Performing statistical population reconstruction using Program PopRecon 2.0

Clawson, Michael V.; Skalski, John R.; Lady, James; Hagen, Christian A.; Millspaugh, Joshua J.; Budeau, David A.; Severson, John P.

doi:10.1002/wsb.790

Cited by 8 publications

(13 citation statements)

References 21 publications

(45 reference statements)

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“…We used tags sold as our measure of hunter effort primarily because it was the only data available for the majority of our study period, although some have suggested that measures such as hunter‐days might better represent hunter effort (Clawson et al ). A regression analysis indicated a surprisingly tight relationship between tags sold and hunter‐days.…”

Section: Discussionmentioning

confidence: 99%

“…If ages were generally underestimated, abundance estimates would also have been underestimated, and vice versa, although SPR analysis has been reported to be robust to errors in aging animals (Conn et al 2009). We used tags sold as our measure of hunter effort primarily because it was the only data available for the majority of our study period, although some have suggested that measures such as hunter-days might better represent hunter effort (Clawson et al 2017b). A regression analysis indicated a surprisingly tight relationship between tags sold and hunter-days.…”

Section: Discussionmentioning

confidence: 99%

“…One advantage of using PopRecon or other SPR models for abundance estimation is the relatively low cost (Skalski et al 2007(Skalski et al , 2011Clawson et al 2017b;Hatter 2019). Ageat-harvest data and basic hunter effort data are collected at low cost by many state agencies.…”

Section: Annual Variationmentioning

confidence: 99%

“…Approaches such as mark‐recapture techniques (Lambert et al , Russell et al ), genetic mark‐recapture using DNA extracted from tissue, scat, or hair samples (Russell et al , Davidson et al , Beausoleil et al ), or identifying individuals from photographs captured by remote cameras (Kelly et al , Smythe , Negrões et al , Rosas‐Rosas and Bender ) have typically been applied to a limited geographic area or population. Recent advances in spatial mark‐resight models using camera trap data provide another means to estimate abundance (Rich et al , Murphy et al ) but still may be cost‐ and labor‐prohibitive for long‐term monitoring across a large landscape (Skalski et al , Clawson et al , Hatter ).…”

mentioning

confidence: 99%

“…In recent years, statistical population reconstruction (SPR; Gove et al , Skalski et al , Gast et al , Clawson et al ) has been used to estimate abundance from age‐at‐harvest data, and auxiliary data such as telemetry and hunter effort data. This approach is appealing because age‐at‐harvest data has been collected at low cost for many game species.…”

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confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Annual Variationmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

et al. 2019

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A statistical population reconstruction model for wildlife populations: A case study with white‐tailed deer and fisher

Bellier,

Ferreira,

Kalb

et al. 2024

Ecosphere

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Harvested wildlife populations should ideally be monitored to inform harvest policies and decision‐making to help achieve management objectives. When the age of harvested individuals can be obtained, these data (i.e., age‐at‐harvest data) can be used to estimate trends of abundances, demographic rates, and harvest probabilities by the statistical reconstruction of the living population. This approach was developed primarily within the frequentist framework and requires the inclusion of auxiliary data (e.g., radiotelemetry data). We developed a novel Bayesian hierarchical approach allowing the population reconstruction from the definition of the species' life cycle without auxiliary data. The hierarchical model assumes that individuals are harvested from an open population whose fluctuations result from demographic processes, and the definition of a superpopulation composed of pseudo‐individuals from which the harvested population is drawn. We evaluated the ability of our model to estimate abundances, survival, recruitment, and harvest probabilities based on simulations guided by the demographic processes of a long‐lived mammal population. We considered model performance across scenarios, including varying age and temporal structures, superpopulation size, and prior information. We showed how prior information selected based on life history characteristics affects the accuracy of estimated parameters. We found that the model estimates accurate demographic parameters and abundances when the age‐at‐harvest matrix comprises more than two age classes. Furthermore, an increase in demographic information (number of age groups and years) increased the precision of the estimated parameters. We apply our model to a population of harvested (2012–2021) white‐tailed deer (Odocoileus virginianus) and a mammalian carnivore, the fisher (Pekania pennanti), from Rhode Island, USA. Our model estimated biologically realistic population size and demographic rates for both species. Our approach provides robustness to track the population abundance of harvested species through time and estimate fundamental demographic parameters. Such results can be used to monitor whether population objectives are being met and whether harvest policy changes are required. Furthermore, this information can be critical for evaluating the effect of harvest on population growth and projecting trajectories of age‐structured populations under different harvest scenarios. Therefore, our framework can help to guide management decisions and species conservation.

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Using statistical population reconstruction to estimate demographics of wild turkey populations

Clawson

Isabelle

Skalski

et al. 2022

Wildlife Society Bulletin

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Although the wild turkey (Meleagris gallopavo; hereafter, turkey) is one of the most popular game species in the United States, there is no consensus on how best to monitor turkey populations. Harvest data are readily available to state wildlife agencies but are often not coupled with other datasets (e.g., hunter effort) for demographic assessments. We described the utility of statistical population reconstruction (SPR) to unite disparate datasets and effectively monitor status and trends of key turkey demographic rates at spatial scales relevant to wildlife managers. We demonstrated SPR model development and associated assumptions, model selection, and model evaluation using male‐only models for turkeys in 2 regions of Missouri, USA, and compared model output to harvest‐based and non‐harvest‐based indices. Use of SPR led to a more comprehensive assessment of population dynamics than did indices alone. Moreover, inference based solely on indices would likely have led to different interpretations of which factors govern turkey population dynamics and ultimately which management practices should be implemented. Disparity of model output between regions demonstrates importance of auxiliary data in SPR model utility. As such, we encourage well‐planned and executed auxiliary studies to improve precision of demographic rates when using SPR models. Statistical population reconstruction provides a viable, useful tool to assess demographics of turkey populations and on which to base management decisions.

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Performing statistical population reconstruction using Program PopRecon 2.0

Cited by 8 publications

References 21 publications

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

A statistical population reconstruction model for wildlife populations: A case study with white‐tailed deer and fisher

Using statistical population reconstruction to estimate demographics of wild turkey populations

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