Integrating wildlife and human-dimensions research methods to study hunters

Richard Stedman, Duane R Diefenbach, Craig B. Swope, James Craig Finley, A. E. Luloff, Harry C. Zinn, Gary J. San Julian, Grace A. Wang

Research output: Contribution to journalArticle

50 Citations (Scopus)

Abstract

Recreational hunting is the primary management tool used by natural resource agencies to control ungulate populations. Although free-ranging ungulates have been studied extensively in North America, relatively little is known about the field behavior of hunters or the factors that influence hunting behavior, except on small study areas where access is limited and controlled. We developed 3 integrated protocols to estimate hunter density, distribution, movements, habitat use, characteristics, and attitudes, which can be used on large areas with unrestricted access. We described how aerial surveys, in conjunction with distance sampling techniques and a Geographic Information System (GIS) database of landscape characteristics, provide estimates of hunter density and a map of hunter distribution and habitat use. We used Global Positioning System (GPS) units issued to hunters to systematically record hunter locations. Hunters also completed a simple questionnaire. We linked these data and used them to obtain detailed information on habitat use, movements, and activity patterns. Whereas aerial surveys are limited to discrete points in time and relate only to aggregations of hunters, data collected on hunters that carry GPS units can be used to study habitat use and distribution at different times of day for individual hunters. Finally, linked responses from a traditional mail or telephone survey to hunter location data collected via GPS units to assess how hunter characteristics (e.g., age, physical condition, attitudes) were related to field behavior. We applied these techniques during a white-tailed deer (Odocoileus virginianus) hunting season on a large tract (45,749 ha) of public land in Pennsylvania, USA, with unrestricted hunter access. We estimated density of 7 hunters/1,000 ha (95% CI: 4.2 to 10.3) in the morning and 6.3 hunters/1,000 ha (95% CI: 3.5 to 10.0) in the afternoon. We found that hunter density was negatively related to distance from roads and slope. Most hunters preferred stand hunting, especially in the early morning hours (0600-0800 hr; 72% stationary); more walked or stalked in the afternoon (1400-1600 hr; 58% stationary). The average maximum distance hunters reached from a road open to public vehicles was 0.84 km (SE = 0.03), and they walked an average of 5.48 km (SE = 0.193) during their daily hunting activities. We believe that the approaches we used for studying hunter behavior will be useful for understanding the connections between hunter attitudes and behavior and hence will allow managers to predict hunter response to changes in harvest regulations. Furthermore, our methods are more accurate than requesting hunters to self-report where they hunted. For example, we found that hunters reported that they walked >2.5 times farther from the nearest road (x̄ = 2.23 km, SE = 0.13) than actual distance recorded via GPS units (x̄ = 0.84 km, SE = 0.03). Our research provides wildlife managers with new knowledge on several levels. At the most basic level, we learned a great deal about what hunters actually do while in the field, rather than simply what they report. Second, linking field behavior with hunter characteristics will provide insights into the likely effects of changing hunter demographics. Finally, linking these data with traditional human-dimensions research topics, such as attitudes toward hunting regulations, may allow managers to better forecast the potential effects of regulation changes on hunter distribution and effort.

Original languageEnglish (US)
Pages (from-to)762-773
Number of pages12
JournalJournal of Wildlife Management
Volume68
Issue number4
DOIs
StatePublished - Oct 1 2004

Fingerprint

research method
research methods
hunting
wildlife
habitat use
GPS
aerial survey
ungulate
road
activity pattern
global positioning systems
deer
hunters
natural resource
roads
distribution
managers
Odocoileus virginianus
ungulates
habitats

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Nature and Landscape Conservation

Cite this

Stedman, R., Diefenbach, D. R., Swope, C. B., Finley, J. C., Luloff, A. E., Zinn, H. C., ... Wang, G. A. (2004). Integrating wildlife and human-dimensions research methods to study hunters. Journal of Wildlife Management, 68(4), 762-773. https://doi.org/10.2193/0022-541X(2004)068[0762:IWAHRM]2.0.CO;2
Stedman, Richard ; Diefenbach, Duane R ; Swope, Craig B. ; Finley, James Craig ; Luloff, A. E. ; Zinn, Harry C. ; San Julian, Gary J. ; Wang, Grace A. / Integrating wildlife and human-dimensions research methods to study hunters. In: Journal of Wildlife Management. 2004 ; Vol. 68, No. 4. pp. 762-773.
@article{4c9fe36532f54a29b3403650c10453ed,
title = "Integrating wildlife and human-dimensions research methods to study hunters",
abstract = "Recreational hunting is the primary management tool used by natural resource agencies to control ungulate populations. Although free-ranging ungulates have been studied extensively in North America, relatively little is known about the field behavior of hunters or the factors that influence hunting behavior, except on small study areas where access is limited and controlled. We developed 3 integrated protocols to estimate hunter density, distribution, movements, habitat use, characteristics, and attitudes, which can be used on large areas with unrestricted access. We described how aerial surveys, in conjunction with distance sampling techniques and a Geographic Information System (GIS) database of landscape characteristics, provide estimates of hunter density and a map of hunter distribution and habitat use. We used Global Positioning System (GPS) units issued to hunters to systematically record hunter locations. Hunters also completed a simple questionnaire. We linked these data and used them to obtain detailed information on habitat use, movements, and activity patterns. Whereas aerial surveys are limited to discrete points in time and relate only to aggregations of hunters, data collected on hunters that carry GPS units can be used to study habitat use and distribution at different times of day for individual hunters. Finally, linked responses from a traditional mail or telephone survey to hunter location data collected via GPS units to assess how hunter characteristics (e.g., age, physical condition, attitudes) were related to field behavior. We applied these techniques during a white-tailed deer (Odocoileus virginianus) hunting season on a large tract (45,749 ha) of public land in Pennsylvania, USA, with unrestricted hunter access. We estimated density of 7 hunters/1,000 ha (95{\%} CI: 4.2 to 10.3) in the morning and 6.3 hunters/1,000 ha (95{\%} CI: 3.5 to 10.0) in the afternoon. We found that hunter density was negatively related to distance from roads and slope. Most hunters preferred stand hunting, especially in the early morning hours (0600-0800 hr; 72{\%} stationary); more walked or stalked in the afternoon (1400-1600 hr; 58{\%} stationary). The average maximum distance hunters reached from a road open to public vehicles was 0.84 km (SE = 0.03), and they walked an average of 5.48 km (SE = 0.193) during their daily hunting activities. We believe that the approaches we used for studying hunter behavior will be useful for understanding the connections between hunter attitudes and behavior and hence will allow managers to predict hunter response to changes in harvest regulations. Furthermore, our methods are more accurate than requesting hunters to self-report where they hunted. For example, we found that hunters reported that they walked >2.5 times farther from the nearest road (x̄ = 2.23 km, SE = 0.13) than actual distance recorded via GPS units (x̄ = 0.84 km, SE = 0.03). Our research provides wildlife managers with new knowledge on several levels. At the most basic level, we learned a great deal about what hunters actually do while in the field, rather than simply what they report. Second, linking field behavior with hunter characteristics will provide insights into the likely effects of changing hunter demographics. Finally, linking these data with traditional human-dimensions research topics, such as attitudes toward hunting regulations, may allow managers to better forecast the potential effects of regulation changes on hunter distribution and effort.",
author = "Richard Stedman and Diefenbach, {Duane R} and Swope, {Craig B.} and Finley, {James Craig} and Luloff, {A. E.} and Zinn, {Harry C.} and {San Julian}, {Gary J.} and Wang, {Grace A.}",
year = "2004",
month = "10",
day = "1",
doi = "10.2193/0022-541X(2004)068[0762:IWAHRM]2.0.CO;2",
language = "English (US)",
volume = "68",
pages = "762--773",
journal = "Journal of Wildlife Management",
issn = "0022-541X",
publisher = "Wiley-Blackwell",
number = "4",

}

Stedman, R, Diefenbach, DR, Swope, CB, Finley, JC, Luloff, AE, Zinn, HC, San Julian, GJ & Wang, GA 2004, 'Integrating wildlife and human-dimensions research methods to study hunters', Journal of Wildlife Management, vol. 68, no. 4, pp. 762-773. https://doi.org/10.2193/0022-541X(2004)068[0762:IWAHRM]2.0.CO;2

Integrating wildlife and human-dimensions research methods to study hunters. / Stedman, Richard; Diefenbach, Duane R; Swope, Craig B.; Finley, James Craig; Luloff, A. E.; Zinn, Harry C.; San Julian, Gary J.; Wang, Grace A.

In: Journal of Wildlife Management, Vol. 68, No. 4, 01.10.2004, p. 762-773.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Integrating wildlife and human-dimensions research methods to study hunters

AU - Stedman, Richard

AU - Diefenbach, Duane R

AU - Swope, Craig B.

AU - Finley, James Craig

AU - Luloff, A. E.

AU - Zinn, Harry C.

AU - San Julian, Gary J.

AU - Wang, Grace A.

PY - 2004/10/1

Y1 - 2004/10/1

N2 - Recreational hunting is the primary management tool used by natural resource agencies to control ungulate populations. Although free-ranging ungulates have been studied extensively in North America, relatively little is known about the field behavior of hunters or the factors that influence hunting behavior, except on small study areas where access is limited and controlled. We developed 3 integrated protocols to estimate hunter density, distribution, movements, habitat use, characteristics, and attitudes, which can be used on large areas with unrestricted access. We described how aerial surveys, in conjunction with distance sampling techniques and a Geographic Information System (GIS) database of landscape characteristics, provide estimates of hunter density and a map of hunter distribution and habitat use. We used Global Positioning System (GPS) units issued to hunters to systematically record hunter locations. Hunters also completed a simple questionnaire. We linked these data and used them to obtain detailed information on habitat use, movements, and activity patterns. Whereas aerial surveys are limited to discrete points in time and relate only to aggregations of hunters, data collected on hunters that carry GPS units can be used to study habitat use and distribution at different times of day for individual hunters. Finally, linked responses from a traditional mail or telephone survey to hunter location data collected via GPS units to assess how hunter characteristics (e.g., age, physical condition, attitudes) were related to field behavior. We applied these techniques during a white-tailed deer (Odocoileus virginianus) hunting season on a large tract (45,749 ha) of public land in Pennsylvania, USA, with unrestricted hunter access. We estimated density of 7 hunters/1,000 ha (95% CI: 4.2 to 10.3) in the morning and 6.3 hunters/1,000 ha (95% CI: 3.5 to 10.0) in the afternoon. We found that hunter density was negatively related to distance from roads and slope. Most hunters preferred stand hunting, especially in the early morning hours (0600-0800 hr; 72% stationary); more walked or stalked in the afternoon (1400-1600 hr; 58% stationary). The average maximum distance hunters reached from a road open to public vehicles was 0.84 km (SE = 0.03), and they walked an average of 5.48 km (SE = 0.193) during their daily hunting activities. We believe that the approaches we used for studying hunter behavior will be useful for understanding the connections between hunter attitudes and behavior and hence will allow managers to predict hunter response to changes in harvest regulations. Furthermore, our methods are more accurate than requesting hunters to self-report where they hunted. For example, we found that hunters reported that they walked >2.5 times farther from the nearest road (x̄ = 2.23 km, SE = 0.13) than actual distance recorded via GPS units (x̄ = 0.84 km, SE = 0.03). Our research provides wildlife managers with new knowledge on several levels. At the most basic level, we learned a great deal about what hunters actually do while in the field, rather than simply what they report. Second, linking field behavior with hunter characteristics will provide insights into the likely effects of changing hunter demographics. Finally, linking these data with traditional human-dimensions research topics, such as attitudes toward hunting regulations, may allow managers to better forecast the potential effects of regulation changes on hunter distribution and effort.

AB - Recreational hunting is the primary management tool used by natural resource agencies to control ungulate populations. Although free-ranging ungulates have been studied extensively in North America, relatively little is known about the field behavior of hunters or the factors that influence hunting behavior, except on small study areas where access is limited and controlled. We developed 3 integrated protocols to estimate hunter density, distribution, movements, habitat use, characteristics, and attitudes, which can be used on large areas with unrestricted access. We described how aerial surveys, in conjunction with distance sampling techniques and a Geographic Information System (GIS) database of landscape characteristics, provide estimates of hunter density and a map of hunter distribution and habitat use. We used Global Positioning System (GPS) units issued to hunters to systematically record hunter locations. Hunters also completed a simple questionnaire. We linked these data and used them to obtain detailed information on habitat use, movements, and activity patterns. Whereas aerial surveys are limited to discrete points in time and relate only to aggregations of hunters, data collected on hunters that carry GPS units can be used to study habitat use and distribution at different times of day for individual hunters. Finally, linked responses from a traditional mail or telephone survey to hunter location data collected via GPS units to assess how hunter characteristics (e.g., age, physical condition, attitudes) were related to field behavior. We applied these techniques during a white-tailed deer (Odocoileus virginianus) hunting season on a large tract (45,749 ha) of public land in Pennsylvania, USA, with unrestricted hunter access. We estimated density of 7 hunters/1,000 ha (95% CI: 4.2 to 10.3) in the morning and 6.3 hunters/1,000 ha (95% CI: 3.5 to 10.0) in the afternoon. We found that hunter density was negatively related to distance from roads and slope. Most hunters preferred stand hunting, especially in the early morning hours (0600-0800 hr; 72% stationary); more walked or stalked in the afternoon (1400-1600 hr; 58% stationary). The average maximum distance hunters reached from a road open to public vehicles was 0.84 km (SE = 0.03), and they walked an average of 5.48 km (SE = 0.193) during their daily hunting activities. We believe that the approaches we used for studying hunter behavior will be useful for understanding the connections between hunter attitudes and behavior and hence will allow managers to predict hunter response to changes in harvest regulations. Furthermore, our methods are more accurate than requesting hunters to self-report where they hunted. For example, we found that hunters reported that they walked >2.5 times farther from the nearest road (x̄ = 2.23 km, SE = 0.13) than actual distance recorded via GPS units (x̄ = 0.84 km, SE = 0.03). Our research provides wildlife managers with new knowledge on several levels. At the most basic level, we learned a great deal about what hunters actually do while in the field, rather than simply what they report. Second, linking field behavior with hunter characteristics will provide insights into the likely effects of changing hunter demographics. Finally, linking these data with traditional human-dimensions research topics, such as attitudes toward hunting regulations, may allow managers to better forecast the potential effects of regulation changes on hunter distribution and effort.

UR - http://www.scopus.com/inward/record.url?scp=12144267498&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=12144267498&partnerID=8YFLogxK

U2 - 10.2193/0022-541X(2004)068[0762:IWAHRM]2.0.CO;2

DO - 10.2193/0022-541X(2004)068[0762:IWAHRM]2.0.CO;2

M3 - Article

AN - SCOPUS:12144267498

VL - 68

SP - 762

EP - 773

JO - Journal of Wildlife Management

JF - Journal of Wildlife Management

SN - 0022-541X

IS - 4

ER -