The utility of auxiliary data in statistical population reconstruction

Clawson, Michael V.; Skalski, John R.; Millspaugh, Joshua J.

doi:10.2981/12-076

Cited by 14 publications

(34 citation statements)

References 10 publications

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“…For example, estimates of the number of prey killed by a predator are difficult to interpret without knowing something about the size of the underlying prey population. Recreational harvest regulations can be based solely on trends, ratios, or hunter effort indices (Crête and Dussault , Rönnegård, et al , Ueno et al ), but some models that guide management require at least the occasional unbiased estimate of abundance (White and Lubow , Clawson et al ). The natural recolonization of wolves ( Canis lupus ) to northeastern Washington (WDFW ) added to existing concerns stemming from a warming climate (Schwab and Pitt , Monteith et al ) and prompted WDFW to increase its focus on estimating moose abundance trends and demographic characteristics in 2013.…”

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confidence: 99%

Hierarchical mark‐recapture distance sampling to estimate moose abundance

et al. 2018

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Estimating the abundance of wide‐ranging wildlife, difficult under any circumstances, is particularly challenging when detection is low and affected by factors that also influence density and distribution. In northeastern Washington, moose (Alces alces) have evidently increased since the 1970s but spend most of their time under coniferous cover that makes detection from the air difficult. We used a Bayesian hierarchical approach to incorporate habitat use (in the form of availability as a function of canopy closure) into a detection model within a mark‐recapture distance sampling framework to estimate moose density. Our model of availability used a latent density surface employing habitat use data obtained from 17 adult female moose wearing global positioning system (GPS) collars. Distance sampling data, obtained from helicopter surveys in winters 2014, 2015, and 2016, consisted of double‐observer detections of 166 moose groups along 2,241 km of systematically placed line transects within 29 survey blocks selected using a stratified‐random design. We estimated moose density over the entire survey area as 0.49/km2 (95% credible interval = 0.33–0.67/km2). Extrapolated to the 10,513‐km2 survey area, we estimated 5,169 moose (95% credible interval = 3,510–7,034). Our methodology allowed us to adjust for availability bias and produce an estimate even where detection was difficult but required many hours of helicopter flights, acceptable weather conditions, and the availability of GPS collared‐moose. © 2018 The Wildlife Society.

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confidence: 99%

Hierarchical mark‐recapture distance sampling to estimate moose abundance

et al. 2018

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“…Also, the accuracy and precision of reconstruction based solely on age‐at‐harvest and catch‐effort is positively correlated with the amount of year‐to‐year variability in catch‐effort (Laake , Clawson et al ). Our simulated and case study data, for example, were based on a less than 3‐fold difference in trapper effort, compared to the 5‐ and 7‐fold differences successfully used before (Skalski et al , ).…”

Section: Discussionmentioning

confidence: 99%

“…Under this framework, all individuals born during the same year constitute a single cohort that is subsequently subjected to annual mortality from harvest and natural causes. Using an age‐at‐harvest matrix (Table ), we can represent each cohort as a separate diagonal, where the observed counts,

h_{i j}

, are a function of the initial abundance of the corresponding cohort and annual rates of harvest mortality and natural survival (Gove et al , Skalski et al , 2012b, Clawson et al ). We model each of these diagonals as independent multinomial distributions, and the joint likelihood for the entire matrix as

L_{A g e - a t - h a r v e s t} = \prod_{i = 1}^{Y} l_{i 1} \times \prod_{j = 2}^{3} l_{1 j},

where

l_{i j}

is the cohort‐specific age‐at‐harvest likelihood (Skalski et al 2012b).…”

Section: Methodsmentioning

confidence: 99%

“…One of the most common sources of this additional information is catch per unit effort (CPUE). Skalski et al () used CPUE data to model the total annual harvest as a binomially distributed function of the yearly trapper effort:

L_{C a t c h - e f f o r t} = \prod_{i = 1}^{Y} true(\begin{array}{c} center & \sum_{j = 1}^{A} N_{i j} \\ center & \sum_{j = 1}^{A} h_{i j} \end{array} true) {true(1 - e^{- c f_{i}} true)}^{\sum_{j = 1}^{A} h_{i j}} {true(e^{- c f_{i}} true)}^{\sum_{j = 1}^{A} true(N_{i j} - h_{i j} true)},

where

f_{i}

is the trapper effort in year

i

c

represents the trapping vulnerability coefficient that converts trapper effort to harvest probability, and

A

is the number of age classes (Skalski et al , , Broms et al , Clawson et al , Gast et al 2013a). Under this framework, the full standard reconstruction model may be written as

L_{0} = L_{A g e - a t - h a r v e s t} \times L_{C a t c h - e f f o r t} .

…”

Section: Methodsmentioning

confidence: 99%

“…More recently, statistical population reconstruction using integrated population models (IPMs) has emerged as a robust and more versatile alternative for estimating population abundance using age‐at‐harvest and annual catch‐effort data (Skalski et al , Fieberg et al , Schaub and Abadi , Clawson et al ). These methods simultaneously estimate several demographic parameters (e.g., annual abundance and recruitment, natural survival, and harvest mortality) and their uncertainty, and provide a flexible framework for incorporating auxiliary information from index surveys, mark‐recapture, or radio‐telemetry studies (Gove et al , Skalski et al , , Broms et al , Fieberg et al , Clawson et al ).…”

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Utility of radio‐telemetry data for improving statistical population reconstruction

et al. 2017

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Statistical population reconstruction using age‐at‐harvest and catch‐effort data has recently emerged as a robust and versatile approach to estimating the demographic dynamics of harvested populations of wildlife. Although there are clear benefits to incorporating radio‐telemetry data into reconstruction efforts, these data are costly and time‐consuming to collect. Managers that consider collecting these data alongside existing efforts could benefit from a comprehensive examination of how such benefits are influenced by the amount of radio‐telemetry data collected. Using a harvested population of American marten (Martes americana) in northeastern Minnesota, USA as a case study, we investigated the performance of population reconstruction using information on natural, harvest, or combined mortality derived from radio‐telemetry data collected over different numbers of years and with different numbers of animals collared each year. We simulated populations under a range of conditions and determined that incorporating radio‐telemetry data on natural and harvest mortality significantly improved model precision, and that each additional animal collared per year yielded a 0.50 ± 0.14% (SE) improvement in precision, whereas every additional year of radio‐telemetry data resulted in a 2.42 ± 0.70% improvement. Thus, including another year of radio‐telemetry resulted in similar gains in precision as including approximately 5 additional animals collared per year. In our applied marten example, incorporating radio‐telemetry data resulted in a significantly higher estimate of trapping vulnerability (0.20 vs. 0.058) and an overall smaller population size than reconstruction based solely on age‐at‐harvest and trapper effort data. These results illustrate the benefits of performing auxiliary studies, caution against relying on the results of population reconstruction based solely on age‐at‐harvest and hunter‐effort data, and demonstrate that improvements from incorporating radio‐telemetry data become evident even after as few as 2 years of data collection. © 2017 The Wildlife Society.

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Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

et al. 2019

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Directly monitoring abundance of cryptic species, such as mountain lions (Puma concolor), over large areas is a challenge for wildlife managers because traditional population estimation techniques may be impractical and expensive. We generated annual estimates of mountain lion abundance in Arizona, USA, for 2004–2018 by employing statistical population reconstruction methods, which use available age‐at‐harvest data and auxiliary information such as estimated survival rates, harvest probabilities, and hunter effort. Using PopRecon 2.0 software, we estimated that the statewide abundance of all mountain lions including kittens ranged from 1,848 (95% CI = 650–3,046) to 4,661 (95% CI = 393–9,030) during 2004–2018. Abundance for subadults and adults was more stable and precisely estimated, ranging from 1,166 (95% CI = 622–1,709) to 1,715 (95% CI = 872–2,558). Our results suggest a stable statewide mountain lion population. This approach provides a practical and cost‐effective option for monitoring Arizona's mountain lion population, and will improve the ability of managers to monitor the population annually to respond to changes in abundance and to evaluate factors that influence mountain lion abundance. © 2019 The Authors. Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

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The utility of auxiliary data in statistical population reconstruction

Cited by 14 publications

References 10 publications

Hierarchical mark‐recapture distance sampling to estimate moose abundance

Hierarchical mark‐recapture distance sampling to estimate moose abundance

Utility of radio‐telemetry data for improving statistical population reconstruction

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

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