Table 1.

1-D and 2-D fits of null model predictions on observed species richness of breeding birds endemic to Africa

Perspective	n	1-D model			2-D GC model used as 1-D			2-D models used as 2-D
								A			GC
		r2	ta=0	tb=1	r2	ta=0	tb=1	r2	ta=0	tb=1	r2	ta=0	tb1
1-D band sum
Latitude	55	0.66	0.84	−1.59	0.66	−4.17	3.88	0.26	−0.97	−1.71	0.44	2.46	−0.86
Longitude	68	0.61	0.38	−1.59	0.62	−4.14	3.62	0.92	−13.53	4.71	0.88	8.22	1.00
1-D band mean
Latitude	55	—	—	—	—	—	—	0.01	0.87	−0.87	0.63	−1.05	1.60
Longitude	68	—	—	—	—	—	—	0.02	1.30	−1.30	0.57	1.69	−1.50
2-D	1,742	—	—	—	—	—	—	0.00	2.50	−2.61	0.21	3.83	−6.19

Different perspectives, measures, and models. 1-D model refers to the one-dimensional Monte Carlo model that uses observed range extents (15); GC and A refer to the geometric constraints and area model, respectively. n is the number of observations for each test; t_a=0 is the t test statistic that indicates the deviation of intercept a from zero for the regression of observed versus predicted data (a measure of fit in magnitude); t_b=1 indicates the deviation of slope b of the regression from unity (a measure of fit in shape). t values in italics indicate a rejection of the tested hypothesis at the P = 0.05 level (two-tailed). Note that in this test as few as ten truly independent observations are sufficient to reject the null hypothesis for any t > 2.3 and not even over 10,000 observations can reject it for any t < 1.96.