Table 1.

Final regression models linking patterns of butterfly species richness and community structure with environmental predictors

Dependent variable	Independent variable	Coefficient (std. coeff.)	Factor P	Adj. R2 (rms)	Model P (n)
(Butterfly species richness)0.5 (5° quadrats)	VGT land cover diversity	0.221 (0.514)	<10−6	0.904	≪10−6
	Maximum PET	0.00332 (0.249)	<10−5	(1.01)	(72)
	Minimum elevation	0.00030 (0.239)	<10−5
	Sampling	0.00478 (0.216)	<10−5
(Butterfly species richness)0.5 (2.5° × 5° quadrats)*	VGT land cover diversity	0.217 (0.390)	<10−6	0.710	≪10−6
	Maximum PET	0.00206 (0.156)	0.00180	(1.61)	(161)
	Minimum elevation	0.00069 (0.168)	0.00040
	Sampling	0.00012 (0.392)	<10−6
(Butterfly species richness)0.5 (2° quadrats)	VGT land cover diversity	0.191 (0.407)	<10−6	0.622	≪10−6
	Maximum PET	0.00386 (0.231)	<10−6	(1.96)	(371)
	Sampling	0.00201 (0.347)	<10−6
Jaccard values for butterflies (community similarity)	Jaccard value for VGT land cover composition	0.0891	<10−4	0.547	<10−4 (23)

Sampling intensity should be interpreted with caution. Standardized coefficients, estimating the relative importance of each variable, are included after the regression coefficient. The rms errors of multiple regression models provide additional evidence that the predictability of butterfly richness increases with sampling grain. 

^*This quadrat system is included to facilitate comparison with previous diversity studies (e.g., refs. 5 and 8). Some quadrats near the southern border region of Canada are of 2.5 × 2.5 degrees.