Table 3. Summary of AIC_c model selection statistics for evaluating (1) the intensity of C. maculata egg predation in soybean fields in Iowa and Michigan, (2) the abundance of all potential egg predators and (3) the abundance of exotic potential egg predators.

Response	Model1 , 2	Log-likelihood	Ki	AICc	Δi	Wi	Adjusted r2
Eggs Remaining	y =  Bo + B1 (PC3)***	−13.15	3	34.70	0.00	0.92	0.53
	y = Bo+B1(Area)*	−17.16	3	42.72	8.02	0.02	0.17
	y = Bo	−18.99	2	43.06	8.36	0.01
	y = Bo+B1Exotic Predators	−17.46	3	43.32	8.62	0.01	0.13
	y = Bo+B1Perimeter	−17.6	3	43.60	8.90	0.01	0.11
	y = Bo+B1All Predators	−17.68	3	43.76	9.06	0.01	0.10
	y = Bo+B1D	−18.12	3	44.64	9.94	0.01	0.04
	y = Bo+B1P(PC1)	−18.82	3	46.04	11.34	0.00	−0.06
	y = Bo+B1(PC2)	−18.96	3	46.32	11.62	0.00	−0.08
	y = Bo+B1(Prey)	−18.99	3	46.38	11.68	0.00	−0.07
Potential Predators	y =  Bo + B1 D*	−7.19	3	22.77	0.00	0.71	0.38
	y = Bo+B1PC2	−9.11	3	26.62	3.85	0.10	0.17
	y = Bo	−11.06	2	27.19	4.42	0.08	−0.05
	y = Bo+B1(PC3)	−10.28	3	28.96	6.19	0.03	0.03
	y = Bo+B1(Perimeter)	−10.74	3	29.88	7.11	0.02	−0.04
	y = Bo+B1(Area)	−10.86	3	30.11	7.34	0.02	−0.06
	y = Bo+B1(Prey)	−10.90	3	30.20	7.43	0.02	0.17
	y = Bo+B1(PC1)	−11.06	3	30.51	7.74	0.01	−0.80
Potential Exotic Predators	y =  Bo + B1 D*	−9.26	3	26.93	0.00	0.41	0.27
	y =  Bo + B1 PC2*	−10.02	3	28.44	1.51	0.19	0.18
	y = Bo+B1(PC3)*	−10.32	3	29.05	2.12	0.14	0.15
	y = Bo	−12.01	2	29.09	2.16	0.14
	y = Bo+B1(Perimeter)	−11.83	3	32.07	5.14	0.03	−0.06
	y = Bo+B1PC1	−11.88	3	32.16	5.23	0.03	−0.06
	y = Bo+B1(Prey)	−11.88	3	32.16	5.23	0.03	−0.06
	y = Bo+B1(Area)	−11.97	3	32.34	5.41	0.03	−0.08

The minimum AIC_c model for each response variable and any competing models (Δ_i<2) are shown in bold.

¹Variables in parentheses indicate a negative relationship with response variable.

²* P<0.1, *** P<0.01.