Comparison of Aerial Thermal Infrared Imagery and Helicopter Surveys of Bison (Bison bison) in Grand Canyon National Park, USA

Hennig, Jacob D.; Schoenecker, Kathryn A.; Terwilliger, Miranda; Holm, Gregory W; Laake, Jeffrey L.

doi:10.3390/s21155087

Cited by 3 publications

(3 citation statements)

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“…Further, thermal clutter was apparent in areas of exposed soil that were exposed to sun radiation, typically in areas with rugged topography. Thermal clutter from exposed rocks was also observed in an IR survey of bison (Bison bison) along the north rim of the Grand Canyon (Hennig et al 2021). Consequently, we recommend that both aerial IR and DOS surveys be conducted with uniform background conditions (i.e., either complete or no snow cover), and when there is a consistently high level of thermal contrast between background and the animals targeted for detection (Figure 4C, D).…”

Section: Discussionmentioning

confidence: 85%

“…Management agencies typically tolerate bias if surveys achieve precise estimates because they can be useful for tracking population fluctuations over time (Cochran 1977) as long as detection probabilities are relatively constant (Pollock and Kendall 1987). However, there is a greater emphasis on accuracy for feral horse (Equus caballus) and burro (E. asinus) abundance estimates on public land in the western United States due to their unique management challenges (Beever et al 2018, Schoenecker et al 2021.…”

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Comparing methods to estimate feral burro abundance

Hennig,

Schoenecker

2023

Wildlife Society Bulletin

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Obtaining precise and unbiased estimates of feral burro (Equus asinus) abundance in the western United States is challenging due to their cryptic pelage and the rugged terrain they inhabit. Management agencies employ helicopter‐based, simultaneous double‐observer sightability surveys (hereafter denoted as DOS) to estimate abundance of burros; but the DOS method routinely produces negatively biased estimates due to residual heterogeneity in detection probability. Consequently, testing alternative methods to improve upon current procedures is warranted. Residual heterogeneity in DOS surveys can be minimized by including radio‐collared individuals in the population. Alternatively, if distance measurements are recorded, residual heterogeneity can also be reduced via a mark‐recapture distance sampling (MRDS) approach. Aerial infrared (IR) surveys offer a safer alternative than helicopter‐based surveys because they can be flown at a higher altitude and require fewer observers in the aircraft. Further, IR surveys using a distance sampling approach have been shown to generate accurate and precise estimates of feral horse (E. caballus) populations. Accordingly, we compared results of surveys using aerial IR distance sampling, the standard DOS survey, a DOS survey incorporating detections of radio‐collared individuals, and an MRDS analysis of a feral burro population with a known minimum population size in central Utah, winter 2015–2016 and spring 2016. The minimum number of burros known alive during the winter and spring surveys were 236 and 136, respectively. The average detection probability of IR surveys was P = 0.88 (SE = 0.16) and distance models produced estimates of 127 burros (95% CIs = 99–175) for the winter survey, and 94 burros (CIs = 72–134) for the spring survey. Mean detection probability of the standard DOS surveys was P = 0.78 (SE = 0.09), and model‐generated abundance estimates were 155 burros (CIs = 133–227) in winter, and 92 burros (CIs = 79–139) in spring. Incorporating detections of radio‐collared individuals in the DOS survey resulted in a decreased detection probability (P = 0.46; SE = 0.06) and increased abundance estimates to 267 (CIs = 169–571) and 155 (CIs = 128–263) for winter and spring, respectively. Mark‐recapture distance sampling produced a mean detection probability of P = 0.48 (SE = 0.12) and resulted in estimates of 282 (CIs = 178–385) and 169 (CIs = 73–310) burros in winter and spring, respectively. Our study demonstrated that aerial IR surveys conducted using standard distance sampling can produce precise estimates of burro population sizes; however, estimates were negatively biased relative to the known population size. Small sample size limits generalization of our results, but the IR‐based distance approach did not improve upon DOS surveys. Accounting for residual heterogeneity through use of radio‐collars and mark‐recapture distance sampling eliminated the negative bias from the standard DOS survey but decreased survey precision. Managers will need to decide whether unbiased but less precise abundance estimates are preferable compared to a more precise, but biased, estimate.

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Comparing methods to estimate feral burro abundance

Hennig,

Schoenecker

2023

Wildlife Society Bulletin

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“…As was true in 5 of 6 data sets in Griffin (2015), regardless of model type, rear seat observers had higher detection probabilities than front seat observers. We expected this result given that helicopter surveys of bison (Bison bison), burros, and elk have also reported greater detection by rear seat observers (Griffin 2015, Schoenecker and Lubow 2016, Bristow et al 2019, Gedir et al 2021, Hennig et al 2021. This result may seem counterintuitive given the larger field of view presented to front seat observers, but a larger field of view also translates into searching a larger area; thus, front seat observers in those studies and in this study spend more effort scanning a wide area closer to the aircraft and need to identify burros from multiple angles compared to rear seat observers who can fully direct their attention to picking out burros in a narrower but longer viewshed.…”

Section: Discussionmentioning

confidence: 86%

Accounting for residual heterogeneity in double‐observer sightability models decreases bias in burro abundance estimates

et al. 2022

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Feral burros (Equus asinus) and horses (E. ferus caballus) inhabiting public land in the western United States are intended to be managed at population levels established to promote a thriving, natural ecological balance. Double‐observer sightability (MDS) models, which use detection records from multiple observers and sighting covariates, perform well for estimating feral horse abundances, but their effectiveness for use in burro populations is less understood. These MDS models help minimize detection bias, yet bias can be further reduced with models that account for unmodeled variation, or residual heterogeneity, in detection probability. In populations containing radio‐marked individuals, residual heterogeneity can be estimated with MDS models by including a covariate that corresponds to the marked status of a group (MH models). Another approach is to use information from detections missed by both observers to account for the characteristics that make groups more or less likely to be detected, or recaptured, by the second observer (MR models). We used aerial survey data from 3 burro populations (Sinbad Herd Management Area, UT [2016–2018], Lake Pleasant Herd Management Area, AZ [2017], and Fort Irwin National Training Center, CA [2016–2017]) to develop MDS models applicable for feral burros in the southwestern United States. Our objectives were to quantify precision and bias of standard MDS surveys for feral burros and to examine which model type for incorporating residual heterogeneity (MH or MR) would result in the least‐biased estimates of burro populations relative to the minimum number known alive (MNKA) within the Sinbad Herd Management Area. Standard MDS model estimates achieved a mean coefficient of variation of 0.08, while underestimating MNKA by an average of 27.1%. Accounting for residual heterogeneity through recapture probability in MR models resulted in estimates closer to MNKA than MH models (9.5% vs. 16.5% less than MNKA). Our results indicate that MDS models can achieve precise enough estimates to monitor feral burro populations, but they routinely produce negatively biased estimates. We encourage the use of radio‐collars to reduce bias in future burro surveys by accounting for residual heterogeneity through MR models.

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Using integrated step selection to determine effects of predation risk on bison habitat selection and movement

Salganek,

Schoenecker,

Terwilliger

2024

Ecosphere

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Animal movement is a fundamental mechanism that shapes communities and ecosystems. Ungulates alter the ecosystems they inhabit and understanding their movements and distribution is critical for linking habitat with population dynamics. Predation risk has been shown to strongly influence ungulate movement patterns, such that ungulates may select habitat where predation risk is lower (refugia), adjust movement rates, temporal patterns, or selection of cover variables in areas with greater predation risk. We evaluated potential predation avoidance behavior in a population of plains bison inhabiting the north rim of Grand Canyon National Park (GRCA) and adjacent Kaibab National Forest (KNF). The KNF has year‐round hunting managed by Arizona Game and Fish Department, whereas hunting is not allowed in GRCA. Human‐maintained water sources on the KNF are particularly important resources for bison wherein they may be exposed to higher predation risk to access these resources. We used 2‐h GPS locations for three years from 31 bison (n = 9 males; n = 22 females), and integrative step selection analysis to test four hypotheses about the potential for bison to reduce their risk from human predation by avoiding areas of high predation risk; moving faster in areas with high predation risk; entering high‐risk areas at night when risk is reduced; and entering high‐risk areas in habitats that provide cover (coniferous forest). The highest performing model indicated bison movement was 1.3 times faster per 2‐h step interval than in areas with no hunting across all vegetation classes (coniferous forest, shrub, quaking aspen, grass‐forb meadow) and across all topography classes (valley, slope, ridge). Bison moved more slowly in grass‐forb meadows than all other vegetation types, and in valleys relative to slopes and ridges. Several radio‐collared individuals had no GPS locations in KNF for the duration of the study. Bison avoided predation risk using two strategies: moving faster while in the KNF, and fully avoiding high‐risk areas by remaining within GRCA. Management that manipulates or reduces timing of hunting seasons may reduce perceived predation risk and encourage bison to distribute into the KNF and across a broader range of available habitat.

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Comparison of Aerial Thermal Infrared Imagery and Helicopter Surveys of Bison (Bison bison) in Grand Canyon National Park, USA

Cited by 3 publications

References 29 publications

Comparing methods to estimate feral burro abundance

Comparing methods to estimate feral burro abundance

Accounting for residual heterogeneity in double‐observer sightability models decreases bias in burro abundance estimates

Using integrated step selection to determine effects of predation risk on bison habitat selection and movement

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