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Statistical ecology deals with the development of new methodologies for analyzing ecological data. Advanced statistical models and techniques are often needed to provide robust analyses of the available data. The statistical models that are developed can often be separated into two distinct processes: a system process that describes the underlying biological system and an observation process that describes the data collection process. The system process is often a function of the demographic parameters of interest, such as survival probabilities, transition rates between states, and/or abundance, whereas the model parameters associated with the observation process are conditional on the underlying state of the system. This review focuses on a number of common forms of ecological data and discusses their associated models and model-fitting approaches, including the incorporation of heterogeneity within the given biological system and the integration of different data sources.
Keyword:
heterogeneity
integrated data
Markov model
observation process
system process
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