Table S3.

Predictive power of the candidate deterministic models

Site	Linear model (9) (Eq. 2a)		Lineal model with interactions (9) (Eq. 2b)		Nonlinear model (18) (Eq. 2c)
	NSE	AIC	NSE	AIC	NSE	AIC
Brazil 1	0.96	−669.2	0.96	−740.2	0.98	−983.2
Brazil 2	0.77	−555.5	−9,066,779	−673.7	0.92	−919.5
Costa Rica 1	−28.2	+1,107.7	−43,311,385	+4,497.4	0.95	−602.1
Costa Rica 2	−18.7	+524.2	0.81	−290.6	0.92	−264.6
Mexico wet	−0.62	−563.7	−29,741,865	+5,776.9	0.89	−626.8
Mexico dry	−10.4	−1,253.0	−2,600,253	+5,534.9	0.98	−1,233.6
Nicaragua	−3.1	+890.5	−11,087,299	+6,302.8	0.93	−1,020.3

Linear model (Eq. 2a), linear model with interactions (Eq. 2b), and nonlinear model (Eq. 2c). For each candidate model, we report the Nash–Sutcliffe efficiency coefficient (NSE) and the Akaike information criterion (AIC), which accounts for the number of parameters of each model (in parentheses). Values in bold indicate the best-fit model for each site.