Using distance sampling and hierarchical models to improve estimates of Dall's sheep abundance

Schmidt, Joshua H.; Rattenbury, Kumi L.; Lawler, James P.; MacCluskie, Margaret C.

doi:10.1002/jwmg.216

Cited by 48 publications

(137 citation statements)

References 22 publications

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“…Each survey team searched the uphill side of each transect for mountain goats. Upon detecting a group, the aircraft would leave the transect line, fly over the group, and mark its initial location using a global positioning system (GPS), thus allowing us to calculate the perpendicular distance from the flight line (Becker and Quang , Schmidt et al ). We also recorded size and composition (i.e., adults vs. juveniles) of each observed group.…”

Section: Methodsmentioning

confidence: 99%

Integrating distance sampling with minimum counts to improve monitoring

et al. 2019

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Minimum counts are commonly used to estimate population size and trend for wildlife conservation and management; however, the scope of inference based on such data is limited by untestable assumptions regarding the detection process. Alternative approaches, such as distance sampling, occupancy surveys, and repeated counts, can be employed to produce detection‐corrected estimates of population parameters. Unfortunately, these approaches can be more complicated and costly to implement, potentially limiting their use. We explored a conceptual framework linking datasets collected at different spatial scales under different survey designs, with the goal of improving inference. Specifically, we link landscape‐scale distance sampling surveys with local‐scale minimum counts in an integrated modeling framework to estimate mountain goat (Oreamnos americanus) abundance at both the local and regional scale in south‐central Alaska, USA, and provide an estimate of detection probability (i.e., sightability) for the minimum counts. Estimated sightability for the minimum count surveys was 0.67 (95% credible interval [CrI] = 0.52–0.83) and abundance for the entire area was 5,600 goats (CV = 9%), both in broad agreement with estimates from previous studies. Abundance estimates at the local scale (i.e., individual min. count unit) were reasonably precise (trueCV̅ = 18%), suggesting the integrated approach can increase the amount of information produced at both spatial scales by linking minimum count approaches with more rigorous survey designs. We propose that our integrated approach may be implemented in the context of a modified split‐panel monitoring design by altering survey protocols to include frequent minimum counts within local count units and intermittent but more rigorous survey designs with inference to the entire study area or population of interest. Doing so would provide estimates of abundance with appropriate measures of uncertainty at multiple spatial scales, thereby improving inference for population monitoring and management. © 2019 The Wildlife Society.

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Section: Methodsmentioning

confidence: 99%

Integrating distance sampling with minimum counts to improve monitoring

et al. 2019

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“…Conventional sampling techniques for mountain ungulates typically consist of aerial or on‐foot surveys (Sumner , Udevitz et al , Liu et al , Schmidt et al ). However, remotely activated cameras or camera traps are an increasingly used tool in the evaluation of wildlife presence and habitat use (Nichols et al ) and may be advantageous in situations where conventional techniques are restricted or too expensive.…”

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confidence: 99%

Evaluating dall's sheep habitat use via camera traps

et al. 2017

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Anthropogenic disturbances, such as military training, potentially affect mountain ungulate populations. Assessing the spatiotemporal occupancy or habitat use of a wildlife population can assist with planning of potentially disrupting activities. Currently, military training is expanding into potential Dall's sheep (Ovis dalli dalli) habitat within 2 training areas of Fort Wainwright, Alaska. The 2 study areas included Molybdenum Ridge, a 10‐km long geologically isolated granite ridgeline on the northern periphery of the Alaska Range and Black Rapids Training Area, a steeper and smaller area within the mountain range and bordered by the Delta River. The United States Army requires a better understanding of sheep habitat use in these 2 areas to identify how training could affect the population and to reduce overlap of training and high sheep occupancy. We used an array of 54 spatially balanced camera traps, taking triggered and hourly time lapse images, to determine Dall's sheep habitat use based on seasonal and site covariates. Camera traps operated for a continuous 15‐month sampling period and captured nearly 8,000 images of sheep. Our occupancy models indicated that abiotic covariates such as slope, snow depth, and distance to escape terrain were the most important factors determining habitat use. Seasonal differences in habitat use indicated higher use during winter and spring for Molybdenum Ridge, and higher habitat use during the summer for Black Rapids Training Area. Detection probabilities were constant temporally and were higher if the camera was positioned on a wildlife trail versus off trail. Our results suggest that the best opportunity to minimize training interactions with Dall's sheep is to conduct training in early‐July to early‐September, specifically in areas with <60% slope and >500 m from escape terrain. © 2017 The Wildlife Society.

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“…7.1;Eguchi and Gerrodette, 2009;Gimenez et al, 2009;Zhang, 2011), a half-normal with covariates (e.g. Gerrodette and Eguchi, 2011;Moore and Barlow, 2011) or a hazard-rate with one covariate (Schmidt et al, 2012). Our approach allows simultaneous exploration of model and parameter space including different detection functions and different covariate combinations for both the detection and count models via an RJMCMC algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…These authors employ a likelihood that combines the detection function and the binomial model using the marginal probability of encounter (estimated from the detection function) and a data augmentation approach for unobserved groups. The data augmentation approach was adopted by Schmidt et al (2012) who added a group size model and Conn et al (2012) who extended the approach for double observer data.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Methods for Hierarchical Distance Sampling Models

Oedekoven

Buckland

MacKenzie

et al. 2014

JABES

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The few distance sampling studies that use Bayesian methods typically consider only line transect sampling with a half-normal detection function. We present a Bayesian approach to analyse distance sampling data applicable to line and point transects, exact and interval distance data and any detection function possibly including covariates affecting detection probabilities. We use an integrated likelihood which combines the detection and count models. For the latter, counts are related to covariates in a log-linear mixed effect Poisson model which accommodates correlated counts. We use a Metropolis-Hastings algorithm for updating parameters and a reversible jump algorithm to include model selection for both the detection function and count models. The approach is applied to a large-scale experimental design study of northern bobwhite coveys where the interest was to assess the effect of establishing herbaceous buffers around agricultural fields in several states in the US on bird densities. Results were compared with those from an existing maximum likelihood approach that analyses the detection and count models in two stages. Both methods revealed an increase of covey densities on buffered fields. Our approach gave estimates with higher precision even though it does not condition on a known detection function for the count 1 model.

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Using distance sampling and hierarchical models to improve estimates of Dall's sheep abundance

Cited by 48 publications

References 22 publications

Integrating distance sampling with minimum counts to improve monitoring

Integrating distance sampling with minimum counts to improve monitoring

Evaluating dall's sheep habitat use via camera traps

Bayesian Methods for Hierarchical Distance Sampling Models

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