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

Abstract

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.

Random Effects Models and Multistage Estimation Procedures for Statistical Population Reconstruction of Small Game Populations

“…Some of the most recent developments require estimation of initial animal cohort abundance as a parameter [3], [4], [7], [8], or as a latent variable [5] in a frequentist or Bayesian framework, respectively. Recently, models for statistical population reconstruction (SPR) of harvested large game animals have been developed that utilize the same likelihood-based inference techniques, but instead consider estimating animal abundance following optimization, outside of the likelihood framework with a Horvitz-Thompson-type estimator, which adjusts the observed harvest count by the estimated probability of harvest in accordance with the assumption of a binomial sampling scheme [9].…”

“…The older two age classes, then, represent a combination of animals born in the previous year that survived to the current year, as well as the adults that survived an additional year or more following their first year. In comparison to fully aged harvests of many big game animals, this represents a considerable loss of cohort data upon which the SPR models of Gove et al [3], Skalski et al [4], Skalski et al [8], and Gast et al [9] rely. On the other hand, Skalski et al [10] found little loss in precision when big game population reconstruction was based on pooling adult harvest information for age classes 3+.…”

“…These models, however, suffer from similar difficulties associated with the big game models of Gove et al [3], Skalski et al [4], and Skalski et al [8] inasmuch as they assume constancy of demographic rates across time periods when additional data are not available to inform estimation of year- or age-specific rates. In addition, models with only fixed effects have proven in fully aged simulation studies to provide biased estimates of annual abundance and poor confidence interval covariate [9].…”

Forest ecosystem vulnerability assessment and synthesis for northern Wisconsin and western Upper Michigan: a report from the Northwoods Climate Change Response Framework project

“…The species remains protected from trapping in Wisconsin despite population sizes sufficient to allow harvesting in neighboring Minnesota and Michigan (Skalski et al 2011, Williams et al 200). The Michigan population has been slowly expanding its range and has supported a modest recreational trapping season since 2000 (Skalski et al 2011). …”

Estimating Mountain Lion Abundance in Arizona Using Statistical Population Reconstruction

“…This approach is appealing because age‐at‐harvest data has been collected at low cost for many game species. Statistical population reconstruction has been applied to a variety of species, including greater sage‐grouse ( Centrocercus urophasianus ; Broms et al ), martens ( Martes americana ; Skalski et al ), wild turkeys ( Meleagris gallopavo ; Clawson et al ), and grizzly bears ( Ursus arctos ; Hatter et al ). Statistical population reconstruction has also been used to estimate mountain lion abundance in British Columbia, Canada (Hatter ), and northeastern Oregon (Clawson ) and North Dakota, USA (Johnson et al ).…”