Recently, I reviewed several papers where the statistical analysis was pseudoreplicated. I pointed this out to the authors and made some suggestions to remedy the problem. The responses to the reviewer (i.e. me) came back with several reasons why the authors thought the design was OK (and even preferred). Continue reading “Virtual ecologist module #4: understanding pseudoreplication…the fun way.”
Ecologists are frequently challenged with the task of making formal predictions about how populations will respond to different forms of management or perturbations. Continue reading “The role of density dependence in manipulating and managing natural populations”
Our recent paper sheds light on the issue of imperfect detection when evaluating patterns in fish abundance. Continue reading “Should we account for imperfect detection when evaluating patterns in fish abundance?”
The Virtual Ecologist refers to the practice of simulating complete experimental designs, that is, the ecological process, the observation process and the statistical analysis. Continue reading “Virtual ecologist module #3: occupancy model power analysis”
Some years back I was challenged with the questions of how to track fish species richness through time in a selection of freshwater lakes in Florida. I first started experimenting with some of the simpler nonparametric species richness estimators as I had seen them commonly used to answer questions about patterns in species richness in the ecological literature.
The Virtual Ecologist method is a form of stochastic simulation where the process of collecting, analyzing, and interpreting data are simulated. This post is the second module of the Virtual Ecologist blog series. Continue reading “Virtual ecologist module #2: occupancy model design trade-offs”
A procedure for model selection has been recently showing up in the ecological literature that seems to have followed the increasing use of Bayesian hierarchical models. Continue reading “Emerging methods for multi-model inference in ecology: inference based on parameter inclusion probabilities”