL GA IN KS KY MI MT NC NE NY OH OR PA SC SD TN TX VA WI Ratio of total respondents Prop. female responses No. female responses Prop. white responses No. white responses 0.037 0.502 324 0.866 559 0.053 0.673 618 0.255 234 0.028 0.747 360 0.702 338 0.035 0.649 386 0.617 366 0.065 0.656 729 0.732 813 0.025 0.665 281 0.871 368 0.049 0.567 479 0.864 730 0.022 0.698 258 0.753 279 0.055 0.330 312 0.855 809 0.028 0.567 271 0.882 422 0.052 0.533 476 0.773 690 0.063 0.540 588 0.812 884 0.043 0.649 484 0.527 393 0.043 0.616 455 0.775 572 0.046 0.566 451 0.724 576 0.031 0.523 277 0.781 414 0.043 0.572 423 0.882 653 0.054 0.509 472 0.958 889 0.063 0.553 602 0.816 888 0.065 0.568 637 0.652 732 0.059 0.410 417 0.721 733 0.041 0.600 423 0.810 571 1.000 0.565 9,722 0.751 12,913 Other Weights: Female Male Gender AVERAGE White Non-white Race AVERAGE School weight Normalized weight 1.233 1.149 0.778 0.725 0.685 0.639 1.929 1.797 0.861 0.802 1.234 1.149 0.683 0.636 1.932 1.800 0.198 0.184 0.561 0.523 0.911 0.849 0.618 0.575 0.658 0.613 2.049 1.908 1.043 0.971 2.459 2.291 0.858 0.800 0.367 0.342 0.677 0.631 1.547 1.441 0.883 0.823 1.452 1.353 1.073 1.000 Actual Normalized 0.892 0.878 1.140 1.122 1.016 1.000 0.877 0.780 1.371 1.220 1.124 1.000 �Table S3. Exploratory factor analysis using principal component factor analysis (PCF) depicting structure of itemsa describing approval of hunting for different reasons among university students in the United States, 2018–2020 Factor Loadingsb Factor (with Items) Mean SD 1 2 3 1. Altruistic motivations 2.616 0.590 (2 items, Cronbach’s α = 0.8233) To control wildlife populations that are 2.705 0.580 0.9045 damaging ecosystems To control wildlife populations that are 2.527 0.695 0.8507 causing problems for people 2. Meat-focused reasons (1 item) To obtain local, free-range meat 2.553 0.698 0.5971 0.5415 3. Egoistic reasons 2.212 0.732 (5 items, Cronbach’s α = 0.9375) 0.8559 To spend time with friends or family 2.348 0.785 0.8552 To be closer to nature and the outdoors 2.373 0.790 0.8317 To seek a new adventure 2.222 0.823 0.4639 0.7626 To relax or escape from everyday life 2.091 0.844 0.5860 0.6553 To engage in sport/recreation 2.030 0.847 4. Trophy reasons (1 item) 0.8888 To harvest a trophy animal 1.577 0.780 Items rated on scale ranging from 1 = disapprove to 3 = approve. Only varimax (orthogonal) rotated factor loadings >0.400 are reported. Meat loaded onto factor 1 and factor 2. As a result, we pulled it out as its own factor. Note: PCF indicated an optimal 2-factor solution that accounted for 81.60% of the variance, with 3 factors containing eigenvalues >0.5 and explaining >10% of the cumulative variance. Kaiser-Myer-Olkin test = 0.904 and Bartlett’s test of spherecity χ2(36) = 1.02e05, P < 0.001. A composite score was created for the factor, based on the mean of the items. Higher scores indicated a greater degree of alignment with the reason for approval. a b �Table S4. Exploratory factor analysis using principal component factor analysis (PCF) depicting one-factor structure of respondents’ beliefs about hunters and hunting among university students in the United States, 2018–2020 Factor Loadingsb Factor (with Items) Mean SD 1 1. Beliefs about hunters and hunting 3.416 0.906 (9 items, Cronbach’s α = 0.9364) 0.8770 Hunting is a wise use of natural resources 3.307 1.131 Hunting provides a direct way to connect to nature and 0.8545 3.497 1.177 ecosystems 0.8471 Hunters care about conserving wildlife and natural resources 3.214 1.117 0.8331 Hunters financially contribute to wildlife conservation 3.416 1.152 Hunting can be an ethical means to acquire locally sourced 0.8162 3.861 1.018 meat People who want to hunt should be provided the opportunity to 0.8131 3.651 1.031 do so 0.7946 Hunting is a safe activity 3.132 1.105 Hunters behave responsibly and follow hunting laws 0.7541 3.195 1.048 0.7489 Hunting is cruel and inhumane to the animals (reverse coded) 3.480 1.218 Items rated on scale ranging from 1 = strongly disagree to 5 = Strongly agree Only varimax (orthogonal) rotated factor loadings > 0.400 are reported Note: PCF indicated an optimal single-factor solution (eigenvalue = 5.999) that accounted for 66.66% of the cumulative variance. Kaiser-Meyer-Olkin test = 0.949 with Bartlett’s test of sphericity χ2-(36) = 1.00e05, p < 0.001. A composite score was created for the factor, based on the mean of the items. Higher scores indicated greater positive beliefs about hunters and hunting. a b �Table S5. Exploratory factor analysis using principal component factor analysis (PCF) depicting structure of items describing motivations for participating in huntinga among university students in the United States, 2018-2020 Factor Loadingsb Factor (with Items) Mean SD 1 2 3 1. Altruistic motivations 2.020 0.859 (2 items, Cronbach’s α = 0.9394) To control wildlife populations that are 0.8962 2.078 0.882 damaging ecosystems To control wildlife populations that are 0.8697 1.963 0.887 causing problems for people 2. Meat-focused motivations (1 item) 2.011 0.896 0.5078 0.6845 To obtain local, free-range meat 2.011 0.894 3. Egoistic motivations 1.842 0.800 (5 items, Cronbach’s α = 0.9455) 0.8013 To be closer to nature and the outdoors 1.927 0.907 0.7985 To seek a new adventure 1.890 0.885 0.7971 To spend time with friends or family 1.981 0.897 0.7646 To relax or escape from everyday life 1.725 0.873 0.6855 To engage in sport/recreation 1.694 0.854 4. Trophy motivations (1 item) 1.391 0.712 0.9104 To harvest a trophy animal 1.391 0.712 Items rated on scale ranging from 1 = no to 3 = yes Only varimax (orthogonal) rotated factor loadings > 0.400 are reported Note: PCF indicated an optimal three-factor solution that accounted for 86.1% of the variance, with 3 factors containing eigenvalues > 0.5 and explaining > 6% of the cumulative variance. Initial factor analysis showed 1 factor with all motivations similar, to discern more differences we chose a more liberal cut off point with min eigenvalues of 0.5. Meat cross-loads so we pulled it out and made it its own factor. Kaiser-Meyer-Olkin test = 0.923 with Bartlett’s test of sphericity χ2(36) = 1.29e05, p < 0.001. A composite score was created for the factor, based on the mean of the items. Higher scores indicated a greater degree of alignment with the motivation. a b �Table S6. Exploratory factor analysis using principal component factor analysis (PCF) depicting structure of itemsa describing perceived constraints to hunting participation among university students across the United States, 2018-2020 Factor Loadingsb Factor (with Items) Mean SD 2 3 4 5 1. Other Activities (1 item) 3.108 1.087 Would rather do other activities 3.108 1.087 0.5760* 2. Moral & Comfort constraints 2.220 1.089 (4 items, Cronbach’s α = 0.9079) Have moral/ethical objections to 0.8864 2.249 1.218 hunting Reluctant to personally kill an 0.8680 2.559 1.298 animal Don’t feel comfortable around 0.8302 2.029 1.191 hunters and hunting culture Don’t feel comfortable around 0.8116 2.044 1.211 firearms or hunting equipment 3. Skills & Knowledge constraints 2.215 1.084 (6 items, Cronbach’s α = 0.9346) 0.9087 Lack knowledge/skills to hunt 2.325 1.233 Lack knowledge about hunting and 0.9033 2.095 1.200 firearm laws Lack knowledge/skills required to 0.8897 2.312 1.257 prepare game meat to eat Have not completed a hunter 0.8543 2.260 1.348 education course Unsure of how/where to store 0.7994 1.863 1.143 equipment and firearms Costs associated with hunting 0.7496 (licenses, tags, equipment, firearms, 2.228 1.197 travel, etc.) 4. Logistical constraints 1.933 0.782 (6 items, Cronbach’s α = 0.8050) Lack of available land where I 0.771 1.805 1.048 currently live Moved away from the area I 0.6733 1.762 1.144 typically hunt to attend college Lack transportation to get to hunting 0.6538 1.422 0.834 areas 0.6334 Don’t know where to go 2.005 1.142 0.6164 Don’t have anyone to hunt with 2.061 1.131 Lack the free time required to go 0.5379 2.588 1.217 hunting 5. Judgment & Experiential constraints 1.291 0.560 3 items, Cronbach’s α = 0.7345) �Feel discouraged or frightened by negative experiences I’ve had in the outdoors Don’t feel comfortable due to the lack of racial and ethnic diversity associated with hunting Worried non-hunting friends and family may judge me 1.209 0.580 0.8131 1.318 0.762 0.7578 1.344 0.716 0.7561 Items rated on scale ranging from 1 = not at all to 4 = very much Only varimax (orthogonal) rotated factor loadings > 0.400 are reported *Note: PCF indicated an optimal four-factor solution that accounted for 68.61% of the variance, with 4 factors containing eigenvalues > 1.0 and explaining > 5% of the cumulative variance. Although initial factor analysis showed four factors retained, we decided to pull out one item (“other activities”) that did not fit well into any other factor, creating five distinct factors. Kaiser-Meyer-Olkin test = 0.903 with Barlett’s test of sphericity χ2(190) = 1.24e05, p < 0.001. A composite score was created for each factor, based on the mean of the items. Higher scores indicated a greater degree of perceived constraint. a b �Table S7. Exploratory factor analysis using principal component factor analysis (PCF) depicting two-factor structure of items used to assess wildlife value orientations among university students across the United States, 2018-2020 Factor Loadingsb Scale (with Items) Mean SD 1 2 1. Mutualistic value orientations 3.676 0.876 (2 items, Cronbach’s α = 0.6469) 0.8594 I view all living things as part of one big family 3.672 1.020 0.8385 I feel a strong bond with animals 3.679 1.016 2. Dominionistic value orientations 2.956 0.951 (2 items, Cronbach’s α = 0.5924) Humans should manage fish and wildlife 0.8774 3.284 1.116 populations so that humans benefit The needs of humans should take priority over fish 0.7848 2.626 1.140 and wildlife protection Items rated on scale ranging from 1 = strongly disagree to 5 = strongly agree; Only varimax (orthogonal) rotated factor loadings > 0.400 are reported Note: PCF indicated an optimal two-factor solution that accounted for 73.04% of the variance, with 2 factors containing eigenvalues > 1.0 and explaining > 20% of the cumulative variance. Kaiser-Meyer-Olkin test = 0.594 with Bartlett’s test of sphericity χ2(6) = 9,233.14, p < 0.001. A composite score was created for each factor, based on the mean of the items. Higher scores indicated a greater degree of agreement with the WVO. a b �Table S8. Exploratory factor analysis using principal component factor analysis (PCF) depicting one-factor structure of items used to assess conservation caring among university students across the United States, 2018-2020 Factor Loadingsb Scale (with Items) Mean SD 1 1. Conservation Caring 4.074 0.667 (4 items, Cronbach’s α = 0.7993) Wildlife conservation is very important to me 0.8421 4.239 0.777 Wildlife conservation and habitat protection 0.8205 3.946 0.922 should be one of society’s highest priorities I am willing to voluntarily spend my own money 0.7740 3.505 0.995 on wildlife conservation Wildlife should be conserved for future 0.7571 4.574 0.609 generations Items rated on scale ranging from 1 = strongly disagree to 5 = strongly agree; Only varimax orthogonal rotated factor loadings > 0.400 are reported Note: PCA indicated an optimal single-factor solution (eigenvalue = 2.55) that accounted for 63.87% of the variance. Kaiser-Meyer-Olkin test = 0.797 with Bartlett’s sphericity χ2(6) = 21593.097, p < 0.001. A composite score was created for the factor, based on the mean of the items. Higher scores indicated a greater degree of conservation caring. a b �Table S9. Distributiona and demographic attributes of college students across 22 universities in the United States, 2018–2020, assigned to 4 future hunting groups based on survey responses: non-hunters (n = 7,820, 50% of sample), potential hunters (n = 3,572, 22% of sample), active hunters (n = 4,421, 26% of sample), and lapsed hunters (n = 718, 3% of sample). Future hunting groups Non-hunters Potential hunters Active hunters Lapsed hunters % of % of % of % of % of % of % of % of hunting hunting hunting hunting variable variable variable variable group group group group within within within within defined defined defined defined hunting hunting hunting hunting by by by by group group group group variable variable variable variable Variable Region Midwest 50.1 27.4 20.1 25.4 26.8 27.8 2.9 31.7 Northeast 55.4 17.5 21.5 15.7 20.5 12.2 2.6 16.5 Southeast 49.7 33.3 21.3 33.0 26.6 33.8 2.4 32.0 West 45.5 21.8 23.5 26.0 28.9 26.3 2.1 19.9 Race* White 43.0 55.4 20.6 62.5 33.6 84.0 2.8 73.6 BIPOCb or 63.6 44.6 22.8 37.5 11.8 16.0 1.8 26.4 mixed race Gender** Male 30.3 28.6 24.6 53.4 41.9 73.7 3.1 59.4 identifying Female identifying or 66.2 71.4 18.8 46.6 13.1 26.3 1.9 40.6 gender nonconforming College major* Ag/NRc 35.6 14.4 22.6 21.0 39.4 29.6 2.4 19.4 Non- Ag/NRc 52.9 85.6 21.2 79.0 23.4 70.4 2.5 80.6 Childhood location* Rural 42.2 39.9 22.9 56.6 35.1 61.6 2.7 51.5 Urban 55.8 60.1 20.0 43.4 19.1 38.4 2.2 48.5 Social support** No social 73.9 55.6 19.7 35.1 4.9 7.3 1.4 21.7 support Extended 56.3 30.4 30.6 39.2 10.8 11.5 2.3 25.1 family Immediate 20.3 13.9 15.9 25.7 60.0 81.2 3.8 53.2 family Note: All chi-square tests are significant at P < 0.001. Effect size denoted as * = small (0.1), ** = medium (0.3), *** = large (0.5). a Distribution represents weighted percentage of students within each variable falling into a certain hunting group (% of variable sub-column) and the percentage of hunting groups defined by each variable (% of hunting group subcolumn). Weights were calculated using Stata fweight procedure accounted for enrollment, gender, and race ratios across schools, and were rounded to nearest integers in chi-square analysis. b BIPOC refers to individuals who are black, indigenous, or people of color c Ag/NR refers to agriculture or natural resource majors (versus students majoring in other fields) �Table S10. Means ratings for beliefs and attitudes, motivations, and constraints to hunting participation among college students across 22 universities in the United States, 2018–2020, assigned to 4 future hunting groups based on survey responses: non-hunters (n = 7,820, 50% of sample), potential hunters (n = 3,572, 22% of sample), active hunters (n = 4,421, 26% of sample), and lapsed hunters (n = 718, 3% of sample). All Welch’s F-statistics comparing group means are significant at P < 0.01. Effect size next to variable names denoted as * = small (0.01), ** = medium (0.06), and *** = large (0.14). Different superscripts for each group’s mean scores represent significant differences based on Games-Howell post hoc tests. Shading indicates highest mean scores (darkest green) to lowest mean scores (lightest green). Variable Future hunting groups NonPotential Active Lapsed hunters hunters hunters hunters Wildlife value orientation: mutualistica* 3.86a 3.60b 3.43c 3.78a a c b a Wildlife value orientation: dominionistic ** 2.65 3.14 3.38 2.97b Conservation caring scorea 4.13a 3.97b 4.14a 4.12a Beliefs about hunters and huntinga*** 2.77a 3.65b 4.23c 3.31d b d b a Approval: egoistic *** 1.72 2.46 2.79 2.16c Approval: altruisticb*** 2.39d 2.72b 2.89a 2.59c b d b a Approval: meat *** 2.21 2.72 2.90 2.54c Approval: trophyb*** 1.17c 1.61b 2.23a 1.41c c d b a Motivation: egoistic *** 1.21 2.13 2.66 1.60c Motivation: altruisticc*** 1.46d 2.33b 2.74a 1.97c Motivation: meatc*** 1.36d 2.36b 2.78a 1.92c c d b a Motivation: trophy *** 1.03 1.34 2.04 1.13c Constraints: other activitiesd*** 3.61a 2.95b 2.34c 3.65a d b a d Constraints: skills and knowledge *** 2.30 2.88 1.60 2.03c Constraints: logisticald*** 1.59d 2.19b 2.41a 1.92c d a d c Constraints: moral and comfort *** 3.06 1.80 1.91 2.20b Constraints: judgment and experientiald** 1.45a 1.31b 1.11c 1.35ab Hunting activities scalee*** 1.31d 1.93b 3.12a 1.79c f d c a Outdoor recreation score * 2.39 2.76 3.23 2.85b Scale: 1 = strongly disagree to 5 = strongly agree. Scale: 1 = disapprove to 3 = approve. c Scale: 1 = no, 2 = maybe, 3 = yes. d Scale: 1 = not at all to 4 = very much. e Nature based recreation index: sum of the activities participated in, minimum = 0 and maximum = 6. f Scale: 1 = never to 5 = very often. a b �Table S11. Odds ratios (OR) from multinomial logistic regression model predicting the likelihood of future hunting participation of college students across 22 universities in the United States, 2018–2020 (n = 15,109). Students were placed into 3 groups based on previous and intended future hunting participation: potential hunters (21.5%), lapsed hunters (2.4%), active hunters (29.1%), and non-hunters (47.0%, reference category). Estimates based on unweighted data. Model Fit Statistics: n = 15,109; Cragg-Uhler (Nagelkerke) R2: 0.762, χ2(54) = 16720.7, P < 0.001. Potential Lapsed Active Variables in model hunters hunters hunters Region (Ref = Midwest) 0.893 0.770 0.693** Northeast 0.968 1.009 0.999 Southeast 1.340** 1.029 1.328** West a Race/ethnicity (Ref=BIPOC /mixed-race) 0.749*** 1.502* 1.095 White Gender (Ref = Female or non-conforming) 1.389*** 3.831*** 4.202*** Male b College Major (Ref = Non Ag/NR ) 1.508*** 1.618** 1.721*** Ag or Natural Resourcesb Childhood Location (Ref = Urban) 0.937 1.246 1.381*** Rural c 1.013 1.078 0.920 Wildlife value orientation: Mutualistic c 1.032 1.094 0.930 Wildlife value orientation: Dominionistic 0.756*** 1.105 0.980 Conservation caringc d 1.378*** 1.237** 1.603*** Overall Approval e 4.703*** 1.667*** 7.336*** Approval: Egoism e 1.347*** 1.226* 1.554*** Approval: Altruism e 1.829*** 1.287** 2.416*** Approval: Meat 1.656*** 1.354 2.899*** Approval: Trophye c 1.622*** 1.142 3.414*** Beliefs/attitudes about hunters/hunting Social Support (Ref = No Support) 1.508*** 2.160*** 1.818*** Extended Support 1.675*** 10.638*** 17.241*** Immediate Family Support *, **, *** denote statistically significant odds ratios (OR) at α = 0.05, 0.01, and 0.001, respectively. a BIPOC refers to individuals who are black, indigenous, or people of color b Ag/NR refers to agriculture or natural resource majors (versus students majoring in other fields) c Scale: 1 = Strongly disagree to 5 = Strongly agree. d Scale: 1 = Strongly disapprove to 5 = Strongly approve. e Scale: 1 = Disapprove to 3 = Approve. � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal Articles Description An account of the resource CPW peer-reviewed journal publications Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Diverse university students across the United States reveal promising pathways to hunter recruitment and retention Description An account of the resource <span>Declining participation in hunting, especially among young adult hunters, affects the ability of state and federal agencies to achieve goals for wildlife management and decreases revenue for conservation. For wildlife agencies hoping to engage diverse audiences in hunter recruitment, retention, and reactivation (R3) efforts, university settings provide unique advantages: they contain millions of young adults who are developmentally primed to explore new activities, and they cultivate a social atmosphere where new identities can flourish. From 2018 to 2020, we surveyed 17,203 undergraduate students at public universities across 22 states in the United States to explore R3 potential on college campuses and assess key demographic, social, and cognitive correlates of past and intended future hunting behavior. After weighting to account for demographic differences between our sample and the larger student population, 29% of students across all states had hunted in the past. Students with previous hunting experience were likely to be white, male, from rural areas or hunting families, and pursuing degrees related to natural resources. When we grouped students into 1 of 4 categories with respect to hunting (i.e., non-hunters [50%], potential hunters [22%], active hunters [26%], and lapsed hunters [3%]), comparisons revealed differences based on demographic attributes, beliefs, attitudes, and behaviors. Compared to active hunters, potential hunters were more likely to be females or racial and ethnic minorities, and less likely to experience social support for hunting. Potential hunters valued game meat and altruistic reasons for hunting, but they faced unique constraints due to lack of hunting knowledge and skills. Findings provide insights for marketing and programming designed to achieve R3 objectives with a focus on university students.</span> Bibliographic Citation A bibliographic reference for the resource. Recommended practice is to include sufficient bibliographic detail to identify the resource as unambiguously as possible. <a href="https://doi.org/10.1002/jwmg.22055" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/jwmg.22055</a> Creator An entity primarily responsible for making the resource Vayer, Victoria R. Larson, Lincoln R. Peterson, Markus Nils Lee, Kangjae Jerry Von Furstenberg, Richard Choi, Daniel Y. Stevenson, Kathryn Ahlers, Adam A. Anhalt-Depies, Christine Bethke, Taniya Bruskotter, Jeremy T. Chizinski, Christopher J. Clark, Brian Dayer, Ashley A. Ghasemi, Benjamin Gigliotti, Larry Graefe, Alan Irwin, Kris Keith, Samuel J. Kelly, Matt Kyle, Gerard Metcalf, Elizabeth Morse, Wayde Needham, Mark D. Poudyal, Neelam C. Quartuch, Michael R. Rodriguez, Shari Romulo, Chelsie Sharp, Ryan L. Siemer, William Springer, Matthew T. Stedman, Richard Stein, Taylor Van Deelen, Timothy R. Whiting, Jason Winkler, Richelle L. Woosnam, Kyle Maurice Subject The topic of the resource Decision-making Forest landowners Behavior change model Landowner behavior Extent The size or duration of the resource. 22 pages Date Created Date of creation of the resource. 2021-05-06 Rights Information about rights held in and over the resource <a href="http://rightsstatements.org/vocab/InC-NC/1.0/" target="_blank" rel="noreferrer noopener">In Copyright - Non-Commercial Use Permitted</a> Format The file format, physical medium, or dimensions of the resource application/pdf Language A language of the resource English Is Part Of A related resource in which the described resource is physically or logically included. The Journal of Wildlife Management Type The nature or genre of the resource Article