Integrating wildlife and human-dimensions research methods to study hunters. Stedman, R., Diefenbach, D., Swope, C., Finley, J., Luloff, A., Zinn, H., San Julian, G., & Wang, G. Journal of Wildlife Management, 68(4):762–773, Western Washington University, Department of Environmental Studies, Bellingham, WA 98225, United States, 2004. abstract bibtex 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 (x? = 2.23 km, SE = 0.13) than actual distance recorded via GPS units (x? = 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.
@ARTICLE{Steetal04,
author = {Stedman, R. and Diefenbach, D.R. and Swope, C.B. and Finley, J.C.
and Luloff, A.E. and Zinn, H.C. and San Julian, G.J. and Wang, G.A.},
title = {Integrating wildlife and human-dimensions research methods to study
hunters},
journal = {Journal of Wildlife Management},
year = {2004},
volume = {68},
pages = {762--773},
number = {4},
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 (x? = 2.23 km, SE = 0.13) than
actual distance recorded via GPS units (x? = 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.},
address = {Western Washington University, Department of Environmental Studies,
Bellingham, WA 98225, United States},
file = {Stedmanetal2004.pdf:C\:\\Documents and Settings\\Tiago\\Os meus documentos\\work\\pdf\\Stedmanetal2004.pdf:PDF},
keywords = {Activity patterns, Aerial surveys, Distribution, Geographic Information
System, Global Positioning System, Human dimension surveys, Hunter
behavior, Hunting, Odocoileus virginianus, Pennsylvania, White-tailed
deer},
owner = {Tiago},
subdatabase = {distance},
timestamp = {2006.11.16}
}
Downloads: 0
{"_id":"f2DhTyZybqnWrmPxA","bibbaseid":"stedman-diefenbach-swope-finley-luloff-zinn-sanjulian-wang-integratingwildlifeandhumandimensionsresearchmethodstostudyhunters-2004","authorIDs":[],"author_short":["Stedman, R.","Diefenbach, D.","Swope, C.","Finley, J.","Luloff, A.","Zinn, H.","San Julian, G.","Wang, G."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Stedman"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Diefenbach"],"firstnames":["D.R."],"suffixes":[]},{"propositions":[],"lastnames":["Swope"],"firstnames":["C.B."],"suffixes":[]},{"propositions":[],"lastnames":["Finley"],"firstnames":["J.C."],"suffixes":[]},{"propositions":[],"lastnames":["Luloff"],"firstnames":["A.E."],"suffixes":[]},{"propositions":[],"lastnames":["Zinn"],"firstnames":["H.C."],"suffixes":[]},{"propositions":[],"lastnames":["San","Julian"],"firstnames":["G.J."],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["G.A."],"suffixes":[]}],"title":"Integrating wildlife and human-dimensions research methods to study hunters","journal":"Journal of Wildlife Management","year":"2004","volume":"68","pages":"762–773","number":"4","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 (x? = 2.23 km, SE = 0.13) than actual distance recorded via GPS units (x? = 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.","address":"Western Washington University, Department of Environmental Studies, Bellingham, WA 98225, United States","file":"Stedmanetal2004.pdf:C\\:\\\\Documents and Settings\\\\Tiago\\\\Os meus documentos\\\\work\\\\pdf\\\\Stedmanetal2004.pdf:PDF","keywords":"Activity patterns, Aerial surveys, Distribution, Geographic Information System, Global Positioning System, Human dimension surveys, Hunter behavior, Hunting, Odocoileus virginianus, Pennsylvania, White-tailed deer","owner":"Tiago","subdatabase":"distance","timestamp":"2006.11.16","bibtex":"@ARTICLE{Steetal04,\r\n author = {Stedman, R. and Diefenbach, D.R. and Swope, C.B. and Finley, J.C.\r\n\tand Luloff, A.E. and Zinn, H.C. and San Julian, G.J. and Wang, G.A.},\r\n title = {Integrating wildlife and human-dimensions research methods to study\r\n\thunters},\r\n journal = {Journal of Wildlife Management},\r\n year = {2004},\r\n volume = {68},\r\n pages = {762--773},\r\n number = {4},\r\n abstract = {Recreational hunting is the primary management tool used by natural\r\n\tresource agencies to control ungulate populations. Although free-ranging\r\n\tungulates have been studied extensively in North America, relatively\r\n\tlittle is known about the field behavior of hunters or the factors\r\n\tthat influence hunting behavior, except on small study areas where\r\n\taccess is limited and controlled. We developed 3 integrated protocols\r\n\tto estimate hunter density, distribution, movements, habitat use,\r\n\tcharacteristics, and attitudes, which can be used on large areas\r\n\twith unrestricted access. We described how aerial surveys, in conjunction\r\n\twith distance sampling techniques and a Geographic Information System\r\n\t(GIS) database of landscape characteristics, provide estimates of\r\n\thunter density and a map of hunter distribution and habitat use.\r\n\tWe used Global Positioning System (GPS) units issued to hunters to\r\n\tsystematically record hunter locations. Hunters also completed a\r\n\tsimple questionnaire. We linked these data and used them to obtain\r\n\tdetailed information on habitat use, movements, and activity patterns.\r\n\tWhereas aerial surveys are limited to discrete points in time and\r\n\trelate only to aggregations of hunters, data collected on hunters\r\n\tthat carry GPS units can be used to study habitat use and distribution\r\n\tat different times of day for individual hunters. Finally, linked\r\n\tresponses from a traditional mail or telephone survey to hunter location\r\n\tdata collected via GPS units to assess how hunter characteristics\r\n\t(e.g., age, physical condition, attitudes) were related to field\r\n\tbehavior. We applied these techniques during a white-tailed deer\r\n\t(Odocoileus virginianus) hunting season on a large tract (45,749\r\n\tha) of public land in Pennsylvania, USA, with unrestricted hunter\r\n\taccess. We estimated density of 7 hunters/1,000 ha (95% CI: 4.2 to\r\n\t10.3) in the morning and 6.3 hunters/1,000 ha (95% CI: 3.5 to 10.0)\r\n\tin the afternoon. We found that hunter density was negatively related\r\n\tto distance from roads and slope. Most hunters preferred stand hunting,\r\n\tespecially in the early morning hours (0600-0800 hr; 72% stationary);\r\n\tmore walked or stalked in the afternoon (1400-1600 hr; 58% stationary).\r\n\tThe average maximum distance hunters reached from a road open to\r\n\tpublic vehicles was 0.84 km (SE = 0.03), and they walked an average\r\n\tof 5.48 km (SE = 0.193) during their daily hunting activities. We\r\n\tbelieve that the approaches we used for studying hunter behavior\r\n\twill be useful for understanding the connections between hunter attitudes\r\n\tand behavior and hence will allow managers to predict hunter response\r\n\tto changes in harvest regulations. Furthermore, our methods are more\r\n\taccurate than requesting hunters to self-report where they hunted.\r\n\tFor example, we found that hunters reported that they walked >2.5\r\n\ttimes farther from the nearest road (x? = 2.23 km, SE = 0.13) than\r\n\tactual distance recorded via GPS units (x? = 0.84 km, SE = 0.03).\r\n\tOur research provides wildlife managers with new knowledge on several\r\n\tlevels. At the most basic level, we learned a great deal about what\r\n\thunters actually do while in the field, rather than simply what they\r\n\treport. Second, linking field behavior with hunter characteristics\r\n\twill provide insights into the likely effects of changing hunter\r\n\tdemographics. 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