Statistical Population Reconstruction Research Articles

The effects of variability in catch effort on the precision of statistical population reconstruction

Statistical population reconstruction (SPR) models have emerged as a robust and versatile framework for estimating the demographic dynamics of harvested wildlife populations using commonly collected age‐at‐harvest and catch‐effort data. Although numerous studies have suggested that higher interannual variability in catch effort may improve the accuracy and precision of reconstructed estimates, particularly in the absence of auxiliary data on annual abundance or survival, the extent and magnitude of these effects has not been explored. We examined the influence of catch‐effort variability, as measured by the ratio between years of highest and lowest effort, on the relative absolute deviation of reconstructed estimates of population abundance, as well as on the actual percent coverage and width of the corresponding confidence intervals. We used a Monte Carlo simulation to generate catch‐effort data with different levels of variability for populations experiencing a wide range of demographic and harvest conditions. For similar amounts of age‐at‐harvest data, using catch‐effort data with higher interannual variability resulted in reconstructed estimates of annual abundance that had significantly lower deviations from reality, better coverage, and narrower confidence intervals (as measured by the margin of error). These improvements were consistent and linear at low to medium levels of catch‐effort variability, but leveled off and became substantially less pronounced at higher levels. We found that the inclusion of auxiliary data largely mediated this relationship, although higher catch‐effort variability still resulted in more accurate and precise estimates of annual abundance even when these data were included. Our research highlights the need to include a thorough investigation of the available catch‐effort data alongside the established practices of assessing the number of years of available data, the average number of animals harvested each year, and the availability of auxiliary data from radio‐telemetry studies or other sources.

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

AbstractHarvested 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.

Utility of Particle Swarm Optimization in Statistical Population Reconstruction

Statistical population reconstruction models based on maximum likelihood and minimum chi-square objective functions provide a robust and versatile approach to estimating the demographic dynamics of harvested populations of wildlife. These models employ numerical optimization techniques to determine which set of model parameters best describes observed age-at-harvest, catch-effort, and other auxiliary field data. Although numerous optimization methods have been used in the past, the benefits of using particle swarm optimization (PSO) have yet to be explored. Using a harvested population of North American river otter (Lontra canadensis) in Indiana as a case study, we investigated the performance of population reconstruction using particle swarm optimization, spectral projected gradient (SPG), Nelder–Mead, and Broyden–Fletcher–Goldfarb–Shanno (BFGS) methods. We used Monte Carlo studies to simulate populations under a wide range of conditions to compare the relative performance of population reconstruction models using each of the four optimization methods. We found that using particle swarm optimization consistently and significantly improved model stability and precision when compared with other numerical optimization methods that may be used in statistical population reconstruction. Given that these models are frequently used to guide management decisions and set harvest limits, we encourage management agencies to adopt this more precise method of estimating model parameters and corresponding population abundance. These results illustrate the benefits of using particle swarm optimization, caution against relying on the results of population reconstruction based on optimization methods that are highly dependent on initial conditions, and reinforce the need to ensure model convergence to a global rather than a local maximum.

Statistical population reconstruction of moose (Alces alces) in northeastern Minnesota using integrated population models.

Given recent and abrupt declines in the abundance of moose (Alces alces) throughout parts of Minnesota and elsewhere in North America, accurately estimating statewide population trends and demographic parameters is a high priority for their continued management and conservation. Statistical population reconstruction using integrated population models provides a flexible framework for combining information from multiple studies to produce robust estimates of population abundance, recruitment, and survival. We used this framework to combine aerial survey data and survival data from telemetry studies to recreate trends and demographics of moose in northeastern Minnesota, USA, from 2005 to 2020. Statistical population reconstruction confirmed the sharp decline in abundance from an estimated 7,841 (90% CI = 6,702-8,933) in 2009 to 3,386 (90% CI = 2,681-4,243) animals in 2013, but also indicated that abundance has remained relatively stable since then, except for a slight decline to 3,163 (90% CI = 2,403-3,718) in 2020. Subsequent stochastic projection of the population from 2021 to 2030 suggests that this modest decline will continue for the next 10 years. Both annual adult survival and per-capita recruitment (number of calves that survived to 1 year per adult female alive during the previous year) decreased substantially in years 2005 and 2019, from 0.902 (SE = 0.043) to 0.689 (SE = 0.061) and from 0.386 (SE = 0.030) to 0.303 (SE = 0.051), respectively. Sensitivity analysis revealed that moose abundance was more sensitive to fluctuations in adult survival than recruitment; thus, we conclude that the steep decline in 2013 was driven primarily by decreasing adult survival. Our analysis demonstrates the potential utility of using statistical population reconstruction to monitor moose population trends and to identify population declines more quickly. Future studies should focus on providing better estimates of per-capita recruitment, using pregnancy rates and calf survival, which can then be incorporated into reconstruction models to help improve estimates of population change through time.

Using statistical population reconstruction to estimate demographics of wild turkey populations

AbstractAlthough 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.

Is unreliable science guiding bobcat management in Wyoming and other western U.S. states?

Abstract Wildlife managers require defensible and transparent population estimates to justify species management. Statistical population reconstruction (SPR) is being widely adopted to estimate wildlife population sizes from hunter harvest data. We assessed factors influencing variation in SPR population estimates produced for bobcats in Wyoming, USA. Specifically, we tested whether prey availability, hunter/trapper effort, the number of bobcats killed or the methods used to classify the age and sex of bobcats (‘classification protocol’) best explained changing SPR abundance estimates. We then quantified the relative magnitude of these effects on SPR model outputs. Classification protocol had the strongest impact on SPR abundance estimates, such that a shift to visual age and sex classifications by trappers/hunters resulted in overestimates of bobcat abundance. The Wyoming bobcat SPR population estimates were likely unreliable and we suggest that spatially explicit integrated population models may be a better approach to obtaining defensible estimates upon which to establish scientific management of this charismatic carnivore.

A COMPARISON OF MULTINOMIAL LIKELIHOOD AND CHI-SQUARE APPROACHES TO STATISTICAL POPULATION RECONSTRUCTION

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 most reconstruction efforts employ the multinomial likelihood approach to identify which set of model parameters best describes the observed age-at-harvest and catch-effort data, using a [Formula: see text] objective function may provide a suitable alternative with a less steep learning curve. Using a harvested population of North American river otter (Lontra canadensis) in Kentucky as a case study, we investigated the performance of population reconstruction using multinomial likelihood and chi-square formulations. We simulated populations under a range of conditions and found that both the accuracy and precision of reconstruction estimates were similar under the two approaches. These results illustrate the potential benefits of using the [Formula: see text] approach, which may also allow agencies to incorporate auxiliary information from studies for which the corresponding likelihood contributions have yet to be developed.

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

ABSTRACTDirectly 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.

Mountain Lion (Puma concolor) Population Characteristics in the Little Missouri Badlands of North Dakota

Mountain lions (Puma concolor) have many impacts on the ecosystems they inhabit, leading to both biological and social ramifications. Yet, due to the relatively recent natural recolonization by mountain lions of the Little Missouri Badlands Region of western North Dakota, detailed data regarding many aspects of this population have been lacking. We studied mountain lions occupying the Badlands Region to improve our understanding of the characteristics of the North Dakota mountain lion population. Our objectives included estimating annual survival rates, documenting sources of mortality in this population, and creating a population model using statistical population reconstruction (SPR) techniques. Between 2012 and 2016, the average annual survival rate estimated from known fate models was 0.456. Sex-specific annual survival was estimated at 0.589 for females and 0.259 for males. We recorded 17 cause-specific mortalities of marked mountain lions over the same 5 y period, plus the probable failures of two litters of marked dependent kittens. Results from our population model indicate annual population abundance estimates between 2005 and 2017 ranged from a low of 27 total mountain lions in 2005–2006 to a high of 165 in 2011–2012. The model indicated an increasing trend in total abundance between 2005 and 2012, then a reversal and sharp drop in abundance from 2012 to 2014, until the trend leveled with similar total abundances from 2014 to 2017. The results from our study can help inform current and future management decisions in North Dakota and may also provide insight for managers faced with potential mountain lion recolonization further eastward in the United States.

Statistical population reconstruction to evaluate grizzly bear trends in British Columbia, Canada

Grizzly bear (Ursus arctos) populations are costly to monitor by traditional survey methods. In British Columbia, Canada, hunter kill data are available and provide relatively inexpensive information that possibly can be used to estimate trends in hunted populations. We applied statistical population reconstruction (SPR) using Program PopRecon 2.0 to evaluate trends in abundance of ≥3-year-old male grizzly bears for 3 large areas in British Columbia. Model inputs included annual estimates of age-at-kill and hunter effort, combined with auxiliary information on population abundance in 2012, and a non-hunting survival rate. Modeled abundance in all 3 areas was sensitive to the auxiliary abundance estimate but less so for the auxiliary survival estimate or the length of the time series. Relative trends in abundance appeared to be primarily affected by kill and effort data and were less affected by the auxiliary data. The gradual increase in abundance within the Temperate Mountains area from 1985 to 2004 followed by an apparent decline was consistent with other independent studies and supported the premise that grizzly bear numbers were recovering from a population low until between 2000 and 2005. Our results suggest that the grizzly bear population in the Boreal–Sub-boreal area was also recovering during this period. Our analysis demonstrates the potential utility of SPR for monitoring grizzly bear population trends, but results from the Coastal area also highlight the importance of sufficient hunter-kill and -effort data, in addition to quality auxiliary data, to detect population change. Future enhancements in Program PopRecon may help improve the performance and utility of SPR for grizzly bears in British Columbia.

Assessment of river otter abundance following reintroduction

Context By the early 1900s, river otters (Lontra canadensis) were extirpated across large parts of their range in North America. Over the last several decades they have made a remarkable recovery through widespread reintroduction programs. River otters were reintroduced in Ohio, USA, between 1988 and 1993, and restricted and limited harvesting of this population began in 2005. While circumstantial evidence points to rapid population growth following the reintroduction, changes in population size over time is unknown. Aims We sought to model river otter population growth following reintroduction, and to assess the impact of harvesting. Methods We used empirical and literature-based data on river otter demographics in Ohio to estimate abundance from 1988–2008 using an age- and sex-specific stochastic Leslie matrix model. Additionally, we used statistical population reconstruction (SPR) methods to estimate population abundance of river otters in Ohio from 2006 to 2008. Results Our Leslie matrix model predicted a population size of 4115 (s.d. = 1169) in 2005, with a population growth rate (λ) of 1.28 in 2005. Using SPR methods we found that both trapper effort and initial population abundance influenced our population estimates from 2006 to 2008. If we assumed that river otter pelt price was an accurate index of trapper effort, and if the initial population was between 2000 and 4000, then we estimated the λ to be 1.27–1.31 in 2008 and the exponential rate to be 0.17–0.21 from 2006 to 2008. Conversely, if the river otter population in 2005 was 1000, then we estimated λ to be 1.20 in 2008 and the exponential rate to be 0.08 from 2006 to 2008. Conclusions The river otter population in Ohio appears to have had the potential to grow rapidly following reintroduction. The ultimate effect of the harvesting regime on population abundance, however, remains clouded by limited data availability and high variability. Implications The considerable uncertainty surrounding population estimates of river otters in Ohio under the harvesting regime was largely driven by lack of additional data. This uncertainty clouds our understanding of the status of river otters in Ohio, but a more robust, long-term monitoring effort would provide the data necessary to more precisely monitor the population.

Performing statistical population reconstruction using Program PopRecon 2.0

ABSTRACTStatistical population reconstruction develops an integrated likelihood model that incorporates age‐at‐harvest composition, harvest totals, hunter effort, and auxiliary data on survival, harvest, or abundance to model demographics of harvested populations. Each statistical population reconstruction model is handcrafted to account for varying levels of available demographic data, hunting regulations, sampling of the harvest process, and biology of the harvested species. PopRecon 2.0 is a software program that facilitates the creation and analysis of these unique demographic models and capable of analyzing either full or pooled age‐at‐harvest data using either fixed or random effects on the annual survival and harvest processes. The program produces annual estimates of abundance, harvest probabilities, natural survival, and recruitment using a user‐friendly, interactive computing environment and AD Model Builder analytical software. Program PopRecon 2.0 is demonstrated using 23 years (1993–2015) of harvest data on greater sage‐grouse (Centrocercus urophasianus) from Oregon, USA. © 2017 The Wildlife Society.

Statistical Population Reconstruction Using Wings from Harvested Northern Bobwhites Can Inform Management

Despite the widespread collection wings from harvested northern bobwhite (Colinus virginianus) by state wildlife agencies and private entities, age-at-harvest information has been largely underutilized for guiding management decisions for this species. Statistical population reconstruction (SPR) techniques can use age-at-harvest information and provide a valuable tool for monitoring trends and the status of bobwhite (and other game bird) populations. However, SPR has not been applied to bobwhite. We evaluated the utility of statistical population reconstruction models, which have been applied successfully to other species (e.g., elk, Cervus elaphus; blacktailed deer, Odocoilues hemonius; and greater sage-grouse, Centrocercus urophasianus), to reconstruct annual abundance and demographic attributes for a bobwhite population in southwest Georgia. During 1998 – 2016, we collected wings from harvested birds (n = 17,448; nannual = 969 ± 104) in conjunction with survival information from mark-recapture. We derived independent estimates of fall abundance from the same site using covey call counts and a standardized measure of hunting success (coveys moved per hour). SPR models suggested that population change was stable (λ = 1.00; CV = 0.19) compared to moderate population growth (λ = 1.05; CV = 0.29) indicated by the covey call quadrat method. Abundance estimates from SPR and covey call counts were moderately correlated (r = 0.48) with only 3 out 18 years statistically different. Abundance estimates from SPR and hunting success (coveys moved per hour) was highly correlated (r = 0.86). SPR provides valid, conservative abundance estimates for bobwhite age-at-harvest data. Therefore, we endorse the use of SPR for bobwhites where age-at-harvest, hunter effort and another source of auxiliary data are readily available. As such, given that the collection of harvested wings is simple and low cost, we recommend managers and state agencies consider incorporating this technique into their management program.

Trends in male wild turkey abundance and harvest following restoration efforts in the southeast region of Missouri, 1960–2010

ABSTRACTLong‐term estimates of abundance can be useful in elucidating wildlife population and hunter dynamics as well as other potential factors affecting populations. We used estimated vulnerability coefficients from a statistical population reconstruction (SPR) analysis (1996–2010), along with a 50‐year time series of harvest and hunter‐effort data to reconstruct a male wild turkey (Meleagris gallopavo) population in southeastern Missouri, USA (1960–2010; Gast et al. 2013). Following restoration efforts, the male wild turkey population in the Ozarks East turkey productivity region grew rapidly following a logistic growth pattern, from 2,932 turkeys in 1960 to 15,764 in 1980, and vacillated around a stable equilibrium from 1980 to 2010. Distance from St. Louis, Missouri, explained 19.8% of the variation in hunter density while turkey density only explained 1.1%, suggesting that factors beyond game density influenced hunter distribution. To explain the high inter‐annual variation of abundance found in the historical reconstruction, we examined the relationship between spring weather, recruitment, and abundance metrics. A multiple linear regression found total precipitation in June to be positively correlated and the number of cold days in April to be negatively correlated with yearling male (1 year old): adult male ratios in the following spring. Our results suggest the current wild turkey population is controlled primarily by extrinsic factors through effects on reproduction. Evidence of a stable population with high inter‐annual variation lends support for using consistent harvest regulations rather than altering regulations annually to accommodate short‐term trends in abundance. Our research highlights the utility of SPR models to assess factors affecting historical wildlife population demographics. © 2014 The Wildlife Society.

Evaluation of fixed- and random-effects models and multistage estimation procedures in statistical population reconstruction

Statistical population reconstruction (SPR) using hunter-supplied, age-at-harvest data provides a flexible framework for estimating population parameters and trends over large geographic areas. Early development of the technique assumed natural survival and the vulnerability coefficients that translate hunter effort into harvest mortality are constant over time. We developed random-effects models that relaxed these assumptions to produce more realistic models for cohort analysis. We compared the statistical performance of fixed-effects and random-effects models and the relative benefits of single-stage and 2-stage estimators of abundance with Monte Carlo simulation studies. In single-stage analyses, initial cohort abundance is estimated directly within the likelihood model. In 2-stage analyses, survival and harvest probabilities are estimated using likelihood conditional on total cohort harvest. Annual abundances are then estimated outside the likelihood using Horvitz–Thompson type estimators. Our simulation results indicate random-effects models conditional on total cohort harvest have low bias and yield asymptotic 95% confidence intervals that have near nominal coverage. The other estimation procedures produced confidence intervals for annual abundance that had far-from-nominal coverage. Our new random-effects, 2-stage model is illustrated using 18 years of age-at-harvest data from an elk (Cervus elaphus) herd in the Lower Peninsula of Michigan. Results indicate the multistage estimation process incorporating random effects provides accurate abundance estimates and confidence interval coverage, and we recommend its use. © 2013 The Wildlife Society.

Random effects models and multistage estimation procedures for statistical population reconstruction of small game populations.

Recently, statistical population models using age-at-harvest data have seen increasing use for monitoring of harvested wildlife populations. Even more recently, detailed evaluation of model performance for long-lived, large game animals indicated that the use of random effects to incorporate unmeasured environmental variation, as well as second-stage Horvitz-Thompson-type estimators of abundance, provided more reliable estimates of total abundance than previous models. We adapt this new modeling framework to small game, age-at-harvest models with only young-of-the-year and adult age classes. Our Monte Carlo simulation results indicate superior model performance for the new modeling framework, evidenced by lower bias and proper confidence interval coverage. We apply this method to male wild turkey harvest in the East Ozarks turkey productivity region, Missouri, USA, where statistical population reconstruction indicates a relatively stationary population for 1996–2010.

The utility of auxiliary data in statistical population reconstruction

Although statistical population reconstruction (SPR) provides a flexible framework for estimating demographics of harvested populations using age‐at‐harvest data, that information alone is insufficient. Auxiliary data are needed to ensure all model parameters are estimable and to improve the precision and accuracy of the estimates. We examined the influence of two types of auxiliary information, independent estimates of annual abundance and annual harvest mortality from radio‐telemetry studies, on the stability and precision of abundance estimates from SPR. Further, we evaluated whether the timing of auxiliary studies in the reconstruction affected the precision of abundance estimates. Monte Carlo studies simulated auxiliary data with precision levels defined by the coefficients of variation (CV) of 0.05, 0.125, 0.25 and 0.50 corresponding to the three levels of precision suggested by Robson & Regier (1964) for accurate research, accurate management and rough management and a minimal information scenario. For comparable levels of precision, radio‐telemetry studies used to estimate harvest mortality stabilized the reconstructed population trends better than independent abundance surveys. However, independent abundance surveys were superior at improving the precision of reconstructed abundance estimates. We found that the timing of auxiliary studies did not influence the stability of SPR estimates, which has important implications for managers designing studies to collect auxiliary data. Our research highlights that different types and quality of auxiliary studies affects the precision and stability of SPR models differently.

Model evaluation in statistical population reconstruction

Statistical population reconstruction can be a valuable tool for monitoring the status and trends of game populations at large spatial scales using age‐at‐harvest data. Despite their utility and increasing use in demographic studies, it is necessary that these models be evaluated before their results are applied. We recommend practitioners evaluate their fitted population models using a variety of approaches, including residual analyses, point‐deletion techniques and sensitivity analyses, and we illustrate these techniques using several case studies. Although we stress the value of these quantitative procedures, the final evaluation criterion should be the biological realism of the estimated demographic parameters and trends. Auxiliary field data should be used whenever possible in this final model check. After investigators are satisfied that the selected model(s) is/are adequate, this auxiliary data can be incorporated in a final stage of the analyses to further improve accuracy and precision of the population projections. The procedures we outline and recommendations we make will improve the credibility and utility of results of population reconstruction modeling.

Comparison of Statistical Population Reconstruction Using Full and Pooled Adult Age-Class Data

BackgroundAge-at-harvest data are among the most commonly collected, yet neglected, demographic data gathered by wildlife agencies. Statistical population construction techniques can use this information to estimate the abundance of wild populations over wide geographic areas and concurrently estimate recruitment, harvest, and natural survival rates. Although current reconstruction techniques use full age-class data (0.5, 1.5, 2.5, 3.5, … years), it is not always possible to determine an animal's age due to inaccuracy of the methods, expense, and logistics of sample collection. The ability to inventory wild populations would be greatly expanded if pooled adult age-class data (e.g., 0.5, 1.5, 2.5+ years) could be successfully used in statistical population reconstruction.Methodology/Principal FindingsWe investigated the performance of statistical population reconstruction models developed to analyze full age-class and pooled adult age-class data. We performed Monte Carlo simulations using a stochastic version of a Leslie matrix model, which generated data over a wide range of abundance levels, harvest rates, and natural survival probabilities, representing medium-to-big game species. Results of full age-class and pooled adult age-class population reconstructions were compared for accuracy and precision. No discernible difference in accuracy was detected, but precision was slightly reduced when using the pooled adult age-class reconstruction. On average, the coefficient of variation increased by 0.059 when the adult age-class data were pooled prior to analyses. The analyses and maximum likelihood model for pooled adult age-class reconstruction are illustrated for a black-tailed deer (Odocoileus hemionus) population in Washington State.Conclusions/SignificanceInventorying wild populations is one of the greatest challenges of wildlife agencies. These new statistical population reconstruction models should expand the demographic capabilities of wildlife agencies that have already collected pooled adult age-class data or are seeking a cost-effective method for monitoring the status and trends of our wild resources.

Abundance trends of American martens in Michigan based on statistical population reconstruction

AbstractEstimating the dynamics of furbearer populations is challenging because their elusive behavior and low densities make observations difficult. Statistical population reconstruction is a flexible approach to demographic assessment for harvested populations, but the technique has not been applied to furbearers. We extended this approach to furbearers and analyzed 8 yr of age‐at‐harvest data for American marten (Martes americana) in the Upper Peninsula of Michigan. Marten abundance estimates showed a general downward trend from an estimate of${\hat {N}}$ = 1,733.3$(\widehat {{\rm SE}} = 861.3)$animals in 2000 to${\hat {N}}$ = 1,163.9$(\widehat {{\rm SE}} = 520.1)$in 2007. The harvest probability of martens increased nearly 5‐fold from 0.0542$(\widehat {{\rm SE}} = 0.0250)$in 2000 to 0.2637$(\widehat {{\rm SE}} = 0.1154)$in 2007, which corresponded to a 5‐fold increase in trap‐nights. Continued monitoring of martens in the Upper Peninsula, Michigan, and a reassessment of current harvest regulations are necessary given the estimated decreases. Moreover, we do not encourage the use of harvest indices as the sole technique to assess the status and trends of marten and fisher populations. Auxiliary studies in the Upper Peninsula, Michigan, will allow for continued use and improvement in the application of these models. © 2011 The Wildlife Society.