Table 5.

The MN and MN-MS generally predict out-of-sample mosquito data better than other competing regression models.

Species	Predictive performance
	MN model				MN-MS model
	Poisson	NB	ZINB	ZIP	Poisson	NB	ZINB	ZIP	MN
A. darlingi	0.86	0.79	0.79	0.64	0.86	0.79	0.79	0.64	0.36
A. nuneztovari	0.86	0.86	0.93	1.00	0.93	0.79	0.93	1.00	0.57
A. triannulatus	0.79	0.71	0.79	0.64	0.79	0.71	0.79	0.64	0.29
A. benarrochi	0.79	0.79	0.86	0.71	0.79	0.86	0.93	0.71	0.79
A. oswaldoi	0.79	0.86	0.71	0.71	0.79	0.79	0.79	0.79	0.64
A. rangeli	0.79	0.93	0.93	0.79	0.71	0.93	0.93	0.79	0.79

Numbers indicate the proportion of cross-validation folds (based on 14 folds) in which the MN and MN-MS models had lower MSE scores when compared to each alternative model and for each mosquito species. “ZI” stands for zero-inflation. The last column on the right shows the proportion of cross-validation folds in which the MN-MS model had lower MSE score relative to the MN model.