Modeling Elk Sightability Bias of Aerial Surveys During Winter in the Central Cascades

Gilbert, Brian A.; Moeller, Barbara J.

doi:10.3955/0029-344x-82.3.222

Cited by 8 publications

(18 citation statements)

References 6 publications

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“…In comparing uncorrected counts to abundance estimates from model M H , the survey crews detected 80–93% of elk present in Mount Rainier National Park subalpine parklands. Elk sightability during helicopter surveys was determined primarily by group size, the amount of concealing vegetation cover, and, to a lesser extent, animal movement and light level; all are factors that have been identified previously in sightability models of detection bias in elk aerial surveys (e.g., Samuel et al , Anderson et al , Gilbert and Moeller , McIntosh et al ). Model M H also allowed us to quantify the negative effect of inexperienced pilot‐observers on the front‐seat observer pair's detection probability, and the negative effect of elk presence along the flightline center on detection probabilities of back‐seat observers.…”

Section: Discussionmentioning

confidence: 95%

“…Sightability models (model M S ) are commonly based on logistic regression equations used to predict the probability that aerial observers detect individuals or groups of animals as functions of sighting covariates such as group size, the amount of concealing vegetation near the group, and animal movement at the time of detection (Samuel et al , Unsworth et al , Gilbert and Moeller , McIntosh et al ). Model M S has been used extensively to estimate observation biases of aerial surveys for elk ( Cervus elaphus ; Samuel et al , Anderson et al , McCorquodale , Gilbert and Moeller , McIntosh et al ), moose ( Alces alces ; Anderson and Lindzey , Giudice et al ), mountain sheep ( Ovis dalli ; Udevitz et al ) mountain goats ( Oreamnos americanus ; Rice et al ), and other species (Krueger et al , Manning and Garton ). Model M S is fit to records of detection or non‐detection of known animal groups present during surveys that are identified using radio‐telemetry or simultaneous observation by independent crews.…”

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confidence: 99%

“…Methods for estimating detection bias in aerial surveys include sightability models (Steinhorst and Samuel 1989), distance models Anderson 1984, Buckland et al 2004), mark-resight models (White 1996, Skalski et al 2005 including double-observer (DO) models (Graham and Bell 1989), and methods that combine 2 or more such techniques (Quang andBecker 1997, Buckland et al 2010). Sightability models (model M S ) are commonly based on logistic regression equations used to predict the probability that aerial observers detect individuals or groups of animals as functions of sighting covariates such as group size, the amount of concealing vegetation near the group, and animal movement at the time of detection (Samuel et al 1987, Unsworth et al 1999, Gilbert and Moeller 2008, McIntosh et al 2009). Model M S has been used extensively to estimate observation biases of aerial surveys for elk (Cervus elaphus; Samuel et al 1987, Anderson et al 1998, McCorquodale 2001, Gilbert and Moeller 2008, McIntosh et al 2009), moose (Alces alces; Anderson and Lindzey 1996, Giudice et al 2012), mountain sheep (Ovis dalli; Udevitz et al 2006) mountain goats (Oreamnos americanus; Rice et al 2008), and other species (Krueger et al 2007, Manning andGarton 2011).…”

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A hybrid double-observer sightability model for aerial surveys

et al. 2013

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Raw counts from aerial surveys make no correction for undetected animals and provide no estimate of precision with which to judge the utility of the counts. Sightability modeling and double‐observer (DO) modeling are 2 commonly used approaches to account for detection bias and to estimate precision in aerial surveys. We developed a hybrid DO sightability model (model MH) that uses the strength of each approach to overcome the weakness in the other, for aerial surveys of elk (Cervus elaphus). The hybrid approach uses detection patterns of 2 independent observer pairs in a helicopter and telemetry‐based detections of collared elk groups. Candidate MH models reflected hypotheses about effects of recorded covariates and unmodeled heterogeneity on the separate front‐seat observer pair and back‐seat observer pair detection probabilities. Group size and concealing vegetation cover strongly influenced detection probabilities. The pilot's previous experience participating in aerial surveys influenced detection by the front pair of observers if the elk group was on the pilot's side of the helicopter flight path. In 9 surveys in Mount Rainier National Park, the raw number of elk counted was approximately 80–93% of the abundance estimated by model MH. Uncorrected ratios of bulls per 100 cows generally were low compared to estimates adjusted for detection bias, but ratios of calves per 100 cows were comparable whether based on raw survey counts or adjusted estimates. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to DO modeling. © 2013 The Wildlife Society.

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Section: Discussionmentioning

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A hybrid double-observer sightability model for aerial surveys

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“…Previous research has shown that the detectability of ungulates is influenced by group size, canopy cover, individual activity (bedded, standing, moving), ground terrain, and light conditions (Thomas and Gray 2002;Gilbert and Moeller 2008;Patterson et al 2014;Peters et al 2014). Of these factors, vegetation cover, which is largely dependent on the habitat, is the most frequently reported factor known to affect the detectability of target wildlife during surveys (Gasaway 1985;Samuel et al 1987;Gilbert and Moeller 2008;McIntosh et al 2009;Jarding 2010;Griffin et al 2013). For this reason we chose to focus on the effect of habitat on the detection of targets.…”

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Evaluation of an unmanned aircraft system for detecting surrogate caribou targets in Labrador

Patterson

Koski²,

Pace³

et al. 2016

J. Unmanned Veh. Sys.

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Regular, standardized population inventories have been suggested as an important component to the recovery of declining populations of boreal caribou (Rangifer tarandus caribou). Current survey methods typically employ manned aircraft, which can be noisy, expensive to operate, and dangerous for the people conducting the surveys. Small unmanned aerial systems (UAS) have garnered attention as a promising alterative to conducting aerial surveys in manned aircraft. Our research investigates the feasibility of using an UAS to conduct aerial surveys and determine which factors affect the detection of surrogate caribou targets, and hence may affect detection of real caribou. In the fall of 2013, we tested the capabilities of the Brican TD100E, a small, electric-powered fixed-wing UAS, to fly beyond visual line of sight near Goose Bay, Labrador. Seven surveys were done using different flight paths to collect aerial images of 26 surrogate caribou targets placed in six different habitats. Mixed effects logistic regression models were used to evaluate how habitat type, distance of the target from the image centerline, photo analysts’ experience level, flight time, and the target contrast against the landscape influenced the detection of surrogate caribou targets. We found that habitat type, target contrast, and the flight time affected target detection. Overall, 77.5% of the targets were detected; the odds of a photo analyst detecting a target in open habitat were roughly 10.5 times higher than in burned habitat and 42 times higher than in heavy forest. Target detection was influenced by the contrast of the target against the landscape, where a higher corrected integrated density was associated with greater target detection. The detection of targets was 87% during evening flights and 75% for morning flights. This study was the first of its kind to successfully fly a UAS beyond line of sight over land for non-military applications in North America and the findings of our research will provide an evaluation for using UAS to survey caribou in the future.

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“…Collecting at least crude distance estimates could improve the ability to estimate sighting probability without depending as much on the residual heterogeneity parameter. Additional covariates that might also help explain visibility differences among groups could be considered, such as indicators for lighting condition (e.g., full sun, patchy sun, and overcast sky) and activity of elk (Griffin et al, 2013;Samuel et al, 1987;Anderson and Lindzey, 1996;Gilbert and Moeller, 2008;McIntosh et al, 2009). If different observers are used in the future, covariates to account for individual observer differences could improve precision and reduce bias.…”

Section: Discussionmentioning

confidence: 98%

Application of a hybrid model to reduce bias and improve precision in population estimates for elk (Cervus elaphus) inhabiting a cold desert ecosystem

Schoenecker

Lubow

2016

Journal of King Saud University - Science

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Accurately estimating the size of wildlife populations is critical to wildlife management and conservation of species. Raw counts or ''minimum counts'' are still used as a basis for wildlife management decisions. Uncorrected raw counts are not only negatively biased due to failure to account for undetected animals, but also provide no estimate of precision on which to judge the utility of counts. We applied a hybrid population estimation technique that combined sightability modeling, radio collar-based mark-resight, and simultaneous double count (double-observer) modeling to estimate the population size of elk in a high elevation desert ecosystem. Combining several models maximizes the strengths of each individual model while minimizing their singular weaknesses. We collected data with aerial helicopter surveys of the elk population in the San Luis Valley and adjacent mountains in Colorado State, USA in 2005 and 2007. We present estimates from 7 alternative analyses: 3 based on different methods for obtaining a raw count and 4 based on different statistical models to correct for sighting probability bias. The most reliable of these approaches is a hybrid double-observer sightability model (model M H ), which uses detection patterns of 2 independent observers in a helicopter plus telemetry-based detections of radio collared elk groups. Data were fit to customized mark-resight models with individual sighting covariates. Error estimates were obtained by a bootstrapping procedure. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to double-observer modeling. The resulting population estimate corrected for multiple

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Modeling Elk Sightability Bias of Aerial Surveys During Winter in the Central Cascades

Cited by 8 publications

References 6 publications

A hybrid double-observer sightability model for aerial surveys

A hybrid double-observer sightability model for aerial surveys

Evaluation of an unmanned aircraft system for detecting surrogate caribou targets in Labrador

Application of a hybrid model to reduce bias and improve precision in population estimates for elk (Cervus elaphus) inhabiting a cold desert ecosystem

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