2.4 Habitat Modeling
We used an ensemble modeling approach to predict habitat suitability for
both species. Ensemble modeling is a powerful approach that combines
predictions from different models (Araújo & New, 2007). Moreover, the
accuracy of the model is increased by fitting several suitability
models, the uncertainty associated with using a single model is
decreased, and finally, a range of predictions is explored across more
than one set of uncertainty sources (Araújo & New, 2007).
Our ensemble models were created by averaging seven different models
using the biomod 2 R package (Thuiller, Lafourcade, Engler, & Araújo,
2009). Biomod2 was chosen because it is a well-known and
well-established software (Hao, Elith, Guillera‐Arroita, &
Lahoz‐Monfort, 2019). These models included two regression-based models
(Generalized Linear Model [GLM], and Multivariate Adaptive
Regression Splines [MARS]) and three machine-learning models
(Maximum Entropy [MaxEnt], Random Forest [RF], and Generalized
Boosting Model [GBM].