Forest restoration and fuel reduction treatments have been recommended for many North American forests that have undergone changes in their fire regimes. I examined how such treatments impact small mammal populations. I further considered potential biases in interpreting historical fire-scar data, which have been used to recommend fire return intervals for treatments in modern forest management, and I developed improved estimators for parameters of historical fire regimes. In Chapter 1, as part of a national experiment, the Fire and Fire Surrogate Program, I evaluated the effects of forest thinning on small mammal population densities and total small mammal biomass in ponderosa pine-dominated forests at 2 study areas in northern Arizona and northern New Mexico, USA. I also evaluated the effects of wildfire on small mammal population densities after a wildfire burned a portion of one study area. I refined statistical methods to efficiently estimate small mammal population densities and to model the impacts of disturbance on densities; these methods involve estimation of abundance and effective trapping area in combined analyses across space and time followed by a weighted regression analysis of treatment effects. I hypothesized that habitat changes post-disturbance would be the largest determinant of population responses to thinning and wildfire within 1 year of disturbances. This hypothesis was largely supported, as predicted positive responses to thinning were documented for deer mice (Peromyscus maniculatus), gray-collared chipmunks (Tamias cinereicollis), and least chipmunks (T. minimus). Predicted positive responses to wildfire were also observed for deer mice, while predicted negative responses to wildfire were not supported for chipmunks. Total biomass of small mammal populations generally increased following both thinning and wildfire. I argue that my statistical methods, combined with rigorous attention to experimental design, provide a template for similar experimental investigations.In Chapter 2, I examined changes in small mammal habitat and densities of 4 small mammal species, including deer mice, gray-collared chipmunks, golden-mantled ground squirrels (Spermophilus lateralis), and Mexican woodrats (Neotoma mexicana), 2-3 years after variable-intensity thinning and prescribed fire treatments in ponderosa pine forests of northern Arizona, USA. These treatments were designed to simultaneously reduce high-severity fire risk while returning forests to conditions more representative of pre-European settlement structure and function. Treatments resulted in increased herbaceous vegetation and decreased woody debris, 2 important components of small mammal habitat in these forests. Small mammal populations varied strongly across years during the course of the study. Small mammal densities were influenced by both treatments and identified habitat variables. Deer mouse densities were negatively related to tree densities. Gray-collared chipmunks were negatively related to tree densities, positively related to woody debris, and negatively related to treatment. Golden-mantled ground squirrels did not appear to vary strongly with treatment or treatment-related habitat changes. Mexican woodrats were positively, but weakly, related to woody debris. Overall, forest thinning can be expected to increase densities of small mammals in these forests, and retention of slash in fuel reduction/restoration treatments may further positively influence small mammal densities in the post-treatment community. However, reduction of woody debris with frequent prescribed fire entries may reduce small mammal densities. Further work is necessary to better understand links between herbaceous vegetation and small mammal populations in southwestern ponderosa pine forests, as well as population dynamics and habitat needs of less common species such golden-mantled ground squirrels and Mexican woodrats.In Chapter 3, I examined general patterns of small mammal responses to mechanical thinning, prescribed fire, and mechanical thinning/prescribed fire combination treatments at 8 different study areas across the United States as a part of the Fire and Fire Surrogate Program. Research questions included 1) do treatments differ in their impact on small mammal densities and biomass, and 2) are effects of treatments consistent across study areas? I modeled taxa-specific densities and total small mammal biomass as functions of treatment types and study area effects, and ranked models based on an informationtheoretic model selection criterion. Small mammal taxa examined, including deer mice, yellow-pine chipmunks (T. amoenus), and golden mantled ground squirrels, as well as all Peromyscus and Tamias species, had top-ranked models with responses varying both by treatment type and study area. However, the top-ranked model of total small mammal biomass was a model with biomass varying only with treatment, not by treatment type or study area. Individual species and taxa appear to have variable responses to fuel reduction treatment types in different areas; however, total small mammal biomass appears to generally increase after any type of fuel reduction. These results highlight the variability in taxa-specific responses to treatments and suggest the importance of adaptive management policies and careful site-specific analyses when applying fuel reduction treatments.In Chapter 4, I describe an analogy between models designed to estimate occupancy of sites by animal species and the estimation of fire size and mean fire return interval. Information on characteristics of historical fire regimes in ponderosa pine forests is increasingly being used to understand ecological function and to set management guidelines for these forests. Better methods for estimating parameters of historical fire regimes from fire-scarred samples are needed. I provide estimators for both size and return interval of fire when detection probabilities of fires are < 1. The sampling method involved requires identification of sites that are geographically closed to fire and the sampling of fire-scar recorder trees within those sites. Simulations suggest that at least 3 recorder trees per site would be necessary to obtain relatively unbiased and precise estimates of parameters. I introduce model assumptions, sampling considerations and ideas for advanced applications of this approach. The model described exists in a likelihood framework, thus facilitating information-theoretic model selection and inference.