intro_mess.qmd

# Introduction to the MESS Model

## Key questions

1. What is the MESS Model?
1. How does MESS differ from other biodiversity models?
1. What are some example applications of MESS?
1. Where can I get a cool MESS logo sticker?

## Lesson objectives

After this lesson, learners should be able to...

1. Describe the (high-level) concept for MESS.
1. Situate MESS in the wider process modeling state space.
1. Formulate scientific questions and decide if/how MESS can be used to
explore them.

## Planned exercises

### [Process-based modeling with the Massive Eco-evolutionary Synthesis Simulations (MESS) Model](https://docs.google.com/presentation/d/1xu2aVlV8sFLJB65rxAN0thclrvGgS-rNFJL8WPeR5jQ/edit?usp=sharing)

![Neutral Assembly Process](images/Forward_Time_Neutral_Assembly.png)

### Overview of MESS simulation and analysis workflow

The basic steps of MESS model simulation-based machine learning inference
are as follows:

* Step 1 - Set model parameters based on prior knowledge of empirical system
* Step 2 - Run many, many simulations
* Step 3 - Use ML to infer community assembly process (neutral/competition/filtering)
* Setp 4 - Use ML to estimate key community assembly parameters
* Step 5 - ???
* Step 6 - Profit!!

## Key points

* MESS is a process-based model in the direct lineage of Island Biogeograpy
Theory and Neutral Biodiversity Theory.
* MESS models the 4 fundamental biodiversity processes: dispersal, speciation,
selection, and drift.
* MESS generates joint predictions of multiple biodiversity patterns:
abundances, trait values, genetic diversities, and phylogenies.