Table 1.

Correlations between climate and estimated yearly intrinsic growth rates for the three grass species studied

Coefficient	Estimate	SE	t	P
B. curtipendula*
(Intercept)	–5.644	1.624	–3.48	0.0020
PPTOct–Dec	0.006	0.003	2.02	0.0556
PPTApr–Jun	0.003	0.001	3.48	0.0020
PPTJul–Sep	0.005	0.001	4.16	0.0004
Lag 1 PPTApr–Sep	–0.001	0.001	–1.68	0.1075
Mean temperatureannual	0.448	0.110	4.07	0.0005
B. hirsuta†
(Intercept)	–4.300	4.571	–0.94	0.3573
PPTOct–Dec	0.0091	0.0047	1.93	0.0678
PPTApr–Jun	0.0081	0.0034	2.37	0.0274
PPTJul–Sep	0.0114	0.0036	3.13	0.0051
Lag 1 PPTApr–Sep	0.0060	0.0027	2.24	0.0358
Mean temperatureApr–Sep	0.3986	0.2534	1.57	0.1306
Mean temperatureannual	–0.5907	0.2337	–2.53	0.0196
PPTannual lag 1 PPTApr–Sep	–0.000013	0.000005	–2.53	0.0194
S. scoparium‡
(Intercept)	–7.08	4.5200	–1.57	0.1307
PPTApr–Jun	0.0084	0.0033	2.518	0.0200
PPTJul–Sep	0.0112	0.0031	3.603	0.0017
Lag 1 PPTApr–Sep	0.0062	0.0025	2.513	0.0202
Mean temperatureApr–Sep	0.4815	0.2651	1.82	0.0836
Mean temperatureannual	–0.4665	0.2461	–1.90	0.0719
PPTannual lag 1 PPTApr–Sep	–0.00001	0.000004	–2.84	0.0098

Variables were selected by using stepwise regression based on Akaike’s information criterion. PPT, precipitation. Approximately 75% of annual precipitation falls during the April–September growing season. Lag 1, conditions in the previous year.

*R² = 0.71; F = 11.32, df = 5, 23; P < 0.0001.

^†R² = 0.49; F = 2.861, df = 7, 21; P < 0.029.

^‡R² = 0.43; F = 2.66, df = 6, 21; P < 0.044.