Community Assembly Model Inference (CAMI)

The Community Assembly Model Inference (CAMI) is an R tool that provides an approach to inferring processes of community formation in ecology. Traditional methods, such as dispersion metrics and statistical hypothesis testing, have limitations due to their reliance on assumptions that are often violated. CAMI addresses these limitations by adapting a phenotypic similarity and repulsion model to simulate the community assembly process through environmental filtering and competitive exclusion.

Key features of CAMI include:

1. Parameterization of ecological processes: CAMI allows users to specify the strength of environmental filtering and competitive exclusion in the simulated community assembly process.

2. Improved accuracy: CAMI distinguishes between different assembly models more accurately than dispersion metrics using random forests and approximate Bayesian computation.

3. Uncertainty accounting: CAMI accounts for uncertainty in model selection, providing a more robust approach to inferring community assembly processes.

4. Parameter estimation: The tool can accurately estimate the parameter that determines the strength of the assembly processes.