Cognitive Processes in Hunters' Recall of Participation and Harvest Estimates

Beaman, Jay; Vaske, Jerry J.; Miller, Craig A.

doi:10.2193/0022-541x(2005)069[0967:cpihro]2.0.co;2

Cited by 22 publications

(14 citation statements)

References 16 publications

(31 reference statements)

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“…We observed artificial peaks in hunting effort due to heaping, similar to observations made by others in wildlife and leisure research (Vaske et al , ; Beaman et al a ). However, with the use of a statistical program developed for heaping (Beaman et al ), we determined the bias in heaping was minimal.…”

Section: Discussionsupporting

confidence: 90%

“…Biases can also arise because of actions by management such as changes in hunting regulations or the permitting process that can alter the meaning or increase the ambiguity of report data (Beaman et al a , Padding and Royle 2012). For example, if regulation changes increase the number of permits issued per hunter over time without changing the bag limit, calculations of success based on permits may become increasingly biased.…”

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confidence: 99%

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Detecting, estimating, and correcting for biases in harvest data

2015

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Hunting is important to many people because it provides food, recreation, and cultural identity, so proper management of wildlife is necessary. Wildlife agencies and researchers often rely on harvest data supplied by hunters, but interpretation of these data can be misleading when biases are not acknowledged, assessed, and corrected. We use harvest information collected by the Alaska Department of Fish and Game (ADF&G) from moose (Alces alces gigas) hunters to examine and correct 3 common biases in harvest data: heaping in responses of estimated effort (i.e., rounding), changes in report design, and nonreporting. We found that bias due to heaping was limited (2.8%). A large increase in special permits in 2004 (6.1% in 2003 and 40.1% in 2004) corresponded with increases in individuals with multiple permits (8.6% and 17.3%), which biased estimates of hunt participation calculated from permit data. Failure to correct for multiple reports per hunter also resulted in an artificial decline in success over time. Road access influenced reporting rates; rural Alaska residents without a road had the lowest reporting rate (67%) and rural with a road the greatest (82%). A statewide trend of 663 additional hunters per year calculated from raw permit data was eliminated once data were corrected for both multiple permits and nonreporting. Late reporters were also less likely to hunt (11.8%) than all reporters. Our research shows that survey data bias can significantly influence data interpretation, and wildlife managers must balance information needs, time constraints, and financial resources when determining which biases to correct. © 2015 The Wildlife Society.

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Section: Discussionsupporting

confidence: 90%

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confidence: 99%

Detecting, estimating, and correcting for biases in harvest data

2015

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“…This social desirability bias may be negative (leading to under-reporting), possibly due to knowledge that killing of orangutans is illegal, or positive (leading to over-reporting) if respondents are inclined to boast about killing or if they perceive that positive responses are related to good hunting skills or knowledge of the forest. We are also aware of incomplete recall over longer time frames [19], [20], but think that our confidence intervals and two different approaches to estimating killing rates sufficiently capture this bias. As with all surveys, it is difficult to quantify the possible magnitude of this bias overall, let alone disentangle the contributions of the direction of the bias.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Killing of Orangutans and Human-Orangutan Conflict in Kalimantan, Indonesia

et al. 2011

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Human-orangutan conflict and hunting are thought to pose a serious threat to orangutan existence in Kalimantan, the Indonesian part of Borneo. No data existed prior to the present study to substantiate these threats. We investigated the rates, spatial distribution and causes of conflict and hunting through an interview-based survey in the orangutan's range in Kalimantan, Indonesia. Between April 2008 and September 2009, we interviewed 6983 respondents in 687 villages to obtain socio-economic information, assess knowledge of local wildlife in general and orangutan encounters specifically, and to query respondents about their knowledge on orangutan conflicts and killing, and relevant laws. This survey revealed estimated killing rates of between 750 and 1800 animals killed in the last year, and between 1950 and 3100 animals killed per year on average within the lifetime of the survey respondents. These killing rates are higher than previously thought and are high enough to pose a serious threat to the continued existence of orangutans in Kalimantan. Importantly, the study contributes to our understanding of the spatial variation in threats, and the underlying causes of those threats, which can be used to facilitate the development of targeted conservation management.

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“…Computation of this aggregate measure involved estimating the total number of respondents who could have used the prototype. In subsequent work (Beaman, Vaske, & Miller, 2005a, 2005b, a formula was derived for estimating the proportion of responses in a heap.…”

Section: Past Researchmentioning

confidence: 99%

Measuring and Correcting Response Heaping Arising From the Use of Prototypes

Beaman

Vaske

Schmidt

et al. 2015

Human Dimensions of Wildlife

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Imprecision in respondent recall can cause response heaping in frequency data for particular values (e.g., 5, 10, 15). In human dimensions research, heaping can occur for variables such as days of participation (e.g., hunting, fishing), animals/fish harvested, or money spent on licenses. Distributions with heaps can bias population estimates because the means and totals can be inflated or deflated. Because bias can result in poor management decisions, determining if the bias is large enough to matter is important. This note introduces the logic and flow of a deheaping program that estimates bias in means and totals when people use approximate responses (i.e., prototypes). The program can make estimates even when spikes occur due to bag limits. The program is available online, and smooths heaps at multiples of 5 (numbers ending in 5 and 0) and 7 (e.g., 7, 14, 21), and produces standard deviations in estimates.

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Cognitive Processes in Hunters' Recall of Participation and Harvest Estimates

Cited by 22 publications

References 16 publications

Detecting, estimating, and correcting for biases in harvest data

Detecting, estimating, and correcting for biases in harvest data

Quantifying Killing of Orangutans and Human-Orangutan Conflict in Kalimantan, Indonesia

Measuring and Correcting Response Heaping Arising From the Use of Prototypes

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