Detection heterogeneity is inherent to ecological data, arising from factors such as varied terrain or weather conditions, inconsistent sampling effort, of individuals themselves. Incorporating additional covariates into a statistical model one approach for addressing heterogeneity, but no guarantee that any set measurable will adequately address the and presence unmodelled has been shown produ...

In recent years, we are seeing the formulation and use of elaborate and complex models in ecological studies. The questions related to the efficient, systematic and error-proof exploration of parameter spaces are of great importance to better understand, estimate confidences and make use of the output from these models. In this work, we investigate some of the relevant questions related to para...

We propose to define the complexity of an ecological model as the statistical complexity of the output it produces. This allows for a direct comparison between data and model complexity. Working with univariate time series, we show that this measure ‘blindly’ discriminates among the different dynamical behaviours a model can exhibit. We then search a model parameter space in order to segment it...

2 Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma 74078 USA The suite of factors that drives where and under what conditions a species occurs has become the focus of intense research interest. Three general categories of methods have emerged by which researchers address questions in this area: mechanistic models of species’ requirements in terms of environment...

insights from the literature on hierarchical models based on Markov chain Monte Carlo algorithms and King’s ecological inference model. The new approach reveals some features of the data that King’s approach does not, can be easily generalized to more complicated problems such as general R × C tables, allows the data analyst to adjust for covariates, and provides a formal evaluation of the sign...