Moose (Alces alces) are a species of management concern in New York State. In some New England states, moose populations are known to be in decline due to mortality from parasitic infection, thermal stress, nutritional deficiency, and moose-vehicle collisions. In contrast, the status of the New York moose population has not been described satisfactorily; abundance has increased since the species' recolonization in 1980, but indices of abundance such as moose-vehicle collisions and public sightings do not reflect the growth of other northeastern U.S. states. In 2015, The New York State Department of Environmental Conservation initiated the project described herein to examine aspects of this population of moose, most notably the size of the population. This thesis offers (a) an advancement of spatial capture-recapture (SCR) methodology to quantify the abundance of rare species through the integration of adaptive sampling principles, and (b) an alternative solution to SCR that estimates population size from scat collections made by detection dogs, without knowledge of individual identity. Rare species present challenges to data collection, particularly when the species is spatially clustered over large areas, such that the encounter frequency of the organism is low. Sampling where the organism is absent consumes resources, and offers relatively low-quality information which are often difficult to model using standard statistical methods. In adaptive sampling, a probabilistic sampling method is employed first, and additional effort is allocated in the vicinity of sites where some measured index variable - assumed to be proportional to local population size - exceeds an a priori threshold. We applied this principle to the spatial capture-recapture (SCR) analytical framework in a Bayesian hierarchical model incorporating capture-recapture (CR) and index information from unsampled sites to estimate density. We assessed the adaptively sampled SCR model (AS-SCR) by simulating CR data and compared performance with a standard SCR baseline (F-SCR), adaptive SCR discarding index information (AS-SCR–), and standard SCR applied at a simple random sample of sites. Under AS-SCR, we observed minimal bias and comparable variance with respect to parameter estimates provided by the standard F-SCR model and sampling implementation, but with substantially reduced effort and significant cost saving potential. This represents the first application of adaptive sampling to SCR, and a useful framework for estimating abundance of low-density species. Obtaining data on individual identification is often expensive to collect, and in the case of genetic identity, sometimes too sparse to perform capture-recapture analysis. We developed a methodology to estimate abundance using detection dog searches along transects for scat without the requirement of individual identity. This method estimates daily accumulation rate of scats during the survey separate from pre-existing scats, accounting for imperfect detection, and accommodates spatial covariates. Daily accumulation rate can be transformed to an estimate of population size using per-capita defecation rate. We applied the method to data collected from a moose scat survey in New York in 2016. We estimated approximately 549 (368 - 850, 95% CI) moose in the Adirondacks under the best-performing model. The method developed is an effective survey method for estimating ungulate abundance from observations of scat and does not require individual identification.