Table 5.

Regression equations used in mapping canonical scores for trait given environmental data^*for a location

	TRAIT1			TRAIT2
Independent variable	Regression coefficient	P-value	Regression coefficient	P-value
Intercept	−3·72476	0·0239	18·34020	<0·0001
ELEV	−0·00112	<0·0001	−0·00033	0·0106
SPRFRST	−0·01343	<0·0001	−0·02281	<0·0001
JULPRE	−0·0085	0·0111	0·00116	0·7814
AUGPRE	0·02117	<0·0001	0·00071	0·8768
FEBMXT	0·17083	<0·0001	−0·15134	<0·0001
MAYMXT	−0·15879	<0·0001	−0·0092	0·8514
LAT	0·09134	0·0044	−0·35691	<0·0001
SEPPRE	−0·00032	0·8568	−0·00815	0·0003
JULMXT	0·11432	0·0002	0·10748	0·0045

Probability of lack of fit for TRAIT1 is <0·0001 (F = 2·44); R² = 0·68.

Probability of lack of fit for TRAIT2 is <0·0001 (F = 2·33); R² = 0·50.

^*Key to environmental variables: ELEV = elevation, SPRFRST = date of first spring frost, JULPRE = July precipitation, AUGPRE = August precipitation, FEBMXT = February average maximum daily temperature, MAYMXT = May average maximum daily temperature, LAT = latitude, SEPPRE = September precipitation, JULMXT = July average maximum daily temperature.