Assessing dispersion

A simple way of assessing dispersion in models for count data

          

It has been some time since I have updated my blog, but I have plenty of good excuses for justifying my lack of activity (or so I like to tell myself). In my post on simple models for abundance, I introduced the Poisson-log GLM as the basic approach for modelling count data. The Poisson-log GLM has an important limitation when it comes to modelling ecological data: it assumes that the variance and the mean of the response (dependent) variable are the same, so it is rather inflexible for modelling response data with a variability exceeding the variation in the mean. When this condition is not met, we obtain over- or under-dispersion in the model. It is important to note that the terms over- and under-dispersion do not refer to the raw data values, but to the mean and variance expected with respect to a Poisson-log GLM. It is a frequent mistake to think that dispersion refers to the raw values of the response variable.

Simple models for distributions

Simple models for distributions

         

In my last post, I said that ecology is fundamentally the study of the distribution and abundance of living things. On that last post, I introduced a very simple model for abundance, so it was just logical to follow-up with an entry on simple models for distribution. I will define distribution as presence/absence of the target species on a given site. I am going to follow the structure of the previous entry to make any comparisons easy.

Simple models for abundance

Simple models for abundance

Ecology is fundamentally the study of the distribution and abundance of living things, and a core goal is to understand the factors that influence the spatial and temporal variation in distribution and abundance patterns. Over the last few months, I have had a few people asking me how to model abundance, so I decided to make a quick post on the topic.

Modelling tips II

A few tips for modelling and analysing ecological data. 

Episode II

Let's continue with the second part of my personal tips for analysing ecological data. Most of you would read through this post and think that it amounts to no more than a collection of motherhood statements and very basic recommendations. That might be true, but it strikes me how important and simple these tips are, yet I keep on finding works that would have benefited from using these ideas to improve the analyses presented.

Modelling tips I

A few tips for modelling and analysing ecological data. 

Episode I 

You do not need to be a professional statistician or mathematician to conduct reliable analyses for ecological systems. I would like share a couple of simple, yet very useful, tips for producing sensible statistical models.

Introduction

About myself and this blog                                                                                                                                     

This is my first post. It is a completely new adventure for me to start writing a blog. I believe that evidence-based conservation is critical to preserving and properly manage nature. Researchers must be able to provide the best available robust and reliable evidence existing to inform decision-makers. However, I think that there are instances where researchers may do better science by appropriately using and understanding statistical methods.