Algorithm Algorithm Algorithm Algorithm Algorithm Algorithm Algorithm Algorithm Algorithm Algorithm Algorithm Algorithm
Features Features A B C D E F G H I J K L
Data Inputs Abundance records can be employed as training data ü ü ü ü
Flexibility to accommodate pools of biotic and abiotic variables as training data ü ü ü
Categorical and continuous variables can be included as training data ü ü ü
Categorical and or continuous predictor data can be employed ü ü ü ü
Large datasets efficiently handled ü ü ü
Traits, phylogeny, and other ancillary data can be incorporated ü
Implementation Model outputs easy to visualize, understand, and interpret ü ü
Readily available tutorials and examples of code ü ü ü ü ü ü ü
Technically challenging to implement and fit models ü ü ü
Computationally demanding and time consuming to run ü ü ü ü ü
Results can be challenging to interpret ü ü ü ü ü ü ü
Inscrutable or “black box” approach ü ü
Analytical Properties Allows for non-linear responses ü ü ü ü ü ü ü ü ü ü ü
Allows for interaction between predictor variables ü ü ü ü ü ü ü ü
Accounts for unexplained co-occurrences ü ü ü
Uncertainty can be explicitly quantified ü ü ü
Predicts compositional differences between locations as a continuous function of environmental gradients ü ü
Dissimilarities between pairs of sites related to differences in environmental conditions and geographic (distance) isolation ü ü
Performance Moderate to high predictive power ü ü