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Hierarchical models, also known as mutl-level and mixed effects models, have advanced the field of population ecology with inferences about population dynamics at broad spatial and temporal scales. The analysis of hierarchical models has been facilitated by recent advances in Bayesian analysis, and computationally intensive techniques such as Markov Chain Monte Carlo (see Mathematical modeling).

Many wildlife studies seek to understand changes or differences in the proportion of sites occupied by a species of interest. These studies are hampered by imperfect detection of these species, which can result in some sites appearing to be unoccupied that are actually occupied. Occupancy models solve this problem and produce unbiased estimates of occupancy (ie, occurrence) and related parameters...

Capture-Mark-Recapture (CMR) can be viewed as an animal survey method in which the count statistic is the total number of animals caught, and the associated detection probability is the probability of capture. The method involves capturing a number of animals, marking them, releasing them back into the population, and then determining the ratio of marked to unmarked animals in the population.

A formal framework that revolves around the construction of a mathematical expression using equations, statements or formulas as representation of a system, behavior, or process to simulate the behavior of the system, a series of relationships, or process under study.

Determining the size of animal populations is fundamental to wildlife management and how such populations change over time and space can provide insights into ecosystem function and associated processes. Patuxent scientists’ utilize a number of robust statistical techniques to generate these estimates and to improve our understanding of population dynamics.