Well this statement certainly need to be qualified – in this post I will explore coupling species distributions models with @LegeDeC13 approach of measuring site-level contribution to β-diversity. In case you are curious, the entire code (100% Julia) is available on GitLab. Specifically, I am interested in finding out where the waterfowl communities are the most distinctive.
The LCBD (Local contribution to β-diversity) is a measure which belongs to a family of approaches, relying on estimating β-diversity through the variance of a community data matrix $\textbf{Y}$, and requires a reduced number of transformations.

I spent last week working on a new projet, which involved generating networks
with fixed structural properties. I experimented with some approaches, but
simulated annealing gave by far the best results. I thought it would be a nice
opportunity to aggregate some notes and code here. We will see the way
temperature decreases to constrain optimization, how to decide on accepting a
change of state as a function of temperature, and then an illustration on
species occurrence data.

I started using the Julia programming language in 2014, and by 2015, it
represented about 75% of my programming time/output. These have been interesting
years, because the language changed a lot, but also because I found a tool that
makes me very productive.