Estimating 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.

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.

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.

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.

Long-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)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(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 (1960-2010Gast 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. Ó