In biological sciences, a Multivariate Generalized Linear Model (GLM) is a statistical analysis technique used to simultaneously analyze multiple dependent variables that may have different distributions.
The choice of Multivariate Generalized Linear Model (GLM) type (Gaussian, Binomial, Poisson) depends on the nature of your ecological data and the assumptions you want to make about the distribution of the dependent variable.
Gaussian (Normal) Distribution:
This is suitable when your dependent variable is continuous and normally distributed. It assumes constant variance and linearity. Gaussian GLM is often used for analyzing data like biomass, plant height, or other continuous measurements that are approximately normally distributed.
Binomial Distribution:
This is suitable when your dependent variable is binary or dichotomous (e.g., presence/absence data, yes/no responses). Binomial GLM is commonly used for analyzing proportions or binary outcomes in ecological studies, such as habitat occupancy or species presence.
Poisson Distribution:
This is suitable when your dependent variable represents counts or frequencies of events (e.g., number of individuals, number of species). Poisson GLM assumes that the variance of the dependent variable is equal to its mean. It's often used for analyzing ecological data such as species abundances, insect counts, or population sizes.
In ecological data analysis, the choice between these distributions depends on the characteristics of your data and the specific research question you're addressing. For example, if you're studying the effect of a treatment on the abundance of a species, you might use a Poisson GLM. If you're examining the effect of habitat type on the presence or absence of a species, a Binomial GLM might be more appropriate. If you're analyzing continuous measurements that are normally distributed, a Gaussian GLM could be suitable.